04_Kaggle_House_Prices_Random_Forest_Ensemble.ipynb

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   "source": [
    "MSDS 422 - Assignment 4 \n",
    "Prepared by Vincent Pun\n",
    "07.18.2020"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "File descriptions\n",
    "train.csv - the training set\n",
    "test.csv - the test set\n",
    "data_description.txt - full description of each column, originally prepared by Dean De Cock but lightly edited to match the column names used here\n",
    "sample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms\n",
    "Data fields\n",
    "Here's a brief version of what you'll find in the data description file.\n",
    "\n",
    "SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.\n",
    "MSSubClass: The building class\n",
    "MSZoning: The general zoning classification\n",
    "LotFrontage: Linear feet of street connected to property\n",
    "LotArea: Lot size in square feet\n",
    "Street: Type of road access\n",
    "Alley: Type of alley access\n",
    "LotShape: General shape of property\n",
    "LandContour: Flatness of the property\n",
    "Utilities: Type of utilities available\n",
    "LotConfig: Lot configuration\n",
    "LandSlope: Slope of property\n",
    "Neighborhood: Physical locations within Ames city limits\n",
    "Condition1: Proximity to main road or railroad\n",
    "Condition2: Proximity to main road or railroad (if a second is present)\n",
    "BldgType: Type of dwelling\n",
    "HouseStyle: Style of dwelling\n",
    "OverallQual: Overall material and finish quality\n",
    "OverallCond: Overall condition rating\n",
    "YearBuilt: Original construction date\n",
    "YearRemodAdd: Remodel date\n",
    "RoofStyle: Type of roof\n",
    "RoofMatl: Roof material\n",
    "Exterior1st: Exterior covering on house\n",
    "Exterior2nd: Exterior covering on house (if more than one material)\n",
    "MasVnrType: Masonry veneer type\n",
    "MasVnrArea: Masonry veneer area in square feet\n",
    "ExterQual: Exterior material quality\n",
    "ExterCond: Present condition of the material on the exterior\n",
    "Foundation: Type of foundation\n",
    "BsmtQual: Height of the basement\n",
    "BsmtCond: General condition of the basement\n",
    "BsmtExposure: Walkout or garden level basement walls\n",
    "BsmtFinType1: Quality of basement finished area\n",
    "BsmtFinSF1: Type 1 finished square feet\n",
    "BsmtFinType2: Quality of second finished area (if present)\n",
    "BsmtFinSF2: Type 2 finished square feet\n",
    "BsmtUnfSF: Unfinished square feet of basement area\n",
    "TotalBsmtSF: Total square feet of basement area\n",
    "Heating: Type of heating\n",
    "HeatingQC: Heating quality and condition\n",
    "CentralAir: Central air conditioning\n",
    "Electrical: Electrical system\n",
    "1stFlrSF: First Floor square feet\n",
    "2ndFlrSF: Second floor square feet\n",
    "LowQualFinSF: Low quality finished square feet (all floors)\n",
    "GrLivArea: Above grade (ground) living area square feet\n",
    "BsmtFullBath: Basement full bathrooms\n",
    "BsmtHalfBath: Basement half bathrooms\n",
    "FullBath: Full bathrooms above grade\n",
    "HalfBath: Half baths above grade\n",
    "Bedroom: Number of bedrooms above basement level\n",
    "Kitchen: Number of kitchens\n",
    "KitchenQual: Kitchen quality\n",
    "TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)\n",
    "Functional: Home functionality rating\n",
    "Fireplaces: Number of fireplaces\n",
    "FireplaceQu: Fireplace quality\n",
    "GarageType: Garage location\n",
    "GarageYrBlt: Year garage was built\n",
    "GarageFinish: Interior finish of the garage\n",
    "GarageCars: Size of garage in car capacity\n",
    "GarageArea: Size of garage in square feet\n",
    "GarageQual: Garage quality\n",
    "GarageCond: Garage condition\n",
    "PavedDrive: Paved driveway\n",
    "WoodDeckSF: Wood deck area in square feet\n",
    "OpenPorchSF: Open porch area in square feet\n",
    "EnclosedPorch: Enclosed porch area in square feet\n",
    "3SsnPorch: Three season porch area in square feet\n",
    "ScreenPorch: Screen porch area in square feet\n",
    "PoolArea: Pool area in square feet\n",
    "PoolQC: Pool quality\n",
    "Fence: Fence quality\n",
    "MiscFeature: Miscellaneous feature not covered in other categories\n",
    "MiscVal: $Value of miscellaneous feature\n",
    "MoSold: Month Sold\n",
    "YrSold: Year Sold\n",
    "SaleType: Type of sale\n",
    "SaleCondition: Condition of sale"
   ]
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    "**Assignment 4 Additional Prompt:**\n",
    "\n",
    "Try **alternative versions of random forests and gradient boosting.** Select a best modeling method .  Employ that method on the full data set, obtaining results that you can report to management.\n",
    "\n",
    "Reviewing the results of the **random forests and gradient boosting model** you have selected to present to management, which **explanatory variables are most important** in predicting home prices?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assignment 2 Prompt: \n",
    "\n",
    "Compete in the House Prices:  Advanced Regression Techniques competition here:  https://www.kaggle.com/c/house-prices-advanced-regression-techniques (Links to an external site.) .  You must establish an account with Kaggle.com (free). \n",
    "\n",
    "Use many explanatory variables for your predictions. <br>\n",
    "Employ at least **two** regression modeling methods selected from those discussed in Chapter 4 of the Géron (2017) textbook: **linear regression, ridge regression, lasso regression, and elastic net.**<br>\n",
    "<br>\n",
    "Evaluate these methods within a **cross-validation design using the root mean-squared error (RMSE) as an index of prediction error.**<br>\n",
    "<br>\n",
    "Submit your models to Kaggle.com for evaluation on the test set.  Python scikit-learn should be your primary environment for conducting this research. Note that it is **not necessary to employ polynomial regression in this assignment.**\n",
    "\n",
    "Regarding the management problem, imagine that you are advising a real estate brokerage firm in its attempt to employ machine learning methods. The firm wants to **use machine learning to complement conventional methods for assessing the market value of residential real estate.**<br>\n",
    "<br>\n",
    "Of the modeling methods examined in your study, **which would you recommend to management, and why?**"
   ]
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  {
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  {
   "cell_type": "code",
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   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Categorical \n",
    "from pandas.plotting import scatter_matrix\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"training_linear_models\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)\n",
    "\n",
    "# Ignore useless warnings (see SciPy issue #5998)\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")\n",
    "\n",
    "# set up notebook to display multiple output in one cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "pd.set_option('display.max_columns', None)  \n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "pd.set_option('max_colwidth', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold  #handsonml_pg83_implementing cross-validation\n",
    "from sklearn.model_selection  import KFold\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "#Might Not Use These\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "#Will Probably Use This\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "#Probably won't use all of these\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/vincentpun/Documents/Northwestern/MSDS 422/Module 4/house-prices-advanced-regression-techniques'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Both Files\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     1452 non-null   object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
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       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold SaleType SaleCondition  SalePrice\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam     2Story            7            5       2003          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace       196.0        Gd        TA      PConc       Gd       TA           No          GLQ         706          Unf           0        150          856    GasA        Ex          Y      SBrkr       856       854             0       1710             1             0         2         1             3             1          Gd             8        Typ           0         NaN     Attchd       2003.0          RFn           2         548         TA         TA          Y           0           61              0          0            0         0    NaN   NaN         NaN        0       2    2008       WD        Normal     208500\n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg         Lvl    AllPub       FR2       Gtl      Veenker      Feedr       Norm     1Fam     1Story            6            8       1976          1976     Gable  CompShg     MetalSd     MetalSd       None         0.0        TA        TA     CBlock       Gd       TA           Gd          ALQ         978          Unf           0        284         1262    GasA        Ex          Y      SBrkr      1262         0             0       1262             0             1         2         0             3             1          TA             6        Typ           1          TA     Attchd       1976.0          RFn           2         460         TA         TA          Y         298            0              0          0            0         0    NaN   NaN         NaN        0       5    2007       WD        Normal     181500\n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1         Lvl    AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam     2Story            7            5       2001          2002     Gable  CompShg     VinylSd     VinylSd    BrkFace       162.0        Gd        TA      PConc       Gd       TA           Mn          GLQ         486          Unf           0        434          920    GasA        Ex          Y      SBrkr       920       866             0       1786             1             0         2         1             3             1          Gd             6        Typ           1          TA     Attchd       2001.0          RFn           2         608         TA         TA          Y           0           42              0          0            0         0    NaN   NaN         NaN        0       9    2008       WD        Normal     223500\n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1         Lvl    AllPub    Corner       Gtl      Crawfor       Norm       Norm     1Fam     2Story            7            5       1915          1970     Gable  CompShg     Wd Sdng     Wd Shng       None         0.0        TA        TA     BrkTil       TA       Gd           No          ALQ         216          Unf           0        540          756    GasA        Gd          Y      SBrkr       961       756             0       1717             1             0         1         0             3             1          Gd             7        Typ           1          Gd     Detchd       1998.0          Unf           3         642         TA         TA          Y           0           35            272          0            0         0    NaN   NaN         NaN        0       2    2006       WD       Abnorml     140000\n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1         Lvl    AllPub       FR2       Gtl      NoRidge       Norm       Norm     1Fam     2Story            8            5       2000          2000     Gable  CompShg     VinylSd     VinylSd    BrkFace       350.0        Gd        TA      PConc       Gd       TA           Av          GLQ         655          Unf           0        490         1145    GasA        Ex          Y      SBrkr      1145      1053             0       2198             1             0         2         1             4             1          Gd             9        Typ           1          TA     Attchd       2000.0          RFn           3         836         TA         TA          Y         192           84              0          0            0         0    NaN   NaN         NaN        0      12    2008       WD        Normal     250000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Rec</td>\n",
       "      <td>468.0</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>144.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>108.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>923.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>791.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>928</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>20.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>602.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>926</td>\n",
       "      <td>678</td>\n",
       "      <td>0</td>\n",
       "      <td>1604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>360</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>StoneBr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>TwnhsE</td>\n",
       "      <td>1Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>263.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea PoolQC  Fence MiscFeature  MiscVal  MoSold  YrSold SaleType SaleCondition\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl        NAmes      Feedr       Norm     1Fam     1Story            5            6       1961          1961     Gable  CompShg     VinylSd     VinylSd       None         0.0        TA        TA     CBlock       TA       TA           No          Rec       468.0          LwQ       144.0      270.0        882.0    GasA        TA          Y      SBrkr       896         0             0        896           0.0           0.0         1         0             2             1          TA             5        Typ           0         NaN     Attchd       1961.0          Unf         1.0       730.0         TA         TA          Y         140            0              0          0          120         0    NaN  MnPrv         NaN        0       6    2010       WD        Normal\n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1         Lvl    AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam     1Story            6            6       1958          1958       Hip  CompShg     Wd Sdng     Wd Sdng    BrkFace       108.0        TA        TA     CBlock       TA       TA           No          ALQ       923.0          Unf         0.0      406.0       1329.0    GasA        TA          Y      SBrkr      1329         0             0       1329           0.0           0.0         1         1             3             1          Gd             6        Typ           0         NaN     Attchd       1958.0          Unf         1.0       312.0         TA         TA          Y         393           36              0          0            0         0    NaN    NaN        Gar2    12500       6    2010       WD        Normal\n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1         Lvl    AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam     2Story            5            5       1997          1998     Gable  CompShg     VinylSd     VinylSd       None         0.0        TA        TA      PConc       Gd       TA           No          GLQ       791.0          Unf         0.0      137.0        928.0    GasA        Gd          Y      SBrkr       928       701             0       1629           0.0           0.0         2         1             3             1          TA             6        Typ           1          TA     Attchd       1997.0          Fin         2.0       482.0         TA         TA          Y         212           34              0          0            0         0    NaN  MnPrv         NaN        0       3    2010       WD        Normal\n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1         Lvl    AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam     2Story            6            6       1998          1998     Gable  CompShg     VinylSd     VinylSd    BrkFace        20.0        TA        TA      PConc       TA       TA           No          GLQ       602.0          Unf         0.0      324.0        926.0    GasA        Ex          Y      SBrkr       926       678             0       1604           0.0           0.0         2         1             3             1          Gd             7        Typ           1          Gd     Attchd       1998.0          Fin         2.0       470.0         TA         TA          Y         360           36              0          0            0         0    NaN    NaN         NaN        0       6    2010       WD        Normal\n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1         HLS    AllPub    Inside       Gtl      StoneBr       Norm       Norm   TwnhsE     1Story            8            5       1992          1992     Gable  CompShg     HdBoard     HdBoard       None         0.0        Gd        TA      PConc       Gd       TA           No          ALQ       263.0          Unf         0.0     1017.0       1280.0    GasA        Ex          Y      SBrkr      1280         0             0       1280           0.0           0.0         2         0             2             1          Gd             5        Typ           0         NaN     Attchd       1992.0          RFn         2.0       506.0         TA         TA          Y           0           82              0          0          144         0    NaN    NaN         NaN        0       1    2010       WD        Normal"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape: (1460, 81)\n",
      "test shape: (1459, 80) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "train.head()\n",
    "\n",
    "test.head()\n",
    "\n",
    "print(\"train shape:\",train.shape)\n",
    "print(\"test shape:\",test.shape,\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Mitchel</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1960</td>\n",
       "      <td>1996</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1224</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1224</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>474</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Mitchel</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>SFoyer</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Mod</td>\n",
       "      <td>Mitchel</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1993</td>\n",
       "      <td>1994</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>94.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>758.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>996</td>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>3.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>190</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea PoolQC  Fence MiscFeature  MiscVal  MoSold  YrSold SaleType SaleCondition\n",
       "2916          20       RL        160.0    20000   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl      Mitchel       Norm       Norm     1Fam     1Story            5            7       1960          1996     Gable  CompShg     VinylSd     VinylSd       None         0.0        TA        TA     CBlock       TA       TA           No          ALQ      1224.0          Unf         0.0        0.0       1224.0    GasA        Ex          Y      SBrkr      1224         0             0       1224           1.0           0.0         1         0             4             1          TA             7        Typ           1          TA     Detchd       1960.0          Unf         2.0       576.0         TA         TA          Y         474            0              0          0            0         0    NaN    NaN         NaN        0       9    2006       WD       Abnorml\n",
       "2917          85       RL         62.0    10441   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl      Mitchel       Norm       Norm     1Fam     SFoyer            5            5       1992          1992     Gable  CompShg     HdBoard     Wd Shng       None         0.0        TA        TA      PConc       Gd       TA           Av          GLQ       337.0          Unf         0.0      575.0        912.0    GasA        TA          Y      SBrkr       970         0             0        970           0.0           1.0         1         0             3             1          TA             6        Typ           0         NaN        NaN          NaN          NaN         0.0         0.0        NaN        NaN          Y          80           32              0          0            0         0    NaN  MnPrv        Shed      700       7    2006       WD        Normal\n",
       "2918          60       RL         74.0     9627   Pave   NaN      Reg         Lvl    AllPub    Inside       Mod      Mitchel       Norm       Norm     1Fam     2Story            7            5       1993          1994     Gable  CompShg     HdBoard     HdBoard    BrkFace        94.0        TA        TA      PConc       Gd       TA           Av          LwQ       758.0          Unf         0.0      238.0        996.0    GasA        Ex          Y      SBrkr       996      1004             0       2000           0.0           0.0         2         1             3             1          TA             9        Typ           1          TA     Attchd       1993.0          Fin         3.0       650.0         TA         TA          Y         190           48              0          0            0         0    NaN    NaN         NaN        0      11    2006       WD        Normal"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(2919, 79)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df\n",
    "\n",
    "#concat train and test sets prior to data processing / feature engineering / normalization  \n",
    "\n",
    "df = pd.concat((train.loc[:,'MSSubClass':'SaleCondition'],\n",
    "                      test.loc[:,'MSSubClass':'SaleCondition'])).reset_index(drop=True)\n",
    "df.tail(3)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2433.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2896.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2917.000000</td>\n",
       "      <td>2917.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57.137718</td>\n",
       "      <td>69.305795</td>\n",
       "      <td>10168.114080</td>\n",
       "      <td>6.089072</td>\n",
       "      <td>5.564577</td>\n",
       "      <td>1971.312778</td>\n",
       "      <td>1984.264474</td>\n",
       "      <td>102.201312</td>\n",
       "      <td>441.423235</td>\n",
       "      <td>49.582248</td>\n",
       "      <td>560.772104</td>\n",
       "      <td>1051.777587</td>\n",
       "      <td>1159.581706</td>\n",
       "      <td>336.483727</td>\n",
       "      <td>4.694416</td>\n",
       "      <td>1500.759849</td>\n",
       "      <td>0.429894</td>\n",
       "      <td>0.061364</td>\n",
       "      <td>1.568003</td>\n",
       "      <td>0.380267</td>\n",
       "      <td>2.860226</td>\n",
       "      <td>1.044536</td>\n",
       "      <td>6.451524</td>\n",
       "      <td>0.597122</td>\n",
       "      <td>1978.113406</td>\n",
       "      <td>1.766621</td>\n",
       "      <td>472.874572</td>\n",
       "      <td>93.709832</td>\n",
       "      <td>47.486811</td>\n",
       "      <td>23.098321</td>\n",
       "      <td>2.602261</td>\n",
       "      <td>16.062350</td>\n",
       "      <td>2.251799</td>\n",
       "      <td>50.825968</td>\n",
       "      <td>6.213087</td>\n",
       "      <td>2007.792737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42.517628</td>\n",
       "      <td>23.344905</td>\n",
       "      <td>7886.996359</td>\n",
       "      <td>1.409947</td>\n",
       "      <td>1.113131</td>\n",
       "      <td>30.291442</td>\n",
       "      <td>20.894344</td>\n",
       "      <td>179.334253</td>\n",
       "      <td>455.610826</td>\n",
       "      <td>169.205611</td>\n",
       "      <td>439.543659</td>\n",
       "      <td>440.766258</td>\n",
       "      <td>392.362079</td>\n",
       "      <td>428.701456</td>\n",
       "      <td>46.396825</td>\n",
       "      <td>506.051045</td>\n",
       "      <td>0.524736</td>\n",
       "      <td>0.245687</td>\n",
       "      <td>0.552969</td>\n",
       "      <td>0.502872</td>\n",
       "      <td>0.822693</td>\n",
       "      <td>0.214462</td>\n",
       "      <td>1.569379</td>\n",
       "      <td>0.646129</td>\n",
       "      <td>25.574285</td>\n",
       "      <td>0.761624</td>\n",
       "      <td>215.394815</td>\n",
       "      <td>126.526589</td>\n",
       "      <td>67.575493</td>\n",
       "      <td>64.244246</td>\n",
       "      <td>25.188169</td>\n",
       "      <td>56.184365</td>\n",
       "      <td>35.663946</td>\n",
       "      <td>567.402211</td>\n",
       "      <td>2.714762</td>\n",
       "      <td>1.314964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1895.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7478.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1953.500000</td>\n",
       "      <td>1965.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>793.000000</td>\n",
       "      <td>876.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>9453.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>368.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>989.500000</td>\n",
       "      <td>1082.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1444.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1979.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11570.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>733.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>805.500000</td>\n",
       "      <td>1302.000000</td>\n",
       "      <td>1387.500000</td>\n",
       "      <td>704.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1743.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>2336.000000</td>\n",
       "      <td>6110.000000</td>\n",
       "      <td>5095.000000</td>\n",
       "      <td>2065.000000</td>\n",
       "      <td>1064.000000</td>\n",
       "      <td>5642.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>1424.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        MSSubClass  LotFrontage        LotArea  OverallQual  OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1   BsmtFinSF2    BsmtUnfSF  TotalBsmtSF     1stFlrSF     2ndFlrSF  LowQualFinSF    GrLivArea  BsmtFullBath  BsmtHalfBath     FullBath     HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd   Fireplaces  GarageYrBlt   GarageCars   GarageArea   WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch     PoolArea       MiscVal       MoSold       YrSold\n",
       "count  2919.000000  2433.000000    2919.000000  2919.000000  2919.000000  2919.000000   2919.000000  2896.000000  2918.000000  2918.000000  2918.000000  2918.000000  2919.000000  2919.000000   2919.000000  2919.000000   2917.000000   2917.000000  2919.000000  2919.000000   2919.000000   2919.000000   2919.000000  2919.000000  2760.000000  2918.000000  2918.000000  2919.000000  2919.000000    2919.000000  2919.000000  2919.000000  2919.000000   2919.000000  2919.000000  2919.000000\n",
       "mean     57.137718    69.305795   10168.114080     6.089072     5.564577  1971.312778   1984.264474   102.201312   441.423235    49.582248   560.772104  1051.777587  1159.581706   336.483727      4.694416  1500.759849      0.429894      0.061364     1.568003     0.380267      2.860226      1.044536      6.451524     0.597122  1978.113406     1.766621   472.874572    93.709832    47.486811      23.098321     2.602261    16.062350     2.251799     50.825968     6.213087  2007.792737\n",
       "std      42.517628    23.344905    7886.996359     1.409947     1.113131    30.291442     20.894344   179.334253   455.610826   169.205611   439.543659   440.766258   392.362079   428.701456     46.396825   506.051045      0.524736      0.245687     0.552969     0.502872      0.822693      0.214462      1.569379     0.646129    25.574285     0.761624   215.394815   126.526589    67.575493      64.244246    25.188169    56.184365    35.663946    567.402211     2.714762     1.314964\n",
       "min      20.000000    21.000000    1300.000000     1.000000     1.000000  1872.000000   1950.000000     0.000000     0.000000     0.000000     0.000000     0.000000   334.000000     0.000000      0.000000   334.000000      0.000000      0.000000     0.000000     0.000000      0.000000      0.000000      2.000000     0.000000  1895.000000     0.000000     0.000000     0.000000     0.000000       0.000000     0.000000     0.000000     0.000000      0.000000     1.000000  2006.000000\n",
       "25%      20.000000    59.000000    7478.000000     5.000000     5.000000  1953.500000   1965.000000     0.000000     0.000000     0.000000   220.000000   793.000000   876.000000     0.000000      0.000000  1126.000000      0.000000      0.000000     1.000000     0.000000      2.000000      1.000000      5.000000     0.000000  1960.000000     1.000000   320.000000     0.000000     0.000000       0.000000     0.000000     0.000000     0.000000      0.000000     4.000000  2007.000000\n",
       "50%      50.000000    68.000000    9453.000000     6.000000     5.000000  1973.000000   1993.000000     0.000000   368.500000     0.000000   467.000000   989.500000  1082.000000     0.000000      0.000000  1444.000000      0.000000      0.000000     2.000000     0.000000      3.000000      1.000000      6.000000     1.000000  1979.000000     2.000000   480.000000     0.000000    26.000000       0.000000     0.000000     0.000000     0.000000      0.000000     6.000000  2008.000000\n",
       "75%      70.000000    80.000000   11570.000000     7.000000     6.000000  2001.000000   2004.000000   164.000000   733.000000     0.000000   805.500000  1302.000000  1387.500000   704.000000      0.000000  1743.500000      1.000000      0.000000     2.000000     1.000000      3.000000      1.000000      7.000000     1.000000  2002.000000     2.000000   576.000000   168.000000    70.000000       0.000000     0.000000     0.000000     0.000000      0.000000     8.000000  2009.000000\n",
       "max     190.000000   313.000000  215245.000000    10.000000     9.000000  2010.000000   2010.000000  1600.000000  5644.000000  1526.000000  2336.000000  6110.000000  5095.000000  2065.000000   1064.000000  5642.000000      3.000000      2.000000     4.000000     2.000000      8.000000      3.000000     15.000000     4.000000  2207.000000     5.000000  1488.000000  1424.000000   742.000000    1012.000000   508.000000   576.000000   800.000000  17000.000000    12.000000  2010.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2919 entries, 0 to 2918\n",
      "Data columns (total 79 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   MSSubClass     2919 non-null   int64  \n",
      " 1   MSZoning       2915 non-null   object \n",
      " 2   LotFrontage    2433 non-null   float64\n",
      " 3   LotArea        2919 non-null   int64  \n",
      " 4   Street         2919 non-null   object \n",
      " 5   Alley          198 non-null    object \n",
      " 6   LotShape       2919 non-null   object \n",
      " 7   LandContour    2919 non-null   object \n",
      " 8   Utilities      2917 non-null   object \n",
      " 9   LotConfig      2919 non-null   object \n",
      " 10  LandSlope      2919 non-null   object \n",
      " 11  Neighborhood   2919 non-null   object \n",
      " 12  Condition1     2919 non-null   object \n",
      " 13  Condition2     2919 non-null   object \n",
      " 14  BldgType       2919 non-null   object \n",
      " 15  HouseStyle     2919 non-null   object \n",
      " 16  OverallQual    2919 non-null   int64  \n",
      " 17  OverallCond    2919 non-null   int64  \n",
      " 18  YearBuilt      2919 non-null   int64  \n",
      " 19  YearRemodAdd   2919 non-null   int64  \n",
      " 20  RoofStyle      2919 non-null   object \n",
      " 21  RoofMatl       2919 non-null   object \n",
      " 22  Exterior1st    2918 non-null   object \n",
      " 23  Exterior2nd    2918 non-null   object \n",
      " 24  MasVnrType     2895 non-null   object \n",
      " 25  MasVnrArea     2896 non-null   float64\n",
      " 26  ExterQual      2919 non-null   object \n",
      " 27  ExterCond      2919 non-null   object \n",
      " 28  Foundation     2919 non-null   object \n",
      " 29  BsmtQual       2838 non-null   object \n",
      " 30  BsmtCond       2837 non-null   object \n",
      " 31  BsmtExposure   2837 non-null   object \n",
      " 32  BsmtFinType1   2840 non-null   object \n",
      " 33  BsmtFinSF1     2918 non-null   float64\n",
      " 34  BsmtFinType2   2839 non-null   object \n",
      " 35  BsmtFinSF2     2918 non-null   float64\n",
      " 36  BsmtUnfSF      2918 non-null   float64\n",
      " 37  TotalBsmtSF    2918 non-null   float64\n",
      " 38  Heating        2919 non-null   object \n",
      " 39  HeatingQC      2919 non-null   object \n",
      " 40  CentralAir     2919 non-null   object \n",
      " 41  Electrical     2918 non-null   object \n",
      " 42  1stFlrSF       2919 non-null   int64  \n",
      " 43  2ndFlrSF       2919 non-null   int64  \n",
      " 44  LowQualFinSF   2919 non-null   int64  \n",
      " 45  GrLivArea      2919 non-null   int64  \n",
      " 46  BsmtFullBath   2917 non-null   float64\n",
      " 47  BsmtHalfBath   2917 non-null   float64\n",
      " 48  FullBath       2919 non-null   int64  \n",
      " 49  HalfBath       2919 non-null   int64  \n",
      " 50  BedroomAbvGr   2919 non-null   int64  \n",
      " 51  KitchenAbvGr   2919 non-null   int64  \n",
      " 52  KitchenQual    2918 non-null   object \n",
      " 53  TotRmsAbvGrd   2919 non-null   int64  \n",
      " 54  Functional     2917 non-null   object \n",
      " 55  Fireplaces     2919 non-null   int64  \n",
      " 56  FireplaceQu    1499 non-null   object \n",
      " 57  GarageType     2762 non-null   object \n",
      " 58  GarageYrBlt    2760 non-null   float64\n",
      " 59  GarageFinish   2760 non-null   object \n",
      " 60  GarageCars     2918 non-null   float64\n",
      " 61  GarageArea     2918 non-null   float64\n",
      " 62  GarageQual     2760 non-null   object \n",
      " 63  GarageCond     2760 non-null   object \n",
      " 64  PavedDrive     2919 non-null   object \n",
      " 65  WoodDeckSF     2919 non-null   int64  \n",
      " 66  OpenPorchSF    2919 non-null   int64  \n",
      " 67  EnclosedPorch  2919 non-null   int64  \n",
      " 68  3SsnPorch      2919 non-null   int64  \n",
      " 69  ScreenPorch    2919 non-null   int64  \n",
      " 70  PoolArea       2919 non-null   int64  \n",
      " 71  PoolQC         10 non-null     object \n",
      " 72  Fence          571 non-null    object \n",
      " 73  MiscFeature    105 non-null    object \n",
      " 74  MiscVal        2919 non-null   int64  \n",
      " 75  MoSold         2919 non-null   int64  \n",
      " 76  YrSold         2919 non-null   int64  \n",
      " 77  SaleType       2918 non-null   object \n",
      " 78  SaleCondition  2919 non-null   object \n",
      "dtypes: float64(11), int64(25), object(43)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.describe()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Understanding Missing/Null Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13965"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how many blanks\n",
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC          2909\n",
       "MiscFeature     2814\n",
       "Alley           2721\n",
       "Fence           2348\n",
       "FireplaceQu     1420\n",
       "LotFrontage      486\n",
       "GarageFinish     159\n",
       "GarageYrBlt      159\n",
       "GarageQual       159\n",
       "GarageCond       159\n",
       "GarageType       157\n",
       "BsmtExposure      82\n",
       "BsmtCond          82\n",
       "BsmtQual          81\n",
       "BsmtFinType2      80\n",
       "BsmtFinType1      79\n",
       "MasVnrType        24\n",
       "MasVnrArea        23\n",
       "MSZoning           4\n",
       "BsmtFullBath       2\n",
       "BsmtHalfBath       2\n",
       "Utilities          2\n",
       "Functional         2\n",
       "Exterior2nd        1\n",
       "Exterior1st        1\n",
       "SaleType           1\n",
       "BsmtFinSF1         1\n",
       "BsmtFinSF2         1\n",
       "BsmtUnfSF          1\n",
       "Electrical         1\n",
       "KitchenQual        1\n",
       "GarageCars         1\n",
       "GarageArea         1\n",
       "TotalBsmtSF        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([0. , 0.2, 0.4, 0.6, 0.8, 1. ]),\n",
       " <a list of 6 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8d19b7910>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percent Missing Data by Feature')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Features')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percent of Missing Values (%)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>4</td>\n",
       "      <td>0.137033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>486</td>\n",
       "      <td>16.649538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Street</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType</th>\n",
       "      <td>1</td>\n",
       "      <td>0.034258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Total    Percent\n",
       "MSSubClass         0   0.000000\n",
       "MSZoning           4   0.137033\n",
       "LotFrontage      486  16.649538\n",
       "LotArea            0   0.000000\n",
       "Street             0   0.000000\n",
       "...              ...        ...\n",
       "MiscVal            0   0.000000\n",
       "MoSold             0   0.000000\n",
       "YrSold             0   0.000000\n",
       "SaleType           1   0.034258\n",
       "SaleCondition      0   0.000000\n",
       "\n",
       "[79 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kPCVJkiRpoA3ZMjhUIggQEatHRFT1Fqo/ZmbeCdwAfDgiJkXEKsB7KdcG/gZ4AvhYRCwTER+pPnbBwsxTkiRJkgbdiO4mGhFbRcQMSqI2KyLePkpxvBV4LTALuA54DPhkZj4KvBnYlXLjmvcDb67KJUmSJEkLaNi7iUbECpn5QK3oAGCrzJwREZsCPwdOW9ggMvPPlMdI9Bp3GfDChZ2HJEmSJGmufi2Dv42IHWvDjwFrRsQkYF3AFjpJkiRJmoD6JYOvBLaLiPMi4hnAvwPfoDwC4qvA7i3HJ0mSJElqwbDdRDPzXmCviNgSOAH4BaWb6CNjEZwkSZIkqR19byBT3TX0H8BWwJ2Ux0ls33ZgkiRJkqT2DJsMRsROlATwL8CNwF+B1wG7RsTpEbFu6xFKkiRJkkZdv5bB/wa2zcy1gNcDh2XmbZn5TuDbwFltByhJkiRJGn39ksGHKXcQBchquAxk/gJ4SUtxSZIkSZJaNOwNZIAPAqdExPLAHcCe9ZE+/F2SJEmSJqZ+dxP9FfC8MYpFkiRJkjRGhuwmGhGbNZlA03qSJEmSpEXHcC2D34qI+yjPF7wwM2/pjIiItYCtgV2BlYBXtBqlJEmSJGlUDZkMZubLI+INlOsEj46IJ4D7KclfAL8EjsjM88YkUkmSJEnSqOl3zeA5wDkRsRSwEbAKMBu4LjMfG+6zkiRJkqRFV7+7iQJQJX5XtRyLJEmSJGmM9HvOoCRJkiRpMWQyKEmSJEkDyGRQkiRJkgbQAiWDEbFhRKw/uqFIkiRJksZKo2QwIn4YES+t3u8GXAlcGRG7txmcJEmSJKkdTVsGtwOmV+8/BbwK2BL4bBtBSZIkSZLa1ejREsDSmfloRKwDrJqZ/wsQEWu0F5okSZIkqS1Nk8E/R8TngGnAuQBVYnhfW4FJkiRJktrTtJvo7sC/AMsB+1Zl/wr8oI2gJEmSJEntatQymJnXA+/qKvsR8KM2gpIkSZIktavp3UQjIj4YEb+KiCuqsq0i4h3thidJkiRJakPTbqIHU7qKfg+YWpXdBHymjaAkSZIkSe1qmgy+D3hDZp4MZFV2A7BhG0FJkiRJktrVNBlcEphTve8kgyvWyiRJkiRJE0jTZPA84GsRsQyUawiBQ4Cz2wpMkiRJktSepsngp4C1gHuBlSktgtPwmkFJkiRJmpCaPlriPuAtEbEG5QYyMzPztlYjkyRJkiS1plEyGBGdFsRZ1eupssx8sp3QJEmSJEltaZQMAo8z98Yx3ZYcpVgkSZIkSWOkaTK4QdfwWsBn8QYykiRJkjQhNb1mcEZX0YyIeC9wKXD0qEclSZIkSWpV07uJ9jIZmDJagUiSJEmSxk7TG8icwLzXDC4PbAWc2EZQkiRJkqR2Nb1m8Lqu4QeAozLzl6McjyRJkiRpDDS9ZvCgtgORJEmSJI2dIZPBiHh/kwlk5jGjF44kSZIkaSwM1zK4S4PPJ2AyKEmSJEkTzJDJYGZuO5aBSJIkSZLGTtMbyDwlIgKIznBmPjmqEUmSJEmSWtfoOYMRsU5EnBERdwGPA4/VXpIkSZKkCabpQ+ePAh4FtgPmAC8AzgL2bCkuSZIkSVKLmnYTfSkwNTMfiIjMzMsjYnfg98D32gtPkiRJktSGpi2DT1C6hwLcExFTKA+eX6eVqCRJkiRJrWqaDF4CvK56/zPgFOB0YHobQUmSJEmS2jVsMhgRz6ne7gJcWL3/BHAB8FfgXe2FJkmSJElqS79rBi+LiCuA44AfAmTmQ8AX2g5MkiRJktSeft1E16YkgrsAN1ePl3hzRIz4+YSSJEmSpEXHsMlgZt6VmUdk5ouBzYArgf8Gbo2Ib0bEi8YiSEmSJEnS6Gp6Axky82+ZuW9mbkC5VvANwMWjGUxE7BwRV0fEAxFxfUS8oirfLiKuiYgHI+LXETFtNOcrSZIkSYOmcTIIEBEviYgjKdcPPgkcPFqBRMSrgS8DuwErAVsB/4iI1Sl3Lt0PWJVyB9NTRmu+kiRJkjSI+l77V7XC7VK91gB+BLwlMy8a5VgOAg7OzE5r483V/PcArszM06rhA4E7I2KTzLxmlGOQJEmSpIHQ79ESFwJ/B15BSdbWyswPjHYiGBFLAlsAUyLiuoi4KSKOiIjlgE2Byzt1M/MB4PqqXJIkSZK0APq1DJ4HvCszb245jjWApYC3URLPx4AzgX2BFYFZXfXvpXQlnUfVirgHwNSpU1sMV5IkSZImtn53E/3yGCSCAA9Vf7+Zmbdm5p3A14DXAXOAyV31JwP3d08kM7+bmVtk5hZTpkxpNWBJkiRJmshGdAOZtmTmbOAmIOvF1d8rKY+1ACAiVgCeUZVLkiRJkhbAIpEMVr4PfDQinh4RTwM+CZwDnAE8NyJ2jIhlgf2BK7x5jCRJkiQtuEUpGTwEuBS4FrgauAw4NDNnATsChwKzgRcDO49XkJIkSZK0OOj7aAmAiHjlEKMeAW7KzBkLG0hmPgbsVb26x/0S2GRh5yFJkiRJKholg8DRwNrV+7uA1ar3dwBrRsQVwM6Z+fdRjk+SJEmS1IKm3USPBr4BrJKZawOrAF8HjqreXwoc2UqEkiRJkqRR17Rl8OOUB84/DpCZD0XEvsAtmXloRHyacjdQSZIkSdIE0LRl8AHgRV1lLwQerN4/OWoRSZIkSZJa17RlcH/g5xFxFjATWBfYAfhoNX474EejH54kSZIkqQ2NksHMPD4iplMe8bA25fEP/5qZV1Xjz6E8E1CSJEmSNAE0bRmkSvyuajEWSZIkSdIYafqcwVWBvYHNgRXr4zJzqxbikiRJkiS1qGnL4EnAMsCpzL1pjCRJkiRpgmqaDL4UmJKZj7QZjCRJkiRpbDR9tMQVlDuISpIkSZIWA01bBi8Azo+I7wO31Udk5jGjHpUkSZIkqVVNk8FXADcBr+4qT8BkUJIkSZImmKbPGdy27UAkSZIkSWNnyGQwIiIzs3o/5LWFmflkG4FJkiRJktozXMvgvcDk6v3jlC6hdVGVLdlCXJIkSZKkFg2XDG5ae79B24FIkiRJksbOkMlgZs6svZ9RHxcRywFP+txBSZIkSZqYGj1nMCK+EhFbVu9fD9wNzI6IHdoMTpIkSZLUjqYPnX838Nfq/f7Ae4A3Aoe1EZQkSZIkqV1NnzO4fGY+GBGrARtm5o8BImJae6FJkiRJktrSNBm8NiLeDTwT+AVARKwOPNRWYJIkSZKk9jRNBvcCDgceBXavyl4D/LyNoCRJkiRJ7WqUDGbmpcBLu8p+APygjaAkSZIkSe1qejfRbSNig+r9WhFxXER8PyLWbDc8SZIkSVIbmt5N9Ejgier9V4GlgCeB77YRlCRJkiSpXU2vGVwnM/8ZEZMo1wpOo1w/eEtrkUmSJEmSWtM0GbwvItYAngtclZlzImJpSguhJEmSJGmCaZoMfhO4FFga+ERV9jLgmjaCkiRJkiS1q+ndRL8cEWcAT2Tm9VXxzcAHWotMkiRJktSapi2DZOa1ww1LkiRp8XD06Xf0rbP7W58+BpFIatOQyWBEXJ2Zz67ezwSyV73MnNpSbJIkSZKklgzXMvjB2vv3tB2IJEmSJGnsDJkMZubvau8vHJtwJEmSJEljYdhrBiNi/34TyMyDRy8cSZIkSdJY6HcDmQOBv1EeKxE9xve8jlCSJEmStGjrlwx+EtgVeCFwPHBiZt7celSSJEmSpFYtMdzIzDw8M18IvB1YFfh9RPwiIt4TEcuMSYSSJEmSpFE3bDLYkZlXZeZngGcAlwHHAi9rMS5JkiRJUosaJYMR8eyI+BJwHaXL6O7A79sMTJIkSZLUnn53E/0o5ZrB5YETgFdk5syxCEySJEmS1J5+N5A5nHI30enAc4BDI+a9qWhm7tpOaJIkSZKktvRLBg/Gx0dIkiRJ0mJn2GQwMw8cozgkSZIkSWOo0Q1kJEmSJEmLF5NBSZIkSRpAJoOSJEmSNICGTAYj4pTa+93GJhxJkiRJ0lgYrmXwNTH3ORKHj0UwkiRJkqSxMdzdRC8C/hAR1wLLRsTxvSr5nEFJkiRJmniGSwbfDrwNmEZ51uD1YxKRJEmSJKl1QyaDmfkwcCJARCyVmQeNWVSSJEmSpFYN+9D5jsw8MCI2At4JrAPcDPwwM//eZnCSJEmSpHY0erREROwA/BHYBLgb2BiYHhFvHM1gImKjiHg4Ik6slb0rImZExAMR8ZOIWHU05ylJkiRJg6hRyyBwGPCmzPx1pyAitgGOAM4axXi+BVxam8emwHeA1wN/Ar4LHAnsPIrzlCRJkqSB0zQZXJdyd9G631XloyIidgbuAX4PPLMqfjdwdmb+tqqzH3B1RKyUmfeP1rwlSZIkadA06iYK/Bn4dFfZp6ryhRYRk4GDq2nWbQpc3hnIzOuBR4FnjcZ8JUmSJGlQNW0Z/DBwdkR8HJgJrAc8COwwSnEcAhydmTfNfc49ACsC93bVvRdYqddEImIPYA+AqVOnjlJokiRJkrT4aXo30Wsi4tnAS4C1gVuASzLzsYUNICI2B14FPL/H6DnA5K6yyUDPLqKZ+V3KdYVsscUWubCxSZIkSdLiqmnLIJn5OOU6wdG2DbA+8M+qVXBFYMmIeA5wPrBZp2JEbAgsA1zbQhySJEmSNDAaJ4Mt+i5wcm14b0py+GHg6cAfIuIVlLuJHgyc7s1jJEmSJGnhjHsymJkPUq4/BCAi5gAPZ+YsYFZE7An8AFgN+CWw27gEKkmSJEmLkXFPBrtl5oFdwycBJ41PNJIkSZK0eGr0aImIuGyI8umjG44kSZIkaSw0fc7gM7sLotztZcPRDUeSJEmSNBaG7SYaEcdXb5euve9YH7iyjaAkSZIkSe3qd83g9UO8T+B/gdNGPSJJkiRJUuuGTQYz8yCAiLg4M382NiFJkiRJktrW6G6imfmziNiY8gD4FbvGHdNGYJIkSZKk9jRKBiPi88D+wOXUnglI6S5qMihJkiRJE0zT5wx+AtgyM69oMxhJkiRJ0tho+miJh4Br2gxEkiRJkjR2miaD+wHfjIi1ImKJ+qvN4CRJkiRJ7WjaTfTY6u8HamVBuWZwydEMSJIkSZLUvqbJ4AatRiFJkiRJGlNNHy0xA6DqFrpGZt7aalSSJEmSpFY1uuYvIlaJiJOAh4HrqrI3RsQX2gxOkiRJktSOpjeAOQq4F5gGPFqV/QHYqY2gJEmSJEntanrN4HbA2pn5WEQkQGbOiointxeaJEmSJKktTVsG7wVWrxdExFTAawclSZIkaQJqmgz+D/DjiNgWWCIi/hU4jtJ9VJIkSZI0wTTtJvpl4CHgW8BSwDHAd4DDW4pLkiRJktSipo+WSEriZ/InSZIkSYuBpo+W+GxEvKirbMuI2KedsCRJkiRJbWp6zeDHgau6yq4CPjG64UiSJEmSxkLTZHBp4LGuskeBZUc3HEmSJEnSWGiaDP4R2KurbE/gT6MbjiRJkiRpLDS9m+gngV9ExC7A9cAzgDWBV7cVmCRJkiSpPX2TwYgIymMlngW8AVgPOB04JzPntBueJEmSJKkNfZPBzMyI+AuwUmaePAYxSZIkSZJa1vSawcsoLYOSJEmSpMVA02sGfwOcHxHHAjOB7IzIzGNGPyxJkiRJUpuaJoMvA24Atu4qT8BkUJIkSZImmEbJYGZu23YgkiRJkqSx0/SaQSJitYjYJSL+vRpeOyLWbS80SZIkSVJbGiWDEbE18Dfg3cD+VfFGwLdbikuSJEmS1KKmLYNfB3bKzNcCj1dllwBbthKVJEmSJKlVTZPB9TPzV9X7zp1EH6X5DWgkSZIkSYuQpsngVRHxmq6yVwF/GeV4JEmSJEljoGnL3qeBcyLiXGC5iPgOsAPwptYikyRJkiS1plHLYGZeDDwPuJLyXMEbgC0z89IWY5MkSZIktWTYlsGIWB7YF3gu8Cfgi5n5yFgEJkmSJElqT7+WwW9RuoNeA7wN+ErrEUmSJEmSWtcvGXwt8G+ZuQ+wPfCG9kOSJEmSJLWtXzK4QmbeCpCZM4GV2w9JkiRJktS2fncTnRQR2wIxxDCZeUFbwUmSJEmS2tEvGbyDcvfQjru6hhPYcLSDkiRJkiS1a9hkMDPXH6M4JEmSJEljqNFzBiVJkiRJixeTQUmSJEkaQF1ds2wAACAASURBVCaDkiRJkjSATAYlSZIkaQCZDEqSJEnSADIZlCRJkqQBZDIoSZIkSQNokUgGI2KZiDg6ImZExP0R8eeI2L42fruIuCYiHoyIX0fEtPGMV5IkSZImukUiGQQmATOBrYGVgX2BUyNi/YhYHTgd2A9YFZgOnDJegUqSJEnS4mDSeAcAkJkPAAfWis6JiBuAFwKrAVdm5mkAEXEgcGdEbJKZ14x1rJIkSZK0OFhUWgbnERFrAM8CrgQ2BS7vjKsSx+urckmSJEnSAljkksGIWAr4AXBc1fK3InBvV7V7gZV6fHaPiJgeEdNnzZrVfrCSJEmSNEEtUslgRCwBnAA8CnykKp4DTO6qOhm4v/vzmfndzNwiM7eYMmVKq7FKkiRJ0kS2yCSDERHA0cAawI6Z+Vg16kpgs1q9FYBnVOWSJEmSpAWwyCSDwLeBZwM7ZOZDtfIzgOdGxI4RsSywP3CFN4+RJEmSpAW3SCSD1XMDPwRsDtwWEXOq17szcxawI3AoMBt4MbDz+EUrSZIkSRPfovJoiRlADDP+l8AmYxeRJEmSJC3eFomWQUmSJEnS2DIZlCRJkqQBZDIoSZIkSQPIZFCSJEmSBtAicQOZiWTWUd/uW2fKnh8eg0gkSZIkacHZMihJkiRJA8hkUJIkSZIGkMmgJEmSJA0gk0FJkiRJGkAmg5IkSZI0gEwGJUmSJGkAmQxKkiRJ0gAyGZQkSZKkAWQyKEmSJEkDyGRQkiRJkgaQyaAkSZIkDSCTQUmSJEkaQCaDkiRJkjSATAYlSZIkaQCZDEqSJEnSADIZlCRJkqQBZDIoSZIkSQPIZFCSJEmSBpDJoCRJkiQNIJNBSZIkSRpAJoOSJEmSNIBMBiVJkiRpAJkMSpIkSdIAMhmUJEmSpAFkMihJkiRJA8hkUJIkSZIGkMmgJEmSJA0gk0FJkiRJGkAmg5IkSZI0gEwGJUmSJGkAmQxKkiRJ0gAyGZQkSZKkAWQyKEmSJEkDyGRQkiRJkgbQpPEOQJIkSYu/M350Z986b3nb6gCce8rwdV+/0+qjEtNQLj521rDjX/K+KQD86eg7+k7rBbs/fVRiktpgMihJkqQFdtKPh0+c3rXjlDGKRNJI2U1UkiRJkgaQyaAkSZIkDSCTQUmSJEkaQCaDkiRJkjSATAYlSZIkaQCZDEqSJEnSADIZlCRJkqQB5HMGJWkRt/2ZH+lb56dvOgKA1/3kM8PWO+/NXy71zjig7zTPe8tBVd3D+tT7fN9pdXv96Yf3rXPuWz8+4ulKkqTmbBmUJEmSpAFkMihJkiRJA8hkUJIkSZIG0IRIBiNi1Yg4IyIeiIgZEfGu8Y5JkiRJkiayiXIDmW8BjwJrAJsD50bE5Zl55fiGJUmSJEkT0yKfDEbECsCOwHMzcw7wu4g4C9gF+Oy4BidpkbP/qa/tW+fgd5wPwN4/Gr7uV952/qjEJEmStCiaCN1EnwU8npnX1souBzYdp3gkSZIkacKLzBzvGIYVEa8ATsvMNWtlHwTenZnbdNXdA9ijGtwY+FvX5FYH7mw466Z125jmeM9/okxzvOfvMk2M+btME2P+LtPEmP9EmeZ4z99lmhjzd5kmxvxdpoWrOy0zpwz5icxcpF/A84EHu8o+DZy9ANOaPtp125jmeM9/okxzvOfvMk2M+btME2P+LtPEmP9EmeZ4z99lmhjzd5kmxvxdpnbqdl4ToZvotcCkiNioVrYZ4M1jJEmSJGkBLfLJYGY+AJwOHBwRK0TEy4A3ASeMb2SSJEmSNHEt8slgZS9gOeAO4IfAh3PBHivx3RbqtjHN8Z7/RJnmeM/fZZoY83eZJsb8XaaJMf+JMs3xnr/LNDHm7zJNjPm7TO3UBSbADWQkSZIkSaNvorQMSpIkSZJGkcmgJEmSJA0gk0FJkiRJGkAmg4uIiHjxEOVbdg0vOTYRaaKKiDOHKD99rGORFjURcXfX8NfHIYYlI+K4iFhmrOetRVtEvLLJa7zjHEpErBcRL+lTZ5HZR0WxxEhf1Wcb120x/r27htdsaT6Nlmksl30iGOt1ZEGn6w1kFlBErAa8DlgrM/8zItYGlsjMmxZwevdl5uQe5Xdn5qq14c4dVU/IzOkNprsU8BJg7cw8JSJWgKce2dGpswSwDfC7zHy0z/S2BW7MzBsiYi3gS8CTwOcy87YGizpuqu/iZ5n5ylrZO4CXUZ5b+f3MfKw27sjM3GsU5rsEsEZm3lormwk0+fFFk3qZObU27WHXpYg4uEncmbl/k3oLKyIuy8zn9yifnplbjEUMi4LuDXdmPjnG855nHZ1oIuLFmXlJg3r3Z+ZKteF5trELMf9VgS0z8/we414LXJKZs2tltwJT69uchZz/3pn5ldrwmr22yRHxjcz8WG14y8z8v9GIoTbNlYDVKdsvADLzH6M5j7FQnXw9ADg0Mx/pU7fxfrTPdG7oKlqHsg+4C1iN8p3elJkbLsQ8pgAPZeacahl3pezHT1jQ7U5ETKUcm2wOZGauGBFvA16bmR/oqtvoeKcq+7dqmivWy7v3TxHxTuDPmXl1RGwMfA94gnL3+Wu66q4NHAFsDazSHQbD73OjWr4lI+LJPnU7sY7oJH6P476vZ+Ynhqg7z3c5WtuzalovAL4FPA9YtlNMtfwjrde2Jse7I6k3SjEt8DoSEa8Gdgaenpk7RMQWwOTMvKCr3kJ//4t9Mth0AzHMmbZHKBveGbW6WwM/BqYDL8vMlaqyvTNzhyHi6LlzrHYgAdwDTK6PB54B/G9mPr02nRcA7wHeWX3mBMoGfGaPef4LcFa1DOtWG+fXAe/NzJ266s5zgDSUiLgaeE1m/jMiTqqKHwKmZOYbu+peRO8fwSPATcDpmXl2VTeAD1TLtXpmPi8itgLWpHzPffU72IhyFv7Bzo+jOqP2EeBM4BWU7/51nQPiXjus4eLMzFO76q4CHAm8DXgsM1eIiDcCWwK/aLJMXV4EvBf4BjADmFbFf3xmfrWW5O0D/GfXZzcENs3M50fE92vlywI7ApdW05xaxffjzHxndwDVd7h/tfyrZebK1c76WcBVTRaix4ZsvnWv+p7vAholg93/++HizMwjuupOojy+Zmvm/41u1TR5bqp+ENNkI97yiYMh19HM3LfJ8kTE1Mz8Z586852IGYkmJ216HBT1PJnQo97szHxan/kPdeD9CHBrZj4ZEV8D7srMQ3t8/nOU9XDvWtk+lAPRA3olhMPMcx6ddb/pQeFIDh67T9RExKcy82tDxRIRzwF+AGxGWRefWie7D0qq5Hlveh/ob9Vj2htX0+2ue0ytztLA+4aY5q49prkysHGPuhfU6txJORjrmyT1248Os0+cR2f5I+LzlARwv8x8MCKWBw6mrGdfrOqc0HCaTy1/RFwC7JmZl0XEl4AdgMeAX2fmJ7tiHnL6XdP8KXAR5QTxXZn5tOr7vSIzp1V1Gu2jatM8AngH8GvgwXlnne/vivN64KWZeXtEnA38DZgDbNW93anGPwh8EbgQ2Ao4EDgP+HmvZe2x7DMiYlqt6PWU7egXmbt//gxlX/rt7s8Pd6A/khNWPer23Z5V9fom2RHxF+BsynHmg131Zoy0Xq3+GpTjjO797THV+JcBb8zMz/T47JeAn2TmxV3ljY53+9Vr+nuirC9NPFF733gdiYiPAh8H/ofS2LJyRGwKfC8zX9pVd0Tffy+DkAw22kBEORu3djXYOQMH5dmGawJXADtn5t8j4jJK4verzg8vIpYFZmTmGl3zH3bn2OeswZOUM5IH9liuJYB/oySGOwB/oqwIp3TObkTE74DvZOYJtThXAK7NzHW6pncucEj3D6zHfO/LzMnVAfTtlJX5UeCWzFy9q+4hlOTlOGAmsB7lDORJ1fewO/BfWVpWDwFeDXwdOCozV6kOhk4Dnt/93XVmUR+uvs/hEsIlgPVqB9nXA9tn5rXV8EHV9/nKakPfK0kZMs7MfGFX3ZOB2ZSd91XV9z8F+H1mbjRMnD1FxF8pifjNtbJ1gfMz87m1JO/dlHWuIyn/q6Mz87oeMZ6WmT+ulb0VePsQyeCRlDPVXwJ+Wi3/OpQd6PINFiOzOqMdEcdXZTsBp3TVW5/y/30ZDf/3TePMzE276n4TeCXl2TyHAv8BfBg4OTMP7Eqem2qUZDfZiEc50TRSw544qM1/odbR7hMsTetF6cr0feCllARv78z8fa3+UwlLNDxp0/SgKCLupyTfnYOQP1G2MUO2YnVtp7vXxScpBxfPpyTRd/aY52rAxfXvtEry16QcLMxi3vV5am2ewdDqJw0aL3/Tg8eRHJRW439D+T4PBm6g/I6/SFmfTuyqez6wDHAq86/7x3XV/Tzl5M7lzJ8U1PfjP6Tsa89m/mke1DXN91FOxMzpMc0Na/W+BlyXmUcOtdy1usPuRyPivf2mUQVwXFV/FqX1on7yYynK/nZKNXxAw2k+tfwRMRtYNTMzIm6i/A7nAFdm5lpdMXdPf03KAe0PstZaFRF3UU4KP1lfTyLinsxcpXo/on1UlC7dm2WPk93datuBZYFbqzgfA+7sXmerWKdm5gOd+KqTE7/PzE36zWuI+V8HbJGZ99TKngZMz8xndNUd9kA/RnDCqkfdvi2DTZPsiLgPWDn7JApN61V13wycCPwd2JSy/X8upUV926rOucCRmXluj8+/Fvh/2dXw0vR4t1+9pr8nSo+BER2bjHAduR7YLjNvrMW5JHBHZq7WVbfx9z+kzFysX8B91d9lKQc9y1CSgru76u0L/BewXDW8HPBlyoHhCsBRwC+qcbNrn7u7+rsE5WxY9/x/A3yNcgZ4NrAy5Uz8e6rx0yg7zBm199MoB4/L9Vm2aVXc1wHXAr8C7gZ26cTJ3IT/7u6Yu6Z1ZFX/WOAQys78YODgrno3AWsA2wEXVWVLA/f2mOYlwLO7yjahdJeCcnB8ffV+JqWl7anvl/Kjmt31+d0oO5GNqvluRDmQfl81/h5Ky912PV7bA0/U1w1K19769D8C/JNytvi+HsvUKM6qfBawVI/vv9d3tTnwUeCgYb77uyk/+HrZKj2+ow+O4PdxL7BkV9mkXjFW424FVuixTPcswG/zgOr1SO39AZSDvg9RDlYa/+8XNE7gZsqBwVPjq/X0wpEuU22aJwM7dpW9FfhhV9l9VL/R0XwBfwXW6SpbF/jrSNdRylnzoV6v7vymgH8M87qx67d3CnA0JYH6BOUE3Ltq4++vvb+e0qLbGT6oKptWr0vX75Ue27mq/ElKAvbkEK8nenxm92pde0a17j2Tsq38ULWunElpWR3q/xE94tt6qNcC/s+bLn+jekPUnW871z2+tj51fksrADf0mjawTMNluwN4XoN6s4FVGk7zZsrJwH71fkdJKm6ktHz9tvPqUbfRfnQE/9MbKb2P6mUvpZx4Xpjtw52UY6F/oSSAUI5h7m/4+S2As7vKrur8Tpl7XPQcSstg9+cb7aMoxzUrNax7ffW7fAvlpB+UE5S99s13dNa96jueUn0f8y0/8Ebgq5ST2sd3Xj3qdRL3etk6lGS0V6zrd9bZ6u+SVMeQwP3ABpTW0g0pxzX14Q1r03q0Hhdlf1of7hXr3ZQT4/2+0+MoJ6BHpV5V96+Uk831Zd8N+Eqtzs10HZfUxk2inAzp9dvve7zbtN4If0+Njk1GuI7c0fkOar+nZSk9URb4+x/qNYnF36yIeCZlo3dpZj4SpatF99nWj1Ou/3scIDMfioh9KSvdoRHxaUoiBHBVRLwmM39W+/yrgL/0mP9mwKsz87GIiMy8NyL+nfKDODHnNuFOg6da/Ia8dqc6i/AOYBfg2ZSzqrtmdVY9Il5EaaU5gbKReyG1bpZRbkhzHfNbDvhJ9X7dXvOufJPS2rE05SAOSuvNNT3qbkI5EKybQUm0yMz/i9JdAMqGcE71Pqu/K9bKOg4BNsrMh6rhv0fEhyg7jWMpZ6UfysxfdQdTtU7U/+8zKC0Ef+4UZOYREfEgJYnvdXOHpnFCSbRWpyQmnRim1oersj2A/6b837YHfkpp9e2+yP4s4KyI+AJlXVwP+FxVVu9S9qsYootZzt+V9jrg/1FakDo+TNlZ9fIozLvdqFqS7uoqOzMz39T94Yg4PTPfWsVyUFV2cddvaSj9/vcjjrOyPCXJB3goIpbPzGsiYr7rGGvT6ndN1PaUs991Z1Faw+rOoPyvmyx/Z96bU1rIuudfv4ZmbeZfJ+dQdjx1TdbR31TD/brJdbr93dBj3NLAObXhbSjJ3MPAZRFxAXBeRKyQmd9j3jOtU6htszLzgKrF5KIoXa06dZeKiN2Y+50sHRHzdCXLzGMyc0Eu1j8IeGYVL8B1EbEX5Wzyd6pWpjsjYuPM/FuPzz+LckBXj+XCBYhjOMvWWtsBVugaJku3vuUj4re14pW6hslaN82ICMp3GkDWhjt16+vFw8BSVC0y1bo0m7k9bequoOxrhtrW1D1E731Mt3/Se7vdyySadQn8XvVqoul+FKBvdzlgP+D8KL2aOr1r3kDZZg81zaUp+9juada75/+UcuywGuXEFZTE7Waa+TPlxEXdV4BzIuKLwKQol+h8ntI7g6b7qC5fBX5QTfP2+oge+7JDgD9STvR0ugW+itKa3O0Syj0fzqBse0+hrGPzXJJStRLtSfmO3g58B3gX8/dkgXJA/ssoN6Tq/K8+VpV3W4m5+5yntl+U/RaUEyjXMe/xSv13kpRjESi9WeoO6zG/bnfStT3qiHm7SS4DnFG1pnVffxxN6uX83bOnZuZpXWXHVZ/rdKOfTNlnPMT8lqJ8f91upNnxbtN6nXFNfk9Nj01Gso78Fvgs8/5/P0Zpze22LM2//54GIRlsuoF4gNK16g+1shcytwm9vsP7NGWjdy6wXER8h9JVc76DXxruHKPr2h3KjrzXtTs3UVaGbwBnZtdF7Zl5acy9U9d+wLkRcRTlwOhzlA3bB7uDzMzdesQ+n8z8ckScQTl73tk43Uxpjev2W+D7EbF/Ffe6lH7Wv6uW+V+Ye9B5HvC1iPhkNS4o/7uzu6a5BKX19Opa2TTmbhgPpvwve3kU2LY2fBxlXfhzvVJmHhMRj1Tz79Y0TihdQH4cEf8BLBER/0rZUB/VVW8fykX2F1XdAd4SEdtTrieo25Py/R1FOdi/lbJDP6ha5qRPlzLmfk8dH6BsRPah/B/XAR6ntGL1chpwXG3516J0mT25q9623R+sbNOj7NGI2CDLTYnWpLTI97opUb///YLESTW9FwH/R9lBHFh1u5jvwCiG6fbdFUfTJHtEG/HROHHQVa/JOjoDeHfWunHW4lmWub+3kZyImURt/5OZV0TENsAvqkS7rulJm0so3dA7LqWcNHvqY8Ax9FBtfzcA/paZD/ao0ln36gnJVOb+zx+g/G6+ERFvrh0UEBHLUf5nP4qI/8jqmsIY5lrUnP/mGMNe11q9bXpQuHvX8NFDxUE50fV4PZTacGfdr6/3F1FOVh4L/Iiyfj4CXFAtRz05v4CS6Hyf+df9Y2LeGyrtB3wzIg6kKylg3m3K8cCZEXF4d72ugzco25l9I+KQHOZ6wOzqsjqcpvtRGL67HHPX0x9Qtklvo2zzrwG+kJk9r8+OiJdTtn3LUA6o72Nu4lFPvj5A6Ub+GOXEMZT16sAe0+y+znd5yr5pnhiq/9ldlNbymdX098vMTnJ8Hc32UdGj3ht61Kt3vwvK8cZ6wOO13/DFzL8fhbJd6Kxfn6Ac061E2UfUvZ9yMv+vEbFbZn4ySlfkXtdT71Mt407M3T8fQe8TCcMe6I/khFV2dX8eSlfyPWSSzfxJUaN7AYyg3h0RsUZm3g7cWO1z7mTe7cg19N6vUZX3OjHU9Hi38XHxCH5PTY9NRrKOfBQ4OyI+SDlh9zdKi3H3bwHKd9/0++9tYZoVJ8qLsvFavjb8dMoNP+p1dq2+6B9QzmSdSDlrvms1/g2U/tz1pt19KNccfJZyIWqveZ/K3C6MX6Js8P9EuQC2Xu9k4NvAWsxtOp8C/L2r3hojXPbnU5LMcykHeC8cot6GQ7161F2K0jKxUzW8AlWXvK56q1bL9SglGX+EcrexTjfLjSn9p6H80M6gJM9PUA6uzqCriwjw75Qf0GGUg+vDgFuAfcZoXWoUZ1U3KC3OV1X1rqbseKKr3n2193dRdV1lIbotjHCZlqJ0+dup+rvUMHWXphzYzqEkbHOq4aWr8Z1uUQ/X3ndeJwKX9Zjm1cztpnlS9ToaOGtB//f94uyq+yLgBdX7jeD/k3fe8XcUVf9/Txq9F8FIQu9gQ9qDgCBNARuoIBCaXUGUJoKKCBZEUbArLXQpYrDxAAGRKqCAgAakJSSUhNCC9PP743M2d+7c2d3ZbwLPT9zXa1/37uzZmS1TTvmcc7gUCRZvz9BeQQPsOxl39yFB7Hr/va9qJ6L7St1e8/7vru6L3jyxHXBqQjcvmm/+hbSr9/jxfAldax9Fi+F+Df3hXv+/OfC2GrpABH9EwuwHMnRj/RljSOmByKcwV+9HgHs69veDgfdHx9uiuf9lNP42rLlmKmLePgF8HSkLDvHz7/Vn+ov30ZO9f57s112PmIgfR3WeXLdn2j8BrR37o768P2KIvvoKzw1j2/aGa4chH+xP0oNsTyzYL3faCs4bQ3pfSspeQpbotv0er3Mysh4+4P9f8H7/QLw77SIotkD1PJXfYrUvljzvyOj/JvTDqUdk3k8jXA4xkv+mEErr1/wFOCCp88vUj59hCBHVVGf6Lv+O5vIVXsm+N4S+OovE7WMu1BnD5R+hB4HOulF0qHdZJOTf533wn368TMM1i6J1Zf6kfCwR/4n43aORIHUoPahhGzw+C5F/Bb7TIfjcj/juZ5HR5aiIZlekIHo/PX5omB9PA3apqbuU3y2lKxpPvEJ8KVo310dW6Q3ndv+O9/+GADLFIZRd6/8BehL7eVajgRvivQxDnXwhhOOO0zvMdhQP/c7XT5jZIkk9ReFmO95bLliBwYADbHGE0uS5lwIeTd95hnZpNLlNtppUFUEOxDsTWccsH869NVLcUGhL77N0CyHcgQJi3BdCuBZFWZuOArssk9C2RtR7tTYfW9MtmkTC0ILYdAlKVPTt2+5zqFtQ4IWlfZxWgQcWQL54KyS0I4GN0MI/DbjW5jCNQOgPrJIN2NChruHIAvExawib78/BnN57UucGiJnOjdvRwD5mNsdRXH2uXBsFb7nWy/6BItVVgaMmIYH3GCQIb2mZqKclfc/f1Tjko7wEEi4vRetNUbqBEMKwzNr0ILCRKYpz1e9WR4EQNnOasYiZm+LH8yOf97UR4uVYM3sphPBW4Dkz+7vTLYUsIhXdgWaWg73H97OYRWkycs/AHKYqCf2RGms3K4yW53VuVljnlUHBwl60Hpz9aaT4A1mRJ5rZEX7ukyhQ3e5+/Aw9SPr8iCnss8AmY7kKEDEMeMg8gngI4Rbk1zi18PmeQOPq5ajOSmETB9GY4yjCNe3vjSI4vx4xw2cDJ82NudfrXxF42czuy5z7M7CvJWkkauopijYdQrgZxWC4PQjK/mukBDzKzJbP1FuUBsNpK0Z/DFJM3FCN+yCkzt1mdoEfb4vmqAW8/XebBykKIVwK/NDMLvTjk5EC4gLk73i+mR3W9k5q3tOhwGVm9peobH1gczP7dle6mjbGIGXRnUn55xHqaR7EDy2JxxewhojGLW0NR9DNNZvWvIi+aDw5bXZ9SNAQtVuOh/P73ZDeeLrOzF5K6Zx2cyTfjEaKyvFmNrGk7eoGXtM70si+2f9Xlrm/Ad+bgzrHkzjo+v5zpNV/Y+aaRg0c0oYv6/8rZ9ExwD8Sus867aG4dgpBTK7J1DkKWWTuQlqzuxCkcd6CZ1wGWSp3Tcr/TBSgxn8XAB70/8tHtMXWRqdfFAkRB/nvYm332XD/e/ozP0xGQzxU2rb7RFEpW/dM+9v5/+38Xl5A6U9iusOQFu16Ek06iiha0fUFOSAJeEC/drx2L/mOdd+UbkFsioMSdewDqyBm+If+u0oD7VbIGjnBj9dLv5OXT8M1s2gcjkHKnYFgQ8nYn71nzm+OhLI/+u87Guq6g17ggWtRsIS3IwYypV0NQfb2jvfM89Ragju+7x8kx+u/mvUi1MG+0fHBiHn4i4+pat6Ktf0rO82CfjwPGWf+V2NHfu3H0h4cIe6DMargUuB90fHJyDJ8LLI8HOPlVwHvjOgu8nf0aTS//yg6twdRYAIfF5ORheGfwGrJfS6GLPvPA7O8bEcEbUyfaQAl4OU3ZsrqLFufT44vqqG7IFO2cw3tTv77d/otLnHguDcQBUbxsfjGGto3IUVQ2s7dONIH+CtSHK1CFIjO+/DN9BQM2TUkon8AD6CD5oo10dz6REJXhETy8uURtPhQ/125pu1ve5/4JPLH+yRCGnw7odvR69oMQcXPRPC7axlcR85CQjbIavpvNJb3ybT/dWRp+6rfZ3bOc9ofoX6/Eb1AR6PxYDoR3btw6zCwgX+zh4iQBRHtich6eA79lv6TMrSfowHlhaz+cdCsScjatwBayy6Pzj1Cz/K+ALKyvdWPVyfP84xm0LK9GIPBTWYHYovKFiSZo0rponOl6LKFgW2QEWUbZPSoe2cXkKB5vI3zkrJJJIH4GuosGk8tdUws2C/PXLcuQvQ8iFxYpvpxTr7Y1/vBMQimfbT303I+rJTwP3WnfxGdgpi3xUki8njZMcgnbICBTmhPRBDS8X7NeOSM+xM0yT5DD15atDiiyfEa5Gv1OJqkJgKfS+gao1AltL9Ei/t23om3Q4zAwORU8+7mIYlaRkskJvqjANbBEnLR+rbw574eQWuv8+MtE7qRSFt0DxKM7vHjUQldUaS4IdA23icdIEsNbYzCmdOkvDaiHv2RGMfV7X5+s2g/EAU++ijC4n8U+dN+oeA7tsJLkKCUjYIW0RyCJt2HUPoW0Di4PvNevE/GcAAAIABJREFUPoYW8j5FTO59IDjdOYhJORtBAXfN0HZRsJTCvt+CmJtZJLC2hK7TJM5cUBwkdAd72wPw2Zr54Gg05qr3tDXwGf9fHKWytM4u9XofWtr/D0OR2yo40nbALf5/Gs5U+Pv8SzK/5CLOFvU9tL78EglMz6I1ZzzSRFc0w6L/SyHI583eR64kI6SgtWF9/z8B9enDgTuT+aGVKcQjSfr/RdHaVEWBXA6hHao6b6VfyLkZMbhrISVLCuXuImTkIjeG3PdN+0BdXyilK6HN1P2tujpJlDEoP3D8TA9n2imBy3VaQ5CFd1f/fyBScE5BiIyYrijSNfItexEJWdf670tIaTUSCXNVv3yExF3G+9Oj0fFX0dg4x+/rPAQ5fRcaJ2mE0kfouSDchoLVrVXTnybW7DlGe65FxY77AwUROp321/6tL0VC7sLJ+WKFVXzPSLH4cFJXLir6X4B1krJ1GFxzZzDIW41icGwU0UXt3IsE3qe97F0oLVpKOz9SgnwYxXeojSzr95CLjD4jKfsUUrxuhqJDN/EmteOJQTeY7D4H/elG5Mta8dsB+DxwU4Z2EomQiITJgXFS295Qb/Q/ZacwhDLwB584PkmGgU5oL2Ew3PNG9FJPbItb9Cj3BSz1L+sSbnYGSZhtJPQW+aJ5Z3o0KfsrPT+/qv31EcQBknQAHb7THcAHk7KdGbSMfg+4GjGMq/nvVSSWXh+02dDEmba70BbdZ8dnH1a3J3T3U8C0d2y7KAXBEOpdw/tKLEi+RL3QuCqwUnKcLlZn+fv/Fi3+dUi42DQpeztwX4a2WMGS+W67E/lEReduQ0LWGjT4WDGHkzhDUBwkdJXf1LMkFuMMbaM2ncE5tTENQUmdXeql30r2ViT8V3NliOr/JZqXd/T+f2h03XrAbZm6W/semo+mI4XinkiTvacfP+l1b4WYig8goe55r/cINF8vXfNsrX6tFDKFSHlVMRjbpt+afoXeTHo+O8t5X1ncjxfKtFGSqqQSpAdC4CMF7FURbWUJm4UURDHCYl9cWUkHX2V6zN/TJIoqxHBOre4fwbBz32Op5PmeJmPZ8HML4kxvy1gYQ5KKaU53NOdtx+Ba0opEQozoJBJLPLKQTQK+j5jqg7z8X+RTH/0rOp5MLyXMKmhNWCh6Tym/Ec8JD+bG+hDfy/3VvUbPv1R8r0lfaUU30SENRvRu9kXInlnA+bjFkQ4KK7TObu7/v4vy48b9NIcaqUsdlSoDLmHQILEfcOlQ6Ly8EV0W0X0GGV1e8Pfxgo+zL0c0sfX0QQaF6kXT56dAoY0ExoEUNcgXeDu09p9csOeswosh5c8X/TeLgENrRircDs/1fbR2jEzK5qGFh+mjn5MB9Z+wI23TRd4Bj/CytRkUMrrkPHqCxCHcB2il2Q70NB7FueYK2z4P+FJcH9Lun5mhvZ18TpPbM7QptPBGBMn4YkK3PRKejvSB+UU0sW5dvceIdmAiaHiuxzMdfwSJpg5pZZZIypZkcCL5PGLWWh1uO9IW3aeX748Hy2mpMw2MMHtP6PZAoa2XpVlo3Ltuz7RdlLswOvee9Plr6K6gINBKx77fJYfY7HEXlY0kYTa8vFjB0uFei/IH0nEST797Qx8oUhzQIdcdLdp0hmYZbNXQl9aLFADL+/8D8VxjfrwgvRxeiyDLxm0I2j9PRHc4Pr927XtI239Uzbmv+zd51PvGdBSK/y0RzTRqhMHCPlfEFCJl2gf9/ylEDAtaH6ZEx4/ibgUowNRt0bkBxoQyIeMrFOYXpWcJSwPF3IOspTs6XcV8PU/CjKGk9ytHdTYhHaYiH1qQsJ4NAIHW3N9Gx9cRQXQT2g+QWFz+r3cKkEgI8lkHyd7Q39fJ9ObOz6Jcx1shJdjWwP8ipr4SolLFTip8pP3pCsRj/AT4Wa6P1txfoBma/x00XldAa+CyyFp5dEKX6ytZpab33d/4u2wUHDPXjkECVZWztVhhhdbkx5HV/pn4myFh8zeZ9u4mgfsiC+Q9SdlaPiZuQqiYm5DQteZQ6Jy2Nc8f4nUeQm4OVf8ajuagh5BV/WTg8Oiak5DSrhKiF0aKoFOGMD5uRvzv+ShTQJGxoKDejby/XeP3erUfb5ShPZtkTkGBys7K0F6EgoxV7gMLoEjmE4rvbW484P/PO2KsPoZM8SO8bHMcjhbR/ZnIMtFS55VIQ1wtklXkvsona0V6EcmKfAG9vNVviQ5RqNCEfyuC/m3n7+EWH0h9vgcMQgp3osbHioZITD5Q1/aB+yTJpEz95HwCScRCtLik/kIPkhcGUwx7Y6S4OaAtuk8vvwgJzBejSSyrbGAwQt+GaKHaJ6GrjaiX0E1M9jsRkzQx0/YpqD/HC/hEkuiUEf0tiEE8EdigYYwUJ5+uuX4UgwvTLRRG00UMxLH0xuh8CFr3xQxtFwVLnb9wChcsTdbbaRLPfP85Uhx02WnRpiM4WaxQeoF2yH2rhr60XgSNvR0JQtOJIs4hGNKfk7azPtzk59LWvkeDwIi0wS+jufEKf6arEAO5mNPUCoNoLn9bUrY+kbBCIVOItNszERPyKJHfH1KMnRMdn4egguuiNfKY6NxaKM9iep+t7g5OW5wkmQwUvIaui6/ylS3n10PW5eNRQI5VkaLk+17+toj2w/793kt/9MP3URP9kGbf7a2I0C6IMY0RFlvW3PMKyC3ljrTOhK4kinBtdE5/tueTsiY3gmq35JpGmC+C8Z2J5tMKAr4TCWTXy0ejAD8zaJgbnbY42nRyXTaWQsvz10boRGPxh0jBfgcu3NBRYeXv6QMkEV5xWG2m3cPQnLY9ciHaAcXSOCzpIysiRdqHUYyED5MgUUrpIvpGdJkf30LN/IAQFy8j4XmhqHwxxJO+iBS8LyKFTpECOdPO2ojPn+zf5/t4/JEa+lWQQuun/jvAPyM0Ryp7fIjI8huV/wopzK5Biopr/PhcEr4DrfPV2viw/15JYgxq2l/z0URLN8/5tAvSNgzkPEpol0cT1HpoQV0cCWQfMeVKWw8xFBd7lKUdkdPvhUgoOwY5uh8f1flZNDn/AjGsi4QQ1kLpLDZO2q+NQpXQ3Vvw6GZmKxbQFW0eVe046hP/Bm+zL/+KRwLbAHXkKt/d0mjwxJ30ZvTsR6JFbiyaHG80s89F9W1Wd4+WJHvuSFt0n+a5v0IIS6CJcTfkt3M+GsB/qmvTr1sETRCrRmVjG+6zMaKeR7Raw8wOSsrnRX4cfVGwUMj6Z9N6/Jo3+vPsghiG8cDpFkV4CyFMQ8qVZ0IIdyPFw0xkwV246V79+nlQFOBhUdkX/D5bc4iFECajhdu83cVQ35uWXDcmKAfhBKRUGI2sDk8B21sSKTYoCXG8LYOYkzOS/ncOWmAb8wd62+fQ0xgujib9XSwTQTDTB5ZFDPgEi6IVenRg6B87A2MvdMt19x2kPT4AaX7XQozy3Wb2pRDCuLq6ojr7cra11ek0xfU67Xoo6trsaLZe/qR5xD0vezLXF3ORWUv6XlBuymUtihIdXb8gsjQv5MdjkcC+Bz2rwGZojObyW05DmvxZSZ2TzOz1UdlKKGDJzWZ2b1T+P0gpc7sfL4SEm0lm9lREtxqy3kz149FofFc5eHc2syf83DeREmO/6PqA4GEfR3PzA4g5+r5lmI1QltC58+bPl9Z5zxDq2RgxhBshAehlNNcfbGZXJ7RfwP3X6Y9++DUzOzZTd7ruLIt4gLORUu77ZnaJ0z6OoM/4vexiZu/O1HktgmueQS9HMjC4lrVtIYT7Uf7bOzPn1kRBy8Z0rPMl+vO3jo6OAxo/Q8p/HUKYgJ75G4gR3hStbb8zs59HdMORJfpoM3sudIw27WvTJDOrXY8L6jgWWb0Mzf9nmdnfMnQjLRPFOYSwqJllk8YXtj8MwYD3QfDvB5Ah4rsxLxlCmIUErgH+MqmviM5pt/e2fuL3UKXr+WjU359GSsKByJlBUcf/jRS9ufPL0PN9Hoj0HkK4iv51cfZmvZytMX1A8PHdUGqL+5Cy/DsRzQ5ozF2MFJxjkKC9u5n9JqKbiYwZ8TsejvrfYkm7Ka+R3SzKMxlCWA7NI1PNo0qXbv8VwqCHTN6MwcUhZsom1lxulgkz7teMwcPGm9kDNTRFi2MI4V9I23dfFMJ2OPCImS2Rq3tubkEh0Q9HPlBVGNvxaKAeZB0SJftgXQY5CK9VQ9snvJQwfL5VCV93je7zLBSQpzVU8JxupfeZMr1+7brona6NhPifo34wEMbdB/Wt6QTh54ahiFYPl0y+0TXTUya3gXZbM/tdC11Azt3HoWe6GvXtsxBD8zszO8WZxh0QY/SAmb3Xr8+GSK6qZ1BwqVNuDCg0mgT8ZLsKIQWuRX7FY2lQsGRvVMqfr5jZDlFZ7URumSTBczSJz4HiIPRSgVTbMmiuvNDMPpK0Mwoxxh9Fjv3PoD58iBWmTcjc+1ypM4RwnZlt6P+/knvHmWueqoSzqGxhZJFOU5q09r0QwmXovZ2YEoUQPoOChbwjc24TJBR+EGmzTzKzgxOaGYhRfj4qG4Wgn4tHZUWh053uaaQ1nyvzZihMVRLR1yZ0zoznhRFjn1vHx0R0ayBF7RvpT2CODSogRyDfoFydmya08yOF0kzrJTTPPdPCwMb00opcWwnPJZszsn/w61cwsxe9fGa1Fvh935MTxFwhsWgB896aCiAoIfk7kB/b1IhuNFJqTjSzL0bl7wEuzjHoEU3JvDzGzMY7/d51RDaopJ/h184KvfQri6NAYKsntNORpbFojk+uXRe9u6Vqzo+hB2WdXEPzIyQAXtXS1q8QpDvmFZdA8SnektCugPi1XGqLeIwUj9NQmK6jlC6ifzOa86v19udmdlN0fhpCHg3w1b62XW9J2q3o/NIMPv890fmUh1sGCcWnW0s6o6D0DSejfhbzJrchxNjEhPZEM1s7KrsBON7MzozKPoyiJa/X1HbmXkLVL5xny27FfdyGYD79T9qRBmgaggA8478Pk4H1DbH+Wmw6gkqeSoEvIg1+S3RMBTDE56gNzMLQEyXXhvJ/Fb57UdTRIdB2xo4jgelkZPm5BKWjeDuCYF3FIPTwPO8PJyT1LOznn0ca6ue8f6U+fyksd0GkeWuLYrouEuymkfGtS2hX8rF1F4IqfwkpEq4lCeNOTaAVBFHbwetK9zWZgwS4JFDiFtqBqIYd2xpBh4AG0AfFKvIBbKlvOep9PIfhUNEO9W1LDUw4olkqfg4veyuwdkJzBoL8/IQa2FBTnaX1+tiqIMGN34LefPoig/PoLOAXQ+wHGyOL8glIwKhghSd6+cYt18+LrO2/z5zrEqChKHS6v79iGBGCrq1Pc5qc4lQldEiQjnx/rkBQ2Kf898/V9RHdFRT6Kvt3uh1Z4572338gVERM92tkFS5JydSYMqDg+sVQTIKniOIS0J++aCQ1AWmQZSKbQDvznRpTASAL528Q3zQRCdkT/XgCg9EjS10IGvkipESs/k+s2XMRQh+hFyX3PjRPzEM+au13gU8VvKeiWApOuyyySD7v7/d5v6Z4jGXqPIt+n96lEXR0wDcZrb2nI/TZZrT7f7eOUwrTdXSgG44s1408MYLiXpyOOeTucTERTxqd2xZZmYthutG1KxMFrkrOjUbom9vR2DwpfadorknjiORiXmyM1qrrkEX4ej8eWBsQnPZz1LsOxPE5cu4jRc9e7a95y6BDHd5tZn+PtEXrI+vSe8xfQBfJOoTweoTz3hQtOjFtqn2chrQIjQmbQwjnoYhnRweHKQUlHn0Tsra0bjYIaSzSpjrtFBTVcEZUtiQKx96XXLN0c831nuQ1VXtk6PdCQkOcNDO1XFQalz1oSK4ZQvgePTjp/UgDdQSCkx4wB7SPoAn6NIs0WTXP/x0EEX0CCXGnWwQBc2vsTOTbFm+zgL+Z2aVJfacgzXkVtGcs0gQ+Y2bjIrqXGYRBPIhgGH9M6lwaCad7IGHQEJN5kmVgoiGET6NvtAqazE4zT37r5+dH1uwF02szdf0B+SflvvEATLTL5rCVS5Gg/RtrsDKFEH6LFtfr6mgi2hQlMD/6xiubW6Yi2s3J9NPQn2w6962yUGqnH5/Qz4/moXPM7LMR3cKIKfswWpReQNba/azFUuFz4UwzWyRzbhVkwaqs8uea2V1+7irgyKrfhhAucrpTkJBzq5l9qkudpfUiJmFLxJBsjKC2A5uZberWiYCifG4Xn0bW9n82vZ+mLYSwEbJ0bkwPVngtCgBxddO1LfWuhYJxTEPM1EpIo72Vmd2R0H4KCUvHoIBbs/uL9WvID0b94/sZuhR2vSda856mH35oFlnxvM5FkUDVaNkN3RI6P4IgtDOidXw0gke/JaKbiZinFyK6BVB05BWSOh9EgRseiGhXB35qZptFdAegfrYaEgzPRJaZnGvGr5G/37VIYXG+mT1Z8/ypFWJ+5Nt6K1ozdzOzGzPXrY/WklUz505EPkgXMghP/3JEV2Rp9vItEUxuSQR/vdTMLqt5plYXAqcr4ou6bEEw0ZPM7MIQwk/RGvVvBGV+R0JbuXs8iJRDcd/fNKIbR/82C/FEdyXl1bd/AAmKs7zfHYMsvDuGEP5gZts6bRFU0a3Av0ZK12+hyKNnmtnXM+0XWYWdtmichkLEXCmd005C/ra161AQ2uUyFNn892jeWxbN11NQntTHk2uqnKqnmtm/6+quaW8+1PcX8eP5kfvHHmh9vRIpMC6wDDLAn/8PZvatqOxg4F1mtnlCuxjwbnrr3e/M7LFMne9HY2kbpFQY7+0/6+eXM7c8hzlwIZrd3n+BMPhE9IEfQWH0X/CFKAyFKQuF2HSnLR10xX5LHZ79dDSYvoc0Rrsh597zzex7Ce2DKBR9Kgzeav0+KQP+NF7+iJktnZSdheA61fuavVkC4wohfAkNvOPoCToHoIXk6IhuXzTB/oIeNnsfFCk29gsoFm470r6F3mL3OBqg4y0DB/GF+VSLoDgZmtWtHFrxEIpM9kxUtiAKtvG6qCydGGaZ2fSkrp3R+94WBQ84Ewm516F38UjNPVyMJsXfWAZiEkI4id4kl93MbG+nXQt4wcwm1bQ11szud+HkQOQMfzvwnXjyDCH81hL/mSA/kF2Q4LoSsraeZmZ/zrTzI6e9iEHGIPWbuze5fBZyvD/C+n20avspWjSGNImHQfjpnCoOUn/h+REEe0eL4C1OuyvwM+SkXz3Tu4GPm9mZQdCr0SY/nEWRpn5tM5sUBIO9xsyW61Kn0xTVGwQ7XB7BTD8x+Eb74dshhPlzC3t0/k4zW8P/9/WLpM4cXG8+5P85MxmvtfUU1Lkg8kNZDvXTiy0PMa9jBvvWskxfjulSmOaDCAb2+6b7Dj1f3ZeQlSgeS6kC8gG05jweQrgDMV8zkD/WIgntdOSH/6LP12uh9fFxi/w+QwdfZRccFzczS66r8yVdBY2NDyML3rkW+UtGdIsiS+IuSOD4A/IpviChS5Vg1VwyHgV52wGNw2nRNa9HwsHFloG0ZeqsNqvmXae7BPErcdyC/by9d+Yq8Hn4dfH91G0h1LsQmIT/ii/6SolAGFqgf06zKEJAPObj7wtoDjw+veeMkBfXO+DiUbJ5H102fp4gpeaDZrZkCGHXaE4rbj/Ir/8PyI3hGxb5qiV0F6P32aikdtricTq3t1CurBqJeJQ+RQTitwZ46RDCYwgR1Di/hkHo8fzIF/AFM9vGaZ5Gc+xp3l6j60aQEmkCCgA3Ga1js4AdLONz69csCqwA/LNlHVocKUt3Q2PpAsQXXx7RHJjrFyGEz5vZd5vufTbtf4EweDNy4rw9hHA5mkhnAkehHE2dmbLQDZveOuh8kt2cGr+l0OCnl9xnyrgWaVOd9njKArPk/GxGIq3KEkn5TKQVa3V0dsZkc+v3ZxqLIgWOjcomoSAGt0Rl6yIBd5WorEi47UobnR+GoLS7oUX7ZrSIn2Me5CGE8IMaZuH46p0GWXD297qWQhPeRIQ1fzi57j4ET4jf0fL+jro68r+MGK+PWX9QjWk0C4ONk47X+08EL8oqP8zsiOi64Sh67Rk54dJpvoUYizOQ4uWtyKfxDj+fZd6i61dDQuFH0Pg7HSVhvt/P1zFQmNledeeatg79dI4n8Zr2SxUHlRKsQg08gxjS/VOmIoRwD7CnRcGPQghvR4vl8kFBLhZz5npbFAo+9lXJzR2Ndfpxp3pDCHtb4k9U845GIIb9zQwymh8LIWxirjwI3YJM1QXkeg5ZK0qEwU7BPl7pLYTwMIK7Nfn5dn1Px6M17swQwoFIUfkC8Ecz2yehvQxFMr0sSMlYRYB8q0W+NiGEc2nxVY5or0HQ2xuCFLx3Ir/Fj5grAWqe8Y3IArGlZaz3Ce0YpBBqpU2uG4n88rZEULKHkGXkbWh9eJ+5P+FQttDN0rwosgrvjBjmBYLiMKxvZodn6l4JrYu7oe90GuIpPoXiK7y/VBjx8f5Lf/Z4sy7vs3QLIawZrStjkCtEujYfUzH4IYRh5pa4EMJdwE6ZOf8CM1s5KusLYJO5hxT9AYI7b4jyOgJCVyW84eIUWIW9jS7jdDE0jiqEywQzm5m571K6ImVV1y0oKM+dbXN/GLRiVkqY71U8YAhhAzO7vmP7I9A3qpTh11eKAVd+3G2uEPJ+fS5ad2YiC2Jte67c+ACKcj4WjZmXEdT50joeKNQYb7Jt/BcIg+9C+Po/hRA2QAzlguglXtB8dW2djwDLmTTV96EJ+kkUoCNldooGXY5Ris7VMqvRNtwS6GUo1KY67Sj6A7M8iGBlX/fnrGANGyGhNd7egHIX7hAXhhBuQfkHH6Zl83e6fIZ5vccii6ML4svYoPZtaiyMlgq3XWmT68YiIWNPNDAnI8ZyfzMb3zBAZ5jZEkFa3qsQA34+PSjEB1AAgc0Q/G1DM/tZCOFwpCn7Lv3W0/HmsBFfpNdE3+hqtBBvjyxqHzHXvAXBvioIxI1oXJyDJsU3NQiDjZNOUBCBPRBM7tfIGpf2l/Tax81s0YbzDyAflGl+vDeycm1vZjc1jR2n3wx9p/chof0B//9tM/tmw3WzF/qobHnEoC2FJuQrzezuzLWl/bRLRMv/E8VBdP2jSCCIn2mkP9NSIYSrUUCkc4Msky9bzwo8Gi2Ob+hSpx8Ppd7VECohFfJOimjORsq33yM4WUx3RHwcQtjZzH6VeSc7mdl5SVmMMqmE7Or4ZaQo+VTbvBiGACt7pbYQwueRleWodEwUXFsakOrt6Hv9MTPuVkT8yr+CrETH+P0cmQovSbsf8TpPsyTKawjhbcin5uYgq9+Pvc4DLQns4QLOLr4vhQLfnGUZpIHTb+K0lbWzD9rnAsGuDI7lMy2CuIUQ3onmmyW9nsvM7H9zbUbXrOJtV0z5WZaHNZZams9GDOvXgDtMcN6lkPJ7lRDCj8zsU6GDC0EHvqgV+udr2bZm9uHMubNQPsjT69oLCsiyK1JKrmNm8/j8cTVwA2Laq7X5gygdxBbIOryOudIuhPBR1C9/SW993guhRn6WtFkbwCZ0iCJZyBvOiVJzI4Ta+Ac95MYayPXq2q50hW0O1fhxFeLj7mdQGB7SHBlC+AHK13pNVLYxCuqT5Q2976yJIjpXCud/IKv7JD++C/WrY5Bf4JbmcNoQwhvMbEoIIaD5YXc0Tq9FBocLzezfIYQPIBTMTsgquT299QaU7uMIK416a0N0bH2t7UircgzyI4kdhnO5sSbgySAR9OFyNBAmFrY1DGkC4rLfIqa/632vixKoTs2cu8w7GQgCeIbf740Z2oHcWnE5mij3REzTuGjfA2GaB5yREUzjOrQw1QYdcNrTkEZrNSQAVWkYxid0RXnZkPP711Cex2f89ygyjssltMD/+O9iKDLsn9Hi/GMi51+kGJiFnKefYTDx+9cRLKB65l/CQCCOgJyU/4Ygce+OyvdGUIk7/Hef6noEVZ6MFuIpCBp5OvJDGZ++I7+m8o+chLTyL/h3Hp7QVd/uGRRhLv6e+wL3J/SLoYAx1yJL4eHU52Abj+AUdX38CQads9+LAkFtQr8j9S7+uxaCcd/v7+oQ4A0R3fLUBBpBAsKxDOauPA4FHbnPn+s+pNk+CQV1GIHndqOln0bvblbh+3w90uDf5t/54/57G9JCroLmgiph9uH+TT+B/Cw+UX2HlvnkHWQCDvi5xtyNdMhfV1rnUOpFObSeRRaVidQEnUAw74Wa3kdEW9dXHsuU7YP69EpoblkZ+Td+HM1rFwHnOe2bUJ7SI9Ec9DWUigCiXGYM5oGdvfv5NNBF3T6kYGR0yMUaXVMFpHqIloBU/z/vKNDNE/5NtyOZixLaY9Gccx/y8XpThmZhtC4+4v3iG/77MBrPyyAF6wdz17fc6w5IMX2m13uG3/uOc/D8j9LLGRsnBn8iHhsosMfONAQHIUqeXtj2YzAYVCqhuY6a/G9IIXRtpnwEUgheiCzHL/n7qnJC/5pMkBY/93X/xo/ifEF0bgtkCf4dPYtwro6iADavYJ8uCppHYV68UrqG+1kLKWehIUhhtJ+UqaNxjoz7VM09PFLT99N3Mk9F699xt+jcHohHeBTxytvFY8X/r+x9bsGovunR+Wo8PQT8HVkDs0GIvI17vf/eG+33IL/54nH/f9IRX4WOvmLJnlzzB8QwfLKpIzntosjXAMS8HI4m/qxAFV1XuziiiGcz0aJwFAljkNAuhSwEN3snuAJB0nLvYSX/vzSanM5BYcdT2iJmB1i9w3e4t2YfiGpJL1JmNTE/68eLJnRznFxziH2qGqCz0KL3QeqjoU3zvvQiCTOKhPINne5R6iNFLYOsCO/scI+TgbH+fxV/jwv58YJpn8tc/z/If6vyscl9y9ykcy01k45/12/7u3hHDU2VXPUKksiqfv5GkkXXy7dFGvXno7LqO81AAVTWb3jer0X/0zF1ZTymkGJjUlof8gmahIJw/BGlYWntpy2yzNjvAAAgAElEQVTvc2ASZy4rDqLrr6Sn6DiEXv7MwzLvqxIInne65/04Fg4mIxjvQsm1q5EZo4V1PoAsNkX1+ntYt2C8XI2Plwaaar14GliB/jXkneSVcFMYjII3Pwo1D1KUTAc+huaTC9F8d6Efn5lcOxytBU1Mdi0jlOybleyZ+oto0TpzAIqE9xIa95/KvI8dUYS+zRBjfiYSYq5hcG0OaD6slF7bIMXKxxK6N9IL5gRS7t7qdS/Z8p3XRoqFrTPnPgjM19afnPZHyP2kieaHSPmbi+b5W7RO3I+smo8gVMfh0J9QvKbu20jmWeSC8vekbAQKFHY+mgOalN930xOSqkjnY4B/+P8ukZQfRvNYa7Rbpz+WJCJlhmZ66XmkrD0Bjb/pKBrxpognWzqim0m98nIxtDavXfIMNXX8Gc1z95EochK6Q1GwlbhsfZTnMq1zD5J5z8fE7hna2ujxCd1M8lHyZw6FLjm/pPfBm9BccfFQ32fHd5+LLjsSmJEpf4T8XD7d/9+DXMaqc1NwIR/Nt9f5/2nAwv5/TyIh2dt+PL0/YL0Oz3TaHL+XV+Plv9o7vTCraYjZ2nCzaCFqTQHhtAOCl5fvlCkrXRxPrtujDvMBZJV8HjF4RyDNWVag6PjOcgNkYTRh7h6VpZau2ftc+nbD/J01hsJHmtP16bf2bBr936Ju70obvx86hA1HENum80/X9TmkLZrl/3dAEe5ydD+hp316Mjn3RHJctGh72x+qOdc66fg33A7BjKchJcRmDfRfqdv9/B4ooEju2i2ILD7RdyoJm103pmakYwpZ1bKCJfITeNnHa2pRHeinXd+n081VxUF07Qx6KW3uRvCW5chbhzoLErSkyxhKnW31IkZ6IC1Mpo4Vkd/UQfQSwO8B7BHRNK0lU0kEEr9mKonSDFkEp/n/UcgqeTcuONBLrbAdmbQeaB4uTg/yau/IIjQBMbi3IiZ2LBr/6Vj6KoUIBsSoT/f3fTdCmTyIIFZTcesNsnw/SS9U+/cRMuZT/o1Pj+pcG63HzyIF1Dv8mhuQG8V+medbFAlnB/nvYi3vYzQSPEbX9I8xNdct7886zo+Ho4BKp/vzXYssyXVzQWmI+6K0Gk57KBLS3+H9diOk3Pycn3+OSHmd26O6lvBvPIUGNEhEf5XXP4kaxJY/8+I111dBnOLx/Kj3ozh1R18/9Xe9QE2dCxLxS4i5P7uG9izyaU3G1e0JXWsKkKj8/rRf+vPfn6GdQpJ+CQlnqRL4BiKEgpd9mARd1oFuJArWcpF/1wqN9ZaGPrAKSjnzU//NpixDSqOPImXKrdH88cGoL1UK2hQxcQ955NT5CHk3zI+HIeX2hVU/iWjXRpa6Kr3JcHrKk18ifmhHZO07NLpuPeC2pO/1paxL97ZxM5R9rlf4n7ojTc1KhbStVjQ6LI6FbT6GFsXvxAOnrT6k8TmY+sm5NecWQ8z74/QjfEDugnLrxRNwZwtudO3SGdq/R+fvrdnv8fPFtE6Tswy03md0/VZIKbBRVHY99ULXh1FwheqdZ7XNCEJ3ea5fMmjVraxmtcIvGUG44ZlWQ1CbsVHZcd6nfufP0Jqbq7D/D0dKh7b8RDkYa52AXzym0FjITsJogn7e/48B9qqh25MaobDg+eeq4iAqm+n3vxIKLlOV55RDRbkbgbcgpnUWLTmPSuvsUi8S6H6K51ekXmj8GhIIbkSMQrXnrCNXdrjPgxHDfzQSUr6OBJhD/Px7kZ9izEjMoMdw5KCnrbAylJooPv58DV06LxxfQ/el5F3VMvr0mOz3JXXkxlIxggFBNPdBCJxPoPG9pp9bHbjP/9+NQyqREPYSsFTVx1CAs6rOKxAkbg3/RjOBTfzcRiiaaXy/WyAh6HokhF7nxwMQQKRIuQqtp4/471X0z5GzqMlXi9bLf9ecmw/NqVcCz9XQTKz6WdIfr0jKHsQFUlxQ9Pc50M8RY7o/UpbNQoF2PkfPUvs83SF92yDY+/m5/hTRjavbI5rfkslN6ee+QD//8lVvdxZiznfwdz6VfmHwMuAzNXV+hsgliCHAVEt3NC+kMMVR5OeImWm/QuvmExnaB8kLg6lrRFFevBI6ZBGfjuaEH9BDSNXysHSAPSNE3XVojFR9ekXgpqgv7Uk3V6c3INTNw0jgfQgpkt7g5yfjBgKEKrwqunZeekq+RRCC5zbk5xe7IB1O/1xbKSBz++z1DgXLiefUlH9/gA75x4fUQf8TdjRxZs34SIJPLXNfQ7lcDqPG4kUHyBAdFkcvb8QxowWsWlg+jmuAWgbSiWhBOoeaBPFI8745WmQ3i/ZNiXxzhvgNVvd3OgUxcVPQor1G0um7WHCHnFh0Dp+l7V5fimjPQmHYq+ODkRbsL2gR2t3Lt0cT2xeQomCU/x6ImI0dnO7hhvsK1Xm/v3giiI8nAy863b3J/rzf31T/fZ4Eyks5Nv5l/+a1Pkw1z7E5miz/6L8DkFIS7XZNPS8hLV/6jNUeC/hXUDimkNZ1jZo218QnXaQ8+WQN3cdRJMy4bGF/tzd5G9lJnLmsOIjKJiBo24UoZQdIMLw3c/0spNHdiQbLG1rwjkHM9th4H2qdXeolGpM0C41P1H3TuTBfVFEQf+/9edsMzR0oaBZofnwfUpg9lKFthZWRCPDUrymldD+O/p9ct/v5PZFG/kXEkH0WKez6mGynLUYw0O9rMxylRhl4lkydtceIaa4EmVHAsy3X3oFbF6KynXGYZFI+ETget+Yg4fZ7RMIYUg5vVfPOtyGyEkTl83qbFZT40prrqzV3KpozphKtuTXvYBo93+ZiyGfd+yq8Zl00511Hi+BYUNf6yKL7A8S7rOq/P0CCxADcDvE3v0BzwHSkFNoyOr+x13lCUueJXh4LOcUwVT+eD1msz0Lr3VlIkBiAIgOX4BbYqGy/3PdHsM+0n+6EQxWT8uORtXcbNJ9ui+aYAcUQgsXuhviY3ai3wjbS0Ysc+3EimDDNPGwR7NnLJ+NwcHpCWGAQ0lrs6uT0wxD6Z2f/HRadOw6N+f3Q+v3J6NxmRJZRegrtRgU5ki/GNu1Ot0nSVnYvfs6uA+8/ZUcws+/VnDsORdmJyybW7DEErRgyRIfF0elbccz0gn3chZj2CWiyG4CiOP1jKOppyfuav2UwtO6Z6y5HsJoQlR1IYaCdmnv5F9IQN/pwABfVlF8wFNrc92lo+4HqG/u7eRT4gB9vh/IXVrQ70QtEUu33E03q/o2zgS6QL1Vl8audEOomBqT8OI4eMzA/spR9MaErxcaPa9sz97AvUlocgxaKo5EG7qMJXWOgmepddexPRWMKaSWvY9A/bbSXfyN6T3XfakESIQvBv65AeZee8t8/AwckdHNVcRCVLeHv/Uh6Du3vJmFAvHwptOj9Bc0tPyNakJL+2hj0oWudXeqlZRGN6CZRAwXL1FnsY9Wh7+1JT4myHWLyXyCjTCgZT2nfp8Zfp5TOzx2YHLf5xVfjqTYgVab9LIKhC+0rUWd0/DiDFpcB6GXUR0cmZaMYhBVOQxD1GH62k5fvFdG+ix5E9DpkkWv7BiOQUqGKfJmzeFxDL9jVBAR9O5zI2hDRNvqt0W1tHIks5TOAT9fQdHZLQRbdyiL7Mj0l30Yt9zMfgv3+0a+5IanzT5k606AxXWCqC9ML+nUqWldO8+PbcL+yiH4tP3cTskrfhJThubgPm3g/Od+/5wVozcj526dB8+4iCpoHvA5Z425FcSyyCI5SOqddHsE870YK5PPRGHiYOYQ9e/lUeoHIKnjmQsDkmvveAUV7zfapmvvpC7Dm/fkraAx9iX5BcX8GeZgShXZn5crc2F/1Bl+1B5M2r05IGo1CJA+17i6QocbFkSHgmP26TRDj9DjSbH07QzOJ8mh5o1BAgx8RBfDwvcls3QQBe4z8IlrLfDjNoihFw4CASkF0MafrEv2vBPbbxUk+Zjzeipj86nuHmolsdRTAZcAai5jPOujhOPqtA8PRIlPq/zo7UlxUNpLBAEet2HiiqI5191tzD5NQbsO4bF3grqSsMdBM1++UuY/aMeXj4zdo4ZyIFsCJfjwBt2r5t66Dkw5n0CrzCL6A0oO2jEZhqdPr55riYE53BBH+Or2gN1+jp7E8FdhmbtY5J/U2tPdJZGnZkBbIN918rGrn0ogmeFsjkusWrLnXDWrK14/+x74m1Zjs8z2J+miMbHk8OV4xrjNpL2tFrLm3bEAqChEMTpv6o/07OX7W6V6kf+2M19OrUH68qs5/088EPk2/oJFaH08g8SNEyt0fZJ75EgaFhY2BS5KyL/h3eB4xsc/78UEJXeXHXOTC0uHbvA2HxiOo7qXIkjiAKqDFb40IhlnQ7h3+jsY20MyJW8p8KPJyUcCf5NrRJBDbqM7R1CjM6QZTPQYpyRdM6BZEvq3HZOpYEKE/DvLfujliGHJTOBRBMg8lYwxAvMb7qfG9c5rz6fncXkaNb3spXea6tyO45OOIf/w5eQF3YvpNyMCevfwXaM6dh97cdzzwo4TuvWjM/9XH3V/RfDExU2dxgLXC5y5RaBcpV2jx0yUDu27aX7N5BkMIT5jZIl3OlybNnIN7+h/EvO+MFpzRIYRxfvonyOJVbYY63uUW5eDK1DkvghftYWbbhf6Ex1shLf83vK5e5Z5vLqrnbMSAT0AMbrydUvJ8FuU08zr/jhbRy6Oyd6C8aGv5cS4Z569QKP6ZKDpinKuoMbFolKPmYKQdi7cVgbXM7M1DoG3MZZfcwz3IN+2+oGTKW5vZ1n5uQcTAL1VSl5m9HELYBmkFj2QwH+ERCD54SdT+NGTFq+03Ee19KP/g1VHZxig31diobDKC2zwcQvgkchZ/u5+b1+8p0EsO3pgIPrmH0px8X6mrw8yOdJqnzGyhXI7Ahvb7aNMxldBugcbVkkhgvNTMLovOT0Ihtm/OtPMWJDDHSeeLc4FG16xWtW9m/0zOXQmcYmYnZ64bB+xjUc4lz+l3OMplVCXLHY8SIj+fa9+v24ya3I1IkbMDsnCm+Z72GEqdZvbNEMI5TfXW5OJ7AY23syzJzxY6JD8OITyIrAwPVHkxQwirI//MzRLas5Cv0MBcWvVTp5uFBPfWflqSjzLJbwgajxb/N7PhEV0gv81+/nTeCyHMNLPF2u43ucd5gPea2Tl+vFnLJZjnmivJo2Zme0XraBPdqV7nFdTkbIy2ERHNMBQ1uGIERyPLwnVm9vYkL9pSKGfdb5Fguxyy7p1pZp+KGwghLIQE5iXQXHKtmT3Z9hzpFkK408zW8P+T657Nhp5fdAaKJvp8VDYKwZnTXKirI/5mGTP7tB+PMrNb/fw+ZvbLgjazeT0Lrqvaf52ZfSZtv4V2NaTgvLVLnSGE9ZEgdDKD6/OeiBe40WlvRYrSmzL381Y0d6+TObccMm5cl57z88ORgLOoZRLZR3R7IsFrJvJj2y33noPyPq9qZo+HEJZE/sjLDZWu4X7mRYLpOAQHHZWcXx3Nowug8TQGISh2MLM7nWYZM3soKA/vqQjyOgpBfy9B6/hTUZ1/R/lJf1XNZyGEvRC/d2DSfhVM7qUQwt0oAMxTwNXpePI6dqcnP4xP1+EQwq+8jmv9eWaP1WptDCFsYlH+0qB8qa8zz7Mclcd1z4v621/o5XlcHzjfzHahYHstC4NTkTZ1cubcGJSoeNmorDhpZghhBQRlexODSY3TDrKMmT2UlM0DvN/MzvIJ4DmkDf1HUELd4xFTeC3SOA0kgm147raF3m9zgNmZiUJXP17aVsG97IgsKBfTS8L6bjQBXeQ0aTLOSUgYnJ2ME1mqYqZmA2QhGUgsGg2QjyBn49mn8bDW5knCO9Iul+tLNc99GD3oyR7AZ83sLD/3LgTN3JhmhmQ28+bX7YTgnHFy7cnAF8zs/KT9g5F19SttAmEIYXekTZtAj3nZHkF4xkd0x6EAHhcibec3zezHfm4zv7dpiBmahDSYZ+faTAWCEMJFiPE/xMyeCSEsgBQYK5jZDk33X/NMRQvjEGkvQ9apLG0I4avo/e1oZlOj8tejd/e7RCC4DGmDL3Mh4mW/n7ea2XqZ+v9qrqBIym80s/WGoDj4Hlo0jqQ3Ro9Avg4HOM0uPlethXxBdkUL8qnAGWY2xemWR3Ch4+reYSS0d6rTzBZuUwbUCAQjkdVrbwR9PqWujqbN58fFzcyClC0reV8dENJK59IQwp+Rb/E/GmiGobngcQQxi+f1lRBTsrTTjh2soX+zRGHXtqXPFwufNfS3IFjj2aXzZUNdnzGzE/3/ytVcPId17mhmv/H/I+vmxxLhEiRglgisTlub+Nu/c0z7spePQsqaXegpa85GyppnnWY289giaA9vOBe3fXl8HEK4BM1bx0dl+6E57p1R2c7IInUBUhYuHEJYD60V7yTZmgTHTL97pOrndZu3/yM07zW2X3qvpXU6//htBC0dhubxaxGUNk7O/gQKbDSgaPNvPT157jHIp/BNiB9Y0HmBbc1s3+T6WxDsfCo1WwjhThTJ8qIQwvuQu9RbMnRF477r/NC0hRBen7v3EMIIhN6o+v/11q84Tu/hdwi6OTnlvVP6SBgchpQbSye0M5GyZgVk3V/Jy1Ml2ZcQr3ccvXX0ABTF+OgQwutMivRWhXZU56Ko7+2EkA0LOE+9vpkdntCeDfwq5gVDCO9HmQ/+64XBXyAtz+6Zc6eil7tvVHY98jE8Oyr7EBLG3pZcfy3yXTuDQc3vlQlto0Y3SJt9pJld6uW/RpqFU9ACcKslGsVXYvOJZGszezhz7mdm9jH/P556zeOA1j+EsCryXagG8rmV4OfnZ1toQwgrIyjWEmb2tAvNDyLho3Uz1/x6XR81s5+XXNeRNmd9AAn0U5Cf4QRnJtZD2uMzouvHITjXgOUot6XMmy+YiyNf0n/mrgnSDi9Dz2E71j4NaIdDCGsiYeH1SHg4z8zuSGhGIiF2PeS7coz55BFC2B+Ng/Fo4hqLfAOOqXmmdNJbFgU52gjBOxZHPi27pItDCGFzNOnG2reJmWdqXRiHSHs/gvI+W3N+JGIctkTRxyphbH0EA9rJzF6M6FdE8/C/ghRBxyA455HpN3D6AQt1CCGg/lBZiLooDqYgiO6MqGxJ5Nc62o+fdCZoBmJMTjOzG2qe/2tm9uXcuYRurtfZ0t56SOu+dgPNisDLZnZf5tw1yI/yhhDCBBRR8UlkVV8joa2dSxO6ryNB+BQGtcQnOU1q7Yu3l5FQ8NWk3gPN7DuZ9j5vZt9tuqfMNc/Tr9T5EBqrs7d43ncGcxdkDbsJKQN/ZWaP+fnYila7mdmXk7WhFmkQQti7sM6TEkawGL1Q0+6auTFaQzuAVAhCCvwQIXLmrYrpVwKehIKXHE2PyTwMQegHnjvUWNR8Tji24FbNzGJ0EUEKm/9Fc9m/kBJiGRQE546I7k6EirglYrJHIoTHUkmdjcJYhtlutUh3bL+ItkudTj8fCqQy08z+nTnfCbEWQvg9gjl/E83xi4UQFkF84djk2oOREvb7iBeJ55PL0/p93XjUzJbM3Me/EfSzUj6dgKDRszcfT6V0xeM+cy8jkPK8WvOvSdbQtK+0KazuRtDPh0MIf/X7n454tSUS2kpJviyKtn1gCGElhAhaIaK7F9g85teClHN/MrOxQQigAyxSsLdtLuDNRHDPO/zbL+XPv0pC+wRSVr4UlY1Afaa2v/XV8RoWBpdBmpkn0YRTMWXvQxrWjWOtQaUBsH642HCkqVksqftJZEkogffkmLeFUVTDJb2TjDaz51wT8CgyV08KggZcYx3M7g33sSjSbvzTzFIYKCGELyAt3fdJIKXIwvoNpyuB6gUEfxqAvPizPxUJEtMQc/1kEITh05Xw7ZPuo2a2qB83WmZCCMtXjFzoh8um93lPF9qkjaMQpOFUepa0PRDTE1AY9GPNLIWdDmkLica44T7jflurHbZEWZG08zoUYCRlWs4xsw/5/70sA0HM1PfVlEktuGY5NEanmluGkvP7ImHpF/Ss9/sg7ebPE9rWhXGItHujSHRfydDG32BLFGF4CRQsoQ9O2nULIZzmfweYceSYH8xhu9E1q1XtW73i4EGUqDgVBm81s9f7cQW9bbKkbGpmf/L/W9Q9R8SUtNY5lHqbtuDwzuj4LOAEM7smCOLzIyRg7WcJlC2E8DbkF31zCGEV4MdIaD/QzK5KaGvnUuuHzA8oMXpktoXTjEXzypWo382mQXNjjuGsVUAiZVvrom8OJW6a7yPaI9OyIAjk++mlFLrMzHYMHaxozqhd7vf8Q+DTNeQDCt98lbZFEPLkB8h37WKEVBlA0dT1pxDCEsiKPQ5Yx8zmaWo0hLAOWh8+Uo2n6NxtCJExnkGl8v1OMwNZoR+PrlscuVa0Wmqi8iFbbPz6BRHiYTm07l1sCWLJ73VJM7PQU3aPQPN5anFpFLLS5yi5/47tF9F2qdPp2yClzwHfaniMg82sUgxU7S9lcheJIeF9c5mX3VtTp5kL+KXvNbRDqavxVEo3JOt56MFE50Xr7XII/hnDRLuiFw5B4+f8EMIeyK/5ZeA4MzsioV0CGSNeQHzd0yGEdyN/y9hS/giKDP1MVLYg4vOXdp7sZyhQz8esTPH8KApY90Ly7XMubjeh/LQ/iMo+iyDJA5bfbHuvVWEQIMgH8AtISz+bKQO+a4kvYAjhBhRW98yo7MNosV8vob0YQfAGcN8RTaXprSxi8bYE8mHZN4TwOD0/q21R6PkxUT3FvmrRNZ388JymdSJxugHYa1oeQjgAwdx2y9CNB/5SddoQwi/9vs5EjP7pZvZNP7ceCl2+TnR9rWUmflehHi5rJr+ZYtqkjeuBPauJyMtWRwNxgyD/gbPMbKUgy+aXEUO0hJktEkLYGml6N6CAKUOWg2I4adctSDg/EQkZI1AghrMRQ/yE08R9tIsv4Cro2SuN3llmdpefC2azFQK1Aq/1C1iTEOzhlqhsXYSLTzVlRf15CLTV/Qz4ZllPm9+mtCi2ZETXVEz5YfRbXCtI82zri9M3wkmj4+PpwUQfQJaHwxFM9HNO8ww1THO0/cDc6lbIlLTWaWaXhxD+3qXeui1IMJ5gZqtGZY+gfFHPO2P+CQTH/HXan7psc3KfDXXOi4TRGB41EgWFec6PK2F5AmLc43e7IoL/xpr3lRB89lR6ypVxKLx/qxBYcM8jkYVwf2BTMxvR8fpVkT/3WBTF76oMmZkLzoV1bowHJkLK0RyUNV3zRiBf1T3Q84xAkMAfWOLH4/SV3+A45Dv6Z+Qn/6uE7kkUYr92fg8h3I4scDHkfDSCrK0VlVX3eyuwDoPf/jQbFEaH04PfPYjgdy8xxC0ITnq6mZ0WCU67IaFv+4S2UcjKCE0HkVg2LbEidWy/iLZjna2Q0hDCKbSs+7FAFEK4A/nbToraXxPBsNdtqie3hRBeQt+62kYnx0P2LX0lthDC5Sg9z3cifuFAxMO+w4/TteTXKCr37DFgDcrCICjuAjFPN4T7PA0pBw+lt44ejWKD7O4086A8l/ui9bYPVZDeY5AF8+1mNi369mPQ2F89oX0zckMZQc+v+UXkjlaEQntNC4NdNl8kLkb+TvcjbfsqwPZmdk1CWzHPFzLot/Zlp9kMdcbfobDhs0mQ9eWfTnc18H0zO9cnipfN4R8+6V9vZjHcq+RZWv3wuiygSd0lgQz+hpj2uzJ0KyPGtQrMsgjykazgh5+JmJvD0cJ8dHR9kWXmldqCzPFLx0x+EDRkmvUsmE+bsP0/RgvtN4Hfm4JOjEZOzecWNnlKCZENwknfhDTyS9I/KaYL6CloEvsiPRhSNYmNc5oJdPQFDCHsgGDUlb/oGMSg7m5mvwn9cK0cFG5AyA2FgWZeyS00+GVZP0SkSWkRW4QCCiLxED1L8zLAn6vFLrl2GzP7Y8F9tsJJvazySdqVnuLqLODr0TisopY2BRxZMYSwgZld33ZvXep02qJ6Q95yOBLN5Z9DiqVvR/SPR2PyBktgsZn6t0L9f2kz28EZvYWbGI2W+y0S2L3sT8hqEAfT2hAxmpv7cSWEjkEMSbVVCoNvmPvLOf11KKDQ7VHZmkgY3LDmfQ5s1m/tDChJ+64IhXM/UvSdbWaTQwMKI6kzRWRcZmZb5miblElJnSni4W4zW7mOPsgavAdSaAGch57lXGRNfySiHYkCQ+yJcrfdjcbR51COv0dItiB3lTPT8Zy89/XRuzyBnmXk037dt6JrmmIFPIRg5z+N6NdFTHNlcXkDsri838z+ltzPCASl24zB9SQORrU6WtvuRULmFUjxuXXKC7QJWaEwcFBSZ5f2i2g71tkJUlqyOb9zKPKh/z5KvXQYGvdnZOhjSOUUFJQohlQWB29K6l2awfgY98wBXa2iOKF7DFlGU/jjo+aIvaAgeG3WyVSpOxJXhJjZOUFxCjCzWSGEL1U8Z2iAt8a8VBhUqr+AeO7PWr9Vf340L2yBoKlN93gomlO+hGSN7RAPf5FFVsnkmTZC6Kpp6Nu3BhGcff1/gzDoAyp1wD7JkocPsiS+O6L7nUXa9oiubqIyS3D8IYT5LQPLjM5vgjS5hny8NokExc8jiOaHih60V2erH57lceKNE4nTNMJe/bgR3992vuXZWi0zr+TmgtFTSMNeLaJfRbDh7YNgQReY2SpBENiVfYJphHjM5Xv8GEp0fAmaQH4PbI0mkV0T2odQKPkU3vAvM3udH89LR1/AIEvLfhb58wX5+51oZmuHKCBPBwFrSIFmUmaxSWlQShtqInxF50vhpCegdx1DTvZH0LD9auoeiywO6aJ7ZhgCnLRtqxOO2uhyQk3XOrvUG/IWuRdRnzkH+Hk85wdBnP6I+vUwM/tYqFHABUFu9kcQ5S+arPxreZ0blzxH5n6LBHYvn22dj8qGOW3qxnCaNURtjeieQH342ahstmKr5n2mWx8T43Pe02iNPdMSbXuL0BLXWTyXh2a/SjnxFyAAACAASURBVBji+uD1zkD5JX9VrYX+jG9MhMHHENTsFPTcN9fRRtfURcd9H/2MYm4bYB69zistiW5b82w3Iqb0u2Zm3u8OQHDWtya0JyDm9WdIUfgllJblbBv0V50fKf3GUgMndbpiIavLVtp+x3stpWuzdg5VEfIeJARW7f/EzH6dab+CVM5HT7HYB6nsugWhyn6JBIzkNvsUtUV0TtuoKE5oWyPSD+GZ1kFpop5DyJAFgwL7jTOzD4UQfmxmn3TaWqWECcqeWlFHIIXJo4ifx8we8Lq2RGPoZuQONTAnJPcZ0NxTffsHgJ8i41ETmqCY3+m77rUuDIYQvo1MxsfTs3zshyBDB7swdnXdyw0e9W4O2m8N3R7kX7EqMMn6Q+CuhvzrWvHFSZvFfnjRNY0TSSiEvXpdjwJrWz4YzeuA203+kkPRPDdFzDudQn+YUB8IZoA2Pg7y1/gR8ocZjpjNC5AGaLp/s4XM7MYg69C6ZvZEtDgshRyVV0rqHYXyrPVpXlE+rJL7jK1ydyOs+FWhp6HcDmkt+6LkBWnVNkuEruWR43Mu2EyRL2CQD+5S1q+VHIF8cGO/rdYInRFtl0AzrcEZhkhbFOGrVGnh72lJ69d8Zn2V/dwXEdzvDpQvrdrM+3UnOKnXuQWJssz602WUCoPFQR86CoNznN6gpt6VUKLlF1B+t0eCAm28zcwOSWj/hRAV90VjajjwiJktETqE9x+KwO7jdEPr93NfFkHuG5Ejzjy9ZO57GZVXeTOPoGd1+iqavzpH8fU617eaQEBDqKv2PSZbkXLDzO7vMu8HRQbeHVnqJyAG9veIKXtTIgxegfKUXovWoXPNbGaLMNjqf991CyG8AZhlkRtMkJJ7PuuHmj6JlAvpvDMzHZehQ1qVjvfaRXBsTQHxf72FdmtnrAgZWBeqg65Ki6j9RkhlGEIQF5/3jkVuMAP+yV3pnLZRUZzQ7oCUFrUR6btuQVGcf2pm46O5fAHEf1fokFaorI+HJkXU7PU+KBDUtui5zxvKfTdtXXiY2jr+C4TBR1By1SlR2XIoqfNS/jEvQwzlgDaujmkJ5Wbu1tDtc3sLHf3wvLxtIimCvfp15wL3m9lBmXv7FrCCmX0wDGqeR3t9M5CAGYAplteADgQ8Cf3hwBv9YbrQpm1H7S+FBOs669F3gJWRxvUmlC7keOTP+aWIbhMEKZgHBTd6EkE3J/s9tW7Wb5WLIZhZB/SI9nAEhfou/SGRx5vZ12ueq7XvB0Eh/2D9UKaDgXeZw9qi8vuB1dsWkYi+MdCM07QGZxgibVGEryalRSJ434kS2F4Ylb0X+JaZrZZ5runI/6oximEoh5N+ASXTPZle398L5fY7zmmK/JbTuTLX36JzXfJ2dg0QUAy/LN18HVnWlG+qYvTmBe41s2VDYXh/M7tyiAL7cSh/437APWjO+i5wm5l9PqG9EvWpq4OCJXweKa1+aGbHRHSpYusl5O/02dxa2PBuhhSMq6DeIkHDaoJi1dQ5rp0KrD8y9aZojtwZKQ4WROtiX0AoH/N7+D4GWb42QzDRPr+soW6+7myHrBgfzJz/C7C3md0Wla0D/MLMNojKzkY5T9N550OWhKIPhWlVGgTtvkjbHZ+3S7qI4vZLaTvW2QVSuhcKMPZVeuvul1GgpVOcZj40F78dKT4fA/6EoiLnAkc1QirDEPLSeZ1LVDxh3VZK57StimJXFBwOrI2QWHdSE5G+65b05xixNZScrX9FBpRTkRJowHDja8Z4YP90Xs/cW2cDiV9XzMM0Vfqa3lEo5EWSskURNAuU22o8Yrw3ylz/VKZsB8Swn4lgamcATyA/vZR2ChokcdmSCKr5Sj3zIsBJwG0oweg80bnDgS9lrnkMGJ6UjUCawrhs/oL2V/X6JiChamv//Q2Cv6ySueYwFA5//qod4DsIkhXTLQycBjyPYDnPoYGYfuPrUFTWuGxNZJVL226kRQ74rXum3lEIrvk0vfxx30PRxWK6v6Cww1TvGy0MBw7x+9+BIluBNNXvQwvKQxnagAThS/26S1GEzlBTd1HfB1ZHkbOmAtcjDPvdiDFK69wbwavGIqZ0WLSHiG5Y3Z6p88m6Z5hD2keBkdWYicqfqKEfhoSIuvq28vavQVaia/19bl1D/08KxqDTjkU+B7vGe0LzILLix2VrIUG7qe6B948slXv599zb+/ve8d61zqHUS37ODsn3CsjiUylEt0FRJj9Wc2/n4fNmVQ8KbnJmhnbnmjp2So63KfmOTjsv0vw+g+aSWchHZd4M7Qx8Lkdjbk1k9Xug4b2/LjeOIpqFkfB5E2IeH4j2pyK6l5FQ+XKyv5Sp8yrE3A7spe8lU+d4tD4M7EOtM6p7PpQ/9hIkXP+lgXYTBAl7HK15366h2wrB6yb48XooSXlK90Z//w+h+eLEmvqerCl/Ijn+FVo7q3nnGj8+N31nfm59/z8BBc85HLgzqfMo7w9HAR/z3/vROvFNNHce3PLt/xcppnZwujuRZRV6a2OFbkqfsUv7RbRd6vR650eptA5C/sUL1nyPKchaS3LtlGi83YbWz1O9vdP8+Dbkq5zW+fe076DAS7dnaM8GPpCUvR8pduOyY2mZt7vQOe1E5OoRlx0MXBEdn+zP+W1/rmx/H+I4/iuwnv+v5vL1kd94TPMPFEthOcSX9O0R7dr+/A8gt4OPpN+2w73dW7Dfkxv3FPIwtW3PrRf8/9NOP5P+WWT52wolkd8aTTifqV6i/34SaSD2T19ypv7bgHckZZsDf8/QPkheGGxktubSe8gyoSgIR1rWOJGQX2B/iYSWNTP1rYQ0JdOQ4DYNLdQDQpPTz2ayo7KBSR8JDecjgXMe//0VgifEdE+QMEpoMX8803YjLf0Mzkv0MzvV8QCzk9S3VN1g9fYrhrpa8EaRURh4+Tr+bbao9oRmT5Q3D6RFnoU02p+cC32qS98fgZiiD/rvyJo6B95ldPxkQvdSji5T56kUMtsdae/GxxW9hWQM8I+EblEkMD+PYFsgwezrmTqXQHC0Q5BVYYmG9rdD42o9b3f2ntB9EQkONyLGq9r/lNA9WNP3c33vLUhYnZV7/0gTPrFhv7xrnV3qpTcvPcfgXPUn4Cqn2xQx6C/799zF38O5iNk6KnOfy/q7vA+NpX/6cW4urWPIH8uUrYbGR5HQjITY2rmkmkOQgLcSrvT08pyQvAhihLagZj5xutP9O7wHrZPvQb5uB8zBXDIu2Q9BQaq+XEP/JrSeH4ks818DvpbQfCXZf4zWluNr6nwdUnDFyoYS5nc0CUNbQzev96/fZ8591vvfobiwhhQx1/j/pZFV9xbvc5f5u1++ob27kZ96XLYyCQOZeU/Z3WnfhtBVoKB6lyIF39uTOq8nUfYhpeD1/n99ekr4UmFsBj2FTTXfjkDw7PTZu7RfRNulzo59f2qm3jWQvy4IMXA5iTCJrNL/i/L8pnXuSM9f91v++xTwngztE+SV/6nS4Co0n06iQWFTShe9v0ZFsZdV6+xyCIExpHkm0/72CIFxpL+vKnje1gldJyEPzbnbIFjr4/iYeTV2OvAwdftrEiYaOjipJ5C6tyGG4Aa0IMzKwURLzNxReWvo9ldqa4C45uCCOyLmNYvNDnn/hpHACmhw7Wpmv03qbE1DEZXdh5zXr47KNkaaqrFRWWvAEy8r9ofpSNsK74ho10QBHh72ezwIMaDHJvf/APItfDwolPROaBGcZP0JaGvhpNYQsj7IH3GU1TvSvx1B0NKAJAOBYjr2/cZksRHd2LQs2l62joFmnLYuOAOWBNfoSFsU4asUTtp1Cwom8HOkUEputc8XsRRO+lEkzH+VXt8/AuW1m53awgQznnMoymD7c63OUvilQ+l+gua7ccjisp6Z3eFQrz+Y2fKZ+gNijMeiufwGixbQ0DG8fwjhMDR33JI8u1km2nMo9J0K5YmS90TWxqcz7ffNJw6TXcPMZoT+KKwTrCWPVZCf+mWW+F/X0K6M3BhSn8nioFiZOtdDgk06l78XCbl3ISHsdsQADkTyddjaygzOkX2RxpNrhiW0aTTTWj9UhFbZCvWlKhrr1NDgg+h1HoZ8Ub9ED058FILWZQN/za0tdIu0XZSiCQlkpakdurRfRIuUU6V1doGUHoQE/ZPpxWjYEyktvh1CuBX5/Q+kLwshvBVBRdfJnFsFff9GSGUozEvXBKu2fih1EV1EP4JeWpOpSLiOo4R3zjPZZQtKxfBRev6qP8+9a6cdhsbinmju2cIy6Rp8Ph6HEDj3Ihni3rl1z01bFx6mdpsTSfK1sJNocRE2+3fIRLxWet5pJtJi5o7KRyGG8G604N6FJucsbGQuP1tOE7wwYtxz9KsiQfVH/rtqYTvvRMJt47uNynMa8t2RFutMpNU6Ewk7uyd09wFjk7LlSSBQ/h3PRpaZl/z3LBSsI227C20jvCMpvwUF8gExnxMREzM+oTseh+8BByLGdQrwy4SuGE5Kz9pUwWJejyJnpXQneHvnIYa82rOwqtK+T0/7NxlZfqZQAxONrqmFVCJoxhVEkOeWPtmo7Z4D2oCiSt6BrFl3ovDxIaErgpMiZcqZXl8MvauD9D2I8hQtSA1sxemK4KQkUL6a48ryVwRFAf5aU56dI0rq7FIvLRrS+Bv4u3smOR/DHmuhjCSa75p3WO1TSSCoiOlft/DZd/Y+9RN6aJb1kICXG/vHIAXkAl72buBzmb60XWH704ER/n8KsigOo2aOT66dhxbkREQ7XzpGvLzKuQW9uW87EkRITZ0jcveJ0DA7J3XuhfzmY7o9vJ9OR/NZtQ+MUQos3cn3r+C8ldVrXmQVedrbOwr5U1fXTEPCSd2zDkNKx3/4PfwDrSk5KH0tRJXEUly3J/VNQHPZyv4cKyNh+2I/vw5wVzUGSeZyEuSOv4PV0Xx4JRKq/ogY7ZyrSZf2i2g71tkVUlpF4Pw9UrxtG89RJO4k0blR9CNm5kfj/TdIqde6RiLl731oLF/vv/fxKlmz0JjcFFnNN8Xnluj8M/Sjn56kAQ1V2OYPkuP1C69bzd/vfYj/WSE6tzhK9XID4gUOI0HpzME7qoXmZ2iLeZja9l6ND///805GYPLyI7wDPp85l5q5p9LC6CbXVwmEX6lnmuyd5kUSBhMtEL+IaAMZ/HnUGUuYv0DiW1j3bmkWRtf09/5j6uGnhyMowicQM/AJxPgeXlNnqz9MF1pa4B1JeQX9CWjhXxIF9hmAuCTXbeLPNiytjwI4KQpaMB34Q/UNvGxCpq3HUK6d0r5V5AuIIC4Hxf0HMSUTM3UWQSrRhNiIxUcpPkqfpZg2umYAFpgrpxxOWkUf3M6/0ey9pp2HSQS/GrpSOOnYkt1pi6AoFPjsReVdILpd6h1Ljb8kgwrAx5LjmNEaF+17IgY1LhuXafvKwue5nxqGL0Nb7DtVc/1wEvhlaV9y2suQFQukKDsDhTkfEPAz12aFQRJoLPAZNG/8MUMbf5MZ9ObB9NulAsv2Pg5yvuJxndU7HUYyPyNN+1aF7+k2xDiukRtDCW2tHypaJ/bw9/4SCkf/BTSvL1VyLy332QZRvbdgT6GnqVL1OSKlKmKqK1+tLkJWqR9el/aLaDvWOdcgpdT4oOfOM+hfd0JhGyNRLIEPIYFswI0DCWtrRM96JRKIVk/oArK0XQ7c6mWbAh/M1NmqKEaCV3HfK3zexnk/05dahTwUcb8636ow6Xi/cx2a37S/JmGi8dZguscUPnp2TpHMte9EAUy2jMqGIVjVDciHIWvmbrmneYB/m9mwVuIhbKFb5M8DgLea2W6ZesYjJ/kfpOcSutEIWrOCHxenoei6OVRrLwaTZOfyRi6CJrAU2tMXiakLbRu8I6F9GC1ya6JIfus5POIx60GThyPhdk1rT61QCif9K7IWXhbBj+ZFEV5fl9R5C5qwZjS1nVzTCPFwmtZksVF5aYTOvWnJ3RdCeBYxGqciP53Z7Weeo5g2uqYIet0BTvokEkrLcgGp/41CPiO1k3cHOOko8xQ3yfWLWRSe3ssaoShhaCkTWuEtXesN7ek3nkPog2o7CPmGVNvBZjYvmS3MpbQWXtcewP8gbX5fGp60P4SWHGYFbc2DLKDxt/88gpkf1db/HAIbzOxfQYmlj/Frj7R2KPJA214+MSGdBfwN+F46H/l89y4TpPJaxPhOR9DfZYK7HoRehGpDa2BV5xGWQLaCUvD8jwnG/1eUWH06EhyXiOgeQBE0W9d3H8+LNI3NiHZZNP8siaD09yCmb3vrTyEyFqE8dkc+e5cgK8fvaupdjXwe0pMimsZUKW333vJcw2iPtF2coumVaL8rbeEzdYGpzoMU3rsgH/FFQghbIzTWiZk5Kt1mz1EOHX6LmU0LirT9J4vg4A3PVJt4PaL5F7Cxj5EJSPH+NHJB2CKiOwpZmo9HeRAX9TnjVzaYt7Ixcn3bfQ91C91SHz2LhM7xCLI9sJnZ5WEICe873G8naH5Qeo496LnljLcofUdre/8FwuC4pGgZFC3xdDP7WsN1A8xQdK44LHrN9dnFcW5voSXhvdP8DUFlcmkxVkaD+c0hHzJ8JGLGDkeM+yF+XbEwGrW1OLIcvYnBRazV1yRT356U+8MU0zr9tgi29XpkGTvXzP6QofsesvIthKJhnRjkC/FzM3tjRDcJ5TZ7ouWZjkd+Smf65HkQCizwRzPbJ6KbPclFjOMwtJAtkdS5HtJqncUgQ/qnmvto9QUMHZLFBuWlfL2ZvRD6wzs/kQi5rbn7QghvBHZDyoIRSPt8mpn9NfMcxbTRNblE4QsjTeWSUVlRwtgQwsUIypH1V8i0PxnNYc8jRcDszaK8kEG5wb6CtNr/TuhiAf025Kt7a1S2HeqjaeL1r9Tdl5kdGYaWMqGxzoSmqN7Q4i8ZQjiFllxzZrZXzbWtvis+Pj6FLLx9eUPjuaykP0e0jTnMnOYk6rcR6DvHwmBRXyrZQnMOsxEIWl6N0Q3M7PqO9e+J1o7fe/88DylF9jOzH6dKmhDChWb2vpY6D0Fpfs53wfxnCNL5XevPGToOWYmOtJaUGyGEU1GE2da0Lk4fkMVoDFIu3tAilGyMLNI75YS2UOiHGlpSpWTqHU5PAfggUgBmlWdBeZPTfp9NK9ImZIUhpKvo2H4RbQmdC0tPofc/BXgDUvQsasozuI7f7yohhB+h9fObSBFZMfqXmNlaXeaoTN8vmaMaE69HdE+a0nlU8OVlEM8x3fqVn5OBN7sgXykXKtRGqvwtVhTPza3Le3olhbzSzdexZczsxRDCFGS5fwpBqdM4JvuitfEX9FKF7IOUYD8vau+1LgzmthA5qfsi8HA1eTtzfCGa9O5GIY4nJdf/FmlTsxqD/9feucfdNlV9/DsOzinXI5fkcg6hm4h6k0QUKUr0SqRci0J5XyrlEtFVNyrphnIplwhJUq65p0iv3EKHgxM5jsMhjst4//jNffZ81l57r7n2s/ezn8v8fT7r8zxrrbHmnGvtueaaY8wxfiPItBAARJgMXDACymBKwvuOlu7oxS4j5XkeTXLPQKxuTxeurVRGI9nfIXeiM2klkjipILsHhSTZFFYGw2T4o+5+YULdybJ1YbL2Pduw0IT+tWRBSdoHuQB8hdYVr7a5uUyEMksgZfAFM3upy4J3Nfo9Loo+9Fug3GObFsr4GLLmPUnrKkrLhNAU5H8+cu1pEI48jd6TmAygSEi0KrAVJcliTRb6jV2WzUZ7p6EP46siuentnoW3Esg0gr53BrZFVveTgJ97K3lRpazVXO22RPIkMzsWrXidQ+vK2GEl12/S4RlcEck9hBTsjqudZrY38GU0KTkOpXN5D3ofWgwcKbDEHIf9KtfM7kATk6Sxp2YbUiZa30MuQj9Gz/YQxFZ9urt/IZKr058rc5iZrNknINfvIhYiUsiCfFJfCrJ7dJA90YbmMGsn127yWjv/oxVIsYpGmpTfqaTMaSjG8rbC8Tejb0xsHGmntHdF5GAVZDOR3IuRwoqXeJIEJW9zr0jIbmZnoRjcL0dj7oHAul4g5DGzdYBzaY75K6Mx/7/d/a+R3GuQ+/DrKCRXbzfXqVKyTCtOu6LxuOGJswv6thia8H7DRbqSXH+qbM0yk1c7Tat5a7hICmMD6GNeIGKrgpk9hWKCG8/wXDSfiJ9p0cOpMvF6kLsbMWSujdjItzCRKT0QzxvN7EFE7Pd01J+WQJ4+qxTqTjYU9xJm9hxKk9LAm5Gb6gJ4FwsP/YKZXYI8gC4xeU89jxYs3lAcL00LCtu7+83RsXVQ3sgkwrqJqgy+GOVcW8rE2rRz4yGa2Y3IqvZNZN1dxd3fW7j+OKSMnIcGqHjyfliQGeKSUgZPWMofDiwh4b1pVea17v5QyfUvRakliq5mqfVXKqOR7OPIWlTlKvl1NNAdE93Tfmjp/MBILmkynCJrna3eC1A2eU+BNVcISopcYE2vdCcNlqT9g9wFYfsAotbfGlFM31C4ZjZKNHxxYluTXTxsKLPZA0iJ/Ly7b1+QS3KpjOQnIUbFWYltXgwl2D0C/c5T6spazdXu4oQ3Ol50J207ifY2q1MpsER30iC7BrIST0cxSrt5WGkzs7d6WCHuZOAqmWxMp9xV7RfdlllVbiSzJaIBPwbF6sZYuF19hfLuadO+lInWAyhn7X3WdO95FZp8tShgqf05TMLeQ5MB7zceMQSbWFK/6O6/Lrn2RcgbZVLYnwa8AY3vRWPnB939tMKxorvRCoip8urGe2/NEIqrO43jJYrbsF1vSxTMTi5gbUNHGvChK7h3Ic+JM2hdZb+7UHblSnck+3rkkbIOUrSAFk+HbyLPkz+Z2bvRiqijMbslgbuZ3YvIVVpcvwtySS6qQfbP4f6/7e5uZoa+Mx/yyAXQzC5HsY1HIqPFqog85Rp3P7VQZqoylsQ66u6r16w/SbZOmdE1KS6l96KQj7mR8rQcTRbZSkRj1AxqrmJZQuL1sL8b8B2kiOzg7n8wGXoP8MiwbGbHIw+D/dEK4jI0cyrvU6h7a9SfSpnrU+69G1gHxtMGvIT5dFCwctf8xZGh/9aC7Gy0ihgzsk5BYQRJbt/jXhm0Vovmoshy86y7vzO8FMu4VlZWQYPiS10U5EsgN5JinFW7CZy7e1sL6kjDtLT8Oo/iL8xsWeDmhvXHzM5EsWSfKbn+KGA1d/9Al/VXKqOR7FXIReHuloKGyj2M/OPvj46tAtzo7stFx+rEw3SU7TRhj1GcvHeadHRjgbIKd9KgsPwYrWh/HBksGhPHU+NnFl1zH7JQdpw8RPIdXTzChPUg5O77D+QmsxwyrmyO3DD3LZSZ6lI5FVle34/e38XCh2l9j9y6CmWviFxBd0GuE2e3U7JSZK3NareZLVIYiJPcSesi9NVL3f2vZrYBWkl/HpGjXBvJpbqTTkIGm/9BxABvRpTYDU+JW9z9teH/dgauIZMNq47Zq11mSrmRXNt4SZqTzsq0QxXt69TOeKI1C8WbPVWisNTuz51gZvsii/25JecWQiRbR5jc3M8ksDKi3K2fbLzT7QwZJWXugWJaPhMdqwyhKHkOndy1hhhby+Du06zG6khhUmhIIRsyYfWhtPkLfs9O7agLS0irUug/16NYybkorrIstUBlHGqktF+LVnwa34hSF1WToXbpwpi/ECLdGaKAo5i5ZyMjyGIoB+1qhTIvJ00ZqxOHV6f+JNk6ZUbXpLiUfhPxCeyP2CLXQsaru9D3c4iC3Lgs3vdheJaZYmT3dK1SNpTR9dHq3PoF2UVDfU+F/eURgVPs4bIkWr3dEoUPPY08GXZx9yeicg5F6VueQMQrHVNgTESYUoc84+63hP3lUN9YG72zn/JCmjAzOw/Nmz4bxorF0Pu0mhdS6rSF94GVZjRttCYo/g3wJUJiZ0T3+6Lw/w7A/0XXLsRQxrFPF8ouZRYcLRsJCe+Rq9Gj6KO0K8rdtCtaKXiEEgrnGvXf36b+soTWRyLl4WA6JGBGeYeWKhybSoGlC33cnkXujx0p++vIFq5bHhkWSllkqZ9UeRVggw717YOotTdBVvmXN7ZIZgoaBP6NGOM6Mlsh8pvjkOIwKd7atOGWYjmI8vnv4f+fIsKGo1COrD+E5/tFSlJ1dHqPiseRq9YPUP60BvvfcgTWuUjuxUip+wOKibgYrU63pFqoIxvk/0Ah/QWy7N8c9aUkJt8g//J2W5v6ZxL6PxrP/he5Sl1fkNuk3VaQuw65HE4P++9GH+fvRzJvqvneP0IJE3BBplaZqeUGuaT0G/3akCvS+uH/89EE/lDgtjr9mYS0FpQkdU5o341oJR/EnvwH9F2cHI6VMmyXlDOJVjbPC+gwhgWZ/zA0yfs82oz5hb67KZpEtvRnhsE+WLyHkvPfRpPa1OfbNmVDQa4yrQpNps9liJhjaZ+2KU4L0za1RepvHPXT9xWObYtW5OJjswjjJlJqpiEja1lajzk0U+88Fv4uRiG5OPVYR+vUnyRbs8zXADdRSMtTfPZBdjJaOZsX5ObRXEmL5XZHK6hrhmvWRMaD3RLezbbfchITr5f06/0RoUw7meVRLtaWbzqtrKfHpvbBXm7Ad4v3gDgQjhlEe0radyVy9W7sn4fSiu2L3M+PK7nmZeh78Gz4XZ9FzK/pTPGDvvFBb8jt4ltoQncVcqtqnFsL+VA39pOpaUfDhqwJ1yCf71ejvDZXFTs9UixODQPf/PD3FNpMSGvUX6mMRscva7NdytBJ8ieRK9s7wj1tgSYznyiUlzQZTpVFrjS/Qnmbfhr6xmzkhjYfETmkPJM1gCsLx6YBVyNlYV449n5aFYey/GVlH/pFwwDyBBUTIhInD5H8e9HH43Sk8J0R6tkmnH+QkAcLxZa8gMg8Oj2TpHyUJOTuQ9bJx8PvdDBy825Xb7JsdM1R4Tf/ALLUfg4ZUz4e9aVNkaU/7ktvJeScLHn+Zfn92j3/Ro65VNkbTwAAIABJREFUJUK9jTxlj1W1vU15B1KYkKJ39Ox2vw8V6QRIyHFYt8zUcoNccsqEfmxoMvT68P+ayMBwPSFPXmp/ptWYVLYlKynF8qP9hcP7fDEaO8pSeEwqbIsj74NieoHj0ET/Z8gAdGRji2Qup/14fxla+W7X9p5/c6vKRN/M+aH/dVTEqUjZUJCtTKuCJoEfQmRQvwjHlkWu6WXy09ttBblKpT2S/SUylF2Dxvtrwv6ZKAShsZ1JUFJQDPLfkeHh3JIyU5WxOqkd6tSfJFuzzMuR4WBqeAeWQu/Dhyue73K0MQpQL69xco7LIL9eaN8FKH/pG6Jzp6G48cb+geHZ3xDK37mkvI65jcNv3ki3tAoFxX+kNjTuFpXuKVSk/BrB9j1CyBUZ+tJ8Qs7v8Nxmdrh2ZeSN15JTumobt26iVqDJDa5oH0VL1Ne6++nh+EpI8XkjepG29+CGZ2ZfQwPWfmG/57EO/YQpyP5QmmkYHkAD6xSP4uv6WP8xNN1E70MfpUPRxO9/a5RTRl5ThHsfCXlMTGEPI+PBDsjl8ZPufk5wS/uiu6+TUM6CeNXo2IXIGvQ1YLbL3XIplK9nes12boZcRW8E9nX3YsxUUb5t+V4gsYiuaRsLWOICVulylupSaQlEM2b2AxRP0pbcKSovWbZw3cZo8mNI+d3F3e8qyCS5k5acXwFN/K70KA4uOv93lM9pLbS6s214Vv/0oXT4Se6kifdba9yzDjF77n5fN2WmlhvkUtNvJLF+9guJ/fm7je9P2P+Iu58Q7Z/t7tvVrHdGqHdmdMzQatarEInIooVrGmNwjAeQq9lFkdxP29Xrw4iBjcqvTQwz3DKtQ6yRt5KbdUzZYErX1HiOU6hOq/JGFLM1H/iIK37oQyhB+c61b7bZzkreg0j28JQyPYqJDK6oOyEF72SP0hWE82cCv3X3n4V51tZI2bjP3bctaW9lHF6JfNv6u5FNuKc6bqrnohW/X3vn+NoHUX+KYyZfjcb2lxVkK92OU2EKH/kvd3843PdDyOB5dhiHv+ZDGdE3Ac4G/ozStSwRjn3ag5tiHffwfsIUajTNI8LD4MJ6nw8jhKNXMLPHkFu2m1z6f+xDQzsWfDvD92Izd28Zd03xnhd7SXhQab3jWBn8DXCGu58S9r+FXOIuRR//o9z9W+FhQqt/9gJEE5hR0ZmHAxORwJPuvpB1ZjxdAC8hckisqytlNExM4olZUg62Qhl1yGtSWFdnI6vW/DBwPBbuw8P5IWkQwrGO8aqR3GwUh/eCJTCLmWIkVyoqMSZq+Xchpq6zUp9VuLYjiYUlxgJavdidugydyUQzwc/+P+4+L0zGdkFW0lOK/amObJDfBbn03IOUjg958O+PZP6AlMRZ0bF1QpmvowNMgd93lhkCTPTfjWD97dz9L2a2E7LUbhnJzUTEUHNN5B+NleK9XMQL57n7NpH8Ee5+eLR/g7u/Mfxfa9yzhByH3YylKeVG954SL5nE+tkNzOwdKEH28u6+tZWzCFf256rnlGJsKWnb8Wji00KMZWY/RH1kUuF4sS8+6YU0C7Ghw8R0HJdxbdEIYmY3uft6JW1oyy7ai29uyXevkhSoRtkdUzZ0o1jVrD9WNotl7hLJ9VRpD+PmJWilsyMJXMm1lcqYVbOOJtefKlv3nmxofOddaGyZg8JiiqkA9kfK+CtR//sF8IeSb1OdvMZ1clx2NBbG44ophu1ylCLj+TA/mxPPTSwht3GduUE/YWZnI0+pA8OcaxIyxK/pFeloRgImNvjvuPuZphQjL3jgIjEtXl3vIe1TGMv/4u4/KCnnY2i1d6+kin0ULIv2Y0NL0lPD/5ORO9jbwv76wO3h/zIXuVIXPDS5iN0ininsnzzo+054LlNC54LWuIr54Z4eDH/n0yHWosv6X0S5D/1KaEI0u/B7lLo4JNRzNHK93AINuFug1beju5GlwkW4eD4cK7o/DYlXjeRupekG8Gj4+xq0MhjLdXQnRQrsS2o+p6noQzQfTfBAE9QvFeR+SkIsIDVid6jvUmmI6OTW8AxuQzFzLS42yC1vvfB/w72nkdB6OLJnIbfSRkzYvshd8zMFuY7upBW/yTpE8UEd5Boxq2sT3A2L/ZE27qQJ/fmJ6P/kGK8gXxmzV7fM1HKjflW6lZQ3rfBcXgVc0c2YE5Wb5CrYpj//T7vfIezP6XQ+sX2T6eBu23gmJcdXR2kt1ig5tzcydDT2n0LeIDPRmP6RkmvK3FEbucka+3sUtier+knC/Xcan4aMUdE1uyND8h3h7+5tyj4LOCR+p5CL3S+G0Z+mozF5p3hrI3t4YfsBcosbVjwUCXGQKObsxQllLYSUiykJsnXi8JLqr9nWOmUmu5RG16wZfqfb0Orwd0tk3hWe/YXAiWhVuKysSrfjSLZj7DkydK4a/v808lZoXLs48mCKy5sT/d/o95NiOYYR19vLDblS/hWtdv4pPPeb6MK1sk/t2wgZER4N7+4ro3MHoEWuxv49KF1JWTmLU8MVd+A33scHGscRbUjrZKfxkb4JTe4OQlaXhYpbdE1xoG3ZBn3fCc9lSpuB9GAUO9nw418Urfoc1If6Xyg5fj6KR1gXsaW9DimHexbkVkPKy610IHuhHnlNpSya3LyNJhnL44X9J4fxTPZAxDK7h3I/iAKtP1SQuzD8TpNoEk4shaxv3dadSspSOxawRhvakbUUFZwkoplwbA5Nz4f7kSL9EsRCNxzZ42iN4XgFcF2J7MboIzcDxdmUTaKLJCF/RorSQQW5WjGraCKyIXIpPTccW5LwcaZVGSwqGTFx1uXUiPEiIWavbpmp5dbsd/HvHscwlcaw1ij3bpoTqcY7tVD07N+CPFPKrj2KKJar5HeqNEL1ekOKyN0MNZLei9ifGzI3IObqlv6ExvRro/22xtTwDlwZyXbqI6X9pA/3fwhSAvdC8fd7ocn7ISWyL0Pv8AxE4nBH2C8boz6H2KHjY+sTYq7C/kHo2/NnNFY0tmTiIKS4nd/ok+FYMnEV6caNPVCs6PTQ3zsRmKQqY5eTGIdXs/4k2TplFq6bhIjJ9ka5K6vu83WIfbMr43co44zQV35ffK9KZKuMhQejb8i3UQzbB6NrtwKuKpR3NUERpakMbgFc3u/3s8tnNQkZtrYPfzv+ngNo3xIo9c8SheOvJCKFQd4+7cj+FqIOUdSgb7qPD/M2lMcF5NJ2XnRuKlGwKIoj/AZSKi5CcSltBypqTEoHdO9v77C9q2zAISIziI4tQsIKRc22tVNGZzcGzWhAeglhBTeSuxaR3WxJtdU/lbwmhXV1BhUW5Tb3uyZwGEqVcBht2FmRu8RvwwB8IbBtm2c0KfwfW8+7Ig8p/u60IWUJ+8VJac8moVQwdFbVSQkBBCEIG62aNZhOJ5UNjimyFCy2FFY6iAhXomO7hN/sBpS79LUlMrsWtveX9RE0mTsh9PuTkNL6vqjvFFeRt0IK/AwCMQBaUbiwze/ZMyUD+AyaQHdkSuxXuch6um74fwM0rv8T5f6L5ZJYP7to58M0J1eNidGLCMYFRNjw7jbXvouw+hL2+2aESryXLZFx7kBE/z8l/P1saMvO6PtaXMG8OvrfiAhPaBpPn2GoMfUwlF6mlndDv7fQd6YXjk2njREu3O+bqJhoIgPEYoVjizP0u5PEoFvR/oXDb/UECgWAoR5RbcnIgmxH40Ykl0xGRroylsQ62kX9qayrqXLJq53RNauj8ebvaMz4PrBRQWYRxLlwD0rXcE/Yn1xS3uHtthLZjsbCsL8L8D1ajdK70souu0HoqychY+aP0PfnjanPY1AbGk83GXQ7umz7nQSyspJzr6dg1O9Y1qBvpo8P6SNo+fdXyLXkndG5HVBgZfGaScjydxqKCWv3kJMnpQO6945KS5uBdAYK/I2PbUgXq050p4w+TJNBaQZanZpC6yTjcRKsOLRnUv3OcGRrPoetQ3t/gdI9/BxNrN5bkEta7SDRnbRmG++iyfDVKHMarUp41aS0hTq9RhuSXCqLfSEcWxJ4pOT4KShO7ipEbgMy+tzejWzxnS++6yXnk9xJazyj2TSp/xdFq4EWnZ/b4doWd1Ja3TSL7ndPlZRzU5vy/1zYr5PWJanMOuWSnn4jifWzi9+qo6sgMj6VvvNo4j5sI1SvNpR6pJQpGXkwzEeGjidpswKCFJx5JceTXNrq9pM+PIOHKXgvhHvqyD5INb3/gnc6OjaZoUa5JAbdSL74vX0PMp5eF84Xx6lzEu+/rXEjkpvebispM1XJqpPaoU79SbI1y6zjUnoDmgucjAwuC7eRSw53qdmnOxoLw/56yDi5KFJ2PxHa8542fX1FNM59H33DR4XbZUlbryDMdZFR6yE0Jh886LZ1cS9fQF4DKxaOr4i+ZYcnlzXom+nzg9oE+BSt1pb3UvLBDy/bV8ILchmwWptykyelY2VDFt4nkOJyVPj7OCUUwglldZy8UK6Mnk9zpeNHKC7jAlpd0H5DRIHcoQ2TEaX5XUiR+UfYL7OoJcvWfA7/R4hTjY5titjF4mP/Rq4vb6koL8mdtGYbP4cU4bchA8ibQ9/fvyA3o+I3HZa/Px1cKqmZuy9cMwW5c+1O+NCGZ98ysU2RLb7zVMRukehOSqLll0RllER3UqrdNC8reU6VMV7h2Cbttm7LrFMuFS5QkVxfPDyocBVE42zppBHlvawdB9ivLbS1dLUjvDfPIsXoOgqrBZHcdhQU8ejcdBLi4er0kz48g5NRyMIrw+/zKsSceEqJbDK9P3Ln+9/Csf2IDNVIUTgVuXpOi7eyPks0Hoe/t4TrVyt7jinPj/7FQZZuBbnacXiD2qjnpvqBdmNAQS453CWcS8px2ebaRWgaCz+CFPEH0Nh1MAon+UkYE+JcoAsh42LyquiAf6fZNL8JdyGD+ipU5JUejVv4zX4dxpvL0Lz9srB/Lm2MDGXbuGUTbQczW9rd50T7L0GT6l3R5OEU4FSPqMoj2Vrsh2MNZvYa9OFeEQ0EZ7n7rSNU91Q0aD5qSr/wKWBpFCMQP+uXoJXdc2il4z7MzN6CVt4+W1LHUcgSel3YT5bt8p7mIJbQ56JjCyOjQczEtR7qgzuiycPp6EP7fyVlboNcqaYjZehH7n7uMNpoaAJSLPOYjhf2GJ0YOgNFtSE32i2jyxy5n90xAu1LYnW0mqkAzOxomulX7kW/wefRisf+kVwVE9v57r6YDU2BsiOwGV2kQCnc+8nh3x1QXEqMVdEK5caDLjMqOzX9RikTZ48YKw2tPDbeqT95+Nia2Q2IoOm8kuu2AQ71wOQ6aJjZI8Cr3f3fJedeCtzqSpmwI3p/90Z0+Q2Wvm2QYeQAdz+tcP1BqK/fiiaTDbiH1B797CepCH3n2NCGRZACfAZibX6sIJtM729mayEX+VnIFXN1xIL7jsZ3twaDbnF8OsdLmBFL5FJSurws3NOyyNh0D1II3uPu/yrIvpfyVC27MEwkso4m158qW0OuwQQaT6qNirRXYawoZU83swdQuNPs6NiyyBtoxUI5n0SEVMejmPOlQh/7ibtvWFLvmmjesRJS+k5z93+Ec7ejdxcUcrWRu18Tzr0T+LoPTS1xM7CluxfnxaMOYV62DOKf+L27rx6Ot6S4Gisws83Rt34ZpOxe7O6X1CpjvCqDYXL5kIccSIHa+xyk6PwDJcm+w8yeRha0U5B1swXepMIf+KR0IiFQEz+JLLOVcPfdzewC4Dh3v6CkvHeh3HuNvDfJsl22/zLgd+5+VHTsQGArd9+0zTWboAF6O+SGs050biF3f77b9tSBmb277Ln0qa6zkFvmLu7+JzPbF7GUftXdvxHJ1crd18uJQQ1lrFYqADO7HxFvFD/2N7v7StGxGbShjI/auprVTIFiCfT+1qTDPxh5TiyoErnY/NLdH42urcxxWLfM1HKDXGr6jaT8lqkwsyup/o3eGtrybZTj8NxIcdoWuVi1KE6DgpmdCjxdZuQ0sxPQ6sZOYf9ThFVtFD+0LIoLPDJ+j6PrH0EkVG0Njt30k34h/EbLImNeabojq0HvH+QXR66cqyAPiN+4+7zo/AMo7ut0hirMxN+CYl9uZ9BIGMcWzHeie94UrXaujYwbM5Fxo5gG4XDg46GtH0MePjsh9sP9KCBhzK2b2iG5/lTZmmVOb9e2EkPAiuhdfyvisIhlYyU/OVezVeS4LMhujcJWfoOMkNNQP9zZ3X8dfyfM7Elg8egbMgmtEMcG7QOR8fE7aDVzQf/3EUgXUQfBYDoTeXDc7e6fNrPVkQK1WuerRzesIkVYR/goWOrsx4bo72N2sxtpuk19H1kvoQv3N2r48I+VDa24fQUpujG7YTJrWR/aVMo8WnFNnXicZNku2/9qZHh4EPlvzyK4JXS45qXIuvcXWl0Dk9xJa7RvTaR0xu/Je0PdHeNhevw7p7pUJhHNhOOHh+d9NLLQH40mj2XU3ZWyCePEP4NcXXfSZKKjxGdZN7axjptmT2jLuymzbrnRNWXxkrXdjhPbt2u07YbSZcTHdo1kD0CrK/PR+DA/7B/Qi3eqVxtaNfgn+p4ejtypD0dxgjMoxAWhcIl3IhK2dzV+rzZlJ8fD1eknfXoOqURgdej9W8aicPyY6P8kBt2S93pOG7kZ7cYv2s93ktyWkVLx2vB/g+xlfcJcqyCbND5TP11Eav1JsnXKrNmfUtnTiyEsdyFDaYtLJomxneF4xxAWNCY1xsvvF+QWo/W7ktyfBr2h1bOvIAV78XDs3RRctsfShgwKP6ciRVinbTyvDM5Bk6wXTEm670Ea86OmBKZ3eUiG2UXZyQnNxwrM7HdI+TqTVveWkwbUpimIyCK2lL28jfgz6OMyF6VA+E9RwOR++rAHC6qZPZEqO4x7WBgxbTX6yXUeuY0GmalIKdsJxexdhEiMfu3uT0dyye6kCe3aDbkfPYoGxwOQm8HaKMXIiWXPpZew+i6VR6G4vk8Av0TB3weiwO8fFsq+F7kI3mJmj7n7VDNbH7ngvbdb2YR7qrsy2LD8HknTTbTU8ptYf+oKZlfud8H6/ToUJ7YA7v6L4j2GcfZe5Cr9fOPZdlNmSrmmhLzfQzEg16LUOH9E78pUtPJ8+kh5eFiFC15YhXwzTdeea9398V7U3UuY2dJofNicsDKGyHaO9mGsypnZlkhpPAZNZBfAy8M0kvpJr5GwihInep+CiMOuojWMoehW2M5NebaHlRxT0vHJwFe8w2St7opfHQQPmi96RchEYTXpYWAld3+26I0QzieNuWa2B1o9O5zWFafiymSd+pNk65QZZFJdSmejuM8no/t/CUrX8apOz7kdTB42N7n7lxvfnbBit66H1ftItmMIS+jTX3H320rq2QHY29t4N2WMPMzsdMS8eyRy3V/azJZD/WnNlDIW7mcDB4zn0CD6NGLFvD36cD2FAsG7xdfRBO7jDI3zWRLYv8N1oxkbosGh0h2jlzCzt3c4Pbnk2F00PwjGULesF5B1fXvKXUu3QMQaDdwejrXE7pTIJqPKXczM8BATE/AgIk35OXJre6zsOne/CeXFPNCa7qSXmtkQd9JEfBbFS14YPmBnA98F3u8lLpd9wm4oXrGBb6Dg9wbeEQu7+2fN7Dfot/06em7ru/tdJWVP9RBzCMw3uZL+KTy34chWYWEzexvNiUBxfyEAM1vBFW9zIFL+jkUGgweQkv+lLuoGTapPjPZnF/Ybk+67o2Px/47Y635ZLNg6xHihwPUGZprZhsgL449BYVsSKWXdlplS7g/D/e2PDCYXAR/1KF4SON3drwh1L+slbsc9REdLa1D8Lupj/T2BK8b+82HrJSajcXanwnEnvCcN1OwnvcZXUFjJZVF7NkXv7K/RNylGxzj7oOCAxoY9CqdfjpTtBvZDcYQHBwViAdx9WrRb9d57KLsb3AtcaGbnoVX1WCE7LJK728zWcve/I+KavYPSMYdWpI65x4e/O0fHGt/9YhxenfpTZZPLtKEupdsTuZSW1P88mqMCPBYm7o+jlfhiuZuiNA+N2L5T4r4Y4ZPA+Wa2J7CEmd1BiO0skf0r4mU4Kjp2QDiOu+9cck0DlyL33biN66K0FDOjY6ugNDE3dyhrRGBmh7j7l8P/R7aTK/TnsYTNEKPos2bmAO7+bzNbPrWA8awMXgF82cxOIrwk0blXUbDa1cT2DI3zucPMbkSuM2NVGfwbSiZ+d5Vgj3FCxfmihXhP5M7wBfRhmoYm09ei3/xU4MdmNo828ThRWUcDPzL51VfJ1sHx0f+GJg37lgkGa9wX0YrcDsBmZnYxIjHqpJTdjgK770MuTHWxortfGP4/H32cPjeCiiBEltPEfYDVkNHlHuSu8qI2ZfdjYpCCVGXsTmDJ4ElwmJmt7e5rdFHfELj7qolyRwCY2XUe4qoT8Cngv7yaVOoziMBmPlrxBk1I/jSMMlPK3ZBmvOQVKF7yXAB3Py9aDW3gWTM7gnHk4dEvmNlNiIH2CuAKj0jYhoHjUCxgSzxcCer0k15jZUTnH+OqcHzBu1QDjYn2ZIYqOY5cJXeNjn04pcDU975LvJjwHhHuuQ0ORavcIJbqX6BV3H1KZFPH3DoxXHXqT5WtU+YeiPznFjPb3d33N7PTQhlFXI/SO5yDDEJnoHfgz7GQmX0UGSOOD9dMA04zs8+7+09iWXefZWZvpElcVRrbGbA3Uhz/J8itghZJKjkSvIRMCs27il40k9F4WtdQ3Q/E/XaVgbWif5iLVqMXxAqa2bR4vwrj2U10JdQR34gUhe3dfW449zUUq9AS1JxYdjLD01hBsJZ8EMVVFt1bTiy9aAAwEW6s4UPdJxcF7nT3lYNLU8N6OYWhRAaHu/u3C+UdgHzHK2WH0eZSdzEzWwrFwU1HieZnoaDmLZGSt3mjzwb5JHfSxDZ1dGccCVS1oeR8EtFMkN0K5Tb7o5m9Ca26Lg7s4+6/6la2V7BEwoeRgKW7ad4BrFdnNS1YJjdCsbO3F40N3ZTZrtwu+lMSk2uNNhW9HHrmqjdohHvbJGzro9WwhmJ4VpdlPoSMUpWkWN32k17AahCBmdnngEvc/Ybo2PrApu7+9YLsl9y9TFFIadOy7v5IteTIwCISLzPbCKVUaOAabw2NiMfc9YmUrG7G3Dr1p8p2cU+VLqVhrnIoUpDuRp4hk5CxYwkULxpP6O9Ec9ebo2PrAGd7iftfMGwXw1JK369giH4zmnM8iGKvuzIGF8fWquODQFCOOsJL3NPHAsK4817gEGRg2BIZEc7zRGb48awMNn74oivhAnT7w1sNhqexgvDBK4O7eydXzhGFmT2I8ubcHh17FcqL9jIzm4xWX6aRGI9jfY7daTfRN7PjUN/5gEc02SaGuTOAe919n+j4U8id9BfAr7yNO2lim55HLicNrFTYL7oh9RyWGN8WyR8HfMqjWEYzewWiGd8g7CcP+IP8OIwGZTzUW0nvH8l2jPGyxJi9OmUGmdRYwLr9KYnJNRVm9s8KEXf3bl31Rg3MrBFj/AlEwNCWNr+inKR4uCBbK76wlwjfl/ORJ8KQVRQvxFSZ2SxkrCyO53e6+4pmZo17DV4opWis5oRnPSfafyly8d/T+0iDb2aruvuM8H/bPuvu95jZ3sCGHlwLw3v4CHrvFgUO9KGx4HWVrCrW0eT6U2Xr3lOQuRHFkf7dzC5F488cFG+5apD5Kcr9dyFaGbzM3T/Z7vmaXINXiJU0E5fCg97KELpOqHMK+pavjMKk3ud9dtU0s1uBD7v7jdGx1yNOg65iIHsNU+qPeJyJdYPKFCCjGWblKcKA71SNrQvgo4AJpx8bIdErQxO/xltLEtgaZZclKf8iyr0y8HsfzxuypD0IfBn5538JDXyfDee3BS4cdDsLbS5N7BvuY1qbc6sSsYAhl+6DkCJ4EVq92p3A+NVFmzap2kbgucwgjaGzyDD3kcL+2dH/8Xs/5H0vvvd1ZPtw708BbwPeHrbHC/tJiYJ70I5H6MBuW5DdBk3ES8dSNGE+AVklT0KED++Lrv1b3TLrlJvan6Jye8rkOp638Oy/ioxRMxHR2D7AWsMocybK2fckBVbXbvpJn+57UngfF0cr0R8If0vHXWRMnFw4Npkmu+Pj0fHiHKUx7jyHVndmhmMPIxKVA8I4cSaKle7nfT+BCPeK7Sx7769lKCP1nOj/dZFxtbG/N4p5a+w/FX7zmeHZFcf2w6lmeq5Tf5JsnTKj41uhVCnQXD3/F/DfkcwsAiM2Mir8s+J3+DUKM1k07C+GYvvPL5H9M1phbCzyWOgzf4netyJ7csvWZX/ZM5T/yfAc9kPeFnv1s5/WbONNKLzmoPDsFypug27jILfxvDJ4E/J1Pwn5M7ckw/Qe5myzkBPPx6hlIUawMpQmQR0NMOUA3B65QswCznT33w22VU2kuouZ8vcsWdYPgwvHXBfzYy130hrtXAjFs+3lI0wcVAd13P/qvPcjPUbEsOq8ge4jsIpUx/3OKnKeWc0chyllBpna5Vbcxwfd/bTx6OHRLwSr+t1IITzZC6s3XZZZJAtZAA8kP5FsUr69fsBqJKM2s98Dv/XINcvM9kNkXZubSDVWdfcrrX1euq8iz5bLkdFvV5RC5RbgY+5+Z/d3k46ScbddIvt/ufsK0f7V7v6W8L8B//LA3G5m1wIf97BSZVEIhYmE5Afu/uaorErW0Zr1J8nWKTMcS3U9TfIIMbMPIDfsSajPb0iT+ftqlFv1gcI1jwNL+9D8kwshRXbJTu9bjOK7lwoz2x7105WRYni8u5/dTVn9gpm9Fr1POyDOhZORl1VfmdP7gZJ5Zik8MTRh3CqDMLI/fFi6/4+7t3X9GM0IrljHUpEENaMaqe5iZvY35Pb4h5Iy3gl8093XtprupDXbOgutTo4kcUwtFCdjVojBLDmf/N6Pp49DN6jjfmcVMV51lPbUMrsttxOsmaJiMlL+dqIZY3Ng3LbOAAATFElEQVQays00ao0jg4CZvQV9G96K4ktvQZPVP7p7kVylH/Unxxf2oe6k1ApBdi1kuJuFlOfVERvoOzyQ35jZo8CW7n59yfXfAt6P4seWdZGaTUarYsv7MFJ51EXJuNpOcZmHVhGfLDm3OFKcFg/7dZWslDi8OvUnydYss46balIKEFOs4OqoD/0RvWu3I2+h+4ttCmWfDpzh7udEx7YFdnD3D5ZdM1yY2RuAZzwww5piuY9Bsf3XovnNvH7UPRyYXLTfgRjNt0SeODd2vGiUIWGeCXWMyj4Klif7vSHryjvRh/4x4PV9qGMKfXZZ6fMzSkqCOqC2HRL9f2S7bdDt7OK+dkOThu2ASeHYJDQZmAXsHo4lu5N20YYDUaBxV+6mI/ScaiVSj44nv/cjMUaMxo0a7neIzfMQghGx5HyV6+uTdcvsttyKe05Kop23ts9veeSmP3c43zzkwrZu+H8DtDr7T+DN3fSTPt7vcSj262coHKTjNwe5lO4Y2rwjIbF1dH5H4N9oRb5Yz91hTO845o3Qfacmsr+O4LZdcm47REzS2J8HLNbhuc0rHLuR4IqMUhrshxhYZ3RZf5JszTLruKnOoLMr+z2R7ArIA+p7KOXDc8j18hSUMqfYrl8i8rtr0FzumrB/JjJynoxW9QF+BWxcuH5j4KyafeRK5JnU2D8XsTvvixh3jxvpfpvY7leiec8M4DJgtUG3adDbuF4ZbMDMXoms/zuhF24Pd0/RqovlVOXEu8DH6Cqa9SEJag/b9gN33zv8/9N2cu6++8i1qjcws0+hNBlFNtMjPTBkWqI7aZf1z0QfnefRBGXBgOB9JpBJRYI19fyy+6/z3vdqjBhrqON+F/WV+Si+J5adZtWur7j7EKr4qjKDTO1yO6GkP5WVNyZZP/sFM3sfSumzCfAK4C+EFQt3/32XZc4EXuvuc00EZuehWLW93P1NJbId+0m/UOebY2bf9RKWcjM7xiPXYzPbFeVVfbvLBfIENBnfzN1nmtl89E42sAOFfHVeSGTea9RYxdoRxfLtjZitGymatkEK7gHufloo8zrgKI9Wr6L6tkOraG+KjlWyjtasP0m2Zpm1Vju7hYkpfU9kRFmuONc05TmshLsfEeZ7y/tQl9KFgYe8QExT0aZH0GrtMya2838j5f1Ok0v0Ne4+KlI5hPnsB9F3fgmkVJ/qY5RBtNcYt8pgP374lGXZOpOS0YTggrFKeKlnoJQcjwOPeB9ZyzLAzJZAMQHLIoVwCJtpqjtpl3Unx+0MCnWUgTrvff441HO/60dfGUT/MzHp3kt7ZdB9HLB+9hJmdjkhlQQan4btRh256y6Bfo/lXLGnj7n71ILsqB+noL3LspnNLk6yzexjyBB4HVKwN3P3f4VzlRN7r5/fsBYSxt0F70kwah6BjOKlRs0gV0fJqpMuIqn+mm1NlUt2Ka2DoEiuS9M9e0PkJXQ1cKW7/zKSXRjlpHwHzXnEJYispyUEJBgBX12YZ0xFaXpWKMp3aONjKE7RTVwOP46NM0VX40HCzJ5Ght5T0DvXgrFqBDQx4n+BctbdJGPZeFYGx+0P3w+Y2fnAie5+jpn9CCUy/w/wYh9FqSUATFTf26MB+BNhVWeKu/9twE3rC8xsN0Qq8AngnOgD+t/IheRgd29rvZ5IqPPe5zECrAa9/3hBuwl7xsjCzP6OVjrWQiQh24ZJzT/rrE70qW1JxgB3vyfI7xEOHYvG6RgvR7niXhlk4+/pPsDmSDl6KCq3MUat0FAQC+0rPT5IWDNF07I0UzS1EJulKFnWXWqHpPprtrVSru5qZwpMcarrAXcgd8urkAL8RIlsbYI5MzsRkad9zN0fD/d5HPCcu+9Wo51Xo/QFZ5rZz4AX3H2PcG4l5E67cqcyRgp1jBtjDWZ2KiLuORqR4X0Yuamf7e5HJ5UxXr//4/mH7weCVWiSuz9qZi9GFMVLA0u5+0cH27omTIxVxwFnI0atJc3sv4Cvufvmg21d/2AJ7qTDKHtd5KJUtCgdNpxyB4Ga1uxk2fGKOu53ZnYAcKm7/9XMNkCxKM+j9/DaLuvveZkJdWZlsCbMbBFEtrMzTbKdU4Avu/v8LsvcCjge9b3t3P0vZrYTytW2ZUF2RPuJNXOSFfMUD9lvuOpZM0/vxiiOaoEIUvK+44GAJsHDKB6j2q00DiQvaa9QpWRZTdbRQaLOameNMv+B0kldhtyxr3T3u9vI1iaYCy6np6I4+UeBlyBFcmevkb84rNiej/r588BG7n5HOHcA8CZ33yG1vIzuEDz7Xu3us60Z5rUSCqF5fVIZ41UZzBg+bBSmyzCz24Ad3f3mxgciTFQedPflBt2+fqLKnbTLMvdCH7LfI0vihcAWwHnuvtPwWpwx2lHH/c5qxHjVqL/nZSbUOWpcl8YKzOxommk47kWTz8+jNBz796D85VH+vn8gV7VnC+dHvJ9Ede+OVu++QPPeDwMucfefFWS/5O6H9rDulr4aFKl73H3ZXtUz2mAjFIfXK9RxU61R5grIuPBWmsbaq5Gx4Sp3/2uQexDYwMtDIVZF84SXdahjFWBmtyvNYV7yCuDOeOUyeGw94e4tKZsyegtT7OYK7v6cmd2PvC2eAB5LNXxmZTCjLWwUpsswBT4vG3zUH3X3lwR/+QfdfflBt2+swczuQqylV0bK9ZZI4d510O3LGD2wGjFegywzo/cIE4zXufvs6NiywM3uvlLNslZC7u2vQUyM30SrH8+jtEa7uPvphWsG1k/Cva/pUZykKd/lne6+spmZh4lUWBEqhdfI1xuUX6e5ChtjGeC00eSx02tYn+Lw+ok6bqpdll9KIGPDIJgLRpghz9GD63PG2IGZXYJCPS4xpRh5HjH3vsHd/yuljIX72cCMcYHRZi34C3JVOjk6tiOiM86oj+W9mSfsBTOb5O4XmtnPB9qqjBFBTfe7mWa2IbI6/jFMxpcM8t2iH2Vm9B7tyHbaMrJ2wA9ROpP90dh9EaLKP8fMtkHpG04vXDPIfjIJpXu4LTo2HWh4zMwFGtb352j9ZhpK11JnvvXhcN1v0feuAUeMj3fUKGss4hbkodISh4dcG/8+ss2pRvDSuahX5YUV0JhAZiNkLPkzcGIkejdKsdNCMAdsBrQodyaylxNQbGEMp9mvM8YO9qQ5Fu+H0mYsBSQzDmdlcILDqtNljDbsB/zezD4CLGZmF6GcMe8YbLPGLO43s1XdfQZwJ7BNcDnoKg4oY8xhfzQpAJEUfRu5lxwDFN3vPgOcRYjxCsfew/AMMf0oM6P3+CVwvpkdgUgppqMYwjO7KGtD4GXuPt/MrkB5Pc8FcPfzzOzkkmsG2U+OBi41pZiYidzqdgvHAdYys42DUa0dm/hX61TYcNE2s2Xd/amuWj22cQxwnJk5rXF4x6LVsXELM/stWmWcDFyPWHyPRauNTxfEvw2cbGZtCeZKqvg+Mrqc5D1gBs4YDMzsDcAz7n5L2F8OjUtrI6+LZGb07CY6wZEQzD7q0mUEF533oAnJfSi/47zBtmpswsRU+lBYDdwSTbgmA/u5+w8G2riMvqNb97uqGK8u29LzMjN6AzObjJS/nZDr4gNo9W6Kux9Ys6whpChFMpR2pCnR+RHvJ2ElZXt077OAM939d9H5R4Et3f36kmu/Bbzf3ad3UW/PiXvGCvoRhzdWYGafQ67TN6T0b6tJMBf66zKeFYAxDTO7EjjC3S8O++ehceJnKG3W34rkQW3Lyn0hYywgkAZUMT9uNlLtGa8Ik77JWbmeGLAEev9uYrwS6u15mRkjC+uSYMyqk5mf34hxGiv9xMQo+T1gC3e/KTp+HHJr3Cx4X9Qtt6/EPaMd/Y7DG0+wGgRzZvYN4DZ3P7HsfMbYQPDiWsmVH3wqcr9/rbvfaWaroHQkqySVlZXBjLGA4BZahpWQ6+ii7r7oCDZp3MDMlgG2Qq5bXzezFVGakfsH3LSMPsMS6P1NOUgfRqvGO6I4lE/GMV7uvk7NenteZsbIoluCMatO6bLAG2U09JNwn4chS/sy7r6UmW0BvMLdj43kdgW+Abzd3W8xsxMQC+Rm7j6zy7p7RtyTkdFAWFFaHxkYhrCIuvtbB9KojNows8eApQOh4ruAH3uUEspqMGdnZTBjTCIoMAehVY0zkDtEVl5qwpRa4GwUlP4Wd18iHPu0u2892NZljCTaud+ZGHwbMV6LohivKRGD4lx3X6pmXT0vM2NkEZSkp+quDNasY+D9JKzurQR8DbjQmzm8fu/uaxVkP4bc9a5DdPub+TCSw5vZA8A6Jcrg39x9xW7LzZjYCIaLMri7l8XsZoxCmNnVKIfpmWb2M+AFd98jnFsJuN7dV04pKxPIZIwpBLeRzwCfAH4DvN7bJGPNSMIxwA6BknhOOHY9shpmjFOkuN/RZHRcpBGf5O5Pmdm8QqxJN4yS/Sgzo8cYBQRjo6GfvA9Yw92fNCWjx90fCO+QGtF8Tv9AueA2R0nIX2NmrwnXXJpaoZl90JWovJfEPRkTHGb2XXffz91PCvsfcfcTovNnM5SpPWN047NofPgh+nZvFJ3bAY1FScgrgxljAmb2YuB/gU8BlwOHu/uoo5cea7CQWzD838jbOAn4dyNmLGP8oY77XZ0Yrxr197zMjN5j0ARjo6GfmNm9aHVubjRGLgdc5+6rB5mq5+Tu/vIadTaInYrEPQ8CpwFfcvdnurqhjAmL4ZI3ZYw+hFjRV6C8p09Ex18JPOHuxTyl5eVkZTBjLMDMHkL5nr6BXBpbUMfyOtFhZi9194eCm8GR7n5RNNHZAjjY3TcdcDMz+oQ67nd1Yrxq1N/zMjPGH0ZDPzGzbwJroDQsf0FkS8cAd7n7IX2qMznWJyMjFcV+FRuDy85nTBxkZTBjTCBhUlDL8jrREVio9ke5BS8I2weQi8jWwDbufsPgWpjRT2QLcUZGGsLq3FEoPn1R4CngJ8Dn+rU6V7Ii2oJs/MyoizzuZ7RDjhnMGBNw91UH3YZxhu2AHwN3AeshprwTUVLl9TMZz7jHwmb2NpqTzeJ+30hBMjLGEkLM4v7A/oGufQPglj67aU4BTqC9MuhANn5m1EUe9zNKkVcGMzImKAIb4BeAjwJHAkNiMLPlefxiNLjfZWSMZgwyx2FeocnoB/K4n9EOWRnMyJjACPFipwFvR4lqG8hutxkZGRMWBZKlHRBD6IjkOMzKYEZGxkgiK4MZGRMUZrYZchW9EdjX3R8ecJMyMjIyRgUGmeMwE3lkZGSMJHLMYEbGBISZnQi8C9jP3c8adHsyMjIyRhkGluMwK4IZGRkjiawMZmRMTCwCvNbdHx10QzIyMjJGITLZRkZGxoRAdhPNyMjIyMjIyIiQyTYyMjImCrIymJGRkZGRkZGRkZGRMQExadANyMjIyMjIyMjIyMjIyBh5ZGUwIyMjIyMjIyMjIyNjAiIrgxkZGRkZGRkZGRkZGRMQWRnMyMjIyJiQMLMZZvYfM5sXbSsOs7zNe9nGjIyMjIyMfiIrgxkZGRkZExlbu/vi0fbgoBpiZjndU0ZGRkbGiCIrgxkZGRkZGQFmtpSZnWBms8zsATP7kpktFM6tbmaXmtlsM3vEzH5uZlPDuVOAacD5YYXxQDPb1MzuL5S/YPXQzL5gZmeZ2alm9jiwW0X9a5jZFWY2N9R/xog+nIyMjIyMcYesDGZkZGRkZDTxM+A5YA1gPWAL4KPhnAFfBVYEXg2sAnwBwN13Bu6judL49cT6tgHOAqYCP6+o/4vA74GlgZWB73V1hxkZGRkZGQHZJSUjIyMjYyLjXDN7Lvx/LfB2YKq7/wd40syOBvYCfuTudwF3Bdl/m9m3gcOHWf+17n4ugJktCWzVrn7gWWA6sKK73w9cNcy6MzIyMjImOLIymJGRkZExkbGtu18MYGbrA+8EZplZ4/wkYGY4/1LgO8DGwBLh3Jxh1j8z+n86sEi7+oED0ergn8xsDvAtdz9xmPVnZGRkZExgZGUwIyMjIyNDmAk8Ayzr7s+VnP8K4MDa7v6omW0LHBud94L8k8CijZ0Q+7dcQSa+pmP97v4vYM9Q1kbAxWb2x7BimZGRkZGRURs5ZjAjIyMjIwNw91koJu9bZrakmU0KpDGbBJElgHnAXDNbCfhMoYiHgJdH+3cCLzKzd5vZIsChwJRu6zez7c1s5SA+BymSLwzrpjMyMjIyJjSyMpiRkZGRkdHELsBk4FakcJ0FvCycOwJ4PTAXuAD4VeHarwKHmtljZvZpd58L7AMcDzyAVgrvpzM61f9G4Hozmwf8Gvgfd7+ny/vMyMjIyMjA3IteLRkZGRkZGRkZGRkZGRnjHXllMCMjIyMjIyMjIyMjYwIiK4MZGRkZGRkZGRkZGRkTEFkZzMjIyMjIyMjIyMjImIDIymBGRkZGRkZGRkZGRsYERFYGMzIyMjIyMjIyMjIyJiCyMpiRkZGRkZGRkZGRkTEBkZXBjIyMjIyMjIyMjIyMCYisDGZkZGRkZGRkZGRkZExAZGUwIyMjIyMjIyMjIyNjAuL/AVRQmJWd7ma5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total = df.isnull().sum()\n",
    "total[total > 0].sort_values(ascending=False)\n",
    "\n",
    "percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
    "\n",
    "missing_data = pd.concat([total, percent*100], axis=1, keys=['Total', 'Percent'])\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "plt.xticks(rotation='90')\n",
    "\n",
    "sns.barplot(x=missing_data.index, \n",
    "            y=missing_data['Percent'])\n",
    "\n",
    "#plt title\n",
    "plt.title('Percent Missing Data by Feature', fontsize=12)\n",
    "\n",
    "#plt x y labels\n",
    "plt.xlabel('Features', fontsize=12)\n",
    "plt.ylabel('Percent of Missing Values (%)', fontsize=12)\n",
    "\n",
    "#Missing Data\n",
    "\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x720 with 0 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8d19a5490>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://heartbeat.fritz.ai/seaborn-heatmaps-13-ways-to-customize-correlation-matrix-visualizations-f1c49c816f07\n",
    "\n",
    "plt.figure(figsize = (16,10))\n",
    "sns.set(font_scale = 1.50)\n",
    "\n",
    "saleprice_corr = train[train.columns[1:]].corr()['SalePrice'][:]\n",
    "saleprice_corr_30 = saleprice_corr[(saleprice_corr >= 0.30)].sort_values(ascending=False)\n",
    "\n",
    "sns.heatmap(train[(saleprice_corr_30.index)].corr(), \n",
    "            annot=True, \n",
    "            cmap = 'coolwarm',\n",
    "            linewidths = 1,\n",
    "            linecolor = 'yellow',\n",
    "            square = True, \n",
    "            cbar_kws = {'orientation': 'vertical'},\n",
    "            fmt = '1.3f',\n",
    "            annot_kws={'size':10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SalePrice        1.000000\n",
      "OverallQual      0.790982\n",
      "GrLivArea        0.708624\n",
      "GarageCars       0.640409\n",
      "GarageArea       0.623431\n",
      "TotalBsmtSF      0.613581\n",
      "1stFlrSF         0.605852\n",
      "FullBath         0.560664\n",
      "TotRmsAbvGrd     0.533723\n",
      "YearBuilt        0.522897\n",
      "YearRemodAdd     0.507101\n",
      "GarageYrBlt      0.486362\n",
      "MasVnrArea       0.477493\n",
      "Fireplaces       0.466929\n",
      "BsmtFinSF1       0.386420\n",
      "LotFrontage      0.351799\n",
      "WoodDeckSF       0.324413\n",
      "2ndFlrSF         0.319334\n",
      "OpenPorchSF      0.315856\n",
      "HalfBath         0.284108\n",
      "LotArea          0.263843\n",
      "BsmtFullBath     0.227122\n",
      "BsmtUnfSF        0.214479\n",
      "BedroomAbvGr     0.168213\n",
      "ScreenPorch      0.111447\n",
      "PoolArea         0.092404\n",
      "MoSold           0.046432\n",
      "3SsnPorch        0.044584\n",
      "BsmtFinSF2      -0.011378\n",
      "BsmtHalfBath    -0.016844\n",
      "MiscVal         -0.021190\n",
      "Id              -0.021917\n",
      "LowQualFinSF    -0.025606\n",
      "YrSold          -0.028923\n",
      "OverallCond     -0.077856\n",
      "MSSubClass      -0.084284\n",
      "EnclosedPorch   -0.128578\n",
      "KitchenAbvGr    -0.135907\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "train_corr = train.corr()\n",
    "train_corr.sort_values(['SalePrice'], ascending=False, inplace=True)\n",
    "print(train_corr.SalePrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Experiment without  dropping these columns\n",
    "#Update - experiment did not go well. need to drop these columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Will drop sparse features to improve random forest performance, and\n",
      "these columns do not appear to be highly correlated with SalePrice.\n"
     ]
    }
   ],
   "source": [
    "print('Will drop sparse features to improve random forest performance, and\\nthese columns do not appear to be highly correlated with SalePrice.')\n",
    "df.drop('PoolQC',axis=1,inplace=True)\n",
    "df.drop('MiscFeature',axis=1,inplace=True)\n",
    "df.drop('Alley',axis=1,inplace=True)\n",
    "df.drop('FireplaceQu',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>160</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>546</td>\n",
       "      <td>546</td>\n",
       "      <td>0</td>\n",
       "      <td>1092</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>160</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>546</td>\n",
       "      <td>546</td>\n",
       "      <td>0</td>\n",
       "      <td>1092</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>20</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1960</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>1224</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1224</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>474</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>85</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>60</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1993</td>\n",
       "      <td>1994</td>\n",
       "      <td>94.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>996</td>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>190</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd  Fireplaces  GarageYrBlt  GarageCars  GarageArea  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold\n",
       "0             60         65.0     8450            7            5       2003          2003       196.0       706.0         0.0      150.0        856.0       856       854             0       1710           1.0           0.0         2         1             3             1             8           0       2003.0         2.0       548.0           0           61              0          0            0         0        0       2    2008\n",
       "1             20         80.0     9600            6            8       1976          1976         0.0       978.0         0.0      284.0       1262.0      1262         0             0       1262           0.0           1.0         2         0             3             1             6           1       1976.0         2.0       460.0         298            0              0          0            0         0        0       5    2007\n",
       "2             60         68.0    11250            7            5       2001          2002       162.0       486.0         0.0      434.0        920.0       920       866             0       1786           1.0           0.0         2         1             3             1             6           1       2001.0         2.0       608.0           0           42              0          0            0         0        0       9    2008\n",
       "3             70         60.0     9550            7            5       1915          1970         0.0       216.0         0.0      540.0        756.0       961       756             0       1717           1.0           0.0         1         0             3             1             7           1       1998.0         3.0       642.0           0           35            272          0            0         0        0       2    2006\n",
       "4             60         84.0    14260            8            5       2000          2000       350.0       655.0         0.0      490.0       1145.0      1145      1053             0       2198           1.0           0.0         2         1             4             1             9           1       2000.0         3.0       836.0         192           84              0          0            0         0        0      12    2008\n",
       "...          ...          ...      ...          ...          ...        ...           ...         ...         ...         ...        ...          ...       ...       ...           ...        ...           ...           ...       ...       ...           ...           ...           ...         ...          ...         ...         ...         ...          ...            ...        ...          ...       ...      ...     ...     ...\n",
       "2914         160         21.0     1936            4            7       1970          1970         0.0         0.0         0.0      546.0        546.0       546       546             0       1092           0.0           0.0         1         1             3             1             5           0       1979.0         0.0         0.0           0            0              0          0            0         0        0       6    2006\n",
       "2915         160         21.0     1894            4            5       1970          1970         0.0       252.0         0.0      294.0        546.0       546       546             0       1092           0.0           0.0         1         1             3             1             6           0       1970.0         1.0       286.0           0           24              0          0            0         0        0       4    2006\n",
       "2916          20        160.0    20000            5            7       1960          1996         0.0      1224.0         0.0        0.0       1224.0      1224         0             0       1224           1.0           0.0         1         0             4             1             7           1       1960.0         2.0       576.0         474            0              0          0            0         0        0       9    2006\n",
       "2917          85         62.0    10441            5            5       1992          1992         0.0       337.0         0.0      575.0        912.0       970         0             0        970           0.0           1.0         1         0             3             1             6           0       1979.0         0.0         0.0          80           32              0          0            0         0      700       7    2006\n",
       "2918          60         74.0     9627            7            5       1993          1994        94.0       758.0         0.0      238.0        996.0       996      1004             0       2000           0.0           0.0         2         1             3             1             9           1       1993.0         3.0       650.0         190           48              0          0            0         0        0      11    2006\n",
       "\n",
       "[2919 rows x 36 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df\n",
    "\n",
    "#Features that are \"object\" = categorical \n",
    "#Features that are not \"object\" = numerical (could be normalized)\n",
    "\n",
    "#Want to fill in numerical features with median (not mean since the data isn't normalized yet)\n",
    "#https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.select_dtypes.html\n",
    "\n",
    "numerical = df.select_dtypes(exclude=['object']).columns\n",
    "categorical = df.select_dtypes(include=['object']).columns\n",
    "\n",
    "df_numerical = df[numerical]\n",
    "df_categorical = df[categorical]\n",
    "\n",
    "df_numerical = df_numerical.fillna(df_numerical.median())\n",
    "df_numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disable this for testing purposes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2919, 75)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The get_dummies() function is used to convert categorical variable into dummy/indicator variables. \n",
    "\n",
    "print(\"disable this for testing purposes\")\n",
    "df_categorical = pd.get_dummies(df_categorical)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASS - NUMERICAL FEATURES REPLACED WITH MEDIAN VALUES\n"
     ]
    }
   ],
   "source": [
    "#Validation\n",
    "if df_numerical.isnull().values.sum() != 0:\n",
    "    print(\"VALIDATION - NUMERICAL FIELDS CONTAIN NULLS\")\n",
    "else: \n",
    "    print(\"PASS - NUMERICAL FEATURES REPLACED WITH MEDIAN VALUES\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASS - CATEGORICAL FEATURES DO NOT CONTAIN NULLS\n"
     ]
    }
   ],
   "source": [
    "#Validation\n",
    "if df_categorical.isnull().values.sum() != 0:\n",
    "    print(\"VALIDATION - CATEGORICAL FIELDS CONTAIN NULLS\")\n",
    "else: \n",
    "    print(\"PASS - CATEGORICAL FEATURES DO NOT CONTAIN NULLS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use to split the data after data cleaning/prep\n",
    "train_shape = train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df2 contains log transformed numerical features + categorical features using dummy variables.\n",
      "df2 does not contain null values.\n",
      "df2 does not have SalePrice (will be included in train df after split using shape).\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>MSZoning_C (all)</th>\n",
       "      <th>MSZoning_FV</th>\n",
       "      <th>MSZoning_RH</th>\n",
       "      <th>MSZoning_RL</th>\n",
       "      <th>MSZoning_RM</th>\n",
       "      <th>Street_Grvl</th>\n",
       "      <th>Street_Pave</th>\n",
       "      <th>LotShape_IR1</th>\n",
       "      <th>LotShape_IR2</th>\n",
       "      <th>LotShape_IR3</th>\n",
       "      <th>LotShape_Reg</th>\n",
       "      <th>LandContour_Bnk</th>\n",
       "      <th>LandContour_HLS</th>\n",
       "      <th>LandContour_Low</th>\n",
       "      <th>LandContour_Lvl</th>\n",
       "      <th>Utilities_AllPub</th>\n",
       "      <th>Utilities_NoSeWa</th>\n",
       "      <th>LotConfig_Corner</th>\n",
       "      <th>LotConfig_CulDSac</th>\n",
       "      <th>LotConfig_FR2</th>\n",
       "      <th>LotConfig_FR3</th>\n",
       "      <th>LotConfig_Inside</th>\n",
       "      <th>LandSlope_Gtl</th>\n",
       "      <th>LandSlope_Mod</th>\n",
       "      <th>LandSlope_Sev</th>\n",
       "      <th>Neighborhood_Blmngtn</th>\n",
       "      <th>Neighborhood_Blueste</th>\n",
       "      <th>Neighborhood_BrDale</th>\n",
       "      <th>Neighborhood_BrkSide</th>\n",
       "      <th>Neighborhood_ClearCr</th>\n",
       "      <th>Neighborhood_CollgCr</th>\n",
       "      <th>Neighborhood_Crawfor</th>\n",
       "      <th>Neighborhood_Edwards</th>\n",
       "      <th>Neighborhood_Gilbert</th>\n",
       "      <th>Neighborhood_IDOTRR</th>\n",
       "      <th>Neighborhood_MeadowV</th>\n",
       "      <th>Neighborhood_Mitchel</th>\n",
       "      <th>Neighborhood_NAmes</th>\n",
       "      <th>Neighborhood_NPkVill</th>\n",
       "      <th>Neighborhood_NWAmes</th>\n",
       "      <th>Neighborhood_NoRidge</th>\n",
       "      <th>Neighborhood_NridgHt</th>\n",
       "      <th>Neighborhood_OldTown</th>\n",
       "      <th>Neighborhood_SWISU</th>\n",
       "      <th>Neighborhood_Sawyer</th>\n",
       "      <th>Neighborhood_SawyerW</th>\n",
       "      <th>Neighborhood_Somerst</th>\n",
       "      <th>Neighborhood_StoneBr</th>\n",
       "      <th>Neighborhood_Timber</th>\n",
       "      <th>Neighborhood_Veenker</th>\n",
       "      <th>Condition1_Artery</th>\n",
       "      <th>Condition1_Feedr</th>\n",
       "      <th>Condition1_Norm</th>\n",
       "      <th>Condition1_PosA</th>\n",
       "      <th>Condition1_PosN</th>\n",
       "      <th>Condition1_RRAe</th>\n",
       "      <th>Condition1_RRAn</th>\n",
       "      <th>Condition1_RRNe</th>\n",
       "      <th>Condition1_RRNn</th>\n",
       "      <th>Condition2_Artery</th>\n",
       "      <th>Condition2_Feedr</th>\n",
       "      <th>Condition2_Norm</th>\n",
       "      <th>Condition2_PosA</th>\n",
       "      <th>Condition2_PosN</th>\n",
       "      <th>Condition2_RRAe</th>\n",
       "      <th>Condition2_RRAn</th>\n",
       "      <th>Condition2_RRNn</th>\n",
       "      <th>BldgType_1Fam</th>\n",
       "      <th>BldgType_2fmCon</th>\n",
       "      <th>BldgType_Duplex</th>\n",
       "      <th>BldgType_Twnhs</th>\n",
       "      <th>BldgType_TwnhsE</th>\n",
       "      <th>HouseStyle_1.5Fin</th>\n",
       "      <th>HouseStyle_1.5Unf</th>\n",
       "      <th>HouseStyle_1Story</th>\n",
       "      <th>HouseStyle_2.5Fin</th>\n",
       "      <th>HouseStyle_2.5Unf</th>\n",
       "      <th>HouseStyle_2Story</th>\n",
       "      <th>HouseStyle_SFoyer</th>\n",
       "      <th>HouseStyle_SLvl</th>\n",
       "      <th>RoofStyle_Flat</th>\n",
       "      <th>RoofStyle_Gable</th>\n",
       "      <th>RoofStyle_Gambrel</th>\n",
       "      <th>RoofStyle_Hip</th>\n",
       "      <th>RoofStyle_Mansard</th>\n",
       "      <th>RoofStyle_Shed</th>\n",
       "      <th>RoofMatl_ClyTile</th>\n",
       "      <th>RoofMatl_CompShg</th>\n",
       "      <th>RoofMatl_Membran</th>\n",
       "      <th>RoofMatl_Metal</th>\n",
       "      <th>RoofMatl_Roll</th>\n",
       "      <th>RoofMatl_Tar&amp;Grv</th>\n",
       "      <th>RoofMatl_WdShake</th>\n",
       "      <th>RoofMatl_WdShngl</th>\n",
       "      <th>Exterior1st_AsbShng</th>\n",
       "      <th>Exterior1st_AsphShn</th>\n",
       "      <th>Exterior1st_BrkComm</th>\n",
       "      <th>Exterior1st_BrkFace</th>\n",
       "      <th>Exterior1st_CBlock</th>\n",
       "      <th>Exterior1st_CemntBd</th>\n",
       "      <th>Exterior1st_HdBoard</th>\n",
       "      <th>Exterior1st_ImStucc</th>\n",
       "      <th>Exterior1st_MetalSd</th>\n",
       "      <th>Exterior1st_Plywood</th>\n",
       "      <th>Exterior1st_Stone</th>\n",
       "      <th>Exterior1st_Stucco</th>\n",
       "      <th>Exterior1st_VinylSd</th>\n",
       "      <th>Exterior1st_Wd Sdng</th>\n",
       "      <th>Exterior1st_WdShing</th>\n",
       "      <th>Exterior2nd_AsbShng</th>\n",
       "      <th>Exterior2nd_AsphShn</th>\n",
       "      <th>Exterior2nd_Brk Cmn</th>\n",
       "      <th>Exterior2nd_BrkFace</th>\n",
       "      <th>Exterior2nd_CBlock</th>\n",
       "      <th>Exterior2nd_CmentBd</th>\n",
       "      <th>Exterior2nd_HdBoard</th>\n",
       "      <th>Exterior2nd_ImStucc</th>\n",
       "      <th>Exterior2nd_MetalSd</th>\n",
       "      <th>Exterior2nd_Other</th>\n",
       "      <th>Exterior2nd_Plywood</th>\n",
       "      <th>Exterior2nd_Stone</th>\n",
       "      <th>Exterior2nd_Stucco</th>\n",
       "      <th>Exterior2nd_VinylSd</th>\n",
       "      <th>Exterior2nd_Wd Sdng</th>\n",
       "      <th>Exterior2nd_Wd Shng</th>\n",
       "      <th>MasVnrType_BrkCmn</th>\n",
       "      <th>MasVnrType_BrkFace</th>\n",
       "      <th>MasVnrType_None</th>\n",
       "      <th>MasVnrType_Stone</th>\n",
       "      <th>ExterQual_Ex</th>\n",
       "      <th>ExterQual_Fa</th>\n",
       "      <th>ExterQual_Gd</th>\n",
       "      <th>ExterQual_TA</th>\n",
       "      <th>ExterCond_Ex</th>\n",
       "      <th>ExterCond_Fa</th>\n",
       "      <th>ExterCond_Gd</th>\n",
       "      <th>ExterCond_Po</th>\n",
       "      <th>ExterCond_TA</th>\n",
       "      <th>Foundation_BrkTil</th>\n",
       "      <th>Foundation_CBlock</th>\n",
       "      <th>Foundation_PConc</th>\n",
       "      <th>Foundation_Slab</th>\n",
       "      <th>Foundation_Stone</th>\n",
       "      <th>Foundation_Wood</th>\n",
       "      <th>BsmtQual_Ex</th>\n",
       "      <th>BsmtQual_Fa</th>\n",
       "      <th>BsmtQual_Gd</th>\n",
       "      <th>BsmtQual_TA</th>\n",
       "      <th>BsmtCond_Fa</th>\n",
       "      <th>BsmtCond_Gd</th>\n",
       "      <th>BsmtCond_Po</th>\n",
       "      <th>BsmtCond_TA</th>\n",
       "      <th>BsmtExposure_Av</th>\n",
       "      <th>BsmtExposure_Gd</th>\n",
       "      <th>BsmtExposure_Mn</th>\n",
       "      <th>BsmtExposure_No</th>\n",
       "      <th>BsmtFinType1_ALQ</th>\n",
       "      <th>BsmtFinType1_BLQ</th>\n",
       "      <th>BsmtFinType1_GLQ</th>\n",
       "      <th>BsmtFinType1_LwQ</th>\n",
       "      <th>BsmtFinType1_Rec</th>\n",
       "      <th>BsmtFinType1_Unf</th>\n",
       "      <th>BsmtFinType2_ALQ</th>\n",
       "      <th>BsmtFinType2_BLQ</th>\n",
       "      <th>BsmtFinType2_GLQ</th>\n",
       "      <th>BsmtFinType2_LwQ</th>\n",
       "      <th>BsmtFinType2_Rec</th>\n",
       "      <th>BsmtFinType2_Unf</th>\n",
       "      <th>Heating_Floor</th>\n",
       "      <th>Heating_GasA</th>\n",
       "      <th>Heating_GasW</th>\n",
       "      <th>Heating_Grav</th>\n",
       "      <th>Heating_OthW</th>\n",
       "      <th>Heating_Wall</th>\n",
       "      <th>HeatingQC_Ex</th>\n",
       "      <th>HeatingQC_Fa</th>\n",
       "      <th>HeatingQC_Gd</th>\n",
       "      <th>HeatingQC_Po</th>\n",
       "      <th>HeatingQC_TA</th>\n",
       "      <th>CentralAir_N</th>\n",
       "      <th>CentralAir_Y</th>\n",
       "      <th>Electrical_FuseA</th>\n",
       "      <th>Electrical_FuseF</th>\n",
       "      <th>Electrical_FuseP</th>\n",
       "      <th>Electrical_Mix</th>\n",
       "      <th>Electrical_SBrkr</th>\n",
       "      <th>KitchenQual_Ex</th>\n",
       "      <th>KitchenQual_Fa</th>\n",
       "      <th>KitchenQual_Gd</th>\n",
       "      <th>KitchenQual_TA</th>\n",
       "      <th>Functional_Maj1</th>\n",
       "      <th>Functional_Maj2</th>\n",
       "      <th>Functional_Min1</th>\n",
       "      <th>Functional_Min2</th>\n",
       "      <th>Functional_Mod</th>\n",
       "      <th>Functional_Sev</th>\n",
       "      <th>Functional_Typ</th>\n",
       "      <th>GarageType_2Types</th>\n",
       "      <th>GarageType_Attchd</th>\n",
       "      <th>GarageType_Basment</th>\n",
       "      <th>GarageType_BuiltIn</th>\n",
       "      <th>GarageType_CarPort</th>\n",
       "      <th>GarageType_Detchd</th>\n",
       "      <th>GarageFinish_Fin</th>\n",
       "      <th>GarageFinish_RFn</th>\n",
       "      <th>GarageFinish_Unf</th>\n",
       "      <th>GarageQual_Ex</th>\n",
       "      <th>GarageQual_Fa</th>\n",
       "      <th>GarageQual_Gd</th>\n",
       "      <th>GarageQual_Po</th>\n",
       "      <th>GarageQual_TA</th>\n",
       "      <th>GarageCond_Ex</th>\n",
       "      <th>GarageCond_Fa</th>\n",
       "      <th>GarageCond_Gd</th>\n",
       "      <th>GarageCond_Po</th>\n",
       "      <th>GarageCond_TA</th>\n",
       "      <th>PavedDrive_N</th>\n",
       "      <th>PavedDrive_P</th>\n",
       "      <th>PavedDrive_Y</th>\n",
       "      <th>Fence_GdPrv</th>\n",
       "      <th>Fence_GdWo</th>\n",
       "      <th>Fence_MnPrv</th>\n",
       "      <th>Fence_MnWw</th>\n",
       "      <th>SaleType_COD</th>\n",
       "      <th>SaleType_CWD</th>\n",
       "      <th>SaleType_Con</th>\n",
       "      <th>SaleType_ConLD</th>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>160</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>546</td>\n",
       "      <td>546</td>\n",
       "      <td>0</td>\n",
       "      <td>1092</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>160</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>546</td>\n",
       "      <td>546</td>\n",
       "      <td>0</td>\n",
       "      <td>1092</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>20</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1960</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>1224</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1224</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>474</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>85</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>60</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1993</td>\n",
       "      <td>1994</td>\n",
       "      <td>94.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>996</td>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>190</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd  Fireplaces  GarageYrBlt  GarageCars  GarageArea  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  MSZoning_C (all)  MSZoning_FV  MSZoning_RH  MSZoning_RL  MSZoning_RM  Street_Grvl  Street_Pave  LotShape_IR1  LotShape_IR2  LotShape_IR3  LotShape_Reg  LandContour_Bnk  LandContour_HLS  LandContour_Low  LandContour_Lvl  Utilities_AllPub  Utilities_NoSeWa  LotConfig_Corner  LotConfig_CulDSac  LotConfig_FR2  LotConfig_FR3  LotConfig_Inside  LandSlope_Gtl  LandSlope_Mod  LandSlope_Sev  Neighborhood_Blmngtn  Neighborhood_Blueste  Neighborhood_BrDale  Neighborhood_BrkSide  Neighborhood_ClearCr  Neighborhood_CollgCr  Neighborhood_Crawfor  Neighborhood_Edwards  Neighborhood_Gilbert  Neighborhood_IDOTRR  Neighborhood_MeadowV  Neighborhood_Mitchel  Neighborhood_NAmes  Neighborhood_NPkVill  Neighborhood_NWAmes  Neighborhood_NoRidge  Neighborhood_NridgHt  Neighborhood_OldTown  Neighborhood_SWISU  Neighborhood_Sawyer  Neighborhood_SawyerW  Neighborhood_Somerst  Neighborhood_StoneBr  Neighborhood_Timber  Neighborhood_Veenker  Condition1_Artery  Condition1_Feedr  Condition1_Norm  Condition1_PosA  Condition1_PosN  Condition1_RRAe  Condition1_RRAn  Condition1_RRNe  Condition1_RRNn  Condition2_Artery  Condition2_Feedr  Condition2_Norm  Condition2_PosA  Condition2_PosN  Condition2_RRAe  Condition2_RRAn  Condition2_RRNn  BldgType_1Fam  BldgType_2fmCon  BldgType_Duplex  BldgType_Twnhs  BldgType_TwnhsE  HouseStyle_1.5Fin  HouseStyle_1.5Unf  HouseStyle_1Story  HouseStyle_2.5Fin  HouseStyle_2.5Unf  HouseStyle_2Story  HouseStyle_SFoyer  HouseStyle_SLvl  RoofStyle_Flat  RoofStyle_Gable  RoofStyle_Gambrel  RoofStyle_Hip  RoofStyle_Mansard  RoofStyle_Shed  RoofMatl_ClyTile  RoofMatl_CompShg  RoofMatl_Membran  RoofMatl_Metal  RoofMatl_Roll  RoofMatl_Tar&Grv  RoofMatl_WdShake  RoofMatl_WdShngl  Exterior1st_AsbShng  Exterior1st_AsphShn  Exterior1st_BrkComm  Exterior1st_BrkFace  Exterior1st_CBlock  Exterior1st_CemntBd  Exterior1st_HdBoard  Exterior1st_ImStucc  Exterior1st_MetalSd  Exterior1st_Plywood  Exterior1st_Stone  Exterior1st_Stucco  Exterior1st_VinylSd  Exterior1st_Wd Sdng  Exterior1st_WdShing  Exterior2nd_AsbShng  Exterior2nd_AsphShn  Exterior2nd_Brk Cmn  Exterior2nd_BrkFace  Exterior2nd_CBlock  Exterior2nd_CmentBd  Exterior2nd_HdBoard  Exterior2nd_ImStucc  Exterior2nd_MetalSd  Exterior2nd_Other  Exterior2nd_Plywood  Exterior2nd_Stone  Exterior2nd_Stucco  Exterior2nd_VinylSd  Exterior2nd_Wd Sdng  Exterior2nd_Wd Shng  MasVnrType_BrkCmn  MasVnrType_BrkFace  MasVnrType_None  MasVnrType_Stone  ExterQual_Ex  ExterQual_Fa  ExterQual_Gd  ExterQual_TA  ExterCond_Ex  ExterCond_Fa  ExterCond_Gd  ExterCond_Po  ExterCond_TA  Foundation_BrkTil  Foundation_CBlock  Foundation_PConc  Foundation_Slab  Foundation_Stone  Foundation_Wood  BsmtQual_Ex  BsmtQual_Fa  BsmtQual_Gd  BsmtQual_TA  BsmtCond_Fa  BsmtCond_Gd  BsmtCond_Po  BsmtCond_TA  BsmtExposure_Av  BsmtExposure_Gd  BsmtExposure_Mn  BsmtExposure_No  BsmtFinType1_ALQ  BsmtFinType1_BLQ  BsmtFinType1_GLQ  BsmtFinType1_LwQ  BsmtFinType1_Rec  BsmtFinType1_Unf  BsmtFinType2_ALQ  BsmtFinType2_BLQ  BsmtFinType2_GLQ  BsmtFinType2_LwQ  BsmtFinType2_Rec  BsmtFinType2_Unf  Heating_Floor  Heating_GasA  Heating_GasW  Heating_Grav  Heating_OthW  Heating_Wall  HeatingQC_Ex  HeatingQC_Fa  HeatingQC_Gd  HeatingQC_Po  HeatingQC_TA  CentralAir_N  CentralAir_Y  Electrical_FuseA  Electrical_FuseF  Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  KitchenQual_Ex  KitchenQual_Fa  KitchenQual_Gd  KitchenQual_TA  Functional_Maj1  Functional_Maj2  Functional_Min1  Functional_Min2  Functional_Mod  Functional_Sev  Functional_Typ  GarageType_2Types  GarageType_Attchd  GarageType_Basment  GarageType_BuiltIn  GarageType_CarPort  GarageType_Detchd  GarageFinish_Fin  GarageFinish_RFn  GarageFinish_Unf  GarageQual_Ex  GarageQual_Fa  GarageQual_Gd  GarageQual_Po  GarageQual_TA  GarageCond_Ex  GarageCond_Fa  GarageCond_Gd  GarageCond_Po  GarageCond_TA  PavedDrive_N  PavedDrive_P  PavedDrive_Y  Fence_GdPrv  Fence_GdWo  Fence_MnPrv  Fence_MnWw  SaleType_COD  SaleType_CWD  SaleType_Con  SaleType_ConLD  SaleType_ConLI  SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial\n",
       "2914         160         21.0     1936            4            7       1970          1970         0.0         0.0         0.0      546.0        546.0       546       546             0       1092           0.0           0.0         1         1             3             1             5           0       1979.0         0.0         0.0           0            0              0          0            0         0        0       6    2006                 0            0            0            0            1            0            1             0             0             0             1                0                0                0                1                 1                 0                 0                  0              0              0                 1              1              0              0                     0                     0                    0                     0                     0                     0                     0                     0                     0                    0                     1                     0                   0                     0                    0                     0                     0                     0                   0                    0                     0                     0                     0                    0                     0                  0                 0                1                0                0                0                0                0                0                  0                 0                1                0                0                0                0                0              0                0                0               1                0                  0                  0                  0                  0                  0                  1                  0                0               0                1                  0              0                  0               0                 0                 1                 0               0              0                 0                 0                 0                    0                    0                    0                    0                   0                    1                    0                    0                    0                    0                  0                   0                    0                    0                    0                    0                    0                    0                    0                   0                    1                    0                    0                    0                  0                    0                  0                   0                    0                    0                    0                  0                   0                1                 0             0             0             0             1             0             0             0             0             1                  0                  1                 0                0                 0                0            0            0            0            1            0            0            0            1                0                0                0                1                 0                 0                 0                 0                 0                 1                 0                 0                 0                 0                 0                 1              0             1             0             0             0             0             0             0             1             0             0             0             1                 0                 0                 0               0                 1               0               0               0               1                0                0                0                0               0               0               1                  0                  0                   0                   0                   0                  0                 0                 0                 0              0              0              0              0              0              0              0              0              0              0             0             0             1            0           0            0           0             0             0             0               0               0               0             0             0            1                      0                      0                     0                     0                     1                      0\n",
       "2915         160         21.0     1894            4            5       1970          1970         0.0       252.0         0.0      294.0        546.0       546       546             0       1092           0.0           0.0         1         1             3             1             6           0       1970.0         1.0       286.0           0           24              0          0            0         0        0       4    2006                 0            0            0            0            1            0            1             0             0             0             1                0                0                0                1                 1                 0                 0                  0              0              0                 1              1              0              0                     0                     0                    0                     0                     0                     0                     0                     0                     0                    0                     1                     0                   0                     0                    0                     0                     0                     0                   0                    0                     0                     0                     0                    0                     0                  0                 0                1                0                0                0                0                0                0                  0                 0                1                0                0                0                0                0              0                0                0               0                1                  0                  0                  0                  0                  0                  1                  0                0               0                1                  0              0                  0               0                 0                 1                 0               0              0                 0                 0                 0                    0                    0                    0                    0                   0                    1                    0                    0                    0                    0                  0                   0                    0                    0                    0                    0                    0                    0                    0                   0                    1                    0                    0                    0                  0                    0                  0                   0                    0                    0                    0                  0                   0                1                 0             0             0             0             1             0             0             0             0             1                  0                  1                 0                0                 0                0            0            0            0            1            0            0            0            1                0                0                0                1                 0                 0                 0                 0                 1                 0                 0                 0                 0                 0                 0                 1              0             1             0             0             0             0             0             0             0             0             1             0             1                 0                 0                 0               0                 1               0               0               0               1                0                0                0                0               0               0               1                  0                  0                   0                   0                   1                  0                 0                 0                 1              0              0              0              0              1              0              0              0              0              1             0             0             1            0           0            0           0             0             0             0               0               0               0             0             0            1                      1                      0                     0                     0                     0                      0\n",
       "2916          20        160.0    20000            5            7       1960          1996         0.0      1224.0         0.0        0.0       1224.0      1224         0             0       1224           1.0           0.0         1         0             4             1             7           1       1960.0         2.0       576.0         474            0              0          0            0         0        0       9    2006                 0            0            0            1            0            0            1             0             0             0             1                0                0                0                1                 1                 0                 0                  0              0              0                 1              1              0              0                     0                     0                    0                     0                     0                     0                     0                     0                     0                    0                     0                     1                   0                     0                    0                     0                     0                     0                   0                    0                     0                     0                     0                    0                     0                  0                 0                1                0                0                0                0                0                0                  0                 0                1                0                0                0                0                0              1                0                0               0                0                  0                  0                  1                  0                  0                  0                  0                0               0                1                  0              0                  0               0                 0                 1                 0               0              0                 0                 0                 0                    0                    0                    0                    0                   0                    0                    0                    0                    0                    0                  0                   0                    1                    0                    0                    0                    0                    0                    0                   0                    0                    0                    0                    0                  0                    0                  0                   0                    1                    0                    0                  0                   0                1                 0             0             0             0             1             0             0             0             0             1                  0                  1                 0                0                 0                0            0            0            0            1            0            0            0            1                0                0                0                1                 1                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 1              0             1             0             0             0             0             1             0             0             0             0             0             1                 0                 0                 0               0                 1               0               0               0               1                0                0                0                0               0               0               1                  0                  0                   0                   0                   0                  1                 0                 0                 1              0              0              0              0              1              0              0              0              0              1             0             0             1            0           0            0           0             0             0             0               0               0               0             0             0            1                      1                      0                     0                     0                     0                      0\n",
       "2917          85         62.0    10441            5            5       1992          1992         0.0       337.0         0.0      575.0        912.0       970         0             0        970           0.0           1.0         1         0             3             1             6           0       1979.0         0.0         0.0          80           32              0          0            0         0      700       7    2006                 0            0            0            1            0            0            1             0             0             0             1                0                0                0                1                 1                 0                 0                  0              0              0                 1              1              0              0                     0                     0                    0                     0                     0                     0                     0                     0                     0                    0                     0                     1                   0                     0                    0                     0                     0                     0                   0                    0                     0                     0                     0                    0                     0                  0                 0                1                0                0                0                0                0                0                  0                 0                1                0                0                0                0                0              1                0                0               0                0                  0                  0                  0                  0                  0                  0                  1                0               0                1                  0              0                  0               0                 0                 1                 0               0              0                 0                 0                 0                    0                    0                    0                    0                   0                    0                    1                    0                    0                    0                  0                   0                    0                    0                    0                    0                    0                    0                    0                   0                    0                    0                    0                    0                  0                    0                  0                   0                    0                    0                    1                  0                   0                1                 0             0             0             0             1             0             0             0             0             1                  0                  0                 1                0                 0                0            0            0            1            0            0            0            0            1                1                0                0                0                 0                 0                 1                 0                 0                 0                 0                 0                 0                 0                 0                 1              0             1             0             0             0             0             0             0             0             0             1             0             1                 0                 0                 0               0                 1               0               0               0               1                0                0                0                0               0               0               1                  0                  0                   0                   0                   0                  0                 0                 0                 0              0              0              0              0              0              0              0              0              0              0             0             0             1            0           0            1           0             0             0             0               0               0               0             0             0            1                      0                      0                     0                     0                     1                      0\n",
       "2918          60         74.0     9627            7            5       1993          1994        94.0       758.0         0.0      238.0        996.0       996      1004             0       2000           0.0           0.0         2         1             3             1             9           1       1993.0         3.0       650.0         190           48              0          0            0         0        0      11    2006                 0            0            0            1            0            0            1             0             0             0             1                0                0                0                1                 1                 0                 0                  0              0              0                 1              0              1              0                     0                     0                    0                     0                     0                     0                     0                     0                     0                    0                     0                     1                   0                     0                    0                     0                     0                     0                   0                    0                     0                     0                     0                    0                     0                  0                 0                1                0                0                0                0                0                0                  0                 0                1                0                0                0                0                0              1                0                0               0                0                  0                  0                  0                  0                  0                  1                  0                0               0                1                  0              0                  0               0                 0                 1                 0               0              0                 0                 0                 0                    0                    0                    0                    0                   0                    0                    1                    0                    0                    0                  0                   0                    0                    0                    0                    0                    0                    0                    0                   0                    0                    1                    0                    0                  0                    0                  0                   0                    0                    0                    0                  0                   1                0                 0             0             0             0             1             0             0             0             0             1                  0                  0                 1                0                 0                0            0            0            1            0            0            0            0            1                1                0                0                0                 0                 0                 0                 1                 0                 0                 0                 0                 0                 0                 0                 1              0             1             0             0             0             0             1             0             0             0             0             0             1                 0                 0                 0               0                 1               0               0               0               1                0                0                0                0               0               0               1                  0                  1                   0                   0                   0                  0                 1                 0                 0              0              0              0              0              1              0              0              0              0              1             0             0             1            0           0            0           0             0             0             0               0               0               0             0             0            1                      0                      0                     0                     0                     1                      0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df2\n",
    "print(\"df2 contains log transformed numerical features + categorical features using dummy variables.\\ndf2 does not contain null values.\\ndf2 does not have SalePrice (will be included in train df after split using shape).\\n\")\n",
    "df2 = pd.concat([df_numerical,\n",
    "                    df_categorical], axis = 1)\n",
    "\n",
    "\n",
    "df2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**train2 and test2 = fillna median and pd.get_dummies performed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape (1460, 81)\n",
      "test shape (1459, 80) \n",
      "\n",
      "df_numerical shape (2919, 36)\n",
      "df_categorical shape (2919, 238) \n",
      "\n",
      "df2 shape (2919, 274) \n",
      "\n",
      "train2 shape (1460, 274)\n",
      "test2 shape (1459, 274)\n"
     ]
    }
   ],
   "source": [
    "#train2\n",
    "\n",
    "#test2 \n",
    "\n",
    "train2 = df2[:train_shape]\n",
    "test2 = df2[train_shape:]\n",
    "\n",
    "print(\"train shape\",train.shape)\n",
    "print(\"test shape\",test.shape,\"\\n\")\n",
    "\n",
    "print(\"df_numerical shape\",df_numerical.shape)\n",
    "print(\"df_categorical shape\",df_categorical.shape,\"\\n\")\n",
    "\n",
    "print(\"df2 shape\",df2.shape,\"\\n\")\n",
    "\n",
    "print(\"train2 shape\",train2.shape)\n",
    "print(\"test2 shape\",test2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>142125</td>\n",
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       "      <th>1459</th>\n",
       "      <td>147500</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "      SalePrice\n",
       "1458     142125\n",
       "1459     147500"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_saleprice = train[['SalePrice']]\n",
    "train_saleprice.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reattach SalePrice to train data (now train2)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>MSZoning_C (all)</th>\n",
       "      <th>MSZoning_FV</th>\n",
       "      <th>MSZoning_RH</th>\n",
       "      <th>MSZoning_RL</th>\n",
       "      <th>MSZoning_RM</th>\n",
       "      <th>Street_Grvl</th>\n",
       "      <th>Street_Pave</th>\n",
       "      <th>LotShape_IR1</th>\n",
       "      <th>LotShape_IR2</th>\n",
       "      <th>LotShape_IR3</th>\n",
       "      <th>LotShape_Reg</th>\n",
       "      <th>LandContour_Bnk</th>\n",
       "      <th>LandContour_HLS</th>\n",
       "      <th>LandContour_Low</th>\n",
       "      <th>LandContour_Lvl</th>\n",
       "      <th>Utilities_AllPub</th>\n",
       "      <th>Utilities_NoSeWa</th>\n",
       "      <th>LotConfig_Corner</th>\n",
       "      <th>LotConfig_CulDSac</th>\n",
       "      <th>LotConfig_FR2</th>\n",
       "      <th>LotConfig_FR3</th>\n",
       "      <th>LotConfig_Inside</th>\n",
       "      <th>LandSlope_Gtl</th>\n",
       "      <th>LandSlope_Mod</th>\n",
       "      <th>LandSlope_Sev</th>\n",
       "      <th>Neighborhood_Blmngtn</th>\n",
       "      <th>Neighborhood_Blueste</th>\n",
       "      <th>Neighborhood_BrDale</th>\n",
       "      <th>Neighborhood_BrkSide</th>\n",
       "      <th>Neighborhood_ClearCr</th>\n",
       "      <th>Neighborhood_CollgCr</th>\n",
       "      <th>Neighborhood_Crawfor</th>\n",
       "      <th>Neighborhood_Edwards</th>\n",
       "      <th>Neighborhood_Gilbert</th>\n",
       "      <th>Neighborhood_IDOTRR</th>\n",
       "      <th>Neighborhood_MeadowV</th>\n",
       "      <th>Neighborhood_Mitchel</th>\n",
       "      <th>Neighborhood_NAmes</th>\n",
       "      <th>Neighborhood_NPkVill</th>\n",
       "      <th>Neighborhood_NWAmes</th>\n",
       "      <th>Neighborhood_NoRidge</th>\n",
       "      <th>Neighborhood_NridgHt</th>\n",
       "      <th>Neighborhood_OldTown</th>\n",
       "      <th>Neighborhood_SWISU</th>\n",
       "      <th>Neighborhood_Sawyer</th>\n",
       "      <th>Neighborhood_SawyerW</th>\n",
       "      <th>Neighborhood_Somerst</th>\n",
       "      <th>Neighborhood_StoneBr</th>\n",
       "      <th>Neighborhood_Timber</th>\n",
       "      <th>Neighborhood_Veenker</th>\n",
       "      <th>Condition1_Artery</th>\n",
       "      <th>Condition1_Feedr</th>\n",
       "      <th>Condition1_Norm</th>\n",
       "      <th>Condition1_PosA</th>\n",
       "      <th>Condition1_PosN</th>\n",
       "      <th>Condition1_RRAe</th>\n",
       "      <th>Condition1_RRAn</th>\n",
       "      <th>Condition1_RRNe</th>\n",
       "      <th>Condition1_RRNn</th>\n",
       "      <th>Condition2_Artery</th>\n",
       "      <th>Condition2_Feedr</th>\n",
       "      <th>Condition2_Norm</th>\n",
       "      <th>Condition2_PosA</th>\n",
       "      <th>Condition2_PosN</th>\n",
       "      <th>Condition2_RRAe</th>\n",
       "      <th>Condition2_RRAn</th>\n",
       "      <th>Condition2_RRNn</th>\n",
       "      <th>BldgType_1Fam</th>\n",
       "      <th>BldgType_2fmCon</th>\n",
       "      <th>BldgType_Duplex</th>\n",
       "      <th>BldgType_Twnhs</th>\n",
       "      <th>BldgType_TwnhsE</th>\n",
       "      <th>HouseStyle_1.5Fin</th>\n",
       "      <th>HouseStyle_1.5Unf</th>\n",
       "      <th>HouseStyle_1Story</th>\n",
       "      <th>HouseStyle_2.5Fin</th>\n",
       "      <th>HouseStyle_2.5Unf</th>\n",
       "      <th>HouseStyle_2Story</th>\n",
       "      <th>HouseStyle_SFoyer</th>\n",
       "      <th>HouseStyle_SLvl</th>\n",
       "      <th>RoofStyle_Flat</th>\n",
       "      <th>RoofStyle_Gable</th>\n",
       "      <th>RoofStyle_Gambrel</th>\n",
       "      <th>RoofStyle_Hip</th>\n",
       "      <th>RoofStyle_Mansard</th>\n",
       "      <th>RoofStyle_Shed</th>\n",
       "      <th>RoofMatl_ClyTile</th>\n",
       "      <th>RoofMatl_CompShg</th>\n",
       "      <th>RoofMatl_Membran</th>\n",
       "      <th>RoofMatl_Metal</th>\n",
       "      <th>RoofMatl_Roll</th>\n",
       "      <th>RoofMatl_Tar&amp;Grv</th>\n",
       "      <th>RoofMatl_WdShake</th>\n",
       "      <th>RoofMatl_WdShngl</th>\n",
       "      <th>Exterior1st_AsbShng</th>\n",
       "      <th>Exterior1st_AsphShn</th>\n",
       "      <th>Exterior1st_BrkComm</th>\n",
       "      <th>Exterior1st_BrkFace</th>\n",
       "      <th>Exterior1st_CBlock</th>\n",
       "      <th>Exterior1st_CemntBd</th>\n",
       "      <th>Exterior1st_HdBoard</th>\n",
       "      <th>Exterior1st_ImStucc</th>\n",
       "      <th>Exterior1st_MetalSd</th>\n",
       "      <th>Exterior1st_Plywood</th>\n",
       "      <th>Exterior1st_Stone</th>\n",
       "      <th>Exterior1st_Stucco</th>\n",
       "      <th>Exterior1st_VinylSd</th>\n",
       "      <th>Exterior1st_Wd Sdng</th>\n",
       "      <th>Exterior1st_WdShing</th>\n",
       "      <th>Exterior2nd_AsbShng</th>\n",
       "      <th>Exterior2nd_AsphShn</th>\n",
       "      <th>Exterior2nd_Brk Cmn</th>\n",
       "      <th>Exterior2nd_BrkFace</th>\n",
       "      <th>Exterior2nd_CBlock</th>\n",
       "      <th>Exterior2nd_CmentBd</th>\n",
       "      <th>Exterior2nd_HdBoard</th>\n",
       "      <th>Exterior2nd_ImStucc</th>\n",
       "      <th>Exterior2nd_MetalSd</th>\n",
       "      <th>Exterior2nd_Other</th>\n",
       "      <th>Exterior2nd_Plywood</th>\n",
       "      <th>Exterior2nd_Stone</th>\n",
       "      <th>Exterior2nd_Stucco</th>\n",
       "      <th>Exterior2nd_VinylSd</th>\n",
       "      <th>Exterior2nd_Wd Sdng</th>\n",
       "      <th>Exterior2nd_Wd Shng</th>\n",
       "      <th>MasVnrType_BrkCmn</th>\n",
       "      <th>MasVnrType_BrkFace</th>\n",
       "      <th>MasVnrType_None</th>\n",
       "      <th>MasVnrType_Stone</th>\n",
       "      <th>ExterQual_Ex</th>\n",
       "      <th>ExterQual_Fa</th>\n",
       "      <th>ExterQual_Gd</th>\n",
       "      <th>ExterQual_TA</th>\n",
       "      <th>ExterCond_Ex</th>\n",
       "      <th>ExterCond_Fa</th>\n",
       "      <th>ExterCond_Gd</th>\n",
       "      <th>ExterCond_Po</th>\n",
       "      <th>ExterCond_TA</th>\n",
       "      <th>Foundation_BrkTil</th>\n",
       "      <th>Foundation_CBlock</th>\n",
       "      <th>Foundation_PConc</th>\n",
       "      <th>Foundation_Slab</th>\n",
       "      <th>Foundation_Stone</th>\n",
       "      <th>Foundation_Wood</th>\n",
       "      <th>BsmtQual_Ex</th>\n",
       "      <th>BsmtQual_Fa</th>\n",
       "      <th>BsmtQual_Gd</th>\n",
       "      <th>BsmtQual_TA</th>\n",
       "      <th>BsmtCond_Fa</th>\n",
       "      <th>BsmtCond_Gd</th>\n",
       "      <th>BsmtCond_Po</th>\n",
       "      <th>BsmtCond_TA</th>\n",
       "      <th>BsmtExposure_Av</th>\n",
       "      <th>BsmtExposure_Gd</th>\n",
       "      <th>BsmtExposure_Mn</th>\n",
       "      <th>BsmtExposure_No</th>\n",
       "      <th>BsmtFinType1_ALQ</th>\n",
       "      <th>BsmtFinType1_BLQ</th>\n",
       "      <th>BsmtFinType1_GLQ</th>\n",
       "      <th>BsmtFinType1_LwQ</th>\n",
       "      <th>BsmtFinType1_Rec</th>\n",
       "      <th>BsmtFinType1_Unf</th>\n",
       "      <th>BsmtFinType2_ALQ</th>\n",
       "      <th>BsmtFinType2_BLQ</th>\n",
       "      <th>BsmtFinType2_GLQ</th>\n",
       "      <th>BsmtFinType2_LwQ</th>\n",
       "      <th>BsmtFinType2_Rec</th>\n",
       "      <th>BsmtFinType2_Unf</th>\n",
       "      <th>Heating_Floor</th>\n",
       "      <th>Heating_GasA</th>\n",
       "      <th>Heating_GasW</th>\n",
       "      <th>Heating_Grav</th>\n",
       "      <th>Heating_OthW</th>\n",
       "      <th>Heating_Wall</th>\n",
       "      <th>HeatingQC_Ex</th>\n",
       "      <th>HeatingQC_Fa</th>\n",
       "      <th>HeatingQC_Gd</th>\n",
       "      <th>HeatingQC_Po</th>\n",
       "      <th>HeatingQC_TA</th>\n",
       "      <th>CentralAir_N</th>\n",
       "      <th>CentralAir_Y</th>\n",
       "      <th>Electrical_FuseA</th>\n",
       "      <th>Electrical_FuseF</th>\n",
       "      <th>Electrical_FuseP</th>\n",
       "      <th>Electrical_Mix</th>\n",
       "      <th>Electrical_SBrkr</th>\n",
       "      <th>KitchenQual_Ex</th>\n",
       "      <th>KitchenQual_Fa</th>\n",
       "      <th>KitchenQual_Gd</th>\n",
       "      <th>KitchenQual_TA</th>\n",
       "      <th>Functional_Maj1</th>\n",
       "      <th>Functional_Maj2</th>\n",
       "      <th>Functional_Min1</th>\n",
       "      <th>Functional_Min2</th>\n",
       "      <th>Functional_Mod</th>\n",
       "      <th>Functional_Sev</th>\n",
       "      <th>Functional_Typ</th>\n",
       "      <th>GarageType_2Types</th>\n",
       "      <th>GarageType_Attchd</th>\n",
       "      <th>GarageType_Basment</th>\n",
       "      <th>GarageType_BuiltIn</th>\n",
       "      <th>GarageType_CarPort</th>\n",
       "      <th>GarageType_Detchd</th>\n",
       "      <th>GarageFinish_Fin</th>\n",
       "      <th>GarageFinish_RFn</th>\n",
       "      <th>GarageFinish_Unf</th>\n",
       "      <th>GarageQual_Ex</th>\n",
       "      <th>GarageQual_Fa</th>\n",
       "      <th>GarageQual_Gd</th>\n",
       "      <th>GarageQual_Po</th>\n",
       "      <th>GarageQual_TA</th>\n",
       "      <th>GarageCond_Ex</th>\n",
       "      <th>GarageCond_Fa</th>\n",
       "      <th>GarageCond_Gd</th>\n",
       "      <th>GarageCond_Po</th>\n",
       "      <th>GarageCond_TA</th>\n",
       "      <th>PavedDrive_N</th>\n",
       "      <th>PavedDrive_P</th>\n",
       "      <th>PavedDrive_Y</th>\n",
       "      <th>Fence_GdPrv</th>\n",
       "      <th>Fence_GdWo</th>\n",
       "      <th>Fence_MnPrv</th>\n",
       "      <th>Fence_MnWw</th>\n",
       "      <th>SaleType_COD</th>\n",
       "      <th>SaleType_CWD</th>\n",
       "      <th>SaleType_Con</th>\n",
       "      <th>SaleType_ConLD</th>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>70</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>1941</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1188</td>\n",
       "      <td>1152</td>\n",
       "      <td>0</td>\n",
       "      <td>2340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1941.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>20</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1078.0</td>\n",
       "      <td>1078</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1078</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>366</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>20</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1965</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1256.0</td>\n",
       "      <td>1256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>736</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd  Fireplaces  GarageYrBlt  GarageCars  GarageArea  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  MSZoning_C (all)  MSZoning_FV  MSZoning_RH  MSZoning_RL  MSZoning_RM  Street_Grvl  Street_Pave  LotShape_IR1  LotShape_IR2  LotShape_IR3  LotShape_Reg  LandContour_Bnk  LandContour_HLS  LandContour_Low  LandContour_Lvl  Utilities_AllPub  Utilities_NoSeWa  LotConfig_Corner  LotConfig_CulDSac  LotConfig_FR2  LotConfig_FR3  LotConfig_Inside  LandSlope_Gtl  LandSlope_Mod  LandSlope_Sev  Neighborhood_Blmngtn  Neighborhood_Blueste  Neighborhood_BrDale  Neighborhood_BrkSide  Neighborhood_ClearCr  Neighborhood_CollgCr  Neighborhood_Crawfor  Neighborhood_Edwards  Neighborhood_Gilbert  Neighborhood_IDOTRR  Neighborhood_MeadowV  Neighborhood_Mitchel  Neighborhood_NAmes  Neighborhood_NPkVill  Neighborhood_NWAmes  Neighborhood_NoRidge  Neighborhood_NridgHt  Neighborhood_OldTown  Neighborhood_SWISU  Neighborhood_Sawyer  Neighborhood_SawyerW  Neighborhood_Somerst  Neighborhood_StoneBr  Neighborhood_Timber  Neighborhood_Veenker  Condition1_Artery  Condition1_Feedr  Condition1_Norm  Condition1_PosA  Condition1_PosN  Condition1_RRAe  Condition1_RRAn  Condition1_RRNe  Condition1_RRNn  Condition2_Artery  Condition2_Feedr  Condition2_Norm  Condition2_PosA  Condition2_PosN  Condition2_RRAe  Condition2_RRAn  Condition2_RRNn  BldgType_1Fam  BldgType_2fmCon  BldgType_Duplex  BldgType_Twnhs  BldgType_TwnhsE  HouseStyle_1.5Fin  HouseStyle_1.5Unf  HouseStyle_1Story  HouseStyle_2.5Fin  HouseStyle_2.5Unf  HouseStyle_2Story  HouseStyle_SFoyer  HouseStyle_SLvl  RoofStyle_Flat  RoofStyle_Gable  RoofStyle_Gambrel  RoofStyle_Hip  RoofStyle_Mansard  RoofStyle_Shed  RoofMatl_ClyTile  RoofMatl_CompShg  RoofMatl_Membran  RoofMatl_Metal  RoofMatl_Roll  RoofMatl_Tar&Grv  RoofMatl_WdShake  RoofMatl_WdShngl  Exterior1st_AsbShng  Exterior1st_AsphShn  Exterior1st_BrkComm  Exterior1st_BrkFace  Exterior1st_CBlock  Exterior1st_CemntBd  Exterior1st_HdBoard  Exterior1st_ImStucc  Exterior1st_MetalSd  Exterior1st_Plywood  Exterior1st_Stone  Exterior1st_Stucco  Exterior1st_VinylSd  Exterior1st_Wd Sdng  Exterior1st_WdShing  Exterior2nd_AsbShng  Exterior2nd_AsphShn  Exterior2nd_Brk Cmn  Exterior2nd_BrkFace  Exterior2nd_CBlock  Exterior2nd_CmentBd  Exterior2nd_HdBoard  Exterior2nd_ImStucc  Exterior2nd_MetalSd  Exterior2nd_Other  Exterior2nd_Plywood  Exterior2nd_Stone  Exterior2nd_Stucco  Exterior2nd_VinylSd  Exterior2nd_Wd Sdng  Exterior2nd_Wd Shng  MasVnrType_BrkCmn  MasVnrType_BrkFace  MasVnrType_None  MasVnrType_Stone  ExterQual_Ex  ExterQual_Fa  ExterQual_Gd  ExterQual_TA  ExterCond_Ex  ExterCond_Fa  ExterCond_Gd  ExterCond_Po  ExterCond_TA  Foundation_BrkTil  Foundation_CBlock  Foundation_PConc  Foundation_Slab  Foundation_Stone  Foundation_Wood  BsmtQual_Ex  BsmtQual_Fa  BsmtQual_Gd  BsmtQual_TA  BsmtCond_Fa  BsmtCond_Gd  BsmtCond_Po  BsmtCond_TA  BsmtExposure_Av  BsmtExposure_Gd  BsmtExposure_Mn  BsmtExposure_No  BsmtFinType1_ALQ  BsmtFinType1_BLQ  BsmtFinType1_GLQ  BsmtFinType1_LwQ  BsmtFinType1_Rec  BsmtFinType1_Unf  BsmtFinType2_ALQ  BsmtFinType2_BLQ  BsmtFinType2_GLQ  BsmtFinType2_LwQ  BsmtFinType2_Rec  BsmtFinType2_Unf  Heating_Floor  Heating_GasA  Heating_GasW  Heating_Grav  Heating_OthW  Heating_Wall  HeatingQC_Ex  HeatingQC_Fa  HeatingQC_Gd  HeatingQC_Po  HeatingQC_TA  CentralAir_N  CentralAir_Y  Electrical_FuseA  Electrical_FuseF  Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  KitchenQual_Ex  KitchenQual_Fa  KitchenQual_Gd  KitchenQual_TA  Functional_Maj1  Functional_Maj2  Functional_Min1  Functional_Min2  Functional_Mod  Functional_Sev  Functional_Typ  GarageType_2Types  GarageType_Attchd  GarageType_Basment  GarageType_BuiltIn  GarageType_CarPort  GarageType_Detchd  GarageFinish_Fin  GarageFinish_RFn  GarageFinish_Unf  GarageQual_Ex  GarageQual_Fa  GarageQual_Gd  GarageQual_Po  GarageQual_TA  GarageCond_Ex  GarageCond_Fa  GarageCond_Gd  GarageCond_Po  GarageCond_TA  PavedDrive_N  PavedDrive_P  PavedDrive_Y  Fence_GdPrv  Fence_GdWo  Fence_MnPrv  Fence_MnWw  SaleType_COD  SaleType_CWD  SaleType_Con  SaleType_ConLD  SaleType_ConLI  SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial\n",
       "1457          70         66.0     9042            7            9       1941          2006         0.0       275.0         0.0      877.0       1152.0      1188      1152             0       2340           0.0           0.0         2         0             4             1             9           2       1941.0         1.0       252.0           0           60              0          0            0         0     2500       5    2010                 0            0            0            1            0            0            1             0             0             0             1                0                0                0                1                 1                 0                 0                  0              0              0                 1              1              0              0                     0                     0                    0                     0                     0                     0                     1                     0                     0                    0                     0                     0                   0                     0                    0                     0                     0                     0                   0                    0                     0                     0                     0                    0                     0                  0                 0                1                0                0                0                0                0                0                  0                 0                1                0                0                0                0                0              1                0                0               0                0                  0                  0                  0                  0                  0                  1                  0                0               0                1                  0              0                  0               0                 0                 1                 0               0              0                 0                 0                 0                    0                    0                    0                    0                   0                    1                    0                    0                    0                    0                  0                   0                    0                    0                    0                    0                    0                    0                    0                   0                    1                    0                    0                    0                  0                    0                  0                   0                    0                    0                    0                  0                   0                1                 0             1             0             0             0             0             0             1             0             0                  0                  0                 0                0                 1                0            0            0            0            1            0            1            0            0                0                0                0                1                 0                 0                 1                 0                 0                 0                 0                 0                 0                 0                 0                 1              0             1             0             0             0             0             1             0             0             0             0             0             1                 0                 0                 0               0                 1               0               0               1               0                0                0                0                0               0               0               1                  0                  1                   0                   0                   0                  0                 0                 1                 0              0              0              0              0              1              0              0              0              0              1             0             0             1            1           0            0           0             0             0             0               0               0               0             0             0            1                      0                      0                     0                     0                     1                      0\n",
       "1458          20         68.0     9717            5            6       1950          1996         0.0        49.0      1029.0        0.0       1078.0      1078         0             0       1078           1.0           0.0         1         0             2             1             5           0       1950.0         1.0       240.0         366            0            112          0            0         0        0       4    2010                 0            0            0            1            0            0            1             0             0             0             1                0                0                0                1                 1                 0                 0                  0              0              0                 1              1              0              0                     0                     0                    0                     0                     0                     0                     0                     0                     0                    0                     0                     0                   1                     0                    0                     0                     0                     0                   0                    0                     0                     0                     0                    0                     0                  0                 0                1                0                0                0                0                0                0                  0                 0                1                0                0                0                0                0              1                0                0               0                0                  0                  0                  1                  0                  0                  0                  0                0               0                0                  0              1                  0               0                 0                 1                 0               0              0                 0                 0                 0                    0                    0                    0                    0                   0                    0                    0                    0                    1                    0                  0                   0                    0                    0                    0                    0                    0                    0                    0                   0                    0                    0                    0                    1                  0                    0                  0                   0                    0                    0                    0                  0                   0                1                 0             0             0             0             1             0             0             0             0             1                  0                  1                 0                0                 0                0            0            0            0            1            0            0            0            1                0                0                1                0                 0                 0                 1                 0                 0                 0                 0                 0                 0                 0                 1                 0              0             1             0             0             0             0             0             0             1             0             0             0             1                 1                 0                 0               0                 0               0               0               1               0                0                0                0                0               0               0               1                  0                  1                   0                   0                   0                  0                 0                 0                 1              0              0              0              0              1              0              0              0              0              1             0             0             1            0           0            0           0             0             0             0               0               0               0             0             0            1                      0                      0                     0                     0                     1                      0\n",
       "1459          20         75.0     9937            5            6       1965          1965         0.0       830.0       290.0      136.0       1256.0      1256         0             0       1256           1.0           0.0         1         1             3             1             6           0       1965.0         1.0       276.0         736           68              0          0            0         0        0       6    2008                 0            0            0            1            0            0            1             0             0             0             1                0                0                0                1                 1                 0                 0                  0              0              0                 1              1              0              0                     0                     0                    0                     0                     0                     0                     0                     1                     0                    0                     0                     0                   0                     0                    0                     0                     0                     0                   0                    0                     0                     0                     0                    0                     0                  0                 0                1                0                0                0                0                0                0                  0                 0                1                0                0                0                0                0              1                0                0               0                0                  0                  0                  1                  0                  0                  0                  0                0               0                1                  0              0                  0               0                 0                 1                 0               0              0                 0                 0                 0                    0                    0                    0                    0                   0                    0                    1                    0                    0                    0                  0                   0                    0                    0                    0                    0                    0                    0                    0                   0                    0                    1                    0                    0                  0                    0                  0                   0                    0                    0                    0                  0                   0                1                 0             0             0             1             0             0             0             0             0             1                  0                  1                 0                0                 0                0            0            0            0            1            0            0            0            1                0                0                0                1                 0                 1                 0                 0                 0                 0                 0                 0                 0                 1                 0                 0              0             1             0             0             0             0             0             0             1             0             0             0             1                 0                 0                 0               0                 1               0               0               0               1                0                0                0                0               0               0               1                  0                  1                   0                   0                   0                  0                 1                 0                 0              0              0              0              0              1              0              0              0              0              1             0             0             1            0           0            0           0             0             0             0               0               0               0             0             0            1                      0                      0                     0                     0                     1                      0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>1941</td>\n",
       "      <td>2006</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>CemntBd</td>\n",
       "      <td>CmentBd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Stone</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>275</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>877</td>\n",
       "      <td>1152</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1188</td>\n",
       "      <td>1152</td>\n",
       "      <td>0</td>\n",
       "      <td>2340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1941.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>1</td>\n",
       "      <td>252</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>1996</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>49</td>\n",
       "      <td>Rec</td>\n",
       "      <td>1029</td>\n",
       "      <td>0</td>\n",
       "      <td>1078</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>FuseA</td>\n",
       "      <td>1078</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>366</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Edwards</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1965</td>\n",
       "      <td>1965</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>BLQ</td>\n",
       "      <td>830</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>290</td>\n",
       "      <td>136</td>\n",
       "      <td>1256</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1256</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>1</td>\n",
       "      <td>276</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>736</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea PoolQC  Fence MiscFeature  MiscVal  MoSold  YrSold SaleType SaleCondition  SalePrice\n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl      Crawfor       Norm       Norm     1Fam     2Story            7            9       1941          2006     Gable  CompShg     CemntBd     CmentBd       None         0.0        Ex        Gd      Stone       TA       Gd           No          GLQ         275          Unf           0        877         1152    GasA        Ex          Y      SBrkr      1188      1152             0       2340             0             0         2         0             4             1          Gd             9        Typ           2          Gd     Attchd       1941.0          RFn           1         252         TA         TA          Y           0           60              0          0            0         0    NaN  GdPrv        Shed     2500       5    2010       WD        Normal     266500\n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam     1Story            5            6       1950          1996       Hip  CompShg     MetalSd     MetalSd       None         0.0        TA        TA     CBlock       TA       TA           Mn          GLQ          49          Rec        1029          0         1078    GasA        Gd          Y      FuseA      1078         0             0       1078             1             0         1         0             2             1          Gd             5        Typ           0         NaN     Attchd       1950.0          Unf           1         240         TA         TA          Y         366            0            112          0            0         0    NaN    NaN         NaN        0       4    2010       WD        Normal     142125\n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg         Lvl    AllPub    Inside       Gtl      Edwards       Norm       Norm     1Fam     1Story            5            6       1965          1965     Gable  CompShg     HdBoard     HdBoard       None         0.0        Gd        TA     CBlock       TA       TA           No          BLQ         830          LwQ         290        136         1256    GasA        Gd          Y      SBrkr      1256         0             0       1256             1             0         1         1             3             1          TA             6        Typ           0         NaN     Attchd       1965.0          Fin           1         276         TA         TA          Y         736           68              0          0            0         0    NaN    NaN         NaN        0       6    2008       WD        Normal     147500"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>MSZoning_C (all)</th>\n",
       "      <th>MSZoning_FV</th>\n",
       "      <th>MSZoning_RH</th>\n",
       "      <th>MSZoning_RL</th>\n",
       "      <th>MSZoning_RM</th>\n",
       "      <th>Street_Grvl</th>\n",
       "      <th>Street_Pave</th>\n",
       "      <th>LotShape_IR1</th>\n",
       "      <th>LotShape_IR2</th>\n",
       "      <th>LotShape_IR3</th>\n",
       "      <th>LotShape_Reg</th>\n",
       "      <th>LandContour_Bnk</th>\n",
       "      <th>LandContour_HLS</th>\n",
       "      <th>LandContour_Low</th>\n",
       "      <th>LandContour_Lvl</th>\n",
       "      <th>Utilities_AllPub</th>\n",
       "      <th>Utilities_NoSeWa</th>\n",
       "      <th>LotConfig_Corner</th>\n",
       "      <th>LotConfig_CulDSac</th>\n",
       "      <th>LotConfig_FR2</th>\n",
       "      <th>LotConfig_FR3</th>\n",
       "      <th>LotConfig_Inside</th>\n",
       "      <th>LandSlope_Gtl</th>\n",
       "      <th>LandSlope_Mod</th>\n",
       "      <th>LandSlope_Sev</th>\n",
       "      <th>Neighborhood_Blmngtn</th>\n",
       "      <th>Neighborhood_Blueste</th>\n",
       "      <th>Neighborhood_BrDale</th>\n",
       "      <th>Neighborhood_BrkSide</th>\n",
       "      <th>Neighborhood_ClearCr</th>\n",
       "      <th>Neighborhood_CollgCr</th>\n",
       "      <th>Neighborhood_Crawfor</th>\n",
       "      <th>Neighborhood_Edwards</th>\n",
       "      <th>Neighborhood_Gilbert</th>\n",
       "      <th>Neighborhood_IDOTRR</th>\n",
       "      <th>Neighborhood_MeadowV</th>\n",
       "      <th>Neighborhood_Mitchel</th>\n",
       "      <th>Neighborhood_NAmes</th>\n",
       "      <th>Neighborhood_NPkVill</th>\n",
       "      <th>Neighborhood_NWAmes</th>\n",
       "      <th>Neighborhood_NoRidge</th>\n",
       "      <th>Neighborhood_NridgHt</th>\n",
       "      <th>Neighborhood_OldTown</th>\n",
       "      <th>Neighborhood_SWISU</th>\n",
       "      <th>Neighborhood_Sawyer</th>\n",
       "      <th>Neighborhood_SawyerW</th>\n",
       "      <th>Neighborhood_Somerst</th>\n",
       "      <th>Neighborhood_StoneBr</th>\n",
       "      <th>Neighborhood_Timber</th>\n",
       "      <th>Neighborhood_Veenker</th>\n",
       "      <th>Condition1_Artery</th>\n",
       "      <th>Condition1_Feedr</th>\n",
       "      <th>Condition1_Norm</th>\n",
       "      <th>Condition1_PosA</th>\n",
       "      <th>Condition1_PosN</th>\n",
       "      <th>Condition1_RRAe</th>\n",
       "      <th>Condition1_RRAn</th>\n",
       "      <th>Condition1_RRNe</th>\n",
       "      <th>Condition1_RRNn</th>\n",
       "      <th>Condition2_Artery</th>\n",
       "      <th>Condition2_Feedr</th>\n",
       "      <th>Condition2_Norm</th>\n",
       "      <th>Condition2_PosA</th>\n",
       "      <th>Condition2_PosN</th>\n",
       "      <th>Condition2_RRAe</th>\n",
       "      <th>Condition2_RRAn</th>\n",
       "      <th>Condition2_RRNn</th>\n",
       "      <th>BldgType_1Fam</th>\n",
       "      <th>BldgType_2fmCon</th>\n",
       "      <th>BldgType_Duplex</th>\n",
       "      <th>BldgType_Twnhs</th>\n",
       "      <th>BldgType_TwnhsE</th>\n",
       "      <th>HouseStyle_1.5Fin</th>\n",
       "      <th>HouseStyle_1.5Unf</th>\n",
       "      <th>HouseStyle_1Story</th>\n",
       "      <th>HouseStyle_2.5Fin</th>\n",
       "      <th>HouseStyle_2.5Unf</th>\n",
       "      <th>HouseStyle_2Story</th>\n",
       "      <th>HouseStyle_SFoyer</th>\n",
       "      <th>HouseStyle_SLvl</th>\n",
       "      <th>RoofStyle_Flat</th>\n",
       "      <th>RoofStyle_Gable</th>\n",
       "      <th>RoofStyle_Gambrel</th>\n",
       "      <th>RoofStyle_Hip</th>\n",
       "      <th>RoofStyle_Mansard</th>\n",
       "      <th>RoofStyle_Shed</th>\n",
       "      <th>RoofMatl_ClyTile</th>\n",
       "      <th>RoofMatl_CompShg</th>\n",
       "      <th>RoofMatl_Membran</th>\n",
       "      <th>RoofMatl_Metal</th>\n",
       "      <th>RoofMatl_Roll</th>\n",
       "      <th>RoofMatl_Tar&amp;Grv</th>\n",
       "      <th>RoofMatl_WdShake</th>\n",
       "      <th>RoofMatl_WdShngl</th>\n",
       "      <th>Exterior1st_AsbShng</th>\n",
       "      <th>Exterior1st_AsphShn</th>\n",
       "      <th>Exterior1st_BrkComm</th>\n",
       "      <th>Exterior1st_BrkFace</th>\n",
       "      <th>Exterior1st_CBlock</th>\n",
       "      <th>Exterior1st_CemntBd</th>\n",
       "      <th>Exterior1st_HdBoard</th>\n",
       "      <th>Exterior1st_ImStucc</th>\n",
       "      <th>Exterior1st_MetalSd</th>\n",
       "      <th>Exterior1st_Plywood</th>\n",
       "      <th>Exterior1st_Stone</th>\n",
       "      <th>Exterior1st_Stucco</th>\n",
       "      <th>Exterior1st_VinylSd</th>\n",
       "      <th>Exterior1st_Wd Sdng</th>\n",
       "      <th>Exterior1st_WdShing</th>\n",
       "      <th>Exterior2nd_AsbShng</th>\n",
       "      <th>Exterior2nd_AsphShn</th>\n",
       "      <th>Exterior2nd_Brk Cmn</th>\n",
       "      <th>Exterior2nd_BrkFace</th>\n",
       "      <th>Exterior2nd_CBlock</th>\n",
       "      <th>Exterior2nd_CmentBd</th>\n",
       "      <th>Exterior2nd_HdBoard</th>\n",
       "      <th>Exterior2nd_ImStucc</th>\n",
       "      <th>Exterior2nd_MetalSd</th>\n",
       "      <th>Exterior2nd_Other</th>\n",
       "      <th>Exterior2nd_Plywood</th>\n",
       "      <th>Exterior2nd_Stone</th>\n",
       "      <th>Exterior2nd_Stucco</th>\n",
       "      <th>Exterior2nd_VinylSd</th>\n",
       "      <th>Exterior2nd_Wd Sdng</th>\n",
       "      <th>Exterior2nd_Wd Shng</th>\n",
       "      <th>MasVnrType_BrkCmn</th>\n",
       "      <th>MasVnrType_BrkFace</th>\n",
       "      <th>MasVnrType_None</th>\n",
       "      <th>MasVnrType_Stone</th>\n",
       "      <th>ExterQual_Ex</th>\n",
       "      <th>ExterQual_Fa</th>\n",
       "      <th>ExterQual_Gd</th>\n",
       "      <th>ExterQual_TA</th>\n",
       "      <th>ExterCond_Ex</th>\n",
       "      <th>ExterCond_Fa</th>\n",
       "      <th>ExterCond_Gd</th>\n",
       "      <th>ExterCond_Po</th>\n",
       "      <th>ExterCond_TA</th>\n",
       "      <th>Foundation_BrkTil</th>\n",
       "      <th>Foundation_CBlock</th>\n",
       "      <th>Foundation_PConc</th>\n",
       "      <th>Foundation_Slab</th>\n",
       "      <th>Foundation_Stone</th>\n",
       "      <th>Foundation_Wood</th>\n",
       "      <th>BsmtQual_Ex</th>\n",
       "      <th>BsmtQual_Fa</th>\n",
       "      <th>BsmtQual_Gd</th>\n",
       "      <th>BsmtQual_TA</th>\n",
       "      <th>BsmtCond_Fa</th>\n",
       "      <th>BsmtCond_Gd</th>\n",
       "      <th>BsmtCond_Po</th>\n",
       "      <th>BsmtCond_TA</th>\n",
       "      <th>BsmtExposure_Av</th>\n",
       "      <th>BsmtExposure_Gd</th>\n",
       "      <th>BsmtExposure_Mn</th>\n",
       "      <th>BsmtExposure_No</th>\n",
       "      <th>BsmtFinType1_ALQ</th>\n",
       "      <th>BsmtFinType1_BLQ</th>\n",
       "      <th>BsmtFinType1_GLQ</th>\n",
       "      <th>BsmtFinType1_LwQ</th>\n",
       "      <th>BsmtFinType1_Rec</th>\n",
       "      <th>BsmtFinType1_Unf</th>\n",
       "      <th>BsmtFinType2_ALQ</th>\n",
       "      <th>BsmtFinType2_BLQ</th>\n",
       "      <th>BsmtFinType2_GLQ</th>\n",
       "      <th>BsmtFinType2_LwQ</th>\n",
       "      <th>BsmtFinType2_Rec</th>\n",
       "      <th>BsmtFinType2_Unf</th>\n",
       "      <th>Heating_Floor</th>\n",
       "      <th>Heating_GasA</th>\n",
       "      <th>Heating_GasW</th>\n",
       "      <th>Heating_Grav</th>\n",
       "      <th>Heating_OthW</th>\n",
       "      <th>Heating_Wall</th>\n",
       "      <th>HeatingQC_Ex</th>\n",
       "      <th>HeatingQC_Fa</th>\n",
       "      <th>HeatingQC_Gd</th>\n",
       "      <th>HeatingQC_Po</th>\n",
       "      <th>HeatingQC_TA</th>\n",
       "      <th>CentralAir_N</th>\n",
       "      <th>CentralAir_Y</th>\n",
       "      <th>Electrical_FuseA</th>\n",
       "      <th>Electrical_FuseF</th>\n",
       "      <th>Electrical_FuseP</th>\n",
       "      <th>Electrical_Mix</th>\n",
       "      <th>Electrical_SBrkr</th>\n",
       "      <th>KitchenQual_Ex</th>\n",
       "      <th>KitchenQual_Fa</th>\n",
       "      <th>KitchenQual_Gd</th>\n",
       "      <th>KitchenQual_TA</th>\n",
       "      <th>Functional_Maj1</th>\n",
       "      <th>Functional_Maj2</th>\n",
       "      <th>Functional_Min1</th>\n",
       "      <th>Functional_Min2</th>\n",
       "      <th>Functional_Mod</th>\n",
       "      <th>Functional_Sev</th>\n",
       "      <th>Functional_Typ</th>\n",
       "      <th>GarageType_2Types</th>\n",
       "      <th>GarageType_Attchd</th>\n",
       "      <th>GarageType_Basment</th>\n",
       "      <th>GarageType_BuiltIn</th>\n",
       "      <th>GarageType_CarPort</th>\n",
       "      <th>GarageType_Detchd</th>\n",
       "      <th>GarageFinish_Fin</th>\n",
       "      <th>GarageFinish_RFn</th>\n",
       "      <th>GarageFinish_Unf</th>\n",
       "      <th>GarageQual_Ex</th>\n",
       "      <th>GarageQual_Fa</th>\n",
       "      <th>GarageQual_Gd</th>\n",
       "      <th>GarageQual_Po</th>\n",
       "      <th>GarageQual_TA</th>\n",
       "      <th>GarageCond_Ex</th>\n",
       "      <th>GarageCond_Fa</th>\n",
       "      <th>GarageCond_Gd</th>\n",
       "      <th>GarageCond_Po</th>\n",
       "      <th>GarageCond_TA</th>\n",
       "      <th>PavedDrive_N</th>\n",
       "      <th>PavedDrive_P</th>\n",
       "      <th>PavedDrive_Y</th>\n",
       "      <th>Fence_GdPrv</th>\n",
       "      <th>Fence_GdWo</th>\n",
       "      <th>Fence_MnPrv</th>\n",
       "      <th>Fence_MnWw</th>\n",
       "      <th>SaleType_COD</th>\n",
       "      <th>SaleType_CWD</th>\n",
       "      <th>SaleType_Con</th>\n",
       "      <th>SaleType_ConLD</th>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>85</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>60</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1993</td>\n",
       "      <td>1994</td>\n",
       "      <td>94.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>996</td>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>190</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd  Fireplaces  GarageYrBlt  GarageCars  GarageArea  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  MSZoning_C (all)  MSZoning_FV  MSZoning_RH  MSZoning_RL  MSZoning_RM  Street_Grvl  Street_Pave  LotShape_IR1  LotShape_IR2  LotShape_IR3  LotShape_Reg  LandContour_Bnk  LandContour_HLS  LandContour_Low  LandContour_Lvl  Utilities_AllPub  Utilities_NoSeWa  LotConfig_Corner  LotConfig_CulDSac  LotConfig_FR2  LotConfig_FR3  LotConfig_Inside  LandSlope_Gtl  LandSlope_Mod  LandSlope_Sev  Neighborhood_Blmngtn  Neighborhood_Blueste  Neighborhood_BrDale  Neighborhood_BrkSide  Neighborhood_ClearCr  Neighborhood_CollgCr  Neighborhood_Crawfor  Neighborhood_Edwards  Neighborhood_Gilbert  Neighborhood_IDOTRR  Neighborhood_MeadowV  Neighborhood_Mitchel  Neighborhood_NAmes  Neighborhood_NPkVill  Neighborhood_NWAmes  Neighborhood_NoRidge  Neighborhood_NridgHt  Neighborhood_OldTown  Neighborhood_SWISU  Neighborhood_Sawyer  Neighborhood_SawyerW  Neighborhood_Somerst  Neighborhood_StoneBr  Neighborhood_Timber  Neighborhood_Veenker  Condition1_Artery  Condition1_Feedr  Condition1_Norm  Condition1_PosA  Condition1_PosN  Condition1_RRAe  Condition1_RRAn  Condition1_RRNe  Condition1_RRNn  Condition2_Artery  Condition2_Feedr  Condition2_Norm  Condition2_PosA  Condition2_PosN  Condition2_RRAe  Condition2_RRAn  Condition2_RRNn  BldgType_1Fam  BldgType_2fmCon  BldgType_Duplex  BldgType_Twnhs  BldgType_TwnhsE  HouseStyle_1.5Fin  HouseStyle_1.5Unf  HouseStyle_1Story  HouseStyle_2.5Fin  HouseStyle_2.5Unf  HouseStyle_2Story  HouseStyle_SFoyer  HouseStyle_SLvl  RoofStyle_Flat  RoofStyle_Gable  RoofStyle_Gambrel  RoofStyle_Hip  RoofStyle_Mansard  RoofStyle_Shed  RoofMatl_ClyTile  RoofMatl_CompShg  RoofMatl_Membran  RoofMatl_Metal  RoofMatl_Roll  RoofMatl_Tar&Grv  RoofMatl_WdShake  RoofMatl_WdShngl  Exterior1st_AsbShng  Exterior1st_AsphShn  Exterior1st_BrkComm  Exterior1st_BrkFace  Exterior1st_CBlock  Exterior1st_CemntBd  Exterior1st_HdBoard  Exterior1st_ImStucc  Exterior1st_MetalSd  Exterior1st_Plywood  Exterior1st_Stone  Exterior1st_Stucco  Exterior1st_VinylSd  Exterior1st_Wd Sdng  Exterior1st_WdShing  Exterior2nd_AsbShng  Exterior2nd_AsphShn  Exterior2nd_Brk Cmn  Exterior2nd_BrkFace  Exterior2nd_CBlock  Exterior2nd_CmentBd  Exterior2nd_HdBoard  Exterior2nd_ImStucc  Exterior2nd_MetalSd  Exterior2nd_Other  Exterior2nd_Plywood  Exterior2nd_Stone  Exterior2nd_Stucco  Exterior2nd_VinylSd  Exterior2nd_Wd Sdng  Exterior2nd_Wd Shng  MasVnrType_BrkCmn  MasVnrType_BrkFace  MasVnrType_None  MasVnrType_Stone  ExterQual_Ex  ExterQual_Fa  ExterQual_Gd  ExterQual_TA  ExterCond_Ex  ExterCond_Fa  ExterCond_Gd  ExterCond_Po  ExterCond_TA  Foundation_BrkTil  Foundation_CBlock  Foundation_PConc  Foundation_Slab  Foundation_Stone  Foundation_Wood  BsmtQual_Ex  BsmtQual_Fa  BsmtQual_Gd  BsmtQual_TA  BsmtCond_Fa  BsmtCond_Gd  BsmtCond_Po  BsmtCond_TA  BsmtExposure_Av  BsmtExposure_Gd  BsmtExposure_Mn  BsmtExposure_No  BsmtFinType1_ALQ  BsmtFinType1_BLQ  BsmtFinType1_GLQ  BsmtFinType1_LwQ  BsmtFinType1_Rec  BsmtFinType1_Unf  BsmtFinType2_ALQ  BsmtFinType2_BLQ  BsmtFinType2_GLQ  BsmtFinType2_LwQ  BsmtFinType2_Rec  BsmtFinType2_Unf  Heating_Floor  Heating_GasA  Heating_GasW  Heating_Grav  Heating_OthW  Heating_Wall  HeatingQC_Ex  HeatingQC_Fa  HeatingQC_Gd  HeatingQC_Po  HeatingQC_TA  CentralAir_N  CentralAir_Y  Electrical_FuseA  Electrical_FuseF  Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  KitchenQual_Ex  KitchenQual_Fa  KitchenQual_Gd  KitchenQual_TA  Functional_Maj1  Functional_Maj2  Functional_Min1  Functional_Min2  Functional_Mod  Functional_Sev  Functional_Typ  GarageType_2Types  GarageType_Attchd  GarageType_Basment  GarageType_BuiltIn  GarageType_CarPort  GarageType_Detchd  GarageFinish_Fin  GarageFinish_RFn  GarageFinish_Unf  GarageQual_Ex  GarageQual_Fa  GarageQual_Gd  GarageQual_Po  GarageQual_TA  GarageCond_Ex  GarageCond_Fa  GarageCond_Gd  GarageCond_Po  GarageCond_TA  PavedDrive_N  PavedDrive_P  PavedDrive_Y  Fence_GdPrv  Fence_GdWo  Fence_MnPrv  Fence_MnWw  SaleType_COD  SaleType_CWD  SaleType_Con  SaleType_ConLD  SaleType_ConLI  SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial\n",
       "2917          85         62.0    10441            5            5       1992          1992         0.0       337.0         0.0      575.0        912.0       970         0             0        970           0.0           1.0         1         0             3             1             6           0       1979.0         0.0         0.0          80           32              0          0            0         0      700       7    2006                 0            0            0            1            0            0            1             0             0             0             1                0                0                0                1                 1                 0                 0                  0              0              0                 1              1              0              0                     0                     0                    0                     0                     0                     0                     0                     0                     0                    0                     0                     1                   0                     0                    0                     0                     0                     0                   0                    0                     0                     0                     0                    0                     0                  0                 0                1                0                0                0                0                0                0                  0                 0                1                0                0                0                0                0              1                0                0               0                0                  0                  0                  0                  0                  0                  0                  1                0               0                1                  0              0                  0               0                 0                 1                 0               0              0                 0                 0                 0                    0                    0                    0                    0                   0                    0                    1                    0                    0                    0                  0                   0                    0                    0                    0                    0                    0                    0                    0                   0                    0                    0                    0                    0                  0                    0                  0                   0                    0                    0                    1                  0                   0                1                 0             0             0             0             1             0             0             0             0             1                  0                  0                 1                0                 0                0            0            0            1            0            0            0            0            1                1                0                0                0                 0                 0                 1                 0                 0                 0                 0                 0                 0                 0                 0                 1              0             1             0             0             0             0             0             0             0             0             1             0             1                 0                 0                 0               0                 1               0               0               0               1                0                0                0                0               0               0               1                  0                  0                   0                   0                   0                  0                 0                 0                 0              0              0              0              0              0              0              0              0              0              0             0             0             1            0           0            1           0             0             0             0               0               0               0             0             0            1                      0                      0                     0                     0                     1                      0\n",
       "2918          60         74.0     9627            7            5       1993          1994        94.0       758.0         0.0      238.0        996.0       996      1004             0       2000           0.0           0.0         2         1             3             1             9           1       1993.0         3.0       650.0         190           48              0          0            0         0        0      11    2006                 0            0            0            1            0            0            1             0             0             0             1                0                0                0                1                 1                 0                 0                  0              0              0                 1              0              1              0                     0                     0                    0                     0                     0                     0                     0                     0                     0                    0                     0                     1                   0                     0                    0                     0                     0                     0                   0                    0                     0                     0                     0                    0                     0                  0                 0                1                0                0                0                0                0                0                  0                 0                1                0                0                0                0                0              1                0                0               0                0                  0                  0                  0                  0                  0                  1                  0                0               0                1                  0              0                  0               0                 0                 1                 0               0              0                 0                 0                 0                    0                    0                    0                    0                   0                    0                    1                    0                    0                    0                  0                   0                    0                    0                    0                    0                    0                    0                    0                   0                    0                    1                    0                    0                  0                    0                  0                   0                    0                    0                    0                  0                   1                0                 0             0             0             0             1             0             0             0             0             1                  0                  0                 1                0                 0                0            0            0            1            0            0            0            0            1                1                0                0                0                 0                 0                 0                 1                 0                 0                 0                 0                 0                 0                 0                 1              0             1             0             0             0             0             1             0             0             0             0             0             1                 0                 0                 0               0                 1               0               0               0               1                0                0                0                0               0               0               1                  0                  1                   0                   0                   0                  0                 1                 0                 0              0              0              0              0              1              0              0              0              0              1             0             0             1            0           0            0           0             0             0             0               0               0               0             0             0            1                      0                      0                     0                     0                     1                      0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"reattach SalePrice to train data (now train2)\")\n",
    "train2 = pd.concat([train2], axis = 1)\n",
    "train2.tail(3)\n",
    "train.tail(3)\n",
    "\n",
    "#will need to put \"Id\" column on this when submitting as CSV to kaggle\n",
    "test2.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**SalePrice**"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Train DF - Understand SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8d5a9fdc0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.882876\n",
      "Kurtosis: 6.536282\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Train DF - Understand SalePrice\n",
    "\n",
    "#SalePrice in the train df shows that the range of housing prices is very extreme. \n",
    "#Perhaps this needs to be normalized\n",
    "train['SalePrice'].describe()\n",
    "\n",
    "sns.distplot(train['SalePrice'] , fit=stats.norm);\n",
    "\n",
    "#if abs(skewness(saleprice)) > 1 and abs(kurtosis(saleprice)) > 3 then dataset is skewed/long tails \n",
    "print(\"Skewness: %f\" % train['SalePrice'].skew())\n",
    "print(\"Kurtosis: %f\" % train['SalePrice'].kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Identifying Important Features (goal = predict SalePrice)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SalePrice        1.000000\n",
      "OverallQual      0.790982\n",
      "GrLivArea        0.708624\n",
      "GarageCars       0.640409\n",
      "GarageArea       0.623431\n",
      "TotalBsmtSF      0.613581\n",
      "1stFlrSF         0.605852\n",
      "FullBath         0.560664\n",
      "TotRmsAbvGrd     0.533723\n",
      "YearBuilt        0.522897\n",
      "YearRemodAdd     0.507101\n",
      "GarageYrBlt      0.486362\n",
      "MasVnrArea       0.477493\n",
      "Fireplaces       0.466929\n",
      "BsmtFinSF1       0.386420\n",
      "LotFrontage      0.351799\n",
      "WoodDeckSF       0.324413\n",
      "2ndFlrSF         0.319334\n",
      "OpenPorchSF      0.315856\n",
      "HalfBath         0.284108\n",
      "LotArea          0.263843\n",
      "BsmtFullBath     0.227122\n",
      "BsmtUnfSF        0.214479\n",
      "BedroomAbvGr     0.168213\n",
      "ScreenPorch      0.111447\n",
      "PoolArea         0.092404\n",
      "MoSold           0.046432\n",
      "3SsnPorch        0.044584\n",
      "BsmtFinSF2      -0.011378\n",
      "BsmtHalfBath    -0.016844\n",
      "MiscVal         -0.021190\n",
      "Id              -0.021917\n",
      "LowQualFinSF    -0.025606\n",
      "YrSold          -0.028923\n",
      "OverallCond     -0.077856\n",
      "MSSubClass      -0.084284\n",
      "EnclosedPorch   -0.128578\n",
      "KitchenAbvGr    -0.135907\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#train\n",
    "\n",
    "#Overall Quality seems to be most correlated to SalePrice\n",
    "#Followed by GrLivArea, GarageCars, GarageArea, and TotalBsmtSF\n",
    "#12 features with greater than .50 correlation \n",
    "\n",
    "train_corr = train.corr()\n",
    "train_corr.sort_values(['SalePrice'], ascending=False, inplace=True)\n",
    "print(train_corr.SalePrice)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define error measure for scoring : RMSE\n",
    "def rmse_cv(model):\n",
    "    rmse= np.sqrt(-cross_val_score(model, X_train, y_train, scoring=\"neg_mean_squared_error\", cv = 5))\n",
    "    return(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split**"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "#training data \n",
    "X_train = train2.copy()\n",
    "X_train.head(2)\n",
    "\n",
    "y_train = train.SalePrice\n",
    "\n",
    "#testing data \n",
    "X_test = test2.copy()\n",
    "\n",
    "X_test.head(2)\n",
    "\n",
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 274)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(1460,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#FOR ASSIGNMENT 4 \n",
    "X = train2.copy()\n",
    "y = train.SalePrice\n",
    "\n",
    "X.shape\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.001,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape : (1458, 274)\n",
      "X_test Shape : (2, 274)\n",
      "y_train Shape : (1458,)\n",
      "y_test Shape : (2,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train Shape :',X_train.shape)\n",
    "print('X_test Shape :',X_test.shape)\n",
    "print('y_train Shape :',y_train.shape)\n",
    "print('y_test Shape :',y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alternatiave Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Linear Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 97113.5501261 , 176633.66499564, 319762.69495671,  77069.25827434,\n",
       "       245068.07688434])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train,y_train)\n",
    "lin_reg.predict(X_train[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34182.808117180386"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(lin_reg).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1299656276.6063626\n"
     ]
    }
   ],
   "source": [
    "MSEs = cross_val_score(lin_reg, X_train, y_train, scoring='neg_mean_squared_error', cv=5)\n",
    "mean_MSE = np.mean(MSEs)\n",
    "print(mean_MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lasso**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "#Lasso = Lasso(alpha=1)\n",
    "#Lasso.fit(X_train, y_train)\n",
    "#Lasso.predict(X_train[0:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#print(\"rmse_cv\",rmse_cv(Lasso).mean(),\"\\nmean SalePrice=\",train['SalePrice'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train2 = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=8, random_state=42, shuffle=True),\n",
       "             estimator=Lasso(), param_grid={'alpha': [157.1]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 157.1}\n",
      "-1051965623.7901013\n"
     ]
    }
   ],
   "source": [
    "#Lasso attempt 2\n",
    "Lasso2 = Lasso()\n",
    "\n",
    "parameters_lasso = {'alpha': [157.1]}\n",
    "\n",
    "kfolds = KFold(n_splits=8, shuffle=True, random_state=42)\n",
    "\n",
    "lasso_regr = GridSearchCV(Lasso2, parameters_lasso, scoring='neg_mean_squared_error', cv = kfolds)\n",
    "\n",
    "lasso_regr.fit(X_train, y_train)\n",
    "\n",
    "print(lasso_regr.best_params_)\n",
    "print(lasso_regr.best_score_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 280961811355.5544, tolerance: 797110913.2903413\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 274473690733.94382, tolerance: 813813982.6104995\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 244126221362.36417, tolerance: 814393926.6565986\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 250508603232.12668, tolerance: 767289982.0110747\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279699416037.25195, tolerance: 846705985.8097527\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 238591404940.97937, tolerance: 791589578.8956363\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 258428876161.03137, tolerance: 771667130.5584\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 272641964565.18036, tolerance: 826469665.5922089\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 276366288661.27783, tolerance: 797110913.2903413\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 270437557970.26205, tolerance: 813813982.6104995\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 240483266765.06122, tolerance: 814393926.6565986\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 244076128937.20917, tolerance: 767289982.0110747\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273374833784.057, tolerance: 846705985.8097527\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 225823723617.6741, tolerance: 791589578.8956363\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 253922847004.0691, tolerance: 771667130.5584\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 267504224831.0781, tolerance: 826469665.5922089\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234537555432.19904, tolerance: 797110913.2903413\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 233127925458.95404, tolerance: 813813982.6104995\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 207123981403.69476, tolerance: 814393926.6565986\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 189657647958.17963, tolerance: 767289982.0110747\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218762949069.13428, tolerance: 846705985.8097527\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 138642582398.53903, tolerance: 791589578.8956363\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213284376031.72293, tolerance: 771667130.5584\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 221096611440.98053, tolerance: 826469665.5922089\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 67693896674.03998, tolerance: 797110913.2903413\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 77868071221.19385, tolerance: 813813982.6104995\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60280220133.55606, tolerance: 814393926.6565986\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20815221633.582397, tolerance: 767289982.0110747\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29306678379.12738, tolerance: 846705985.8097527\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24806777899.874207, tolerance: 791589578.8956363\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44564043117.24536, tolerance: 771667130.5584\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60300952655.7594, tolerance: 826469665.5922089\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1616861630.9557495, tolerance: 797110913.2903413\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1346992910.0706787, tolerance: 814393926.6565986\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1382499156.6990356, tolerance: 767289982.0110747\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2288775759.663513, tolerance: 846705985.8097527\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 967591685.5213013, tolerance: 771667130.5584\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1111497613.2695312, tolerance: 826469665.5922089\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2024062711.225586, tolerance: 797110913.2903413\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1394076803.8513794, tolerance: 813813982.6104995\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1731933230.3701172, tolerance: 814393926.6565986\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1720568059.6153564, tolerance: 767289982.0110747\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1210236111.5793457, tolerance: 846705985.8097527\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1705579608.3999634, tolerance: 791589578.8956363\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1250086461.8483887, tolerance: 771667130.5584\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1058189144.1428223, tolerance: 826469665.5922089\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2530408586.721863, tolerance: 814393926.6565986\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2372350578.7337646, tolerance: 767289982.0110747\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1736017159.930359, tolerance: 846705985.8097527\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2435930955.128113, tolerance: 791589578.8956363\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1739461229.2418213, tolerance: 771667130.5584\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=8, random_state=42, shuffle=True),\n",
       "             estimator=Lasso(),\n",
       "             param_grid={'alpha': [0.001, 0.01, 0.1, 1, 10, 20, 50, 100, 157.1,\n",
       "                                   160]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 160}\n",
      "-1127796175.3245733\n"
     ]
    }
   ],
   "source": [
    "#Lasso attempt 3\n",
    "Lasso2 = Lasso()\n",
    "\n",
    "parameters_lasso2 = {'alpha': [0.001,0.01,.1,1,10,20,50,100,157.1,160]}\n",
    "\n",
    "lasso_regr2 = GridSearchCV(Lasso2, parameters_lasso2, scoring='neg_mean_squared_error', cv = kfolds)\n",
    "\n",
    "lasso_regr2.fit(X_train2, y_train)\n",
    "\n",
    "print(lasso_regr2.best_params_)\n",
    "print(lasso_regr2.best_score_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171935829916.56213, tolerance: 617683706.588452\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 152487649109.66498, tolerance: 613417944.4136952\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 163679641450.9565, tolerance: 599547395.8355926\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 201011873444.89938, tolerance: 628489378.2305526\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203540563390.2729, tolerance: 639788343.0062565\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 172115618864.13208, tolerance: 566005285.0903338\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202872926986.83966, tolerance: 614044406.7496655\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198534150631.1603, tolerance: 596230419.693288\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 89424714585.05074, tolerance: 617683706.588452\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 143734846981.27124, tolerance: 613417944.4136952\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 163605643529.68506, tolerance: 599547395.8355926\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 192694474266.39148, tolerance: 628489378.2305526\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194658448546.24197, tolerance: 639788343.0062565\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 138990321430.95526, tolerance: 566005285.0903338\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198499134933.18237, tolerance: 614044406.7496655\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191692570900.74142, tolerance: 596230419.693288\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 160599267170.98328, tolerance: 617683706.588452\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 153132914915.27295, tolerance: 613417944.4136952\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 69567031341.39296, tolerance: 599547395.8355926\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 196028793537.24557, tolerance: 628489378.2305526\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 189179366681.19556, tolerance: 639788343.0062565\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 118440489753.07518, tolerance: 566005285.0903338\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194353728211.3397, tolerance: 614044406.7496655\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 199411640555.22928, tolerance: 596230419.693288\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 87828185570.6947, tolerance: 617683706.588452\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 147895876515.64597, tolerance: 613417944.4136952\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 89484177870.05011, tolerance: 599547395.8355926\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 196750578504.63516, tolerance: 628489378.2305526\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198171962226.2476, tolerance: 639788343.0062565\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 84892617785.57664, tolerance: 566005285.0903338\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 199884054746.31393, tolerance: 614044406.7496655\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202411170819.15662, tolerance: 596230419.693288\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 196621559037.40085, tolerance: 617683706.588452\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211784252808.551, tolerance: 651780596.8397408\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 175556409095.7709, tolerance: 634013018.0074481\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168827908648.05685, tolerance: 576646589.4277672\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213854373281.67096, tolerance: 643373066.1330749\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218657255712.47354, tolerance: 662326214.69797\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 208806007664.3826, tolerance: 607062816.1204522\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 226807704401.79477, tolerance: 648014818.983428\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202990803462.17593, tolerance: 610735753.7975781\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211667241725.11835, tolerance: 651780596.8397408\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 175531501079.19302, tolerance: 634013018.0074481\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168660086683.26218, tolerance: 576646589.4277672\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213730963846.39398, tolerance: 643373066.1330749\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218453261161.33774, tolerance: 662326214.69797\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 208394375445.76755, tolerance: 607062816.1204522\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 226553856798.0388, tolerance: 648014818.983428\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202909701186.47708, tolerance: 610735753.7975781\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 210202740182.1032, tolerance: 651780596.8397408\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 175154229954.0074, tolerance: 634013018.0074481\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 167315783048.49902, tolerance: 576646589.4277672\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211946517662.54745, tolerance: 643373066.1330749\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 217334766292.65088, tolerance: 662326214.69797\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 208779429715.82584, tolerance: 607062816.1204522\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 223971448139.35077, tolerance: 648014818.983428\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 201978123746.8925, tolerance: 610735753.7975781\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 209998696487.69653, tolerance: 651780596.8397408\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 178298172322.859, tolerance: 634013018.0074481\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129080159436.84938, tolerance: 576646589.4277672\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 216580005785.333, tolerance: 643373066.1330749\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1591400317.046875, tolerance: 662326214.69797\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211327322341.95197, tolerance: 607062816.1204522\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27065805053.363647, tolerance: 648014818.983428\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 156705577289.5608, tolerance: 610735753.7975781\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5654081897.590088, tolerance: 643373066.1330749\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12225907335.664795, tolerance: 651780596.8397408\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190005823964.0984, tolerance: 683291044.4062437\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180526830732.0243, tolerance: 668884561.5546032\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 172643751264.26447, tolerance: 626223056.6230835\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 188680725900.5976, tolerance: 684332562.4053749\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190975109719.40976, tolerance: 669145085.9924138\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 192595874650.29272, tolerance: 659769050.7723776\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183765562259.7583, tolerance: 650782550.1950731\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 177892675440.8435, tolerance: 649546034.845088\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 189928532143.1034, tolerance: 683291044.4062437\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180487429609.09555, tolerance: 668884561.5546032\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 172585492353.20764, tolerance: 626223056.6230835\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 188590074483.11057, tolerance: 684332562.4053749\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190854916989.91513, tolerance: 669145085.9924138\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 192502556942.31763, tolerance: 659769050.7723776\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183689775091.3425, tolerance: 650782550.1950731\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 177829556256.50952, tolerance: 649546034.845088\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 188001741291.8325, tolerance: 683291044.4062437\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180011436989.46216, tolerance: 668884561.5546032\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171917750721.7192, tolerance: 626223056.6230835\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 187450839098.3815, tolerance: 684332562.4053749\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 189482198310.54413, tolerance: 669145085.9924138\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191181253199.09732, tolerance: 659769050.7723776\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 182806632518.6789, tolerance: 650782550.1950731\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 177433356612.3844, tolerance: 649546034.845088\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191436945698.40665, tolerance: 683291044.4062437\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 181796925179.74023, tolerance: 668884561.5546032\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166620725108.7245, tolerance: 626223056.6230835\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 137382737242.90198, tolerance: 684332562.4053749\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 160573345821.4927, tolerance: 669145085.9924138\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171050076741.05316, tolerance: 659769050.7723776\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164453107718.2185, tolerance: 650782550.1950731\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126200702145.55215, tolerance: 649546034.845088\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 770296636.8344727, tolerance: 683291044.4062437\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5150479349.542236, tolerance: 668884561.5546032\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17706113886.83618, tolerance: 659769050.7723776\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26390437006.88492, tolerance: 650782550.1950731\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21325044088.062378, tolerance: 659769050.7723776\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171768953719.5289, tolerance: 642410942.8901265\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183978944535.29892, tolerance: 670257464.5833496\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169197966214.1893, tolerance: 616922780.2375839\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 170366811440.82394, tolerance: 650285749.1542294\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 182230808531.92557, tolerance: 659647291.616064\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 186533574490.96103, tolerance: 648178862.5207988\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 178592674951.0034, tolerance: 627716914.3251145\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 176396710985.1849, tolerance: 639449722.8045771\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171741854004.8407, tolerance: 642410942.8901265\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183964653143.49097, tolerance: 670257464.5833496\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 169166444562.60318, tolerance: 616922780.2375839\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 170302157866.85278, tolerance: 650285749.1542294\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 182124567710.38467, tolerance: 659647291.616064\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 186374370801.907, tolerance: 648178862.5207988\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 178579393582.81192, tolerance: 627716914.3251145\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 176360951414.16864, tolerance: 639449722.8045771\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171377853558.94772, tolerance: 642410942.8901265\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 183753117591.42715, tolerance: 670257464.5833496\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168732308305.2572, tolerance: 616922780.2375839\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 168264730607.73062, tolerance: 650285749.1542294\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180960691542.63644, tolerance: 659647291.616064\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 186356768776.57443, tolerance: 648178862.5207988\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 178696978347.11905, tolerance: 627716914.3251145\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 175904292879.35376, tolerance: 639449722.8045771\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 153045391027.72855, tolerance: 642410942.8901265\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 176514680934.8133, tolerance: 670257464.5833496\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 167393893047.95273, tolerance: 616922780.2375839\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 146331748996.8259, tolerance: 650285749.1542294\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 134914606017.12903, tolerance: 659647291.616064\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 156894894395.11948, tolerance: 648178862.5207988\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 172338089024.11768, tolerance: 627716914.3251145\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 173884626968.00046, tolerance: 639449722.8045771\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30062491445.40155, tolerance: 642410942.8901265\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 36145255908.18036, tolerance: 670257464.5833496\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2474545889.102356, tolerance: 650285749.1542294\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1495913458.8309326, tolerance: 648178862.5207988\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7295544870.5737915, tolerance: 627716914.3251145\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 214299208947.09268, tolerance: 672302081.7970605\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191433242720.30536, tolerance: 697933557.9578884\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 225950086932.75696, tolerance: 652743750.0944573\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 216027618016.99362, tolerance: 667887528.5879089\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 232879704183.01672, tolerance: 681381444.179361\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191923938564.76724, tolerance: 673956635.0870641\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219712875499.969, tolerance: 630047685.85182\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 225304811788.9551, tolerance: 685958803.5800102\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 214257511350.4593, tolerance: 672302081.7970605\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191432235484.89948, tolerance: 697933557.9578884\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 225911080135.9869, tolerance: 652743750.0944573\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 215954526751.61542, tolerance: 667887528.5879089\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 232804275945.50647, tolerance: 681381444.179361\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191765022873.35703, tolerance: 673956635.0870641\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219698324664.56662, tolerance: 630047685.85182\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 225279545120.66516, tolerance: 685958803.5800102\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 213723049219.49722, tolerance: 672302081.7970605\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191620526435.80142, tolerance: 697933557.9578884\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 225530572874.57224, tolerance: 652743750.0944573\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 215364238396.2125, tolerance: 667887528.5879089\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 232089541551.19745, tolerance: 681381444.179361\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190171801511.3474, tolerance: 673956635.0870641\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219506193286.89352, tolerance: 630047685.85182\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 225540330440.0697, tolerance: 685958803.5800102\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 192294677048.18164, tolerance: 672302081.7970605\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 188532711589.53577, tolerance: 697933557.9578884\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220619694615.22086, tolerance: 652743750.0944573\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 184021190688.3069, tolerance: 667887528.5879089\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220684324284.80057, tolerance: 681381444.179361\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59617230909.696075, tolerance: 673956635.0870641\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 221335656737.2348, tolerance: 630047685.85182\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 228382088146.4768, tolerance: 685958803.5800102\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 71405398440.88293, tolerance: 697933557.9578884\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1378239694.744751, tolerance: 673956635.0870641\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2863295074.387024, tolerance: 685958803.5800102\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 36006554739.547, tolerance: 697933557.9578884\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([28061.94705357, 27075.45713985, 54704.01484333, 28538.92562554,\n",
       "       19597.60497214])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(lasso_regr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ridge**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=20, random_state=42, solver='cholesky')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([104015.97299901, 179718.82955483, 325929.31792345,  69229.49426468,\n",
       "       252674.96283453])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg = Ridge(alpha=20, solver=\"cholesky\", random_state=42)\n",
    "ridge_reg.fit(X_train, y_train)\n",
    "ridge_reg.predict(X_train[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32966.77074953915"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(ridge_reg).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor(max_iter=50, random_state=42, tol=-inf)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([-1.13934997e+16, -5.48009101e+15, -1.88791834e+16,  7.01346482e+15,\n",
       "       -4.11087837e+16])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_reg = SGDRegressor(max_iter=50, tol=-np.infty, penalty=\"l2\", random_state=42)\n",
    "sgd_reg.fit(X_train, y_train.ravel())\n",
    "sgd_reg.predict(X_train[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.54889195190418e+16"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(sgd_reg).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Ridge(), param_grid={'alpha': [5, 10, 11, 12, 13]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 5}\n",
      "-1220132057.6083472\n"
     ]
    }
   ],
   "source": [
    "#https://towardsdatascience.com/how-to-perform-lasso-and-ridge-regression-in-python-3b3b75541ad8\n",
    "#find best parameter and best MSE\n",
    "\n",
    "parameters = {'alpha': [5, 10, 11, 12, 13]}\n",
    "\n",
    "ridge = Ridge()\n",
    "\n",
    "ridge_regressor = GridSearchCV(ridge, parameters, scoring='neg_mean_squared_error', cv=5)\n",
    "\n",
    "ridge_regressor.fit(X_train, y_train)\n",
    "\n",
    "print(ridge_regressor.best_params_)\n",
    "print(ridge_regressor.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([160674.18440436, 343110.08655164])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sanity check\n",
    "ridgepredict = ridge_regressor.predict(X_test)\n",
    "ridgepredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 4 - Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features='sqrt', n_estimators=2000, random_state=42)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestRegressor(n_estimators=2000, max_features='sqrt',random_state=42)\n",
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_test_pred = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gradient Boosting Regressor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=0.03, max_depth=4, max_features='sqrt',\n",
       "                          n_estimators=2000, random_state=42)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Regressor\n",
      "\n",
      "Mean Absolute Error Accuracy:  8626.012051026162\n",
      "Mean Squared Error Accuracy:  78254337.84933998\n"
     ]
    }
   ],
   "source": [
    "grad_regr = GradientBoostingRegressor(n_estimators=2000, learning_rate = 0.03, max_depth = 4, max_features='sqrt',random_state=42)\n",
    "grad_regr.fit(X_train, y_train)\n",
    "grad_regr_predict = grad_regr.predict(X_test)\n",
    "print('Gradient Boosting Regressor\\n')\n",
    "print('Mean Absolute Error Accuracy: ',mean_absolute_error(y_test,grad_regr_predict))\n",
    "print('Mean Squared Error Accuracy: ',mean_squared_error(y_test,grad_regr_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27247.336823649723"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(grad_regr).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Feature  Importance\n",
      "25             GarageCars    8.622866\n",
      "15              GrLivArea    7.629505\n",
      "11            TotalBsmtSF    6.549349\n",
      "3             OverallQual    5.545874\n",
      "5               YearBuilt    5.493297\n",
      "..                    ...         ...\n",
      "102       Condition2_RRNn    0.000000\n",
      "62   Neighborhood_Blueste    0.000000\n",
      "213          HeatingQC_Po    0.000000\n",
      "220        Electrical_Mix    0.000000\n",
      "125        RoofMatl_Metal    0.000000\n",
      "\n",
      "[274 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/42128545/how-to-print-the-order-of-important-features-in-random-forest-regression-using-p\n",
    "\n",
    "featureImp= []\n",
    "\n",
    "for feat, importance in zip(X.columns, grad_regr.feature_importances_):  \n",
    "    temp = [feat, importance*100]\n",
    "    featureImp.append(temp)\n",
    "\n",
    "fT_df = pd.DataFrame(featureImp, columns = ['Feature', 'Importance'])\n",
    "\n",
    "print (fT_df.sort_values('Importance', ascending = False))\n",
    "\n",
    "fT_df2 = fT_df.sort_values('Importance', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize Top 20 Features\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0. , 0.2, 0.4, 0.6, 0.8, 1. ]),\n",
       " <a list of 6 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd17b86f5b0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gradient Boosting Regressor - Top 20 Features')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Features')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Importance')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Visualize Top 20 Features')\n",
    "\n",
    "Top20_GB = fT_df2.iloc[0:20]\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "plt.xticks(rotation='90')\n",
    "\n",
    "sns.barplot(x=Top20_GB['Feature'], \n",
    "            y=Top20_GB['Importance'])\n",
    "\n",
    "#plt title\n",
    "plt.title('Gradient Boosting Regressor - Top 20 Features', fontsize=12)\n",
    "\n",
    "#plt x y labels\n",
    "plt.xlabel('Features', fontsize=12)\n",
    "plt.ylabel('Importance', fontsize=12)\n",
    "\n",
    "#Missing Data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Stacking Regressor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StackingRegressor(estimators=[('gbr',\n",
       "                               GradientBoostingRegressor(learning_rate=0.03,\n",
       "                                                         max_depth=4,\n",
       "                                                         max_features='sqrt',\n",
       "                                                         n_estimators=2000,\n",
       "                                                         random_state=42)),\n",
       "                              ('lasso',\n",
       "                               GridSearchCV(cv=KFold(n_splits=8, random_state=42, shuffle=True),\n",
       "                                            estimator=Lasso(),\n",
       "                                            param_grid={'alpha': [0.001, 0.01,\n",
       "                                                                  0.1, 1, 10,\n",
       "                                                                  20, 50, 100,\n",
       "                                                                  157.1, 160]},\n",
       "                                            scoring='neg_mean_squared_error')),\n",
       "                              ('lr', LinearRegression()),\n",
       "                              ('ridge',\n",
       "                               GridSearchCV(cv=5, estimator=Ridge(),\n",
       "                                            param_grid={'alpha': [5, 10, 11, 12,\n",
       "                                                                  13]},\n",
       "                                            scoring='neg_mean_squared_error')),\n",
       "                              ('Random Forest',\n",
       "                               RandomForestRegressor(max_features='sqrt',\n",
       "                                                     n_estimators=2000,\n",
       "                                                     random_state=42))],\n",
       "                  final_estimator=ExtraTreesRegressor(n_estimators=2000),\n",
       "                  n_jobs=-1)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stacking Ensemble\n",
      "\n",
      "Mean Absolute Error Accuracy:  19568.046749999994\n",
      "Mean Squared Error Accuracy:  449803108.276868\n"
     ]
    }
   ],
   "source": [
    "#https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingRegressor.html\n",
    "\n",
    "from sklearn.ensemble import StackingRegressor\n",
    "\n",
    "estimators = [('gbr', grad_regr), \n",
    "              ('lasso', lasso_regr2), \n",
    "              ('lr', lin_reg),\n",
    "              ('ridge', ridge_regressor),\n",
    "             ]\n",
    "\n",
    "stack_ensemble = StackingRegressor(estimators=estimators, final_estimator=ExtraTreesRegressor(n_estimators=2000), n_jobs=-1)\n",
    "\n",
    "stack_ensemble.fit(X_train, y_train)\n",
    "stack_ensemble_predict = stack_ensemble.predict(X_test)\n",
    "\n",
    "print('Stacking Ensemble\\n')\n",
    "print('Mean Absolute Error Accuracy: ',mean_absolute_error(y_test,stack_ensemble_predict))\n",
    "print('Mean Squared Error Accuracy: ',mean_squared_error(y_test,stack_ensemble_predict))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('rmse_cv: ',rmse_cv(stack_ensemble).mean())\n",
    "#this equals approx. 31k (takes very long to run though)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model Predict on Test2 (Goes to Output)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest \n",
    "forest_test_predict = forest.predict(test2)\n",
    "\n",
    "#Gradient Boosting Regressor\n",
    "gb_test_predict = grad_regr.predict(test2)\n",
    "\n",
    "#Ensemble (Linear Regression + Ridge + Gradient Boosting Regressor + GridSearchCV Lasso)\n",
    "stack_ensemble_predict = stack_ensemble.predict(test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id\n",
       "0  1461"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
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       "      <td>2916</td>\n",
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       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
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       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "        Id\n",
       "1454  2915\n",
       "1455  2916\n",
       "1456  2917\n",
       "1457  2918\n",
       "1458  2919"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflist = pd.DataFrame(data = range(1461,2920), columns=['Id'])\n",
    "dflist.head(1)\n",
    "dflist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.124598e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.894769e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SalePrice\n",
       "0  1.124598e+08\n",
       "1  1.894769e+08"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Submission 4 (refined submission 3)\n",
    "output_lasso = pd.DataFrame(lasso_regr2.predict(X_test), columns=['SalePrice'])\n",
    "output_lasso.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "        Id  SalePrice\n",
       "1454  2915        NaN\n",
       "1455  2916        NaN\n",
       "1456  2917        NaN\n",
       "1457  2918        NaN\n",
       "1458  2919        NaN"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Submission 4\n",
    "output3_lasso = pd.concat([dflist,output_lasso], axis = 1)\n",
    "output3_lasso.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Submission 4\n",
    "#/Users/vincentpun/Documents/Northwestern/MSDS 422/Module 4/house-prices-advanced-regression-techniques/Output\n",
    "output3_lasso.to_csv(r'output-submission-REFINED2.csv')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest\n",
    "submission_forest = pd.DataFrame({'Id':test.Id, 'SalePrice': forest_test_predict})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest\n",
    "submission_forest.to_csv('forest_submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gradient Boosting Regressor\n",
    "submission_GB = pd.DataFrame({'Id':test.Id, 'SalePrice': gb_test_predict})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gradient Boosting Regressor\n",
    "#Currently Rank 1278 / 5028 (Top 25%)\n",
    "submission_GB.to_csv('gb_submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ensemble\n",
    "submission_ensemble = pd.DataFrame({'Id':test.Id, 'SalePrice': stack_ensemble_predict})\n",
    "submission_ensemble.to_csv('ensemble_submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Retired Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#ATTEMPT 1 - forest \n",
    "#this gave me a score of .21... .way worse than .13 from other models \n",
    "\n",
    "#https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/\n",
    "#https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74\n",
    "\n",
    "forest = RandomForestClassifier(n_estimators=100, #number of trees in the forest\n",
    "                                random_state=42,\n",
    "                                max_features='sqrt',\n",
    "                                n_jobs=-1, #number of jobs to run in parallel\n",
    "                                )\n",
    "forest.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "def plot_feature_importances_saleprice(model):\n",
    "    n_features = X.shape[1]\n",
    "    plt.barh(np.arange(n_features), model.feature_importances_, align='center')\n",
    "    plt.yticks(np.arange(n_features), X.columns)\n",
    "    plt.xlabel(\"Feature importance\")\n",
    "    plt.ylabel(\"Feature\")\n",
    "    plt.ylim(-1, n_features)\n",
    "\n",
    "plot_feature_importances_saleprice(grad_regr)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "features=X.columns\n",
    "importances = grad_regr.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.title('feature importances')\n",
    "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
    "plt.yticks(range(len(indices)), feature[indices])\n",
    "plt.xlabel('Relative Importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "for name, importance in zip(X.columns, grad_regr.feature_importances_): print(name, \"=\", importance)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "#too many features to visualize \n",
    "\n",
    "features = X.columns\n",
    "importances = grad_regr.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
    "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#Feature Importance\n",
    "#didn't show names though \n",
    "\n",
    "important_features_dict = {}\n",
    "for x,i in enumerate(grad_regr.feature_importances_):\n",
    "    important_features_dict[x]=i\n",
    "\n",
    "\n",
    "important_features_list = sorted(important_features_dict,\n",
    "                                 key=important_features_dict.get,\n",
    "                                 reverse=True)\n",
    "\n",
    "print('Most important features: %s' %important_features_list)"
   ]
  }
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