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Car_Price_Predictor.ipynb

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+{
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+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPmt2lmbu+FwSqN/2ioK1mu"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
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+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "I6tnizR9KmGN"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car = pd.read_csv('https://raw.githubusercontent.com/rajtilakls2510/car_price_predictor/master/quikr_car.csv')\n",
+        "car.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "_QB98RY-L_DI",
+        "outputId": "85adff85-d29f-46d3-9b5c-c721a0dcc7f8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                     name   company  year          Price  \\\n",
+              "0    Hyundai Santro Xing XO eRLX Euro III   Hyundai  2007         80,000   \n",
+              "1                 Mahindra Jeep CL550 MDI  Mahindra  2006       4,25,000   \n",
+              "2              Maruti Suzuki Alto 800 Vxi    Maruti  2018  Ask For Price   \n",
+              "3  Hyundai Grand i10 Magna 1.2 Kappa VTVT   Hyundai  2014       3,25,000   \n",
+              "4        Ford EcoSport Titanium 1.5L TDCi      Ford  2014       5,75,000   \n",
+              "\n",
+              "   kms_driven fuel_type  \n",
+              "0  45,000 kms    Petrol  \n",
+              "1      40 kms    Diesel  \n",
+              "2  22,000 kms    Petrol  \n",
+              "3  28,000 kms    Petrol  \n",
+              "4  36,000 kms    Diesel  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-940caad2-31d8-4e4a-afe4-711d22c64a75\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>company</th>\n",
+              "      <th>year</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>kms_driven</th>\n",
+              "      <th>fuel_type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Hyundai Santro Xing XO eRLX Euro III</td>\n",
+              "      <td>Hyundai</td>\n",
+              "      <td>2007</td>\n",
+              "      <td>80,000</td>\n",
+              "      <td>45,000 kms</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Mahindra Jeep CL550 MDI</td>\n",
+              "      <td>Mahindra</td>\n",
+              "      <td>2006</td>\n",
+              "      <td>4,25,000</td>\n",
+              "      <td>40 kms</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Maruti Suzuki Alto 800 Vxi</td>\n",
+              "      <td>Maruti</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>Ask For Price</td>\n",
+              "      <td>22,000 kms</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Hyundai Grand i10 Magna 1.2 Kappa VTVT</td>\n",
+              "      <td>Hyundai</td>\n",
+              "      <td>2014</td>\n",
+              "      <td>3,25,000</td>\n",
+              "      <td>28,000 kms</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Ford EcoSport Titanium 1.5L TDCi</td>\n",
+              "      <td>Ford</td>\n",
+              "      <td>2014</td>\n",
+              "      <td>5,75,000</td>\n",
+              "      <td>36,000 kms</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-940caad2-31d8-4e4a-afe4-711d22c64a75')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  </svg>\n",
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+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
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+              "      fill: #174EA6;\n",
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+              "\n",
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+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-940caad2-31d8-4e4a-afe4-711d22c64a75 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-940caad2-31d8-4e4a-afe4-711d22c64a75');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
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+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wwy0Ve_wNG9M",
+        "outputId": "43958f8c-46a1-4274-9c6b-7fb0f4e9c1d3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(892, 6)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xh6mrkM0NLeU",
+        "outputId": "e2b7a481-1583-46aa-e547-7da1f3d6ae82"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 892 entries, 0 to 891\n",
+            "Data columns (total 6 columns):\n",
+            " #   Column      Non-Null Count  Dtype \n",
+            "---  ------      --------------  ----- \n",
+            " 0   name        892 non-null    object\n",
+            " 1   company     892 non-null    object\n",
+            " 2   year        892 non-null    object\n",
+            " 3   Price       892 non-null    object\n",
+            " 4   kms_driven  840 non-null    object\n",
+            " 5   fuel_type   837 non-null    object\n",
+            "dtypes: object(6)\n",
+            "memory usage: 41.9+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car['year'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1U_wrin0NO0F",
+        "outputId": "7ec4499e-c071-4ae0-9ea6-7a854185852e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['2007', '2006', '2018', '2014', '2015', '2012', '2013', '2016',\n",
+              "       '2010', '2017', '2008', '2011', '2019', '2009', '2005', '2000',\n",
+              "       '...', '150k', 'TOUR', '2003', 'r 15', '2004', 'Zest', '/-Rs',\n",
+              "       'sale', '1995', 'ara)', '2002', 'SELL', '2001', 'tion', 'odel',\n",
+              "       '2 bs', 'arry', 'Eon', 'o...', 'ture', 'emi', 'car', 'able', 'no.',\n",
+              "       'd...', 'SALE', 'digo', 'sell', 'd Ex', 'n...', 'e...', 'D...',\n",
+              "       ', Ac', 'go .', 'k...', 'o c4', 'zire', 'cent', 'Sumo', 'cab',\n",
+              "       't xe', 'EV2', 'r...', 'zest'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In year column has alphbet values (Non-year value)"
+      ],
+      "metadata": {
+        "id": "y93Rrz_YNhKo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car['Price'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "H_n8jcMMNbfk",
+        "outputId": "10cd6ade-1ba5-4998-d3ef-b00c00e4cc39"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['80,000', '4,25,000', 'Ask For Price', '3,25,000', '5,75,000',\n",
+              "       '1,75,000', '1,90,000', '8,30,000', '2,50,000', '1,82,000',\n",
+              "       '3,15,000', '4,15,000', '3,20,000', '10,00,000', '5,00,000',\n",
+              "       '3,50,000', '1,60,000', '3,10,000', '75,000', '1,00,000',\n",
+              "       '2,90,000', '95,000', '1,80,000', '3,85,000', '1,05,000',\n",
+              "       '6,50,000', '6,89,999', '4,48,000', '5,49,000', '5,01,000',\n",
+              "       '4,89,999', '2,80,000', '3,49,999', '2,84,999', '3,45,000',\n",
+              "       '4,99,999', '2,35,000', '2,49,999', '14,75,000', '3,95,000',\n",
+              "       '2,20,000', '1,70,000', '85,000', '2,00,000', '5,70,000',\n",
+              "       '1,10,000', '4,48,999', '18,91,111', '1,59,500', '3,44,999',\n",
+              "       '4,49,999', '8,65,000', '6,99,000', '3,75,000', '2,24,999',\n",
+              "       '12,00,000', '1,95,000', '3,51,000', '2,40,000', '90,000',\n",
+              "       '1,55,000', '6,00,000', '1,89,500', '2,10,000', '3,90,000',\n",
+              "       '1,35,000', '16,00,000', '7,01,000', '2,65,000', '5,25,000',\n",
+              "       '3,72,000', '6,35,000', '5,50,000', '4,85,000', '3,29,500',\n",
+              "       '2,51,111', '5,69,999', '69,999', '2,99,999', '3,99,999',\n",
+              "       '4,50,000', '2,70,000', '1,58,400', '1,79,000', '1,25,000',\n",
+              "       '2,99,000', '1,50,000', '2,75,000', '2,85,000', '3,40,000',\n",
+              "       '70,000', '2,89,999', '8,49,999', '7,49,999', '2,74,999',\n",
+              "       '9,84,999', '5,99,999', '2,44,999', '4,74,999', '2,45,000',\n",
+              "       '1,69,500', '3,70,000', '1,68,000', '1,45,000', '98,500',\n",
+              "       '2,09,000', '1,85,000', '9,00,000', '6,99,999', '1,99,999',\n",
+              "       '5,44,999', '1,99,000', '5,40,000', '49,000', '7,00,000', '55,000',\n",
+              "       '8,95,000', '3,55,000', '5,65,000', '3,65,000', '40,000',\n",
+              "       '4,00,000', '3,30,000', '5,80,000', '3,79,000', '2,19,000',\n",
+              "       '5,19,000', '7,30,000', '20,00,000', '21,00,000', '14,00,000',\n",
+              "       '3,11,000', '8,55,000', '5,35,000', '1,78,000', '3,00,000',\n",
+              "       '2,55,000', '5,49,999', '3,80,000', '57,000', '4,10,000',\n",
+              "       '2,25,000', '1,20,000', '59,000', '5,99,000', '6,75,000', '72,500',\n",
+              "       '6,10,000', '2,30,000', '5,20,000', '5,24,999', '4,24,999',\n",
+              "       '6,44,999', '5,84,999', '7,99,999', '4,44,999', '6,49,999',\n",
+              "       '9,44,999', '5,74,999', '3,74,999', '1,30,000', '4,01,000',\n",
+              "       '13,50,000', '1,74,999', '2,39,999', '99,999', '3,24,999',\n",
+              "       '10,74,999', '11,30,000', '1,49,000', '7,70,000', '30,000',\n",
+              "       '3,35,000', '3,99,000', '65,000', '1,69,999', '1,65,000',\n",
+              "       '5,60,000', '9,50,000', '7,15,000', '45,000', '9,40,000',\n",
+              "       '1,55,555', '15,00,000', '4,95,000', '8,00,000', '12,99,000',\n",
+              "       '5,30,000', '14,99,000', '32,000', '4,05,000', '7,60,000',\n",
+              "       '7,50,000', '4,19,000', '1,40,000', '15,40,000', '1,23,000',\n",
+              "       '4,98,000', '4,80,000', '4,88,000', '15,25,000', '5,48,900',\n",
+              "       '7,25,000', '99,000', '52,000', '28,00,000', '4,99,000',\n",
+              "       '3,81,000', '2,78,000', '6,90,000', '2,60,000', '90,001',\n",
+              "       '1,15,000', '15,99,000', '1,59,000', '51,999', '2,15,000',\n",
+              "       '35,000', '11,50,000', '2,69,000', '60,000', '4,30,000',\n",
+              "       '85,00,003', '4,01,919', '4,90,000', '4,24,000', '2,05,000',\n",
+              "       '5,49,900', '3,71,500', '4,35,000', '1,89,700', '3,89,700',\n",
+              "       '3,60,000', '2,95,000', '1,14,990', '10,65,000', '4,70,000',\n",
+              "       '48,000', '1,88,000', '4,65,000', '1,79,999', '21,90,000',\n",
+              "       '23,90,000', '10,75,000', '4,75,000', '10,25,000', '6,15,000',\n",
+              "       '19,00,000', '14,90,000', '15,10,000', '18,50,000', '7,90,000',\n",
+              "       '17,25,000', '12,25,000', '68,000', '9,70,000', '31,00,000',\n",
+              "       '8,99,000', '88,000', '53,000', '5,68,500', '71,000', '5,90,000',\n",
+              "       '7,95,000', '42,000', '1,89,000', '1,62,000', '35,999',\n",
+              "       '29,00,000', '39,999', '50,500', '5,10,000', '8,60,000',\n",
+              "       '5,00,001'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Price column has 'Ask for price' string"
+      ],
+      "metadata": {
+        "id": "VYVx8iFOOG5K"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car['kms_driven'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UUVyLDZ2ODAc",
+        "outputId": "82a219be-00b2-46b3-c279-324ecca81c06"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['45,000 kms', '40 kms', '22,000 kms', '28,000 kms', '36,000 kms',\n",
+              "       '59,000 kms', '41,000 kms', '25,000 kms', '24,530 kms',\n",
+              "       '60,000 kms', '30,000 kms', '32,000 kms', '48,660 kms',\n",
+              "       '4,000 kms', '16,934 kms', '43,000 kms', '35,550 kms',\n",
+              "       '39,522 kms', '39,000 kms', '55,000 kms', '72,000 kms',\n",
+              "       '15,975 kms', '70,000 kms', '23,452 kms', '35,522 kms',\n",
+              "       '48,508 kms', '15,487 kms', '82,000 kms', '20,000 kms',\n",
+              "       '68,000 kms', '38,000 kms', '27,000 kms', '33,000 kms',\n",
+              "       '46,000 kms', '16,000 kms', '47,000 kms', '35,000 kms',\n",
+              "       '30,874 kms', '15,000 kms', '29,685 kms', '1,30,000 kms',\n",
+              "       '19,000 kms', nan, '54,000 kms', '13,000 kms', '38,200 kms',\n",
+              "       '50,000 kms', '13,500 kms', '3,600 kms', '45,863 kms',\n",
+              "       '60,500 kms', '12,500 kms', '18,000 kms', '13,349 kms',\n",
+              "       '29,000 kms', '44,000 kms', '42,000 kms', '14,000 kms',\n",
+              "       '49,000 kms', '36,200 kms', '51,000 kms', '1,04,000 kms',\n",
+              "       '33,333 kms', '33,600 kms', '5,600 kms', '7,500 kms', '26,000 kms',\n",
+              "       '24,330 kms', '65,480 kms', '28,028 kms', '2,00,000 kms',\n",
+              "       '99,000 kms', '2,800 kms', '21,000 kms', '11,000 kms',\n",
+              "       '66,000 kms', '3,000 kms', '7,000 kms', '38,500 kms', '37,200 kms',\n",
+              "       '43,200 kms', '24,800 kms', '45,872 kms', '40,000 kms',\n",
+              "       '11,400 kms', '97,200 kms', '52,000 kms', '31,000 kms',\n",
+              "       '1,75,430 kms', '37,000 kms', '65,000 kms', '3,350 kms',\n",
+              "       '75,000 kms', '62,000 kms', '73,000 kms', '2,200 kms',\n",
+              "       '54,870 kms', '34,580 kms', '97,000 kms', '60 kms', '80,200 kms',\n",
+              "       '3,200 kms', '0,000 kms', '5,000 kms', '588 kms', '71,200 kms',\n",
+              "       '1,75,400 kms', '9,300 kms', '56,758 kms', '10,000 kms',\n",
+              "       '56,450 kms', '56,000 kms', '32,700 kms', '9,000 kms', '73 kms',\n",
+              "       '1,60,000 kms', '84,000 kms', '58,559 kms', '57,000 kms',\n",
+              "       '1,70,000 kms', '80,000 kms', '6,821 kms', '23,000 kms',\n",
+              "       '34,000 kms', '1,800 kms', '4,00,000 kms', '48,000 kms',\n",
+              "       '90,000 kms', '12,000 kms', '69,900 kms', '1,66,000 kms',\n",
+              "       '122 kms', '0 kms', '24,000 kms', '36,469 kms', '7,800 kms',\n",
+              "       '24,695 kms', '15,141 kms', '59,910 kms', '1,00,000 kms',\n",
+              "       '4,500 kms', '1,29,000 kms', '300 kms', '1,31,000 kms',\n",
+              "       '1,11,111 kms', '59,466 kms', '25,500 kms', '44,005 kms',\n",
+              "       '2,110 kms', '43,222 kms', '1,00,200 kms', '65 kms',\n",
+              "       '1,40,000 kms', '1,03,553 kms', '58,000 kms', '1,20,000 kms',\n",
+              "       '49,800 kms', '100 kms', '81,876 kms', '6,020 kms', '55,700 kms',\n",
+              "       '18,500 kms', '1,80,000 kms', '53,000 kms', '35,500 kms',\n",
+              "       '22,134 kms', '1,000 kms', '8,500 kms', '87,000 kms', '6,000 kms',\n",
+              "       '15,574 kms', '8,000 kms', '55,800 kms', '56,400 kms',\n",
+              "       '72,160 kms', '11,500 kms', '1,33,000 kms', '2,000 kms',\n",
+              "       '88,000 kms', '65,422 kms', '1,17,000 kms', '1,50,000 kms',\n",
+              "       '10,750 kms', '6,800 kms', '5 kms', '9,800 kms', '57,923 kms',\n",
+              "       '30,201 kms', '6,200 kms', '37,518 kms', '24,652 kms', '383 kms',\n",
+              "       '95,000 kms', '3,528 kms', '52,500 kms', '47,900 kms',\n",
+              "       '52,800 kms', '1,95,000 kms', '48,008 kms', '48,247 kms',\n",
+              "       '9,400 kms', '64,000 kms', '2,137 kms', '10,544 kms', '49,500 kms',\n",
+              "       '1,47,000 kms', '90,001 kms', '48,006 kms', '74,000 kms',\n",
+              "       '85,000 kms', '29,500 kms', '39,700 kms', '67,000 kms',\n",
+              "       '19,336 kms', '60,105 kms', '45,933 kms', '1,02,563 kms',\n",
+              "       '28,600 kms', '41,800 kms', '1,16,000 kms', '42,590 kms',\n",
+              "       '7,400 kms', '54,500 kms', '76,000 kms', '00 kms', '11,523 kms',\n",
+              "       '38,600 kms', '95,500 kms', '37,458 kms', '85,960 kms',\n",
+              "       '12,516 kms', '30,600 kms', '2,550 kms', '62,500 kms',\n",
+              "       '69,000 kms', '28,400 kms', '68,485 kms', '3,500 kms',\n",
+              "       '85,455 kms', '63,000 kms', '1,600 kms', '77,000 kms',\n",
+              "       '26,500 kms', '2,875 kms', '13,900 kms', '1,500 kms', '2,450 kms',\n",
+              "       '1,625 kms', '33,400 kms', '60,123 kms', '38,900 kms',\n",
+              "       '1,37,495 kms', '91,200 kms', '1,46,000 kms', '1,00,800 kms',\n",
+              "       '2,100 kms', '2,500 kms', '1,32,000 kms', 'Petrol'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "All numeric data has camma, and kms.\n",
+        "also column has 'Petrol' string"
+      ],
+      "metadata": {
+        "id": "OW2xuYDdOeTa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car['fuel_type'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8G0rfwtgOaeJ",
+        "outputId": "562ebc56-52af-46da-9325-1bc708d0aa01"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['Petrol', 'Diesel', nan, 'LPG'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Quality of data\n",
+        "\n",
+        "1. year has many non-year values\n",
+        "2. Year object to int\n",
+        "3. price ask for price\n",
+        "4. price object to int\n",
+        "5. kms_driven has kms with intergers\n",
+        "6. kms_driven object to int\n",
+        "7. kms_driven has nan values\n",
+        "8. fuel_type has nan value\n",
+        "9. keep first 3 word of name"
+      ],
+      "metadata": {
+        "id": "b6QNQf33PFZ0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Cleaning"
+      ],
+      "metadata": {
+        "id": "GWwGOVJ_QGmQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "backup = car.copy()"
+      ],
+      "metadata": {
+        "id": "iXGXeDukPC2J"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car=car[car['year'].str.isnumeric()]"
+      ],
+      "metadata": {
+        "id": "_MNBGYqUQMvM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car['year']=car['year'].astype(int)"
+      ],
+      "metadata": {
+        "id": "fYHyyMyNQnJj",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dc46662d-8a98-44e2-f135-c85fda9edc65"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-11-c95edc1f455b>:1: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\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",
+            "  car['year']=car['year'].astype(int)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9Al6QL_oQ9e3",
+        "outputId": "8d95c8a8-92a0-4fbd-c847-c1bd056fedda"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 842 entries, 0 to 891\n",
+            "Data columns (total 6 columns):\n",
+            " #   Column      Non-Null Count  Dtype \n",
+            "---  ------      --------------  ----- \n",
+            " 0   name        842 non-null    object\n",
+            " 1   company     842 non-null    object\n",
+            " 2   year        842 non-null    int64 \n",
+            " 3   Price       842 non-null    object\n",
+            " 4   kms_driven  840 non-null    object\n",
+            " 5   fuel_type   837 non-null    object\n",
+            "dtypes: int64(1), object(5)\n",
+            "memory usage: 46.0+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "O7ivSz7URJIR",
+        "outputId": "1d3282b9-118a-4173-9bc2-3acc5fd54a57"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(842, 6)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car=car[car['Price'] != 'Ask For Price']"
+      ],
+      "metadata": {
+        "id": "3JQg_JyRRNL7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car['Price']=car['Price'].str.replace(',','').astype(int)"
+      ],
+      "metadata": {
+        "id": "mA4qN2hvRiXQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car['kms_driven']=car['kms_driven'].str.split(' ').str.get(0).str.replace(',','')"
+      ],
+      "metadata": {
+        "id": "uZ1Dc2d5R6i8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car=car[car['kms_driven'].str.isnumeric()]"
+      ],
+      "metadata": {
+        "id": "4quYafuESjHB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car['kms_driven']=car['kms_driven'].astype(int)"
+      ],
+      "metadata": {
+        "id": "5uYORIqaWXWy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car=car[~car['fuel_type'].isna()]"
+      ],
+      "metadata": {
+        "id": "RhHcSCgsTVur"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VTzBHi3DT7lS",
+        "outputId": "a8c30b5c-bba9-48e9-f5c7-0da650f8a001"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(816, 6)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car['name']=car['name'].str.split(' ').str.slice(0,3).str.join(' ')"
+      ],
+      "metadata": {
+        "id": "1qStv-CNVVvB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car.reset_index(drop=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "wUzYTdroVjuh",
+        "outputId": "d20835c7-583c-4e75-f9fd-2c64734e5de1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                       name   company  year   Price  kms_driven fuel_type\n",
+              "0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
+              "1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
+              "2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
+              "3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
+              "4                 Ford Figo      Ford  2012  175000       41000    Diesel\n",
+              "..                      ...       ...   ...     ...         ...       ...\n",
+              "811      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n",
+              "812          Tata Indica V2      Tata  2009  110000       30000    Diesel\n",
+              "813    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n",
+              "814            Tata Zest XM      Tata  2018  260000       27000    Diesel\n",
+              "815      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n",
+              "\n",
+              "[816 rows x 6 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a30ccbef-fa5b-4c7c-b69a-4fffbe233fad\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>company</th>\n",
+              "      <th>year</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>kms_driven</th>\n",
+              "      <th>fuel_type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Hyundai Santro Xing</td>\n",
+              "      <td>Hyundai</td>\n",
+              "      <td>2007</td>\n",
+              "      <td>80000</td>\n",
+              "      <td>45000</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Mahindra Jeep CL550</td>\n",
+              "      <td>Mahindra</td>\n",
+              "      <td>2006</td>\n",
+              "      <td>425000</td>\n",
+              "      <td>40</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Hyundai Grand i10</td>\n",
+              "      <td>Hyundai</td>\n",
+              "      <td>2014</td>\n",
+              "      <td>325000</td>\n",
+              "      <td>28000</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Ford EcoSport Titanium</td>\n",
+              "      <td>Ford</td>\n",
+              "      <td>2014</td>\n",
+              "      <td>575000</td>\n",
+              "      <td>36000</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Ford Figo</td>\n",
+              "      <td>Ford</td>\n",
+              "      <td>2012</td>\n",
+              "      <td>175000</td>\n",
+              "      <td>41000</td>\n",
+              "      <td>Diesel</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",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>811</th>\n",
+              "      <td>Maruti Suzuki Ritz</td>\n",
+              "      <td>Maruti</td>\n",
+              "      <td>2011</td>\n",
+              "      <td>270000</td>\n",
+              "      <td>50000</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>812</th>\n",
+              "      <td>Tata Indica V2</td>\n",
+              "      <td>Tata</td>\n",
+              "      <td>2009</td>\n",
+              "      <td>110000</td>\n",
+              "      <td>30000</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>813</th>\n",
+              "      <td>Toyota Corolla Altis</td>\n",
+              "      <td>Toyota</td>\n",
+              "      <td>2009</td>\n",
+              "      <td>300000</td>\n",
+              "      <td>132000</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>814</th>\n",
+              "      <td>Tata Zest XM</td>\n",
+              "      <td>Tata</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>260000</td>\n",
+              "      <td>27000</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>815</th>\n",
+              "      <td>Mahindra Quanto C8</td>\n",
+              "      <td>Mahindra</td>\n",
+              "      <td>2013</td>\n",
+              "      <td>390000</td>\n",
+              "      <td>40000</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>816 rows × 6 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a30ccbef-fa5b-4c7c-b69a-4fffbe233fad')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-a30ccbef-fa5b-4c7c-b69a-4fffbe233fad button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-a30ccbef-fa5b-4c7c-b69a-4fffbe233fad');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zCqFEY-_V6Ki",
+        "outputId": "ce435213-a276-4d46-d644-d4f0f23597b3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 816 entries, 0 to 889\n",
+            "Data columns (total 6 columns):\n",
+            " #   Column      Non-Null Count  Dtype \n",
+            "---  ------      --------------  ----- \n",
+            " 0   name        816 non-null    object\n",
+            " 1   company     816 non-null    object\n",
+            " 2   year        816 non-null    int64 \n",
+            " 3   Price       816 non-null    int64 \n",
+            " 4   kms_driven  816 non-null    int64 \n",
+            " 5   fuel_type   816 non-null    object\n",
+            "dtypes: int64(3), object(3)\n",
+            "memory usage: 44.6+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "97ilwJBOWEWj",
+        "outputId": "8841325e-30cb-47f2-82da-16a2bf9210e4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              year         Price     kms_driven\n",
+              "count   816.000000  8.160000e+02     816.000000\n",
+              "mean   2012.444853  4.117176e+05   46275.531863\n",
+              "std       4.002992  4.751844e+05   34297.428044\n",
+              "min    1995.000000  3.000000e+04       0.000000\n",
+              "25%    2010.000000  1.750000e+05   27000.000000\n",
+              "50%    2013.000000  2.999990e+05   41000.000000\n",
+              "75%    2015.000000  4.912500e+05   56818.500000\n",
+              "max    2019.000000  8.500003e+06  400000.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-28996f39-6ff2-4d3a-ba71-0ff17ab1a9d0\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>year</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>kms_driven</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>816.000000</td>\n",
+              "      <td>8.160000e+02</td>\n",
+              "      <td>816.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>2012.444853</td>\n",
+              "      <td>4.117176e+05</td>\n",
+              "      <td>46275.531863</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>4.002992</td>\n",
+              "      <td>4.751844e+05</td>\n",
+              "      <td>34297.428044</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1995.000000</td>\n",
+              "      <td>3.000000e+04</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>2010.000000</td>\n",
+              "      <td>1.750000e+05</td>\n",
+              "      <td>27000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>2013.000000</td>\n",
+              "      <td>2.999990e+05</td>\n",
+              "      <td>41000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>2015.000000</td>\n",
+              "      <td>4.912500e+05</td>\n",
+              "      <td>56818.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2019.000000</td>\n",
+              "      <td>8.500003e+06</td>\n",
+              "      <td>400000.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28996f39-6ff2-4d3a-ba71-0ff17ab1a9d0')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-28996f39-6ff2-4d3a-ba71-0ff17ab1a9d0 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-28996f39-6ff2-4d3a-ba71-0ff17ab1a9d0');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car=car[car['Price']<6e6]"
+      ],
+      "metadata": {
+        "id": "I6ZlcuaYWn_4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car.to_csv('cleaned car.csv')"
+      ],
+      "metadata": {
+        "id": "OSATadwKngdW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Model"
+      ],
+      "metadata": {
+        "id": "cY-nLpwXoz09"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x=car.drop(columns='Price')\n",
+        "y=car.Price"
+      ],
+      "metadata": {
+        "id": "KGCSWc8HoeGZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "as2r7WYxpFs5",
+        "outputId": "ead73d06-997f-4c07-998a-6e6dd74f1761"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                       name   company  year  kms_driven fuel_type\n",
+              "0       Hyundai Santro Xing   Hyundai  2007       45000    Petrol\n",
+              "1       Mahindra Jeep CL550  Mahindra  2006          40    Diesel\n",
+              "3         Hyundai Grand i10   Hyundai  2014       28000    Petrol\n",
+              "4    Ford EcoSport Titanium      Ford  2014       36000    Diesel\n",
+              "6                 Ford Figo      Ford  2012       41000    Diesel\n",
+              "..                      ...       ...   ...         ...       ...\n",
+              "883      Maruti Suzuki Ritz    Maruti  2011       50000    Petrol\n",
+              "885          Tata Indica V2      Tata  2009       30000    Diesel\n",
+              "886    Toyota Corolla Altis    Toyota  2009      132000    Petrol\n",
+              "888            Tata Zest XM      Tata  2018       27000    Diesel\n",
+              "889      Mahindra Quanto C8  Mahindra  2013       40000    Diesel\n",
+              "\n",
+              "[815 rows x 5 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6d2e5997-e433-44e9-bdfb-c792d491d011\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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",
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+              "\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>company</th>\n",
+              "      <th>year</th>\n",
+              "      <th>kms_driven</th>\n",
+              "      <th>fuel_type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Hyundai Santro Xing</td>\n",
+              "      <td>Hyundai</td>\n",
+              "      <td>2007</td>\n",
+              "      <td>45000</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Mahindra Jeep CL550</td>\n",
+              "      <td>Mahindra</td>\n",
+              "      <td>2006</td>\n",
+              "      <td>40</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Hyundai Grand i10</td>\n",
+              "      <td>Hyundai</td>\n",
+              "      <td>2014</td>\n",
+              "      <td>28000</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Ford EcoSport Titanium</td>\n",
+              "      <td>Ford</td>\n",
+              "      <td>2014</td>\n",
+              "      <td>36000</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Ford Figo</td>\n",
+              "      <td>Ford</td>\n",
+              "      <td>2012</td>\n",
+              "      <td>41000</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>883</th>\n",
+              "      <td>Maruti Suzuki Ritz</td>\n",
+              "      <td>Maruti</td>\n",
+              "      <td>2011</td>\n",
+              "      <td>50000</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>885</th>\n",
+              "      <td>Tata Indica V2</td>\n",
+              "      <td>Tata</td>\n",
+              "      <td>2009</td>\n",
+              "      <td>30000</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>886</th>\n",
+              "      <td>Toyota Corolla Altis</td>\n",
+              "      <td>Toyota</td>\n",
+              "      <td>2009</td>\n",
+              "      <td>132000</td>\n",
+              "      <td>Petrol</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>888</th>\n",
+              "      <td>Tata Zest XM</td>\n",
+              "      <td>Tata</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>27000</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>889</th>\n",
+              "      <td>Mahindra Quanto C8</td>\n",
+              "      <td>Mahindra</td>\n",
+              "      <td>2013</td>\n",
+              "      <td>40000</td>\n",
+              "      <td>Diesel</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>815 rows × 5 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d2e5997-e433-44e9-bdfb-c792d491d011')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-6d2e5997-e433-44e9-bdfb-c792d491d011 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-6d2e5997-e433-44e9-bdfb-c792d491d011');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EYo6tsKQpIPz",
+        "outputId": "8a5237c2-a990-4df9-bfb6-c86013be1092"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0       80000\n",
+              "1      425000\n",
+              "3      325000\n",
+              "4      575000\n",
+              "6      175000\n",
+              "        ...  \n",
+              "883    270000\n",
+              "885    110000\n",
+              "886    300000\n",
+              "888    260000\n",
+              "889    390000\n",
+              "Name: Price, Length: 815, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "x_train, x_test,y_train,y_test = train_test_split(x,y, test_size=0.2, random_state=1)"
+      ],
+      "metadata": {
+        "id": "GU4fNOrzpLnE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.metrics import r2_score\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.compose import make_column_transformer\n",
+        "from sklearn.pipeline import make_pipeline"
+      ],
+      "metadata": {
+        "id": "nZB719Azps7t"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ohe = OneHotEncoder()\n",
+        "\n",
+        "ohe.fit(x[['name','company','fuel_type']])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "-3CjyNTrqDq0",
+        "outputId": "13aa2a5a-5430-46bf-c9bc-80bc5b173237"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "OneHotEncoder()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "column_trans = make_column_transformer((OneHotEncoder(categories=ohe.categories_),['name','company','fuel_type']), remainder='passthrough')"
+      ],
+      "metadata": {
+        "id": "Lz5Oa5CVryOD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lr=LinearRegression()"
+      ],
+      "metadata": {
+        "id": "h2-fyidUsBgq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pipe=make_pipeline(column_trans,lr)"
+      ],
+      "metadata": {
+        "id": "ItK-P-f_uPL4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pipe.fit(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 192
+        },
+        "id": "lFvb7w-hulX3",
+        "outputId": "d755c75f-4aec-4f90-cd4e-c096b17e002a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('columntransformer',\n",
+              "                 ColumnTransformer(remainder='passthrough',\n",
+              "                                   transformers=[('onehotencoder',\n",
+              "                                                  OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n",
+              "       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n",
+              "       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n",
+              "       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n",
+              "                                                                            array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n",
+              "       'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n",
+              "       'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n",
+              "       'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n",
+              "      dtype=object),\n",
+              "                                                                            array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n",
+              "                                                  ['name', 'company',\n",
+              "                                                   'fuel_type'])])),\n",
+              "                ('linearregression', LinearRegression())])"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
+              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
+              "                                                  OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n",
+              "       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n",
+              "       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n",
+              "       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat...\n",
+              "                                                                            array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n",
+              "       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n",
+              "       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n",
+              "       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n",
+              "      dtype=object),\n",
+              "                                                                            array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n",
+              "                                                  [&#x27;name&#x27;, &#x27;company&#x27;,\n",
+              "                                                   &#x27;fuel_type&#x27;])])),\n",
+              "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
+              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
+              "                                                  OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n",
+              "       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n",
+              "       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n",
+              "       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat...\n",
+              "                                                                            array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n",
+              "       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n",
+              "       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n",
+              "       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n",
+              "      dtype=object),\n",
+              "                                                                            array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n",
+              "                                                  [&#x27;name&#x27;, &#x27;company&#x27;,\n",
+              "                                                   &#x27;fuel_type&#x27;])])),\n",
+              "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+              "                  transformers=[(&#x27;onehotencoder&#x27;,\n",
+              "                                 OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n",
+              "       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n",
+              "       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n",
+              "       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat Diesel&#x27;,\n",
+              "       &#x27;Chevrolet Beat LS&#x27;, &#x27;Chevrolet B...\n",
+              "       &#x27;Volkswagen Vento Konekt&#x27;, &#x27;Volvo S80 Summum&#x27;], dtype=object),\n",
+              "                                                           array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n",
+              "       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n",
+              "       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n",
+              "       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n",
+              "      dtype=object),\n",
+              "                                                           array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)]),\n",
+              "                                 [&#x27;name&#x27;, &#x27;company&#x27;, &#x27;fuel_type&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;name&#x27;, &#x27;company&#x27;, &#x27;fuel_type&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(categories=[array([&#x27;Audi A3 Cabriolet&#x27;, &#x27;Audi A4 1.8&#x27;, &#x27;Audi A4 2.0&#x27;, &#x27;Audi A6 2.0&#x27;,\n",
+              "       &#x27;Audi A8&#x27;, &#x27;Audi Q3 2.0&#x27;, &#x27;Audi Q5 2.0&#x27;, &#x27;Audi Q7&#x27;, &#x27;BMW 3 Series&#x27;,\n",
+              "       &#x27;BMW 5 Series&#x27;, &#x27;BMW 7 Series&#x27;, &#x27;BMW X1&#x27;, &#x27;BMW X1 sDrive20d&#x27;,\n",
+              "       &#x27;BMW X1 xDrive20d&#x27;, &#x27;Chevrolet Beat&#x27;, &#x27;Chevrolet Beat Diesel&#x27;,\n",
+              "       &#x27;Chevrolet Beat LS&#x27;, &#x27;Chevrolet Beat LT&#x27;, &#x27;Chevrolet Beat PS&#x27;,\n",
+              "       &#x27;Chevrolet Cruze LTZ&#x27;, &#x27;Chevrolet Enjoy&#x27;, &#x27;Chevrolet E...\n",
+              "       &#x27;Volkswagen Vento Comfortline&#x27;, &#x27;Volkswagen Vento Highline&#x27;,\n",
+              "       &#x27;Volkswagen Vento Konekt&#x27;, &#x27;Volvo S80 Summum&#x27;], dtype=object),\n",
+              "                          array([&#x27;Audi&#x27;, &#x27;BMW&#x27;, &#x27;Chevrolet&#x27;, &#x27;Datsun&#x27;, &#x27;Fiat&#x27;, &#x27;Force&#x27;, &#x27;Ford&#x27;,\n",
+              "       &#x27;Hindustan&#x27;, &#x27;Honda&#x27;, &#x27;Hyundai&#x27;, &#x27;Jaguar&#x27;, &#x27;Jeep&#x27;, &#x27;Land&#x27;,\n",
+              "       &#x27;Mahindra&#x27;, &#x27;Maruti&#x27;, &#x27;Mercedes&#x27;, &#x27;Mini&#x27;, &#x27;Mitsubishi&#x27;, &#x27;Nissan&#x27;,\n",
+              "       &#x27;Renault&#x27;, &#x27;Skoda&#x27;, &#x27;Tata&#x27;, &#x27;Toyota&#x27;, &#x27;Volkswagen&#x27;, &#x27;Volvo&#x27;],\n",
+              "      dtype=object),\n",
+              "                          array([&#x27;Diesel&#x27;, &#x27;LPG&#x27;, &#x27;Petrol&#x27;], dtype=object)])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;year&#x27;, &#x27;kms_driven&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred=pipe.predict(x_test)"
+      ],
+      "metadata": {
+        "id": "sGdoVcQ7uyL7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "r2_score(y_test,y_pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "w8Tj31iru6v-",
+        "outputId": "6f411965-f819-45f4-f97d-28fcabaf1f2f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.4786019553698676"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "r2_scores= []\n",
+        "random_i=[]\n",
+        "for i in range(500):\n",
+        "  x_train, x_test,y_train,y_test = train_test_split(x,y,test_size=0.2, random_state=i)\n",
+        "  lr=LinearRegression()\n",
+        "  pipe=make_pipeline(column_trans,lr)\n",
+        "  pipe.fit(x_train,y_train)\n",
+        "  y_pred=pipe.predict(x_test)\n",
+        "  r2_scores.append(r2_score(y_test,y_pred))\n",
+        "  random_i.append(i)"
+      ],
+      "metadata": {
+        "id": "Kdo3Pd9Wv2EE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "random_data= pd.DataFrame({'Score':r2_scores,'Random_value':random_i})"
+      ],
+      "metadata": {
+        "id": "IO3IvSfVxsBj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.argmax(r2_scores)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fChp9sltzzpj",
+        "outputId": "3077a482-b60c-43da-a3f9-3b1f0fe1998c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "433"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 54
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "r2_scores[433]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bWaARfLmz6Yh",
+        "outputId": "93925ab2-dffc-40d2-d1e2-87866b8b51bf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8456515104452564"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 55
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2, random_state=np.argmax(r2_scores))\n",
+        "lr=LinearRegression()\n",
+        "pipe=make_pipeline(column_trans,lr)\n",
+        "pipe.fit(x_train,y_train)\n",
+        "y_pred=pipe.predict(x_test)\n",
+        "r2_score(y_test,y_pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JUb6oA4x0dTR",
+        "outputId": "759bdbd3-ccc8-43c6-85f3-5d8ea4047482"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8456515104452564"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pickle"
+      ],
+      "metadata": {
+        "id": "R4j2B3Xx7Bm_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pickle.dump(pipe,open('LinearRegressionModel.pkl','wb'))"
+      ],
+      "metadata": {
+        "id": "JOTFnco_7H65"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pipe.predict(pd.DataFrame([['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']],columns=['name','company','year','kms_driven','fuel_type']))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "L6PM14NG7ZWI",
+        "outputId": "bc568fdb-bb40-436c-c6ce-caf4e9e19026"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([459113.49353657])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "UKYqrC2n8ZzO"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

LinearRegressionModel.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7458627b95185a80cb137cfe4d335f586e9bb20fa840b4aee82d7a44b614307b
+size 11316

application.py

@@ -0,0 +1,36 @@
+# flask, pandas, sci-kit learn, pickle-mixin
+from flask import Flask, render_template, request
+import pandas as pd
+import pickle
+import sklearn
+import numpy as np
+
+app =Flask(__name__)
+
+model = pickle.load(open('LinearRegressionModel.pkl','rb'))
+car = pd.read_csv('cleaned car.csv')
+
+@app.route('/')
+def index():
+    companies = sorted(car['company'].unique())
+    car_models = sorted(car['name'].unique())
+    year = sorted(car['year'].unique(), reverse=True)
+    fuel_type = car['fuel_type'].unique()
+    companies.insert(0,'Select Company')
+    return render_template('index.html', companies=companies, car_models=car_models,years=year,fuel_types=fuel_type)
+
+@app.route('/predict',methods=['POST'])
+def predict():
+    company= request.form.get('company')
+    car_model = request.form.get('car_model')
+    year = int(request.form.get('year'))
+    fuel_type = request.form.get('fuel_type')
+    kms_driven = int(request.form.get('kilo_driven'))
+
+    prediction = model.predict(pd.DataFrame([[car_model, company,year,kms_driven,fuel_type]], columns=['name','company','year','kms_driven','fuel_type']))
+    
+    return str(np.round(prediction[0],2))
+
+
+if __name__=='__main__':
+    app.run(debug=True)

cleaned car.csv

@@ -0,0 +1,816 @@
+,name,company,year,Price,kms_driven,fuel_type
+0,Hyundai Santro Xing,Hyundai,2007,80000,45000,Petrol
+1,Mahindra Jeep CL550,Mahindra,2006,425000,40,Diesel
+3,Hyundai Grand i10,Hyundai,2014,325000,28000,Petrol
+4,Ford EcoSport Titanium,Ford,2014,575000,36000,Diesel
+6,Ford Figo,Ford,2012,175000,41000,Diesel
+7,Hyundai Eon,Hyundai,2013,190000,25000,Petrol
+8,Ford EcoSport Ambiente,Ford,2016,830000,24530,Diesel
+9,Maruti Suzuki Alto,Maruti,2015,250000,60000,Petrol
+10,Skoda Fabia Classic,Skoda,2010,182000,60000,Petrol
+11,Maruti Suzuki Stingray,Maruti,2015,315000,30000,Petrol
+12,Hyundai Elite i20,Hyundai,2014,415000,32000,Petrol
+13,Mahindra Scorpio SLE,Mahindra,2015,320000,48660,Diesel
+14,Hyundai Santro Xing,Hyundai,2007,80000,45000,Petrol
+15,Mahindra Jeep CL550,Mahindra,2006,425000,40,Diesel
+16,Audi A8,Audi,2017,1000000,4000,Petrol
+17,Audi Q7,Audi,2014,500000,16934,Diesel
+18,Mahindra Scorpio S10,Mahindra,2016,350000,43000,Diesel
+19,Maruti Suzuki Alto,Maruti,2014,160000,35550,Petrol
+20,Mahindra Scorpio S10,Mahindra,2016,350000,43000,Diesel
+21,Mahindra Scorpio S10,Mahindra,2016,310000,39522,Diesel
+22,Maruti Suzuki Alto,Maruti,2015,75000,39000,Petrol
+23,Hyundai i20 Sportz,Hyundai,2012,100000,55000,Petrol
+24,Hyundai i20 Sportz,Hyundai,2012,100000,55000,Petrol
+25,Hyundai i20 Sportz,Hyundai,2012,100000,55000,Petrol
+26,Maruti Suzuki Alto,Maruti,2017,190000,72000,Petrol
+27,Maruti Suzuki Vitara,Maruti,2016,290000,15975,Diesel
+28,Maruti Suzuki Alto,Maruti,2008,95000,70000,Petrol
+29,Mahindra Bolero DI,Mahindra,2017,180000,23452,Diesel
+30,Maruti Suzuki Swift,Maruti,2014,385000,35522,Diesel
+31,Mahindra Scorpio S10,Mahindra,2015,250000,48508,Diesel
+32,Maruti Suzuki Swift,Maruti,2017,180000,15487,Petrol
+33,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+34,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+35,Toyota Innova 2.0,Toyota,2012,650000,82000,Diesel
+36,Renault Lodgy 85,Renault,2018,689999,20000,Diesel
+37,Skoda Yeti Ambition,Skoda,2012,448000,68000,Diesel
+38,Maruti Suzuki Baleno,Maruti,2017,549000,32000,Diesel
+39,Renault Duster 110,Renault,2012,501000,38000,Diesel
+40,Renault Duster 85,Renault,2013,489999,27000,Diesel
+41,Honda City 1.5,Honda,2011,280000,33000,Petrol
+42,Maruti Suzuki Alto,Maruti,2015,250000,60000,Petrol
+43,Maruti Suzuki Dzire,Maruti,2013,349999,46000,Diesel
+44,Honda Amaze,Honda,2013,284999,46000,Diesel
+45,Honda Amaze 1.5,Honda,2015,345000,36000,Diesel
+46,Honda City,Honda,2015,499999,55000,Petrol
+47,Datsun Redi GO,Datsun,2017,235000,16000,Petrol
+48,Maruti Suzuki SX4,Maruti,2010,249999,36000,Petrol
+49,Mitsubishi Pajero Sport,Mitsubishi,2015,1475000,47000,Diesel
+50,Mahindra Bolero DI,Mahindra,2017,180000,23452,Diesel
+51,Maruti Suzuki Swift,Maruti,2014,385000,35522,Diesel
+52,Mahindra Scorpio S10,Mahindra,2015,250000,48508,Diesel
+53,Maruti Suzuki Swift,Maruti,2017,180000,15487,Petrol
+54,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+55,Maruti Suzuki Wagon,Maruti,2013,105000,39000,Petrol
+56,Mahindra Scorpio S10,Mahindra,2015,395000,35000,Diesel
+57,Maruti Suzuki Swift,Maruti,2017,220000,30874,Petrol
+58,Honda City ZX,Honda,2017,170000,15000,Diesel
+59,Maruti Suzuki Wagon,Maruti,2013,85000,29685,Petrol
+60,Ford Figo,Ford,2012,175000,41000,Diesel
+61,Hyundai Eon,Hyundai,2013,190000,25000,Petrol
+62,Tata Indigo eCS,Tata,2017,200000,130000,Diesel
+63,Ford EcoSport Ambiente,Ford,2016,830000,24530,Diesel
+64,Tata Indigo eCS,Tata,2017,200000,130000,Diesel
+65,Mahindra Scorpio SLE,Mahindra,2012,570000,19000,Diesel
+66,Volkswagen Polo Highline,Volkswagen,2014,315000,60000,Petrol
+67,Skoda Fabia Classic,Skoda,2010,182000,60000,Petrol
+68,Maruti Suzuki Stingray,Maruti,2015,315000,30000,Petrol
+70,Chevrolet Spark LS,Chevrolet,2010,110000,41000,Petrol
+71,Renault Duster 110PS,Renault,2012,501000,35000,Diesel
+72,Honda City,Honda,2015,448999,54000,Petrol
+73,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+74,Datsun Redi GO,Datsun,2017,235000,16000,Petrol
+75,Skoda Fabia 1.2L,Skoda,2011,159500,38200,Diesel
+76,Honda Amaze,Honda,2015,344999,22000,Petrol
+77,Honda Amaze,Honda,2015,344999,22000,Petrol
+78,Renault Duster,Renault,2014,449999,50000,Diesel
+79,Mini Cooper S,Mini,2013,1891111,13500,Petrol
+80,Mahindra Scorpio S4,Mahindra,2015,865000,30000,Diesel
+81,Mahindra Scorpio VLX,Mahindra,2014,699000,50000,Diesel
+82,Mahindra Quanto C8,Mahindra,2013,375000,20000,Diesel
+83,Ford EcoSport,Ford,2017,489999,39000,Petrol
+84,Honda Brio,Honda,2012,224999,30000,Petrol
+86,Volkswagen Vento Highline,Volkswagen,2019,1200000,3600,Diesel
+87,Hyundai i20 Magna,Hyundai,2009,195000,32000,Petrol
+88,Toyota Corolla Altis,Toyota,2010,351000,38000,Diesel
+89,Hyundai Verna Transform,Hyundai,2008,160000,45000,Petrol
+90,Toyota Corolla Altis,Toyota,2009,240000,35000,Petrol
+91,Honda City 1.5,Honda,2005,90000,50000,Petrol
+92,Hyundai Elite i20,Hyundai,2014,415000,32000,Petrol
+93,Skoda Fabia 1.2L,Skoda,2011,155000,45863,Diesel
+94,BMW 3 Series,BMW,2011,600000,60500,Petrol
+95,Maruti Suzuki A,Maruti,2011,189500,12500,Petrol
+96,Toyota Etios GD,Toyota,2013,350000,60000,Diesel
+97,Ford Figo Diesel,Ford,2012,210000,35000,Diesel
+98,Maruti Suzuki Swift,Maruti,2014,390000,35000,Petrol
+99,Chevrolet Beat LT,Chevrolet,2012,135000,45000,Diesel
+100,BMW 7 Series,BMW,2009,1600000,35000,Petrol
+101,Mahindra XUV500 W8,Mahindra,2013,701000,38000,Diesel
+102,Hyundai i10 Magna,Hyundai,2014,265000,18000,Petrol
+103,Hyundai Verna Fluidic,Hyundai,2015,525000,35000,Diesel
+104,Maruti Suzuki Swift,Maruti,2013,372000,13349,Petrol
+105,Maruti Suzuki Ertiga,Maruti,2016,635000,29000,Petrol
+106,Ford EcoSport Titanium,Ford,2014,550000,44000,Diesel
+107,Maruti Suzuki Ertiga,Maruti,2016,575000,29000,Petrol
+108,Maruti Suzuki Ertiga,Maruti,2013,485000,42000,Diesel
+109,Maruti Suzuki Alto,Maruti,2012,155000,14000,Petrol
+110,Hyundai Grand i10,Hyundai,2014,345000,49000,Diesel
+111,Honda Amaze 1.2,Honda,2014,325000,42000,Petrol
+112,Hyundai i20 Asta,Hyundai,2012,329500,36200,Diesel
+113,Ford Figo Diesel,Ford,2014,195000,50000,Diesel
+114,Maruti Suzuki Eeco,Maruti,2015,251111,55000,Petrol
+115,Maruti Suzuki Ertiga,Maruti,2014,569999,45000,Petrol
+116,Maruti Suzuki Esteem,Maruti,2007,69999,51000,Petrol
+117,Maruti Suzuki Ritz,Maruti,2014,299999,19000,Petrol
+118,Maruti Suzuki Dzire,Maruti,2009,220000,46000,Petrol
+119,Maruti Suzuki Ritz,Maruti,2013,399999,33000,Diesel
+120,Maruti Suzuki Swift,Maruti,2013,372000,13349,Petrol
+121,Maruti Suzuki Dzire,Maruti,2015,450000,104000,Diesel
+122,Toyota Etios Liva,Toyota,2014,270000,55000,Petrol
+123,Hyundai i20 Sportz,Hyundai,2011,350000,33333,Diesel
+124,Chevrolet Spark,Chevrolet,2012,158400,33600,Petrol
+125,Maruti Suzuki Alto,Maruti,2017,350000,5600,Petrol
+126,Nissan Micra XV,Nissan,2011,179000,41000,Petrol
+127,Maruti Suzuki Swift,Maruti,2007,125000,70000,Petrol
+128,Maruti Suzuki Alto,Maruti,2018,200000,7500,Petrol
+129,Honda Amaze 1.5,Honda,2013,299000,45000,Diesel
+130,Maruti Suzuki Alto,Maruti,2015,220000,38000,Petrol
+131,Chevrolet Beat,Chevrolet,2015,150000,30000,Petrol
+133,Honda City 1.5,Honda,2010,285000,35000,Petrol
+134,Ford EcoSport Trend,Ford,2016,830000,24330,Diesel
+135,Hyundai i20 Asta,Hyundai,2009,210000,65480,Petrol
+136,Maruti Suzuki Swift,Maruti,2013,340000,41000,Petrol
+137,Tata Indica V2,Tata,2006,90000,20000,Petrol
+139,Hindustan Motors Ambassador,Hindustan,2000,70000,200000,Diesel
+140,Toyota Corolla Altis,Toyota,2010,289999,70000,Petrol
+141,Toyota Corolla Altis,Toyota,2012,349999,59000,Petrol
+142,Toyota Innova 2.5,Toyota,2012,849999,99000,Diesel
+143,Volkswagen Jetta Highline,Volkswagen,2014,749999,46000,Diesel
+144,Volkswagen Polo Comfortline,Volkswagen,2015,399999,2800,Petrol
+145,Volkswagen Polo,Volkswagen,2014,274999,32000,Petrol
+146,Mahindra Scorpio,Mahindra,2015,984999,22000,Diesel
+147,Renault Duster,Renault,2014,449999,50000,Diesel
+148,Honda Amaze,Honda,2015,344999,22000,Petrol
+149,Nissan Sunny,Nissan,2012,224999,45000,Petrol
+150,Hyundai Elite i20,Hyundai,2018,599999,21000,Petrol
+151,Renault Kwid,Renault,2016,244999,11000,Petrol
+152,Renault Duster,Renault,2013,399999,41000,Diesel
+153,Ford EcoSport,Ford,2017,489999,39000,Petrol
+154,Renault Duster,Renault,2014,474999,50000,Diesel
+155,Mahindra Scorpio VLX,Mahindra,2011,499999,66000,Diesel
+156,Maruti Suzuki Alto,Maruti,2018,310000,3000,Petrol
+157,Chevrolet Spark LT,Chevrolet,2010,85000,45000,Petrol
+158,Datsun Redi GO,Datsun,2016,245000,7000,Petrol
+159,Maruti Suzuki Swift,Maruti,2010,189500,38500,Diesel
+160,Fiat Punto Emotion,Fiat,2012,169500,37200,Diesel
+161,Maruti Suzuki Swift,Maruti,2010,159500,43200,Diesel
+162,Toyota Etios GD,Toyota,2013,275000,24800,Petrol
+163,Hyundai i20 Sportz,Hyundai,2014,370000,60000,Diesel
+164,Hyundai i10 Sportz,Hyundai,2010,168000,45872,Petrol
+165,Chevrolet Beat LT,Chevrolet,2011,150000,40000,Diesel
+166,Chevrolet Beat LS,Chevrolet,2011,145000,45000,Diesel
+167,Chevrolet Beat LT,Chevrolet,2012,98500,38000,Diesel
+168,Mahindra Scorpio VLX,Mahindra,2014,699000,50000,Diesel
+169,Tata Indigo CS,Tata,2011,85000,11400,Diesel
+170,Toyota Corolla Altis,Toyota,2015,575000,42000,Petrol
+171,Honda City 1.5,Honda,2014,549000,39000,Petrol
+172,Maruti Suzuki Swift,Maruti,2011,209000,47000,Diesel
+173,Hyundai Eon Era,Hyundai,2013,185000,27000,Petrol
+174,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+175,Mahindra XUV500,Mahindra,2014,699999,52000,Diesel
+176,Honda Brio,Honda,2012,224999,30000,Petrol
+177,Ford Fiesta,Ford,2011,274999,55000,Diesel
+178,Honda Amaze,Honda,2013,284999,46000,Diesel
+179,Honda City,Honda,2015,599999,30000,Diesel
+180,Maruti Suzuki Wagon,Maruti,2012,199999,44000,Petrol
+181,Honda City,Honda,2014,544999,45000,Diesel
+182,Hyundai i20,Hyundai,2009,199000,31000,Petrol
+183,Tata Indigo eCS,Tata,2016,320000,175430,Diesel
+184,Hyundai Fluidic Verna,Hyundai,2015,540000,38000,Diesel
+186,Mahindra Quanto C8,Mahindra,2013,340000,37000,Diesel
+187,Fiat Petra ELX,Fiat,2008,75000,65000,Petrol
+188,Skoda Fabia 1.2L,Skoda,2011,159500,38200,Diesel
+189,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+190,Hyundai Santro Xing,Hyundai,2005,49000,7500,Petrol
+191,Maruti Suzuki Ciaz,Maruti,2016,700000,3350,Petrol
+192,Maruti Suzuki Zen,Maruti,2000,55000,60000,Petrol
+193,Honda City,Honda,2015,448999,54000,Petrol
+194,Hyundai Creta 1.6,Hyundai,2017,895000,32000,Petrol
+196,Mahindra Scorpio SLX,Mahindra,2007,355000,75000,Diesel
+197,Mahindra Scorpio SLE,Mahindra,2012,565000,62000,Diesel
+198,Toyota Innova 2.5,Toyota,2006,365000,73000,Diesel
+199,Maruti Suzuki Alto,Maruti,2011,145000,41000,Petrol
+200,Maruti Suzuki Wagon,Maruti,2011,210000,35000,Petrol
+201,Tata Nano Cx,Tata,2013,40000,2200,Petrol
+202,Maruti Suzuki Alto,Maruti,2013,125000,39000,Petrol
+203,Maruti Suzuki Wagon,Maruti,2009,135000,45000,Petrol
+204,Maruti Suzuki Swift,Maruti,2006,135000,45000,Petrol
+205,Tata Sumo Victa,Tata,2012,285000,65000,Diesel
+207,Maruti Suzuki Wagon,Maruti,2010,145000,54870,Petrol
+208,Maruti Suzuki Alto,Maruti,2010,135000,34580,Petrol
+209,Volkswagen Passat Diesel,Volkswagen,2009,450000,97000,Diesel
+210,Renault Scala RxL,Renault,2015,375000,25000,Diesel
+211,Mahindra Quanto C8,Mahindra,2013,375000,20000,Diesel
+212,Hyundai Grand i10,Hyundai,2014,365000,20000,Petrol
+213,Hyundai i20 Active,Hyundai,2015,500000,18000,Petrol
+214,Mahindra Xylo E4,Mahindra,2012,400000,35000,Diesel
+215,Mahindra Jeep MM,Mahindra,2019,390000,60,Diesel
+216,Renault Duster 110PS,Renault,2012,501000,35000,Diesel
+217,Mahindra Bolero SLE,Mahindra,2013,330000,80200,Diesel
+218,Force Motors Force,Force,2015,580000,3200,Diesel
+219,Maruti Suzuki SX4,Maruti,2012,265000,46000,Diesel
+220,Mahindra Jeep CL550,Mahindra,2019,379000,0,Diesel
+221,Maruti Suzuki Alto,Maruti,2015,219000,5000,Petrol
+222,Mahindra Jeep CL550,Mahindra,2018,385000,588,Diesel
+223,Toyota Etios,Toyota,2011,275000,36000,Diesel
+224,Volkswagen Polo,Volkswagen,2015,330000,38000,Diesel
+225,Honda City ZX,Honda,2008,110000,45000,Petrol
+226,Maruti Suzuki Wagon,Maruti,2006,80000,71200,Petrol
+227,Honda City VX,Honda,2016,519000,52000,Diesel
+228,Mahindra Thar CRDe,Mahindra,2016,730000,29000,Diesel
+229,Mitsubishi Pajero Sport,Mitsubishi,2015,1475000,47000,Diesel
+230,Audi A4 1.8,Audi,2009,699000,47000,Petrol
+231,Mercedes Benz GLA,Mercedes,2015,2000000,20000,Diesel
+232,Land Rover Freelander,Land,2015,2100000,30000,Diesel
+233,Renault Kwid RXT,Renault,2017,340000,5000,Petrol
+234,Tata Aria Pleasure,Tata,2014,390000,35000,Diesel
+235,Mercedes Benz B,Mercedes,2014,1400000,31000,Petrol
+236,Datsun GO T,Datsun,2016,245000,7000,Petrol
+237,Tata Indigo eCS,Tata,2016,320000,175430,Diesel
+238,Tata Indigo eCS,Tata,2016,320000,175400,Diesel
+239,Honda Jazz VX,Honda,2016,450000,41000,Petrol
+240,Honda Amaze 1.2,Honda,2014,311000,33000,Petrol
+241,Honda Amaze,Honda,2013,284999,46000,Diesel
+242,Honda City,Honda,2012,399999,45000,Petrol
+243,Honda City,Honda,2015,599999,39000,Diesel
+244,Honda Amaze,Honda,2015,344999,22000,Petrol
+245,Audi A4 1.8,Audi,2009,699000,47000,Petrol
+246,Force Motors Force,Force,2015,580000,3200,Diesel
+247,Mahindra Scorpio S4,Mahindra,2015,855000,30000,Diesel
+248,Hyundai i20 Active,Hyundai,2015,535000,37000,Diesel
+249,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+250,Maruti Suzuki Ciaz,Maruti,2017,699000,14000,Petrol
+251,Chevrolet Tavera Neo,Chevrolet,2013,375000,55000,Diesel
+252,Honda Amaze,Honda,2013,284999,46000,Diesel
+253,Hyundai Eon Sportz,Hyundai,2012,178000,30000,Petrol
+254,Tata Sumo Gold,Tata,2013,300000,50000,Diesel
+255,Maruti Suzuki Wagon,Maruti,2003,90000,45000,Petrol
+256,Maruti Suzuki Esteem,Maruti,2006,95000,45000,Petrol
+257,Maruti Suzuki Eeco,Maruti,2015,255000,9300,Petrol
+258,Chevrolet Enjoy 1.4,Chevrolet,2013,245000,55000,Diesel
+259,Hyundai i20 Asta,Hyundai,2012,329500,36200,Diesel
+260,Ford Figo Diesel,Ford,2014,195000,50000,Diesel
+261,Maruti Suzuki Eeco,Maruti,2015,251111,55000,Petrol
+262,Maruti Suzuki Ertiga,Maruti,2014,569999,45000,Petrol
+263,Maruti Suzuki Esteem,Maruti,2007,69999,51000,Petrol
+264,Maruti Suzuki Ritz,Maruti,2014,299999,19000,Petrol
+265,Maruti Suzuki Dzire,Maruti,2009,220000,46000,Petrol
+266,Maruti Suzuki Ritz,Maruti,2013,399999,33000,Diesel
+267,Maruti Suzuki SX4,Maruti,2010,249999,36000,Petrol
+268,Maruti Suzuki Wagon,Maruti,2015,289999,22000,Petrol
+269,Mini Cooper S,Mini,2013,1891111,13500,Petrol
+270,Nissan Terrano XL,Nissan,2015,499999,60000,Diesel
+271,Renault Duster 85,Renault,2013,489999,27000,Diesel
+272,Renault Duster 85,Renault,2014,489999,59000,Diesel
+273,Renault Duster 85,Renault,2015,549999,19000,Diesel
+274,Maruti Suzuki Dzire,Maruti,2013,380000,30000,Petrol
+275,Renault Kwid RXT,Renault,2018,325000,15000,Petrol
+276,Maruti Suzuki Maruti,Maruti,2003,57000,56758,Petrol
+277,Renault Kwid 1.0,Renault,2018,349999,10000,Petrol
+278,Renault Lodgy 85,Renault,2018,689999,20000,Diesel
+279,Renault Scala RxL,Renault,2014,349999,49000,Diesel
+280,Hyundai Grand i10,Hyundai,2014,410000,41000,Petrol
+281,Maruti Suzuki Swift,Maruti,2011,225000,45000,Petrol
+282,Chevrolet Beat LS,Chevrolet,2010,120000,43000,Petrol
+283,Tata Indigo eCS,Tata,2016,320000,175430,Diesel
+284,Hyundai Santro Xing,Hyundai,2000,59000,56450,Petrol
+285,Hyundai Fluidic Verna,Hyundai,2015,540000,38000,Diesel
+287,Chevrolet Beat LS,Chevrolet,2010,80000,56000,Petrol
+288,Mahindra Quanto C8,Mahindra,2013,340000,37000,Diesel
+289,Fiat Petra ELX,Fiat,2008,75000,65000,Petrol
+290,Chevrolet Beat LS,Chevrolet,2015,220000,32700,Petrol
+291,Skoda Fabia 1.2L,Skoda,2011,159500,38200,Diesel
+292,Ford EcoSport Titanium,Ford,2016,599000,30000,Diesel
+293,Hyundai Accent GLX,Hyundai,2006,80000,56000,Petrol
+296,Mahindra TUV300 T4,Mahindra,2016,675000,9000,Diesel
+297,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+298,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+299,Tata Indica V2,Tata,2008,150000,11000,Petrol
+300,Mini Cooper S,Mini,2013,1891111,13000,Petrol
+301,Tata Indigo CS,Tata,2009,72500,46000,Diesel
+302,Maruti Suzuki Swift,Maruti,2019,610000,73,Petrol
+303,Mahindra Scorpio VLX,Mahindra,2004,230000,160000,Diesel
+305,Honda Accord,Honda,2009,175000,58559,Petrol
+306,Mahindra Scorpio S4,Mahindra,2015,855000,30000,Diesel
+307,Chevrolet Tavera Neo,Chevrolet,2013,375000,55000,Diesel
+308,Ford EcoSport Titanium,Ford,2014,520000,57000,Diesel
+309,Maruti Suzuki Ertiga,Maruti,2015,524999,50000,Diesel
+310,Honda Amaze,Honda,2014,299999,37000,Petrol
+311,Maruti Suzuki Dzire,Maruti,2012,299999,40000,Petrol
+312,Honda City,Honda,2011,284999,55000,Petrol
+313,Mahindra Scorpio 2.6,Mahindra,2007,220000,170000,Diesel
+314,Maruti Suzuki Dzire,Maruti,2014,424999,55000,Diesel
+315,Honda City,Honda,2015,644999,39000,Petrol
+316,Honda Mobilio,Honda,2014,399999,44000,Petrol
+317,Toyota Corolla Altis,Toyota,2009,199999,65000,Petrol
+318,Honda City,Honda,2014,584999,39000,Petrol
+319,Skoda Laura,Skoda,2012,349999,44000,Diesel
+320,Renault Duster,Renault,2015,449999,49000,Diesel
+321,Maruti Suzuki Ertiga,Maruti,2018,799999,9000,Diesel
+322,Maruti Suzuki Dzire,Maruti,2015,444999,45000,Diesel
+323,Mahindra XUV500,Mahindra,2014,649999,47000,Diesel
+324,Hyundai Verna Fluidic,Hyundai,2012,444999,40000,Diesel
+325,Maruti Suzuki Vitara,Maruti,2016,689999,29000,Diesel
+326,Maruti Suzuki Wagon,Maruti,2016,344999,15000,Petrol
+327,Mahindra Scorpio,Mahindra,2015,944999,45000,Diesel
+328,Honda Amaze,Honda,2014,274999,35000,Petrol
+329,Mahindra XUV500,Mahindra,2013,689999,80000,Diesel
+330,Mahindra Scorpio,Mahindra,2013,574999,68000,Diesel
+331,Skoda Laura,Skoda,2013,374999,50000,Diesel
+332,Volkswagen Polo,Volkswagen,2010,199999,60000,Diesel
+333,Hyundai Elite i20,Hyundai,2016,549999,9000,Petrol
+334,Tata Manza Aura,Tata,2012,130000,72000,Diesel
+335,Chevrolet Sail UVA,Chevrolet,2013,210000,60000,Petrol
+336,Renault Duster 110,Renault,2012,501000,38000,Diesel
+337,Hyundai Verna Fluidic,Hyundai,2013,401000,45000,Diesel
+338,Audi A4 2.0,Audi,2012,1350000,40000,Diesel
+339,Hyundai Elantra SX,Hyundai,2013,600000,20000,Petrol
+340,Mahindra Scorpio VLX,Mahindra,2013,610000,35000,Diesel
+341,Mahindra KUV100 K8,Mahindra,2016,400000,20000,Diesel
+342,Renault Scala RxL,Renault,2015,375000,25000,Diesel
+343,Mahindra Quanto C8,Mahindra,2013,375000,20000,Diesel
+344,Hyundai Grand i10,Hyundai,2014,365000,20000,Petrol
+345,Hyundai i20 Active,Hyundai,2015,500000,18000,Petrol
+346,Mahindra Xylo E4,Mahindra,2012,400000,35000,Diesel
+347,Hyundai Grand i10,Hyundai,2017,524999,6821,Petrol
+348,Hyundai i20,Hyundai,2014,449999,23000,Petrol
+349,Hyundai Eon,Hyundai,2014,174999,14000,Petrol
+350,Hyundai i10,Hyundai,2012,244999,38000,Petrol
+351,Hyundai i20 Active,Hyundai,2015,574999,35000,Diesel
+352,Datsun Redi GO,Datsun,2017,244999,22000,Petrol
+353,Toyota Etios Liva,Toyota,2011,239999,41000,Petrol
+354,Hyundai Accent,Hyundai,2010,99999,45000,Petrol
+355,Hyundai Verna,Hyundai,2014,489999,44000,Diesel
+356,Maruti Suzuki Swift,Maruti,2013,324999,45000,Diesel
+357,Toyota Fortuner,Toyota,2011,1074999,52000,Diesel
+358,Hyundai i10 Sportz,Hyundai,2012,230000,34000,Petrol
+359,Mahindra Bolero Power,Mahindra,2018,699000,1800,Diesel
+361,Mahindra XUV500,Mahindra,2015,1000000,15000,Diesel
+362,Honda City 1.5,Honda,2010,240000,400000,Petrol
+363,Chevrolet Spark LT,Chevrolet,2009,110000,44000,Petrol
+364,Mahindra Jeep MM,Mahindra,2019,390000,60,Diesel
+365,Renault Duster 110PS,Renault,2012,501000,35000,Diesel
+366,Mahindra XUV500,Mahindra,2016,1130000,72000,Diesel
+367,Tata Indigo eCS,Tata,2014,250000,40000,Diesel
+369,Mahindra Bolero SLE,Mahindra,2013,330000,80200,Diesel
+370,Force Motors Force,Force,2015,580000,3200,Diesel
+371,Skoda Rapid Elegance,Skoda,2013,340000,48000,Diesel
+372,Tata Vista Quadrajet,Tata,2011,120000,90000,Diesel
+373,Maruti Suzuki Alto,Maruti,2015,265000,12000,Petrol
+374,Maruti Suzuki SX4,Maruti,2012,265000,46000,Diesel
+375,Maruti Suzuki Zen,Maruti,2003,85000,69900,Petrol
+376,Mahindra Jeep CL550,Mahindra,2019,379000,0,Diesel
+377,Hyundai i10 Magna,Hyundai,2011,175000,45000,Petrol
+378,Maruti Suzuki Alto,Maruti,2015,219000,5000,Petrol
+379,Maruti Suzuki Swift,Maruti,2016,350000,166000,Diesel
+380,Honda City ZX,Honda,2008,149000,42000,Petrol
+381,Mahindra Jeep CL550,Mahindra,2018,385000,588,Diesel
+382,Mahindra Jeep MM,Mahindra,2006,425000,122,Diesel
+383,Chevrolet Beat Diesel,Chevrolet,2017,150000,62000,Diesel
+384,Honda City 1.5,Honda,2010,225000,70000,Petrol
+386,Hyundai Verna 1.4,Hyundai,2014,375000,36000,Petrol
+387,Toyota Innova 2.5,Toyota,2012,770000,0,Diesel
+389,Maruti Suzuki Maruti,Maruti,1995,30000,55000,Petrol
+390,Toyota Etios,Toyota,2011,275000,36000,Diesel
+391,Volkswagen Polo,Volkswagen,2015,330000,38000,Diesel
+392,Maruti Suzuki Swift,Maruti,2014,335000,55000,Diesel
+393,Hyundai Elite i20,Hyundai,2015,450000,20000,Diesel
+394,Maruti Suzuki Swift,Maruti,2012,225000,40000,Petrol
+396,Maruti Suzuki Versa,Maruti,2004,80000,50000,Petrol
+397,Tata Indigo LX,Tata,2016,130000,104000,Diesel
+398,Volkswagen Vento Konekt,Volkswagen,2011,245000,65000,Diesel
+399,Mercedes Benz C,Mercedes,2002,399000,41000,Petrol
+400,Maruti Suzuki Ertiga,Maruti,2013,450000,90000,Diesel
+402,Honda City,Honda,2000,65000,80000,Petrol
+403,Hyundai Santro Xing,Hyundai,2006,75000,46000,Petrol
+404,Maruti Suzuki Omni,Maruti,2001,70000,70000,Petrol
+405,Hyundai Sonata Transform,Hyundai,2017,190000,36469,Diesel
+406,Hyundai Elite i20,Hyundai,2018,600000,7800,Petrol
+407,Volkswagen Vento Konekt,Volkswagen,2011,245000,65000,Diesel
+408,Maruti Suzuki Alto,Maruti,2017,240000,60000,Petrol
+409,Maruti Suzuki Alto,Maruti,2011,155000,32000,Petrol
+410,Honda Jazz S,Honda,2009,169999,24695,Petrol
+411,Hyundai Grand i10,Hyundai,2017,450000,15141,Petrol
+412,Maruti Suzuki Zen,Maruti,2001,40000,40000,Petrol
+413,Mahindra Scorpio W,Mahindra,2012,165000,65000,Diesel
+415,Maruti Suzuki Alto,Maruti,2014,270000,22000,Petrol
+416,Hyundai Grand i10,Hyundai,2016,280000,59910,Diesel
+417,Mahindra XUV500 W8,Mahindra,2012,560000,100000,Diesel
+418,Hyundai Creta 1.6,Hyundai,2016,950000,25000,Petrol
+419,Hyundai i20 Magna,Hyundai,2013,310000,35000,Petrol
+420,Renault Duster 85,Renault,2015,715000,65000,Diesel
+421,Hyundai Grand i10,Hyundai,2014,340000,35000,Petrol
+422,Honda Brio V,Honda,2012,235000,33000,Petrol
+423,Mahindra TUV300 T4,Mahindra,2017,610000,68000,Diesel
+424,Chevrolet Spark LS,Chevrolet,2010,95000,23000,Petrol
+425,Mahindra TUV300 T8,Mahindra,2018,1000000,4500,Diesel
+426,Maruti Suzuki Swift,Maruti,2015,220000,129000,Diesel
+427,Nissan X Trail,Nissan,2019,1200000,300,Diesel
+428,Maruti Suzuki Alto,Maruti,2015,230000,5000,Petrol
+429,Ford Ikon 1.3,Ford,2001,45000,65000,Petrol
+430,Toyota Fortuner 3.0,Toyota,2010,940000,131000,Diesel
+431,Tata Manza ELAN,Tata,2010,155555,111111,Petrol
+434,Mercedes Benz A,Mercedes,2013,1500000,14000,Petrol
+435,Chevrolet Beat LS,Chevrolet,2016,210000,22000,Diesel
+436,Ford EcoSport Trend,Ford,2013,495000,38000,Diesel
+437,Tata Indigo LS,Tata,2016,125000,70000,Diesel
+438,Hyundai i20 Magna,Hyundai,2010,195000,36000,Petrol
+439,Volkswagen Vento Highline,Volkswagen,2015,550000,34000,Diesel
+440,Renault Kwid RXT,Renault,2015,270000,43000,Petrol
+442,Ford EcoSport Titanium,Ford,2014,500000,40000,Diesel
+443,Honda Amaze 1.5,Honda,2016,240000,160000,Diesel
+444,Hyundai Verna 1.6,Hyundai,2017,800000,12000,Petrol
+445,BMW 5 Series,BMW,2011,1299000,49000,Diesel
+446,Skoda Superb 1.8,Skoda,2011,530000,68000,Petrol
+447,Audi Q3 2.0,Audi,2013,1499000,37000,Diesel
+448,Mahindra Bolero DI,Mahindra,2012,220000,59466,Diesel
+450,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+451,Ford Figo Duratorq,Ford,2012,250000,99000,Diesel
+452,Maruti Suzuki Wagon,Maruti,2018,395000,25500,Petrol
+453,Mahindra Logan Diesel,Mahindra,2009,130000,66000,Petrol
+454,Tata Nano GenX,Tata,2010,32000,44005,Petrol
+455,Mahindra TUV300 T4,Mahindra,2016,540000,35000,Diesel
+456,Mahindra TUV300 T4,Mahindra,2016,540000,35000,Diesel
+457,Hyundai Elite i20,Hyundai,2015,405000,28000,Petrol
+458,Hyundai Elite i20,Hyundai,2015,400000,30000,Petrol
+459,Honda City SV,Honda,2017,760000,4000,Petrol
+460,Maruti Suzuki Baleno,Maruti,2016,500000,28000,Petrol
+461,Ford Figo Petrol,Ford,2011,175000,75000,Petrol
+462,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+463,Honda City,Honda,2017,750000,3000,Petrol
+464,Hyundai Elite i20,Hyundai,2015,419000,20000,Petrol
+465,Maruti Suzuki Versa,Maruti,2004,90000,50000,Petrol
+466,Hyundai Eon Era,Hyundai,2018,140000,2110,Petrol
+467,Mitsubishi Pajero Sport,Mitsubishi,2015,1540000,43222,Petrol
+468,Hyundai i10 Magna,Hyundai,2008,275000,100200,Petrol
+469,Toyota Corolla H2,Toyota,2003,150000,100000,Petrol
+470,Maruti Suzuki Swift,Maruti,2011,230000,65,Petrol
+471,Tata Indigo CS,Tata,2015,123000,100000,Diesel
+472,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+473,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel
+474,Hyundai Xcent Base,Hyundai,2016,300000,140000,Diesel
+475,Honda City,Honda,2015,499999,55000,Petrol
+476,Hyundai Accent Executive,Hyundai,2009,165000,48000,Petrol
+477,Maruti Suzuki Baleno,Maruti,2016,498000,22000,Petrol
+478,Tata Zest XE,Tata,2018,480000,103553,Diesel
+479,Maruti Suzuki Dzire,Maruti,2017,488000,80000,Diesel
+480,Tata Sumo Gold,Tata,2014,250000,99000,Diesel
+481,Toyota Corolla Altis,Toyota,2010,220000,58000,Petrol
+482,Maruti Suzuki Eeco,Maruti,2013,290000,70000,LPG
+483,Toyota Fortuner 3.0,Toyota,2015,1525000,120000,Diesel
+484,Mahindra XUV500 W6,Mahindra,2013,548900,49800,Diesel
+485,Tata Tigor Revotron,Tata,2019,650000,100,Diesel
+486,Maruti Suzuki 800,Maruti,2001,55000,81876,Petrol
+487,Maruti Suzuki Ertiga,Maruti,2015,550000,75000,Petrol
+488,Maruti Suzuki Versa,Maruti,2004,90000,50000,Petrol
+489,Honda Mobilio S,Honda,2014,399000,44000,Diesel
+490,Maruti Suzuki Ertiga,Maruti,2016,730000,55000,Diesel
+491,Maruti Suzuki Vitara,Maruti,2017,725000,36000,Diesel
+492,Hyundai Verna 1.6,Hyundai,2016,195000,56000,Diesel
+493,Maruti Suzuki Swift,Maruti,2007,130000,62000,Petrol
+494,Toyota Fortuner 3.0,Toyota,2015,1525000,120000,Diesel
+495,Maruti Suzuki Omni,Maruti,2014,190000,6020,Petrol
+496,Honda Amaze,Honda,2013,250000,55700,Diesel
+497,Tata Indica,Tata,2005,80000,42000,Petrol
+498,Hyundai Santro Xing,Hyundai,2003,120000,50000,Petrol
+499,Maruti Suzuki Zen,Maruti,2010,149000,35000,Petrol
+500,Maruti Suzuki Wagon,Maruti,2014,250000,18500,Petrol
+501,Maruti Suzuki Wagon,Maruti,2007,120000,7000,Petrol
+502,Honda Brio VX,Honda,2017,450000,11000,Petrol
+504,Maruti Suzuki Zen,Maruti,2003,99999,53000,Petrol
+505,Maruti Suzuki Zen,Maruti,2008,135000,23000,Petrol
+506,Maruti Suzuki Wagon,Maruti,2016,225000,35500,Diesel
+507,Maruti Suzuki Alto,Maruti,2010,99000,22134,Petrol
+508,Renault Kwid RXT,Renault,2019,370000,1000,Petrol
+509,Tata Nano Lx,Tata,2010,52000,9000,Petrol
+510,Jaguar XE XE,Jaguar,2016,2800000,8500,Petrol
+512,Hyundai Eon Magna,Hyundai,2014,190000,35000,Petrol
+513,Honda City 1.5,Honda,2014,499000,22000,Petrol
+514,Hindustan Motors Ambassador,Hindustan,2002,90000,25000,Diesel
+515,Maruti Suzuki Ritz,Maruti,2010,149000,40000,Petrol
+516,Hyundai Grand i10,Hyundai,2017,400000,20000,Petrol
+517,Hyundai Eon D,Hyundai,2016,120000,87000,Petrol
+518,Maruti Suzuki Swift,Maruti,2015,250000,55000,Petrol
+519,Maruti Suzuki Wagon,Maruti,2017,375000,23000,Petrol
+520,Honda Amaze 1.2,Honda,2014,381000,6000,Petrol
+521,Maruti Suzuki Estilo,Maruti,2013,180000,65000,Petrol
+522,Maruti Suzuki Vitara,Maruti,2016,580000,25000,Diesel
+523,Maruti Suzuki Eeco,Maruti,2015,278000,39000,Petrol
+525,Hyundai Creta 1.6,Hyundai,2016,1000000,8000,Petrol
+526,Mahindra Scorpio Vlx,Mahindra,2013,690000,75000,Diesel
+527,Maruti Suzuki Ertiga,Maruti,2012,480000,51000,Diesel
+528,Mitsubishi Lancer 1.8,Mitsubishi,2006,85000,50000,Petrol
+529,Maruti Suzuki Maruti,Maruti,2001,40000,75000,Petrol
+530,Maruti Suzuki Alto,Maruti,2015,90000,55800,Petrol
+531,Hyundai Grand i10,Hyundai,2015,340000,53000,Petrol
+532,Hyundai Eon D,Hyundai,2018,260000,25000,Petrol
+533,Ford Fiesta SXi,Ford,2009,250000,56400,Petrol
+534,Maruti Suzuki Ritz,Maruti,2010,180000,72160,Diesel
+535,Hyundai Verna Fluidic,Hyundai,2012,350000,10000,Diesel
+536,Maruti Suzuki Wagon,Maruti,2006,90001,48000,Petrol
+537,Maruti Suzuki Estilo,Maruti,2007,115000,36000,Petrol
+538,Audi A6 2.0,Audi,2012,1599000,11500,Diesel
+539,Maruti Suzuki Wagon,Maruti,2003,130000,133000,Petrol
+540,Maruti Suzuki Wagon,Maruti,2009,159000,27000,Petrol
+541,Maruti Suzuki Wagon,Maruti,2009,160000,35000,Petrol
+542,Maruti Suzuki Alto,Maruti,2010,110000,55000,Petrol
+543,Maruti Suzuki Baleno,Maruti,2016,425000,40000,Petrol
+544,Hyundai Verna 1.6,Hyundai,2019,900000,2000,Petrol
+545,Maruti Suzuki Swift,Maruti,2009,150000,45000,Petrol
+546,Hyundai Getz Prime,Hyundai,2009,110000,20000,Petrol
+547,Hyundai Santro,Hyundai,2000,51999,88000,Petrol
+548,Hyundai Getz Prime,Hyundai,2009,115000,20000,Petrol
+549,Chevrolet Beat PS,Chevrolet,2012,215000,65422,Diesel
+550,Ford EcoSport Trend,Ford,2017,580000,10000,Petrol
+551,Maruti Suzuki Dzire,Maruti,2013,380000,35000,Petrol
+552,Hyundai Fluidic Verna,Hyundai,2013,350000,117000,Diesel
+553,Tata Indica V2,Tata,2005,35000,150000,Diesel
+554,BMW X1 xDrive20d,BMW,2011,1150000,72000,Diesel
+555,Hyundai i20 Asta,Hyundai,2010,300000,10750,Petrol
+556,Honda City 1.5,Honda,2009,269000,55000,Petrol
+557,Tata Nano,Tata,2013,60000,6800,Petrol
+558,Chevrolet Cruze LTZ,Chevrolet,2014,400000,41000,Diesel
+559,Hyundai Verna Fluidic,Hyundai,2015,430000,73000,Diesel
+561,Maruti Suzuki Swift,Maruti,2011,140000,65000,Diesel
+563,Mahindra XUV500 W10,Mahindra,2018,1299000,40000,Diesel
+564,Maruti Suzuki Alto,Maruti,2014,199000,37000,Petrol
+565,Hyundai Accent GLE,Hyundai,2006,90000,55000,Petrol
+566,Force Motors One,Force,2013,550000,140000,Diesel
+568,Maruti Suzuki Alto,Maruti,2019,265000,9800,Petrol
+569,Chevrolet Spark 1.0,Chevrolet,2011,100000,27000,Petrol
+570,Hyundai i10,Hyundai,2009,215000,27000,Petrol
+571,Toyota Etios Liva,Toyota,2012,380000,20000,Diesel
+572,Renault Duster 85PS,Renault,2013,401919,57923,Diesel
+573,Chevrolet Enjoy,Chevrolet,2014,490000,30201,Diesel
+574,Maruti Suzuki Alto,Maruti,2017,280000,6200,Petrol
+575,BMW 5 Series,BMW,2009,650000,37518,Petrol
+576,Toyota Etios Liva,Toyota,2014,160000,24652,Petrol
+577,Mahindra Jeep MM,Mahindra,2004,424000,383,Diesel
+578,Chevrolet Beat LS,Chevrolet,2016,225000,95000,Diesel
+579,Chevrolet Cruze LTZ,Chevrolet,2011,350000,35000,Diesel
+580,Jeep Wrangler Unlimited,Jeep,2015,950000,3528,Diesel
+581,Maruti Suzuki Ertiga,Maruti,2013,485000,52500,Diesel
+582,Hyundai Verna VGT,Hyundai,2010,205000,47900,Diesel
+583,Maruti Suzuki Omni,Maruti,2012,160000,14000,Petrol
+584,Maruti Suzuki Celerio,Maruti,2018,310000,37000,Petrol
+585,Tata Zest Quadrajet,Tata,2017,180000,90000,Diesel
+586,Mahindra XUV500 W6,Mahindra,2013,549900,52800,Diesel
+587,Tata Indigo CS,Tata,2016,150000,104000,Diesel
+588,Hyundai i10 Era,Hyundai,2011,175000,30000,Petrol
+589,Tata Indigo eCS,Tata,2014,95000,195000,Diesel
+590,Tata Indigo LX,Tata,2016,230000,104000,Diesel
+591,Tata Indigo eCS,Tata,2016,230000,104000,Diesel
+592,Tata Indigo Marina,Tata,2004,180000,70000,Diesel
+594,Hyundai Xcent SX,Hyundai,2015,400000,43000,Diesel
+595,Hyundai Eon Magna,Hyundai,2013,185000,23000,Petrol
+596,Renault Duster 85,Renault,2015,385000,51000,Diesel
+597,Maruti Suzuki Alto,Maruti,2009,90000,62000,Petrol
+598,Tata Nano LX,Tata,2010,32000,48008,Petrol
+600,Renault Duster 110,Renault,2013,435000,39000,Diesel
+601,Maruti Suzuki Wagon,Maruti,2010,225000,40000,Petrol
+602,Maruti Suzuki Swift,Maruti,2006,189700,48247,Petrol
+603,Maruti Suzuki Ertiga,Maruti,2012,389700,39000,Diesel
+604,Maruti Suzuki Swift,Maruti,2014,365000,23000,Petrol
+605,Maruti Suzuki Alto,Maruti,2017,360000,9400,Petrol
+606,Hyundai i20 Magna,Hyundai,2010,210000,50000,Petrol
+607,Hyundai i10 Magna,Hyundai,2009,170000,75000,Petrol
+609,Tata Zest XE,Tata,2017,380000,70000,Diesel
+610,Mahindra Xylo E8,Mahindra,2009,295000,64000,Diesel
+611,Toyota Corolla Altis,Toyota,2010,185000,55000,Petrol
+612,Tata Manza Aqua,Tata,2014,160000,200000,Diesel
+615,Renault Kwid 1.0,Renault,2018,290000,2137,Petrol
+617,Tata Venture EX,Tata,2013,100000,30000,Diesel
+618,Maruti Suzuki Swift,Maruti,2014,315000,44000,Petrol
+620,Skoda Octavia Classic,Skoda,2006,114990,65000,Diesel
+621,Maruti Suzuki Omni,Maruti,2012,120000,160000,LPG
+622,Chevrolet Beat Diesel,Chevrolet,2011,125000,56000,Diesel
+623,Tata Sumo Gold,Tata,2012,210000,75000,Diesel
+625,Hyundai Verna 1.6,Hyundai,2018,855000,42000,Diesel
+626,Tata Sumo Gold,Tata,2012,210000,75000,Diesel
+627,Mahindra Scorpio 2.6,Mahindra,2007,260000,56000,Diesel
+628,Maruti Suzuki Zen,Maruti,2002,95000,10544,Petrol
+629,Maruti Suzuki Swift,Maruti,2011,255000,64000,Petrol
+630,Mahindra Scorpio SLX,Mahindra,2008,300000,70000,Diesel
+631,Hyundai Grand i10,Hyundai,2014,340000,25000,Petrol
+632,Hyundai Elite i20,Hyundai,2017,550000,15000,Petrol
+633,Ford Ikon 1.6,Ford,2003,60000,50000,Petrol
+636,Toyota Innova 2.5,Toyota,2011,750000,147000,Diesel
+637,Nissan Sunny XL,Nissan,2011,230000,52000,Petrol
+638,Chevrolet Beat LT,Chevrolet,2012,130000,90001,Diesel
+639,Maruti Suzuki Alto,Maruti,2017,270000,21000,Petrol
+640,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel
+641,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel
+642,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel
+644,Toyota Innova 2.0,Toyota,2012,600000,80000,Diesel
+646,Maruti Suzuki Swift,Maruti,2010,190000,74000,Diesel
+647,Hyundai Elite i20,Hyundai,2015,500000,22000,Petrol
+648,Mahindra XUV500 W10,Mahindra,2016,1065000,41000,Diesel
+649,Volkswagen Polo Trendline,Volkswagen,2015,350000,25000,Diesel
+650,Toyota Etios Liva,Toyota,2012,350000,85000,Diesel
+651,Mahindra TUV300 T4,Mahindra,2016,540000,29500,Diesel
+652,Hyundai Elite i20,Hyundai,2015,470000,30000,Petrol
+653,Hyundai Santro Xing,Hyundai,2014,179000,57000,Petrol
+654,Maruti Suzuki Zen,Maruti,2003,48000,60000,Petrol
+655,Maruti Suzuki Ciaz,Maruti,2016,650000,50000,Petrol
+656,Hyundai Eon Era,Hyundai,2013,190000,39700,Petrol
+657,Hyundai Elantra 1.8,Hyundai,2012,500000,65000,Petrol
+658,Maruti Suzuki Swift,Maruti,2010,270000,67000,Diesel
+659,Maruti Suzuki Zen,Maruti,2008,125000,46000,Petrol
+660,Hyundai Eon Era,Hyundai,2012,188000,38000,Petrol
+661,Hyundai Grand i10,Hyundai,2016,380000,27000,Petrol
+662,Hyundai Verna Fluidic,Hyundai,2011,365000,43000,Diesel
+663,Ford EcoSport Trend,Ford,2014,465000,47000,Petrol
+664,Hyundai i20 Magna,Hyundai,2011,240000,42000,Petrol
+665,Chevrolet Beat Diesel,Chevrolet,2016,179999,19336,Diesel
+666,Tata Indica eV2,Tata,2015,140000,60105,Diesel
+667,Jaguar XF 2.2,Jaguar,2013,2190000,29000,Diesel
+668,Audi Q5 2.0,Audi,2014,2390000,34000,Diesel
+669,BMW 3 Series,BMW,2011,1075000,35000,Diesel
+670,Maruti Suzuki Swift,Maruti,2015,475000,22000,Petrol
+671,BMW X1 sDrive20d,BMW,2012,1025000,41000,Diesel
+672,Maruti Suzuki S,Maruti,2016,615000,21000,Diesel
+673,Maruti Suzuki Ertiga,Maruti,2013,475000,48000,Diesel
+674,Maruti Suzuki Alto,Maruti,2016,270000,38000,Petrol
+675,Honda City SV,Honda,2014,475000,34000,Diesel
+676,Volkswagen Vento Comfortline,Volkswagen,2011,240000,45933,Petrol
+677,Honda City 1.5,Honda,2005,120000,68000,Petrol
+678,Audi A4 2.0,Audi,2016,1900000,44000,Diesel
+679,Mahindra KUV100,Mahindra,2017,360000,35000,Diesel
+680,Tata Zest XE,Tata,2018,450000,102563,Diesel
+681,Mahindra XUV500 W8,Mahindra,2015,900000,28600,Diesel
+682,Maruti Suzuki Swift,Maruti,2017,650000,41800,Diesel
+683,Tata Sumo Gold,Tata,2014,275000,116000,Diesel
+684,Maruti Suzuki Swift,Maruti,2009,210000,59000,Petrol
+685,Mahindra Scorpio 2.6,Mahindra,2004,175000,58000,Diesel
+686,Maruti Suzuki Omni,Maruti,2009,85000,45000,Petrol
+687,Mitsubishi Pajero Sport,Mitsubishi,2015,1490000,42590,Diesel
+688,Renault Duster,Renault,2014,800000,7400,Diesel
+689,Volkswagen Jetta Comfortline,Volkswagen,2009,450000,54500,Diesel
+690,Maruti Suzuki Ertiga,Maruti,2012,1000000,200000,Diesel
+691,Audi A4 2.0,Audi,2013,1510000,27000,Diesel
+692,Volvo S80 Summum,Volvo,2015,1850000,42000,Diesel
+693,Toyota Corolla Altis,Toyota,2014,790000,29000,Petrol
+694,Mitsubishi Pajero Sport,Mitsubishi,2015,1725000,37000,Diesel
+695,Chevrolet Beat LT,Chevrolet,2012,135000,36000,Petrol
+696,BMW X1,BMW,2011,1000000,34000,Diesel
+697,Datsun Redi GO,Datsun,2018,299999,7000,Petrol
+698,Mercedes Benz C,Mercedes,2009,1225000,76000,Diesel
+699,Mahindra Scorpio SLX,Mahindra,2004,175000,60000,Diesel
+700,Volkswagen Vento Comfortline,Volkswagen,2011,200000,95000,Diesel
+701,Tata Indigo CS,Tata,2017,270000,50000,Diesel
+702,Ford Figo Petrol,Ford,2019,525000,0,Petrol
+703,Honda City ZX,Honda,2006,180000,50000,Petrol
+704,Maruti Suzuki Wagon,Maruti,2008,140000,68000,Petrol
+705,Ford EcoSport Trend,Ford,2014,400000,16000,Petrol
+706,Maruti Suzuki Swift,Maruti,2016,499000,51000,Diesel
+707,Maruti Suzuki Omni,Maruti,2009,85000,56000,Petrol
+708,Maruti Suzuki Zen,Maruti,2004,70000,100000,Petrol
+709,Renault Duster RxL,Renault,2015,550000,36000,Petrol
+710,Maruti Suzuki Swift,Maruti,2014,370000,11523,Petrol
+711,Maruti Suzuki Baleno,Maruti,2018,690000,1000,Petrol
+712,Honda WR V,Honda,2009,250000,60000,Petrol
+713,Tata Indigo CS,Tata,2016,110000,85000,Diesel
+714,Renault Duster 110,Renault,2013,490000,38600,Diesel
+715,Mahindra Scorpio LX,Mahindra,2009,320000,95500,Diesel
+716,Maruti Suzuki Zen,Maruti,2004,68000,56000,Petrol
+717,Maruti Suzuki Wagon,Maruti,2014,130000,37458,Petrol
+718,Maruti Suzuki SX4,Maruti,2016,970000,85960,Diesel
+719,Audi A3 Cabriolet,Audi,2015,3100000,12516,Petrol
+720,Hyundai Eon D,Hyundai,2018,280000,35000,Petrol
+721,Maruti Suzuki Zen,Maruti,2009,125000,0,Petrol
+722,Mahindra Scorpio SLX,Mahindra,2008,285000,80000,Diesel
+724,Hyundai Santro AE,Hyundai,2011,165000,45000,Petrol
+726,Maruti Suzuki Swift,Maruti,2009,250000,51000,Diesel
+727,Mahindra Scorpio S4,Mahindra,2015,865000,30000,Diesel
+729,Mahindra Xylo D2,Mahindra,2011,390000,48000,Diesel
+730,Hyundai Santro,Hyundai,2003,60000,51000,Petrol
+731,Chevrolet Beat LT,Chevrolet,2015,215000,90000,Diesel
+732,Maruti Suzuki Swift,Maruti,2015,475000,43000,Diesel
+733,Mahindra XUV500 W8,Mahindra,2015,899000,53000,Diesel
+734,Toyota Fortuner 3.0,Toyota,2013,1499000,97000,Diesel
+735,Maruti Suzuki Alto,Maruti,2013,240000,20000,Petrol
+736,Hyundai Getz GLE,Hyundai,2007,99000,55000,Petrol
+737,Maruti Suzuki Swift,Maruti,2014,260000,120000,Diesel
+738,Hyundai Creta 1.6,Hyundai,2019,1200000,0,Petrol
+739,Hyundai Santro Xing,Hyundai,2007,115000,46000,Petrol
+740,Hyundai Santro Xing,Hyundai,2009,88000,43200,Petrol
+741,Mahindra Xylo D2,Mahindra,2011,390000,56000,Diesel
+742,Hyundai Santro Xing,Hyundai,2007,135000,42000,Petrol
+743,Tata Indica V2,Tata,2009,90000,30600,Diesel
+744,Hyundai i10 Sportz,Hyundai,2011,220000,38000,Petrol
+745,Hyundai Grand i10,Hyundai,2017,424999,2550,Petrol
+746,Hyundai Santro Xing,Hyundai,2007,135000,47000,Petrol
+747,Honda City 1.5,Honda,2005,95000,41000,Petrol
+748,Nissan Micra XL,Nissan,2017,430000,62500,Diesel
+749,Honda City 1.5,Honda,2005,115000,68000,Petrol
+750,Maruti Suzuki Alto,Maruti,2015,215000,50000,Petrol
+751,Maruti Suzuki Wagon,Maruti,2004,53000,69000,Petrol
+752,Maruti Suzuki Ertiga,Maruti,2012,500000,48000,Diesel
+753,Tata Indica eV2,Tata,2012,85000,55000,Diesel
+754,Maruti Suzuki Omni,Maruti,2013,165000,25000,Petrol
+755,Hyundai Eon Era,Hyundai,2014,200000,28400,Petrol
+756,Hyundai Eon,Hyundai,2014,200000,28000,Petrol
+757,Maruti Suzuki Swift,Maruti,2015,425000,42000,Diesel
+759,Hyundai Verna 1.6,Hyundai,2012,600000,29000,Diesel
+760,Chevrolet Tavera LS,Chevrolet,2005,130000,68485,Diesel
+761,Tata Tiago Revotron,Tata,2018,430000,3500,Petrol
+762,Tata Tiago Revotorq,Tata,2019,568500,0,Petrol
+765,Maruti Suzuki Zen,Maruti,2006,71000,32000,Petrol
+766,Mahindra KUV100 K8,Mahindra,2018,560000,8000,Diesel
+767,Ford EcoSport Titanium,Ford,2014,590000,34000,Diesel
+768,Hindustan Motors Ambassador,Hindustan,1995,750000,37000,Petrol
+769,Ford Fusion 1.4,Ford,2007,125000,85455,Diesel
+770,Hyundai Santro Xing,Hyundai,2007,135000,46000,Petrol
+771,Hyundai Santro,Hyundai,2002,60000,47000,Petrol
+772,Fiat Linea Emotion,Fiat,2009,120000,64000,Petrol
+773,Ford Ikon 1.3,Ford,2008,95000,46000,Petrol
+774,Maruti Suzuki Omni,Maruti,2017,240000,8000,Petrol
+775,Tata Indica V2,Tata,2012,115000,64000,Diesel
+776,Mahindra Scorpio S4,Mahindra,2015,795000,63000,Diesel
+777,Hyundai Santro Xing,Hyundai,2007,55000,65000,Petrol
+778,Mahindra Xylo D2,Mahindra,2009,300000,62000,Diesel
+779,Hyundai Grand i10,Hyundai,2014,320000,41000,Petrol
+780,Maruti Suzuki Alto,Maruti,2015,265000,14000,Petrol
+781,Toyota Corolla,Toyota,2006,160000,40000,Petrol
+782,Hyundai Eon Magna,Hyundai,2017,300000,1600,Petrol
+783,Tata Sumo Grande,Tata,2010,130000,90000,Diesel
+784,Maruti Suzuki Swift,Maruti,2011,250000,58000,Diesel
+785,Volkswagen Polo Highline1.2L,Volkswagen,2013,380000,27000,Petrol
+786,Maruti Suzuki Alto,Maruti,2003,42000,60000,Petrol
+787,Tata Tiago Revotron,Tata,2017,400000,31000,Petrol
+788,Maruti Suzuki Swift,Maruti,2009,120000,90000,Diesel
+789,Maruti Suzuki Swift,Maruti,2009,120000,90000,Diesel
+790,Tata Indigo eCS,Tata,2016,130000,150000,Diesel
+791,Chevrolet Beat LS,Chevrolet,2014,189000,31000,Diesel
+793,Mahindra Xylo E8,Mahindra,2011,365000,43000,Diesel
+794,Hyundai Eon D,Hyundai,2013,170000,20000,Petrol
+804,Tata Sumo Gold,Tata,2013,215000,100000,Petrol
+813,Tata Nano,Tata,2013,60000,7000,Petrol
+814,Hyundai Elite i20,Hyundai,2017,599999,31000,Petrol
+815,Hyundai i10 Magna,Hyundai,2009,400000,33000,Petrol
+816,Hyundai Creta,Hyundai,2016,900000,60000,Diesel
+817,Volkswagen Polo,Volkswagen,2013,299999,48000,Diesel
+818,Maruti Suzuki Dzire,Maruti,2014,374999,33000,Petrol
+819,Tata Bolt XM,Tata,2015,600000,15000,Petrol
+820,Maruti Suzuki Alto,Maruti,2005,70000,47000,Petrol
+821,Maruti Suzuki Alto,Maruti,2005,100000,40000,Petrol
+823,Maruti Suzuki Ritz,Maruti,2010,150000,38000,Diesel
+824,Maruti Suzuki Alto,Maruti,2017,225000,12500,Petrol
+825,Maruti Suzuki Dzire,Maruti,2009,210000,42000,Petrol
+827,Hyundai i20 Asta,Hyundai,2014,425000,31000,Petrol
+828,Maruti Suzuki Swift,Maruti,2008,162000,60000,Diesel
+829,Tata Indica V2,Tata,2005,60000,80000,Diesel
+830,Mahindra Scorpio VLX,Mahindra,2014,650000,77000,Diesel
+831,Toyota Innova 2.5,Toyota,2012,750000,75000,Diesel
+832,Mahindra Xylo E8,Mahindra,2010,375000,40000,Diesel
+833,Hyundai i20 Magna,Hyundai,2011,230000,47000,Petrol
+834,Maruti Suzuki Omni,Maruti,2000,35999,60000,Petrol
+835,Mahindra KUV100,Mahindra,2016,380000,26500,Petrol
+836,Mahindra KUV100 K8,Mahindra,2019,560000,2875,Petrol
+837,Datsun Go Plus,Datsun,2016,285000,13900,Petrol
+838,Ford Endeavor 4x4,Ford,2019,2900000,9000,Diesel
+839,Tata Indica V2,Tata,2005,39999,80000,Diesel
+840,Hyundai Santro Xing,Hyundai,2006,85000,60000,Petrol
+841,Maruti Suzuki Wagon,Maruti,2016,395000,20000,Petrol
+842,Maruti Suzuki Swift,Maruti,2008,175000,58000,Diesel
+843,Maruti Suzuki Alto,Maruti,2019,400000,1500,Petrol
+844,Toyota Innova 2.5,Toyota,2011,750000,75000,Diesel
+846,Maruti Suzuki Alto,Maruti,2016,250000,2450,Petrol
+847,Maruti Suzuki Alto,Maruti,2019,425000,1625,Petrol
+849,Volkswagen Polo Highline1.2L,Volkswagen,2017,525000,45000,Petrol
+850,Mahindra Logan,Mahindra,2009,130000,65000,Diesel
+851,Maruti Suzuki 800,Maruti,2000,30000,33400,Petrol
+852,Mahindra Scorpio,Mahindra,2011,475000,60123,Diesel
+853,Chevrolet Sail 1.2,Chevrolet,2013,300000,28000,Petrol
+855,Hyundai Santro AE,Hyundai,2003,60000,70000,Petrol
+856,Maruti Suzuki Wagon,Maruti,2006,100000,7000,Petrol
+857,Hyundai Eon,Hyundai,2018,260000,25000,Petrol
+858,Tata Manza,Tata,2015,100000,100000,Diesel
+860,Toyota Etios G,Toyota,2013,265000,42000,Petrol
+861,Hyundai Getz Prime,Hyundai,2009,115000,20000,Petrol
+862,Toyota Qualis,Toyota,2003,180000,100000,Diesel
+863,Hyundai Santro Xing,Hyundai,2004,45000,137495,Petrol
+864,Tata Indica eV2,Tata,2016,50500,91200,Diesel
+865,Honda City 1.5,Honda,2009,270000,55000,Petrol
+866,Tata Zest XE,Tata,2017,290000,120000,Diesel
+867,Mahindra Quanto C4,Mahindra,2013,325000,63000,Diesel
+868,Tata Indigo eCS,Tata,2016,160000,104000,Diesel
+869,Maruti Suzuki Swift,Maruti,2016,350000,146000,Diesel
+870,Hyundai Elite i20,Hyundai,2011,290000,40000,Petrol
+871,Hyundai i20 Select,Hyundai,2011,290000,40000,Petrol
+872,Chevrolet Tavera Neo,Chevrolet,2007,465000,100800,Diesel
+873,Maruti Suzuki Dzire,Maruti,2016,325000,150000,Diesel
+874,Hyundai Elite i20,Hyundai,2018,510000,2100,Petrol
+875,Honda City VX,Honda,2016,860000,95000,Petrol
+876,Maruti Suzuki Dzire,Maruti,2016,450000,2500,Diesel
+877,Hyundai Getz,Hyundai,2006,125000,80000,Petrol
+878,Mercedes Benz C,Mercedes,2006,500001,15000,Petrol
+879,Maruti Suzuki Alto,Maruti,2005,95000,65000,Petrol
+880,Maruti Suzuki Swift,Maruti,2009,250000,51000,Diesel
+881,Skoda Fabia,Skoda,2009,110000,45000,Petrol
+883,Maruti Suzuki Ritz,Maruti,2011,270000,50000,Petrol
+885,Tata Indica V2,Tata,2009,110000,30000,Diesel
+886,Toyota Corolla Altis,Toyota,2009,300000,132000,Petrol
+888,Tata Zest XM,Tata,2018,260000,27000,Diesel
+889,Mahindra Quanto C8,Mahindra,2013,390000,40000,Diesel

requirements.txt

@@ -0,0 +1,18 @@
+blinker==1.6.2
+click==8.1.3
+Flask==2.3.1
+itsdangerous==2.1.2
+Jinja2==3.1.2
+joblib==1.2.0
+MarkupSafe==2.1.2
+numpy==1.24.3
+pandas==2.0.1
+python-dateutil==2.8.2
+pytz==2023.3
+scikit-learn==1.2.2
+scipy==1.10.1
+six==1.16.0
+sklearn==0.0.post4
+threadpoolctl==3.1.0
+tzdata==2023.3
+Werkzeug==2.3.2