CO2_data_preprocessed2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "b156729f-fa21-408c-ac3b-c281b3d36064",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "e6d901f8-4572-4181-9705-03015f43eddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read csv file\n",
    "df = pd.read_csv(\"FDM1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "9c7b5dc8-5e3d-4eba-9897-17aad1b48d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "0  ACURA         ILX       COMPACT             2.0        4.0          AS5   \n",
       "1  ACURA         ILX       COMPACT            15.0        4.0          AS5   \n",
       "2  ACURA         ILX       COMPACT             2.4        4.0           M6   \n",
       "3  ACURA  ILX HYBRID       COMPACT             1.5        4.0          AV7   \n",
       "4  ACURA     MDX 4WD   SUV - SMALL             3.5        6.0          AS6   \n",
       "\n",
       "  Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "0         Z                               9.9   \n",
       "1         Z                               9.9   \n",
       "2         Z                              11.2   \n",
       "3         Z                               6.0   \n",
       "4         Z                              12.7   \n",
       "\n",
       "   Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "0                              6.7                               8.5   \n",
       "1                              6.7                               8.5   \n",
       "2                              7.7                               9.6   \n",
       "3                              5.8                               5.9   \n",
       "4                              9.1                              11.1   \n",
       "\n",
       "   Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "0                         33.0                196.0  \n",
       "1                         33.0                196.0  \n",
       "2                         29.0                221.0  \n",
       "3                         48.0                136.0  \n",
       "4                         25.0                255.0  "
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the first few rows of the DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "9d64e6bb-2456-42a5-bafd-ab61f6f1f1e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                 0\n",
       "Model                                1\n",
       "Vehicle Class                        3\n",
       "Engine Size(L)                       3\n",
       "Cylinders                            2\n",
       "Transmission                         8\n",
       "Fuel Type                            4\n",
       "Fuel Consumption City (L/100 km)    10\n",
       "Fuel Consumption Hwy (L/100 km)      9\n",
       "Fuel Consumption Comb (L/100 km)     9\n",
       "Fuel Consumption Comb (mpg)          5\n",
       "CO2 Emissions(g/km)                 10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking wheater there is null value\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "92b6d594-ac8f-49e9-9d1e-201dea1a3a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS7</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "9  ACURA   NaN      MID-SIZE             3.5        6.0          AS7   \n",
       "\n",
       "  Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "9         Z                              12.6   \n",
       "\n",
       "   Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "9                              9.2                              10.1   \n",
       "\n",
       "   Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "9                         25.0                259.0  "
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking specific rows where the \"Model\" column is null\n",
    "df[df[[\"Model\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "42646fed-bbaf-462a-a431-a1d5ba9e9310",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.60</td>\n",
       "      <td>27.0</td>\n",
       "      <td>244.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10.00</td>\n",
       "      <td>28.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TL</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.10</td>\n",
       "      <td>28.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TL AWD</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS7</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>10.10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TL AWD</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>13.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>11.60</td>\n",
       "      <td>24.0</td>\n",
       "      <td>267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TL AWD</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.95</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TSX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.40</td>\n",
       "      <td>27.0</td>\n",
       "      <td>239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.60</td>\n",
       "      <td>27.0</td>\n",
       "      <td>244.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10.00</td>\n",
       "      <td>28.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX HYBRID</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.70</td>\n",
       "      <td>37.0</td>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>9.20</td>\n",
       "      <td>31.0</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.60</td>\n",
       "      <td>29.0</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD ELITE</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>10.70</td>\n",
       "      <td>26.0</td>\n",
       "      <td>251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>10.70</td>\n",
       "      <td>26.0</td>\n",
       "      <td>249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10.00</td>\n",
       "      <td>28.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX HYBRID</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.70</td>\n",
       "      <td>37.0</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.60</td>\n",
       "      <td>29.0</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD ELITE</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.70</td>\n",
       "      <td>26.0</td>\n",
       "      <td>251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX HYBRID AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>NSX</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.8</td>\n",
       "      <td>11.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.70</td>\n",
       "      <td>26.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX HYBRID</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.10</td>\n",
       "      <td>35.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.60</td>\n",
       "      <td>29.0</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD ELITE</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.70</td>\n",
       "      <td>26.0</td>\n",
       "      <td>251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX HYBRID AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>NSX</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.8</td>\n",
       "      <td>11.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.70</td>\n",
       "      <td>26.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX HYBRID</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.40</td>\n",
       "      <td>34.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.80</td>\n",
       "      <td>29.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.60</td>\n",
       "      <td>30.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD A-SPEC</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.30</td>\n",
       "      <td>27.0</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.80</td>\n",
       "      <td>26.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD A-SPEC</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>11.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX Hybrid AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>NSX</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.8</td>\n",
       "      <td>11.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX Hybrid</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.40</td>\n",
       "      <td>34.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.80</td>\n",
       "      <td>29.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD A-SPEC</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.30</td>\n",
       "      <td>27.0</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>10.90</td>\n",
       "      <td>26.0</td>\n",
       "      <td>254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD A-SPEC</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>11.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX Hybrid AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX Hybrid</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.40</td>\n",
       "      <td>34.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.80</td>\n",
       "      <td>29.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD A-SPEC/Limited Edition</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.30</td>\n",
       "      <td>27.0</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make                              Model Vehicle Class  Engine Size(L)  \\\n",
       "4   ACURA                            MDX 4WD   SUV - SMALL             3.5   \n",
       "5   ACURA                            RDX AWD   SUV - SMALL             3.5   \n",
       "6   ACURA                                RLX      MID-SIZE             3.5   \n",
       "7   ACURA                                 TL      MID-SIZE             3.5   \n",
       "8   ACURA                             TL AWD      MID-SIZE             3.7   \n",
       "9   ACURA                                NaN      MID-SIZE             3.5   \n",
       "10  ACURA                             TL AWD      MID-SIZE             3.7   \n",
       "11  ACURA                             TL AWD      MID-SIZE             3.5   \n",
       "14  ACURA                                TSX       COMPACT             3.5   \n",
       "19  ACURA                         MDX SH-AWD   SUV - SMALL             3.5   \n",
       "20  ACURA                            RDX AWD   SUV - SMALL             3.5   \n",
       "24  ACURA                                RLX      MID-SIZE             3.5   \n",
       "25  ACURA                         RLX HYBRID      MID-SIZE             3.5   \n",
       "27  ACURA                                TLX       COMPACT             3.5   \n",
       "28  ACURA                         TLX SH-AWD       COMPACT             3.5   \n",
       "30  ACURA                         MDX SH-AWD   SUV - SMALL             3.5   \n",
       "31  ACURA                   MDX SH-AWD ELITE   SUV - SMALL             3.5   \n",
       "32  ACURA                            RDX AWD   SUV - SMALL             3.5   \n",
       "33  ACURA                                RLX      MID-SIZE             3.5   \n",
       "34  ACURA                         RLX HYBRID      MID-SIZE             3.5   \n",
       "36  ACURA                         TLX SH-AWD       COMPACT             3.5   \n",
       "38  ACURA                         MDX SH-AWD   SUV - SMALL             3.5   \n",
       "39  ACURA                   MDX SH-AWD ELITE   SUV - SMALL             3.5   \n",
       "40  ACURA                     MDX HYBRID AWD   SUV - SMALL             3.0   \n",
       "41  ACURA                                NSX    TWO-SEATER             3.5   \n",
       "42  ACURA                            RDX AWD   SUV - SMALL             3.5   \n",
       "43  ACURA                         RLX HYBRID      MID-SIZE             3.5   \n",
       "45  ACURA                         TLX SH-AWD       COMPACT             3.5   \n",
       "47  ACURA                         MDX SH-AWD   SUV - SMALL             3.5   \n",
       "48  ACURA                   MDX SH-AWD ELITE   SUV - SMALL             3.5   \n",
       "49  ACURA                     MDX HYBRID AWD   SUV - SMALL             3.0   \n",
       "50  ACURA                                NSX    TWO-SEATER             3.5   \n",
       "51  ACURA                            RDX AWD   SUV - SMALL             3.5   \n",
       "52  ACURA                         RLX HYBRID      MID-SIZE             3.5   \n",
       "55  ACURA                         TLX SH-AWD           NaN             3.5   \n",
       "56  ACURA                         TLX SH-AWD       COMPACT            -5.6   \n",
       "57  ACURA                  TLX SH-AWD A-SPEC       COMPACT             3.5   \n",
       "59  ACURA                         MDX SH-AWD   SUV - SMALL             3.5   \n",
       "60  ACURA                  MDX SH-AWD A-SPEC   SUV - SMALL             3.5   \n",
       "61  ACURA                     MDX Hybrid AWD   SUV - SMALL             3.0   \n",
       "62  ACURA                                NSX    TWO-SEATER             3.5   \n",
       "65  ACURA                         RLX Hybrid      MID-SIZE             3.5   \n",
       "68  ACURA                         TLX SH-AWD       COMPACT             3.5   \n",
       "69  ACURA                  TLX SH-AWD A-SPEC       COMPACT             3.5   \n",
       "71  ACURA                         MDX SH-AWD   SUV - SMALL             3.5   \n",
       "72  ACURA                  MDX SH-AWD A-SPEC   SUV - SMALL             3.5   \n",
       "73  ACURA                     MDX Hybrid AWD   SUV - SMALL             3.0   \n",
       "76  ACURA                         RLX Hybrid      MID-SIZE             3.5   \n",
       "78  ACURA                         TLX SH-AWD       COMPACT             3.5   \n",
       "79  ACURA  TLX SH-AWD A-SPEC/Limited Edition       COMPACT             3.5   \n",
       "\n",
       "    Cylinders Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "4         6.0          AS6         Z                              12.7   \n",
       "5         6.0          AS6         Z                              12.1   \n",
       "6         6.0          AS6         Z                              11.9   \n",
       "7         6.0          AS6         Z                              11.8   \n",
       "8         6.0          AS6         Z                              12.8   \n",
       "9         6.0          AS7         Z                              12.6   \n",
       "10        6.0           M6         Z                              13.4   \n",
       "11        6.0          AS6         Z                              11.9   \n",
       "14        6.0          AS5         Z                              12.1   \n",
       "19        6.0          AS6         Z                              12.7   \n",
       "20        6.0          AS6         Z                              12.1   \n",
       "24        6.0          AS6         Z                              11.9   \n",
       "25        6.0          AM7         Z                               8.0   \n",
       "27        6.0          AS9         Z                              11.2   \n",
       "28        6.0          AS9         Z                              11.2   \n",
       "30        6.0          AS9         Z                              12.7   \n",
       "31        6.0          AS9         Z                              12.2   \n",
       "32        6.0          AS6         Z                              12.4   \n",
       "33        6.0          AS6         Z                              11.9   \n",
       "34        6.0          AM7         Z                               8.0   \n",
       "36        6.0          AS9         Z                              11.2   \n",
       "38        6.0          AS9         Z                              12.6   \n",
       "39        6.0          AS9         Z                              12.2   \n",
       "40        6.0          AM7         Z                               9.1   \n",
       "41        6.0          AM9         Z                              11.1   \n",
       "42        6.0          AS6         Z                              12.4   \n",
       "43        6.0          AM7         Z                               8.2   \n",
       "45        6.0          AS9         Z                              11.2   \n",
       "47        6.0          AS9         Z                              12.6   \n",
       "48        6.0          AS9         Z                              12.2   \n",
       "49        6.0          AM7         Z                               9.1   \n",
       "50        6.0          AM9         Z                              11.1   \n",
       "51        6.0          AS6         Z                              12.4   \n",
       "52        6.0          AM7         Z                               8.4   \n",
       "55        6.0          AS9         Z                              11.4   \n",
       "56        6.0         AS10         Z                              10.0   \n",
       "57        6.0          AS9         Z                              12.0   \n",
       "59        6.0          AS9         Z                              12.2   \n",
       "60        6.0          AS9         Z                              12.2   \n",
       "61        6.0          AM7         Z                               9.1   \n",
       "62        6.0          AM9         Z                              11.1   \n",
       "65        6.0          AM7         Z                               8.4   \n",
       "68        6.0          AS9         Z                              11.4   \n",
       "69        6.0          AS9         Z                              12.0   \n",
       "71        6.0          AS9         Z                              12.3   \n",
       "72        6.0          AS9         Z                              12.2   \n",
       "73        6.0          AM7         Z                               9.1   \n",
       "76        6.0          AM7         Z                               8.4   \n",
       "78        6.0          AS9         Z                              11.4   \n",
       "79        6.0          AS9         Z                              12.0   \n",
       "\n",
       "    Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "4                               9.1                             11.10   \n",
       "5                               8.7                             10.60   \n",
       "6                               7.7                             10.00   \n",
       "7                               8.1                             10.10   \n",
       "8                               9.0                             11.10   \n",
       "9                               9.2                             10.10   \n",
       "10                              9.5                             11.60   \n",
       "11                              8.0                              9.95   \n",
       "14                              8.3                             10.40   \n",
       "19                              9.1                             11.10   \n",
       "20                              8.7                             10.60   \n",
       "24                              7.7                             10.00   \n",
       "25                              7.5                              7.70   \n",
       "27                              6.9                              9.20   \n",
       "28                              7.5                              9.60   \n",
       "30                              9.1                             11.10   \n",
       "31                              9.1                             10.70   \n",
       "32                              8.6                             10.70   \n",
       "33                              7.7                             10.00   \n",
       "34                              7.5                              7.70   \n",
       "36                              7.5                              9.60   \n",
       "38                              9.0                             11.00   \n",
       "39                              9.0                             10.70   \n",
       "40                              9.0                              9.00   \n",
       "41                             10.8                             11.00   \n",
       "42                              8.7                             10.70   \n",
       "43                              7.8                              8.10   \n",
       "45                              7.5                              9.60   \n",
       "47                              9.0                             11.00   \n",
       "48                              9.0                             10.70   \n",
       "49                              9.0                              9.00   \n",
       "50                             10.8                             11.00   \n",
       "51                              8.7                             10.70   \n",
       "52                              8.2                              8.40   \n",
       "55                              7.7                              9.80   \n",
       "56                              1.2                              5.60   \n",
       "57                              8.2                             10.30   \n",
       "59                              9.0                             10.80   \n",
       "60                              9.5                             11.00   \n",
       "61                              9.0                              9.00   \n",
       "62                             10.8                             11.00   \n",
       "65                              8.2                              8.40   \n",
       "68                              7.7                              9.80   \n",
       "69                              8.2                             10.30   \n",
       "71                              9.2                             10.90   \n",
       "72                              9.5                             11.00   \n",
       "73                              9.0                              9.00   \n",
       "76                              8.2                              8.40   \n",
       "78                              7.7                              9.80   \n",
       "79                              8.2                             10.30   \n",
       "\n",
       "    Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "4                          25.0                255.0  \n",
       "5                          27.0                244.0  \n",
       "6                          28.0                230.0  \n",
       "7                          28.0                232.0  \n",
       "8                          25.0                255.0  \n",
       "9                          25.0                259.0  \n",
       "10                         24.0                267.0  \n",
       "11                        -25.0                  NaN  \n",
       "14                         27.0                239.0  \n",
       "19                         25.0                255.0  \n",
       "20                         27.0                244.0  \n",
       "24                         28.0                230.0  \n",
       "25                         37.0                177.0  \n",
       "27                         31.0                212.0  \n",
       "28                         29.0                221.0  \n",
       "30                         25.0                254.0  \n",
       "31                         26.0                251.0  \n",
       "32                         26.0                249.0  \n",
       "33                         28.0                235.0  \n",
       "34                         37.0                180.0  \n",
       "36                         29.0                226.0  \n",
       "38                         26.0                259.0  \n",
       "39                         26.0                251.0  \n",
       "40                         31.0                210.0  \n",
       "41                         26.0                261.0  \n",
       "42                         26.0                250.0  \n",
       "43                         35.0                190.0  \n",
       "45                         29.0                226.0  \n",
       "47                         26.0                259.0  \n",
       "48                         26.0                251.0  \n",
       "49                         31.0                210.0  \n",
       "50                         26.0                261.0  \n",
       "51                         26.0                250.0  \n",
       "52                         34.0                196.0  \n",
       "55                         29.0                228.0  \n",
       "56                         30.0                228.0  \n",
       "57                         27.0                240.0  \n",
       "59                         26.0                252.0  \n",
       "60                         26.0                258.0  \n",
       "61                         31.0                210.0  \n",
       "62                         26.0                261.0  \n",
       "65                         34.0                196.0  \n",
       "68                         29.0                228.0  \n",
       "69                         27.0                240.0  \n",
       "71                         26.0                254.0  \n",
       "72                         26.0                258.0  \n",
       "73                         31.0                210.0  \n",
       "76                         34.0                196.0  \n",
       "78                         29.0                228.0  \n",
       "79                         27.0                240.0  "
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering rows based on specific conditions (ACURA and Cylinders)\n",
    "df[(df[\"Make\"] == \"ACURA\") & (df[\"Cylinders\"] == 6.0) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "266a58f1-850d-4b01-aeef-379ee0e8bb3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop model null value\n",
    "df = df.dropna(subset=[\"Model\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "a6c2d3eb-192c-49c9-ab3a-7afd9900b442",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                 0\n",
       "Model                                0\n",
       "Vehicle Class                        3\n",
       "Engine Size(L)                       3\n",
       "Cylinders                            2\n",
       "Transmission                         8\n",
       "Fuel Type                            4\n",
       "Fuel Consumption City (L/100 km)    10\n",
       "Fuel Consumption Hwy (L/100 km)      9\n",
       "Fuel Consumption Comb (L/100 km)     9\n",
       "Fuel Consumption Comb (mpg)          5\n",
       "CO2 Emissions(g/km)                 10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rechecking wheater there is null value\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "edc21e98-5aca-4290-841b-6b190c59b2be",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TL AWD</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.95</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>Tsx</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>AstON MARTIN</td>\n",
       "      <td>RAPIDE S</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>AstON MARTIN</td>\n",
       "      <td>RAPIDE S</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>AstON MARTIN</td>\n",
       "      <td>RAPIDE S</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A6 QUATTRO</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A6 QUATTRO</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A7</td>\n",
       "      <td>Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>s7</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>A7</td>\n",
       "      <td>Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>s7</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>14.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Make       Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "11          ACURA      TL AWD      MID-SIZE             3.5        6.0   \n",
       "15          ACURA         Tsx       COMPACT             2.7        4.0   \n",
       "22          ACURA     RDX AWD           NaN             NaN        8.0   \n",
       "148  AstON MARTIN    RAPIDE S    SUBCOMPACT             5.0       11.0   \n",
       "149  AstON MARTIN    RAPIDE S    SUBCOMPACT             4.0        8.0   \n",
       "150  AstON MARTIN    RAPIDE S    SUBCOMPACT             6.0       12.0   \n",
       "212          AUDI  A6 QUATTRO    SUBCOMPACT             4.2        4.0   \n",
       "213          AUDI  A6 QUATTRO      MID-SIZE             2.0        6.0   \n",
       "215          AUDI          s7      MID-SIZE             3.0        8.0   \n",
       "216          AUDI          s7    SUBCOMPACT             3.0        4.0   \n",
       "\n",
       "    Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "11           AS6         Z                              11.9   \n",
       "15           NaN         Z                              15.5   \n",
       "22           NaN         Z                               NaN   \n",
       "148           A8         Z                              15.2   \n",
       "149          NaN       NaN                               NaN   \n",
       "150          NaN         Z                               NaN   \n",
       "212          AS8         Z                               NaN   \n",
       "213           A7         Z                               NaN   \n",
       "215           A7         Z                               NaN   \n",
       "216          AS8         Z                              14.2   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "11                               8.0                              9.95   \n",
       "15                               NaN                               NaN   \n",
       "22                               NaN                              6.70   \n",
       "148                              NaN                               NaN   \n",
       "149                              NaN                               NaN   \n",
       "150                              NaN                             12.20   \n",
       "212                              9.5                               NaN   \n",
       "213                              8.1                               NaN   \n",
       "215                              NaN                             12.00   \n",
       "216                              NaN                               NaN   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "11                         -25.0                  NaN  \n",
       "15                          25.0                  NaN  \n",
       "22                           NaN                  NaN  \n",
       "148                         15.0                  NaN  \n",
       "149                         19.0                  NaN  \n",
       "150                          NaN                  NaN  \n",
       "212                         11.0                  NaN  \n",
       "213                         15.0                  NaN  \n",
       "215                         23.0                  NaN  \n",
       "216                         21.0                  NaN  "
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking specific rows where the \"CO2 Emissions(g/km)\" column is null\n",
    "df[df[[\"CO2 Emissions(g/km)\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "e415c846-c8f6-4edd-a058-7bb337fdd47d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop model null value\n",
    "df = df.dropna(subset=[\"CO2 Emissions(g/km)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "e597fe1c-85ed-4bd9-8be3-92340b8cb359",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>s7</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>BMW</td>\n",
       "      <td>335i SEDAN</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "21   ACURA         ILX       COMPACT             NaN        NaN          NaN   \n",
       "214   AUDI          s7    SUBCOMPACT             2.0       10.0          AS8   \n",
       "590    BMW  335i SEDAN       COMPACT            10.0        2.0          NaN   \n",
       "\n",
       "    Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "21        NaN                               NaN   \n",
       "214         Z                               NaN   \n",
       "590         Z                              11.5   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "21                               5.8                               NaN   \n",
       "214                              8.8                               NaN   \n",
       "590                              8.0                               NaN   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "21                          28.0                140.0  \n",
       "214                          NaN                235.0  \n",
       "590                         72.0                 85.0  "
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking specific rows where the \"Fuel Consumption Comb (L/100 km)\" column is null\n",
    "df[df[[\"Fuel Consumption Comb (L/100 km)\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "bf5c9740-8731-44d0-9b3c-b1a6420650b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       2\n",
       "Engine Size(L)                      2\n",
       "Cylinders                           2\n",
       "Transmission                        4\n",
       "Fuel Type                           3\n",
       "Fuel Consumption City (L/100 km)    4\n",
       "Fuel Consumption Hwy (L/100 km)     2\n",
       "Fuel Consumption Comb (L/100 km)    3\n",
       "Fuel Consumption Comb (mpg)         3\n",
       "CO2 Emissions(g/km)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rechecking wheater there is null value\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "3ec312e7-33a6-4c19-9aef-411afe16b750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([21, 23], dtype='int64')"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding rows with more than 4 missing values and storing their indices\n",
    "result_df =df[ df.isnull().sum(axis=1) > 4].index\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "d78ffa72-ac8b-446e-8da6-4311c6af0d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows with more than 4 missing values\n",
    "df.drop(result_df,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "5152fc53-4d1d-4650-89dd-d6dc3e211266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([21, 23], dtype='int64')"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "3da12e44-f48b-4bc5-997c-3ab0b90f5a0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       1\n",
       "Engine Size(L)                      0\n",
       "Cylinders                           0\n",
       "Transmission                        2\n",
       "Fuel Type                           1\n",
       "Fuel Consumption City (L/100 km)    2\n",
       "Fuel Consumption Hwy (L/100 km)     1\n",
       "Fuel Consumption Comb (L/100 km)    2\n",
       "Fuel Consumption Comb (mpg)         2\n",
       "CO2 Emissions(g/km)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rechecking wheater there is null value\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "id": "5007f252-7a3f-4002-a243-96c1a1183e10",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 578,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values in the \"Fuel Type\" column\n",
    "df[df[[\"Fuel Type\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "id": "72b3b90d-d7fd-4e9f-a15a-d00e0e479ae3",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB11 V12</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>5.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.5</td>\n",
       "      <td>11.4</td>\n",
       "      <td>13.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB11 V12</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>5.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.5</td>\n",
       "      <td>11.4</td>\n",
       "      <td>13.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Make     Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "138  ASTON MARTIN  DB11 V12   MINICOMPACT             5.2       12.0   \n",
       "144  ASTON MARTIN  DB11 V12   MINICOMPACT             5.2       12.0   \n",
       "\n",
       "    Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "138           A8         Z                              15.5   \n",
       "144           A8         Z                              15.5   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "138                             11.4                              13.7   \n",
       "144                             11.4                              13.7   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "138                         21.0                322.0  \n",
       "144                         21.0                322.0  "
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the DataFrame to select rows where the \"Make\" is \"ASTON MARTIN\" and the \"Model\" is \"DB11 V12\"\n",
    "df[(df[\"Make\"] == \"ASTON MARTIN\") & (df[\"Model\"] == \"DB11 V12\") ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "fec2c9dc-fd49-4392-bf1f-254e7a88c51a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FORD                                              631\n",
       "CHEVROLET                                         594\n",
       "BMW                                               529\n",
       "MERCEDES-BENZ                                     423\n",
       "PORSCHE                                           377\n",
       "TOYOTA                                            336\n",
       "GMC                                               331\n",
       "AUDI                                              295\n",
       "NISSAN                                            259\n",
       "JEEP                                              253\n",
       "DODGE                                             249\n",
       "KIA                                               231\n",
       "HONDA                                             214\n",
       "HYUNDAI                                           210\n",
       "MINI                                              205\n",
       "VOLKSWAGEN                                        197\n",
       "MAZDA                                             181\n",
       "LEXUS                                             178\n",
       "JAGUAR                                            160\n",
       "CADILLAC                                          158\n",
       "SUBARU                                            141\n",
       "VOLVO                                             124\n",
       "INFINITI                                          108\n",
       "BUICK                                             103\n",
       "RAM                                                98\n",
       "LINCOLN                                            96\n",
       "MITSUBISHI                                         96\n",
       "CHRYSLER                                           88\n",
       "LAND ROVER                                         86\n",
       "ACURA                                              74\n",
       "FIAT                                               73\n",
       "MASERATI                                           62\n",
       "ROLLS-ROYCE                                        51\n",
       "BENTLEY                                            47\n",
       "ASTON MARTIN                                       47\n",
       "LAMBORGHINI                                        41\n",
       "ALFA ROMEO                                         31\n",
       "GENESIS                                            25\n",
       "SCION                                              22\n",
       "SMART                                               7\n",
       "BUGATTI                                             3\n",
       "SRT                                                 2\n",
       "LEXUS UX 250h AWD                                   1\n",
       "LAND ROVER Range Rover SVAutobiography Dynamic      1\n",
       "LAMBORGHINI Aventador Coupe                         1\n",
       "AstON MARTIN                                        1\n",
       "Name: Make, dtype: int64"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning and standardizing values in the \"Make\" column\n",
    "df[\"Make\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "id": "6a14d56c-6037-49a6-b2ab-71692c24ab17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing inconsistent values in the \"Make\" column\n",
    "df[\"Make\"].replace(\"AstON MARTIN\", \"ASTON MARTIN\", inplace=True)\n",
    "df[\"Make\"].replace(\"LEXUS UX 250h AWD\", \"LEXUS\", inplace=True)\n",
    "df[\"Make\"].replace(\"LAND ROVER Range Rover SVAutobiography Dynamic\", \"LAND ROVER\", inplace=True)\n",
    "df[\"Make\"].replace(\"LAMBORGHINI Aventador Coupe\", \"LAMBORGHINI\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "id": "0b342a62-671e-4047-a484-e44cf3aab52c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SUV - SMALL                  1220\n",
       "MID-SIZE                     1136\n",
       "COMPACT                      1026\n",
       "SUV - STANDARD                741\n",
       "FULL-SIZE                     642\n",
       "SUBCOMPACT                    610\n",
       "PICKUP TRUCK - STANDARD       539\n",
       "TWO-SEATER                    460\n",
       "MINICOMPACT                   326\n",
       "STATION WAGON - SMALL         252\n",
       "PICKUP TRUCK - SMALL          160\n",
       "MINIVAN                        81\n",
       "SPECIAL PURPOSE VEHICLE        77\n",
       "VAN - PASSENGER                66\n",
       "STATION WAGON - MID-SIZE       53\n",
       "VAN - CARGO                    22\n",
       " COMPACT                        5\n",
       " MID-SIZE                       4\n",
       " TWO-SEATER                     3\n",
       " SUBCOMPACT                     2\n",
       " PICKUP TRUCK - STANDARD        2\n",
       " MINICOMPACT                    2\n",
       " SUV - SMALL                    2\n",
       " SUV - STANDARD                 1\n",
       "PICKUP TRUCK - SMA              1\n",
       " FULL-SIZE                      1\n",
       "MINICOMPACT4                    1\n",
       " STATION WAGON - MID-SIZE       1\n",
       "Aventador Coupe                 1\n",
       " SPECIAL PURPOSE VEHICLE        1\n",
       " MINIVAN                        1\n",
       "Name: Vehicle Class, dtype: int64"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning and standardizing values in the \"Vehicle Class\" column\n",
    "df[\"Vehicle Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "7fc677a5-35ff-4a9d-a20e-700ed6cebd6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing variations in the \"Vehicle Class\" column for consistency\n",
    "df[\"Vehicle Class\"].replace(\" COMPACT\", \"COMPACT\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" MID-SIZE\", \"MID-SIZE\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" TWO-SEATER\", \"TWO-SEATER\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" MINICOMPACT\", \"MINICOMPACT\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" PICKUP TRUCK - STANDARD\", \"PICKUP TRUCK - STANDARD\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" SUBCOMPACT\", \"SUBCOMPACT\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" SUV - SMALL\", \"SUV - SMALL\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" MINICOMPACT4\", \"MINICOMPACT\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" SPECIAL PURPOSE VEHICLE\", \"SPECIAL PURPOSE VEHICLE\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" FULL-SIZE\", \"FULL-SIZE\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" SUV - STANDARD\", \"SUV - STANDARD\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\"PICKUP TRUCK - SMA\", \"PICKUP TRUCK - SMALL\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\"MINICOMPACT4\", \"MINICOMPACT\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" MINIVAN\", \"MINIVAN\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\" STATION WAGON - MID-SIZE\", \"STATION WAGON - MID-SIZE\", inplace=True)\n",
    "df[\"Vehicle Class\"].replace(\"Aventador Coupe\", \"TWO-SEATER\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "id": "eadb7563-fc20-4e33-962e-4ca3cdc1975f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F-150 FFV 4X4                32\n",
       "F-150 FFV                    32\n",
       "MUSTANG                      27\n",
       "FOCUS FFV                    24\n",
       "F-150                        20\n",
       "                             ..\n",
       "AVENTADOR COUPE LP 740        1\n",
       "AVENTADOR ROADSTER LP 740     1\n",
       "AVENTADOR S COUPE             1\n",
       "AVENTADOR S ROADSTER          1\n",
       "XC40 T4 AWD                   1\n",
       "Name: Model, Length: 2074, dtype: int64"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning and standardizing values in the \"Model\" column\n",
    "df[\"Model\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "800a8d6f-7056-4e75-8f3f-04f645b13c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' 1500 eTorque',\n",
       " ' 4C',\n",
       " ' 4Runner 4WD',\n",
       " ' 911 Carrera S',\n",
       " ' A4 QUATTRO',\n",
       " ' A6 QUATTRO',\n",
       " ' AMG C 43 4MATIC',\n",
       " ' CX-3',\n",
       " ' Cooper 3 Door',\n",
       " ' Crosstrek AWD',\n",
       " ' E 550 4MATIC',\n",
       " ' E 550 CABRIOLET',\n",
       " ' E 63 AMG 4MATIC WAGON',\n",
       " ' Eclipse Cross 4WD',\n",
       " ' F-150 FFV (Payload Pkg)',\n",
       " ' GT',\n",
       " ' Ghibli',\n",
       " ' Phantom',\n",
       " ' Sienna',\n",
       " ' Transit Connect Van FFV',\n",
       " '124 SPIDER',\n",
       " '124 Spider',\n",
       " '1500',\n",
       " '1500 (MDS)',\n",
       " '1500 4X4',\n",
       " '1500 4X4 (MDS)',\n",
       " '1500 4X4 DIESEL',\n",
       " '1500 4X4 ECODIESEL',\n",
       " '1500 4X4 EcoDiesel',\n",
       " '1500 4X4 FFV',\n",
       " '1500 4X4 eTorque',\n",
       " '1500 Classic',\n",
       " '1500 Classic 4X4',\n",
       " '1500 Classic 4X4 EcoDiesel',\n",
       " '1500 Classic 4X4 FFV',\n",
       " '1500 Classic EcoDiesel',\n",
       " '1500 Classic FFV',\n",
       " '1500 DIESEL',\n",
       " '1500 ECODIESEL',\n",
       " '1500 ECODIESEL HFE',\n",
       " '1500 EcoDiesel',\n",
       " '1500 FFV',\n",
       " '1500 HFE',\n",
       " '1500 HFE eTorque',\n",
       " '1500 eTorque',\n",
       " '200',\n",
       " '200 AWD',\n",
       " '200 AWD FFV',\n",
       " '200 CONVERTIBLE',\n",
       " '200 CONVERTIBLE FFV',\n",
       " '200 FFV',\n",
       " '200 SEDAN',\n",
       " '200 SEDAN FFV',\n",
       " '228i',\n",
       " '228i CABRIOLET',\n",
       " '228i COUPE',\n",
       " '228i xDRIVE',\n",
       " '228i xDRIVE CABRIOLET',\n",
       " '228i xDRIVE COUPE',\n",
       " '230i CABRIOLET',\n",
       " '230i COUPE',\n",
       " '230i Cabriolet',\n",
       " '230i Coupe',\n",
       " '230i xDRIVE CABRIOLET',\n",
       " '230i xDRIVE COUPE',\n",
       " '230i xDrive Cabriolet',\n",
       " '230i xDrive Coupe',\n",
       " '300',\n",
       " '300 (MDS)',\n",
       " '300 AWD',\n",
       " '300 AWD (MDS)',\n",
       " '300 AWD FFV',\n",
       " '300 FFV',\n",
       " '300 SRT (MDS)',\n",
       " '320i',\n",
       " '320i SEDAN',\n",
       " '320i xDRIVE',\n",
       " '320i xDRIVE SEDAN',\n",
       " '328d xDRIVE',\n",
       " '328d xDRIVE SEDAN',\n",
       " '328d xDRIVE TOURING',\n",
       " '328i',\n",
       " '328i SEDAN',\n",
       " '328i xDRIVE',\n",
       " '328i xDRIVE GRAN TURISMO',\n",
       " '328i xDRIVE SEDAN',\n",
       " '328i xDRIVE TOURING',\n",
       " '330i xDRIVE',\n",
       " '330i xDRIVE GRAN TURISMO',\n",
       " '330i xDRIVE TOURING',\n",
       " '330i xDrive',\n",
       " '330i xDrive Gran Turismo',\n",
       " '330i xDrive Sedan',\n",
       " '330i xDrive Touring',\n",
       " '335i',\n",
       " '335i SEDAN',\n",
       " '335i xDRIVE',\n",
       " '335i xDRIVE GRAN TURISMO',\n",
       " '335i xDRIVE SEDAN',\n",
       " '340i',\n",
       " '340i xDRIVE',\n",
       " '340i xDRIVE GRAN TURISMO',\n",
       " '370Z',\n",
       " '370Z COUPE',\n",
       " '370Z ROADSTER',\n",
       " '370Z Roadster',\n",
       " '428i CABRIOLET',\n",
       " '428i COUPE',\n",
       " '428i GRAN COUPE',\n",
       " '428i xDRIVE CABRIOLET',\n",
       " '428i xDRIVE COUPE',\n",
       " '428i xDRIVE GRAN COUPE',\n",
       " '430i Coupe',\n",
       " '430i xDRIVE CABRIOLET',\n",
       " '430i xDRIVE COUPE',\n",
       " '430i xDRIVE GRAN COUPE',\n",
       " '430i xDrive Cabriolet',\n",
       " '430i xDrive Coupe',\n",
       " '430i xDrive Gran Coupe',\n",
       " '435i CABRIOLET',\n",
       " '435i COUPE',\n",
       " '435i GRAN COUPE',\n",
       " '435i xDRIVE CABRIOLET',\n",
       " '435i xDRIVE COUPE',\n",
       " '435i xDRIVE GRAN COUPE',\n",
       " '440i COUPE',\n",
       " '440i Coupe',\n",
       " '440i xDRIVE CABRIOLET',\n",
       " '440i xDRIVE COUPE',\n",
       " '440i xDRIVE GRAN COUPE',\n",
       " '440i xDrive Cabriolet',\n",
       " '440i xDrive Coupe',\n",
       " '440i xDrive Gran Coupe',\n",
       " '4C',\n",
       " '4C COUPE',\n",
       " '4C Coupe',\n",
       " '4C SPIDER',\n",
       " '4C Spider',\n",
       " '4RUNNER (Part-Time 4WD)',\n",
       " '4RUNNER 4WD',\n",
       " '4RUNNER 4WD (Part-Time 4WD)',\n",
       " '4Runner 4WD',\n",
       " '4Runner 4WD (Part-Time 4WD)',\n",
       " '500',\n",
       " '500 ABARTH',\n",
       " '500 ABARTH CABRIO',\n",
       " '500 ABARTH HATCHBACK',\n",
       " '500 CABRIO',\n",
       " '500 CABRIO TURBO',\n",
       " '500 HATCHBACK',\n",
       " '500 HATCHBACK TURBO',\n",
       " '500L',\n",
       " '500L TURBO',\n",
       " '500X',\n",
       " '500X AWD',\n",
       " '528i',\n",
       " '528i SEDAN',\n",
       " '528i xDRIVE',\n",
       " '528i xDRIVE SEDAN',\n",
       " '530i xDRIVE',\n",
       " '530i xDrive',\n",
       " '530i xDrive Sedan',\n",
       " '535d xDRIVE',\n",
       " '535d xDRIVE SEDAN',\n",
       " '535i xDRIVE',\n",
       " '535i xDRIVE GRAN TURISMO',\n",
       " '535i xDRIVE SEDAN',\n",
       " '540i xDRIVE',\n",
       " '540i xDrive',\n",
       " '540i xDrive Sedan',\n",
       " '550i xDRIVE',\n",
       " '550i xDRIVE GRAN TURISMO',\n",
       " '550i xDRIVE SEDAN',\n",
       " '640i xDRIVE CABRIOLET',\n",
       " '640i xDRIVE GRAN COUPE',\n",
       " '640i xDRIVE GRAN TURISMO',\n",
       " '640i xDrive Gran Coupe',\n",
       " '640i xDrive Gran Turismo',\n",
       " '650i xDRIVE CABRIOLET',\n",
       " '650i xDRIVE COUPE',\n",
       " '650i xDRIVE GRAN COUPE',\n",
       " '650i xDrive Gran Coupe',\n",
       " '740Ld xDRIVE SEDAN',\n",
       " '740Li xDRIVE',\n",
       " '740Li xDRIVE SEDAN',\n",
       " '750Li xDRIVE',\n",
       " '750Li xDRIVE SEDAN',\n",
       " '750Li xDrive',\n",
       " '750Li xDrive Sedan',\n",
       " '750i xDRIVE',\n",
       " '750i xDRIVE SEDAN',\n",
       " '750i xDRIVE SWB',\n",
       " '750i xDrive',\n",
       " '750i xDrive Sedan',\n",
       " '760Li',\n",
       " '760Li SEDAN',\n",
       " '86',\n",
       " '911 CARRERA',\n",
       " '911 CARRERA 4',\n",
       " '911 CARRERA 4 CABRIOLET',\n",
       " '911 CARRERA 4 GTS',\n",
       " '911 CARRERA 4 GTS CABRIOLET',\n",
       " '911 CARRERA 4S',\n",
       " '911 CARRERA 4S CABRIOLET',\n",
       " '911 CARRERA CABRIOLET',\n",
       " '911 CARRERA GTS',\n",
       " '911 CARRERA GTS CABRIOLET',\n",
       " '911 CARRERA S',\n",
       " '911 CARRERA S CABRIOLET',\n",
       " '911 Carrera',\n",
       " '911 Carrera 4',\n",
       " '911 Carrera 4 Cabriolet',\n",
       " '911 Carrera 4 GTS',\n",
       " '911 Carrera 4 GTS Cabriolet',\n",
       " '911 Carrera 4S',\n",
       " '911 Carrera 4S Cabriolet',\n",
       " '911 Carrera Cabriolet',\n",
       " '911 Carrera GTS',\n",
       " '911 Carrera GTS Cabriolet',\n",
       " '911 Carrera S',\n",
       " '911 Carrera S Cabriolet',\n",
       " '911 Carrera T',\n",
       " '911 GT2 RS',\n",
       " '911 GT3',\n",
       " '911 GT3 RS',\n",
       " '911 GT3 Touring',\n",
       " '911 GT3RS',\n",
       " '911 Speedster',\n",
       " '911 TARGA 4',\n",
       " '911 TARGA 4 GTS',\n",
       " '911 TARGA 4S',\n",
       " '911 TURBO',\n",
       " '911 TURBO CABRIOLET',\n",
       " '911 TURBO S',\n",
       " '911 TURBO S CABRIOLET',\n",
       " '911 TURBO S EXCLUSIVE',\n",
       " '911 Targa 4',\n",
       " '911 Targa 4 GTS',\n",
       " '911 Targa 4S',\n",
       " '911 Turbo',\n",
       " '911 Turbo Cabriolet',\n",
       " '911 Turbo S',\n",
       " '911 Turbo S Cabriolet',\n",
       " '911 Turbo S Exclusive Cabriolet',\n",
       " 'A 220',\n",
       " 'A 220 4MATIC',\n",
       " 'A 250',\n",
       " 'A 250 4MATIC',\n",
       " 'A3',\n",
       " 'A3 CABRIOLET QUATTRO',\n",
       " 'A3 Cabriolet quattro',\n",
       " 'A3 QUATTRO',\n",
       " 'A3 TDI (modified)',\n",
       " 'A3 quattro',\n",
       " 'A4',\n",
       " 'A4 ALLROAD QUATTRO',\n",
       " 'A4 QUATTRO',\n",
       " 'A4 ULTRA',\n",
       " 'A4 allroad quattro',\n",
       " 'A4 quattro',\n",
       " 'A5 CABRIOLET QUATTRO',\n",
       " 'A5 Cabriolet quattro',\n",
       " 'A5 QUATTRO',\n",
       " 'A5 SPORTBACK QUATTRO',\n",
       " 'A5 Sportback quattro',\n",
       " 'A5 quattro',\n",
       " 'A6 QUATTRO',\n",
       " 'A6 QUATTRO TDI (modified)',\n",
       " 'A6 allroad',\n",
       " 'A6 quattro',\n",
       " 'A7 QUATTRO',\n",
       " 'A7 QUATTRO TDI (modified)',\n",
       " 'A7 quattro',\n",
       " 'A8',\n",
       " 'A8 QUATTRO',\n",
       " 'A8 QUATTRO TDI (modified)',\n",
       " 'A8 TDI (modified)',\n",
       " 'A8L',\n",
       " 'A8L QUATTRO',\n",
       " 'A8L TDI (modified)',\n",
       " 'ACADIA',\n",
       " 'ACADIA AWD',\n",
       " 'ACCENT',\n",
       " 'ACCORD',\n",
       " 'ACCORD COUPE',\n",
       " 'ACCORD HYBRID',\n",
       " 'ACCORD SEDAN',\n",
       " 'ACCORD SPORT',\n",
       " 'ACCORD SPORT/TOURING',\n",
       " 'ACTIVEHYBRID 3',\n",
       " 'ACTIVEHYBRID 5',\n",
       " 'ACTIVEHYBRID 7L',\n",
       " 'ALLROAD QUATTRO',\n",
       " 'ALPINA B6 xDRIVE GRAN COUPE',\n",
       " 'ALPINA B7 xDRIVE',\n",
       " 'ALPINA B7 xDRIVE LWB',\n",
       " 'ALPINA B7 xDRIVE SWB',\n",
       " 'ALTIMA',\n",
       " 'ALTIMA 3.5',\n",
       " 'ALTIMA SR',\n",
       " 'AMG C 43 4MATIC',\n",
       " 'AMG C 43 4MATIC CABRIOLET',\n",
       " 'AMG C 43 4MATIC COUPE',\n",
       " 'AMG C 43 4MATIC Cabriolet',\n",
       " 'AMG C 43 4MATIC Coupe',\n",
       " 'AMG C 43 4MATIC Wagon',\n",
       " 'AMG C 63',\n",
       " 'AMG C 63 CABRIOLET',\n",
       " 'AMG C 63 COUPE',\n",
       " 'AMG C 63 S',\n",
       " 'AMG C 63 S CABRIOLET',\n",
       " 'AMG C 63 S COUPE',\n",
       " 'AMG C 63 S Cabriolet',\n",
       " 'AMG C 63 S Coupe',\n",
       " 'AMG CLA 45',\n",
       " 'AMG CLA 45 4MATIC',\n",
       " 'AMG CLS 53 4MATIC+',\n",
       " 'AMG CLS 63 S 4MATIC',\n",
       " 'AMG E 43 4MATIC',\n",
       " 'AMG E 53 4MATIC+',\n",
       " 'AMG E 53 4MATIC+ Cabriolet',\n",
       " 'AMG E 53 4MATIC+ Coupe',\n",
       " 'AMG E 53 4MATIC+ Wagon',\n",
       " 'AMG E 63 S 4MATIC',\n",
       " 'AMG E 63 S 4MATIC WAGON',\n",
       " 'AMG E 63 S 4MATIC+',\n",
       " 'AMG E 63 S 4MATIC+ Wagon',\n",
       " 'AMG G 63',\n",
       " 'AMG G 65',\n",
       " 'AMG GL 63 S',\n",
       " 'AMG GLA 45',\n",
       " 'AMG GLA 45 4MATIC',\n",
       " 'AMG GLC 43 4MATIC',\n",
       " 'AMG GLC 43 4MATIC COUPE',\n",
       " 'AMG GLC 43 4MATIC Coupe',\n",
       " 'AMG GLC 63 S 4MATIC+',\n",
       " 'AMG GLC 63 S 4MATIC+ Coupe',\n",
       " 'AMG GLE 43 4MATIC',\n",
       " 'AMG GLE 43 4MATIC COUPE',\n",
       " 'AMG GLE 43 4MATIC Coupe',\n",
       " 'AMG GLE 63 S 4MATIC',\n",
       " 'AMG GLE 63 S 4MATIC COUPE',\n",
       " 'AMG GLE 63 S 4MATIC Coupe',\n",
       " 'AMG GLE 63 S COUPE 4MATIC',\n",
       " 'AMG GLS 63',\n",
       " 'AMG GLS 63 4MATIC',\n",
       " 'AMG GT 53 4MATIC+ Coupe',\n",
       " 'AMG GT 63 4MATIC+ Coupe',\n",
       " 'AMG GT 63 S 4MATIC+ Coupe',\n",
       " 'AMG GT C COUPE',\n",
       " 'AMG GT C Coupe',\n",
       " 'AMG GT C ROADSTER',\n",
       " 'AMG GT C Roadster',\n",
       " 'AMG GT COUPE',\n",
       " 'AMG GT R COUPE',\n",
       " 'AMG GT R Coupe',\n",
       " 'AMG GT ROADSTER',\n",
       " 'AMG GT S',\n",
       " 'AMG GT S COUPE',\n",
       " 'AMG S 63',\n",
       " 'AMG S 63 4MATIC',\n",
       " 'AMG S 63 4MATIC CABRIOLET',\n",
       " 'AMG S 63 4MATIC COUPE',\n",
       " 'AMG S 63 4MATIC+',\n",
       " 'AMG S 63 4MATIC+ Cabriolet',\n",
       " 'AMG S 63 4MATIC+ Coupe',\n",
       " 'AMG S 63 CABRIOLET',\n",
       " 'AMG S 63 COUPE',\n",
       " 'AMG S 65',\n",
       " 'AMG S 65 CABRIOLET',\n",
       " 'AMG S 65 COUPE',\n",
       " 'AMG S 65 Cabriolet',\n",
       " 'AMG S 65 Coupe',\n",
       " 'AMG SL 63',\n",
       " 'AMG SL 65',\n",
       " 'AMG SLC 43',\n",
       " 'AMG SLK 55',\n",
       " 'ARMADA 4WD',\n",
       " 'ATLAS',\n",
       " 'ATLAS 4MOTION',\n",
       " 'ATS',\n",
       " 'ATS AWD',\n",
       " 'ATS-V',\n",
       " 'AVALON',\n",
       " 'AVENGER',\n",
       " 'AVENGER FFV',\n",
       " 'AVENTADOR COUPE',\n",
       " 'AVENTADOR COUPE LP 740',\n",
       " 'AVENTADOR ROADSTER',\n",
       " 'AVENTADOR ROADSTER LP 740',\n",
       " 'AVENTADOR S COUPE',\n",
       " 'AVENTADOR S ROADSTER',\n",
       " 'Acadia',\n",
       " 'Acadia AWD',\n",
       " 'Accent',\n",
       " 'Accord',\n",
       " 'Accord Hybrid',\n",
       " 'Accord Sport/Touring',\n",
       " 'Alpina B6 xDrive Gran Coupe',\n",
       " 'Alpina B7 xDrive',\n",
       " 'Altima',\n",
       " 'Altima AWD',\n",
       " 'Altima AWD SR/Platinum',\n",
       " 'Altima SR/Platinum',\n",
       " 'Armada 4WD',\n",
       " 'Arteon 4MOTION',\n",
       " 'Ascent AWD',\n",
       " 'Atlas',\n",
       " 'Atlas 4MOTION',\n",
       " 'Atlas Cross Sport 4MOTION',\n",
       " 'Avalon',\n",
       " 'Aventador Coupe',\n",
       " 'Aventador Roadster',\n",
       " 'Aviator',\n",
       " 'B 250',\n",
       " 'B 250 4MATIC',\n",
       " 'BEETLE',\n",
       " 'BEETLE CONVERTIBLE',\n",
       " 'BEETLE DUNE',\n",
       " 'BEETLE DUNE CONVERTIBLE',\n",
       " 'BEETLE TDI (modified)',\n",
       " 'BENTAYGA',\n",
       " 'BOXSTER',\n",
       " 'BOXSTER GTS',\n",
       " 'BOXSTER S',\n",
       " 'BOXSTER SPYDER',\n",
       " 'BRZ',\n",
       " 'BRZ tS',\n",
       " 'Beetle',\n",
       " 'Beetle Convertible',\n",
       " 'Beetle Dune',\n",
       " 'Beetle Dune Convertible',\n",
       " 'Bentayga',\n",
       " 'Blazer',\n",
       " 'Blazer AWD',\n",
       " 'Boxster',\n",
       " 'Boxster GTS',\n",
       " 'Boxster S',\n",
       " 'C 250',\n",
       " 'C 250 COUPE',\n",
       " 'C 300 4MATIC',\n",
       " 'C 300 4MATIC CABRIOLET',\n",
       " 'C 300 4MATIC COUPE',\n",
       " 'C 300 4MATIC Cabriolet',\n",
       " 'C 300 4MATIC Coupe',\n",
       " 'C 300 4MATIC FFV',\n",
       " 'C 300 4MATIC WAGON',\n",
       " 'C 300 4MATIC Wagon',\n",
       " 'C 350',\n",
       " 'C 350 4MATIC',\n",
       " 'C 350 4MATIC COUPE',\n",
       " 'C 350 COUPE',\n",
       " 'C 400 4MATIC',\n",
       " 'C 450 AMG SPORT 4MATIC',\n",
       " 'C 63 AMG',\n",
       " 'C 63 AMG COUPE',\n",
       " 'C-HR',\n",
       " 'C-MAX HYBRID',\n",
       " 'CADENZA',\n",
       " 'CAMARO',\n",
       " 'CAMARO 2LS',\n",
       " 'CAMARO SS',\n",
       " 'CAMARO Z/28',\n",
       " 'CAMARO ZL1',\n",
       " 'CAMRY',\n",
       " 'CAMRY HYBRID LE',\n",
       " 'CAMRY HYBRID XLE/SE',\n",
       " 'CAMRY LE/SE',\n",
       " 'CAMRY XLE/XSE',\n",
       " 'CAMRY XSE',\n",
       " 'CANYON',\n",
       " 'CANYON 4WD',\n",
       " 'CARGO VAN FFV',\n",
       " 'CAYENNE',\n",
       " 'CAYENNE DIESEL (modified)',\n",
       " 'CAYENNE GTS',\n",
       " 'CAYENNE PLATINUM',\n",
       " 'CAYENNE S',\n",
       " 'CAYENNE S HYBRID',\n",
       " 'CAYENNE TURBO',\n",
       " 'CAYENNE TURBO S',\n",
       " 'CAYMAN',\n",
       " 'CAYMAN GT4',\n",
       " 'CAYMAN GTS',\n",
       " 'CAYMAN S',\n",
       " 'CC',\n",
       " 'CC 4MOTION',\n",
       " 'CHALLENGER',\n",
       " 'CHALLENGER (MDS)',\n",
       " 'CHALLENGER GT',\n",
       " 'CHALLENGER GT AWD',\n",
       " 'CHALLENGER GT AWD FFV',\n",
       " 'CHALLENGER SRT',\n",
       " 'CHALLENGER SRT (MDS)',\n",
       " 'CHALLENGER SRT DEMON',\n",
       " 'CHALLENGER SRT HELLCAT',\n",
       " 'CHARGER',\n",
       " 'CHARGER (MDS)',\n",
       " 'CHARGER AWD',\n",
       " 'CHARGER AWD (MDS)',\n",
       " 'CHARGER AWD FFV',\n",
       " 'CHARGER FFV',\n",
       " 'CHARGER SRT (MDS)',\n",
       " 'CHARGER SRT 392 HEMI',\n",
       " 'CHARGER SRT HELLCAT',\n",
       " 'CHEROKEE',\n",
       " 'CHEROKEE 4X4',\n",
       " 'CHEROKEE 4X4 ACTIVE DRIVE I',\n",
       " 'CHEROKEE 4X4 ACTIVE DRIVE I FFV',\n",
       " 'CHEROKEE 4X4 ACTIVE DRIVE II',\n",
       " 'CHEROKEE 4X4 ACTIVE DRIVE LOCK',\n",
       " 'CHEROKEE 4X4 FFV',\n",
       " 'CHEROKEE FFV',\n",
       " 'CHEROKEE TRAILHAWK 4X4',\n",
       " 'CHIRON',\n",
       " 'CITY EXPRESS',\n",
       " 'CIVIC',\n",
       " 'CIVIC COUPE',\n",
       " 'CIVIC COUPE Si',\n",
       " 'CIVIC HATCHBACK',\n",
       " 'CIVIC HATCHBACK SPORT',\n",
       " 'CIVIC HYBRID',\n",
       " 'CIVIC SEDAN',\n",
       " 'CIVIC SEDAN Si',\n",
       " 'CIVIC Si',\n",
       " 'CIVIC TYPE R',\n",
       " 'CL 550 4MATIC',\n",
       " 'CL 63 AMG',\n",
       " 'CLA 250',\n",
       " 'CLA 250 4MATIC',\n",
       " 'CLA 250 4MATIC FFV',\n",
       " 'CLA 45 AMG',\n",
       " 'CLA 45 AMG 4MATIC',\n",
       " 'CLS 400 4MATIC',\n",
       " 'CLS 450 4MATIC',\n",
       " 'CLS 550 4MATIC',\n",
       " 'CLS 63 AMG 4MATIC',\n",
       " 'CLS 63 AMG S 4MATIC',\n",
       " 'COLORADO',\n",
       " 'COLORADO 4WD',\n",
       " 'COLORADO ZR2 4WD',\n",
       " 'COMPASS',\n",
       " 'COMPASS 4X4',\n",
       " 'COMPASS 4X4 TRAIL RATED',\n",
       " 'CONTINENTAL AWD',\n",
       " 'CONTINENTAL GT',\n",
       " 'CONTINENTAL GT CONVERTIBLE',\n",
       " 'CONTINENTAL GT SPEED CONVERTIBLE',\n",
       " 'CONTINENTAL GT3-R',\n",
       " 'CONTINENTAL GTC',\n",
       " 'CONTINENTAL SUPERSPORTS',\n",
       " 'COOPER 3 DOOR',\n",
       " 'COOPER 5 DOOR',\n",
       " 'COOPER CLUBMAN',\n",
       " 'COOPER CLUBMAN ALL4',\n",
       " 'COOPER CONVERTIBLE',\n",
       " 'COOPER COUNTRYMAN',\n",
       " 'COOPER COUNTRYMAN ALL4',\n",
       " 'COOPER COUPE',\n",
       " 'COOPER PACEMAN',\n",
       " 'COOPER ROADSTER',\n",
       " 'COOPER S 3 DOOR',\n",
       " 'COOPER S 5 DOOR',\n",
       " 'COOPER S CLUBMAN',\n",
       " 'COOPER S CLUBMAN ALL4',\n",
       " 'COOPER S CONVERTIBLE',\n",
       " 'COOPER S COUNTRYMAN  ALL4',\n",
       " 'COOPER S COUNTRYMAN ALL4',\n",
       " 'COOPER S COUPE',\n",
       " 'COOPER S PACEMAN ALL4',\n",
       " 'COOPER S ROADSTER',\n",
       " 'COROLLA',\n",
       " 'COROLLA LE ECO (1-mode)',\n",
       " 'COROLLA LE ECO (2-mode)',\n",
       " 'COROLLA MATRIX',\n",
       " 'COROLLA iM',\n",
       " 'CORVETTE',\n",
       " 'CORVETTE Z06',\n",
       " 'CR-V',\n",
       " 'CR-V AWD',\n",
       " 'CR-Z',\n",
       " 'CROSSTOUR AWD',\n",
       " 'CROSSTREK AWD',\n",
       " 'CRUZE',\n",
       " 'CRUZE DIESEL',\n",
       " 'CRUZE ECO',\n",
       " 'CRUZE HATCHBACK',\n",
       " 'CRUZE HATCHBACK DIESEL',\n",
       " 'CRUZE HATCHBACK PREMIER',\n",
       " 'CRUZE LIMITED',\n",
       " 'CRUZE LIMITED ECO',\n",
       " 'CRUZE PREMIER',\n",
       " 'CT 200h',\n",
       " 'CT4',\n",
       " 'CT4 AWD',\n",
       " 'CT4-V',\n",
       " 'CT4-V AWD',\n",
       " 'CT5',\n",
       " 'CT5 AWD',\n",
       " 'CT5-V',\n",
       " 'CT5-V AWD',\n",
       " 'CT6',\n",
       " 'CT6 AWD',\n",
       " 'CTS',\n",
       " 'CTS AWD',\n",
       " 'CTS COUPE',\n",
       " 'CTS COUPE AWD',\n",
       " 'CTS SEDAN',\n",
       " 'CTS SEDAN AWD',\n",
       " 'CTS SEDAN Vsport',\n",
       " 'CTS SPORT WAGON',\n",
       " 'CTS SPORT WAGON AWD',\n",
       " 'CTS Vsport',\n",
       " 'CTS-V',\n",
       " 'CTS-V COUPE',\n",
       " 'CTS-V SEDAN',\n",
       " 'CTS-V SPORT WAGON',\n",
       " 'CX-3',\n",
       " 'CX-3 (SIL)',\n",
       " 'CX-3 4WD',\n",
       " 'CX-30',\n",
       " 'CX-30 4WD',\n",
       " 'CX-30 4WD (Cylinder Deactivation)',\n",
       " 'CX-5',\n",
       " 'CX-5 ',\n",
       " 'CX-5 (Cylinder Deactivation)',\n",
       " 'CX-5 (SIL)',\n",
       " 'CX-5 4WD',\n",
       " 'CX-5 4WD (Cylinder Deactivation)',\n",
       " 'CX-5 Diesel 4WD',\n",
       " 'CX-5 Turbo 4WD',\n",
       " 'CX-9',\n",
       " 'CX-9 4WD',\n",
       " 'Cadenza',\n",
       " 'Camaro',\n",
       " 'Camaro SS',\n",
       " 'Camaro ZL1',\n",
       " 'Camry',\n",
       " 'Camry AWD LE/SE',\n",
       " 'Camry AWD XLE/XSE',\n",
       " 'Camry Hybrid LE',\n",
       " 'Camry Hybrid XLE/SE',\n",
       " 'Camry LE/SE',\n",
       " 'Camry TRD',\n",
       " 'Camry XLE/XSE',\n",
       " 'Camry XSE',\n",
       " 'Canyon',\n",
       " 'Canyon 4WD',\n",
       " 'Cayenne',\n",
       " 'Cayenne S',\n",
       " 'Cayenne Turbo',\n",
       " 'Cayman',\n",
       " 'Cayman GTS',\n",
       " 'Cayman S',\n",
       " 'Challenger',\n",
       " 'Challenger (MDS)',\n",
       " 'Challenger AWD',\n",
       " 'Challenger GT AWD',\n",
       " 'Challenger SRT Hellcat',\n",
       " 'Challenger SRT Hellcat Redeye',\n",
       " 'Challenger SRT Hellcat Widebody',\n",
       " 'Challenger Widebody (MDS)',\n",
       " 'Charger',\n",
       " 'Charger (MDS)',\n",
       " 'Charger AWD',\n",
       " 'Charger AWD (MDS)',\n",
       " 'Charger AWD FFV',\n",
       " 'Charger FFV',\n",
       " 'Charger SRT Hellcat',\n",
       " 'Charger SRT Hellcat Widebody',\n",
       " 'Charger Widebody (MDS)',\n",
       " 'Cherokee',\n",
       " 'Cherokee 4X4 Active Drive I',\n",
       " 'Cherokee 4X4 Active Drive II',\n",
       " 'Cherokee 4X4 Active Drive Lock',\n",
       " 'Chiron',\n",
       " 'Civic Coupe',\n",
       " 'Civic Coupe Si',\n",
       " 'Civic Hatchback',\n",
       " 'Civic Hatchback Sport',\n",
       " 'Civic Sedan',\n",
       " 'Civic Sedan Si',\n",
       " 'Civic Type R',\n",
       " 'Colorado',\n",
       " 'Colorado 4WD',\n",
       " 'Colorado ZR2 4WD',\n",
       " 'Compass',\n",
       " 'Compass 4X4',\n",
       " 'Continental AWD',\n",
       " 'Continental GT',\n",
       " 'Continental GT Convertible',\n",
       " 'Cooper 3 Door',\n",
       " 'Cooper 5 Door',\n",
       " 'Cooper Clubman ALL4',\n",
       " 'Cooper Convertible',\n",
       " 'Cooper Countryman ALL4',\n",
       " 'Cooper S 3 Door',\n",
       " 'Cooper S 5 Door',\n",
       " 'Cooper S Clubman ALL4',\n",
       " 'Cooper S Convertible',\n",
       " 'Cooper S Countryman ALL4',\n",
       " 'Corolla',\n",
       " 'Corolla Hatchback',\n",
       " 'Corolla Hybrid',\n",
       " 'Corolla LE Eco',\n",
       " 'Corolla XLE',\n",
       " 'Corolla XSE',\n",
       " 'Corsair AWD',\n",
       " 'Corvette',\n",
       " 'Corvette Z06',\n",
       " 'Corvette ZR1',\n",
       " 'Crosstrek AWD',\n",
       " 'Cruze',\n",
       " 'Cruze Diesel',\n",
       " 'Cruze Hatchback',\n",
       " 'Cruze Hatchback Diesel',\n",
       " 'Cruze Hatchback Premier',\n",
       " 'Cruze Premier',\n",
       " 'Cullinan',\n",
       " 'Cullinan Black Badge',\n",
       " 'DART',\n",
       " 'DART FFV',\n",
       " 'DART GT',\n",
       " 'DART TURBO AERO',\n",
       " 'DAWN',\n",
       " 'DB11 AMR',\n",
       " 'DB11 V12',\n",
       " 'DB11 V8',\n",
       " 'DB9',\n",
       " 'DB9 GT',\n",
       " 'DBS Superleggera',\n",
       " 'DISCOVERY SPORT',\n",
       " 'DURANGO AWD',\n",
       " 'DURANGO AWD (MDS)',\n",
       " 'DURANGO AWD FFV',\n",
       " 'DURANGO AWD SRT',\n",
       " 'Dawn',\n",
       " 'Discovery',\n",
       " 'Discovery Sport',\n",
       " 'Discovery Sport P250',\n",
       " 'Discovery Sport P290',\n",
       " 'Discovery TD6 Diesel',\n",
       " 'Durango AWD',\n",
       " 'Durango AWD SRT',\n",
       " 'E 250 BLUETEC 4MATIC',\n",
       " 'E 300 4MATIC',\n",
       " 'E 350 4MATIC',\n",
       " 'E 350 4MATIC COUPE',\n",
       " 'E 350 4MATIC WAGON',\n",
       " 'E 350 CABRIOLET',\n",
       " 'E 350 COUPE',\n",
       " 'E 400 4MATIC',\n",
       " 'E 400 4MATIC CABRIOLET',\n",
       " 'E 400 4MATIC COUPE',\n",
       " 'E 400 4MATIC WAGON',\n",
       " 'E 400 CABRIOLET',\n",
       " 'E 400 COUPE',\n",
       " 'E 400 HYBRID',\n",
       " 'E 450 4MATIC',\n",
       " 'E 450 4MATIC Cabriolet',\n",
       " 'E 450 4MATIC Coupe',\n",
       " 'E 450 4MATIC Wagon',\n",
       " 'E 550 4MATIC',\n",
       " 'E 550 CABRIOLET',\n",
       " 'E 550 COUPE',\n",
       " 'E 63 AMG 4MATIC',\n",
       " 'E 63 AMG 4MATIC WAGON',\n",
       " 'E 63 AMG S 4MATIC',\n",
       " 'E 63 AMG S 4MATIC WAGON',\n",
       " 'E-PACE P250',\n",
       " 'E-PACE P300',\n",
       " 'E150 VAN FFV',\n",
       " 'E150 WAGON FFV',\n",
       " 'E350 WAGON',\n",
       " 'E350 WAGON FFV',\n",
       " 'ECLIPSE CROSS',\n",
       " 'ECLIPSE CROSS 4WD',\n",
       " 'ECOSPORT',\n",
       " 'ECOSPORT AWD',\n",
       " 'EDGE',\n",
       " 'EDGE (Start/Stop)',\n",
       " 'EDGE AWD',\n",
       " 'EDGE SPORT AWD',\n",
       " 'ELANTRA',\n",
       " 'ELANTRA COUPE',\n",
       " 'ELANTRA GT',\n",
       " 'ENCLAVE',\n",
       " 'ENCLAVE AWD',\n",
       " 'ENCORE',\n",
       " 'ENCORE (LE2 Engine)',\n",
       " 'ENCORE (LUV Engine)',\n",
       " 'ENCORE (SIDI with Stop/Start)',\n",
       " 'ENCORE AWD',\n",
       " 'ENCORE AWD (LE2 Engine)',\n",
       " 'ENCORE AWD (LUV Engine)',\n",
       " 'ENCORE AWD (SIDI with Stop/Start)',\n",
       " 'ENCORE SPORT TOURING',\n",
       " 'ENCORE SPORT TOURING AWD',\n",
       " 'ENVISION AWD',\n",
       " 'EOS',\n",
       " 'EQUINOX',\n",
       " 'EQUINOX AWD',\n",
       " 'EQUINOX AWD FFV',\n",
       " 'EQUINOX FFV',\n",
       " 'EQUUS',\n",
       " 'ES 300h',\n",
       " 'ES 350',\n",
       " 'ES 350 F SPORT',\n",
       " 'ESCALADE 4WD',\n",
       " 'ESCALADE AWD',\n",
       " 'ESCALADE ESV 4WD',\n",
       " 'ESCALADE ESV AWD',\n",
       " 'ESCAPE',\n",
       " 'ESCAPE AWD',\n",
       " 'ESCAPE FFV',\n",
       " 'EXPEDITION 4X4',\n",
       " 'EXPEDITION 4X4 FFV',\n",
       " 'EXPEDITION EL 4X4',\n",
       " 'EXPEDITION MAX 4X4',\n",
       " 'EXPLORER',\n",
       " 'EXPLORER AWD',\n",
       " 'EXPLORER AWD (EcoBoost)',\n",
       " 'EXPLORER FFV',\n",
       " 'EXPLORER FFV AWD',\n",
       " 'EXPRESS 1500 CARGO',\n",
       " 'EXPRESS 1500 CARGO AWD',\n",
       " 'EXPRESS 1500 CARGO CONV',\n",
       " 'EXPRESS 1500 CARGO CONV AWD',\n",
       " 'EXPRESS 1500 PASSENGER',\n",
       " 'EXPRESS 1500 PASSENGER AWD',\n",
       " 'EXPRESS 2500 PASSENGER',\n",
       " 'EXPRESS 2500 PASSENGER FFV',\n",
       " 'EXPRESS 3500 PASSENGER',\n",
       " 'EXPRESS 3500 PASSENGER FFV',\n",
       " 'Eclipse Cross 4WD',\n",
       " 'EcoSport',\n",
       " 'EcoSport AWD',\n",
       " 'Edge',\n",
       " 'Edge AWD',\n",
       " 'Elantra',\n",
       " 'Elantra GT',\n",
       " 'Enclave',\n",
       " 'Enclave AWD',\n",
       " 'Encore',\n",
       " 'Encore (SIDI with Stop/Start)',\n",
       " 'Encore AWD',\n",
       " 'Encore AWD (SIDI with Stop/Start)',\n",
       " 'Encore GX',\n",
       " 'Encore GX AWD',\n",
       " 'Envision AWD',\n",
       " 'Equinox',\n",
       " 'Equinox AWD',\n",
       " 'Escalade 4WD',\n",
       " 'Escape',\n",
       " 'Escape AWD',\n",
       " 'Escape FFV',\n",
       " 'Escape Hybrid',\n",
       " 'Escape Hybrid AWD',\n",
       " 'Expedition 4X4',\n",
       " 'Expedition MAX 4X4',\n",
       " 'Explorer AWD',\n",
       " 'Explorer FFV AWD',\n",
       " 'Explorer Hybrid AWD',\n",
       " 'F-150',\n",
       " 'F-150 (LT Tire Pkg)',\n",
       " 'F-150 (Payload Pkg)',\n",
       " 'F-150 4X4',\n",
       " 'F-150 4X4 (LT Tire Pkg)',\n",
       " 'F-150 4X4 (Payload Pkg)',\n",
       " 'F-150 4X4 Limited',\n",
       " 'F-150 4X4 XL/XLT',\n",
       " 'F-150 FFV',\n",
       " 'F-150 FFV (LT Tire Pkg)',\n",
       " 'F-150 FFV (Payload Pkg)',\n",
       " 'F-150 FFV 4X4',\n",
       " 'F-150 FFV 4X4 (LT Tire Pkg)',\n",
       " 'F-150 FFV 4X4 (Payload Pkg)',\n",
       " 'F-150 RAPTOR 4WD',\n",
       " 'F-150 RAPTOR 4X4',\n",
       " 'F-150 Raptor 4X4',\n",
       " 'F-PACE 20d',\n",
       " 'F-PACE 25t',\n",
       " 'F-PACE 30t',\n",
       " 'F-PACE 35t',\n",
       " 'F-PACE S',\n",
       " 'F-PACE SVR',\n",
       " 'F-TYPE CONVERTIBLE',\n",
       " 'F-TYPE CONVERTIBLE R-DYNAMIC',\n",
       " 'F-TYPE CONVERTIBLE R-DYNAMIC AWD',\n",
       " 'F-TYPE COUPE',\n",
       " 'F-TYPE COUPE R-DYNAMIC',\n",
       " 'F-TYPE COUPE R-DYNAMIC AWD',\n",
       " 'F-TYPE Convertible',\n",
       " 'F-TYPE Convertible R-Dynamic',\n",
       " 'F-TYPE Convertible R-Dynamic AWD',\n",
       " 'F-TYPE Coupe',\n",
       " 'F-TYPE Coupe R-Dynamic',\n",
       " 'F-TYPE Coupe R-Dynamic AWD',\n",
       " 'F-TYPE P300 Convertible',\n",
       " 'F-TYPE P300 Coupe',\n",
       " 'F-TYPE PROJECT 7 CONVERTIBLE',\n",
       " 'F-TYPE R AWD CONVERTIBLE',\n",
       " 'F-TYPE R AWD COUPE',\n",
       " 'F-TYPE R AWD Convertible',\n",
       " 'F-TYPE R AWD Coupe',\n",
       " 'F-TYPE R CONVERTIBLE AWD',\n",
       " 'F-TYPE R COUPE AWD',\n",
       " 'F-TYPE S CONVERTIBLE',\n",
       " 'F-TYPE S CONVERTIBLE AWD',\n",
       " 'F-TYPE S COUPE',\n",
       " 'F-TYPE S COUPE AWD',\n",
       " 'F-TYPE SVR AWD Convertible',\n",
       " 'F-TYPE SVR AWD Coupe',\n",
       " 'F-TYPE V8 R COUPE',\n",
       " 'F-TYPE V8 S CONVERTIBLE',\n",
       " 'FIESTA',\n",
       " 'FIESTA SFE',\n",
       " 'FIESTA ST',\n",
       " 'FIT',\n",
       " 'FJ CRUISER 4WD',\n",
       " 'FLEX',\n",
       " 'FLEX AWD',\n",
       " 'FLEX AWD (EcoBoost)',\n",
       " 'FLEX AWD GTDI',\n",
       " 'FLYING SPUR',\n",
       " 'FOCUS',\n",
       " 'FOCUS (Start/Stop)',\n",
       " 'FOCUS FFV',\n",
       " 'FOCUS RS AWD',\n",
       " 'FOCUS SFE FFV',\n",
       " 'FOCUS ST',\n",
       " 'FORESTER AWD',\n",
       " 'FORTE',\n",
       " 'FORTE (GDI)',\n",
       " 'FORTE (MPI)',\n",
       " 'FORTE 5',\n",
       " 'FORTE KOUP',\n",
       " 'FORTWO CABRIOLET',\n",
       " 'FORTWO COUPE',\n",
       " 'FR-S',\n",
       " 'FRONTIER',\n",
       " 'FRONTIER 4WD',\n",
       " 'FUSION',\n",
       " 'FUSION (Start/Stop)',\n",
       " 'FUSION AWD',\n",
       " 'FUSION HYBRID',\n",
       " 'Fiesta',\n",
       " 'Fiesta ST',\n",
       " 'Fit',\n",
       " 'Flex',\n",
       " 'Flex AWD',\n",
       " 'Flex AWD GTDI',\n",
       " 'Flying Spur',\n",
       " 'Forester AWD',\n",
       " 'Forte',\n",
       " 'Forte 5',\n",
       " 'Frontier',\n",
       " 'Frontier 4WD',\n",
       " 'Fusion',\n",
       " 'Fusion Hybrid',\n",
       " 'G 550',\n",
       " 'G 63 AMG',\n",
       " 'G70',\n",
       " 'G70 AWD',\n",
       " 'G80 AWD',\n",
       " 'G90 AWD',\n",
       " 'GALLARDO COUPE',\n",
       " 'GENESIS',\n",
       " 'GENESIS AWD',\n",
       " 'GENESIS COUPE',\n",
       " 'GHIBLI',\n",
       " 'GHIBLI AWD',\n",
       " 'GHIBLI S',\n",
       " 'GHIBLI SQ4',\n",
       " 'GHOST',\n",
       " 'GHOST EWB',\n",
       " 'GIULIA',\n",
       " 'GIULIA AWD',\n",
       " 'GIULIA QUADRIFOGLIO',\n",
       " 'GL 350 BLUETEC 4MATIC',\n",
       " 'GL 450 4MATIC',\n",
       " 'GL 550 4MATIC',\n",
       " 'GL 63 AMG',\n",
       " 'GLA 250 4MATIC',\n",
       " 'GLA 45 AMG 4MATIC',\n",
       " 'GLC 300 4MATIC',\n",
       " 'GLC 300 4MATIC COUPE',\n",
       " 'GLC 300 4MATIC Coupe',\n",
       " 'GLE 350d 4MATIC',\n",
       " 'GLE 350d 4MATIC COUPE',\n",
       " 'GLE 400 4MATIC',\n",
       " 'GLE 450 AMG SPORT 4MATIC COUPE',\n",
       " 'GLE 550 4MATIC',\n",
       " 'GLK 250 BLUETEC 4MATIC',\n",
       " 'GLK 350 4MATIC',\n",
       " 'GLS 450 4MATIC',\n",
       " 'GLS 550 4MATIC',\n",
       " 'GOLF',\n",
       " 'GOLF ALLTRACK',\n",
       " 'GOLF GTI',\n",
       " ...]"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get unique values from the 'Model' column and store them in 'unique_values'\n",
    "unique_values = df['Model'].unique()\n",
    "\n",
    "# Create an empty list 'array1' to hold the unique values\n",
    "array1 = []\n",
    "\n",
    "\n",
    "# Iterate through each unique value and append it to 'array1'\n",
    "for value in unique_values:\n",
    "    # print(value)\n",
    "    array1.append(value)\n",
    "\n",
    "# Sort the 'array1' to get a list of unique values in alphabetical order\n",
    "sorted_list = sorted(array1)\n",
    "sorted_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "84deeb26-6ce1-4b5f-a46c-06fb337755ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing inconsistent values in the \"Model\" column\n",
    "df['Model'] = df['Model'].replace(' 4C', '4C')\n",
    "df['Model'] = df['Model'].replace(' A4 QUATTRO', 'A4 QUATTRO')\n",
    "df['Model'] = df['Model'].replace(' A6 QUATTRO', 'A6 QUATTRO')\n",
    "df['Model'] = df['Model'].replace(' s7', 'S7')\n",
    "df['Model'] = df['Model'].replace(' F-150 FFV (Payload Pkg)', 'F-150 FFV (Payload Pkg)')\n",
    "df['Model'] = df['Model'].replace(' 911 Carrera S', '911 Carrera S')\n",
    "df['Model'] = df['Model'].replace(' Transit Connect Van FFV', 'Transit Connect Van FFV')\n",
    "df['Model'] = df['Model'].replace(' Phantom', 'Phantom')\n",
    "df['Model'] = df['Model'].replace(' 1500 eTorque', '1500 eTorque')\n",
    "df['Model'] = df['Model'].replace(' Crosstrek AWD', 'Crosstrek AWD')\n",
    "df['Model'] = df['Model'].replace(' 4Runner 4WD', '4Runner 4WD')\n",
    "df['Model'] = df['Model'].replace(' Sienna', 'Sienna')\n",
    "df['Model'] = df['Model'].replace(' Eclipse Cross 4WD', 'Eclipse Cross 4WD')\n",
    "df['Model'] = df['Model'].replace(' Cooper 3 Door', 'Cooper 3 Door')\n",
    "df['Model'] = df['Model'].replace(' AMG C 43 4MATIC', 'AMG C 43 4MATIC')\n",
    "df['Model'] = df['Model'].replace(' CX-3', 'CX-3')\n",
    "df['Model'] = df['Model'].replace(' E 550 4MATIC', 'E 550 CABRIOLET')\n",
    "df['Model'] = df['Model'].replace(' E 550 CABRIOLET', 'E 550 CABRIOLET')\n",
    "df['Model'] = df['Model'].replace(' E 63 AMG 4MATIC WAGON', 'E 63 AMG 4MATIC WAGON')\n",
    "df['Model'] = df['Model'].replace(' GT', 'GT')\n",
    "df['Model'] = df['Model'].replace(' Ghibli', 'Ghibli')\n",
    "df['Model'] = df['Model'].replace('s7', 'S7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "db181317-5ed5-42a7-a199-a1fb0753907a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2053"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Model'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "5ffaeaeb-da78-434e-94e1-f3ee9cf968f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       1\n",
       "Engine Size(L)                      0\n",
       "Cylinders                           0\n",
       "Transmission                        2\n",
       "Fuel Type                           1\n",
       "Fuel Consumption City (L/100 km)    2\n",
       "Fuel Consumption Hwy (L/100 km)     1\n",
       "Fuel Consumption Comb (L/100 km)    2\n",
       "Fuel Consumption Comb (mpg)         2\n",
       "CO2 Emissions(g/km)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "09184fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>30.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "28  ACURA  TLX SH-AWD       COMPACT             3.5        6.0          AS9   \n",
       "36  ACURA  TLX SH-AWD       COMPACT             3.5        6.0          AS9   \n",
       "45  ACURA  TLX SH-AWD       COMPACT             3.5        6.0          AS9   \n",
       "55  ACURA  TLX SH-AWD           NaN             3.5        6.0          AS9   \n",
       "56  ACURA  TLX SH-AWD       COMPACT            -5.6        6.0         AS10   \n",
       "68  ACURA  TLX SH-AWD       COMPACT             3.5        6.0          AS9   \n",
       "78  ACURA  TLX SH-AWD       COMPACT             3.5        6.0          AS9   \n",
       "\n",
       "   Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "28         Z                              11.2   \n",
       "36         Z                              11.2   \n",
       "45         Z                              11.2   \n",
       "55         Z                              11.4   \n",
       "56         Z                              10.0   \n",
       "68         Z                              11.4   \n",
       "78         Z                              11.4   \n",
       "\n",
       "    Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "28                              7.5                               9.6   \n",
       "36                              7.5                               9.6   \n",
       "45                              7.5                               9.6   \n",
       "55                              7.7                               9.8   \n",
       "56                              1.2                               5.6   \n",
       "68                              7.7                               9.8   \n",
       "78                              7.7                               9.8   \n",
       "\n",
       "    Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "28                         29.0                221.0  \n",
       "36                         29.0                226.0  \n",
       "45                         29.0                226.0  \n",
       "55                         29.0                228.0  \n",
       "56                         30.0                228.0  \n",
       "68                         29.0                228.0  \n",
       "78                         29.0                228.0  "
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the DataFrame to select rows where both \"Make\" is \"ACURA\" and \"Model\" is \"TLX SH-AWD\"\n",
    "df[(df[\"Make\"] == \"ACURA\") & (df[\"Model\"] == \"TLX SH-AWD\") ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "1d5acd51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in the \"Vehicle Class\" column with the value \"COMPACT\" after research\n",
    "df.loc[df[\"Vehicle Class\"].isnull(), \"Vehicle Class\"] = \"COMPACT\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "c682daf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       0\n",
       "Engine Size(L)                      0\n",
       "Cylinders                           0\n",
       "Transmission                        2\n",
       "Fuel Type                           1\n",
       "Fuel Consumption City (L/100 km)    2\n",
       "Fuel Consumption Hwy (L/100 km)     1\n",
       "Fuel Consumption Comb (L/100 km)    2\n",
       "Fuel Consumption Comb (mpg)         2\n",
       "CO2 Emissions(g/km)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "dd449c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
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       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB11 V12</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>12.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Make     Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "147  ASTON MARTIN  DB11 V12   MINICOMPACT             4.0       14.0   \n",
       "\n",
       "    Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "147          NaN       NaN                               NaN   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "147                             14.4                              12.1   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "147                         18.0                363.0  "
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values in the \"Fuel Type\" column\n",
    "df[df[[\"Fuel Type\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "b4242321",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB11 V12</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>5.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.5</td>\n",
       "      <td>11.4</td>\n",
       "      <td>13.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB11 V12</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>5.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.5</td>\n",
       "      <td>11.4</td>\n",
       "      <td>13.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB11 V12</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.4</td>\n",
       "      <td>12.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Make     Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "138  ASTON MARTIN  DB11 V12   MINICOMPACT             5.2       12.0   \n",
       "144  ASTON MARTIN  DB11 V12   MINICOMPACT             5.2       12.0   \n",
       "147  ASTON MARTIN  DB11 V12   MINICOMPACT             4.0       14.0   \n",
       "\n",
       "    Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "138           A8         Z                              15.5   \n",
       "144           A8         Z                              15.5   \n",
       "147          NaN       NaN                               NaN   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "138                             11.4                              13.7   \n",
       "144                             11.4                              13.7   \n",
       "147                             14.4                              12.1   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "138                         21.0                322.0  \n",
       "144                         21.0                322.0  \n",
       "147                         18.0                363.0  "
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the DataFrame to select rows where the \"Make\" is \"ASTON MARTIN\" and the \"Model\" is\n",
    "df[(df[\"Make\"] == \"ASTON MARTIN\") & (df[\"Model\"] == \"DB11 V12\") ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "e9454348",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in the \"Transmission\" column with \"A8\" and in the \"Fuel Type\" column with \"Z\" after research\n",
    "df.loc[df[\"Fuel Type\"].isnull(), \"Transmission\"] = \"A8\"\n",
    "df.loc[df[\"Fuel Type\"].isnull(), \"Fuel Type\"] = \"Z\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "id": "0d6e24b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB11 V12</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>5.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.5</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>40.954357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB11 V12</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>5.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.5</td>\n",
       "      <td>11.4</td>\n",
       "      <td>13.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>40.954357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB11 V12</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.7</td>\n",
       "      <td>14.4</td>\n",
       "      <td>12.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>46.908714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Make     Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "138  ASTON MARTIN  DB11 V12   MINICOMPACT             5.2       12.0   \n",
       "144  ASTON MARTIN  DB11 V12   MINICOMPACT             5.2       12.0   \n",
       "147  ASTON MARTIN  DB11 V12   MINICOMPACT             4.0       14.0   \n",
       "\n",
       "    Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "138           A8         Z                              15.5   \n",
       "144           A8         Z                              15.5   \n",
       "147           A8         Z                              10.7   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "138                             11.4                              13.7   \n",
       "144                             11.4                              13.7   \n",
       "147                             14.4                              12.1   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "138                         21.0            40.954357  \n",
       "144                         21.0            40.954357  \n",
       "147                         18.0            46.908714  "
      ]
     },
     "execution_count": 580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the DataFrame to select rows where the \"Make\" is \"ASTON MARTIN\" and the \"Model\" is \"DB11 V12\"\n",
    "df[(df[\"Make\"] == \"ASTON MARTIN\") & (df[\"Model\"] == \"DB11 V12\") ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "97d3e47c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       0\n",
       "Engine Size(L)                      0\n",
       "Cylinders                           0\n",
       "Transmission                        1\n",
       "Fuel Type                           0\n",
       "Fuel Consumption City (L/100 km)    2\n",
       "Fuel Consumption Hwy (L/100 km)     1\n",
       "Fuel Consumption Comb (L/100 km)    2\n",
       "Fuel Consumption Comb (mpg)         2\n",
       "CO2 Emissions(g/km)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "id": "c8abe5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>BMW</td>\n",
       "      <td>335i SEDAN</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "590  BMW  335i SEDAN       COMPACT            10.0        2.0          NaN   \n",
       "\n",
       "    Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "590         Z                              11.5   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "590                              8.0                               NaN   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "590                         72.0                 85.0  "
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values in the \"Transmission\" column\n",
    "df[df[[\"Transmission\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "id": "3ef426f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
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       "      <th>Cylinders</th>\n",
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       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>BMW</td>\n",
       "      <td>335i SEDAN</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>Z</td>\n",
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       "      <td>29.0</td>\n",
       "      <td>221.0</td>\n",
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       "      <th>589</th>\n",
       "      <td>BMW</td>\n",
       "      <td>335i SEDAN</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>BMW</td>\n",
       "      <td>335i SEDAN</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "588  BMW  335i SEDAN       COMPACT             3.0        6.0          AS8   \n",
       "589  BMW  335i SEDAN       COMPACT             3.0        6.0           M6   \n",
       "590  BMW  335i SEDAN       COMPACT            10.0        2.0          NaN   \n",
       "\n",
       "    Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "588         Z                              11.4   \n",
       "589         Z                              11.5   \n",
       "590         Z                              11.5   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "588                              7.4                               9.6   \n",
       "589                              7.8                               9.8   \n",
       "590                              8.0                               NaN   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "588                         29.0                221.0  \n",
       "589                         29.0                225.0  \n",
       "590                         72.0                 85.0  "
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering rows for a specific make and model (BMW 335i SEDAN)\n",
    "df[(df[\"Make\"] == \"BMW\") & (df[\"Model\"] == \"335i SEDAN\") ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "08667b21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows with missing values in the \"Transmission\" column\n",
    "df.dropna(subset=[\"Transmission\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "id": "91b6ab0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
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       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
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       "  </tbody>\n",
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       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ReChecking for missing values in the \"Transmission\" column\n",
    "df[df[[\"Transmission\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "c36ffee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       0\n",
       "Engine Size(L)                      0\n",
       "Cylinders                           0\n",
       "Transmission                        0\n",
       "Fuel Type                           0\n",
       "Fuel Consumption City (L/100 km)    2\n",
       "Fuel Consumption Hwy (L/100 km)     1\n",
       "Fuel Consumption Comb (L/100 km)    1\n",
       "Fuel Consumption Comb (mpg)         2\n",
       "CO2 Emissions(g/km)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "1c5874d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
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       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>S7</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "214  AUDI    S7    SUBCOMPACT             2.0       10.0          AS8   \n",
       "\n",
       "    Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "214         Z                               NaN   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "214                              8.8                               NaN   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "214                          NaN                235.0  "
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values in the \"Fuel Consumption Comb (L/100 km)\" column\n",
    "df[df[[\"Fuel Consumption Comb (L/100 km)\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "d5d13ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the mean fuel consumption for AUDI vehicles\n",
    "audi_fule = df.loc[df[\"Make\"] == \"AUDI\", \"Fuel Consumption Comb (L/100 km)\"].mean().round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "309bf037",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling missing fuel consumption values for AUDI vehicles with the calculated mean\n",
    "df.loc[df[\"Fuel Consumption Comb (L/100 km)\"].isnull(), \"Fuel Consumption Comb (L/100 km)\"] = audi_fule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "cfdbb27d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
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       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ReChecking for missing values in the \"Fuel Consumption City (L/100 km)\" column\n",
    "df[df[[\"Fuel Consumption Comb (L/100 km)\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "079ee999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       0\n",
       "Engine Size(L)                      0\n",
       "Cylinders                           0\n",
       "Transmission                        0\n",
       "Fuel Type                           0\n",
       "Fuel Consumption City (L/100 km)    2\n",
       "Fuel Consumption Hwy (L/100 km)     1\n",
       "Fuel Consumption Comb (L/100 km)    0\n",
       "Fuel Consumption Comb (mpg)         2\n",
       "CO2 Emissions(g/km)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "id": "4e4cbfd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 581,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[df[[\"Fuel Consumption City (L/100 km)\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "effc070c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df[\"Fuel Consumption City (L/100 km)\"].isnull(), \"Fuel Consumption City (L/100 km)\"] = audi_fule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "id": "bba3df5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating and filling missing values for the \"Fuel Consumption City (L/100 km)\" column based on a percentage of the combined consumption\n",
    "mask = df[\"Fuel Consumption City (L/100 km)\"].isnull()\n",
    "df.loc[mask, \"Fuel Consumption City (L/100 km)\"] = df.loc[mask, \"Fuel Consumption Comb (L/100 km)\"] * 0.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "id": "3708c159",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[[\"Fuel Consumption City (L/100 km)\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "id": "4a64bcae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       0\n",
       "Engine Size(L)                      0\n",
       "Cylinders                           0\n",
       "Transmission                        0\n",
       "Fuel Type                           0\n",
       "Fuel Consumption City (L/100 km)    0\n",
       "Fuel Consumption Hwy (L/100 km)     1\n",
       "Fuel Consumption Comb (L/100 km)    0\n",
       "Fuel Consumption Comb (mpg)         2\n",
       "CO2 Emissions(g/km)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "2a79aabd",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>S7</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A7</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "211  AUDI    S7      MID-SIZE             4.0        6.0           A7   \n",
       "\n",
       "    Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "211         Z                              12.0   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "211                              NaN                              10.4   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "211                         18.0                240.0  "
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values in the \"Fuel Consumption Hwy (L/100 km)\" column\n",
    "df[df[[\"Fuel Consumption Hwy (L/100 km)\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "id": "638749fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "7448    False\n",
       "7449    False\n",
       "7450    False\n",
       "7451    False\n",
       "7452    False\n",
       "Name: Fuel Consumption Hwy (L/100 km), Length: 7439, dtype: bool"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating and filling missing values for the \"Fuel Consumption Hwy (L/100 km)\" column based on a percentage of the combined consumption\n",
    "mask = df[\"Fuel Consumption Hwy (L/100 km)\"].isnull()\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "id": "7555d429",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[mask, \"Fuel Consumption Hwy (L/100 km)\"] = df.loc[mask, \"Fuel Consumption Comb (L/100 km)\"] * 0.45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "id": "1d6e326e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       0\n",
       "Engine Size(L)                      0\n",
       "Cylinders                           0\n",
       "Transmission                        0\n",
       "Fuel Type                           0\n",
       "Fuel Consumption City (L/100 km)    0\n",
       "Fuel Consumption Hwy (L/100 km)     0\n",
       "Fuel Consumption Comb (L/100 km)    0\n",
       "Fuel Consumption Comb (mpg)         2\n",
       "CO2 Emissions(g/km)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "92f8b9b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AS6      1326\n",
       "AS8      1215\n",
       "M6        902\n",
       "A6        795\n",
       "A8        495\n",
       "AM7       445\n",
       "A9        341\n",
       "AS7       320\n",
       "AV        295\n",
       "M5        194\n",
       "AS10      169\n",
       "AM6       132\n",
       "AV7       118\n",
       "AV6       113\n",
       "M7         91\n",
       "A5         85\n",
       "AS9        79\n",
       "A4         65\n",
       "AM8        62\n",
       "A7         55\n",
       "AV8        39\n",
       "A10        31\n",
       "AS5        27\n",
       "AV10       11\n",
       " AS8        7\n",
       "AM5         4\n",
       " AS7        3\n",
       " AM7        3\n",
       "AM9         3\n",
       "AS4         2\n",
       " AV8        2\n",
       " AS6        2\n",
       " AM6        1\n",
       " A9         1\n",
       " AM9        1\n",
       " AM8        1\n",
       " A8         1\n",
       " AV6        1\n",
       " AS10       1\n",
       " AS5        1\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standardizing and replacing values in the \"Transmission\" column for consistency\n",
    "df[\"Transmission\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "id": "25d6e3ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing variations in the \"Transmission\" column for consistency\n",
    "df[\"Transmission\"].replace(\" AS8\", \"AS8\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" AS7\", \"AS7\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" AM7\", \"AM7\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" AV8\", \"AV8\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" AS6\", \"AS6\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" AM6\", \"AM6\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" A9\", \"A9\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" AM9\", \"AM9\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" AM8\", \"AM8\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" A8\", \"A8\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" AV6\", \"AV6\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" AS10\", \"AS10\", inplace=True)\n",
    "df[\"Transmission\"].replace(\" AS5\", \"AS5\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "808e503b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AS6     1328\n",
       "AS8     1222\n",
       "M6       902\n",
       "A6       795\n",
       "A8       496\n",
       "AM7      448\n",
       "A9       342\n",
       "AS7      323\n",
       "AV       295\n",
       "M5       194\n",
       "AS10     170\n",
       "AM6      133\n",
       "AV7      118\n",
       "AV6      114\n",
       "M7        91\n",
       "A5        85\n",
       "AS9       79\n",
       "A4        65\n",
       "AM8       63\n",
       "A7        55\n",
       "AV8       41\n",
       "A10       31\n",
       "AS5       28\n",
       "AV10      11\n",
       "AM9        4\n",
       "AM5        4\n",
       "AS4        2\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Transmission\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "id": "83446045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COMPACT', 'SUV - SMALL', 'MID-SIZE', 'TWO-SEATER', 'MINICOMPACT',\n",
       "       'SUBCOMPACT', 'FULL-SIZE', 'STATION WAGON - SMALL',\n",
       "       'SUV - STANDARD', 'VAN - CARGO', 'VAN - PASSENGER',\n",
       "       'PICKUP TRUCK - STANDARD', 'SPECIAL PURPOSE VEHICLE',\n",
       "       'PICKUP TRUCK - SMALL', 'MINIVAN', 'STATION WAGON - MID-SIZE'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Vehicle Class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "id": "d1693603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['124 SPIDER',\n",
       " '124 Spider',\n",
       " '1500',\n",
       " '1500 (MDS)',\n",
       " '1500 4X4',\n",
       " '1500 4X4 (MDS)',\n",
       " '1500 4X4 DIESEL',\n",
       " '1500 4X4 ECODIESEL',\n",
       " '1500 4X4 EcoDiesel',\n",
       " '1500 4X4 FFV',\n",
       " '1500 4X4 eTorque',\n",
       " '1500 Classic',\n",
       " '1500 Classic 4X4',\n",
       " '1500 Classic 4X4 EcoDiesel',\n",
       " '1500 Classic 4X4 FFV',\n",
       " '1500 Classic EcoDiesel',\n",
       " '1500 Classic FFV',\n",
       " '1500 DIESEL',\n",
       " '1500 ECODIESEL',\n",
       " '1500 ECODIESEL HFE',\n",
       " '1500 EcoDiesel',\n",
       " '1500 FFV',\n",
       " '1500 HFE',\n",
       " '1500 HFE eTorque',\n",
       " '1500 eTorque',\n",
       " '200',\n",
       " '200 AWD',\n",
       " '200 AWD FFV',\n",
       " '200 CONVERTIBLE',\n",
       " '200 CONVERTIBLE FFV',\n",
       " '200 FFV',\n",
       " '200 SEDAN',\n",
       " '200 SEDAN FFV',\n",
       " '228i',\n",
       " '228i CABRIOLET',\n",
       " '228i COUPE',\n",
       " '228i xDRIVE',\n",
       " '228i xDRIVE CABRIOLET',\n",
       " '228i xDRIVE COUPE',\n",
       " '230i CABRIOLET',\n",
       " '230i COUPE',\n",
       " '230i Cabriolet',\n",
       " '230i Coupe',\n",
       " '230i xDRIVE CABRIOLET',\n",
       " '230i xDRIVE COUPE',\n",
       " '230i xDrive Cabriolet',\n",
       " '230i xDrive Coupe',\n",
       " '300',\n",
       " '300 (MDS)',\n",
       " '300 AWD',\n",
       " '300 AWD (MDS)',\n",
       " '300 AWD FFV',\n",
       " '300 FFV',\n",
       " '300 SRT (MDS)',\n",
       " '320i',\n",
       " '320i SEDAN',\n",
       " '320i xDRIVE',\n",
       " '320i xDRIVE SEDAN',\n",
       " '328d xDRIVE',\n",
       " '328d xDRIVE SEDAN',\n",
       " '328d xDRIVE TOURING',\n",
       " '328i',\n",
       " '328i SEDAN',\n",
       " '328i xDRIVE',\n",
       " '328i xDRIVE GRAN TURISMO',\n",
       " '328i xDRIVE SEDAN',\n",
       " '328i xDRIVE TOURING',\n",
       " '330i xDRIVE',\n",
       " '330i xDRIVE GRAN TURISMO',\n",
       " '330i xDRIVE TOURING',\n",
       " '330i xDrive',\n",
       " '330i xDrive Gran Turismo',\n",
       " '330i xDrive Sedan',\n",
       " '330i xDrive Touring',\n",
       " '335i',\n",
       " '335i SEDAN',\n",
       " '335i xDRIVE',\n",
       " '335i xDRIVE GRAN TURISMO',\n",
       " '335i xDRIVE SEDAN',\n",
       " '340i',\n",
       " '340i xDRIVE',\n",
       " '340i xDRIVE GRAN TURISMO',\n",
       " '370Z',\n",
       " '370Z COUPE',\n",
       " '370Z ROADSTER',\n",
       " '370Z Roadster',\n",
       " '428i CABRIOLET',\n",
       " '428i COUPE',\n",
       " '428i GRAN COUPE',\n",
       " '428i xDRIVE CABRIOLET',\n",
       " '428i xDRIVE COUPE',\n",
       " '428i xDRIVE GRAN COUPE',\n",
       " '430i Coupe',\n",
       " '430i xDRIVE CABRIOLET',\n",
       " '430i xDRIVE COUPE',\n",
       " '430i xDRIVE GRAN COUPE',\n",
       " '430i xDrive Cabriolet',\n",
       " '430i xDrive Coupe',\n",
       " '430i xDrive Gran Coupe',\n",
       " '435i CABRIOLET',\n",
       " '435i COUPE',\n",
       " '435i GRAN COUPE',\n",
       " '435i xDRIVE CABRIOLET',\n",
       " '435i xDRIVE COUPE',\n",
       " '435i xDRIVE GRAN COUPE',\n",
       " '440i COUPE',\n",
       " '440i Coupe',\n",
       " '440i xDRIVE CABRIOLET',\n",
       " '440i xDRIVE COUPE',\n",
       " '440i xDRIVE GRAN COUPE',\n",
       " '440i xDrive Cabriolet',\n",
       " '440i xDrive Coupe',\n",
       " '440i xDrive Gran Coupe',\n",
       " '4C',\n",
       " '4C COUPE',\n",
       " '4C Coupe',\n",
       " '4C SPIDER',\n",
       " '4C Spider',\n",
       " '4RUNNER (Part-Time 4WD)',\n",
       " '4RUNNER 4WD',\n",
       " '4RUNNER 4WD (Part-Time 4WD)',\n",
       " '4Runner 4WD',\n",
       " '4Runner 4WD (Part-Time 4WD)',\n",
       " '500',\n",
       " '500 ABARTH',\n",
       " '500 ABARTH CABRIO',\n",
       " '500 ABARTH HATCHBACK',\n",
       " '500 CABRIO',\n",
       " '500 CABRIO TURBO',\n",
       " '500 HATCHBACK',\n",
       " '500 HATCHBACK TURBO',\n",
       " '500L',\n",
       " '500L TURBO',\n",
       " '500X',\n",
       " '500X AWD',\n",
       " '528i',\n",
       " '528i SEDAN',\n",
       " '528i xDRIVE',\n",
       " '528i xDRIVE SEDAN',\n",
       " '530i xDRIVE',\n",
       " '530i xDrive',\n",
       " '530i xDrive Sedan',\n",
       " '535d xDRIVE',\n",
       " '535d xDRIVE SEDAN',\n",
       " '535i xDRIVE',\n",
       " '535i xDRIVE GRAN TURISMO',\n",
       " '535i xDRIVE SEDAN',\n",
       " '540i xDRIVE',\n",
       " '540i xDrive',\n",
       " '540i xDrive Sedan',\n",
       " '550i xDRIVE',\n",
       " '550i xDRIVE GRAN TURISMO',\n",
       " '550i xDRIVE SEDAN',\n",
       " '640i xDRIVE CABRIOLET',\n",
       " '640i xDRIVE GRAN COUPE',\n",
       " '640i xDRIVE GRAN TURISMO',\n",
       " '640i xDrive Gran Coupe',\n",
       " '640i xDrive Gran Turismo',\n",
       " '650i xDRIVE CABRIOLET',\n",
       " '650i xDRIVE COUPE',\n",
       " '650i xDRIVE GRAN COUPE',\n",
       " '650i xDrive Gran Coupe',\n",
       " '740Ld xDRIVE SEDAN',\n",
       " '740Li xDRIVE',\n",
       " '740Li xDRIVE SEDAN',\n",
       " '750Li xDRIVE',\n",
       " '750Li xDRIVE SEDAN',\n",
       " '750Li xDrive',\n",
       " '750Li xDrive Sedan',\n",
       " '750i xDRIVE',\n",
       " '750i xDRIVE SEDAN',\n",
       " '750i xDRIVE SWB',\n",
       " '750i xDrive',\n",
       " '750i xDrive Sedan',\n",
       " '760Li',\n",
       " '760Li SEDAN',\n",
       " '86',\n",
       " '911 CARRERA',\n",
       " '911 CARRERA 4',\n",
       " '911 CARRERA 4 CABRIOLET',\n",
       " '911 CARRERA 4 GTS',\n",
       " '911 CARRERA 4 GTS CABRIOLET',\n",
       " '911 CARRERA 4S',\n",
       " '911 CARRERA 4S CABRIOLET',\n",
       " '911 CARRERA CABRIOLET',\n",
       " '911 CARRERA GTS',\n",
       " '911 CARRERA GTS CABRIOLET',\n",
       " '911 CARRERA S',\n",
       " '911 CARRERA S CABRIOLET',\n",
       " '911 Carrera',\n",
       " '911 Carrera 4',\n",
       " '911 Carrera 4 Cabriolet',\n",
       " '911 Carrera 4 GTS',\n",
       " '911 Carrera 4 GTS Cabriolet',\n",
       " '911 Carrera 4S',\n",
       " '911 Carrera 4S Cabriolet',\n",
       " '911 Carrera Cabriolet',\n",
       " '911 Carrera GTS',\n",
       " '911 Carrera GTS Cabriolet',\n",
       " '911 Carrera S',\n",
       " '911 Carrera S Cabriolet',\n",
       " '911 Carrera T',\n",
       " '911 GT2 RS',\n",
       " '911 GT3',\n",
       " '911 GT3 RS',\n",
       " '911 GT3 Touring',\n",
       " '911 GT3RS',\n",
       " '911 Speedster',\n",
       " '911 TARGA 4',\n",
       " '911 TARGA 4 GTS',\n",
       " '911 TARGA 4S',\n",
       " '911 TURBO',\n",
       " '911 TURBO CABRIOLET',\n",
       " '911 TURBO S',\n",
       " '911 TURBO S CABRIOLET',\n",
       " '911 TURBO S EXCLUSIVE',\n",
       " '911 Targa 4',\n",
       " '911 Targa 4 GTS',\n",
       " '911 Targa 4S',\n",
       " '911 Turbo',\n",
       " '911 Turbo Cabriolet',\n",
       " '911 Turbo S',\n",
       " '911 Turbo S Cabriolet',\n",
       " '911 Turbo S Exclusive Cabriolet',\n",
       " 'A 220',\n",
       " 'A 220 4MATIC',\n",
       " 'A 250',\n",
       " 'A 250 4MATIC',\n",
       " 'A3',\n",
       " 'A3 CABRIOLET QUATTRO',\n",
       " 'A3 Cabriolet quattro',\n",
       " 'A3 QUATTRO',\n",
       " 'A3 TDI (modified)',\n",
       " 'A3 quattro',\n",
       " 'A4',\n",
       " 'A4 ALLROAD QUATTRO',\n",
       " 'A4 QUATTRO',\n",
       " 'A4 ULTRA',\n",
       " 'A4 allroad quattro',\n",
       " 'A4 quattro',\n",
       " 'A5 CABRIOLET QUATTRO',\n",
       " 'A5 Cabriolet quattro',\n",
       " 'A5 QUATTRO',\n",
       " 'A5 SPORTBACK QUATTRO',\n",
       " 'A5 Sportback quattro',\n",
       " 'A5 quattro',\n",
       " 'A6 QUATTRO',\n",
       " 'A6 QUATTRO TDI (modified)',\n",
       " 'A6 allroad',\n",
       " 'A6 quattro',\n",
       " 'A7 QUATTRO',\n",
       " 'A7 QUATTRO TDI (modified)',\n",
       " 'A7 quattro',\n",
       " 'A8',\n",
       " 'A8 QUATTRO',\n",
       " 'A8 QUATTRO TDI (modified)',\n",
       " 'A8 TDI (modified)',\n",
       " 'A8L',\n",
       " 'A8L QUATTRO',\n",
       " 'A8L TDI (modified)',\n",
       " 'ACADIA',\n",
       " 'ACADIA AWD',\n",
       " 'ACCENT',\n",
       " 'ACCORD',\n",
       " 'ACCORD COUPE',\n",
       " 'ACCORD HYBRID',\n",
       " 'ACCORD SEDAN',\n",
       " 'ACCORD SPORT',\n",
       " 'ACCORD SPORT/TOURING',\n",
       " 'ACTIVEHYBRID 3',\n",
       " 'ACTIVEHYBRID 5',\n",
       " 'ACTIVEHYBRID 7L',\n",
       " 'ALLROAD QUATTRO',\n",
       " 'ALPINA B6 xDRIVE GRAN COUPE',\n",
       " 'ALPINA B7 xDRIVE',\n",
       " 'ALPINA B7 xDRIVE LWB',\n",
       " 'ALPINA B7 xDRIVE SWB',\n",
       " 'ALTIMA',\n",
       " 'ALTIMA 3.5',\n",
       " 'ALTIMA SR',\n",
       " 'AMG C 43 4MATIC',\n",
       " 'AMG C 43 4MATIC CABRIOLET',\n",
       " 'AMG C 43 4MATIC COUPE',\n",
       " 'AMG C 43 4MATIC Cabriolet',\n",
       " 'AMG C 43 4MATIC Coupe',\n",
       " 'AMG C 43 4MATIC Wagon',\n",
       " 'AMG C 63',\n",
       " 'AMG C 63 CABRIOLET',\n",
       " 'AMG C 63 COUPE',\n",
       " 'AMG C 63 S',\n",
       " 'AMG C 63 S CABRIOLET',\n",
       " 'AMG C 63 S COUPE',\n",
       " 'AMG C 63 S Cabriolet',\n",
       " 'AMG C 63 S Coupe',\n",
       " 'AMG CLA 45',\n",
       " 'AMG CLA 45 4MATIC',\n",
       " 'AMG CLS 53 4MATIC+',\n",
       " 'AMG CLS 63 S 4MATIC',\n",
       " 'AMG E 43 4MATIC',\n",
       " 'AMG E 53 4MATIC+',\n",
       " 'AMG E 53 4MATIC+ Cabriolet',\n",
       " 'AMG E 53 4MATIC+ Coupe',\n",
       " 'AMG E 53 4MATIC+ Wagon',\n",
       " 'AMG E 63 S 4MATIC',\n",
       " 'AMG E 63 S 4MATIC WAGON',\n",
       " 'AMG E 63 S 4MATIC+',\n",
       " 'AMG E 63 S 4MATIC+ Wagon',\n",
       " 'AMG G 63',\n",
       " 'AMG G 65',\n",
       " 'AMG GL 63 S',\n",
       " 'AMG GLA 45',\n",
       " 'AMG GLA 45 4MATIC',\n",
       " 'AMG GLC 43 4MATIC',\n",
       " 'AMG GLC 43 4MATIC COUPE',\n",
       " 'AMG GLC 43 4MATIC Coupe',\n",
       " 'AMG GLC 63 S 4MATIC+',\n",
       " 'AMG GLC 63 S 4MATIC+ Coupe',\n",
       " 'AMG GLE 43 4MATIC',\n",
       " 'AMG GLE 43 4MATIC COUPE',\n",
       " 'AMG GLE 43 4MATIC Coupe',\n",
       " 'AMG GLE 63 S 4MATIC',\n",
       " 'AMG GLE 63 S 4MATIC COUPE',\n",
       " 'AMG GLE 63 S 4MATIC Coupe',\n",
       " 'AMG GLE 63 S COUPE 4MATIC',\n",
       " 'AMG GLS 63',\n",
       " 'AMG GLS 63 4MATIC',\n",
       " 'AMG GT 53 4MATIC+ Coupe',\n",
       " 'AMG GT 63 4MATIC+ Coupe',\n",
       " 'AMG GT 63 S 4MATIC+ Coupe',\n",
       " 'AMG GT C COUPE',\n",
       " 'AMG GT C Coupe',\n",
       " 'AMG GT C ROADSTER',\n",
       " 'AMG GT C Roadster',\n",
       " 'AMG GT COUPE',\n",
       " 'AMG GT R COUPE',\n",
       " 'AMG GT R Coupe',\n",
       " 'AMG GT ROADSTER',\n",
       " 'AMG GT S',\n",
       " 'AMG GT S COUPE',\n",
       " 'AMG S 63',\n",
       " 'AMG S 63 4MATIC',\n",
       " 'AMG S 63 4MATIC CABRIOLET',\n",
       " 'AMG S 63 4MATIC COUPE',\n",
       " 'AMG S 63 4MATIC+',\n",
       " 'AMG S 63 4MATIC+ Cabriolet',\n",
       " 'AMG S 63 4MATIC+ Coupe',\n",
       " 'AMG S 63 CABRIOLET',\n",
       " 'AMG S 63 COUPE',\n",
       " 'AMG S 65',\n",
       " 'AMG S 65 CABRIOLET',\n",
       " 'AMG S 65 COUPE',\n",
       " 'AMG S 65 Cabriolet',\n",
       " 'AMG S 65 Coupe',\n",
       " 'AMG SL 63',\n",
       " 'AMG SL 65',\n",
       " 'AMG SLC 43',\n",
       " 'AMG SLK 55',\n",
       " 'ARMADA 4WD',\n",
       " 'ATLAS',\n",
       " 'ATLAS 4MOTION',\n",
       " 'ATS',\n",
       " 'ATS AWD',\n",
       " 'ATS-V',\n",
       " 'AVALON',\n",
       " 'AVENGER',\n",
       " 'AVENGER FFV',\n",
       " 'AVENTADOR COUPE',\n",
       " 'AVENTADOR COUPE LP 740',\n",
       " 'AVENTADOR ROADSTER',\n",
       " 'AVENTADOR ROADSTER LP 740',\n",
       " 'AVENTADOR S COUPE',\n",
       " 'AVENTADOR S ROADSTER',\n",
       " 'Acadia',\n",
       " 'Acadia AWD',\n",
       " 'Accent',\n",
       " 'Accord',\n",
       " 'Accord Hybrid',\n",
       " 'Accord Sport/Touring',\n",
       " 'Alpina B6 xDrive Gran Coupe',\n",
       " 'Alpina B7 xDrive',\n",
       " 'Altima',\n",
       " 'Altima AWD',\n",
       " 'Altima AWD SR/Platinum',\n",
       " 'Altima SR/Platinum',\n",
       " 'Armada 4WD',\n",
       " 'Arteon 4MOTION',\n",
       " 'Ascent AWD',\n",
       " 'Atlas',\n",
       " 'Atlas 4MOTION',\n",
       " 'Atlas Cross Sport 4MOTION',\n",
       " 'Avalon',\n",
       " 'Aventador Coupe',\n",
       " 'Aventador Roadster',\n",
       " 'Aviator',\n",
       " 'B 250',\n",
       " 'B 250 4MATIC',\n",
       " 'BEETLE',\n",
       " 'BEETLE CONVERTIBLE',\n",
       " 'BEETLE DUNE',\n",
       " 'BEETLE DUNE CONVERTIBLE',\n",
       " 'BEETLE TDI (modified)',\n",
       " 'BENTAYGA',\n",
       " 'BOXSTER',\n",
       " 'BOXSTER GTS',\n",
       " 'BOXSTER S',\n",
       " 'BOXSTER SPYDER',\n",
       " 'BRZ',\n",
       " 'BRZ tS',\n",
       " 'Beetle',\n",
       " 'Beetle Convertible',\n",
       " 'Beetle Dune',\n",
       " 'Beetle Dune Convertible',\n",
       " 'Bentayga',\n",
       " 'Blazer',\n",
       " 'Blazer AWD',\n",
       " 'Boxster',\n",
       " 'Boxster GTS',\n",
       " 'Boxster S',\n",
       " 'C 250',\n",
       " 'C 250 COUPE',\n",
       " 'C 300 4MATIC',\n",
       " 'C 300 4MATIC CABRIOLET',\n",
       " 'C 300 4MATIC COUPE',\n",
       " 'C 300 4MATIC Cabriolet',\n",
       " 'C 300 4MATIC Coupe',\n",
       " 'C 300 4MATIC FFV',\n",
       " 'C 300 4MATIC WAGON',\n",
       " 'C 300 4MATIC Wagon',\n",
       " 'C 350',\n",
       " 'C 350 4MATIC',\n",
       " 'C 350 4MATIC COUPE',\n",
       " 'C 350 COUPE',\n",
       " 'C 400 4MATIC',\n",
       " 'C 450 AMG SPORT 4MATIC',\n",
       " 'C 63 AMG',\n",
       " 'C 63 AMG COUPE',\n",
       " 'C-HR',\n",
       " 'C-MAX HYBRID',\n",
       " 'CADENZA',\n",
       " 'CAMARO',\n",
       " 'CAMARO 2LS',\n",
       " 'CAMARO SS',\n",
       " 'CAMARO Z/28',\n",
       " 'CAMARO ZL1',\n",
       " 'CAMRY',\n",
       " 'CAMRY HYBRID LE',\n",
       " 'CAMRY HYBRID XLE/SE',\n",
       " 'CAMRY LE/SE',\n",
       " 'CAMRY XLE/XSE',\n",
       " 'CAMRY XSE',\n",
       " 'CANYON',\n",
       " 'CANYON 4WD',\n",
       " 'CARGO VAN FFV',\n",
       " 'CAYENNE',\n",
       " 'CAYENNE DIESEL (modified)',\n",
       " 'CAYENNE GTS',\n",
       " 'CAYENNE PLATINUM',\n",
       " 'CAYENNE S',\n",
       " 'CAYENNE S HYBRID',\n",
       " 'CAYENNE TURBO',\n",
       " 'CAYENNE TURBO S',\n",
       " 'CAYMAN',\n",
       " 'CAYMAN GT4',\n",
       " 'CAYMAN GTS',\n",
       " 'CAYMAN S',\n",
       " 'CC',\n",
       " 'CC 4MOTION',\n",
       " 'CHALLENGER',\n",
       " 'CHALLENGER (MDS)',\n",
       " 'CHALLENGER GT',\n",
       " 'CHALLENGER GT AWD',\n",
       " 'CHALLENGER GT AWD FFV',\n",
       " 'CHALLENGER SRT',\n",
       " 'CHALLENGER SRT (MDS)',\n",
       " 'CHALLENGER SRT DEMON',\n",
       " 'CHALLENGER SRT HELLCAT',\n",
       " 'CHARGER',\n",
       " 'CHARGER (MDS)',\n",
       " 'CHARGER AWD',\n",
       " 'CHARGER AWD (MDS)',\n",
       " 'CHARGER AWD FFV',\n",
       " 'CHARGER FFV',\n",
       " 'CHARGER SRT (MDS)',\n",
       " 'CHARGER SRT 392 HEMI',\n",
       " 'CHARGER SRT HELLCAT',\n",
       " 'CHEROKEE',\n",
       " 'CHEROKEE 4X4',\n",
       " 'CHEROKEE 4X4 ACTIVE DRIVE I',\n",
       " 'CHEROKEE 4X4 ACTIVE DRIVE I FFV',\n",
       " 'CHEROKEE 4X4 ACTIVE DRIVE II',\n",
       " 'CHEROKEE 4X4 ACTIVE DRIVE LOCK',\n",
       " 'CHEROKEE 4X4 FFV',\n",
       " 'CHEROKEE FFV',\n",
       " 'CHEROKEE TRAILHAWK 4X4',\n",
       " 'CHIRON',\n",
       " 'CITY EXPRESS',\n",
       " 'CIVIC',\n",
       " 'CIVIC COUPE',\n",
       " 'CIVIC COUPE Si',\n",
       " 'CIVIC HATCHBACK',\n",
       " 'CIVIC HATCHBACK SPORT',\n",
       " 'CIVIC HYBRID',\n",
       " 'CIVIC SEDAN',\n",
       " 'CIVIC SEDAN Si',\n",
       " 'CIVIC Si',\n",
       " 'CIVIC TYPE R',\n",
       " 'CL 550 4MATIC',\n",
       " 'CL 63 AMG',\n",
       " 'CLA 250',\n",
       " 'CLA 250 4MATIC',\n",
       " 'CLA 250 4MATIC FFV',\n",
       " 'CLA 45 AMG',\n",
       " 'CLA 45 AMG 4MATIC',\n",
       " 'CLS 400 4MATIC',\n",
       " 'CLS 450 4MATIC',\n",
       " 'CLS 550 4MATIC',\n",
       " 'CLS 63 AMG 4MATIC',\n",
       " 'CLS 63 AMG S 4MATIC',\n",
       " 'COLORADO',\n",
       " 'COLORADO 4WD',\n",
       " 'COLORADO ZR2 4WD',\n",
       " 'COMPASS',\n",
       " 'COMPASS 4X4',\n",
       " 'COMPASS 4X4 TRAIL RATED',\n",
       " 'CONTINENTAL AWD',\n",
       " 'CONTINENTAL GT',\n",
       " 'CONTINENTAL GT CONVERTIBLE',\n",
       " 'CONTINENTAL GT SPEED CONVERTIBLE',\n",
       " 'CONTINENTAL GT3-R',\n",
       " 'CONTINENTAL GTC',\n",
       " 'CONTINENTAL SUPERSPORTS',\n",
       " 'COOPER 3 DOOR',\n",
       " 'COOPER 5 DOOR',\n",
       " 'COOPER CLUBMAN',\n",
       " 'COOPER CLUBMAN ALL4',\n",
       " 'COOPER CONVERTIBLE',\n",
       " 'COOPER COUNTRYMAN',\n",
       " 'COOPER COUNTRYMAN ALL4',\n",
       " 'COOPER COUPE',\n",
       " 'COOPER PACEMAN',\n",
       " 'COOPER ROADSTER',\n",
       " 'COOPER S 3 DOOR',\n",
       " 'COOPER S 5 DOOR',\n",
       " 'COOPER S CLUBMAN',\n",
       " 'COOPER S CLUBMAN ALL4',\n",
       " 'COOPER S CONVERTIBLE',\n",
       " 'COOPER S COUNTRYMAN  ALL4',\n",
       " 'COOPER S COUNTRYMAN ALL4',\n",
       " 'COOPER S COUPE',\n",
       " 'COOPER S PACEMAN ALL4',\n",
       " 'COOPER S ROADSTER',\n",
       " 'COROLLA',\n",
       " 'COROLLA LE ECO (1-mode)',\n",
       " 'COROLLA LE ECO (2-mode)',\n",
       " 'COROLLA MATRIX',\n",
       " 'COROLLA iM',\n",
       " 'CORVETTE',\n",
       " 'CORVETTE Z06',\n",
       " 'CR-V',\n",
       " 'CR-V AWD',\n",
       " 'CR-Z',\n",
       " 'CROSSTOUR AWD',\n",
       " 'CROSSTREK AWD',\n",
       " 'CRUZE',\n",
       " 'CRUZE DIESEL',\n",
       " 'CRUZE ECO',\n",
       " 'CRUZE HATCHBACK',\n",
       " 'CRUZE HATCHBACK DIESEL',\n",
       " 'CRUZE HATCHBACK PREMIER',\n",
       " 'CRUZE LIMITED',\n",
       " 'CRUZE LIMITED ECO',\n",
       " 'CRUZE PREMIER',\n",
       " 'CT 200h',\n",
       " 'CT4',\n",
       " 'CT4 AWD',\n",
       " 'CT4-V',\n",
       " 'CT4-V AWD',\n",
       " 'CT5',\n",
       " 'CT5 AWD',\n",
       " 'CT5-V',\n",
       " 'CT5-V AWD',\n",
       " 'CT6',\n",
       " 'CT6 AWD',\n",
       " 'CTS',\n",
       " 'CTS AWD',\n",
       " 'CTS COUPE',\n",
       " 'CTS COUPE AWD',\n",
       " 'CTS SEDAN',\n",
       " 'CTS SEDAN AWD',\n",
       " 'CTS SEDAN Vsport',\n",
       " 'CTS SPORT WAGON',\n",
       " 'CTS SPORT WAGON AWD',\n",
       " 'CTS Vsport',\n",
       " 'CTS-V',\n",
       " 'CTS-V COUPE',\n",
       " 'CTS-V SEDAN',\n",
       " 'CTS-V SPORT WAGON',\n",
       " 'CX-3',\n",
       " 'CX-3 (SIL)',\n",
       " 'CX-3 4WD',\n",
       " 'CX-30',\n",
       " 'CX-30 4WD',\n",
       " 'CX-30 4WD (Cylinder Deactivation)',\n",
       " 'CX-5',\n",
       " 'CX-5 ',\n",
       " 'CX-5 (Cylinder Deactivation)',\n",
       " 'CX-5 (SIL)',\n",
       " 'CX-5 4WD',\n",
       " 'CX-5 4WD (Cylinder Deactivation)',\n",
       " 'CX-5 Diesel 4WD',\n",
       " 'CX-5 Turbo 4WD',\n",
       " 'CX-9',\n",
       " 'CX-9 4WD',\n",
       " 'Cadenza',\n",
       " 'Camaro',\n",
       " 'Camaro SS',\n",
       " 'Camaro ZL1',\n",
       " 'Camry',\n",
       " 'Camry AWD LE/SE',\n",
       " 'Camry AWD XLE/XSE',\n",
       " 'Camry Hybrid LE',\n",
       " 'Camry Hybrid XLE/SE',\n",
       " 'Camry LE/SE',\n",
       " 'Camry TRD',\n",
       " 'Camry XLE/XSE',\n",
       " 'Camry XSE',\n",
       " 'Canyon',\n",
       " 'Canyon 4WD',\n",
       " 'Cayenne',\n",
       " 'Cayenne S',\n",
       " 'Cayenne Turbo',\n",
       " 'Cayman',\n",
       " 'Cayman GTS',\n",
       " 'Cayman S',\n",
       " 'Challenger',\n",
       " 'Challenger (MDS)',\n",
       " 'Challenger AWD',\n",
       " 'Challenger GT AWD',\n",
       " 'Challenger SRT Hellcat',\n",
       " 'Challenger SRT Hellcat Redeye',\n",
       " 'Challenger SRT Hellcat Widebody',\n",
       " 'Challenger Widebody (MDS)',\n",
       " 'Charger',\n",
       " 'Charger (MDS)',\n",
       " 'Charger AWD',\n",
       " 'Charger AWD (MDS)',\n",
       " 'Charger AWD FFV',\n",
       " 'Charger FFV',\n",
       " 'Charger SRT Hellcat',\n",
       " 'Charger SRT Hellcat Widebody',\n",
       " 'Charger Widebody (MDS)',\n",
       " 'Cherokee',\n",
       " 'Cherokee 4X4 Active Drive I',\n",
       " 'Cherokee 4X4 Active Drive II',\n",
       " 'Cherokee 4X4 Active Drive Lock',\n",
       " 'Chiron',\n",
       " 'Civic Coupe',\n",
       " 'Civic Coupe Si',\n",
       " 'Civic Hatchback',\n",
       " 'Civic Hatchback Sport',\n",
       " 'Civic Sedan',\n",
       " 'Civic Sedan Si',\n",
       " 'Civic Type R',\n",
       " 'Colorado',\n",
       " 'Colorado 4WD',\n",
       " 'Colorado ZR2 4WD',\n",
       " 'Compass',\n",
       " 'Compass 4X4',\n",
       " 'Continental AWD',\n",
       " 'Continental GT',\n",
       " 'Continental GT Convertible',\n",
       " 'Cooper 3 Door',\n",
       " 'Cooper 5 Door',\n",
       " 'Cooper Clubman ALL4',\n",
       " 'Cooper Convertible',\n",
       " 'Cooper Countryman ALL4',\n",
       " 'Cooper S 3 Door',\n",
       " 'Cooper S 5 Door',\n",
       " 'Cooper S Clubman ALL4',\n",
       " 'Cooper S Convertible',\n",
       " 'Cooper S Countryman ALL4',\n",
       " 'Corolla',\n",
       " 'Corolla Hatchback',\n",
       " 'Corolla Hybrid',\n",
       " 'Corolla LE Eco',\n",
       " 'Corolla XLE',\n",
       " 'Corolla XSE',\n",
       " 'Corsair AWD',\n",
       " 'Corvette',\n",
       " 'Corvette Z06',\n",
       " 'Corvette ZR1',\n",
       " 'Crosstrek AWD',\n",
       " 'Cruze',\n",
       " 'Cruze Diesel',\n",
       " 'Cruze Hatchback',\n",
       " 'Cruze Hatchback Diesel',\n",
       " 'Cruze Hatchback Premier',\n",
       " 'Cruze Premier',\n",
       " 'Cullinan',\n",
       " 'Cullinan Black Badge',\n",
       " 'DART',\n",
       " 'DART FFV',\n",
       " 'DART GT',\n",
       " 'DART TURBO AERO',\n",
       " 'DAWN',\n",
       " 'DB11 AMR',\n",
       " 'DB11 V12',\n",
       " 'DB11 V8',\n",
       " 'DB9',\n",
       " 'DB9 GT',\n",
       " 'DBS Superleggera',\n",
       " 'DISCOVERY SPORT',\n",
       " 'DURANGO AWD',\n",
       " 'DURANGO AWD (MDS)',\n",
       " 'DURANGO AWD FFV',\n",
       " 'DURANGO AWD SRT',\n",
       " 'Dawn',\n",
       " 'Discovery',\n",
       " 'Discovery Sport',\n",
       " 'Discovery Sport P250',\n",
       " 'Discovery Sport P290',\n",
       " 'Discovery TD6 Diesel',\n",
       " 'Durango AWD',\n",
       " 'Durango AWD SRT',\n",
       " 'E 250 BLUETEC 4MATIC',\n",
       " 'E 300 4MATIC',\n",
       " 'E 350 4MATIC',\n",
       " 'E 350 4MATIC COUPE',\n",
       " 'E 350 4MATIC WAGON',\n",
       " 'E 350 CABRIOLET',\n",
       " 'E 350 COUPE',\n",
       " 'E 400 4MATIC',\n",
       " 'E 400 4MATIC CABRIOLET',\n",
       " 'E 400 4MATIC COUPE',\n",
       " 'E 400 4MATIC WAGON',\n",
       " 'E 400 CABRIOLET',\n",
       " 'E 400 COUPE',\n",
       " 'E 400 HYBRID',\n",
       " 'E 450 4MATIC',\n",
       " 'E 450 4MATIC Cabriolet',\n",
       " 'E 450 4MATIC Coupe',\n",
       " 'E 450 4MATIC Wagon',\n",
       " 'E 550 4MATIC',\n",
       " 'E 550 CABRIOLET',\n",
       " 'E 550 COUPE',\n",
       " 'E 63 AMG 4MATIC',\n",
       " 'E 63 AMG 4MATIC WAGON',\n",
       " 'E 63 AMG S 4MATIC',\n",
       " 'E 63 AMG S 4MATIC WAGON',\n",
       " 'E-PACE P250',\n",
       " 'E-PACE P300',\n",
       " 'E150 VAN FFV',\n",
       " 'E150 WAGON FFV',\n",
       " 'E350 WAGON',\n",
       " 'E350 WAGON FFV',\n",
       " 'ECLIPSE CROSS',\n",
       " 'ECLIPSE CROSS 4WD',\n",
       " 'ECOSPORT',\n",
       " 'ECOSPORT AWD',\n",
       " 'EDGE',\n",
       " 'EDGE (Start/Stop)',\n",
       " 'EDGE AWD',\n",
       " 'EDGE SPORT AWD',\n",
       " 'ELANTRA',\n",
       " 'ELANTRA COUPE',\n",
       " 'ELANTRA GT',\n",
       " 'ENCLAVE',\n",
       " 'ENCLAVE AWD',\n",
       " 'ENCORE',\n",
       " 'ENCORE (LE2 Engine)',\n",
       " 'ENCORE (LUV Engine)',\n",
       " 'ENCORE (SIDI with Stop/Start)',\n",
       " 'ENCORE AWD',\n",
       " 'ENCORE AWD (LE2 Engine)',\n",
       " 'ENCORE AWD (LUV Engine)',\n",
       " 'ENCORE AWD (SIDI with Stop/Start)',\n",
       " 'ENCORE SPORT TOURING',\n",
       " 'ENCORE SPORT TOURING AWD',\n",
       " 'ENVISION AWD',\n",
       " 'EOS',\n",
       " 'EQUINOX',\n",
       " 'EQUINOX AWD',\n",
       " 'EQUINOX AWD FFV',\n",
       " 'EQUINOX FFV',\n",
       " 'EQUUS',\n",
       " 'ES 300h',\n",
       " 'ES 350',\n",
       " 'ES 350 F SPORT',\n",
       " 'ESCALADE 4WD',\n",
       " 'ESCALADE AWD',\n",
       " 'ESCALADE ESV 4WD',\n",
       " 'ESCALADE ESV AWD',\n",
       " 'ESCAPE',\n",
       " 'ESCAPE AWD',\n",
       " 'ESCAPE FFV',\n",
       " 'EXPEDITION 4X4',\n",
       " 'EXPEDITION 4X4 FFV',\n",
       " 'EXPEDITION EL 4X4',\n",
       " 'EXPEDITION MAX 4X4',\n",
       " 'EXPLORER',\n",
       " 'EXPLORER AWD',\n",
       " 'EXPLORER AWD (EcoBoost)',\n",
       " 'EXPLORER FFV',\n",
       " 'EXPLORER FFV AWD',\n",
       " 'EXPRESS 1500 CARGO',\n",
       " 'EXPRESS 1500 CARGO AWD',\n",
       " 'EXPRESS 1500 CARGO CONV',\n",
       " 'EXPRESS 1500 CARGO CONV AWD',\n",
       " 'EXPRESS 1500 PASSENGER',\n",
       " 'EXPRESS 1500 PASSENGER AWD',\n",
       " 'EXPRESS 2500 PASSENGER',\n",
       " 'EXPRESS 2500 PASSENGER FFV',\n",
       " 'EXPRESS 3500 PASSENGER',\n",
       " 'EXPRESS 3500 PASSENGER FFV',\n",
       " 'Eclipse Cross 4WD',\n",
       " 'EcoSport',\n",
       " 'EcoSport AWD',\n",
       " 'Edge',\n",
       " 'Edge AWD',\n",
       " 'Elantra',\n",
       " 'Elantra GT',\n",
       " 'Enclave',\n",
       " 'Enclave AWD',\n",
       " 'Encore',\n",
       " 'Encore (SIDI with Stop/Start)',\n",
       " 'Encore AWD',\n",
       " 'Encore AWD (SIDI with Stop/Start)',\n",
       " 'Encore GX',\n",
       " 'Encore GX AWD',\n",
       " 'Envision AWD',\n",
       " 'Equinox',\n",
       " 'Equinox AWD',\n",
       " 'Escalade 4WD',\n",
       " 'Escape',\n",
       " 'Escape AWD',\n",
       " 'Escape FFV',\n",
       " 'Escape Hybrid',\n",
       " 'Escape Hybrid AWD',\n",
       " 'Expedition 4X4',\n",
       " 'Expedition MAX 4X4',\n",
       " 'Explorer AWD',\n",
       " 'Explorer FFV AWD',\n",
       " 'Explorer Hybrid AWD',\n",
       " 'F-150',\n",
       " 'F-150 (LT Tire Pkg)',\n",
       " 'F-150 (Payload Pkg)',\n",
       " 'F-150 4X4',\n",
       " 'F-150 4X4 (LT Tire Pkg)',\n",
       " 'F-150 4X4 (Payload Pkg)',\n",
       " 'F-150 4X4 Limited',\n",
       " 'F-150 4X4 XL/XLT',\n",
       " 'F-150 FFV',\n",
       " 'F-150 FFV (LT Tire Pkg)',\n",
       " 'F-150 FFV (Payload Pkg)',\n",
       " 'F-150 FFV 4X4',\n",
       " 'F-150 FFV 4X4 (LT Tire Pkg)',\n",
       " 'F-150 FFV 4X4 (Payload Pkg)',\n",
       " 'F-150 RAPTOR 4WD',\n",
       " 'F-150 RAPTOR 4X4',\n",
       " 'F-150 Raptor 4X4',\n",
       " 'F-PACE 20d',\n",
       " 'F-PACE 25t',\n",
       " 'F-PACE 30t',\n",
       " 'F-PACE 35t',\n",
       " 'F-PACE S',\n",
       " 'F-PACE SVR',\n",
       " 'F-TYPE CONVERTIBLE',\n",
       " 'F-TYPE CONVERTIBLE R-DYNAMIC',\n",
       " 'F-TYPE CONVERTIBLE R-DYNAMIC AWD',\n",
       " 'F-TYPE COUPE',\n",
       " 'F-TYPE COUPE R-DYNAMIC',\n",
       " 'F-TYPE COUPE R-DYNAMIC AWD',\n",
       " 'F-TYPE Convertible',\n",
       " 'F-TYPE Convertible R-Dynamic',\n",
       " 'F-TYPE Convertible R-Dynamic AWD',\n",
       " 'F-TYPE Coupe',\n",
       " 'F-TYPE Coupe R-Dynamic',\n",
       " 'F-TYPE Coupe R-Dynamic AWD',\n",
       " 'F-TYPE P300 Convertible',\n",
       " 'F-TYPE P300 Coupe',\n",
       " 'F-TYPE PROJECT 7 CONVERTIBLE',\n",
       " 'F-TYPE R AWD CONVERTIBLE',\n",
       " 'F-TYPE R AWD COUPE',\n",
       " 'F-TYPE R AWD Convertible',\n",
       " 'F-TYPE R AWD Coupe',\n",
       " 'F-TYPE R CONVERTIBLE AWD',\n",
       " 'F-TYPE R COUPE AWD',\n",
       " 'F-TYPE S CONVERTIBLE',\n",
       " 'F-TYPE S CONVERTIBLE AWD',\n",
       " 'F-TYPE S COUPE',\n",
       " 'F-TYPE S COUPE AWD',\n",
       " 'F-TYPE SVR AWD Convertible',\n",
       " 'F-TYPE SVR AWD Coupe',\n",
       " 'F-TYPE V8 R COUPE',\n",
       " 'F-TYPE V8 S CONVERTIBLE',\n",
       " 'FIESTA',\n",
       " 'FIESTA SFE',\n",
       " 'FIESTA ST',\n",
       " 'FIT',\n",
       " 'FJ CRUISER 4WD',\n",
       " 'FLEX',\n",
       " 'FLEX AWD',\n",
       " 'FLEX AWD (EcoBoost)',\n",
       " 'FLEX AWD GTDI',\n",
       " 'FLYING SPUR',\n",
       " 'FOCUS',\n",
       " 'FOCUS (Start/Stop)',\n",
       " 'FOCUS FFV',\n",
       " 'FOCUS RS AWD',\n",
       " 'FOCUS SFE FFV',\n",
       " 'FOCUS ST',\n",
       " 'FORESTER AWD',\n",
       " 'FORTE',\n",
       " 'FORTE (GDI)',\n",
       " 'FORTE (MPI)',\n",
       " 'FORTE 5',\n",
       " 'FORTE KOUP',\n",
       " 'FORTWO CABRIOLET',\n",
       " 'FORTWO COUPE',\n",
       " 'FR-S',\n",
       " 'FRONTIER',\n",
       " 'FRONTIER 4WD',\n",
       " 'FUSION',\n",
       " 'FUSION (Start/Stop)',\n",
       " 'FUSION AWD',\n",
       " 'FUSION HYBRID',\n",
       " 'Fiesta',\n",
       " 'Fiesta ST',\n",
       " 'Fit',\n",
       " 'Flex',\n",
       " 'Flex AWD',\n",
       " 'Flex AWD GTDI',\n",
       " 'Flying Spur',\n",
       " 'Forester AWD',\n",
       " 'Forte',\n",
       " 'Forte 5',\n",
       " 'Frontier',\n",
       " 'Frontier 4WD',\n",
       " 'Fusion',\n",
       " 'Fusion Hybrid',\n",
       " 'G 550',\n",
       " 'G 63 AMG',\n",
       " 'G70',\n",
       " 'G70 AWD',\n",
       " 'G80 AWD',\n",
       " 'G90 AWD',\n",
       " 'GALLARDO COUPE',\n",
       " 'GENESIS',\n",
       " 'GENESIS AWD',\n",
       " 'GENESIS COUPE',\n",
       " 'GHIBLI',\n",
       " 'GHIBLI AWD',\n",
       " 'GHIBLI S',\n",
       " 'GHIBLI SQ4',\n",
       " 'GHOST',\n",
       " 'GHOST EWB',\n",
       " 'GIULIA',\n",
       " 'GIULIA AWD',\n",
       " 'GIULIA QUADRIFOGLIO',\n",
       " 'GL 350 BLUETEC 4MATIC',\n",
       " 'GL 450 4MATIC',\n",
       " 'GL 550 4MATIC',\n",
       " 'GL 63 AMG',\n",
       " 'GLA 250 4MATIC',\n",
       " 'GLA 45 AMG 4MATIC',\n",
       " 'GLC 300 4MATIC',\n",
       " 'GLC 300 4MATIC COUPE',\n",
       " 'GLC 300 4MATIC Coupe',\n",
       " 'GLE 350d 4MATIC',\n",
       " 'GLE 350d 4MATIC COUPE',\n",
       " 'GLE 400 4MATIC',\n",
       " 'GLE 450 AMG SPORT 4MATIC COUPE',\n",
       " 'GLE 550 4MATIC',\n",
       " 'GLK 250 BLUETEC 4MATIC',\n",
       " 'GLK 350 4MATIC',\n",
       " 'GLS 450 4MATIC',\n",
       " 'GLS 550 4MATIC',\n",
       " 'GOLF',\n",
       " 'GOLF ALLTRACK',\n",
       " 'GOLF GTI',\n",
       " 'GOLF R',\n",
       " 'GOLF SPORTWAGEN',\n",
       " 'GOLF SPORTWAGEN 4MOTION',\n",
       " 'GOLF SPORTWAGON',\n",
       " 'GOLF SPORTWAGON TDI (modified)',\n",
       " 'GOLF TDI (modified)',\n",
       " 'GOLF WAGON',\n",
       " 'GOLF WAGON TDI (modified)',\n",
       " 'GR Supra',\n",
       " 'GRAND CARAVAN FFV',\n",
       " 'GRAND CHEROKEE 4X4',\n",
       " 'GRAND CHEROKEE 4X4 (MDS)',\n",
       " 'GRAND CHEROKEE 4X4 DIESEL',\n",
       " 'GRAND CHEROKEE 4X4 FFV',\n",
       " 'GRAND CHEROKEE 4X4 SRT',\n",
       " 'GRAND CHEROKEE 4X4 TRACKHAWK',\n",
       " 'GRAND CHEROKEE SRT 4X4 (MDS)',\n",
       " 'GRAND CHEROKEE SRT8',\n",
       " 'GRANTURISMO',\n",
       " 'GRANTURISMO CONVERTIBLE',\n",
       " ...]"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting and sorting unique values from the \"Model\" column\n",
    "unique_values_models = df['Model'].unique()\n",
    "\n",
    "arrayModel=[]\n",
    "\n",
    "for value in unique_values_models:\n",
    "    # print(value)\n",
    "    arrayModel.append(value)\n",
    "\n",
    "sorted_list = sorted(arrayModel)\n",
    "sorted_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "599f8fba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Model'] = df['Model'].str.upper()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "b2b15058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1596"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Model'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "id": "2af0db39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Make'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "id": "a12b36c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Vehicle Class'] = df['Vehicle Class'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "a1fc2889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Vehicle Class'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "bdf8d04b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' E', ' X', ' Z', 'D', 'E', 'N', 'X', 'Z']"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_values_models = df['Fuel Type'].unique()\n",
    "\n",
    "arrayModel=[]\n",
    "\n",
    "for value in unique_values_models:\n",
    "    # print(value)\n",
    "    arrayModel.append(value)\n",
    "\n",
    "sorted_list = sorted(arrayModel)\n",
    "sorted_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "19a73a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fuel Type'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "7dae9757",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Fuel Type\"].replace(\" E\", \"E\", inplace=True)\n",
    "df[\"Fuel Type\"].replace(\" X\", \"X\", inplace=True)\n",
    "df[\"Fuel Type\"].replace(\" Z\", \"Z\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "id": "e44f2089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['D', 'E', 'N', 'X', 'Z']"
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_values_models = df['Fuel Type'].unique()\n",
    "\n",
    "arrayModel=[]\n",
    "\n",
    "for value in unique_values_models:\n",
    "    # print(value)\n",
    "    arrayModel.append(value)\n",
    "\n",
    "sorted_list = sorted(arrayModel)\n",
    "sorted_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "bca15d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fuel Type'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "a1767e50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Transmission'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "9895f1cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A10',\n",
       " 'A4',\n",
       " 'A5',\n",
       " 'A6',\n",
       " 'A7',\n",
       " 'A8',\n",
       " 'A9',\n",
       " 'AM5',\n",
       " 'AM6',\n",
       " 'AM7',\n",
       " 'AM8',\n",
       " 'AM9',\n",
       " 'AS10',\n",
       " 'AS4',\n",
       " 'AS5',\n",
       " 'AS6',\n",
       " 'AS7',\n",
       " 'AS8',\n",
       " 'AS9',\n",
       " 'AV',\n",
       " 'AV10',\n",
       " 'AV6',\n",
       " 'AV7',\n",
       " 'AV8',\n",
       " 'M5',\n",
       " 'M6',\n",
       " 'M7']"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_values_models = df['Transmission'].unique()\n",
    "\n",
    "arrayModel=[]\n",
    "\n",
    "for value in unique_values_models:\n",
    "    # print(value)\n",
    "    arrayModel.append(value)\n",
    "\n",
    "sorted_list = sorted(arrayModel)\n",
    "sorted_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "68ec7b39",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A8L</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>S7</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>10.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "180  AUDI   A8L     FULL-SIZE             3.0        6.0          AS8   \n",
       "214  AUDI    S7    SUBCOMPACT             2.0       10.0          AS8   \n",
       "\n",
       "    Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "180         Z                              12.6   \n",
       "214         Z                              10.7   \n",
       "\n",
       "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "180                              7.7                              21.0   \n",
       "214                              8.8                              10.7   \n",
       "\n",
       "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "180                          NaN                321.0  \n",
       "214                          NaN                235.0  "
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[[\"Fuel Consumption Comb (mpg)\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "id": "adb31cb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating and filling missing values for the \"Fuel Consumption Comb (mpg)\" column based on a conversion formula\n",
    "mask = df[\"Fuel Consumption Comb (mpg)\"].isnull()\n",
    "df.loc[mask, \"Fuel Consumption Comb (mpg)\"] = 282.481/df.loc[mask, \"Fuel Consumption Comb (L/100 km)\"].round(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "id": "d53c1c5a",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[[\"Fuel Consumption Comb (mpg)\"]].isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "id": "671e9ecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                       AUDI\n",
       "Model                                 Q5 HYBRID\n",
       "Vehicle Class                       SUV - SMALL\n",
       "Engine Size(L)                              2.0\n",
       "Cylinders                                   4.0\n",
       "Transmission                                AS8\n",
       "Fuel Type                                     Z\n",
       "Fuel Consumption City (L/100 km)            9.9\n",
       "Fuel Consumption Hwy (L/100 km)             8.3\n",
       "Fuel Consumption Comb (L/100 km)            9.2\n",
       "Fuel Consumption Comb (mpg)                31.0\n",
       "CO2 Emissions(g/km)                       212.0\n",
       "Name: 189, dtype: object"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the content of a specific row (row 180)\n",
    "df.iloc[180]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "id": "cc1fb103",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.626556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.593361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.012448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AM8</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>22.074689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.804979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7408</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC90 T5 AWD</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.900415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7412</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>S60 POLESTAR</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.190871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7413</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>S60 CC T5 AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>X</td>\n",
       "      <td>10.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>26.141079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7418</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>V60 POLESTAR</td>\n",
       "      <td>STATION WAGON - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.190871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7419</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>V60 CC T5 AWD</td>\n",
       "      <td>STATION WAGON - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>X</td>\n",
       "      <td>10.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>26.141079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1402 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Make          Model          Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "20    ACURA        RDX AWD            SUV - SMALL             3.5        6.0   \n",
       "24    ACURA            RLX               MID-SIZE             3.5        6.0   \n",
       "45    ACURA     TLX SH-AWD                COMPACT             3.5        6.0   \n",
       "46    ACURA            ILX                COMPACT             2.4        4.0   \n",
       "47    ACURA     MDX SH-AWD            SUV - SMALL             3.5        6.0   \n",
       "...     ...            ...                    ...             ...        ...   \n",
       "7408  VOLVO    XC90 T5 AWD         SUV - STANDARD             2.0        4.0   \n",
       "7412  VOLVO   S60 POLESTAR                COMPACT             2.0        4.0   \n",
       "7413  VOLVO  S60 CC T5 AWD                COMPACT             2.0        4.0   \n",
       "7418  VOLVO   V60 POLESTAR  STATION WAGON - SMALL             2.0        4.0   \n",
       "7419  VOLVO  V60 CC T5 AWD  STATION WAGON - SMALL             2.0        4.0   \n",
       "\n",
       "     Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "20            AS6         Z                              12.1   \n",
       "24            AS6         Z                              11.9   \n",
       "45            AS9         Z                              11.2   \n",
       "46            AM8         Z                               9.4   \n",
       "47            AS9         Z                              12.6   \n",
       "...           ...       ...                               ...   \n",
       "7408          AS8         Z                              10.8   \n",
       "7412          AS8         Z                              11.8   \n",
       "7413          AS8         X                              10.8   \n",
       "7418          AS8         Z                              11.8   \n",
       "7419          AS8         X                              10.8   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "20                                8.7                              10.6   \n",
       "24                                7.7                              10.0   \n",
       "45                                7.5                               9.6   \n",
       "46                                6.8                               8.2   \n",
       "47                                9.0                              11.0   \n",
       "...                               ...                               ...   \n",
       "7408                              9.5                              10.2   \n",
       "7412                              8.5                              10.3   \n",
       "7413                              7.8                               9.4   \n",
       "7418                              8.5                              10.3   \n",
       "7419                              7.8                               9.4   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "20                           27.0            29.626556  \n",
       "24                           28.0            27.593361  \n",
       "45                           29.0            27.012448  \n",
       "46                           34.0            22.074689  \n",
       "47                           26.0            31.804979  \n",
       "...                           ...                  ...  \n",
       "7408                         28.0            28.900415  \n",
       "7412                         27.0            29.190871  \n",
       "7413                         30.0            26.141079  \n",
       "7418                         27.0            29.190871  \n",
       "7419                         30.0            26.141079  \n",
       "\n",
       "[1402 rows x 12 columns]"
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying and displaying duplicate rows in the DataFrame\n",
    "duplicate_rows = df[df.duplicated()]\n",
    "duplicate_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "c32b55d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "all_duplicated_rows = df.apply(lambda row: row.duplicated(keep=False).all(), axis=1)\n",
    "\n",
    "df_all_duplicated = df[all_duplicated_rows]\n",
    "\n",
    "print(df_all_duplicated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "77661088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Transmission\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "6372b8b5",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>30.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "56  ACURA  TLX SH-AWD       COMPACT            -5.6        6.0         AS10   \n",
       "\n",
       "   Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "56         Z                              10.0   \n",
       "\n",
       "    Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "56                              1.2                               5.6   \n",
       "\n",
       "    Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "56                         30.0                228.0  "
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying rows with negative values in the \"Engine Size(L)\" column\n",
    "negative_rows = df[df['Engine Size(L)'] < 0]\n",
    "\n",
    "negative_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ff74d92",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "1ce0b94e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Engine Size(L)' 'Cylinders' 'Fuel Consumption City (L/100 km)'\n",
      " 'Fuel Consumption Hwy (L/100 km)' 'Fuel Consumption Comb (L/100 km)'\n",
      " 'Fuel Consumption Comb (mpg)' 'CO2 Emissions(g/km)' 'Make' 'Model'\n",
      " 'Vehicle Class' 'Transmission' 'Fuel Type']\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 4 is out of bounds for axis 1 with size 4",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[530], line 14\u001b[0m\n\u001b[0;32m     12\u001b[0m y \u001b[38;5;241m=\u001b[39m i\u001b[38;5;241m%\u001b[39m\u001b[38;5;241m7\u001b[39m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i\u001b[38;5;241m<\u001b[39m\u001b[38;5;241m8\u001b[39m:\n\u001b[1;32m---> 14\u001b[0m     sns\u001b[38;5;241m.\u001b[39mboxplot(data\u001b[38;5;241m=\u001b[39mdf, y\u001b[38;5;241m=\u001b[39mcol, ax\u001b[38;5;241m=\u001b[39m\u001b[43max\u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43my\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[0;32m     15\u001b[0m     ax[x,y]\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mlabel\u001b[38;5;241m.\u001b[39mset_size(\u001b[38;5;241m15\u001b[39m)\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[1;31mIndexError\u001b[0m: index 4 is out of bounds for axis 1 with size 4"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the style and color palette for Seaborn plots\n",
    "sns.set_style('darkgrid')\n",
    "colors = ['#0055ff', '#ff7000', '#23bf00']\n",
    "CustomPalette = sns.set_palette(sns.color_palette(colors))\n",
    "\n",
    "# Defining the order of columns for plotting\n",
    "OrderedCols = np.concatenate([df.select_dtypes(exclude='object').columns.values,  df.select_dtypes(include='object').columns.values])\n",
    "\n",
    "print(OrderedCols)\n",
    "\n",
    "# Creating subplots for box plots\n",
    "fig, ax = plt.subplots(2, 4, figsize=(15,7),dpi=100)\n",
    "\n",
    "# Looping through columns for plotting\n",
    "for i,col in enumerate(OrderedCols):\n",
    "    x = i//7\n",
    "    y = i%7\n",
    "    if i<8:\n",
    "        sns.boxplot(data=df, y=col, ax=ax[x,y])\n",
    "        ax[x,y].yaxis.label.set_size(15)\n",
    "    else:\n",
    "        sns.boxplot(data=df, x=col, y='CO2 Emissions(g/km)', ax=ax[x,y])\n",
    "        ax[x,y].xaxis.label.set_size(15)\n",
    "        ax[x,y].yaxis.label.set_size(15)\n",
    "\n",
    "# Adjusting the layout for better visualization\n",
    "plt.tight_layout()    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "id": "a1e50d5d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Fuel Type', ylabel='CO2 Emissions(g/km)'>"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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trKiDSkpKtGrVCuXl5WrVqhUqKSkxOhIAAADgVNW6TlVOTo6OHTumkJAQtWjRosZDffvtt5o/f75++OEH+fj46M4779TUqVPl5eWlvXv36rnnnlN2drb8/f0VFxene+65x3LfjRs3aunSpTpx4oSuueYazZo1S127drXr8TmnynYbNqzRli0bLbtBDhoUo5iYYUbHAgAAABxS4+dUvfbaazp//rzl/aCgIHXu3LlSoSorK9Orr75qR9TKKioq9Mgjj6hfv3766quvtH79en366adavny5Tp06pXHjxmnIkCFKT0/XvHnztGDBAmVmZkqS0tLSNHfuXCUkJCg9PV2DBg1SXFyczp0751AmVC0n55hSUjbrQi83m81KTt6knJxjBicDAAAAnMemUtWwYUMNGjRIK1asUE5OTqXxw4cPa8WKFRowYIAaNGjgUKBTp07pxIkTqqiosLxY9/DwkK+vr7Zt2yY/Pz+NGDFCDRo0UM+ePTVw4EAlJSVJktatW6fo6GhFRESoYcOGGj16tPz9/ZWamupQJlRmNpu1evXr+vVE528dBwAAAFyVTQ3owQcfVK9evbRkyRItXrxYQUFBCgoKUkVFhXJycpSXl6fbb79dS5cuVdu2bR0K5O/vr9GjR2vhwoV6/vnnVV5erttvv12jR49WQkKC2rVrZ3X70NBQrV+/XpKUnZ2toUOHVhrft2+fXRm4iPnlHTlyWFlZmZWOV1RUKCsrU0ePHlbr1lcakAwAAABwnD2dwOZppbZt2+qll15Sbm6uvvrqKx09elQeHh4KCQlRjx491KxZs+pkraSiokI+Pj6aNWuWYmNj9fPPP+tPf/qTlixZoqKiIvn6+lrd3sfHR2fPnpWky47bKjDQtrWT7iww8Fp169ZNe/bssdpG38PDQ127dlV4+LWWCxoCAAAArszutXrNmzdX//79JUmFhYXy8vKSl5dXjQX64IMPtHXrVr3//vuSpLCwMMXHx2vevHkaOHCgzpw5Y3X74uJiNWrUSNIv18kqLi6uNO7v729Xhrw8NqqwxX33jdLevXutjplMJt177wPKyys0KBUAAADgOJPJ9skWu0rVjz/+qMWLF+tvf/ubPvjgA02cOFGNGjXS0qVLFRFRM9dXOHr0qEpLS61DNmighg0bql27dvrss8+sxrKzsxUWFibplwK2f//+SuO9evWyK4PZLEqVDVq2bKXo6MFWu/8NGDBELVu24usHAAAAt2HXdarmz5+vpk2bymw2a/HixXrsscf02GOPKSEhocYCRUZG6sSJE/r73/+u8vJyHTx4UMuWLdPAgQMVFRWl3NxcJSYmqqysTF9++aW2bNliOY8qNjZWW7Zs0ZdffqmysjIlJiYqLy9PUVFRNZYP1gYMGCI/v19mAv38/BUdPdjgRAAAAIBz2XWdqsjISG3fvl05OTnq16+f0tLS1KhRI0VEROjrr7+usVCff/65XnzxRf30009q0qSJBg0apPj4eHl5eembb77RvHnz9O9//1sBAQEaP368YmJiLPfdvHmzli1bppycHIWGhmrmzJnq3LmzXY/Pdarss3t3ht58c6VGjnyIK8IDAADAJdhznSq7SlWPHj20Y8cObdy4URs2bNDatWt18uRJRUdH64svvqh24LqGUgUAAAC4N3tKlV3nVN18882aMGGC9u3bpzFjxujgwYOaOnWqbrvtturkBAAAAIB6z66ZqqKiIq1cuVLe3t4aN26c9u3bp/Xr12vSpEm64oorajOnUzFTBQAAALi3Wlv+5y4oVQAAAIB7q7Xlfzk5OVq2bJn++9//Wl3wVZJWr15tz4cCAAAAAJdgV6maPn26cnNz1bt3bzVs2LC2MgEAAABAvWFXqfrmm2+0detWBQQE1FYeAAAAAKhX7Lr4b5MmTeTl5VVbWQAAAACg3rGrVI0fP17Tp09XZmamjhw5YvUf3Nfu3RmaNCleu3dnGB0FAAAAcDq7dv+79tprre9sMslsNstkMun777+v8XBGYfc/25WUlGjatCeUn39S/v4BWrjwRXl7exsdCwAAAHBIre3+99FHH1UrEFxXcvImFRTkS5IKCvKVkrJZMTHDDE4FAAAAOI9dy/9at24tPz8/7d27V6mpqdq1a5eaNGmi1q1b11Y+1GE5OceUkrJZFyY7zWazkpM3KSfnmMHJAAAAAOexa/nfzz//rNGjR6usrEwhISE6cuSIKioqtGrVKoWFhdVmTqdi+d/lmc1m/eUv8/Xdd1lW1yzz8PDQ9dd31JQpT8tkMhmYEAAAAKg+e5b/2TVTtWDBAt15553asWOH1q5dqx07dmjw4MFKSEioVlDUX0ePHlZWVmali0BXVFQoKytTR48eNigZAAAA4Fx2laq9e/dq4sSJ8vD45W4eHh56/PHHtXfv3loJh7orOLi1OnYMt3wvXODh4aFOnTorOJgloQAAAHAPdpUqT09PFRYWWh0rLCyUr69vjYZC3WcymfTAA2MqLfEzmUwaOfIhlv4BAADAbdhVqnr37q3Jkyfrp59+UmlpqX788Uc9+eST6t27d23lQx0WFNRK0dGDLQXKZDJpwIAhCgpqZXAyAAAAwHnsKlWTJ0/W+fPn1b9/f3Xu3FkDBgyQl5eXpkyZUlv5UMcNGDBEfn7+kiQ/P39FRw82OBEAAADgXHbt/nfBwYMHlZeXp9atW6tFixa1kctQ7P5nn927M/Tmmys1cuRD6to1wug4AAAAgMPs2f3PplKVkZGhiIgIpaen/+ZtbrzxRtsT1nGUKgAAAMC91Xip6tatm77++mtde+21v/GAJn3//ff2pazDKFUAAACAe6vxUuVuKFUAAACAe6u1i/9KUlZWliTpzJkzWrRokV5//XWdP3/e3g8DAAAAAC6hgT03XrZsmVasWKGMjAzNnTtXWVlZ8vDw0LFjxzRjxozayggAAAAAdZZdM1XJyclKSkpSaWmptm7dqsWLF2vVqlVKTU2trXwAAAAAUKfZNVN1/PhxXXvttfriiy/UpEkTy8YV586dq5VwAAAAAFDX2TVTFRQUpPT0dG3atEk9e/aU9MvsVZs2bWo0VEFBgaZOnaru3bvrxhtv1Pjx43X8+HFJ0t69e3XPPfeoa9eu6tOnj9atW2d1340bNyoqKkpdunRRTEyMdu/eXaPZAAAAAOBidpWqCRMmaOzYsfr4448VFxenL774QtOnT9fEiRNrNNSECRN09uxZffDBB9q+fbs8PT01a9YsnTp1SuPGjdOQIUOUnp6uefPmacGCBcrMzJQkpaWlae7cuUpISFB6eroGDRqkuLg4ZtIAAAAA1Bq7t1QvKSmRJHl7e6uoqEhFRUVq2bJljQXKysrSH//4R33++edq3LixpF9mrk6cOKE9e/ZoxYoV2rp1q+X2s2fPVnFxsRYuXKgpU6bI19dXc+fOtYzfddddGjt2rIYOHWpzBrZUBwAAANybPVuq23VOVUVFhXbs2KGoqCjl5ORowYIFCggI0KRJkywFyFGZmZkKDQ3V2rVr9c477+jcuXP6wx/+oGnTpmn//v1q166d1e1DQ0O1fv16SVJ2dnal8hQaGqp9+/bZlcFkcuxzAAAAAFC/2dMJ7CpVCQkJ2rp1q6KiojR79mwVFhaqoKBAzz77rJ5//nl7c1bp1KlT+uGHH9SxY0dt3LhRxcXFmjp1qqZNm6bmzZvL19fX6vY+Pj46e/asJKmoqOiS47YKDLStkQIAAACAXaXqk08+0TvvvKOioiJ9+umnSklJUWBgoG6//fYaC+Tl5SVJmjFjhry9vdW4cWM98cQTGjZsmGJiYlRcXGx1++LiYjVq1EiS5OvrW+W4v7+/XRny8lj+BwAAALgzk8n2yRa7SlV+fr5CQkL08ccfq2XLlrr66qtVXl6u8vLyagWtSmhoqCoqKlRWViZvb29Jvyw7lKTrrrtOb7/9ttXts7OzFRYWJkkKCwvT/v37K4336tXLrgxmsyhVAAAAAGxi1+5/bdq00aZNm/SPf/xDkZGRqqio0MqVKxUaGlpjgW6++Wa1adNGTz/9tIqKinTy5Em98MILuuOOOzRgwADl5uYqMTFRZWVl+vLLL7VlyxbLeVSxsbHasmWLvvzyS5WVlSkxMVF5eXmKioqqsXwAAAAAcDG7dv9LT0/XtGnT5OPjo8TERGVnZ2vSpEn6+9//ri5dutRYqJycHMu26CUlJerTp49mzJihpk2b6ptvvtG8efP073//WwEBARo/frxiYmIs9928ebOWLVumnJwchYaGaubMmercubNdj8/ufwAAAIB7s2f3P7u3VL9YaWmppP+dB+UqKFUAAACAe6vxLdWTk5M1YMAAbdq06TdvM2TIEJseEAAAAABciU0zVQMGDFBycrL69OlT9QcxmfTRRx/VeDijMFNln927M/Tmmys1cuRD6to1wug4AAAAgMOctvzPVVGqbFdSUqJp055Qfv5J+fsHaOHCFy27NgIAAAD1VY0v/7vYrl27dPjwYf26i7H8zz0lJ29Sfv5JSVJ+/kmlpGxWTMwwg1MBAAAAzmNXqZo9e7bWr1+vli1bymQyWY6bTCZKlRvKyTmm99571+rY5s0bdMstvRQU1MqgVAAAAIBz2VWqUlNTtWbNGnXs2LG28qCeMJvNWr369SrHVq9+XVOmPG1VvAEAAABXZdfFf5s0aaJ27drVVhbUI0ePHlZWVmaVY1lZmTp69LCTEwEAAADGsGumKi4uTjNmzNCYMWPUtGlTq7GQkJAaDYa6rWXLSy/vu9w4AAAA4CrsKlUlJSVKTU1VcnKy5ZjZbJbJZNL3339f4+FQd33yyb8uO3777X2dlAYAAAAwjl1bqt98882aMGGCIiMj5eFhvXKwdevWNR7OKGypfnnl5eV6+OGRKi8vrzTm6emp5cvflKenpwHJAMBxXH8PAFBrW6qXl5frvvvuq1YouBZPT08NHXqv1q5NqjQWG3svhQpAvVVSUqJVq1YoP/+kVq1aoeuv78j19wAAl2TXRhUxMTFavXp1bWVBPWI2m/XVV19UOZaW9kWl65gBQH2RnLxJBQX5kqSCgnylpGw2OBEAoK6za6YqMzNTb7zxhl566SU1a9bMasvsjz76qMbDoe46fPiQ/vvfn6oc++9/f9Lhw4d05ZVtnJwKAByTk3NMKSmbLX8YMpvNSk7exPX3AACXZFepio2NVWxsbG1lQT1y7NjRy45TqgDUJxeuv/frmfYLx7n+HgDgt9hVqu6+++7ayoF6pqDgpEPjAFDX/Nb19yoqKizX3wsJudKAZACAus6mc6rGjRtn9X5KSorV+zfddFPNJUK9cOuttzs0DgB1TXBwa3XsGF7lWKdOnRUc7Dq73AIAapZNpWrXrl1W78+ZM8fq/bKysppLhHohK2uvQ+MAUNeYTCb17BlZ5VjPnpEs/QMA/Ca7dv+74NfrzXmicT/+/gEOjQNAXVNRUaF33ql6h9u3316liooKJycCANQX1SpVlChs337p3R4vNw4AdU1m5m4VFhZWOVZYWKjMzN1OTgQAqC+qVaqA4cNHODQOAHVNeHhX+fj4VDnm4+Or8PCuTk4EAKgvKFWolrVr33ZoHADqot9a4ldRUe7kJACA+sSmLdXPnTun22//325uZ86csXq/uLi45pOhThsxYpS2b//gkuMAUJ/s2fO1SktLqxwrLS3Vnj1fq1u3G5ycCq5uw4Y1Sk7epAEDhmjo0OFGxwFQTTaVqvnz59d2DtQznp6e8vT0VHl55b/eXhgDgPqkpKTEoXHAXqdPn9Z7770rSXrvvXcVFXWXmjZtanAqANVhU6nior/4tT17MqosVJJUXl6uPXsy1K3bjU5OBQDVt3Vr6mXHe/a8xUlp4A4WLpxr9f7zz8/Vc88tMigNAEdwThWqpbz80lsLX24cAOoaP79LzxBcbhywx7fffqNDhw5YHTt48IC+/fYbgxIBcIRNM1VGKC8v1+jRo9W6dWslJCRIkvbu3avnnntO2dnZ8vf3V1xcnO655x7LfTZu3KilS5fqxIkTuuaaazRr1ix17eoeuzWZzWaVljpvacrRo4cvO15S4pxz7by8vNnmH4DDBgyI0e7dX19yHKgJFRUVWrw4ocqxxYsTtHz5m/Lw4O/eQH1SZ0vVK6+8ol27dql169aSpFOnTmncuHF67LHHNHz4cKWnpys+Pl7t27dXeHi40tLSNHfuXC1fvlzh4eFKSkpSXFyctm/fLl9fX4M/m9plNpv13HPPKDv730ZHsdiwYY02bFjjlMcKC2uvGTPmUKwAOCQj46vLjoeGhjkpDVxZRka6zp8/X+XY+fPnlZGRrhtv7O7kVAAcUSf/DPLFF19o27Zt6tu3r+XYtm3b5OfnpxEjRqhBgwbq2bOnBg4cqKSkJEnSunXrFB0drYiICDVs2FCjR4+Wv7+/UlMvvUbeVVAoAMAxRUVVX/jX1nHAVmlpnzs0DqDusWmmqri4WH/5y1+0a9cudezYUY8//rhatGhhGR84cKC2bNlSI4Hy8vI0Y8YMLV26VImJiZbj+/fvV7t27axuGxoaqvXr10uSsrOzNXTo0Erj+/btsztDfesnJpNJM2fOceryP0latep1ffrpjkrHe/W6TSNHPui0HCz/A1yTs5c1Dxt2nz755F+XHC8tZVkzHPfII/FKT//ykuP88wPGs+fn0KZS9Ze//EV79uzR0KFDtW3bNg0fPlzvvPOOgoKCJEmHDh2qVtBfq6io0JNPPqkHH3xQ1157rdVYUVFRpWV8Pj4+Onv2rE3j9ggMbGL3fdzR008/pejonTKbzZZjJpNJTz011cBUAFyB2WzWlClT9P333xsdxSI+/mGnPdb111+vRYsWUaxcWL9+/bR169ZKx/v376+QkEADEgFwhE2l6sMPP9SaNWsUFBSk+++/X9OnT9ejjz6qNWvWyMvLq8Z+6b/66qvy8vLSyJEjK435+vrqzJkzVseKi4vVqFEjy/ivL0JcXFwsf39/u3Pk5Z3RRT0BlzB16tNauHCe5f0ZM+YoN/fMJe4BAJdnNpt1/rz77iJaVlau3NwzlCoXdscd/assVb179+N5FKgjTCbbJ1tsKlVFRUVq2bLl/39wk5577jmNGjVKs2bN0sKFC61mKhyxefNmHT9+XDfc8MsV6y+UpA8//FBTp07VZ599ZnX77OxshYX9ctJwWFiY9u/fX2m8V69educwm0WpslHbtv9bkunn56+wsPZ87QDUAJNmzHDusmaz2axFi+ZXuelPWFg7TZnytNNKjpeXtyQTv09dlNls1qpVr1c5tmrV6079XgNQM2wqVW3bttWGDRsUGxv7y50aNNALL7ygmJgYvfzyyzX2g//+++9bvf/UU09JkhISEpSfn69FixYpMTFRI0aMUEZGhrZs2aKlS5dKkmJjYxUfH6+77rpLERERSkpKUl5enqKiomokGy7v+edfNDoCABdiMpnk7e3j1MccNy5e06Y9UWlZ88MPx8vHx7V3koXzHD16WFlZmVWOZWVl6ujRwwoJudLJqQA4wqZSNXHiRMXFxSktLU2LFv1ype+WLVtq6dKlGjNmjM6dO1erISXJ399fK1eu1Lx587RkyRIFBARo5syZ6tGjhySpZ8+emj17tv785z8rJydHoaGhWr58ufz8/Go9GwDANQQFtVLfvv21dWuK5Vj//oMUFNTKwFSobc7eFCUgIFDXX99R+/Z9p4qK/y1z9fDw0HXXdVBAQCDXegTqGZPZxrV7OTk5OnjwoGVp3gUHDx7UqlWrNHPmzFoJaITcXM6pslVJSbHGjRslSXrttVVO/6syANS006dPacKEcZKkRo0a64UXlsrb29vgVKgtdfFaj87EtR6B32YySc2b1+A5VZIUFBSkoKAgHTt2TMePH1eLFi0UHBysNm3auFShAgC4t4sL1OjRYylUboBCAcBRNpeq/fv3a+bMmcrMzJTZbJbJZFKHDh00b948tW/fvjYzAgBgiM6duxodAbXMZHL+pigXnDlzWpMnT5D0y2ZPc+cudHqJZ/kfUDNsKlVHjx7ViBEj9Ic//EETJ05Uy5YtdfDgQa1fv14jRozQe++9p5CQkNrOCgAAUOOM2BTl10aMGKWmTZsZmgFA9XnYcqNly5bprrvu0l//+lf16NFD11xzjW699Va9/PLLuvPOO/X3v/+9tnMCAAC4LGZFgfrNplL16aef6tFHH61y7JFHHtHOnTtrNBQAAAAA1Bc2Lf/Lz89XcHBwlWNXXnmlCgoKajITgDrM2VsP//qxJeNOKufcAwAAUBWbSlXjxo11+PBhtW7dutLYkSNH1KwZa4ABd8DWw2w9DAAAKrNp+d8tt9yi5cuXVzm2fPlyRUZG1mgoAHUXhQIAAMCaTTNVcXFxiomJUUVFhQYNGqQWLVroyJEjWr9+vXbu3KmNGzfWdk4AdYCRWw+XlJRYLsj68suvGXLtIJb/AQCAqthUqq6++molJibqmWee0dq1a2UymWQ2m9WhQwetXr26ymWBAFxTXdh62Nvb2/AMAAAAF9h88d9OnTpp48aNOnz4sI4fP64WLVroyiuvrM1s9YqRJ+8bqaSkpMq33QmzFwDgGJ5DeQ4F6jubS5XZbNbBgwd11VVXWWamUlNT1a9fP3l6etZawPqitLRE48aNMjqGoS4szXI3r722ilkTAHAAz6E8hwL1nU0bVZw9e1b33Xefnn/+ecuxvLw8PfXUUxo5cqTOnj1bawEBAAAAoC6zaaZq2bJlatiwoebMmWM5FhgYqO3btysuLk6vvvqqJk6cWGsh65uX+8bI29PmScB6z+hrBxmhpPy8Jmx71+gYAOByGg4bL7nVc+gv/3ejp1Cp/LzK1i41OgVQo2z6rbV161YtX75cgYGBVscDAwM1Z84cPfHEE5Sqi3h7NpB3A/d5QgDgmjjPhfNcnOXCH+ck8WLbzVz8bw/UZza98s/Ly9PVV19d5dh1112nEydO1GgoAL+NF7q80HUWznPhPBcAgG1sKlWNGzdWfn6+/P39K40VFBTI19e3xoMBqBovdHmhC7iai/9Y0PC+x2Rq6GVgGtQ2c1mpyt5ZIsm9Th2Aa7OpVPXs2VNJSUn605/+VGns7bffVpcuXWo6FwCgLrn/BqmBG+306o4nupwvl97aZXQKmRp6UaoA1Ds2lapHHnlEMTExys/PV//+/dWiRQsdP35c//znP7Vhwwa99dZbtZ2zXik5f97oCKhldeXf+L6BFXKn0/fc8nXueemdLTZt1Fq7GnhKDd2oVAEAYAebXo79/ve/1+uvv67Zs2crKSlJJpNJZrNZ7dq10/Lly9WxY8fazlnnXXyi5YQP2BXOnRh5kq27nd7rTmXqAnf7NwZ0vsytvu/dcQddnS8zOgFQ42z+G3e3bt20ZcsWHTx4UCdPnlSLFi0UEhJSm9kAVOHiEvePujCDAadhlyy4g9K3XzI6AgDYze6FQ23atFGbNm1qI0u9dvFfmF6OimFLdRdXcv68ZUbSrf66CAAAgEp45V8LvBtwnSrUnotL3L0DK9SQbzWXVnb+fzOShhb4snLjHhvOYeC/sZeXt157bZVhj2+UkpISy26mL7/8mry9vQ1O5HxeXu73OcM18XKsFpSU141NDJzFHdeD15V/44YNRKlCrbFabphk/K5wcB5nLzU1mUxuf7kAb29vt/8aAPUZL8dqwYRtbFQBAAAAuIs6War27dunhQsX6ttvv1XDhg11yy236KmnnlJAQID27t2r5557TtnZ2fL391dcXJzuuecey303btyopUuX6sSJE7rmmms0a9Ysde3a1cDPBgDqJ6vZ5xE3sKW6qysrt8xIutPKA7gfs9msM2dOG/LYFRUVKiwsNOSxjda4cWN5eBizwVaTJk1r/fdanStVxcXFGjt2rIYNG6ZXX31VRUVFmjZtmp5++mktXLhQ48aN02OPPabhw4crPT1d8fHxat++vcLDw5WWlqa5c+dq+fLlCg8PV1JSkuLi4rR9+3b5+vrWam7Wg7MeHHBpDblOFYD6z2w267nnnlF29r+NjgInCgtrrxkz5tRqsapzperIkSO69tprFR8fL09PT3l5eWn48OGaOnWqtm3bJj8/P40YMUKS1LNnTw0cOFBJSUkKDw/XunXrFB0drYiICEnS6NGjtWbNGqWmpmro0KG1mpv14KwHBwAAdR8zsagNda5UXXPNNVqxYoXVsa1bt6pDhw7av3+/2rVrZzUWGhqq9evXS5Kys7MrlafQ0FDt27fPrgz8rNnu4q+VycTXzhku/hqfrxv7ZTjNhXPn3en77OJ/Y2f/jLnT1xnW+H3uHDyHOp/JZNLMmXNY/meA+rj8z5671LlSdTGz2awXX3xR27dv11tvvaXVq1dXWsbn4+Ojs2fPSpKKioouOW6rwMAmjgV3I8XFDS1vBwY2kY8PM1W17eKv+Ttc/NetOPtn7OLvNZ13sy3V3bLB/+/fmN/nzsFzqHFatGhqdAS4mDpbqgoLCzV9+nR9++23euutt9S+fXv5+vrqzJkzVrcrLi5Wo0aNJEm+vr4qLi6uNO7v72/XY+flnZGTd5Ott0pK/vf1zss7I2/vMgPTuIeLv+ZwL87+GbP6XnuLLdXdCb/PnePin7H58xP02GOTDUwD4NdMJtsnW+pkqTpw4IAefvhhhYSEaP369QoICJAktWvXTp999pnVbbOzsxUWFiZJCgsL0/79+yuN9+rVy67HN5tFqbLRxV8nvm7O0bAhm6K466YoDRt6O/VnjJ9n98Xvc+fIzc21vL1r11c6ceKEmjdvYWAiANVV50rVqVOnNGrUKPXo0UPz5s2zWnsZFRWlRYsWKTExUSNGjFBGRoa2bNmipUuXSpJiY2MVHx+vu+66SxEREUpKSlJeXp6ioqKM+nSAGsemKGyK4izsauq+Bd7ddjU1m80qLS1x+uMmJMy1en/u3Fl6/vkXnZrBy8ubjRuAGlDnStW7776rI0eO6J///Kfef/99q7Hdu3dr5cqVmjdvnpYsWaKAgADNnDlTPXr0kPTLboCzZ8/Wn//8Z+Xk5Cg0NFTLly+Xn5+fAZ8J4JqMevFRUlJS5dvO5G4vPijwFHh3UJe22C4oyNe4caOc+pjO2GoacAd1rlQ9+OCDevDBB39zvFOnTvrHP/7xm+ODBw/W4MGDayMa4PbqyouPC7MIzsaLD8A18TMNwFF1rlQBqNt48QHAlZhMJs2YMcepM/Dbt3+kd95Z/Zvj9933gHr3vt0pWdxtBh6oLZQqADYz4sXHxcz/f+a8US8AePEBuCZnL3Xt0KHTZcdZdgrUL5QqAHbhPBsAcMyVV7ZR69ZtdPjwwSrGrtKVV7YxIBUAR3DlUAAAACcymUx6/PEpVY499thkZsSBeohSBQAA4GRBQa30+9+3tTr2+9+3VVBQK4MSAXAEpQoAAMDJcnKO6eef/2t17MCB/yon55gxgQA4hHOqXATXDmKpBACgfjCbzVq9+nVJ5iqPT5nyNM9rQD1DqXIBXDuIawcBAOqPo0cPKysrs9LxiooKZWVl6ujRwwoJudKAZACqi+V/LoJCAQBA/RAc3FodO4bLw8P6ZZiHh4c6deqs4ODWBiUDUF3MVLkArh3E8j8AQP1hMpn0wANjNH36pErHR458iOc0oB6iVLkIrh0EwNVwrigvrF1ZUFArRUcP1pYtG2U2m2UymTRgwBB2/wPqKZP5wjQDLHJzz4ivCgAYp66cK2oUzhV1DyUlJZo27Qnl55+Uv3+AFi58Ud7e3kbHAvD/TCapefMmNt2Wc6oAAHUShQKuztvbW6NGjVVgYHONGjWWQgXUY8xUVYGZKgAwnlHL/yRpz56v9Y9/vKX77hupzp27Ov3xWf4HAMazZ6aKc6oAAHWSUeeKlpSU6J133lR+/km9/fZqde7cjRkEAMAlsfwPAICLJCdvUkFBviSpoCBfKSmbDU4EAKjrKFUAAPy/nJxjSknZbLlUhNlsVnLyJuXkHDM4GQCgLqNUAQCgXwrU6tWv69enGv/WcQAALqBUAQAg6ejRw8rKylRFRYXV8YqKCmVlZero0cMGJQMA1HWUKgAAJAUHt1bHjuGVdt0zmUzq1KmzgoNbG5QMAFDXUaoAANAv5al//0FVLv/r338QW5wDAH4TpQoAAP1SnlJT36ty7OLNKwAA+DVKFQAA+t85VVXhnCoAwKVQqgAA0P/OqfLwsH5q9PDw4JwqAMAlUaoAANAv51Q98MCYKjeqGDnyIc6pAgD8JpcrVXl5eRo/frxuuOEGde/eXfPmzdP58+eNjgUAqAeCglopOnqwpUCZTCYNGDBEQUGtDE4GAKjLXK5UPfHEE7riiiu0c+dOrV+/Xl988YUSExONjgUAqCcGDBgiPz9/SZKfn7+iowcbnAgAUNe5VKn6+eef9dVXX+nJJ5+Ur6+v2rRpo/HjxyspKcnoaACAesLb21ujRo1VYGBzjRo1Vt7e3kZHAgDUcQ2MDlCT9u/fLz8/PwUFBVmOtW3bVkeOHNHp06fVtGlTmz4Oy+YBwL116xahbt0ijI4BADCQPZ3ApUpVUVGRfH19rY5deP/s2bM2l6rAwCY1ng0AAACAa3KpUnXFFVfo3LlzVscuvN+oUSObP05e3hlxjUcAAADAfZlMtk+2uFSpCgsLU0FBgXJzc9W8eXNJ0o8//qhWrVqpSRPbZ5/MZlGqAAAAANjEpUrV7373O0VERGj+/Pl69tlnlZ+fr6VLlyo2Ntauj8M5VQAAAIB7s6cTmMxm15qTyc3N1bPPPqu0tDR5eHhoyJAhmjJlijw9PY2OBgAAAMAFuVypAgAAAABncqnrVAEAAACAs1GqAAAAAMABlCoAAAAAcAClCgAAAAAcQKkCAAAAAAdQqgAAAADAAZQqAAAAAHAApQoAAAAAHNDA6ACon44cOaLo6OhKx0tLS3X+/Hn98MMPBqSCK+rTp49OnDihBg1++XVlNpt19dVX6/7779c999xjcDq4iv/85z+KjY3VI488onHjxlmOnzx5Uvfcc4/uvvtu/elPfzIwIVzJr3+vXWz58uW64YYbDEgFV9S+fXvdeuutevXVV2UymSzH3333Xb3yyiv617/+ZWA610KpQrWEhIRo9+7dVsd+/PFH/fGPf9Tw4cMNSgVXNWfOHMXExEj6pbh//PHHmj59uvLz861eAAPV9fvf/14LFy7U448/rk6dOqlnz54qLS1VfHy8OnbsqPj4eKMjwsVc/HsNqE2ffPKJVqxYoYcfftjoKC6N5X+oETk5ORozZoxuueUWTZw40eg4cGFeXl7q27evpk2bpldeeUWFhYVGR4KLuOOOOzR27FhNnDhRR48e1ezZs1VcXKyEhASrv/ACQH0ycuRIvfTSS/r666+NjuLSKFVwWFFRkR599FGFhITw4gNOc9ttt6mkpIQnCdSoxx9/XB06dNAf//hH7dixQ0uXLpWvr6/RsQCg2qKiojR8+HBNmjRJBQUFRsdxWZQqOKS8vFyTJk3S2bNn9be//U1eXl5GR4Kb8Pf3lySeIFCjPDw8NGzYMB05ckTdu3dXcHCw0ZHgoubMmaMbbrjB6r+BAwcaHQsuatq0aQoICNBTTz0ls9lsdByXxDlVcMjcuXO1d+9erV271vIiF3CGkydPSpICAwMNTgJXcuDAAT3zzDMaPXq03n77ba1du1bDhg0zOhZc0OzZszmnCk7j5eWlF198UXfffbdWrlzJa7ZaQKlCtb3++ut69913lZiYqKuuusroOHAz//rXv3TFFVeoc+fORkeBiygsLFRcXJxuu+02TZ8+XW3bttWzzz6r9u3b830GoN676qqrNHfuXE2dOpVCXwtY/odq2bZtmxYvXqxFixapW7duRseBGyktLVVqaqoWL16siRMnqnHjxkZHgguoqKjQlClT5O3trWeffVaSNGzYMA0cOFATJkxQbm6uwQkBwHH9+/fX0KFDtWbNGqOjuByTmYWVqIaRI0cqPT39N0/gTklJUUhIiJNTwRX9+nou3t7euuaaa3T//ferf//+BqeDq/jrX/+qdevW6d1337X63VVSUqJ7771XjRo1UmJiYpXXFQLsdanrVI0fP56tr1Fj2rdvr9WrV6t79+6WYyUlJRo+fLhOnz7NdapqEKUKAAAAABzA8j8AAAAAcAClCgAAAAAcQKkCAAAAAAdQqgAAAADAAZQqAAAAAHAApQoAAAAAHECpAgAAAAAHcBVDAECd9lsXSu3atatWrlzp0MdOS0vTAw88oB9++KHSWNeuXS1vFxcXy9PTUw0bNpQkhYSEKCUlxaHHBgC4DkoVAKDOmzNnjmJiYpz6mLt377a8PXLkSN10002aMGGCUzMAAOoHlv8BAOq1kSNH6uWXX7a8f+jQIbVv316HDh2SJOXm5mrKlCm65ZZbFBkZqWeeeUaFhYUOPeaePXt03XXX6dixY5Zj33zzjbp06aLCwkKNHDlSCQkJiomJUZcuXRQTE6Ndu3ZZbnvgwAE9+uij6t69u3r37q0XXnhBpaWlDmUCABiHUgUAcFkVFRUaP368PDw8tHXrVm3ZskXHjx/XM88849DH7dKli6655hq99957lmObNm1Sv3791LhxY0nSmjVrNHXqVH311VeKiopSXFyc8vPzdfbsWY0ePVphYWHasWOH3n77bX3++edWxRAAUL9QqgAAdd6cOXN0ww03WP139uzZy94vKytL3377rWbPnq3GjRvL399f06ZNU0pKivLz8x3KFBMTYylVZWVlSk5O1tChQy3jQ4cOVY8ePeTl5aVHH31Uvr6+2r59uz7++GOVlpZq0qRJ8vb2VnBwsB5//HElJSU5lAcAYBzOqQIA1HmzZ8+u1jlVhw4dUnl5uW699Var415eXjp48KBDmQYPHqzFixfru+++06FDh9SkSRPdeOONlvHf/e53lrdNJpNatWqlEydOyMPDQydPnrS6rdlsVllZmfLy8hQYGOhQLgCA81GqAAD1moeHh8rKyizvXzwD1apVK/n4+CgtLU2enp6SpNLSUh08eFBXX321MjIyqv24zZs3V69evZSSkqJDhw4pJiZGJpPJMp6Tk2N5u6KiQkeOHFFwcLBMJpOuuuoqvf/++5bxwsJC5eXlKSAgoNp5AADGYfkfAKBea9u2rXbu3KnTp0/rzJkzWr58uWUsPDxcV199tRISElRUVKTi4mLNnz9fo0ePVnl5ucOPPXToUH3wwQf6/PPPdffdd1uNrVu3TllZWSotLdXf/vY3mc1m9e7dW71791ZRUZFWrFih0tJSnT59WtOmTdPEiROtShkAoP6gVAEA6rVHHnlEgYGBuv322zV48GD16dPHMtagQQO9+uqrys3NVd++fRUZGakDBw7ojTfekLe3t8OPfdttt6moqEjh4eEKDg62Grvpppv07LPPqkePHkpLS9PKlSvVpEkTNW7cWImJiUpLS1OvXr10xx13yMPDQ8uWLXM4DwDAGCaz2Ww2OgQAAPXV3XffrYcfflj9+/e3HOO6VgDgXjinCgCAavjPf/6jtLQ0nThxQnfccYfRcQAABqJUAQBQDbNmzdKPP/6ohIQEeXl5GR0HAGAglv8BAAAAgAPYqAIAAAAAHECpAgAAAAAHUKoAAAAAwAGUKgAAAABwAKUKAAAAABxAqQIAAAAAB1CqAAAAAMABlCoAAAAAcAClCgAAAAAc8H+u0zTUZ5GQIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a box plot to visualize the relationship between \"Fuel Type\" and \"CO2 Emissions(g/km)\"\n",
    "plt.figure(figsize=(10, 3))\n",
    "sns.boxplot(data=df, x='Fuel Type', y='CO2 Emissions(g/km)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "3dd06044",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a violin plot with categories on the x-axis\n",
    "#sns.box(x=\"Make\",y='CO2 Emissions(g/km)', data=df)\n",
    "\n",
    "#sns.displot(df['Make'])\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "b046d876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2200x1300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a large box plot for all numerical columns in the DataFrame\n",
    "plt.figure(figsize=(22, 13))\n",
    "df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "8eb34257",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>30.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "56  ACURA  TLX SH-AWD       COMPACT            -5.6        6.0         AS10   \n",
       "\n",
       "   Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "56         Z                              10.0   \n",
       "\n",
       "    Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "56                              1.2                               5.6   \n",
       "\n",
       "    Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "56                         30.0                228.0  "
      ]
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying rows with negative values in numerical columns\n",
    "negative_samples = []\n",
    "\n",
    "for col in df.select_dtypes(exclude='object').columns:\n",
    "    negative_samples_in_col = df[df[col] < 0]\n",
    "    negative_samples.append(negative_samples_in_col)\n",
    "\n",
    "# Concatenate the list of DataFrames into a single DataFrame\n",
    "if negative_samples:\n",
    "    negative_samples = pd.concat(negative_samples)\n",
    "\n",
    "# Display the rows with negative values\n",
    "negative_samples\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "bbfa4ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows with negative values from the DataFrame\n",
    "df = df.drop(negative_samples.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "24945d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_samples = []\n",
    "\n",
    "for col in df.select_dtypes(exclude='object').columns:\n",
    "    negative_samples_in_col = df[df[col] < 0]\n",
    "    negative_samples.append(negative_samples_in_col)\n",
    "\n",
    "# Concatenate the list of DataFrames into a single DataFrame\n",
    "if negative_samples:\n",
    "    negative_samples = pd.concat(negative_samples)\n",
    "\n",
    "# Display the rows with negative values\n",
    "negative_samples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "id": "3c571918",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.25\n",
      "-0.5500000000000003\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
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       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A5 CABRIOLET QUATTRO</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS7</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A8L</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>6.3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>18.7</td>\n",
       "      <td>11.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A8L</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>6.3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>17.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>14.6</td>\n",
       "      <td>19.0</td>\n",
       "      <td>344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A8L QUATTRO</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>6.3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>17.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>14.6</td>\n",
       "      <td>19.0</td>\n",
       "      <td>344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>ROLLS-ROYCE</td>\n",
       "      <td>WRAITH</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>19.6</td>\n",
       "      <td>12.8</td>\n",
       "      <td>16.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6623</th>\n",
       "      <td>ROLLS-ROYCE</td>\n",
       "      <td>PHANTOM</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>6.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>20.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6653</th>\n",
       "      <td>SRT</td>\n",
       "      <td>VIPER COUPE</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>8.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6654</th>\n",
       "      <td>SRT</td>\n",
       "      <td>VIPER GTS COUPE</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>8.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7129</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>4RUNNER 4WD</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>99.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>X</td>\n",
       "      <td>14.8</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Make                 Model   Vehicle Class  Engine Size(L)  \\\n",
       "1           ACURA                   ILX         COMPACT            15.0   \n",
       "165          AUDI  A5 CABRIOLET QUATTRO      SUBCOMPACT            12.0   \n",
       "182          AUDI                   A8L       FULL-SIZE             6.3   \n",
       "245          AUDI                   A8L       FULL-SIZE             6.3   \n",
       "299          AUDI           A8L QUATTRO       FULL-SIZE             6.3   \n",
       "...           ...                   ...             ...             ...   \n",
       "6622  ROLLS-ROYCE                WRAITH        MID-SIZE             6.6   \n",
       "6623  ROLLS-ROYCE               PHANTOM       FULL-SIZE             6.7   \n",
       "6653          SRT           VIPER COUPE      TWO-SEATER             8.4   \n",
       "6654          SRT       VIPER GTS COUPE      TWO-SEATER             8.4   \n",
       "7129       TOYOTA           4RUNNER 4WD  SUV - STANDARD            99.0   \n",
       "\n",
       "      Cylinders Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "1           4.0          AS5         Z                               9.9   \n",
       "165         4.0          AS7         Z                              11.5   \n",
       "182        12.0          AS8         Z                              18.7   \n",
       "245        12.0          AS8         Z                              17.8   \n",
       "299        12.0          AS8         Z                              17.8   \n",
       "...         ...          ...       ...                               ...   \n",
       "6622       12.0          AS8         Z                              19.6   \n",
       "6623       12.0          AS8         Z                              20.0   \n",
       "6653       10.0           M6         Z                              20.0   \n",
       "6654       10.0           M6         Z                              20.0   \n",
       "7129        6.0          AS5         X                              14.8   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "1                                 6.7                               8.5   \n",
       "165                               8.1                              10.0   \n",
       "182                              11.5                              15.5   \n",
       "245                              10.8                              14.6   \n",
       "299                              10.8                              14.6   \n",
       "...                               ...                               ...   \n",
       "6622                             12.8                              16.5   \n",
       "6623                            999.0                              16.3   \n",
       "6653                             13.0                              16.9   \n",
       "6654                             13.0                              16.9   \n",
       "7129                             12.5                              13.8   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "1                            33.0                196.0  \n",
       "165                          28.0                220.0  \n",
       "182                          18.0                363.0  \n",
       "245                          19.0                344.0  \n",
       "299                          19.0                344.0  \n",
       "...                           ...                  ...  \n",
       "6622                         17.0                387.0  \n",
       "6623                         17.0                382.0  \n",
       "6653                         17.0                389.0  \n",
       "6654                         17.0                389.0  \n",
       "7129                         20.0                321.0  \n",
       "\n",
       "[146 rows x 12 columns]"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying outliers in the \"Engine Size(L)\" column using the IQR method\n",
    "q1 = df[\"Engine Size(L)\"].quantile(0.25)\n",
    "q3 = df[\"Engine Size(L)\"].quantile(0.75)\n",
    "iqr = q3-q1\n",
    "maximum = q3 + (1.5 * iqr)\n",
    "minimum = q1 - (1.5 * iqr)\n",
    "outlier_samples = df[(df[\"Engine Size(L)\"] < minimum) | (df[\"Engine Size(L)\"] > maximum)]\n",
    "\n",
    "# Displaying the maximum and minimum values for identifying outliers\n",
    "print(maximum)\n",
    "print(minimum)\n",
    "\n",
    "outlier_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "id": "dcfcd1ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
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       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
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       "      <td>33.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A5 CABRIOLET QUATTRO</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS7</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>BMW</td>\n",
       "      <td>ALPINA B7 XDRIVE LWB</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>24.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>12.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2162</th>\n",
       "      <td>DODGE</td>\n",
       "      <td>CHALLENGER</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>13.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>X</td>\n",
       "      <td>12.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>10.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5742</th>\n",
       "      <td>MINI</td>\n",
       "      <td>COOPER 3 DOOR</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>88.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7129</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>4RUNNER 4WD</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>99.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>X</td>\n",
       "      <td>14.8</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Make                 Model   Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "1      ACURA                   ILX         COMPACT            15.0        4.0   \n",
       "165     AUDI  A5 CABRIOLET QUATTRO      SUBCOMPACT            12.0        4.0   \n",
       "549      BMW  ALPINA B7 XDRIVE LWB       FULL-SIZE            24.4        8.0   \n",
       "2162   DODGE            CHALLENGER        MID-SIZE            13.6        6.0   \n",
       "5742    MINI         COOPER 3 DOOR      SUBCOMPACT            88.0        3.0   \n",
       "7129  TOYOTA           4RUNNER 4WD  SUV - STANDARD            99.0        6.0   \n",
       "\n",
       "     Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "1             AS5         Z                               9.9   \n",
       "165           AS7         Z                              11.5   \n",
       "549            A8         Z                              15.0   \n",
       "2162           A8         X                              12.4   \n",
       "5742          AM7         Z                               8.3   \n",
       "7129          AS5         X                              14.8   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "1                                 6.7                               8.5   \n",
       "165                               8.1                              10.0   \n",
       "549                               9.8                              12.7   \n",
       "2162                              7.8                              10.3   \n",
       "5742                              6.4                               7.5   \n",
       "7129                             12.5                              13.8   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "1                            33.0                196.0  \n",
       "165                          28.0                220.0  \n",
       "549                          23.0                297.0  \n",
       "2162                         27.0                242.0  \n",
       "5742                         38.0                174.0  \n",
       "7129                         20.0                321.0  "
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying and displaying rows where \"Engine Size(L)\" is greater than 10\n",
    "df[(df[\"Engine Size(L)\"] > 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "752daff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows where \"Engine Size(L)\" is greater than 10 from the DataFrame\n",
    "df = df.drop(df[(df[\"Engine Size(L)\"] > 10)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "id": "2c515c67",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.0\n",
      "1.0\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB9</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>5.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>Z</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>18.0</td>\n",
       "      <td>359.0</td>\n",
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       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>RAPIDE</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>5.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>Z</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>15.6</td>\n",
       "      <td>18.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>VANQUISH</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>5.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>Z</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>15.6</td>\n",
       "      <td>18.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB9</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>5.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>Z</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>15.6</td>\n",
       "      <td>18.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>RAPIDE S</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>16.8</td>\n",
       "      <td>10.90</td>\n",
       "      <td>14.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>327.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>ROLLS-ROYCE</td>\n",
       "      <td>WRAITH</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>19.6</td>\n",
       "      <td>12.80</td>\n",
       "      <td>16.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6623</th>\n",
       "      <td>ROLLS-ROYCE</td>\n",
       "      <td>PHANTOM</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>6.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>20.0</td>\n",
       "      <td>999.00</td>\n",
       "      <td>16.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6653</th>\n",
       "      <td>SRT</td>\n",
       "      <td>VIPER COUPE</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>8.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>16.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6654</th>\n",
       "      <td>SRT</td>\n",
       "      <td>VIPER GTS COUPE</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>8.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>16.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6963</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>SIENNA</td>\n",
       "      <td>MINIVAN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>X</td>\n",
       "      <td>14.4</td>\n",
       "      <td>10.21</td>\n",
       "      <td>12.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>288.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Make            Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "111   ASTON MARTIN              DB9   MINICOMPACT             5.9       12.0   \n",
       "112   ASTON MARTIN           RAPIDE    SUBCOMPACT             5.9       12.0   \n",
       "117   ASTON MARTIN         VANQUISH   MINICOMPACT             5.9       12.0   \n",
       "118   ASTON MARTIN              DB9   MINICOMPACT             5.9       12.0   \n",
       "119   ASTON MARTIN         RAPIDE S    SUBCOMPACT             6.0       12.0   \n",
       "...            ...              ...           ...             ...        ...   \n",
       "6622   ROLLS-ROYCE           WRAITH      MID-SIZE             6.6       12.0   \n",
       "6623   ROLLS-ROYCE          PHANTOM     FULL-SIZE             6.7       12.0   \n",
       "6653           SRT      VIPER COUPE    TWO-SEATER             8.4       10.0   \n",
       "6654           SRT  VIPER GTS COUPE    TWO-SEATER             8.4       10.0   \n",
       "6963        TOYOTA           SIENNA       MINIVAN             3.5       56.0   \n",
       "\n",
       "     Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "111            A6         Z                              18.0   \n",
       "112            A6         Z                              18.0   \n",
       "117            A6         Z                              18.0   \n",
       "118            A6         Z                              18.0   \n",
       "119            A8         Z                              16.8   \n",
       "...           ...       ...                               ...   \n",
       "6622          AS8         Z                              19.6   \n",
       "6623          AS8         Z                              20.0   \n",
       "6653           M6         Z                              20.0   \n",
       "6654           M6         Z                              20.0   \n",
       "6963          AS6         X                              14.4   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "111                             12.60                              15.6   \n",
       "112                             12.60                              15.6   \n",
       "117                             12.60                              15.6   \n",
       "118                             12.60                              15.6   \n",
       "119                             10.90                              14.2   \n",
       "...                               ...                               ...   \n",
       "6622                            12.80                              16.5   \n",
       "6623                           999.00                              16.3   \n",
       "6653                            13.00                              16.9   \n",
       "6654                            13.00                              16.9   \n",
       "6963                            10.21                              12.5   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "111                          18.0                359.0  \n",
       "112                          18.0                359.0  \n",
       "117                          18.0                359.0  \n",
       "118                          18.0                359.0  \n",
       "119                          20.0                327.0  \n",
       "...                           ...                  ...  \n",
       "6622                         17.0                387.0  \n",
       "6623                         17.0                382.0  \n",
       "6653                         17.0                389.0  \n",
       "6654                         17.0                389.0  \n",
       "6963                         23.0                288.0  \n",
       "\n",
       "[204 rows x 12 columns]"
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying outliers in the \"Cylinders\" column using the IQR method\n",
    "q1 = df[\"Cylinders\"].quantile(0.25)\n",
    "q3 = df[\"Cylinders\"].quantile(0.75)\n",
    "iqr = q3-q1\n",
    "maximum = q3 + (1.5 * iqr)\n",
    "minimum = q1 - (1.5 * iqr)\n",
    "outlier_samples = df[(df[\"Cylinders\"] < minimum) | (df[\"Cylinders\"] > maximum)]\n",
    "\n",
    "# Printing the maximum and minimum values for identifying outliers\n",
    "print(maximum)\n",
    "print(minimum)\n",
    "\n",
    "# Displaying the rows identified as outliers\n",
    "outlier_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "id": "8e135118",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Make</th>\n",
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       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3179</th>\n",
       "      <td>GMC</td>\n",
       "      <td>TERRAIN AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>A9</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>1239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4123</th>\n",
       "      <td>JEEP</td>\n",
       "      <td>GRAND CHEROKEE 4X4</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>3.6</td>\n",
       "      <td>56.0</td>\n",
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       "      <td>X</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.60</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5535</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>E 550 CABRIOLET</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>4.7</td>\n",
       "      <td>88.0</td>\n",
       "      <td>AS7</td>\n",
       "      <td>Z</td>\n",
       "      <td>13.7</td>\n",
       "      <td>9.30</td>\n",
       "      <td>11.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6963</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>SIENNA</td>\n",
       "      <td>MINIVAN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>X</td>\n",
       "      <td>14.4</td>\n",
       "      <td>10.21</td>\n",
       "      <td>12.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>288.0</td>\n",
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      ],
      "text/plain": [
       "               Make               Model   Vehicle Class  Engine Size(L)  \\\n",
       "3179            GMC         TERRAIN AWD     SUV - SMALL             2.0   \n",
       "4123           JEEP  GRAND CHEROKEE 4X4  SUV - STANDARD             3.6   \n",
       "5535  MERCEDES-BENZ     E 550 CABRIOLET      SUBCOMPACT             4.7   \n",
       "6963         TOYOTA              SIENNA         MINIVAN             3.5   \n",
       "\n",
       "      Cylinders Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "3179       54.0           A9         Z                              11.2   \n",
       "4123       56.0           A8         X                              12.7   \n",
       "5535       88.0          AS7         Z                              13.7   \n",
       "6963       56.0          AS6         X                              14.4   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "3179                             9.00                              10.2   \n",
       "4123                             9.60                              11.3   \n",
       "5535                             9.30                              11.7   \n",
       "6963                            10.21                              12.5   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "3179                         28.0               1239.0  \n",
       "4123                         25.0                265.0  \n",
       "5535                         24.0                269.0  \n",
       "6963                         23.0                288.0  "
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Cylinders\"] > 16)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "id": "6de7b524",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows where \"Cylinders\" is greater than 16 from the DataFrame\n",
    "df = df.drop(df[(df[\"Cylinders\"] > 16)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "id": "176069a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.35\n",
      "3.3499999999999996\n"
     ]
    },
    {
     "data": {
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       "      <th>Model</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>V12 VANTAGE S</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>M7</td>\n",
       "      <td>Z</td>\n",
       "      <td>23.2</td>\n",
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       "      <td>19.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>450.0</td>\n",
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       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>Q5 HYBRID</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>39.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>CONTINENTAL GTC</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>97.4</td>\n",
       "      <td>210.2</td>\n",
       "      <td>14.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>532.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>MULSANNE</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>MULSANNE</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.3</td>\n",
       "      <td>13.3</td>\n",
       "      <td>18.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4933</th>\n",
       "      <td>MASERATI</td>\n",
       "      <td>GHIBLI</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM9</td>\n",
       "      <td>Z</td>\n",
       "      <td>114.1</td>\n",
       "      <td>9.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5238</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>AMG G 65</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS7</td>\n",
       "      <td>Z</td>\n",
       "      <td>24.3</td>\n",
       "      <td>17.9</td>\n",
       "      <td>21.4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>476.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5299</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>AMG G 65</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS7</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5365</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>AMG G 65</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A7</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6572</th>\n",
       "      <td>RAM</td>\n",
       "      <td>1500 ETORQUE</td>\n",
       "      <td>PICKUP TRUCK - STANDARD</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>X</td>\n",
       "      <td>999.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>26.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Make            Model            Vehicle Class  Engine Size(L)  \\\n",
       "142    ASTON MARTIN    V12 VANTAGE S               TWO-SEATER             6.0   \n",
       "188            AUDI        Q5 HYBRID              SUV - SMALL             3.0   \n",
       "464         BENTLEY  CONTINENTAL GTC               SUBCOMPACT             4.0   \n",
       "468         BENTLEY         MULSANNE                 MID-SIZE             6.8   \n",
       "476         BENTLEY         MULSANNE                 MID-SIZE             6.8   \n",
       "...             ...              ...                      ...             ...   \n",
       "4933       MASERATI           GHIBLI                 MID-SIZE             3.0   \n",
       "5238  MERCEDES-BENZ         AMG G 65           SUV - STANDARD             6.0   \n",
       "5299  MERCEDES-BENZ         AMG G 65           SUV - STANDARD             6.0   \n",
       "5365  MERCEDES-BENZ         AMG G 65           SUV - STANDARD             6.0   \n",
       "6572            RAM     1500 ETORQUE  PICKUP TRUCK - STANDARD             3.6   \n",
       "\n",
       "      Cylinders Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "142        12.0           M7         Z                              23.2   \n",
       "188         4.0          AS8         Z                              39.9   \n",
       "464         8.0          AS9         Z                              97.4   \n",
       "468         8.0          AS8         Z                              22.3   \n",
       "476         8.0          AS8         Z                              22.3   \n",
       "...         ...          ...       ...                               ...   \n",
       "4933        6.0          AM9         Z                             114.1   \n",
       "5238       12.0          AS7         Z                              24.3   \n",
       "5299       12.0          AS7         Z                              22.2   \n",
       "5365       12.0           A7         Z                              22.2   \n",
       "6572        6.0           A8         X                             999.0   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "142                              14.4                              19.2   \n",
       "188                               8.3                               9.2   \n",
       "464                             210.2                              14.2   \n",
       "468                              14.9                              19.0   \n",
       "476                              13.3                              18.2   \n",
       "...                               ...                               ...   \n",
       "4933                              9.8                              12.2   \n",
       "5238                             17.9                              21.4   \n",
       "5299                             18.0                              20.3   \n",
       "5365                             18.0                              20.3   \n",
       "6572                              9.4                              10.8   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "142                          15.0                450.0  \n",
       "188                          31.0                212.0  \n",
       "464                          20.0                532.0  \n",
       "468                          15.0                437.0  \n",
       "476                          16.0                419.0  \n",
       "...                           ...                  ...  \n",
       "4933                         23.0                286.0  \n",
       "5238                         13.0                476.0  \n",
       "5299                         14.0                467.0  \n",
       "5365                         14.0                473.0  \n",
       "6572                         26.0                253.0  \n",
       "\n",
       "[142 rows x 12 columns]"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = df[\"Fuel Consumption City (L/100 km)\"].quantile(0.25)\n",
    "q3 = df[\"Fuel Consumption City (L/100 km)\"].quantile(0.75)\n",
    "iqr = q3-q1\n",
    "maximum = q3 + (1.5 * iqr)\n",
    "minimum = q1 - (1.5 * iqr)\n",
    "outlier_samples = df[(df[\"Fuel Consumption City (L/100 km)\"] < minimum) | (df[\"Fuel Consumption City (L/100 km)\"] > maximum)]\n",
    "\n",
    "print(maximum)\n",
    "print(minimum)\n",
    "\n",
    "outlier_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "id": "0c86cfd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
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       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>Q5 HYBRID</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>39.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>CONTINENTAL GTC</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS9</td>\n",
       "      <td>Z</td>\n",
       "      <td>97.4</td>\n",
       "      <td>210.2</td>\n",
       "      <td>14.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>532.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>SONIC RS</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>X</td>\n",
       "      <td>118.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2164</th>\n",
       "      <td>DODGE</td>\n",
       "      <td>DURANGO AWD SRT</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>6.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>218.3</td>\n",
       "      <td>12.2</td>\n",
       "      <td>15.6</td>\n",
       "      <td>18.0</td>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4522</th>\n",
       "      <td>LAND ROVER</td>\n",
       "      <td>RANGE ROVER SPORT V8 5.0 SC FFV</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>116.6</td>\n",
       "      <td>11.3</td>\n",
       "      <td>14.7</td>\n",
       "      <td>29.0</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4933</th>\n",
       "      <td>MASERATI</td>\n",
       "      <td>GHIBLI</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM9</td>\n",
       "      <td>Z</td>\n",
       "      <td>114.1</td>\n",
       "      <td>9.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6572</th>\n",
       "      <td>RAM</td>\n",
       "      <td>1500 ETORQUE</td>\n",
       "      <td>PICKUP TRUCK - STANDARD</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>X</td>\n",
       "      <td>999.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>26.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make                            Model            Vehicle Class  \\\n",
       "188         AUDI                        Q5 HYBRID              SUV - SMALL   \n",
       "464      BENTLEY                  CONTINENTAL GTC               SUBCOMPACT   \n",
       "1366   CHEVROLET                         SONIC RS                  COMPACT   \n",
       "2164       DODGE                  DURANGO AWD SRT           SUV - STANDARD   \n",
       "4522  LAND ROVER  RANGE ROVER SPORT V8 5.0 SC FFV           SUV - STANDARD   \n",
       "4933    MASERATI                           GHIBLI                 MID-SIZE   \n",
       "6572         RAM                     1500 ETORQUE  PICKUP TRUCK - STANDARD   \n",
       "\n",
       "      Engine Size(L)  Cylinders Transmission Fuel Type  \\\n",
       "188              3.0        4.0          AS8         Z   \n",
       "464              4.0        8.0          AS9         Z   \n",
       "1366             1.4        4.0           M6         X   \n",
       "2164             6.4        8.0           A8         Z   \n",
       "4522             5.0        8.0          AS8         Z   \n",
       "4933             3.0        6.0          AM9         Z   \n",
       "6572             3.6        6.0           A8         X   \n",
       "\n",
       "      Fuel Consumption City (L/100 km)  Fuel Consumption Hwy (L/100 km)  \\\n",
       "188                               39.9                              8.3   \n",
       "464                               97.4                            210.2   \n",
       "1366                             118.8                              7.0   \n",
       "2164                             218.3                             12.2   \n",
       "4522                             116.6                             11.3   \n",
       "4933                             114.1                              9.8   \n",
       "6572                             999.0                              9.4   \n",
       "\n",
       "      Fuel Consumption Comb (L/100 km)  Fuel Consumption Comb (mpg)  \\\n",
       "188                                9.2                         31.0   \n",
       "464                               14.2                         20.0   \n",
       "1366                               8.0                         35.0   \n",
       "2164                              15.6                         18.0   \n",
       "4522                              14.7                         29.0   \n",
       "4933                              12.2                         23.0   \n",
       "6572                              10.8                         26.0   \n",
       "\n",
       "      CO2 Emissions(g/km)  \n",
       "188                 212.0  \n",
       "464                 532.0  \n",
       "1366                184.0  \n",
       "2164                363.0  \n",
       "4522                338.0  \n",
       "4933                286.0  \n",
       "6572                253.0  "
      ]
     },
     "execution_count": 544,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Fuel Consumption City (L/100 km)\"] > 32)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "id": "26f9bba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows where \"Fuel Consumption City (L/100 km)\" is greater than 32 from the DataFrame\n",
    "df = df.drop(df[(df[\"Fuel Consumption City (L/100 km)\"] > 32)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "id": "6f6a9867",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 546,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Fuel Consumption City (L/100 km)\"] > 32)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "id": "00437489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.249999999999998\n",
      "3.450000000000001\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>V12 VANTAGE S</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>M7</td>\n",
       "      <td>Z</td>\n",
       "      <td>23.2</td>\n",
       "      <td>14.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>DB11 V12</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.7</td>\n",
       "      <td>14.4</td>\n",
       "      <td>12.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>TT COUPE QUATTRO</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.4</td>\n",
       "      <td>57.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>MULSANNE</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>MULSANNE</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>24.5</td>\n",
       "      <td>14.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6524</th>\n",
       "      <td>RAM</td>\n",
       "      <td>1500 4X4 FFV</td>\n",
       "      <td>PICKUP TRUCK - STANDARD</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>E</td>\n",
       "      <td>20.7</td>\n",
       "      <td>14.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6535</th>\n",
       "      <td>RAM</td>\n",
       "      <td>1500 4X4 FFV</td>\n",
       "      <td>PICKUP TRUCK - STANDARD</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>E</td>\n",
       "      <td>20.7</td>\n",
       "      <td>14.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6554</th>\n",
       "      <td>RAM</td>\n",
       "      <td>1500 CLASSIC 4X4 FFV</td>\n",
       "      <td>PICKUP TRUCK - STANDARD</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A8</td>\n",
       "      <td>E</td>\n",
       "      <td>20.7</td>\n",
       "      <td>14.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6623</th>\n",
       "      <td>ROLLS-ROYCE</td>\n",
       "      <td>PHANTOM</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>6.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>20.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7131</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>SIENNA</td>\n",
       "      <td>MINIVAN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>X</td>\n",
       "      <td>12.6</td>\n",
       "      <td>999.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>218 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Make                 Model            Vehicle Class  \\\n",
       "142   ASTON MARTIN         V12 VANTAGE S               TWO-SEATER   \n",
       "147   ASTON MARTIN              DB11 V12              MINICOMPACT   \n",
       "220           AUDI      TT COUPE QUATTRO               SUBCOMPACT   \n",
       "468        BENTLEY              MULSANNE                 MID-SIZE   \n",
       "500        BENTLEY              MULSANNE                 MID-SIZE   \n",
       "...            ...                   ...                      ...   \n",
       "6524           RAM          1500 4X4 FFV  PICKUP TRUCK - STANDARD   \n",
       "6535           RAM          1500 4X4 FFV  PICKUP TRUCK - STANDARD   \n",
       "6554           RAM  1500 CLASSIC 4X4 FFV  PICKUP TRUCK - STANDARD   \n",
       "6623   ROLLS-ROYCE               PHANTOM                FULL-SIZE   \n",
       "7131        TOYOTA                SIENNA                  MINIVAN   \n",
       "\n",
       "      Engine Size(L)  Cylinders Transmission Fuel Type  \\\n",
       "142              6.0       12.0           M7         Z   \n",
       "147              4.0       14.0           A8         Z   \n",
       "220              2.0        4.0           A6         Z   \n",
       "468              6.8        8.0          AS8         Z   \n",
       "500              6.8        8.0          AS8         Z   \n",
       "...              ...        ...          ...       ...   \n",
       "6524             3.6        6.0           A8         E   \n",
       "6535             3.6        6.0           A8         E   \n",
       "6554             3.6        6.0           A8         E   \n",
       "6623             6.7       12.0          AS8         Z   \n",
       "7131             3.5        6.0          AS8         X   \n",
       "\n",
       "      Fuel Consumption City (L/100 km)  Fuel Consumption Hwy (L/100 km)  \\\n",
       "142                               23.2                             14.4   \n",
       "147                               10.7                             14.4   \n",
       "220                               10.4                             57.6   \n",
       "468                               22.3                             14.9   \n",
       "500                               24.5                             14.4   \n",
       "...                                ...                              ...   \n",
       "6524                              20.7                             14.7   \n",
       "6535                              20.7                             14.7   \n",
       "6554                              20.7                             14.7   \n",
       "6623                              20.0                            999.0   \n",
       "7131                              12.6                            999.0   \n",
       "\n",
       "      Fuel Consumption Comb (L/100 km)  Fuel Consumption Comb (mpg)  \\\n",
       "142                               19.2                         15.0   \n",
       "147                               12.1                         18.0   \n",
       "220                                9.1                         31.0   \n",
       "468                               19.0                         15.0   \n",
       "500                               20.0                         14.0   \n",
       "...                                ...                          ...   \n",
       "6524                              18.0                         16.0   \n",
       "6535                              18.0                         16.0   \n",
       "6554                              18.0                         16.0   \n",
       "6623                              16.3                         17.0   \n",
       "7131                              11.0                         26.0   \n",
       "\n",
       "      CO2 Emissions(g/km)  \n",
       "142                 450.0  \n",
       "147                 363.0  \n",
       "220                 809.0  \n",
       "468                 437.0  \n",
       "500                 465.0  \n",
       "...                   ...  \n",
       "6524                300.0  \n",
       "6535                300.0  \n",
       "6554                300.0  \n",
       "6623                382.0  \n",
       "7131                259.0  \n",
       "\n",
       "[218 rows x 12 columns]"
      ]
     },
     "execution_count": 547,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = df[\"Fuel Consumption Hwy (L/100 km)\"].quantile(0.25)\n",
    "q3 = df[\"Fuel Consumption Hwy (L/100 km)\"].quantile(0.75)\n",
    "iqr = q3-q1\n",
    "maximum = q3 + (1.5 * iqr)\n",
    "minimum = q1 - (1.5 * iqr)\n",
    "outlier_samples = df[(df[\"Fuel Consumption Hwy (L/100 km)\"] < minimum) | (df[\"Fuel Consumption Hwy (L/100 km)\"] > maximum)]\n",
    "\n",
    "print(maximum)\n",
    "print(minimum)\n",
    "\n",
    "outlier_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "id": "c9411a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
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       "      <th>Fuel Consumption Comb (mpg)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>TT COUPE QUATTRO</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5537</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>AMG C 43 4MATIC</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>A9</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.4</td>\n",
       "      <td>343.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6623</th>\n",
       "      <td>ROLLS-ROYCE</td>\n",
       "      <td>PHANTOM</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>6.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>20.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7131</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>SIENNA</td>\n",
       "      <td>MINIVAN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>X</td>\n",
       "      <td>12.6</td>\n",
       "      <td>999.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Make             Model Vehicle Class  Engine Size(L)  \\\n",
       "220            AUDI  TT COUPE QUATTRO    SUBCOMPACT             2.0   \n",
       "5537  MERCEDES-BENZ   AMG C 43 4MATIC       COMPACT             3.0   \n",
       "6623    ROLLS-ROYCE           PHANTOM     FULL-SIZE             6.7   \n",
       "7131         TOYOTA            SIENNA       MINIVAN             3.5   \n",
       "\n",
       "      Cylinders Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "220         4.0           A6         Z                              10.4   \n",
       "5537        6.0           A9         Z                              12.4   \n",
       "6623       12.0          AS8         Z                              20.0   \n",
       "7131        6.0          AS8         X                              12.6   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "220                              57.6                               9.1   \n",
       "5537                            343.0                              10.7   \n",
       "6623                            999.0                              16.3   \n",
       "7131                            999.0                              11.0   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "220                          31.0                809.0  \n",
       "5537                         26.0                253.0  \n",
       "6623                         17.0                382.0  \n",
       "7131                         26.0                259.0  "
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Fuel Consumption Hwy (L/100 km)\"] > 25)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "id": "ae444d1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows where \"Fuel Consumption Hwy (L/100 km)\" is greater than 25 from the DataFrame\n",
    "df = df.drop(df[(df[\"Fuel Consumption Hwy (L/100 km)\"] > 25)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "id": "ba2b4413",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Fuel Consumption Hwy (L/100 km)\"] > 25)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "id": "0c315ed4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.15\n",
      "3.3500000000000014\n"
     ]
    },
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>V12 VANTAGE S</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>M7</td>\n",
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       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A8L</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.451476</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>MULSANNE</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>MULSANNE</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.3</td>\n",
       "      <td>13.3</td>\n",
       "      <td>18.2</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>MULSANNE EWB</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.2</td>\n",
       "      <td>13.6</td>\n",
       "      <td>18.3</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5299</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>AMG G 65</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AS7</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5365</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>AMG G 65</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A7</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5838</th>\n",
       "      <td>MITSUBISHI</td>\n",
       "      <td>ECLIPSE CROSS 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV8</td>\n",
       "      <td>X</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>689.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6795</th>\n",
       "      <td>SUBARU</td>\n",
       "      <td>CROSSTREK AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV8</td>\n",
       "      <td>X</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7130</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>4RUNNER 4WD</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>999.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Make              Model   Vehicle Class  Engine Size(L)  \\\n",
       "142    ASTON MARTIN      V12 VANTAGE S      TWO-SEATER             6.0   \n",
       "180            AUDI                A8L       FULL-SIZE             3.0   \n",
       "468         BENTLEY           MULSANNE        MID-SIZE             6.8   \n",
       "476         BENTLEY           MULSANNE        MID-SIZE             6.8   \n",
       "492         BENTLEY       MULSANNE EWB        MID-SIZE             6.8   \n",
       "...             ...                ...             ...             ...   \n",
       "5299  MERCEDES-BENZ           AMG G 65  SUV - STANDARD             6.0   \n",
       "5365  MERCEDES-BENZ           AMG G 65  SUV - STANDARD             6.0   \n",
       "5838     MITSUBISHI  ECLIPSE CROSS 4WD     SUV - SMALL             1.5   \n",
       "6795         SUBARU      CROSSTREK AWD     SUV - SMALL             2.0   \n",
       "7130         TOYOTA        4RUNNER 4WD     MINICOMPACT             2.0   \n",
       "\n",
       "      Cylinders Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "142        12.0           M7         Z                              23.2   \n",
       "180         6.0          AS8         Z                              12.6   \n",
       "468         8.0          AS8         Z                              22.3   \n",
       "476         8.0          AS8         Z                              22.3   \n",
       "492         8.0          AS8         Z                              22.2   \n",
       "...         ...          ...       ...                               ...   \n",
       "5299       12.0          AS7         Z                              22.2   \n",
       "5365       12.0           A7         Z                              22.2   \n",
       "5838        4.0          AV8         X                               9.6   \n",
       "6795        4.0          AV8         X                               8.5   \n",
       "7130        4.0          AS6         Z                               9.9   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "142                              14.4                              19.2   \n",
       "180                               7.7                              21.0   \n",
       "468                              14.9                              19.0   \n",
       "476                              13.3                              18.2   \n",
       "492                              13.6                              18.3   \n",
       "...                               ...                               ...   \n",
       "5299                             18.0                              20.3   \n",
       "5365                             18.0                              20.3   \n",
       "5838                              8.9                             689.0   \n",
       "6795                              7.0                             999.0   \n",
       "7130                              7.3                             999.0   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "142                     15.000000                450.0  \n",
       "180                     13.451476                321.0  \n",
       "468                     15.000000                437.0  \n",
       "476                     16.000000                419.0  \n",
       "492                     15.000000                430.0  \n",
       "...                           ...                  ...  \n",
       "5299                    14.000000                467.0  \n",
       "5365                    14.000000                473.0  \n",
       "5838                    30.000000                216.0  \n",
       "6795                    36.000000                185.0  \n",
       "7130                    32.000000                204.0  \n",
       "\n",
       "[153 rows x 12 columns]"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = df[\"Fuel Consumption Comb (L/100 km)\"].quantile(0.25)\n",
    "q3 = df[\"Fuel Consumption Comb (L/100 km)\"].quantile(0.75)\n",
    "iqr = q3-q1\n",
    "maximum = q3 + (1.5 * iqr)\n",
    "minimum = q1 - (1.5 * iqr)\n",
    "outlier_samples = df[(df[\"Fuel Consumption Comb (L/100 km)\"] < minimum) | (df[\"Fuel Consumption Comb (L/100 km)\"] > maximum)]\n",
    "\n",
    "print(maximum)\n",
    "print(minimum)\n",
    "\n",
    "outlier_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "id": "e9465854",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
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       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3178</th>\n",
       "      <td>GMC</td>\n",
       "      <td>CANYON</td>\n",
       "      <td>PICKUP TRUCK - SMALL</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>D</td>\n",
       "      <td>10.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>129.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4596</th>\n",
       "      <td>LAND ROVER</td>\n",
       "      <td>AMG C 63 S</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>17.1</td>\n",
       "      <td>12.6</td>\n",
       "      <td>115.1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5114</th>\n",
       "      <td>MAZDA</td>\n",
       "      <td>CX-3</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>X</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>444.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5838</th>\n",
       "      <td>MITSUBISHI</td>\n",
       "      <td>ECLIPSE CROSS 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV8</td>\n",
       "      <td>X</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>689.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6795</th>\n",
       "      <td>SUBARU</td>\n",
       "      <td>CROSSTREK AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV8</td>\n",
       "      <td>X</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7130</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>4RUNNER 4WD</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>999.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make              Model         Vehicle Class  Engine Size(L)  \\\n",
       "3178         GMC             CANYON  PICKUP TRUCK - SMALL             2.8   \n",
       "4596  LAND ROVER         AMG C 63 S               COMPACT             5.0   \n",
       "5114       MAZDA               CX-3               COMPACT             2.0   \n",
       "5838  MITSUBISHI  ECLIPSE CROSS 4WD           SUV - SMALL             1.5   \n",
       "6795      SUBARU      CROSSTREK AWD           SUV - SMALL             2.0   \n",
       "7130      TOYOTA        4RUNNER 4WD           MINICOMPACT             2.0   \n",
       "\n",
       "      Cylinders Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "3178        4.0           A6         D                              10.8   \n",
       "4596        8.0          AS8         Z                              17.1   \n",
       "5114        4.0          AS6         X                               8.3   \n",
       "5838        4.0          AV8         X                               9.6   \n",
       "6795        4.0          AV8         X                               8.5   \n",
       "7130        4.0          AS6         Z                               9.9   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "3178                              8.0                             129.6   \n",
       "4596                             12.6                             115.1   \n",
       "5114                              6.9                             444.0   \n",
       "5838                              8.9                             689.0   \n",
       "6795                              7.0                             999.0   \n",
       "7130                              7.3                             999.0   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "3178                         29.0                257.0  \n",
       "4596                         19.0                354.0  \n",
       "5114                         37.0                179.0  \n",
       "5838                         30.0                216.0  \n",
       "6795                         36.0                185.0  \n",
       "7130                         32.0                204.0  "
      ]
     },
     "execution_count": 552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Fuel Consumption Comb (L/100 km)\"] > 30)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "id": "5519db49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows where \"Fuel Consumption Comb (L/100 km)\" is greater than 30 from the DataFrame\n",
    "df = df.drop(df[(df[\"Fuel Consumption Comb (L/100 km)\"] > 30)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "16c4f817",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Fuel Consumption Comb (L/100 km)\"] > 30)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "id": "197a7064",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.0\n",
      "7.0\n"
     ]
    },
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
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       "      <th>171</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A6 QUATTRO</td>\n",
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       "      <td>Z</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>235.0</td>\n",
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       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A6 QUATTRO</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1518</th>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>MALIBU HYBRID</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV</td>\n",
       "      <td>X</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1611</th>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>MALIBU HYBRID</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV</td>\n",
       "      <td>X</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7111</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>PRIUS AWD</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV</td>\n",
       "      <td>X</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>60.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7112</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>PRIUS C</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV</td>\n",
       "      <td>X</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7160</th>\n",
       "      <td>VOLKSWAGEN</td>\n",
       "      <td>JETTA TURBO HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7204</th>\n",
       "      <td>VOLKSWAGEN</td>\n",
       "      <td>JETTA HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7234</th>\n",
       "      <td>VOLKSWAGEN</td>\n",
       "      <td>JETTA HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AM7</td>\n",
       "      <td>Z</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make               Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "3          ACURA          ILX HYBRID       COMPACT             1.5        4.0   \n",
       "171         AUDI          A6 QUATTRO      MID-SIZE             2.0        4.0   \n",
       "210         AUDI          A6 QUATTRO      MID-SIZE             2.0        4.0   \n",
       "1518   CHEVROLET       MALIBU HYBRID      MID-SIZE             1.8        4.0   \n",
       "1611   CHEVROLET       MALIBU HYBRID      MID-SIZE             1.8        4.0   \n",
       "...          ...                 ...           ...             ...        ...   \n",
       "7111      TOYOTA           PRIUS AWD      MID-SIZE             1.8        4.0   \n",
       "7112      TOYOTA             PRIUS C       COMPACT             1.5        4.0   \n",
       "7160  VOLKSWAGEN  JETTA TURBO HYBRID       COMPACT             1.4        4.0   \n",
       "7204  VOLKSWAGEN        JETTA HYBRID       COMPACT             1.4        4.0   \n",
       "7234  VOLKSWAGEN        JETTA HYBRID       COMPACT             1.4        4.0   \n",
       "\n",
       "     Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "3             AV7         Z                               6.0   \n",
       "171           AS8         Z                              12.0   \n",
       "210           AS8         Z                              12.0   \n",
       "1518           AV         X                               5.0   \n",
       "1611           AV         X                               4.8   \n",
       "...           ...       ...                               ...   \n",
       "7111           AV         X                               4.5   \n",
       "7112           AV         X                               4.9   \n",
       "7160          AM7         Z                               5.6   \n",
       "7204          AM7         Z                               5.6   \n",
       "7234          AM7         Z                               5.7   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "3                                 5.8                               5.9   \n",
       "171                               8.1                              10.2   \n",
       "210                               8.1                              10.2   \n",
       "1518                              5.1                               5.1   \n",
       "1611                              5.5                               5.1   \n",
       "...                               ...                               ...   \n",
       "7111                              4.9                               4.7   \n",
       "7112                              5.5                               5.1   \n",
       "7160                              5.2                               5.4   \n",
       "7204                              4.9                               5.3   \n",
       "7234                              4.9                               5.3   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "3                            48.0                136.0  \n",
       "171                         128.0                235.0  \n",
       "210                         128.0                235.0  \n",
       "1518                         55.0                122.0  \n",
       "1611                         55.0                121.0  \n",
       "...                           ...                  ...  \n",
       "7111                         60.0                109.0  \n",
       "7112                         55.0                120.0  \n",
       "7160                         52.0                124.0  \n",
       "7204                         53.0                122.0  \n",
       "7234                         53.0                124.0  \n",
       "\n",
       "[119 rows x 12 columns]"
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = df[\"Fuel Consumption Comb (mpg)\"].quantile(0.25)\n",
    "q3 = df[\"Fuel Consumption Comb (mpg)\"].quantile(0.75)\n",
    "iqr = q3-q1\n",
    "maximum = q3 + (1.5 * iqr)\n",
    "minimum = q1 - (1.5 * iqr)\n",
    "outlier_samples = df[(df[\"Fuel Consumption Comb (mpg)\"] < minimum) | (df[\"Fuel Consumption Comb (mpg)\"] > maximum)]\n",
    "\n",
    "print(maximum)\n",
    "print(minimum)\n",
    "\n",
    "outlier_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "596c8d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
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       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A6 QUATTRO</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>AUDI</td>\n",
       "      <td>A6 QUATTRO</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4122</th>\n",
       "      <td>JEEP</td>\n",
       "      <td>CHEROKEE</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>A9</td>\n",
       "      <td>X</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.6</td>\n",
       "      <td>229.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5534</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>E 550 CABRIOLET</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>4.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS7</td>\n",
       "      <td>Z</td>\n",
       "      <td>13.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.6</td>\n",
       "      <td>999.0</td>\n",
       "      <td>267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6962</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>TACOMA</td>\n",
       "      <td>PICKUP TRUCK - SMALL</td>\n",
       "      <td>5.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>X</td>\n",
       "      <td>18.8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>217.0</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Make            Model         Vehicle Class  Engine Size(L)  \\\n",
       "171            AUDI       A6 QUATTRO              MID-SIZE             2.0   \n",
       "210            AUDI       A6 QUATTRO              MID-SIZE             2.0   \n",
       "4122           JEEP         CHEROKEE           SUV - SMALL             2.4   \n",
       "5534  MERCEDES-BENZ  E 550 CABRIOLET              MID-SIZE             4.7   \n",
       "6962         TOYOTA           TACOMA  PICKUP TRUCK - SMALL             5.7   \n",
       "\n",
       "      Cylinders Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "171         4.0          AS8         Z                              12.0   \n",
       "210         4.0          AS8         Z                              12.0   \n",
       "4122        4.0           A9         X                              11.0   \n",
       "5534        8.0          AS7         Z                              13.6   \n",
       "6962        8.0          AS6         X                              18.8   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "171                               8.1                              10.2   \n",
       "210                               8.1                              10.2   \n",
       "4122                              7.8                               9.6   \n",
       "5534                              9.1                              11.6   \n",
       "6962                             14.0                              16.6   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "171                         128.0                235.0  \n",
       "210                         128.0                235.0  \n",
       "4122                        229.0                225.0  \n",
       "5534                        999.0                267.0  \n",
       "6962                        217.0                382.0  "
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Fuel Consumption Comb (mpg)\"] > 70)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "e12118ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows where \"Fuel Consumption Comb (mpg)\" is greater than 70 from the DataFrame\n",
    "df = df.drop(df[(df[\"Fuel Consumption Comb (mpg)\"] > 70)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "id": "de1e7f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Cylinders</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Fuel Consumption Comb (mpg)\"] > 70)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "e2acabca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "409.875\n",
      "86.875\n"
     ]
    },
    {
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>ASTON MARTIN</td>\n",
       "      <td>V12 VANTAGE S</td>\n",
       "      <td>TWO-SEATER</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>M7</td>\n",
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       "      <th>468</th>\n",
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       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>437.0</td>\n",
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       "      <th>476</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>MULSANNE</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.3</td>\n",
       "      <td>13.3</td>\n",
       "      <td>18.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>419.0</td>\n",
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       "      <th>492</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>MULSANNE EWB</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.2</td>\n",
       "      <td>13.6</td>\n",
       "      <td>18.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>BENTLEY</td>\n",
       "      <td>MULSANNE</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>24.5</td>\n",
       "      <td>14.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5365</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>AMG G 65</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A7</td>\n",
       "      <td>Z</td>\n",
       "      <td>22.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5400</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>G 550</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>A7</td>\n",
       "      <td>Z</td>\n",
       "      <td>18.2</td>\n",
       "      <td>16.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5536</th>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>E 63 AMG 4MATIC WAGON</td>\n",
       "      <td>STATION WAGON - MID-SIZE</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AS7</td>\n",
       "      <td>Z</td>\n",
       "      <td>15.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6474</th>\n",
       "      <td>PORSCHE</td>\n",
       "      <td>911 CARRERA S</td>\n",
       "      <td>MINICOMPACT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AM8</td>\n",
       "      <td>Z</td>\n",
       "      <td>13.1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>11.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6964</th>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>YARIS</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>M5</td>\n",
       "      <td>X</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>41.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Make                  Model             Vehicle Class  \\\n",
       "142    ASTON MARTIN          V12 VANTAGE S                TWO-SEATER   \n",
       "468         BENTLEY               MULSANNE                  MID-SIZE   \n",
       "476         BENTLEY               MULSANNE                  MID-SIZE   \n",
       "492         BENTLEY           MULSANNE EWB                  MID-SIZE   \n",
       "500         BENTLEY               MULSANNE                  MID-SIZE   \n",
       "...             ...                    ...                       ...   \n",
       "5365  MERCEDES-BENZ               AMG G 65            SUV - STANDARD   \n",
       "5400  MERCEDES-BENZ                  G 550            SUV - STANDARD   \n",
       "5536  MERCEDES-BENZ  E 63 AMG 4MATIC WAGON  STATION WAGON - MID-SIZE   \n",
       "6474        PORSCHE          911 CARRERA S               MINICOMPACT   \n",
       "6964         TOYOTA                  YARIS                   COMPACT   \n",
       "\n",
       "      Engine Size(L)  Cylinders Transmission Fuel Type  \\\n",
       "142              6.0       12.0           M7         Z   \n",
       "468              6.8        8.0          AS8         Z   \n",
       "476              6.8        8.0          AS8         Z   \n",
       "492              6.8        8.0          AS8         Z   \n",
       "500              6.8        8.0          AS8         Z   \n",
       "...              ...        ...          ...       ...   \n",
       "5365             6.0       12.0           A7         Z   \n",
       "5400             4.0        8.0           A7         Z   \n",
       "5536             5.5        8.0          AS7         Z   \n",
       "6474             3.0        6.0          AM8         Z   \n",
       "6964             1.5        4.0           M5         X   \n",
       "\n",
       "      Fuel Consumption City (L/100 km)  Fuel Consumption Hwy (L/100 km)  \\\n",
       "142                               23.2                             14.4   \n",
       "468                               22.3                             14.9   \n",
       "476                               22.3                             13.3   \n",
       "492                               22.2                             13.6   \n",
       "500                               24.5                             14.4   \n",
       "...                                ...                              ...   \n",
       "5365                              22.2                             18.0   \n",
       "5400                              18.2                             16.5   \n",
       "5536                              15.5                             11.0   \n",
       "6474                              13.1                              9.7   \n",
       "6964                               7.8                              5.8   \n",
       "\n",
       "      Fuel Consumption Comb (L/100 km)  Fuel Consumption Comb (mpg)  \\\n",
       "142                               19.2                         15.0   \n",
       "468                               19.0                         15.0   \n",
       "476                               18.2                         16.0   \n",
       "492                               18.3                         15.0   \n",
       "500                               20.0                         14.0   \n",
       "...                                ...                          ...   \n",
       "5365                              20.3                         14.0   \n",
       "5400                              17.5                         16.0   \n",
       "5536                              13.5                         21.0   \n",
       "6474                              11.5                         25.0   \n",
       "6964                               6.9                         41.0   \n",
       "\n",
       "      CO2 Emissions(g/km)  \n",
       "142                 450.0  \n",
       "468                 437.0  \n",
       "476                 419.0  \n",
       "492                 430.0  \n",
       "500                 465.0  \n",
       "...                   ...  \n",
       "5365                473.0  \n",
       "5400                410.0  \n",
       "5536                500.0  \n",
       "6474                699.0  \n",
       "6964                 62.0  \n",
       "\n",
       "[84 rows x 12 columns]"
      ]
     },
     "execution_count": 559,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = df[\"CO2 Emissions(g/km)\"].quantile(0.25)\n",
    "q3 = df[\"CO2 Emissions(g/km)\"].quantile(0.75)\n",
    "iqr = q3-q1\n",
    "maximum = q3 + (1.5 * iqr)\n",
    "minimum = q1 - (1.5 * iqr)\n",
    "outlier_samples = df[(df[\"CO2 Emissions(g/km)\"] < minimum) | (df[\"CO2 Emissions(g/km)\"] > maximum)]\n",
    "\n",
    "print(maximum)\n",
    "print(minimum)\n",
    "\n",
    "outlier_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "id": "e800129e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Make</th>\n",
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       "      <th>Cylinders</th>\n",
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       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2163</th>\n",
       "      <td>DODGE</td>\n",
       "      <td>CHARGER FFV</td>\n",
       "      <td>FULL-SIZE</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>E</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6474</th>\n",
       "      <td>PORSCHE</td>\n",
       "      <td>911 CARRERA S</td>\n",
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       "      <td>699.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Make          Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "2163    DODGE    CHARGER FFV     FULL-SIZE             3.6        6.0   \n",
       "6474  PORSCHE  911 CARRERA S   MINICOMPACT             3.0        6.0   \n",
       "\n",
       "     Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "2163           A5         E                              18.9   \n",
       "6474          AM8         Z                              13.1   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "2163                             12.7                              16.1   \n",
       "6474                              9.7                              11.5   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "2163                         18.0                868.0  \n",
       "6474                         25.0                699.0  "
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"CO2 Emissions(g/km)\"] > 550)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "id": "6bef7235",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows where \"CO2 Emissions(g/km)\" is greater than 550 from the DataFrame\n",
    "df = df.drop(df[(df[\"CO2 Emissions(g/km)\"] > 550)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "id": "e0e12012",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2 Emissions(g/km)]\n",
       "Index: []"
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"CO2 Emissions(g/km)\"] > 550)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "id": "192e6c08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7404 entries, 0 to 7452\n",
      "Data columns (total 12 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   Make                              7404 non-null   object \n",
      " 1   Model                             7404 non-null   object \n",
      " 2   Vehicle Class                     7404 non-null   object \n",
      " 3   Engine Size(L)                    7404 non-null   float64\n",
      " 4   Cylinders                         7404 non-null   float64\n",
      " 5   Transmission                      7404 non-null   object \n",
      " 6   Fuel Type                         7404 non-null   object \n",
      " 7   Fuel Consumption City (L/100 km)  7404 non-null   float64\n",
      " 8   Fuel Consumption Hwy (L/100 km)   7404 non-null   float64\n",
      " 9   Fuel Consumption Comb (L/100 km)  7404 non-null   float64\n",
      " 10  Fuel Consumption Comb (mpg)       7404 non-null   float64\n",
      " 11  CO2 Emissions(g/km)               7404 non-null   float64\n",
      "dtypes: float64(7), object(5)\n",
      "memory usage: 752.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "id": "1fcf80ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Make</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>33.0</td>\n",
       "      <td>196.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA</td>\n",
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       "      <td>2.4</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
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       "      <td>9.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>244.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7448</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC40 T5 AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7449</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC60 T5 AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7450</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC60 T6 AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>10.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7451</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC90 T5 AWD</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7452</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC90 T6 AWD</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7404 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Make        Model   Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "0     ACURA          ILX         COMPACT             2.0        4.0   \n",
       "2     ACURA          ILX         COMPACT             2.4        4.0   \n",
       "3     ACURA   ILX HYBRID         COMPACT             1.5        4.0   \n",
       "4     ACURA      MDX 4WD     SUV - SMALL             3.5        6.0   \n",
       "5     ACURA      RDX AWD     SUV - SMALL             3.5        6.0   \n",
       "...     ...          ...             ...             ...        ...   \n",
       "7448  VOLVO  XC40 T5 AWD     SUV - SMALL             2.0        4.0   \n",
       "7449  VOLVO  XC60 T5 AWD     SUV - SMALL             2.0        4.0   \n",
       "7450  VOLVO  XC60 T6 AWD     SUV - SMALL             2.0        4.0   \n",
       "7451  VOLVO  XC90 T5 AWD  SUV - STANDARD             2.0        4.0   \n",
       "7452  VOLVO  XC90 T6 AWD  SUV - STANDARD             2.0        4.0   \n",
       "\n",
       "     Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "0             AS5         Z                               9.9   \n",
       "2              M6         Z                              11.2   \n",
       "3             AV7         Z                               6.0   \n",
       "4             AS6         Z                              12.7   \n",
       "5             AS6         Z                              12.1   \n",
       "...           ...       ...                               ...   \n",
       "7448          AS8         Z                              10.7   \n",
       "7449          AS8         Z                              11.2   \n",
       "7450          AS8         Z                              11.7   \n",
       "7451          AS8         Z                              11.2   \n",
       "7452          AS8         Z                              12.2   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "0                                 6.7                               8.5   \n",
       "2                                 7.7                               9.6   \n",
       "3                                 5.8                               5.9   \n",
       "4                                 9.1                              11.1   \n",
       "5                                 8.7                              10.6   \n",
       "...                               ...                               ...   \n",
       "7448                              7.7                               9.4   \n",
       "7449                              8.3                               9.9   \n",
       "7450                              8.6                              10.3   \n",
       "7451                              8.3                               9.9   \n",
       "7452                              8.7                              10.7   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "0                            33.0                196.0  \n",
       "2                            29.0                221.0  \n",
       "3                            48.0                136.0  \n",
       "4                            25.0                255.0  \n",
       "5                            27.0                244.0  \n",
       "...                           ...                  ...  \n",
       "7448                         30.0                219.0  \n",
       "7449                         29.0                232.0  \n",
       "7450                         27.0                240.0  \n",
       "7451                         29.0                232.0  \n",
       "7452                         26.0                248.0  \n",
       "\n",
       "[7404 rows x 12 columns]"
      ]
     },
     "execution_count": 564,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "id": "6f22d3c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Create an instance of the MinMaxScaler\n",
    "# scaler = MinMaxScaler()\n",
    "\n",
    "# # Assuming 'data' is your dataset\n",
    "# # 'column_to_scale' is the column you want to scale, and 'reference_column' is the reference column\n",
    "# column_to_scale = df['CO2 Emissions(g/km)'].values.reshape(-1, 1)\n",
    "# reference_column = df['Fuel Consumption Comb (mpg)'].values.reshape(-1, 1)\n",
    "\n",
    "# # Fit and transform the 'column_to_scale' using the range of values in 'reference_column'\n",
    "# scaler.fit(reference_column)\n",
    "# scaled_column = scaler.transform(column_to_scale)\n",
    "\n",
    "# # Replace the original 'column_to_scale' in the dataset with the scaled values\n",
    "# df['CO2 Emissions(g/km)'] = scaled_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "id": "c15ebcf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Create an instance of the MinMaxScaler\n",
    "# scaler = MinMaxScaler()\n",
    "\n",
    "# # Assuming 'data' is your dataset\n",
    "# # 'column_to_scale' is the column you want to scale, and 'reference_column' is the reference column\n",
    "# column_to_scale = df['Fuel Consumption Comb (mpg)'].values.reshape(-1, 1)\n",
    "# reference_column = df['Fuel Consumption Comb (L/100 km)'].values.reshape(-1, 1)\n",
    "\n",
    "# # Fit and transform the 'column_to_scale' using the range of values in 'reference_column'\n",
    "# scaler.fit(reference_column)\n",
    "# scaled_column = scaler.transform(column_to_scale)\n",
    "\n",
    "# # Replace the original 'column_to_scale' in the dataset with the scaled values\n",
    "# df['Fuel Consumption Comb (mpg)'] = scaled_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "id": "0a7f675c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(22, 13))\n",
    "# df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "id": "3ef8e497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 570,
     "metadata": {},
     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 2200x1300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(22, 13))\n",
    "df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "id": "f7833767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7404, 12)"
      ]
     },
     "execution_count": 569,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "id": "64322de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Create an instance of the MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 70))\n",
    "\n",
    "# Assuming 'data' is your dataset and 'column' is the column you want to scale\n",
    "# You need to reshape the column to a 2D array before scaling\n",
    "column_to_scale = df['CO2 Emissions(g/km)'].values.reshape(-1, 1)\n",
    "\n",
    "# Fit and transform the column using the scaler\n",
    "scaled_column = scaler.fit_transform(column_to_scale)\n",
    "\n",
    "# Replace the original column in the dataset with the scaled values\n",
    "df['CO2 Emissions(g/km)'] = scaled_column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "id": "d70b4c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 576,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2200x1300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(22, 13))\n",
    "df.boxplot()"
   ]
  },
  {
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   "execution_count": 577,
   "id": "bb7935ab",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.655602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>26.286307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>13.941909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.224066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.626556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7448</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC40 T5 AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.995851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7449</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC60 T5 AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.883817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7450</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC60 T6 AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>10.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.045643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7451</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC90 T5 AWD</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.883817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7452</th>\n",
       "      <td>VOLVO</td>\n",
       "      <td>XC90 T6 AWD</td>\n",
       "      <td>SUV - STANDARD</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.207469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7404 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Make        Model   Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "0     ACURA          ILX         COMPACT             2.0        4.0   \n",
       "2     ACURA          ILX         COMPACT             2.4        4.0   \n",
       "3     ACURA   ILX HYBRID         COMPACT             1.5        4.0   \n",
       "4     ACURA      MDX 4WD     SUV - SMALL             3.5        6.0   \n",
       "5     ACURA      RDX AWD     SUV - SMALL             3.5        6.0   \n",
       "...     ...          ...             ...             ...        ...   \n",
       "7448  VOLVO  XC40 T5 AWD     SUV - SMALL             2.0        4.0   \n",
       "7449  VOLVO  XC60 T5 AWD     SUV - SMALL             2.0        4.0   \n",
       "7450  VOLVO  XC60 T6 AWD     SUV - SMALL             2.0        4.0   \n",
       "7451  VOLVO  XC90 T5 AWD  SUV - STANDARD             2.0        4.0   \n",
       "7452  VOLVO  XC90 T6 AWD  SUV - STANDARD             2.0        4.0   \n",
       "\n",
       "     Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "0             AS5         Z                               9.9   \n",
       "2              M6         Z                              11.2   \n",
       "3             AV7         Z                               6.0   \n",
       "4             AS6         Z                              12.7   \n",
       "5             AS6         Z                              12.1   \n",
       "...           ...       ...                               ...   \n",
       "7448          AS8         Z                              10.7   \n",
       "7449          AS8         Z                              11.2   \n",
       "7450          AS8         Z                              11.7   \n",
       "7451          AS8         Z                              11.2   \n",
       "7452          AS8         Z                              12.2   \n",
       "\n",
       "      Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "0                                 6.7                               8.5   \n",
       "2                                 7.7                               9.6   \n",
       "3                                 5.8                               5.9   \n",
       "4                                 9.1                              11.1   \n",
       "5                                 8.7                              10.6   \n",
       "...                               ...                               ...   \n",
       "7448                              7.7                               9.4   \n",
       "7449                              8.3                               9.9   \n",
       "7450                              8.6                              10.3   \n",
       "7451                              8.3                               9.9   \n",
       "7452                              8.7                              10.7   \n",
       "\n",
       "      Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "0                            33.0            22.655602  \n",
       "2                            29.0            26.286307  \n",
       "3                            48.0            13.941909  \n",
       "4                            25.0            31.224066  \n",
       "5                            27.0            29.626556  \n",
       "...                           ...                  ...  \n",
       "7448                         30.0            25.995851  \n",
       "7449                         29.0            27.883817  \n",
       "7450                         27.0            29.045643  \n",
       "7451                         29.0            27.883817  \n",
       "7452                         26.0            30.207469  \n",
       "\n",
       "[7404 rows x 12 columns]"
      ]
     },
     "execution_count": 577,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "500d86ef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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