Homework 0601.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, recall_score, precision_score, balanced_accuracy_score, f1_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 100)\n",
    "RANDOM_STATE = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1 [10]\n",
    "- Read in the dataset\n",
    "- How many rows and variables are there?\n",
    "- What is the average value of AP for those points who have CO levels above 3 $mg/m^3$?\n",
    "- Create a correlation map between the variables. Which variables have high correlation (> |0.9|)?\n",
    "- Create a scatter plot of 'TEY' vs 'CDP'\n",
    "- Create histograms for CO and NOX.\n",
    "- Create a sctter plot of CO vs NOX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\wodnj\\\\OneDrive\\\\바탕 화면\\\\Advanced Machine Learning\\\\DSC 540 - Week 6\\\\Data File\\\\gas_emissions.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>AT</th>\n",
       "      <th>AP</th>\n",
       "      <th>AH</th>\n",
       "      <th>AFDP</th>\n",
       "      <th>GTEP</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "      <th>TEY</th>\n",
       "      <th>CDP</th>\n",
       "      <th>CO</th>\n",
       "      <th>NOX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.95320</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>84.985</td>\n",
       "      <td>2.5304</td>\n",
       "      <td>20.116</td>\n",
       "      <td>1048.7</td>\n",
       "      <td>544.92</td>\n",
       "      <td>116.27</td>\n",
       "      <td>10.799</td>\n",
       "      <td>7.4491</td>\n",
       "      <td>113.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.21910</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>87.523</td>\n",
       "      <td>2.3937</td>\n",
       "      <td>18.584</td>\n",
       "      <td>1045.5</td>\n",
       "      <td>548.50</td>\n",
       "      <td>109.18</td>\n",
       "      <td>10.347</td>\n",
       "      <td>6.4684</td>\n",
       "      <td>112.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.94915</td>\n",
       "      <td>1022.2</td>\n",
       "      <td>78.335</td>\n",
       "      <td>2.7789</td>\n",
       "      <td>22.264</td>\n",
       "      <td>1068.8</td>\n",
       "      <td>549.95</td>\n",
       "      <td>125.88</td>\n",
       "      <td>11.256</td>\n",
       "      <td>3.6335</td>\n",
       "      <td>88.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00750</td>\n",
       "      <td>1021.7</td>\n",
       "      <td>76.942</td>\n",
       "      <td>2.8170</td>\n",
       "      <td>23.358</td>\n",
       "      <td>1075.2</td>\n",
       "      <td>549.63</td>\n",
       "      <td>132.21</td>\n",
       "      <td>11.702</td>\n",
       "      <td>3.1972</td>\n",
       "      <td>87.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.28580</td>\n",
       "      <td>1021.6</td>\n",
       "      <td>76.732</td>\n",
       "      <td>2.8377</td>\n",
       "      <td>23.483</td>\n",
       "      <td>1076.2</td>\n",
       "      <td>549.68</td>\n",
       "      <td>133.58</td>\n",
       "      <td>11.737</td>\n",
       "      <td>2.3833</td>\n",
       "      <td>82.515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        AT      AP      AH    AFDP    GTEP     TIT     TAT     TEY     CDP  \\\n",
       "0  1.95320  1020.1  84.985  2.5304  20.116  1048.7  544.92  116.27  10.799   \n",
       "1  1.21910  1020.1  87.523  2.3937  18.584  1045.5  548.50  109.18  10.347   \n",
       "2  0.94915  1022.2  78.335  2.7789  22.264  1068.8  549.95  125.88  11.256   \n",
       "3  1.00750  1021.7  76.942  2.8170  23.358  1075.2  549.63  132.21  11.702   \n",
       "4  1.28580  1021.6  76.732  2.8377  23.483  1076.2  549.68  133.58  11.737   \n",
       "\n",
       "       CO      NOX  \n",
       "0  7.4491  113.250  \n",
       "1  6.4684  112.020  \n",
       "2  3.6335   88.147  \n",
       "3  3.1972   87.078  \n",
       "4  2.3833   82.515  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7384, 11)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AT      0\n",
       "AP      0\n",
       "AH      0\n",
       "AFDP    0\n",
       "GTEP    0\n",
       "TIT     0\n",
       "TAT     0\n",
       "TEY     0\n",
       "CDP     0\n",
       "CO      0\n",
       "NOX     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A. The Average value is 1015.690017349063.\n"
     ]
    }
   ],
   "source": [
    "df_CO = df[df[\"CO\"] > 3]\n",
    "avg_value = (df_CO[\"AP\"].mean())\n",
    "print(f\"A. The Average value is {avg_value}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "\n",
    "plt.figure(figsize = (10, 10))\n",
    "sns.heatmap(corr, annot = True, fmt = \".2f\")\n",
    "plt.title(\"Correlation Map\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The variables that have high correlation are like below:\n",
      "AFDP  TIT     0.915128\n",
      "      CDP     0.922991\n",
      "GTEP  TEY     0.932337\n",
      "      CDP     0.938142\n",
      "TIT   AFDP    0.915128\n",
      "      TEY     0.951813\n",
      "      CDP     0.951590\n",
      "TEY   GTEP    0.932337\n",
      "      TIT     0.951813\n",
      "      CDP     0.991207\n",
      "CDP   AFDP    0.922991\n",
      "      GTEP    0.938142\n",
      "      TIT     0.951590\n",
      "      TEY     0.991207\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "high_correlated_vars = corr.stack().loc[(corr.stack() > 0.9) & (corr.stack() < 1) |\n",
    "                                        (corr.stack() < -0.9) & (corr.stack() > -1)]\n",
    "\n",
    "print(f\"The variables that have high correlation are like below:\\n{high_correlated_vars}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 3))\n",
    "plt.scatter(df[\"CDP\"], df[\"TEY\"], edgecolors = 'white', linewidths = 0.5)\n",
    "plt.xlabel(\"CDP\")\n",
    "plt.ylabel(\"TEY\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 3))  \n",
    "plt.hist(df['CO'], bins = 30)\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 3))  \n",
    "plt.hist(df['NOX'], bins = 30)\n",
    "plt.tight_layout()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 3))\n",
    "plt.scatter(df[\"NOX\"], df[\"CO\"], edgecolors = 'white', linewidths = 0.5)\n",
    "plt.xlabel(\"CO\")\n",
    "plt.ylabel(\"NOX\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.CO\n",
    "X = df.drop(['CO', 'NOX'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.10, random_state = RANDOM_STATE)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.20, random_state = RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss = StandardScaler() # Standardization of the data\n",
    "ss.fit(X_train)\n",
    "X_train = ss.transform(X_train)\n",
    "X_val = ss.transform(X_val)\n",
    "X_test = ss.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5316, 9)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1329, 9)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(739, 9)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q2 [10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Apply Linear Regression. Predict CO emission using other variables.\n",
    "    - What is the model $R^2$ on training data?\n",
    "    - What is the $R^2$ on validation data?\n",
    "    - What is the Root Mean Squared Error (RMSE) on training data?\n",
    "    - What is the RMSE on validation data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression() # Initialize the model\n",
    "lr.fit(X_train, y_train) # Fit the model\n",
    "lr_pred_train = lr.predict(X_train) # Predict the target values\n",
    "lr_pred_val = lr.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the training data is 0.664\n",
      "The R-Squared value on the validation data is 0.646\n",
      "The RMSE value on the training data is 1.272\n",
      "The RMSE value on the validation data is 1.388\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the training data is {r2_score(y_train, lr_pred_train):.3f}\")\n",
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, lr_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the training data is {np.sqrt(mean_squared_error(y_train, lr_pred_train)):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, lr_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q3 [10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Apply Random Forest Regression with 100 trees. Predict CO emission using other variables.\n",
    "    - What is the model $R^2$?\n",
    "    - What is the $R^2$ on validation data?\n",
    "    - What is the RMSE on training data?\n",
    "    - What is the RMSE on validation data?\n",
    "\n",
    "- Is training and validation score close? If not what might be the problem?\n",
    "- If there is a large gap, try to decrease the gap by changing some hyperparameters of the Random Forest model. (In this analysis, any difference more than 0.10 $R^2$ difference can be assumed to be large.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "estimator = RandomForestRegressor(n_estimators = 100, random_state = RANDOM_STATE)  # Initialize the model\n",
    "estimator.fit(X_train, y_train) # Fit the model\n",
    "rfr_pred_train = estimator.predict(X_train) # Predict the target values\n",
    "rfr_pred_val = estimator.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the training data is 0.964\n",
      "The R-Squared value on the validation data is 0.756\n",
      "The RMSE value on the training data is 0.419\n",
      "The RMSE value on the validation data is 1.154\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the training data is {r2_score(y_train, rfr_pred_train):.3f}\")\n",
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, rfr_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the training data is {np.sqrt(mean_squared_error(y_train, rfr_pred_train)):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, rfr_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A. There is a large gap between training and validation score. This discrepancy indicates that the model is likely overfitting to the training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wodnj\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n",
      "135 fits failed out of a total of 405.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "135 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\wodnj\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\wodnj\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\base.py\", line 1467, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\Users\\wodnj\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\Users\\wodnj\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\wodnj\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1051: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan 0.73687849 0.73683223 0.73679855\n",
      " 0.73727532 0.73727532 0.73727532 0.73967602 0.73967602 0.73967602\n",
      " 0.73687849 0.73683223 0.73679855 0.73727532 0.73727532 0.73727532\n",
      " 0.73967602 0.73967602 0.73967602        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.75997651 0.75942761 0.75928314 0.7608104  0.7608104  0.76061151\n",
      " 0.7647489  0.7647489  0.7647489  0.75997651 0.75942761 0.75928314\n",
      " 0.7608104  0.7608104  0.76061151 0.7647489  0.7647489  0.7647489\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan 0.77319506 0.77332334 0.7742928\n",
      " 0.7758239  0.7758239  0.77571908 0.77800117 0.77800117 0.77800117\n",
      " 0.77319506 0.77332334 0.7742928  0.7758239  0.7758239  0.77571908\n",
      " 0.77800117 0.77800117 0.77800117]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyper parameters are {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2}.\n"
     ]
    }
   ],
   "source": [
    "estimator = RandomForestRegressor(n_estimators = 100, random_state = RANDOM_STATE) # Initialize the model\n",
    "\n",
    "# Define the hyperparameters\n",
    "hyperparameters_rfr = {'max_depth': [3, 5, 10],\n",
    "                        'min_samples_split': [2, 3, 5],\n",
    "                        'min_samples_leaf': [1, 2, 4],\n",
    "                        'max_features': ['auto', 'sqrt', 'log2']}\n",
    "\n",
    "grid_search_rfr = GridSearchCV(estimator = estimator, param_grid = hyperparameters_rfr, cv = 5) # Set up the grid search\n",
    "grid_search_rfr.fit(X_train, y_train) # Fit the grid search\n",
    "print(f\"The best hyper parameters are {grid_search_rfr.best_params_}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a random forest regressor with hyperparameters\n",
    "rfr_with_hyperparameters = RandomForestRegressor(n_estimators = 100,\n",
    "                                                 max_depth = 10,\n",
    "                                                 min_samples_split = 2,\n",
    "                                                 min_samples_leaf = 4,\n",
    "                                                 max_features = 'sqrt',\n",
    "                                                 random_state = RANDOM_STATE)\n",
    "rfr_with_hyperparameters.fit(X_train, y_train) # Fit the model\n",
    "rfr_pred_train = rfr_with_hyperparameters.predict(X_train)  # Predict the target values\n",
    "rfr_pred_val = rfr_with_hyperparameters.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the training data is 0.850\n",
      "The R-Squared value on the validation data is 0.760\n",
      "The gap between training and validation R-squared values reached less than 0.10.\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the training data is {r2_score(y_train, rfr_pred_train):.3f}\")\n",
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, rfr_pred_val):.3f}\")\n",
    "print(\"The gap between training and validation R-squared values reached less than 0.10.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q4 [10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Apply Support Vector Regression. Predict CO emission using other variables.\n",
    "    - What is the model $R^2$?\n",
    "    - What is the $R^2$ on validation data?\n",
    "    - What is the RMSE on training data?\n",
    "    - What is the RMSE on validation data?\n",
    "    \n",
    "- Do this for each of the models listed below using the default parameters:  \n",
    "    1. **Linear kernel SVR**  \n",
    "    2. **Polynomial kernel SVR**  \n",
    "    3. **RBF kernel SVR** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear SVM Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "estimator = SVR(kernel = 'linear') # Initialize the model\n",
    "estimator.fit(X_train, y_train) # Fit the model\n",
    "linear_svr_pred_train = estimator.predict(X_train) # Predict the target values\n",
    "linear_svr_pred_val = estimator.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the training data is 0.624\n",
      "The R-Squared value on the validation data is 0.602\n",
      "The RMSE value on the training data is 1.345\n",
      "The RMSE value on the validation data is 1.473\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the training data is {r2_score(y_train, linear_svr_pred_train):.3f}\")\n",
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, linear_svr_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the training data is {np.sqrt(mean_squared_error(y_train, linear_svr_pred_train)):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, linear_svr_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Kernel SVM Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "estimator = SVR(kernel = 'poly') # Initialize the model\n",
    "estimator.fit(X_train, y_train) # Fit the model\n",
    "polynomial_svr_pred_train = estimator.predict(X_train) # Predict the target values\n",
    "polynomial_svr_pred_val = estimator.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the training data is 0.740\n",
      "The R-Squared value on the validation data is 0.731\n",
      "The RMSE value on the training data is 1.118\n",
      "The RMSE value on the validation data is 1.211\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the training data is {r2_score(y_train, polynomial_svr_pred_train):.3f}\")\n",
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, polynomial_svr_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the training data is {np.sqrt(mean_squared_error(y_train, polynomial_svr_pred_train)):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, polynomial_svr_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RBF Kernel SVM Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "estimator = SVR(kernel = 'rbf') # Initialize the model\n",
    "estimator.fit(X_train, y_train) # Fit the model\n",
    "rbf_svr_pred_train = estimator.predict(X_train) # Predict the target values\n",
    "rbf_svr_pred_val = estimator.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the training data is 0.765\n",
      "The R-Squared value on the validation data is 0.748\n",
      "The RMSE value on the training data is 1.063\n",
      "The RMSE value on the validation data is 1.173\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the training data is {r2_score(y_train, rbf_svr_pred_train):.3f}\")\n",
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, rbf_svr_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the training data is {np.sqrt(mean_squared_error(y_train, rbf_svr_pred_train)):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, rbf_svr_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q5 [10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Take RBF Kernel SVM and perform hyperparameter tuning. See if you can increase the $R^2$ score.   \n",
    "    - Use:  \n",
    "          'C': [1, 10, 100, 1000]\n",
    "          'gamma': [0.001, 0.01, 0.1, 1]  \n",
    "\n",
    "    - What is the model $R^2$?\n",
    "    - What is the $R^2$ on validation data?\n",
    "    - What is the RMSE on training data?\n",
    "    - What is the RMSE on validation data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-11 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-11 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-11 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-11 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-11 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-11 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-11 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVR(),\n",
       "             param_grid={&#x27;C&#x27;: [1, 10, 100, 1000],\n",
       "                         &#x27;gamma&#x27;: [0.001, 0.01, 0.1, 1]},\n",
       "             scoring=&#x27;r2&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=SVR(),\n",
       "             param_grid={&#x27;C&#x27;: [1, 10, 100, 1000],\n",
       "                         &#x27;gamma&#x27;: [0.001, 0.01, 0.1, 1]},\n",
       "             scoring=&#x27;r2&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: SVR</label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVR(),\n",
       "             param_grid={'C': [1, 10, 100, 1000],\n",
       "                         'gamma': [0.001, 0.01, 0.1, 1]},\n",
       "             scoring='r2')"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator = SVR(kernel = 'rbf') # Initialize the model\n",
    "\n",
    "# Define the hyperparameters\n",
    "hyperparameters_rbf_svr = {\"C\": [1, 10, 100, 1000],\n",
    "                           \"gamma\": [0.001, 0.01, 0.1, 1]}\n",
    "\n",
    "grid_search_rbf_svr = GridSearchCV(estimator = estimator, param_grid = hyperparameters_rbf_svr, cv = 5, scoring = \"r2\")  # Set up the grid search\n",
    "grid_search_rbf_svr.fit(X_train, y_train) # Fit the grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rbf_svr = grid_search_rbf_svr.best_estimator_ # Retrieve the best model found by the grid search\n",
    "rbf_svr_pred_train = best_rbf_svr.predict(X_train) # Predict the target values\n",
    "rbf_svr_pred_val = best_rbf_svr.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 1000, 'gamma': 0.01}\n",
      "The R-Squared value on the training data is 0.792\n",
      "The R-Squared value on the validation data is 0.757\n",
      "The RMSE value on the training data is 1.000\n",
      "The RMSE value on the validation data is 1.151\n"
     ]
    }
   ],
   "source": [
    "print(f\"Best parameters: {grid_search_rbf_svr.best_params_}\")\n",
    "print(f\"The R-Squared value on the training data is {r2_score(y_train, rbf_svr_pred_train):.3f}\")\n",
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, rbf_svr_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the training data is {np.sqrt(mean_squared_error(y_train, rbf_svr_pred_train)):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, rbf_svr_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A. Increasing the R-Squared value is observed with hyperparameter tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q6 [10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Apply PCA and transform the data. \n",
    "\n",
    "- Use the PCA transformed data to perform regression. Use the models: \n",
    "    - Linear Regression\n",
    "    - Random Forest Regression (Optional to do hyper parameter tuning)\n",
    "    - RBF Kernel Support Vector Regression (Optional to do hyper parameter tuning)\n",
    "  \n",
    "- For each model:  \n",
    "    - What is the model $R^2$?\n",
    "    - What is the $R^2$ on validation data?\n",
    "    - What is the RMSE on training data?\n",
    "    - What is the RMSE on validation data?\n",
    "    \n",
    "- Compare these models with their counterparts obtained before. Do you observe any change in performance? Please explain. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components = None) # Initialize PCA object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca.fit(X_train)  # Fit the model\n",
    "\n",
    "# Transform the data\n",
    "X_train_pca = pca.transform(X_train)\n",
    "X_val_pca = pca.transform(X_val)\n",
    "X_test_pca = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.58158952 0.78924196 0.88751722 0.94337792 0.98704348 0.9959942\n",
      " 0.99975821 0.99992456 1.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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B4AiM6hIsdSwiIrPDckNkARJOZmPamiMoKquEt0qJZc90QIfGHlLHIiIySyw3RGZMqxXw6Y6z+OjPVABAx8Ye+GJ0e3i7cX4NEVFtWG6IzFRhWQXi1hzFnyezAQBjoxpj9sAwONjJJU5GRGTeWG6IzNDZnCI8990hnL9aAgc7Od4aGoERHYOkjkVEZBFYbojMzLbjWYhbcwQl5Rr4uTti+TMd0DaogdSxiIgsBssNkZnQaAUs/TMVn+44CwDo0sQTn49uDy9XpcTJiIgsC8sNkRkoKK3A1DWHsev0VQDAxO5NMHNAK9grOL+GiMhQLDdEEjudVYTnvjuIS9dLobST490nIzHsoUCpYxERWSyWGyIJbTqWidd+OYrScg0CGjjhyzEdEBHgLnUsIiKLxnJDJAGNVsB7207hy93nAQDdmzfEp0+3h6eLg8TJiIgsH8sNkYnllZRjyurD2HPmGgDg/x5uitf6tYQd59cQEYmC5YbIhI5fKcD/fXcIl/NuwMlegff+1QYxbf2ljkVEZFVYbohM5PcjGXjj12Moq9Ai2NMZX43tgFa+blLHIiKyOiw3REZWqdFi8ZZT+PbvCwCAXi0a4ZOnHoK7s73EyYiIrBPLDZERXS9WI/bHJPxzPhcAENu7GeIeawmFXCZxMiIi68VyQ2QkyZcL8H/fHcSVgjK4OCjw4Yi26B/hJ3UsIiKrx3JDZAT/PZiON39LQXmlFk29XPDlmA4I9VFJHYuIyCaw3BCJqEKjxVsbT2DV/ksAgOjW3ogf2Q5ujpxfQ0RkKiw3RCLJKSrDSz8cRuLFqvk106JDMeXRUMg5v4aIyKQkv2rY559/jpCQEDg6OqJLly5ITEysdd+KigosXLgQzZo1g6OjI9q2bYutW7eaMC1RzZLS8hDz6d9IvJgLldIO34ztiGnRLVhsiIgkIGm5WbNmDeLi4jBv3jwkJSWhbdu26NevH3Jycmrcf/bs2fjyyy/x6aef4sSJE3j++ecxbNgwHD582MTJiW5ZnZiGp778B9mFajT3dsVvL3VHdJiP1LGIiGyWTBAEQaoP79KlCzp16oTPPvsMAKDVahEUFISXX34ZM2bMuGt/f39/vPnmm4iNjdVte/LJJ+Hk5ITvv/++xs9Qq9VQq9W6x4WFhQgKCkJBQQHc3HgBNbp/6koN5q8/gZ8S0wAA/cN98cGItnBVcrSXiEhshYWFcHd3r9f3t2RnbsrLy3Ho0CFER0ffCiOXIzo6Gvv376/xNWq1Go6OjnrbnJyc8Pfff9f6OYsXL4a7u7vuJygoSJwDIJuWXViGp776Bz8lpkEmA17r1xLLnmnPYkNEZAYkKzfXrl2DRqOBj4/+6XsfHx9kZWXV+Jp+/fohPj4eZ86cgVarxfbt27F27VpkZmbW+jkzZ85EQUGB7ic9PV3U4yDbc/BiLgZ9+jcOp+XDzdEOK8Z3Qmzv5pDJOL+GiMgcWNR/Zn788cd49tln0apVK8hkMjRr1gwTJkzAihUran2NUqmEUqk0YUqyVoIg4Pt/LmHBhhOo1Apo5avCl2M6oHFDF6mjERHRbSQ7c+Pl5QWFQoHs7Gy97dnZ2fD19a3xNY0aNcJvv/2GkpISXLp0CadOnYKrqyuaNm1qishkw8oqNHj9l2OY8/txVGoFDGzjh7UvdmOxISIyQ5KVGwcHB3To0AEJCQm6bVqtFgkJCYiKiqrztY6OjggICEBlZSV+/fVXDBkyxNhxyYZdyb+BEV/ux38PXYZcBsx8vBU+e/ohODtY1IlPIiKbIem/znFxcRg3bhw6duyIzp07Y+nSpSgpKcGECRMAAGPHjkVAQAAWL14MAPjf//6HjIwMtGvXDhkZGZg/fz60Wi1ef/11KQ+DrIRGKyDxQi5yisrgrXJE5yaeOHAxF7E/JOF6STkaONvjs6fbo0eol9RRiYioDpKWm5EjR+Lq1auYO3cusrKy0K5dO2zdulU3yTgtLQ1y+a2TS2VlZZg9ezbOnz8PV1dXDBgwAN999x0aNGgg0RGQtdiakokFG04gs6BMt83N0Q7F6kpoBSDMzw1fjumAIE9nCVMSEVF9SHqdGykYsk6ebMPWlEy88H0Savt/hM4hHlg1sQucHBQmzUVERLdYxHVuiMyBRitgwYYTtRYbAEjPuwEHO/6/ChGRpeC/2GTTEi/k6g1F1SSzoAyJF3JNlIiIiB4Uyw3ZtJyiuouNofsREZH0WG7IpnmrHO+9kwH7ERGR9FhuyKZ1buKJBk72tT4vA+DnXrUsnIiILAPLDdm0zIIbKKvU1Phc9Z2i5sWEQSHnfaOIiCwFyw3ZLI1WQNyaoyir0KKJlzN83fSHnnzdHbHsmfboH+EnUUIiIrofvH482awvdp5F4sVcuCrtsGpCFwR4ON11hWKesSEisjwsN2STDqflYWnCGQDAwiHhCG5YdeXhqGYNpYxFREQi4LAU2ZxidSWmrj4CjVZATFt/DHsoQOpIREQkIpYbsjnzfj+OtNxSBDRwwltDIyCTceiJiMiasNyQTdlw9Ap+TboMuQxY+lQ7uNexDJyIiCwTyw3ZjMt5pZi1LhkA8FLv5ugUwmvXEBFZI5YbsgnVy76LyirxUHADTOkTKnUkIiIyEpYbsgnLdlUt+3ZxUODjkQ/BTsH/6RMRWSv+C09W73BaHj76s3rZd4Ru2TcREVmnel3nJi4urt5vGB8ff99hiMR257LvJ9pz2TcRkbWrV7k5fPiw3uOkpCRUVlaiZcuWAIDU1FQoFAp06NBB/IRED4DLvomIbE+9ys3OnTt1v8fHx0OlUmHVqlXw8PAAAOTl5WHChAno2bOncVIS3Qcu+yYisk0yQRAEQ14QEBCAP/74A+Hh4XrbU1JS0LdvX1y5ckXUgGIrLCyEu7s7CgoK4ObmJnUcMpLLeaV4/OM9KCqrxJRHmyOub0upIxER0QMw5Pvb4AnFhYWFuHr16l3br169iqKiIkPfjkh0XPZNRGTbDC43w4YNw4QJE7B27VpcvnwZly9fxq+//opJkybhiSeeMEZGIoPcvux76ch2XPZNRGRjDL4r+PLly/Hqq69i1KhRqKioqHoTOztMmjQJ77//vugBiQxx57Lvxg1dJE5ERESmZvCcm2olJSU4d+4cAKBZs2ZwcbGMLxHOubFexepKDPh4D9JySxHT1h+fPNWOq6OIiKyEUefcVMvMzERmZiZCQ0Ph4uKC++xIRKLhsm8iIgLuo9xcv34dffr0QYsWLTBgwABkZmYCACZNmoTp06eLHpCoPrjsm4iIqhlcbl555RXY29sjLS0Nzs63LmM/cuRIbN26VdRwRPWRkX+Dd/smIiIdgycU//HHH9i2bRsCAwP1toeGhuLSpUuiBSOqD41WwCurj6CorBLtghrgZS77JiKyeQafuSkpKdE7Y1MtNzcXSqVSlFBE9aV3t++n2sGey76JiGyewd8EPXv2xH/+8x/dY5lMBq1Wi/feew+9e/cWNRxRXbjsm4iIamLwsNR7772HPn364ODBgygvL8frr7+O48ePIzc3F3v37jVGRqK78G7fRERUG4PP3ERERCA1NRU9evTAkCFDUFJSgieeeAKHDx9Gs2bNjJGR6C5c9k1ERLUx+MwNALi7u+PNN98UOwtRvdy+7PujkVz2TURE+u6r3OTn5yMxMRE5OTnQarV6z40dO1aUYEQ1uX3Zd2zv5ujchMu+iYhIn8HlZsOGDRg9ejSKi4vh5uamNxwgk8lYbsho7lz2zbt9ExFRTQyeczN9+nRMnDgRxcXFyM/PR15enu4nNzfXGBmJAHDZNxER1Y/B3w4ZGRmYMmVKjde6ITIWLvsmIqL6Mrjc9OvXDwcPHjRGFqIacdk3EREZwuA5NwMHDsRrr72GEydOIDIyEvb2+itVBg8eLFo4IgCYv57LvomIqP5kgiAIhrxALq/9ZI9MJoNGo3ngUMZUWFgId3d3FBQUwM3NTeo4dA8bj13BSz8ehlwGrH4uiqujiIhslCHf3wafublz6TeRsWTk38DMtVz2TUREhuFyEzJLXPZNRET3q15nbj755BM899xzcHR0xCeffFLnvlOmTBElGNk2LvsmIqL7Va85N02aNMHBgwfRsGFDNGnSpPY3k8lw/vx5UQOKjXNuzN/htDz8a/l+aLQCPhzeFk92CJQ6EhERSUz0OTcXLlyo8XcisRWrKzFtTdWy70Ft/Ljsm4iIDMZz/WRW5q8/jkvXq5Z9vz0sksu+iYjIYPd148zLly9j/fr1SEtLQ3l5ud5z8fHxogQj27Px2BX8coh3+yYiogdjcLlJSEjA4MGD0bRpU5w6dQoRERG4ePEiBEFA+/btjZGRbACXfRMRkVgMHpaaOXMmXn31VSQnJ8PR0RG//vor0tPT0atXLwwfPtwYGcnKcdk3ERGJyeByc/LkSYwdOxYAYGdnhxs3bsDV1RULFy7EkiVLRA9I1o/LvomISEwGf4u4uLjo5tn4+fnh3LlzuueuXbsmXjKyCUfS83V3+17Au30TEZEIDJ5z07VrV/z9999o3bo1BgwYgOnTpyM5ORlr165F165djZGRrFTV3b4P65Z9P8ll30REJAKDy018fDyKi4sBAAsWLEBxcTHWrFmD0NBQrpQig3DZNxERGYPB5aZp06a6311cXLB8+XJRA5Ft4LJvIiIyFs7cJJPjsm8iIjKmep258fDwqPeQQW5u7gMFIuvGZd9ERGRs9So3S5cuNXIMshXLd5/jsm8iIjKqepWbcePGGTsH2YAj6fn4aHsqAC77JiIi47mve0tpNBqsW7cOJ0+eBACEhYVhyJAhsLO7r7cjG1C97LuSy76JiMjIDG4jx48fx+DBg5GVlYWWLVsCAJYsWYJGjRphw4YNiIiIED0kWT4u+yYiIlMxeMLD5MmTER4ejsuXLyMpKQlJSUlIT09HmzZt8NxzzxkjI1k4LvsmIiJTMvjMzZEjR3Dw4EF4eHjotnl4eODtt99Gp06dRA1Hlu/2Zd8vPsJl30REZHwGn7lp0aIFsrOz79qek5OD5s2bixKKrINGK+CVNbeWfU+N5rJvIiIyPoPLzeLFizFlyhT88ssvuHz5Mi5fvoxffvkF06ZNw5IlS1BYWKj7Idu2fPc5JF7gsm8iIjItmSAIgiEvkMtvfUFVTwqtfovbH8tkMmg0GrFyiqawsBDu7u4oKCiAm5ub1HGs1pH0fPxr2T5UagV8MLwt/tUhUOpIRERkwQz5/jZ4zs3OnTvvOxjZBi77JiIiKRlcbnr16mWMHGRFuOybiIikZPAkiPnz50Or1d61vaCgAE8//bQoochy3b7sO35EWy77JiIikzO43Hz77bfo0aMHzp8/r9u2a9cuREZG4ty5c6KGI8uSkX8Ds25b9t2laUOJExERkS0yuNwcO3YMgYGBaNeuHb7++mu89tpr6Nu3L8aMGYN9+/YZHODzzz9HSEgIHB0d0aVLFyQmJta5/9KlS9GyZUs4OTkhKCgIr7zyCsrKygz+XBJX9bLvQi77JiIiiRk858bDwwM///wzZs2ahf/7v/+DnZ0dtmzZgj59+hj84WvWrEFcXByWL1+OLl26YOnSpejXrx9Onz4Nb2/vu/b/8ccfMWPGDKxYsQLdunVDamoqxo8fD5lMhvj4eIM/n8TDZd9ERGQu7usb6NNPP8XHH3+Mp59+Gk2bNsWUKVNw9OhRg98nPj4ezz77LCZMmICwsDAsX74czs7OWLFiRY3779u3D927d8eoUaMQEhKCvn374umnn77n2R4yLt7tm4iIzInB5aZ///5YsGABVq1ahR9++AGHDx/Gww8/jK5du+K9996r9/uUl5fj0KFDiI6OvhVGLkd0dDT2799f42u6deuGQ4cO6crM+fPnsXnzZgwYMKDWz1Gr1XoXFuTFBcXFZd9ERGRuDC43Go0Gx44dw7/+9S8AgJOTE5YtW4ZffvkFH330Ub3f59q1a9BoNPDx8dHb7uPjg6ysrBpfM2rUKCxcuBA9evSAvb09mjVrhkceeQSzZs2q9XMWL14Md3d33U9QUFC9M9K9VS/79nd3xNtDueybiIikZ3C52b59O/z9/e/aPnDgQCQnJ4sSqja7du3CO++8gy+++AJJSUlYu3YtNm3ahEWLFtX6mpkzZ6KgoED3k56ebtSMtuSuu307c9k3ERFJr97lJjExsc7bKajVauzYsaPeH+zl5QWFQnHXTTizs7Ph6+tb42vmzJmDMWPGYPLkyYiMjMSwYcPwzjvvYPHixTVeewcAlEol3Nzc9H7owXHZNxERmat6l5uoqChcv35d99jNzU3vWjf5+fkGXcTPwcEBHTp0QEJCgm6bVqtFQkICoqKianxNaWmp3r2tAEChUAC4dX8rMg6NVsD+c9fx+5EM7D17DdNWH+aybyIiMkv1Xgp+Z3moqUwYWjDi4uIwbtw4dOzYEZ07d8bSpUtRUlKCCRMmAADGjh2LgIAALF68GAAQExOD+Ph4PPTQQ+jSpQvOnj2LOXPmICYmRldySHxbUzKxYMMJZBboX09IaSfnsm8iIjI7Bl/npi6GTiYdOXIkrl69irlz5yIrKwvt2rXD1q1bdZOM09LS9M7UzJ49GzKZDLNnz0ZGRgYaNWqEmJgYvP3222IeBt1ma0omXvg+CTXVVnWlFiczC7n0m4iIzIpMqOfpFrlcjqysLN3F9VQqFY4ePYqmTZsCqJor4+/vX+e8HHNgyC3TbZ1GK6DHkh13nbGpJgPg6+6Iv994FAo5V0kREZHxGPL9bdCZmxMnTuiWaQuCgFOnTqG4uBhA1dJusi6JF3JrLTYAIADILChD4oVcRDXjhGIiIjIPBpWbPn366M2rGTRoEICq4ShBEHiNEyuTU1S/e3bVdz8iIiJTqHe5uXDhgjFzkBnyVjmKuh8REZEp1LvcNG7c2Jg5yAx1buIJP3dHZBWU1TihuHrOTecmnqaORkREVCuu4aVaKeQyzIsJq/G56gHIeTFhnExMRERmheWG6tQ/wg/PRN191s7X3RHLnmmP/hF+EqQiIiKqnajXuSHrdOl6KQBgRIdAdA/1greqaiiKZ2yIiMgcsdxQnfJKyrH3bNUy/+cfaYamjVwlTkRERFS3+xqWqqysxJ9//okvv/wSRUVFAIArV67ornlD1mPr8SxotALC/NxYbIiIyCIYfObm0qVL6N+/P9LS0qBWq/HYY49BpVJhyZIlUKvVWL58uTFykkQ2HcsEAAxqy7k1RERkGQw+czN16lR07NgReXl5cHJy0m0fNmyY3h2+yfJdK1Zj37mqIalBkf4SpyEiIqofg8/c7NmzB/v27YODg4Pe9pCQEGRkZIgWjKS3JSULWgFoE+iO4IbOUschIiKqF4PP3Gi12hpvjnn58mWoVCpRQpF52HTsCgBgYCSHpIiIyHIYXG769u2LpUuX6h7LZDIUFxdj3rx5GDBggJjZSEI5hWX434VcAMDANiw3RERkOQwelvrwww/Rr18/hIWFoaysDKNGjcKZM2fg5eWFn376yRgZSQJbUrIgCEC7oAYI9OCQFBERWQ6Dy01gYCCOHj2KNWvW4OjRoyguLsakSZMwevRovQnGZNk23hySGsSzNkREZGHu6yJ+dnZ2GD16NEaPHi12HjIDWQVlOHAxDwAwgPNtiIjIwhg852bx4sVYsWLFXdtXrFiBJUuWiBKKpLUpueraNh0be8C/Ac/GERGRZTG43Hz55Zdo1arVXdvDw8N5AT8roVslxSEpIiKyQAaXm6ysLPj53f2l16hRI2RmZooSiqSTkX8DSWn5kMk4JEVERJbJ4HITFBSEvXv33rV979698PfnVWwt3eabt1voFOIJHzdHidMQEREZzuAJxc8++yymTZuGiooKPProowCAhIQEvP7665g+fbroAcm0qldJxXBIioiILJTB5ea1117D9evX8eKLL6K8vBwA4OjoiDfeeAMzZ84UPSCZTnpuKY5eLoBcBvSPYLkhIiLLZHC5kclkWLJkCebMmYOTJ0/CyckJoaGhUCqVxshHJrTx5pBU16YN0UjFv08iIrJM93WdGwBwdXVFp06dxMxCEtuUXH3hPs6dIiIiy2VwuSkpKcG7776LhIQE5OTkQKvV6j1//vx50cKR6Vy8VoKUjEIo5DL0j/CVOg4REdF9M7jcTJ48Gbt378aYMWPg5+cHmUxmjFxkYtUX7uvWrCE8XRwkTkNERHT/DC43W7ZswaZNm9C9e3dj5CGJbDjKe0kREZF1MPg6Nx4eHvD09DRGFpLI2ZxinMoqgp1chn7hHJIiIiLLZnC5WbRoEebOnYvS0lJj5CEJbLq5SqpHqBcaOHNIioiILJvBw1Iffvghzp07Bx8fH4SEhMDe3l7v+aSkJNHCkWlUX7iPq6SIiMgaGFxuhg4daoQYJJXU7CKcySmGg0KOx8J8pI5DRET0wAwuN/PmzTNGDpLIxpsTiR9u4QV3J/t77E1ERGT+DJ5zQ9ZDEARsvLkEfCBXSRERkZUw+MyNRqPBRx99hJ9//hlpaWm6+0tVy83NFS0cGdfJzCKcv1oCBzs5oltzSIqIiKyDwWduFixYgPj4eIwcORIFBQWIi4vDE088Ablcjvnz5xshIhlL9e0WHmnRCCpHDkkREZF1MLjc/PDDD/j6668xffp02NnZ4emnn8Y333yDuXPn4p9//jFGRjICQRB0N8oc1JarpIiIyHoYXG6ysrIQGRkJoOrmmQUFBQCAQYMGYdOmTeKmI6M5fqUQl66XwtFejj6tvKWOQ0REJBqDy01gYCAyM6v+i79Zs2b4448/AAAHDhyAUqkUNx0ZzYab17Z5tJU3XJT3fXN4IiIis2NwuRk2bBgSEhIAAC+//DLmzJmD0NBQjB07FhMnThQ9IIlPEATdVYl54T4iIrI2Bv8n+7vvvqv7feTIkQgODsb+/fsRGhqKmJgYUcORcRy9XIDLeTfg7KBA75YckiIiIuvywOMRUVFRiIqKEiMLmcimm0NSfVr7wMlBIXEaIiIicdWr3Kxfvx6PP/447O3tsX79+jr3HTx4sCjByDi02ltDUgMjeeE+IiKyPvUqN0OHDkVWVha8vb3rvLeUTCaDRqMRKxsZweH0fFwpKIOLgwKPtGwkdRwiIiLR1avcaLXaGn8ny1N9B/DHwnzgaM8hKSIisj4GrZaqqKhAnz59cObMGWPlISPSagVsTuYqKSIism4GlRt7e3scO3bMWFnIyA5eykN2oRoqRzv0bOEldRwiIiKjMPg6N8888wy+/fZbY2QhI6teJdU3zBdKOw5JERGRdTJ4KXhlZSVWrFiBP//8Ex06dICLi4ve8/Hx8aKFI/FotAI2p2QBAAa14SopIiKyXgaXm5SUFLRv3x4AkJqaqvecTCYTJxWJ7n8XruNqkRruTvbo3pxDUkREZL0MLjc7d+40Rg4ysupr2/QL94GDncGjkURERBaD33I2oFKjxVbdkBRXSRERkXW7r9svHDx4ED///DPS0tJQXl6u99zatWtFCUbi+ed8Lq6XlMPD2R5RzRpKHYeIiMioDD5zs3r1anTr1g0nT57EunXrUFFRgePHj2PHjh1wd3c3RkZ6QNUX7usf4Qd7BU/WERGRdTP4m+6dd97BRx99hA0bNsDBwQEff/wxTp06hREjRiA4ONgYGekBVGi02Hq8akgqhqukiIjIBhhcbs6dO4eBAwcCABwcHFBSUgKZTIZXXnkFX331legB6cHsPXsN+aUV8HJ1QOcmnlLHISIiMjqDy42HhweKiooAAAEBAUhJSQEA5Ofno7S0VNx09MCqV0k9HuEHOw5JERGRDTB4QvHDDz+M7du3IzIyEsOHD8fUqVOxY8cObN++HX369DFGRrpP5ZVabLs5JDWQQ1JERGQj6l1uUlJSEBERgc8++wxlZWUAgDfffBP29vbYt28fnnzyScyePdtoQclwf5+9isKySnirlOgUwiEpIiKyDfUuN23atEGnTp0wefJkPPXUUwAAuVyOGTNmGC0cPZiNR6uGpAZE+kEh59WjiYjINtR7Esbu3bsRHh6O6dOnw8/PD+PGjcOePXuMmY0eQFmFBttPZAPgvaSIiMi21Lvc9OzZEytWrEBmZiY+/fRTXLx4Eb169UKLFi2wZMkSZGVlGTMnGeiv1KsoUlfC180R7YM9pI5DRERkMgYvn3FxccGECROwe/dupKamYvjw4fj8888RHByMwYMHGyMj3YdNyVVDUgPb+EHOISkiIrIhD7Q2uHnz5pg1axZmz54NlUqFTZs2iZWLHkBZhQZ/3hyS4iopIiKyNfd1bykA+Ouvv7BixQr8+uuvkMvlGDFiBCZNmiRmNrpPu07noKRcg4AGTngoqIHUcYiIiEzKoHJz5coVrFy5EitXrsTZs2fRrVs3fPLJJxgxYgRcXFyMlZEMtOHYrSEpmYxDUkREZFvqXW4ef/xx/Pnnn/Dy8sLYsWMxceJEtGzZ0pjZ6D6Ulldix8kcAFwlRUREtqne5cbe3h6//PILBg0aBIVCYcxM9AB2nMrBjQoNgj2dERnAu7QTEZHtqXe5Wb9+vTFzkEiqL9zHISkiIrJVZnEnxc8//xwhISFwdHREly5dkJiYWOu+jzzyCGQy2V0/1Xcqt2XF6krsPF01JDUwkkNSRERkmyQvN2vWrEFcXBzmzZuHpKQktG3bFv369UNOTk6N+69duxaZmZm6n5SUFCgUCgwfPtzEyc1PwslsqCu1aOLlgnB/N6njEBERSULychMfH49nn30WEyZMQFhYGJYvXw5nZ2esWLGixv09PT3h6+ur+9m+fTucnZ1ZbgBsvLlKahCHpIiIyIZJWm7Ky8tx6NAhREdH67bJ5XJER0dj//799XqPb7/9Fk899VStS9HVajUKCwv1fqxRYVkFdp++CoAX7iMiItsmabm5du0aNBoNfHx89Lb7+PjU615ViYmJSElJweTJk2vdZ/HixXB3d9f9BAUFPXBuc/TniWyUa7Ro7u2Klj4qqeMQERFJRvJhqQfx7bffIjIyEp07d651n5kzZ6KgoED3k56ebsKEplM9JDUwkkNSRERk2+779gti8PLygkKhQHZ2tt727Oxs+Pr61vnakpISrF69GgsXLqxzP6VSCaVS+cBZzVlBaQX2nKkakuKF+4iIyNZJeubGwcEBHTp0QEJCgm6bVqtFQkICoqKi6nztf//7X6jVajzzzDPGjmn2tp3IQoVGQEsfFUI5JEVERDZO0jM3ABAXF4dx48ahY8eO6Ny5M5YuXYqSkhJMmDABADB27FgEBARg8eLFeq/79ttvMXToUDRs2FCK2GZl022rpIiIiGyd5OVm5MiRuHr1KubOnYusrCy0a9cOW7du1U0yTktLg1yuf4Lp9OnT+Pvvv/HHH39IEdms5JWUY+/ZawC4SoqIiAgAZIIgCFKHMKXCwkK4u7ujoKAAbm6Wf6G71YlpmLE2GWF+btg8tafUcYiIiIzCkO9vi14tRbetkuJZGyIiIgAsNxbterEa+85VDUlxvg0REVEVlhsLtiUlC1oBiAxwR+OGNV+hmYiIyNaw3FgwrpIiIiK6G8uNhcopKsP/LlwHAAyIZLkhIiKqxnJjobYkVw1JtQtqgCBPZ6njEBERmQ2WGwvFISkiIqKasdxYoKyCMhy4lAuAQ1JERER3YrmxQJuTMyEIQMfGHvBv4CR1HCIiIrPCcmOBNh67AoAX7iMiIqoJy42Fyci/gaS0fMhkHJIiIiKqCcuNhdl8cyJxpxBP+Lg5SpyGiIjI/LDcWJiNyVXlJoZDUkRERDViubEg6bmlOJqeD7kM6B/BckNERFQTlhsLsunmWZuuTRuikUopcRoiIiLzxHJjQbhKioiI6N5YbizExWslSMkohEIuQ/9wX6njEBERmS2WGwtRPSTVrVlDNHTlkBQREVFtWG4sxEbeS4qIiKheWG4swLmrxTiZWQg7uQz9OCRFRERUJ5YbC1B9B/AeoV5o4OwgcRoiIiLzxnJjAXSrpHi7BSIiontiuTFzqdlFSM0uhoNCjr4ckiIiIronlhszVz2R+OEWXnB3spc4DRERkfljuTFjgiDwwn1EREQGYrkxY6eyinD+agkc7OSIbu0jdRwiIiKLwHJjxqrP2jzSohFUjhySIiIiqg+WGzMlCIJuCfigtv4SpyEiIrIcLDdm6viVQly8XgpHezn6tPKWOg4REZHFYLkxU9WrpB5t5Q0XpZ3EaYiIiCwHy40Z0lslFckhKSIiIkOw3JihY5cLcDnvBpzsFXiUQ1JEREQGYbkxQ9Vnbfq09oaTg0LiNERERJaF5cbM6K2SasMhKSIiIkOx3JiZpLR8XCkog4uDAo+0bCR1HCIiIovDcmNmqs/aPBbmA0d7DkkREREZiuXGjGi1AjYnV5WbgRySIiIiui8sN2bkUFoesgrLoFLa4eEWXlLHISIiskgsN2Zk49GqVVKPhftAacchKSIiovvBcmMmNFoBm1OyAAAxHJIiIiK6byw3ZiLxQi6uFqnh7mSP7s05JEVERHS/WG7MRPWF+/qF+8DBjn8tRERE94vfomagUqPF1ptDUrxwHxER0YNhuTED/5zPxfWScng42yOqWUOp4xAREVk0lhszsCm5akiqf4Qf7BX8KyEiInoQ/CaVWIVGiy26ISk/idMQERFZPpYbie07dx35pRXwcnVAlyaeUschIiKyeCw3Equ+cF//CF/YcUiKiIjogfHbVELllVpsO85VUkRERGJiuZHQ32evorCsEo1USnQK4ZAUERGRGFhuJLTx2M07gEf6QSGXSZyGiIjIOrDcSKSsQoPtx7MBAAO5SoqIiEg0LDcS2XPmGorUlfB1c0SHYA+p4xAREVkNlhuJVN9LakCkH+QckiIiIhINy40Eyio0+PNE1ZDUoLYckiIiIhITy40Edp3OQUm5BgENnPBQUAOp4xAREVkVlhsJ6FZJtfGDTMYhKSIiIjGx3JhYaXklEk7mAOC9pIiIiIyB5cbEdpzKwY0KDYI9nREZ4C51HCIiIqvDcmNimzgkRUREZFQsNyZUrK7EjlNVQ1IDIzkkRUREZAwsNyaUcDIb6kotmni5INzfTeo4REREVonlxoRuv5cUh6SIiIiMg+XGRIrKKrD79FUAvHAfERGRMbHcmMj2E9ko12jRrJELWvqopI5DRERktVhuTKR6ldSgNv4ckiIiIjIilhsTKCitwF9nbg5J8cJ9RERERsVyYwJ/nMhChUZASx8VQjkkRUREZFQsNyZw+72kiIiIyLhYbowsr6Qce89eA8ByQ0REZAqSl5vPP/8cISEhcHR0RJcuXZCYmFjn/vn5+YiNjYWfnx+USiVatGiBzZs3myit4bYdz0KlVkBrPzc0a+QqdRwiIiKrZyflh69ZswZxcXFYvnw5unTpgqVLl6Jfv344ffo0vL2979q/vLwcjz32GLy9vfHLL78gICAAly5dQoMGDUwfvp42JVevkuJZGyIiIlOQtNzEx8fj2WefxYQJEwAAy5cvx6ZNm7BixQrMmDHjrv1XrFiB3Nxc7Nu3D/b29gCAkJCQOj9DrVZDrVbrHhcWFop3APdwvViNfeeuA2C5ISIiMhXJhqXKy8tx6NAhREdH3wojlyM6Ohr79++v8TXr169HVFQUYmNj4ePjg4iICLzzzjvQaDS1fs7ixYvh7u6u+wkKChL9WGqz9XgWNFoBkQHuaNzQxWSfS0REZMskKzfXrl2DRqOBj4+P3nYfHx9kZWXV+Jrz58/jl19+gUajwebNmzFnzhx8+OGHeOutt2r9nJkzZ6KgoED3k56eLupx1GXjUQ5JERERmZqkw1KG0mq18Pb2xldffQWFQoEOHTogIyMD77//PubNm1fja5RKJZRKpYmTAjlFZfjfhaohqQGRLDdERESmIlm58fLygkKhQHZ2tt727Oxs+Pr61vgaPz8/2NvbQ6FQ6La1bt0aWVlZKC8vh4ODg1EzG2JrSha0AtAuqAGCPJ2ljkNERGQzJBuWcnBwQIcOHZCQkKDbptVqkZCQgKioqBpf0717d5w9exZarVa3LTU1FX5+fmZVbAAOSREREUlF0uvcxMXF4euvv8aqVatw8uRJvPDCCygpKdGtnho7dixmzpyp2/+FF15Abm4upk6ditTUVGzatAnvvPMOYmNjpTqEGmUVlOHApVwAHJIiIiIyNUnn3IwcORJXr17F3LlzkZWVhXbt2mHr1q26ScZpaWmQy2/1r6CgIGzbtg2vvPIK2rRpg4CAAEydOhVvvPGGVIdQo83JmRAEoENjD/g3cJI6DhERkU2RCYIgSB3ClAoLC+Hu7o6CggK4ubkZ5TOeXLYPhy7lYV5MGCZ0b2KUzyAiIrIlhnx/S377BWtzJf8GDl3Kg0zGISkiIiIpsNyIbPPN2y10CvGEj5ujxGmIiIhsj0Vd58acabQCEi/k4j/7LwEABkTWvJydiIiIjIvlRgRbUzKxYMMJZBaU6bZ9sfMcfN0c0T+CQ1NERESmxGGpB7Q1JRMvfJ+kV2wA4GqRGi98n4StKZkSJSMiIrJNLDcPQKMVsGDDCdS03Kx624INJ6DR2tSCNCIiIkmx3DyAxAu5d52xuZ0AILOgDIkXck0XioiIyMax3DyAnKLai8397EdEREQPjuXmAXir6rfUu777ERER0YNjuXkAnZt4ws/dEbJanpcB8HN3ROcmnqaMRUREZNNYbh6AQi7DvJgwALir4FQ/nhcTBoW8tvpDREREYmO5eUD9I/yw7Jn28HXXH3rydXfEsmfa8zo3REREJsaL+Imgf4QfHgvzReKFXOQUlcFbVTUUxTM2REREpsdyIxKFXIaoZg2ljkFERGTzOCxFREREVoXlhoiIiKwKyw0RERFZFZYbIiIisiosN0RERGRVWG6IiIjIqrDcEBERkVVhuSEiIiKrwnJDREREVsXmrlAsCAIAoLCwUOIkREREVF/V39vV3+N1sblyU1RUBAAICgqSOAkREREZqqioCO7u7nXuIxPqU4GsiFarxZUrV6BSqSCTiXtjy8LCQgQFBSE9PR1ubm6ivrc5sPbjA6z/GHl8ls/aj5HHZ/mMdYyCIKCoqAj+/v6Qy+ueVWNzZ27kcjkCAwON+hlubm5W+z9awPqPD7D+Y+TxWT5rP0Yen+UzxjHe64xNNU4oJiIiIqvCckNERERWheVGREqlEvPmzYNSqZQ6ilFY+/EB1n+MPD7LZ+3HyOOzfOZwjDY3oZiIiIisG8/cEBERkVVhuSEiIiKrwnJDREREVoXlhoiIiKwKy40I/vrrL8TExMDf3x8ymQy//fab1JFEtXjxYnTq1AkqlQre3t4YOnQoTp8+LXUs0Sxbtgxt2rTRXXAqKioKW7ZskTqW0bz77ruQyWSYNm2a1FFEM3/+fMhkMr2fVq1aSR1LVBkZGXjmmWfQsGFDODk5ITIyEgcPHpQ6lmhCQkLu+juUyWSIjY2VOpooNBoN5syZgyZNmsDJyQnNmjXDokWL6nWfJEtRVFSEadOmoXHjxnByckK3bt1w4MABSbLY3BWKjaGkpARt27bFxIkT8cQTT0gdR3S7d+9GbGwsOnXqhMrKSsyaNQt9+/bFiRMn4OLiInW8BxYYGIh3330XoaGhEAQBq1atwpAhQ3D48GGEh4dLHU9UBw4cwJdffok2bdpIHUV04eHh+PPPP3WP7eys55+3vLw8dO/eHb1798aWLVvQqFEjnDlzBh4eHlJHE82BAweg0Wh0j1NSUvDYY49h+PDhEqYSz5IlS7Bs2TKsWrUK4eHhOHjwICZMmAB3d3dMmTJF6niimDx5MlJSUvDdd9/B398f33//PaKjo3HixAkEBASYNoxAogIgrFu3TuoYRpWTkyMAEHbv3i11FKPx8PAQvvnmG6ljiKqoqEgIDQ0Vtm/fLvTq1UuYOnWq1JFEM2/ePKFt27ZSxzCaN954Q+jRo4fUMUxq6tSpQrNmzQStVit1FFEMHDhQmDhxot62J554Qhg9erREicRVWloqKBQKYePGjXrb27dvL7z55psmz8NhKTJYQUEBAMDT01PiJOLTaDRYvXo1SkpKEBUVJXUcUcXGxmLgwIGIjo6WOopRnDlzBv7+/mjatClGjx6NtLQ0qSOJZv369ejYsSOGDx8Ob29vPPTQQ/j666+ljmU05eXl+P777zFx4kTRb3AslW7duiEhIQGpqakAgKNHj+Lvv//G448/LnEycVRWVkKj0cDR0VFvu5OTE/7++2+T57Ge87ZkElqtFtOmTUP37t0REREhdRzRJCcnIyoqCmVlZXB1dcW6desQFhYmdSzRrF69GklJSZKNfxtbly5dsHLlSrRs2RKZmZlYsGABevbsiZSUFKhUKqnjPbDz589j2bJliIuLw6xZs3DgwAFMmTIFDg4OGDdunNTxRPfbb78hPz8f48ePlzqKaGbMmIHCwkK0atUKCoUCGo0Gb7/9NkaPHi11NFGoVCpERUVh0aJFaN26NXx8fPDTTz9h//79aN68uekDmfxckZWDlQ9LPf/880Ljxo2F9PR0qaOISq1WC2fOnBEOHjwozJgxQ/Dy8hKOHz8udSxRpKWlCd7e3sLRo0d126xtWOpOeXl5gpubm9UMLdrb2wtRUVF6215++WWha9euEiUyrr59+wqDBg2SOoaofvrpJyEwMFD46aefhGPHjgn/+c9/BE9PT2HlypVSRxPN2bNnhYcfflgAICgUCqFTp07C6NGjhVatWpk8C8uNyKy53MTGxgqBgYHC+fPnpY5idH369BGee+45qWOIYt26dbp/bKp/AAgymUxQKBRCZWWl1BGNomPHjsKMGTOkjiGK4OBgYdKkSXrbvvjiC8Hf31+iRMZz8eJFQS6XC7/99pvUUUQVGBgofPbZZ3rbFi1aJLRs2VKiRMZTXFwsXLlyRRAEQRgxYoQwYMAAk2fgnBu6J0EQ8NJLL2HdunXYsWMHmjRpInUko9NqtVCr1VLHEEWfPn2QnJyMI0eO6H46duyI0aNH48iRI1AoFFJHFF1xcTHOnTsHPz8/qaOIonv37nddfiE1NRWNGzeWKJHx/Pvf/4a3tzcGDhwodRRRlZaWQi7X/8pVKBTQarUSJTIeFxcX+Pn5IS8vD9u2bcOQIUNMnoFzbkRQXFyMs2fP6h5fuHABR44cgaenJ4KDgyVMJo7Y2Fj8+OOP+P3336FSqZCVlQUAcHd3h5OTk8TpHtzMmTPx+OOPIzg4GEVFRfjxxx+xa9cubNu2TepoolCpVHfNj3JxcUHDhg2tZt7Uq6++ipiYGDRu3BhXrlzBvHnzoFAo8PTTT0sdTRSvvPIKunXrhnfeeQcjRoxAYmIivvrqK3z11VdSRxOVVqvFv//9b4wbN86qlvIDQExMDN5++20EBwcjPDwchw8fRnx8PCZOnCh1NNFs27YNgiCgZcuWOHv2LF577TW0atUKEyZMMH0Yk58rskI7d+4UANz1M27cOKmjiaKmYwMg/Pvf/5Y6migmTpwoNG7cWHBwcBAaNWok9OnTR/jjjz+kjmVU1jbnZuTIkYKfn5/g4OAgBAQECCNHjhTOnj0rdSxRbdiwQYiIiBCUSqXQqlUr4auvvpI6kui2bdsmABBOnz4tdRTRFRYWClOnThWCg4MFR0dHoWnTpsKbb74pqNVqqaOJZs2aNULTpk0FBwcHwdfXV4iNjRXy8/MlySITBCu6PCIRERHZPM65ISIiIqvCckNERERWheWGiIiIrArLDREREVkVlhsiIiKyKiw3REREZFVYboiIiMiqsNwQERGRVWG5IbJyISEhWLp0qWjvN378eAwdOlS09wOAXbt2QSaTIT8/X9T3JSLbxHJDZCHGjx8PmUwGmUwGBwcHNG/eHAsXLkRlZWWdrztw4ACee+450XJ8/PHHWLlypWjvZ4jDhw9j+PDh8PHxgaOjI0JDQ/Hss88iNTVVkjzmSuxCS2RpWG6ILEj//v2RmZmJM2fOYPr06Zg/fz7ef//9GvctLy8HADRq1AjOzs6iZXB3d0eDBg1Ee7/62rhxI7p27Qq1Wo0ffvgBJ0+exPfffw93d3fMmTPH5HmIyHyx3BBZEKVSCV9fXzRu3BgvvPACoqOjsX79egC3hovefvtt+Pv7o2XLlgDu/q94mUyGb775BsOGDYOzszNCQ0N171Ht+PHjGDRoENzc3KBSqdCzZ0+cO3dO73OqPfLII3jppZfw0ksvwd3dHV5eXpgzZw5uv23dd999h44dO0KlUsHX1xejRo1CTk5OvY+7tLQUEyZMwIABA7B+/XpER0ejSZMm6NKlCz744AN8+eWXun13796Nzp07Q6lUws/PDzNmzNA7u/XII4/g5ZdfxrRp0+Dh4QEfHx98/fXXKCkpwYQJE6BSqdC8eXNs2bJF95rqYbNNmzahTZs2cHR0RNeuXZGSkqKX89dff0V4eDiUSiVCQkLw4Ycf6j0fEhKCd955BxMnToRKpUJwcPBdd/ZOT0/HiBEj0KBBA3h6emLIkCG4ePGi7vnqP/8PPvgAfn5+aNiwIWJjY1FRUaE7vkuXLuGVV17RnekDgEuXLiEmJgYeHh5wcXFBeHg4Nm/eXO+/AyJLwnJDZMGcnJx0Z2gAICEhAadPn8b27duxcePGWl+3YMECjBgxAseOHcOAAQMwevRo5ObmAgAyMjLw8MMPQ6lUYseOHTh06BAmTpxY5/DXqlWrYGdnh8TERHz88ceIj4/HN998o3u+oqICixYtwtGjR/Hbb7/h4sWLGD9+fL2Pc9u2bbh27Rpef/31Gp+vPpOUkZGBAQMGoFOnTjh69CiWLVuGb7/9Fm+99dZdeb28vJCYmIiXX34ZL7zwAoYPH45u3bohKSkJffv2xZgxY1BaWqr3utdeew0ffvghDhw4gEaNGiEmJkZXKg4dOoQRI0bgqaeeQnJyMubPn485c+bcNYT34YcfomPHjjh8+DBefPFFvPDCCzh9+rTuz6lfv35QqVTYs2cP9u7dC1dXV/Tv31/v73nnzp04d+4cdu7ciVWrVmHlypW6z1m7di0CAwOxcOFCZGZmIjMzEwAQGxsLtVqNv/76C8nJyViyZAlcXV3r/XdAZFEkuRc5ERls3LhxwpAhQwRBEAStVits375dUCqVwquvvqp73sfHR1Cr1Xqva9y4sfDRRx/pHgMQZs+erXtcXFwsABC2bNkiCIIgzJw5U2jSpIlQXl5+zxyCIAi9evUSWrduLWi1Wt22N954Q2jdunWtx3LgwAEBgFBUVCQIgiDs3LlTACDk5eXVuP+SJUsEAEJubm6t7ykIgjBr1iyhZcuWelk+//xzwdXVVdBoNLq8PXr00D1fWVkpuLi4CGPGjNFty8zMFAAI+/fv18u3evVq3T7Xr18XnJychDVr1giCIAijRo0SHnvsMb08r732mhAWFqZ73LhxY+GZZ57RPdZqtYK3t7ewbNkyQRAE4bvvvrsrv1qtFpycnIRt27YJglD159+4cWOhsrJSt8/w4cOFkSNH6n3O7X/ngiAIkZGRwvz58+v88yOyFjxzQ2RBNm7cCFdXVzg6OuLxxx/HyJEjMX/+fN3zkZGRcHBwuOf7tGnTRve7i4sL3NzcdMNER44cQc+ePWFvb1/vXF27dtUNfwBAVFQUzpw5A41GA6DqrEZMTAyCg4OhUqnQq1cvAEBaWlq93l+4bYirLidPnkRUVJRelu7du6O4uBiXL1/Wbbv9+BUKBRo2bIjIyEjdNh8fHwC4a+gsKipK97unpydatmyJkydP6j67e/fuevt3795d78/hzs+WyWTw9fXVfc7Ro0dx9uxZqFQquLq6wtXVFZ6enigrK9MNCwJAeHg4FAqF7rGfn989h/mmTJmCt956C927d8e8efNw7NixOvcnsmQsN0QWpHfv3jhy5AjOnDmDGzduYNWqVXBxcdE9f/vvdbmzuMhkMmi1WgBVQ11iKikpQb9+/eDm5oYffvgBBw4cwLp16wBAb6ilLi1atAAAnDp1SpRMNR3/7duqy1H1n4mY6vqzLy4uRocOHXDkyBG9n9TUVIwaNape71GbyZMn4/z58xgzZgySk5PRsWNHfPrppyIdFZF5YbkhsiAuLi5o3rw5goODYWdnZ5TPaNOmDfbs2aObS1If//vf//Qe//PPPwgNDYVCocCpU6dw/fp1vPvuu+jZsydatWpl0GRiAOjbty+8vLzw3nvv1fh89fVxWrdujf379+ud6dm7dy9UKhUCAwMN+sya/PPPP7rf8/LykJqaitatW+s+e+/evXr77927Fy1atNA7y1KX9u3b48yZM/D29kbz5s31ftzd3eud08HBQe9sUbWgoCA8//zzWLt2LaZPn46vv/663u9JZElYbohIz0svvYTCwkI89dRTOHjwIM6cOYPvvvtON+m1JmlpaYiLi8Pp06fx008/4dNPP8XUqVMBAMHBwXBwcMCnn36K8+fPY/369Vi0aJFBmVxcXPDNN99g06ZNGDx4MP78809cvHgRBw8exOuvv47nn38eAPDiiy8iPT0dL7/8Mk6dOoXff/8d8+bNQ1xcHOTyB//nbuHChUhISEBKSgrGjx8PLy8v3cqx6dOnIyEhAYsWLUJqaipWrVqFzz77DK+++mq933/06NHw8vLCkCFDsGfPHly4cAG7du3ClClT9IbV7iUkJAR//fUXMjIycO3aNQDAtGnTsG3bNly4cAFJSUnYuXOnrpgRWRuWGyLS07BhQ+zYsQPFxcXo1asXOnTogK+//rrOOThjx47FjRs30LlzZ8TGxmLq1Km6Cwc2atQIK1euxH//+1+EhYXh3XffxQcffGBwriFDhmDfvn2wt7fHqFGj0KpVKzz99NMoKCjQrYYKCAjA5s2bkZiYiLZt2+L555/HpEmTMHv27Pv7w7jDu+++i6lTp6JDhw7IysrChg0bdHOc2rdvj59//hmrV69GREQE5s6di4ULFxq0KszZ2Rl//fUXgoOD8cQTT6B169aYNGkSysrK4ObmVu/3WbhwIS5evIhmzZqhUaNGAACNRoPY2Fi0bt0a/fv3R4sWLfDFF18YdPxElkIm1HemHhFRDR555BG0a9fOqq+Iu2vXLvTu3Rt5eXmSXMCQiAzDMzdERERkVVhuiIiIyKpwWIqIiIisCs/cEBERkVVhuSEiIiKrwnJDREREVoXlhoiIiKwKyw0RERFZFZYbIiIisiosN0RERGRVWG6IiIjIqvw/ky/Jb76iwqQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pca.explained_variance_ratio_.cumsum())\n",
    "plt.plot(pca.explained_variance_ratio_.cumsum(), '-o');\n",
    "plt.xticks(ticks= range(X_train_pca.shape[1]), labels = [i + 1 for i in range(X_train_pca.shape[1])])\n",
    "plt.xlabel('Principal Components')\n",
    "plt.ylabel('Variance Explained')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pca2 = X_train_pca[:, 0:5]\n",
    "X_val_pca2 = X_val_pca[:, 0:5]\n",
    "X_test_pca2 = X_test_pca[:, 0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Linear Regression with PCA transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_pca = LinearRegression() # Initialize the model\n",
    "lr_pca.fit(X_train_pca2, y_train) # Fit the model\n",
    "lr_pca_pred_train = lr_pca.predict(X_train_pca2) # Predict the target values\n",
    "lr_pca_pred_val = lr_pca.predict(X_val_pca2) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the training data is 0.656\n",
      "The R-Squared value on the validation data is 0.633\n",
      "The RMSE value on the training data is 1.287\n",
      "The RMSE value on the validation data is 1.414\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the training data is {r2_score(y_train, lr_pca_pred_train):.3f}\")\n",
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, lr_pca_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the training data is {np.sqrt(mean_squared_error(y_train, lr_pca_pred_train)):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, lr_pca_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. RF Regression with PCA transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the model with hyperparameters\n",
    "rfr_pca = RandomForestRegressor(n_estimators = 100,\n",
    "                                max_depth = 10,\n",
    "                                min_samples_split = 2,\n",
    "                                min_samples_leaf = 4,\n",
    "                                max_features = 'sqrt',\n",
    "                                random_state = RANDOM_STATE)\n",
    "rfr_pca.fit(X_train_pca2, y_train) # Fit the model\n",
    "rfr_pca_pred_train = rfr_pca.predict(X_train_pca2) # Predict the target values\n",
    "rfr_pca_pred_val = rfr_pca.predict(X_val_pca2) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the training data is 0.836\n",
      "The R-Squared value on the validation data is 0.744\n",
      "The RMSE value on the training data is 0.889\n",
      "The RMSE value on the validation data is 1.181\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the training data is {r2_score(y_train, rfr_pca_pred_train):.3f}\")\n",
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, rfr_pca_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the training data is {np.sqrt(mean_squared_error(y_train, rfr_pca_pred_train)):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, rfr_pca_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. SVM Regression with PCA transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the model with hyperparameters\n",
    "rbf_svr_pca = SVR(kernel = 'rbf',\n",
    "                  C = 1000,\n",
    "                  gamma = 0.01)\n",
    "rbf_svr_pca.fit(X_train_pca2, y_train) # Fit the model\n",
    "rbf_svr_pca_pred_train = rbf_svr_pca.predict(X_train_pca2) # Predict the target values\n",
    "rbf_svr_pca_pred_val = rbf_svr_pca.predict(X_val_pca2) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the training data is 0.785\n",
      "The R-Squared value on the validation data is 0.752\n",
      "The RMSE value on the training data is 1.017\n",
      "The RMSE value on the validation data is 1.163\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the training data is {r2_score(y_train, rbf_svr_pca_pred_train):.3f}\")\n",
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, rbf_svr_pca_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the training data is {np.sqrt(mean_squared_error(y_train, rbf_svr_pca_pred_train)):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, rbf_svr_pca_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Linear Regression**\n",
    "- R-Squared Value Without PCA\n",
    "    - Training: 0.664\n",
    "    - Validation: 0.646\n",
    "- R-Squared Value With PCA\n",
    "    - Training: 0.656\n",
    "    - Validation: 0.633\n",
    "\n",
    "**Random Forest Regression**\n",
    "- R-Squared Value Without PCA\n",
    "    - Training: 0.850\n",
    "    - Validation: 0.760\n",
    "- R-Squared Value With PCA\n",
    "    - Training: 0.836\n",
    "    - Validation: 0.744\n",
    "\n",
    "**RBF Kernel Support Vectior Regression**\n",
    "- R-Squared Value Without PCA\n",
    "    - Training: 0.792\n",
    "    - Validation: 0.757\n",
    "- R-Squared Value With PCA\n",
    "    - Training: 0.785\n",
    "    - Validation: 0.752\n",
    "\n",
    "A. Based on the R-Squared differences of each model, it is hard to say that PCA affected change in performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q7 [10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit RBF Kernel SVM Classifier\n",
    "\n",
    "#### 1. Standardized training data \n",
    "- Using the standardized training data perform grid search:\n",
    "    - Find the best combination of the `C` and `gamma` hyper-parameters.  \n",
    "- For the best model:\n",
    "    - Either using the confusion matrix or the methods in `sklearn.metrics` get the following metrics on the validation set:\n",
    "     - Create confusion matrix\n",
    "     - Recall (Sensitivity)\n",
    "     - Specificity\n",
    "     - Precision\n",
    "     - Balanced Accuracy\n",
    "     - F1 Score\n",
    "     \n",
    "\n",
    "#### 2. PCA transformed training data \n",
    "- Using the **pca** transformed training data `X_train_pca2` perform grid search:\n",
    "    - Find the best combination of the `C` and `gamma` hyper-parameters.  \n",
    "- For the best model:\n",
    "    - Either using the confusion matrix or the methods in `sklearn.metrics` get the following metrics on the validation set `X_val_pca2`:\n",
    "         - Create confusion matrix\n",
    "         - Recall (Sensitivity)\n",
    "         - Specificity\n",
    "         - Precision\n",
    "         - Balanced Accuracy\n",
    "         - F1 Score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Binarizer\n",
    "\n",
    "binarizer = Binarizer(threshold = 3) # Initialize Binarizer object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Binarize the target variables\n",
    "y_train_cat = binarizer.transform(y_train.values.reshape(-1, 1)).ravel()\n",
    "y_val_cat = binarizer.transform(y_val.values.reshape(-1, 1)).ravel()\n",
    "y_test_cat = binarizer.transform(y_test.values.reshape(-1, 1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "estimator = SVC(kernel = 'rbf') # Initialize the model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVC Model 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-12 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-12 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-12 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-12 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-12 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-12 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-12 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-12 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-12 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [1, 10, 100, 1000],\n",
       "                         &#x27;gamma&#x27;: [0.001, 0.01, 0.1, 1]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [1, 10, 100, 1000],\n",
       "                         &#x27;gamma&#x27;: [0.001, 0.01, 0.1, 1]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={'C': [1, 10, 100, 1000],\n",
       "                         'gamma': [0.001, 0.01, 0.1, 1]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the hyperparameters\n",
    "hyperparameters_rbf_svr = {\"C\": [1, 10, 100, 1000],\n",
    "                           \"gamma\": [0.001, 0.01, 0.1, 1]}\n",
    "\n",
    "grid_search_rbf_svr1 = GridSearchCV(estimator = estimator, param_grid = hyperparameters_rbf_svr, cv = 5, scoring = \"accuracy\") # Set up the grid search\n",
    "grid_search_rbf_svr1.fit(X_train, y_train_cat) # Fit the grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rbf_svr1 = grid_search_rbf_svr1.best_estimator_ # Retrieve the best model found by the grid search\n",
    "rbf_svr_pred_val1 = best_rbf_svr1.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e5155682b0>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the confusion matrix\n",
    "cm = confusion_matrix(y_val_cat, rbf_svr_pred_val1)\n",
    "\n",
    "# Display the confusion matrix\n",
    "display = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = [0, 1])\n",
    "display.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.87\n",
      "Specificity: 0.938\n",
      "Precision: 0.90\n",
      "Balanced Accuracy: 0.90\n",
      "F1 Score: 0.88\n"
     ]
    }
   ],
   "source": [
    "specificity = cm[0, 0] / (cm[0, 0] + cm[0, 1])\n",
    "print(f\"Recall: {recall_score(y_val_cat, rbf_svr_pred_val1):.2f}\")\n",
    "print(f\"Specificity: {specificity:.3f}\")\n",
    "print(f\"Precision: {precision_score(y_val_cat, rbf_svr_pred_val1):.2f}\")\n",
    "print(f\"Balanced Accuracy: {balanced_accuracy_score(y_val_cat, rbf_svr_pred_val1):.2f}\")\n",
    "print(f\"F1 Score: {f1_score(y_val_cat, rbf_svr_pred_val1):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVC Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-13 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-13 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-13 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-13 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-13 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-13 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-13 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-13 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-13 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-13 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [1, 10, 100, 1000],\n",
       "                         &#x27;gamma&#x27;: [0.001, 0.01, 0.1, 1]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [1, 10, 100, 1000],\n",
       "                         &#x27;gamma&#x27;: [0.001, 0.01, 0.1, 1]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={'C': [1, 10, 100, 1000],\n",
       "                         'gamma': [0.001, 0.01, 0.1, 1]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the hyperparameters\n",
    "hyperparameters_rbf_svr = {\"C\": [1, 10, 100, 1000],\n",
    "                           \"gamma\": [0.001, 0.01, 0.1, 1]}\n",
    "\n",
    "grid_search_rbf_svr2 = GridSearchCV(estimator = estimator, param_grid = hyperparameters_rbf_svr, cv = 5, scoring = \"accuracy\") # Set up the grid search\n",
    "grid_search_rbf_svr2.fit(X_train_pca2, y_train_cat) # Fit the grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rbf_svr2 = grid_search_rbf_svr2.best_estimator_ # Retrieve the best model found by the grid search\n",
    "rbf_svr_pred_val2 = best_rbf_svr2.predict(X_val_pca2) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e517393af0>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the confusion matrix\n",
    "cm = confusion_matrix(y_val_cat, rbf_svr_pred_val2)\n",
    "\n",
    "# Display the confusion matrix\n",
    "display = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = [0, 1])\n",
    "display.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.86\n",
      "Specificity: 0.941\n",
      "Precision: 0.90\n",
      "Balanced Accuracy: 0.90\n",
      "F1 Score: 0.88\n"
     ]
    }
   ],
   "source": [
    "specificity = cm[0, 0] / (cm[0, 0] + cm[0, 1])\n",
    "print(f\"Recall: {recall_score(y_val_cat, rbf_svr_pred_val2):.2f}\")\n",
    "print(f\"Specificity: {specificity:.3f}\")\n",
    "print(f\"Precision: {precision_score(y_val_cat, rbf_svr_pred_val2):.2f}\")\n",
    "print(f\"Balanced Accuracy: {balanced_accuracy_score(y_val_cat, rbf_svr_pred_val2):.2f}\")\n",
    "print(f\"F1 Score: {f1_score(y_val_cat, rbf_svr_pred_val2):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q8 [10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- For regression:\n",
    "    - Think of other ways of improving the regression performance and list at least two possibilities.\n",
    "    - You can use any of the datasets we have been using (standardized data, PCA transformed data), or re-create your own set from the original.\n",
    "    - Implement both of the methods you proposed. \n",
    "    - Is the validation performance increasing? For performance comparison you can use any regression performance metric that you deem to be appropriate. \n",
    "    - Optional: Apply different methods, ideas as you see fit and see if the validation performance is improving."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Original Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression() # Initialize the model\n",
    "lr.fit(X_train, y_train) # Fit the model\n",
    "lr_pred_val = lr.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the validation data is 0.646\n",
      "The RMSE value on the validation data is 1.388\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, lr_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, lr_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.feature_selection import SelectKBest, f_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize PolynomialFeatures object\n",
    "poly = PolynomialFeatures(degree = 2)\n",
    "\n",
    "# Transform the features to polynomial features\n",
    "X_train_poly = poly.fit_transform(X_train)\n",
    "X_val_poly = poly.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize SelectKBest object\n",
    "k_best = SelectKBest(score_func = f_regression, k = 20)\n",
    "\n",
    "# Transform the features\n",
    "X_train_selected = k_best.fit_transform(X_train_poly, y_train)\n",
    "X_val_selected = k_best.transform(X_val_poly)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression() # Initialize the model\n",
    "lr.fit(X_train_selected, y_train) # Fit the model\n",
    "lr_pred_val = lr.predict(X_val_selected) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the validation data is 0.735\n",
      "The RMSE value on the validation data is 1.202\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, lr_pred_val):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, lr_pred_val)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Lasso Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "lasso = Lasso() # Initialize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-14 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-14 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-14 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-14 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-14 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-14 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-14 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-14 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-14 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-14 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=Lasso(),\n",
       "             param_grid={&#x27;alpha&#x27;: [0.01, 0.1, 1, 10, 100]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=Lasso(),\n",
       "             param_grid={&#x27;alpha&#x27;: [0.01, 0.1, 1, 10, 100]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Lasso</label><div class=\"sk-toggleable__content fitted\"><pre>Lasso()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;Lasso<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Lasso(),\n",
       "             param_grid={'alpha': [0.01, 0.1, 1, 10, 100]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the hyperparameters\n",
    "hyperparameters_lasso = {'alpha': [0.01, 0.1, 1, 10, 100]}\n",
    "\n",
    "grid_search_lasso = GridSearchCV(estimator = lasso, param_grid = hyperparameters_lasso, cv = 5, scoring = \"neg_mean_squared_error\") # Set up the grid search\n",
    "grid_search_lasso.fit(X_train, y_train) # Fit the grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_lasso = grid_search_lasso.best_estimator_ # Retrieve the best model found by the grid search\n",
    "best_lasso.fit(X_train, y_train) # Fit the model\n",
    "lasso_pred_val1 = best_lasso.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-Squared value on the validation data is 0.642\n",
      "The RMSE value on the validation data is 1.397\n"
     ]
    }
   ],
   "source": [
    "print(f\"The R-Squared value on the validation data is {r2_score(y_val, lasso_pred_val1):.3f}\")\n",
    "print(f\"The RMSE value on the validation data is {np.sqrt(mean_squared_error(y_val, lasso_pred_val1)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Original Model Performance**\n",
    "- R-Squared: 0.646\n",
    "- RMSE: 1.388\n",
    "\n",
    "**Model Performance With Feature Engineering**\n",
    "- R-Squared: 0.735\n",
    "- RMSE: 1.202\n",
    "\n",
    "**Model Performance With Lasso Regularization**\n",
    "- R-Squared: 0.642\n",
    "- RMSE: 1.397\n",
    "\n",
    "\n",
    "The validation performance has increased after applying feature engineering. On the other hand, it appears the slight decrease in R-Squared and increase in RMSE with Lasso regularization. This demonstrates that applying feature engineering is better way of improving the regression performance on this validation dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q9 [10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- For classification:\n",
    "    - Try at least one classifier other than SVM (RF, GBM, Logistic Regression, etc). \n",
    "    - Think of other ways of improving the regression performance and list at least two possibilities.\n",
    "    - You can use any of the datasets we have been using (standardized data, PCA transformed data), or re-create your own set from the original.\n",
    "    - Implement both of the methods you proposed. \n",
    "    - Is the validation performance increasing? For performance comparison you can use any regression performance metric that you deem to be appropriate. \n",
    "    - Optional: Apply different methods, ideas as you see fit and see if the validation performance is improving."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Original Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(random_state = 42) # Initialize the model\n",
    "rf.fit(X_train, y_train_cat) # Fit the model\n",
    "rf_pred_val = rf.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_val_cat, rf_pred_val) # Calculate the confusion matrix\n",
    "specificity = cm[0, 0] / (cm[0, 0] + cm[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.859\n",
      "Specificity: 0.953\n",
      "Precision: 0.921\n",
      "Balanced Accuracy: 0.906\n",
      "F1 Score: 0.889\n"
     ]
    }
   ],
   "source": [
    "print(f\"Recall: {recall_score(y_val_cat, rf_pred_val):.3f}\")\n",
    "print(f\"Specificity: {specificity:.3f}\")\n",
    "print(f\"Precision: {precision_score(y_val_cat, rf_pred_val):.3f}\")\n",
    "print(f\"Balanced Accuracy: {balanced_accuracy_score(y_val_cat, rf_pred_val):.3f}\")\n",
    "print(f\"F1 Score: {f1_score(y_val_cat, rf_pred_val):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize PolynomialFeatures object\n",
    "poly = PolynomialFeatures(degree = 2)\n",
    "\n",
    "# Transform the features to polynomial features\n",
    "X_train_poly = poly.fit_transform(X_train)\n",
    "X_val_poly = poly.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize SelectKBest object\n",
    "k_best = SelectKBest(score_func = f_regression, k = 20)\n",
    "\n",
    "# Transform the features\n",
    "X_train_selected = k_best.fit_transform(X_train_poly, y_train)\n",
    "X_val_selected = k_best.transform(X_val_poly)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(random_state = 42) # Initialize the mode\n",
    "rf.fit(X_train_selected, y_train_cat) # Fit the model\n",
    "rf_pred_val = rf.predict(X_val_selected) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_val_cat, rf_pred_val) # Calculate the confusion matrix\n",
    "specificity = cm[0, 0] / (cm[0, 0] + cm[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.851\n",
      "Specificity: 0.941\n",
      "Precision: 0.902\n",
      "Balanced Accuracy: 0.896\n",
      "F1 Score: 0.876\n"
     ]
    }
   ],
   "source": [
    "print(f\"Recall: {recall_score(y_val_cat, rf_pred_val):.3f}\")\n",
    "print(f\"Specificity: {specificity:.3f}\")\n",
    "print(f\"Precision: {precision_score(y_val_cat, rf_pred_val):.3f}\")\n",
    "print(f\"Balanced Accuracy: {balanced_accuracy_score(y_val_cat, rf_pred_val):.3f}\")\n",
    "print(f\"F1 Score: {f1_score(y_val_cat, rf_pred_val):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Hyperparameters Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(random_state = 42) # Initialize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-15 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-15 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-15 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-15 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-15 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-15 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-15 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-15 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-15 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-15 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-15 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-15 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [3, 5, 7],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [3, 5, 7],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={'max_depth': [3, 5, 7],\n",
       "                         'n_estimators': [50, 100, 200]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the hyperparameters\n",
    "hyperparameters_rf = {'n_estimators': [50, 100, 200],\n",
    "                      'max_depth': [3, 5, 7]}\n",
    "\n",
    "grid_search_rf = GridSearchCV(estimator = rf, param_grid = hyperparameters_rf, cv = 5, scoring = \"accuracy\") # Set up the grid search\n",
    "grid_search_rf.fit(X_train, y_train_cat) # Fit the grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rf = grid_search_rf.best_estimator_ # Retrieve the best model found by the grid search\n",
    "best_rf.fit(X_train, y_train_cat) # Fit the model\n",
    "rf_pred_val = best_rf.predict(X_val) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_val_cat, rf_pred_val) # Calculate the confusion matrix\n",
    "specificity = cm[0, 0] / (cm[0, 0] + cm[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.855\n",
      "Specificity: 0.948\n",
      "Precision: 0.913\n",
      "Balanced Accuracy: 0.902\n",
      "F1 Score: 0.883\n"
     ]
    }
   ],
   "source": [
    "print(f\"Recall: {recall_score(y_val_cat, rf_pred_val):.3f}\")\n",
    "print(f\"Specificity: {specificity:.3f}\")\n",
    "print(f\"Precision: {precision_score(y_val_cat, rf_pred_val):.3f}\")\n",
    "print(f\"Balanced Accuracy: {balanced_accuracy_score(y_val_cat, rf_pred_val):.3f}\")\n",
    "print(f\"F1 Score: {f1_score(y_val_cat, rf_pred_val):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Original Model Performance**\n",
    "- Recall: 0.859\n",
    "- Specificity: 0.953\n",
    "- Precision: 0.921\n",
    "- Balanced Accuracy: 0.906\n",
    "- F1 Score: 0.889\n",
    "\n",
    "**Model Performance With Feature Engineering**\n",
    "- Recall: 0.851\n",
    "- Specificity: 0.941\n",
    "- Precision: 0.902\n",
    "- Balanced Accuracy: 0.896\n",
    "- F1 Score: 0.876\n",
    "\n",
    "**Model Performance With Hyperparameters Tuning**\n",
    "- Recall: 0.855\n",
    "- Specificity: 0.948\n",
    "- Precision: 0.913\n",
    "- Balanced Accuracy: 0.902\n",
    "- F1 Score: 0.883\n",
    "\n",
    "The classification performance has not improved after applying feature engineering or hyperparameter tuning. If we could find the optimal number of features or add more hyperparameters, however, the model's performance still could be improved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q10 [10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Choose the best 2 regressors among the trained regressions models:\n",
    "- Create a table summarizing results on the **test** set for regression (with target variables in `y_test`):\n",
    "    - Show the R^2 and RMSE performance metrics on test data for each model.\n",
    "- Which model is best for regression? Is this in line with your analysis (the validation performance of the models) or is this not expected?\n",
    "\n",
    "\n",
    "\n",
    "- Choose the best 2 classifiers among the trained classification models:\n",
    "- Create a table summarizing results on the **test** set for classification (with target variables in `y_test_cat`):\n",
    "    - Show the classification performance metrics (accuracy, recall, precision, f1-score, etc.) on test data for each model. \n",
    "- Which model is best for classification? Is this in line with your analysis (the validation performance of the models) or is this not expected?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Best 2 Regressors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the model with hyperparameters\n",
    "rfr_with_hyperparameters = RandomForestRegressor(n_estimators = 100,\n",
    "                                                 max_depth = 10,\n",
    "                                                 min_samples_split = 2,\n",
    "                                                 min_samples_leaf = 4,\n",
    "                                                 max_features = 'sqrt',\n",
    "                                                 random_state = RANDOM_STATE)\n",
    "rfr_with_hyperparameters.fit(X_train, y_train) # Fit the model\n",
    "rfr_pred_test = rfr_with_hyperparameters.predict(X_test) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the model with hyperparameters\n",
    "rfr_pca = RandomForestRegressor(n_estimators = 100,\n",
    "                                max_depth = 10,\n",
    "                                min_samples_split = 2,\n",
    "                                min_samples_leaf = 4,\n",
    "                                max_features = 'sqrt',\n",
    "                                random_state = RANDOM_STATE)\n",
    "rfr_pca.fit(X_train_pca2, y_train) # Fit the model\n",
    "rfr_pca_pred_test = rfr_pca.predict(X_test_pca2) # Predict the target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                        Model  R2 Score      RMSE\n",
      "0        Random Forest (Hyperparamter Tuning)  0.574155  1.522971\n",
      "1  Random Forest (Hyperparamter Tuning + PCA)  0.553123  1.560127\n"
     ]
    }
   ],
   "source": [
    "evaluation_results = {\n",
    "    'Model': ['Random Forest (Hyperparamter Tuning)', 'Random Forest (Hyperparamter Tuning + PCA)'],\n",
    "    'R2 Score': [r2_score(y_test, rfr_pred_test), r2_score(y_test, rfr_pca_pred_test)],\n",
    "    'RMSE': [np.sqrt(mean_squared_error(y_test, rfr_pred_test)), np.sqrt(mean_squared_error(y_test, rfr_pca_pred_test))]\n",
    "    }\n",
    "\n",
    "summary_df = pd.DataFrame(evaluation_results)\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A. The Random Forest regressor with the hyperparameter tuning is the best for regression, and this is in line with the previous analyses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Best 2 Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rbf_svr1 = grid_search_rbf_svr1.best_estimator_ # Retrieve the best model found by the grid search\n",
    "rbf_svr_pred_test1 = best_rbf_svr1.predict(X_test) # Predict the target values\n",
    "\n",
    "cm1 = confusion_matrix(y_test_cat, rbf_svr_pred_test1) # Calculate the confusion matrix\n",
    "specificity1 = cm1[0, 0] / (cm1[0, 0] + cm1[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(random_state = 42) # Initialize the model\n",
    "rf.fit(X_train, y_train_cat) # Fit the model\n",
    "rf_pred_test = rf.predict(X_test) # Predict the target values\n",
    "\n",
    "cm2 = confusion_matrix(y_test_cat, rf_pred_test) # Calculate the confusion matrix\n",
    "specificity2 = cm2[0, 0] / (cm2[0, 0] + cm2[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   Model    Recall  Specificity  Precision  \\\n",
      "0  RBF Kernel SVM (Hyperparamter Tuning)  0.878676     0.940043   0.895131   \n",
      "1               Random Forest (Original)  0.889706     0.950749   0.913208   \n",
      "\n",
      "   Balanced Accuracy  F1-score  \n",
      "0           0.909360  0.886827  \n",
      "1           0.920228  0.901304  \n"
     ]
    }
   ],
   "source": [
    "evaluation_results = {\n",
    "    'Model': ['RBF Kernel SVM (Hyperparamter Tuning)', 'Random Forest (Original)'],\n",
    "    'Recall': [recall_score(y_test_cat, rbf_svr_pred_test1), recall_score(y_test_cat, rf_pred_test)],\n",
    "    'Specificity': [specificity1, specificity2],\n",
    "    'Precision': [precision_score(y_test_cat, rbf_svr_pred_test1), precision_score(y_test_cat, rf_pred_test)],\n",
    "    'Balanced Accuracy': [balanced_accuracy_score(y_test_cat, rbf_svr_pred_test1), balanced_accuracy_score(y_test_cat, rf_pred_test)],\n",
    "    'F1-score': [f1_score(y_test_cat, rbf_svr_pred_test1), f1_score(y_test_cat, rf_pred_test)]\n",
    "    }\n",
    "\n",
    "summary_df = pd.DataFrame(evaluation_results)\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A. The Random Forest classifier with default set up is the best for classification, and this is in line with the previous analyses."
   ]
  }
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