Car Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "<h1 style=\"color:blue\" align=\"center\">Car Price Prediction</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af033bb",
   "metadata": {
    "papermill": {
     "duration": 0.029148,
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     "start_time": "2023-06-04T17:05:10.957337",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Importing Important Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d633dabd",
   "metadata": {
    "papermill": {
     "duration": 1.627725,
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     "start_time": "2023-06-04T17:05:11.014508",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13568272",
   "metadata": {
    "papermill": {
     "duration": 0.028292,
     "end_time": "2023-06-04T17:05:12.699441",
     "exception": false,
     "start_time": "2023-06-04T17:05:12.671149",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Reading CSV File**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d474ba6c",
   "metadata": {
    "papermill": {
     "duration": 0.090177,
     "end_time": "2023-06-04T17:05:12.818627",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>kms Driven</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Suspension</th>\n",
       "      <th>Price</th>\n",
       "      <th>Car Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>19006 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>9.29 Lakh</td>\n",
       "      <td>Honda City V CVT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>11936 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>13.95 Lakh</td>\n",
       "      <td>Honda City ZX CVT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>29635 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>9.95 Lakh</td>\n",
       "      <td>Honda City i-VTEC CVT ZX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>16163 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>13.26 Lakh</td>\n",
       "      <td>Honda City ZX CVT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>105114 kms</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>5.68 Lakh</td>\n",
       "      <td>Honda City i VTEC V</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year   kms Driven Fuel Type  Suspension        Price  \\\n",
       "0  2019   19006 kms    Petrol    Automatic    9.29 Lakh   \n",
       "1  2021   11936 kms    Petrol    Automatic   13.95 Lakh   \n",
       "2  2018   29635 kms    Petrol    Automatic    9.95 Lakh   \n",
       "3  2020   16163 kms    Petrol    Automatic   13.26 Lakh   \n",
       "4  2015  105114 kms    Petrol       Manual    5.68 Lakh   \n",
       "\n",
       "                  Car Model  \n",
       "0          Honda City V CVT  \n",
       "1         Honda City ZX CVT  \n",
       "2  Honda City i-VTEC CVT ZX  \n",
       "3         Honda City ZX CVT  \n",
       "4       Honda City i VTEC V  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"honda_car_selling.csv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e7af3ba",
   "metadata": {
    "papermill": {
     "duration": 0.028868,
     "end_time": "2023-06-04T17:05:12.875765",
     "exception": false,
     "start_time": "2023-06-04T17:05:12.846897",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h1 style=\"color:blue\" align=\"center\">Data Cleaning</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6fc0a5",
   "metadata": {
    "papermill": {
     "duration": 0.027158,
     "end_time": "2023-06-04T17:05:12.930686",
     "exception": false,
     "start_time": "2023-06-04T17:05:12.903528",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Creating Backup of Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2cc90bfa",
   "metadata": {
    "papermill": {
     "duration": 0.040818,
     "end_time": "2023-06-04T17:05:12.999785",
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     "start_time": "2023-06-04T17:05:12.958967",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "backup = df1.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26926f7b",
   "metadata": {
    "papermill": {
     "duration": 0.027786,
     "end_time": "2023-06-04T17:05:13.055995",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.028209",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Info of the Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "19940cdf",
   "metadata": {
    "papermill": {
     "duration": 0.065574,
     "end_time": "2023-06-04T17:05:13.149510",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.083936",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 999 entries, 0 to 998\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Year        999 non-null    int64 \n",
      " 1   kms Driven  999 non-null    object\n",
      " 2   Fuel Type   999 non-null    object\n",
      " 3   Suspension  999 non-null    object\n",
      " 4   Price       999 non-null    object\n",
      " 5   Car Model   999 non-null    object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 47.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7dc2406",
   "metadata": {
    "papermill": {
     "duration": 0.027441,
     "end_time": "2023-06-04T17:05:13.206145",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.178704",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Shape of Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "df09a21f",
   "metadata": {
    "papermill": {
     "duration": 0.043006,
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     "exception": false,
     "start_time": "2023-06-04T17:05:13.234666",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(999, 6)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ddfbad",
   "metadata": {
    "papermill": {
     "duration": 0.029203,
     "end_time": "2023-06-04T17:05:13.335747",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.306544",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Cleaning Year Column**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c91b4b",
   "metadata": {
    "papermill": {
     "duration": 0.031598,
     "end_time": "2023-06-04T17:05:13.397352",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.365754",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*As, we see Year Column has no Non-Year Values and it is in (int) DataType so It is already Clean*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a967a30a",
   "metadata": {
    "papermill": {
     "duration": 0.046511,
     "end_time": "2023-06-04T17:05:13.474365",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.427854",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2019, 2021, 2018, 2020, 2015, 2016, 2014, 2017, 2022, 2013, 2012,\n",
       "       2010, 2011, 2008, 2009, 2023, 2007, 2005, 2004, 2003, 2006, 2000],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"Year\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88691c0f",
   "metadata": {
    "papermill": {
     "duration": 0.03223,
     "end_time": "2023-06-04T17:05:13.540371",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.508141",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Cleaning kms Driven Column**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6430ffed",
   "metadata": {
    "papermill": {
     "duration": 0.03001,
     "end_time": "2023-06-04T17:05:13.599914",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.569904",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Firstly, we will strip the White Spaces from both the ends from the String*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0c4200f4",
   "metadata": {
    "papermill": {
     "duration": 0.041275,
     "end_time": "2023-06-04T17:05:13.670351",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.629076",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1[\"kms Driven\"] = df1[\"kms Driven\"].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ff4617",
   "metadata": {
    "papermill": {
     "duration": 0.031805,
     "end_time": "2023-06-04T17:05:13.733426",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.701621",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Then, we will Split the String with Space Delimiter and keep the First Numeric Part*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a635abbe",
   "metadata": {
    "papermill": {
     "duration": 0.045907,
     "end_time": "2023-06-04T17:05:13.810356",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.764449",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1[\"kms Driven\"] = df1[\"kms Driven\"].str.split().str.get(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902932ed",
   "metadata": {
    "papermill": {
     "duration": 0.029797,
     "end_time": "2023-06-04T17:05:13.869068",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.839271",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*And, finally we will convert the kms Driven into (int) DataType*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8393b34b",
   "metadata": {
    "papermill": {
     "duration": 0.043329,
     "end_time": "2023-06-04T17:05:13.941647",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.898318",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1[\"kms Driven\"] = df1[\"kms Driven\"].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51591132",
   "metadata": {
    "papermill": {
     "duration": 0.027984,
     "end_time": "2023-06-04T17:05:13.999145",
     "exception": false,
     "start_time": "2023-06-04T17:05:13.971161",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Cleaning the Fuel Type Column**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3374591e",
   "metadata": {
    "papermill": {
     "duration": 0.028149,
     "end_time": "2023-06-04T17:05:14.056996",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.028847",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Here, we can see Extra White Spaces in Fuel Type Column*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "36574508",
   "metadata": {
    "papermill": {
     "duration": 0.044734,
     "end_time": "2023-06-04T17:05:14.130701",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.085967",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Petrol ', ' Diesel ', ' CNG '], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"Fuel Type\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a662c86",
   "metadata": {
    "papermill": {
     "duration": 0.029858,
     "end_time": "2023-06-04T17:05:14.189913",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.160055",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*We will Strip the Extra White Spaces from both ends in Fuel Type Column*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8b9cf592",
   "metadata": {
    "papermill": {
     "duration": 0.04264,
     "end_time": "2023-06-04T17:05:14.261170",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.218530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1[\"Fuel Type\"] = df1[\"Fuel Type\"].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f2acdd6",
   "metadata": {
    "papermill": {
     "duration": 0.028699,
     "end_time": "2023-06-04T17:05:14.318186",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.289487",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Cleaning Suspension Column**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2aae059",
   "metadata": {
    "papermill": {
     "duration": 0.027649,
     "end_time": "2023-06-04T17:05:14.374703",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.347054",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Here, we can see Extra White Spaces in Suspension Column*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b3e99269",
   "metadata": {
    "papermill": {
     "duration": 0.04681,
     "end_time": "2023-06-04T17:05:14.450502",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.403692",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Automatic', ' Manual'], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"Suspension\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d577ab6",
   "metadata": {
    "papermill": {
     "duration": 0.028638,
     "end_time": "2023-06-04T17:05:14.507723",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.479085",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*We will Strip the Extra White Spaces from both ends in Suspension Column*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6ab54145",
   "metadata": {
    "papermill": {
     "duration": 0.043225,
     "end_time": "2023-06-04T17:05:14.579635",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.536410",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1[\"Suspension\"] = df1[\"Suspension\"].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e17093",
   "metadata": {
    "papermill": {
     "duration": 0.029082,
     "end_time": "2023-06-04T17:05:14.637809",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.608727",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Cleaning Price Column**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd90308c",
   "metadata": {
    "papermill": {
     "duration": 0.027938,
     "end_time": "2023-06-04T17:05:14.694081",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.666143",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Firstly, we will Strip the Extra White Spaces from both ends in Price Column*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "426fd35d",
   "metadata": {
    "papermill": {
     "duration": 0.044513,
     "end_time": "2023-06-04T17:05:14.767958",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.723445",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1[\"Price\"] = df1[\"Price\"].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ffb69a9",
   "metadata": {
    "papermill": {
     "duration": 0.033604,
     "end_time": "2023-06-04T17:05:14.837971",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.804367",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Here, we have created a Function which will convert the Price from (6.35 Lakh to 635000) and store it back to Price Column*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b4cfecb8",
   "metadata": {
    "papermill": {
     "duration": 0.07675,
     "end_time": "2023-06-04T17:05:14.945051",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.868301",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def price_cleaner(values):\n",
    "    output = values.split()\n",
    "    result = [eval(i) for i in output]\n",
    "    return np.prod(result)\n",
    "\n",
    "df1[\"Price\"] = df1[\"Price\"].str.strip()\n",
    "df1[\"Price\"] = df1[\"Price\"].str.replace(',','')\n",
    "df1[\"Price\"] = df1[\"Price\"].str.replace('Lakh','100000')\n",
    "df1[\"Price\"] = df1[\"Price\"].map(price_cleaner)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7c363f",
   "metadata": {
    "papermill": {
     "duration": 0.028364,
     "end_time": "2023-06-04T17:05:15.003987",
     "exception": false,
     "start_time": "2023-06-04T17:05:14.975623",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*And, finally we will convert the Price into (int) DataType*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5edeff49",
   "metadata": {
    "papermill": {
     "duration": 0.041126,
     "end_time": "2023-06-04T17:05:15.073429",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.032303",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1[\"Price\"] = df1[\"Price\"].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecaf51ef",
   "metadata": {
    "papermill": {
     "duration": 0.027986,
     "end_time": "2023-06-04T17:05:15.132178",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.104192",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Cleaning Car Model Column**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c55ae2d",
   "metadata": {
    "papermill": {
     "duration": 0.028263,
     "end_time": "2023-06-04T17:05:15.189712",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.161449",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Firstly, we will Strip the Extra White Spaces from both ends in Car Model Column*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f8d7bf94",
   "metadata": {
    "papermill": {
     "duration": 0.042086,
     "end_time": "2023-06-04T17:05:15.259978",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.217892",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1[\"Car Model\"] = df1[\"Car Model\"].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c03226",
   "metadata": {
    "papermill": {
     "duration": 0.028137,
     "end_time": "2023-06-04T17:05:15.316393",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.288256",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Then, we will Split the String with Space Delimiter and keep the First Three Words*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "db7c23fa",
   "metadata": {
    "papermill": {
     "duration": 0.0477,
     "end_time": "2023-06-04T17:05:15.392397",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.344697",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1[\"Car Model\"] = df1[\"Car Model\"].str.split(\" \").str.slice(start = 0,stop = 3).str.join(\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c44d11",
   "metadata": {
    "papermill": {
     "duration": 0.028283,
     "end_time": "2023-06-04T17:05:15.450152",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.421869",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Saving Cleaned Data ot CSV File**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a6bdb7ba",
   "metadata": {
    "papermill": {
     "duration": 0.053791,
     "end_time": "2023-06-04T17:05:15.533647",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.479856",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1.to_csv(\"Cleaned_Car_Data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd67670",
   "metadata": {
    "papermill": {
     "duration": 0.031772,
     "end_time": "2023-06-04T17:05:15.595080",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.563308",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h1 style=\"color:blue\" align=\"center\">Data Visualization</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dbf0c38",
   "metadata": {
    "papermill": {
     "duration": 0.034009,
     "end_time": "2023-06-04T17:05:15.659545",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.625536",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Relationship of Year with Price**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a32fb9a4",
   "metadata": {
    "papermill": {
     "duration": 2.043134,
     "end_time": "2023-06-04T17:05:17.736866",
     "exception": false,
     "start_time": "2023-06-04T17:05:15.693732",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,15))\n",
    "ax = sns.swarmplot(x = 'Year',y = 'Price',data = df1)\n",
    "ax.set_xticklabels(ax.get_xticklabels())\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dac94bfc",
   "metadata": {
    "papermill": {
     "duration": 0.030795,
     "end_time": "2023-06-04T17:05:17.797873",
     "exception": false,
     "start_time": "2023-06-04T17:05:17.767078",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Relationship of kms Driven with Price**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3cb5e5eb",
   "metadata": {
    "papermill": {
     "duration": 0.542734,
     "end_time": "2023-06-04T17:05:18.371003",
     "exception": false,
     "start_time": "2023-06-04T17:05:17.828269",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x = 'kms Driven',y = 'Price',data = df1,height = 7,aspect = 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7173a335",
   "metadata": {
    "papermill": {
     "duration": 0.032594,
     "end_time": "2023-06-04T17:05:18.436229",
     "exception": false,
     "start_time": "2023-06-04T17:05:18.403635",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Relationship of Price with Car Model, Suspension as Hue**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ab94751b",
   "metadata": {
    "papermill": {
     "duration": 2.54199,
     "end_time": "2023-06-04T17:05:21.011397",
     "exception": false,
     "start_time": "2023-06-04T17:05:18.469407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1521.38x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.relplot(x = 'Car Model',y = 'Price',data = df1,hue = 'Suspension',height = 7,aspect = 2)\n",
    "ax.set_xticklabels(rotation = 40,ha = 'right')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79767156",
   "metadata": {
    "papermill": {
     "duration": 0.035591,
     "end_time": "2023-06-04T17:05:21.084814",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.049223",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h1 style=\"color:blue\" align=\"center\">Dummy Variable</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9a8dea2",
   "metadata": {
    "papermill": {
     "duration": 0.034033,
     "end_time": "2023-06-04T17:05:21.157073",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.123040",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Creating Dummy Variable for Fuel Type Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ba1c72bb",
   "metadata": {
    "papermill": {
     "duration": 0.050993,
     "end_time": "2023-06-04T17:05:21.244118",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.193125",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dummies1 = pd.get_dummies(df1[\"Fuel Type\"],dtype = \"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51fb8c0b",
   "metadata": {
    "papermill": {
     "duration": 0.035906,
     "end_time": "2023-06-04T17:05:21.315536",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.279630",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Converting dummies1 to DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d325805b",
   "metadata": {
    "papermill": {
     "duration": 0.056552,
     "end_time": "2023-06-04T17:05:21.406835",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.350283",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CNG</th>\n",
       "      <th>Diesel</th>\n",
       "      <th>Petrol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CNG  Diesel  Petrol\n",
       "0    0       0       1\n",
       "1    0       0       1\n",
       "2    0       0       1\n",
       "3    0       0       1\n",
       "4    0       0       1"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_dummies = pd.DataFrame(dummies1)\n",
    "fuel_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2dbd0c",
   "metadata": {
    "papermill": {
     "duration": 0.037486,
     "end_time": "2023-06-04T17:05:21.482028",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.444542",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Merging df1 and fuel_dummies and store it into a New DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "90a0f1cf",
   "metadata": {
    "papermill": {
     "duration": 0.053531,
     "end_time": "2023-06-04T17:05:21.577802",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.524271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df2 = pd.concat([df1,fuel_dummies],axis = \"columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2804749",
   "metadata": {
    "papermill": {
     "duration": 0.036146,
     "end_time": "2023-06-04T17:05:21.652792",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.616646",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Creating Dummy Variable for Suspension Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ce551f5b",
   "metadata": {
    "papermill": {
     "duration": 0.061028,
     "end_time": "2023-06-04T17:05:21.753435",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.692407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dummies2 = pd.get_dummies(df2[\"Suspension\"],dtype = \"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625d8884",
   "metadata": {
    "papermill": {
     "duration": 0.043231,
     "end_time": "2023-06-04T17:05:21.843712",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.800481",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Converting dummies2 to DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c1b7c48b",
   "metadata": {
    "papermill": {
     "duration": 0.052719,
     "end_time": "2023-06-04T17:05:21.933343",
     "exception": false,
     "start_time": "2023-06-04T17:05:21.880624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Automatic</th>\n",
       "      <th>Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Automatic  Manual\n",
       "0          1       0\n",
       "1          1       0\n",
       "2          1       0\n",
       "3          1       0\n",
       "4          0       1"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suspension_dummies = pd.DataFrame(dummies2)\n",
    "suspension_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84694eb",
   "metadata": {
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   },
   "source": [
    "- **Merging df2 and suspension_dummies and store it into a New DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a87cffae",
   "metadata": {
    "papermill": {
     "duration": 0.048803,
     "end_time": "2023-06-04T17:05:22.094988",
     "exception": false,
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     "status": "completed"
    },
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   "outputs": [],
   "source": [
    "df3 = pd.concat([df2,suspension_dummies],axis = \"columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba64fdb",
   "metadata": {
    "papermill": {
     "duration": 0.034484,
     "end_time": "2023-06-04T17:05:22.167332",
     "exception": false,
     "start_time": "2023-06-04T17:05:22.132848",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Creating Dummy Variable for Car Model Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "29a2f1da",
   "metadata": {
    "papermill": {
     "duration": 0.051086,
     "end_time": "2023-06-04T17:05:22.253041",
     "exception": false,
     "start_time": "2023-06-04T17:05:22.201955",
     "status": "completed"
    },
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   "outputs": [],
   "source": [
    "dummies3 = pd.get_dummies(df3[\"Car Model\"],dtype = \"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0868ff6f",
   "metadata": {
    "papermill": {
     "duration": 0.038544,
     "end_time": "2023-06-04T17:05:22.326848",
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     "start_time": "2023-06-04T17:05:22.288304",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Converting dummies3 to DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b2d3dc5b",
   "metadata": {
    "papermill": {
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     "start_time": "2023-06-04T17:05:22.366623",
     "status": "completed"
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       "   Honda Accord 2.4  Honda Accord VTi-L  Honda Amaze E  Honda Amaze EX  \\\n",
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     "metadata": {},
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   "source": [
    "car_dummies = pd.DataFrame(dummies3)\n",
    "car_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a5ecac7",
   "metadata": {
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   },
   "source": [
    "- **Merging df3 and car_dummies and store it into a New DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "091b75c6",
   "metadata": {
    "papermill": {
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     "status": "completed"
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   "source": [
    "df4 = pd.concat([df3,car_dummies],axis = \"columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63b828a",
   "metadata": {
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   "source": [
    "- **Dropping Fuel Type, Suspension, Car Model Columns after Converting it into Dummies**"
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  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0ffe5967",
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       "   Year  kms Driven    Price  CNG  Diesel  Petrol  Automatic  Manual  \\\n",
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     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.drop([\"Fuel Type\",\"Suspension\",\"Car Model\"],axis = \"columns\")\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8baed74",
   "metadata": {
    "papermill": {
     "duration": 0.043696,
     "end_time": "2023-06-04T17:05:22.869059",
     "exception": false,
     "start_time": "2023-06-04T17:05:22.825363",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h1 style=\"color:blue\" align=\"center\">Outlier Removal</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c9815a",
   "metadata": {
    "papermill": {
     "duration": 0.035954,
     "end_time": "2023-06-04T17:05:22.940657",
     "exception": false,
     "start_time": "2023-06-04T17:05:22.904703",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Describing the Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4622b734",
   "metadata": {
    "papermill": {
     "duration": 0.049227,
     "end_time": "2023-06-04T17:05:23.037966",
     "exception": false,
     "start_time": "2023-06-04T17:05:22.988739",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Here, we can see our 75% of Cars has driven 85000 kms and our Maximum Value in kms Driven is 11 Lakh kms which is an Outlier*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd38e4f",
   "metadata": {
    "papermill": {
     "duration": 0.037102,
     "end_time": "2023-06-04T17:05:23.128933",
     "exception": false,
     "start_time": "2023-06-04T17:05:23.091831",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Here, we can see our 75% of Cars has Price 7 Lakh and our Maximum Price is 26 Lakh which is an Outlier*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "51b140ce",
   "metadata": {
    "papermill": {
     "duration": 0.256573,
     "end_time": "2023-06-04T17:05:23.423987",
     "exception": false,
     "start_time": "2023-06-04T17:05:23.167414",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>kms Driven</th>\n",
       "      <th>Price</th>\n",
       "      <th>CNG</th>\n",
       "      <th>Diesel</th>\n",
       "      <th>Petrol</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>Manual</th>\n",
       "      <th>Honda Accord 2.4</th>\n",
       "      <th>Honda Accord VTi-L</th>\n",
       "      <th>...</th>\n",
       "      <th>Honda Jazz X</th>\n",
       "      <th>Honda Mobilio E</th>\n",
       "      <th>Honda Mobilio RS</th>\n",
       "      <th>Honda Mobilio S</th>\n",
       "      <th>Honda Mobilio V</th>\n",
       "      <th>Honda WR-V Edge</th>\n",
       "      <th>Honda WR-V SV</th>\n",
       "      <th>Honda WR-V VX</th>\n",
       "      <th>Honda WR-V i-DTEC</th>\n",
       "      <th>Honda WR-V i-VTEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>999.000000</td>\n",
       "      <td>9.990000e+02</td>\n",
       "      <td>9.990000e+02</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.717718</td>\n",
       "      <td>6.607667e+04</td>\n",
       "      <td>5.530370e+05</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.201201</td>\n",
       "      <td>0.797798</td>\n",
       "      <td>0.223223</td>\n",
       "      <td>0.776777</td>\n",
       "      <td>0.013013</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004004</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.004004</td>\n",
       "      <td>0.005005</td>\n",
       "      <td>0.009009</td>\n",
       "      <td>0.002002</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>0.019019</td>\n",
       "      <td>0.018018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.713756</td>\n",
       "      <td>5.310468e+04</td>\n",
       "      <td>3.030851e+05</td>\n",
       "      <td>0.031639</td>\n",
       "      <td>0.401099</td>\n",
       "      <td>0.401843</td>\n",
       "      <td>0.416615</td>\n",
       "      <td>0.416615</td>\n",
       "      <td>0.113387</td>\n",
       "      <td>0.031639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063182</td>\n",
       "      <td>0.031639</td>\n",
       "      <td>0.063182</td>\n",
       "      <td>0.070604</td>\n",
       "      <td>0.094535</td>\n",
       "      <td>0.044721</td>\n",
       "      <td>0.031639</td>\n",
       "      <td>0.054745</td>\n",
       "      <td>0.136660</td>\n",
       "      <td>0.133083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1.425000e+03</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>3.534100e+04</td>\n",
       "      <td>3.265000e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>5.990000e+04</td>\n",
       "      <td>5.099990e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>8.528750e+04</td>\n",
       "      <td>7.000000e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2023.000000</td>\n",
       "      <td>1.100000e+06</td>\n",
       "      <td>2.650000e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year    kms Driven         Price         CNG      Diesel  \\\n",
       "count   999.000000  9.990000e+02  9.990000e+02  999.000000  999.000000   \n",
       "mean   2014.717718  6.607667e+04  5.530370e+05    0.001001    0.201201   \n",
       "std       3.713756  5.310468e+04  3.030851e+05    0.031639    0.401099   \n",
       "min    2000.000000  1.425000e+03  4.500000e+04    0.000000    0.000000   \n",
       "25%    2013.000000  3.534100e+04  3.265000e+05    0.000000    0.000000   \n",
       "50%    2015.000000  5.990000e+04  5.099990e+05    0.000000    0.000000   \n",
       "75%    2017.000000  8.528750e+04  7.000000e+05    0.000000    0.000000   \n",
       "max    2023.000000  1.100000e+06  2.650000e+06    1.000000    1.000000   \n",
       "\n",
       "           Petrol   Automatic      Manual  Honda Accord 2.4  \\\n",
       "count  999.000000  999.000000  999.000000        999.000000   \n",
       "mean     0.797798    0.223223    0.776777          0.013013   \n",
       "std      0.401843    0.416615    0.416615          0.113387   \n",
       "min      0.000000    0.000000    0.000000          0.000000   \n",
       "25%      1.000000    0.000000    1.000000          0.000000   \n",
       "50%      1.000000    0.000000    1.000000          0.000000   \n",
       "75%      1.000000    0.000000    1.000000          0.000000   \n",
       "max      1.000000    1.000000    1.000000          1.000000   \n",
       "\n",
       "       Honda Accord VTi-L  ...  Honda Jazz X  Honda Mobilio E  \\\n",
       "count          999.000000  ...    999.000000       999.000000   \n",
       "mean             0.001001  ...      0.004004         0.001001   \n",
       "std              0.031639  ...      0.063182         0.031639   \n",
       "min              0.000000  ...      0.000000         0.000000   \n",
       "25%              0.000000  ...      0.000000         0.000000   \n",
       "50%              0.000000  ...      0.000000         0.000000   \n",
       "75%              0.000000  ...      0.000000         0.000000   \n",
       "max              1.000000  ...      1.000000         1.000000   \n",
       "\n",
       "       Honda Mobilio RS  Honda Mobilio S  Honda Mobilio V  Honda WR-V Edge  \\\n",
       "count        999.000000       999.000000       999.000000       999.000000   \n",
       "mean           0.004004         0.005005         0.009009         0.002002   \n",
       "std            0.063182         0.070604         0.094535         0.044721   \n",
       "min            0.000000         0.000000         0.000000         0.000000   \n",
       "25%            0.000000         0.000000         0.000000         0.000000   \n",
       "50%            0.000000         0.000000         0.000000         0.000000   \n",
       "75%            0.000000         0.000000         0.000000         0.000000   \n",
       "max            1.000000         1.000000         1.000000         1.000000   \n",
       "\n",
       "       Honda WR-V SV  Honda WR-V VX  Honda WR-V i-DTEC  Honda WR-V i-VTEC  \n",
       "count     999.000000     999.000000         999.000000         999.000000  \n",
       "mean        0.001001       0.003003           0.019019           0.018018  \n",
       "std         0.031639       0.054745           0.136660           0.133083  \n",
       "min         0.000000       0.000000           0.000000           0.000000  \n",
       "25%         0.000000       0.000000           0.000000           0.000000  \n",
       "50%         0.000000       0.000000           0.000000           0.000000  \n",
       "75%         0.000000       0.000000           0.000000           0.000000  \n",
       "max         1.000000       1.000000           1.000000           1.000000  \n",
       "\n",
       "[8 rows x 68 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5cfe98f",
   "metadata": {
    "papermill": {
     "duration": 0.039962,
     "end_time": "2023-06-04T17:05:23.505791",
     "exception": false,
     "start_time": "2023-06-04T17:05:23.465829",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*So we will filter all the Cars with kms Driven less than 1.5 Lakh kms for best possible Result*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9abe276f",
   "metadata": {
    "papermill": {
     "duration": 0.052933,
     "end_time": "2023-06-04T17:05:23.596770",
     "exception": false,
     "start_time": "2023-06-04T17:05:23.543837",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df5 = df5[df5[\"kms Driven\"]<150000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017a3453",
   "metadata": {
    "papermill": {
     "duration": 0.03685,
     "end_time": "2023-06-04T17:05:23.670525",
     "exception": false,
     "start_time": "2023-06-04T17:05:23.633675",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*So we will filter all the Cars with Price less than 10 Lakh for best possible Result*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c8e4945a",
   "metadata": {
    "papermill": {
     "duration": 0.050264,
     "end_time": "2023-06-04T17:05:23.757053",
     "exception": false,
     "start_time": "2023-06-04T17:05:23.706789",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df5 = df5[df5[\"Price\"]<1000000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3407ef",
   "metadata": {
    "papermill": {
     "duration": 0.03631,
     "end_time": "2023-06-04T17:05:23.831121",
     "exception": false,
     "start_time": "2023-06-04T17:05:23.794811",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h1 style=\"color:blue\" align=\"center\">Model Building</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36edd2c9",
   "metadata": {
    "papermill": {
     "duration": 0.036015,
     "end_time": "2023-06-04T17:05:23.903850",
     "exception": false,
     "start_time": "2023-06-04T17:05:23.867835",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Defining Dependent and Independent Variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b194814e",
   "metadata": {
    "papermill": {
     "duration": 0.051311,
     "end_time": "2023-06-04T17:05:23.992628",
     "exception": false,
     "start_time": "2023-06-04T17:05:23.941317",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df5.drop([\"Price\"],axis = \"columns\")\n",
    "y = df5.Price"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "686d1a04",
   "metadata": {
    "papermill": {
     "duration": 0.037512,
     "end_time": "2023-06-04T17:05:24.069034",
     "exception": false,
     "start_time": "2023-06-04T17:05:24.031522",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Train Test Split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "771eb37b",
   "metadata": {
    "papermill": {
     "duration": 0.339322,
     "end_time": "2023-06-04T17:05:24.445263",
     "exception": false,
     "start_time": "2023-06-04T17:05:24.105941",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e04f2d15",
   "metadata": {
    "papermill": {
     "duration": 0.051337,
     "end_time": "2023-06-04T17:05:24.535556",
     "exception": false,
     "start_time": "2023-06-04T17:05:24.484219",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd079a9",
   "metadata": {
    "papermill": {
     "duration": 0.037601,
     "end_time": "2023-06-04T17:05:24.610286",
     "exception": false,
     "start_time": "2023-06-04T17:05:24.572685",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Importing LinearRegression Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f4073a7b",
   "metadata": {
    "papermill": {
     "duration": 0.15793,
     "end_time": "2023-06-04T17:05:24.804909",
     "exception": false,
     "start_time": "2023-06-04T17:05:24.646979",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0705af71",
   "metadata": {
    "papermill": {
     "duration": 0.049329,
     "end_time": "2023-06-04T17:05:24.891020",
     "exception": false,
     "start_time": "2023-06-04T17:05:24.841691",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b43897",
   "metadata": {
    "papermill": {
     "duration": 0.037031,
     "end_time": "2023-06-04T17:05:24.965204",
     "exception": false,
     "start_time": "2023-06-04T17:05:24.928173",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Fitting the Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8973ee69",
   "metadata": {
    "papermill": {
     "duration": 0.087368,
     "end_time": "2023-06-04T17:05:25.089641",
     "exception": false,
     "start_time": "2023-06-04T17:05:25.002273",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461c0cc3",
   "metadata": {
    "papermill": {
     "duration": 0.044858,
     "end_time": "2023-06-04T17:05:25.212239",
     "exception": false,
     "start_time": "2023-06-04T17:05:25.167381",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Score of the Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "26b26ea2",
   "metadata": {
    "papermill": {
     "duration": 0.060656,
     "end_time": "2023-06-04T17:05:25.309482",
     "exception": false,
     "start_time": "2023-06-04T17:05:25.248826",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8211897942749485"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6054797",
   "metadata": {
    "papermill": {
     "duration": 0.051395,
     "end_time": "2023-06-04T17:05:25.439478",
     "exception": false,
     "start_time": "2023-06-04T17:05:25.388083",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Importing KFold for measuring Model Accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "45e3959c",
   "metadata": {
    "papermill": {
     "duration": 0.049821,
     "end_time": "2023-06-04T17:05:25.527083",
     "exception": false,
     "start_time": "2023-06-04T17:05:25.477262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "dba9af1e",
   "metadata": {
    "papermill": {
     "duration": 0.05433,
     "end_time": "2023-06-04T17:05:25.619037",
     "exception": false,
     "start_time": "2023-06-04T17:05:25.564707",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=5, random_state=None, shuffle=False)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits = 5)\n",
    "kf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df427b1",
   "metadata": {
    "papermill": {
     "duration": 0.038095,
     "end_time": "2023-06-04T17:05:25.696058",
     "exception": false,
     "start_time": "2023-06-04T17:05:25.657963",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Importing CrossValScore for Best Score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c44da62a",
   "metadata": {
    "papermill": {
     "duration": 0.053217,
     "end_time": "2023-06-04T17:05:25.788828",
     "exception": false,
     "start_time": "2023-06-04T17:05:25.735611",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d67a4783",
   "metadata": {
    "papermill": {
     "duration": 0.156435,
     "end_time": "2023-06-04T17:05:25.984422",
     "exception": false,
     "start_time": "2023-06-04T17:05:25.827987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cross_val = cross_val_score(LinearRegression(),X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afb66ac0",
   "metadata": {
    "papermill": {
     "duration": 0.053784,
     "end_time": "2023-06-04T17:05:26.124554",
     "exception": false,
     "start_time": "2023-06-04T17:05:26.070770",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Mean of CrossValScore**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3536263c",
   "metadata": {
    "papermill": {
     "duration": 0.054043,
     "end_time": "2023-06-04T17:05:26.217070",
     "exception": false,
     "start_time": "2023-06-04T17:05:26.163027",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8335835128642257"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f77e43a",
   "metadata": {
    "papermill": {
     "duration": 0.036925,
     "end_time": "2023-06-04T17:05:26.292778",
     "exception": false,
     "start_time": "2023-06-04T17:05:26.255853",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- **Predicting the Output**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "230a8144",
   "metadata": {
    "papermill": {
     "duration": 0.060735,
     "end_time": "2023-06-04T17:05:26.390369",
     "exception": false,
     "start_time": "2023-06-04T17:05:26.329634",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_predicted = lr.predict(X_test)"
   ]
  }
 ],
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  "papermill": {
   "default_parameters": {},
   "duration": 32.940944,
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   "environment_variables": {},
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