Car price Hackathon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54e626a4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff819e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Training_Data_Set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca2d4a3b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Maker</th>\n",
       "      <th>model</th>\n",
       "      <th>Location</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Owner Type</th>\n",
       "      <th>manufacture_year</th>\n",
       "      <th>Age of car</th>\n",
       "      <th>engine_displacement</th>\n",
       "      <th>engine_power</th>\n",
       "      <th>body_type</th>\n",
       "      <th>Vroom Audit Rating</th>\n",
       "      <th>transmission</th>\n",
       "      <th>door_count</th>\n",
       "      <th>seat_count</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25001</td>\n",
       "      <td>skoda</td>\n",
       "      <td>octavia</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Second</td>\n",
       "      <td>1964</td>\n",
       "      <td>55</td>\n",
       "      <td>1964</td>\n",
       "      <td>147.0</td>\n",
       "      <td>compact</td>\n",
       "      <td>8</td>\n",
       "      <td>man</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>petrol</td>\n",
       "      <td>543764.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25002</td>\n",
       "      <td>fiat</td>\n",
       "      <td>panda</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>27750.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>1242</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>man</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>petrol</td>\n",
       "      <td>401819.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25003</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>1995</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>auto</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2392855.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25004</td>\n",
       "      <td>nissan</td>\n",
       "      <td>juke</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>43949.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>1618</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>man</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>petrol</td>\n",
       "      <td>958606.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25005</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x5</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>59524.0</td>\n",
       "      <td>Fourth &amp; Above</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>2993</td>\n",
       "      <td>180.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>auto</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>3085561.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id   Maker    model   Location  Distance       Owner Type  \\\n",
       "0  25001   skoda  octavia  Ahmedabad        NaN          Second   \n",
       "1  25002    fiat    panda  Ahmedabad    27750.0           Third   \n",
       "2  25003     bmw       x1  Hyderabad    46000.0           Third   \n",
       "3  25004  nissan     juke     Mumbai    43949.0           Third   \n",
       "4  25005     bmw       x5     Jaipur    59524.0  Fourth & Above   \n",
       "\n",
       "   manufacture_year  Age of car  engine_displacement  engine_power body_type  \\\n",
       "0              1964          55                 1964         147.0   compact   \n",
       "1              2012           7                 1242          51.0       NaN   \n",
       "2              2014           5                 1995         105.0       NaN   \n",
       "3              2011           8                 1618         140.0       NaN   \n",
       "4              2012           7                 2993         180.0       NaN   \n",
       "\n",
       "   Vroom Audit Rating transmission door_count seat_count fuel_type       Price  \n",
       "0                   8          man       None       None    petrol   543764.25  \n",
       "1                   6          man          4          4    petrol   401819.25  \n",
       "2                   7         auto          4          5    diesel  2392855.50  \n",
       "3                   7          man          4          5    petrol   958606.50  \n",
       "4                   7         auto          4          5    diesel  3085561.50  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "928ccc78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53515 entries, 0 to 53514\n",
      "Data columns (total 17 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Id                   53515 non-null  int64  \n",
      " 1   Maker                53515 non-null  object \n",
      " 2   model                53515 non-null  object \n",
      " 3   Location             53515 non-null  object \n",
      " 4   Distance             52304 non-null  float64\n",
      " 5   Owner Type           53515 non-null  object \n",
      " 6   manufacture_year     53515 non-null  int64  \n",
      " 7   Age of car           53515 non-null  int64  \n",
      " 8   engine_displacement  53515 non-null  int64  \n",
      " 9   engine_power         52076 non-null  float64\n",
      " 10  body_type            4136 non-null   object \n",
      " 11  Vroom Audit Rating   53515 non-null  int64  \n",
      " 12  transmission         53515 non-null  object \n",
      " 13  door_count           53515 non-null  object \n",
      " 14  seat_count           53515 non-null  object \n",
      " 15  fuel_type            53515 non-null  object \n",
      " 16  Price                53515 non-null  float64\n",
      "dtypes: float64(3), int64(5), object(9)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ec1f80e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                         0\n",
       "Maker                      0\n",
       "model                      0\n",
       "Location                   0\n",
       "Distance                1211\n",
       "Owner Type                 0\n",
       "manufacture_year           0\n",
       "Age of car                 0\n",
       "engine_displacement        0\n",
       "engine_power            1439\n",
       "body_type              49379\n",
       "Vroom Audit Rating         0\n",
       "transmission               0\n",
       "door_count                 0\n",
       "seat_count                 0\n",
       "fuel_type                  0\n",
       "Price                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "164427cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'Maker', 'model', 'Location', 'Distance ', 'Owner Type',\n",
       "       'manufacture_year', 'Age of car', 'engine_displacement', 'engine_power',\n",
       "       'body_type', 'Vroom Audit Rating', 'transmission', 'door_count',\n",
       "       'seat_count', 'fuel_type', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "051c1a7d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02263"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round( df['Distance '].isnull().sum()/len(df), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47d223d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02689"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round( df['engine_power'].isnull().sum()/len(df), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "945a8c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92271"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round( df['body_type'].isnull().sum()/len(df), 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e85325",
   "metadata": {},
   "source": [
    "* Both 'distance' and 'engine power' has less than 5% of missing values so we drop the missing values.\n",
    "* 94% of the values are missing in 'body type' so we are not considering it further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8fcf5ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset = ['Distance '],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "255b07fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Distance '].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4981940",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset = ['engine_power'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd3fe289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['engine_power'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ba3069ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                         0\n",
       "Maker                      0\n",
       "model                      0\n",
       "Location                   0\n",
       "Distance                   0\n",
       "Owner Type                 0\n",
       "manufacture_year           0\n",
       "Age of car                 0\n",
       "engine_displacement        0\n",
       "engine_power               0\n",
       "body_type              48430\n",
       "Vroom Audit Rating         0\n",
       "transmission               0\n",
       "door_count                 0\n",
       "seat_count                 0\n",
       "fuel_type                  0\n",
       "Price                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a355ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f171a295",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Maker</th>\n",
       "      <th>model</th>\n",
       "      <th>Location</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Owner Type</th>\n",
       "      <th>manufacture_year</th>\n",
       "      <th>Age of car</th>\n",
       "      <th>engine_displacement</th>\n",
       "      <th>engine_power</th>\n",
       "      <th>body_type</th>\n",
       "      <th>Vroom Audit Rating</th>\n",
       "      <th>transmission</th>\n",
       "      <th>door_count</th>\n",
       "      <th>seat_count</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25002</td>\n",
       "      <td>fiat</td>\n",
       "      <td>panda</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>27750.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>1242</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>man</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>petrol</td>\n",
       "      <td>401819.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25003</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>1995</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>auto</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2392855.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25004</td>\n",
       "      <td>nissan</td>\n",
       "      <td>juke</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>43949.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>1618</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>man</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>petrol</td>\n",
       "      <td>958606.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25005</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x5</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>59524.0</td>\n",
       "      <td>Fourth &amp; Above</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>2993</td>\n",
       "      <td>180.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>auto</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>3085561.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25006</td>\n",
       "      <td>skoda</td>\n",
       "      <td>octavia</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>12015.0</td>\n",
       "      <td>First</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1968</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>man</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>1543728.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id   Maker    model   Location  Distance       Owner Type  \\\n",
       "1  25002    fiat    panda  Ahmedabad    27750.0           Third   \n",
       "2  25003     bmw       x1  Hyderabad    46000.0           Third   \n",
       "3  25004  nissan     juke     Mumbai    43949.0           Third   \n",
       "4  25005     bmw       x5     Jaipur    59524.0  Fourth & Above   \n",
       "5  25006   skoda  octavia    Chennai    12015.0           First   \n",
       "\n",
       "   manufacture_year  Age of car  engine_displacement  engine_power body_type  \\\n",
       "1              2012           7                 1242          51.0       NaN   \n",
       "2              2014           5                 1995         105.0       NaN   \n",
       "3              2011           8                 1618         140.0       NaN   \n",
       "4              2012           7                 2993         180.0       NaN   \n",
       "5              2015           4                 1968         110.0       NaN   \n",
       "\n",
       "   Vroom Audit Rating transmission door_count seat_count fuel_type       Price  \n",
       "1                   6          man          4          4    petrol   401819.25  \n",
       "2                   7         auto          4          5    diesel  2392855.50  \n",
       "3                   7          man          4          5    petrol   958606.50  \n",
       "4                   7         auto          4          5    diesel  3085561.50  \n",
       "5                   4          man          4          5    diesel  1543728.75  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8cf872fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.rename(columns = {'Distance ':'Distance','Owner Type':'Owner_Type','Age of car':'Age_of_car','Vroom Audit Rating':'Vroom_Audit_Rating'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dc554fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d2233ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Maker'] = label_encoder.fit_transform(df['Maker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "754a6924",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['model'] = label_encoder.fit_transform(df['model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0152791c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Location'] = label_encoder.fit_transform(df['Location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d6c24e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Owner_Type'] = label_encoder.fit_transform(df1['Owner_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "95d48a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['transmission'] = label_encoder.fit_transform(df['transmission'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8b0fdb5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['fuel_type'] = label_encoder.fit_transform(df['fuel_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e6596e9b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'Maker', 'model', 'Location', 'Distance', 'Owner_Type',\n",
       "       'manufacture_year', 'Age_of_car', 'engine_displacement', 'engine_power',\n",
       "       'body_type', 'Vroom_Audit_Rating', 'transmission', 'door_count',\n",
       "       'seat_count', 'fuel_type', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7689ed01",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4', '5', 'None', '2', '3', '7', '6', '1', '9', '8'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['seat_count'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ab154e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['seat_count'].replace({'None':0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9b7c952c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4', '5', 0, '2', '3', '7', '6', '1', '9', '8'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['seat_count'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "75cd8e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4', '5', 'None', '2', '3', '1', '6'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['door_count'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "483a2bbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['door_count'].replace({'None':0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dbc08b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4', '5', 0, '2', '3', '1', '6'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['door_count'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3bd41db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['door_count'] = df1['door_count'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bf38f031",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['seat_count'] = df1['seat_count'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d1e6b627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(df1.corr(),annot =True, cmap='YlGnBu')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d2239c63",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df1[['Id','Maker','model','Location','Distance','Owner_Type','manufacture_year','Age_of_car','engine_displacement','engine_power','Vroom_Audit_Rating','transmission','door_count','seat_count','fuel_type']]\n",
    "y = df[['Price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "30d571da",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5a1349a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0493f6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pre = pd.DataFrame(model.predict(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "32f90e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = mean_squared_error(y,y_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3b1900a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "463562.4250181664"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b268a578",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('Test_Data_Set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1e49fe43",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Maker</th>\n",
       "      <th>model</th>\n",
       "      <th>Location</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Owner Type</th>\n",
       "      <th>manufacture_year</th>\n",
       "      <th>Age of car</th>\n",
       "      <th>engine_displacement</th>\n",
       "      <th>engine_power</th>\n",
       "      <th>body_type</th>\n",
       "      <th>Vroom Audit Rating</th>\n",
       "      <th>transmission</th>\n",
       "      <th>door_count</th>\n",
       "      <th>seat_count</th>\n",
       "      <th>fuel_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11001</td>\n",
       "      <td>skoda</td>\n",
       "      <td>octavia</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>First</td>\n",
       "      <td>2007</td>\n",
       "      <td>12</td>\n",
       "      <td>1595</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>man</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11002</td>\n",
       "      <td>skoda</td>\n",
       "      <td>rapid</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>29376.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>1598</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>man</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11003</td>\n",
       "      <td>skoda</td>\n",
       "      <td>octavia</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>30563.0</td>\n",
       "      <td>Second</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>1968</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>man</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11004</td>\n",
       "      <td>audi</td>\n",
       "      <td>q3</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>8650.0</td>\n",
       "      <td>Fourth &amp; Above</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1968</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>auto</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11005</td>\n",
       "      <td>audi</td>\n",
       "      <td>tt</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>6400.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1984</td>\n",
       "      <td>169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>auto</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>petrol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  Maker    model    Location  Distance       Owner Type  \\\n",
       "0  11001  skoda  octavia      Mumbai   150000.0           First   \n",
       "1  11002  skoda    rapid  Coimbatore    29376.0           Third   \n",
       "2  11003  skoda  octavia  Coimbatore    30563.0          Second   \n",
       "3  11004   audi       q3       Delhi     8650.0  Fourth & Above   \n",
       "4  11005   audi       tt     Chennai     6400.0           Third   \n",
       "\n",
       "   manufacture_year  Age of car  engine_displacement  engine_power body_type  \\\n",
       "0              2007          12                 1595          75.0       NaN   \n",
       "1              2014           5                 1598          77.0       NaN   \n",
       "2              2014           5                 1968         110.0       NaN   \n",
       "3              2015           4                 1968         110.0       NaN   \n",
       "4              2015           4                 1984         169.0       NaN   \n",
       "\n",
       "   Vroom Audit Rating transmission door_count seat_count fuel_type  \n",
       "0                   5          man          4          5    petrol  \n",
       "1                   4          man          4          5    diesel  \n",
       "2                   5          man          5          5    diesel  \n",
       "3                   8         auto          4          5    diesel  \n",
       "4                   4         auto          2       None    petrol  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "767951df",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Maker'] = label_encoder.fit_transform(test['Maker'])\n",
    "test['model'] = label_encoder.fit_transform(test['model'])\n",
    "test['Location'] = label_encoder.fit_transform(test['Location'])\n",
    "test['Owner Type'] = label_encoder.fit_transform(test['Owner Type'])\n",
    "test['transmission'] = label_encoder.fit_transform(test['transmission'])\n",
    "test['fuel_type'] = label_encoder.fit_transform(test['fuel_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ecd1c1e1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Maker</th>\n",
       "      <th>model</th>\n",
       "      <th>Location</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Owner Type</th>\n",
       "      <th>manufacture_year</th>\n",
       "      <th>Age of car</th>\n",
       "      <th>engine_displacement</th>\n",
       "      <th>engine_power</th>\n",
       "      <th>body_type</th>\n",
       "      <th>Vroom Audit Rating</th>\n",
       "      <th>transmission</th>\n",
       "      <th>door_count</th>\n",
       "      <th>seat_count</th>\n",
       "      <th>fuel_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11001</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>12</td>\n",
       "      <td>1595</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11002</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>29376.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>1598</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11003</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>30563.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>1968</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11004</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>8650.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1968</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11005</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>6400.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1984</td>\n",
       "      <td>169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  Maker  model  Location  Distance   Owner Type  manufacture_year  \\\n",
       "0  11001      6      8         9   150000.0           0              2007   \n",
       "1  11002      6     14         3    29376.0           3              2014   \n",
       "2  11003      6      8         3    30563.0           2              2014   \n",
       "3  11004      0     10         4     8650.0           1              2015   \n",
       "4  11005      0     17         2     6400.0           3              2015   \n",
       "\n",
       "   Age of car  engine_displacement  engine_power body_type  \\\n",
       "0          12                 1595          75.0       NaN   \n",
       "1           5                 1598          77.0       NaN   \n",
       "2           5                 1968         110.0       NaN   \n",
       "3           4                 1968         110.0       NaN   \n",
       "4           4                 1984         169.0       NaN   \n",
       "\n",
       "   Vroom Audit Rating  transmission door_count seat_count  fuel_type  \n",
       "0                   5             1          4          5          1  \n",
       "1                   4             1          4          5          0  \n",
       "2                   5             1          5          5          0  \n",
       "3                   8             0          4          5          0  \n",
       "4                   4             0          2       None          1  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "60be4ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.rename(columns = {'Distance ':'Distance','Owner Type':'Owner_Type','Age of car':'Age_of_car','Vroom Audit Rating':'Vroom_Audit_Rating'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bacc4fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['door_count'].replace({'None':0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1f9fb9f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['door_count'] = test['door_count'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "12b2a823",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['seat_count'].replace({'None':0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "33c791e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['seat_count'] = test['seat_count'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2703f4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test[['Id','Maker','model','Location','Distance','Owner_Type','manufacture_year','Age_of_car','engine_displacement','engine_power','Vroom_Audit_Rating','transmission','door_count','seat_count','fuel_type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9d1bb0c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                       0\n",
       "Maker                    0\n",
       "model                    0\n",
       "Location                 0\n",
       "Distance               128\n",
       "Owner_Type               0\n",
       "manufacture_year         0\n",
       "Age_of_car               0\n",
       "engine_displacement      0\n",
       "engine_power           168\n",
       "Vroom_Audit_Rating       0\n",
       "transmission             0\n",
       "door_count               0\n",
       "seat_count               0\n",
       "fuel_type                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f8370ef7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X_test['Distance'] = X_test['Distance'].fillna(X_test['Distance'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a077bdfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test['engine_power'] = X_test['engine_power'].fillna(X_test['engine_power'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d118a2c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                     0\n",
       "Maker                  0\n",
       "model                  0\n",
       "Location               0\n",
       "Distance               0\n",
       "Owner_Type             0\n",
       "manufacture_year       0\n",
       "Age_of_car             0\n",
       "engine_displacement    0\n",
       "engine_power           0\n",
       "Vroom_Audit_Rating     0\n",
       "transmission           0\n",
       "door_count             0\n",
       "seat_count             0\n",
       "fuel_type              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d7416b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "Id = test['Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c822903f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pre = pd.DataFrame(model.predict(X_test),index = Id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ab0d9c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pre.columns = ['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "61b8f4bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#y_test_pre.to_csv('Anoop_hackathon.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "67b84a4a",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11001</th>\n",
       "      <td>3.437063e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11002</th>\n",
       "      <td>1.087846e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11003</th>\n",
       "      <td>1.248386e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11004</th>\n",
       "      <td>2.137594e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11005</th>\n",
       "      <td>2.372644e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16611</th>\n",
       "      <td>9.946402e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16612</th>\n",
       "      <td>4.397079e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16613</th>\n",
       "      <td>2.461991e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16614</th>\n",
       "      <td>1.034804e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16615</th>\n",
       "      <td>9.763969e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5615 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price\n",
       "Id                 \n",
       "11001  3.437063e+05\n",
       "11002  1.087846e+06\n",
       "11003  1.248386e+06\n",
       "11004  2.137594e+06\n",
       "11005  2.372644e+06\n",
       "...             ...\n",
       "16611  9.946402e+05\n",
       "16612  4.397079e+05\n",
       "16613  2.461991e+05\n",
       "16614  1.034804e+06\n",
       "16615  9.763969e+05\n",
       "\n",
       "[5615 rows x 1 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b0d81114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Price</td>      <th>  R-squared:         </th>  <td>   0.702</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.702</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   8600.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 08 May 2023</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:14:16</td>     <th>  Log-Likelihood:    </th> <td>-7.3947e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 51119</td>      <th>  AIC:               </th>  <td>1.479e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 51104</td>      <th>  BIC:               </th>  <td>1.479e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    14</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>           <td>  -39.7953</td> <td>    0.237</td> <td> -167.789</td> <td> 0.000</td> <td>  -40.260</td> <td>  -39.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Id</th>                  <td>    0.0858</td> <td>    0.133</td> <td>    0.646</td> <td> 0.518</td> <td>   -0.175</td> <td>    0.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Maker</th>               <td> -7.81e+04</td> <td> 1006.497</td> <td>  -77.601</td> <td> 0.000</td> <td>-8.01e+04</td> <td>-7.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>model</th>               <td> 3691.6172</td> <td>  346.210</td> <td>   10.663</td> <td> 0.000</td> <td> 3013.042</td> <td> 4370.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Location</th>            <td>  481.7368</td> <td>  649.786</td> <td>    0.741</td> <td> 0.458</td> <td> -791.851</td> <td> 1755.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distance</th>            <td>   -0.2014</td> <td>    0.009</td> <td>  -22.981</td> <td> 0.000</td> <td>   -0.219</td> <td>   -0.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Owner_Type</th>          <td>-3628.6238</td> <td> 1833.462</td> <td>   -1.979</td> <td> 0.048</td> <td>-7222.228</td> <td>  -35.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>manufacture_year</th>    <td>  844.0141</td> <td>    8.952</td> <td>   94.282</td> <td> 0.000</td> <td>  826.468</td> <td>  861.560</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Age_of_car</th>          <td>-8.119e+04</td> <td>  480.180</td> <td> -169.084</td> <td> 0.000</td> <td>-8.21e+04</td> <td>-8.02e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>engine_displacement</th> <td>    1.3575</td> <td>    2.057</td> <td>    0.660</td> <td> 0.509</td> <td>   -2.674</td> <td>    5.389</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>engine_power</th>        <td> 6853.4258</td> <td>   64.254</td> <td>  106.661</td> <td> 0.000</td> <td> 6727.486</td> <td> 6979.365</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vroom_Audit_Rating</th>  <td> 1596.5415</td> <td> 1445.345</td> <td>    1.105</td> <td> 0.269</td> <td>-1236.350</td> <td> 4429.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transmission</th>        <td>-2.689e+05</td> <td> 5337.060</td> <td>  -50.381</td> <td> 0.000</td> <td>-2.79e+05</td> <td>-2.58e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>door_count</th>          <td>-4.896e+04</td> <td> 1869.500</td> <td>  -26.190</td> <td> 0.000</td> <td>-5.26e+04</td> <td>-4.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>seat_count</th>          <td> 3.076e+04</td> <td> 1440.450</td> <td>   21.357</td> <td> 0.000</td> <td> 2.79e+04</td> <td> 3.36e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>fuel_type</th>           <td>-1.251e+05</td> <td> 4458.487</td> <td>  -28.063</td> <td> 0.000</td> <td>-1.34e+05</td> <td>-1.16e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>54236.749</td> <th>  Durbin-Watson:     </th>   <td>   1.986</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>27373455.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 4.648</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>115.983</td>  <th>  Cond. No.          </th>   <td>1.18e+20</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 2.52e-25. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Price   R-squared:                       0.702\n",
       "Model:                            OLS   Adj. R-squared:                  0.702\n",
       "Method:                 Least Squares   F-statistic:                     8600.\n",
       "Date:                Mon, 08 May 2023   Prob (F-statistic):               0.00\n",
       "Time:                        12:14:16   Log-Likelihood:            -7.3947e+05\n",
       "No. Observations:               51119   AIC:                         1.479e+06\n",
       "Df Residuals:                   51104   BIC:                         1.479e+06\n",
       "Df Model:                          14                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "Intercept             -39.7953      0.237   -167.789      0.000     -40.260     -39.330\n",
       "Id                      0.0858      0.133      0.646      0.518      -0.175       0.346\n",
       "Maker                -7.81e+04   1006.497    -77.601      0.000   -8.01e+04   -7.61e+04\n",
       "model                3691.6172    346.210     10.663      0.000    3013.042    4370.192\n",
       "Location              481.7368    649.786      0.741      0.458    -791.851    1755.324\n",
       "Distance               -0.2014      0.009    -22.981      0.000      -0.219      -0.184\n",
       "Owner_Type          -3628.6238   1833.462     -1.979      0.048   -7222.228     -35.019\n",
       "manufacture_year      844.0141      8.952     94.282      0.000     826.468     861.560\n",
       "Age_of_car          -8.119e+04    480.180   -169.084      0.000   -8.21e+04   -8.02e+04\n",
       "engine_displacement     1.3575      2.057      0.660      0.509      -2.674       5.389\n",
       "engine_power         6853.4258     64.254    106.661      0.000    6727.486    6979.365\n",
       "Vroom_Audit_Rating   1596.5415   1445.345      1.105      0.269   -1236.350    4429.433\n",
       "transmission        -2.689e+05   5337.060    -50.381      0.000   -2.79e+05   -2.58e+05\n",
       "door_count          -4.896e+04   1869.500    -26.190      0.000   -5.26e+04   -4.53e+04\n",
       "seat_count           3.076e+04   1440.450     21.357      0.000    2.79e+04    3.36e+04\n",
       "fuel_type           -1.251e+05   4458.487    -28.063      0.000   -1.34e+05   -1.16e+05\n",
       "==============================================================================\n",
       "Omnibus:                    54236.749   Durbin-Watson:                   1.986\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         27373455.206\n",
       "Skew:                           4.648   Prob(JB):                         0.00\n",
       "Kurtosis:                     115.983   Cond. No.                     1.18e+20\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.52e-25. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "model = smf.ols(formula='Price~Id+Maker+model+Location+Distance+Owner_Type+manufacture_year+Age_of_car+engine_displacement+engine_power+Vroom_Audit_Rating+transmission+door_count+seat_count+fuel_type',data=df1).fit()\n",
    "model.summary()"
   ]
  }
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