Chapter 3 Script Part 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from sklearn import datasets, linear_model\n",
    "from scipy import linalg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('automobile.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6019 entries, 0 to 6018\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Price         6019 non-null   float64\n",
      " 1   Make          6019 non-null   object \n",
      " 2   Location      6019 non-null   object \n",
      " 3   Age           6019 non-null   int64  \n",
      " 4   Odometer      6019 non-null   int64  \n",
      " 5   FuelType      6019 non-null   object \n",
      " 6   Transmission  6019 non-null   object \n",
      " 7   OwnerType     6019 non-null   object \n",
      " 8   Mileage       6019 non-null   object \n",
      " 9   EngineCC      6019 non-null   object \n",
      " 10  PowerBhp      6019 non-null   object \n",
      "dtypes: float64(1), int64(2), object(8)\n",
      "memory usage: 517.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Make</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>FuelType</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>OwnerType</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineCC</th>\n",
       "      <th>PowerBhp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.75</td>\n",
       "      <td>marutiwagonr</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6</td>\n",
       "      <td>998</td>\n",
       "      <td>58.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.50</td>\n",
       "      <td>hyundaicreta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>5</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582</td>\n",
       "      <td>126.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.50</td>\n",
       "      <td>hondajazz</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>9</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2</td>\n",
       "      <td>1199</td>\n",
       "      <td>88.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.00</td>\n",
       "      <td>marutiertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>8</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248</td>\n",
       "      <td>88.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.74</td>\n",
       "      <td>audia4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>7</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2</td>\n",
       "      <td>1968</td>\n",
       "      <td>140.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price          Make    Location  Age  Odometer FuelType Transmission  \\\n",
       "0   1.75  marutiwagonr      Mumbai   10     72000      CNG       Manual   \n",
       "1  12.50  hyundaicreta        Pune    5     41000   Diesel       Manual   \n",
       "2   4.50     hondajazz     Chennai    9     46000   Petrol       Manual   \n",
       "3   6.00  marutiertiga     Chennai    8     87000   Diesel       Manual   \n",
       "4  17.74        audia4  Coimbatore    7     40670   Diesel    Automatic   \n",
       "\n",
       "  OwnerType Mileage EngineCC PowerBhp  \n",
       "0     First    26.6      998    58.16  \n",
       "1     First   19.67     1582    126.2  \n",
       "2     First    18.2     1199     88.7  \n",
       "3     First   20.77     1248    88.76  \n",
       "4    Second    15.2     1968    140.8  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.distplot(df.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.distplot(df.Age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Odometer'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.distplot(df.Odometer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Price'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mNAxj7+K6KdwGZIGfAP+dgj7RJ+u9qHoS9dSezeZ5adcQW8ZsskPZPEt6OpyGwfOEt8/r5O41K8sMSj2Dr54nZHPK393z5B5DdE5fXefM5oJdVtkQWU+mR2UY0wOXL2GZqq5W1RuA04A/nYI11ZWoaZ47hzIMpcsvSUPpHDuH3EHUXM5n87bdnLn+Ed699iHOXP8Im7ftrmuK6I7BDBd+bZyb7Gv1DfpGratolW54htEMuG4K2dE/qGquUGvW3ERtTK/AUCZfdvJee9phhNm2tu1O88UHni0LwH7xgWe54qRD2W9Oe9WxUU/QjQhuR3WTNSLOYxhGMC6jcPiYymWhoGz6ZvHPGrWArdFEaUxfyV9+15qVIUYr5x2zlE9/a08A9upTD0McJqWWTKlGBLejGtxaDJi5nQxj7+KqaI6Nq2KO11rR3Kz4vgZuXH6IfhSqlAzC6LhPf+tXuLwj2wfTgSfo7YPuWoNGNROKUldhbifDmD7ULz9xhtGWDN642kJkHykEGhQXI9ngE/RI1h2LaFTntamU87BiOcPY+4RWSW115nWmAv3l8zrdbqhURFdOTCRwXCzkvh7FTQZTL+fhecIBvV1suOhosnmfRMwLlZ1lKrKGsfcxoxCSqP5ygNmpGNetXsHFY3SIrlu9gtmp6kahMxVj3arlXHL7Y6Vx61Ytp9MxbpQom3stcYyoAWPfV57r3z3pORsRNzGMmY65jyZBVB2i/sEMXypmH929ZiV/98FlfOmBZ+l3uDkyeeXLP3yubNyXf/gcmbzbJRPV316LSybqyT3qnI2KmxjGTMZuClNAzlfuf3ob9z9d3jfosx9YVnVcNucHjvvch9wxhain9lpcMlFP7lHnrOX2ZhhGMHZTmALingQGqeMh00rHjwvjHom60dYyZ9STey1zRr29GYYRjLOfwnQmaj+FqEQNwO4aHGHLG+kJMYVFc1L0dLZVne+lHYP8bsdQSVrjrXM7WDK30zlv/0Caz37nV5y6Yv9Swdy3Nr3Mlacc5vTv16IiO9VxDMMwJk+1fgoNMQoiMgf4N+BQChmbHwU2A3cDS4CXgDNUdVe1v2cqjUItG9fvdgxy7y9f4eTli1BVRIR7HtvCyUcuZPHczupzvj5QkqsY1S866C3uOWtpstOIgjArQjOMqWM6GoXbgJ+o6r+JSBLoAP4W2KmqV4nI5UCPqn662t8zlZ3Xtg2M8BfrfjbBX/7tS45hfnfl0z7AK7uGOHP9IxPG3r1mJQt7OuoyZ/9AmlPWPTxhrElHtB5mcI3x1NR5rQ6LmQW8C7gJQFUzqvoGcDIFVVaKXz9cj/mjZuWkKxSSpUMUkqXiHutWLS/zta9btZyU48ReS/Ga5fAbYFXfxuRpRKD5bUA/cIuI/FJE/k1EOoEFqvoaQPHr/KDBIrJGRDaKyMb+/v5JTx41/XG0kGwsYQvJFOE/nniFW85/Jz/81Lu55fx38h9PvIJSfXAtc9YSvDVmDlb1bUyWRhiFOLAcuE5Vj6TQ3vPysINVdb2q9qlqX29v76QnryX9ce1ph5Wd9teedlioa/jcziSrj17CqMqsiLD66CXOrJxEzOPaMw4vm/PaMw4nEaJ7muXwG2A3RmPyNKJOYQuwRVV/Xvz+mxSMwlYR2VdVXxORfYFtFf+GGoieS+/zhfs2l8lff+G+zfzr2Uc45/R95Q/DuQlB331naXWjIsqcjgSfP/nQUvbRnI4EjgsGUC4dkcv7xENKRxgzC6v6NibLlBsFVX1dRF4WkYNUdTNwAvB08b/zgKuKX++px/xz2uKBkhNz2qq/FTFP6N+d5qKvbSo9K7hy3Jtsf4V+Cv9w0qHsW6WfQi6nXHDrxgn/oDeEkOuOKh1hzCwa0QrWaG4aVdF8KXB7MfPoBeACCq6sDSLyMeD3wOn1mHis5MToBv2lEA1vRODqUw+b2BMhVEwhuJ8Cjn4K2Qpy3bkQQcJKstthMpeMmYNVfRuTpSFGQVUfB4LSoU6o99zZfLB0xP/7geoZPapw289eLDMmt/3sRT73oUOcc1bqp3C348Qf84JVUsP8g64lc8mYWURVyzVak5bTPkrEvMCNNu4I3namYlx6/AFcPEax9LqQiqV5jdagJ1EMbo92fBsNbidCGIVaZbenGsulN4zpQcsZhfldKa5fvWJC0Hd+V/WTVFcyQXsyUxb0bU/G6EomnHPGvWBDFPOqGyLxYG5XsmzOuV1JJMRm2Zbw+Pr/OIpcHjwBXyEeKzx3MdUbtMlcGMb0oeWMQjzucdD8Lu5es5Kcr8Q9YX5Xyin9sGs4y/m3/CJShbCgXHP64XzqG0+UNr1rTj/c2aM5l4d/+v5mTl2xPx3EyOR9/un7m7nipEPdv6jCwHBuws2mO1n9f3kt0hpRiaroahjG3qflVFJ9X3l++yBnrn+Ed699iDPXP8Lz2wfdFc21NJdXuOmnL5T1Rbjppy84ezSrKv0D5UVG/QMZwkiTjOT8kkEYXevFtz/GSK56TGH77nTJIIyOu/BrG9m+290XGqK146wllz7KfIZhVKblbgpRT6UCgS6gMMQ9Ccw+cklntydjrD39HYCHJzC3K8Xa099Be9Idx8hFzFwarhigDrdBR3EDRc2lN7eTYex9Wu6mEPVUOpqSOrZC+OpTDyPM3uN5Hj/evLVM5uLHm7fiuWIKKMMZn/NveZTjr/kR59/yKMMZ3+l2gkJA/X3L5nPDOSu4e81KbjhnBe9bNt9ZDT2a8TSWsBlPU91BzSQcDGPv03I3hcin0gopqX8fIiW1pz3Bh45YxAW3/qIsuN3TXj1IPZzx+VKxHWeppuKHz3HFhw6hp7LiNgCdKY//ecKBEwLqnanqRiEZ8wIznpIhpDWmuoOaSTgYxt6n5YxC1ArPZMzjgmOXRtosdxUrmMdXNLsa3iAEup3CFMwNpf2SQYDCZvnxr2/iGxcdzewqXq9YDDqSsbKMp45kjFgIVYREPDjLKuEI4kO0XHqpkHYrYd4gwzACaTmjEPVUqqr0dCa49YKjSimeOT8fKujr+37g5u777oK5KEVvAJm8H3iKzuarzzmS8bni3qf5+HF/VMp4uuLep/nyR44Ex+0kXqGuwhU7iUoyJqxbtZxLxmRYrVu1nOR0LcYwjCag5YxCVBJxD1/h5Z17WmMumJ0KdQrOV9jcN1x0dPVxFYLF+RAZNlGroZPxWKDGUxgBteFMPlA0MIxBiUImr3x5nHvtyz98LlzKrmEYgbScUYiasaKqDGfy/N09T5bG/etZR4S7KUSsaI5HrL6G6Kf2uZ1J7rzwv5HOaelGlIpLKAG1ZDzGR49dzCH7zSLvKwt72vnosYtDGZRczmfb7jTZvE+iqOjqqh1R1UDJks99yNJSDSMqLWcUoqakpnM+n7zr8bJxn7zrcedpH8Cr4Pv2HL7vzqQEVl93Jt3uESE4NuAaGVnmG5iVjLGkdxZnFVuPjirQznKk0EbtJ22y0Iax92k5oxA1YyWbr5D37/DRA8QEbli9nG0DmdIGPb876dQh2p32+T+Pb+GW899JzBPyvvLNjb/n3GOWMrtya2egIJExpyNBZypROvEnYjglMrbtTgcGqDdcdHRVFVmA7UOZkiT56NiLv76p0Is6VfmjFnXOWmWhTW/JMCbSckYh6umyFsVSkUJLzrGup+tXr3BmEeV85dGX3mD5krkln/mjL73BR1aGcI/4sH13pnS7GXV37T+n+u+ZjRigHl1vlIK5SnOGMbipuFd2G3L1vR7FCt8MI5iWK16LWig1mr8/vh1nmJTUXF4DT8K5fPXNsi3uccVJy0pzJGOF79tCbHw5XwPdXa4NOl6heC1MBlHUsaPKtRPGOd7bHYMZzr35US649Recuf4RLrj1F5x786Ohites8M0wgmm5m0LUlNSe9gTzulNlp9J53SlnARpUbpaTdWzQIsLQuOD22tMOC5WHH3XOZNzjulXLJwjpJUMYoo6kF9jVriNZfWxU5dpaitcaVfhmLitjutMwoyAiMWAj8IqqflBE9gHuBpYALwFnqOqueswdpVDqjZEca+97pkyxdO19zxQK0BLRXE8xV8Vu3i9lD0Fh07rsm7/irhB1CvEKc7pO7XEPZnWU12Mk4kIYr0zeh30649x54Up8VTwRPE9xeYHicY+DF3RP6Cftyj6qJdDciCC1uayMZqCRN4VPAr8BZhW/vxx4QFWvEpHLi99/uh4TRzmtpbP5QMXSdIiTZdRmOXlf6e1KleXhX//Qb0MpgXa1BZ/au9qqb7RDGZ9VN/58wmZ595qVTmmNTF4584aJY0NlaHlCIuahqiRiXqhNspZAc9Re3bVgEuFGM9AQoyAii4APAFcC/0/x8cnAccU/3wY8RB2Mgu8rL+0Y5Hc79hShvXVuB0vmdlbdiBIx4apTDyXmxUqKpVedemioLmh5VdrHpYe2J2POOoXOZIy/OfGgCcakI4RKajoLL/W/yV1rVpL3lZgn/PJ3O1jQ3QtVkoiiBosB8r4faMRcxXZRT9C19B+O2qu7FkyryWgGGnVT+Bfgb4DuMc8WqOprAKr6mojMDxooImuANQCLFy+e9MRvDGfY+ubIBD/9nI4E+3RWPq3FPCGdUy65/dEySQWXC6jw+8C6B58vcz2te/B5Z3/nTC7YfbQhhPsIlLfO6y6rGVi3ajk4FFbjnvC+ZfM5dcX+pc3yW5teDhVoTsa8QCPmkp2o5QQdtf9w1F7dtWB1FUYzMOXZRyLyQWCbqm5y/nAAqrpeVftUta+3t3fS44czeR56plzG+qFntjKcqX5aS+f8ksYOFDauS25/jLSjaQ0UNtoLjl3K57/7NGeuf4TPf/dpLjh2qXOjjRoshkJdRdB6s66Mp4THpSccWLbWS084MFwbTyXQiLmW24gTdNSMp1qImvlmGFNJI24KxwInicifA23ALBH5OrBVRPYt3hL2BbZV/VsikogLHzh8YZmM9bpVy50aRrW4VdJ5n40v7uSOMQHYex7bwkJHk55aTu05XzlzxSJOXr6obE7XekeyfsUCNBf5iHIetairRiVqxhNEzyCqxd1lGFPFlBsFVf0M8BkAETkO+GtVXS0ia4HzgKuKX++px/zZXLCImsuVEzWbB6AzEeO4g+fzkRvHyD+sWk6nI2upu71wah8fDO1ud2+Ws9piHPfHC8rnXL2CWW3V51QI3NjDqAnFIsp5xDwCA/F1PLRHzniqNYMoqrvLMKaK6VS8dhXwXhF5Dnhv8fu9jhR7FIx1j5x3zFJndXFnKsZ1q1aUXf2vW7WCzpTbHxy1X/JQWgNP7UNp9xY9lAk+8Q9lqs+ZrOBWCVOk5wlce8bhZe/RtWcc7uxON5jeo6462sP6C/dtZjBd3wBsPO6x35x2Fs/tZL857U6DAFb0Zsx8Glq8pqoPUcgyQlV3ACfUf85oPQpmtSWZ150vy8GPxwrPXUR1PaVzwfIPmRBxjFrcXUGn9jD4KIlxshOJuIdrtZ5IoFy364bRCCyDyJjpTKebwpRQye+dDyGB/cZglrNvfIR3r32Is298hDcGs6HmjCr/UEu/5KhzDmfzpfjHQ5cdxx0XrmTjizsZzro3Pd8vZFllitVqo1lWrrqK0TqO8RIiYdJ9p5rRDKKxWAaRMZOQMP0Apit9fX26cePGSY15/Q/DnHb9f03we3/z40fzlip9Kre9OcJfXPezCeO+ffExzJ/VVnXObW8O8/qb6Qkdwt4yK8X8WZXn3PqHYV7YPjjh1P62eZ0sqNZTE9ixe4SdQ1m27BwundoX7dPOPh0J5nZVXu/2gRF2DWXYsmtkz7ieNno6kszrrv57vvbGEFsH0uwazJbG9nQmWNCdYt85lWVddw6m6R8YKdWA+Ap5P09vd1vVNOFaidLDwaqSjZmAiGxS1b6g11pO+0go+L3/asMTpX/U155xuLPPwHA22G0wEuIEncsr//HEKxMksM87ZmnVcTFPmNuVLHPHzO1KhqqNyOWV3SO5snqML519JLOqSFhDYUPevjszoY5jdrvbTeaJTGhEtPa0w5xuoFmpBK++McLHbttTA3L96hXMSrl1paJmAuVyPi/tHOTlMUZzaJ8cS/bprGoYLIPImOm0nFHIa7Df2+U+qkU6Ox4TPnjEorI02OtWryDuKOoazOT53fZBlu03m5yvxD3h6Vf/QOotMeY55sz5yqV3/rIsdnLpnb90xk5q0VvK+hpp7K7hLF8cV138xQeeLehKVcnUiVqdDrBzKEP/QHqCAZvVlnDe/CyDyJjJtJxRUIW/vOOXEzZ312aZ8ISvfORIdo5xjezTmQjl91YF3/fLDJHv+7g8d91tMd4yp4Mzx3Uy63aklUL0/s6V9JbC9IWO2nbU933OO2ZpKQFgUU87V596GL5fPUS9azgdWJ0+uyPO3M7qG3sm73PLwy+W/Z63PPwif+9ITTaMmU7LGYWom2UiLsQ8b0KjnEQ8nIz1V8bJXHzlweedG9BwhbTSu9esBIc4XdT+zu3xYKmK9hDpmm3xGBf96RJO61tc5iZrcwRhc75y28/KN+jbfvais3ZkOFPlVuN4f7xiavJ4Q+TqhtcoTHLbmCpazih4Ed1Awxk/sFFOmA06FnEDqiWt1JPg1FLXPpLTYBfQhovc7qO2ZLCbrM3RU9rzgt8fz2GHKhn4MCqyUVOTG4EFt42ppOVSUtsTwR3U2h3aPqNulRvOWcHda1Zywzkr6O1KhUpl9StsQK69q5YuaOmcH1gQ5tJqqtyL2v17DgznS8qjo3N+6YFnGRiuHoz3/Qrvj6PAoVKhXSJEoV1lV5dz6JRjBXPGVNJyN4XuZDywg1p3svpbkargVkmF2IAquqycmkDCulXLJ6SyhnFZJWJeYEGYa8NMVnA7hdlopcKNKEwv6iguvZgXnEkWJjvLqyjJ4RxaE1HcQFYwZ0wlLXdT6B/MsPa+Z8oKrNbe9wz9jlNXvoJbJcxNIVYUtht7y3jfsvnEHLtlLk8plXVU0fU/nniFMHtBKi5cv7pcluP61StIOQxKrOh2Gn+TCuNrV6UUGxi9Kdz2sxedAfVE5EI7n3/83jNl8/3j954JlSbsCVx9avnvefWpbvcaFDb2/oE0r+waon8gHcpdNTpu89YBTln3MMde/SCnrHuYzVsHnOOtYM6YSlruppDztYKO/rKq42pxqyTiwl8ef8CkT/wxgT9b9pYyH/21ZxweaoP2FZJxKbsRJePidI+M5H2+81h5TcWNP36BS094u3POqLGBVMILvBGlHC69mFdBHiPEzu6PMWCTCW7X4t+P2jeilg5zhjFZWs4oVFI7dbkcoo6DgjJrUG8DV1DTr1BT4UrxhEKDno/eunHCel1zpmIeq1YuZsuuPUVdq1YuDiWIVyk24JpzOOsHKte6srOSFdqcJkPKgAQZMNftpJaGQFHdQFYwZ0wlLWcUPK/gNpjsabY9GQvcgNpDtMbMa4Xcf1f+fsSaCoieuSQCQwFVyWG06aKm+xbcMRP7X7vcKrG4MLsjUWY0Z3ckiIWIuXieF3hTuPKU6uJ/tfj3a+m8ZgVzxlTRckbB9+HHm7dOkJxYMre65MSc9iQLZrWVbUALZrUxJ4T8Q6pCm0pXkDpqgBqiN+jJ5KNVJUP0dN+2RIy//fODJwSM2xz9JmIi7Dc7xey2RKniuzPlgVO0pOCS+av3HjRpl0wtG7u5gYxmoOWMQiIunNq3f5l75NS+/UN1+UrEhP336SiJtiVCVjrlKsg/uE78sQobuytADYW2mpedeDBbdhbmTMYK37vaavoVbjVhXFbJmMdN562YIGzncj3lfC0ZBCi8P3+14Qk2XHR01XGep/xuR3pCE6K3znWfqD1POKC3iw0XHV0miOcyYD3ticCObT3tbp0mcwMZzUDLGQX1YUeQ4Ftb9X/UbwxnSpv52HGdqbhTyTNqymUiLvz1+w/ilV0jQGHT/ev3HxQqJTWXV7YHafs4BPGi3mqgkCKaycPFY4Ttrlu9wtlBLZsP7huRzVcvVBgYrlzx7ZAvwveV5/p3TzpgHFWnaRRzAxnTnSlPSRWR/UXkQRH5jYg8JSKfLD7fR0R+ICLPFb/21GP+rK8lzZvRNMZbHn6RrGODHs7kA8cNZ0L4kiMWWanPhL9/OJNH3T12yPrKQ89sLUtnfeiZrc7fs9KtJkwVdaX+ziPZ6guu1DfCFcSvpeJ7+2A6MGC8fTBddVwmlw+Mf1jNgDFTaMRNIQd8SlUfE5FuYJOI/AA4H3hAVa8SkcuBy4FP7+3Jo0pOVE63DFMoFVxk5RqqBAd9w2TFJ2PCBw5fWJbOum7VcpKOX7SWjTbq2KhZRIkKMYwwIoUjFaXQqxuw9mRw/CNMwkGjMN0kYzJMuVFQ1deA14p/HhCR3wALgZOB44o/dhuFNp173ShUkpxwpodGTLeEQo/mb2zcMiH3/xPHV8/9r3RqDyVjnY+WBlvLRlspbdcV3E4lCi6VsUH83u4UqYTDKMQ9bli9nG0DmdK4+d3JUPGhWIWKZqceVT44/vHti49xzglTv0GbbpIxWRoaUxCRJcCRwM+BBUWDgaq+JiLzK4xZA6wBWLx48aTnjJrRU0sAti3ucfF73kbcixX6OcQK37c5Nq9aBN+intrjMeG61SsmBG9dvR8A2pMet1zwzgnd3tqT7k26MxXj7fO7yvpfu1BVFJmgXBumm2BbUQNr/O3EFYgfqZCSmg7hPqplg45qTGqpqzBak4YZBRHpAr4F/C9VfVNCNmlX1fXAeii045zsvFEzepIxj7WnvwPw8ATmdqVYe/o7QhV1JePCm2l4afvuss0y6QgYJ2IVTt4hNuiop3ZVSMbg1guOKssgCtO1NZuL1u0t5xfcenkBtFArEZPC86rj8hqoXLshpNLpvHFd7eZ1uVNDK90wXN3lIPoGXYsxMd0kY7I0RPtIRBIUDMLtqvrt4uOtIrJv8fV9gW2VxtdCIi5cevwBfP67T3Pm+kf4/Hef5tLjD3Bm9MRjwlDG5/xbHuX4a37E+bc8ylDGD7VBZ3J7MoHOXP8If3fPk2wfSJPJOU7tIlx7xuFl+jzXnnE48RAbUHvS47px2kfXrV7hPLXnfOWa+5/lt/276R9I89v+3Vxz/7OhYwpB3d5cY2MeDOeUkaxP3i98Hc6pO2upwm3IFUyHQlB87fc3l2tgfX+zM6YgFTSTwpxpom7QtaikNptuUlRdKWPvMeU3BSlcCW4CfqOq/zzmpXuB84Cril/vqcf8uZxy8Thf+8W3P+Y8XY5UkGJwaeVA9DaVI7k9gm+jc/7j957hX886wjmnJ7DfnBR3r1lZKuxqS3rO4LYncMl73s6uwSxQuCFd8p63hxKKi5p6m8vDH4ayE7SPOh3Fa1FvQ6NrDcoicnaYq6CZdEWIz0HUwrdaTvvNVDBn8Y/pQSPcR8cC5wC/FpHHi8/+loIx2CAiHwN+D5xej8lrkX/42J+8jU99Y0/WyTWnH16T/IPrFOTVIPiWz8OrfxgpGcBFPe1ct2o5+8129B8WYTgg4ymMeyRqH+t0zo8UFI9XyFoKYxQqSaEnHXGernaP/3nCgROK17ra3ZfuqIVvtcpjNEvBnMU/pgeNyD76KZV1CE6o9/yVNi6nsJ3CTT99oeyEeNNPXwh3QozYo6A7FRy47U65N6CRnB94I3JttGPrOCbbuzhRYZN2ZS7lKvSFdhnqTH5PI6HRcV+4bzNfPPsI51pVCby9fcNRRT2cVtoSUhZzAZ/htDoL5nYNZ/k/j2+ZILGy4F1vr6tKarMUzFn8Y3rQchXNUU+X4gW7VSREVCaVEG44ZwUXfW3PCfGGc1Y4Uy7TWeXN4WzZqf1fzzqC2Y7ALUR35dTSuzivSnsyVha8bU/GnJldUftCe1LhJhXiVlOxitrR7k0puJnGr7Ur5Za58H2fdx20oKx25OpTD8N3zNlMp/1aqOVGZOw9Ws4o5FXZb04bd164krwqMRF89Z0bl0cFt0oI8bW8D12pWNnpMhErPK9G1lc+edfjZafZT971eKjaiEr1BmGyj6L0GSgMhnUPPs+pK/angxiZvM+6B5933qZyFU7tLu2j0aDvhILCEHtlxRujq/FRDbUjeQ0WY1w6723Osc1y2q+FZop/zGRazii0JTx2DGa5+Ovl+jwL51R/K6IGi6EQQHv1jZEJp8u3zeusOi6qFDUUNpGgG5HrdBm1UQ4UMrSC/O2uDK1Kp/acy2oiPPf6H7jjwpWl+oYfPv0aSx3vK0B3mxdYj9Hd5hYMPOZtc7nwXW8rK0QMUxuRjAmnv3MxL49xB57+zsXOKnOAbDbPtt3pUtLA/K4UCUcgfpRczmfb7nSZ8F88RIHfVNMqN6LpTssZheGMz3cD/LrnHrMUquwltRSSRfXTR45/UAjeBvnbXZlLtVRuiwipcd3eUnHBVYMStYFRW0I49sD5/HbbnvqPYw+cT5vDLQcwMOKz6cXt3HHhSlQVKRqUfTr2ZVZ75XEdiRjnHrOkzAV0/eoVtIfYoAs1H36Z0m7ezzu74WWzeZ7ZtnuCATt4fpfTMORyPs9sHZhgqA9e0O00DI0wJq1wI5ruTL/jQp1JjNEEOv6aH3HBrb/gA4cvdMpgp+LBonaubBXYo7c0tjbivGOWhtJbCuwjHObUPiZz6cz1j3DR1zbRvzvt3GhruZ1kcsG5/xlHFVrU3zObU4bSubJnQ+kcWUf9BxQ+B8uXzOUjNz7Cu9c+xEdufITlS+Y6PwfZCgVz2RBtWUUgndOyWpd0Tp0ZbNt2pwOFBrftri7eNzo2aL2usaPG5Iwb/ot3r32IM274L57ZOkDOVVFoND0td1OIqgkU9yQwnTBM+mP0fsDR/fupuMct5/exZdfInsylnjZSDiNWseI7xO8pFYLUrk1PKzQ+WupofKQUDNHYOM+1ZxweSjAw6ucgE1Hmm+Jao8xZi6HORXTNVTImGy46mv3mVLlKGU1PyxmFqHUKOV+Z1R7jzjH+a8UPVenrCVz2/oMQ2SORcdn7D3IGRKVCIVnY2gjPK28KJKLOjaQ9GZyHH0a/KGqQui3p8cEjFpW5ZK5bvYI2x5z5Cs15wri6om60UWsxoPAZCopHRJ0zjKGOOjZ6nMdodlrOKEStgo17wraB3ISq2/1mu9/CZMxjOOtzye0by8bu01F904t7HtmAk3A8hP8o5gmD6fyE9bqaCY1kK+gJOTKBIHqQejhTuVlOlDhPmBN01M2yLe6xbtXyCe+rS9wQoCsZ48J3LWXLrj2B5gvftZROh+y2V0OWVdwTvvyRI9k1mC3N2dOZcH7eExVqa+IhtL4guoCfyXw3npaLKUTVBErn9shcjDbZ+fIPnyMdwsc6UsFtMOIYm8v7gSfhMKe1SlXCrvXWckKsFKR2pOFHV3St0JwnjEsvahyjls9BzlfeGMqWaWC9MZR1/p5j3Y+jc972sxedAWooyIuPLx5MeOKUF5/fleL6cf9Orl+9gvld7iDwqFzFKese5tirH+SUdQ+zeeuAMykj6jhj79JyN4WhCtlH5xyzlH2qnEqj+sshesVuxc0yRPpjpTldp+h4DTGFqCf3qLe3ZIVTe5jgv+/Dzt3D3LVmJXlfiXnCL3+3gyVzO6qOy/nK/U9v4/6ny/UaP/uBZc45K/Widrm7OpIen/3gMrI5LbkfP/vBZXSEcukpb45Trl172mG8xfEZisc9DprfVaadFTb7KKpcRaNkLux2Uk7LGYW8r9zwk5e44ScvlT1ftXJJ1XG1FHW1VajYdbkcKm6WISxRpTldgeZU3AvsC+0aBwWXzEV/uoTT+haXGVyXQemo0IfBtemN5IJFCsNIcnQkPZb2zuKs9Y+UxU5ccyZjXqDRDCOhHrXKXIE3h3MTaypS7jTY4awfqb7G95XXB0ZIFw1RNl/4fuGcjrrJdTdC5sJE+CbSckYhEQs+CbsKrDwvWBAvTHqoH1Fnx/MkuI1nmFO7BtdGXHGSo7rYV3bszkw4Wc5yxCKg4JoLKs5yueYyuWA5D1cfhnxUpVMKcYxKvRh6qtwY2xLBBXphaiOiBqmjxlwgen3NmyMZ0jm/7P/l/vu08+ZIhjkd1U/tUqHnhKtepVaZiygn/mYU4av3zabljEIy5nHp8QdMUA91nfTUp2QQoPDh+dQ3ngjV0KWizo7DT++rkoh7ZcVgibgXqnpWqODucoyrRcYhl1MGx9UNDKZzzs096ys3/Oi3ZQbshh/91nnib497XHXqocS8WMmtctWphzo1k6ByJpDLpRfVmAC0J7zAtOZ2R7e3qO5HiC7GmMlpYKGdqwcIFAxn0M3PZTjndib56keP4nc7hkrj3jq3I5TMRdQTf7OJ8E3FzabljEIl9VDXplepoUuYf5i1dEH7yzt+OWFcmJRLP2Iv6loqt31gKEAfyhWCjVXIWnJ5ZGIxIZ1TLrl9j2TJulXLiYWQjehIxjjn6LeWp8GuWk6HIxOols9BzIN9A3pcuJbbFvf43x8+hG3FW1EyVvg+TMZTzJPAPtYul55I4cT/8s49G/TCnrZQMbRsTslkyzfVTDZPNhdiuxn/NoaMMe8YzPDz3/ZPkDyZ15WqeuJPxmPB7sA63k5qYSpuNi1nFPyIm14t+ekIXHP64RNcT65je01FSxHH1pKKGPWWEVVaYyQbrRgMCkVogc2WLqo+tqbPAbBrMDvBJTOvq7przvMkMFg836XVDYDS3Z4glYiXTvyFNrDuTKDhTPnGPpzJM7vN/dlTCoev8e5A18hdw2l2DWcmqgIPx5nbWf13jXlK39J5fOTG8hhRzKs+a097gk+ecCAXjbm93RCixwU0pud2JpcPvDXuzZvNtEtJFZETRWSziDwvIpfv7b8/ahpjMlZo9D42RW/taYeFCjDqmF4MoymFN/30BWffY6/CWsN8eCr9nq4TYiImgamILvkHiH7LiNyxrYZTezZfoZWnQ65iVHp9/OcgTBpsJhvsXstkq8+ZzgUHi8OkwcY84Q9D2TJpjT8MZZ2fA6Xw/o5Nn835Gurg7muwuq/v+MCns37guLSjRSpUrq8Zcby3O4cyJYMwOu6ir29i55C71en2wTT//tjL3HL+O/nhp97NLee/k39/7GW2D1aXEKkl9bY9GeOKk5aV9p1kzOOKk5bR7rjhToZpdVMQkRjwFeC9wBbgFyJyr6o+vbfmSMQ9rlu1fEJMwZW33ZksCHWN9e/3dqfoTLo3g+52j0tPOHBi9oijW1cyFqx0GkZVM1Xh93RlEWlR1nuszHfezzsNWGG90fzXUQXxamnHGbV4TSi4nsZ+DjqSsRAC6tHda7W49NLZCjci1+2twsbuuklBZYObcxjcqOMgupxHukJMIR3q5K0lHbWx7ksX2wfTgS6gb19yDPO7q9+IVDXwMxQmzhiW6XZTOAp4XlVfUNUMcBdw8t6dQvjuE6+UWffvPvEKLl9OezLJvrMSHLigi7fMbuPABV3sOytBe9IdBMvlYWHRl/yjy47j7jUrWTgnhftzJ8zuSPD5kw/l7jUr+fzJhzK7I+FcKxT817M6Etx6wVH88FPv5tYLjmJWR8Lpp0/FIeZ5vLyz0Dj95Z1DxDyPEH198AS+dPaRZafoL519pLPytqNCQaErPXTU8JWNC2H4IPrNL68TT8tafO6iknstapFemNqRSjGQrGPOWjboWMQbbtRx1ca63iNPKswZplFTLlg/K+u4wY1kgw3RSIgbUaUU4+EQY8MyrW4KwELg5THfbwH+29gfEJE1wBqAxYsXT3qCuZ1JPrx8/zLrHqaRh+cJ7ckUQ9kMFP2A7clwfsA57SleeWOoLOfbV2XhnOqFUrOScd4cyZY9S8Y8ZiXd/9tiscJNw0MKWUwixGOF59UYzCiv7hrkwAWzSsHQ3257k47kbGZXXy6phNDVFi87RXe1xZ0d5mIxmNeVKNOVCrPWRGyP4Ss1L4oLYdoMdCQJvPl1OGy8VmgkFKZeJeqJP1EhNdnV5hSi34hqiZ1EbcsadRxEb7hUaVzYotQo7stYhZTdUN0Na7g1hmW6GYWgt6Xst1XV9cB6gL6+vkm/E7U08oiq9e55wsI5HZMOLLW1xVlEO+2JWGmDntuepK3N/b+tK5kim0/j+z6+Fk7xybhHV7L6+ud1JNjZ1c6Z44q65nW4A28d8ThdKZ+3z+8q29w74tXXO5yG13YNsWB2R2mtr+0aYmFPV9W+x55XSPOMFYX+kp6QjEuo2hEBersTZe9tV5u7j96cCq7AOQ5XIOyRXx+/GbgqsFNJoaczWWbAejqTpEK4LkdvRBNdkA7XZYUNOhni30lXSpg3zuDO607RlXLdxoPHtYf4PQUJLC79h5MOrTqulqLUSkkZLndpezIW+N6GiQtEddFOBtmbvqhaEZGjgStU9f3F7z8DoKr/X9DP9/X16caNG6dwhc1F5AyHTI7+wUxps+ztTJIMcTuJOtb3lWdef5M1Y3pYrz9nBQe/ZVbV9eZyPgOZDENpvzRfR8qjO5l0yjEUsmvSvDG8Z+ycdo/2ZKrqnFHHja732W0DE37PA+dXb3iTy/nsHE4XawcKkhzJuLBPu1t2IpfzeWnn4ISMpyX7dFYdm8nk+P0bwxPGLZ7T7vz/mcv5DOUyDIx5j7rbPTri1f+/ZLN53sxkGcn4ZSm7s5IJZzOhTCbHs/2DE2pADuztrLreTCbH5v7BCUb+IMe40d8zSgMj31de2jE4oR5jydzOUJ+hqE2TxiIim1S1L/C1aWYU4sCzwAnAK8AvgI+o6lNBP29GYeYQ1YCNdgfL5X3ik+wO1gglz6jrreX3jDq2lsNB1DlraTsadb2N+D0b8RkaS9MYBQAR+XPgX4AYcLOqXlnpZ80oGIZhTJ5qRmG6xRRQ1e8B32v0OgzDMFqR6ZaSahiGYTQQMwqGYRhGCTMKhmEYRgkzCoZhGEaJaZd9NBlEpB8YBLY3ei3TnHnYe1QNe3+qY++Pm2Z7j96qqr1BLzS1UQAQkY2VUquMAvYeVcfen+rY++NmJr1H5j4yDMMwSphRMAzDMErMBKOwvtELaALsPaqOvT/VsffHzYx5j5o+pmAYhmHsPWbCTcEwDMPYS5hRMAzDMEo0rVEQkRNFZLOIPC8ilzd6PdMREXlJRH4tIo+LiMnJAiJys4hsE5EnxzzbR0R+ICLPFb/2NHKNjaTC+3OFiLxS/Bw9XlQybklEZH8ReVBEfiMiT4nIJ4vPZ8xnqCmNgojEgK8A/x1YBpwtIssau6ppy3tU9YiZkkO9F7gVOHHcs8uBB1T1AOCB4vetyq1MfH8Ari1+jo4oKhm3KjngU6r6x8BK4BPFvWfGfIaa0igARwHPq+oLqpoB7gJObvCajCZAVX8M7Bz3+GTgtuKfbwM+PJVrmk5UeH+MIqr6mqo+VvzzAPAbCr3lZ8xnqFmNwkLg5THfbyk+M8pR4H4R2SQiaxq9mGnMAlV9DQr/6IH5DV7PdOQvReRXRfdS07pG9iYisgQ4Evg5M+gz1KxGIahvneXWTuRYVV1Owc32CRF5V6MXZDQl1wF/BBwBvAZc09DVTANEpAv4FvC/VPXNRq9nb9KsRmELsP+Y7xcBrzZoLdMWVX21+HUb8B0KbjdjIltFZF+A4tdtDV7PtEJVt6pqXlV94EZa/HMkIgkKBuF2Vf128fGM+Qw1q1H4BXCAiCwVkSRwFnBvg9c0rRCRThHpHv0z8D7gyeqjWpZ7gfOKfz4PuKeBa5l2jG52RU6hhT9HIiLATcBvVPWfx7w0Yz5DTVvRXEyL+xcgBtysqlc2dkXTCxF5G4XbARR6cd9h7xGIyJ3AcRSkjrcCnwP+HdgALAZ+D5yuqi0ZbK3w/hxHwXWkwEvARaP+81ZDRP4E+Anwa8AvPv5bCnGFGfEZalqjYBiGYex9mtV9ZBiGYdQBMwqGYRhGCTMKhmEYRgkzCoZhGEYJMwqGYRhGCTMKhhERETlFRFREDm70Wgxjb2FGwTCiczbwUwrFk4YxIzCjYBgRKGrfHAt8jKJREBFPRNYVdfa/KyLfE5HTiq+tEJEfFcUJvz+uStgwpg1mFAwjGh8G7lPVZ4GdIrIc+AtgCfAO4H8AR0NJK+dLwGmqugK4GWj56nJjehJv9AIMo0k5m4LMChT6eZwNJIBvFIXjXheRB4uvHwQcCvygIJ1DjILaqGFMO8woGMYkEZG5wPHAoSKiFDZ5ZY/W1IQhwFOqevQULdEwImPuI8OYPKcBX1XVt6rqElXdH3gR2A6cWowtLKAgJAewGegVkZI7SUQOacTCDcOFGQXDmDxnM/FW8C1gPwq9Pp4EbqCgnPmHYsvY04CrReQJ4HHgmClbrWFMAlNJNYy9iIh0qeruoovpUQrd715v9LoMIywWUzCMvct3RWQOkAQ+bwbBaDbspmAYhmGUsJiCYRiGUcKMgmEYhlHCjIJhGIZRwoyCYRiGUcKMgmEYhlHi/wKLjvwGfGqFLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df.Age,y=df.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6019 entries, 0 to 6018\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Price         6019 non-null   float64\n",
      " 1   Make          6019 non-null   object \n",
      " 2   Location      6019 non-null   object \n",
      " 3   Age           6019 non-null   int64  \n",
      " 4   Odometer      6019 non-null   int64  \n",
      " 5   FuelType      6019 non-null   object \n",
      " 6   Transmission  6019 non-null   object \n",
      " 7   OwnerType     6019 non-null   object \n",
      " 8   Mileage       6019 non-null   object \n",
      " 9   EngineCC      6019 non-null   object \n",
      " 10  PowerBhp      6019 non-null   object \n",
      "dtypes: float64(1), int64(2), object(8)\n",
      "memory usage: 517.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "marutiswift           353\n",
       "hondacity             270\n",
       "hyundaii20            247\n",
       "hyundaiverna          170\n",
       "hyundaii10            156\n",
       "                     ... \n",
       "mahindraxuv300          1\n",
       "bentleycontinental      1\n",
       "ambassadorclassic       1\n",
       "fordclassic             1\n",
       "fiatgrandepuntoevo      1\n",
       "Name: Make, Length: 217, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Make.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "marutiswift           0.058648\n",
       "hondacity             0.044858\n",
       "hyundaii20            0.041037\n",
       "hyundaiverna          0.028244\n",
       "hyundaii10            0.025918\n",
       "                        ...   \n",
       "mahindraxuv300        0.000166\n",
       "bentleycontinental    0.000166\n",
       "ambassadorclassic     0.000166\n",
       "fordclassic           0.000166\n",
       "fiatgrandepuntoevo    0.000166\n",
       "Name: Make, Length: 217, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Make.value_counts()/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(df.Make))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ambassadorclassic',\n",
       " 'audia3',\n",
       " 'audia4',\n",
       " 'audia6',\n",
       " 'audia7',\n",
       " 'audia8',\n",
       " 'audiq3',\n",
       " 'audiq5',\n",
       " 'audiq7',\n",
       " 'audirs5',\n",
       " 'auditt',\n",
       " 'bentleycontinental',\n",
       " 'bmw1',\n",
       " 'bmw3',\n",
       " 'bmw5',\n",
       " 'bmw6',\n",
       " 'bmw7',\n",
       " 'bmwx1',\n",
       " 'bmwx3',\n",
       " 'bmwx5',\n",
       " 'bmwx6',\n",
       " 'bmwz4',\n",
       " 'chevroletaveo',\n",
       " 'chevroletbeat',\n",
       " 'chevroletcaptiva',\n",
       " 'chevroletcruze',\n",
       " 'chevroletenjoy',\n",
       " 'chevroletoptra',\n",
       " 'chevroletsail',\n",
       " 'chevroletspark',\n",
       " 'chevrolettavera',\n",
       " 'datsungo',\n",
       " 'datsunredigo',\n",
       " 'fiatavventura',\n",
       " 'fiatgrandepunto',\n",
       " 'fiatgrandepuntoevo',\n",
       " 'fiatlinea',\n",
       " 'fiatpetra',\n",
       " 'fiatpunto',\n",
       " 'fiatpuntoevo',\n",
       " 'fiatsiena',\n",
       " 'forceone',\n",
       " 'fordaspire',\n",
       " 'fordclassic',\n",
       " 'fordecosport',\n",
       " 'fordendeavour',\n",
       " 'fordfiesta',\n",
       " 'fordfigo',\n",
       " 'fordfreestyle',\n",
       " 'fordfusion',\n",
       " 'fordikon',\n",
       " 'fordmustang',\n",
       " 'hondaaccord',\n",
       " 'hondaamaze',\n",
       " 'hondabrio',\n",
       " 'hondabrv',\n",
       " 'hondacity',\n",
       " 'hondacivic',\n",
       " 'hondacrv',\n",
       " 'hondajazz',\n",
       " 'hondamobilio',\n",
       " 'hondawrv',\n",
       " 'hyundaiaccent',\n",
       " 'hyundaicreta',\n",
       " 'hyundaielantra',\n",
       " 'hyundaielite',\n",
       " 'hyundaieon',\n",
       " 'hyundaigetz',\n",
       " 'hyundaigrand',\n",
       " 'hyundaii10',\n",
       " 'hyundaii20',\n",
       " 'hyundaisanta',\n",
       " 'hyundaisantro',\n",
       " 'hyundaisonata',\n",
       " 'hyundaitucson',\n",
       " 'hyundaiverna',\n",
       " 'hyundaixcent',\n",
       " 'isuzudmax',\n",
       " 'isuzumux',\n",
       " 'jaguarf',\n",
       " 'jaguarxe',\n",
       " 'jaguarxf',\n",
       " 'jaguarxj',\n",
       " 'jeepcompass',\n",
       " 'lamborghinigallardo',\n",
       " 'landroverdiscovery',\n",
       " 'landroverfreelander',\n",
       " 'landroverrangerover',\n",
       " 'mahindrabolero',\n",
       " 'mahindrae',\n",
       " 'mahindrajeep',\n",
       " 'mahindrakuv',\n",
       " 'mahindralogan',\n",
       " 'mahindranuvosport',\n",
       " 'mahindraquanto',\n",
       " 'mahindrarenaultlogan',\n",
       " 'mahindrascorpio',\n",
       " 'mahindrassangyong',\n",
       " 'mahindrathar',\n",
       " 'mahindratuv',\n",
       " 'mahindraverito',\n",
       " 'mahindraxuv300',\n",
       " 'mahindraxuv500',\n",
       " 'mahindraxylo',\n",
       " 'maruti1000',\n",
       " 'maruti800',\n",
       " 'marutialto',\n",
       " 'marutiastar',\n",
       " 'marutibaleno',\n",
       " 'maruticelerio',\n",
       " 'maruticiaz',\n",
       " 'marutidzire',\n",
       " 'marutieeco',\n",
       " 'marutiertiga',\n",
       " 'marutiesteem',\n",
       " 'marutiestilo',\n",
       " 'marutigrand',\n",
       " 'marutiignis',\n",
       " 'marutiomni',\n",
       " 'marutiritz',\n",
       " 'marutiscross',\n",
       " 'marutiswift',\n",
       " 'marutisx4',\n",
       " 'marutiversa',\n",
       " 'marutivitarabrezza',\n",
       " 'marutiwagonr',\n",
       " 'marutizen',\n",
       " 'mercedesbenza',\n",
       " 'mercedesbenzb',\n",
       " 'mercedesbenzcclass',\n",
       " 'mercedesbenzcla',\n",
       " 'mercedesbenzclsclass',\n",
       " 'mercedesbenzeclass',\n",
       " 'mercedesbenzgla',\n",
       " 'mercedesbenzglc',\n",
       " 'mercedesbenzglclass',\n",
       " 'mercedesbenzgle',\n",
       " 'mercedesbenzgls',\n",
       " 'mercedesbenzmclass',\n",
       " 'mercedesbenznewclass',\n",
       " 'mercedesbenzrclass',\n",
       " 'mercedesbenzs',\n",
       " 'mercedesbenzsclass',\n",
       " 'mercedesbenzslc',\n",
       " 'mercedesbenzslclass',\n",
       " 'mercedesbenzslkclass',\n",
       " 'miniclubman',\n",
       " 'minicooper',\n",
       " 'minicountryman',\n",
       " 'mitsubishicedia',\n",
       " 'mitsubishilancer',\n",
       " 'mitsubishimontero',\n",
       " 'mitsubishioutlander',\n",
       " 'mitsubishipajero',\n",
       " 'nissanevalia',\n",
       " 'nissanmicra',\n",
       " 'nissansunny',\n",
       " 'nissanteana',\n",
       " 'nissanterrano',\n",
       " 'nissanxtrail',\n",
       " 'porscheboxster',\n",
       " 'porschecayenne',\n",
       " 'porschecayman',\n",
       " 'porschepanamera',\n",
       " 'renaultcaptur',\n",
       " 'renaultduster',\n",
       " 'renaultfluence',\n",
       " 'renaultkoleos',\n",
       " 'renaultkwid',\n",
       " 'renaultlodgy',\n",
       " 'renaultpulse',\n",
       " 'renaultscala',\n",
       " 'skodafabia',\n",
       " 'skodalaura',\n",
       " 'skodaoctavia',\n",
       " 'skodarapid',\n",
       " 'skodasuperb',\n",
       " 'skodayeti',\n",
       " 'smartfortwo',\n",
       " 'tatabolt',\n",
       " 'tatahexa',\n",
       " 'tataindica',\n",
       " 'tataindigo',\n",
       " 'tatamanza',\n",
       " 'tatanano',\n",
       " 'tatanew',\n",
       " 'tatanexon',\n",
       " 'tatasafari',\n",
       " 'tatasumo',\n",
       " 'tatatiago',\n",
       " 'tatatigor',\n",
       " 'tataventure',\n",
       " 'tataxenon',\n",
       " 'tatazest',\n",
       " 'toyotacamry',\n",
       " 'toyotacorolla',\n",
       " 'toyotaetios',\n",
       " 'toyotaetioscross',\n",
       " 'toyotafortuner',\n",
       " 'toyotainnova',\n",
       " 'toyotainnovacrysta',\n",
       " 'toyotaplatinum',\n",
       " 'toyotaprius',\n",
       " 'toyotaqualis',\n",
       " 'volkswagenameo',\n",
       " 'volkswagenbeetle',\n",
       " 'volkswagencrosspolo',\n",
       " 'volkswagenjetta',\n",
       " 'volkswagenpassat',\n",
       " 'volkswagenpolo',\n",
       " 'volkswagentiguan',\n",
       " 'volkswagenvento',\n",
       " 'volvos60',\n",
       " 'volvos80',\n",
       " 'volvov40',\n",
       " 'volvoxc60',\n",
       " 'volvoxc90'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.Make)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['make'] = df['Make'].str.replace('\\d+', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(df.make))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ambassadorclassic',\n",
       " 'audia',\n",
       " 'audiq',\n",
       " 'audirs',\n",
       " 'auditt',\n",
       " 'bentleycontinental',\n",
       " 'bmw',\n",
       " 'bmwx',\n",
       " 'bmwz',\n",
       " 'chevroletaveo',\n",
       " 'chevroletbeat',\n",
       " 'chevroletcaptiva',\n",
       " 'chevroletcruze',\n",
       " 'chevroletenjoy',\n",
       " 'chevroletoptra',\n",
       " 'chevroletsail',\n",
       " 'chevroletspark',\n",
       " 'chevrolettavera',\n",
       " 'datsungo',\n",
       " 'datsunredigo',\n",
       " 'fiatavventura',\n",
       " 'fiatgrandepunto',\n",
       " 'fiatgrandepuntoevo',\n",
       " 'fiatlinea',\n",
       " 'fiatpetra',\n",
       " 'fiatpunto',\n",
       " 'fiatpuntoevo',\n",
       " 'fiatsiena',\n",
       " 'forceone',\n",
       " 'fordaspire',\n",
       " 'fordclassic',\n",
       " 'fordecosport',\n",
       " 'fordendeavour',\n",
       " 'fordfiesta',\n",
       " 'fordfigo',\n",
       " 'fordfreestyle',\n",
       " 'fordfusion',\n",
       " 'fordikon',\n",
       " 'fordmustang',\n",
       " 'hondaaccord',\n",
       " 'hondaamaze',\n",
       " 'hondabrio',\n",
       " 'hondabrv',\n",
       " 'hondacity',\n",
       " 'hondacivic',\n",
       " 'hondacrv',\n",
       " 'hondajazz',\n",
       " 'hondamobilio',\n",
       " 'hondawrv',\n",
       " 'hyundaiaccent',\n",
       " 'hyundaicreta',\n",
       " 'hyundaielantra',\n",
       " 'hyundaielite',\n",
       " 'hyundaieon',\n",
       " 'hyundaigetz',\n",
       " 'hyundaigrand',\n",
       " 'hyundaii',\n",
       " 'hyundaisanta',\n",
       " 'hyundaisantro',\n",
       " 'hyundaisonata',\n",
       " 'hyundaitucson',\n",
       " 'hyundaiverna',\n",
       " 'hyundaixcent',\n",
       " 'isuzudmax',\n",
       " 'isuzumux',\n",
       " 'jaguarf',\n",
       " 'jaguarxe',\n",
       " 'jaguarxf',\n",
       " 'jaguarxj',\n",
       " 'jeepcompass',\n",
       " 'lamborghinigallardo',\n",
       " 'landroverdiscovery',\n",
       " 'landroverfreelander',\n",
       " 'landroverrangerover',\n",
       " 'mahindrabolero',\n",
       " 'mahindrae',\n",
       " 'mahindrajeep',\n",
       " 'mahindrakuv',\n",
       " 'mahindralogan',\n",
       " 'mahindranuvosport',\n",
       " 'mahindraquanto',\n",
       " 'mahindrarenaultlogan',\n",
       " 'mahindrascorpio',\n",
       " 'mahindrassangyong',\n",
       " 'mahindrathar',\n",
       " 'mahindratuv',\n",
       " 'mahindraverito',\n",
       " 'mahindraxuv',\n",
       " 'mahindraxylo',\n",
       " 'maruti',\n",
       " 'marutialto',\n",
       " 'marutiastar',\n",
       " 'marutibaleno',\n",
       " 'maruticelerio',\n",
       " 'maruticiaz',\n",
       " 'marutidzire',\n",
       " 'marutieeco',\n",
       " 'marutiertiga',\n",
       " 'marutiesteem',\n",
       " 'marutiestilo',\n",
       " 'marutigrand',\n",
       " 'marutiignis',\n",
       " 'marutiomni',\n",
       " 'marutiritz',\n",
       " 'marutiscross',\n",
       " 'marutiswift',\n",
       " 'marutisx',\n",
       " 'marutiversa',\n",
       " 'marutivitarabrezza',\n",
       " 'marutiwagonr',\n",
       " 'marutizen',\n",
       " 'mercedesbenza',\n",
       " 'mercedesbenzb',\n",
       " 'mercedesbenzcclass',\n",
       " 'mercedesbenzcla',\n",
       " 'mercedesbenzclsclass',\n",
       " 'mercedesbenzeclass',\n",
       " 'mercedesbenzgla',\n",
       " 'mercedesbenzglc',\n",
       " 'mercedesbenzglclass',\n",
       " 'mercedesbenzgle',\n",
       " 'mercedesbenzgls',\n",
       " 'mercedesbenzmclass',\n",
       " 'mercedesbenznewclass',\n",
       " 'mercedesbenzrclass',\n",
       " 'mercedesbenzs',\n",
       " 'mercedesbenzsclass',\n",
       " 'mercedesbenzslc',\n",
       " 'mercedesbenzslclass',\n",
       " 'mercedesbenzslkclass',\n",
       " 'miniclubman',\n",
       " 'minicooper',\n",
       " 'minicountryman',\n",
       " 'mitsubishicedia',\n",
       " 'mitsubishilancer',\n",
       " 'mitsubishimontero',\n",
       " 'mitsubishioutlander',\n",
       " 'mitsubishipajero',\n",
       " 'nissanevalia',\n",
       " 'nissanmicra',\n",
       " 'nissansunny',\n",
       " 'nissanteana',\n",
       " 'nissanterrano',\n",
       " 'nissanxtrail',\n",
       " 'porscheboxster',\n",
       " 'porschecayenne',\n",
       " 'porschecayman',\n",
       " 'porschepanamera',\n",
       " 'renaultcaptur',\n",
       " 'renaultduster',\n",
       " 'renaultfluence',\n",
       " 'renaultkoleos',\n",
       " 'renaultkwid',\n",
       " 'renaultlodgy',\n",
       " 'renaultpulse',\n",
       " 'renaultscala',\n",
       " 'skodafabia',\n",
       " 'skodalaura',\n",
       " 'skodaoctavia',\n",
       " 'skodarapid',\n",
       " 'skodasuperb',\n",
       " 'skodayeti',\n",
       " 'smartfortwo',\n",
       " 'tatabolt',\n",
       " 'tatahexa',\n",
       " 'tataindica',\n",
       " 'tataindigo',\n",
       " 'tatamanza',\n",
       " 'tatanano',\n",
       " 'tatanew',\n",
       " 'tatanexon',\n",
       " 'tatasafari',\n",
       " 'tatasumo',\n",
       " 'tatatiago',\n",
       " 'tatatigor',\n",
       " 'tataventure',\n",
       " 'tataxenon',\n",
       " 'tatazest',\n",
       " 'toyotacamry',\n",
       " 'toyotacorolla',\n",
       " 'toyotaetios',\n",
       " 'toyotaetioscross',\n",
       " 'toyotafortuner',\n",
       " 'toyotainnova',\n",
       " 'toyotainnovacrysta',\n",
       " 'toyotaplatinum',\n",
       " 'toyotaprius',\n",
       " 'toyotaqualis',\n",
       " 'volkswagenameo',\n",
       " 'volkswagenbeetle',\n",
       " 'volkswagencrosspolo',\n",
       " 'volkswagenjetta',\n",
       " 'volkswagenpassat',\n",
       " 'volkswagenpolo',\n",
       " 'volkswagentiguan',\n",
       " 'volkswagenvento',\n",
       " 'volvos',\n",
       " 'volvov',\n",
       " 'volvoxc'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.make)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['make']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "      <th>Odometer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6019.000000</td>\n",
       "      <td>6019.000000</td>\n",
       "      <td>6.019000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.479468</td>\n",
       "      <td>6.641801</td>\n",
       "      <td>5.873838e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.187917</td>\n",
       "      <td>3.269742</td>\n",
       "      <td>9.126884e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.440000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.710000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.400000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.640000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.300000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.950000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.300000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>160.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>6.500000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price          Age      Odometer\n",
       "count  6019.000000  6019.000000  6.019000e+03\n",
       "mean      9.479468     6.641801  5.873838e+04\n",
       "std      11.187917     3.269742  9.126884e+04\n",
       "min       0.440000     1.000000  1.710000e+02\n",
       "25%       3.500000     4.000000  3.400000e+04\n",
       "50%       5.640000     6.000000  5.300000e+04\n",
       "75%       9.950000     9.000000  7.300000e+04\n",
       "max     160.000000    22.000000  6.500000e+06"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Price', 'Make', 'Location', 'Age', 'Odometer', 'FuelType',\n",
       "       'Transmission', 'OwnerType', 'Mileage', 'EngineCC', 'PowerBhp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'+AC0-1',\n",
       " '10',\n",
       " '10.1',\n",
       " '10.13',\n",
       " '10.2',\n",
       " '10.37',\n",
       " '10.4',\n",
       " '10.5',\n",
       " '10.6',\n",
       " '10.8',\n",
       " '10.9',\n",
       " '10.91',\n",
       " '10.93',\n",
       " '10.98',\n",
       " '11',\n",
       " '11.05',\n",
       " '11.07',\n",
       " '11.1',\n",
       " '11.18',\n",
       " '11.2',\n",
       " '11.25',\n",
       " '11.3',\n",
       " '11.33',\n",
       " '11.36',\n",
       " '11.4',\n",
       " '11.49',\n",
       " '11.5',\n",
       " '11.56',\n",
       " '11.57',\n",
       " '11.62',\n",
       " '11.68',\n",
       " '11.7',\n",
       " '11.72',\n",
       " '11.74',\n",
       " '11.78',\n",
       " '11.79',\n",
       " '11.8',\n",
       " '11.9',\n",
       " '12',\n",
       " '12.05',\n",
       " '12.07',\n",
       " '12.1',\n",
       " '12.19',\n",
       " '12.3',\n",
       " '12.35',\n",
       " '12.37',\n",
       " '12.38',\n",
       " '12.39',\n",
       " '12.4',\n",
       " '12.5',\n",
       " '12.51',\n",
       " '12.55',\n",
       " '12.6',\n",
       " '12.62',\n",
       " '12.63',\n",
       " '12.65',\n",
       " '12.7',\n",
       " '12.8',\n",
       " '12.81',\n",
       " '12.83',\n",
       " '12.85',\n",
       " '12.86',\n",
       " '12.9',\n",
       " '12.95',\n",
       " '12.97',\n",
       " '12.98',\n",
       " '12.99',\n",
       " '13',\n",
       " '13.01',\n",
       " '13.06',\n",
       " '13.1',\n",
       " '13.14',\n",
       " '13.17',\n",
       " '13.2',\n",
       " '13.22',\n",
       " '13.24',\n",
       " '13.29',\n",
       " '13.33',\n",
       " '13.4',\n",
       " '13.44',\n",
       " '13.45',\n",
       " '13.49',\n",
       " '13.5',\n",
       " '13.53',\n",
       " '13.58',\n",
       " '13.6',\n",
       " '13.68',\n",
       " '13.7',\n",
       " '13.73',\n",
       " '13.8',\n",
       " '13.9',\n",
       " '13.93',\n",
       " '14',\n",
       " '14.02',\n",
       " '14.07',\n",
       " '14.1',\n",
       " '14.16',\n",
       " '14.2',\n",
       " '14.21',\n",
       " '14.24',\n",
       " '14.28',\n",
       " '14.3',\n",
       " '14.33',\n",
       " '14.36',\n",
       " '14.39',\n",
       " '14.4',\n",
       " '14.42',\n",
       " '14.45',\n",
       " '14.47',\n",
       " '14.49',\n",
       " '14.53',\n",
       " '14.57',\n",
       " '14.59',\n",
       " '14.6',\n",
       " '14.62',\n",
       " '14.66',\n",
       " '14.67',\n",
       " '14.69',\n",
       " '14.7',\n",
       " '14.74',\n",
       " '14.75',\n",
       " '14.8',\n",
       " '14.81',\n",
       " '14.83',\n",
       " '14.84',\n",
       " '14.9',\n",
       " '14.94',\n",
       " '14.95',\n",
       " '15',\n",
       " '15.04',\n",
       " '15.06',\n",
       " '15.1',\n",
       " '15.11',\n",
       " '15.15',\n",
       " '15.17',\n",
       " '15.2',\n",
       " '15.26',\n",
       " '15.29',\n",
       " '15.3',\n",
       " '15.4',\n",
       " '15.41',\n",
       " '15.42',\n",
       " '15.5',\n",
       " '15.6',\n",
       " '15.63',\n",
       " '15.64',\n",
       " '15.68',\n",
       " '15.7',\n",
       " '15.72',\n",
       " '15.73',\n",
       " '15.74',\n",
       " '15.8',\n",
       " '15.85',\n",
       " '15.87',\n",
       " '15.9',\n",
       " '15.96',\n",
       " '15.97',\n",
       " '16',\n",
       " '16.02',\n",
       " '16.05',\n",
       " '16.07',\n",
       " '16.09',\n",
       " '16.1',\n",
       " '16.12',\n",
       " '16.2',\n",
       " '16.25',\n",
       " '16.3',\n",
       " '16.36',\n",
       " '16.38',\n",
       " '16.4',\n",
       " '16.46',\n",
       " '16.47',\n",
       " '16.5',\n",
       " '16.51',\n",
       " '16.52',\n",
       " '16.55',\n",
       " '16.6',\n",
       " '16.7',\n",
       " '16.73',\n",
       " '16.76',\n",
       " '16.77',\n",
       " '16.78',\n",
       " '16.8',\n",
       " '16.82',\n",
       " '16.9',\n",
       " '16.93',\n",
       " '16.95',\n",
       " '16.96',\n",
       " '16.98',\n",
       " '17',\n",
       " '17.01',\n",
       " '17.05',\n",
       " '17.06',\n",
       " '17.09',\n",
       " '17.1',\n",
       " '17.11',\n",
       " '17.16',\n",
       " '17.19',\n",
       " '17.2',\n",
       " '17.21',\n",
       " '17.24',\n",
       " '17.3',\n",
       " '17.32',\n",
       " '17.4',\n",
       " '17.43',\n",
       " '17.44',\n",
       " '17.45',\n",
       " '17.5',\n",
       " '17.54',\n",
       " '17.57',\n",
       " '17.6',\n",
       " '17.67',\n",
       " '17.68',\n",
       " '17.7',\n",
       " '17.71',\n",
       " '17.72',\n",
       " '17.8',\n",
       " '17.84',\n",
       " '17.85',\n",
       " '17.88',\n",
       " '17.9',\n",
       " '17.92',\n",
       " '17.97',\n",
       " '18',\n",
       " '18.06',\n",
       " '18.1',\n",
       " '18.12',\n",
       " '18.15',\n",
       " '18.16',\n",
       " '18.18',\n",
       " '18.19',\n",
       " '18.2',\n",
       " '18.23',\n",
       " '18.25',\n",
       " '18.3',\n",
       " '18.33',\n",
       " '18.4',\n",
       " '18.42',\n",
       " '18.44',\n",
       " '18.48',\n",
       " '18.49',\n",
       " '18.5',\n",
       " '18.51',\n",
       " '18.53',\n",
       " '18.56',\n",
       " '18.59',\n",
       " '18.6',\n",
       " '18.69',\n",
       " '18.7',\n",
       " '18.78',\n",
       " '18.8',\n",
       " '18.86',\n",
       " '18.88',\n",
       " '18.9',\n",
       " '19',\n",
       " '19.01',\n",
       " '19.08',\n",
       " '19.09',\n",
       " '19.1',\n",
       " '19.12',\n",
       " '19.15',\n",
       " '19.16',\n",
       " '19.2',\n",
       " '19.27',\n",
       " '19.3',\n",
       " '19.33',\n",
       " '19.34',\n",
       " '19.4',\n",
       " '19.44',\n",
       " '19.49',\n",
       " '19.5',\n",
       " '19.59',\n",
       " '19.6',\n",
       " '19.63',\n",
       " '19.64',\n",
       " '19.67',\n",
       " '19.68',\n",
       " '19.69',\n",
       " '19.7',\n",
       " '19.71',\n",
       " '19.72',\n",
       " '19.81',\n",
       " '19.83',\n",
       " '19.87',\n",
       " '19.91',\n",
       " '19.98',\n",
       " '20',\n",
       " '20.14',\n",
       " '20.3',\n",
       " '20.34',\n",
       " '20.36',\n",
       " '20.37',\n",
       " '20.38',\n",
       " '20.4',\n",
       " '20.45',\n",
       " '20.46',\n",
       " '20.5',\n",
       " '20.51',\n",
       " '20.54',\n",
       " '20.58',\n",
       " '20.62',\n",
       " '20.63',\n",
       " '20.64',\n",
       " '20.65',\n",
       " '20.68',\n",
       " '20.7',\n",
       " '20.73',\n",
       " '20.77',\n",
       " '20.85',\n",
       " '20.86',\n",
       " '20.89',\n",
       " '20.92',\n",
       " '21',\n",
       " '21.02',\n",
       " '21.03',\n",
       " '21.1',\n",
       " '21.12',\n",
       " '21.13',\n",
       " '21.14',\n",
       " '21.19',\n",
       " '21.2',\n",
       " '21.21',\n",
       " '21.27',\n",
       " '21.38',\n",
       " '21.4',\n",
       " '21.43',\n",
       " '21.5',\n",
       " '21.56',\n",
       " '21.64',\n",
       " '21.66',\n",
       " '21.7',\n",
       " '21.72',\n",
       " '21.76',\n",
       " '21.79',\n",
       " '21.8',\n",
       " '21.9',\n",
       " '22',\n",
       " '22.07',\n",
       " '22.1',\n",
       " '22.24',\n",
       " '22.3',\n",
       " '22.32',\n",
       " '22.48',\n",
       " '22.5',\n",
       " '22.54',\n",
       " '22.69',\n",
       " '22.7',\n",
       " '22.71',\n",
       " '22.74',\n",
       " '22.77',\n",
       " '22.8',\n",
       " '22.9',\n",
       " '22.95',\n",
       " '23',\n",
       " '23.01',\n",
       " '23.03',\n",
       " '23.08',\n",
       " '23.1',\n",
       " '23.19',\n",
       " '23.2',\n",
       " '23.3',\n",
       " '23.4',\n",
       " '23.5',\n",
       " '23.57',\n",
       " '23.59',\n",
       " '23.65',\n",
       " '23.8',\n",
       " '23.84',\n",
       " '23.9',\n",
       " '24',\n",
       " '24.04',\n",
       " '24.07',\n",
       " '24.2',\n",
       " '24.3',\n",
       " '24.4',\n",
       " '24.5',\n",
       " '24.52',\n",
       " '24.6',\n",
       " '24.7',\n",
       " '24.8',\n",
       " '25',\n",
       " '25.01',\n",
       " '25.1',\n",
       " '25.17',\n",
       " '25.2',\n",
       " '25.32',\n",
       " '25.4',\n",
       " '25.44',\n",
       " '25.47',\n",
       " '25.5',\n",
       " '25.6',\n",
       " '25.8',\n",
       " '25.83',\n",
       " '26',\n",
       " '26.1',\n",
       " '26.2',\n",
       " '26.21',\n",
       " '26.3',\n",
       " '26.59',\n",
       " '26.6',\n",
       " '26.8',\n",
       " '26.83',\n",
       " '27.03',\n",
       " '27.28',\n",
       " '27.3',\n",
       " '27.39',\n",
       " '27.4',\n",
       " '27.62',\n",
       " '28.09',\n",
       " '28.4',\n",
       " '30.46',\n",
       " '31.79',\n",
       " '32.26',\n",
       " '33.44',\n",
       " '33.54',\n",
       " '6.4',\n",
       " '7.5',\n",
       " '7.81',\n",
       " '7.94',\n",
       " '8',\n",
       " '8.1',\n",
       " '8.2',\n",
       " '8.3',\n",
       " '8.45',\n",
       " '8.5',\n",
       " '8.6',\n",
       " '8.7',\n",
       " '8.77',\n",
       " '9',\n",
       " '9.1',\n",
       " '9.3',\n",
       " '9.43',\n",
       " '9.5',\n",
       " '9.52',\n",
       " '9.7',\n",
       " '9.74',\n",
       " '9.8',\n",
       " '9.9'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.Mileage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['mileage'] = pd.to_numeric(df.Mileage.replace('+AC0-1',None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mileage', ylabel='Price'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(x=df.mileage,y=df.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'+AC0-1',\n",
       " '1014.45',\n",
       " '1047',\n",
       " '1061',\n",
       " '1077.42',\n",
       " '1086',\n",
       " '1120',\n",
       " '1150',\n",
       " '1172',\n",
       " '1186',\n",
       " '1193',\n",
       " '1194',\n",
       " '1196',\n",
       " '1197',\n",
       " '1198',\n",
       " '1199',\n",
       " '1240.47',\n",
       " '1242',\n",
       " '1248',\n",
       " '1272.33',\n",
       " '1298',\n",
       " '1299',\n",
       " '1341',\n",
       " '1343',\n",
       " '1364',\n",
       " '1368',\n",
       " '1373',\n",
       " '1374.44',\n",
       " '1388',\n",
       " '1390',\n",
       " '1395',\n",
       " '1396',\n",
       " '1399',\n",
       " '1405',\n",
       " '1422',\n",
       " '1461',\n",
       " '1462',\n",
       " '1468',\n",
       " '1489',\n",
       " '1493',\n",
       " '1494.72',\n",
       " '1495',\n",
       " '1496',\n",
       " '1497',\n",
       " '1498',\n",
       " '1499',\n",
       " '1582',\n",
       " '1586',\n",
       " '1590',\n",
       " '1591',\n",
       " '1595',\n",
       " '1596',\n",
       " '1597',\n",
       " '1598',\n",
       " '1599',\n",
       " '1781',\n",
       " '1794',\n",
       " '1796',\n",
       " '1797',\n",
       " '1798',\n",
       " '1799',\n",
       " '1896',\n",
       " '1948',\n",
       " '1950',\n",
       " '1956',\n",
       " '1968',\n",
       " '1969',\n",
       " '1978',\n",
       " '1984',\n",
       " '1985',\n",
       " '1991',\n",
       " '1995',\n",
       " '1997',\n",
       " '1998',\n",
       " '1999',\n",
       " '2092',\n",
       " '2112',\n",
       " '2143',\n",
       " '2147',\n",
       " '2148',\n",
       " '2149',\n",
       " '2179',\n",
       " '2198',\n",
       " '2199',\n",
       " '2200',\n",
       " '2216.69',\n",
       " '2349',\n",
       " '2354',\n",
       " '2359',\n",
       " '2359.43',\n",
       " '2360',\n",
       " '2362',\n",
       " '2393',\n",
       " '2400',\n",
       " '2446',\n",
       " '2477',\n",
       " '2487',\n",
       " '2489',\n",
       " '2494',\n",
       " '2495',\n",
       " '2496',\n",
       " '2497',\n",
       " '2498',\n",
       " '2499',\n",
       " '2523',\n",
       " '2609',\n",
       " '2689.36',\n",
       " '2694',\n",
       " '2696',\n",
       " '2698',\n",
       " '2706',\n",
       " '2720',\n",
       " '2755',\n",
       " '2771',\n",
       " '2773',\n",
       " '2835',\n",
       " '2894',\n",
       " '2925',\n",
       " '2953',\n",
       " '2956',\n",
       " '2967',\n",
       " '2979',\n",
       " '2982',\n",
       " '2987',\n",
       " '2993',\n",
       " '2995',\n",
       " '2996',\n",
       " '2997',\n",
       " '2999',\n",
       " '3197',\n",
       " '3198',\n",
       " '3200',\n",
       " '3436',\n",
       " '3498',\n",
       " '3597',\n",
       " '4134',\n",
       " '4367',\n",
       " '4395',\n",
       " '4806',\n",
       " '4951',\n",
       " '5000',\n",
       " '5204',\n",
       " '5461',\n",
       " '5998',\n",
       " '624',\n",
       " '72',\n",
       " '793',\n",
       " '796',\n",
       " '799',\n",
       " '814',\n",
       " '936',\n",
       " '970',\n",
       " '993',\n",
       " '995',\n",
       " '998',\n",
       " '999'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.EngineCC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['engineCC'] = pd.to_numeric(df.EngineCC.replace('+AC0-1',None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'+AC0-1',\n",
       " '100',\n",
       " '100.6',\n",
       " '101',\n",
       " '102',\n",
       " '102.5',\n",
       " '102.53',\n",
       " '102.57',\n",
       " '103',\n",
       " '103.2',\n",
       " '103.25',\n",
       " '103.3',\n",
       " '103.5',\n",
       " '103.52',\n",
       " '103.6',\n",
       " '104',\n",
       " '104.68',\n",
       " '105',\n",
       " '105.5',\n",
       " '106',\n",
       " '107.2',\n",
       " '107.3',\n",
       " '108.4',\n",
       " '108.45',\n",
       " '108.49',\n",
       " '108.495',\n",
       " '108.5',\n",
       " '108.62',\n",
       " '110',\n",
       " '110.4',\n",
       " '110.5',\n",
       " '110.78',\n",
       " '112',\n",
       " '112.2',\n",
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       " '113.98',\n",
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       " '114.4',\n",
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       " '116.3',\n",
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       " '123.24',\n",
       " '123.37',\n",
       " '123.7',\n",
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       " '125.79',\n",
       " '126.2',\n",
       " '126.24',\n",
       " '126.3',\n",
       " '126.32',\n",
       " '127',\n",
       " '130',\n",
       " '130.2',\n",
       " '130.3',\n",
       " '132',\n",
       " '134',\n",
       " '135.1',\n",
       " '136',\n",
       " '138',\n",
       " '138.02',\n",
       " '138.03',\n",
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       " '138.1',\n",
       " '139.01',\n",
       " '139.07',\n",
       " '140',\n",
       " '140.8',\n",
       " '141',\n",
       " '141.1',\n",
       " '142',\n",
       " '143',\n",
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       " '147.5',\n",
       " '147.51',\n",
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       " '148',\n",
       " '148.31',\n",
       " '149.92',\n",
       " '150',\n",
       " '152',\n",
       " '152.88',\n",
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       " '99.6'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df.PowerBhp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['powerBhp'] = pd.to_numeric(df.PowerBhp.replace('+AC0-1',None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6019 entries, 0 to 6018\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Price         6019 non-null   float64\n",
      " 1   Make          6019 non-null   object \n",
      " 2   Location      6019 non-null   object \n",
      " 3   Age           6019 non-null   int64  \n",
      " 4   Odometer      6019 non-null   int64  \n",
      " 5   FuelType      6019 non-null   object \n",
      " 6   Transmission  6019 non-null   object \n",
      " 7   OwnerType     6019 non-null   object \n",
      " 8   Mileage       6019 non-null   object \n",
      " 9   EngineCC      6019 non-null   object \n",
      " 10  PowerBhp      6019 non-null   object \n",
      " 11  mileage       6019 non-null   float64\n",
      " 12  engineCC      6019 non-null   float64\n",
      " 13  powerBhp      6019 non-null   float64\n",
      "dtypes: float64(4), int64(2), object(8)\n",
      "memory usage: 658.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Make</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>FuelType</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>OwnerType</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineCC</th>\n",
       "      <th>PowerBhp</th>\n",
       "      <th>mileage</th>\n",
       "      <th>engineCC</th>\n",
       "      <th>powerBhp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.75</td>\n",
       "      <td>marutiwagonr</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6</td>\n",
       "      <td>998</td>\n",
       "      <td>58.16</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.50</td>\n",
       "      <td>hyundaicreta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>5</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582</td>\n",
       "      <td>126.2</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.50</td>\n",
       "      <td>hondajazz</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>9</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2</td>\n",
       "      <td>1199</td>\n",
       "      <td>88.7</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.00</td>\n",
       "      <td>marutiertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>8</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248</td>\n",
       "      <td>88.76</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.74</td>\n",
       "      <td>audia4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>7</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2</td>\n",
       "      <td>1968</td>\n",
       "      <td>140.8</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price          Make    Location  Age  Odometer FuelType Transmission  \\\n",
       "0   1.75  marutiwagonr      Mumbai   10     72000      CNG       Manual   \n",
       "1  12.50  hyundaicreta        Pune    5     41000   Diesel       Manual   \n",
       "2   4.50     hondajazz     Chennai    9     46000   Petrol       Manual   \n",
       "3   6.00  marutiertiga     Chennai    8     87000   Diesel       Manual   \n",
       "4  17.74        audia4  Coimbatore    7     40670   Diesel    Automatic   \n",
       "\n",
       "  OwnerType Mileage EngineCC PowerBhp  mileage  engineCC  powerBhp  \n",
       "0     First    26.6      998    58.16    26.60     998.0     58.16  \n",
       "1     First   19.67     1582    126.2    19.67    1582.0    126.20  \n",
       "2     First    18.2     1199     88.7    18.20    1199.0     88.70  \n",
       "3     First   20.77     1248    88.76    20.77    1248.0     88.76  \n",
       "4    Second    15.2     1968    140.8    15.20    1968.0    140.80  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f8a48bbb250>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[['Price','Age','Odometer','mileage','engineCC','powerBhp']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f8a4ad8f2b0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TPFsystjHMW1NVdceidLR7OID559YEbvsqPIY32ixCCmM69p7nsDXrzkbACeyzkdp18LfvOUUXLLt4bL9YtNtPfj61Wdj6YJUnUdIc9l4C5G/rcFrvxMll2VE5AhVLXT0fTuAJ2vwmkSzQrWzG/tGPAxn/bL7D2d9HMzmkM2NWXvk0q6qr7N/1I+8tn/fNeegIxF9hiPnm8giKecbTmSdp1SBXBC9X4zmeMmOpuZwqZkUgKsAvBrAfwP4kqr61R4zHiLSBOANADaVbP6ciJyO8NLM82NuI2oohcmlYz/Q25td7BkcxYgXlBUc169fjWzO4Mb/2FU2d+PG/9iF//P238fS1lTkmYrJ9LSpViQdrpsvNaasb/D83pHI/YIFKE3V4c6IfAVADsB/IryUcjKAD031RVV1BED7mG2XTvV5ieaKakuvx10auXvTWrz77JX42L2H5m5ct241AqOxZyqWLUhWnVcSpVqRVG3CLC/ZNK5coHiydz+2dHdWXOZrcsfbsowo2uEKkZNV9fcBQES+BOCR2g+JaH6IW3rd5BMKpXoHMlBFsQgpbPvYvU/gro1rsXc4G3mm4utXnx1bVFQb1wkdLbh701nwAwMnvwiaZUns2ZJqa5ywGJn7bEvwptVH4ms/ewE3X34GbEsQGMU9O1/EZWevBJrrPUKayw5XiOQK/1BVX5jVIqq5VCL6wz6IKVCMAqO56Eswo76p2vQu6iwGADzdNxRZVMQ164tb44SXbBqDa1sQAd60+kj0DhyaRP2m1UcywktTPht6uELkNBE5mP+3AEjnv570yqpEVN2SliS2X9qFK28rKQQu7UIydu6GhSAwkbc5VY4F1S7nxBUV7c1uZLO+uAmuXHukMTg2oCrIRMxdslmJzGvTMYH9cCur2mNWU3WmeWVVIhrDsgSrXhWexfjRx16P+645B6te1QpAcf361WWrp16/fjWgCse2Im9z7PCSyds3/wjnXPcDvH3zj7DrlcHibzBRBUfGi5/gGveYQFF87YLDzUWhuSPjGWR9Ezl3Kcd+M/PadKzEPO6md0RUX6O+wee+W56a+dx3d+EfLzkdjiWRt33hna/Bjd8fk7T5/i585u2rYxM1haIiaoJr3GNUdcJzUWju8I0WV1ct1TuQKU6W5lyg+WkyybyxWIgQzTJxpzoXNyXQN5TFptt6ivdd3paGk59E2tFa/qHf0erCFkQmbYwxsRNPUwkrtqjoH/Zii5RVy9Kxc1Fobks5FkZyQfREZdtC/7DHuUDz1OFWfB4P5q6I6sQYRd9gFi8NjKBvMAuTP8Udd6rTKLClu7Ps8suW7k6kEhYWpRx84PwT8elvPYV3bHsYn/7WU/jA+ScimbAikzaBxjfeW9KcLE5wLV4ayl/vrdasr5ACOqqtCR2tyRkvQuJ+njR1CuDvvvNL3HDRaWX/91u7OyGinAs0j1U7JowXz4gQ1UG1CV7ZmFOdvjHoeW4v7rhyLVQVIoIHnnoZi085Alnfi1xB9c6Na2MvpVRbywRA5G+4h3tMvXDF19ryfIO+QQ/JRHmjxlTCQmCAJOcCzVvTcUxgIUJUB9VWKLUQPUfDFsE5J3TgmT1DxQ+Cc07oQHPSxoGMH3v9fqKN8g5nMo+pNa74WluWJfjg+Sfg/Xf8vGJfumvjWs4FmuemekzgpRmiOqg2wcu2JDIBY1sCL1B84htP4h3bHsYnvvEkvEDRnHCQsCQytZKwrSmfNp0LpmPCHMVzLMHR7U0xk5t5CYymhmdEiOogYVuxE/8yuSAyAfP5S07H5//j12XbP/8fv8bfvvXUYny3EK8sFC9JR5B0yk+nJ53G+/1jOibMUTxBOGH15svPQJNrY38mh60PPoO+oSwcEex6ZZCXwWjSWIgQ1UlU4QCEy2lHpWNcx4pMwACKTC462vvFd70Gl335kYoP6K9fczaWtjZO6/Zq/XFo6sJ90qtYzKyjNQnJr6rLy2A0WSxEiOqg2lmPpBN9dsMYje01E1e8xC0LP5XW7bOxud1snUTbKEZ9g6vGTIa+9p5w3/MDw8tgNCUsRIjqIGFb0WuC2BZSroUlrcmyyylLWpMINH5BqbjiJWELNv3RsVjfdXRZo7LCsty+b7BnKItcYJDIN7dzqly6mc3plNk4ibZRxBW0Wd9gxAvwxpOX8jIYTVpdChEReR7AIIAAgK+qXSKyGMBdAI4F8DyAi1V1oB7jI6q1pS1JbO3uLP6WWViTYWlLEvtGPFz/3V9hXecKNMGGFxhc/91f4ZNvOSU6TWMJRASLmxO45YozYQlgFAhMgCbXwoWnL8cVt/ysrHV72rXg+wa/emWwYgwn5YuKqLMe1dIphQXPeEai8cTNaXJswRfu/zX+15tP5mUwmrR6nhF5varuLfn64wDuV9XPisjH819/rD5DI6otyxI0uXbZWY8m14ZlCUZzAb731B5876k9ZY/527eeghsvPg0fvvvxYuFw48WnwbEEqoq060AgCFSREIFCMJw1keuL3LVxLUZz2YrT7Vft6MHXNp2F/Zlc5FmPuHSKMWbWnimh6aC46dJObLqtvGgNjMEV56yEbQn/n2nSZtOlmbcBODf/768AeBAsRKhB9Q97kZNI77vmHFj5KO7Y2wIFWtNO2VkP34TX5V1HcDBr0LvvUIv25YvTWJhycPZx7bjytccVL81sf+hZ+EYBRJ9u9wITe9YjLp0SKCIf02iTYucrWwRHLUqGc0KMwrYEQNh/ZuWSJtgWL8vQ5NUrx6cAviciPSKyMb9tmaq+DAD5v5dGPVBENorIThHZ2dfXN0PDJZqcuP212roXTsw6IknHQhAodu8LlzDfvW8EQaBIOBY8X7F3MFu2xsjewSwcW9B91jG44paf4bwbfogrbvkZus86Bs2uDSdm7RHbktixxS3nHBgTMynWTNvPkmqr2rHVtgQv7c/iHdsexuuufxCXbHsYu/dl4PkGjmXxsgxNSb0KkXNUdQ2ANwF4n4i8drwPVNVtqtqlql0dHR21GyHRNIjbXwtnFkoV1r2wSuZ7PPCR1+GWK87E4uYEVIGDo35ZsXFw1IdCkTMa2aI94xlcc/ujZduvuf1RZH2DhCPYvGFNWVGxecMauDEFioiUpVNK+9DYEv0Yh2fr54xqx9asb4qXZYBD+9dLA6PI+oaXZWhK6nJpRlV/m/97j4jcB+BMAK+IyBGq+rKIHAFgT9UnIZrD2tKJyMmqbekE9mc8LEgn4AcozvdwbAu5wEQWG3dtXBubaohr3e4bRQIWXtg7iDvzj7ctwc9f6EdHq4vb//xMWGIhUIUtAqMGdv6zxhhFLjDwjUICA2M0dkE1x268xdPmo8J+9JoVi3DVuccXI+dLWtz8ZT6iyZvxQkREmgFYqjqY//cbAfxvAN8E8G4An83//Y2ZHhvRTBnI5PCF+8tXSf3C/b/GZ96+Go4FvHwwV5xkWki6LFvgxsZ33bhUQ8x8E8cStKUSOLZjAS7Z9nDZ67QmLTw3lMPVOx4p297WnohN2ixqSkSui/JP73rNjP1MqXYSluCNJy+tWFBv84Y1SCc4P4Smph5nRJYBuE/CdQwcAHeo6ndF5GcA7haR9wJ4EcBFdRgb0Yzw/OhkzCffEsADYpMukWuCWALXkcgzLM1JC1s2rMHV+cszy9vS2LJhDZKOhb7h6I69d21cG7t9SCoXtroqf1v0arDhh9RsXASNxs+xBB9/0++VTbAuXOa7e+PaOo+O5roZL0RU9VkAp0Vs7wdw/kyPh6geqvVGGfFiOumqRq4J0uRaODga4KmX9pddZvnx031ob+lAekxMOO3asC0g50VPMK12OScuaWMJYi81zeZF0Gh8soHBgUyuyn5BNHmzKb5LNG9U643i+UH0wmUisWcqmlwbJx25sOwyy+YNa6AquPzmn1U8190b11a9bBO3XST6NqOIvdQEREd7q/Um4RmU2cUSwf6RXOyCekRTwZlkRHUQlz4JFzqzItMsIoj9jdTzo9MxuSD6rEfOaHjZpruz7HW2dHdiQTp6e3PSKq4IW3rb1u5OOJbge0/twabbevCObQ9j0209+N5Te+D5QdWocpTCGZS3b/4RzrnuB3j75h9h1yuDMPzNu24sAVYsTlfEym+46LRwTRv+39AU8IwI0SwznDX44a/24I4r10JVISL4xqO9+LPO5bFnKnyj6GhJlp2R2PrgM7CrnN0Yzhp867Fe3Hz5GWVzTi47eyV6nttb9voPPPUyljQfiUVNFlYtbSkubOVYgqUtSewf9WMvNalq5G2JmERNtWXk2UumPowCrmPBsSzc9p4zEajidwdG8aX/ehaffMsp6B/2+H9Dk8YzIkR1YIzi+f5hPPnSAfQOZPDkSwfwfP8wjFEkbMEbT10GzzcI8mc73njqsth1PxKOIOVY+OgFq/Dpbz2Fd2x7GJ/+1lP46AWrkHKsyDMYSceCbxSPPL8fz+4dRt9gFs/uHcYjz++HbxTfePx3+OXLB/G7A6P45csH8Y3HfwffKIxR/GbvcHFhq3dsexi/2TuMtnQicqGzwkJXUQu0xZnoGRSqPUuAwBikXBuXfvkR/PE/PISPf/2/8cHzT0Rz0oIxXLiOJo9nRIjqYH/GQy4IsGJxU3G59lwQYH/Gg20Jsr7imtsfKZvvAQM8+nx/xZmKN5xyBEQEN//oubIzIjf/6Dn87VtPxeKWBL565VoYVVgisG3AqBaLl7FrfzS7duR2167e9K5wqWnsvI5MLoiM9n7+ktMj54JUm8hL9SIQCCwAt//5H8ASgVHFHQ8/j7e+ZjmWcGVVmgIWIkR1YExYSOzeN1JMsxzVloIximzMfI+vX3UWulYuwbu2H5qQurW7EwlH4AdascbDdetWA6LoH/Qq4rtHLkwha+IXSIvafvems5CrcrbCsiTy9HzCtiKjvamEHZmmOaGjJXYiL9VHKiHYNxJgU0kq6saLT8ObVh+Jz//Hr/E3bzml3kOkOYyXZojqIDCK/iGvbLn2/iEPgdHY+KxnNHINjyAAVFEsQgq3fezeJ2AMikVIYfvV+SXeq8V0I7cHBgJELuVeTdwEVyA6TTOQycVO5KX6yOS0Yon3D9/9OEZzBus6V3CyKk0JCxGiOojrDZPLTwCN+rCPW8Y9F5jY22IfYzS+P0yVZngiwHXryud7XLduNaRKjeA4Fk5a1oq7N52Fh649F3dvOgsnLWuNTfSUnl05qq0JHa1JFiF1Fvd/taTFRXuzywgvTQkvzRDVQVyBYIxiaXMSW7o7K5Z4j1vfo/AhHXfbG09einWdK4rzM+7t2V0sKqJ6yiRswecvOR0fuvOx4ut//pLTi+mch3a9UpG0ObZ95bi+79LfmzkXZO6IS1/Z+YIxLgFFNB4sRIjqIFElVtuf8fDFMYuDffH+X+N/v+3U6MZylkAA/PO7XoN9w7ninJPFzQkkLMH7zzuhOOekMPE15VhwbGD3QERPmWYHrSmnbDXW1pQDEaAlZUeu7tqSsmMXIfN9g+f3DWP3vkzx+UYW+zimrYlzQeaIhCW4fv1q3Pyj57CucwXam10sbnbR7Ibdohck+VFCk8e9h6gOHFsie8A4tiCTM5F9aP7PnwJNY5Zrb8p/ENgWYFsWPvGNJ8smsrqORE58vXvjWmR9RBY8n3zLKXjPLTsriqS7Nq4FRCMf83frfh/P9w/jhf5Dk2+PaW/Cse3N2DfiYTjrl30vw1m/bC4IV1Cd3WxLcGRbuqKovenSThy1KFxHZqnLjxOaHO45RHWQCxRffODp8g/0B57GJ99yCpyYyymWCBY2JdCcTBQjv44NiABZP3oi610b18b2rbEtiUzaiABnH9eOK197XPHyy/aHng0XMDOKa17/agwM5wAArm3hmte/GkEAvHJwtKwQun79aixscmBUMeIFFbcZ1dikTTVc/n3mZX2DoYxfUdRuuq0Hd29aC85VpalgIUJUB4HRyLMe/+vNJ2Nh2sYHzj+xYo6IAug7mMWH7368LEKZWmzHTiYMTPSqppZIbNLmro1r0X3WMWWXXzZvWINm10ZgFJmYoiJq8u2d+c6s1W6bCDbQqw9LgJaUE5OmUrAOoangDCOiOrBikimWJRjKmuLlj7s2rsUnLjwZX7z/1/B8UyxCgEMRSj/fbTfu+eJSLkZjJsyq4p/yZ2sKr/9PDzyNrG9i0z5xkV+TX4017rY4xij6BrN4aWAEfYPZ4n3jFlTrH/bG9XOnyTGK2DSVZQkSLAJpCma8EBGRFSLyAxH5pYj8QkQ+lN/+KRF5SUQey//5HzM9NqKZ4tpW5LLnrm3BEuDdZ68sW6793WevhAjQ0ZLETZd24q6Na3HTpZ3oaEnC5CO/Uc/nWIKv/Pi5sqLiKz9+DqqAY1kx8V0LH/rjE+HmkxCuHX4tUj3tExcFjvsAcyyJLDiqNb3j8u/1IQJkckHFPnbjxafBsQRpl7/T0uTV49KMD+AjqvqoiLQC6BGR7+dvu1FV/74OYyKaUaoaOfFUVWFiLpncvXFt5NLrKccCRCKfz7Yk8jJPS8qC5wM3da/BnkGv+JilrS5cR3Awk6u4/NLRkoQgvoHdDRedho987dBloxsuOq24vkjUbY4tkZdZli1Ixi4jz8hvfagCl9/8s7LGiiNegOWLwwiv5/PiDE3ejJexqvqyqj6a//cggF8COGqmx0FUT6O+wae++RS8IGwW5gXh11k/fnEy32ixn0zh7MbNP3ouvzhZmKgp1eTaECDyMs/QqCkWPaWru4Y9b6Ivv3iBgSXAjRefVvFbsSXAl/7r2bLX+dJ/PYtAw9P6UbflAo0sODJe/FmP9ma3anM9qo3CPvnz3fux6bYevGPbw7jilp9hNGfQP5RFjrNVaQrqOllVRI4F8BoAPwVwDoD3i8hlAHYiPGsyEPGYjQA2AsDRRx89c4MlmoS4/dW2JLL/imUJLIlfPCoq5WJJWNhc/++7sK5zBZpgwwvCrz/5llPQN1g+f6Jv0IOf/+CISu78zVtOib38EgiQSpSfeUklbBhVfOj8E7An/1qubeFD558A17agGt0HJ3Y1WI1enM11bFiWMPJbI9WOrYU5TWP/TwSClqRTvKTG/weajLoVIiLSAuBeAH+hqgdFZAuATyNcfPHTAG4A8J6xj1PVbQC2AUBXVxfLcJrV4vbXlGPhy5d34aWB0bKmdynHwsJUInZl1biUi28Ui9IujlvSDNsSLG52sSjtwhLgU289GftK4rafeuvJSDkWTEyBYFvApj86Fuu7ji5bPdW2BNDy3jVA+IH0tU1nQSEV65ikXWA0Z+Hp3x3AHSUdgB946mWsXNIc+eGWcqyqC51NJvJLh1ft2OpYgpsu7Sz2mylcrjuQ8fCqBamwd9Kwx/8XmpS6FCIikkBYhNyuql8HAFV9peT27QC+VY+xEc2EwCgOjJTPw7jx4tOwMJXAoOejxbVwyxVnFtcLsSXsyhsX0W1ybfz5a1eid+DQ6qV//tqVsC0LzUm7bO0RwEAknCgaV9hErZ7a5Fo4OBp92SQXmNh1TJa2JNE5pmtwYZ5K1Gqwjs2zHrONJYL2Zhc3Xnx6sdgQAfYczMIASCYsjOY4YZgmpx6pGQHwJQC/VNV/KNl+RMnd3g7gyZkeG9FMyRnF9v8snzex/T+fRc4osjmDOx95EarhL6WqijsfeTE2omvn0ydR3XxtCxjxDC6/+RGcd8MPcfnNj2DEC+d6xEVuA6PFszGFbVfv6MGIZ6rGhOOeq2/Yi3y+oUyAXKBlY84FipxvJtX0Li7yS1MXGAOjCqOK7i/9FOf+/YPY8C8/hW8MhrM+AgNOGKZJq0fm6hwAlwI4b0xU93Mi8t8i8gSA1wP4cB3GRjQj4iK6tgC2BazvWoHegQz6BrPoHchgfdcKuI6FLRvWlE3U3LJhDVzHil3fYzRnIpd4z/qm6roQUTFh3yiSTnTsOJFfDbb0MW88eSlsS2IXW8sZxV/c9VjZ2P7irsdgdOJFRbXIL02dIkzOFJJPwKF9bO+QBy8wWJTi+pg0OTO+56jqfwGI+vXmOzM9FqJ6qbaqqa3hae8Vi5vKLqfkAoNlC5PFOSGOJUg44Qd9taRN3PZm18bmDWsqGuKlHQv/83+cVLmCq2MhlQCOWdKEr165ttix17aBlBsdE065FrKeiZ18G12gmKqrp0Yt8R630Nl915zDeQvTwLUtjMSkmTpak7AEGMjksDTBsyI0cSxhieogMBrZzyXIn3UY8QyuuX1nWYHQ0WLhxYFsxYf90W1JDMd82DsxaQfHEiiARU2JsrkoriPIGY1cwfVrm85CNoewiir9XUIVI9noyzl3b1yLpGNFFjxxHYhtkdiior3ZjSxSFjclpn2hM/a0OSQoWb137P/Xi/0jOHFZC4zy7BNNDpfDI6qDtGsX+7mcd8MPccUtP0P3Wccg7drI+gbffvwl3Hz5GXjgI6/DzZefgW8//hKGsiZ6rkU2XIvkhovK1/e44aLT0ORa2NLdWX45Jz/xVBXIeOVdcTOeHzspNhcYWBbQP+zjndsfxrnXP4h3bn8Y/cM+XCf67IZvFENegB/+ag/uuHItHrz2XNxx5Vr88Fd7EKjGLj8fV1TEnfkoRH5LTWWhM17qKZcz4fyQsZcGr1u3Gl+4/2kY1WIknGiieEaEqA48P3ruxl0b1yJhS3GOSCFNsr5rRdXLLI4leOCXv8PNl59RFrldueR4LEw7FWc9gLDXzL7hHK69pzySuWJxIrpRniXIeIf64BTXHrn/1/jkW06JvfziWIKulYvLUjPXr18NRw4tP194rq/8+Dl8Kua5Eo4Vu8S7qlaN/E4UL/WUc21BJqcYyvrFNWT2Z3L4+3/fhb6hLCwJ9zmiyWAhQlQH1YqKhCUY8covKYx4ARakogsExxKkXAsXnnZUeeR2wxoAwLu2/7TiMXdvXAsDFFdqLRQCN//oOXz6bafi+vWrK5aST1iCIGbtERHgunWrIxdbC2Im0n5t01n44PknFmO/hbVHmpNW5NLzjiWQKku8r1qWnrbIL3valPODcP2Xm3/0HN599sqy5fq3dndCBEjkexPxkhZNFAsRojpwLIlcNMyxBAZh4TG214tlCW6+4gz07jt0pmT54jQWpC0czJiyhcZ6BzK4+vZHcefGtfFnUWzBNa9/NQZKFju75vWvRqCK9Ji+NWnXRqBadZJt1NmNv3nLKbFdfnPG4Atjzq58IX92pTWdQDLhlJ3FMUbR3pzEre85Ey/0jxTHdkx707Qv8c6eNmMIkLAEV5yzsli8tje7WNzsoiXlwPMNko5gdNTHcwMjsRONiaJwjghRHbSmLaw/4+jyiO4ZR6M1bcGPOYPg+WFyplQuMBjJ6oS74tqWwBZBJl/wFNbxyHgBBILNP/hNWR+czT/4DQQSW1QYVfzln6wq69j7l3+yCumEVbXL70Wdy3HqkQvwqoUpnHrkAlzUuRyBhou37d4XRnd37xtB1jcQCX/bjhpzEJiqczp83+C3+zN4oX8Yv92fge+X/xzHYk+bcqph993PfXcXrjhnJU5c2oJFTYnwZxkYpBMWXhrI4On+Yfzro7srLmn1D3uHeQWaz3hGZIxjP/7tCT/m+c++uQYjoUaWySr2D3sVK6suyPftiFscbOxqrNevX432Jjc20WBbEnnJRASxa4/cc9VZ2PS64/GhOx8rPubzl5wOSxDbB8cSgRUROQ4CRSohkUvWNycFr1rUhHdsK19xdWHaxr5hg1cvbSlGhH0TIAg07M8zZsLuph09uHvTWZFzOr5+zdlYnHbxq1cGKy4BnbSsFY4T/bsYe9qUM6rwA8WZxy7CsgUp9A976B/2cG/Pbnzw/BOxtMXF0gVJXHzTw7j58jNw038+X3zsfL6kRePDQoSoDvyYiOxdG9fCsa3YomLXy5U9Wwof/nFzNPYNZXDnxrXFCObPX+jHyiXNCIyJjBAbVSxIlU9wVTUIVGFZYTqndI7ADRedhoQjGBhRXLXjkbIP+4VpwWiVCa5RKaBwnRTg+b1DZZegHEcwGrPEvB+zaFo2Z7AnyEYuP3/3prNw5KLyMzWl2NPmEEsEL+8fxltPX47LvvxI2T4WXl4LL8G9o3N52JOoxLy+pEXjwkKEqA6qLa++IG1ha3dnxW/wTa6Fc05cimf2HPqAPufEpUgnLGRyBg/teqUiNfPqjuOwsmMBLik567C1uxPNrsA3Ni7NR4hLJ7imEhZe6PcqJqsuTCUAAKmENab7rgU/QOyHfc4ovvfUHnzvqT1l3+9fv/nk2PkrxpiysyuBCZDz49eysPMru67rXFEsdu7t2Q1LELuyqx8YTqwcpybXwvFLFxTPXgGH5gd94sKToaq44+Hn8ZbTl6PJPTS/pvSSFn/WFIeFCFEdxH2gWpYgm1MkbJSdkQhMAM9X7B3MVlyaWZB0kHKsyEZ1IhJdIGxcC4hETnC9a+PayEs2d25cCwvA++74ecW4b//zP4j9sI8rEuIWNEvYFg5mFFff/khZgWRZAlHFP73rNRgoaZTX1hw29PvLP1mFlwZGARyao5JwLEigsa8z0RVc5+sHp+fHz0Nqbw4vDb75tKPw/x7rxeXnHFdxScsYxW8PZLBnMFu8pPPhN6ziJFYCwEKEqC4SlsRGZBXAaM5g33C2rCttLqmRcdswmYLIyxx3blyLjpZk2WO2PvgMfFUgZnVX32jkY4xRBEDkbXEruCZsC64j+Os3/x5yQdhjp70lib9+8+/BsaO779qCyALp7vxlq8BoWTH2+UtOh2tbxaZ/pT/PRWkXS1uSuKm7szi3ZHlbGjd1d8J1oldw/fo1Z2NJc7JqkRKnUYuXnFG4joU7N67FkhYXtgh+d3AUN//oOXS0JpGwBdfc/ihuvvwM5AKDnDHh8v9WfnG4PYPYdNuhn/9161bjxu/vwmfevpqXv4iFCFE9BKpoSTpllzhakg6MKkQEnm8qJrIWGuWNnQdiV7n8YAvwf/70FOwZDFMLrm3h//zpKUjZFixLiqu7Fp5v84Y1aHFtXH/R7wOwioXD9Rf9PlIJG6qKj16wKrKAiiqsCp/BBzN+sbgonOFoSToYzZV/nzdcdBqC2LivQiwtTqItbP/QnY9VPYsDAK0Ri7rF9U4ZzZmqC5oVetuMLTYKq7FOV3R1NhU1ri3oG/Lwl197HB0tSXzw/BNwdHsT/teFp2BhykIuyO9vluCZvmFcccvPivtAR2sSn/+PX0de0uEkVgJYiBDVhWNJRW8Oo5rvVhs/kTVuDY+4MxKWCA6O+hVnCjpaU8hWWd3VscN5H8Xx2hZsC8j60Yug/fWbT8bu/uGySbE/froPRy5KI2EEPc/3V0yyXXzKERXdXD/ytfD7jFu4zfOjJ9jGzbkxRrFvxIMlQNKxEKgiIQIRhSVW5CWj8HWCyNfx/CC22Ogf9nDj93eV/Wwm+1v/dBc1U+UHik239eCDrz8eZ5/QAd8c+n88Y+USvGpBMj8p1cIX7n8awKFi8NNvOxXrOleUzREqXNLhJFYCWIgQ1YUfaORci7vznXXjJrLGbXcdK/KMRFxEt1AwRD2fJeEZjLFN6lpcB4LolVUXpm383lGLyibFbunuxIKUDaNA58olZUu8b+nurLomyR1X/gE8X8vOYLSmLGR9RE6wbXbt6AXibAsJGzgwaioWglvWauOvLzwZufzrtLck8dcXngzXFji2FTuR95k9I2UdkJ/ZcxBLWpIwxuC9f3hcRaLIGAPfN9gzlEUuMEjYFpa2JGOjw8DsW2I+Z8JETNT/8c+e24s3nHIE7r5qbUVb9d6B8GfeapV/1CxvS2Npa3LerstC5WbdgmYicoGI7BKR34jIx+s9HqJayMUUAbmSLqelCsmQuO1Z3+Bz3w1/G79r41p84sKT8bnv7qpavDgxz2cUkWdKsr6BiVlZdTimId+IZzCai76tsCbJ2Ne3RCIXNFMFcn70CrKeb/Cus46FSPhRKCJ411nHIpUQeL7CsYBXL23BsoUpvHppCxwLyPnAwZEcLr/5EZx3ww9x+c2P4OBIDiLAiBf9OoECi1vSeMe2h/G66x/EO7Y9jMUtabiOwigiz/AYBX57MIMRL0BgFCNegN8ePLSoWtRia4UzMt//8GvxwEdeh+9/+LU4+7j2ul3KsC3Bn65ZHvn/eN7JR8A3ioGhHG75r+dw1bnHFx+3vC2dn/vjli0Od9OlnThyYboh5s/Q1M2qMyIiYgP4ZwBvANAL4Gci8k1Vfaq+IyOaXnGXUpx8k7iosxtJx8KNF59WvGxTmDvi5A/mfUNZbLqtp+z5YpMplsB1LGzZsKZi7kZc8VLorhp320QfU60/TVw6KK6A8zVc7G3sWZzmhI2EIxgaMrh6R3miqNmNnxQb9/14vomNKQcmep4ORLE/amyujYWp6MXWjlqUjJy/05Ssz++Orm0hk4tvOJiwBJt29ODmy8/AgUzYMqDw/5Z2bWx98JniJavlbWkcwSKESsy2MyJnAviNqj6rqh6AOwG8rc5jIpp2adfClu7Ost8St3R3Iu1aaElaWNKaxKffdiru2rgWn37bqVjSmkTCARY1u2XbFzW7SDiChCW48eLTyp7vxotPQyrmdZKOBZFDkzgf+MjrcMsVZ6I17cSeeSkUSXG3TfQxqij2pymcxfnKj5+DUUReTspVOYtji8Sexcl40WdkvJgJvtXOSsUWaYGJPcNjTPQZptFceLkmqrAZ8aLn74xkqy9NXyuBmsP+Hxcmqy5udvHQtefizo1r4VgW/vabT+Hunl5suq0HH/na43Adm0UIlZlthchRAHaXfN2b30bUUAIj6HluL+64ci1+eO25uOPKteh5bi8CI0i7Lo5ckMCJy1rwqoUpnLisBUcuCBcTa07aePXScPurl7agOWnDtoDmpKBtTJHS1uwiaQMLxhQbC9IO3ATgOuGKmaUskbBI2rCmvHjZsAYp18KidHRhE7e9JWWhKRlzW9LC+887AZ/+1lN4x7aH8elvPYX3n3cCRKLPoBijSDoWNo8Z2+YNa2If4xutOucm7lJX4WxN6etct2517IexVeUx1c4WxS62dpgzTDNOJfb/2LGAjG+Kk1U/cvfjsC3BkQvTaEk56BvKFu8/n/v1ULxZdWkGqJjrBABl7zwR2QhgIwAcffTRMzGmw5pMfxqAPWrmg7j9tb3ZxR8c31E2gbNwkLYsQdpNYiTnAfnoZtoND95ZfzRs5KaASHiJpzWZhDGKIxYA6YRdnES5KG3BhoMFKWAka+AbRdISNCUtJOBgJPDRmrQxIiW3uRYsC1jYlKiIuzbb4WOOa0+WTdZclLbgBcCyBW5ZasZ1BJ4PNCWAJS0JfLUkNePYgGMBiyJeJ66fjeuEE0/Hji3hCKCIvdRV+HfU5amoS2CuJfCNRnYT/vTbTo1d/yXuMZ98yymxa6zEja3apbtaqXZsbUkJBjKm4v8eUKzb+jC+euVabOnuxHA2h76hbPGsB/v10HiIap0q7AgichaAT6nqn+S//isAUNW/i7p/V1eX7ty5M/K5JlsczCQWIrNSzY6SY/fXyawTUe0xhWSGHxg4JcmM0VEf/Rmv+OHRnnaRSjnhJMmDmYp0ypEL0vB9E/kYYxQv7R9BtuQxSUewtDmJ3gMZvFiSTDl6cRor8r1ccsbH/oypKJKGAx+j3qHtKddCs+3gmX3DZQtg3XRpJ45f3AwAOJjLFVf6LBQ8CxIJPN0/XNFY74T28DFxt/UezGB3yZhXLE5j+YJwzL/pH66Yu/Hq9mbsPpipSOCsWJCGCPDrvdGvE/VcJy1rBYDIOSInLGmOfK6TlrYgkSiLvNZkfx27r46O+gjg42DWYO9QrmJcyxclkfUN/uYbv+CKqRQndoeYbYWIA+DXAM4H8BKAnwF4l6r+Iur+LESoBmasEJkN4oqXauKKIc/z0Td8qHjpaHbhuuFJ17jb4oqkuO0AZvVjslkfe0cO3bakyUUy6VT9OcfdlssF4fb8cy1tSY4tQoAZKkQKP48APrwgTBWVFpXZIFwG3rYsnvWgOLE7xay6NKOqvoi8H8C/A7ABfDmuCCGiqXMcq2oH2ihxXWld18FRbvQhJe62VMrBUanxb5/tj0kmHRyVrLyt2s857rZEwsZRbU2Rj6mHsNhy0Aygrbn8tuaoBxCN06wqRABAVb8D4Dv1HgcRERHV3qwrROaTyVw+4uUcIiJqJLNqjshEiUgfgBdibl4CYO8MDmey5so4gbkz1qmMc6+qXjCdgykQkUEAu2rx3HPIXNmHamW6v/+a7K88ts6YuTBGYHrGGbuvzulCpBoR2amqXfUex+HMlXECc2ess3Wcs3VcM2m+/wwa4fufK9/DXBjnXBgjUPtxzrYFzYiIiGgeYSFCREREddPIhci2eg9gnObKOIG5M9bZOs7ZOq6ZNN9/Bo3w/c+V72EujHMujBGo8Tgbdo4IERERzX6NfEaEiIiIZjkWIkRERFQ3LESIiIiobliIEBERUd3M6ULkggsuUAD8wz/T+admuL/yTw3+1AT3Vf6pwZ9Yc7oQ2bt3LqyMSxTi/kpzBfdVmklzuhAhIiKiuY2FCBEREdUNCxEiIiKqG6feA5huuVyAPUNZ+EbhWIKmpIWRrIHr2GhvdmFZUu8hEhHNScYoDoxmMZI1xWPs0pYkEgm73kOjOayhCpFcLsCv9gzh6h096B3IYHlbGps3rMGOn7yAHz/bj+2XdWHVslYWI0REE2SM4qX9IxgYyeGa2x8tHmO3dHfipKUtLEZo0hrq0syeoWyxCAGA3oEMrrn9UVz52uPQO5DBlbfuRP+wV+dREhHNPf3DHrK+FosQIDzGXr2jB3uGsnUeHc1lDXVGxDdafIMU9A5kYOfPgPQOZOD5QT2GRjTtjv34tyf1uOc/++ZpHgnNB54fwBJEHmN9U3WZCKKqGuqMiGMJlrely7Ytb0sjyL9Jlrel4To8fUhENFGuY8MoIo+xDi930xQ0VCGytCWJLd2dxTdKYY7I9oeexfK2NLZf1oX2ZrfOoyQimnvam10kHcHmDWvKjrFbujuxtCVZ59HRXNZQl2YSCRsnLW3BXRvXlqVmPvTHJ+Ba5ySmZoiIJsmyBEctakJLKlt2jGVqhqaqoQoRABARiAgAhYig1XXR1tRQJ36IpmQyc0s4r4QK/EDgWOFxVlWxf9RHu23xlzyatIYqRHzf4FevDOKqkvju1u5OnLSsFY7DYoSIaLKMUex6ZRA3fn8X3n32Snzs3ieKx1kujUBT0VCfznuGssUiBAhnc1/FaBkR0ZT1D3u48tadWNe5oliEAODSCDRlDVWI5AITHS0LTJ1GRETUGDw/QO9ABovSicjjLJdGoMlqqEIkYVvR0TK7ob5NIqIZ5zo2lrelsT+TizzOcmkEmqyG+oRe2pLE1jHx3a2MlhERTVl7s4vtl3Xh3p7duG7d6rLjLJdGoKmY8cmqIrICwK0AXgXAANimqp8XkcUA7gJwLIDnAVysqgMTeW7HsXDSslbcveks+IGBY1tY2pLkRFUioimyLMGqZa34zNtXwxiDuzedBVVlQ1Gasnp8QvsAPqKqvwdgLYD3icjJAD4O4H5VPQHA/fmvJ00BqCoOjHrwfc4RISKaKssSLEo58I0il5975zZU9pLqYcZ3IVV9GcDL+X8PisgvARwF4G0Azs3f7SsAHgTwsYk8d1R8d/OGNRhuCnDkgjTPjBARTUFUh/Mt3Z1Y0hJgWWuaZ0VoUur6ySwixwJ4DYCfAliWL1IKxcrSiT5fVHz3mtsfhecrI7xERFMU1eH86h098AMwvkuTVrdCRERaANwL4C9U9eAEHrdRRHaKyM6+vr6y2+Liu5aAEV6qi2r7K9FsMp59Na7DuVFlfJcmrS6FiIgkEBYht6vq1/ObXxGRI/K3HwFgT9RjVXWbqnapaldHR0fZbXHxXaNghJfqotr+SjSbjGdfjetwbokwvkuTNuOfzhI2gvkSgF+q6j+U3PRNAO/O//vdAL4x0eeOiu9u3rAGriOM8BIRTVFUh/Mt3Z1wbDC+S5NWj/nO5wC4FMB/i8hj+W3/E8BnAdwtIu8F8CKAiyb6xKXx3Vxg4FiCVMLCwpTLiapERFMU1eG8JWWjNcn4Lk1ePVIz/wUgbo89f6rPb4yBqiJhC1SBES+AF2RhAbAsi3l3IqJJMkYxkMkhYYfHUBFgaDTAwcwIkgkbS5qTPL7ShDXUaQLP87Grbxh/+/9+gef2juAd2x7GH33uQVy09SfYPZDBX9/3BHa9MghjtN5DJSKaU4xR7PrdIL70n8/gdwezFcfZP9v8Yx5faVIaqhDpG/Zw9Y6eyO6Qf3HXY1jXuYJdIomIJqF/2MOVt+3E+q6jcc3tj7ILL02bhipECtGyuO6Qhe2MmRERTUyh+65tCbvw0rRqqEKkEC2L6w5Z2M6YGRHRxBS67wZG2YWXplVDFSIdzS62dHdGdof8x3ecjnt7drNLJBHRJLQ3u9h+aRfu2fkiNm9Ywy68NG1Ede5OLOrq6tKdO3eWbfM8H33DHkQAVcCowrEtpmZovGq2c0Ttr1Nx7Me/PW3PdTjPf/bNM/ZaNCE12V/j9lVjFHuHsgAUuUBLjrNAMmExNUPVxO4YDdc3UTVcYdULDAKjSFiClqRgNCcsQoiIpsDzAuQCU1xDJOlYaEk4SCYb7qOEZlBD7T3ZrI/dBzLoG8zi2nueKHaH3NrdCVsM9g5lsWpZK4sRIqIJGh318XT/cFnn3c0b1mBRUwLLABYjNGkNNUdk74iH3fsyxSIECGdxX7WjBwvSSUbLiIgmqT/jVXTeLXQ33zvC4ypNXkMVIr5RNLl2ZKSsEO1ltIyIaOLiOu9aEt5GNFkNVYg4lmDECyIjZYVoL6NlREQTF9d512h4G9FkNVQhsqTJxYrFaVy/vjxStrW7EwczWUbLiIgmqT3tVnTeLXQ3X9LE4ypNXkPNLkomHaxYmMaCVAJ3blxbTM20pi2mZoiIpiCVcnBCe3NZ591UgqkZmrqG23tUAT8wsAQQS+AbxeCooskVvHwgA9exKwoSYxT9wx48P4i8nYiIQgnbgm0pfKMYygbwA6AjYfOYSZPWUIXI6KiP3/QP4wv3/xrvPntlsSHT8rY0tnR34rYfP48fP9uP7Zd1FWO8xih2vTKIK2/dWbxv6e1ERBQeX3sPZjCc9THiBWVLJGy7tBMnvWoBj5k0KQ01R6Q/4+GqmO67V+/owZWvPa6iQ2T/sFcsQgr3ZcyXiKhcfyZcHmHfcK5iiYSNt/XwmEmT1lBnRA7XfdfOV+ulMd5CR8mx92XMl4jokMLyCAB4zKRp1VBnRA7XfTfIZ91LY7yFjpJj78uYLxHRIYXlEeKWSOAxkyaroQqR9rSLrTHdd7d0d2L7Q89WdIhsb3ax/bIudpAkIqqiPR0uj7C4OVGxRMK2Szt5zKRJa7juu6OjPvZlwu67RoHAhN13m1wLw9noVAxTM1SC3XcjsPvurDWj3XdHR30c9HwYDVMzxiiSjo2OVnbdpcOaP913ASDlWsh4hzpEphMCP1BYIvD8AP3DXlmxYVmCjtZknUdNRDR7+b5BDj4cG8h4WlwiIRsE+N3BUSxpSsB1G/IjhWqsofaa0VEf+7Ie+odyZR0it3R3YklLAp/85i/wvaf2MKJLRDQBvm8w6HnI5Az6h3L4YsQSCVu7O3FiRzOLEZqwhpoj0p/xEASo6BB59Y4e+AGwrnNFcRsjukRE47NnKIuRrCkeX6OWSLhqRw/6eEylSWio0tU3CkV0tMyoYlE6UbaNcTMiosPLBQYAisfXuCUS2IWXJqOhzog4lsCW6A6Rlgj2Z3Jl2xg3IyI6vIRtlR1f45ZIYBdemoyGKkTa0y5sGxUdIrd0d8KxgXt7dhe3MaJLRDQ+S1uSaEpaxeNr1BIJW7s70cFjKk1CXS7NiMiXAVwIYI+qnprf9ikAVwLoy9/tf6rqdybyvKmUg8UAUo5V1iGyybUQGOBv33oqPvkWZUSXiGgCHMdCK1w4loeUk8Qn33IKRIC7Nq5FoArHspiaoUmr115zC4B/AnDrmO03qurfT/XJy6K7rgXPV7iOwKigPZXA3pEcdg+MIGFbWNqShOM01Ikhomk32TVLuP5I41BVZDxAJJwr4geKhCVY1GQjFwB9wx7SuQCL0vwljyamLoWIqj4kIsdO9/OOjvp4un+4IrrbkrRwMAu0Jm38um8YV5XcvrW7Eycta2UxQkQUI5cL8MLACIwq+oe8ss67W7s7cVRbEt//xctYdcRCLFuQwrHtzSxGaNxm26fv+0XkCRH5soi0TfTB/RkvMrrrWDZ692XgGxSLkMLtV+3owZ6h7PR+F0REDWTPUBa792Xw0sBoRefdq3b0YCRrcN7JR+Dae57AC/0jXBqBJmQ2FSJbABwP4HQALwO4IepOIrJRRHaKyM6+vr6y2wrdd0sVortNro0g5nY/H00jmm7V9lei2eRwx9Ym10aTa8fGdlXD42uTa3NpBJqQWVOIqOorqhqoqgGwHcCZMffbpqpdqtrV0dFRdluh+26pQnR3xAtgx9zu2LPmx0ANptr+SjSbHO7YWq3zrmMJJB/tHfECLo1AEzJrPoFF5IiSL98O4MmJPkd72o2M7vomwPLFaTgWsHXM7Vu7O7G0hX1miIjiLG1JYsXiNI5qS1V03t3a3YmmpIUHnnoZ169fjWPam7g0Ak1IveK7XwVwLoAlItIL4JMAzhWR0xFOyH4ewKaJPm8q5eCE9uay6G7atZDLp2ZyAXBiRzPu3nQW/MDAYWqGZoGZ7KJLNBmJhI1j2powkMmhrcnFnRvXIjBhaqYlbSEXAG845QikXZupGZqweqVm3hmx+UvT8dwi4SqAijDCO+oZNKcsDGcVfmCwJzBwLYHtWMj6BnuGsmXFiDGK/mEPnh/Udb2R2TIOIiIAsG0LzS4wlC1Zxl0EQ6MGlgha0zZaXB6naOIaavWZbNbH7gMZ9A1mK+JlO5/bi09961dY3pbG9etXo8m18alvPoW+oWwxwmtZgl2vDOLKW3cWH1uPLr3G6KwYBxEREB6TMl4Wvz2Yw94xx9d/fMfp2PbQM/jQH5+IjlaDJc0pHqdoQhrqmsTeEQ+792Ui42XnnXxE8etr73kC+4ZzuOrc48sivP3DXvHDv3DfenTpnS3jICICwmPS/oxBb8Tx9S/uegzrOldg02098HzlcYomrKHOiBQiZlHxMlUt+7rJtdEEu/i1H5hi/GzsY2c6iub5wawYBxEREB6Tqh1fC914A6M8TtGENdQZkULELCpeJiJlX494QbEbbyHC6zp25GNnOoo2W8ZBRASEx6Rqx9dCN17bEh6naMIaqhBZ0uRixeJ0ZLzsgadeLn59/frVWNycwNYHnymL8LY3u9h+WVfZY+vRpXe2jIOICAiPSYvSFpZHHF//8R2n496e3bjp0k64jvA4RRMmpZcs5pquri7duXNn2bZs1seBrA8vMMV4WTE1Y8LZ3a4lUABZvzLCO1vSKrNlHPNQzX7IUfsr0NjxXTa9q7ma7K9R+2phwupQVpEzCmMUjm1BoBARNCUtpmaomtgdo6HmiABhfDftAlYujOfmjGJo1CBhC1QFLSkL2RyQCwwKJZiqVnzwH7EwHfmGmqkCwbIEHa1caI2IZgdjFDkDGACBUdiWwBbAdSz47JJBU9BQhYjn+Rj2fRwcDXBgJIdrbn/0UBfeDWvwrcdfwltOXw6BYtOOR8vivQvTDt65/adV47KM1RLRfOT7Bn3Do9g3nMOm2w51L79+/WosaU0i5Vg4mDEYTTO+SxPXUHNE+oY9jGQNcr4WixAg34X39kexvuvoMKo76FXEe7O+HjYuy1gtEc1He4ay8AMUixDg0FIIvfsyAAQv7sswvkuT0lBnRHwTXmyxBJERM9uSYnR37G1jC/iouCxjtUQ0HxUuZUcd/5pcu6zDOY+HNFENdUbEsQSOJTCKyIhZYLQY3R17mxkzZzcqLstYLRHNRwnbgi3R3ctHvKCswzmPhzRRDVWIdDS7aEpaSDiCzRvWlHfh3bAG9+x8MYzqtroV8d6kI4eNyzJWS0Tz0dKWJBwbuOnS8u7l169fjeWL0wAURy9OM75LkzKl+K6ILAPwfwEcqapvEpGTAZylqtPSwO5woiJmnucja3xkc2E8t9CFN2ELcoEWUzPFeK9toaPZhW1b40rDMFbb8BjfnUaM79bcjMV3fd9gyPOQySlyvoFlCRKWwHUEvgGSCWF8l6qJ3TGmekbkFgD/DuDI/Ne/BvAXU3zOKRvOKoa9AAog5VhoSVpIJcKfwf6RAH5gkHat8E1kCzJ+Dn2Do0jYQMKx4PkB+oc9mJLrNcYo+gazePlAeI30iIVpdLQmY990hfu/NDCCvsFs2XMREc01qoqEFX5oWJYgMAqjwKhv4AeKkaypOG4SjcdUJ6suUdW7ReSvAEBVfRGp20wlz/Pxm73D2BgRL0s6Ft5VEs8tdOTtPLYdC5oSaEpY2DOYxXtuqYzmAphQbJcxXyJqJLlcAC/IVe2+e9XrXo2tP/wNPvyGVTzW0YRM9YzIsIi0A+HaYCKyFsCBKY9qkvqGvWIRApTHy7wx8dxCR96rb38UOV/h+YqXBkYjo7kTje0y5ktEjWTPUPaw3Xc/eOfPsa5zBY91NGFTPSPy/wH4JoDjReRHADoArJ/yqCbJN9Hdc5tcOzKeW+i2a8mhzr1j71OIok0ktsuYLxE1ksLSCIfrvlv4m8c6mogpnRFR1UcBvA7A2QA2AThFVZ+YjoFNhmPFx8ui4rmSj6MZPdS5d+x9XMeecGyXMV8iaiSFpREO13238DePdTQRUypEROTPALwVwCoAJwJ4i4icLyJLp2NwE9XR7GJbTLzMHRPPLXTk3bJhDRJOOPP7qLZUZDR3orFdxnyJqJEsbUketvvuFy55De7t2c1jHU3YVOO73wZwFoAf5DedC+BhhEXJ/1bV26Y6wGri4rv7Mjl4JfGyVEIgAgyOhnHehCVIuhaynoHrWHAdIOMpkgkLXhBG08ZGcyca22XMd85ifHcaMb5bczMW3y1MWC3tvpuwLUAU0PAYa1sWj3UUp2bddw2A31PVV4DiuiJbAPwBgIcA1LQQidOaFIzaNkZzBoEqhrIKowpLwjMfFgSjXliUSBBG0AKjECh830AkP89j/wgcETQl7dgCpSCq8GD3XCJqFEEQ/tIaGC3rvgtLYAxgiaAtnahoFMpfyOhwplqIHFsoQvL2ADhRVfeJSG6Kzz1hnucjZ3z0jwTYP5LDPz3wNN599kp87N5DUbMbLjoNqYSF993x84oob9fKJfjC/b+ueMyWDWvwxQeexvee2hMZxWVcl4ga2eiojwA+nuvP4qodY5ZHaHFxz87deOvpy3FwNIdjFzfDcSweF2ncphrf/U8R+ZaIvFtE3g3gGwAeEpFmAPunPLoJ6hv2sD9j4OW7767rXFEsKIBwdvdHvvY49g3nIqO8V+3oiXzM1fnnKnw9Np7GuC4RNbL+THhsLRQhQMnyCAOjWN91NDbt6MHufRnsGcqGj+FxkcZpqmdE3gfgzwD8Yf7rRwAcoarDAF4/xeeesLHddwtRslJx3XcLUd64xyxKJ8q+Lo2nMa5LRI2scGyNO56Wdjb3AwOAx0Uav6nGdxXAMwByAN4O4HwAv5yGcU3K2O67hShZqbjuu4Uob9xj9mdyZV+XxtMY1yWiRlY4tsYdT0s7mzt2+LHC4yKN16QKERE5UUT+RkR+CeCfAOxGmMB5var+07SOcAI6ml0sSltw89137+3ZjevWlUfNbrjoNCxuTkRGebd2d0Y+Zkv+uQpfj42nMa5LRI2sPR0eW7d2RyyP0JbCPTtfxE3dnVixOI2lLeEkfR4XabwmFd8VEQPgPwG8V1V/k9/2rKoeN87HfxnAhQD2qOqp+W2LAdwF4FgAzwO4WFUHqj1PXHw3Z3yM+sBoLkzAqKKYmrEswILAz8/8TtgWbAvwA0Vz0sJw1gD5xwSqU0rNcELWnMT47jRifLfmZiy+W5iweiBjkMunZlxLAAvF1MziJheOc+j3Wx4XqcS0x3fXAbgEwA9E5LsA7qz2IhFuQXgm5daSbR8HcL+qflZEPp7/+mMTHZhlWYABrPx7IRcoHEvQ5NrI5gyaXQsHR01YhFiCZCKM8gaqGPHCN1eg4WNgAF8VXqBYnHZxIOsh4wX43cEMAIEfhGuVOJaUvAnD3wZ83+B3B0cBKFTDZjxJvhGJaI4r/OoqAGxbkHIA3wAjXuUvtZYlXMaADmtShYiq3gfgvnw65k8BfBjAMhHZAuA+Vf3eYR7/kIgcO2bz2xAuiAYAXwHwICZYiPi+Qdb3cDBrsHcoh6tLYmZbujvRk4/oPtd3EB+48wm88eSl+MD5J5bd78aLT0Nr2sHAcK6sw+RN3Z3wjcE//+A3eO8fHoePfO3xsghbk2sXo2sA8KtXBiOjwIyvEdFcUzgb8mx/tux4uXnDGixqSmBRk42nXzmA/uE0TlrWWnZWhOhwpjpZdVhVb1fVCwEsB/AYwjMZk7FMVV/OP+/LACa8THyhQ6QfoPhmAfIR3JKI7muOaQcArOtcUXG/D9/9OBzLrugwuWlHD/YN57Cuc0WxCCncdu09T2DfcK4YXdszlI2NAjO+RkRzTSG+O/Z4ec3tj8LzFYMZg+OXLsBVO3qK8V2i8Zq2slVV96nqTap63nQ9ZxQR2SgiO0VkZ19fX9ltuSBcLTXQ6C68hYhukI+ixUV1C/HfsdubXLtqJLgQXcsFpmoUmPG1+aPa/ko0m1TbV32jsd3NC93LC7cX4rtE4zWbzp+9IiJHAED+7z1Rd1LVbarapapdHR0dZbclbAuOJbAlOmZWiOja+csicVHdQvx37PYRL6gaCS5E1xK2VTUKzPja/FFtfyWaTartq9Xiu4Xu5YXbC/FdovGaTXvMNwG8O//vwiqtE1LoEOnYwJYxMbMtJRHdn7/QDwC4t2d3xf1uvPg0+Cao6DB5U3cnFjcncG/Pbtxw0WkVEbbFzYlidG1pSzI2Csz4GhHNNYX47tjj5eYNa+A6gta0hWf2HMTW7s5ifJdovKbUfXfSLyryVYQTU5cAeAXAJwH8K4C7ARwN4EUAF6nqvmrPExUxK0xY9Qwwkg0v1TiWIJWwkM0ZtKTKUzMp1yqmZhzLgiVhbNeWfMRXgVTCKknNGNgCaGxqxiqOY89QFoKwqR4AxtfmBsZ3pxHjuzU34/Hd/Zny42ppaqajJcmJqhSnZt13J0VV3xlz0/nT8NwYzCpak4KRku2WADmjyHhhkRFAAQm7RqZdC8OegULhm3wnXjssMHzfwPcNRn0PjoQ/SQ1fqBhjSziCtnSyrMBwHAtHLkqX5eiBMFe/dziL0VyApG3B5Nc4mUiRMhez+XNxzERUnZv/BAk0PI7yPU2TUZdCpFZyuQBP9w3hmMXJipjZlu5O/PKl/VjR3lwWy93a3YnWtIM7fvI8XrtqWVnU9vr1q/G57+5C31AWW7s7kUpY+Nx3fxUZ3122wMex7c0VLbBLu0++8eSl+OD5J+KqHT3oaEnioxesKhvLeKK9c7Gj5VwcMxEdEhffDS/VJOHawMCIj8FRH0ctauL7miakoc6h7RnKYuNtPZExs6t39ODsEzoqYrlX7ehBzles7zq6Imp77T1P4Kpzjy/eb/e+TGx894X+kYpY7tjuk+s6VxS7V1517vEVYxlPtHcudrSci2MmokPi4rtX7+jBSNbgQMYg6yuyvvJ9TRPWUGdEivGxmJhZUCV+BpGqXXeLEV3YsfHdsbHcsd0nS+O8k432zsWOlnNxzER0SLXuu6VdzwHwfU0T1lBnRIrxsZiYmV0lflboHjn2tkLX3fHEd8fGcsd2nyx97GSjvXOxo+VcHDMRHVItvlva9dwo+L6mCWuoQmRpSxLbLu2MjJlt6e7Ej5/uq4jlbu3uRMIR3LPzxYqo7fXrV2Prg88U77dicTo2vntMe1NFLHds98l7e3YXu1duffCZirGMJ9o7FztazsUxE9EhcfHdLd2daEpaWJi2kHQESUf4vqYJq0t8d7pERcxyuQD9Ix5ak1IWM2tyLRwcDZBywqRKLjBwbAuuLbAtYDgfyy2cHUnYYZR31DdIWIIFaQuBAYayBla+O69vFJYlSLtWRWqmYGxapC2dwL6Mh9GcQdIWpmZmH8Z3pxHjuzVX1/jugrQFC0A2CLvvLkjN2vc11d/siu/Wkm1bcJ3K73c0Z5DIr/iXCwxsS+DYQNY3MKqwRIpx3JaUHcZ9A8C2BDmjODiqYXy3pFixLEHCEuR8xSsHR5GwBaO+gW0BqgItKTCMUewZyuKlAxkkbAtHLkjBcayyD+j+Ya/qB/TYD/NlrSkMZHJ4+UBmtn+4swsnUQMyCngGMAbwYbB3OIslzdG/lBHFaahCxBjFS/tHsLjJroiZXbduNb7y4+dwxTkr8bnv7kJHq4v3n3cCrrn90Yr7fOD8E7GkJYF9Qx427Th0+03dazCUDSqiu2nXxuYf/AbvP+8EfPvxlypiwLe+50yMeEExMVO41LNqaQt+s3d4XLHWqAjs1u5OfOH+X+N7T+1hJJaIaqZafPeIhS6GsgECo/jaz17En65ZweMQTUhDzRHpH/aQ9TUyZvaxe5/Aus4VxUjuus4VxSJk7H2u3tEDPwD2DHplt+8Z9CKjuwP5rrzX3P5oZAz4hf6RYhFS2FboUjneWGtUBLbQ4fdwjyUimopq8d1sThEYYPe+DNZ3Hc3jEE1YQ50R8fyg2AkyLoo7NpIbdx+jiia3fPZ3kxsf3S3Eem2rMgYc97i4cUbF3+IisIXvpdpjieplMvNfOK9k9jlcfNeS8DhXOP7xOEQT0VBnRFzHLnaCjIviFv6Oi88WtlsiGPHK30wjXnDYrrxRMeC4x8WNMyr+FheBLcSLqz2WiGgqDhffNRoe5wrHPx6HaCIaqhBpb3aRdCQyZnbdutW4t2d3MZJ7b89ubN6wJvI+W7o74djA0la37PalrW5kdLct35V384Y1kTHgY9qbirHdwrZCl8rxxlqjIrCFDr+HeywR0VRUi+8mE2HycMXiNO7Z+SKPQzRhDRffNUaxP5NF0gb2Z8Iuu7YlsPJJFlvCpIxtCRJOmHgppGYkH8tNu1YxNZP1TTHO6wjgq0LHpGaAcPb4eFIzfj42vDTfpXIisdaoKPBAJjcXIrFzCeO7dcZLMxNS1/huS8qCn0/NhG1AhakZijN/4ruWJWiyHYwEPgAc6pBrWRABAhNuU4RvnqRjIRcoFGFX3pwqBkcDuPl1RIL8G861BcNeAKdQ1CAsQmzr0PbCZSFjFIEaWCLwgwC/OziKJjcsTBRhh+Ahz4PJCnyjyPlmXIWEZYWLBRWKkYEMir959A97ePlABiJhsWVZFgsTIppWY39tNQawBRjJBbAtC0uanMhfrEq7jtsiSLs2FqV5fKJQwxUio6M++kay2D+Sw9Ul0dzNG9ZgQdqBHxhc+7X/Rt9QFjdefBoWNSVw/b/vwjWvfzUyXlDWDffGi0/D//3Or9A3lMXmDWuw4ycv4MfP9pdFgdtbXPzLQ8/hx8/248uXd2Fw1MeH7nysLBK8byiDYzsWVMTeFqZtvGv7I+PuSBsV4b31PWci65uybYXxffgNqxijI6IpGx314cHHCxHx3SUtCfzroy/hrp7eyCUFTuhowdN9Q2XHqLBjeaqiYznNTw01RwQIY2ZZX4tFCBDO7L7m9kcRTuS2ih11P3z34+gdGMW6zhUYGM5VdMP98N2PF+97ze2P4srXHlcRBX5pYLS4/aWB0WIRUniOj937BF5zTHtk7E1gTagjbVSE94X+kYpthfExRkdE06E/42EwJr7rB8Db1iyPXVIgapmCuI7lND813BmRQpQstssuENlRt/D12MeU3tfOP0FpzLcQWQPiY7pxXX/NmPk5h4u9RUV4416zMD7G6IhoqqrFd0uPY1FLCviBGXfHcpqfGu6MSGGuRlyXXaOI7KgbF7EtvW+QfzOWxnwLkTUgPqYb1/XXEqnYVi32FhXhrTZuxuiIaDpUi++GE/2l+PXYJQUc2xp3x3KanxquEGlPhxHeLWOiuZs3rEG4z5tiR90bLz4Ny9tSuLdnN9qaExXdcG+8+LTifTdvWIPtDz1bEQU+qi1V3H5UWwqfv+T0ikjwz1/oj4y9KcyEOtJGRXiPaW+q2FYYH2N0RDQd2tMuWmPiu44NfOPR3tglBaKWKYjrWE7zU8PFd4FwYtVI4CPjHYqZJZ1DqZnRXAArX+E7lpSnZozCjOm+W3j8iBcUo8CBQUVqJlHopmsUgQKWhH8UgiZXMJw9NJ7mpAWjE0vNANFdbAEUtzE1M2WM79YZ47sTMqPxXR8+Dozpai4CDI4WUjMJHMgGVVIzYZdzpmbmpfkT3y1I2IAmwjdJxjPFYiHphCeBLAkLh6FsWFw4Vth917EElm3BdYDhrCk+X86Y4v1EAAOFFxhYJnweza8j4vkK27Lg+wGQ7+hrVGH00P9BeBpTkPPD51cAI54PVcWSlvgMfrU1Rybb2XYi65gQ0fw2lFX4+bWZwmUNwu0J20LCFjiOjQ638mPFsgRLW1MzPFqaKxquEBkd9ZExfr5CB/YN+2Vxs9IY7pYNa3Bb/t/Xr19d7C3T3uLilUE/snvvptcdj9aUg/fcUhmX/cD5J1ZEcq9btxpP/+4AOlcuKY+9bViDlpSN3x3IlkWGt1/ahVWvGl/33al2263FcxJR4xkd9fHsvmFsvO3QMez69auxpDWJIxck0JoU/KYvgwMjPo5dwkguTUzDzRHpz3gYyRp4vsIYqYiblcZwry7597X3PIF9wznsG84BqHxcIRL7oTsfw0sDo7Fde8dGcj927xM47+QjKmNvtz8Kx7IrIsNX3jb+7rtTjefW4jmJqPH0Z7xiEQIciuD27stgf8Zgf8Zg33AOL+xjJJcmruHOiBRiZpYAgUbHZktjuKX/LpwRiYvblkZ2426LiuRqzDhMzPaJdN+dSvytFs9JRI0nrlN4k2sXj7mF4yKPHzRRDXdGpDAB1ShgS3TcrDSGW/rvES8oTkitFokd25V3bNfesbdJzDismO0T6b47lfhbLZ6TiBpPXHR3JD/3zrGkePzk8YMmquEKkfa0i6akBdcRWJZWxM1KY7hbSv59/frVWNycwOLmBIDKxxUisZ+/5HQc1ZaK7do7NpJ73brVeOCplytjbxvWwDdBRWR4+6Xj77471XhuLZ6TiBpPe9rFtkvLj2HXr1+N5YvTWJS2sChtYXFzAscsZiSXJm7WxXdF5HkAgwACAL6qdsXdt1p8Nwcfno9iaqY0xjviBXBsC0lHMJw9FOUtsESKqZnS7r3GoJiayQWVXXubXAueH3agzPr5qK8AQf62wjgStoUm10I2F6ZmCpHhVMKedGpmspiaqcD4bp0xvjshMxrf3Z/NwfNNsfN4SzJ8+cGsImEL2prYeZdizbn47utVde9kH2xZgGUAKDCaM8W4WcIS5AJT7KYLBZx89NbLrxfSmrZwMGOQyYWFSyoRdudNWBaG89c+064FPwifU7XQoTcsQgrrjjQnbYzmDNKuheGsweBogIQlWJS2kQuA4WwQFiWWIOVYGPYC5AKDfcNZDHsB0gkbi5tcDGRy8PwACceCY0lkPr9QSCTs8ARXJhf+e2lLEo5TftIrqvA4XPSXxQoRAUBrUjAECzkTHusUFlqTgtakIO2GRYjvG+wZyiIXmNjjEFGp2VqITJrn+cgZH3tHAhzMlEdwr1+/Gi1JB1984Gl84LwTsKApgYMlXXo3/dGxuPD05RVx328//hLefNpR2PGTF7A/4+ED55+IL97/a7z77JX42L1PlD3/5767C31DWWzp7sTzfQcruu7ecsUZyHhBRWfgQqS48O/9GQ8fPP9EXDVm/IXnr9bVsnCfrd2dOGlZa/EgMJm4LiO+RFTovvvKwRz2DpYvOXDTpZ04dnESQ14WaTuBXXuGyo5bY49DRGPNxj1DAXxPRHpEZONEH9w37GF/xiDna0Vk9tp7nsDeIS+M2t7+KHK+Yu+QV7zP+q6jI+O+67uOLsZ+CzHddZ0rikVI6fMXuvVevaMnsuvu7n2ZyM7AhRhx6etcFTH+wvNX62pZuM9VO3qwZyhb/NlMJq7LiC8RFbrv9u7LVCw5sOm2HuzPGAxmwjMhY49bY49DRGPNxjMi56jqb0VkKYDvi8ivVPWhwo354mQjABx99NEVDy6N78bFzZpgF7vxlkZxbUti476Fvwsx3cLfY+9b2q03KgYc1y13bKR4PM+fi+lqWXofPzi0Ouxk4rqM+E7N4fZXotmi2r5aGtGNOh4Ubi98XXF7yXGIaKxZd0ZEVX+b/3sPgPsAnDnm9m2q2qWqXR0dHRWPL43vxsXNClFboyiL4gZGY+O+hb8Ljy38Pfa+pd16o2LAcd1yx0aKx/P8iZiulqX3cexD/8WTiesy4js1h9tfiWaLavtqaUQ36nhQuD0u5lt6HCIaa1btHSLSLCKthX8DeCOAJyfyHB3NLhalLSQcqYjMXr9+NZa0uGHUdsMaJBzBkha3eJ97dr4YGfe9Z+eLxdhvIaZ7b89uXLdudcXzF7r1bunujOy6u2JxOrIzcCFGXPo6WyPGX3j+al0tC/fZ2t2JpS2HJqJOJq7LiC8RFbrvLl+crlhy4KZLO7EobaE1HU5MHXvcGnscIhprVsV3ReQ4hGdBgPCy0R2q+pm4+8fFIQsTVkdzQNY3yOVTM64lCKBQA7iOBQHgBQammHw5lJopxH0TdtidN5lPtjiWIO1aGPUMkI/tGg1TM6XdelMJC6M5g+akVey6m8h33c0F4bgK29yS5y7Ei1MxqZmMd/jUzGgujCePNzUzmY6/DTxRlfHdOmN8d0JmNL4bwMdQVotLDrhOmJoBUJGa8QMTexyieWluxHdV9VkAp031eRzHRs7zERhFocxyLIFvws6Rjm3BqIbR23wRkrAFyURYhCTs8OdVuO6ZdCx4gUHCEji2YDRn4OQLlOL6JAnBiBdeB7UtgR8oAqMYzhqIhK/vOhayvhZvcywBJIwOtzXbGB4N1y2xLEEmF2DfiAc/CAsWCRRtqQQCAxhj8PKBcA7KoTd65W8cxij6BrMVBcREO/VO5jFEkzXZwowFTO0Ne+G6R0H+l6ikIxj1gZQTHm8sS+A4Fo5clD78kxHlzapCZDoYo8h4Wbx8MIeBYQ8fvvtxdLQk8dELVpVFzrZ2dyIwBu+74+eHOuJ2d6Lnub1Yc2w7rhkTr3UdwX09vbjwtKPwrXyct/Q+W7o78cX7f43vPbWnIkZb6M577Z+sQtbXsmhb4bYPnn8ilrQk8OzeYVx7zxORY97S3YlvPdaL165aVhYbjorHMXZLRNNldNRH78EM+sZEd7d2d6K9JYFhT7GoyUMTXJ79oAlruD2mPx/f3b0vgw/f/XgYHzv3+IrI2VU7erBvOFfeEXdHD847+YhigVHYfk2+U+76rqNxdUmcd+xj13WuKH5dGqMtdOftHRitiLYVbrtqRw8Cg+I4o8Z89Y4erO86uiI2HBWPY+yWiKZLf8bD7ojo7lU7euAHgOdrMb5LNFENd0bE88MVS0tjZnFR2KguunGdci0BIIdivNVis2O/HvvvqMcVInCHG3Pca4+NxzF2S0TTZewxtaB3IJNvdRHeRzB75hzS3NFwZ0Rcx66ImcVFYaO66MZ1yi1MaC2N8469TyE2O/brwr/jom+F8ZVG3+LGHPfaY+NxjN0S0XSpFt21JFwuwbGEMV2alIbba9rz8d0Vi9O48eLTwuuYDz5TETnb2t2Jxc2J8o643Z144KmXsTkiXuubIIz3lsR5xz723p7dxa9LY7SF7rzL21IV0bbCbVu7O2FbKI4zasxbujtxz84XK2LDUfE4xm6JaLq0p12siIjubu3uhGMDriPF+C7RRM2q+O5ExcUhCxNWh/MxsyAfMzMlqRnHBtQc6nzr5FMzo54pRnZLO/bmAgPNN8krbC+9T9q1MOKZYqRNAGQDA7ukO2/SCdM6fv5xhe68qkBLOoz5WpCybrx+EMaPE7aFjmYX+0d9GBMmacpTM5U15TyL3U4XxnfnqHmampnR+O5Bz4cXmGJqJu1aMBqmZpIOJ6pSVXMjvjudcgZIJgSav1ri+QZ2Pn5rVKFGkHAExlcA+XU/PFOM7LYkD0VtR7yg2L13NBc+j5+PBifz63touKwIFOElnFTCgp9/jZwfFh7wDZrcsJtvgZuwYIsgkzUwBsiZQ+MMjKKjJYnhnI/hbICD+Rbckv/vtESgqtgzNApAioXKRDrrlpoPhct8+B6JaiWXL0IcS9CStmADGMwqWt0Ei5B5bKrH1YYrRIxRvDIYTuo8kPGxPx/hHRuXveKclVjSmoRjAX/3nV/h/eedgH964Oli/PbmK86A5xtsui26+23hea78o+NwZFsK/UO5ijjv830HccyS1ort33qsFzf95/PF5zxiUQr9Qx4+dOdjFeP80B+fCNcW/OjpPnSuXBLZ9bdw3w+cf2LZc08krjsf4r7z4XskqoXRUR9P9w+XdRIPV41OojUpeHrvME5Y0gzXbbiPFDqM6TiuNlwJ2z/sFeNkvSURXqA8LnvtPU+gd18GjmVjXecKXHP7o2Xx2959mWIRUtgWFcn98N2PwxiJjPO+5pj2yO3ru44ue04/QLEIGTvOcAyjOO/kI2K7/hbuO/a5JxLXnQ9x3/nwPRLVQn/Gq+gkfvWOHoxkDfZnwl/Y+vg+mpem47jacOWr5wcIVCEa3ymyEI1tcm0Y1bKOugXVHjv2eYKYyG9U993STruFr+M6BY8d5+G6/kY993jjuvMh7jsfvkeiWvBjjmWFS9ljO/DS/DEdx9WGOyPiOjZsEdhV4maFaOyIF8ASKeuoW1DtsWOfx46J/EZ13y3ttFv4Oq5T8Nhxlm6Lu+/Y5x5vXHc+xH3nw/dIVAuxXXVLOu46vLw5L03HcbXhCpH2ZrcYJ1teEuEFyuOy169fjeWL0/BNgHt7dmPzhjVl8dvli8OuknGdbQvPc+PFp8GyNDLO+/MX+iO337PzxbLndGzg85ecHjnOcAwpPPDUy7Fdfwv3HfvcE4nrzoe473z4HolqoT3tVnQS39LdiaakhUVpCzdd2okOvo/mpek4rjZsfPfgaBYCYDSnxahrIS4bKJCwwkRL1jcIDNCU76ibK8wIL0nNFKK2CUswmo/kFp6n8BuBIOy8W4jzFrrvlqZmHEvQlI/5BqqwRZByw9RMNmcqxgkIljS7xdRM4TkLcWBVQARhBDgmNTORSZjzIVEyju+R8d05ivHd6RMX3+3PeMVjWWtJamZxOsGJqvPYOD875ld817IEC1JJZDwPQBizLZRbIoKEoDiXQk0Yt814BinHgqOKXKDIBQAgEAnXBVmcTmDvSK744W+LhGuLSBizHfUNHEuwuNnBYCbAUDaAYwksCCxLscC1MeIZHBwNt7embAQBkPMVGWOK64a0pKRYEAXGYN+IV+wibFRwxMI0LEtgjGLvUBajuQBqCRwLGMjksLjJLXbijeu+O5+xkzDRNAlXPsCS5uh1jGj+mOpxtSELkcKCZr89mMPeMd0ib7z4NLiOhUVNCQyOBmWdcDdvWIOFaQd3PPwC3nzaUWhyw7MiP3t2LzpXLimLrpVGeQv/7mh18YHzTyy73+YNa7C4OYHegVFcPSbGu6w1Udac740nL8W1F5xUHHNUB97tl3XhhI4WPL1nCFfetrNsPE2ujYOjORy7uBmWJROKVDHaSkRx4uK7Ry5KAvAAdt2lKWjIPafQgbc3olvkh+9+HPuGc8gFqOiEe83tjyIXoNhdF7Cw52C2GJ2Ni/IW/l2I0I59TkCKRUhh+9U7euAFKIsXr+tcUTbmqA68V966E3uGssUipHQ8+4Zz2L0vgz1D2QlHqhhtJaI4cfHdUS+M77LrLk1FQ54RierAW1CIw8ZFZku77FqCsujs2PuOt7tuXIx37PaxzxEX1fUDE/t9AYAfmNguwnGRKkZbaa6bzFybeTqvZMIOF99l112aioY8IxLVgbegEIeNi8yWdtk1irLo7Nj7jo3yxkVr42K8Y7eP7dAb93yObcV+XyNeAMe2JhypYrSViOIcLr7Lrrs0FQ259xQ68C6P6BZ548WnYXFzAgkbFZ1wN29Yg4SNYnddwGDpgmQxOhsX5S38uxChHfucgGJLRIzXtVEWL763Z3fZmKM68G6/rAtLW5LYfmlXxXgWNyewYnEaS1uSE45UMdpKRHHi4rspN4zvsusuTUVDxneBwoRVD0NZU+xma1vhQmeCMDVjWSiL7KYcC0E+NRN2ygUCYyAixdRMLjBI5H8LGPUNErYFS1BMzSxI2xjMBMXnTDoWciZM5Ix4h+K9TUkLQRCe8vRLuu0mE4dSM8aE4wiMwgBIliRfSlMzVn48lkg+NWMVfwYTiePOh/juODC+O480wKWZ+sV3UxZsYdddGrf5Fd8tfKDa1qE1RBJ2+KYprN8x6h/qIplyLCQcwYgXxmgdK4zmGg3X6PCNYiCTQ9IWGBVAwuSalb9foQNvYBQHMwFEUCxCLEuwrDmM3C5MH/qg9wNBWzqBgUwOgQmQdh0sSjnoG/aQCwzSCRuww26XUUWBZQmWLkhVfM+vDI4W7x/3c4krNEojWCxKiKgam4cDmiYNV4gUYqg/faavInL7z+96DSwRDGX9ikjvomYX9/zsxWLn2hsuOg1f+q9n8e6zVxa77C5sSuDv/30Xrnn9q5HxgtiIbaEb7vvPOwFJR7B/JIej25rwdN9QMZnyxpOX4oPnn1hM7rzx5KXF6G9cbHci0dtb33Mmsr4p27a1uxNfuP/XxQ7Dcc/JKC8RlYqL7x7XnkTWZ3yXpqbh9pxCDDUqcrtvOIe9Q15kpLd3X6asc+1HvvZ4sdNtocvuSwOjWNe5AgPDuaoR28Jjrrn9UdiWjRf6R8LIbUk8dl3nirL4cGn0Ny62O5Ho7Qv9IxXbrsp37z3cczLKS0Sl4uK7+zOM79LUNdwZkUIMNSpyW4i3xkVfx3auLe3KW7hPE8qf43DdcAsR4NyYyO3Yx5V+HfecE4neHq57cLXnZJSXiEoxvku11HBnRAox1KjIbSHeGhd9Hdu5trQrb+E+443YFrYXIsCJMZHbsY8r/TruOScSvT1c9+Bqz8koLxGVYnyXaqnhUjOzcY5IwrY5R2TuYGqGDmsWpW1mJDVTbY4IwOQMjUvsvjrrChERuQDA5wHYAP5FVT8bd99q3XcLqZlMPjI7NjVTGul18p14S1MzIiimZgrdeh1LkPENrELTu5KIbWnn3EJ33EJqZlH6UOS2NIlSSM0Uvi6kZvzAIJUIzz7EpWbivufSlAuAqq9X7TnncWqGhQgd1nwrRIDK+O6idFh4sAihcZob8V0RsQH8M4A3AOgF8DMR+aaqPjWR5ynrBNg8/sctnsB9JyOqQ+HYr49cVH76cyrPHfX84+2QyC61RFQqlXJwVGpWfWRQg5hte9WZAH6jqs8CgIjcCeBtACZUiBAR1RL72hBNn9lWiBwFYHfJ170A/qBOYyEimjaTvQTHAoYa3WwrRKKuIZVNYhGRjQA25r8cEpEsgIUAfAC/KLnrEgB7I56vDcCR+X+PAHhuKgOeBnHjnI3mylinMs7vquoF0zWQMftrVkSenK7nnqPmyj5UKxP+/uW6qjdP2/4acWzdFXPXufJ/OBfGORfGCEzPOGP31Vk1WVVEzgLwKVX9k/zXfwUAqvp3VR7zWgBDAG5V1VNLtu9U1a4x9z0BwN0AzlPVARFZqqp7avCtjFvUOGeruTLW2TrO2TqumTTffwaN8P3Ple9hLoxzLowRqP04Z9tU558BOEFEVoqIC+ASAN+s9gBVfQjAvtJtInJ8/nl6ROQ/ReSk/E1XAvhnVR3IP7auRQgREdF8N6sKEVX1AbwfwL8D+CWAu1X1F9UfFWkbgBdVtRPAXwLYnN9+IoATReRHIvJwPipMREREdTLb5ohAVb8D4DuTfbyItAA4G8BeEXksv7mQQ3UAnADgXADLAfyniJyqqvsn+3rTYFsdX3ui5spYZ+s4Z+u4ZtJ8/xk0wvc/V76HuTDOuTBGoMbjnFVzRCZLRI4F8C1VPVVEFgDYpapHRNxvK4CHVfWW/Nf3A/i4qv5sJsdLREREoVl1aWY6qOpBAM+JyEUAIKHT8jf/K4DX57cvQXip5tl6jJOIiIgaoBARka8C+AmAVSLSKyLvBbABwHtF5HGEkd635e/+7wD6ReQpAD8AcK2q9tdj3ERERNQgl2aIiIhobprzZ0SIiIho7prThcgFF1ygCFde5R/+ma4/NcP9lX9q8KcmuK/yTw3+xJrThcjevXNhZVyiEPdXmiu4r9JMmtOFCBEREc1tLESIiIiobmbdyqoUzxhF/7AHzw/gOjbam11YVlTDYmpU3AeIqNGwEJkjjFHsemUQV966E70DGSxvS2P7ZV1YtayVH0TzBPcBImpEvDQzR/QPe8UPIADoHcjgylt3on/Yq/PIaKZwHyCiRsRCZI7w/KD4AVTQO5CB5wd1GhHNNO4DRNSIeGlmjnAdG8vb0mUfRMvb0nAdu46jopnEfYBmg2M//u0JP+b5z765BiOhRsEzInNEe7OL7Zd1YXlbGgCK8wPam906j4xmCvcBImpEPCMyR1iWYNWyVtx3zTlMTMxT3AeIqBGxEJlDLEvQ0Zqs9zCojrgPEFGj4aUZIiIiqhsWIkRERFQ3LESIiIiobmZdISIii0TkHhH5lYj8UkTOqveYiIiIqDZm42TVzwP4rqquFxEXQFO9B0RERES1MasKERFZAOC1AC4HAFX1AHD9aiIiogY12y7NHAegD8DNIvJzEfkXEWmu96CIiIioNmZbIeIAWANgi6q+BsAwgI+X3kFENorIThHZ2dfXV48xEo0b91eaK7ivUr3MtkKkF0Cvqv40//U9CAuTIlXdpqpdqtrV0dEx4wMkmgjurzRXcF+leplVhYiq/g7AbhFZld90PoCn6jgkIiIiqqFZNVk17wMAbs8nZp4FcEWdx0NEREQ1MusKEVV9DEBXvcdBREREtTerLs0QERHR/MJChIiIiOqGhQgRERHVDQsRIiIiqhsWIkRERFQ3LESIiIiobliIEBERUd2wECEiIqK6YSFCREREdcNChIiIiOqGhQgRERHVDQsRIiIiqhsWIkRERFQ3LESIiIiobpx6D2AsEXkewCCAAICvql31HRERERHVyqwrRPJer6p76z0IIiIiqi1emiEiIqK6mY2FiAL4noj0iMjGsTeKyEYR2SkiO/v6+uowPKLx4/5KcwX3VaqX2ViInKOqawC8CcD7ROS1pTeq6jZV7VLVro6OjvqMkGicuL/SXMF9lepl1hUiqvrb/N97ANwH4Mz6joiIiIhqZVYVIiLSLCKthX8DeCOAJ+s7KiIiIqqV2ZaaWQbgPhEBwrHdoarfre+QiIiIqFZmVSGiqs8COK3e4yAiIqKZMasuzRAREdH8wkKEiIiI6oaFCBEREdUNCxEiIiKqGxYiREREVDcsRIiIiKhuWIgQERFR3bAQISIiorphIUJERER1w0KEiIiI6oaFCBEREdXNtBciImKJCDvmEhER0WFNeyGiqgbA4yJy9HQ/NxERETWWWnXfPQLAL0TkEQDDhY2q+tbDPVBEbAA7AbykqhfWaHxEREQ0C9SqEPnbKTz2QwB+CWDBNI2FiIiIZqmaTFZV1R8CeB5AIv/vnwF49HCPE5HlAN4M4F9qMS4iIiKaXWpSiIjIlQDuAXBTftNRAP51HA/9RwAfBWBqMS4iIiKaXWoV330fgHMAHAQAVX0awNJqDxCRCwHsUdWew9xvo4jsFJGdfX190zVeoprg/kpzBfdVqpdaFSJZVfUKX4iIA0AP85hzALxVRJ4HcCeA80Rkx9g7qeo2Ve1S1a6Ojo7pHDPRtOP+SnMF91Wql1oVIj8Ukf8JIC0ibwDwNQD/r9oDVPWvVHW5qh4L4BIAD6hqd43GR0RERLNArQqRjwPoA/DfADYB+I6q/nWNXouIiIjmqFrFdz+gqp8HsL2wQUQ+lN92WKr6IIAHazM0IiIimi1qdUbk3RHbLq/RaxEREdEcNa1nRETknQDeBWCliHyz5KZWAP3T+VpEREQ09033pZkfA3gZwBIAN5RsHwTwxDS/FhEREc1x01qIqOoLAF4AcJaIHAPgBFX9DxFJA0gjLEiIiIiIAMzcyqrLMb6VVYmIiGgemTUrqxIREdH8M5tWViUiIqJ5ZtasrEpERETzz4ytrArgf9XotYiIiGiOqsnKqqpqEK6quv1w9yUiIqL5q1apmQtF5Ocisk9EDorIoIgcrMVrERER0dxVq14z/wjgzwD8t6pykioRERFFqtUckd0AnmQRQkRERNXU6ozIRwF8R0R+CCBb2Kiq/1Cj1yMiIqI5qFaFyGcADAFIAXDH+yARSQF4CEAS4djuUdVP1mSEREREVHe1KkQWq+obJ/G4LIDzVHVIRBIA/ktE/k1VH57m8REREdEsUKs5Iv8hIhMuRDQ0lP8ykf/DeSZEREQNqpa9Zr4rIqP56O6447siYovIYwD2APi+qv60RmMkIiKiOqtJIaKqrapqqWoq/+9WVV0wzscGqno6wo69Z4rIqaW3i8hGEdkpIjv7+vpqMHqi6cP9leYK7qtUL7U6IwIReauI/H3+z4UTfbyq7gfwIIALxmzfpqpdqtrV0dExPYMlqhHurzRXcF+leqnVyqqfBfAhAE/l/3wov+1wj+sQkUX5f6cB/DGAX9VijERERFR/tUrN/A8Ap+d7zkBEvgLg5wib4VVzBICviIiNsEi6W1W/VaMxEhERUZ3VqhABgEUA9uX/vXA8D1DVJwC8plYDIiIiotmlVoXI3wH4uYj8AIAAeC2Av6rRaxEREdEcVZNCRFW/KiIPAjgDYSHyMVX9XS1ei4iIiOauaS1ERGTNmE29+b+PFJEjVfXR6Xw9IiIimtum+4zIDfm/UwC6ADyO8IzIagA/BfCH0/x6RERENIdNa3xXVV+vqq8H8AKANflMeifCCai/mc7XIiIiormvVguanaSq/134QlWfBHB6jV6LiIiI5qhapWZ+JSL/AmAHwqZ13QB+WaPXIiIiojmqVmdErkB4eeavEcZ2f5HfRkRERFQ03akZB8D/RVh07EY4UXUFgP8GEEznaxEREdHcN91nRK4HsBjAcaq6RlVfA2AlwpVV/36aX4uIiIjmuOkuRC4EcKWqDhY25P99NcL+M0RERERF012IqKpqxMYA4aRVIiIioqLpLkSeEpHLxm4UkW4Av5rm1yIiIqI5brrju+8D8HUReQ+AHoRnQc4AkAbw9ml+LSIiIprjprUQUdWXAPyBiJwH4BSEqZl/U9X7x/N4EVkB4FYArwJgAGxT1c9P5xiJiIho9qhV990HADwwiYf6AD6iqo+KSCuAHhH5vqo+Nb0jJCIiotmgVguaTYqqvlzo0JtP2/wSwFH1HRURERHVyqwqREqJyLEIm+X9dMz2jSKyU0R29vX11WVsROPF/ZXmCu6rVC+zshARkRYA9wL4C1U9WHqbqm7Ld/Xt6ujoqM8AicaJ+yvNFdxXqV5mXSEiIgmERcjtqvr1eo+HiIiIamdWFSIiIgC+BOCXqvoP9R4PERER1dasKkQAnAPgUgDnichj+T9cGp6IiKhB1SS+O1mq+l8I1x4hIiKieWC2nREhIiKieYSFCBEREdUNCxEiIiKqGxYiREREVDcsRIiIiKhuWIgQERFR3bAQISIiorphIUJERER1w0KEiIiI6oaFCBEREdUNCxEiIiKqGxYiREREVDcsRIiIiKhuZlUhIiJfFpE9IvJkvcdCREREtTerChEAtwC4oN6DICIiopkxqwoRVX0IwL56j4OIiIhmxqwqRIiIiGh+mXOFiIhsFJGdIrKzr6+v3sMhqor7K80V3FepXuZcIaKq21S1S1W7Ojo66j0coqq4v9JcwX2V6sWp9wBmijGK/mEPnh/AdWy0N7uwLKn3sIhmJb5fiGimzKozIiLyVQA/AbBKRHpF5L3T8by+b9A7MIIX+ofx5G8P4q/vewK7XhmEMTodT0/UUIxRPN8/jCdfOoDegQyefOkAnu8f5vuFiGpiVp0RUdV3TvdzGqPYtWcQm27rQe9ABsvb0rhu3Wrc+P1d+MzbV6OjNTndL0k0p+3PeHjl4Cg+8Y0ni++Z69evxqKmBBY38/1CRNNrVp0RmU7GKPoGs3j5QKZYhABA70AGH7v3CazrXAHPD+o8SqLZJ+MFuPaeJ8reM9fe8wQy3vS/Xwrv05cGRtA3mOVZF6J5qCELEWMUu14ZxNs3/wi9A5niAbWgdyCD9mYXrmPXaYREs1egGvmeCaa5Rih9n55z3Q/w9s0/4iVTonmoIQuR/mEPV966E70DGezP5LC8LV12+/K2NJa2JtHe7NZphESzVyphR75nUonpPVyUvk+BsNi58tad6B/2pvV1iGh2a8hCxPOD4sFt64PP4Lp1q4sH1uVtadx0aSeOXJhmCoAowpLmJLZf1lX2ntl+WReWTPP8kNL3aUHvQIaXTInmmVk1WXW6uE74G13vQAYnLG3BqxamsOO9f4CELWhO2liQCqOIjCjSfOD7BnuGssgFBgnbwtKWJBwn/ncQyxKc0NGCuzedVfaY6X5vlL5PC5a3pXnJlGieachCpL3ZxfbLuvCvj+7Gm087Cu/+8iPF2f9buztx0rIEAGDXK4PFU8OF3/pWLWtlMUINw/cNfvXKIK7a0TPmPdAaW4wYo3i6b6jm743C+3Ts6/CSKdH80pCXZixLsGpZKy47eyWuuf3RsmvQV+3owZ6hLK9P07ywZyhbLEKA8vdAnJl6bxTep/ddcw5+9LHX475rzuEvAkTzUEOeEQHCg5xvomf/+4GBxiQDeH2aGkkuMLHvgTgzOXfDsoRr+RDNcw15RqSwNoFtSeTsf8e2itenx97G69PUSBK2FfseiHO498ZMrP0x3a/B9UqIZq+GOyNSWJvgxu/vwsbXHo/b3nsmnt87gn/775fxpt8/AscuaYIlgDEGt//5HyAXKLb98Bn8+Nl+Xp+mhrO0JYmt3Z0Vc0SWtoRnIaImbLc3u7j1ijPxwr4RNLk2RrwAxyxuQnuzW3x/Rc0fARA7+TtuwmzUdsuSqvO34p4rbvJ5Ycn6F/pLvp/2Jhzb3jztl4E4AZ5o4hquEBkYySJhC/7mLafA8w38QPHC3kG87/xXI+crVIGhbADXEXzz5y/hrp5ebOnuxEcvWIWFqQQPGjRnxX1Ar1ragrs2roVvFI4lZR/cz+8drig4li9KYSQXlC3xvrW7E0FgMDCaw78+uhs3X34GbEsQGMU9O1/Ee//oeGT9AFlfYQmQyRmM5nwctagJxmjkhNkTO5rx4v4Mdu/LFF9/ZLGPtuYEbvz+LnziwpOxKJ3A/kyu2JKhLZ3Abw9m4OVfJxcE+O3BDI5oTWFg1IPnKwKjUD/A3uFRLGlOVV2yviVho2/YK/5sOppduO7kDovVijQeV4jiNVQh4nk+vMAgkzO4/OafFQ8GN3V3ImELNmz/aXHb5g1r8I4zV2Bd13J4vsJA8bvBLIwqbEvgWIKELRARZDz+dkOzQ9xv3L5vMOh5UA0vOagqBj0PLZrAUC5X9hxDuRwW2hYGRrIYzJbfNpjNYe+IHTnB9e5NZ8GxgYvOOLqseLjojKNh24A3asq2H704jYOjHjKewb6hTFkx9Myeg9jf4iKbM2UFwk3dnTAGuOp1r8YH7/x5cfsXLnkNAmNwYNSD55e/zorFaWT8HPoGvbKeUjdd2olUwkLGC3Dzj54rK2xu/tFz+Lu3/z5+3TccWSC5rjPhsxt7h7ORk3y/fs3ZWNKcjH0unkWh+a6hCpEDWR8Cwd7BLL74ztfgVQuTMAbwjUIN8I1rzoYCgADZXFiwOJagNWVjKBsg6VhQFXj5iXwCwMDAtgSAYs/gKHwT/sbl2hZsATJ+eLtrW2hLJzDo+ch4AQJVpBI2ljSPf/2FaqeWp+tAxYPe7FHt/yLqNgAYzGbDM31GAT/AYDaL1mQSGd/D4GhQcqZAEajCtQDXAkZKXte1gBEvi1ygOL4jjSUtyWKBsDBtYWAkwNnHtePK1x5XPOux/aFnERiDhG1DBFixOH+JUwERRWCA5qSNE5a1IDCFYj4cR5MrOGX5Iox6hybInrJ8EXK+4puP9VacXXn32SuLRQgQfqB/8M6f42ubzoJRIJsrnzSbzQUYzSUqekptuq0H91x1FiwL+Lt1v49RL/y5LW9Lo+vY30c2Z2ILrlc5Nnb97iCuLClstl/aiVWvWgDfDyLPomRz0ZN8s7kAL+0fiTxbBMQvIxAE4Rmu0jNZiQTnsFHjaahCxLaAAxkfAHD80jQGMwY5o7BE8P1fvIwzVi7BivYkBoYD9A2GEd57e3bjg+efiCMXJfHy/iw2lfx2dP361VjS4mL/SHi6OJ2wMZozUDXFg23hj0gYlfQCg+f3juAL9z+NvqEsbrniDDQlbOTyB5OELbBFMJr/MHEsCX9ryxkkLIFjSzhmY/C7g6PwAxMWI6pQVagP7B4YKZ61EQC2JVDIYYsKnjqePYxRvDKYgR+EvV1MLsArgxksaw0niWa88oIj42WhAMb+N1kSFhWeDxzM+BW/3bc12XihP1uxfWV7Eq3Jyv9zQVhQbHzdcWVnHTa+7jg0J22oAi1JG7kAYUFuCRI24FiAj3xlUhibJXBtQMI6vkLaFazvWoHegUOvs75rBRxb8I7O5XjbmuUwGr5/v/FoL3LGwBILMuaHIJZAVSMf4/kGi5ptvBjxM2hvTsQmivqGssUipLD9ytt6cO9VZyMblBd8vxvK4lUtgG1ZkQu0OZaFwWwOLw2MFr/Po9pSODjqIRcg8izKtz94Dl7cN4qrS8a8pbsTJy1tYTFCDWfWFSIicgGAzwOwAfyLqn52vI8NDNA3mMUxi5N4oT9b8Sb+2XN70Zo+Ap/59lP43lN7sLwtjevWrcYX7v81PnHhKdi0owcdLcniKdwRL8D+ER9DWR8LmxyIhIUEYOOOh5/HmmPb0d7sYumCFGwrPJMSGEXatfHJt56MOx5+EX2DWVx7zxM4+7h2bHzd8fBNWEAMZX083z+Ce3t24wPnn4iBoQxWtLfA8gUJSzCc9csuL91yxRnI+gabbnukrFBqcm0sanaRsCz07h+BIwIIoAq0p130Zw795ubYVuRB7+5NZ0FVkcgnKTK5AAnbQpNrYTgbIO3a8I0i55tJn0WZLWdiZss4BkezWJC0sD9jYDQsKBYkLQyOZhG36GnM5zkEQNY3eK7vIO7cuLZYJP/8hX4sbl4S+Vv/1zadhQUpwW8P5tBbUnAsX5zGwnQCBzK5sksmn7/kdCxIJeA6wG8P5CreWyvbk3hpv1ex/ei2cFLsb/dXvh9ftSCJvUNexdyNhWkX5/7eMrxr+8Nl91+QsuH5igMjuYrHHLEgGfmYlpSNoVGN/BnctXFtZOFgW4LRmLMbRg32j+SK6xMVLvM2uzYsAa5fv7rYubgwtoQt6I/6PlOJ2CUGhkaD4s+rsO3q/JiPamua+A5HNIvNqviuiNgA/hnAmwCcDOCdInLyeB+f9Q2uvecJJBOJyDfxeScfAT8wuOKclcXtH7v3CazrXIFcYNDRksRf/skqfPpbT+Ed2x7GJ77xJFIJC8vbUugf8nDxTT/B665/EJ/59lN482lH4dPfegrrt/4E79r+MH53YBR/951f4o//4SH85dceR843eN95ry4WId1nHYPLb34Er7v+Qbxj28PYOxSejXn32Svxxft/jWOWtOLSLx26vW8wi458sqF3IJzQN/bU87X3PFE8MF9800/w2s89iIu3PYzd+zL42//3CzzdP4zv/+Ll4nOOeH7kQe+3+zM457of4M+2/BjP9A3hL+58DBff9BO8uC+Dr/f0YtfvBvFnm3886Q6ps6XL6mwZBxCeQXi2P4t3bHu4+P/zbP+hIiTqNgB4fsz25/Pb066FYzsW4JL8bZdsexjHdiyAH7OOSC4wGMoq9g5m8YlvPFnc3/cOZhEEig/d+VjZvvahOx+DbwyGsybyvXUgE719KGswOBp9W+H9WrFPB9H3H84aeIFGPmbEi35MxjOxa6kERiv6UF23bjUsQWz03ygqFkm85vZHMZozyPoGn/tuOMn2ro1r8YkLT8bnvrsLozHfZ84oRKJfJ3YNJMaOqQHNqkIEwJkAfqOqz6qqB+BOAG8b74OD/Js37k2sqnihfwSvWpAq297e7CIwig+efwI+dm/5AePq2x9FMuGUHUjWda6IPBit61xR/PrDdz8OSyQ86/Da4yruXyiACn/3DWYrDlRXnXt8cZxNrh35PS1pcSue+yNfexzrOlcUi6/C9uf3jkQe9AorZpa+buF7etua5RUH0YmusjlbVrGdLeMAgP0xH9z7M6bqbXHbR2IKBCvmA9WywkuAcYVA9CULrfoBGbc97rZggtsL87Mm+vpxRYVlCb7y4+fKCoev/Pg5BAo4luD69eVFyvXrV1d9HccS9A1lsem2Hrxj28PYdFsP+oaysWM2RmELIoshJ24NJF5CpQY02wqRowDsLvm6N7+tSEQ2ishOEdnZ19dX9uBE/s0b9ya2LcEX7n8agWrZ9iUtSdyz80Uc3d4UfWAcc2BelI6+trwonSh/nGrxdePuXyiExn4Yjn2+ES+I/J4CRdXn1pLv9Qv3P42t3Z0VB72tDz4T+brhqeipr0A7W7qs1mMccfvrZD64J/OYuA9UJz85NOox1T64q31Axm2Puy0xwe2OJVVvq/aYqJ+BawmuOGdl8Qzop7/1FK44ZyVcS2AhLP4//bZTcdfGtfj0205Fk2tXfZ1UwsKNF59W9jo3Xnwa3JhF5VIJG5ZlRRZDqYSFzRvWlD3X5g1r0JSs3SG72rGVqJZmWyESVe6XnYtU1W2q2qWqXR0dHWV3bElb2NrdibRrYcuYD9wt3Z348dN96BvKYu+QV9y+ecMa/L/HXsK5Jy1D32A29sO+dPv+TC7yfvszubKvocANF50GM+bxpfdf3pZGR2sS9/bsrrh9xAuK/25rTuCGi06rOJjGHRgLzy1y6EfaN5RFa8opHvRuueJMfOXHz+Hnu/dHfh/L29KwYk4dT2QF2tmyim09xhG3v07mg3syjxFEf6BaIrEfkNU+uFuS0e+tuO1NroUF6ejbFubfr6Xbt+a3R91/UdpCSyr6MYtiHrMgbcF1BEtak2U/gyWtSSTdcHn50u0drUk0JwULkgk0J8un0DUnHbSn3cjXaU+7aGtKor25/Pnam5PoaEli+6VdZY/ZfmkXlrQk0d7s4sNvWFVWDH34DauwMOWirSmBW644Ew985HW45Yoz0daUwMJU7ZbDr3ZsJaolKf2Nud5E5CwAn1LVP8l//VcAoKp/F3X/rq4u3blzZ/HrbNZHVn3kcoBnDPwAxRn0//poL+7q6c2vR6DhgcgRvDQwWrzP8sVp9A95ZbPrt2xYg/+/vXMPkquq8/jn29PzIiSGhGgpoQxqgkAMMSAuoIhkfYCWgQJcRqy1CsrHlrKCT7LuVlklVsFarKJrKBEh+Fhx8QFWTCW4iBhfPDfkQQhBgjII5AEYIPPq6Z9/nNOTefTtmUm6597b+X2qurrn3HPu+faZX5/+9Xn9ymb0l8pc9r8P0v1cD+889uVcsnTBiMV3Ky5cwn//atvQIthrLljMFau2MGd6G1ecvZBnXhi5kO+qcxdx0++3869LF7B95x7mzZkx4nrY8dBKX6nMn3eHXThzprex/KxjgLBotlgQhvHkc71c+qP1Q2WvPv94vvPbx7hk6QLu376LL656eKjDXLW+m2+te5y5h3Xy3YtOoq9UHrGL5ivnLeI/12xl54t9rLhwCXc9vIMTj5o1YgHeZHfaZGW3zgR1NEzQcHvt7S2xbfdLYxZwzp89DSDx2v6U6d7TM+bcjbkzwpfio7vHnqPxutnTeGJPz5hFrEfGMnsGBoYODmspiLaimNHayrN9/QwO+8y1tMCs9rDtuKdcYm/fvp1ih7QX6CwUeWmwNLSttlgQHW0FprUUGaTE8z370md2FmiJa+uTylSr49DWMLq3a2/fiP6g2AKzOtrotxJ7htUzo7NAZ7GNYrFAb29pxGLv2Z1tdHQUE9MrNjbZLfgHuG2/IfY6um8dzrzLfzHp+z1+5XsOVJKTfxJtNWuOSBF4BFgKPAncC3zAzDZXy1/tw9LXV6JnsETZoKe/jOIOkkoHVPm7s61AawsjOqG2YgEB/aUyg2a0KGzN7SuVaYsnUVbmnDtaC/QOhF0yrS3hV1fPQJly2Si2FCgo3KcYt+u2FcXe/n3bfgsFKJcZeu5oLdA3ELQUCqKjWAgnRBKG8ctlG9LXWwpbfdvjtt+WuGty0Kzmrpk509r4W9/gmLMpKp1dZddM78AgxYN318yUOCJAzS+0/fkSbKYy+3OvgYHBxHM3kq5VTqMtDZYpDjuNNke4I+LkhURbzdT2XTMrSfoEsJawffeGJCckifb2Iu2VtzVt/PzTO8bPUy8Om4CeenNEx8h/8Zwqx1fXin46s047BbMSZTUrOgA6Oopj/j/jXTtYyuzPvVpbWxK3tiZdKxYLvGpmZ5USjuNMFZlyRADMbDWwOm0djuM4juM0nlyNQTqO4ziO01y4I+I4juM4TmpkarHqZJG0E/hzwuXDgV1TKGd/yYtOyI/WA9G5y8zeXU8xFSS9AGxtxL1zRF5sqFHU+/03xF69b50y8qAR6qMz0VZz7YjUQtJ9ZnZi2jrGIy86IT9as6ozq7qmkoO9DZrh/eflPeRBZx40QuN1+tSM4ziO4zip4Y6I4ziO4zip0cyOyHVpC5ggedEJ+dGaVZ1Z1TWVHOxt0AzvPy/vIQ8686ARGqyzadeIOI7jOI6TfZp5RMRxHMdxnIzTdI6IpHdL2irpUUmXp61nNJIel7RR0npJ98W0WZJ+KWlbfD4sBV03SNohadOwtERdkpbHNt4q6V0Z0PpFSU/Gdl0v6awsaI31Z9omG8Fk7anZkHSkpDslbZG0WdInY3pu2yAPdpzU7llFUouk/5e0Km0tSUiaKenHkh6O7XpyvetoKkdEUgvwTeBM4FigS9Kx6aqqytvNbPGw7VCXA3eY2Xzgjvj3VLMSGL3Hu6qu2KYXAMfFMiti208VKxmrFeCrsV0Xx1ABqWvNkU3Wm5VM0J6alBLwaTM7BvgH4OPx/57LNsiRHSe1e1b5JLAlbRHjcA2wxsxeDxxPA/Q2lSMCnAQ8amaPmVk/cDOwLGVNE2EZcFN8fRNw9lQLMLPfAM+OSk7StQy42cz6zGw78Cih7aeEBK1JpKqV/NrkATFJe2o6zOwpM3sgvn6B0HkfQX7bIBd2XKPdM4ekucB7gOvT1pKEpBnAacB3AMys38yer3c9zeaIHAE8MezvbrJnhAbcLul+SR+Jaa8ws6cgfJCAl6embiRJurLazp+QtCFOC1SGvNPWmnb9WSKrdt5QJM0D3gjcTX7bIHd2PKrds8jXgM8B5ZR11OI1wE7gxjiFdL2kuseRbzZHRFXSsrYt6FQzW0IY4vy4pNPSFrQfZLGdrwVeCywGngKujulpa027fidFJB0K/AS41Mz2pK3nAMiVHWe93SW9F9hhZvenrWUcisAS4FozeyPwEg2YTmw2R6QbOHLY33OBv6akpSpm9tf4vAP4GWHI8xlJrwSIzzvSUziCJF2Za2cze8bMBs2sDHybfdMvaWtNu/4skVU7bwiSWglfhj8ws5/G5Ly2QW7sOKHds8apwPskPU6Y5jpD0vfTlVSVbqDbzCqjSj8mOCZ1pdkckXuB+ZKOktRGWKT485Q1DSFpmqTpldfAO4FNBI0fitk+BNyWjsIxJOn6OXCBpHZJRwHzgXtS0DdEpXOPnENoV0hfa6ZtcorJqp3XHUkizKtvMbP/GnYpr22QCzuu0e6ZwsyWm9lcM5tHaMtfmdkHU5Y1BjN7GnhC0tExaSnwUCMqaqoHcBbwCPAn4Atp6xml7TXAg/GxuaIPmE1YQb8tPs9KQdsPCVMaAwQv+OJauoAvxDbeCpyZAa3fAzYCGwgd5CuzoDXrNpkVe2q2B/AWwtTFBmB9fJyV5zbIgx0ntXvausbRfDqwKm0dNfQtBu6LbXorcFi96/CTVR3HcRzHSY1mm5pxHMdxHCdHuCPiOI7jOE5quCPiOI7jOE5quCPiOI7jOE5quCPiOI7jOE5VqgWwHCf/+yU9FIMO/s9EyrgjkmMkDcZIs5sk3SLpkIR8v59qbY5TC0nnSDJJr09bi9PcSJor6bYY8fhPkq6JZ6GMzvdrSSdWu0eddMyT9IFG3b+BrKR6kNExSJoPLCecIH4ccOlEyrkjkm96LESaXQj0Ax8bfrESZdbMTklDnOPUoAv4LeEwJ8dpCPGAs58Ct1qIeLwAOBT4cgpy5gGTckSmOKp5VaxKAEtJr5W0JsZMWzfsB8WHgW+a2XOx7IRODnZHpHlYB7xO0umS7oxDYhsBJL1YySTpc5I2SnpQ0pUxLcmoHKfuxDggpxIOObsgphUkrYjDuaskrZZ0Xrx2gqS7on2uHXWKruPU4gyg18xuBDCzQeAy4KJ40vXNMVDmj4DOSiFJXbGf3CTpqmHpL0q6Ktri/0k6KY6kPCbpfTFPi6SvSLo33vujsfiVwFvjKPZlSfmq9eEZ5DrgEjM7AfgMsCKmLwAWSPqdpD9KmtBISrFBIp0pRFKREERvTUw6CVhoIez98HxnEsKOv9nM9kqaFS9dB3zMzLZJejPBqM6YEvHOwcjZwBoze0TSs5KWEE4dnge8gRCRdgtwg0LckG8Ay8xsp6R/IvyavSgV5U7eOA4YEVjOzPZI+gvwaWCvmS2StAh4AEDSq4CrgBOA5wjR0s82s1uBacCvzezzkn4GXAG8AzgWuIlwqvPFwN/M7E2S2oHfSbqdECzuM2b23ljPRxLyQUIfngXiD4lTgFvCgBMA7fG5SAijcTohHtE6SQvN7Pla93RHJN90SlofX68jxFg4BbgnwYD/EbjRzPYCmNmz4xiV4zSCLkIIdAgBv7qAVuAWC0ELn5Z0Z7x+NLAQ+GW0zxbC0fGOMxFE9SjBAt5GnKIxsw2SNsRrbyI4GzsBJP0AOI1wvHk/+37wbQT6zGxA0kaCIw0hhtiiyoge8DLCl3P/KA218iX14VmgADxvZourXOsG/mhmA8B2SVsJ7+neWjd0RyTf9Iw2hthZv5SQv9qHspZROU5dkTSbMNq2UJIRHAsjRKKuWgTYbGYnT5FEp7nYDJw7PEHSDEIk4R0kOylJDNi+uChloA/AzMpxZLpS/hIzWzuq3tOr1JOUL6kPT504orRd0vlmdktch7PIzB4kOGtdwEpJhxOmah4b756+RuTg4nbC3OghAJJmmdkegud6fkyTpOPTFOk0NecB3zWzV5vZPDM7EtgO7ALOjWtFXkEY2oUQqHCOpJMhhHiXdFwawp1ccgdwiKR/hqHFn1cTdoKsAS6M6QuBRbHM3cDbJB0e83cBd02izrXAv8RpRSQtUIi2/gIwfQL5MoWkHwJ/AI6W1C3pYkK7XSypEsB1Wcy+Ftgt6SHgTuCzZrZ7vDp8ROQgwszWSFoM3CepH1gN/BvBqK6V9O+EIfKbCRGCHafedBEW7Q3nJ8AxhGHdTYQIr3cT5s/749D11yW9jNBnfY3Q+TlOTczMJJ0DrJD0H4Qf35V+rwDcGKdk1gP3xDJPSVpO+CIVsNrMbptEtdcTpmkeiKMFOwnrojYApfjlvRK4JiFfpjCzroRLYxaixtGiT8XHhPHou47jZAJJh5rZi3H65h7CWQRPp63LcZzG4iMijuNkhVWSZgJtwJfcCXGcgwMfEXEcx3EcJzV8sarjOI7jOKnhjojjOI7jOKnhjojjOI7jOKnhjojjOI7jOKnhjojjOI7jOKnhjojjOI7jOKnxd2hsCZjs4hGcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[['Price','Age','Odometer']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>mileage</th>\n",
       "      <th>engineCC</th>\n",
       "      <th>powerBhp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.305327</td>\n",
       "      <td>-0.011493</td>\n",
       "      <td>-0.334989</td>\n",
       "      <td>0.659230</td>\n",
       "      <td>0.771140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.305327</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173048</td>\n",
       "      <td>-0.295045</td>\n",
       "      <td>0.050181</td>\n",
       "      <td>-0.028722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odometer</th>\n",
       "      <td>-0.011493</td>\n",
       "      <td>0.173048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.065223</td>\n",
       "      <td>0.090721</td>\n",
       "      <td>0.031543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mileage</th>\n",
       "      <td>-0.334989</td>\n",
       "      <td>-0.295045</td>\n",
       "      <td>-0.065223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.641136</td>\n",
       "      <td>-0.545009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engineCC</th>\n",
       "      <td>0.659230</td>\n",
       "      <td>0.050181</td>\n",
       "      <td>0.090721</td>\n",
       "      <td>-0.641136</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.863728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>powerBhp</th>\n",
       "      <td>0.771140</td>\n",
       "      <td>-0.028722</td>\n",
       "      <td>0.031543</td>\n",
       "      <td>-0.545009</td>\n",
       "      <td>0.863728</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price       Age  Odometer   mileage  engineCC  powerBhp\n",
       "Price     1.000000 -0.305327 -0.011493 -0.334989  0.659230  0.771140\n",
       "Age      -0.305327  1.000000  0.173048 -0.295045  0.050181 -0.028722\n",
       "Odometer -0.011493  0.173048  1.000000 -0.065223  0.090721  0.031543\n",
       "mileage  -0.334989 -0.295045 -0.065223  1.000000 -0.641136 -0.545009\n",
       "engineCC  0.659230  0.050181  0.090721 -0.641136  1.000000  0.863728\n",
       "powerBhp  0.771140 -0.028722  0.031543 -0.545009  0.863728  1.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrl = (df[['Price','Age','Odometer','mileage','engineCC','powerBhp']]).corr()\n",
    "corrl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }#T_07add0f2_50fe_11eb_8e89_acde48001122row5_col1{\n",
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       "            background-color:  #5673e0;\n",
       "            color:  #000000;\n",
       "        }#T_07add0f2_50fe_11eb_8e89_acde48001122row5_col3{\n",
       "            background-color:  #4b64d5;\n",
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       "            background-color:  #d1493f;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_07add0f2_50fe_11eb_8e89_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Price</th>        <th class=\"col_heading level0 col1\" >Age</th>        <th class=\"col_heading level0 col2\" >Odometer</th>        <th class=\"col_heading level0 col3\" >mileage</th>        <th class=\"col_heading level0 col4\" >engineCC</th>        <th class=\"col_heading level0 col5\" >powerBhp</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_07add0f2_50fe_11eb_8e89_acde48001122level0_row0\" class=\"row_heading level0 row0\" >Price</th>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row0_col1\" class=\"data row0 col1\" >-0.305327</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row0_col2\" class=\"data row0 col2\" >-0.011493</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row0_col3\" class=\"data row0 col3\" >-0.334989</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row0_col4\" class=\"data row0 col4\" >0.659230</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row0_col5\" class=\"data row0 col5\" >0.771140</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_07add0f2_50fe_11eb_8e89_acde48001122level0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row1_col0\" class=\"data row1 col0\" >-0.305327</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row1_col2\" class=\"data row1 col2\" >0.173048</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row1_col3\" class=\"data row1 col3\" >-0.295045</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row1_col4\" class=\"data row1 col4\" >0.050181</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row1_col5\" class=\"data row1 col5\" >-0.028722</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_07add0f2_50fe_11eb_8e89_acde48001122level0_row2\" class=\"row_heading level0 row2\" >Odometer</th>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row2_col0\" class=\"data row2 col0\" >-0.011493</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row2_col1\" class=\"data row2 col1\" >0.173048</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row2_col3\" class=\"data row2 col3\" >-0.065223</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row2_col4\" class=\"data row2 col4\" >0.090721</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row2_col5\" class=\"data row2 col5\" >0.031543</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_07add0f2_50fe_11eb_8e89_acde48001122level0_row3\" class=\"row_heading level0 row3\" >mileage</th>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row3_col0\" class=\"data row3 col0\" >-0.334989</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row3_col1\" class=\"data row3 col1\" >-0.295045</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row3_col2\" class=\"data row3 col2\" >-0.065223</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row3_col4\" class=\"data row3 col4\" >-0.641136</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row3_col5\" class=\"data row3 col5\" >-0.545009</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_07add0f2_50fe_11eb_8e89_acde48001122level0_row4\" class=\"row_heading level0 row4\" >engineCC</th>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row4_col0\" class=\"data row4 col0\" >0.659230</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row4_col1\" class=\"data row4 col1\" >0.050181</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row4_col2\" class=\"data row4 col2\" >0.090721</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row4_col3\" class=\"data row4 col3\" >-0.641136</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row4_col5\" class=\"data row4 col5\" >0.863728</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_07add0f2_50fe_11eb_8e89_acde48001122level0_row5\" class=\"row_heading level0 row5\" >powerBhp</th>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row5_col0\" class=\"data row5 col0\" >0.771140</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row5_col1\" class=\"data row5 col1\" >-0.028722</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row5_col2\" class=\"data row5 col2\" >0.031543</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row5_col3\" class=\"data row5 col3\" >-0.545009</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row5_col4\" class=\"data row5 col4\" >0.863728</td>\n",
       "                        <td id=\"T_07add0f2_50fe_11eb_8e89_acde48001122row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8a4aeb23d0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrl.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Yes', 'No', 'No', 'No', 'Yes', 'Yes'],\n",
       "       ['No', 'Yes', 'No', 'No', 'No', 'No'],\n",
       "       ['No', 'No', 'Yes', 'No', 'No', 'No'],\n",
       "       ['No', 'No', 'No', 'Yes', 'No', 'No'],\n",
       "       ['Yes', 'No', 'No', 'No', 'Yes', 'Yes'],\n",
       "       ['Yes', 'No', 'No', 'No', 'Yes', 'Yes']], dtype='<U3')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where((df[['Price','Age','Odometer','mileage','engineCC','powerBhp']]).corr()>0.6,'Yes','No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Power BHP is highly positively correlated with the Price\n",
    "# EngineCC is also highly correlated with the Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# created dummy variables for those\n",
    "# Location\n",
    "# FuelType\n",
    "# Transmission\n",
    "# OwnerType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6019 entries, 0 to 6018\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Price         6019 non-null   float64\n",
      " 1   Make          6019 non-null   object \n",
      " 2   Location      6019 non-null   object \n",
      " 3   Age           6019 non-null   int64  \n",
      " 4   Odometer      6019 non-null   int64  \n",
      " 5   FuelType      6019 non-null   object \n",
      " 6   Transmission  6019 non-null   object \n",
      " 7   OwnerType     6019 non-null   object \n",
      " 8   Mileage       6019 non-null   object \n",
      " 9   EngineCC      6019 non-null   object \n",
      " 10  PowerBhp      6019 non-null   object \n",
      " 11  mileage       6019 non-null   float64\n",
      " 12  engineCC      6019 non-null   float64\n",
      " 13  powerBhp      6019 non-null   float64\n",
      "dtypes: float64(4), int64(2), object(8)\n",
      "memory usage: 658.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Location_dummy = pd.get_dummies(df.Location,prefix='Location',drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "FuelType_dummy = pd.get_dummies(df.FuelType,prefix='FuelType',drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "Transmission_dummy = pd.get_dummies(df.Transmission,prefix='Transmission',drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "OwnerType_dummy = pd.get_dummies(df.OwnerType,prefix='OwnerType',drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine_all_dummy = pd.concat([df,Location_dummy,FuelType_dummy,Transmission_dummy,OwnerType_dummy],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Make</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>FuelType</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>OwnerType</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineCC</th>\n",
       "      <th>...</th>\n",
       "      <th>Location_Mumbai</th>\n",
       "      <th>Location_Pune</th>\n",
       "      <th>FuelType_Diesel</th>\n",
       "      <th>FuelType_Electric</th>\n",
       "      <th>FuelType_LPG</th>\n",
       "      <th>FuelType_Petrol</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <th>OwnerType_Fourth +ACY- Above</th>\n",
       "      <th>OwnerType_Second</th>\n",
       "      <th>OwnerType_Third</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.75</td>\n",
       "      <td>marutiwagonr</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6</td>\n",
       "      <td>998</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.50</td>\n",
       "      <td>hyundaicreta</td>\n",
       "      <td>Pune</td>\n",
       "      <td>5</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.50</td>\n",
       "      <td>hondajazz</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>9</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2</td>\n",
       "      <td>1199</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.00</td>\n",
       "      <td>marutiertiga</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>8</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.74</td>\n",
       "      <td>audia4</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>7</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2</td>\n",
       "      <td>1968</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price          Make    Location  Age  Odometer FuelType Transmission  \\\n",
       "0   1.75  marutiwagonr      Mumbai   10     72000      CNG       Manual   \n",
       "1  12.50  hyundaicreta        Pune    5     41000   Diesel       Manual   \n",
       "2   4.50     hondajazz     Chennai    9     46000   Petrol       Manual   \n",
       "3   6.00  marutiertiga     Chennai    8     87000   Diesel       Manual   \n",
       "4  17.74        audia4  Coimbatore    7     40670   Diesel    Automatic   \n",
       "\n",
       "  OwnerType Mileage EngineCC  ... Location_Mumbai  Location_Pune  \\\n",
       "0     First    26.6      998  ...               1              0   \n",
       "1     First   19.67     1582  ...               0              1   \n",
       "2     First    18.2     1199  ...               0              0   \n",
       "3     First   20.77     1248  ...               0              0   \n",
       "4    Second    15.2     1968  ...               0              0   \n",
       "\n",
       "   FuelType_Diesel  FuelType_Electric  FuelType_LPG  FuelType_Petrol  \\\n",
       "0                0                  0             0                0   \n",
       "1                1                  0             0                0   \n",
       "2                0                  0             0                1   \n",
       "3                1                  0             0                0   \n",
       "4                1                  0             0                0   \n",
       "\n",
       "   Transmission_Manual  OwnerType_Fourth +ACY- Above  OwnerType_Second  \\\n",
       "0                    1                             0                 0   \n",
       "1                    1                             0                 0   \n",
       "2                    1                             0                 0   \n",
       "3                    1                             0                 0   \n",
       "4                    0                             0                 1   \n",
       "\n",
       "   OwnerType_Third  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_all_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Price', 'Make', 'Location', 'Age', 'Odometer', 'FuelType',\n",
       "       'Transmission', 'OwnerType', 'Mileage', 'EngineCC', 'PowerBhp',\n",
       "       'mileage', 'engineCC', 'powerBhp', 'Location_Bangalore',\n",
       "       'Location_Chennai', 'Location_Coimbatore', 'Location_Delhi',\n",
       "       'Location_Hyderabad', 'Location_Jaipur', 'Location_Kochi',\n",
       "       'Location_Kolkata', 'Location_Mumbai', 'Location_Pune',\n",
       "       'FuelType_Diesel', 'FuelType_Electric', 'FuelType_LPG',\n",
       "       'FuelType_Petrol', 'Transmission_Manual',\n",
       "       'OwnerType_Fourth +ACY- Above', 'OwnerType_Second', 'OwnerType_Third'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_all_dummy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df = combine_all_dummy.drop(columns=['Make','Location','FuelType','Transmission','OwnerType','Mileage', 'EngineCC', 'PowerBhp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Price', 'Age', 'Odometer', 'mileage', 'engineCC', 'powerBhp',\n",
       "       'Location_Bangalore', 'Location_Chennai', 'Location_Coimbatore',\n",
       "       'Location_Delhi', 'Location_Hyderabad', 'Location_Jaipur',\n",
       "       'Location_Kochi', 'Location_Kolkata', 'Location_Mumbai',\n",
       "       'Location_Pune', 'FuelType_Diesel', 'FuelType_Electric', 'FuelType_LPG',\n",
       "       'FuelType_Petrol', 'Transmission_Manual',\n",
       "       'OwnerType_Fourth +ACY- Above', 'OwnerType_Second', 'OwnerType_Third'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6019 entries, 0 to 6018\n",
      "Data columns (total 24 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Price                         6019 non-null   float64\n",
      " 1   Age                           6019 non-null   int64  \n",
      " 2   Odometer                      6019 non-null   int64  \n",
      " 3   mileage                       6019 non-null   float64\n",
      " 4   engineCC                      6019 non-null   float64\n",
      " 5   powerBhp                      6019 non-null   float64\n",
      " 6   Location_Bangalore            6019 non-null   uint8  \n",
      " 7   Location_Chennai              6019 non-null   uint8  \n",
      " 8   Location_Coimbatore           6019 non-null   uint8  \n",
      " 9   Location_Delhi                6019 non-null   uint8  \n",
      " 10  Location_Hyderabad            6019 non-null   uint8  \n",
      " 11  Location_Jaipur               6019 non-null   uint8  \n",
      " 12  Location_Kochi                6019 non-null   uint8  \n",
      " 13  Location_Kolkata              6019 non-null   uint8  \n",
      " 14  Location_Mumbai               6019 non-null   uint8  \n",
      " 15  Location_Pune                 6019 non-null   uint8  \n",
      " 16  FuelType_Diesel               6019 non-null   uint8  \n",
      " 17  FuelType_Electric             6019 non-null   uint8  \n",
      " 18  FuelType_LPG                  6019 non-null   uint8  \n",
      " 19  FuelType_Petrol               6019 non-null   uint8  \n",
      " 20  Transmission_Manual           6019 non-null   uint8  \n",
      " 21  OwnerType_Fourth +ACY- Above  6019 non-null   uint8  \n",
      " 22  OwnerType_Second              6019 non-null   uint8  \n",
      " 23  OwnerType_Third               6019 non-null   uint8  \n",
      "dtypes: float64(4), int64(2), uint8(18)\n",
      "memory usage: 388.1 KB\n"
     ]
    }
   ],
   "source": [
    "clean_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "clean_df[['Location_Bangalore', 'Location_Chennai', 'Location_Coimbatore',\n",
    "       'Location_Delhi', 'Location_Hyderabad', 'Location_Jaipur',\n",
    "       'Location_Kochi', 'Location_Kolkata', 'Location_Mumbai',\n",
    "       'Location_Pune', 'FuelType_Diesel', 'FuelType_Electric', 'FuelType_LPG',\n",
    "       'FuelType_Petrol', 'Transmission_Manual',\n",
    "       'OwnerType_Fourth +ACY- Above', 'OwnerType_Second', 'OwnerType_Third']] = clean_df[['Location_Bangalore', 'Location_Chennai', 'Location_Coimbatore',\n",
    "       'Location_Delhi', 'Location_Hyderabad', 'Location_Jaipur',\n",
    "       'Location_Kochi', 'Location_Kolkata', 'Location_Mumbai',\n",
    "       'Location_Pune', 'FuelType_Diesel', 'FuelType_Electric', 'FuelType_LPG',\n",
    "       'FuelType_Petrol', 'Transmission_Manual',\n",
    "       'OwnerType_Fourth +ACY- Above', 'OwnerType_Second', 'OwnerType_Third']].apply(pd.Categorical) \n",
    "print(clean_df.dtypes) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Price', 'Age', 'Odometer', 'mileage', 'engineCC', 'powerBhp',\n",
       "       'Location_Bangalore', 'Location_Chennai', 'Location_Coimbatore',\n",
       "       'Location_Delhi', 'Location_Hyderabad', 'Location_Jaipur',\n",
       "       'Location_Kochi', 'Location_Kolkata', 'Location_Mumbai',\n",
       "       'Location_Pune', 'FuelType_Diesel', 'FuelType_Electric', 'FuelType_LPG',\n",
       "       'FuelType_Petrol', 'Transmission_Manual',\n",
       "       'OwnerType_Fourth +ACY- Above', 'OwnerType_Second', 'OwnerType_Third'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before proceeding to creating a Linear Regression Model, we need to verify the assumptions of LR\n",
    "# investigating a linear relationship\n",
    "# All variables follow a normal distribution\n",
    "# There is very little or no multicollinearity\n",
    "# There is little or no autocorrelation\n",
    "# Data is homoscedastic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Verify Linear Relationship')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#investigating the linear relationship\n",
    "plt.plot(clean_df.mileage, clean_df.Price, 'o', color='black');\n",
    "plt.title(\"Verify Linear Relationship\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Verify Linear Relationship')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#understanding the linear relationship\n",
    "plt.plot(clean_df.Odometer, clean_df.Price, 'o', color='black');\n",
    "plt.title(\"Verify Linear Relationship\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Verify Linear Relationship')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#understanding the linear relationship\n",
    "plt.plot(clean_df.Age, clean_df.Price, 'o', color='black');\n",
    "plt.title(\"Verify Linear Relationship\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Verify Linear Relationship')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#understanding the linear relationship\n",
    "plt.plot(clean_df.engineCC, clean_df.Price, 'o', color='black');\n",
    "plt.title(\"Verify Linear Relationship\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Verify Linear Relationship')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#understanding the linear relationship\n",
    "plt.plot(clean_df.powerBhp, clean_df.Price, 'o', color='black');\n",
    "plt.title(\"Verify Linear Relationship\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# there is no evident of strong linear relatiosnhip among the dependent variable and other numeric independent variables\n",
    "# hence making a linear regression won't help the purpose\n",
    "# If Business wants a solution yes, we can proceed with caution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# All variables follow a normal distribution\n",
    "sns.kdeplot(clean_df.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(clean_df.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q3 + 1.5*IQR\n",
    "def find_outliers(x):\n",
    "    IQR = x.describe()[6]-x.describe()[4] #Q3-Q1\n",
    "    UCL = x.describe()[6] + 1.5*IQR # Q3+1.5*IQR\n",
    "    LCL = x.describe()[4] - 1.5*IQR #Q1-1.5*IQR\n",
    "    H_values = x[x > UCL] # outliers on the higher side\n",
    "    L_values = x[x < LCL] #outliers on the lower side\n",
    "    return(len(H_values),len(L_values)) #number of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(718, 0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_outliers(clean_df.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# there are many outliers in the dependent variable, we need to treat the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.kdeplot(clean_df.Odometer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Odometer'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(clean_df.Odometer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(202, 0)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_outliers(clean_df.Odometer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.kdeplot(clean_df.mileage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mileage seems to be approximately normal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mileage'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(clean_df.mileage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 1)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_outliers(clean_df.mileage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# few outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.kdeplot(clean_df.engineCC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='engineCC'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(clean_df.engineCC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 0)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_outliers(clean_df.engineCC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# less outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.kdeplot(clean_df.powerBhp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='powerBhp'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(clean_df.powerBhp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(239, 0)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_outliers(clean_df.powerBhp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have three options:\n",
    "# 1. Be cognizant about outliers, considering them as reality, go ahead with modeling\n",
    "# 2. Delete the outliers from the dataset, this will lead to significant data loss, information loss\n",
    "# 3. Cap the Outliers to their threshold value, anything greater than UCL = Q3, and less than LCL = Q1, \n",
    "# we can build a good model, but it will lack reality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cognizant about the all the three options i have decided to go with option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price                           float64\n",
       "Age                               int64\n",
       "Odometer                          int64\n",
       "mileage                         float64\n",
       "engineCC                        float64\n",
       "powerBhp                        float64\n",
       "Location_Bangalore                uint8\n",
       "Location_Chennai                  uint8\n",
       "Location_Coimbatore               uint8\n",
       "Location_Delhi                    uint8\n",
       "Location_Hyderabad                uint8\n",
       "Location_Jaipur                   uint8\n",
       "Location_Kochi                    uint8\n",
       "Location_Kolkata                  uint8\n",
       "Location_Mumbai                   uint8\n",
       "Location_Pune                     uint8\n",
       "FuelType_Diesel                   uint8\n",
       "FuelType_Electric                 uint8\n",
       "FuelType_LPG                      uint8\n",
       "FuelType_Petrol                   uint8\n",
       "Transmission_Manual               uint8\n",
       "OwnerType_Fourth +ACY- Above      uint8\n",
       "OwnerType_Second                  uint8\n",
       "OwnerType_Third                   uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There is very little or no multicollinearity\n",
    "# multicollinearity is handled later after creation of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data is homoscedastic\n",
    "# this is a post modeling exercise, done later in this script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the dataset into training and testig \n",
    "data_train, data_test = train_test_split(clean_df,test_size=0.25,random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4514, 24), (1505, 24))"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape,data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "XTrain = np.array(data_train.iloc[:,0:(clean_df.shape[1]-1)])\n",
    "YTrain = np.array(data_train['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "XTest = np.array(data_test.iloc[:,0:(clean_df.shape[1]-1)])\n",
    "YTest = np.array(data_test['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4514, 23), (1505, 23))"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XTrain.shape, XTest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#multiple linear regression model\n",
    "reg = linear_model.LinearRegression()\n",
    "reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(XTrain,YTrain) #training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 1.  0. -0. -0. -0.  0.  0.  0.  0.  0. -0. -0.  0.  0. -0. -0. -0.  0.\n",
      " -0.  0.  0. -0.  0.]\n"
     ]
    }
   ],
   "source": [
    "print('Coefficients: \\n', np.round(reg.coef_,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 0.0\n"
     ]
    }
   ],
   "source": [
    "print('Intercept: \\n', np.round(reg.intercept_,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(XTrain,YTrain) # R-square value from the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(XTest,YTest) # R-square value from the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Statistical API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(clean_df['Price'])\n",
    "xx = np.array(clean_df[['Price', 'Age', 'Odometer', 'mileage', 'engineCC', 'powerBhp',\n",
    "       'Location_Bangalore', 'Location_Chennai', 'Location_Coimbatore',\n",
    "       'Location_Delhi', 'Location_Hyderabad', 'Location_Jaipur',\n",
    "       'Location_Kochi', 'Location_Kolkata', 'Location_Mumbai',\n",
    "       'Location_Pune', 'FuelType_Diesel', 'FuelType_Electric', 'FuelType_LPG',\n",
    "       'FuelType_Petrol', 'Transmission_Manual',\n",
    "       'OwnerType_Fourth +ACY- Above', 'OwnerType_Second', 'OwnerType_Third']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.75, 12.5 ,  4.5 , ...,  2.9 ,  2.65,  2.5 ])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                      y   R-squared (uncentered):                   1.000\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              1.000\n",
      "Method:                 Least Squares   F-statistic:                          4.310e+28\n",
      "Date:                Thu, 07 Jan 2021   Prob (F-statistic):                        0.00\n",
      "Time:                        21:06:01   Log-Likelihood:                      1.5711e+05\n",
      "No. Observations:                6019   AIC:                                 -3.142e+05\n",
      "Df Residuals:                    5995   BIC:                                 -3.140e+05\n",
      "Df Model:                          24                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1             1.0000   2.35e-15   4.26e+14      0.000       1.000       1.000\n",
      "x2         -1.376e-16   5.69e-15     -0.024      0.981   -1.13e-14     1.1e-14\n",
      "x3          1.039e-17   1.63e-19     63.663      0.000    1.01e-17    1.07e-17\n",
      "x4           7.04e-16   4.04e-15      0.174      0.862   -7.22e-15    8.63e-15\n",
      "x5          8.491e-18   5.76e-17      0.147      0.883   -1.05e-16    1.22e-16\n",
      "x6          -4.36e-16   7.03e-16     -0.620      0.535   -1.81e-15    9.42e-16\n",
      "x7          1.335e-15   9.35e-14      0.014      0.989   -1.82e-13    1.85e-13\n",
      "x8         -2.705e-17   8.86e-14     -0.000      1.000   -1.74e-13    1.74e-13\n",
      "x9          3.288e-16   8.49e-14      0.004      0.997   -1.66e-13    1.67e-13\n",
      "x10        -1.768e-16   8.59e-14     -0.002      0.998   -1.68e-13    1.68e-13\n",
      "x11         7.511e-16   8.32e-14      0.009      0.993   -1.62e-13    1.64e-13\n",
      "x12         1.383e-15    9.1e-14      0.015      0.988   -1.77e-13     1.8e-13\n",
      "x13         1.673e-15   8.43e-14      0.020      0.984   -1.64e-13    1.67e-13\n",
      "x14         5.117e-16   8.73e-14      0.006      0.995   -1.71e-13    1.72e-13\n",
      "x15         1.394e-16   8.18e-14      0.002      0.999    -1.6e-13    1.61e-13\n",
      "x16         2.181e-16   8.47e-14      0.003      0.998   -1.66e-13    1.66e-13\n",
      "x17        -5.129e-15   1.22e-13     -0.042      0.966   -2.44e-13    2.33e-13\n",
      "x18        -5.794e-15   7.98e-13     -0.007      0.994   -1.57e-12    1.56e-12\n",
      "x19        -2.194e-16   3.71e-13     -0.001      1.000   -7.28e-13    7.28e-13\n",
      "x20        -3.559e-15    1.1e-13     -0.032      0.974   -2.19e-13    2.12e-13\n",
      "x21        -8.857e-16   4.31e-14     -0.021      0.984   -8.54e-14    8.36e-14\n",
      "x22        -5.022e-16   3.76e-13     -0.001      0.999   -7.37e-13    7.36e-13\n",
      "x23        -3.409e-16   4.24e-14     -0.008      0.994   -8.35e-14    8.29e-14\n",
      "x24        -1.623e-15   1.12e-13     -0.015      0.988   -2.21e-13    2.17e-13\n",
      "==============================================================================\n",
      "Omnibus:                    20140.957   Durbin-Watson:                   1.435\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       4287312734.603\n",
      "Skew:                         -58.830   Prob(JB):                         0.00\n",
      "Kurtosis:                    4135.950   Cond. No.                     6.01e+06\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 6.01e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "mod = sm.OLS(y, xx)\n",
    "\n",
    "results = mod.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This might indicate that there are strong multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters:  [ 1.00000000e+00 -1.37558151e-16  1.03864475e-17  7.03986023e-16\n",
      "  8.49108178e-18 -4.35957694e-16  1.33514077e-15 -2.70508442e-17\n",
      "  3.28838519e-16 -1.76752059e-16  7.51135265e-16  1.38344197e-15\n",
      "  1.67270711e-15  5.11743425e-16  1.39428399e-16  2.18141477e-16\n",
      " -5.12892680e-15 -5.79397641e-15 -2.19442520e-16 -3.55921889e-15\n",
      " -8.85684755e-16 -5.02202446e-16 -3.40873163e-16 -1.62348433e-15]\n",
      "R2:  1.0\n"
     ]
    }
   ],
   "source": [
    "print('Parameters: ', results.params)\n",
    "print('R2: ', results.rsquared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters:  [ 1.00000000e+00 -1.37558151e-16  1.03864475e-17  7.03986023e-16\n",
      "  8.49108178e-18 -4.35957694e-16  1.33514077e-15 -2.70508442e-17\n",
      "  3.28838519e-16 -1.76752059e-16  7.51135265e-16  1.38344197e-15\n",
      "  1.67270711e-15  5.11743425e-16  1.39428399e-16  2.18141477e-16\n",
      " -5.12892680e-15 -5.79397641e-15 -2.19442520e-16 -3.55921889e-15\n",
      " -8.85684755e-16 -5.02202446e-16 -3.40873163e-16 -1.62348433e-15]\n",
      "Standard errors:  [2.34509635e-15 5.69350797e-15 1.63148501e-19 4.04152348e-15\n",
      " 5.76471760e-17 7.02891824e-16 9.34709592e-14 8.86316495e-14\n",
      " 8.48716143e-14 8.58573933e-14 8.32022055e-14 9.10459447e-14\n",
      " 8.43089431e-14 8.73171557e-14 8.18409968e-14 8.46587046e-14\n",
      " 1.21698314e-13 7.98019501e-13 3.71341015e-13 1.09800792e-13\n",
      " 4.31152096e-14 3.75616207e-13 4.24457220e-14 1.11753313e-13]\n",
      "Predicted values:  [ 1.75 12.5   4.5  ...  2.9   2.65  2.5 ]\n"
     ]
    }
   ],
   "source": [
    "print('Parameters: ', results.params)\n",
    "print('Standard errors: ', results.bse)\n",
    "print('Predicted values: ', results.predict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "infl = results.get_influence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        dfb_x1    dfb_x2      dfb_x3    dfb_x4    dfb_x5    dfb_x6    dfb_x7  \\\n",
      "0     0.151695 -0.017794  -13.810035  0.181078  0.633106 -0.377082 -0.000112   \n",
      "1     0.121790  0.000941   79.197995  0.033253  0.088498 -0.125366  0.010155   \n",
      "2     0.254088 -0.013534  -43.050008  0.042685  0.070246 -0.216151  0.010261   \n",
      "3     0.602685 -0.070600    8.509360  0.443401  0.753396 -0.821829  0.027867   \n",
      "4     0.134413 -0.027763  -33.335333  0.054800  0.150178 -0.173144 -0.004729   \n",
      "...        ...       ...         ...       ...       ...       ...       ...   \n",
      "6014  0.050787 -0.044406  -29.468499 -0.012233  0.402592 -0.389577 -0.008852   \n",
      "6015  0.110266  0.014468  -47.662511  0.054265  0.438912 -0.210908  0.001297   \n",
      "6016  0.291430 -0.093376  160.927292  0.183029  0.520267 -1.051591  0.072698   \n",
      "6017  0.628444 -0.060210  166.062286  0.114033  0.828792 -0.673667  0.012664   \n",
      "6018  0.040520  0.000407   76.746030  0.011672  0.063727 -0.181062  0.004551   \n",
      "\n",
      "        dfb_x8    dfb_x9   dfb_x10  ...   dfb_x15   dfb_x16   dfb_x17  \\\n",
      "0     0.004465 -0.005660  0.005628  ...  0.001737 -0.007035 -0.035653   \n",
      "1    -0.002550  0.001576  0.000776  ...  0.003738  0.004995 -0.016532   \n",
      "2    -0.002690 -0.002433 -0.003041  ...  0.004550 -0.003364 -0.003475   \n",
      "3    -0.016614  0.008620 -0.010608  ...  0.008712  0.010384 -0.023976   \n",
      "4     0.006719 -0.002293 -0.001165  ...  0.003578  0.003709 -0.027521   \n",
      "...        ...       ...       ...  ...       ...       ...       ...   \n",
      "6014 -0.020883 -0.012090 -0.015932  ... -0.011134 -0.014258  0.021304   \n",
      "6015  0.003756  0.004695  0.006910  ...  0.000511  0.000758 -0.017199   \n",
      "6016  0.040854  0.053037  0.005975  ...  0.013625  0.035112 -0.124883   \n",
      "6017 -0.010365  0.008311 -0.026079  ... -0.038008 -0.018161 -0.002306   \n",
      "6018  0.002096  0.005676  0.000994  ... -0.002174  0.003082 -0.003702   \n",
      "\n",
      "       dfb_x18   dfb_x19   dfb_x20   dfb_x21   dfb_x22   dfb_x23   dfb_x24  \n",
      "0    -0.006364 -0.003693 -0.030229 -0.022253 -0.000514 -0.013472 -0.013073  \n",
      "1    -0.004621 -0.000499 -0.017896  0.000150 -0.001961 -0.004964 -0.003981  \n",
      "2    -0.001120  0.001307 -0.003626 -0.006410 -0.003256 -0.001398 -0.003991  \n",
      "3    -0.009130  0.001010 -0.015521  0.013107  0.004874  0.004431 -0.032949  \n",
      "4    -0.012721 -0.000038 -0.015022 -0.000166 -0.000565 -0.005137 -0.006367  \n",
      "...        ...       ...       ...       ...       ...       ...       ...  \n",
      "6014 -0.007686  0.014395  0.022044  0.002143 -0.002125  0.005641 -0.003829  \n",
      "6015 -0.004759 -0.004448 -0.021823 -0.006109  0.002195  0.000695 -0.003296  \n",
      "6016 -0.006605 -0.024460 -0.093307 -0.007198 -0.012160 -0.036947 -0.016604  \n",
      "6017  0.034925  0.028630  0.050058 -0.017274  0.012897  0.037129 -0.010770  \n",
      "6018 -0.004053  0.002204 -0.002727 -0.010034 -0.000536  0.001360 -0.003584  \n",
      "\n",
      "[6019 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "print(infl.summary_frame().filter(regex=\"dfb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_vif(X):\n",
    "\n",
    "    # Calculating VIF\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"variables\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "    return(vif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>engineCC</td>\n",
       "      <td>47.739114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FuelType_Diesel</td>\n",
       "      <td>37.931247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>29.552494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mileage</td>\n",
       "      <td>27.715907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>26.455456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          variables        VIF\n",
       "3          engineCC  47.739114\n",
       "15  FuelType_Diesel  37.931247\n",
       "4          powerBhp  29.552494\n",
       "2           mileage  27.715907\n",
       "18  FuelType_Petrol  26.455456"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = clean_df.drop('Price',axis=1)\n",
    "\n",
    "vif_df = calc_vif(X)\n",
    "\n",
    "vif_df.sort_values(by='VIF', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FuelType_Diesel</td>\n",
       "      <td>31.231801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>23.955044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mileage</td>\n",
       "      <td>23.103542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>10.462183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>7.297420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          variables        VIF\n",
       "14  FuelType_Diesel  31.231801\n",
       "17  FuelType_Petrol  23.955044\n",
       "2           mileage  23.103542\n",
       "3          powerBhp  10.462183\n",
       "0               Age   7.297420"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = clean_df.drop(['Price','engineCC'],axis=1)\n",
    "\n",
    "vif_df = calc_vif(X)\n",
    "\n",
    "vif_df.sort_values(by='VIF', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mileage</td>\n",
       "      <td>14.916291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>6.843925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>6.840977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>5.612177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>3.797697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              variables        VIF\n",
       "2               mileage  14.916291\n",
       "3              powerBhp   6.843925\n",
       "0                   Age   6.840977\n",
       "17  Transmission_Manual   5.612177\n",
       "12      Location_Mumbai   3.797697"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = clean_df.drop(['Price','engineCC','FuelType_Diesel'],axis=1)\n",
    "\n",
    "vif_df = calc_vif(X)\n",
    "\n",
    "vif_df.sort_values(by='VIF', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>6.834173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>6.782311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>4.479880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>2.797853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>2.619884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              variables       VIF\n",
       "0                   Age  6.834173\n",
       "2              powerBhp  6.782311\n",
       "16  Transmission_Manual  4.479880\n",
       "11      Location_Mumbai  2.797853\n",
       "7    Location_Hyderabad  2.619884"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = clean_df.drop(['Price','engineCC','FuelType_Diesel','mileage'],axis=1)\n",
    "\n",
    "vif_df = calc_vif(X)\n",
    "\n",
    "vif_df.sort_values(by='VIF', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# VIF less than 10 is acceptable\n",
    "# the more your VIF increases, the less reliable your regression results are going to be. \n",
    "# In general, a VIF above 10 indicates high correlation and is cause for concern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are losing two important variables enigneCC and mileage, we can consider verifying them by converting them\n",
    "# into buckets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>mileage</th>\n",
       "      <th>engineCC</th>\n",
       "      <th>powerBhp</th>\n",
       "      <th>Location_Bangalore</th>\n",
       "      <th>Location_Chennai</th>\n",
       "      <th>Location_Coimbatore</th>\n",
       "      <th>Location_Delhi</th>\n",
       "      <th>...</th>\n",
       "      <th>Location_Mumbai</th>\n",
       "      <th>Location_Pune</th>\n",
       "      <th>FuelType_Diesel</th>\n",
       "      <th>FuelType_Electric</th>\n",
       "      <th>FuelType_LPG</th>\n",
       "      <th>FuelType_Petrol</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <th>OwnerType_Fourth +ACY- Above</th>\n",
       "      <th>OwnerType_Second</th>\n",
       "      <th>OwnerType_Third</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1.75</td>\n",
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       "      <td>4.50</td>\n",
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       "      <td>46000</td>\n",
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       "      <th>3</th>\n",
       "      <td>6.00</td>\n",
       "      <td>8</td>\n",
       "      <td>87000</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
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       "      <td>17.74</td>\n",
       "      <td>7</td>\n",
       "      <td>40670</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price  Age  Odometer  mileage  engineCC  powerBhp  Location_Bangalore  \\\n",
       "0   1.75   10     72000    26.60     998.0     58.16                   0   \n",
       "1  12.50    5     41000    19.67    1582.0    126.20                   0   \n",
       "2   4.50    9     46000    18.20    1199.0     88.70                   0   \n",
       "3   6.00    8     87000    20.77    1248.0     88.76                   0   \n",
       "4  17.74    7     40670    15.20    1968.0    140.80                   0   \n",
       "\n",
       "   Location_Chennai  Location_Coimbatore  Location_Delhi  ...  \\\n",
       "0                 0                    0               0  ...   \n",
       "1                 0                    0               0  ...   \n",
       "2                 1                    0               0  ...   \n",
       "3                 1                    0               0  ...   \n",
       "4                 0                    1               0  ...   \n",
       "\n",
       "   Location_Mumbai  Location_Pune  FuelType_Diesel  FuelType_Electric  \\\n",
       "0                1              0                0                  0   \n",
       "1                0              1                1                  0   \n",
       "2                0              0                0                  0   \n",
       "3                0              0                1                  0   \n",
       "4                0              0                1                  0   \n",
       "\n",
       "   FuelType_LPG  FuelType_Petrol  Transmission_Manual  \\\n",
       "0             0                0                    1   \n",
       "1             0                0                    1   \n",
       "2             0                1                    1   \n",
       "3             0                0                    1   \n",
       "4             0                0                    0   \n",
       "\n",
       "   OwnerType_Fourth +ACY- Above  OwnerType_Second  OwnerType_Third  \n",
       "0                             0                 0                0  \n",
       "1                             0                 0                0  \n",
       "2                             0                 0                0  \n",
       "3                             0                 0                0  \n",
       "4                             0                 1                0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.qcut(clean_df.mileage,q=[0.,0.25,0.50,0.75,1.]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hml_mileage = pd.get_dummies(pd.qcut(clean_df.mileage,q=[0.,0.25,0.50,0.75,1.]),prefix='mileage',drop_first=True)\n",
    "# high, medium and low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.qcut(clean_df.mileage,q=[0.,0.25,0.50,0.75,1.]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.qcut(clean_df.engineCC,q=[0.,0.25,0.50,0.75,1.]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hml_engineCC = pd.get_dummies(pd.qcut(clean_df.engineCC,q=[0.,0.25,0.50,0.75,1.]),prefix='engineCC',drop_first=True)\n",
    "# high, medium and low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean_df = pd.concat([clean_df,hml_mileage,hml_engineCC],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Price  Age  Odometer  mileage  engineCC  powerBhp  Location_Bangalore  \\\n",
       "0   1.75   10     72000    26.60     998.0     58.16                   0   \n",
       "1  12.50    5     41000    19.67    1582.0    126.20                   0   \n",
       "2   4.50    9     46000    18.20    1199.0     88.70                   0   \n",
       "3   6.00    8     87000    20.77    1248.0     88.76                   0   \n",
       "4  17.74    7     40670    15.20    1968.0    140.80                   0   \n",
       "\n",
       "   Location_Chennai  Location_Coimbatore  Location_Delhi  ...  \\\n",
       "0                 0                    0               0  ...   \n",
       "1                 0                    0               0  ...   \n",
       "2                 1                    0               0  ...   \n",
       "3                 1                    0               0  ...   \n",
       "4                 0                    1               0  ...   \n",
       "\n",
       "   Location_Mumbai  Location_Pune  FuelType_Diesel  FuelType_Electric  \\\n",
       "0                1              0                0                  0   \n",
       "1                0              1                1                  0   \n",
       "2                0              0                0                  0   \n",
       "3                0              0                1                  0   \n",
       "4                0              0                1                  0   \n",
       "\n",
       "   FuelType_LPG  FuelType_Petrol  Transmission_Manual  \\\n",
       "0             0                0                    1   \n",
       "1             0                0                    1   \n",
       "2             0                1                    1   \n",
       "3             0                0                    1   \n",
       "4             0                0                    0   \n",
       "\n",
       "   OwnerType_Fourth +ACY- Above  OwnerType_Second  OwnerType_Third  \n",
       "0                             0                 0                0  \n",
       "1                             0                 0                0  \n",
       "2                             0                 0                0  \n",
       "3                             0                 0                0  \n",
       "4                             0                 1                0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>variables</th>\n",
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       "  <tbody>\n",
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       "      <th>13</th>\n",
       "      <td>FuelType_Diesel</td>\n",
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       "      <th>16</th>\n",
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       "      <td>16.654208</td>\n",
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       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
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       "      <td>7.077965</td>\n",
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       "      <td>5.582305</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "              variables        VIF\n",
       "13      FuelType_Diesel  20.164122\n",
       "16      FuelType_Petrol  16.654208\n",
       "2              powerBhp   9.591883\n",
       "0                   Age   7.077965\n",
       "17  Transmission_Manual   5.582305"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = clean_df.drop(['Price','engineCC','mileage'],axis=1)\n",
    "\n",
    "vif_df = calc_vif(X)\n",
    "\n",
    "vif_df.sort_values(by='VIF', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>6.834173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>6.782311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>4.479880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>2.797853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>2.619884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              variables       VIF\n",
       "0                   Age  6.834173\n",
       "2              powerBhp  6.782311\n",
       "16  Transmission_Manual  4.479880\n",
       "11      Location_Mumbai  2.797853\n",
       "7    Location_Hyderabad  2.619884"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = clean_df.drop(['Price','engineCC','FuelType_Diesel','mileage'],axis=1)\n",
    "\n",
    "vif_df = calc_vif(X)\n",
    "\n",
    "vif_df.sort_values(by='VIF', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.qcut(clean_df.powerBhp,q=[0.,0.25,0.50,0.75,1.]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hml_powerBhp = pd.get_dummies(pd.qcut(clean_df.powerBhp,q=[0.,0.25,0.50,0.75,1.]),prefix='powerBhp',drop_first=True)\n",
    "# high, medium and low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean_df = pd.concat([clean_df,hml_powerBhp],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>6.322638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>3.482934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>1.997771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>1.838072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Location_Pune</td>\n",
       "      <td>1.760061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              variables       VIF\n",
       "0                   Age  6.322638\n",
       "15  Transmission_Manual  3.482934\n",
       "14      FuelType_Petrol  1.997771\n",
       "6    Location_Hyderabad  1.838072\n",
       "11        Location_Pune  1.760061"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = clean_df.drop(['Price','engineCC','FuelType_Diesel','mileage','powerBhp'],axis=1)\n",
    "\n",
    "vif_df = calc_vif(X)\n",
    "\n",
    "vif_df.sort_values(by='VIF', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numeric values of engineCC, mileage and powerBhp lead to high multicollinearity, hence quantile based\n",
    "# buckets created and then dummy variables introduced, which reduced the multicollinearity\n",
    "\n",
    "# diesel fuel type also contributed to high multicollinearity, hence dropped from the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7000714797069869\n",
      "0.6902967954209108\n"
     ]
    }
   ],
   "source": [
    "y = clean_df['Price']\n",
    "x = clean_df.drop(['Price','engineCC','FuelType_Diesel','mileage'],axis=1)\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.25,random_state=1234)\n",
    "\n",
    "xtrain.shape,ytrain.shape,xtest.shape,ytest.shape\n",
    "\n",
    "new_model = LinearRegression()\n",
    "\n",
    "new_model.fit(xtrain,ytrain)\n",
    "\n",
    "print(new_model.score(xtrain,ytrain))\n",
    "\n",
    "print(new_model.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FuelType_Electric</td>\n",
       "      <td>9.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>OwnerType_Fourth +ACY- Above</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Coimbatore</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Location_Bangalore</td>\n",
       "      <td>1.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OwnerType_Third</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FuelType_LPG</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location_Chennai</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_Jaipur</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Location_Pune</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Odometer</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Location_Kochi</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Delhi</td>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>OwnerType_Second</td>\n",
       "      <td>-0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>-0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Location_Kolkata</td>\n",
       "      <td>-0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>-1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>-2.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Variables  coefficients\n",
       "13             FuelType_Electric          9.02\n",
       "17  OwnerType_Fourth +ACY- Above          4.70\n",
       "5            Location_Coimbatore          2.35\n",
       "3             Location_Bangalore          1.96\n",
       "7             Location_Hyderabad          1.92\n",
       "19               OwnerType_Third          1.66\n",
       "14                  FuelType_LPG          1.50\n",
       "4               Location_Chennai          1.05\n",
       "8                Location_Jaipur          0.65\n",
       "12                 Location_Pune          0.21\n",
       "2                       powerBhp          0.14\n",
       "1                       Odometer          0.00\n",
       "9                 Location_Kochi         -0.06\n",
       "6                 Location_Delhi         -0.12\n",
       "18              OwnerType_Second         -0.53\n",
       "11               Location_Mumbai         -0.60\n",
       "0                            Age         -0.93\n",
       "10              Location_Kolkata         -0.97\n",
       "15               FuelType_Petrol         -1.31\n",
       "16           Transmission_Manual         -2.68"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsDF = pd.DataFrame()\n",
    "resultsDF['Variables'] = pd.Series(xtrain.columns)\n",
    "resultsDF['coefficients'] = pd.Series(np.round(new_model.coef_,2))\n",
    "resultsDF.sort_values(by='coefficients',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.53"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(new_model.intercept_,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mod = sm.OLS(np.array(ytrain), np.array(xtrain))\n",
    "\n",
    "#results = mod.fit()\n",
    "#print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how good the model is?\n",
    "\n",
    "# R-square, coefficient of determination, \n",
    "# the value ranges from 0-1, \n",
    "# 0- there is no predictive power in the model\n",
    "# 1- abosulte predictive power\n",
    "# 0.8- a good model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adjusted R square\n",
    "def AdjustedRSquare(model,X,Y):\n",
    "    YHat = model.predict(X)\n",
    "    n,k = X.shape\n",
    "    sse = np.sum(np.square(YHat-Y),axis=0) #sum of suare error\n",
    "    sst = np.sum(np.square(Y-np.mean(Y)),axis=0) # sum of square total\n",
    "    R2 = 1- sse/sst #explained sum of squares\n",
    "    adjR2 = R2-(1-R2)*(float(k)/(n-k-1))\n",
    "    return adjR2, R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ReturnPValue(model,X,Y):\n",
    "    YHat = model.predict(X)\n",
    "    n,k = X.shape\n",
    "    sse = np.sum(np.square(YHat-Y),axis=0)\n",
    "    x = np.hstack((np.ones((n,1)),np.matrix(X)))\n",
    "    df = float(n-k-1)\n",
    "    sampleVar = sse/df\n",
    "    sampleVarianceX = x.T*x\n",
    "    covarianceMatrix = linalg.sqrtm(sampleVar*sampleVarianceX.I)\n",
    "    se = covarianceMatrix.diagonal()[1:]\n",
    "    betasTstat = np.zeros(len(se))\n",
    "    for i in range(len(se)):\n",
    "        betasTstat[i] = model.coef_[i]/se[i]\n",
    "    betasPvalue = 1- stats.t.cdf(abs(betasTstat),df)\n",
    "    return betasPvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Variables</th>\n",
       "      <th>coefficients</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FuelType_Electric</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>OwnerType_Fourth +ACY- Above</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Coimbatore</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Location_Bangalore</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OwnerType_Third</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FuelType_LPG</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location_Chennai</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_Jaipur</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Location_Pune</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Odometer</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Location_Kochi</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Delhi</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>OwnerType_Second</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Location_Kolkata</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Variables  coefficients  p_value\n",
       "13             FuelType_Electric          9.02     0.02\n",
       "17  OwnerType_Fourth +ACY- Above          4.70     0.03\n",
       "5            Location_Coimbatore          2.35     0.00\n",
       "3             Location_Bangalore          1.96     0.00\n",
       "7             Location_Hyderabad          1.92     0.00\n",
       "19               OwnerType_Third          1.66     0.01\n",
       "14                  FuelType_LPG          1.50     0.29\n",
       "4               Location_Chennai          1.05     0.01\n",
       "8                Location_Jaipur          0.65     0.08\n",
       "12                 Location_Pune          0.21     0.31\n",
       "2                       powerBhp          0.14     0.00\n",
       "1                       Odometer          0.00     0.04\n",
       "9                 Location_Kochi         -0.06     0.44\n",
       "6                 Location_Delhi         -0.12     0.38\n",
       "18              OwnerType_Second         -0.53     0.02\n",
       "11               Location_Mumbai         -0.60     0.06\n",
       "0                            Age         -0.93     0.00\n",
       "10              Location_Kolkata         -0.97     0.01\n",
       "15               FuelType_Petrol         -1.31     0.00\n",
       "16           Transmission_Manual         -2.68     0.00"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsDF['p_value'] = pd.Series(np.round(ReturnPValue(new_model,xtrain,ytrain),2))\n",
    "resultsDF.sort_values(by='coefficients',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6987363872507527 0.7000714797069869\n"
     ]
    }
   ],
   "source": [
    "reg.adjR2, reg.R2 = AdjustedRSquare(new_model,xtrain,ytrain)\n",
    "print (reg.adjR2, reg.R2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ErrorMetric(model,X,Y):\n",
    "    Yhat = model.predict(X)\n",
    "    MAPE = np.mean(abs(Y-Yhat)/Y)*100\n",
    "    MSSE = np.mean(np.square(Y-Yhat))\n",
    "    Error = sns.distplot(Y-Yhat)\n",
    "    return MAPE, MSSE, Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74.52047160093976, 37.417570616183106, <AxesSubplot:xlabel='Price'>)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ErrorMetric(new_model,xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84.9500508446753, 39.11742237999155, <AxesSubplot:xlabel='Price'>)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ErrorMetric(new_model,xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# error from the model seems to be normally distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price'>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "Yhat = new_model.predict(xtest)\n",
    "Y = ytest\n",
    "sns.scatterplot(Y,Yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.83164586],\n",
       "       [0.83164586, 1.        ]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(Y,Yhat) # a good correlation between actual and predicted using test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>coefficients</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Location_Kochi</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Delhi</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Location_Pune</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FuelType_LPG</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_Jaipur</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Odometer</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>OwnerType_Fourth +ACY- Above</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>OwnerType_Second</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FuelType_Electric</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Location_Kolkata</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OwnerType_Third</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location_Chennai</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Coimbatore</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Location_Bangalore</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Variables  coefficients  p_value\n",
       "9                 Location_Kochi         -0.06     0.44\n",
       "6                 Location_Delhi         -0.12     0.38\n",
       "12                 Location_Pune          0.21     0.31\n",
       "14                  FuelType_LPG          1.50     0.29\n",
       "8                Location_Jaipur          0.65     0.08\n",
       "11               Location_Mumbai         -0.60     0.06\n",
       "1                       Odometer          0.00     0.04\n",
       "17  OwnerType_Fourth +ACY- Above          4.70     0.03\n",
       "18              OwnerType_Second         -0.53     0.02\n",
       "13             FuelType_Electric          9.02     0.02\n",
       "10              Location_Kolkata         -0.97     0.01\n",
       "19               OwnerType_Third          1.66     0.01\n",
       "4               Location_Chennai          1.05     0.01\n",
       "7             Location_Hyderabad          1.92     0.00\n",
       "5            Location_Coimbatore          2.35     0.00\n",
       "15               FuelType_Petrol         -1.31     0.00\n",
       "16           Transmission_Manual         -2.68     0.00\n",
       "3             Location_Bangalore          1.96     0.00\n",
       "2                       powerBhp          0.14     0.00\n",
       "0                            Age         -0.93     0.00"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsDF.sort_values(by='p_value',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7000706125623979\n",
      "0.6903132829996341\n"
     ]
    }
   ],
   "source": [
    "y = clean_df['Price']\n",
    "x = clean_df.drop(['Price','engineCC','FuelType_Diesel','mileage','Location_Kochi'],axis=1)\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.25,random_state=1234)\n",
    "\n",
    "xtrain.shape,ytrain.shape,xtest.shape,ytest.shape\n",
    "\n",
    "new_model = LinearRegression()\n",
    "\n",
    "new_model.fit(xtrain,ytrain)\n",
    "\n",
    "print(new_model.score(xtrain,ytrain))\n",
    "\n",
    "print(new_model.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>coefficients</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Delhi</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FuelType_LPG</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Location_Pune</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_Jaipur</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Odometer</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>OwnerType_Fourth +ACY- Above</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FuelType_Electric</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>OwnerType_Second</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>OwnerType_Third</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>1.97</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Coimbatore</td>\n",
       "      <td>2.39</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location_Chennai</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Location_Bangalore</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Location_Kolkata</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Variables  coefficients  p_value\n",
       "6                 Location_Delhi         -0.08     0.41\n",
       "13                  FuelType_LPG          1.50     0.29\n",
       "11                 Location_Pune          0.26     0.23\n",
       "8                Location_Jaipur          0.70     0.04\n",
       "1                       Odometer          0.00     0.04\n",
       "10               Location_Mumbai         -0.55     0.04\n",
       "16  OwnerType_Fourth +ACY- Above          4.70     0.03\n",
       "12             FuelType_Electric          9.02     0.02\n",
       "17              OwnerType_Second         -0.53     0.02\n",
       "18               OwnerType_Third          1.66     0.01\n",
       "15           Transmission_Manual         -2.68     0.00\n",
       "14               FuelType_Petrol         -1.31     0.00\n",
       "0                            Age         -0.93     0.00\n",
       "7             Location_Hyderabad          1.97     0.00\n",
       "5            Location_Coimbatore          2.39     0.00\n",
       "4               Location_Chennai          1.10     0.00\n",
       "3             Location_Bangalore          2.01     0.00\n",
       "2                       powerBhp          0.14     0.00\n",
       "9               Location_Kolkata         -0.93     0.00"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsDF = pd.DataFrame()\n",
    "resultsDF['Variables'] = pd.Series(xtrain.columns)\n",
    "resultsDF['coefficients'] = pd.Series(np.round(new_model.coef_,2))\n",
    "resultsDF['p_value'] = pd.Series(np.round(ReturnPValue(new_model,xtrain,ytrain),2))\n",
    "resultsDF.sort_values(by='p_value',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7000680439037767\n",
      "0.6902474066716742\n"
     ]
    }
   ],
   "source": [
    "y = clean_df['Price']\n",
    "x = clean_df.drop(['Price','engineCC','FuelType_Diesel','mileage','Location_Kochi','Location_Delhi'],axis=1)\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.25,random_state=1234)\n",
    "\n",
    "xtrain.shape,ytrain.shape,xtest.shape,ytest.shape\n",
    "\n",
    "new_model = LinearRegression()\n",
    "\n",
    "new_model.fit(xtrain,ytrain)\n",
    "\n",
    "print(new_model.score(xtrain,ytrain))\n",
    "\n",
    "print(new_model.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>coefficients</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FuelType_LPG</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Location_Pune</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Odometer</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>OwnerType_Fourth +ACY- Above</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Jaipur</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>OwnerType_Second</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FuelType_Electric</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>OwnerType_Third</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_Kolkata</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Coimbatore</td>\n",
       "      <td>2.42</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location_Chennai</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Location_Bangalore</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Variables  coefficients  p_value\n",
       "12                  FuelType_LPG          1.50     0.29\n",
       "10                 Location_Pune          0.29     0.20\n",
       "9                Location_Mumbai         -0.52     0.04\n",
       "1                       Odometer          0.00     0.04\n",
       "15  OwnerType_Fourth +ACY- Above          4.70     0.03\n",
       "7                Location_Jaipur          0.73     0.03\n",
       "16              OwnerType_Second         -0.53     0.02\n",
       "11             FuelType_Electric          9.02     0.02\n",
       "17               OwnerType_Third          1.66     0.01\n",
       "8               Location_Kolkata         -0.89     0.00\n",
       "6             Location_Hyderabad          2.00     0.00\n",
       "5            Location_Coimbatore          2.42     0.00\n",
       "4               Location_Chennai          1.13     0.00\n",
       "13               FuelType_Petrol         -1.31     0.00\n",
       "14           Transmission_Manual         -2.68     0.00\n",
       "3             Location_Bangalore          2.04     0.00\n",
       "2                       powerBhp          0.14     0.00\n",
       "0                            Age         -0.93     0.00"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsDF = pd.DataFrame()\n",
    "resultsDF['Variables'] = pd.Series(xtrain.columns)\n",
    "resultsDF['coefficients'] = pd.Series(np.round(new_model.coef_,2))\n",
    "resultsDF['p_value'] = pd.Series(np.round(ReturnPValue(new_model,xtrain,ytrain),2))\n",
    "resultsDF.sort_values(by='p_value',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7000482628071347\n",
      "0.6900697617882772\n"
     ]
    }
   ],
   "source": [
    "y = clean_df['Price']\n",
    "x = clean_df.drop(['Price','engineCC','FuelType_Diesel','mileage','Location_Kochi','Location_Delhi',\n",
    "                  'FuelType_LPG'],axis=1)\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.25,random_state=1234)\n",
    "\n",
    "xtrain.shape,ytrain.shape,xtest.shape,ytest.shape\n",
    "\n",
    "new_model = LinearRegression()\n",
    "\n",
    "new_model.fit(xtrain,ytrain)\n",
    "\n",
    "print(new_model.score(xtrain,ytrain))\n",
    "\n",
    "print(new_model.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>coefficients</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Location_Pune</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Odometer</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>OwnerType_Fourth +ACY- Above</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Jaipur</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>OwnerType_Second</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FuelType_Electric</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>OwnerType_Third</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Coimbatore</td>\n",
       "      <td>2.42</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location_Chennai</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Location_Bangalore</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_Kolkata</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Variables  coefficients  p_value\n",
       "10                 Location_Pune          0.29     0.20\n",
       "1                       Odometer          0.00     0.04\n",
       "9                Location_Mumbai         -0.52     0.04\n",
       "14  OwnerType_Fourth +ACY- Above          4.70     0.03\n",
       "7                Location_Jaipur          0.74     0.03\n",
       "15              OwnerType_Second         -0.53     0.02\n",
       "11             FuelType_Electric          9.02     0.02\n",
       "16               OwnerType_Third          1.66     0.01\n",
       "13           Transmission_Manual         -2.68     0.00\n",
       "12               FuelType_Petrol         -1.31     0.00\n",
       "0                            Age         -0.93     0.00\n",
       "6             Location_Hyderabad          2.01     0.00\n",
       "5            Location_Coimbatore          2.42     0.00\n",
       "4               Location_Chennai          1.13     0.00\n",
       "3             Location_Bangalore          2.04     0.00\n",
       "2                       powerBhp          0.14     0.00\n",
       "8               Location_Kolkata         -0.90     0.00"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsDF = pd.DataFrame()\n",
    "resultsDF['Variables'] = pd.Series(xtrain.columns)\n",
    "resultsDF['coefficients'] = pd.Series(np.round(new_model.coef_,2))\n",
    "resultsDF['p_value'] = pd.Series(np.round(ReturnPValue(new_model,xtrain,ytrain),2))\n",
    "resultsDF.sort_values(by='p_value',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.70000423172522\n",
      "0.6898005338656239\n"
     ]
    }
   ],
   "source": [
    "y = clean_df['Price']\n",
    "x = clean_df.drop(['Price','engineCC','FuelType_Diesel','mileage','Location_Kochi','Location_Delhi',\n",
    "                  'FuelType_LPG','Location_Pune'],axis=1)\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.25,random_state=1234)\n",
    "\n",
    "xtrain.shape,ytrain.shape,xtest.shape,ytest.shape\n",
    "\n",
    "new_model = LinearRegression()\n",
    "\n",
    "new_model.fit(xtrain,ytrain)\n",
    "\n",
    "print(new_model.score(xtrain,ytrain))\n",
    "\n",
    "print(new_model.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>coefficients</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Jaipur</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Odometer</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OwnerType_Fourth +ACY- Above</td>\n",
       "      <td>4.71</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>OwnerType_Second</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FuelType_Electric</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>OwnerType_Third</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Location_Bangalore</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location_Chennai</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Coimbatore</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_Kolkata</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Variables  coefficients  p_value\n",
       "7                Location_Jaipur          0.65     0.05\n",
       "1                       Odometer          0.00     0.04\n",
       "13  OwnerType_Fourth +ACY- Above          4.71     0.03\n",
       "14              OwnerType_Second         -0.52     0.03\n",
       "9                Location_Mumbai         -0.61     0.02\n",
       "10             FuelType_Electric          9.02     0.02\n",
       "15               OwnerType_Third          1.70     0.01\n",
       "0                            Age         -0.92     0.00\n",
       "2                       powerBhp          0.14     0.00\n",
       "3             Location_Bangalore          1.95     0.00\n",
       "4               Location_Chennai          1.04     0.00\n",
       "5            Location_Coimbatore          2.35     0.00\n",
       "6             Location_Hyderabad          1.92     0.00\n",
       "8               Location_Kolkata         -0.98     0.00\n",
       "11               FuelType_Petrol         -1.31     0.00\n",
       "12           Transmission_Manual         -2.69     0.00"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsDF = pd.DataFrame()\n",
    "resultsDF['Variables'] = pd.Series(xtrain.columns)\n",
    "resultsDF['coefficients'] = pd.Series(np.round(new_model.coef_,2))\n",
    "resultsDF['p_value'] = pd.Series(np.round(ReturnPValue(new_model,xtrain,ytrain),2))\n",
    "resultsDF.sort_values(by='p_value',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.699771002502553\n",
      "0.6906731401107624\n"
     ]
    }
   ],
   "source": [
    "y = clean_df['Price']\n",
    "x = clean_df.drop(['Price','engineCC','FuelType_Diesel','mileage','Location_Kochi','Location_Delhi',\n",
    "                  'FuelType_LPG','Location_Pune','OwnerType_Fourth +ACY- Above'],axis=1)\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.25,random_state=1234)\n",
    "\n",
    "xtrain.shape,ytrain.shape,xtest.shape,ytest.shape\n",
    "\n",
    "new_model = LinearRegression()\n",
    "\n",
    "new_model.fit(xtrain,ytrain)\n",
    "\n",
    "print(new_model.score(xtrain,ytrain))\n",
    "\n",
    "print(new_model.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>coefficients</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Jaipur</td>\n",
       "      <td>0.652</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Odometer</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>-0.594</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OwnerType_Second</td>\n",
       "      <td>-0.540</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FuelType_Electric</td>\n",
       "      <td>8.994</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>OwnerType_Third</td>\n",
       "      <td>1.657</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_Kolkata</td>\n",
       "      <td>-0.989</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location_Chennai</td>\n",
       "      <td>1.037</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.917</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Location_Bangalore</td>\n",
       "      <td>1.970</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Coimbatore</td>\n",
       "      <td>2.353</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>1.908</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>-1.323</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>-2.684</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Variables  coefficients  p_value\n",
       "7       Location_Jaipur         0.652    0.044\n",
       "1              Odometer         0.000    0.036\n",
       "9       Location_Mumbai        -0.594    0.020\n",
       "13     OwnerType_Second        -0.540    0.020\n",
       "10    FuelType_Electric         8.994    0.019\n",
       "14      OwnerType_Third         1.657    0.011\n",
       "8      Location_Kolkata        -0.989    0.002\n",
       "4      Location_Chennai         1.037    0.001\n",
       "0                   Age        -0.917    0.000\n",
       "2              powerBhp         0.141    0.000\n",
       "3    Location_Bangalore         1.970    0.000\n",
       "5   Location_Coimbatore         2.353    0.000\n",
       "6    Location_Hyderabad         1.908    0.000\n",
       "11      FuelType_Petrol        -1.323    0.000\n",
       "12  Transmission_Manual        -2.684    0.000"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsDF = pd.DataFrame()\n",
    "resultsDF['Variables'] = pd.Series(xtrain.columns)\n",
    "resultsDF['coefficients'] = pd.Series(np.round(new_model.coef_,3))\n",
    "resultsDF['p_value'] = pd.Series(np.round(ReturnPValue(new_model,xtrain,ytrain),3))\n",
    "resultsDF.sort_values(by='p_value',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6996154616418608\n",
      "0.6912643086728383\n"
     ]
    }
   ],
   "source": [
    "y = clean_df['Price']\n",
    "x = clean_df.drop(['Price','engineCC','FuelType_Diesel','mileage','Location_Kochi','Location_Delhi',\n",
    "                  'FuelType_LPG','Location_Pune','OwnerType_Fourth +ACY- Above','Odometer'],axis=1)\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.25,random_state=1234)\n",
    "\n",
    "xtrain.shape,ytrain.shape,xtest.shape,ytest.shape\n",
    "\n",
    "new_model = LinearRegression()\n",
    "\n",
    "new_model.fit(xtrain,ytrain)\n",
    "\n",
    "print(new_model.score(xtrain,ytrain))\n",
    "\n",
    "print(new_model.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>coefficients</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Jaipur</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FuelType_Electric</td>\n",
       "      <td>8.972</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>OwnerType_Second</td>\n",
       "      <td>-0.540</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>-0.610</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OwnerType_Third</td>\n",
       "      <td>1.654</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Location_Chennai</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Kolkata</td>\n",
       "      <td>-1.010</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.911</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Location_Bangalore</td>\n",
       "      <td>1.963</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location_Coimbatore</td>\n",
       "      <td>2.348</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>1.915</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>-1.351</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>-2.671</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Variables  coefficients  p_value\n",
       "6       Location_Jaipur         0.663    0.042\n",
       "9     FuelType_Electric         8.972    0.020\n",
       "12     OwnerType_Second        -0.540    0.020\n",
       "8       Location_Mumbai        -0.610    0.017\n",
       "13      OwnerType_Third         1.654    0.011\n",
       "3      Location_Chennai         1.073    0.001\n",
       "7      Location_Kolkata        -1.010    0.001\n",
       "0                   Age        -0.911    0.000\n",
       "1              powerBhp         0.141    0.000\n",
       "2    Location_Bangalore         1.963    0.000\n",
       "4   Location_Coimbatore         2.348    0.000\n",
       "5    Location_Hyderabad         1.915    0.000\n",
       "10      FuelType_Petrol        -1.351    0.000\n",
       "11  Transmission_Manual        -2.671    0.000"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsDF = pd.DataFrame()\n",
    "resultsDF['Variables'] = pd.Series(xtrain.columns)\n",
    "resultsDF['coefficients'] = pd.Series(np.round(new_model.coef_,3))\n",
    "resultsDF['p_value'] = pd.Series(np.round(ReturnPValue(new_model,xtrain,ytrain),3))\n",
    "resultsDF.sort_values(by='p_value',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Null: beta estimates are = 0\n",
    "# Alt : beta estimates are != 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>coefficients</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FuelType_Electric</td>\n",
       "      <td>8.972</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location_Coimbatore</td>\n",
       "      <td>2.348</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Location_Bangalore</td>\n",
       "      <td>1.963</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Hyderabad</td>\n",
       "      <td>1.915</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OwnerType_Third</td>\n",
       "      <td>1.654</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Location_Chennai</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Jaipur</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>powerBhp</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>OwnerType_Second</td>\n",
       "      <td>-0.540</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_Mumbai</td>\n",
       "      <td>-0.610</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.911</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Kolkata</td>\n",
       "      <td>-1.010</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FuelType_Petrol</td>\n",
       "      <td>-1.351</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Transmission_Manual</td>\n",
       "      <td>-2.671</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Variables  coefficients  p_value\n",
       "9     FuelType_Electric         8.972    0.020\n",
       "4   Location_Coimbatore         2.348    0.000\n",
       "2    Location_Bangalore         1.963    0.000\n",
       "5    Location_Hyderabad         1.915    0.000\n",
       "13      OwnerType_Third         1.654    0.011\n",
       "3      Location_Chennai         1.073    0.001\n",
       "6       Location_Jaipur         0.663    0.042\n",
       "1              powerBhp         0.141    0.000\n",
       "12     OwnerType_Second        -0.540    0.020\n",
       "8       Location_Mumbai        -0.610    0.017\n",
       "0                   Age        -0.911    0.000\n",
       "7      Location_Kolkata        -1.010    0.001\n",
       "10      FuelType_Petrol        -1.351    0.000\n",
       "11  Transmission_Manual        -2.671    0.000"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsDF.sort_values(by='coefficients',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Above table has the best result so far, we can explore additional modeling options and see how much \n",
    "# improvement we are getting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7\n",
      "0.69\n"
     ]
    }
   ],
   "source": [
    "print(np.round(new_model.score(xtrain,ytrain),2))\n",
    "\n",
    "print(np.round(new_model.score(xtest,ytest),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the difference between training score and test score is within +/- 2%, the model is acceptable\n",
    "# there is no sign of overfitting here\n",
    "# all the assumptions are also met\n",
    "# the correlation between actual and predicted on the test is close to 82%\n",
    "# hence this is the final model"
   ]
  }
 ],
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