Major project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import r2_score\n",
    "import category_encoders as ce\n",
    "from sklearn.neighbors import KNeighborsRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('Data_Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Location', 'Year', 'Kilometers_Driven', 'Fuel_Type',\n",
       "       'Transmission', 'Owner_Type', 'Mileage', 'Engine', 'Power', 'Seats',\n",
       "       'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   Price  \n",
       "0   1.75  \n",
       "1  12.50  \n",
       "2   4.50  \n",
       "3   6.00  \n",
       "4  17.74  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6019.000000</td>\n",
       "      <td>6.019000e+03</td>\n",
       "      <td>5977.000000</td>\n",
       "      <td>6019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.358199</td>\n",
       "      <td>5.873838e+04</td>\n",
       "      <td>5.278735</td>\n",
       "      <td>9.479468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.269742</td>\n",
       "      <td>9.126884e+04</td>\n",
       "      <td>0.808840</td>\n",
       "      <td>11.187917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.710000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>3.400000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>5.300000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>7.300000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>6.500000e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Kilometers_Driven        Seats        Price\n",
       "count  6019.000000       6.019000e+03  5977.000000  6019.000000\n",
       "mean   2013.358199       5.873838e+04     5.278735     9.479468\n",
       "std       3.269742       9.126884e+04     0.808840    11.187917\n",
       "min    1998.000000       1.710000e+02     0.000000     0.440000\n",
       "25%    2011.000000       3.400000e+04     5.000000     3.500000\n",
       "50%    2014.000000       5.300000e+04     5.000000     5.640000\n",
       "75%    2016.000000       7.300000e+04     5.000000     9.950000\n",
       "max    2019.000000       6.500000e+06    10.000000   160.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  0\n",
       "Location              0\n",
       "Year                  0\n",
       "Kilometers_Driven     0\n",
       "Fuel_Type             0\n",
       "Transmission          0\n",
       "Owner_Type            0\n",
       "Mileage               2\n",
       "Engine               36\n",
       "Power                36\n",
       "Seats                42\n",
       "Price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Location\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mumbai', 'Pune', 'Chennai', 'Coimbatore', 'Hyderabad', 'Jaipur',\n",
       "       'Kochi', 'Kolkata', 'Delhi', 'Bangalore', 'Ahmedabad'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Location.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "data.Location.value_counts().plot(kind='bar')\n",
    "plt.title('Distribution w.r.t cities')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "brand_name=[]\n",
    "model_name=[]\n",
    "for index, rows in data.iterrows():\n",
    "    brand_name.append(rows['Name'].split()[0].lower())\n",
    "    model_name.append(rows['Name'].split()[1].lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Brand'] = brand_name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [15,7]\n",
    "data.Brand.value_counts().plot(kind='bar')\n",
    "plt.xlabel('Brand Name')\n",
    "plt.ylabel('No of cars')\n",
    "plt.title('Distribution according to the brand')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.Year.value_counts().plot(kind='bar')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('No of cars')\n",
    "plt.title('Distribution according year of the car')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most of the cars are bought in 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Identification and Null Value Filling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kilometers Driven"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "p=[]\n",
    "for i in range(1,101,1):\n",
    "    p.append(np.percentile(data.Kilometers_Driven, i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oz9mkvP77JP++qo4keVaSH0hyQ5KH7HLfAAAAVtJoMkmS1Q8ok2R4yuufS/KiJE9JcuNudgoAAGCVjcadJKs/D2VV/UqSZ2T6pNefTfLu7p7sdscAAABW1WhzdofyYI+hPJs7lL+Q5C9293i3OwMAAHAYjMZDQLm+oncoq+pZ3f3OJBckubZqa+Tc3b+2y30DAABYSRuzgHJtRQPKJP9jkndmOnZyu04ioAQAAFjC5jCG8uj6iqa8dvdrqmotyTu6+y172CcAAICVdiLl9YA/lOe0vR8evvODe9QXAACAQ2HjMASUg1uq6u9U1eVV9fDZa9d7BgAAsKIOzbQhSf7q8P6KubJO8rhz3x0AAIDVtyopr2cMKLv7sXvREQAAgMNiNg/l+irPQ1lVj0jyF5N881D08ST/srvv2u2OAQAArKrRZHjK6wG/Q3nK3lfVtyT5SJKnJvn/knwiydOSfKSqvvlU+wEAAHB6szuUqzyG8ieSvHL7lCFV9ReS/GSSv7CbHQMAAFhVJ8ZQHvB5KE8XDv93O80/2d2/muRJu9clAACA1XYYpg25e8ltAAAAnMZhmDbkUVX1IzuUV5JLdqk/AAAAK2+W8rrKT3n950kuOsW2N+xCXwAAAA6FzRVJeT1lQNndP7aXHQEAADgsNsYrPm0IAAAAu2OW8nrQx1Ae7N4DAAAcQLN5KI8e8DGUAkoAAIA9NhpPUpUcWVvxgLKqXllVD6mpN1bVb1fVc/aicwAAAKtoY9w5uraWqhUPKJP81e7+cpLnZDpdyEuT/NSu9goAAGCFjcaTA5/umpxdQDk7y+cl+YXu/p25MgAAABa0OZ7k6PrBH4F4Nmfw/qr6jUwDypur6qIkk93tFgAAwOraGPeBnzIkOc08lElS04Tef5hpqusnu/ueqnpEpmmvAAAALGE0nhz4KUOSMwSU3d1V9W+6+6lzZZ9P8vld7xkAAMCKOkxjKH+rqp626z0BAAA4JKYB5YrfoRx8Z5K/UVV/kOTuTB/I0939J3ezYwAAAKtqY7OzfkgCyufuei8AAAAOkc3JJMcOQ8prd/9hksuTPGtYvuds9gMAAGBnq5LyesYzqKrXJPnRJK8eio4m+Re72SkAAIBVNtpcjWlDzuYM/nyS7810/GS6+zNJLtrNTgEAAKyyjfEkR9cPR0C50d2dpJOkqi7c3S4BAACstuk8lIdgDGWSt1TVzye5uKp+IMm/S/KG3e0WAADA6hqNJ1lfO/h3KM/4lNfu/sdV9d1JvpzkCUn+YXffsus9AwAAWFGb416JlNczBpRV9dPd/aNJbtmhDAAAgAVtjCc5ekhSXr97hzJzUwIAACxpOoby4N+hPOUZVNXLq+rDSZ5QVR+ae/1+kg+d6cBV9aCqem9V/U5VfbSqfmwof2xVvaeqPlFVv1JVx4by84b124ftV8wd69VD+X+pqu+ZK79mKLu9ql41V75wGwAAAHtlNF79aUP+ZZI/l+Sm4X32emp3f/9ZHPveJM/q7m9N8uQk11TV1Ul+OsnPdPeVSb6Q5GVD/Zcl+UJ3/7EkPzPUS1U9McmLk/yJJNck+bmqOlJVR5L8bKZ3S5+Y5PuGulm0DQAAgL002pysdkDZ3V/q7j/o7u9LcnmmweEfJlmrqsee6cA99dVh9ejw6iTPSvLWofzGJM8flq8d1jNsf3ZV1VD+5u6+t7t/P8ntSZ4+vG7v7k9290aSNye5dthn0TYAAAD2zKEZQ1lVr0nyo0lePRQdS/Ivzubgw53EDya5M9OH+vxeki929+ZQ5Y4klw7Llyb5dJIM27+U5BHz5dv2OVX5I5ZoY3u/r6+q26rqtuPHj5/NqQIAAJy1zcnqp7zO/Pkk35vk7iTp7s8kuehsDt7d4+5+cpLLMr2j+C07VRvedwrP+xyWn66NrQXdr+/uq7r7qksuuWSHXQAAAJYznnTGhyig3OjuzhB4VdWFizbS3V9M8u4kVye5uKpm05VcluQzw/IdmabWZtj+0CR3zZdv2+dU5Z9bog0AAIA9MRpPkiRH1w9BymuSt1TVz2capP1Akn+X5J+faaequqSqLh6Wz0/yXUk+nuRdSV4wVLsuyduG5ZuG9Qzb3zkEsjclefHwhNbHJrkyyXuTvC/JlcMTXY9l+uCem4Z9Fm0DAABgT8wCylWYNmT9TBW6+x9X1Xcn+XKSJyT5h919y1kc+zFJbhyexrqW5C3d/W+r6mNJ3lxV/yjJB5K8caj/xiT/d1XdnuldwxcP7X+0qt6S5GNJNpO8orvHSVJVP5jk5iRHktzQ3R8djvWji7QBAACwV0bj6T2tVUh5PWNAmSTdfUtVvWdWv6oe3t2nTRXt7g8l+bYdyj+Z6XjK7eVfT/LCUxzrJ5P85A7lb0/y9nPRBgAAwF6Y3aFcX4GnvJ4xoKyqv57kx5N8Lckk0wfbdJLH7W7XAAAAVs+JMZSH5A7l30nyJ7r7c7vdGQAAgFU3S3ldhTGUZ3MGv5fknt3uCAAAwGFw2O5QvjrJfx7GUN47K+zuH9q1XgEAAKyojc1ZQHkIxlAm+fkk70zy4UzHUAIAALCkk/NQHo47lJvd/SO73hMAAIBD4LCNoXxXVV1fVY+pqofPXrveMwAAgBV0YtqQtcOR8voXh/dXz5WZNgQAAGAJhyrltbsfuxcdAQAAOAxWKeX1jAFlVR1N8vIk3zEUvTvJz3f3aBf7BQAAsJIO27Qhr0tyNMnPDet/eSj7a7vVKQAAgFV1MqA8HGMon9bd3zq3/s6q+p3d6hAAAMAqOzkP5cG/Q3k2ZzCuqsfPVqrqcUnGu9clAACA1TUbQ7kKAeXZ3KH8u5lOHfLJJJXkm5K8dFd7BQAAsKI2J4co5bW7b62qK5M8IdOA8ne7+95d7xkAAMAKOpHyusrThlTV/3SKTY+vqnT3r+1SnwAAAFbWYZk25M+dZlsnEVACAAAs6FBMG9LdxkkCAACcY6PxJGuVHFlb4TGUVfX93f0vqupHdtre3f9k97oFAACwmjbGk6yvwN3J5PQprxcO7xftsK13oS8AAAArb3PcKzF+Mjl9QPn/Jkl3/9j2DVV1uvGVAAAAnMJoPFmJKUOS5HRh8a1VdcX2wqp6aZJ/ulsdAgAAWGXTgHI17lCe7iz+VpJbhjkokyRV9eokP5Lkf9ztjgEAAKyijc1emYDydE95fXtV3ZvkHVX1/CR/LcnTknxHd39hrzoIAACwSkbjSY6tr0ZAedqz6O5bk/yVJO9O8rgkzxZMAgAALG80nmR9BaYMSU4/bchXMn2aayU5L8mzk9xZVZWku/she9NFAACA1TEaH46U152mCwEAAOB+GI0nOXoYUl4BAAA4t0bjSY4dgmlDAAAAOMcOy7QhAAAAnGMbKzSGcjXOAgAA4IAYbbpDCQAAwBI2J5McNYYSAACARa3StCGrcRYAAAAHxIaUVwAAAJYxGk9ybF3KKwAAAAsybQgAAABLMYYSAACApWyMJ1n3lFcAAAAWtTme5Jg7lAAAACxiPOlMOlJeAQAAWMxoPEkioAQAAGBBGycCSmMoAQAAWMBocxpQHltfjVBsNc4CAADgABiNO0myvrYaodiunUVVXV5V76qqj1fVR6vqlUP5w6vqlqr6xPD+sKG8quq1VXV7VX2oqp4yd6zrhvqfqKrr5sqfWlUfHvZ5bVXVsm0AAADstpGU17O2meRvd/e3JLk6ySuq6olJXpXk1u6+Msmtw3qSPDfJlcPr+iSvS6bBYZLXJHlGkqcnec0sQBzqXD+33zVD+UJtAAAA7IVZQCnl9Qy6+7Pd/dvD8leSfDzJpUmuTXLjUO3GJM8flq9N8qae+q0kF1fVY5J8T5Jbuvuu7v5CkluSXDNse0h3/2Z3d5I3bTvWIm0AAADsulnKq6e8LqCqrkjybUnek+TR3f3ZZBp0JnnUUO3SJJ+e2+2Ooex05XfsUJ4l2tje3+ur6raquu348eOLnCoAAMApmTZkQVX14CS/muSHu/vLp6u6Q1kvUX7a7pzNPt39+u6+qruvuuSSS85wSAAAgLNj2pAFVNXRTIPJX+ruXxuK/2iWZjq83zmU35Hk8rndL0vymTOUX7ZD+TJtAAAA7LrZtCHuUJ7B8MTVNyb5eHf/k7lNNyWZPan1uiRvmyt/yfAk1quTfGlIV705yXOq6mHDw3iek+TmYdtXqurqoa2XbDvWIm0AAADsus3Jao2hXN/FY397kr+c5MNV9cGh7O8n+akkb6mqlyX5VJIXDtvenuR5SW5Pck+SlyZJd99VVT+R5H1DvR/v7ruG5Zcn+cUk5yd5x/DKom0AAADshVVLed21gLK7/1N2HrOYJM/eoX4necUpjnVDkht2KL8tyZN2KP/8om0AAADsNimvAAAALGU2bYh5KAEAAFiIaUMAAABYyqqNoRRQAgAA7JHN8Wo95XU1zgIAAOAAkPIKAADAUkZSXgEAAFjGhjuUAAAALGO0aQwlAAAASxiNJzmyVjmyJuUVAACABYwmk6yvSDCZCCgBAAD2zGizc2xF0l0TASUAAMCeGY0nObq+OmHY6pwJAADAA9xoPFmZKUMSASUAAMCe2RhPVuYJr4mAEgAAYM+MxsZQAgAAsITR5iTrUl4BAABY1OZEyisAAABL2Bi3gBIAAIDFjTYnxlACAACwuOk8lMZQAgAAsKCRaUMAAABYxsa4s762OmHY6pwJAADAA9zmeJJjUl4BAABYlJRXAAAAljIybQgAAADL2HCHEgAAgGWMxpMcO2IMJQAAAAsabU6y7g4lAAAAixpNjKEEAABgQd0t5RUAAIDFjSed7rhDCQAAwGJG406SHF1fnTBsdc4EAADgAWxjPEniDiUAAAALGg0BpTGUAAAALGRzSHk1bQgAAAALGUl5BQAAYBknx1BKeQUAAGABJ8dQrk4YtjpnAgAA8AA22hymDRFQAgAAsIgTKa/moQQAAGARm7OAcs0YSgAAABYwGqYNcYcSAACAhZg2BAAAgKWYNmQBVXVDVd1ZVR+ZK3t4Vd1SVZ8Y3h82lFdVvbaqbq+qD1XVU+b2uW6o/4mqum6u/KlV9eFhn9dWVS3bBgAAwG4zbchifjHJNdvKXpXk1u6+Msmtw3qSPDfJlcPr+iSvS6bBYZLXJHlGkqcnec0sQBzqXD+33zXLtAEAALAXpLwuoLv/Q5K7thVfm+TGYfnGJM+fK39TT/1Wkour6jFJvifJLd19V3d/IcktSa4Ztj2ku3+zuzvJm7Yda5E2AAAAdt1sHsp1Ka9Le3R3fzZJhvdHDeWXJvn0XL07hrLTld+xQ/kybdxHVV1fVbdV1W3Hjx9f6AQBAAB2MppIed0tO4XovUT5Mm3ct7D79d19VXdfdckll5zhsAAAAGc22pTyen/90SzNdHi/cyi/I8nlc/UuS/KZM5RftkP5Mm0AAADsOvNQ3n83JZk9qfW6JG+bK3/J8CTWq5N8aUhXvTnJc6rqYcPDeJ6T5OZh21eq6urh6a4v2XasRdoAAADYdas4bcj6bh24qn45yTOTPLKq7sj0aa0/leQtVfWyJJ9K8sKh+tuTPC/J7UnuSfLSJOnuu6rqJ5K8b6j34909e9DPyzN9kuz5Sd4xvLJoGwAAAHvhxFNe11bnDuWuBZTd/X2n2PTsHep2klec4jg3JLlhh/Lbkjxph/LPL9oGAADAbhuNJzmyVllbW507lKsTGgMAADyAbY57pdJdEwElAADAntgYT1bqCa+JgBIAAGBPjMaTlZqDMhFQAgAA7InRZrtDCQAAwOJG40mOrhtDCQAAwIKMoQQAAGApm+NeqTkoEwElAADAnpDyCgAAwFKkvAIAALCUkYASAACAZYzGbR5KAAAAFje9Q2kMJQAAAAsajTvr7lACAACwqNF4IuUVAACAxUl5BQAAYCmjTU95BQAAYAkb487R9dUKwVbrbAAAAB6gjKEEAABgKZvjSdbXjKEEAABgQSMprwAAACyqu7Mx9lAeAAAAFrQ56STJMdOGAAAAsIjReJIk7lACAACwmNHm9A6lgBIAAICFbJy4QynlFQAAgAVsTqS8AgAAsAQprwAAACzlRMqreSgBAABYxOwpr6YNAQAAYCGmDQEAAGApAkoAAACWMhpPH8qzLuUVAACARZwcQ3MHKBgAAA/gSURBVLlaIdhqnQ0AAMADkJRXAAAAlrJhHkoAAACWcSLldd0YSgAAABYg5RUAAICFdXe+eM8oSbK+YgHl+n53AAAAYBXcuznOH37+nvzenV/N7Xd+Nb93/Kv5veN35/eOfzX3bIxTlTz42GqFYKt1NgAAAOfI5niSL399M1/62ujE6667783nvrKR41+9N8e/Mvf66r256+6NLft/w0MflMc/6sF50dMuz+MveXCefPnFeegFR/fpbHaHgBIAADjQJpPO10bj3L2xmXvuHedro3G+Phrn66NJvr45zr3D8r2b0/e7NzZz972bufvece7ZmL7Pyr567zhfHoLHr967eco2z1tfyyUXnZdLLjov3/SIC3LVFQ/LJRedlysecWEef8mD87hLLsyF561+uLX6ZwgAAJxT3Z3RuDMaT7I57myMJxnNvTY2h22TyZZ60+2dzcl0fTzpjLuzOemMx5OMOxkP+3x9NM49G9PX1zY287XRbHlWvpm7N8a5+97N3LMxXvgc1iq58Lz1PPi89Vxw7EguPG89Fx5bz6UXH8sTH/OQPPT8o3no+UfzkPPXTyw/9PyjediFx/Koi87Lg89bT9VqPbF1GQJKAAAOpO5OdzLpTmd4H9YnPd0+/z6edCY9BDFzy9P3k9tPHqNPHGsyOXmsWVuTWdvdmUwy14eT22b7bG0zGXcPx5yWz9ocz9qfzB9jtu+wPgRhszqbk+ny5qQznkyG9972Pg3gNretbz2P+77PAr/ReJLxpDOadDbHk0x69/99q5Lzjx7JBceO5PxjR3LB0fXp+7EjufiCo7ng2PoQBB7JBXPvFwz7POjokZx3dG36vj59f9DRI3nQ+louPG89562vCQjPAQElABxyZ7oon61vvUjupLNlvXv7ceeWs7WNk21ubS87tD+9TD9d/4cL+cnW4GG+X5M+Q/tzZZ2twUG2fTYngoo+9fosqJh9Br2tv/OfSQ+Fk57/nKbLmet7D/vOPqcego8T5zzZHkj1ln1mH+Vk/jx3CLq2/zt0b+3rfb8vJ9ub9WG2/+kClZ3+necDpvm+n6izff2QqEqOVGVtrbI2W55fX1vL+lrlyFpl/cjwvlY5sraWI2vJ+tz2Bx1d27K+VtP3qmSttr5XpsdZP1I5emTtxPGPrk2Xjw7lR4+s5ej6Wo7Nrx+prK9Ny2f11tdObp8dZ23o35GhP7PX+loJ+A4IASXAnD7NBdCJi7PJDhdX2XrRdbqLzuTkhWFOrOXEReT8RfD2i6ntF35b+jt3DieOOnfhuv1i81QXx/PvJy845855/nznlk+e18mrvK2fybZjzJVNhoPMr2+5kN8SeGzv+7TlybZ/l/sERZOTdxumU4Gd+mp0/t92+wX2rJ37fP47XJCf7vjbA5ydjjG/frqL/+3/ZmdzXlsv5Nlv04v3pGoaIFSmBbPl2fa1YWFW98gQUKzNAoyalt8nOEiG49Ww72z7dH1tbet6bau7pT81DQJm/Zq1e2L/od6sT1Unz2vtRHt14nzW1qadmx57GizNn8Nsvwz9OLI237eaO8/7rs/6tDZ8ZkfWpkHYLDg7UicDmGlgNfdZrp083pEdPtO1ufOafW7zgdn2f49ZWyfbP1l2qn/L+c8PHqgOXUBZVdck+WdJjiR5Q3f/1D53iTM4eRG4Q9rHLFXkVBfD9/lL5vxfP0/1V9STF6nb/7p96r9C3/fCvrfsu/Xi+D4Xh5Md/sp7imPPX3jfJziYnDz29s/jxPlPtvYh0+Idg5Hefj47BCQnL3a3/nX/dJ/ZWQVt2z7/7fVmfUy2BmY7BgI7fO7Zdm695Tjsh/kL5vmL5p0urOYv5JK5i8m5C9wkJy7YZvvOLhpP7nv6/my54N72V/u1tWRtbe2UF8xnuv7bemF68hgnLkozf66nv1jffpF++vPadrGdU1+Ub//cT1yQDyd4sh8nP/v5dqb/OnPnPBeE7PR5nejHrO5c/850Xtsv8Gd3bk7++81/zif/DWsukJmvu/V7VFu+i1sCttn2+wRWWwPB+c9k/nMRLAAH3aEKKKvqSJKfTfLdSe5I8r6quqm7P7a/PVvM7Xd+NXd++evD4OdpHvv88vxg59Ew2HlzPMloyKMfjScn8uxnwdl87v32IGyngG4yl/s/nqs3PlH3vvtOxwdsrT9rv3s43g77cV87XUCe7i/Ts4vuLRd/s4u9ytb3uYvC7X/Jvk8qzFw/ZheX8xd/Jy/o1u7z1+/tfzk+8dfgIYo4cqoLt7m2Zz3e2v+t/Z2dx5YLxu0XsTl5gTx/Mb/9InH+QvjkviePOX9hPt/elvUzXHQf2ekieHbRu3ayH9svnE8GMDt9HtOVU/+FfduF/PZ/3y3nvvUieetncbLR7e2v7XCM7X/dBwAOnkMVUCZ5epLbu/uTSVJVb05ybZIDFVD+3Ltuz6994L8uvN/REzn107z1WbrH+lz+/Prazn/Fn6VjzC62Zxemx9bXTtRdm9tvez7+luWaz/k/WX++nfsszy6AZ2ki246xJSg5xcX4fJCxU6ByqvED84HbTn+5T7L1M8vOKTCZP0bu24ctAcJceZItn7ELbwAAHigOW0B5aZJPz63fkeQZ2ytV1fVJrk+Sb/zGb9ybni3g5c98fF70tMuzfmQtx46s5ej6NEicXz56pLI+DH6eDboWiAAAAOfSYQsod4qo7pNU2d2vT/L6JLnqqqsecEmXVz76ov3uAgAAQNb2uwN77I4kl8+tX5bkM/vUFwAAgAPtsAWU70tyZVU9tqqOJXlxkpv2uU8AAAAH0qFKee3uzar6wSQ3ZzptyA3d/dF97hYAAMCBdKgCyiTp7rcneft+9wMAAOCgO2wprwAAAJwjAkoAAACWIqAEAABgKQJKAAAAliKgBAAAYCkCSgAAAJYioAQAAGApAkoAAACWIqAEAABgKQJKAAAAliKgBAAAYCnV3fvdhwe0qjqe5A/3ux87eGSSz+13J1h5vmfsBd8zdpvvGHvB94y9sF/fs2/q7kt22iCgPKCq6rbuvmq/+8Fq8z1jL/iesdt8x9gLvmfshQfi90zKKwAAAEsRUAIAALAUAeXB9fr97gCHgu8Ze8H3jN3mO8Ze8D1jLzzgvmfGUAIAALAUdygBAABYioASAACApQgoD6Cquqaq/ktV3V5Vr9rv/nDwVdXlVfWuqvp4VX20ql45lD+8qm6pqk8M7w/b775y8FXVkar6QFX922H9sVX1nuF79itVdWy/+8jBVlUXV9Vbq+p3h9+1P+X3jHOpqv7W8P/lR6rql6vqQX7LuL+q6oaqurOqPjJXtuNvV029dogHPlRVT9mvfgsoD5iqOpLkZ5M8N8kTk3xfVT1xf3vFCthM8re7+1uSXJ3kFcP36lVJbu3uK5PcOqzD/fXKJB+fW//pJD8zfM++kORl+9IrVsk/S/Lr3f3NSb410++b3zPOiaq6NMkPJbmqu5+U5EiSF8dvGfffLya5ZlvZqX67npvkyuF1fZLX7VEf70NAefA8Pcnt3f3J7t5I8uYk1+5znzjguvuz3f3bw/JXMr34ujTT79aNQ7Ubkzx/f3rIqqiqy5L8mSRvGNYrybOSvHWo4nvG/VJVD0nyHUnemCTdvdHdX4zfM86t9STnV9V6kguSfDZ+y7ifuvs/JLlrW/GpfruuTfKmnvqtJBdX1WP2pqdbCSgPnkuTfHpu/Y6hDM6JqroiybcleU+SR3f3Z5Np0JnkUfvXM1bEP03y95JMhvVHJPlid28O637TuL8el+R4kl8YUqvfUFUXxu8Z50h3/9ck/zjJpzINJL+U5P3xW8buONVv1wMmJhBQHjy1Q5m5XzgnqurBSX41yQ9395f3uz+slqr6s0nu7O73zxfvUNVvGvfHepKnJHldd39bkrsjvZVzaBjDdm2Sxyb5hiQXZpp+uJ3fMnbTA+b/TwHlwXNHksvn1i9L8pl96gsrpKqOZhpM/lJ3/9pQ/Eez9Inh/c796h8r4duTfG9V/UGm6frPyvSO5cVD2ljiN437744kd3T3e4b1t2YaYPo941z5riS/393Hu3uU5NeS/On4LWN3nOq36wETEwgoD573JblyeJLYsUwHgd+0z33igBvGsb0xyce7+5/MbbopyXXD8nVJ3rbXfWN1dPeru/uy7r4i09+ud3b3X0ryriQvGKr5nnG/dPd/S/LpqnrCUPTsJB+L3zPOnU8lubqqLhj+/5x9x/yWsRtO9dt1U5KXDE97vTrJl2apsXutut2NP2iq6nmZ/lX/SJIbuvsn97lLHHBV9d8n+Y9JPpyTY9v+fqbjKN+S5Bsz/Q/0hd29fbA4LKyqnpnk73T3n62qx2V6x/LhST6Q5Pu7+9797B8HW1U9OdMHPx1L8skkL830j+h+zzgnqurHkrwo06ekfyDJX8t0/JrfMpZWVb+c5JlJHpnkj5K8Jsm/yQ6/XcMfM/6vTJ8Ke0+Sl3b3bfvSbwElAAAAy5DyCgAAwFIElAAAACxFQAkAAMBSBJQAAAAsRUAJAADAUgSUAHA/VdW4qj5YVR+pqn9VVRfsUz9+eL7tqnp7VV08LH91P/oEwGoTUALA/fe17n5ydz8pyUaSv3G2O1bVkXPYjx9OciKg7O7ndfcXz+HxAWALASUAnFv/MckfS5Kq+v6qeu9w9/LnZ8FjVX21qn68qt6T5E9V1dOq6j9X1e8M9S+qqiNV9b9X1fuq6kNV9deHfZ9ZVe+uqrdW1e9W1S/V1A8l+YYk76qqdw11/6CqHrm9g1X1d+eO+2N79cEAsHoElABwjlTVepLnJvlwVX1Lkhcl+fbufnKScZK/NFS9MMlHuvsZSd6b5FeSvLK7vzXJdyX5WpKXJflSdz8tydOS/EBVPXbY/9syvRv5xCSPG9p4bZLPJPnO7v7O0/TxOUmuTPL0JE9O8tSq+o5z9RkAcLis73cHAGAFnF9VHxyW/2OSNya5PslTk7yvqpLk/CR3DnXGSX51WH5Cks929/uSpLu/nJwI/P5kVb1gqPfQTAPBjSTv7e47hnofTHJFkv90ln19zvD6wLD+4OG4/+HsTxcApgSUAHD/fW24C3lCTaPIG7v71TvU/3p3j2dVk/QOdSrJ3+zum7cd95lJ7p0rGmex/88ryf/W3T+/wD4AsCMprwCwO25N8oKqelSSVNXDq+qbdqj3u0m+oaqeNtS7aEidvTnJy6vq6FD+x6vqwjO0+ZUkF52hzs1J/mpVPXg47qWzPgLAotyhBIBd0N0fq6p/kOQ3qmotySjJK5L84bZ6G1X1oiT/Z1Wdn+n4ye9K8oZMU1l/e7jbeTzJ88/Q7OuTvKOqPnuqcZTd/RvD+M7fHFJxv5rk+3MyHRcAzlp175RlAwAAAKcn5RUAAIClCCgBAABYioASAACApQgoAQAAWIqAEgAAgKUIKAEAAFiKgBIAAICl/P8R24KwXTEdRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Kilometers Driven')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see some kind of outlier in between 90th to 100th percentile so let's Zoom in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=[]\n",
    "for i in range(90,101,1):\n",
    "    p.append(np.percentile(data.Kilometers_Driven, i))\n",
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Kilometers Driven')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Skoda Octavia Ambition Plus 2.0 TDI AT</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2013</td>\n",
       "      <td>775000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>19.3 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>141 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>skoda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>Hyundai i10 Magna 1.2</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2009</td>\n",
       "      <td>620000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.36 kmpl</td>\n",
       "      <td>1197 CC</td>\n",
       "      <td>78.9 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>hyundai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>Volkswagen Vento Diesel Highline</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2013</td>\n",
       "      <td>720000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.54 kmpl</td>\n",
       "      <td>1598 CC</td>\n",
       "      <td>103.6 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>BMW X5 xDrive 30d M Sport</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2017</td>\n",
       "      <td>6500000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>15.97 kmpl</td>\n",
       "      <td>2993 CC</td>\n",
       "      <td>258 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>bmw</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name Location  Year  \\\n",
       "340   Skoda Octavia Ambition Plus 2.0 TDI AT  Kolkata  2013   \n",
       "358                    Hyundai i10 Magna 1.2  Chennai  2009   \n",
       "1860        Volkswagen Vento Diesel Highline  Chennai  2013   \n",
       "2328               BMW X5 xDrive 30d M Sport  Chennai  2017   \n",
       "\n",
       "      Kilometers_Driven Fuel_Type Transmission Owner_Type     Mileage  \\\n",
       "340              775000    Diesel    Automatic      First   19.3 kmpl   \n",
       "358              620000    Petrol       Manual      First  20.36 kmpl   \n",
       "1860             720000    Diesel       Manual      First  20.54 kmpl   \n",
       "2328            6500000    Diesel    Automatic      First  15.97 kmpl   \n",
       "\n",
       "       Engine      Power  Seats  Price       Brand  \n",
       "340   1968 CC    141 bhp    5.0    7.5       skoda  \n",
       "358   1197 CC   78.9 bhp    5.0    2.7     hyundai  \n",
       "1860  1598 CC  103.6 bhp    5.0    5.9  volkswagen  \n",
       "2328  2993 CC    258 bhp    5.0   65.0         bmw  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Kilometers_Driven']>600000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "q=[]\n",
    "for i in range(1,101,1):\n",
    "    q.append(np.percentile(data.Price, i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(q)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see some kind of outlier in between 90th to 100th percentile so let's Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=[]\n",
    "for i in range(80,101,1):\n",
    "    p.append(np.percentile(data.Price, i))\n",
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4079</th>\n",
       "      <td>Land Rover Range Rover 3.0 Diesel LWB Vogue</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017</td>\n",
       "      <td>25000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>13.33 kmpl</td>\n",
       "      <td>2993 CC</td>\n",
       "      <td>255 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>land</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Name   Location  Year  \\\n",
       "4079  Land Rover Range Rover 3.0 Diesel LWB Vogue  Hyderabad  2017   \n",
       "\n",
       "      Kilometers_Driven Fuel_Type Transmission Owner_Type     Mileage  \\\n",
       "4079              25000    Diesel    Automatic      First  13.33 kmpl   \n",
       "\n",
       "       Engine    Power  Seats  Price Brand  \n",
       "4079  2993 CC  255 bhp    5.0  160.0  land  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Price']>150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fuel_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Diesel      3205\n",
       "Petrol      2746\n",
       "CNG           56\n",
       "LPG           10\n",
       "Electric       2\n",
       "Name: Fuel_Type, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Fuel_Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x201faa7e388>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.Fuel_Type.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Diesel cars are more in number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transmission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manual       4299\n",
       "Automatic    1720\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Transmission.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x201fb175a48>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.Transmission.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Manual cars are more than Automatic cars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Owner_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "First             4929\n",
       "Second             968\n",
       "Third              113\n",
       "Fourth & Above       9\n",
       "Name: Owner_Type, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Owner_Type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Mileage"
   ]
  },
  {
   "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",
       "    .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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4446</th>\n",
       "      <td>Mahindra E Verito D4</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2016</td>\n",
       "      <td>50000</td>\n",
       "      <td>Electric</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72 CC</td>\n",
       "      <td>41 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>mahindra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4904</th>\n",
       "      <td>Toyota Prius 2009-2016 Z4</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2011</td>\n",
       "      <td>44000</td>\n",
       "      <td>Electric</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1798 CC</td>\n",
       "      <td>73 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.75</td>\n",
       "      <td>toyota</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name Location  Year  Kilometers_Driven Fuel_Type  \\\n",
       "4446       Mahindra E Verito D4  Chennai  2016              50000  Electric   \n",
       "4904  Toyota Prius 2009-2016 Z4   Mumbai  2011              44000  Electric   \n",
       "\n",
       "     Transmission Owner_Type Mileage   Engine   Power  Seats  Price     Brand  \n",
       "4446    Automatic      First     NaN    72 CC  41 bhp    5.0  13.00  mahindra  \n",
       "4904    Automatic      First     NaN  1798 CC  73 bhp    5.0  12.75    toyota  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Mileage'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['is_electric']=data['Fuel_Type'].apply(lambda x: 1 if x=='Electric' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG   Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option     Pune  2015              41000   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "\n",
       "   Price    Brand  is_electric  \n",
       "0   1.75   maruti            0  \n",
       "1  12.50  hyundai            0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Removig the CC,bhp,km/kg Units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mileage_cleaned=[]\n",
    "Engine_cleaned=[]\n",
    "power_cleaned=[]\n",
    "for index, rows in data.iterrows():\n",
    "    try:\n",
    "        Mileage_cleaned.append(rows['Mileage'].split()[0])\n",
    "    except:\n",
    "        Mileage_cleaned.append(0)\n",
    "    try:\n",
    "        Engine_cleaned.append(rows['Engine'].split()[0])\n",
    "    except:\n",
    "        Engine_cleaned.append(0)\n",
    "    try:\n",
    "        if rows['Power'].split()[0]=='null':\n",
    "            power_cleaned.append(0)\n",
    "        else:\n",
    "            power_cleaned.append(rows['Power'].split()[0])\n",
    "    except:\n",
    "        power_cleaned.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Mileage_cleaned']=Mileage_cleaned\n",
    "data.Mileage_cleaned = data.Mileage_cleaned.astype(float)\n",
    "data['Engine_cleaned']=Engine_cleaned\n",
    "data.Engine_cleaned = data.Engine_cleaned.astype(float)\n",
    "data['Power_cleaned']=power_cleaned\n",
    "data.Power_cleaned = data.Power_cleaned.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</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>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG   Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option     Pune  2015              41000   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "\n",
       "   Price    Brand  is_electric  Mileage_cleaned  Engine_cleaned  Power_cleaned  \n",
       "0   1.75   maruti            0            26.60           998.0          58.16  \n",
       "1  12.50  hyundai            0            19.67          1582.0         126.20  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filling null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mileage\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mileage_missing=[]\n",
    "for i,j in data[(data['Mileage_cleaned']==0)].iterrows():\n",
    "    if j.Engine_cleaned!=0:\n",
    "        if j.Power_cleaned!=0:\n",
    "            Mileage_missing.append(data[(data.Power_cleaned.between(j.Power_cleaned-50,j.Power_cleaned+50)) & (data.Engine_cleaned.between(j.Engine_cleaned-100,j.Engine_cleaned+100))]['Mileage_cleaned'].mean())\n",
    "        else:\n",
    "            Mileage_missing.append(data[(data.Engine_cleaned.between(j.Engine_cleaned-100,j.Engine_cleaned+100))]['Mileage_cleaned'].mean())\n",
    "    else:\n",
    "        if j.Power_cleaned!=0:\n",
    "            Mileage_missing.append(data[(data.Power_cleaned.between(j.Power_cleaned-50,j.Power_cleaned+50))]['Mileage_cleaned'].mean())\n",
    "        else:\n",
    "            Mileage_missing.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:670: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "j=0\n",
    "for i in data[(data['Mileage_cleaned']==0)]['Mileage_cleaned'].index.tolist():\n",
    "    data['Mileage_cleaned'].iloc[i]=Mileage_missing[j]\n",
    "    j+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data.Mileage_cleaned[data.Mileage_cleaned == 0] = data['Mileage_cleaned'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6019.000000\n",
       "mean       18.328940\n",
       "std         4.169318\n",
       "min         6.400000\n",
       "25%        15.300000\n",
       "50%        18.200000\n",
       "75%        21.100000\n",
       "max        33.540000\n",
       "Name: Mileage_cleaned, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Mileage_cleaned'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "mileage_mean=data['Mileage_cleaned'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Mileage\"].fillna(mileage_mean, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=[]\n",
    "for i in range(1,101,1):\n",
    "    p.append(np.percentile(data.Mileage_cleaned, i))\n",
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Mileage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see some kind of outlier in between 90th to 100th percentile so let's Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=[]\n",
    "for i in range(98,101,1):\n",
    "    p.append(np.percentile(data.Mileage_cleaned, i))\n",
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Mileage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>Maruti Alto 800 2016-2019 CNG LXI</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2018</td>\n",
       "      <td>10600</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>33.44 km/kg</td>\n",
       "      <td>796 CC</td>\n",
       "      <td>40.3 bhp</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>33.44</td>\n",
       "      <td>796.0</td>\n",
       "      <td>40.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>Maruti Wagon R CNG LXI</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2013</td>\n",
       "      <td>79494</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>33.54 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>67.04 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.96</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>33.54</td>\n",
       "      <td>998.0</td>\n",
       "      <td>67.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>Maruti Wagon R CNG LXI</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2013</td>\n",
       "      <td>54000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>33.54 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>67.04 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.05</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>33.54</td>\n",
       "      <td>998.0</td>\n",
       "      <td>67.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>Maruti Wagon R CNG LXI</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2014</td>\n",
       "      <td>29202</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>33.54 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>67.04 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.90</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>33.54</td>\n",
       "      <td>998.0</td>\n",
       "      <td>67.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3129</th>\n",
       "      <td>Maruti Wagon R CNG LXI</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2014</td>\n",
       "      <td>74663</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>33.54 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>67.04 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>33.54</td>\n",
       "      <td>998.0</td>\n",
       "      <td>67.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3553</th>\n",
       "      <td>Maruti Alto 800 2016-2019 CNG LXI</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>18000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>33.44 km/kg</td>\n",
       "      <td>796 CC</td>\n",
       "      <td>40.3 bhp</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>33.44</td>\n",
       "      <td>796.0</td>\n",
       "      <td>40.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3869</th>\n",
       "      <td>Maruti Alto 800 2016-2019 CNG LXI</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2012</td>\n",
       "      <td>65537</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>33.44 km/kg</td>\n",
       "      <td>796 CC</td>\n",
       "      <td>40.3 bhp</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>33.44</td>\n",
       "      <td>796.0</td>\n",
       "      <td>40.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4141</th>\n",
       "      <td>Maruti Wagon R CNG LXI</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2014</td>\n",
       "      <td>47200</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>33.54 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>67.04 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>33.54</td>\n",
       "      <td>998.0</td>\n",
       "      <td>67.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4769</th>\n",
       "      <td>Maruti Alto 800 2016-2019 CNG LXI</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>2017</td>\n",
       "      <td>24310</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>33.44 km/kg</td>\n",
       "      <td>796 CC</td>\n",
       "      <td>40.3 bhp</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.70</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>33.44</td>\n",
       "      <td>796.0</td>\n",
       "      <td>40.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Name Location  Year  Kilometers_Driven  \\\n",
       "1269  Maruti Alto 800 2016-2019 CNG LXI   Mumbai  2018              10600   \n",
       "1332             Maruti Wagon R CNG LXI     Pune  2013              79494   \n",
       "2059             Maruti Wagon R CNG LXI   Mumbai  2013              54000   \n",
       "2371             Maruti Wagon R CNG LXI     Pune  2014              29202   \n",
       "3129             Maruti Wagon R CNG LXI    Delhi  2014              74663   \n",
       "3553  Maruti Alto 800 2016-2019 CNG LXI     Pune  2015              18000   \n",
       "3869  Maruti Alto 800 2016-2019 CNG LXI    Delhi  2012              65537   \n",
       "4141             Maruti Wagon R CNG LXI   Mumbai  2014              47200   \n",
       "4769  Maruti Alto 800 2016-2019 CNG LXI    Kochi  2017              24310   \n",
       "\n",
       "     Fuel_Type Transmission Owner_Type      Mileage  Engine      Power  Seats  \\\n",
       "1269       CNG       Manual      First  33.44 km/kg  796 CC   40.3 bhp    4.0   \n",
       "1332       CNG       Manual      First  33.54 km/kg  998 CC  67.04 bhp    5.0   \n",
       "2059       CNG       Manual      First  33.54 km/kg  998 CC  67.04 bhp    5.0   \n",
       "2371       CNG       Manual      First  33.54 km/kg  998 CC  67.04 bhp    5.0   \n",
       "3129       CNG       Manual      First  33.54 km/kg  998 CC  67.04 bhp    5.0   \n",
       "3553       CNG       Manual      First  33.44 km/kg  796 CC   40.3 bhp    4.0   \n",
       "3869       CNG       Manual     Second  33.44 km/kg  796 CC   40.3 bhp    4.0   \n",
       "4141       CNG       Manual      First  33.54 km/kg  998 CC  67.04 bhp    5.0   \n",
       "4769       CNG       Manual      First  33.44 km/kg  796 CC   40.3 bhp    4.0   \n",
       "\n",
       "      Price   Brand  is_electric  Mileage_cleaned  Engine_cleaned  \\\n",
       "1269   3.50  maruti            0            33.44           796.0   \n",
       "1332   2.96  maruti            0            33.54           998.0   \n",
       "2059   3.05  maruti            0            33.54           998.0   \n",
       "2371   3.90  maruti            0            33.54           998.0   \n",
       "3129   3.25  maruti            0            33.54           998.0   \n",
       "3553   3.00  maruti            0            33.44           796.0   \n",
       "3869   2.10  maruti            0            33.44           796.0   \n",
       "4141   2.85  maruti            0            33.54           998.0   \n",
       "4769   3.70  maruti            0            33.44           796.0   \n",
       "\n",
       "      Power_cleaned  \n",
       "1269          40.30  \n",
       "1332          67.04  \n",
       "2059          67.04  \n",
       "2371          67.04  \n",
       "3129          67.04  \n",
       "3553          40.30  \n",
       "3869          40.30  \n",
       "4141          67.04  \n",
       "4769          40.30  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Mileage_cleaned']>33]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "manual_petrol = data[(data['Fuel_Type']=='Petrol') & (data['Transmission']=='Manual')].Engine_cleaned.mean()\n",
    "manual_diesel = data[(data['Fuel_Type']=='Diesel') & (data['Transmission']=='Manual')].Engine_cleaned.mean()\n",
    "auto_diesel = data[(data['Fuel_Type']=='Diesel') & (data['Transmission']=='Automatic')].Engine_cleaned.mean()\n",
    "auto_petrol = data[(data['Fuel_Type']=='Petrol') & (data['Transmission']=='Automatic')].Engine_cleaned.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:670: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "data.Engine_cleaned.loc[(data['Fuel_Type']=='Petrol') & (data['Transmission']=='Manual') & (data['Engine_cleaned']==0)] = manual_petrol\n",
    "data.Engine_cleaned.loc[(data['Fuel_Type']=='Diesel') & (data['Transmission']=='Manual') & (data['Engine_cleaned']==0)] = manual_diesel\n",
    "data.Engine_cleaned.loc[(data['Fuel_Type']=='Diesel') & (data['Transmission']=='Automatic') & (data['Engine_cleaned']==0)] = auto_diesel\n",
    "data.Engine_cleaned.loc[(data['Fuel_Type']=='Petrol') & (data['Transmission']=='Automatic') & (data['Engine_cleaned']==0)] = auto_petrol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6019.000000\n",
       "mean     1620.273483\n",
       "std       600.566802\n",
       "min        72.000000\n",
       "25%      1198.000000\n",
       "50%      1493.000000\n",
       "75%      1984.000000\n",
       "max      5998.000000\n",
       "Name: Engine_cleaned, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Engine_cleaned'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine_mean=data['Engine_cleaned'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Engine\"].fillna(engine_mean, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=[]\n",
    "for i in range(1,101,1):\n",
    "    p.append(np.percentile(data.Engine_cleaned, i))\n",
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Engine')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see some kind of outlier in between 90th to 100th percentile so let's Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=[]\n",
    "for i in range(90,101,1):\n",
    "    p.append(np.percentile(data.Engine_cleaned, i))\n",
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Engine')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5521</th>\n",
       "      <td>Bentley Continental Flying Spur</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2006</td>\n",
       "      <td>48000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>8.6 kmpl</td>\n",
       "      <td>5998 CC</td>\n",
       "      <td>552 bhp</td>\n",
       "      <td>4.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>bentley</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>5998.0</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Name   Location  Year  Kilometers_Driven  \\\n",
       "5521  Bentley Continental Flying Spur  Hyderabad  2006              48000   \n",
       "\n",
       "     Fuel_Type Transmission Owner_Type   Mileage   Engine    Power  Seats  \\\n",
       "5521    Petrol    Automatic      First  8.6 kmpl  5998 CC  552 bhp    4.0   \n",
       "\n",
       "      Price    Brand  is_electric  Mileage_cleaned  Engine_cleaned  \\\n",
       "5521   59.0  bentley            0              8.6          5998.0   \n",
       "\n",
       "      Power_cleaned  \n",
       "5521          552.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Engine_cleaned']>5500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Ford Fiesta 1.4 SXi TDCi</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>2008</td>\n",
       "      <td>111111</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.8 kmpl</td>\n",
       "      <td>1399 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>ford</td>\n",
       "      <td>0</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Hyundai Santro Xing XL</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2005</td>\n",
       "      <td>87591</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>0.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>19.988571</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Hyundai Santro Xing XO</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2007</td>\n",
       "      <td>73745</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Hyundai Santro Xing XL eRLX Euro III</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2005</td>\n",
       "      <td>102000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Hyundai Santro Xing XO eRLX Euro II</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>2008</td>\n",
       "      <td>80759</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third</td>\n",
       "      <td>17.0 kmpl</td>\n",
       "      <td>1086 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.67</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Name   Location  Year  Kilometers_Driven  \\\n",
       "76               Ford Fiesta 1.4 SXi TDCi     Jaipur  2008             111111   \n",
       "79                 Hyundai Santro Xing XL  Hyderabad  2005              87591   \n",
       "89                 Hyundai Santro Xing XO  Hyderabad  2007              73745   \n",
       "120  Hyundai Santro Xing XL eRLX Euro III     Mumbai  2005             102000   \n",
       "143   Hyundai Santro Xing XO eRLX Euro II      Kochi  2008              80759   \n",
       "\n",
       "    Fuel_Type Transmission Owner_Type    Mileage   Engine     Power  Seats  \\\n",
       "76     Diesel       Manual      First  17.8 kmpl  1399 CC  null bhp    5.0   \n",
       "79     Petrol       Manual      First   0.0 kmpl  1086 CC  null bhp    5.0   \n",
       "89     Petrol       Manual      First  17.0 kmpl  1086 CC  null bhp    5.0   \n",
       "120    Petrol       Manual     Second  17.0 kmpl  1086 CC  null bhp    5.0   \n",
       "143    Petrol       Manual      Third  17.0 kmpl  1086 CC  null bhp    5.0   \n",
       "\n",
       "     Price    Brand  is_electric  Mileage_cleaned  Engine_cleaned  \\\n",
       "76    2.00     ford            0        17.800000          1399.0   \n",
       "79    1.30  hyundai            0        19.988571          1086.0   \n",
       "89    2.10  hyundai            0        17.000000          1086.0   \n",
       "120   0.85  hyundai            0        17.000000          1086.0   \n",
       "143   1.67  hyundai            0        17.000000          1086.0   \n",
       "\n",
       "     Power_cleaned  \n",
       "76             0.0  \n",
       "79             0.0  \n",
       "89             0.0  \n",
       "120            0.0  \n",
       "143            0.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Power_cleaned == 0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "Power_missing=[]\n",
    "for i,j in data[(data['Power_cleaned']==0)].iterrows():\n",
    "    Power_missing.append(data[(data.Mileage_cleaned.between(j.Mileage_cleaned-20,j.Mileage_cleaned+20)) \n",
    "                                      & (data.Engine_cleaned.between(j.Engine_cleaned-200,j.Engine_cleaned+200))]\n",
    "                           ['Power_cleaned'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6019.000000\n",
       "mean      110.562372\n",
       "std        55.956041\n",
       "min         0.000000\n",
       "25%        74.000000\n",
       "50%        91.100000\n",
       "75%       138.030000\n",
       "max       560.000000\n",
       "Name: Power_cleaned, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Power_cleaned'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:670: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "j=0\n",
    "for i in data[(data['Power_cleaned']==0)]['Power_cleaned'].index.tolist():\n",
    "    data['Power_cleaned'].iloc[i]=Power_missing[j]\n",
    "    j+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "power_mean=data['Power_cleaned'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Power\"].fillna(power_mean, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=[]\n",
    "for i in range(1,101,1):\n",
    "    p.append(np.percentile(data.Power_cleaned, i))\n",
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Power')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see some kind of outlier in between 90th to 100th percentile so let's Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=[]\n",
    "for i in range(90,101,1):\n",
    "    p.append(np.percentile(data.Power_cleaned, i))\n",
    "plt.plot(p)\n",
    "plt.xlabel('Percentile')\n",
    "plt.ylabel('Power')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5521</th>\n",
       "      <td>Bentley Continental Flying Spur</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2006</td>\n",
       "      <td>48000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>8.6 kmpl</td>\n",
       "      <td>5998 CC</td>\n",
       "      <td>552 bhp</td>\n",
       "      <td>4.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>bentley</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>5998.0</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>Lamborghini Gallardo Coupe</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2011</td>\n",
       "      <td>6500</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Third</td>\n",
       "      <td>6.4 kmpl</td>\n",
       "      <td>5204 CC</td>\n",
       "      <td>560 bhp</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>lamborghini</td>\n",
       "      <td>0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Name   Location  Year  Kilometers_Driven  \\\n",
       "5521  Bentley Continental Flying Spur  Hyderabad  2006              48000   \n",
       "5781       Lamborghini Gallardo Coupe      Delhi  2011               6500   \n",
       "\n",
       "     Fuel_Type Transmission Owner_Type   Mileage   Engine    Power  Seats  \\\n",
       "5521    Petrol    Automatic      First  8.6 kmpl  5998 CC  552 bhp    4.0   \n",
       "5781    Petrol    Automatic      Third  6.4 kmpl  5204 CC  560 bhp    2.0   \n",
       "\n",
       "      Price        Brand  is_electric  Mileage_cleaned  Engine_cleaned  \\\n",
       "5521   59.0      bentley            0              8.6          5998.0   \n",
       "5781  120.0  lamborghini            0              6.4          5204.0   \n",
       "\n",
       "      Power_cleaned  \n",
       "5521          552.0  \n",
       "5781          560.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Power_cleaned']>550]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.278735151413753"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Seats.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "seats_missing=[]\n",
    "for i,j in data[(data.Seats.isnull()) | (data.Seats== 0)].iterrows():\n",
    "    seats_missing.append(data[data['Name'].str.match(\" \".join((j['Name'].split()[0],j['Name'].split()[1])))]['Seats'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:670: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "j=0\n",
    "for i in data[(data.Seats.isnull()) | (data.Seats== 0)].index.tolist():\n",
    "    data['Seats'].iloc[i]=seats_missing[j]\n",
    "    j+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data.Seats[data.Seats == 0] = data['Seats'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "seats_list=data[\"Seats\"].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "seats_mean = round(np.nanmean(seats_list, axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Seats\"].fillna(seats_mean, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The null values were replaced by the mean of their respective columns as this is a better solution than removing rows or columns(loss of data), so this method yields better results.\n",
    "### As the given data sets does not have a large number of outliers in each column(less than 1%), so replacing the null values with mean made more sense."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One Hot Encoding (Categorical variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</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>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</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>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>honda</td>\n",
       "      <td>0</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>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</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>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>audi</td>\n",
       "      <td>0</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": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   Price    Brand  is_electric  Mileage_cleaned  Engine_cleaned  Power_cleaned  \n",
       "0   1.75   maruti            0            26.60           998.0          58.16  \n",
       "1  12.50  hyundai            0            19.67          1582.0         126.20  \n",
       "2   4.50    honda            0            18.20          1199.0          88.70  \n",
       "3   6.00   maruti            0            20.77          1248.0          88.76  \n",
       "4  17.74     audi            0            15.20          1968.0         140.80  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cop = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_target = data['Price']\n",
    "data_final = data.drop(['Name','Mileage','Engine','Power','Price'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Location', 'Year', 'Kilometers_Driven', 'Fuel_Type', 'Transmission',\n",
       "       'Owner_Type', 'Seats', 'Brand', 'is_electric', 'Mileage_cleaned',\n",
       "       'Engine_cleaned', 'Power_cleaned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</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>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</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>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>honda</td>\n",
       "      <td>0</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>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>7.0</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</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>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>5.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>0</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": [
       "     Location  Year  Kilometers_Driven Fuel_Type Transmission Owner_Type  \\\n",
       "0      Mumbai  2010              72000       CNG       Manual      First   \n",
       "1        Pune  2015              41000    Diesel       Manual      First   \n",
       "2     Chennai  2011              46000    Petrol       Manual      First   \n",
       "3     Chennai  2012              87000    Diesel       Manual      First   \n",
       "4  Coimbatore  2013              40670    Diesel    Automatic     Second   \n",
       "\n",
       "   Seats    Brand  is_electric  Mileage_cleaned  Engine_cleaned  Power_cleaned  \n",
       "0    5.0   maruti            0            26.60           998.0          58.16  \n",
       "1    5.0  hyundai            0            19.67          1582.0         126.20  \n",
       "2    5.0    honda            0            18.20          1199.0          88.70  \n",
       "3    7.0   maruti            0            20.77          1248.0          88.76  \n",
       "4    5.0     audi            0            15.20          1968.0         140.80  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Scaling and Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['Kilometers_Driven','Mileage_cleaned','Engine_cleaned', 'Power_cleaned']:\n",
    "    data_final[i] = MinMaxScaler(feature_range=(0,1)).fit_transform(data_final[i].values.astype(float).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_final, data_target, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Brand</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>Jaipur</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.009765</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>honda</td>\n",
       "      <td>0</td>\n",
       "      <td>0.770081</td>\n",
       "      <td>0.240634</td>\n",
       "      <td>0.122480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.010743</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>0.171110</td>\n",
       "      <td>0.075707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.008435</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>0</td>\n",
       "      <td>0.316875</td>\n",
       "      <td>0.307796</td>\n",
       "      <td>0.134652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.005205</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mahindra</td>\n",
       "      <td>0</td>\n",
       "      <td>0.353721</td>\n",
       "      <td>0.355552</td>\n",
       "      <td>0.201217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5711</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.011722</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>0.648489</td>\n",
       "      <td>0.176848</td>\n",
       "      <td>0.068087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2634</th>\n",
       "      <td>Pune</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.013974</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>0.383198</td>\n",
       "      <td>0.214141</td>\n",
       "      <td>0.092811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237</th>\n",
       "      <td>Kochi</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.008177</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>0</td>\n",
       "      <td>0.346352</td>\n",
       "      <td>0.256328</td>\n",
       "      <td>0.165652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.001512</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>tata</td>\n",
       "      <td>0</td>\n",
       "      <td>0.411570</td>\n",
       "      <td>0.189166</td>\n",
       "      <td>0.103652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.010282</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>tata</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585851</td>\n",
       "      <td>0.198448</td>\n",
       "      <td>0.075694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5483</th>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.004884</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>0.651069</td>\n",
       "      <td>0.156261</td>\n",
       "      <td>0.062571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4030 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Location  Year  Kilometers_Driven Fuel_Type Transmission Owner_Type  \\\n",
       "564       Jaipur  2015           0.009765    Diesel       Manual      First   \n",
       "631      Chennai  2002           0.010743    Petrol       Manual      Third   \n",
       "2173     Chennai  2012           0.008435    Diesel       Manual      First   \n",
       "509    Bangalore  2015           0.005205    Diesel    Automatic      First   \n",
       "5711   Hyderabad  2015           0.011722    Diesel       Manual      First   \n",
       "...          ...   ...                ...       ...          ...        ...   \n",
       "2634        Pune  2005           0.013974    Petrol       Manual      Third   \n",
       "5237       Kochi  2018           0.008177    Petrol       Manual      First   \n",
       "5999     Chennai  2016           0.001512    Petrol       Manual      First   \n",
       "448    Hyderabad  2014           0.010282    Diesel       Manual      First   \n",
       "5483  Coimbatore  2018           0.004884    Petrol    Automatic      First   \n",
       "\n",
       "      Seats       Brand  is_electric  Mileage_cleaned  Engine_cleaned  \\\n",
       "564     5.0       honda            0         0.770081        0.240634   \n",
       "631     5.0     hyundai            0         0.500684        0.171110   \n",
       "2173    5.0  volkswagen            0         0.316875        0.307796   \n",
       "509     7.0    mahindra            0         0.353721        0.355552   \n",
       "5711    5.0     hyundai            0         0.648489        0.176848   \n",
       "...     ...         ...          ...              ...             ...   \n",
       "2634    5.0     hyundai            0         0.383198        0.214141   \n",
       "5237    5.0     hyundai            0         0.346352        0.256328   \n",
       "5999    5.0        tata            0         0.411570        0.189166   \n",
       "448     5.0        tata            0         0.585851        0.198448   \n",
       "5483    5.0      maruti            0         0.651069        0.156261   \n",
       "\n",
       "      Power_cleaned  \n",
       "564        0.122480  \n",
       "631        0.075707  \n",
       "2173       0.134652  \n",
       "509        0.201217  \n",
       "5711       0.068087  \n",
       "...             ...  \n",
       "2634       0.092811  \n",
       "5237       0.165652  \n",
       "5999       0.103652  \n",
       "448        0.075694  \n",
       "5483       0.062571  \n",
       "\n",
       "[4030 rows x 12 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Kilometers_Driven</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3512</th>\n",
       "      <td>Kochi</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.009481</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
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       "    <tr>\n",
       "      <th>2366</th>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.004128</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>toyota</td>\n",
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       "      <td>0.291259</td>\n",
       "      <td>0.197471</td>\n",
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       "    <tr>\n",
       "      <th>3389</th>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.004141</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>0</td>\n",
       "      <td>0.446942</td>\n",
       "      <td>0.319946</td>\n",
       "      <td>0.292012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3761</th>\n",
       "      <td>Pune</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.005821</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>0.615328</td>\n",
       "      <td>0.156261</td>\n",
       "      <td>0.062457</td>\n",
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       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.006942</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>0.689020</td>\n",
       "      <td>0.198448</td>\n",
       "      <td>0.103271</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5382</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.005666</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>land</td>\n",
       "      <td>0</td>\n",
       "      <td>0.232130</td>\n",
       "      <td>0.355552</td>\n",
       "      <td>0.291936</td>\n",
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       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>0</td>\n",
       "      <td>0.501105</td>\n",
       "      <td>0.223928</td>\n",
       "      <td>0.064283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3774</th>\n",
       "      <td>Kochi</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>maruti</td>\n",
       "      <td>0</td>\n",
       "      <td>0.558585</td>\n",
       "      <td>0.234560</td>\n",
       "      <td>0.131324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Jaipur</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.013513</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>0</td>\n",
       "      <td>0.701548</td>\n",
       "      <td>0.145798</td>\n",
       "      <td>0.044504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.008767</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>5.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>0</td>\n",
       "      <td>0.459838</td>\n",
       "      <td>0.324502</td>\n",
       "      <td>0.284899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1986 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location  Year  Kilometers_Driven Fuel_Type Transmission Owner_Type  \\\n",
       "3512      Kochi  2017           0.009481    Petrol       Manual      First   \n",
       "2366    Kolkata  2014           0.004128    Petrol       Manual      First   \n",
       "3389    Kolkata  2012           0.004141    Diesel    Automatic      First   \n",
       "3761       Pune  2016           0.005821    Petrol    Automatic      First   \n",
       "3651    Kolkata  2016           0.006942    Diesel       Manual      First   \n",
       "...         ...   ...                ...       ...          ...        ...   \n",
       "5382      Delhi  2015           0.005666    Diesel    Automatic      First   \n",
       "3949    Kolkata  2013           0.003358    Diesel       Manual      First   \n",
       "3774      Kochi  2018           0.001601    Petrol       Manual      First   \n",
       "179      Jaipur  2012           0.013513    Diesel       Manual      First   \n",
       "1483  Hyderabad  2012           0.008767    Diesel    Automatic     Second   \n",
       "\n",
       "      Seats      Brand  is_electric  Mileage_cleaned  Engine_cleaned  \\\n",
       "3512    5.0      honda            0         0.420044        0.190010   \n",
       "2366    5.0     toyota            0         0.290346        0.291259   \n",
       "3389    5.0       audi            0         0.446942        0.319946   \n",
       "3761    5.0     maruti            0         0.615328        0.156261   \n",
       "3651    5.0     maruti            0         0.689020        0.198448   \n",
       "...     ...        ...          ...              ...             ...   \n",
       "5382    5.0       land            0         0.232130        0.355552   \n",
       "3949    5.0       ford            0         0.501105        0.223928   \n",
       "3774    5.0     maruti            0         0.558585        0.234560   \n",
       "179     5.0  chevrolet            0         0.701548        0.145798   \n",
       "1483    5.0        bmw            0         0.459838        0.324502   \n",
       "\n",
       "      Power_cleaned  \n",
       "3512       0.099848  \n",
       "2366       0.197471  \n",
       "3389       0.292012  \n",
       "3761       0.062457  \n",
       "3651       0.103271  \n",
       "...             ...  \n",
       "5382       0.291936  \n",
       "3949       0.064283  \n",
       "3774       0.131324  \n",
       "179        0.044504  \n",
       "1483       0.284899  \n",
       "\n",
       "[1986 rows x 12 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4030, 12), (1986, 12), (4030,), (1986,))"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting train to one hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(cols=['Location', 'Fuel_Type', 'Transmission', 'Owner_Type',\n",
       "                    'Brand'],\n",
       "              drop_invariant=False, handle_missing='value',\n",
       "              handle_unknown='ignore', return_df=True, use_cat_names=True,\n",
       "              verbose=0)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded = ce.OneHotEncoder(handle_unknown='ignore',use_cat_names=True)\n",
    "encoded.fit(data_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_encoded=encoded.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>Location_Mumbai</th>\n",
       "      <th>Location_Pune</th>\n",
       "      <th>Location_Chennai</th>\n",
       "      <th>Location_Coimbatore</th>\n",
       "      <th>Location_Hyderabad</th>\n",
       "      <th>Location_Jaipur</th>\n",
       "      <th>Location_Kochi</th>\n",
       "      <th>Location_Kolkata</th>\n",
       "      <th>Location_Delhi</th>\n",
       "      <th>Location_Bangalore</th>\n",
       "      <th>...</th>\n",
       "      <th>Brand_smart</th>\n",
       "      <th>Brand_ambassador</th>\n",
       "      <th>Brand_isuzu</th>\n",
       "      <th>Brand_force</th>\n",
       "      <th>Brand_bentley</th>\n",
       "      <th>Brand_lamborghini</th>\n",
       "      <th>is_electric</th>\n",
       "      <th>Mileage_cleaned</th>\n",
       "      <th>Engine_cleaned</th>\n",
       "      <th>Power_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
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       "      <td>0.770081</td>\n",
       "      <td>0.240634</td>\n",
       "      <td>0.122480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>0</td>\n",
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       "      <td>0.500684</td>\n",
       "      <td>0.171110</td>\n",
       "      <td>0.075707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.316875</td>\n",
       "      <td>0.307796</td>\n",
       "      <td>0.134652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.353721</td>\n",
       "      <td>0.355552</td>\n",
       "      <td>0.201217</td>\n",
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       "    <tr>\n",
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       "      <td>0.648489</td>\n",
       "      <td>0.176848</td>\n",
       "      <td>0.068087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Location_Mumbai  Location_Pune  Location_Chennai  Location_Coimbatore  \\\n",
       "564                 0              0                 0                    0   \n",
       "631                 0              0                 1                    0   \n",
       "2173                0              0                 1                    0   \n",
       "509                 0              0                 0                    0   \n",
       "5711                0              0                 0                    0   \n",
       "\n",
       "      Location_Hyderabad  Location_Jaipur  Location_Kochi  Location_Kolkata  \\\n",
       "564                    0                1               0                 0   \n",
       "631                    0                0               0                 0   \n",
       "2173                   0                0               0                 0   \n",
       "509                    0                0               0                 0   \n",
       "5711                   1                0               0                 0   \n",
       "\n",
       "      Location_Delhi  Location_Bangalore  ...  Brand_smart  Brand_ambassador  \\\n",
       "564                0                   0  ...            0                 0   \n",
       "631                0                   0  ...            0                 0   \n",
       "2173               0                   0  ...            0                 0   \n",
       "509                0                   1  ...            0                 0   \n",
       "5711               0                   0  ...            0                 0   \n",
       "\n",
       "      Brand_isuzu  Brand_force  Brand_bentley  Brand_lamborghini  is_electric  \\\n",
       "564             0            0              0                  0            0   \n",
       "631             0            0              0                  0            0   \n",
       "2173            0            0              0                  0            0   \n",
       "509             0            0              0                  0            0   \n",
       "5711            0            0              0                  0            0   \n",
       "\n",
       "      Mileage_cleaned  Engine_cleaned  Power_cleaned  \n",
       "564          0.770081        0.240634       0.122480  \n",
       "631          0.500684        0.171110       0.075707  \n",
       "2173         0.316875        0.307796       0.134652  \n",
       "509          0.353721        0.355552       0.201217  \n",
       "5711         0.648489        0.176848       0.068087  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_encoded=encoded.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1986, 59)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_encoded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in X_train_encoded.columns:\n",
    "    if len(X_train_encoded[X_train_encoded[i].isnull()].index)>0:\n",
    "        print(i,' Has Null values count : ',len(X_train_encoded[X_train_encoded[i].isnull()].index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7810514707853553"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegression = LinearRegression()\n",
    "linearRegression.fit(X_train_encoded, y_train)\n",
    "y_pred = linearRegression.predict(X_test_encoded)\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error metrics when Linear Regression is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\tError Table\n",
      "Mean Absolute Error      :  2.830130837739174\n",
      "Mean Squared  Error      :  24.678546309973033\n",
      "Root Mean Squared  Error :  4.967750628803043\n",
      "R Squared Error          :  0.7810514707853553\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "print(\"\\t\\tError Table\")\n",
    "print('Mean Absolute Error      : ', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared  Error      : ', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared  Error : ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "print('R Squared Error          : ', metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9066130476571647"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators = 100)\n",
    "rf.fit(X_train_encoded, y_train)\n",
    "y_pred = rf.predict(X_test_encoded)\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error metric when random forest method is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\tError Table\n",
      "Mean Absolute Error      :  1.43242937868652\n",
      "Mean Squared  Error      :  10.526009178534169\n",
      "Root Mean Squared  Error :  3.2443811703519314\n",
      "R Squared Error          :  0.9066130476571647\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "print(\"\\t\\tError Table\")\n",
    "print('Mean Absolute Error      : ', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared  Error      : ', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared  Error : ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "print('R Squared Error          : ', metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest give better accuracy as compared to Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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