car_prices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c88e7910",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e592fc8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('germany-cars-zenrows.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d8f63893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mileage</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>fuel</th>\n",
       "      <th>gear</th>\n",
       "      <th>offerType</th>\n",
       "      <th>price</th>\n",
       "      <th>hp</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>235000</td>\n",
       "      <td>BMW</td>\n",
       "      <td>316</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Used</td>\n",
       "      <td>6800</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92800</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Used</td>\n",
       "      <td>6877</td>\n",
       "      <td>122.0</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149300</td>\n",
       "      <td>SEAT</td>\n",
       "      <td>Exeo</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Used</td>\n",
       "      <td>6900</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>96200</td>\n",
       "      <td>Renault</td>\n",
       "      <td>Megane</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Used</td>\n",
       "      <td>6950</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>156000</td>\n",
       "      <td>Peugeot</td>\n",
       "      <td>308</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Used</td>\n",
       "      <td>6950</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mileage        make   model      fuel    gear offerType  price     hp  year\n",
       "0   235000         BMW     316    Diesel  Manual      Used   6800  116.0  2011\n",
       "1    92800  Volkswagen    Golf  Gasoline  Manual      Used   6877  122.0  2011\n",
       "2   149300        SEAT    Exeo  Gasoline  Manual      Used   6900  160.0  2011\n",
       "3    96200     Renault  Megane  Gasoline  Manual      Used   6950  110.0  2011\n",
       "4   156000     Peugeot     308  Gasoline  Manual      Used   6950  156.0  2011"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "abe44382",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 46405 entries, 0 to 46404\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   mileage    46405 non-null  int64  \n",
      " 1   make       46405 non-null  object \n",
      " 2   model      46262 non-null  object \n",
      " 3   fuel       46405 non-null  object \n",
      " 4   gear       46223 non-null  object \n",
      " 5   offerType  46405 non-null  object \n",
      " 6   price      46405 non-null  int64  \n",
      " 7   hp         46376 non-null  float64\n",
      " 8   year       46405 non-null  int64  \n",
      "dtypes: float64(1), int64(3), object(5)\n",
      "memory usage: 3.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c5dbff88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mileage        0\n",
       "make           0\n",
       "model        143\n",
       "fuel           0\n",
       "gear         182\n",
       "offerType      0\n",
       "price          0\n",
       "hp            29\n",
       "year           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b3c79310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46405, 9)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "b5ea1373",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets drop null values of model, gear and hp\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "524ab6ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = 2024 - df['year']\n",
    "df.drop('year',axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a8a9e2c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 46071 entries, 0 to 46404\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   mileage    46071 non-null  int64  \n",
      " 1   make       46071 non-null  object \n",
      " 2   model      46071 non-null  object \n",
      " 3   fuel       46071 non-null  object \n",
      " 4   gear       46071 non-null  object \n",
      " 5   offerType  46071 non-null  object \n",
      " 6   price      46071 non-null  int64  \n",
      " 7   hp         46071 non-null  float64\n",
      " 8   Age        46071 non-null  int64  \n",
      "dtypes: float64(1), int64(3), object(5)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "272d2190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mileage</th>\n",
       "      <th>price</th>\n",
       "      <th>hp</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mileage</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.303550</td>\n",
       "      <td>-0.014901</td>\n",
       "      <td>0.678053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.303550</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.746715</td>\n",
       "      <td>-0.405698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp</th>\n",
       "      <td>-0.014901</td>\n",
       "      <td>0.746715</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.166900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.678053</td>\n",
       "      <td>-0.405698</td>\n",
       "      <td>-0.166900</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mileage     price        hp       Age\n",
       "mileage  1.000000 -0.303550 -0.014901  0.678053\n",
       "price   -0.303550  1.000000  0.746715 -0.405698\n",
       "hp      -0.014901  0.746715  1.000000 -0.166900\n",
       "Age      0.678053 -0.405698 -0.166900  1.000000"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_df = df.select_dtypes(include=[np.number])\n",
    "numeric_df.corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "9652065f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(numeric_df.corr(), annot=True, cmap=\"Set2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c5441c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mileage        int64\n",
       "make          object\n",
       "model         object\n",
       "fuel          object\n",
       "gear          object\n",
       "offerType     object\n",
       "price          int64\n",
       "hp           float64\n",
       "Age            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "622e76db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Used', 'Demonstration', \"Employee's car\", 'Pre-registered', 'New'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.offerType.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "54158c5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "offerType\n",
       "Used              39822\n",
       "Pre-registered     2768\n",
       "Demonstration      2351\n",
       "Employee's car     1117\n",
       "New                  13\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.offerType.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "9d486b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop 'new'by filtering out value counts below as we have only afew data values\n",
    "df = df.groupby(\"offerType\").filter(lambda x: len(x) > 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "e56ec124",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"offerType\", y=\"price\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "c041f5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    46058.000000\n",
       "mean       132.969538\n",
       "std         74.991707\n",
       "min          1.000000\n",
       "25%         86.000000\n",
       "50%        116.000000\n",
       "75%        150.000000\n",
       "max        850.000000\n",
       "Name: hp, dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.hp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "3ff629d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for horsepower below 5 and drop\n",
    "hp_drop = df[df.hp<5]\n",
    "df.drop(hp_drop.index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "45c02ace",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Diesel', 'Gasoline', 'Electric/Gasoline', '-/- (Fuel)',\n",
       "       'Electric', 'Electric/Diesel', 'CNG', 'LPG', 'Others', 'Hydrogen',\n",
       "       'Ethanol'], dtype=object)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fuel.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "cf649519",
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for fuel with value counts above 50\n",
    "df = df.groupby(\"fuel\").filter(lambda x: len(x) > 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "34a4a24c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10,4))\n",
    "sns.barplot(x=\"fuel\", y=\"price\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d360943b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Manual', 'Automatic', 'Semi-automatic'], dtype=object)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.gear.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "22fe2e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"gear\", y=\"price\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e8e24333",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the make and model of car as \n",
    "df.drop(labels=['make','model'],axis= 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "cc7eaf60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuel</th>\n",
       "      <th>gear</th>\n",
       "      <th>offerType</th>\n",
       "      <th>price</th>\n",
       "      <th>hp</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>235000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Used</td>\n",
       "      <td>6800</td>\n",
       "      <td>116.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92800</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Used</td>\n",
       "      <td>6877</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149300</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Used</td>\n",
       "      <td>6900</td>\n",
       "      <td>160.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>96200</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Used</td>\n",
       "      <td>6950</td>\n",
       "      <td>110.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>156000</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Used</td>\n",
       "      <td>6950</td>\n",
       "      <td>156.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mileage      fuel    gear offerType  price     hp  Age\n",
       "0   235000    Diesel  Manual      Used   6800  116.0   13\n",
       "1    92800  Gasoline  Manual      Used   6877  122.0   13\n",
       "2   149300  Gasoline  Manual      Used   6900  160.0   13\n",
       "3    96200  Gasoline  Manual      Used   6950  110.0   13\n",
       "4   156000  Gasoline  Manual      Used   6950  156.0   13"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "4e8a2881",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(data = df,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "e1e9260f",
   "metadata": {},
   "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",
       "        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>mileage</th>\n",
       "      <th>price</th>\n",
       "      <th>hp</th>\n",
       "      <th>Age</th>\n",
       "      <th>fuel_Diesel</th>\n",
       "      <th>fuel_Electric</th>\n",
       "      <th>fuel_Electric_Diesel</th>\n",
       "      <th>fuel_Electric_Gasoline</th>\n",
       "      <th>fuel_Gasoline</th>\n",
       "      <th>fuel_LPG</th>\n",
       "      <th>gear_Manual</th>\n",
       "      <th>gear_Semi_automatic</th>\n",
       "      <th>offerType_Employee_car</th>\n",
       "      <th>offerType_Pre_registered</th>\n",
       "      <th>offerType_Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>235000</td>\n",
       "      <td>6800</td>\n",
       "      <td>116.0</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92800</td>\n",
       "      <td>6877</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149300</td>\n",
       "      <td>6900</td>\n",
       "      <td>160.0</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>96200</td>\n",
       "      <td>6950</td>\n",
       "      <td>110.0</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>156000</td>\n",
       "      <td>6950</td>\n",
       "      <td>156.0</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mileage  price     hp  Age  fuel_Diesel  fuel_Electric  \\\n",
       "0   235000   6800  116.0   13         True          False   \n",
       "1    92800   6877  122.0   13        False          False   \n",
       "2   149300   6900  160.0   13        False          False   \n",
       "3    96200   6950  110.0   13        False          False   \n",
       "4   156000   6950  156.0   13        False          False   \n",
       "\n",
       "   fuel_Electric_Diesel  fuel_Electric_Gasoline  fuel_Gasoline  fuel_LPG  \\\n",
       "0                 False                   False          False     False   \n",
       "1                 False                   False           True     False   \n",
       "2                 False                   False           True     False   \n",
       "3                 False                   False           True     False   \n",
       "4                 False                   False           True     False   \n",
       "\n",
       "   gear_Manual  gear_Semi_automatic  offerType_Employee_car  \\\n",
       "0         True                False                   False   \n",
       "1         True                False                   False   \n",
       "2         True                False                   False   \n",
       "3         True                False                   False   \n",
       "4         True                False                   False   \n",
       "\n",
       "   offerType_Pre_registered  offerType_Used  \n",
       "0                     False            True  \n",
       "1                     False            True  \n",
       "2                     False            True  \n",
       "3                     False            True  \n",
       "4                     False            True  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "1b62eed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mileage', 'price', 'hp', 'Age', 'fuel_Diesel', 'fuel_Electric',\n",
       "       'fuel_Electric_Diesel', 'fuel_Electric_Gasoline', 'fuel_Gasoline',\n",
       "       'fuel_LPG', 'gear_Manual', 'gear_Semi_automatic',\n",
       "       'offerType_Employee_car', 'offerType_Pre_registered', 'offerType_Used'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "620afc97",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'fuel_Electric/Diesel':'fuel_Electric_Diesel',\\\n",
    "           'fuel_Electric/Gasoline':'fuel_Electric_Gasoline',\\\n",
    "           \"offerType_Employee's car\":'offerType_Employee_car',\\\n",
    "           'offerType_Pre-registered':'offerType_Pre_registered',\\\n",
    "           'gear_Semi-automatic':'gear_Semi_automatic'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "223b7ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mileage</th>\n",
       "      <th>price</th>\n",
       "      <th>hp</th>\n",
       "      <th>Age</th>\n",
       "      <th>fuel_Diesel</th>\n",
       "      <th>fuel_Electric</th>\n",
       "      <th>fuel_Electric_Diesel</th>\n",
       "      <th>fuel_Electric_Gasoline</th>\n",
       "      <th>fuel_Gasoline</th>\n",
       "      <th>fuel_LPG</th>\n",
       "      <th>gear_Manual</th>\n",
       "      <th>gear_Semi_automatic</th>\n",
       "      <th>offerType_Employee_car</th>\n",
       "      <th>offerType_Pre_registered</th>\n",
       "      <th>offerType_Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>235000</td>\n",
       "      <td>6800</td>\n",
       "      <td>116.0</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92800</td>\n",
       "      <td>6877</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149300</td>\n",
       "      <td>6900</td>\n",
       "      <td>160.0</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>96200</td>\n",
       "      <td>6950</td>\n",
       "      <td>110.0</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>156000</td>\n",
       "      <td>6950</td>\n",
       "      <td>156.0</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mileage  price     hp  Age  fuel_Diesel  fuel_Electric  \\\n",
       "0   235000   6800  116.0   13         True          False   \n",
       "1    92800   6877  122.0   13        False          False   \n",
       "2   149300   6900  160.0   13        False          False   \n",
       "3    96200   6950  110.0   13        False          False   \n",
       "4   156000   6950  156.0   13        False          False   \n",
       "\n",
       "   fuel_Electric_Diesel  fuel_Electric_Gasoline  fuel_Gasoline  fuel_LPG  \\\n",
       "0                 False                   False          False     False   \n",
       "1                 False                   False           True     False   \n",
       "2                 False                   False           True     False   \n",
       "3                 False                   False           True     False   \n",
       "4                 False                   False           True     False   \n",
       "\n",
       "   gear_Manual  gear_Semi_automatic  offerType_Employee_car  \\\n",
       "0         True                False                   False   \n",
       "1         True                False                   False   \n",
       "2         True                False                   False   \n",
       "3         True                False                   False   \n",
       "4         True                False                   False   \n",
       "\n",
       "   offerType_Pre_registered  offerType_Used  \n",
       "0                     False            True  \n",
       "1                     False            True  \n",
       "2                     False            True  \n",
       "3                     False            True  \n",
       "4                     False            True  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "7d17e90d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\"mileage\", \"hp\", \"Age\", \"fuel_Diesel\", \"fuel_Electric\", \"fuel_Electric_Diesel\",\n",
    "  \"fuel_Electric_Gasoline\", \"fuel_Gasoline\", \"fuel_LPG\", \"gear_Manual\", \"gear_Semi_automatic\",\n",
    "  \"offerType_Employee_car\", \"offerType_Pre_registered\", \"offerType_Used\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "ad252365",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.DataFrame(df, columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "20fc0267",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('price',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "6cd87aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "a21f3576",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "03b3fb36",
   "metadata": {},
   "outputs": [],
   "source": [
    "steps=[('scale',StandardScaler()),('model',LinearRegression())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "5a4ee5fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()), (&#x27;model&#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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()), (&#x27;model&#x27;, LinearRegression())])</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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scale', StandardScaler()), ('model', LinearRegression())])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe=Pipeline(steps)\n",
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "13a1e5e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "ae6748d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "95f97b24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mileage</th>\n",
       "      <th>hp</th>\n",
       "      <th>Age</th>\n",
       "      <th>fuel_Diesel</th>\n",
       "      <th>fuel_Electric</th>\n",
       "      <th>fuel_Electric_Diesel</th>\n",
       "      <th>fuel_Electric_Gasoline</th>\n",
       "      <th>fuel_Gasoline</th>\n",
       "      <th>fuel_LPG</th>\n",
       "      <th>gear_Manual</th>\n",
       "      <th>gear_Semi_automatic</th>\n",
       "      <th>offerType_Employee_car</th>\n",
       "      <th>offerType_Pre_registered</th>\n",
       "      <th>offerType_Used</th>\n",
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       "  <tbody>\n",
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       "      <td>1.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>156000.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46401</th>\n",
       "      <td>99.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46402</th>\n",
       "      <td>99.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46403</th>\n",
       "      <td>99.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46404</th>\n",
       "      <td>99.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45985 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mileage     hp   Age  fuel_Diesel  fuel_Electric  \\\n",
       "0      235000.0  116.0  13.0          1.0            0.0   \n",
       "1       92800.0  122.0  13.0          0.0            0.0   \n",
       "2      149300.0  160.0  13.0          0.0            0.0   \n",
       "3       96200.0  110.0  13.0          0.0            0.0   \n",
       "4      156000.0  156.0  13.0          0.0            0.0   \n",
       "...         ...    ...   ...          ...            ...   \n",
       "46400      99.0   71.0   3.0          0.0            0.0   \n",
       "46401      99.0   71.0   3.0          0.0            0.0   \n",
       "46402      99.0   71.0   3.0          0.0            0.0   \n",
       "46403      99.0   71.0   3.0          0.0            0.0   \n",
       "46404      99.0   71.0   3.0          0.0            0.0   \n",
       "\n",
       "       fuel_Electric_Diesel  fuel_Electric_Gasoline  fuel_Gasoline  fuel_LPG  \\\n",
       "0                       0.0                     0.0            0.0       0.0   \n",
       "1                       0.0                     0.0            1.0       0.0   \n",
       "2                       0.0                     0.0            1.0       0.0   \n",
       "3                       0.0                     0.0            1.0       0.0   \n",
       "4                       0.0                     0.0            1.0       0.0   \n",
       "...                     ...                     ...            ...       ...   \n",
       "46400                   0.0                     1.0            0.0       0.0   \n",
       "46401                   0.0                     1.0            0.0       0.0   \n",
       "46402                   0.0                     1.0            0.0       0.0   \n",
       "46403                   0.0                     1.0            0.0       0.0   \n",
       "46404                   0.0                     1.0            0.0       0.0   \n",
       "\n",
       "       gear_Manual  gear_Semi_automatic  offerType_Employee_car  \\\n",
       "0              1.0                  0.0                     0.0   \n",
       "1              1.0                  0.0                     0.0   \n",
       "2              1.0                  0.0                     0.0   \n",
       "3              1.0                  0.0                     0.0   \n",
       "4              1.0                  0.0                     0.0   \n",
       "...            ...                  ...                     ...   \n",
       "46400          1.0                  0.0                     0.0   \n",
       "46401          1.0                  0.0                     0.0   \n",
       "46402          1.0                  0.0                     0.0   \n",
       "46403          1.0                  0.0                     0.0   \n",
       "46404          1.0                  0.0                     0.0   \n",
       "\n",
       "       offerType_Pre_registered  offerType_Used  \n",
       "0                           0.0             1.0  \n",
       "1                           0.0             1.0  \n",
       "2                           0.0             1.0  \n",
       "3                           0.0             1.0  \n",
       "4                           0.0             1.0  \n",
       "...                         ...             ...  \n",
       "46400                       1.0             0.0  \n",
       "46401                       1.0             0.0  \n",
       "46402                       1.0             0.0  \n",
       "46403                       1.0             0.0  \n",
       "46404                       1.0             0.0  \n",
       "\n",
       "[45985 rows x 14 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "5b01d194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mileage', 'hp', 'Age', 'fuel_Diesel', 'fuel_Electric',\n",
       "       'fuel_Electric_Diesel', 'fuel_Electric_Gasoline', 'fuel_Gasoline',\n",
       "       'fuel_LPG', 'gear_Manual', 'gear_Semi_automatic',\n",
       "       'offerType_Employee_car', 'offerType_Pre_registered', 'offerType_Used'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "7f5719d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(X[[\"mileage\", \"hp\", \"Age\", \"fuel_Diesel\", \"fuel_Electric\", \"fuel_Electric_Diesel\",\n",
    "  \"fuel_Electric_Gasoline\", \"fuel_Gasoline\", \"fuel_LPG\", \"gear_Manual\", \"gear_Semi_automatic\",\n",
    "  \"offerType_Employee_car\", \"offerType_Pre_registered\", \"offerType_Used\"]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "9ad64f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.350e+05, 1.160e+02, 1.300e+01, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [9.280e+04, 1.220e+02, 1.300e+01, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [1.493e+05, 1.600e+02, 1.300e+01, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       ...,\n",
       "       [9.900e+01, 7.100e+01, 3.000e+00, ..., 0.000e+00, 1.000e+00,\n",
       "        0.000e+00],\n",
       "       [9.900e+01, 7.100e+01, 3.000e+00, ..., 0.000e+00, 1.000e+00,\n",
       "        0.000e+00],\n",
       "       [9.900e+01, 7.100e+01, 3.000e+00, ..., 0.000e+00, 1.000e+00,\n",
       "        0.000e+00]])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "38ec9297",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "a98510f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.350e+05, 1.160e+02, 1.300e+01, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [9.280e+04, 1.220e+02, 1.300e+01, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [1.493e+05, 1.600e+02, 1.300e+01, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       ...,\n",
       "       [9.900e+01, 7.100e+01, 3.000e+00, ..., 0.000e+00, 1.000e+00,\n",
       "        0.000e+00],\n",
       "       [9.900e+01, 7.100e+01, 3.000e+00, ..., 0.000e+00, 1.000e+00,\n",
       "        0.000e+00],\n",
       "       [9.900e+01, 7.100e+01, 3.000e+00, ..., 0.000e+00, 1.000e+00,\n",
       "        0.000e+00]])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "01f51e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()), (&#x27;model&#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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()), (&#x27;model&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scale', StandardScaler()), ('model', LinearRegression())])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#split train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=2)\n",
    "#fit model\n",
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "4507bac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8604.61717796,  8460.55561335, 13969.59435509, 77368.61938263])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypipe_train=pipe.predict(X_train)\n",
    "ypipe_train[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "d1be64ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-square is: 0.6147109751229876\n"
     ]
    }
   ],
   "source": [
    "print('The R-square is:', pipe.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "ecf86d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9120.90565041,  6804.21965019, 12266.65835745, 29299.39937683])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypipe_test=pipe.predict(X_test)\n",
    "ypipe_test[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "6dec0c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y_predicted')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,ypipe_test)\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_predicted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "ce20d7f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-square is: 0.7501257226694673\n"
     ]
    }
   ],
   "source": [
    "print('The R-square is:', pipe.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "755b209c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.displot((y1_train-ypipe_train),hist = False)\n",
    "sns.displot((y_train-ypipe_train))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "b559efa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "20486125",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('regression_model.pkl','wb') as file:\n",
    "    pickle.dump(pipe, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "659df3bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickled_model=pickle.load(open('regression_model.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "2c817385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9120.90565041,  6804.21965019, 12266.65835745, 29299.39937683])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=pickled_model.predict(X_test)\n",
    "test[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "985ef997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypipe_test==test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9e0268d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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