Fuel_Consumption_prediction_EDA.ipynb

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   "source": [
    "-----\n",
    "<center>\n",
    "<h1 style=\"font-size: 50px; font-family: serif;\">Fuel Consumption Ratings Regression !</h1>\n",
    "</center>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "459c402d",
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    "id": "459c402d"
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   "source": [
    "<center>\n",
    "<img src=\"https://media.tenor.com/GRq2qp1DU0wAAAAC/fuel-gas.gif\" width=\"550\" height='100'>\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "951bf2fe"
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   "source": [
    "---\n",
    "# Metadata:\n",
    "---    \n",
    "**1. Dataset**   : Given dataset contain 2022 Fuel Consumption Ratings information.\n",
    "\n",
    "**2. Model Year**: Represent the year in which a particular model of a vehicle was produced. \n",
    "\n",
    "**3. Make**\t  : It describe the brand or manufacturer of a vehicle.\n",
    "\n",
    "**4. Model**     : It refers to a specific version of a vehicle produced by a manufacturer under a particular make or brand.\n",
    "\n",
    "**5. Vehicle Class**: Represent the categorization system used in the automotive industry to group vehicles based on their                        size, body style, and intended use. \n",
    "\n",
    "**6. Engine Size**  : It tell us about the total volume of air and fuel that can be drawn into the engine cylinders of a                          vehicle's engine during one complete cycle. It is typically measured in either cubic centimeters (cc)                      or liters (L).\n",
    "\n",
    "**7. Cylinders**    : It refer to the individual chambers within a vehicle's engine where the combustion process takes place.\n",
    "\n",
    "**8. Transmission** : It represent the system in which power transmission take place, from the engine to the wheels, which                        allowing the vehicle to move forward or backward\n",
    "\n",
    "**9. Fuel Type**    : It refers to the specific type of fuel that is used to power a vehicle, machine,\n",
    "                      or other device.\n",
    "                      Z=  Premium gasoline | D=  Diesel | E=  Ethanol \n",
    "                      |  X=Regular gasoline\n",
    "                   \n",
    "\n",
    "**10. Fuel Consumption(City (L/100 km)** : it represent fuel consumption in the city.\n",
    "\n",
    "**11. Fuel Consumption(Hwy (L/100 km)** : it represent fuel consumption on the Highway.\n",
    "\n",
    "**12. Fuel Consumption(Comb (L/100 km)** : it represent fuel consumption for both city and highway.\n",
    "\n",
    "**13. Fuel Consumption(Comb (mpg))**: it represent fuel consumption for both city & highway, in miles per imperial gallon(mpg).\n",
    "\n",
    "**14. CO2 Emissions(g/km)** : It refer to the amount of CO2 that is emitted by a vehicle for every kilometer that it is driven.\n",
    "\n",
    "**15. CO2 Rating**  : It's measure a vehicle's carbon dioxide (CO2) emissions, typically expressed in grams of CO2 per                           kilometer(g/km).\n",
    "\n",
    "**16. Smog Rating** : It's measure a vehicle's emissions of pollutants that contribute to the formation of smog. "
   ]
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    }
   },
   "source": [
    "------------------------------\n",
    "<center id=\"report\">\n",
    "<h1 style=\"font-size: 30px; font-family: serif;\">Report content</h1>\n",
    "</center>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e815b83",
   "metadata": {
    "id": "2e815b83"
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   "source": [
    "\n",
    "1. <a href=\"#Importmodules\">Import Modules</a><br>\n",
    "    1.1 <a href=\"#Importdata\">Importing data into Dataframe</a><br>\n",
    "    \n",
    "2. <a href=\"#Datatcleaning\">Data cleaning</a><br>\n",
    "    2.1 <a href=\"#df1\">Creating new DataFrame with necessary columns</a><br>\n",
    "    2.2 <a href=\"#nullcheck\">Checking null values</a><br>\n",
    "    2.3 <a href=\"#treatfuel\">Treating Null's in \"Fuel type\"</a><br>\n",
    "    2.4 <a href=\"#nullco2\">Dealing with the nulls present in feature \"CO2 Rating\"</a><br>\n",
    "    \n",
    "<a href=\"#eda\"># Exploratory Data Analysis (E.D.A.)</a><br>\n",
    "\n",
    "3. <a href=\"#univar\">Univariate analysis:-</a><br>\n",
    "    3.1  <a href=\"#mean\">Checking Mean, Median, Standard Deviation and Quantile</a><br>\n",
    "    3.2 <a href=\"#distribute\">Frequency Distribution for the categorical Columns</a><br>\n",
    "    3.3 <a href=\"#numerical\">Frequency Distribution on Numerical columns</a><br>\n",
    "    \n",
    "4. <a href=\"#bivariate\">Bivariate analysis:-</a><br>\n",
    "    4.1 <a href=\"#compare\">Comparison Between all columns</a><br>\n",
    "    4.2  <a href=\"#comparecylinder\">Comparison between \"No. of cylinders\" & \"Fuel consumption\"</a><br>\n",
    "    4.3 <a href=\"#chi\">Performing Chi Square analysis</a><br>\n",
    "    4.4 <a href=\"#heatcorr\">Heatmap showing \"Pearson correlation\"</a><br>\n",
    "    \n",
    "\n",
    "5. <a href=\"#prepro\">Data Preprocessing</a><br>\n",
    "    5.1 <a href=\"#outlier\">Outlier analysis</a><br>\n",
    "    5.2 <a href=\"#ordinal\">Ordinal encoding</a><br>\n",
    "    5.3 <a href=\"#onehot\">One Hot encoding on categorical columns</a><br>\n",
    "    5.4 <a href=\"#traintest\">Split into train and test set</a><br>\n",
    "    5.5 <a href=\"#scailing\">Feature Scaling</a><br>\n",
    "    \n",
    "\n",
    "6. <a href=\"#model\">Training Models</a><br>\n",
    "    6.1 <a href=\"#linear\">Linear Regression</a><br>\n",
    "    6.2  <a href=\"#decision\">Decision Tree Regressor</a><br>\n",
    "    6.3 <a href=\"#rf\">Random Forest Regressor</a><br>\n",
    "    \n",
    "    \n",
    "    \n",
    "7. <a href=\"#deployment\">Model Deployment</a><br>\n",
    "    7.1 <a href=\"#pickling\">Pickling Model and Scaler object for Deployment</a><br>  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a90f39ad",
   "metadata": {
    "id": "a90f39ad"
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   "source": [
    "---\n",
    "# 1. Import Modules \n",
    "<span id=\"Importmodules\"></span>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c5cb13c",
   "metadata": {},
   "source": [
    "## Pandas Module"
   ]
  },
  {
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   "execution_count": 22,
   "id": "81e1d46a",
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    "ExecuteTime": {
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     "start_time": "2023-03-19T19:52:04.629055Z"
    }
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sys\n",
    "import warnings\n",
    "if not sys.warnoptions:\n",
    "    warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8f1150",
   "metadata": {},
   "source": [
    "## M.L. Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9c35fc36",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:23:08.970709Z",
     "start_time": "2023-03-19T20:23:08.965287Z"
    },
    "id": "9c35fc36"
   },
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "#data transformation\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Linear Regression Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "# Decision Tree Regressor Model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn import tree\n",
    "\n",
    "# Random Forest Regressor Model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2ad91e",
   "metadata": {
    "id": "ce2ad91e"
   },
   "source": [
    "\n",
    "# 1.1 Creating Dataframe & Basic Observation\n",
    "<span id=\"Importdata\"></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "516a41b3",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57f4ca49",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.692785Z",
     "start_time": "2023-03-19T19:52:04.662951Z"
    },
    "id": "57f4ca49",
    "outputId": "27d2a125-38db-49b4-aa6c-33eecbee6bf1"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Year</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",
       "      <th>CO2 Rating</th>\n",
       "      <th>Smog Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX</td>\n",
       "      <td>Compact</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>AM8</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>Acura</td>\n",
       "      <td>MDX SH-AWD</td>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>11.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>Acura</td>\n",
       "      <td>RDX SH-AWD</td>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>Acura</td>\n",
       "      <td>RDX SH-AWD A-SPEC</td>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>10.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>Acura</td>\n",
       "      <td>TLX SH-AWD</td>\n",
       "      <td>Compact</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>2022</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC40 T5 AWD</td>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</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",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>2022</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC60 B5 AWD</td>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>2022</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC60 B6 AWD</td>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>9.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>2022</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC90 T5 AWD</td>\n",
       "      <td>SUV: Standard</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.5</td>\n",
       "      <td>8.4</td>\n",
       "      <td>10.1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>2022</td>\n",
       "      <td>Volvo</td>\n",
       "      <td>XC90 T6 AWD</td>\n",
       "      <td>SUV: Standard</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.8</td>\n",
       "      <td>26.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
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      "text/plain": [
       "     Model Year   Make              Model  Vehicle Class  Engine Size(L)  \\\n",
       "0          2022  Acura                ILX        Compact             2.4   \n",
       "1          2022  Acura         MDX SH-AWD     SUV: Small             3.5   \n",
       "2          2022  Acura         RDX SH-AWD     SUV: Small             2.0   \n",
       "3          2022  Acura  RDX SH-AWD A-SPEC     SUV: Small             2.0   \n",
       "4          2022  Acura         TLX SH-AWD        Compact             2.0   \n",
       "..          ...    ...                ...            ...             ...   \n",
       "940        2022  Volvo        XC40 T5 AWD     SUV: Small             2.0   \n",
       "941        2022  Volvo        XC60 B5 AWD     SUV: Small             2.0   \n",
       "942        2022  Volvo        XC60 B6 AWD     SUV: Small             2.0   \n",
       "943        2022  Volvo        XC90 T5 AWD  SUV: Standard             2.0   \n",
       "944        2022  Volvo        XC90 T6 AWD  SUV: Standard             2.0   \n",
       "\n",
       "     Cylinders Transmission Fuel Type  Fuel Consumption (City (L/100 km)  \\\n",
       "0            4          AM8         Z                                NaN   \n",
       "1            6         AS10         Z                               12.6   \n",
       "2            4         AS10         Z                               11.0   \n",
       "3            4         AS10         Z                               11.3   \n",
       "4            4         AS10         Z                               11.2   \n",
       "..         ...          ...       ...                                ...   \n",
       "940          4          AS8         Z                               10.7   \n",
       "941          4          AS8         Z                               10.5   \n",
       "942          4          AS8         Z                               11.0   \n",
       "943          4          AS8         Z                               11.5   \n",
       "944          4          AS8         Z                                NaN   \n",
       "\n",
       "     Fuel Consumption(Hwy (L/100 km))  Fuel Consumption(Comb (L/100 km))  \\\n",
       "0                                 7.0                                8.6   \n",
       "1                                 9.4                               11.2   \n",
       "2                                 8.6                                9.9   \n",
       "3                                 9.1                               10.3   \n",
       "4                                 8.0                                9.8   \n",
       "..                                ...                                ...   \n",
       "940                               7.7                                9.4   \n",
       "941                               8.1                                9.4   \n",
       "942                               8.7                                9.9   \n",
       "943                               8.4                               10.1   \n",
       "944                               8.9                               10.8   \n",
       "\n",
       "     Fuel Consumption(Comb (mpg))  CO2 Emissions(g/km)  CO2 Rating  \\\n",
       "0                            33.0                200.0         6.0   \n",
       "1                            25.0                263.0         4.0   \n",
       "2                            29.0                232.0         5.0   \n",
       "3                            27.0                242.0         5.0   \n",
       "4                            29.0                230.0         5.0   \n",
       "..                            ...                  ...         ...   \n",
       "940                          30.0                219.0         5.0   \n",
       "941                          30.0                219.0         5.0   \n",
       "942                          29.0                232.0         5.0   \n",
       "943                          28.0                236.0         5.0   \n",
       "944                          26.0                252.0         5.0   \n",
       "\n",
       "     Smog Rating  \n",
       "0            3.0  \n",
       "1            5.0  \n",
       "2            6.0  \n",
       "3            6.0  \n",
       "4            7.0  \n",
       "..           ...  \n",
       "940          5.0  \n",
       "941          5.0  \n",
       "942          NaN  \n",
       "943          5.0  \n",
       "944          7.0  \n",
       "\n",
       "[945 rows x 15 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\user\\Downloads\\projects\\ML-project\\MY2022 Fuel Consumption Ratings.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "04c1fb91",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.708262Z",
     "start_time": "2023-03-19T19:52:04.694783Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 945 entries, 0 to 944\n",
      "Data columns (total 15 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   Model Year                         945 non-null    int64  \n",
      " 1   Make                               945 non-null    object \n",
      " 2   Model                              945 non-null    object \n",
      " 3   Vehicle Class                      945 non-null    object \n",
      " 4   Engine Size(L)                     945 non-null    float64\n",
      " 5   Cylinders                          945 non-null    int64  \n",
      " 6   Transmission                       945 non-null    object \n",
      " 7   Fuel Type                          944 non-null    object \n",
      " 8   Fuel Consumption (City (L/100 km)  931 non-null    float64\n",
      " 9   Fuel Consumption(Hwy (L/100 km))   930 non-null    float64\n",
      " 10  Fuel Consumption(Comb (L/100 km))  945 non-null    float64\n",
      " 11  Fuel Consumption(Comb (mpg))       931 non-null    float64\n",
      " 12  CO2 Emissions(g/km)                934 non-null    float64\n",
      " 13  CO2 Rating                         932 non-null    float64\n",
      " 14  Smog Rating                        927 non-null    float64\n",
      "dtypes: float64(8), int64(2), object(5)\n",
      "memory usage: 110.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c685e6d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.723239Z",
     "start_time": "2023-03-19T19:52:04.709259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Model Year', 'Make', 'Model', 'Vehicle Class', 'Engine Size(L)',\n",
       "       'Cylinders', 'Transmission', 'Fuel Type',\n",
       "       'Fuel Consumption (City (L/100 km)', 'Fuel Consumption(Hwy (L/100 km))',\n",
       "       'Fuel Consumption(Comb (L/100 km))', 'Fuel Consumption(Comb (mpg))',\n",
       "       'CO2 Emissions(g/km)', 'CO2 Rating', 'Smog Rating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3e527a31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.739235Z",
     "start_time": "2023-03-19T19:52:04.724238Z"
    }
   },
   "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>CO2 Rating</th>\n",
       "      <th>Smog Rating</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CO2 Rating</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.503795</td>\n",
       "      <td>-0.955018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smog Rating</th>\n",
       "      <td>0.503795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.520441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "      <td>-0.955018</td>\n",
       "      <td>-0.520441</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CO2 Rating  Smog Rating  CO2 Emissions(g/km)\n",
       "CO2 Rating             1.000000     0.503795            -0.955018\n",
       "Smog Rating            0.503795     1.000000            -0.520441\n",
       "CO2 Emissions(g/km)   -0.955018    -0.520441             1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['CO2 Rating','Smog Rating','CO2 Emissions(g/km)']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4089c831",
   "metadata": {
    "id": "4089c831"
   },
   "source": [
    "-----\n",
    "<center>\n",
    "<h1 style=\"font-size: 40px; font-family: serif;\">2. Data cleaning</h1>\n",
    "</center>\n",
    "<span id=\"Datatcleaning\"></span>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6621f4a",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7a22a5",
   "metadata": {
    "id": "de7a22a5"
   },
   "source": [
    "# 2.1 Creating new DataFrame with necessary columns\n",
    "<span id=\"df1\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c1b943ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.755293Z",
     "start_time": "2023-03-19T19:52:04.740811Z"
    },
    "id": "c1b943ed"
   },
   "outputs": [],
   "source": [
    "df= data.drop(['Model Year','Make','Model','Fuel Consumption (City (L/100 km)','Fuel Consumption(Hwy (L/100 km))','Fuel Consumption(Comb (mpg))','CO2 Emissions(g/km)','Smog Rating'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e42988e",
   "metadata": {
    "id": "4e42988e"
   },
   "source": [
    "**New DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0d083c7c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.770781Z",
     "start_time": "2023-03-19T19:52:04.756290Z"
    },
    "id": "0d083c7c",
    "outputId": "f733d33d-7a6b-4779-e39a-546d87f633e3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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(Comb (L/100 km))</th>\n",
       "      <th>CO2 Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Compact</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>AM8</td>\n",
       "      <td>Z</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Compact</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>SUV: Standard</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>SUV: Standard</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS8</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.8</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>945 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Vehicle Class  Engine Size(L)  Cylinders Transmission Fuel Type  \\\n",
       "0          Compact             2.4          4          AM8         Z   \n",
       "1       SUV: Small             3.5          6         AS10         Z   \n",
       "2       SUV: Small             2.0          4         AS10         Z   \n",
       "3       SUV: Small             2.0          4         AS10         Z   \n",
       "4          Compact             2.0          4         AS10         Z   \n",
       "..             ...             ...        ...          ...       ...   \n",
       "940     SUV: Small             2.0          4          AS8         Z   \n",
       "941     SUV: Small             2.0          4          AS8         Z   \n",
       "942     SUV: Small             2.0          4          AS8         Z   \n",
       "943  SUV: Standard             2.0          4          AS8         Z   \n",
       "944  SUV: Standard             2.0          4          AS8         Z   \n",
       "\n",
       "     Fuel Consumption(Comb (L/100 km))  CO2 Rating  \n",
       "0                                  8.6         6.0  \n",
       "1                                 11.2         4.0  \n",
       "2                                  9.9         5.0  \n",
       "3                                 10.3         5.0  \n",
       "4                                  9.8         5.0  \n",
       "..                                 ...         ...  \n",
       "940                                9.4         5.0  \n",
       "941                                9.4         5.0  \n",
       "942                                9.9         5.0  \n",
       "943                               10.1         5.0  \n",
       "944                               10.8         5.0  \n",
       "\n",
       "[945 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b9389fb",
   "metadata": {
    "id": "6b9389fb"
   },
   "source": [
    "**Renaming the columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "57cf3a0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.786601Z",
     "start_time": "2023-03-19T19:52:04.771961Z"
    },
    "id": "57cf3a0b"
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns = {'Vehicle Class':'Vehicle Class', 'Engine Size(L)':'Engine Size', 'Cylinders': 'Cylinders', 'Transmission':'Transmission','Fuel Type':'Fuel Type', 'Fuel Consumption(Comb (L/100 km))':'Fuel Consumption', 'CO2 Rating':'CO2 Rating'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee4098be",
   "metadata": {
    "id": "ee4098be"
   },
   "source": [
    "## 2.2 Checking null values\n",
    "<span id=\"nullcheck\"></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "520f5fc1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.801583Z",
     "start_time": "2023-03-19T19:52:04.789594Z"
    },
    "id": "520f5fc1",
    "outputId": "820b1fd8-d20a-40d2-b960-e559b0e9a836"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vehicle Class        0\n",
       "Engine Size          0\n",
       "Cylinders            0\n",
       "Transmission         0\n",
       "Fuel Type            1\n",
       "Fuel Consumption     0\n",
       "CO2 Rating          13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b73eb2",
   "metadata": {
    "id": "85b73eb2"
   },
   "source": [
    "## 2.3.1  Fuel type\n",
    "<span id=\"treatfuel\"></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f322321a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.816571Z",
     "start_time": "2023-03-19T19:52:04.803054Z"
    },
    "id": "f322321a"
   },
   "outputs": [],
   "source": [
    "df['Fuel Type'].fillna((df['Fuel Type'].mode()[0]), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3f0ec3bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.832130Z",
     "start_time": "2023-03-19T19:52:04.817570Z"
    },
    "id": "3f0ec3bb",
    "outputId": "68bb9bf8-bf76-4cba-c2e1-ddd2461695e7"
   },
   "outputs": [
    {
     "data": {
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       "      <td>SUV: Small</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Compact</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS10</td>\n",
       "      <td>Z</td>\n",
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       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Vehicle Class  Engine Size  Cylinders Transmission Fuel Type  \\\n",
       "0       Compact          2.4          4          AM8         Z   \n",
       "1    SUV: Small          3.5          6         AS10         Z   \n",
       "2    SUV: Small          2.0          4         AS10         Z   \n",
       "3    SUV: Small          2.0          4         AS10         Z   \n",
       "4       Compact          2.0          4         AS10         Z   \n",
       "\n",
       "   Fuel Consumption  CO2 Rating  \n",
       "0               8.6         6.0  \n",
       "1              11.2         4.0  \n",
       "2               9.9         5.0  \n",
       "3              10.3         5.0  \n",
       "4               9.8         5.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ec4244",
   "metadata": {
    "id": "64ec4244"
   },
   "source": [
    "## 2.3.2 CO2 Rating\n",
    "<span id=\"nullco2\"></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "56471e18",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.862926Z",
     "start_time": "2023-03-19T19:52:04.848906Z"
    },
    "id": "56471e18"
   },
   "outputs": [],
   "source": [
    "df['CO2 Rating'].fillna(0,inplace=True)\n",
    "new_ratting = []\n",
    "\n",
    "for fuel,co2 in zip(df['Fuel Consumption'], df['CO2 Rating']):\n",
    "    if co2==0:\n",
    "        if 20 <= fuel:\n",
    "            new_ratting.append(1)\n",
    "        elif 16.0 <= fuel < 20.0:\n",
    "            new_ratting.append(2) \n",
    "        elif 14.0 <= fuel < 16.0: \n",
    "            new_ratting.append(3)\n",
    "        elif 12.0 <= fuel < 14.0: \n",
    "            new_ratting.append(4)\n",
    "        elif 10.0 <= fuel < 12.0: \n",
    "            new_ratting.append(5)\n",
    "        elif 8.0 <= fuel < 10.0: \n",
    "            new_ratting.append(6)\n",
    "        elif 7.0 <= fuel < 8.0: \n",
    "            new_ratting.append(7)\n",
    "        elif 6.0 <= fuel < 7.0: \n",
    "            new_ratting.append(8)\n",
    "        elif 5.0 <= fuel < 6.0: \n",
    "            new_ratting.append(9)\n",
    "        elif fuel < 5.0: \n",
    "            new_ratting.append(10)            \n",
    "    else:\n",
    "        new_ratting.append(co2)\n",
    "        \n",
    "        \n",
    "df['CO2 Rating'] = new_ratting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f49176a4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.878401Z",
     "start_time": "2023-03-19T19:52:04.864923Z"
    },
    "id": "f49176a4",
    "outputId": "4a5d36d1-6bf8-421c-dbad-173bdf804ab4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vehicle Class       0\n",
       "Engine Size         0\n",
       "Cylinders           0\n",
       "Transmission        0\n",
       "Fuel Type           0\n",
       "Fuel Consumption    0\n",
       "CO2 Rating          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd46f223",
   "metadata": {
    "id": "dd46f223"
   },
   "source": [
    "## Checking the correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9821fe45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.894353Z",
     "start_time": "2023-03-19T19:52:04.879401Z"
    },
    "id": "9821fe45",
    "outputId": "fe00d8fb-ac65-4a33-a4aa-ce45966de7b3"
   },
   "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>Engine Size</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Fuel Consumption</th>\n",
       "      <th>CO2 Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Engine Size</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920672</td>\n",
       "      <td>0.818655</td>\n",
       "      <td>-0.766032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>0.920672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.821719</td>\n",
       "      <td>-0.761408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Consumption</th>\n",
       "      <td>0.818655</td>\n",
       "      <td>0.821719</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.927449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2 Rating</th>\n",
       "      <td>-0.766032</td>\n",
       "      <td>-0.761408</td>\n",
       "      <td>-0.927449</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Engine Size  Cylinders  Fuel Consumption  CO2 Rating\n",
       "Engine Size          1.000000   0.920672          0.818655   -0.766032\n",
       "Cylinders            0.920672   1.000000          0.821719   -0.761408\n",
       "Fuel Consumption     0.818655   0.821719          1.000000   -0.927449\n",
       "CO2 Rating          -0.766032  -0.761408         -0.927449    1.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670cf35e",
   "metadata": {
    "id": "670cf35e"
   },
   "source": [
    "**Checking Corelation of each feature against target variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "16b76ed5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.909343Z",
     "start_time": "2023-03-19T19:52:04.896348Z"
    },
    "id": "16b76ed5",
    "outputId": "69c93b63-1bb8-4991-9d01-f69839895c89"
   },
   "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>Fuel Consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Engine Size</th>\n",
       "      <td>0.818655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>0.821719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Consumption</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2 Rating</th>\n",
       "      <td>-0.927449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Fuel Consumption\n",
       "Engine Size               0.818655\n",
       "Cylinders                 0.821719\n",
       "Fuel Consumption          1.000000\n",
       "CO2 Rating               -0.927449"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[\"Fuel Consumption\"].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "850f25e3",
   "metadata": {
    "id": "850f25e3"
   },
   "source": [
    "## Looking for noise in all the features, including Target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2412b4eb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.924968Z",
     "start_time": "2023-03-19T19:52:04.910851Z"
    },
    "id": "2412b4eb",
    "outputId": "7ae8807e-bb9f-42ad-c8ba-af9e6ae784f4",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vehicle Class\n",
      "['Compact' 'SUV: Small' 'Mid-size' 'Minicompact' 'SUV: Standard'\n",
      " 'Two-seater' 'Subcompact' 'Station wagon: Small'\n",
      " 'Station wagon: Mid-size' 'Full-size' 'Pickup truck: Small'\n",
      " 'Pickup truck: Standard' 'Minivan' 'Special purpose vehicle'] \n",
      "\n",
      "Engine Size\n",
      "[2.4 3.5 2.  3.  2.9 4.  5.2 2.5 6.  4.4 6.6 8.  3.6 1.4 1.2 1.3 2.7 6.2\n",
      " 2.8 1.5 5.3 5.7 6.4 2.3 3.3 5.  1.8 1.6 3.8 5.6 3.2 6.5 4.6 3.4 3.7 6.7] \n",
      "\n",
      "Cylinders\n",
      "[ 4  6  8 12 10  5 16  3] \n",
      "\n",
      "Transmission\n",
      "['AM8' 'AS10' 'A8' 'A9' 'AM7' 'AS8' 'M6' 'AS6' 'AV' 'AS9' 'A10' 'A6' 'M5'\n",
      " 'M7' 'AV7' 'AV1' 'AM6' 'AS7' 'AV8' 'AV6' 'AV10' 'AS5' 'A7'] \n",
      "\n",
      "Fuel Type\n",
      "['Z' 'X' 'D' 'E'] \n",
      "\n",
      "Fuel Consumption\n",
      "[ 8.6 11.2  9.9 10.3  9.8 11.   8.7  9.2 11.6  9.3  9.6 12.3 11.5 13.8\n",
      " 14.6  7.6  8.2  8.8  8.9  9.1 10.  10.6  9.4 10.8 11.7 15.3 11.4 13.7\n",
      " 13.5 15.4  9.7 10.9 13.9 10.1  9.  13.2 16.3 12.2 16.2 12.5 17.  16.\n",
      "  8.3 12.  12.6 12.7 12.9 15.1 10.4 15.7 11.1  8.5 22.2 26.1 11.8  7.8\n",
      "  7.9  9.5 10.2 13.  10.7 14.8 15.2 10.5 14.7 14.9 11.3 12.1 14.1  8.4\n",
      "  7.5 14.5 19.1 13.4 13.1 14.  19.5 21.5 14.4 15.5 15.  16.4  7.   7.2\n",
      " 14.3 15.6 12.4  5.8  5.9 12.8 17.9 17.4 18.1 17.8 15.8 13.3 16.7  6.3\n",
      " 13.6  5.   5.5  7.1  8.   6.9  7.7  8.1  6.7  6.4  4.4  4.2  4.   7.4\n",
      " 16.5  4.6  5.3  6.6 11.9 21.2 16.9  6.   7.3  6.2  6.5 14.2 19.8  4.9\n",
      "  5.1  6.8  4.5  4.8  6.1] \n",
      "\n",
      "CO2 Rating\n",
      "[ 6.  4.  5.  3.  7.  2.  1.  8.  9. 10.] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(i)\n",
    "    print(df[i].unique(),'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc7d63c",
   "metadata": {
    "id": "afc7d63c"
   },
   "source": [
    "## Removed the Numbers from Transmission columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "71206f73",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.940752Z",
     "start_time": "2023-03-19T19:52:04.926960Z"
    },
    "id": "71206f73",
    "outputId": "c83ec934-b19b-4a44-96ed-958b16932090",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption</th>\n",
       "      <th>CO2 Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Compact</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>AM</td>\n",
       "      <td>Z</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Compact</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>SUV: Standard</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>SUV: Standard</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.8</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>945 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Vehicle Class  Engine Size  Cylinders Transmission Fuel Type  \\\n",
       "0          Compact          2.4          4           AM         Z   \n",
       "1       SUV: Small          3.5          6           AS         Z   \n",
       "2       SUV: Small          2.0          4           AS         Z   \n",
       "3       SUV: Small          2.0          4           AS         Z   \n",
       "4          Compact          2.0          4           AS         Z   \n",
       "..             ...          ...        ...          ...       ...   \n",
       "940     SUV: Small          2.0          4           AS         Z   \n",
       "941     SUV: Small          2.0          4           AS         Z   \n",
       "942     SUV: Small          2.0          4           AS         Z   \n",
       "943  SUV: Standard          2.0          4           AS         Z   \n",
       "944  SUV: Standard          2.0          4           AS         Z   \n",
       "\n",
       "     Fuel Consumption  CO2 Rating  \n",
       "0                 8.6         6.0  \n",
       "1                11.2         4.0  \n",
       "2                 9.9         5.0  \n",
       "3                10.3         5.0  \n",
       "4                 9.8         5.0  \n",
       "..                ...         ...  \n",
       "940               9.4         5.0  \n",
       "941               9.4         5.0  \n",
       "942               9.9         5.0  \n",
       "943              10.1         5.0  \n",
       "944              10.8         5.0  \n",
       "\n",
       "[945 rows x 7 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.replace({'Transmission' : {'AM8':'AM', 'AS10': 'AS', 'A8':'A', 'A9':'A', 'AM7':'AM', 'AS8':'AS', 'M6':'M', 'AS6':'AS', 'AS9':'AS', 'A10':'A', 'A6':'A', 'M5':'M', 'M7':'M', 'AV7':'AV', 'AV1':'AV', 'AM6':'AM', 'AS7':'AS', 'AV8':'AV', 'AV6':'AV', 'AV10':'AV', 'AS5':'AS', 'A7':'A'}})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "37529fd4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.956329Z",
     "start_time": "2023-03-19T19:52:04.942843Z"
    },
    "id": "37529fd4",
    "outputId": "2ad59922-7ea3-4342-eedc-ebc22c191708"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AM', 'AS', 'A', 'M', 'AV'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Transmission'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266391f3",
   "metadata": {
    "id": "266391f3"
   },
   "source": [
    "-----\n",
    "<center>\n",
    "<h1 style=\"font-size: 40px; font-family: serif;\">Exploratory Data Analysis (E.D.A.)</h1>\n",
    "</center>\n",
    "<span id=\"eda\"></span>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "797b52ef",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3643f9c",
   "metadata": {
    "id": "e3643f9c"
   },
   "source": [
    "\n",
    "# 3. Univariate analysis:-\n",
    "<span id=\"univar\"></span>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4688c8d2",
   "metadata": {
    "id": "4688c8d2"
   },
   "source": [
    "## 3.1 Statistical Summary :\n",
    "<span id=\"mean\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6eeb8607",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:04.987119Z",
     "start_time": "2023-03-19T19:52:04.957327Z"
    },
    "id": "6eeb8607",
    "outputId": "2e5113af-3c1f-4a15-e3d2-e176cef1b6eb"
   },
   "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>Engine Size</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Fuel Consumption</th>\n",
       "      <th>CO2 Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>945.000000</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>945.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.199683</td>\n",
       "      <td>5.669841</td>\n",
       "      <td>11.093439</td>\n",
       "      <td>4.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.375231</td>\n",
       "      <td>1.932930</td>\n",
       "      <td>2.877491</td>\n",
       "      <td>1.471665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Engine Size   Cylinders  Fuel Consumption  CO2 Rating\n",
       "count   945.000000  945.000000        945.000000  945.000000\n",
       "mean      3.199683    5.669841         11.093439    4.542857\n",
       "std       1.375231    1.932930          2.877491    1.471665\n",
       "min       1.200000    3.000000          4.000000    1.000000\n",
       "25%       2.000000    4.000000          9.100000    3.000000\n",
       "50%       3.000000    6.000000         10.800000    5.000000\n",
       "75%       3.800000    6.000000         12.900000    5.000000\n",
       "max       8.000000   16.000000         26.100000   10.000000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Engine Size','Cylinders','Fuel Consumption','CO2 Rating']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06a92d21",
   "metadata": {
    "id": "06a92d21"
   },
   "source": [
    "## 3.2 Frequency Distribution for the categorical Columns\n",
    "<span id=\"distribute\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3a0005e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:05.313757Z",
     "start_time": "2023-03-19T19:52:04.988117Z"
    },
    "id": "3a0005e8",
    "outputId": "008cdbe6-e419-41fc-86d2-891a323d8fbc",
    "scrolled": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1950x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,6), dpi=150)\n",
    "chart1=sns.histplot(data=df, x='Transmission', color='DarkOliveGreen')\n",
    "chart1.bar_label(chart1.containers[0],size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fc419e8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:05.437603Z",
     "start_time": "2023-03-19T19:52:05.314772Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,4), dpi=150)\n",
    "chart1=sns.histplot(data=df, x='Fuel Type', color='PaleVioletRed')\n",
    "chart1.bar_label(chart1.containers[0],size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41aba91c",
   "metadata": {
    "id": "41aba91c"
   },
   "source": [
    "## 3.3 Frequency Distribution on Numerical columns\n",
    "<span id=\"numerical\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e02ac473",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:06.506283Z",
     "start_time": "2023-03-19T19:52:05.438599Z"
    },
    "id": "e02ac473",
    "outputId": "1aeb4350-081f-416a-ede3-8c4c56b76f4a",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1950x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LL0+SvPa1r83w4cPXsmIAAAB6q74WrvwqySVJfpvkiSRbJDkorWHL50spc2qt/902dumfIq7qN/3zkrykbeyLhiullHtW0rXyZ0/0MS0tLf4KtAHNzc1+sQMAneCVr3xl3v3ud+fHP/5xfvzjH+cDH/hAJkyYkK233jqLFi3K7bffnuOOOy4PPfRQktZ9WQAAAOi7+tSeK7XWU2utV9RaH6q1zq+13l9r/XKSA9qGnFZKGdp2vHQ5QH3Bif6PJQMAAOuJb3/72xk9enSS5IorrsirX/3qNDU1ZdiwYXnzm9+cO+64I0kyfvz4HH744T1ZKgAAAD2sr61c6VCt9ZZSyh1Jdk3ypiS/zP+tRFnVsoBhbe/Nq3mdnTpqb1vR0vGDvfuwaw45JEMG+J/oihYsWpRDrrmmp8sAgPXOS1/60vz+97/Pd77znfzgBz/In//85zzzzDMZMGBAttxyy+yxxx456qijsueee/Z0qQAAAPQwv7n+Pw+kNVx5edvnpRvcv6KjwaWU4Wl9JNiz9lvpGkMGDMjQgQN7ugwAYD0xYcKETJgwYZVjBgwYkCOPPDJHHnlk9xQFAABAr9SnHgv2IjZqe1+6CuW+JAuTbFpK6ShgWbrr6V+6ujAAAAAAAGDdYeVKklLKpknGtH28K0lqrfNLKb9IMjatm96fu8K0g9reJ3VHjQAA64Na/287u3nz5vVgJeu+YcOGpRRb/AEAAKyL+ky4Ukp5U5KhSSbXZX6qL6W8KskVad1b5cZa6+PLTDs7reHKZ0spP6m1PtA2Z/ckRyWZk+TS7rkDAIDeb+Hixe3HI0eO7MFK1n3Nzc0ZPnxV2/8BAADQU/pMuJJkhySXJXmilHJ/kifTup/K6CRDktyT5CPLTqi1/ryU8t9Jjkvy51LK/yQZlOSdaX2k2mG11me67xYAAAAAAICe1pfClT8kuSDJ/0uyY5I3J5mX5M9JfpDkglrr/BUn1VqPL6X8OcnH0xqqPJ/k1iRfrLXe1i2VAwCsh64+6KAMHTSop8tYpyxYtCiHXHNNT5cBAADAi+gz4Uqt9d4kH1vDuROTTOzMegBgWV/96lfzmc98pv3zsvtSrOipp57KGWeckUmTJuXRRx/N0KFDs9NOO2XcuHE58sgj7dFArzFkwIAMHTiwp8sAAACAhvWZcAUA1lX33XdfTjvttNUae+edd2afffbJzJkzkyRNTU2ZO3dubrvtttx22235wQ9+kBtvvDGDBw/uypIBAAAA+rR+PV0AAPRlS5YsyZFHHpkFCxZk9913X+XY2bNnZ7/99svMmTOzww475Pbbb8/cuXMzb968nHfeeRk4cGBuueWWnHDCCd1UPQAAAEDfJFwBgB70jW98I7/5zW9y2GGHZe+9917l2LPOOitPPvlkhg4dmp/+9KfZddddkySDBg3KMccc07765aKLLsr999/f5bUDAAAA9FXCFQDoIQ8//HBOPvnkbLLJJjnnnHNedPzll1+eJDn00EOz1VZbvaD/2GOPTVNTUxYvXpwrr7yy0+sFAAAAoJVwBQB6yEc+8pHMmzcvZ599djbddNNVjr3vvvvy6KOPJknGjh3b4ZimpqaMGTMmSXLLLbd0brEAAAAAtBOuAEAPuPjii3PrrbfmHe94Rw4//PAXHT9lypT245133nml45b2TZ06de2LBAAAAKBDwhUA6GbTp0/PSSedlKFDh+bCCy9crTl///vf248333zzlY5b2jdnzpw0NzevXaEAAAAAdEi4AgDd7Kijjsrs2bMzYcKEbL311qs1Z+7cue3Hw4YNW+m4ZfuWnQMAAABA5xGuAEA3uuKKK/KTn/wkr3/96/OJT3yip8sBAGAdc9ddd+W0007Lv/7rv2aHHXbIJptskoEDB2aTTTbJm9/85nzpS1/KM88884J506ZNSylltV8f+tCHeuDuAGD9MaCnCwCAvuLpp5/O8ccfn/79++fiiy/OgAGr/5/hESNGtB+3tLRkgw026HBcS0tLh3MAAOgdvv3tb+f8889v/zxkyJAMHTo0zzzzTH7729/mt7/9bc4999zceOON2X333dvH9e/fPyNHjlzluRcsWJDZs2cnSf7pn/6pa24AAPoI4QoAdJNPfepTmTlzZo4++ujssMMOL9gT5bnnnms/Xto3aNCgDBo0KJtttll73/Tp01carkyfPj1JssEGG6SpqamzbwEAgC6222675VWvelX23HPP7LDDDnnJS16SpPX74bXXXpuTTjop//jHP3LAAQfk/vvvz4Ybbpgk2WKLLfLkk0+u8tzHHntszjvvvAwdOjTve9/7uvpWAGC9JlwBgG7y8MMPJ0kuuOCCXHDBBascu3TVyXHHHZdzzz03O++8c3vflClT8prXvKbDeVOmTEmS7Ljjjp1RMgAA3ezwww/vsL2pqSnjxo3Ly1/+8uyzzz55+umnM2nSpBx22GGrdd4FCxbkyiuvTJIceOCB7aENALBm7LkCAL3A9ttvny233DJJcvPNN3c4Zt68efn1r3+dJNl77727rTYAALrPm970pvbjxx9/fLXnXXfddZk1a1aS5MMf/nCn1wUAfY1wBQC6yeTJk1NrXenrc5/7XPvYpW3nnntue9vSv2L8/ve/n2nTpr3g/Oeff36am5vTv3//1f4LRgAAepelf0yTJNtss81qz7v00kuTJNtuu2322muvTq8LAPoa4QoA9BLjx4/PqFGj0tLSkn333Td33nlnkta9Wi644IKccsopSZKPfvSj2W677XqyVAAAOtHChQszbdq0nHfeefnABz6QJHn1q1+dd7/73as1/6GHHsovf/nLJMmRRx7ZZXUCQF9izxUA6CU23HDDTJo0Kfvss0+mTp2aXXfdNSNGjMiCBQvy/PPPJ2l9HNg555zTw5UCANAZhgwZkoULF76g/c1vfnO+973vZfDgwat1nm9/+9uptWbAgAEZN25cZ5cJAH2SlSsA0IuMHj0699xzT0444YRsu+22ef755zN8+PDsueeeufjii3PTTTet9g/ZAACs20aNGpWRI0dm+PDh7W1ve9vbcu6557bvx/diFi9enIkTJyZJ9t1334waNaorSgWAPke4AgDriAkTJrTvtbIqI0eOzNlnn537778/8+fPz6xZs/LrX/86H/7wh9Ovn/+0AwCsL6ZNm5Ynn3wyzc3Neeqpp3LWWWflz3/+c3bbbbeceuqpq3WOm2++OdOnT09iI3sA6Ex+AwMAAACwjnvZy16WE088MTfffHNKKfnCF76QSZMmvei8Sy65JEmy+eabZ+zYsV1dJgD0GcIVAAAAgF5it912y5577pkkueiii1Y59umnn85PfvKTJMkHP/jB9O/fv8vrA4C+wob2ALCMZR/JNW/evB6sZN3k3wQAoOdtvvnmSZK//e1vqxz3ne98J88//3xKKTniiCO6ozQA6DOEKwCwjIWLF7cfjxw5sgcrWffVJUt6ugQAgD7poYceSpKMGDFileMuvfTSJMnb3va2bL311l1eFwD0JR4LBgAAALAOWLx48XIrqTty66235o9//GOS5K1vfetKx91222257777ktjIHgC6gpUrALASVx90UIYOGtTTZaxTnl2wIIdfd11PlwEAsF567LHHcsABB+Too4/OO9/5zmy11VYppbT3XXnllfniF7+YWms23njjnHDCCSs919KN7DfeeOO8973v7Zb6AaAvEa4AL3DXXXflxz/+ce68887cf//9+cc//pE5c+Zkgw02yA477JB3vetdOfroo7Pxxhu/YO706dNzww035Je//GX+9Kc/Zfr06UmSUaNG5U1velM+8pGP5J//+Z+7+5ZgjQwZMCBDBw7s6TLWKQsWLerpEgDoYb4rQte6++678x//8R9JkkGDBmWDDTbI/Pnzl9v7bquttsq1116bUaNGdXiOuXPn5gc/+EGS5P3vf38GDx7c9YUDQB8jXAFe4Nvf/nbOP//89s9DhgzJ0KFD88wzz+S3v/1tfvvb3+bcc8/NjTfemN1337193GOPPZZXvvKVyy1jHzZsWGqtmTZtWqZNm5bvf//7OeKII3LRRRelf//+3XpfAACsPd8VoetsttlmueaaazJ58uT84Q9/yBNPPJEZM2akf//+2XLLLfO6170u+++/f973vvdl6NChKz3PVVddlZaWliQeCQYAXcWeK8AL7LbbbjnzzDPzu9/9LrNmzcr8+fMzZ86czJ07NxMnTsymm26aGTNm5IADDsjs2bPb5y19PvDb3/72fOc738n06dMzb968NDc355577sn++++fpPUH8gkTJvTQ3QEAsDZ8V4SuM2jQoBx88ME5//zzc8cdd2T69OlZuHBhWlpa8sgjj+TGG2/MkUceucpgJUk++tGPptaaWmt22WWXbqoeAPoWK1eAFzj88MM7bG9qasq4cePy8pe/PPvss0+efvrpTJo0KYcddliSZKONNsqdd96ZN77xjcvN69evX3bcccdcf/31ede73pWbb7455557bk4++eQMGTKky+8HAIDO47siAABYuQKsgTe96U3tx48//nj78YYbbviCH5aXVUrJEUcckSRpbm7Ovffe23VFAgDQI3xXBACgL7ByBWjYr3/96/bjbbbZpqG5y/714eLFizutJgAA1g2+K7K+qrW272PCixs2bFhKKT1dBgB0GeEKsFoWLlyYJ554IpMmTcqpp56aJHn1q1+dd7/73Q2dZ/LkyUlanyW83XbbdXaZAAD0AN8V6QtaWlrS1NTU02X0Gs3NzRk+fHhPlwEAXUa4AqzSkCFDsnDhwhe0v/nNb873vve9DB48eLXP9fDDD+db3/pWkuTf/u3fssEGG3RanQAAdD/fFQEA6KuEK8AqjRo1KgsWLEhzc3PmzZuXJHnb296WM844I1tuueVqn2f+/Pk5+OCD09LSkk022SRf+cpXuqpkAAC6ie+K9FXXHHJIhgzwK5UVLVi0KIdcc01PlwEA3cI3AWCVpk2b1n789NNP57vf/W6+9KUvZbfddstnP/vZfP7zn3/RcyxatCjve9/7cuedd2bgwIH53ve+l80337wLqwYAoDv4rkhfNWTAgAwdOLCnywAAelC/ni4A6D1e9rKX5cQTT8zNN9+cUkq+8IUvZNKkSaucs3jx4rz//e/Pj370owwYMCDf+973svfee3dTxQAAdBffFQEA6EuEK0DDdtttt+y5555Jkosuumil45b+sHz11Venf//+ueKKK3LQQQd1V5kAAPQA3xUBAOgLPBYMWCNLH9Xwt7/9rcP+xYsX57DDDlvuh+V/+7d/684SAQDoIb4rAgCwvrNyBVgjDz30UJJkxIgRL+jr6IflQw89tLtLBACgh/iuCADA+k64Aixn8eLFqbWucsytt96aP/7xj0mSt771rS+Y/773vS9XX311BgwYkCuvvNIPywAA6wnfFQEAoJVwBVjOY489lje84Q258MIL89BDDy33w/Njjz2Wr371q9l///1Ta83GG2+cE044ob1/8eLF+cAHPpBrrrmmfUNSj3cAAFh/+K4IAACt7LkCvMDdd9+d//iP/0iSDBo0KBtssEHmz5+fefPmtY/Zaqutcu2112bUqFHtbb/5zW9y1VVXJUlKKTn22GNz7LHHrvQ6//3f/+0HagCAXsZ3RQAAEK4AK9hss81yzTXXZPLkyfnDH/6QJ554IjNmzEj//v2z5ZZb5nWve13233//vO9978vQoUOXm7tkyZL24+effz5PPfXUKq81f/78LrkHAAC6hu+KAADQSrgCLGfQoEE5+OCDc/DBBzc8961vfeuLPoMbAIDey3dFAABoZc8VAAAAAACABli5Ar3Msn/tt+xzrWnl3wQA6KtqrWlpaenpMtZpvisCANBZhCvQyyxcvLj9eOTIkT1YybqvLvNcbwCg1cyZM3PjjTfm1ltvzV133ZVHHnkkixYtyqabbppdd90148aNy3ve854O506YMCGnnXbai17jgQceyKtf/erOLp0X0dLSkqampp4uo9fwXREAgLUhXAEAgD5k1KhRWbRoUfvnIUOGZODAgZk+fXqmT5+eG264IWPHjs0Pf/jDDBs2rMNzDBw4MBtvvPFKrzFggB8zAACA9ZufeqAXu/qggzJ00KCeLmOd8uyCBTn8uut6ugwAWGctWrQou+22Wz74wQ9mn332ydZbb50kmTZtWr74xS/m0ksvzU033ZSjjjoq3/3udzs8xx577JHJkyd3Y9U06ppDDskQIdcL+K4IAEBn8W0berEhAwZk6MCBPV3GOmXBMn+JCwC80C9+8Yu87W1ve0H7q171qlxyySUZMGBALrzwwlxxxRX58pe/nC222KIHqmRt+Z7YMd8VAQDoLP16ugAAAKD7dBSsLOvII49sP77jjju6uhwAAIBeSbgCAAC0GzJkSPvx4sWLe7ASAACAdZdwBQAAaLfsXiq77LJLh2Puueee7Lzzzhk6dGiampqy/fbb5yMf+Uj+9Kc/dVOVAAAAPUu4AgAAJEmeffbZfOUrX0mSjBkzJttvv32H42bMmJF77703w4YNy8KFC3P//ffnkksuyejRo/PZz362O0sGAADoEcIVAAAgS5YsyQc+8IE88cQTGTx4cL7xjW+8YMy2226bM844I/fdd18WLFiQmTNnZt68efnZz36W0aNHp9aaL33pS/na177WA3cAAADQfYQrAABAjjvuuEyaNClJ8s1vfjOve93rXjDmsMMOy0knnZTtttsuAwcOTJIMGjQoe++9d2677bb80z/9U5JkwoQJmT17dvcVDwAA0M2EKwAA0MeNHz8+5513XpLknHPOyRFHHNHwOYYMGZIvf/nLSZLm5ubceuutnVojAADAukS4AgAAfdgnP/nJ9sd4nXnmmTn++OPX+Fy77757+/FDDz20tqUBAACsswb0dAEAAEDPOOmkk3LWWWclSc4444yMHz++hysCAADoHYQrAADQB40fP759xcoZZ5yRk046aa3P+fvf/779eKuttlrr8wEAAKyrhCsAANDHLBusnHXWWTnxxBNfdE6tNaWUlfYvXLgwJ598cpJk+PDhefvb3945xQIAAKyD+syeK6WUYaWUA0opl5ZS/lJKmVNKmVdKubuUcmoppamDORNKKXUVr6/2xL0AAMCa+tSnPtUerJx99tmrFawkya9+9au84x3vyBVXXJHHH3+8vf3555/PrbfemjFjxuQPf/hDkuTUU0/NS17ykk6vHQAAYF3Rl1auvC/JxW3H9yS5OckGSfZIclqSfy+l7FVrfbqDub9J8rcO2u/sikIBAKArPProoznjjDOSJP369cvpp5+e008/faXjx48f374PS601t956a2699dYkydChQzN8+PDMnj07zz//fPs5P/3pT+eTn/xkF98JAABAz+pL4cpzSS5Ick6t9YGljaWUlyf5SZI3JDk3rSHMii6ptU7shhoBAKDLLFmyZLnjp556apXjm5ub24932WWXnHXWWfnd736Xv/71r5kxY0aeffbZDBs2LDvuuGPGjBmTj370o9lll126rH4AAIB1RZ8JV2qtlye5vIP2J0opxyT5bZL3llIG1Vqf6/YCAQCgi73qVa9KrXWN5m6yySar/QgxAACA9V2f2XPlRdzd9j44ySY9WQgAAAAAALBu6zMrV17E1m3vzyd5poP+fy6lvD7JkCSPJ7mp1mq/FQAAAAAA6IOEK62Oa3u/uda6sIP+D6zw+QullGuTfLDW2tzBeAAAaNiyj+yaN29eD1ay7hs2bFhKKT1dBgAA0Ef1+XCllPKuJEemddXKKSt0/y3J+CQ3JXkkyUZJ3pLkjCQHJumf5D0NXOuelXRt01jVAACsjxYuXtx+PHLkyB6sZN3X3Nyc4cOH93QZAABAH9Wnw5VSymuSXJGkJDmp1nr3sv211itWmDIvyfdKKb9M8tckB5RS9qi1/rZbCgYAAAD4/9u79zCry3rv4+8v5xlBBUPxLCKKmgfM8yFhYxakYQoqeEBN27qTdmr2uLMUM3cHKms/ZWkeoBQEPGWWmJl47JEEUzMVFUYQQTZBBgyMMHM/f6w144BzhJn5rTXzfl3Xun6/9Tt+1i3rGtf6rvu+JUlS5jpscSUidgFmkuuN8qOU0k+aem5KaUlE3EGuV8ungSYVV1JK+9eT5RVgv6beX5IkSe3ftFGjKOnWLesYBWXdhg2cPn161jEkSZIkqWMWVyLiY8CjwG5AdZGkud7IL3dsqVySJElStR5dulDStWvWMSSpRZWXl/PEE08wZ84c5s6dy5w5c1i4cCEA1157LRMmTKjzvLKyMvr379/k+5x33nnccccdLRFZkiSpTh2uuBIRvcjNoTIIuA+4KNWeObTpeueXTmgvSZIkSVITzJ49mxEjRjT7vM6dOzc6F9W6det4//33ATjssMM2K58kSVJTdajiSkR0B34DHAo8AoxJKVU2fFad1wk+nMh+TssllCRJkiSpfevduzeHHHJIzeOyyy5j6dKlDZ6z6667NnrM+PHj+elPf0pJSQljx45tyciSJEkf0WGKKxHRGZgKDAWeAk5NKX3QwPEfA0YA01JKFbW29wR+ABwBLAXub83ckiRJkiS1F8cddxwrVqzYaNtVV121xdddt24dd911FwCnnXYa22677RZfU5IkqSEdprgCXMqHvU2WAzflOqB8xFdTSsuBnsBk4P9GxKvAQmBb4BBgO+CfwKiUUnnrxpYkSZIkqX3o3Llzq1z3vvvuY+XKlQBceOGFrXIPSZKk2jpScaV3rfXP13sUTCBXfPkH8D3gSGAv4GCgElgATAJuTCktboWckiRJkiSpGW677TYABg4cyPHHH59xGkmS1BF0mOJKSmkCucJJU49fBWx532RJkiRJktRq5s+fz+OPPw7AF77whYzTSJKkjqJT1gEkSZIkSZI21+23305KiS5dujBu3Lis40iSpA7C4ookSZIkSSpKlZWVTJo0CYDPfvaz9OvXL9tAkiSpw7C4IkmSJEmSitLMmTNZvDg3HaoT2UuSpLZkcUWSJEmSJBWlW2+9FYCdd96Z4cOHZ5xGkiR1JBZXJEmSJElS0Vm2bBm/+93vADjvvPPo3LlzxokkSVJHYnFFkiRJkiQVncmTJ7N+/XoiggsuuCDrOJIkqYOxuCJJkiRJkorObbfdBsDQoUPZc889M04jSZI6GosrkiRJkiSpqDz99NO8/vrrgBPZS5KkbHTJOoAkSZIkSeo4Vq5cSWVlZc3zqqoqAMrLy1m+fHnN9h49etCzZ886r1E9kX2fPn049dRTWzGtJElS3ey5IkmSJEmS2szgwYPp27dvzWPRokUATJw4caPtl156aZ3nr1q1ihkzZgBw9tln07179zbLruyVl5fz8MMP8+1vf5tTTz2V3XffnYggIpgwYUKD5y5evJibbrqJ0aNHs9dee1FSUkJJSQn9+/dnzJgx/OlPf2qbFyFJahfsuSJJkiRJkorG1KlTKS8vBxwSrCOaPXs2I0aMaPZ5ixYtYvfddyelVLOttLSUlBJlZWWUlZVx9913c8EFF3DLLbfQuXPnlowtSWqH7LkiSZIkSZLaTFlZGSmlRh+TJk2q8/wvfvGLNccccMABbRteBaF3794MGzaMK6+8kqlTp9KvX79Gz6msrCSlxLBhw5g8eTKLFy9mzZo1rF69mldeeYWRI0cCcPvttzfaA0aSJLDniiRJkiRJkorEcccdx4oVKzbadtVVVzV6Xu/evZkzZw6HHHLIRts7derEfvvtx/3338+IESOYOXMmP/7xj7n66qvp0aNHi2aXJLUv9lyRJEmSJElSUdjc4bq22WabjxRWaosILrjgAgBWr17Nq6++uln3kSR1HPZckSRJkiRJG81FsWbNmgyTFCbbpP2r3VOlsrIywySSpGJgcUWSJEmSJFFR68vkHXbYIcMkhS9VVWUdQa1g1qxZAHTr1o2999472zCSpILnsGCSJEmSJEnq0BYsWMAvfvELAM444wy23nrrjBNJkgqdPVckSZIkSdJGpo0aRUm3blnHKCj/XLeOc++7L+sYagVr165l9OjRlJeXs9122/Gd73wn60iSpCJgcUWSJEmSJG2kR5culHTtmnWMgrJuw4asI6gVbNiwgbFjxzJnzhy6du3KlClT2HnnnbOOJUkqAg4LJkmSJEmSpA6nsrKSs88+mwceeIAuXbowZcoUTjzxxKxjSZKKhMUVSZIkSZIkdSjVhZVp06bRuXNn7rzzTkaNGpV1LElSEbG4IkmSJEmSpA6jsrKSs846i7vvvrumsHLGGWdkHatNPfroo5x++unsvvvu9OjRg5KSEvbcc0/OOussnnjiiazjSVJRsLgiSZIkSZKkDqG6sFK7x8qZZ56Zdaw2k1Li4osv5sQTT2TGjBksXLiQiCAiWLBgAVOmTGHIkCFcfvnlWUeVpIJncUWSJEmSJEntXmVlJWPHjmXatGl06dKFu+66q0MVVgAmTZrEzTffDMCoUaOYN28ea9eupby8nNdee42RI0cCcOONN3L//fdnGVWSCl6XrANIkiRJkiRJTbVy5UoqKytrnldVVQFQXl7O8uXLa7b36NGDnj17ArnCyjnnnMP06dNrJq8fPXp02wYvAL/61a8A2GuvvZg6dSpdunz41eA+++zDjBkzGDRoEPPnz2f69Ol8/vOfzyqqJBU8e65IkiRJkiSpaAwePJi+ffvWPBYtWgTAxIkTN9p+6aWX1pzzzDPPMHXqVAAigvHjx9OvX796H9OmTcvktbW2JUuWAHDQQQdtVFip1rVrVw4++GAAVq9e3ZbRJKno2HNFkiRJkiRJ7Vp17xaA9evX89577zV4/Nq1a1s7Uib23HNPXn/9dV588UU2bNjwkQLL+vXr+etf/wrAoYcemkFCSSoe9lyRJEmSJElS0SgrKyOl1Ohj0qRJNecMGTKkSedUP84777zMXl9ruuSSSwB48803GTNmDG+++WbNvtdff53TTz+d+fPnM2DAAC677LKsYkpSUbC4IkmSJEmSJHUAJ598MjfeeCPdunXjnnvuYeDAgZSWllJaWsqgQYOYNWsWl1xyCbNnz2brrbfOOq4kFTSHBZMkSZIkSdIWSynVrK9ZsybDJIWvtLSUiMjk3l/5ylcYOHAgF1xwAcuWLdtoCLSKigpWrVrF+++/T58+fTLJJ0nFwuKKJEmSJEmStlhFZWXN+g477JBhksK3evVqttpqqza/b3l5Oeeffz7Tp0/n0EMP5c477+SQQw4hpcQLL7zA17/+de68805mzpzJY489xoEHHtjmGSWpWFhckSRJkiRJkjqAK6+8kunTp7P33nvz5JNPUlJSUrPvU5/6FMceeywHH3ww8+bN40tf+hJPPfVUhmklqbBZXJEkSZIkSVKLmjZqFCXdumUdo6Cs27CB06dPz+z+q1at4pZbbgHg0ksv3aiwUq2kpIRLL72UL3/5yzz99NMsW7aM7bffvq2jSlJRsLgiSZIkSZKkFtWjSxdKunbNOoZqmTdvHhs2bABgwIAB9R43cODAmvUFCxZYXJGkenTKOoAkSZIkSZKk1tWp04dfA7799tv1Hvfee+/VrPfq1atVM0lSMbO4IkmSJEmSJLVzgwYNqhkK7NZbb63pxVJbZWVlzdBhvXv3Zp999mnTjJJUTCyuSJIkSZIkSe1cSUkJF154IQBz587l5JNP5uWXX6aqqoqqqipeeuklRowYwbPPPgvAV77yFTp37pxlZKnozJ07l+uuu47Pfe5zDBo0iO22246uXbuy3Xbbccwxx3DDDTewYsWKrGOqhTjniiRJkiRJktQBfO973+ONN95g5syZNY/u3bsDUFFRUXPcmDFjuPrqq7OKKRWt22+/nZ/97Gc1z3v06EFJSQkrVqzg2Wef5dlnn+XHP/4xDz74IEcddVSGSdUS7LkiSZIkSZIkdQAlJSX8/ve/Z8aMGYwcOZJddtmFlBIAu+66K6eddhoPPfQQU6ZMsdeKtBkOP/xwJk6cyJ///GdWrlzJ2rVr+de//sWqVauYNGkSffv2Zfny5Zxyyim8//77WcfVFrLniiRJkiRJktRBRASjRo1i1KhRWUeR2p1zzz23zu09e/Zk3Lhx7Ljjjnz6059m2bJlPPTQQ5x11lltnFAtyZ4rkiRJkiRJkiS1siOPPLJm/Z133skwiVqCPVckSZIkSZKkVlY9/BbAmjVrMkxS+EpLS4mIrGNILe6pp56qWR8wYECGSdQSLK5IkiRJUhOVl5fzxBNPMGfOHObOncucOXNYuHAhANdeey0TJkzINqAkqWBVVFbWrO+www4ZJil8q1evZquttso6htQiKioqWLJkCQ899BDXXHMNAHvttRcnn3xyxsm0pSyuSJIkSVITzZ49mxEjRmQdQ5IkSQWuR48eVFRUfGT7Mcccw5QpU+jevXsGqdSSLK5IkiRJUjP07t2bQw45pOZx2WWXsXTp0qxjSZKKyLRRoyjp1i3rGAVl3YYNnD59etYxpBbTr18/1q1bx+rVq2uGAhw6dCjf//732W233TJOp5ZgcUWSJEmSmui4445jxYoVG2276qqrMkojSSpWPbp0oaRr16xjSGpFZWVlNevLli3j17/+NTfccAOHH3443/jGN/jWt76VXTi1iE5ZB5AkSZKkYtG5c+esI0iSpFa2atUqJkyYwAEHHEDPnj3ZZpttOOyww/jhD3/IBx98kHW8zNk+zbf99ttzxRVXMHPmTCKC66+/noceeijrWNpC9lyRJEmSJEmSJODtt99myJAhNb0OSktLqaio4Pnnn+f555/nrrvu4rHHHqN3797ZBs2I7bNlDj/8cI499liefPJJbrnlFk466aSsI2kL2HNFkiRJkiRJUodXWVnJySefTFlZGTvuuCOPPvooa9asoby8nLvvvptevXrxwgsvcNZZZ2UdNRO2T8vYeeedAXjzzTczTqItZXFFkiRJkiRJUoc3adIkXn75ZQDuvfdeTjjhBAA6derEGWecwc033wzAww8/zGOPPZZZzqzYPi1j/vz5APTq1SvjJNpSFlckSZIkSZIkdXiTJ08GYOjQoRx11FEf2X/mmWfSv39/AH71q1+1abZCYPs0rLKykpRSg8c89thjzJ49G4AhQ4a0QSq1JosrkiRJkiRJkjq08vJynnnmGQCGDx9e5zERwWc+8xkA/vCHP7RZtkJg+zRu0aJFDB48mJtvvpn58+dvVGhZtGgR3/3udxk5ciQpJfr06cNll12WYVq1BCe0lyRJkiRJktShvfrqq1RVVQHw8Y9/vN7jqvctXbqUFStW0KdPnzbJlzXbp2lefPFFLr74YgC6devG1ltvzdq1a1mzZk3NMf379+fee++lX79+WcVUC7G4IkmSJEmSJKlDe/fdd2vWqyccr0vtfe+++26HKR7YPo3baaedmD59OrNmzeK5555jyZIlLF++nM6dO7Pbbrtx0EEHMXLkSMaOHUtJSUnWcdUCLK5IkiRJkiRJ6tBWrVpVs15aWlrvcbX31T6nvbN9GtetWzdGjx7N6NGjs46iNuKcK5IkSZIkSZIkSc1gcUWSJEmSJElSh9arV6+a9fLy8nqPq72v9jntne0jfZTDgkmSJElSM6xcuZLKysqa59WTu5aXl7N8+fKa7T169KBnz55tnk+SJDXfTjvtVLO+ePFiDjzwwDqPW7x4cZ3ntHfF0D4ppQYLP/pQaWkpEZF1jKJncUWSJEmSmmHw4MG8/fbbH9k+ceJEJk6cWPN83LhxTJo0qQ2TSZKkzbXvvvvSqVMnqqqq+Nvf/sbw4cPrPO5vf/sbAP369etQk7UXQ/uUl5f7w5YmWr16NVtttVXWMYqew4JJkiRJkiRJ6tBKS0s55phjAJg5c2adx6SUeOSRRwA48cQT2yxbIbB9pI+y50oTREQP4L+AMcBuwApgJnBNSumdLLNJkiRJaltlZWVZR5AkSa1g3LhxPPXUUzz++OM899xzHHHEERvtnzFjBvPnzwfg3HPPzSJipoqpfaaffjo9uvjVd23rNmzg9OnTs47RrvgvrBH5wspjwNHAEuA3wB7A+cBJEXFUSumt7BJKkiRJkiRJ2lLjxo3jJz/5CS+//DKnnXYakydPZtiwYVRVVXHvvfdy0UUXATB8+HCGDRuWcdq2V0zt06NLF0q6ds00g9o/iyuN+zq5wsqfgRNTSqsBIuJy4IfA7cDx2cWTJEmSJEmStKW6dOnCgw8+yNChQykrK+OEE06gtLSUqqoq1q1bB+TmXrvrrrsyTpoN20famMWVBkREV2B8/umXqgsrACmlH0XEOOCTEfGJlNKcTEJKkiRJHURKqWZ9zZo1GSYpTLaJJElbbo899uCll17iBz/4Affddx8LFiyga9eu7L///owZM4bx48fTrVu3rGNmxvaRPmRxpWHHAtsCb6WUXqhj/z3AgcDJgMUVSZIkqRVVVFbWrO+www4ZJil8qaoq6wiSJBWtXr16cd1113HddddlHaUg2T5STqesAxS4g/LLufXsn7vJcZIkSZIkSZIkqZ2z50rDdssv36ln/zubHKcWtG7DhqwjFKTa7bJuwwaikzXS2myfhtk+jbONGmb7NMz2aZxt1DDbp2G122fyKadQ4pATG3l/3TouevBBwH8/9fE91jDbp2G2T+Nso4bZPg2r3T4OdanNUfvfjd8rfpRt0vKi9rjF2lhE3AJcBNyQUvpGHfv3At4A5qWU9mnC9V6pZ9eg7t27dxowYMAW5W0PqqqqeO2117KOIUmSJEmSJEnt0qBBg+hkgReAt956i4qKilUppa2be649VxoW+WV9FaioZ3tzVVVUVKz5+9//vqiFrteY6irOW210P6kY+T6RGuf7RGqc7xOpcb5PpMb5PpEa5/tEatwAgNdee833yYd2Bco350SLKw1blV9uVc/+0vxydVMullLaf4sTtYDqHjSFkkcqRL5PpMb5PpEa5/tEapzvE6lxvk+kxvk+kRrn+6Rl2fenYQvzy13q2b/LJsdJkiRJkiRJkqR2zuJKw17MLw+pZ3/19pfaIIskSZIkSZIkSSoAFlca9gzwPjAgIgbXsX9UfvlQ20WSJEmSJEmSJElZsrjSgJTSB8BP809/GhE1c69ExOXAgcDTKaW/ZJFPkiRJkiRJkiS1PSe0b9y3gROAo4E3IuIpYHfgCOAfwPkZZpMkSZIkSZIkSW0sUkpZZyh4EVEC/BcwFtgVWAnMBL6ZUlqUZTZJkiRJkiRJktS2LK5IkiRJkiRJkiQ1g3OuSJIkSZIkSZIkNYPFFUmSJEmSJEmSpGawuCJJkiRJkiRJktQMFlckSZIkSZIkSZKaweKKJEmSJEmSJElSM1hckSRJkiRJkiRJagaLKx1ERHwiIq6KiPsiYnFEpIhYl3UuqVBERGlEnBIRt0XESxHxr4hYExEvRsQ1EdEz64xSIYiIy/N/S96IiPcjoiIi3o6IyRGxf9b5pEITEX0iYln+/71eyzqPVCgiYlb+fVHf4zNZZ5QKRUT0i4gbI2JeRKyNiBURMScivp91NilLETGkkb8l1Y9rss4qZS0ijoyIeyNiaUSsz/8teSwiRmWdrZhFSinrDGoDEfEAMHKTzRUppR4ZxJEKTkRcCPwy//QV4O/A1sDRQC/gNeD4lNKybBJKhSEilgNbAS8Bi/Ob9wf2Bj4ATkkpPZxRPKngRMQk4FwggNdTSoOyTSQVhoiYBRwP3AusruOQH6aUXm7TUFIBioijgN8D25L7jPI3cp9P9gN2SSl1yS6dlK2IGARcVc/uzsDZ+fV/Syk93jappMITEaOBu8l1tHgeeAvYCTgmv+17KaX63ktqgMWVDiIi/g9QCvwl/1iKxRWpRkScCxwJ3JhSeqPW9h2B3wGDgakppbEZRZQKQkQcA8xJKa3bZPslwE3Au8BuKaXKLPJJhSQihgF/BG4BvojFFalGreJK/5RSWbZppMIUETuR++FXd+CslNL9m+w/PKU0O5NwUoGLiOHkCpOLgD1SSlUZR5IyERFdyH1O7wucmVKaVmvfUcCfyP2dGZhSeiublMXL4koHFREJiytSk+T/2DwLVABbp5Q+yDiSVJAi4g1gL2D/lNLfs84jZSkiSsj18PoAOAWYh8UVqYbFFalxEfEr4BxgfErpp1nnkYpJRNwFjAW+m1L6r6zzSFmJiI8DLwOvpZT2rWP/A+RGOzojpTS9jeMVPbuPSlLjXswvuwPbAUsyzCIVsureKhYgJbgWGAAMAdZnG0WSVGwiojdwOvA+cGvGcaSiEhFb8eHQ+HdmmUUqABVNPG5Fq6ZopyyuSFLj9swv1+MfG6lO+aH19iH36/z5GceRMhURBwJXAHeklJ6MiD0yjiQVsi9ExHZAFbm/IQ+klBZmnEkqBMeQ+3HXH4H1+QmHjwW6kpsPcnpK6b0M80mF7FRy80S+kFJ6JeswUsbm5x+DIuL02r1T8iO1fBpYADyZUb6iZnFFkhr3n/nlzJRSUyv+UrsWEVeSm8h+K2Df/Pq7wFjHM1ZHFhGdgF8C/wS+lm0aqSh8Y5PnP4iI61NK12eSRioc++eX7wFPAUdtsv87EXF+SmlG28aSikL1RPa/zjSFVABSSpURcR7wW2Ba/rP8W8CO5Ir2s4FzHAJ/83TKOoAkFbKIGAF8gVyvlW9mHEcqJJ8GxgGjyH34X0SusDIn01RS9sYDhwNXppT+kXUYqYA9SW4uiQFAKbnej1cDG4BvRcR/NnCu1BH0zi/PBQ4k95mkL9Af+BG5H7jcme8tKSkvIvoBw8gNWTw14zhSQUgpPUVurrsFwKHAGcAngTXkeki+m1264mZxRZLqERH7khufNch9SfZiI6dIHUZK6YSUUpD74P9J4HVgVkRcnW0yKTsRsSvwbeCJlNKkjONIBS2ldE1K6c6U0vyU0tqU0ryU0n8Dp+QPuS4iSjKMKGWtc37ZBbg8pXR7Sml5SqkspXQFcA/QDXtJSpsaS+7982hKaWnWYaRCEBFjgOeAhcARQE9gb3IFyG8Af4yIrtklLF4WVySpDhGxCzCT3BfHP0op/STjSFJBSin9M/8rmBHAHOD6iDgs41hSVm4i90XXJVkHkYpVSukPwPPANsCRGceRsrQqv6wCJtex//b8ckibpJGKh0OCSbVExEByf0f+F/hsSml2SmlNSumNlNK/kxsu7Cjg/CxzFiuLK5K0iYj4GPAosBtwB/DVbBNJhS+ltB6YRq6n18kZx5GychJQDvw8ImZVP4C78/t3q7W9Z2YppcL3Rn65Y6YppGyV5ZdL65n3sXr/9m2SRioC+dEnBgOrgQeyTSMVjDOBruTmEV5Tx/7qCe6HtFmidsQJ7SWplojoBTwMDALuAy5KKaVsU0lFY3l+2TfTFFK2tiU3nnFdSmrt8//DpfpVzzWxOtMUUrZeyC97R0TU8Zlku/zS94n0oXPyy/tSSuWZJpEKxy755b/q2V+9vU8bZGl37LkiSXkR0R34DbnJvR4BxqSUKrNNJRWV6i+N38o0hZSRlFLU9SA3+TDA67W2/zPDqFLBioi+wHH5p3OzzCJlKaX0MrmJh0vIjY+/qSH5pe8TCYiIIDffCjgkmFRb9dxDh9azv3pY77LWj9L+WFyRJCAiOpObyGso8BRwakrpg2xTSYUlIo6LiDMiossm27tGxHhyvxRbS254MEmS6hQRR0bE0PwXYbW37wHcD2wFPJhSeieLfFIB+V5++T/5oYsBiIhPAFfkn/6izVNJhek4YHfgXeBPGWeRCslv8stPRsRGc0NGxJHAZfmn97RpqnbC4Qg6iIj4LPDNTTZ3i4j/V+v59Sml37VhLKmQXAp8Pr++HLhpk8/71b6aUlpe1w6pAxhAbh6i5RExB/gH8DHgAHLj4q8DzkspLcouoiSpCAwi9/dkSUTMI/eLyl2ATwA9gFeAi7KLJxWMXwLDgNHA6xHxLNATOBroBvwypeSXYVJO9UT2d6WUqjJNIhWQlNLciPgBufmEb4qILwF/B3YiN5F9J+CWlNIfM4xZtCyudBx9+WhX4thkm2PkqyPrXWv98/UeBRP4cF4JqaN5AvhvcsN/HUiusPIBue7D9wD/k1J6M7N0kqRi8Rzwc3KfRfYDjgHWAH8FZgA/TymtzSydVCBSSlURcSYwC7gQ+DcgAc8Dv0gpOfSRRM0Q36PyT+/MMotUiFJKV+YL9BeT+zHLPsAqcp/xb00pTckyXzEL52mWJEmSJEmSJElqOudckSRJkiRJkiRJagaLK5IkSZIkSZIkSc1gcUWSJEmSJEmSJKkZLK5IkiRJkiRJkiQ1g8UVSZIkSZIkSZKkZrC4IkmSJEmSJEmS1AwWVyRJkiRJkiRJkprB4ookSZIkSZIkSVIzWFyRJEmSJEmSJElqBosrkiRJkiRJkiRJzWBxRZIkSZIkSZIkqRksrkiSJElqFRGRmvCYlFG2Sfn7D8ni/g2JiM4RcVFEPBkRKyPig4hYEhFzIuJnEXHSJsfvkX8tszKKLEmSJHU4XbIOIEmSJKndm9zAvqfbLEURiIhuwG+BE4FK4DlgEdADOBj4D2AY8FBGESVJkiQBkVLKOoMkSZKkdigiEkBKKbLOsqmI2BHYBliYUirPOk+1iLgC+AFQBpyQUnprk/2Dgc+mlL5da1tXYABQnlJa2IZxJUmSpA7L4ookSZKkVlHIxZVCFRHPAEcDF6aUbss6jyRJkqS6OeeKJEmSpIKRnzukLD/vyNciYl5EVETEooj4XkR0r+e8wRHxcES8n388EhGHRcR5+WtO2OT4Oudcyd875dcvjIiXImJtRCyNiJsjYtt67t8tIv4zIv4SEasiYk1EzI6IL0REc4pLffPL/23qCXXNuVJrW0OPWXVc6+R82/0jItbl2//6iOjZjNcgSZIktXvOuSJJkiSpEN0FnATMBl4HjgO+BuwMnF37wIg4GvgjUAK8kD9+P3LzudyxOTePiO8D/wn8BZhJrjfJF4F9I+L4VGsIgIjYCng4n3F5/r5VwFHArcBhwMVNvPU7wEDgCxHx+5TShs3JD6ym/rluDgYOIjenS42I+CFwObCOXLsvBz4BfAMYnn/dazYzjyRJktSuWFyRJEmSVGh2B8qBj6eUygAioj8wBzgrIq6tnoskIjqRK6CUAF9LKU2svkhEfBP41mZmOBs4IqX01/y1Pgb8mVwBZSjwp1rHTsxv/zXwHyml1flz+pKbnP7fI+K3KaXfNeG+t+av/zngrYi4H3gWeDal9E5Tw6eUlgPnbbo9IgaQK5x8AFxTa/vp5AorLwCn1mr3rsBPyRWWJgBXNjWDJEmS1J45LJgkSZKkVtXI0FSn1HPa+Oov+AFSSguAO/NPj6t13L8BewOvkZsIvrb/BhZsZuxvVhdW8vdfDvw8//ST1dsjYnvgwvx9LqourOTP+V/g3/NPq5cNSilNIddDZy2wG7neM9OARRHx94j4j4jovDkvKCK2Bh4E+gCXpJSeqbX76/nlmE3afX0+w1LgwnwxS5IkSerw7LkiSZIkqbXVNzwVwMI6tq0HZtWxfV5+uWOtbUfnl/fUHqoLIKVUGRH3AVc0MWdtf2ji/Y8HugIzU0oVm56QUnoxIlaRGxqsSVJKEyPiDmAUuV4shwN7APsCPwM+FRGnpZSqmnrNfFFkCrnh0n6cUrq91r7tyQ0T9mpK6fU68qyLiOfJDdM2kNywa5IkSVKHZnFFkiRJUqtKKZ3XzFOWpJQq69he3Suk9qT2O+WXi+q5Vl3Fm6aoawiuuu6/R355SURc0sD1Sppz83xPmV/kH0TE3sBXgYuAU4Ax5OalaarvAp8lVzT66ib7ds8v942IRMM+hsUVSZIkyeKKJEmSpILT2Bf8zTknNivAJr1gGlA9RNcLwEubc6+mSCnNA74YEX2A08gVSppUXImIc8jNlTIPOKOOwlX1a1hC3T12avtHk0NLkiRJ7ZjFFUmSJEnFbEl+uVs9+3dt5ftX93CZlVK6vJXvBbnh0k4j14OkURFxBPBL4J/A51JK/6zjsOrXsHQzehlJkiRJHZKTEUqSJEkqZs/ml6dFxEa9VPLzjHy+le//OFAJnLS5E83XtulrqMOA/PLdJlxrZ+ABcj+qO7Ou+VQAUkrvkBvq68CI6N/0tJIkSVLHZXFFkiRJUjH7E/AmucneL9tk31XAnq1585TSYmASuYnefx0RH+lREhFHR8SIJl7ywYgYHxHb1nGd4cDF+af3NXSRiCgBfgP0A76aUnqkkft+m9zwYPdGxMfruN6AiLigCfklSZKkDsFhwSRJkiS1qoiY1MDuhSmlazb32imlyog4H3gU+GFEnEWuF8Z+5AouvyQ3CfwHm3uPJvgyuSLOGHI9WP5KrmdJP2AvYGfgJ8Dvm3CtXYH/IfdaXgDKgK7AIHKvB+DWlNKDjVxnFPAJYDVwcD3/DV5LKX0XIKV0Z0QcAHwN+Gv+3guArclNeD8IeBG4vQmvQZIkSWr3LK5IkiRJam3jGtj3IrDZxRWAlNLTEXEscANwNLleJLOB44Fh+cNabSL2lFJ5RJxI7nWeAxwIHAEsA94iV1iZ2sTLjSI3Wf2ngL3JFYm65a91P3BHSum3TbhO9RBlPam//Z8AvlvrdfyfiHgEuBQ4CjgIWEluTpaJwN1NfA2SJElSuxcppawzSJIkSVKriIiHgc8AR6aUnss6jyRJkqT2wTlXJEmSJBW1iOgTEbtvsi0iYjy5wsqb5HqySJIkSVKLcFgwSZIkScVub+DZiHgJmE9uSKyPk5sHZS1wUbLLviRJkqQWZM8VSZIkScVuPvALoDu5OVaG59enAEeklGZlF02SJElSe+ScK5IkSZIkSZIkSc1gzxVJkiRJkiRJkqRmsLgiSZIkSZIkSZLUDBZXJEmSJEmSJEmSmsHiiiRJkiRJkiRJUjNYXJEkSZIkSZIkSWoGiyuSJEmSJEmSJEnNYHFFkiRJkiRJkiSpGSyuSJIkSZIkSZIkNYPFFUmSJEmSJEmSpGawuCJJkiRJkiRJktQMFlckSZIkSZIkSZKaweKKJEmSJEmSJElSM1hckSRJkiRJkiRJagaLK5IkSZIkSZIkSc3w/wF75k5bpb7bygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1950x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HZ2uKcGXlXtb/fu8K2u9drh/DqHdR+yfHy85x4cKFLaxkzVh2jtaX0czPcvuytrSTTvp57rSf5U5a26Tz1reT+Flub9aXduFnuX21+/wYeW4LthKllK8kOTLJP9VaPzFI+yuS/C7JrbXWrYZwvhXd9+tV48ePH7PFFlusVr3tYMmSJfnf//3fVpcBAAAAANCWXvWqV2XMGI9jT5LbbrstTz755OO11vWbjrVzZeWW3nhuRQnUcN2YbsmTTz65cM6cOfcM0/mAlVuaZN7W0ioAVo3fYcBo5/cYMJr5HQaMZlskyf/+7//6HfYnL02yStvShCsr93j/+4puQLf0CVY9QznZqjwUBxh+S3eR+f9JYDTyOwwY7fweA0Yzv8OA0czvsOFl78/K3d3/PnUF7VOX6wcAAAAAALQ54crK/ar/fbsVtC89/us1UAsAAAAAALAWEK6s3E+TLEiyRSll20Ha39H/fuWaKwkAAAAAAGgl4cpK1FqfSvLF/q9fLKUMPHullPLRJK9NckOt9b9bUR8AAAAAALDmeaD98zs5yZuT7JTkd6WUnyTZLMn2SR5O8t4W1gYAAAAAAKxhpdba6hrWeqWUiUn+X5KDkrw0ySNJrknyyVrrPa2sDQAAAAAAWLOEKwAAAAAAAA145goAAAAAAEADwhUAAAAAAIAGhCsAAAAAAAANCFcAAAAAAAAaEK4AAAAAAAA0IFwBAAAAAABoQLgCtL1SSlcp5W2llHNKKb8upTxWSllYSvlVKeVTpZTuVtcI0EQp5YWllD+UUmop5X9bXQ/AUJRSNiqlfLaUcmspZVEpZX4pZVYp5dRW1wbwfEopO5RSLi2lzCulPN3/O+y6Uso7Wl0bQClleinlxFLKZaWU+/r/W/GJIYx7TynlxlJKT//vte+WUnZaEzW3g1JrbXUNACOqlPK+JF/t/zo7yZwk6yfZKcl6Sf43yW611j+0pkKAZkopM5O8J0lJ8tta66taWxHAypVSdkzy3SQvSN+/i/0mff8eNi3J1FrruNZVB7BypZQDknwzfX+kfFOS25JskmTn/mP/Ums9sXUVAp2ulPKdJH+z3OEna60TVjLmzCTHJFmU5PtJJiSZkb7/zjyg1vrtkam2fQhXgLZXSnlPkh2SfLbW+rtljm+c5Kok2yb5Rq31oBaVCDBkpZQZSa5N8pUkfxvhCrCWK6Vskr4/cBmf5ODl/0O9lPIXtdYbW1IcwPMopYxLcn+SKUneWWv91jJtOyb5Yfp+v72y1npba6oEOl0p5WNJupL8d/9rXlYSrpRS3pTkuiQPJ9lx6f9e1v977f9v787D5SzLO45/f8GwRZBVCxcgIFSoilRAlhbEBWkjFKlbcCmxqAhXFZFFrdpSsUVoxaWtIiJEpVQBcasWC2LUCC5UCaCCa6SIQRaDEGig5u4f73suhsmck5kkcyYn5/u5rrnuzPs8z7z38EcyzD3Pc8+nKbjsVFW/GX72U5fFFUnTWvuPxtXAMmDTqnpwxClJ0riSbARcDzwIPB/4ERZXJK3lknwMeAXwuqr6l1HnI0mDSPJk4Abgpqravcf4Z2h+Lf6Sqrp4ktOTpJ6SFBMXV74AzAZOrKr3do29D3g9cHJVvXvYuU5l9lyRNN0tbOMGwJajTESS+vC3wBOA44CHRpyLJK1Uks2BFwP3AOeNOB1JWhXL+px391CzkKQ1JMnY8V8Al/aYMnbt8MnJaOryXFtJ093ObXwIPwxLWosl2QM4Cbigqr6WZMcRpyRJ/fgjmh+xXAk81DZ+/mNgJk3fu4ur6vYR5idJK/Oz9rFbkhd37k5pT0I4FPg58LUR5SdJg9qN5vPZHVV1a4/x77Zxj8lLaWqyuCJpujuhjZdXVb+/SJKkSZVkBvBhYAlw6mizkaSBPKmNtwNfB/bvGj8jySur6pLJTUuS+lNVv0syF/g88Mkkp9A0tN+Gplj8beAVHjEtaQrZoY29CitU1dIkS4DNk2xSVfdOWmZTjMeCSZq2kswGjqHZtfL2EacjSRN5HfB04JSqumvUyUjSADZv41/Q/PrxGJqm0DsBZwOzgAvb3XmStFaqqq8Dz6DZobI38BLgIGApzc6820aXnSQN7NFtvH+COUu75qoHiyuSpqUkuwMXAqH5snLhSpZI0kgk2R54J/DVqpo34nQkaVDrtfFRwBur6vyqurOqFlXVSTRneq+Pu/IkrcWSHAV8C7gF2Jfmy8bfB/4deBtwZZKZo8tQkgaSNlYfczQBiyuSpp0k2wGX0/yS8uyqet+IU5KkiXyA5ovH40adiCStgrFjJJYDH+0xfn4bD56UbCRpQEl2pfn76w7geVX17apaWlU/rqpjaY4L2x945SjzlKQBjH0+mzXBnI3beN+Qc5nS7LkiaVpJshVwBc35khcAJ482I0laqcNoeq18MHnEj4c2bOMOSeaPza0qP/xKWpssauPicfrbjY0/dlKykaTBzQFm0vTpXNpj/GLgcJoi8bmTmJckrapb2rhdr8Eks4DNgCX2W5mYxRVJ00aSTYD/BHYDLgNeXVUTbYGUpLXFZjTnfPeyUceYn+0krW2+18bNk6THZ68t22hhWNLaauzLx9+OMz52fYtJyEWS1oSbgWXA1km2q6ruxvZPa+P1k5vW1OOxYJKmhSQbAJ+laT74JeCoqvrdaLOSpJWrqvR60DSDBri54/qSEaYqSSuoqhtoGkBvRNOnoNvBbfzuZOUkSQNa3Ma9xxnfp42Lhp+KJK2+qnoAuKp9+sIeU8au/cfkZDR1WVyRtM5Lsh5No8FnAl8H/ryqHhxtVpIkSdPGmW18f3tEKwBJ9gJOap+eM+lZSVJ/PtvGg5I8ogdekv2AE9unl05qVpK0es5u49va3lIAJNkfOJZmV95HRpHYVBJPxJG0rktyAvDe9umnGX8798lVdeekJCVJqynJjjS/Br+5qnYbcTqSNK4kM4BPAC8C7gauBh4NHACsD3y4ql4zugwlaWJJ/pGH+3V+H/gBsC1NI/sZwLltc3tJGokkzwPe3nFpX6CAb3dcO72qvtCx5r3ACcD9NP2J1wcOofl77cVV9akhpz3leS63pOlg844/HznBvNMAiyuSJElrUFUtTzIHmA+8CngWzf/sXwucU1UfH2F6krRSVXVKkquB1wJ7AU8E7gW+CpxXVReNMj9JArZmxSNY03Vt687BqnpDkuuAv6IpqjwEfBl4Z1UtGF6q6w53rkiSJEmSJEmSJA3AniuSJEmSJEmSJEkDsLgiSZIkSZIkSZI0AIsrkiRJkiRJkiRJA7C4IkmSJEmSJEmSNACLK5IkSZIkSZIkSQOwuCJJkiRJkiRJkjQAiyuSJEmSJEmSJEkDsLgiSZIkSZIkSZI0AIsrkiRJkiRJkiRJA7C4IkmSJEmSJEmSNACLK5IkSZIkSZIkSQOwuCJJkiRpUiSZleTEJF9JcnuSB5P8Jsk1Sd6RZIcJ1s5OcmmSXyZZluSOJPOTHJ9k5jhrHpfkmCSfTvKjJA8kWZLkq0mOTpIB85+bpLoeDya5LcllSQ4a9L/Jqkoyr73/wZN1T0mSJEkPS1WNOgdJkiRJ67gk+wGXAdsA9wPfBG4HHgPsA2wNLAMOq6orO9ZtCHwceCFQwLeARcAWwIHARsAPgNlV9Yuue14IvAx4CPgOcAuwHXAAzQ/NLgXmVNXv+nwPc4ELgJ8CC9rLs4A9gV3a/F5VVef383orudci4PFV1bMAlGQecDTwzKqav7r3kyRJkjSYR406AUmSJEnrtiR7AFfRFELOBE6vqqUd4zOA5wNn0RQ/Ol0EHAlcT1MI+WHHus2Ac4CXAPOT7FlV93SsvQt4E3BeVd3dsW4f4Eqags0xwLkDvqUFVTW3K/+zgJOAs5N8svP9DclbgHfRFIwkSZIkTTKPBZMkSZI0NO3RWxfSFFZOq6o3dxceqmp5VV0G7AVc27F2Dk1hZTHw7M7CSrtuCfBS4ApgR5piQ+f4CVV1Vmdhpb3+nY65R63ue6yq5cBbgXtoduLst7qv2cc9f1VVN1XV/cO+lyRJkqQVWVyRJEmSNEyHAk8BbgX+fqKJVXVPVd3YcemNbXxHVd05zprlwAnt07lJtugzr4Vt3LbP+ROqqmXAT9qnj+0cS7JNklPbXi+/bPu0LG77tOzTNffgJAU8vn3e2d9lUce8nj1Xkixq15PkVUmub3vNLE7yoXa3zwqS7Jzk4iR3J7kvyYIkh47l0x5DJkmSJKllcUWSJEnSMD2vjZdU1f/1uyjJVjS9WAr4xERz2x0t1wEbAs/s8xY7t3Fxvzn1YZM2/rrr+hE0x6FtC9wAfAa4jWZXzjeSPLdj7mLgo8DY7p6Pdjwu7TeRJGcB/wr8FrgcCPAa4HPtbqLOubvS9LJ5UZv754H1gC8CL+j3npIkSdJ0Ys8VSZIkScP0h2387oDr9mzjz6rqN33M/+92zZ7ApyaamGQmcHz79LMD5jXea+4KPAFYAnyza/gbwFOr6vquNYcCnwM+kGTXatxEswPnYGBWZ2+XAb0c2LeqrmvvtRVwDXAgTQHqqo65HwS2Av4ZeEO7G4gkrwA+tor3lyRJktZp7lyRJEmSNExbtvGOVVzXvQtkPGOvv1Ufc08Hdgd+DpwzYF6PkGRWkmfwcEHnuB49ZW7oLqy0178EXEJTlHny6uTRw9vHCivtve6kKaIAHDR2PckuwLOBu4G3jBVW2jUfB762hvOSJEmS1gnuXJEkSZI0TFn5lAnX9bt+bF5NOCk5CjgV+F/gpavYEP7oJEd3XVsG/GlVXTHOfTcA/gR4OrA1sH479JQ27kpzZNia8l89rv2ojdt0XDugjV/sLgq1LqGjGCNJkiSpYXFFkiRJ0jDdCTyRpqAw6Droag4/gbHXv2u8CUkOAeYBy4Gjqqr7+K5+/RRY0P55S5riw6bAvCT7VtWtXfd9Cs3xXztO8JqbTDC2Km7tce2+Nm7QcW3bNv7POK9zyxrLSJIkSVqHeCyYJEmSpGG6ro1PG3DdwjbulGTzPubv1XW/R0iyL/BpYCbw6qr6zID5dFpQVXPbx+HA42n6rGwLfKjrvgEupimsnEPTE2ZTYEZVBThjbOpq5LOCqppwB0+vJeNcX6N5SZIkSesKiyuSJEmShukLbXxRkr53zlfVHTRN6gPMmWhukj8Ankpz1Nf8HuNPAr4IzAJOqqoL+s2jz1yXAC+lORpsdpLOY7R2ax/XVtVxVbWwqu7tKH7svCZzWQW/auMO44xvP1mJSJIkSVOJxRVJkiRJw3Q58H1gO+CtE01MsmlbCBlzdhv/JknPRvVJZgDvaZ9+rKru6hrfkab/yBbAaVX1Hoagqn5OszMF4G0dQ2O7blY4pqvdkXPIOC/5YDtn2Ec5X93G2Uk27jH+wiHfX5IkSZqSLK5IkiRJGpp2h8bLaXaVnJbkjCSzOuek8WfAtcA+HWsvoulV8nvAlUl271q3GfBvwHNpeoO8qWv8scAVNMd1vbuq/m7NvrsVvAt4ADgkydj7+AlNj5dnJdm1I7cNaYoxW4zzWre18YlDyhWAqvox8JU2j39ojzEby/FlwDOGeX9JkiRpqrKhvSRJkqShqqrrkjwH+BTwZuD1Sa4BbgceA+wNPI6mANPdWH0OcBHwfODGJN8CFtEUAw4ENgZuAma3x3N1+hCwC3A/sFWSeT3Su7OqTl69d9ioqsVJzgFOBP4aOLKqfp3kI8CrgYVJrqIpwBwIrAfMA+b2eLnP0RQ2vpzkK8DSNtc3r4lcuxxHs4PlBODQJN+j6RGzH/AB4HjanTSSJEmSGhZXJEmSJA1dVX0jyS7AscDhwB40R2bdB9xMs4vjvKq6tWvdA8CRSQ4D/pLmC/+9gXtperJcDJxbVb2+/B87kmtj4OhxUvsFsEaKK60zad7jEUmeXFU30hQvbgKOAZ4N3ANcSXNM2ivHeZ330+R/FPACYGab6xovrlTVzUn2Bc4AngMcASwEDmtzOB64a/xXkCRJkqafPNxHUZIkSZKkhyX5IPBaYE5VfXLU+UiSJElrC4srkiRJkjSNtf1fdqqqH3ZdfwHwCZojybarqvtGkZ8kSZK0NvJYMEmSJEma3jYDfpDkJuDHwEPA7u1jOXC8hRVJkiTpkdy5IkmSJEnTWJKNgNOBQ4DtgU2Au4FrgH+qqgUjTE+SJElaK1lckSRJkiRJkiRJGsCMUScgSZIkSZIkSZI0lVhckSRJkiRJkiRJGoDFFUmSJEmSJEmSpAFYXJEkSZIkSZIkSRqAxRVJkiRJkiRJkqQBWFyRJEmSJEmSJEkagMUVSZIkSZIkSZKkAVhckSRJkiRJkiRJGoDFFUmSJEmSJEmSpAFYXJEkSZIkSZIkSRqAxRVJkiRJkiRJkqQBWFyRJEmSJEmSJEkagMUVSZIkSZIkSZKkAVhckSRJkiRJkiRJGsD/A4wZF4L3/tMhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1950x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1950x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "column_set = ['Cylinders','Engine Size','CO2 Rating','Fuel Consumption']\n",
    "color_set = ['pink','brown','Thistle', 'SaddleBrown']\n",
    "for colors,col in zip(color_set,column_set):\n",
    "    plt.figure(figsize=(13,6), dpi=150)\n",
    "    chart1=sns.histplot(data=df, x=col, color=colors)\n",
    "    chart1.bar_label(chart1.containers[0],size=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcb4ea6",
   "metadata": {
    "id": "1fcb4ea6"
   },
   "source": [
    "---\n",
    "# 4 Bivariate analysis\n",
    "<span id=\"bivariate\"></span>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a504ee01",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7341c387",
   "metadata": {
    "id": "7341c387"
   },
   "source": [
    "## 4.1 Comparison Between all columns\n",
    "<span id=\"compare\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3ddb9eb4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.045855Z",
     "start_time": "2023-03-19T19:52:06.507284Z"
    },
    "id": "3ddb9eb4",
    "outputId": "44d02e89-e105-43f3-ed97-b0e35d549646",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x283285b8f40>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1950x900 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 774.5x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,6), dpi=150)\n",
    "sns.pairplot(df, hue='Fuel Type',diag_kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8cecf81",
   "metadata": {
    "id": "a8cecf81"
   },
   "source": [
    "## 4.2 Comparison between \"No. of cylinders\" & \"Fuel consumption\"\n",
    "<span id=\"comparecylinder\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fc46b05c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.264193Z",
     "start_time": "2023-03-19T19:52:11.047848Z"
    },
    "id": "fc46b05c",
    "outputId": "981f8d55-e908-4972-bb42-a708d3147c67",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1950x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,6), dpi=150)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Cylinders vs Consumption',size=20)\n",
    "chart1=sns.barplot( data=df, x=\"Cylinders\", y=\"Fuel Consumption\",palette='mako_r', ci=None)\n",
    "plt.xlabel('Cylinders',size=20)\n",
    "plt.ylabel('Fuel Consumption',size=20)\n",
    "chart1.bar_label(chart1.containers[0],size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e5ce80",
   "metadata": {
    "id": "62e5ce80"
   },
   "source": [
    "## 4.4 Heatmap showing \"Pearson correlation\" :\n",
    "<span id=\"heatcorr\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "846b8c37",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.497934Z",
     "start_time": "2023-03-19T19:52:11.266190Z"
    },
    "id": "846b8c37",
    "outputId": "44070421-7737-4bc7-c06a-e28195681831",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1950x900 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,6), dpi=150)\n",
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d77a2ee",
   "metadata": {
    "id": "6d77a2ee"
   },
   "source": [
    "# 5 Performing Chi Square analysis:-\n",
    "<span id=\"chi\"></span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92011c67",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb047c7",
   "metadata": {
    "id": "fcb047c7"
   },
   "source": [
    "## 5.1 Transmission and Fuel Type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8865c278",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.528906Z",
     "start_time": "2023-03-19T19:52:11.503916Z"
    },
    "id": "8865c278"
   },
   "outputs": [],
   "source": [
    "# from scipy.stats import chi2_contingency\n",
    "fuel_type= pd.crosstab(df['Transmission'],df['Fuel Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c0fa9d7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.544361Z",
     "start_time": "2023-03-19T19:52:11.529903Z"
    },
    "id": "c0fa9d7f",
    "outputId": "862b3815-9e70-44cd-8f78-8bec1407c0df",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Fuel Type</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>X</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>141</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>150</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AV</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Fuel Type      D  E    X    Z\n",
       "Transmission                 \n",
       "A             28  6  141   87\n",
       "AM             0  0   25  107\n",
       "AS             0  8  150  207\n",
       "AV             0  0   87    9\n",
       "M              0  0   41   49"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2c015328",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.559885Z",
     "start_time": "2023-03-19T19:52:11.547359Z"
    },
    "id": "2c015328"
   },
   "outputs": [],
   "source": [
    "Chi_square_statistic,p,dof,expec = chi2_contingency(fuel_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f4a79d24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.575000Z",
     "start_time": "2023-03-19T19:52:11.560882Z"
    },
    "id": "f4a79d24",
    "outputId": "66177dee-2d73-4deb-d7b3-a6e1a69abbdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_value is 3.762904200058271e-41\n",
      "Dependent\n",
      "dof is 12\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "print(\"p_value is \" + str(p))\n",
    "if p <= alpha:\n",
    "    print('Dependent')\n",
    "    print('dof is ' +str(dof))\n",
    "else:\n",
    "    print('Independent')\n",
    "    print('dof is ' +str(dof))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f794d978",
   "metadata": {
    "id": "f794d978"
   },
   "source": [
    "## 5.2 Transmission and Vehicle Class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "58b655b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.606226Z",
     "start_time": "2023-03-19T19:52:11.576997Z"
    },
    "id": "58b655b3"
   },
   "outputs": [],
   "source": [
    "Class=pd.crosstab(df['Transmission'],df['Vehicle Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "68f4ada1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.621234Z",
     "start_time": "2023-03-19T19:52:11.607222Z"
    },
    "id": "68f4ada1",
    "outputId": "f53ede67-a552-4242-fd7a-c0c1b37784d4",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Compact</th>\n",
       "      <th>Full-size</th>\n",
       "      <th>Mid-size</th>\n",
       "      <th>Minicompact</th>\n",
       "      <th>Minivan</th>\n",
       "      <th>Pickup truck: Small</th>\n",
       "      <th>Pickup truck: Standard</th>\n",
       "      <th>SUV: Small</th>\n",
       "      <th>SUV: Standard</th>\n",
       "      <th>Special purpose vehicle</th>\n",
       "      <th>Station wagon: Mid-size</th>\n",
       "      <th>Station wagon: Small</th>\n",
       "      <th>Subcompact</th>\n",
       "      <th>Two-seater</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>74</td>\n",
       "      <td>47</td>\n",
       "      <td>67</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>91</td>\n",
       "      <td>69</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AV</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Vehicle Class  Compact  Full-size  Mid-size  Minicompact  Minivan  \\\n",
       "Transmission                                                        \n",
       "A                    3         16        18            3        3   \n",
       "AM                   8         18        12           25        0   \n",
       "AS                  34         19        46            4        2   \n",
       "AV                  11          9        22            0        2   \n",
       "M                   13          2        19           16        0   \n",
       "\n",
       "Vehicle Class  Pickup truck: Small  Pickup truck: Standard  SUV: Small  \\\n",
       "Transmission                                                             \n",
       "A                               15                      74          47   \n",
       "AM                               1                       0          16   \n",
       "AS                               1                      38          91   \n",
       "AV                               1                       0          36   \n",
       "M                                2                       1           7   \n",
       "\n",
       "Vehicle Class  SUV: Standard  Special purpose vehicle  \\\n",
       "Transmission                                            \n",
       "A                         67                        4   \n",
       "AM                         0                        0   \n",
       "AS                        69                        8   \n",
       "AV                         5                        0   \n",
       "M                          0                        0   \n",
       "\n",
       "Vehicle Class  Station wagon: Mid-size  Station wagon: Small  Subcompact  \\\n",
       "Transmission                                                               \n",
       "A                                    1                     0          10   \n",
       "AM                                   2                     8          16   \n",
       "AS                                   4                     2          34   \n",
       "AV                                   1                     7           2   \n",
       "M                                    0                     2          17   \n",
       "\n",
       "Vehicle Class  Two-seater  \n",
       "Transmission               \n",
       "A                       1  \n",
       "AM                     26  \n",
       "AS                     13  \n",
       "AV                      0  \n",
       "M                      11  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4aba7908",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.636945Z",
     "start_time": "2023-03-19T19:52:11.623412Z"
    },
    "id": "4aba7908",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Chi_square_statistic,p,dof,expec = chi2_contingency(Class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "979ebd7e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.652297Z",
     "start_time": "2023-03-19T19:52:11.637943Z"
    },
    "id": "979ebd7e",
    "outputId": "21d3e8b4-9ddc-4ea8-e984-e31d154ee5b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_value is 3.356375805194314e-79\n",
      "Dependent\n",
      "dof is 52\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "print(\"p_value is \" + str(p))\n",
    "if p <= alpha:\n",
    "    print('Dependent')\n",
    "    print('dof is ' +str(dof))\n",
    "else:\n",
    "    print('Independent')\n",
    "    print('dof is ' +str(dof))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f477fd",
   "metadata": {
    "id": "21f477fd"
   },
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "992323c3",
   "metadata": {
    "id": "992323c3"
   },
   "source": [
    "-----\n",
    "<center>\n",
    "<h1 style=\"font-size: 40px; font-family: serif;\">5. Data Preprocessing</h1>\n",
    "</center>\n",
    "<span id=\"prepro\"></span>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9842562b",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e17c7a",
   "metadata": {
    "id": "e4e17c7a"
   },
   "source": [
    "\n",
    "# 5.1 Outlier analysis\n",
    "<span id=\"outlier\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7e62646b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:11.962364Z",
     "start_time": "2023-03-19T19:52:11.654299Z"
    },
    "id": "7e62646b",
    "outputId": "f03e77da-49fb-43cb-cedf-82bdc097f309",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dApjBhREx4/p2soIHcmK7HhG1qnMAc8ESDQBkioIHgExR8EA5o1UHAOaKNXgAyBQzeADIFAUPAJmi4AEgUxQ8AGSKggeATP0/nPVjYwqGrHMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0UEXEUGa2ms4hzYZbPZJUGItfkgpj8UtvzmDTAaTZco9fkgrjil+SCmPxS1JhLH5JKozFL0mFsfglqTD/DzkuVohgddKHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    if df[col].dtypes!= object:\n",
    "        sns.boxplot(y=col,data=df)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1a057310",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:12.270601Z",
     "start_time": "2023-03-19T19:52:11.963380Z"
    },
    "id": "1a057310",
    "outputId": "f3e80202-c0bc-4ae5-8cd3-c803fb388753",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    if df[col].dtypes!= object:\n",
    "        percentile_25=df[col].quantile(0.25)\n",
    "        percentile_75=df[col].quantile(0.75)\n",
    "        IQR=percentile_75-percentile_25\n",
    "        upper_limit=percentile_75+(1.5*IQR)\n",
    "        lower_limit=percentile_25-(1.5*IQR)\n",
    "        df=df[df[col]<=upper_limit]\n",
    "        df=df[df[col]>=lower_limit]\n",
    "        plt.figure()\n",
    "        sns.boxplot(y=col,data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3f2026c2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:12.286079Z",
     "start_time": "2023-03-19T19:52:12.271602Z"
    },
    "id": "3f2026c2",
    "outputId": "6d2c8ab6-4e56-4429-dd31-1268303df391"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782, 7)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b523b4d8",
   "metadata": {
    "id": "b523b4d8"
   },
   "source": [
    "## 5.3 Ordinal encoding\n",
    "<span id=\"ordinal\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "90813a8f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:12.301087Z",
     "start_time": "2023-03-19T19:52:12.287077Z"
    },
    "id": "90813a8f"
   },
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder\n",
    "\n",
    "order = ['AV', 'AM', 'M', 'AS', 'A']\n",
    "\n",
    "od = OrdinalEncoder(categories = [order])\n",
    "\n",
    "df[\"Transmission_X\"] = od.fit_transform(df[[\"Transmission\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ee71b250",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:12.315770Z",
     "start_time": "2023-03-19T19:52:12.303258Z"
    },
    "id": "ee71b250"
   },
   "outputs": [],
   "source": [
    "order = ['Two-seater','Minicompact','Compact','Subcompact','Mid-size','Full-size','SUV: Small','SUV: Standard','Minivan',\\\n",
    "         'Station wagon: Small','Station wagon: Mid-size', 'Pickup truck: Small', 'Special purpose vehicle',\\\n",
    "         'Pickup truck: Standard']\n",
    "\n",
    "od = OrdinalEncoder(categories = [order])\n",
    "\n",
    "df[\"Vehicle Class_X\"] = od.fit_transform(df[[\"Vehicle Class\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9687f108",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:52:12.330808Z",
     "start_time": "2023-03-19T19:52:12.317762Z"
    }
   },
   "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>Vehicle Class</th>\n",
       "      <th>Engine Size</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption</th>\n",
       "      <th>CO2 Rating</th>\n",
       "      <th>Transmission_X</th>\n",
       "      <th>Vehicle Class_X</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Compact</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>AM</td>\n",
       "      <td>Z</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SUV: Small</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Compact</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Vehicle Class  Engine Size  Cylinders Transmission Fuel Type  \\\n",
       "0       Compact          2.4          4           AM         Z   \n",
       "1    SUV: Small          3.5          6           AS         Z   \n",
       "2    SUV: Small          2.0          4           AS         Z   \n",
       "3    SUV: Small          2.0          4           AS         Z   \n",
       "4       Compact          2.0          4           AS         Z   \n",
       "\n",
       "   Fuel Consumption  CO2 Rating  Transmission_X  Vehicle Class_X  \n",
       "0               8.6         6.0             1.0              2.0  \n",
       "1              11.2         4.0             3.0              6.0  \n",
       "2               9.9         5.0             3.0              6.0  \n",
       "3              10.3         5.0             3.0              6.0  \n",
       "4               9.8         5.0             3.0              2.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46c04de3",
   "metadata": {
    "id": "46c04de3"
   },
   "source": [
    "## 5.4 One Hot encoding on categorical columns\n",
    "<span id=\"onehot\"></span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f5a85d",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c69c3f94",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:53:40.606628Z",
     "start_time": "2023-03-19T19:53:40.587636Z"
    }
   },
   "outputs": [],
   "source": [
    "new_df = df['Fuel Type'].str.get_dummies()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4e0d4b53",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:53:41.414537Z",
     "start_time": "2023-03-19T19:53:41.397002Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>D</th>\n",
       "      <th>E</th>\n",
       "      <th>X</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>782 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     D  E  X  Z\n",
       "0    0  0  0  1\n",
       "1    0  0  0  1\n",
       "2    0  0  0  1\n",
       "3    0  0  0  1\n",
       "4    0  0  0  1\n",
       "..  .. .. .. ..\n",
       "940  0  0  0  1\n",
       "941  0  0  0  1\n",
       "942  0  0  0  1\n",
       "943  0  0  0  1\n",
       "944  0  0  0  1\n",
       "\n",
       "[782 rows x 4 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "53fc3756",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:53:42.858050Z",
     "start_time": "2023-03-19T19:53:42.851064Z"
    }
   },
   "outputs": [],
   "source": [
    "df= pd.concat([df,new_df], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a58afb6c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:54:31.526264Z",
     "start_time": "2023-03-19T19:54:31.509068Z"
    }
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "  Vehicle Class  Engine Size  Cylinders Transmission Fuel Type  \\\n",
       "0       Compact          2.4          4           AM         Z   \n",
       "1    SUV: Small          3.5          6           AS         Z   \n",
       "2    SUV: Small          2.0          4           AS         Z   \n",
       "3    SUV: Small          2.0          4           AS         Z   \n",
       "4       Compact          2.0          4           AS         Z   \n",
       "\n",
       "   Fuel Consumption  CO2 Rating  Transmission_X  Vehicle Class_X  D  E  X  Z  \n",
       "0               8.6         6.0             1.0              2.0  0  0  0  1  \n",
       "1              11.2         4.0             3.0              6.0  0  0  0  1  \n",
       "2               9.9         5.0             3.0              6.0  0  0  0  1  \n",
       "3              10.3         5.0             3.0              6.0  0  0  0  1  \n",
       "4               9.8         5.0             3.0              2.0  0  0  0  1  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285a0dd1",
   "metadata": {
    "id": "285a0dd1"
   },
   "source": [
    "## 5.5 Split Into Train and Test \n",
    "<span id=\"traintest\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d0c64f1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:03.698976Z",
     "start_time": "2023-03-19T19:55:03.685013Z"
    },
    "id": "d0c64f1c"
   },
   "outputs": [],
   "source": [
    "x = df.drop([\"Fuel Type\",'Fuel Consumption',\"Vehicle Class\",\"Transmission\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "31456e2d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:04.056198Z",
     "start_time": "2023-03-19T19:55:04.037446Z"
    }
   },
   "outputs": [
    {
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   Engine Size  Cylinders  CO2 Rating  Transmission_X  Vehicle Class_X  D  E  \\\n",
       "0          2.4          4         6.0             1.0              2.0  0  0   \n",
       "1          3.5          6         4.0             3.0              6.0  0  0   \n",
       "2          2.0          4         5.0             3.0              6.0  0  0   \n",
       "3          2.0          4         5.0             3.0              6.0  0  0   \n",
       "4          2.0          4         5.0             3.0              2.0  0  0   \n",
       "\n",
       "   X  Z  \n",
       "0  0  1  \n",
       "1  0  1  \n",
       "2  0  1  \n",
       "3  0  1  \n",
       "4  0  1  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c6cec24c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:05.841784Z",
     "start_time": "2023-03-19T19:55:05.835747Z"
    }
   },
   "outputs": [],
   "source": [
    "y= df['Fuel Consumption']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6d9bbb6f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:06.122734Z",
     "start_time": "2023-03-19T19:55:06.113229Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "   Fuel Consumption\n",
       "0               8.6\n",
       "1              11.2\n",
       "2               9.9\n",
       "3              10.3\n",
       "4               9.8"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "eee3328c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:12.838983Z",
     "start_time": "2023-03-19T19:55:12.828448Z"
    },
    "id": "eee3328c"
   },
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x,y, test_size= 0.30 , random_state= 51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f1ba16ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:15.776066Z",
     "start_time": "2023-03-19T19:55:15.749293Z"
    },
    "id": "f1ba16ff",
    "outputId": "58911f4c-7453-46a9-8009-1493229108d3"
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   "outputs": [
    {
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       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>5.7</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>2.5</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>547 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Engine Size  Cylinders  CO2 Rating  Transmission_X  Vehicle Class_X  D  \\\n",
       "454          2.0          4         6.0             2.0              4.0  0   \n",
       "153          3.0          6         5.0             3.0              4.0  0   \n",
       "593          3.4          6         4.0             3.0              4.0  0   \n",
       "275          5.7          8         3.0             4.0              7.0  0   \n",
       "151          3.6          6         3.0             2.0              2.0  0   \n",
       "..           ...        ...         ...             ...              ... ..   \n",
       "631          2.0          4         6.0             3.0              2.0  0   \n",
       "834          5.7          8         3.0             4.0             13.0  0   \n",
       "872          2.5          4         6.0             3.0              4.0  0   \n",
       "584          3.5          6         5.0             3.0              4.0  0   \n",
       "66           4.0          8         4.0             1.0              3.0  0   \n",
       "\n",
       "     E  X  Z  \n",
       "454  0  1  0  \n",
       "153  0  0  1  \n",
       "593  0  0  1  \n",
       "275  0  1  0  \n",
       "151  0  0  1  \n",
       "..  .. .. ..  \n",
       "631  0  1  0  \n",
       "834  0  1  0  \n",
       "872  0  1  0  \n",
       "584  0  1  0  \n",
       "66   0  0  1  \n",
       "\n",
       "[547 rows x 9 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed14f7e6",
   "metadata": {
    "id": "ed14f7e6"
   },
   "source": [
    "## 5.6 Feature Scaling\n",
    "<span id=\"scailing\"></span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f30214",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c774adf3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:25.113062Z",
     "start_time": "2023-03-19T19:55:25.109093Z"
    },
    "id": "c774adf3"
   },
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "775442de",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:25.768187Z",
     "start_time": "2023-03-19T19:55:25.757705Z"
    },
    "id": "775442de"
   },
   "outputs": [],
   "source": [
    "xtrain = sc.fit_transform(xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "45ec62e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:27.660994Z",
     "start_time": "2023-03-19T19:55:27.644109Z"
    },
    "id": "45ec62e2",
    "outputId": "eff082d7-04e1-44e7-e5f8-4fc797476e90"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.94659202, -1.01098082,  1.58914929, ..., -0.07426107,\n",
       "         1.14795323, -1.05449946],\n",
       "       [-0.12731064,  0.29174358,  0.59342803, ..., -0.07426107,\n",
       "        -0.87111563,  0.94831722],\n",
       "       [ 0.20040192,  0.29174358, -0.40229323, ..., -0.07426107,\n",
       "        -0.87111563,  0.94831722],\n",
       "       ...,\n",
       "       [-0.53695133, -1.01098082,  1.58914929, ..., -0.07426107,\n",
       "         1.14795323, -1.05449946],\n",
       "       [ 0.28233006,  0.29174358,  0.59342803, ..., -0.07426107,\n",
       "         1.14795323, -1.05449946],\n",
       "       [ 0.69197075,  1.59446798, -0.40229323, ..., -0.07426107,\n",
       "        -0.87111563,  0.94831722]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0418858b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:30.716542Z",
     "start_time": "2023-03-19T19:55:30.710543Z"
    },
    "id": "0418858b"
   },
   "outputs": [],
   "source": [
    "xtest = sc.transform(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d323363f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:55:31.168561Z",
     "start_time": "2023-03-19T19:55:31.151579Z"
    },
    "id": "d323363f",
    "outputId": "6f3a038e-9826-4f99-d6cf-372e83b0e8e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.28233006,  0.29174358, -0.40229323, ..., -0.07426107,\n",
       "         1.14795323, -1.05449946],\n",
       "       [ 0.36425819,  0.29174358,  0.59342803, ..., -0.07426107,\n",
       "         1.14795323, -1.05449946],\n",
       "       [ 0.69197075,  1.59446798, -1.3980145 , ..., -0.07426107,\n",
       "        -0.87111563,  0.94831722],\n",
       "       ...,\n",
       "       [-0.94659202, -1.01098082,  0.59342803, ..., -0.07426107,\n",
       "         1.14795323, -1.05449946],\n",
       "       [ 0.69197075,  0.29174358, -0.40229323, ..., -0.07426107,\n",
       "        -0.87111563,  0.94831722],\n",
       "       [ 1.75703655,  1.59446798, -1.3980145 , ..., -0.07426107,\n",
       "         1.14795323, -1.05449946]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5644c47",
   "metadata": {
    "id": "f5644c47"
   },
   "source": [
    "-----\n",
    "<center>\n",
    "<h1 style=\"font-size: 40px; font-family: serif;\">6. Training Models</h1>\n",
    "</center>\n",
    "<span id=\"model\"></span>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48628f93",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "091f9a13",
   "metadata": {
    "id": "091f9a13"
   },
   "source": [
    "# 6.1 Linear Regression\n",
    "<span id=\"linear\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b425cd78",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:56:43.415896Z",
     "start_time": "2023-03-19T19:56:43.333925Z"
    },
    "id": "b425cd78"
   },
   "outputs": [],
   "source": [
    "#from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c114c350",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:57:43.805297Z",
     "start_time": "2023-03-19T19:57:43.764655Z"
    },
    "id": "c114c350",
    "outputId": "3db5ff46-b9c8-43ef-e0b9-673f4fd0e333"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a817ee71",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:57:46.046058Z",
     "start_time": "2023-03-19T19:57:46.036916Z"
    },
    "id": "a817ee71",
    "outputId": "ce8ce4a3-5acc-4c92-b3ae-33d8438f45fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.109872029250457"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa95b28",
   "metadata": {
    "id": "faa95b28"
   },
   "source": [
    "**Checking if model is overfitted or underfitted**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8cb4eef7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:58:11.643171Z",
     "start_time": "2023-03-19T19:58:11.628164Z"
    },
    "id": "8cb4eef7",
    "outputId": "d379851a-35f0-41ad-f67a-b5871756b6b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score =  0.927142068087607\n",
      "testing score =  0.9179795499509192\n"
     ]
    }
   ],
   "source": [
    "print(\"training score = \",lr.score(xtrain,ytrain))\n",
    "print(\"testing score = \",lr.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ec7ae667",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:58:35.825323Z",
     "start_time": "2023-03-19T19:58:35.819830Z"
    },
    "id": "ec7ae667"
   },
   "outputs": [],
   "source": [
    "ypred = lr.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "806fc17a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T19:58:36.261871Z",
     "start_time": "2023-03-19T19:58:36.250616Z"
    },
    "id": "806fc17a",
    "outputId": "3294f995-3b4f-4ecb-bb7d-11a09ad87dd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9179795499509192"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(ytest, ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "826d1ceb",
   "metadata": {
    "id": "826d1ceb"
   },
   "source": [
    "# 6.2 Decision Tree Regressor\n",
    "<span id=\"decision\"></span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c56bd1",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e970b7a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:15:36.712543Z",
     "start_time": "2023-03-19T20:15:36.708553Z"
    },
    "id": "0014feb2"
   },
   "outputs": [],
   "source": [
    "# from sklearn.tree import DecisionTreeRegressor\n",
    "dc=DecisionTreeRegressor(max_depth=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "50c4bc7d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:15:37.007283Z",
     "start_time": "2023-03-19T20:15:36.988299Z"
    },
    "id": "50c4bc7d",
    "outputId": "64b36d53-deab-409c-8612-0eda61b85677"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=4)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "11c7e078",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:15:37.221084Z",
     "start_time": "2023-03-19T20:15:37.207577Z"
    },
    "id": "11c7e078"
   },
   "outputs": [],
   "source": [
    "ypred=dc.predict(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c68f8448",
   "metadata": {
    "id": "c68f8448"
   },
   "source": [
    "**Checking if model is overfitted or underfitted**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "21e3acd0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:15:43.485210Z",
     "start_time": "2023-03-19T20:15:43.467143Z"
    },
    "id": "21e3acd0",
    "outputId": "711dd32b-582e-46d2-c291-69d92bfd1fe9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score =  0.9317518865681176\n",
      "testing score =  0.8680850127640054\n"
     ]
    }
   ],
   "source": [
    "print(\"training score = \",dc.score(xtrain,ytrain))\n",
    "print(\"testing score = \",dc.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7b2f5126",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:16:38.785345Z",
     "start_time": "2023-03-19T20:16:38.767432Z"
    },
    "id": "7b2f5126",
    "outputId": "2798003e-9ced-4962-8b08-9ffa44e6318e",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8680850127640054"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(ytest, ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f03bce7c",
   "metadata": {
    "id": "f03bce7c"
   },
   "source": [
    "**Visualising the tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "502c8f8f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:16:40.257426Z",
     "start_time": "2023-03-19T20:16:40.253168Z"
    },
    "id": "502c8f8f"
   },
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "4bf9fa3f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:16:40.705274Z",
     "start_time": "2023-03-19T20:16:40.679714Z"
    },
    "id": "4bf9fa3f"
   },
   "outputs": [],
   "source": [
    "new = df[[\"Vehicle Class\",\"Engine Size\",\"Cylinders\",\"Transmission\",\"Fuel Type\",\"CO2 Rating\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "06649b7e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:17:24.165352Z",
     "start_time": "2023-03-19T20:17:22.880164Z"
    },
    "id": "06649b7e",
    "outputId": "43285834-bd76-47a0-c860-240b5907b829",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(50,30))\n",
    "tree.plot_tree(dc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "420858ac",
   "metadata": {
    "id": "420858ac"
   },
   "source": [
    "# 6.3 Random Forest Regressor\n",
    "<span id=\"rf\"></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0608ad1b",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ac6ae5b4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:18:33.599488Z",
     "start_time": "2023-03-19T20:18:33.588697Z"
    },
    "id": "ac6ae5b4"
   },
   "outputs": [],
   "source": [
    "#from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5c403d4e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:18:41.505009Z",
     "start_time": "2023-03-19T20:18:41.500025Z"
    },
    "id": "5c403d4e"
   },
   "outputs": [],
   "source": [
    "no_of_decision_tree = [10,20,30,40,50,60,70,80,90,100]\n",
    "max_no_of_features = ['sqrt','log2']\n",
    "max_depth = [6,7,8,9,10,11,12,13,14,15]\n",
    "criterion_of_decision_tree = [\"squared_error\", \"poisson\"]\n",
    "min_sample_split=[2,3,4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "83a7fea9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:18:44.544277Z",
     "start_time": "2023-03-19T20:18:44.540032Z"
    },
    "id": "83a7fea9"
   },
   "outputs": [],
   "source": [
    "random_grid = {\n",
    "    'n_estimators' : no_of_decision_tree,\n",
    "    'max_features' : max_no_of_features,\n",
    "    'max_depth' : max_depth,\n",
    "    'criterion' : criterion_of_decision_tree,\n",
    "    'min_samples_split' : min_sample_split\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc6b8f8",
   "metadata": {
    "id": "5dc6b8f8"
   },
   "source": [
    "### Hyper Parameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c4c25019",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:18:56.845877Z",
     "start_time": "2023-03-19T20:18:49.934178Z"
    },
    "id": "c4c25019",
    "outputId": "0fbf54f9-1cc8-46f3-f297-9e40c9f2c0cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=25,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['squared_error',\n",
       "                                                      'poisson'],\n",
       "                                        'max_depth': [6, 7, 8, 9, 10, 11, 12,\n",
       "                                                      13, 14, 15],\n",
       "                                        'max_features': ['sqrt', 'log2'],\n",
       "                                        'min_samples_split': [2, 3, 4, 5, 6],\n",
       "                                        'n_estimators': [10, 20, 30, 40, 50, 60,\n",
       "                                                         70, 80, 90, 100]})"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "rscv = RandomizedSearchCV(estimator = rf , param_distributions = random_grid , n_iter = 25 , cv = 5 ,n_jobs=-1)\n",
    "rscv.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d60736a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:19:11.570371Z",
     "start_time": "2023-03-19T20:19:11.558314Z"
    },
    "id": "d60736a2",
    "outputId": "340bf448-534d-4da5-f978-47442c4c9c3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 50,\n",
       " 'min_samples_split': 2,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 10,\n",
       " 'criterion': 'squared_error'}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "680558cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:21:28.594096Z",
     "start_time": "2023-03-19T20:21:28.581589Z"
    },
    "id": "680558cc"
   },
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor(n_estimators = 50 , min_samples_split = 3, max_features =  'sqrt', max_depth = 11, criterion='squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f1c580e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:21:29.073733Z",
     "start_time": "2023-03-19T20:21:28.999084Z"
    },
    "id": "f1c580e5"
   },
   "outputs": [],
   "source": [
    "rf.fit(xtrain,ytrain)\n",
    "ypred = rf.predict(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4648a3ea",
   "metadata": {
    "id": "4648a3ea"
   },
   "source": [
    "**Checking if model is overfitted or underfitted**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "cb8292b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:21:30.040815Z",
     "start_time": "2023-03-19T20:21:30.019786Z"
    },
    "id": "cb8292b0",
    "outputId": "f7d615c1-2e5d-4e4c-aac5-512048bc021c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score =  0.9673641526924741\n",
      "testing score =  0.8939955106371547\n"
     ]
    }
   ],
   "source": [
    "print(\"training score = \",rf.score(xtrain,ytrain))\n",
    "print(\"testing score = \",rf.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "44256146",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-19T20:21:46.696343Z",
     "start_time": "2023-03-19T20:21:46.687825Z"
    },
    "id": "44256146",
    "outputId": "dcebb546-dbc5-4337-d6e5-3dbeb18283ff",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8939955106371547"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(ytest,ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc2cfbf3",
   "metadata": {
    "id": "cc2cfbf3"
   },
   "source": [
    "-----\n",
    "<center>\n",
    "<h1 style=\"font-size: 40px; font-family: serif;\">7. Model Deployment</h1>\n",
    "</center>\n",
    "<span id=\"deployment\"></span>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a9045b1",
   "metadata": {},
   "source": [
    "<a href=\"#report\"><button>REPORT CONTENT</button></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65dfa3f2",
   "metadata": {
    "id": "65dfa3f2"
   },
   "source": [
    "# 7.1 Pickling Model and Scaler object for Deployment\n",
    "<span id=\"pickling\"></span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d681873c",
   "metadata": {
    "id": "d681873c"
   },
   "outputs": [],
   "source": [
    "import pickle as pk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ce8b388c",
   "metadata": {
    "id": "ce8b388c"
   },
   "outputs": [],
   "source": [
    "filename = \"scaled_data.sav\"\n",
    "pk.dump(sc, open(filename, \"wb\")) #write binary = wb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1ca66715",
   "metadata": {
    "id": "1ca66715"
   },
   "outputs": [],
   "source": [
    "loaded_scaler = pk.load(open(\"scaled_data.sav\", \"rb\")) #read binary = rb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "82b1f143",
   "metadata": {
    "id": "82b1f143"
   },
   "outputs": [],
   "source": [
    "filename = \"trained_model_rf.sav\"\n",
    "pk.dump(rf, open(filename, \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "868b5f73",
   "metadata": {
    "id": "868b5f73"
   },
   "outputs": [],
   "source": [
    "loaded_model = pk.load(open(\"trained_model_rf.sav\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a75ccd1f",
   "metadata": {
    "id": "a75ccd1f"
   },
   "outputs": [],
   "source": [
    "inp = [\"Compact\",2.4,4,\"AM\",6.0,\"X\"]    # input example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "747a3cfe",
   "metadata": {
    "id": "747a3cfe",
    "outputId": "2d36fc47-18b2-4230-df9e-8f60a9065920",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Fuel Consumption L/100km is 11.54'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def input_converter(inp):\n",
    "    vcl = ['Two-seater','Minicompact','Compact','Subcompact','Mid-size','Full-size','SUV: Small','SUV: Standard','Minivan','Station wagon: Small','Station wagon: Mid-size','Pickup truck: Small','Special purpose vehicle','Pickup truck: Standard']\n",
    "    trans = ['AV','AM','M','AS','A']\n",
    "    fuel = [\"D\",\"E\",\"X\",\"Z\"]\n",
    "    lst = []\n",
    "    for i in range(9):\n",
    "        if(type(inp[i]) == str):\n",
    "            if(inp[i] in vcl):\n",
    "                lst.append(vcl.index(inp[i]))\n",
    "            elif(inp[i] in trans):\n",
    "                lst.append(trans.index(inp[i]))\n",
    "            elif(inp[i] in fuel):\n",
    "                if(fuel.index(inp[i]) == 0):\n",
    "                    lst.extend([1,0,0,0])\n",
    "                    break\n",
    "                elif(fuel.index(inp[i]) == 1):\n",
    "                    lst.extend([0,1,0,0])\n",
    "                    break\n",
    "                elif(fuel.index(inp[i]) == 2):\n",
    "                    lst.extend([0,0,1,0])\n",
    "                    break\n",
    "                elif(fuel.index(inp[i]) == 3):\n",
    "                    lst.extend([0,0,0,1])\n",
    "        else:\n",
    "            lst.append(inp[i])\n",
    "\n",
    "    arr = np.asarray(lst)\n",
    "    arr = arr.reshape(1, -1)\n",
    "    arr = loaded_scaler.transform(arr)\n",
    "    prediction = loaded_model.predict(arr)\n",
    "    \n",
    "    return(f\"The Fuel Consumption L/100km is {round(prediction[0],2)}\")\n",
    "\n",
    "\n",
    "\n",
    "input_converter(inp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554f3776",
   "metadata": {
    "id": "554f3776"
   },
   "source": [
    "-------\n",
    "-------\n",
    "-------\n",
    "<center>\n",
    "<h1 style=\"font-size: 30px; font-family: serif;\">Thank you</h1>\n",
    "</center>"
   ]
  }
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