ames_house_price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case study: Feature Engineering - Ames house price prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# used for manipulating directory paths\n",
    "import os\n",
    "\n",
    "# For N-dimensional array manipulation\n",
    "import numpy as np\n",
    "\n",
    "# For advanced mathematical routines\n",
    "import scipy\n",
    "\n",
    "# Plotting library\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# For data analysis and data structures in DataFrames \n",
    "import pandas as pd\n",
    "\n",
    "# For data visualization\n",
    "import seaborn as sns\n",
    "\n",
    "# For machine learning algorithms and evaluation metrics\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "# tells matplotlib to embed plots within the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Steps\n",
    "\n",
    "- *Step 1: Load dataset*<br>\n",
    "- *Step 2: Exploratory data analysis and process dataset for ML*<br>\n",
    "- *Step 3: Linear regression*<br>\n",
    "- *Step 4: Train and evaluate a simple linear regression model*<br>\n",
    "- *Step 5: Refine model using regularization*<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1 Load dataset \n",
    "## Implement a function to load dataset\n",
    "def loadDataset(dirname, filename):\n",
    "    df = pd.read_csv(dirname + filename)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape after dropping ID: \n",
      " (1460, 80)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "house_data_df = loadDataset('Data/','train.csv')\n",
    "\n",
    "## Brielfy look at the data using 'shape', 'head', 'info' etc, functions. \n",
    "\n",
    "house_data_df.shape\n",
    "\n",
    "#house_data_df.info()\n",
    "\n",
    "#house_data_df.head(10)\n",
    "\n",
    "#house_data_df.describe()\n",
    "\n",
    "# Print the count of values in 'Street' using value_counts() \n",
    "#house_data_df['Street'].value_counts()\n",
    "\n",
    "# Drop the 'Id' column \n",
    "\n",
    "house_data_df.drop(['Id'],axis=1, inplace = True)\n",
    "\n",
    "# print the new dataframe shape\n",
    "\n",
    "print('Shape after dropping ID: \\n', house_data_df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.02, 'Histograms of numerical features')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Step 2 Visualize data to gain insights (Histograms)\n",
    "\n",
    "# Plot histogram of numerical features using DataFrame.hist(figsize=(30,20)). \n",
    "\n",
    "house_data_df.hist(figsize=(30, 20), bins = 15, edgecolor = 'black', grid = False, color = 'royalblue')\n",
    "plt.suptitle('Histograms of numerical features', x = 0.5, y = 1.02, size = 35)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>0.800858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>0.720516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>0.649256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>0.646584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>0.636964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>0.625235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullBath</th>\n",
       "      <td>0.559048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>0.537462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>0.535279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>0.521428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>0.499230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0.478862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>0.466765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>0.395923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>0.356773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>0.330361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>0.322538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>0.297301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath</th>\n",
       "      <td>0.282925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>0.269866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>0.235697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>0.220678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>0.160542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>0.118324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>0.056797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>0.047414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>0.032819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>-0.008900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>-0.021097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>-0.023694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <td>-0.025351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>-0.036792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>-0.080202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>-0.088160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>-0.129774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>-0.138849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SalePrice\n",
       "SalePrice       1.000000\n",
       "OverallQual     0.800858\n",
       "GrLivArea       0.720516\n",
       "GarageCars      0.649256\n",
       "TotalBsmtSF     0.646584\n",
       "GarageArea      0.636964\n",
       "1stFlrSF        0.625235\n",
       "FullBath        0.559048\n",
       "TotRmsAbvGrd    0.537462\n",
       "YearBuilt       0.535279\n",
       "YearRemodAdd    0.521428\n",
       "GarageYrBlt     0.499230\n",
       "MasVnrArea      0.478862\n",
       "Fireplaces      0.466765\n",
       "BsmtFinSF1      0.395923\n",
       "LotFrontage     0.356773\n",
       "OpenPorchSF     0.330361\n",
       "WoodDeckSF      0.322538\n",
       "2ndFlrSF        0.297301\n",
       "HalfBath        0.282925\n",
       "LotArea         0.269866\n",
       "BsmtFullBath    0.235697\n",
       "BsmtUnfSF       0.220678\n",
       "BedroomAbvGr    0.160542\n",
       "ScreenPorch     0.118324\n",
       "MoSold          0.056797\n",
       "3SsnPorch       0.047414\n",
       "PoolArea        0.032819\n",
       "BsmtFinSF2     -0.008900\n",
       "MiscVal        -0.021097\n",
       "YrSold         -0.023694\n",
       "LowQualFinSF   -0.025351\n",
       "BsmtHalfBath   -0.036792\n",
       "OverallCond    -0.080202\n",
       "MSSubClass     -0.088160\n",
       "EnclosedPorch  -0.129774\n",
       "KitchenAbvGr   -0.138849"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Step 2 Visualize data to gain insights  (Correlations)\n",
    "\n",
    "# Check correlation of 'SalePrice' with others features using DataFrame.corr()\n",
    "# check each column value \n",
    "feature_corr = house_data_df[['SalePrice','MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'Alley',\n",
    "       'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope',\n",
    "       'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle',\n",
    "       'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'RoofStyle',\n",
    "       'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea',\n",
    "       'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond',\n",
    "       'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2',\n",
    "       'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC',\n",
    "       'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n",
    "       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n",
    "       'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual', 'TotRmsAbvGrd',\n",
    "       'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType', 'GarageYrBlt',\n",
    "       'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual', 'GarageCond',\n",
    "       'PavedDrive', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',\n",
    "       'ScreenPorch', 'PoolArea', 'PoolQC', 'Fence', 'MiscFeature', 'MiscVal',\n",
    "       'MoSold', 'YrSold', 'SaleType', 'SaleCondition']]\n",
    "\n",
    "feature_corr = feature_corr.corr()\n",
    "#feature_corr.sort_values(by='SalePrice', ascending = False)\n",
    "feature_corr[['SalePrice']].sort_values('SalePrice', ascending = False)\n",
    "\n",
    "# or \n",
    "#feature_corr = house_data_df.corr()\n",
    "#descending_feature_corr = feature_corr.unstack()\n",
    "#descending_feature_corr = descending_feature_corr.sort_values(kind=\"quicksort\", ascending=False)\n",
    "#print(descending_feature_corr[\"SalePrice\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = feature_corr.corr()\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(correlation,vmax=1, center=0,square=True,cmap = 'GnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Step 2 Visualize data to gain insights (Scatterplots)\n",
    "\n",
    "from pandas.plotting import scatter_matrix\n",
    "scatter_features = ['SalePrice','OverallQual','GrLivArea','GarageArea','TotalBsmtSF','LotArea']\n",
    "pd.plotting.scatter_matrix(house_data_df[scatter_features], alpha=0.4)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Step2 Visualize data to gain insights (scatterplot-detailed)\n",
    "# other solution\n",
    "#data = pd.concat([house_data_df['SalePrice'], house_data_df['GrLivArea']], axis=1)\n",
    "#data.plot.scatter(x='GrLivArea', y='SalePrice',ylim=(0,800000));\n",
    "\n",
    "sns.scatterplot(x='GrLivArea', y='SalePrice', data = house_data_df)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1456, 80)\n"
     ]
    }
   ],
   "source": [
    "# Removing houses with GrLivArea of more than 4000, as these contain outliers\n",
    "df_outlier = house_data_df.loc[house_data_df['GrLivArea'] > 4000]\n",
    "house_data_df = house_data_df.drop(df_outlier.index)\n",
    "print(house_data_df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Step 2 Visualize data to gain insights (Pivot table)\n",
    "\n",
    "# Plot pivot tables using DataFrame.pivot_table() \n",
    "\n",
    "# Pivot table to investigate relationship of OveralQual and SalePrice\n",
    "quality_pivot = house_data_df.pivot_table(index='OverallQual', values='SalePrice', aggfunc=np.median)\n",
    "quality_pivot.plot(kind='bar',label='Overall Quality',color='blue')\n",
    "\n",
    "# Pivot table to evaluate relationship of SaleCondition and SalePrice\n",
    "quality_pivot = house_data_df.pivot_table(index='SaleCondition', values='SalePrice', aggfunc=np.median)\n",
    "quality_pivot.plot(kind='bar',label='SaleCondition',color='red')\n",
    "\n",
    "# Pivot table to evaluate relationship of Neighborhood and SalePrice\n",
    "quality_pivot = house_data_df.pivot_table(index='Neighborhood', values='SalePrice', aggfunc=np.median)\n",
    "quality_pivot.plot(kind='bar',label='Neighborhood',color='black')\n",
    "\n",
    "# Pivot table to evaluate relationship of MSZoning and SalePrice\n",
    "quality_pivot = house_data_df.pivot_table(index='MSZoning', values='SalePrice', aggfunc=np.median)\n",
    "quality_pivot.plot(kind='bar',label='MSZoning',color='green')\n",
    "\n",
    "plt.ylabel('Median Sale Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of features: \n",
      " (1456, 79)\n",
      "Shape of target: \n",
      " (1456,)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1456 entries, 0 to 1459\n",
      "Data columns (total 79 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   MSSubClass     1456 non-null   int64  \n",
      " 1   MSZoning       1456 non-null   object \n",
      " 2   LotFrontage    1197 non-null   float64\n",
      " 3   LotArea        1456 non-null   int64  \n",
      " 4   Street         1456 non-null   object \n",
      " 5   Alley          91 non-null     object \n",
      " 6   LotShape       1456 non-null   object \n",
      " 7   LandContour    1456 non-null   object \n",
      " 8   Utilities      1456 non-null   object \n",
      " 9   LotConfig      1456 non-null   object \n",
      " 10  LandSlope      1456 non-null   object \n",
      " 11  Neighborhood   1456 non-null   object \n",
      " 12  Condition1     1456 non-null   object \n",
      " 13  Condition2     1456 non-null   object \n",
      " 14  BldgType       1456 non-null   object \n",
      " 15  HouseStyle     1456 non-null   object \n",
      " 16  OverallQual    1456 non-null   int64  \n",
      " 17  OverallCond    1456 non-null   int64  \n",
      " 18  YearBuilt      1456 non-null   int64  \n",
      " 19  YearRemodAdd   1456 non-null   int64  \n",
      " 20  RoofStyle      1456 non-null   object \n",
      " 21  RoofMatl       1456 non-null   object \n",
      " 22  Exterior1st    1456 non-null   object \n",
      " 23  Exterior2nd    1456 non-null   object \n",
      " 24  MasVnrType     1448 non-null   object \n",
      " 25  MasVnrArea     1448 non-null   float64\n",
      " 26  ExterQual      1456 non-null   object \n",
      " 27  ExterCond      1456 non-null   object \n",
      " 28  Foundation     1456 non-null   object \n",
      " 29  BsmtQual       1419 non-null   object \n",
      " 30  BsmtCond       1419 non-null   object \n",
      " 31  BsmtExposure   1418 non-null   object \n",
      " 32  BsmtFinType1   1419 non-null   object \n",
      " 33  BsmtFinSF1     1456 non-null   int64  \n",
      " 34  BsmtFinType2   1418 non-null   object \n",
      " 35  BsmtFinSF2     1456 non-null   int64  \n",
      " 36  BsmtUnfSF      1456 non-null   int64  \n",
      " 37  TotalBsmtSF    1456 non-null   int64  \n",
      " 38  Heating        1456 non-null   object \n",
      " 39  HeatingQC      1456 non-null   object \n",
      " 40  CentralAir     1456 non-null   object \n",
      " 41  Electrical     1455 non-null   object \n",
      " 42  1stFlrSF       1456 non-null   int64  \n",
      " 43  2ndFlrSF       1456 non-null   int64  \n",
      " 44  LowQualFinSF   1456 non-null   int64  \n",
      " 45  GrLivArea      1456 non-null   int64  \n",
      " 46  BsmtFullBath   1456 non-null   int64  \n",
      " 47  BsmtHalfBath   1456 non-null   int64  \n",
      " 48  FullBath       1456 non-null   int64  \n",
      " 49  HalfBath       1456 non-null   int64  \n",
      " 50  BedroomAbvGr   1456 non-null   int64  \n",
      " 51  KitchenAbvGr   1456 non-null   int64  \n",
      " 52  KitchenQual    1456 non-null   object \n",
      " 53  TotRmsAbvGrd   1456 non-null   int64  \n",
      " 54  Functional     1456 non-null   object \n",
      " 55  Fireplaces     1456 non-null   int64  \n",
      " 56  FireplaceQu    766 non-null    object \n",
      " 57  GarageType     1375 non-null   object \n",
      " 58  GarageYrBlt    1375 non-null   float64\n",
      " 59  GarageFinish   1375 non-null   object \n",
      " 60  GarageCars     1456 non-null   int64  \n",
      " 61  GarageArea     1456 non-null   int64  \n",
      " 62  GarageQual     1375 non-null   object \n",
      " 63  GarageCond     1375 non-null   object \n",
      " 64  PavedDrive     1456 non-null   object \n",
      " 65  WoodDeckSF     1456 non-null   int64  \n",
      " 66  OpenPorchSF    1456 non-null   int64  \n",
      " 67  EnclosedPorch  1456 non-null   int64  \n",
      " 68  3SsnPorch      1456 non-null   int64  \n",
      " 69  ScreenPorch    1456 non-null   int64  \n",
      " 70  PoolArea       1456 non-null   int64  \n",
      " 71  PoolQC         5 non-null      object \n",
      " 72  Fence          280 non-null    object \n",
      " 73  MiscFeature    54 non-null     object \n",
      " 74  MiscVal        1456 non-null   int64  \n",
      " 75  MoSold         1456 non-null   int64  \n",
      " 76  YrSold         1456 non-null   int64  \n",
      " 77  SaleType       1456 non-null   object \n",
      " 78  SaleCondition  1456 non-null   object \n",
      "dtypes: float64(3), int64(33), object(43)\n",
      "memory usage: 910.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Separating features and target from house dataset \n",
    "house_data_features = house_data_df.drop(columns='SalePrice', axis =1) \n",
    "house_data_target = house_data_df[\"SalePrice\"].copy() \n",
    "house_data_target.columns = ['SalePrice']\n",
    "\n",
    "# Print DataFrame shape of features and target variable\n",
    "print('Shape of features: \\n', house_data_features.shape)\n",
    "print('Shape of target: \\n', house_data_target.shape)\n",
    "house_data_features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PoolQC          1451\n",
      "MiscFeature     1402\n",
      "Alley           1365\n",
      "Fence           1176\n",
      "FireplaceQu      690\n",
      "LotFrontage      259\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "GarageFinish      81\n",
      "BsmtFinType2      38\n",
      "BsmtExposure      38\n",
      "BsmtCond          37\n",
      "BsmtFinType1      37\n",
      "BsmtQual          37\n",
      "MasVnrArea         8\n",
      "MasVnrType         8\n",
      "Electrical         1\n",
      "dtype: int64\n",
      "Shape of features (1456, 79): \n",
      " (1456, 79)\n",
      "Shape of target (1456,): \n",
      " (1456,)\n"
     ]
    }
   ],
   "source": [
    "# Function to check for missing values\n",
    "def checkMissing(df):\n",
    "    \"\"\"\n",
    "    Checks missing value in a panda's dataframe and returns count\n",
    "    \n",
    "    Parameters\n",
    "     ----------\n",
    "    df: Object\n",
    "        A panda's DataFrame to check for missing values\n",
    "        \n",
    "    Returns\n",
    "     ----------\n",
    "    missing: Object\n",
    "            A panda's object with count of missing values\n",
    "            \n",
    "    \"\"\"\n",
    "    missing_count = df.isna().sum().sort_values(ascending= False)\n",
    "    missing_count = missing_count[missing_count != 0]\n",
    "    return missing_count\n",
    "    \n",
    "\n",
    "# Checking for missing values in features\n",
    "print(checkMissing(house_data_features))\n",
    "\n",
    "# Print DataFrame shape of features and target variable\n",
    "print('Shape of features (1456, 79): \\n', house_data_features.shape)\n",
    "print('Shape of target (1456,): \\n', house_data_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1456, 36)\n",
      "LotFrontage    259\n",
      "GarageYrBlt     81\n",
      "MasVnrArea       8\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## Step 3.1 Numeric features (Data cleaning - Handling missing values)\n",
    "\n",
    "numeric_features= house_data_features.select_dtypes(include=[np.number])\n",
    "\n",
    "print(numeric_features.shape)\n",
    "\n",
    "# Print numeric_features with missing values using the 'checkMissing()' function created above \n",
    "print(checkMissing(numeric_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PoolQC          1451\n",
      "MiscFeature     1402\n",
      "Alley           1365\n",
      "Fence           1176\n",
      "FireplaceQu      690\n",
      "GarageType        81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "GarageFinish      81\n",
      "BsmtFinType2      38\n",
      "BsmtExposure      38\n",
      "BsmtCond          37\n",
      "BsmtFinType1      37\n",
      "BsmtQual          37\n",
      "MasVnrType         8\n",
      "Electrical         1\n",
      "dtype: int64\n",
      "Shape of features (1456, 79): \n",
      " (1456, 79)\n",
      "Shape of target (1456,): \n",
      " (1456,)\n"
     ]
    }
   ],
   "source": [
    "## Step 3.1.1 Handling missing values of numeric features\n",
    "\n",
    "# Fill nulls for 'LotFrontage' with median value calculated after grouping by 'Neighborhood'\n",
    "house_data_features[\"LotFrontage\"] = house_data_features.groupby(\"Neighborhood\")[\"LotFrontage\"].transform(\n",
    "    lambda x: x.fillna(x.median()))\n",
    "\n",
    "# Fill nulls for 'GarageYrBlt','MasVnrArea' with 0\n",
    "\n",
    "for col in ('GarageYrBlt', 'MasVnrArea'):\n",
    "    house_data_features[col] = house_data_features[col].fillna(0)\n",
    "\n",
    "# Check that features LotFrontage, GarageYrBl and MasVnrArea are no longer in missing value list \n",
    "print(checkMissing(house_data_features))\n",
    "\n",
    "# Print DataFrame shape of features and target variable\n",
    "print('Shape of features (1456, 79): \\n', house_data_features.shape)\n",
    "print('Shape of target (1456,): \\n', house_data_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skew (no log transform): 1.565959 \n",
      " 1.5659592925562151\n",
      "Skew (log transform): 0.065448 \n",
      " 0.06544882031646646\n",
      "Shape of features (1456, 79): \n",
      " (1456, 79)\n",
      "Shape of target (1456,): \n",
      " (1456,)\n"
     ]
    }
   ],
   "source": [
    "## Step 3.1.2 Feature Engineering for Numeric features (log-transformation)\n",
    "\n",
    "# Check skewness of the target variable\n",
    "print(\"Skew (no log transform): 1.565959 \\n\", house_data_target.skew())\n",
    "\n",
    "# Apply log-transform on target feature 'SalePrice' using 'np.log()' function (Execute only once)\n",
    "house_data_target = np.log(house_data_target)\n",
    "\n",
    "# Check skewness of the target variable after log-transform\n",
    "print(\"Skew (log transform): 0.065448 \\n\", house_data_target.skew())\n",
    "\n",
    "# Print DataFrame shape of features and target variable\n",
    "print('Shape of features (1456, 79): \\n', house_data_features.shape)\n",
    "print('Shape of target (1456,): \\n', house_data_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PoolQC          1451\n",
      "MiscFeature     1402\n",
      "Alley           1365\n",
      "Fence           1176\n",
      "FireplaceQu      690\n",
      "GarageType        81\n",
      "GarageCond        81\n",
      "GarageQual        81\n",
      "GarageFinish      81\n",
      "BsmtFinType2      38\n",
      "BsmtExposure      38\n",
      "BsmtFinType1      37\n",
      "BsmtQual          37\n",
      "BsmtCond          37\n",
      "MasVnrType         8\n",
      "Electrical         1\n",
      "dtype: int64\n",
      "Numeric features shape (1456, 36): \n",
      " (1456, 36)\n",
      "Categorical features shape (1456, 43): \n",
      " (1456, 43)\n",
      "House data features shape (1456,79): \n",
      " (1456, 79)\n"
     ]
    }
   ],
   "source": [
    "## Step 3.2 Feature Engineering for Categorical features (Handling missing values)\n",
    "\n",
    "# Transform some numerical features that are actually categorical\n",
    "for col in ['OverallQual','MSSubClass','OverallCond']:\n",
    "    house_data_features[col].apply(str)\n",
    "\n",
    "# Create a new object 'categorical_features' from house_data_features that contains only categorical features\n",
    "\n",
    "categorical_features= house_data_features.select_dtypes(exclude=[np.number])\n",
    "\n",
    "\n",
    "#for col in ('PoolQC', 'MiscFeature','Alley','Fence','FireplaceQu','GarageCond','GarageType','GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','MasVnrType'):\n",
    "    #house_data_features[col] = house_data_features[col].fillna('None')\n",
    "\n",
    "print(checkMissing(categorical_features))\n",
    "\n",
    "# Print missing values in categorical features using the checkMissing() function\n",
    "#print(categorical_features.isnull().sum().sort_values(ascending=False))\n",
    "\n",
    "print(\"Numeric features shape (1456, 36): \\n\", numeric_features.shape)\n",
    "print(\"Categorical features shape (1456, 43): \\n\", categorical_features.shape)\n",
    "print(\"House data features shape (1456,79): \\n\", house_data_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BsmtFinType1    37\n",
      "MasVnrType       8\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## Step 3.2 Feature Engineering for Categorical features (Handling missing values)\n",
    "\n",
    "# Fill nulls for the missing values in the specified categorical features according to the \n",
    "# data description above (3.2.1).\n",
    "# Note the special description for feature 'Electrical'.\n",
    "\n",
    "for col in ('PoolQC', 'MiscFeature','Alley','Fence','FireplaceQu','GarageCond','GarageType','GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual'):\n",
    "    house_data_features[col].fillna('None',inplace= True)\n",
    "\n",
    "house_data_features['Electrical'].fillna('SBrkr', inplace = True)\n",
    "\n",
    "# Check that there are no nulls in the dataframe \n",
    "print(checkMissing(house_data_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of features before one-hot encoding (1456,79):\n",
      " (1456, 79)\n",
      "0            Good\n",
      "1            Good\n",
      "2            Good\n",
      "3            Good\n",
      "4       Excellent\n",
      "          ...    \n",
      "1455         Good\n",
      "1456         Good\n",
      "1457         Good\n",
      "1458      Average\n",
      "1459      Average\n",
      "Name: OverallQual, Length: 1456, dtype: object\n",
      "0         Average\n",
      "1       Excellent\n",
      "2         Average\n",
      "3         Average\n",
      "4         Average\n",
      "          ...    \n",
      "1455      Average\n",
      "1456         Good\n",
      "1457    Excellent\n",
      "1458         Good\n",
      "1459         Good\n",
      "Name: OverallCond, Length: 1456, dtype: object\n",
      "Shape of features after one-hot encoding:\n",
      " (1456, 308)\n"
     ]
    }
   ],
   "source": [
    "## Step 3.2 Feature Engineering for Categorical features (one-hot encoding)\n",
    "\n",
    "# Print the shape of dataset before one-hot encoding\n",
    "print('Shape of features before one-hot encoding (1456,79):\\n', house_data_features.shape)\n",
    "\n",
    "# Perform binning and convert values of 'OverallQual and OverallCond' features\n",
    "\n",
    "house_data_features['OverallQual']= house_data_features['OverallQual'].map({1:\"Poor\",2:'Poor',3:'Fair',4:'Average',5:'Average',6:'Good',7:'Good',8:'Excellent',9:'Excellent',10:'Excellent'})\n",
    "print(house_data_features['OverallQual'])\n",
    "house_data_features['OverallCond']= house_data_features['OverallCond'].map({1:\"Poor\",2:'Poor',3:'Fair',4:'Average',5:'Average',6:'Good',7:'Good',8:'Excellent',9:'Excellent',10:'Excellent'})\n",
    "print(house_data_features['OverallCond'])\n",
    "\n",
    "# another solution\n",
    "'''\n",
    "house_data_features['OverallQual'].replace([1, 2], \"Poor\", inplace = True)\n",
    "house_data_features['OverallQual'].replace([3], \"Fair\", inplace = True)\n",
    "house_data_features['OverallQual'].replace([4, 5], \"Average\", inplace = True)\n",
    "house_data_features['OverallQual'].replace([6, 7], \"Good\", inplace = True)\n",
    "house_data_features['OverallQual'].replace([8, 9, 10], \"Excellent\", inplace = True)\n",
    "\n",
    "house_data_features['OverallCond'].replace([1, 2], \"Poor\", inplace = True)\n",
    "house_data_features['OverallCond'].replace([3], \"Fair\", inplace = True)\n",
    "house_data_features['OverallCond'].replace([4, 5], \"Average\", inplace = True)\n",
    "house_data_features['OverallCond'].replace([6, 7], \"Good\", inplace = True)\n",
    "house_data_features['OverallCond'].replace([8, 9, 10], \"Excellent\", inplace = True)\n",
    "'''\n",
    "# Perform one-hot encoding on all categorical features\n",
    "categorical = []\n",
    "for col in categorical_features.columns:\n",
    "    categorical.append(col)\n",
    "categorical.append('OverallCond')\n",
    "categorical.append('OverallQual')\n",
    "\n",
    "for col in categorical:\n",
    "    house_data_features = pd.get_dummies(house_data_features, columns=[col])\n",
    "\n",
    "# Print DataFrame shape of features and target variable\n",
    "print('Shape of features after one-hot encoding:\\n', house_data_features.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1164, 308) (1164,) (292, 308) (292,)\n",
      "Model intercept (lr.intercept): \n",
      "  8.605960452411612\n",
      "Model weights (lr.coef_) : \n",
      "  [ 6.91121756e-05  5.45954841e-04  2.84307531e-06  1.89583496e-03\n",
      "  1.22112414e-03  1.39526024e-05  5.50417955e-05  2.17697781e-05\n",
      "  1.07858452e-05  8.75977115e-05  1.02033490e-04  9.29969288e-05\n",
      " -5.36810986e-05  1.41349358e-04  3.00886446e-02  2.08016996e-02\n",
      "  2.02551983e-02  1.80834661e-02  1.30806447e-03 -4.42751708e-02\n",
      "  5.44491984e-03  1.89580723e-02 -1.57787780e-04  2.24921439e-02\n",
      "  9.50033743e-05  1.22988979e-04  1.02730126e-04  9.62861572e-05\n",
      "  1.95062143e-04  2.66868625e-04  2.49715750e-04  1.30880250e-05\n",
      " -4.60758535e-04 -1.97202010e-03 -3.48822662e-01  1.30352712e-01\n",
      "  9.86048793e-02  8.77788121e-02  3.20862587e-02 -4.58175322e-02\n",
      "  4.58175322e-02 -1.68664212e-03 -6.49116875e-03  8.17781086e-03\n",
      "  1.21847891e-02  1.12016229e-02 -2.98400274e-02  6.45361542e-03\n",
      "  3.13488791e-02 -2.04460475e-02 -2.38947049e-02  1.29918733e-02\n",
      "  1.28629384e-01 -1.28629384e-01  2.00039563e-02  4.65365174e-02\n",
      " -3.33728403e-03 -7.22141947e-02  9.01100511e-03  4.58708644e-02\n",
      "  6.84366482e-02 -1.14307513e-01  1.97082048e-02 -4.79046674e-02\n",
      " -2.59636315e-02  6.50356161e-02  2.22272540e-02 -1.37100804e-02\n",
      "  1.44669728e-01 -7.11429235e-02 -1.49639464e-02  1.38820051e-02\n",
      " -1.57209660e-01 -5.99190510e-02 -2.36286227e-02 -1.90190642e-03\n",
      " -4.86735004e-02  2.15024158e-02  4.73192785e-02 -7.41799083e-03\n",
      "  3.98045051e-02 -2.22293919e-02 -1.84001967e-02  7.66751298e-03\n",
      "  9.48984628e-02  4.81644876e-03  3.15341370e-02 -5.33053527e-02\n",
      "  4.56922288e-03  4.66436017e-02  5.93448883e-03  4.63735831e-02\n",
      " -6.14297100e-02  1.86192801e-02 -3.43571832e-02  2.69520693e-02\n",
      "  8.02714128e-02  8.61888711e-02  7.01349382e-02  2.21591500e-01\n",
      " -2.07109031e-02 -5.10670360e-01 -1.00052301e-01  1.73246842e-01\n",
      "  3.60279875e-02 -1.15647743e-02  4.20591837e-02 -5.51737128e-02\n",
      " -1.13486841e-02  2.09677337e-02  2.25704546e-02 -4.24900672e-03\n",
      " -6.65850545e-02  5.16995518e-02 -4.21791611e-03 -1.74676747e-02\n",
      " -2.71808804e-03 -3.86669484e-02 -8.98410930e-02 -1.15357616e-01\n",
      " -8.04997551e-02 -1.78046829e-02  3.42170096e-01 -7.64547559e-02\n",
      "  3.02398584e-01  1.07026694e-01 -8.14645083e-02 -7.64506169e-02\n",
      " -1.46005662e-01 -2.90497347e-02 -3.11178885e-02 -1.10568840e-01\n",
      " -1.01637592e-01  1.10505839e-01 -4.91836608e-02 -2.08304080e-02\n",
      "  1.64469823e-02  4.66811083e-02  6.07135571e-02  1.38296343e-02\n",
      "  1.73238673e-02  4.84955154e-02 -4.54461977e-03 -1.53761930e-02\n",
      "  1.92626977e-02  1.07210534e-02  1.41793279e-01  2.53116523e-02\n",
      " -7.03288269e-02 -4.91836608e-02  5.48637633e-02 -1.04335718e-02\n",
      "  2.56708086e-03 -3.36285260e-02 -6.11235965e-02 -1.83727065e-02\n",
      " -5.88342591e-02  2.32122112e-02  2.73141223e-02  2.17726252e-02\n",
      " -5.65063951e-03  2.64764351e-02  5.15185334e-02  4.39568733e-02\n",
      "  7.89555104e-02  2.75258394e-02 -3.24931395e-02  4.22705871e-03\n",
      "  7.40241383e-04  1.51192448e-01 -4.30973599e-02  5.29232567e-03\n",
      " -1.34896058e-01  2.15086446e-02  2.14614074e-02  4.27117524e-02\n",
      "  5.50881100e-02  3.14119055e-02  1.46902151e-01 -2.97575327e-01\n",
      " -3.74622411e-03  3.07408911e-02 -2.64401786e-02  4.30877784e-02\n",
      " -4.36422668e-02 -3.65541631e-02 -2.12081076e-02  4.30877784e-02\n",
      "  2.32153887e-02 -8.54089638e-03 -3.70297478e-03  4.09996284e-02\n",
      "  1.04314133e-02 -5.69013934e-05 -4.76711655e-02  6.57436499e-04\n",
      " -5.08360457e-03  1.14384519e-02 -1.94862224e-02 -1.20787925e-02\n",
      " -1.85350474e-02  4.83115351e-02 -3.17034720e-02  5.81199957e-02\n",
      "  6.98815882e-03 -9.81436737e-02  1.19513484e-02  4.47610771e-03\n",
      " -8.89909743e-02  1.35802432e-02  4.70061920e-02 -9.87179289e-02\n",
      "  5.73578912e-02  6.97645768e-02  3.81263708e-02  3.46896136e-02\n",
      "  2.86086281e-02 -1.05843907e-01  4.41929478e-03 -3.00187683e-02\n",
      "  3.00187683e-02  6.14240942e-02  1.03483935e-01 -6.83717789e-02\n",
      " -1.48741619e-01  5.22053683e-02  5.93856154e-02 -2.21522891e-02\n",
      " -1.80704395e-02 -1.91628868e-02  8.58321963e-02 -2.40069105e-01\n",
      "  1.14257105e-01  9.49271274e-02  9.29001797e-03 -2.20538232e-01\n",
      "  1.56300890e-01 -1.69533130e-02 -1.46711359e-02 -2.83571524e-03\n",
      " -1.29887775e-03  3.11040372e-02  4.65500462e-03 -9.61635327e-02\n",
      "  4.83396102e-02  4.77962910e-02  3.93821946e-02  5.84745183e-05\n",
      "  4.63027579e-02 -8.57157955e-02  2.71270271e-02 -8.57157955e-02\n",
      "  3.55952607e-02  2.29935076e-02  4.12310977e-01 -1.38588368e-01\n",
      " -2.81284538e-02 -8.57157955e-02 -8.76468396e-02 -7.22315199e-02\n",
      "  0.00000000e+00 -2.23326095e-02  8.36425746e-03 -8.57157955e-02\n",
      "  8.15351459e-02  1.81490016e-02  1.91258854e-03 -5.72137033e-03\n",
      "  3.80878180e-03 -1.72864950e-01 -5.37925606e-02  1.67160818e-01\n",
      "  5.94966930e-02  1.16993413e-02 -3.98890249e-02  1.21662682e-02\n",
      " -8.19302748e-03  2.42164429e-02 -1.18621424e-01  4.91197674e-02\n",
      "  4.35538944e-02  2.59477618e-02  0.00000000e+00 -5.19248337e-02\n",
      "  7.68903901e-02 -1.15857868e-02  1.38638944e-01 -1.51508501e-01\n",
      " -9.15991731e-03  1.12600966e-01 -1.54371400e-02 -8.85141222e-02\n",
      " -2.40768732e-02  7.42611574e-02  3.11395773e-02 -5.70359080e-03\n",
      "  4.26845780e-02 -1.18304849e-01 -3.37128057e-02  6.44411726e-02\n",
      " -1.59132925e-01  1.99239627e-02  1.08480595e-01  4.00466453e-02\n",
      "  1.46199243e-01 -8.55790930e-02  7.67684015e-02 -1.77435196e-01]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "## Task 4 Train and Evaluate a simple linear regression model\n",
    "\n",
    "#  Split dataset in training set (X_train, y_train) and test set (X_test, y_test)\n",
    "\n",
    "X = house_data_features\n",
    "y = house_data_target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0,test_size=0.2)\n",
    "\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n",
    "\n",
    "# Train a simple linear regression model 'lr' with training set\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "#lr.score(X_train, y_train)\n",
    "\n",
    "# Third, print model weights(coefficients) and intercept\n",
    "print(\"Model intercept (lr.intercept): \\n \", lr.intercept_)\n",
    "print(\"Model weights (lr.coef_) : \\n \", lr.coef_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score on trainig set:  0.946090999554027\n",
      "MSE score on trainig set:  0.008083320085922519\n",
      "R^2 score on test set: 0.8913688710715012\n",
      "MSE score on test set:  0.014723634506256793\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Step 4 Train and Evaluate a simple linear regression mode\n",
    "\n",
    "# Use the trained simple model to make predictions 'pred_test' on test set 'X_test' using the trained model \n",
    "\n",
    "pred_test = lr.predict(X_test)\n",
    "\n",
    "# Evaluate model performance using R^2 and mean squared error on training set i.e., pred_train, y_train\n",
    "pred_train = lr.predict(X_train)\n",
    "R_square_train = r2_score(pred_train,y_train)\n",
    "MSE_train = mean_squared_error(pred_train,y_train)\n",
    "\n",
    "print(\"R^2 score on trainig set: \", R_square_train)\n",
    "print(\"MSE score on trainig set: \", MSE_train)\n",
    "\n",
    "\n",
    "# Evaluate model performance using R^2 and mean squared error on test set i.e., pred_test, y_test\n",
    "\n",
    "R_square_test = r2_score(pred_test, y_test)\n",
    "MSE_test = mean_squared_error(pred_test, y_test)\n",
    "\n",
    "print(\"R^2 score on test set:\", R_square_test)\n",
    "print(\"MSE score on test set: \", MSE_test)\n",
    "\n",
    "\n",
    "# Plot the linear model\n",
    "plt.scatter(pred_test, y_test, color='red', marker='.', label = \"validation set\")\n",
    "plt.scatter(pred_train, y_train, color='blue', marker='.', label = \"Training set\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score on training set:  0.945988601327299\n",
      "R^2 score on test set:  0.8941027857741486\n"
     ]
    }
   ],
   "source": [
    "## Step 5 Model refinement with regularization (Ridge regression)\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# Train a ridge regression model 'ridge' with training set\n",
    "lr_ridge = Ridge(alpha=0.05, random_state=0)\n",
    "lr_ridge.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "# Make predictions 'ridge_pred_train' on train set 'X_train' using the trained model and calculate the R^2\n",
    "ridge_pred_train = lr_ridge.predict(X_train)\n",
    "R_square_train_ridge = r2_score(ridge_pred_train,y_train)\n",
    "\n",
    "print(\"R^2 score on training set: \", R_square_train_ridge)\n",
    "\n",
    "\n",
    "# Make predictions 'ridge_pred_test' on test set 'X_test' using the trained model and calculate the R^2\n",
    "ridge_pred_test = lr_ridge.predict(X_test)\n",
    "R_square_test_ridge =  r2_score(ridge_pred_test,y_test)\n",
    "\n",
    "print(\"R^2 score on test set: \", R_square_test_ridge)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score on trainig set:  0.9416930296104757\n",
      "R^2 score on test set:  0.9084362411685808\n",
      "Number of features used(Lasso): 210\n"
     ]
    }
   ],
   "source": [
    "## Step 5 Model refinement with regularization (Lasso)\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# Train a Lasso model 'lasso' with training set\n",
    "lsr= Lasso(alpha= 0.0001)\n",
    "lsr.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions 'ridge_pred_train' on train set 'X_train' using the trained model and calculate the R^2\n",
    "lasso_pred_train = lsr.predict(X_train)\n",
    "R_square_train_lasso = r2_score(lasso_pred_train, y_train)\n",
    "\n",
    "print(\"R^2 score on trainig set: \", R_square_train_lasso)\n",
    "\n",
    "\n",
    "# Make predictions 'ridge_pred_test' on test set 'X_test' using the trained model and calculate the R^2\n",
    "lasso_pred_test = lsr.predict(X_test)\n",
    "R_square_test_lasso = r2_score(lasso_pred_test,y_test)\n",
    "\n",
    "print(\"R^2 score on test set: \", R_square_test_lasso)\n",
    "\n",
    "# Check the number of features the model is training with\n",
    "print(\"Number of features used(Lasso):\", np.sum(lsr.coef_!=0))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How is the model performing on training set and test set with Lasso? Less scores indicate that you are underfitting and that it is using few number of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Regression</th>\n",
       "      <th>Ridge</th>\n",
       "      <th>Lasso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.946091</td>\n",
       "      <td>0.945989</td>\n",
       "      <td>0.941693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.891369</td>\n",
       "      <td>0.894103</td>\n",
       "      <td>0.908436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Regression     Ridge     Lasso\n",
       "0    0.946091  0.945989  0.941693\n",
       "1    0.891369  0.894103  0.908436"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print all scores\n",
    "all_scores = {'Regression':[R_square_train, R_square_test], \n",
    "              'Ridge':[R_square_train_ridge, R_square_test_ridge], \n",
    "              'Lasso':[R_square_train_lasso, R_square_test_lasso]}\n",
    "\n",
    "score_df = pd.DataFrame(data=all_scores)\n",
    "score_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: in practice, ridge regression is usually the first choice between two models. However, if you have a large amount of features and expect only a few of them to be important, Lasso might be a better choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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