Car Price Predictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a84930b3",
   "metadata": {},
   "source": [
    "## Import Necessary Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "40564eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "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\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a141bc9",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "276a6e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset/car_price.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fb2ae49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>"
      ],
      "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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "289131fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46622053",
   "metadata": {},
   "source": [
    "## Create Backup For Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fcd1635e",
   "metadata": {},
   "outputs": [],
   "source": [
    "backup = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a4de438",
   "metadata": {},
   "source": [
    "## Clean \"year\" Field Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21f1c768",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['year'].str.isnumeric()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e35b39e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = pd.to_numeric(df['year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1cba16",
   "metadata": {},
   "source": [
    "## Clean \"price\" Field Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a45d424",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Price'] != 'Ask For Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7cbfec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Price'] = pd.to_numeric(df['Price'].str.replace(',', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cdd749d",
   "metadata": {},
   "source": [
    "## Clean \"kms_driven\" Field Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a60b490",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['kms_driven'] = df['kms_driven'].str.split().str.get(0).str.replace(',', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cbda8609",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['kms_driven'].str.isnumeric()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "09aa69bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['kms_driven'] = pd.to_numeric(df['kms_driven'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb81481",
   "metadata": {},
   "source": [
    "## Clean \"fuel_type\" Field Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dca06d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[~df['fuel_type'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a65aa7",
   "metadata": {},
   "source": [
    "## Slice \"name\" Field To 3 Words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6d213188",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['name'] = df['name'].str.split().str.slice(0, 3).str.join(' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f82d8ef",
   "metadata": {},
   "source": [
    "## Check For Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5c28f535",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>816.0</td>\n",
       "      <td>2012.444853</td>\n",
       "      <td>4.002992</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>816.0</td>\n",
       "      <td>411717.615196</td>\n",
       "      <td>475184.422264</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>299999.0</td>\n",
       "      <td>491250.0</td>\n",
       "      <td>8500003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kms_driven</th>\n",
       "      <td>816.0</td>\n",
       "      <td>46275.531863</td>\n",
       "      <td>34297.428044</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>56818.5</td>\n",
       "      <td>400000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count           mean            std      min       25%       50%  \\\n",
       "year        816.0    2012.444853       4.002992   1995.0    2010.0    2013.0   \n",
       "Price       816.0  411717.615196  475184.422264  30000.0  175000.0  299999.0   \n",
       "kms_driven  816.0   46275.531863   34297.428044      0.0   27000.0   41000.0   \n",
       "\n",
       "                 75%        max  \n",
       "year          2015.0     2019.0  \n",
       "Price       491250.0  8500003.0  \n",
       "kms_driven   56818.5   400000.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ed1f93c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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,
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Price', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1f8f204e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='kms_driven'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='kms_driven', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e7f6b7d",
   "metadata": {},
   "source": [
    "## Exclude Outliers Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a9bf9248",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Price'] < 6e6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2efb6687",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['kms_driven'] < 300000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5594e7c",
   "metadata": {},
   "source": [
    "## Reset Data Frame Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "09907e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e99ffd",
   "metadata": {},
   "source": [
    "## Split Dataset Into Training And Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "25152f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Price', axis=1)\n",
    "y = df['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b549d39f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a9d0cd",
   "metadata": {},
   "source": [
    "## Apply One Hot Encoder To Object Type Fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4bb8fd2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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>"
      ],
      "text/plain": [
       "OneHotEncoder()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = OneHotEncoder()\n",
    "ohe.fit(X[['name', 'company', 'fuel_type']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c83f7cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_trans = make_column_transformer(\n",
    "    (\n",
    "        OneHotEncoder(categories=ohe.categories_),\n",
    "        ['name', 'company', 'fuel_type']\n",
    "    ),\n",
    "    remainder='passthrough'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14836a0",
   "metadata": {},
   "source": [
    "## Create Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "64b01d87",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cd7b6ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = make_pipeline(column_trans, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8d54011e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" 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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" 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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" 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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" 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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" 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>"
      ],
      "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())])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0603dfba",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3d5a9efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.658349908356938"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a378bfe1",
   "metadata": {},
   "source": [
    "## Improve Model Performance By Deciding Random State When Splitting Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cfbcea96",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []\n",
    "\n",
    "for i in range(1000):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=i)\n",
    "    model = LinearRegression()\n",
    "    pipe = make_pipeline(column_trans, model)\n",
    "    pipe.fit(X_train, y_train)\n",
    "    y_predict = pipe.predict(X_test)\n",
    "    scores.append(r2_score(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "eaa47afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_random_state = np.argmax(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0a2e766f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9038169185099911"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores[optimal_random_state]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bc34432a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9038169185099911"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=optimal_random_state)\n",
    "model = LinearRegression()\n",
    "pipe = make_pipeline(column_trans, model)\n",
    "pipe.fit(X_train, y_train)\n",
    "y_predict = pipe.predict(X_test)\n",
    "r2_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a3112c",
   "metadata": {},
   "source": [
    "## Test Predict Car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "429b50d4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([335333.36430919])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.predict(pd.DataFrame([['Maruti Suzuki Swift', 'Maruti', 2015, 10000, 'Petrol']], columns=['name', 'company', 'year', 'kms_driven', 'fuel_type']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e2c591",
   "metadata": {},
   "source": [
    "## Generate File That Should Be Used On Project Development Using Flask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ef827569",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(pipe, open('CarPricePredictorModel.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "55d770d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('cleaned_car_price.csv')"
   ]
  }
 ],
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