assignment5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Import Data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11304, 41)\n",
      "Represented Test Veh Make          object\n",
      "Aftertreatment Device Desc         object\n",
      "Transmission Overdrive Desc        object\n",
      "Equivalent Test Weight (lbs.)       int64\n",
      "Represented Test Veh Model         object\n",
      "Drive System Description           object\n",
      "Test Veh Displacement (L)         float64\n",
      "Shift Indicator Light Use Desc     object\n",
      "N/V Ratio                         float64\n",
      "Rated Horsepower                    int64\n",
      "Test Veh Configuration #            int64\n",
      "# of Cylinders and Rotors         float64\n",
      "Target Coef B (lbf/mph)           float64\n",
      "Target Coef A (lbf)               float64\n",
      "Test Number                        object\n",
      "CO (g/mi)                         float64\n",
      "Test Category                      object\n",
      "Aftertreatment Device Cd           object\n",
      "THC (g/mi)                        float64\n",
      "CH4 (g/mi)                        float64\n",
      "Test Fuel Type Cd                   int64\n",
      "Model Year                          int64\n",
      "FE_UNIT                            object\n",
      "Set Coef C (lbf/mph**2)           float64\n",
      "Police - Emergency Vehicle?        object\n",
      "Axle Ratio                        float64\n",
      "Test Procedure Description         object\n",
      "Test Fuel Type Description         object\n",
      "Tested Transmission Type           object\n",
      "# of Gears                          int64\n",
      "Set Coef A (lbf)                  float64\n",
      "Test Vehicle ID                    object\n",
      "NOx (g/mi)                        float64\n",
      "RND_ADJ_FE                        float64\n",
      "CO2 (g/mi)                        float64\n",
      "Tested Transmission Type Code      object\n",
      "PM (g/mi)                         float64\n",
      "Vehicle Type                       object\n",
      "Set Coef B (lbf/mph)              float64\n",
      "N2O (g/mi)                        float64\n",
      "Target Coef C (lbf/mph**2)        float64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": "  Represented Test Veh Make Aftertreatment Device Desc  \\\n0              Aston Martin         Three-way catalyst   \n1              Aston Martin         Three-way catalyst   \n2              Aston Martin         Three-way catalyst   \n3              Aston Martin         Three-way catalyst   \n4              Aston Martin         Three-way catalyst   \n\n  Transmission Overdrive Desc  Equivalent Test Weight (lbs.)  \\\n0          Top gear ratio < 1                           4500   \n1          Top gear ratio < 1                           4500   \n2          Top gear ratio < 1                           4500   \n3          Top gear ratio < 1                           4500   \n4          Top gear ratio < 1                           5500   \n\n  Represented Test Veh Model Drive System Description  \\\n0                    DB11 V8      2-Wheel Drive, Rear   \n1                    DB11 V8      2-Wheel Drive, Rear   \n2                        DBS      2-Wheel Drive, Rear   \n3                        DBS      2-Wheel Drive, Rear   \n4                        DBX            4-Wheel Drive   \n\n   Test Veh Displacement (L) Shift Indicator Light Use Desc  N/V Ratio  \\\n0                        4.0                    Not eqipped       22.2   \n1                        4.0                    Not eqipped       22.2   \n2                        5.2                    Not eqipped       23.0   \n3                        5.2                    Not eqipped       23.0   \n4                        4.0                    Not eqipped       21.0   \n\n   Rated Horsepower  ...  Test Vehicle ID  NOx (g/mi)  RND_ADJ_FE  CO2 (g/mi)  \\\n0               503  ...        562TT5348      0.0290        22.7      386.66   \n1               503  ...        562TT5348      0.0020        33.8      259.74   \n2               715  ...       7002PT7056      0.0262        17.7      499.69   \n3               715  ...       7002PT7056      0.0070        30.6      290.25   \n4               550  ...       8001PT8342      0.0090        17.2      521.26   \n\n  Tested Transmission Type Code  PM (g/mi) Vehicle Type Set Coef B (lbf/mph)  \\\n0                            SA        NaN          Car               0.0919   \n1                            SA        NaN          Car               0.0919   \n2                            SA        NaN          Car               0.0807   \n3                            SA        NaN          Car               0.0807   \n4                             A        NaN         Both              -0.5318   \n\n   N2O (g/mi)  Target Coef C (lbf/mph**2)  \n0      0.0064                      0.0271  \n1         NaN                      0.0271  \n2      0.0000                      0.0271  \n3         NaN                      0.0271  \n4         NaN                      0.0373  \n\n[5 rows x 41 columns]",
      "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>Represented Test Veh Make</th>\n      <th>Aftertreatment Device Desc</th>\n      <th>Transmission Overdrive Desc</th>\n      <th>Equivalent Test Weight (lbs.)</th>\n      <th>Represented Test Veh Model</th>\n      <th>Drive System Description</th>\n      <th>Test Veh Displacement (L)</th>\n      <th>Shift Indicator Light Use Desc</th>\n      <th>N/V Ratio</th>\n      <th>Rated Horsepower</th>\n      <th>...</th>\n      <th>Test Vehicle ID</th>\n      <th>NOx (g/mi)</th>\n      <th>RND_ADJ_FE</th>\n      <th>CO2 (g/mi)</th>\n      <th>Tested Transmission Type Code</th>\n      <th>PM (g/mi)</th>\n      <th>Vehicle Type</th>\n      <th>Set Coef B (lbf/mph)</th>\n      <th>N2O (g/mi)</th>\n      <th>Target Coef C (lbf/mph**2)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>Aston Martin</td>\n      <td>Three-way catalyst</td>\n      <td>Top gear ratio &lt; 1</td>\n      <td>4500</td>\n      <td>DB11 V8</td>\n      <td>2-Wheel Drive, Rear</td>\n      <td>4.0</td>\n      <td>Not eqipped</td>\n      <td>22.2</td>\n      <td>503</td>\n      <td>...</td>\n      <td>562TT5348</td>\n      <td>0.0290</td>\n      <td>22.7</td>\n      <td>386.66</td>\n      <td>SA</td>\n      <td>NaN</td>\n      <td>Car</td>\n      <td>0.0919</td>\n      <td>0.0064</td>\n      <td>0.0271</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>Aston Martin</td>\n      <td>Three-way catalyst</td>\n      <td>Top gear ratio &lt; 1</td>\n      <td>4500</td>\n      <td>DB11 V8</td>\n      <td>2-Wheel Drive, Rear</td>\n      <td>4.0</td>\n      <td>Not eqipped</td>\n      <td>22.2</td>\n      <td>503</td>\n      <td>...</td>\n      <td>562TT5348</td>\n      <td>0.0020</td>\n      <td>33.8</td>\n      <td>259.74</td>\n      <td>SA</td>\n      <td>NaN</td>\n      <td>Car</td>\n      <td>0.0919</td>\n      <td>NaN</td>\n      <td>0.0271</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>Aston Martin</td>\n      <td>Three-way catalyst</td>\n      <td>Top gear ratio &lt; 1</td>\n      <td>4500</td>\n      <td>DBS</td>\n      <td>2-Wheel Drive, Rear</td>\n      <td>5.2</td>\n      <td>Not eqipped</td>\n      <td>23.0</td>\n      <td>715</td>\n      <td>...</td>\n      <td>7002PT7056</td>\n      <td>0.0262</td>\n      <td>17.7</td>\n      <td>499.69</td>\n      <td>SA</td>\n      <td>NaN</td>\n      <td>Car</td>\n      <td>0.0807</td>\n      <td>0.0000</td>\n      <td>0.0271</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>Aston Martin</td>\n      <td>Three-way catalyst</td>\n      <td>Top gear ratio &lt; 1</td>\n      <td>4500</td>\n      <td>DBS</td>\n      <td>2-Wheel Drive, Rear</td>\n      <td>5.2</td>\n      <td>Not eqipped</td>\n      <td>23.0</td>\n      <td>715</td>\n      <td>...</td>\n      <td>7002PT7056</td>\n      <td>0.0070</td>\n      <td>30.6</td>\n      <td>290.25</td>\n      <td>SA</td>\n      <td>NaN</td>\n      <td>Car</td>\n      <td>0.0807</td>\n      <td>NaN</td>\n      <td>0.0271</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>Aston Martin</td>\n      <td>Three-way catalyst</td>\n      <td>Top gear ratio &lt; 1</td>\n      <td>5500</td>\n      <td>DBX</td>\n      <td>4-Wheel Drive</td>\n      <td>4.0</td>\n      <td>Not eqipped</td>\n      <td>21.0</td>\n      <td>550</td>\n      <td>...</td>\n      <td>8001PT8342</td>\n      <td>0.0090</td>\n      <td>17.2</td>\n      <td>521.26</td>\n      <td>A</td>\n      <td>NaN</td>\n      <td>Both</td>\n      <td>-0.5318</td>\n      <td>NaN</td>\n      <td>0.0373</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 41 columns</p>\n</div>"
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "import pandas as pd\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df = pd.read_csv('cars_10_16_23.csv', index_col=0)\n",
    "\n",
    "print(df.shape)\n",
    "print(df.dtypes)\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## EDA"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x720 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "\n",
    "# Show the missing values.\n",
    "msno.matrix(df);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x720 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop features which are less meaningful.\n",
    "\n",
    "df_meaningful = df.drop(['Represented Test Veh Make', 'Represented Test Veh Model', 'Test Veh Configuration #', 'Test Number', 'Test Category', 'Aftertreatment Device Cd', 'Model Year', 'FE_UNIT', 'Police - Emergency Vehicle?', 'Test Procedure Description', 'Test Fuel Type Description', 'Test Vehicle ID', 'Test Fuel Type Cd'], axis=1)\n",
    "\n",
    "# Show missing values after dropping above features.\n",
    "msno.matrix(df_meaningful);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9153, 25)\n",
      "Aftertreatment Device Desc         object\n",
      "Transmission Overdrive Desc        object\n",
      "Equivalent Test Weight (lbs.)       int64\n",
      "Drive System Description           object\n",
      "Test Veh Displacement (L)         float64\n",
      "Shift Indicator Light Use Desc     object\n",
      "N/V Ratio                         float64\n",
      "Rated Horsepower                    int64\n",
      "# of Cylinders and Rotors         float64\n",
      "Target Coef B (lbf/mph)           float64\n",
      "Target Coef A (lbf)               float64\n",
      "CO (g/mi)                         float64\n",
      "THC (g/mi)                        float64\n",
      "Set Coef C (lbf/mph**2)           float64\n",
      "Axle Ratio                        float64\n",
      "Tested Transmission Type           object\n",
      "# of Gears                          int64\n",
      "Set Coef A (lbf)                  float64\n",
      "NOx (g/mi)                        float64\n",
      "RND_ADJ_FE                        float64\n",
      "CO2 (g/mi)                        float64\n",
      "Tested Transmission Type Code      object\n",
      "Vehicle Type                       object\n",
      "Set Coef B (lbf/mph)              float64\n",
      "Target Coef C (lbf/mph**2)        float64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": "  Aftertreatment Device Desc Transmission Overdrive Desc  \\\n0         Three-way catalyst          Top gear ratio < 1   \n1         Three-way catalyst          Top gear ratio < 1   \n2         Three-way catalyst          Top gear ratio < 1   \n3         Three-way catalyst          Top gear ratio < 1   \n4         Three-way catalyst          Top gear ratio < 1   \n\n   Equivalent Test Weight (lbs.) Drive System Description  \\\n0                           4500      2-Wheel Drive, Rear   \n1                           4500      2-Wheel Drive, Rear   \n2                           4500      2-Wheel Drive, Rear   \n3                           4500      2-Wheel Drive, Rear   \n4                           5500            4-Wheel Drive   \n\n   Test Veh Displacement (L) Shift Indicator Light Use Desc  N/V Ratio  \\\n0                        4.0                    Not eqipped       22.2   \n1                        4.0                    Not eqipped       22.2   \n2                        5.2                    Not eqipped       23.0   \n3                        5.2                    Not eqipped       23.0   \n4                        4.0                    Not eqipped       21.0   \n\n   Rated Horsepower  # of Cylinders and Rotors  Target Coef B (lbf/mph)  ...  \\\n0               503                        8.0                   0.0169  ...   \n1               503                        8.0                   0.0169  ...   \n2               715                       12.0                   0.0169  ...   \n3               715                       12.0                   0.0169  ...   \n4               550                        8.0                  -0.3286  ...   \n\n   Tested Transmission Type  # of Gears  Set Coef A (lbf)  NOx (g/mi)  \\\n0            Semi-Automatic           8             11.26      0.0290   \n1            Semi-Automatic           8             11.26      0.0020   \n2            Semi-Automatic           8              6.81      0.0262   \n3            Semi-Automatic           8              6.81      0.0070   \n4                 Automatic           9             -4.88      0.0090   \n\n   RND_ADJ_FE CO2 (g/mi)  Tested Transmission Type Code  Vehicle Type  \\\n0        22.7     386.66                             SA           Car   \n1        33.8     259.74                             SA           Car   \n2        17.7     499.69                             SA           Car   \n3        30.6     290.25                             SA           Car   \n4        17.2     521.26                              A          Both   \n\n   Set Coef B (lbf/mph)  Target Coef C (lbf/mph**2)  \n0                0.0919                      0.0271  \n1                0.0919                      0.0271  \n2                0.0807                      0.0271  \n3                0.0807                      0.0271  \n4               -0.5318                      0.0373  \n\n[5 rows x 25 columns]",
      "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>Aftertreatment Device Desc</th>\n      <th>Transmission Overdrive Desc</th>\n      <th>Equivalent Test Weight (lbs.)</th>\n      <th>Drive System Description</th>\n      <th>Test Veh Displacement (L)</th>\n      <th>Shift Indicator Light Use Desc</th>\n      <th>N/V Ratio</th>\n      <th>Rated Horsepower</th>\n      <th># of Cylinders and Rotors</th>\n      <th>Target Coef B (lbf/mph)</th>\n      <th>...</th>\n      <th>Tested Transmission Type</th>\n      <th># of Gears</th>\n      <th>Set Coef A (lbf)</th>\n      <th>NOx (g/mi)</th>\n      <th>RND_ADJ_FE</th>\n      <th>CO2 (g/mi)</th>\n      <th>Tested Transmission Type Code</th>\n      <th>Vehicle Type</th>\n      <th>Set Coef B (lbf/mph)</th>\n      <th>Target Coef C (lbf/mph**2)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>Three-way catalyst</td>\n      <td>Top gear ratio &lt; 1</td>\n      <td>4500</td>\n      <td>2-Wheel Drive, Rear</td>\n      <td>4.0</td>\n      <td>Not eqipped</td>\n      <td>22.2</td>\n      <td>503</td>\n      <td>8.0</td>\n      <td>0.0169</td>\n      <td>...</td>\n      <td>Semi-Automatic</td>\n      <td>8</td>\n      <td>11.26</td>\n      <td>0.0290</td>\n      <td>22.7</td>\n      <td>386.66</td>\n      <td>SA</td>\n      <td>Car</td>\n      <td>0.0919</td>\n      <td>0.0271</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>Three-way catalyst</td>\n      <td>Top gear ratio &lt; 1</td>\n      <td>4500</td>\n      <td>2-Wheel Drive, Rear</td>\n      <td>4.0</td>\n      <td>Not eqipped</td>\n      <td>22.2</td>\n      <td>503</td>\n      <td>8.0</td>\n      <td>0.0169</td>\n      <td>...</td>\n      <td>Semi-Automatic</td>\n      <td>8</td>\n      <td>11.26</td>\n      <td>0.0020</td>\n      <td>33.8</td>\n      <td>259.74</td>\n      <td>SA</td>\n      <td>Car</td>\n      <td>0.0919</td>\n      <td>0.0271</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>Three-way catalyst</td>\n      <td>Top gear ratio &lt; 1</td>\n      <td>4500</td>\n      <td>2-Wheel Drive, Rear</td>\n      <td>5.2</td>\n      <td>Not eqipped</td>\n      <td>23.0</td>\n      <td>715</td>\n      <td>12.0</td>\n      <td>0.0169</td>\n      <td>...</td>\n      <td>Semi-Automatic</td>\n      <td>8</td>\n      <td>6.81</td>\n      <td>0.0262</td>\n      <td>17.7</td>\n      <td>499.69</td>\n      <td>SA</td>\n      <td>Car</td>\n      <td>0.0807</td>\n      <td>0.0271</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>Three-way catalyst</td>\n      <td>Top gear ratio &lt; 1</td>\n      <td>4500</td>\n      <td>2-Wheel Drive, Rear</td>\n      <td>5.2</td>\n      <td>Not eqipped</td>\n      <td>23.0</td>\n      <td>715</td>\n      <td>12.0</td>\n      <td>0.0169</td>\n      <td>...</td>\n      <td>Semi-Automatic</td>\n      <td>8</td>\n      <td>6.81</td>\n      <td>0.0070</td>\n      <td>30.6</td>\n      <td>290.25</td>\n      <td>SA</td>\n      <td>Car</td>\n      <td>0.0807</td>\n      <td>0.0271</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>Three-way catalyst</td>\n      <td>Top gear ratio &lt; 1</td>\n      <td>5500</td>\n      <td>4-Wheel Drive</td>\n      <td>4.0</td>\n      <td>Not eqipped</td>\n      <td>21.0</td>\n      <td>550</td>\n      <td>8.0</td>\n      <td>-0.3286</td>\n      <td>...</td>\n      <td>Automatic</td>\n      <td>9</td>\n      <td>-4.88</td>\n      <td>0.0090</td>\n      <td>17.2</td>\n      <td>521.26</td>\n      <td>A</td>\n      <td>Both</td>\n      <td>-0.5318</td>\n      <td>0.0373</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 25 columns</p>\n</div>"
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop features with too many missing values and then drop NA.\n",
    "df_dna = df_meaningful.drop(['CH4 (g/mi)', 'PM (g/mi)', 'N2O (g/mi)'], axis=1)\n",
    "df_dna.dropna(inplace=True)\n",
    "\n",
    "# Show number of rows and columns\n",
    "print(df_dna.shape)\n",
    "print(df_dna.dtypes)\n",
    "df_dna.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0    8194\n",
      "-1     608\n",
      "-2     287\n",
      "-3      64\n",
      "Name: all_out, dtype: int64\n",
      "(8802, 25)\n",
      "Aftertreatment Device Desc         object\n",
      "Transmission Overdrive Desc        object\n",
      "Equivalent Test Weight (lbs.)       int64\n",
      "Drive System Description           object\n",
      "Test Veh Displacement (L)         float64\n",
      "Shift Indicator Light Use Desc     object\n",
      "N/V Ratio                         float64\n",
      "Rated Horsepower                    int64\n",
      "# of Cylinders and Rotors         float64\n",
      "Target Coef B (lbf/mph)           float64\n",
      "Target Coef A (lbf)               float64\n",
      "CO (g/mi)                         float64\n",
      "THC (g/mi)                        float64\n",
      "Set Coef C (lbf/mph**2)           float64\n",
      "Axle Ratio                        float64\n",
      "Tested Transmission Type           object\n",
      "# of Gears                          int64\n",
      "Set Coef A (lbf)                  float64\n",
      "NOx (g/mi)                        float64\n",
      "RND_ADJ_FE                        float64\n",
      "CO2 (g/mi)                        float64\n",
      "Tested Transmission Type Code      object\n",
      "Vehicle Type                       object\n",
      "Set Coef B (lbf/mph)              float64\n",
      "Target Coef C (lbf/mph**2)        float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Drop outliers by features in assignment 4.\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "\n",
    "df_ass4 = df_dna[['Rated Horsepower', 'Equivalent Test Weight (lbs.)', 'RND_ADJ_FE', 'CO2 (g/mi)']]\n",
    "\n",
    "# Identify outliers using LocalOutlierFactor.\n",
    "lof = LocalOutlierFactor(n_neighbors=20, contamination=0.05)\n",
    "y_pred_lof = lof.fit_predict(df_ass4)\n",
    "\n",
    "# Identify outliers using IsolationForest.\n",
    "iforest = IsolationForest(n_estimators=100,  contamination=0.05)\n",
    "y_pred_if = iforest.fit_predict(df_ass4)\n",
    "\n",
    "# Identify outliers using EllipticEnvelope.\n",
    "rob_cov = EllipticEnvelope(contamination=0.05)\n",
    "y_pred_rob = rob_cov.fit_predict(df_ass4)\n",
    "\n",
    "df_ass4[\"y_pred_lof\"] = y_pred_lof\n",
    "df_ass4[\"y_pred_if\"] = y_pred_if\n",
    "df_ass4[\"y_pred_rob\"] = y_pred_rob\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# Converting them to a binary -1, 0.\n",
    "# Where -1 denotes outlier\n",
    "# The purpose is to then add these columns and find out which rows were identified as outliers from multiple methods\n",
    "# -----------------------------------------------------------------------------\n",
    "df_ass4[\"y_pred_lof_2\"] = np.where(df_ass4[\"y_pred_lof\"]<0, -1, 0)\n",
    "df_ass4[\"y_pred_if_2\"] = np.where(df_ass4[\"y_pred_if\"]<0, -1, 0)\n",
    "df_ass4[\"y_pred_rob_2\"] = np.where(df_ass4[\"y_pred_rob\"]<0, -1, 0)\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# List of food items identified as outliers based by at least two methods\n",
    "# -----------------------------------------------------------------------------\n",
    "df_ass4[\"all_out\"] = df_ass4.loc[:,[\"y_pred_if_2\",\"y_pred_rob_2\",\"y_pred_lof_2\"]].sum(axis = 1)\n",
    "print(df_ass4[\"all_out\"].value_counts())\n",
    "\n",
    "# Dop outliers.\n",
    "df_dol = df_dna[df_ass4['all_out'] >= -1]\n",
    "\n",
    "# Show number of rows and columns after dropping outliers\n",
    "print(df_dol.shape)\n",
    "print(df_dol.dtypes)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Before Training"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "outputs": [
    {
     "data": {
      "text/plain": "(8800, 25)"
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove all vehicles with mpg > 120\n",
    "df_dmpl = df_dol.drop(df_dol['RND_ADJ_FE'] > 120)\n",
    "\n",
    "df_dmpl.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "outputs": [
    {
     "data": {
      "text/plain": "(8800, 23)"
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify features and responses.\n",
    "# In both y_CO2 and y_CO, 0 means Other and 1 means High.\n",
    "X = df_dmpl.drop(['CO2 (g/mi)', 'CO (g/mi)'], axis=1)\n",
    "y_CO2 = np.where(df_dmpl['CO2 (g/mi)'] > 600, 1, 0)\n",
    "y_CO = np.where(df_dmpl['CO (g/mi)'] > 1, 1, 0)\n",
    "X.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8800, 15) (8800, 38) (8800, 53)\n"
     ]
    }
   ],
   "source": [
    "# Encode categorical features.\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ohe = OneHotEncoder(sparse=False)\n",
    "X_num = df_dmpl.select_dtypes(include=[np.float])\n",
    "X_num.reset_index(inplace=True, drop=True)\n",
    "scaler = StandardScaler()\n",
    "X_num = pd.DataFrame(scaler.fit_transform(X_num))\n",
    "\n",
    "X_cat = df_dmpl.select_dtypes(include=[np.object])\n",
    "X_cat.reset_index(inplace=True, drop=True)\n",
    "\n",
    "X_cat = pd.DataFrame(ohe.fit_transform(X_cat))\n",
    "X_final = pd.concat([X_num, X_cat], axis=1)\n",
    "X_final.reset_index(inplace=True, drop=True)\n",
    "X_final.set_axis(list(range(X_final.shape[1])), inplace=True, axis=1)\n",
    "print(X_num.shape, X_cat.shape, X_final.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Train model with CO2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "outputs": [],
   "source": [
    "# Train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_final, y_CO2, test_size = 0.2, random_state=42)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## <font color = green> Decision tree"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1757\n",
      "           1       1.00      1.00      1.00         3\n",
      "\n",
      "    accuracy                           1.00      1760\n",
      "   macro avg       1.00      1.00      1.00      1760\n",
      "weighted avg       1.00      1.00      1.00      1760\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "y_test        0  1\ny_pred_dt         \n0          1757  0\n1             0  3",
      "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>y_test</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>y_pred_dt</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>1757</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.utils import class_weight\n",
    "\n",
    "# ---------------------------------------------------------------\n",
    "# Train a decision tree classifier on the balanced dataset\n",
    "# ---------------------------------------------------------------\n",
    "\n",
    "clf = DecisionTreeClassifier(criterion='gini',\n",
    "                             min_samples_split=5,\n",
    "                             min_samples_leaf=5)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# --------------------------------------------\n",
    "# Evaluate the classifier on the testing set\n",
    "# --------------------------------------------\n",
    "y_pred_dt = clf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred_dt))\n",
    "\n",
    "# confusion_matrix(y_test, y_pred_dt)\n",
    "\n",
    "# Confusion Matrix\n",
    "pd.crosstab(y_pred_dt, y_test, rownames =['y_pred_dt'], colnames = ['y_test'] )\n",
    "\n",
    "\n",
    "# -------------------------------------------------------\n",
    "# Notes for reading the classification report\n",
    "# -------------------------------------------------------\n",
    "\n",
    "# --------------\n",
    "# Precision:\n",
    "# --------------\n",
    "# Measures the proportion of true positives (TP) out of all predicted positives (TP + false positives (FP)).\n",
    "# A high precision means the model makes few false positive predictions.\n",
    "\n",
    "# --------------\n",
    "# Recall:\n",
    "# --------------\n",
    "# Measures the proportion of true positives (TP) out of all actual positives (TP + false negatives (FN)). \\\n",
    "# A high recall indicates that the model makes few false negative predictions.\n",
    "\n",
    "# --------------\n",
    "# F1-score:\n",
    "# --------------\n",
    "# It is the harmonic mean of precision and recall, calculated as (2 * precision * recall) / (precision + recall).\n",
    "# It is useful especially when the classes are imbalanced\n",
    "\n",
    "# --------------\n",
    "# Support:\n",
    "# --------------\n",
    "# It is the number of observations in each class.\n",
    "\n",
    "# --------------\n",
    "# Weighted Average:\n",
    "# --------------\n",
    "# It is calculated as the average of the precision, recall etc. weighted by the number of samples in each class.\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## <font color = green> Random Forest"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.9988636363636364\n",
      "Random Forest Precision: 1.0\n",
      "Random Forest Recall: 0.3333333333333333\n",
      "Random Forest F1 Score: 0.5\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1757\n",
      "           1       1.00      0.33      0.50         3\n",
      "\n",
      "    accuracy                           1.00      1760\n",
      "   macro avg       1.00      0.67      0.75      1760\n",
      "weighted avg       1.00      1.00      1.00      1760\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "y_test        0  1\ny_pred_rf         \n0          1757  2\n1             0  1",
      "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>y_test</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>y_pred_rf</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>1757</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "# ------------------\n",
    "# Define the RF model\n",
    "# ------------------\n",
    "rf = RandomForestClassifier(n_estimators=1000,\n",
    "                            criterion='entropy',\n",
    "                            min_samples_split=5,\n",
    "                            min_samples_leaf=5,\n",
    "                            random_state=100)\n",
    "\n",
    "# ------------------\n",
    "# Train the models\n",
    "# ------------------\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# ------------------------------------\n",
    "# Make predictions on the test set\n",
    "# ------------------------------------\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "\n",
    "# ------------------------------------------------------------------------\n",
    "# Evaluate the model using accuracy, precision, recall, and F1 score\n",
    "# ------------------------------------------------------------------------\n",
    "print('Random Forest Accuracy:', accuracy_score(y_test, y_pred_rf))\n",
    "print('Random Forest Precision:', precision_score(y_test, y_pred_rf))\n",
    "print('Random Forest Recall:', recall_score(y_test, y_pred_rf))\n",
    "print('Random Forest F1 Score:', f1_score(y_test, y_pred_rf))\n",
    "\n",
    "print(classification_report(y_test, y_pred_rf))\n",
    "\n",
    "# Confusion Matrix\n",
    "pd.crosstab(y_pred_rf, y_test, rownames =['y_pred_rf'], colnames = ['y_test'] )"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## <font color = darkgreen> Gradient Boosting Classifier"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Accuracy: 1.0\n",
      "Gradient Boosting Precision: 1.0\n",
      "Gradient Boosting Recall: 1.0\n",
      "Gradient Boosting F1 Score: 1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1757\n",
      "           1       1.00      1.00      1.00         3\n",
      "\n",
      "    accuracy                           1.00      1760\n",
      "   macro avg       1.00      1.00      1.00      1760\n",
      "weighted avg       1.00      1.00      1.00      1760\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "y_test        0  1\ny_pred_gb         \n0          1757  0\n1             0  3",
      "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>y_test</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>y_pred_gb</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>1757</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# ------------------\n",
    "# Define the GB model\n",
    "# ------------------\n",
    "gb = GradientBoostingClassifier(n_estimators=500,\n",
    "                            min_samples_split=5,\n",
    "                            learning_rate=0.5,\n",
    "                            min_samples_leaf=5,\n",
    "                            random_state=100)\n",
    "\n",
    "# ------------------\n",
    "# Train the model\n",
    "# ------------------\n",
    "gb.fit(X_train, y_train)\n",
    "\n",
    "# ------------------------------------\n",
    "# Make predictions on the test set\n",
    "# ------------------------------------\n",
    "y_pred_gb = gb.predict(X_test)\n",
    "\n",
    "# ------------------------------------------------------------------------\n",
    "# Evaluate the model using accuracy, precision, recall, and F1 score\n",
    "# ------------------------------------------------------------------------\n",
    "\n",
    "print('Gradient Boosting Accuracy:', accuracy_score(y_test, y_pred_gb))\n",
    "print('Gradient Boosting Precision:', precision_score(y_test, y_pred_gb))\n",
    "print('Gradient Boosting Recall:', recall_score(y_test, y_pred_gb))\n",
    "print('Gradient Boosting F1 Score:', f1_score(y_test, y_pred_gb))\n",
    "\n",
    "print(classification_report(y_test, y_pred_gb))\n",
    "# Confusion Matrix\n",
    "pd.crosstab(y_pred_gb, y_test, rownames =['y_pred_gb'], colnames = ['y_test'] )"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## <font color = Green> XGBoost"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB: 0.9982954545454545\n",
      "XGB: 0.0\n",
      "XGB: 0.0\n",
      "XGB: 0.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1757\n",
      "           1       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           1.00      1760\n",
      "   macro avg       0.50      0.50      0.50      1760\n",
      "weighted avg       1.00      1.00      1.00      1760\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "y_test         0  1\ny_pred_xgb         \n0           1757  3",
      "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>y_test</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>y_pred_xgb</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>1757</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "# ------------------\n",
    "# Define the XGB model\n",
    "# ------------------\n",
    "xgb = xgb.XGBClassifier(n_estimators=100,\n",
    "                        max_depth = 10,\n",
    "                        eta= 0.01,\n",
    "                        min_child_weight = 5,\n",
    "                        random_state=100)\n",
    "\n",
    "# ------------------\n",
    "# Train the model\n",
    "# ------------------\n",
    "xgb.fit(X_train, y_train)\n",
    "\n",
    "# ------------------------------------\n",
    "# Make predictions on the test set\n",
    "# ------------------------------------\n",
    "y_pred_xgb = xgb.predict(X_test)\n",
    "\n",
    "# ------------------------------------------------------------------------\n",
    "# Evaluate the model using accuracy, precision, recall, and F1 score\n",
    "# ------------------------------------------------------------------------\n",
    "\n",
    "print('XGB:', accuracy_score(y_test, y_pred_xgb))\n",
    "print('XGB:', precision_score(y_test, y_pred_xgb))\n",
    "print('XGB:', recall_score(y_test, y_pred_xgb))\n",
    "print('XGB:', f1_score(y_test, y_pred_xgb))\n",
    "\n",
    "print(classification_report(y_test, y_pred_xgb))\n",
    "# Confusion Matrix\n",
    "pd.crosstab(y_pred_xgb, y_test, rownames =['y_pred_xgb'], colnames = ['y_test'] )"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Analysis\n",
    "\n",
    "Decision Tree and Gradient Boost performs perfect in this problem while Random Forest predicts every observation to be Other and XGBoost makes some mistake on predicting some Other to be High."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Train model with CO"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "outputs": [],
   "source": [
    "# Train test split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_final, y_CO, test_size=0.2, random_state=42)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "## <font color = green> Decision tree"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1671\n",
      "           1       1.00      1.00      1.00        89\n",
      "\n",
      "    accuracy                           1.00      1760\n",
      "   macro avg       1.00      1.00      1.00      1760\n",
      "weighted avg       1.00      1.00      1.00      1760\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "y_test        0   1\ny_pred_dt          \n0          1671   0\n1             0  89",
      "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>y_test</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>y_pred_dt</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>1671</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0</td>\n      <td>89</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# ---------------------------------------------------------------\n",
    "# Train a decision tree classifier on the balanced dataset\n",
    "# ---------------------------------------------------------------\n",
    "\n",
    "clf = DecisionTreeClassifier(criterion='gini',\n",
    "\t\t\t\t\t\t\t min_samples_split=5,\n",
    "\t\t\t\t\t\t\t min_samples_leaf=5)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# --------------------------------------------\n",
    "# Evaluate the classifier on the testing set\n",
    "# --------------------------------------------\n",
    "y_pred_dt = clf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred_dt))\n",
    "\n",
    "# confusion_matrix(y_test, y_pred_dt)\n",
    "\n",
    "# Confusion Matrix\n",
    "pd.crosstab(y_pred_dt, y_test, rownames=['y_pred_dt'], colnames=['y_test'])\n",
    "\n",
    "# -------------------------------------------------------\n",
    "# Notes for reading the classification report\n",
    "# -------------------------------------------------------\n",
    "\n",
    "# --------------\n",
    "# Precision:\n",
    "# --------------\n",
    "# Measures the proportion of true positives (TP) out of all predicted positives (TP + false positives (FP)).\n",
    "# A high precision means the model makes few false positive predictions.\n",
    "\n",
    "# --------------\n",
    "# Recall:\n",
    "# --------------\n",
    "# Measures the proportion of true positives (TP) out of all actual positives (TP + false negatives (FN)). \\\n",
    "# A high recall indicates that the model makes few false negative predictions.\n",
    "\n",
    "# --------------\n",
    "# F1-score:\n",
    "# --------------\n",
    "# It is the harmonic mean of precision and recall, calculated as (2 * precision * recall) / (precision + recall).\n",
    "# It is useful especially when the classes are imbalanced\n",
    "\n",
    "# --------------\n",
    "# Support:\n",
    "# --------------\n",
    "# It is the number of observations in each class.\n",
    "\n",
    "# --------------\n",
    "# Weighted Average:\n",
    "# --------------\n",
    "# It is calculated as the average of the precision, recall etc. weighted by the number of samples in each class."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "## <font color = green> Random Forest"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 1.0\n",
      "Random Forest Precision: 1.0\n",
      "Random Forest Recall: 1.0\n",
      "Random Forest F1 Score: 1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1671\n",
      "           1       1.00      1.00      1.00        89\n",
      "\n",
      "    accuracy                           1.00      1760\n",
      "   macro avg       1.00      1.00      1.00      1760\n",
      "weighted avg       1.00      1.00      1.00      1760\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "y_test        0   1\ny_pred_rf          \n0          1671   0\n1             0  89",
      "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>y_test</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>y_pred_rf</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>1671</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0</td>\n      <td>89</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ------------------\n",
    "# Define the RF model\n",
    "# ------------------\n",
    "rf = RandomForestClassifier(n_estimators=1000,\n",
    "\t\t\t\t\t\t\tcriterion='entropy',\n",
    "\t\t\t\t\t\t\tmin_samples_split=5,\n",
    "\t\t\t\t\t\t\tmin_samples_leaf=5,\n",
    "\t\t\t\t\t\t\trandom_state=100)\n",
    "\n",
    "# ------------------\n",
    "# Train the models\n",
    "# ------------------\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# ------------------------------------\n",
    "# Make predictions on the test set\n",
    "# ------------------------------------\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "\n",
    "# ------------------------------------------------------------------------\n",
    "# Evaluate the model using accuracy, precision, recall, and F1 score\n",
    "# ------------------------------------------------------------------------\n",
    "print('Random Forest Accuracy:', accuracy_score(y_test, y_pred_rf))\n",
    "print('Random Forest Precision:', precision_score(y_test, y_pred_rf))\n",
    "print('Random Forest Recall:', recall_score(y_test, y_pred_rf))\n",
    "print('Random Forest F1 Score:', f1_score(y_test, y_pred_rf))\n",
    "\n",
    "print(classification_report(y_test, y_pred_rf))\n",
    "\n",
    "# Confusion Matrix\n",
    "pd.crosstab(y_pred_rf, y_test, rownames=['y_pred_rf'], colnames=['y_test'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "## <font color = darkgreen> Gradient Boosting Classifier"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Accuracy: 1.0\n",
      "Gradient Boosting Precision: 1.0\n",
      "Gradient Boosting Recall: 1.0\n",
      "Gradient Boosting F1 Score: 1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1671\n",
      "           1       1.00      1.00      1.00        89\n",
      "\n",
      "    accuracy                           1.00      1760\n",
      "   macro avg       1.00      1.00      1.00      1760\n",
      "weighted avg       1.00      1.00      1.00      1760\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "y_test        0   1\ny_pred_gb          \n0          1671   0\n1             0  89",
      "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>y_test</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>y_pred_gb</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>1671</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0</td>\n      <td>89</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ------------------\n",
    "# Define the GB model\n",
    "# ------------------\n",
    "gb = GradientBoostingClassifier(n_estimators=500,\n",
    "\t\t\t\t\t\t\t\tmin_samples_split=5,\n",
    "\t\t\t\t\t\t\t\tlearning_rate=0.5,\n",
    "\t\t\t\t\t\t\t\tmin_samples_leaf=5,\n",
    "\t\t\t\t\t\t\t\trandom_state=100)\n",
    "\n",
    "# ------------------\n",
    "# Train the model\n",
    "# ------------------\n",
    "gb.fit(X_train, y_train)\n",
    "\n",
    "# ------------------------------------\n",
    "# Make predictions on the test set\n",
    "# ------------------------------------\n",
    "y_pred_gb = gb.predict(X_test)\n",
    "\n",
    "# ------------------------------------------------------------------------\n",
    "# Evaluate the model using accuracy, precision, recall, and F1 score\n",
    "# ------------------------------------------------------------------------\n",
    "\n",
    "print('Gradient Boosting Accuracy:', accuracy_score(y_test, y_pred_gb))\n",
    "print('Gradient Boosting Precision:', precision_score(y_test, y_pred_gb))\n",
    "print('Gradient Boosting Recall:', recall_score(y_test, y_pred_gb))\n",
    "print('Gradient Boosting F1 Score:', f1_score(y_test, y_pred_gb))\n",
    "\n",
    "print(classification_report(y_test, y_pred_gb))\n",
    "# Confusion Matrix\n",
    "pd.crosstab(y_pred_gb, y_test, rownames=['y_pred_gb'], colnames=['y_test'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "## <font color = Green> XGBoost"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB: 1.0\n",
      "XGB: 1.0\n",
      "XGB: 1.0\n",
      "XGB: 1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1671\n",
      "           1       1.00      1.00      1.00        89\n",
      "\n",
      "    accuracy                           1.00      1760\n",
      "   macro avg       1.00      1.00      1.00      1760\n",
      "weighted avg       1.00      1.00      1.00      1760\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "y_test         0   1\ny_pred_xgb          \n0           1671   0\n1              0  89",
      "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>y_test</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>y_pred_xgb</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>1671</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0</td>\n      <td>89</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "# ------------------\n",
    "# Define the XGB model\n",
    "# ------------------\n",
    "xgb = xgb.XGBClassifier(n_estimators=100,\n",
    "\t\t\t\t\t\tmax_depth=10,\n",
    "\t\t\t\t\t\teta=0.01,\n",
    "\t\t\t\t\t\tmin_child_weight=5,\n",
    "\t\t\t\t\t\trandom_state=100)\n",
    "\n",
    "# ------------------\n",
    "# Train the model\n",
    "# ------------------\n",
    "xgb.fit(X_train, y_train)\n",
    "\n",
    "# ------------------------------------\n",
    "# Make predictions on the test set\n",
    "# ------------------------------------\n",
    "y_pred_xgb = xgb.predict(X_test)\n",
    "\n",
    "# ------------------------------------------------------------------------\n",
    "# Evaluate the model using accuracy, precision, recall, and F1 score\n",
    "# ------------------------------------------------------------------------\n",
    "\n",
    "print('XGB:', accuracy_score(y_test, y_pred_xgb))\n",
    "print('XGB:', precision_score(y_test, y_pred_xgb))\n",
    "print('XGB:', recall_score(y_test, y_pred_xgb))\n",
    "print('XGB:', f1_score(y_test, y_pred_xgb))\n",
    "\n",
    "print(classification_report(y_test, y_pred_xgb))\n",
    "# Confusion Matrix\n",
    "pd.crosstab(y_pred_xgb, y_test, rownames=['y_pred_xgb'], colnames=['y_test'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Compare Pollutant Level"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2877, 2) (4213, 2) (2984, 2)\n"
     ]
    }
   ],
   "source": [
    "# Get pollutants of year 2023, 2016, and 2010.\n",
    "\n",
    "pollutant_2023 = df.loc[df['Model Year'] == 2023, ['CO2 (g/mi)', 'CO (g/mi)']].copy()\n",
    "pollutant_2023.dropna(inplace=True)\n",
    "pollutant_2016 = df.loc[df['Model Year'] == 2016, ['CO2 (g/mi)', 'CO (g/mi)']].copy()\n",
    "pollutant_2016.dropna(inplace=True)\n",
    "pollutant_2010 = df.loc[df['Model Year'] == 2010, ['CO2 (g/mi)', 'CO (g/mi)']].copy()\n",
    "pollutant_2010.dropna(inplace=True)\n",
    "\n",
    "print(pollutant_2023.shape, pollutant_2016.shape, pollutant_2010.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([4.36271707, 0.84888014]), array([0.99999358, 0.80202601]))\n",
      "(array([-18.08112184,  -2.97461647]), array([2.24428186e-73, 1.46677536e-03]))\n"
     ]
    }
   ],
   "source": [
    "# Perform the one-sided z test on data of 2023, 2016, and 2010.\n",
    "\n",
    "from statsmodels.stats.weightstats import ztest\n",
    "\n",
    "print(ztest(pollutant_2023, pollutant_2016, value=0, alternative='smaller'))\n",
    "print(ztest(pollutant_2016, pollutant_2010, value=0, alternative='smaller'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Conclusion on Pollutant Level\n",
    "\n",
    "The z test above shows that from 2010 to 2016, the pollutant level for both CO2 and CO decreased. From 2016 to 2023, the pollutant level of CO2 increased while there isn't sufficient evidence to determine the change on CO."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Nearest neighbor"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "outputs": [
    {
     "data": {
      "text/plain": "['Test Veh Displacement (L)',\n 'N/V Ratio',\n '# of Cylinders and Rotors',\n 'Target Coef B (lbf/mph)',\n 'Target Coef A (lbf)',\n 'CO (g/mi)',\n 'THC (g/mi)',\n 'Set Coef C (lbf/mph**2)',\n 'Axle Ratio',\n 'Set Coef A (lbf)',\n 'NOx (g/mi)',\n 'RND_ADJ_FE',\n 'CO2 (g/mi)',\n 'Set Coef B (lbf/mph)',\n 'Target Coef C (lbf/mph**2)',\n 'Represented Test Veh Model',\n 'Model Year']"
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the name of numeric features\n",
    "\n",
    "feature_name = list(df_dmpl.select_dtypes(include=[np.float]).columns)\n",
    "feature_name.append('Represented Test Veh Model')\n",
    "feature_name.append('Model Year')\n",
    "feature_name"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MALIBU', 'MAZDA3', 'C 300', 'C 300 4MATIC', 'E 350', 'E 350 4MATIC', 'Cayenne', 'Cayenne GTS', 'Cayenne S', 'Panamera 4S', 'GX 460', '4RUNNER 2WD', '4RUNNER 4WD', 'CAMRY', 'SEQUOIA 2WD', 'SEQUOIA 4WD', 'A3', 'A4', 'A6', 'Q5', 'GTI', 'Jetta']\n"
     ]
    }
   ],
   "source": [
    "# Find the models that appear in all three years.\n",
    "df_nn = df[feature_name].copy()\n",
    "df_nn = df_nn.reset_index(drop=True)\n",
    "df_nn = df_nn.dropna()\n",
    "\n",
    "memo = {}\n",
    "for idx, row in df_nn.iterrows():\n",
    "    if row['Represented Test Veh Model'] in memo:\n",
    "        memo[row['Represented Test Veh Model']].add(row['Model Year'])\n",
    "    else:\n",
    "        memo[row['Represented Test Veh Model']] = {row['Model Year']}\n",
    "\n",
    "print([x for x in memo if len(memo[x]) == 3])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4RUNNER 4WD\n",
      "2716     2023\n",
      "2717     2023\n",
      "2718     2023\n",
      "2719     2023\n",
      "2720     2023\n",
      "2721     2023\n",
      "6171     2010\n",
      "6172     2010\n",
      "10605    2016\n",
      "10606    2016\n",
      "10607    2016\n",
      "10608    2016\n",
      "10609    2016\n",
      "Name: Model Year, dtype: int64\n",
      "\n",
      "CAMRY\n",
      "2734     2023\n",
      "2735     2023\n",
      "6175     2010\n",
      "6176     2010\n",
      "6177     2010\n",
      "6178     2010\n",
      "6179     2010\n",
      "6180     2010\n",
      "6181     2010\n",
      "6182     2010\n",
      "10610    2016\n",
      "10611    2016\n",
      "10612    2016\n",
      "10613    2016\n",
      "Name: Model Year, dtype: int64\n",
      "\n",
      "Cayenne\n",
      "2373     2023\n",
      "2374     2023\n",
      "5909     2010\n",
      "5910     2010\n",
      "5911     2010\n",
      "5912     2010\n",
      "10155    2016\n",
      "10156    2016\n",
      "Name: Model Year, dtype: int64\n",
      "\n",
      "SEQUOIA 4WD\n",
      "2892     2023\n",
      "2893     2023\n",
      "2894     2023\n",
      "2895     2023\n",
      "6292     2010\n",
      "6293     2010\n",
      "6294     2010\n",
      "6295     2010\n",
      "6296     2010\n",
      "6297     2010\n",
      "6298     2010\n",
      "6299     2010\n",
      "6300     2010\n",
      "6301     2010\n",
      "6302     2010\n",
      "6303     2010\n",
      "6304     2010\n",
      "6305     2010\n",
      "10792    2016\n",
      "10793    2016\n",
      "10794    2016\n",
      "10795    2016\n",
      "Name: Model Year, dtype: int64\n",
      "\n",
      "MAZDA3\n",
      "2027    2023\n",
      "2028    2023\n",
      "5074    2010\n",
      "5075    2010\n",
      "5076    2010\n",
      "5077    2010\n",
      "5078    2010\n",
      "5079    2010\n",
      "5084    2010\n",
      "5085    2010\n",
      "5086    2010\n",
      "5087    2010\n",
      "5088    2010\n",
      "5089    2010\n",
      "9347    2016\n",
      "9348    2016\n",
      "9349    2016\n",
      "9350    2016\n",
      "9351    2016\n",
      "9352    2016\n",
      "9353    2016\n",
      "9354    2016\n",
      "9355    2016\n",
      "9356    2016\n",
      "9357    2016\n",
      "9358    2016\n",
      "9359    2016\n",
      "9360    2016\n",
      "9361    2016\n",
      "9362    2016\n",
      "9363    2016\n",
      "9364    2016\n",
      "9365    2016\n",
      "9366    2016\n",
      "9367    2016\n",
      "9368    2016\n",
      "9369    2016\n",
      "9370    2016\n",
      "9371    2016\n",
      "9372    2016\n",
      "9373    2016\n",
      "9374    2016\n",
      "9375    2016\n",
      "9376    2016\n",
      "Name: Model Year, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Find the indices of interested vehicles.\n",
    "\n",
    "vehicles_name = ['4RUNNER 4WD', 'CAMRY', 'Cayenne', 'SEQUOIA 4WD', 'MAZDA3']\n",
    "\n",
    "for name in vehicles_name:\n",
    "    print(name)\n",
    "    print(df_nn[df_nn['Represented Test Veh Model'] == name]['Model Year'])\n",
    "    print()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "outputs": [],
   "source": [
    "# Split data into three years.\n",
    "\n",
    "data = {}\n",
    "data[2010] = df_nn.loc[df_nn['Model Year'] == 2010]\n",
    "data[2016] = df_nn.loc[df_nn['Model Year'] == 2016]\n",
    "data[2023] = df_nn.loc[df_nn['Model Year'] == 2023]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now for vehicle: 4RUNNER 4WD\n",
      "\n",
      "In year: 2010\n",
      "Ball tree: the nearest vehicles to Row 6171 are:\n",
      "6171       4RUNNER 4WD\n",
      "6604        V50 T5 AWD\n",
      "6221    FJ CRUISER 4WD\n",
      "3771             535iA\n",
      "4474             1LH26\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         4.57916824 5.95252186 6.26356923 6.43569375]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 6171 are:\n",
      "6171       4RUNNER 4WD\n",
      "6604        V50 T5 AWD\n",
      "6221    FJ CRUISER 4WD\n",
      "3771             535iA\n",
      "4474             1LH26\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         4.57916824 5.95252186 6.26356923 6.43569375]]\n",
      "\n",
      "In year: 2016\n",
      "Ball tree: the nearest vehicles to Row 10605 are:\n",
      "10605              4RUNNER 4WD\n",
      "10608              4RUNNER 4WD\n",
      "10606              4RUNNER 4WD\n",
      "10946             Passat wagon\n",
      "10664    HIGHLANDER HYBRID AWD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         2.62836455 2.6502302  5.23868343 5.25450975]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 10605 are:\n",
      "10605              4RUNNER 4WD\n",
      "10608              4RUNNER 4WD\n",
      "10606              4RUNNER 4WD\n",
      "10946             Passat wagon\n",
      "10664    HIGHLANDER HYBRID AWD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         2.62836455 2.6502302  5.23868343 5.25450975]]\n",
      "\n",
      "In year: 2023\n",
      "Ball tree: the nearest vehicles to Row 2716 are:\n",
      "2716        4RUNNER 4WD\n",
      "1408                G80\n",
      "1406                G80\n",
      "1439               GV70\n",
      "758     F150 Tremor 4WD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         4.96598023 5.28512661 5.38695338 5.82412211]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 2716 are:\n",
      "2716        4RUNNER 4WD\n",
      "1408                G80\n",
      "1406                G80\n",
      "1439               GV70\n",
      "758     F150 Tremor 4WD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         4.96598023 5.28512661 5.38695338 5.82412211]]\n",
      "\n",
      "Now for vehicle: CAMRY\n",
      "\n",
      "In year: 2010\n",
      "Ball tree: the nearest vehicles to Row 6175 are:\n",
      "6175      CAMRY\n",
      "6527     Passat\n",
      "4470      1LH26\n",
      "4952    BORREGO\n",
      "6388         CC\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         3.13722194 3.92650377 4.27480024 4.78735184]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 6175 are:\n",
      "6175      CAMRY\n",
      "6527     Passat\n",
      "4470      1LH26\n",
      "4952    BORREGO\n",
      "6388         CC\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         3.13722194 3.92650377 4.27480024 4.78735184]]\n",
      "\n",
      "In year: 2016\n",
      "Ball tree: the nearest vehicles to Row 10610 are:\n",
      "10610           CAMRY\n",
      "11208          Passat\n",
      "11212          Passat\n",
      "7824     Renegade 4x4\n",
      "7826     Renegade 4x4\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         2.69605459 2.78549475 3.20762477 3.26033484]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 10610 are:\n",
      "10610           CAMRY\n",
      "11208          Passat\n",
      "11212          Passat\n",
      "7824     Renegade 4x4\n",
      "7826     Renegade 4x4\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         2.69605459 2.78549475 3.20762477 3.26033484]]\n",
      "\n",
      "In year: 2023\n",
      "Ball tree: the nearest vehicles to Row 2734 are:\n",
      "2734          CAMRY\n",
      "2614         ES 350\n",
      "2778      CAMRY XSE\n",
      "1207     ACADIA FWD\n",
      "2555    OUTBACK AWD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         2.444801   4.92464757 5.06005547 5.16027698]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 2734 are:\n",
      "2734          CAMRY\n",
      "2614         ES 350\n",
      "2778      CAMRY XSE\n",
      "1207     ACADIA FWD\n",
      "2555    OUTBACK AWD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         2.444801   4.92464757 5.06005547 5.16027698]]\n",
      "\n",
      "Now for vehicle: Cayenne\n",
      "\n",
      "In year: 2010\n",
      "Ball tree: the nearest vehicles to Row 5909 are:\n",
      "5909                     Cayenne\n",
      "6385                          Q7\n",
      "3808    550i xDrive Gran Turismo\n",
      "5203              GL 350 BLUETEC\n",
      "5204              GL 350 BLUETEC\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[ 0.         23.13319182 23.45804359 23.54934913 23.54934913]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 5909 are:\n",
      "5909                     Cayenne\n",
      "6385                          Q7\n",
      "3808    550i xDrive Gran Turismo\n",
      "5203              GL 350 BLUETEC\n",
      "5204              GL 350 BLUETEC\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[ 0.         23.13319182 23.45804359 23.54934913 23.54934913]]\n",
      "\n",
      "In year: 2016\n",
      "Ball tree: the nearest vehicles to Row 10155 are:\n",
      "10155                     Cayenne\n",
      "10071    911 Carrera 4S Cabriolet\n",
      "10079          911 Carrera 4S Kit\n",
      "10076    911 Carrera 4S Cabriolet\n",
      "10085          911 Carrera 4S Kit\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         6.65698618 6.89904574 7.31814602 7.60491787]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 10155 are:\n",
      "10155                     Cayenne\n",
      "10071    911 Carrera 4S Cabriolet\n",
      "10079          911 Carrera 4S Kit\n",
      "10076    911 Carrera 4S Cabriolet\n",
      "10085          911 Carrera 4S Kit\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         6.65698618 6.89904574 7.31814602 7.60491787]]\n",
      "\n",
      "In year: 2023\n",
      "Ball tree: the nearest vehicles to Row 2373 are:\n",
      "2373              Cayenne\n",
      "1978    Ghibli Modena AWD\n",
      "2645           LS 500 AWD\n",
      "2037               Artura\n",
      "2643           LS 500 AWD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         7.09871824 8.48004895 8.54893136 8.74702218]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 2373 are:\n",
      "2373              Cayenne\n",
      "1978    Ghibli Modena AWD\n",
      "2645           LS 500 AWD\n",
      "2037               Artura\n",
      "2643           LS 500 AWD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         7.09871824 8.48004895 8.54893136 8.74702218]]\n",
      "\n",
      "Now for vehicle: SEQUOIA 4WD\n",
      "\n",
      "In year: 2010\n",
      "Ball tree: the nearest vehicles to Row 6292 are:\n",
      "6292                SEQUOIA 4WD\n",
      "5048    GRANTURISMO CONVERTIBLE\n",
      "5052    GRANTURISMO S AUTOMATIC\n",
      "5210              GL 550 4MATIC\n",
      "4077    Grand Cherokee SRT8 AWD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[ 0.          8.34610934 11.15884188 11.25947175 14.2041047 ]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 6292 are:\n",
      "6292                SEQUOIA 4WD\n",
      "5048    GRANTURISMO CONVERTIBLE\n",
      "5052    GRANTURISMO S AUTOMATIC\n",
      "5210              GL 550 4MATIC\n",
      "4077    Grand Cherokee SRT8 AWD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[ 0.          8.34610934 11.15884188 11.25947175 14.2041047 ]]\n",
      "\n",
      "In year: 2016\n",
      "Ball tree: the nearest vehicles to Row 10792 are:\n",
      "10792                 SEQUOIA 4WD\n",
      "10797             SEQUOIA 4WD FFV\n",
      "7214     535i xDrive Gran Turismo\n",
      "7197                  535i xDrive\n",
      "7480                 X6 xDrive35i\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         4.50329585 5.3706433  6.04531433 6.10682885]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 10792 are:\n",
      "10792                 SEQUOIA 4WD\n",
      "10797             SEQUOIA 4WD FFV\n",
      "7214     535i xDrive Gran Turismo\n",
      "7197                  535i xDrive\n",
      "7480                 X6 xDrive35i\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         4.50329585 5.3706433  6.04531433 6.10682885]]\n",
      "\n",
      "In year: 2023\n",
      "Ball tree: the nearest vehicles to Row 2892 are:\n",
      "2892    SEQUOIA 4WD\n",
      "2895    SEQUOIA 4WD\n",
      "2952     TUNDRA 4WD\n",
      "2954     TUNDRA 4WD\n",
      "2927     TACOMA 4WD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         0.6273606  3.6633712  5.30168816 7.44712877]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 2892 are:\n",
      "2892    SEQUOIA 4WD\n",
      "2895    SEQUOIA 4WD\n",
      "2952     TUNDRA 4WD\n",
      "2954     TUNDRA 4WD\n",
      "2927     TACOMA 4WD\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         0.6273606  3.6633712  5.30168816 7.44712877]]\n",
      "\n",
      "Now for vehicle: MAZDA3\n",
      "\n",
      "In year: 2010\n",
      "Ball tree: the nearest vehicles to Row 5074 are:\n",
      "5074    MAZDA3\n",
      "5084    MAZDA3\n",
      "4915    TUCSON\n",
      "4922    TUCSON\n",
      "5362    GALANT\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         3.21463109 4.65020849 4.8481099  4.9519973 ]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 5074 are:\n",
      "5074    MAZDA3\n",
      "5084    MAZDA3\n",
      "4915    TUCSON\n",
      "4922    TUCSON\n",
      "5362    GALANT\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         3.21463109 4.65020849 4.8481099  4.9519973 ]]\n",
      "\n",
      "In year: 2016\n",
      "Ball tree: the nearest vehicles to Row 9347 are:\n",
      "9347      MAZDA3\n",
      "9349      MAZDA3\n",
      "10632    COROLLA\n",
      "9361      MAZDA3\n",
      "9315        CX-3\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         4.04583022 4.64347996 6.69156824 7.04665015]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 9347 are:\n",
      "9347      MAZDA3\n",
      "9349      MAZDA3\n",
      "10632    COROLLA\n",
      "9361      MAZDA3\n",
      "9315        CX-3\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         4.04583022 4.64347996 6.69156824 7.04665015]]\n",
      "\n",
      "In year: 2023\n",
      "Ball tree: the nearest vehicles to Row 2027 are:\n",
      "2027        MAZDA3\n",
      "2481           BRZ\n",
      "3324    Tiguan FWD\n",
      "2550        LEGACY\n",
      "2548        LEGACY\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are: [[0.         3.6646077  5.44441868 5.4916505  5.59667232]]\n",
      "\n",
      "KD Tree: the nearest indices to Row 2027 are:\n",
      "2027        MAZDA3\n",
      "2481           BRZ\n",
      "3324    Tiguan FWD\n",
      "2550        LEGACY\n",
      "2548        LEGACY\n",
      "Name: Represented Test Veh Model, dtype: object\n",
      "\n",
      "And the respective distances are:[[0.         3.6646077  5.44441868 5.4916505  5.59667232]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import BallTree\n",
    "from sklearn.neighbors import KDTree\n",
    "\n",
    "# Train for 5 models\n",
    "for name in vehicles_name:\n",
    "    print(\"Now for vehicle: \" + name + '\\n')\n",
    "    # Train for 3 years.\n",
    "    for year in [2010, 2016, 2023]:\n",
    "        X = data[year].iloc[:, :-2]\n",
    "        print('In year: ' + str(year))\n",
    "        ball = BallTree(X, leaf_size=2)\n",
    "        idx = X[data[year]['Represented Test Veh Model'] == name].index[0]\n",
    "\n",
    "        q = [X.loc[idx]]\n",
    "        dist_ball, ind_ball = ball.query(q, k=5)\n",
    "        print(f\"Ball tree: the nearest vehicles to Row {idx} are:\")\n",
    "        print(data[year]['Represented Test Veh Model'].iloc[ind_ball[0]])\n",
    "        print(f\"\\nAnd the respective distances are: {dist_ball}\")\n",
    "\n",
    "        print()\n",
    "        kd = KDTree(X, leaf_size=2)\n",
    "        dist_kd, ind_kd = kd.query(q, k=5)\n",
    "        print(f'KD Tree: the nearest indices to Row {idx} are:')\n",
    "        print(data[year]['Represented Test Veh Model'].iloc[ind_ball[0]])\n",
    "        print(f'\\nAnd the respective distances are:{dist_kd}\\n')"
   ],
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   "source": [
    "## Analysis\n",
    "The result is generally as expected. The nearest neighbors for each vehicle and each year are the same for both methods. The nearest neighbors change over time, but this is because not all vehicles are tested in these three years. Beyond that, the neighbors are of the same type as the chosen vehicles: neighbors of cars are cars, neighbors of SUVs are SUVs, and neighbors of sports cars are sports cars."
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