3_mpg_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3738c539",
   "metadata": {},
   "source": [
    "## Import needed libraries and pakages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7c63fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad0456af",
   "metadata": {},
   "source": [
    "## Read data and explore it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5807bca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the data: (398, 9)\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           398 non-null    float64\n",
      " 1   cylinders     398 non-null    int64  \n",
      " 2   displacement  398 non-null    float64\n",
      " 3   horsepower    398 non-null    object \n",
      " 4   weight        398 non-null    int64  \n",
      " 5   acceleration  398 non-null    float64\n",
      " 6   model_year    398 non-null    int64  \n",
      " 7   origin        398 non-null    int64  \n",
      " 8   name          398 non-null    object \n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 28.1+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model_year  \\\n",
       "0  18.0          8         307.0        130    3504          12.0          70   \n",
       "1  15.0          8         350.0        165    3693          11.5          70   \n",
       "2  18.0          8         318.0        150    3436          11.0          70   \n",
       "3  16.0          8         304.0        150    3433          12.0          70   \n",
       "4  17.0          8         302.0        140    3449          10.5          70   \n",
       "\n",
       "   origin                       name  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/mpg.csv\")\n",
    "print(f\"Shape of the data: {data.shape}\\n\")\n",
    "print(data.info())\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223860d9",
   "metadata": {},
   "source": [
    "### Notes: \n",
    "- there is no missing values \n",
    "- the `horsepower` column's data type is `object` and it should be numerical and the `origin` should be categorical value\n",
    "- we have one categorical value which is the `car name`, we will check its unique values and see if we could apply encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5abe9df4",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e3ad775",
   "metadata": {},
   "source": [
    "### Convert data type of the `horsepower` column to numerical value and `origin` to categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd605c4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           398 non-null    float64\n",
      " 1   cylinders     398 non-null    int64  \n",
      " 2   displacement  398 non-null    float64\n",
      " 3   horsepower    392 non-null    float64\n",
      " 4   weight        398 non-null    int64  \n",
      " 5   acceleration  398 non-null    float64\n",
      " 6   model_year    398 non-null    int64  \n",
      " 7   origin        398 non-null    object \n",
      " 8   name          398 non-null    object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data[\"horsepower\"] = pd.to_numeric(data[\"horsepower\"], errors=\"coerce\")\n",
    "data[\"origin\"] = data[\"origin\"].astype(\"object\")\n",
    "\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47def1c6",
   "metadata": {},
   "source": [
    "#### Note: we have missing values in `horsepower` column now, we have to deal with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7046e5e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2046</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>ford pinto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2875</td>\n",
       "      <td>17.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>ford maverick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>40.9</td>\n",
       "      <td>4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1835</td>\n",
       "      <td>17.3</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>renault lecar deluxe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>23.6</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2905</td>\n",
       "      <td>14.3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang cobra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>34.5</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2320</td>\n",
       "      <td>15.8</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>renault 18i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3035</td>\n",
       "      <td>20.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>amc concord dl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "32   25.0          4          98.0         NaN    2046          19.0   \n",
       "126  21.0          6         200.0         NaN    2875          17.0   \n",
       "330  40.9          4          85.0         NaN    1835          17.3   \n",
       "336  23.6          4         140.0         NaN    2905          14.3   \n",
       "354  34.5          4         100.0         NaN    2320          15.8   \n",
       "374  23.0          4         151.0         NaN    3035          20.5   \n",
       "\n",
       "     model_year origin                  name  \n",
       "32           71      1            ford pinto  \n",
       "126          74      1         ford maverick  \n",
       "330          80      2  renault lecar deluxe  \n",
       "336          80      1    ford mustang cobra  \n",
       "354          81      2           renault 18i  \n",
       "374          82      1        amc concord dl  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[pd.isnull(data).any(axis=1) == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "722f2a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([130., 165., 150., 140., 198., 220., 215., 225., 190., 170., 160.,\n",
       "        95.,  97.,  85.,  88.,  46.,  87.,  90., 113., 200., 210., 193.,\n",
       "        nan, 100., 105., 175., 153., 180., 110.,  72.,  86.,  70.,  76.,\n",
       "        65.,  69.,  60.,  80.,  54., 208., 155., 112.,  92., 145., 137.,\n",
       "       158., 167.,  94., 107., 230.,  49.,  75.,  91., 122.,  67.,  83.,\n",
       "        78.,  52.,  61.,  93., 148., 129.,  96.,  71.,  98., 115.,  53.,\n",
       "        81.,  79., 120., 152., 102., 108.,  68.,  58., 149.,  89.,  63.,\n",
       "        48.,  66., 139., 103., 125., 133., 138., 135., 142.,  77.,  62.,\n",
       "       132.,  84.,  64.,  74., 116.,  82.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"horsepower\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29dc71fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1eca0ce1c10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data, x=\"horsepower\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25d2d283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    392.000000\n",
       "mean     104.469388\n",
       "std       38.491160\n",
       "min       46.000000\n",
       "25%       75.000000\n",
       "50%       93.500000\n",
       "75%      126.000000\n",
       "max      230.000000\n",
       "Name: horsepower, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"horsepower\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af5f6614",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           398 non-null    float64\n",
      " 1   cylinders     398 non-null    int64  \n",
      " 2   displacement  398 non-null    float64\n",
      " 3   horsepower    398 non-null    float64\n",
      " 4   weight        398 non-null    int64  \n",
      " 5   acceleration  398 non-null    float64\n",
      " 6   model_year    398 non-null    int64  \n",
      " 7   origin        398 non-null    object \n",
      " 8   name          398 non-null    object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# filling the null values with the median value \n",
    "data[\"horsepower\"].fillna(data[\"horsepower\"].median(), inplace=True)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93f9d0a3",
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.514573</td>\n",
       "      <td>5.454774</td>\n",
       "      <td>193.425879</td>\n",
       "      <td>104.304020</td>\n",
       "      <td>2970.424623</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>76.010050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.815984</td>\n",
       "      <td>1.701004</td>\n",
       "      <td>104.269838</td>\n",
       "      <td>38.222625</td>\n",
       "      <td>846.841774</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>3.697627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>2223.750000</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>17.175000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement  horsepower       weight  \\\n",
       "count  398.000000  398.000000    398.000000  398.000000   398.000000   \n",
       "mean    23.514573    5.454774    193.425879  104.304020  2970.424623   \n",
       "std      7.815984    1.701004    104.269838   38.222625   846.841774   \n",
       "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
       "25%     17.500000    4.000000    104.250000   76.000000  2223.750000   \n",
       "50%     23.000000    4.000000    148.500000   93.500000  2803.500000   \n",
       "75%     29.000000    8.000000    262.000000  125.000000  3608.000000   \n",
       "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
       "\n",
       "       acceleration  model_year  \n",
       "count    398.000000  398.000000  \n",
       "mean      15.568090   76.010050  \n",
       "std        2.757689    3.697627  \n",
       "min        8.000000   70.000000  \n",
       "25%       13.825000   73.000000  \n",
       "50%       15.500000   76.000000  \n",
       "75%       17.175000   79.000000  \n",
       "max       24.800000   82.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91de5f3e",
   "metadata": {},
   "source": [
    "### Dealing with the categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45e2ae55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values of the categorical variables: \n",
      "origin 3\n",
      "name 305\n"
     ]
    }
   ],
   "source": [
    "cat_cols = [col for col in data.columns if data[col].dtype==\"object\"]\n",
    "print(\"Unique values of the categorical variables: \")\n",
    "\n",
    "for col in cat_cols:\n",
    "    print(col, data[col].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a547f0",
   "metadata": {},
   "source": [
    "#### encoding the `object` column using one hot encoding and dropping the `car name` column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef305eb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\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"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = make_column_transformer((OneHotEncoder(), [\"origin\"]), remainder=\"passthrough\")\n",
    "\n",
    "transformed = transformer.fit_transform(data[[\"origin\"]])\n",
    "transformed_df = pd.DataFrame(transformed, index=data.index)\n",
    "transformed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2a7ba56",
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  model_year\n",
       "0  18.0          8         307.0       130.0    3504          12.0          70\n",
       "1  15.0          8         350.0       165.0    3693          11.5          70\n",
       "2  18.0          8         318.0       150.0    3436          11.0          70\n",
       "3  16.0          8         304.0       150.0    3433          12.0          70\n",
       "4  17.0          8         302.0       140.0    3449          10.5          70"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns=[\"name\", \"origin\"], axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4c0011",
   "metadata": {},
   "source": [
    "### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ce83410",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.630870</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.503514</td>\n",
       "      <td>1.589958</td>\n",
       "      <td>0.854333</td>\n",
       "      <td>-1.477038</td>\n",
       "      <td>-1.627426</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.196232</td>\n",
       "      <td>1.197027</td>\n",
       "      <td>0.550470</td>\n",
       "      <td>-1.658577</td>\n",
       "      <td>-1.627426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.061796</td>\n",
       "      <td>1.197027</td>\n",
       "      <td>0.546923</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.042591</td>\n",
       "      <td>0.935072</td>\n",
       "      <td>0.565841</td>\n",
       "      <td>-1.840117</td>\n",
       "      <td>-1.627426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cylinders  displacement  horsepower    weight  acceleration  model_year\n",
       "0   1.498191      1.090604    0.673118  0.630870     -1.295498   -1.627426\n",
       "1   1.498191      1.503514    1.589958  0.854333     -1.477038   -1.627426\n",
       "2   1.498191      1.196232    1.197027  0.550470     -1.658577   -1.627426\n",
       "3   1.498191      1.061796    1.197027  0.546923     -1.295498   -1.627426\n",
       "4   1.498191      1.042591    0.935072  0.565841     -1.840117   -1.627426"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_cols = [\"cylinders\", \"displacement\", \"horsepower\", \"weight\", \"acceleration\", \"model_year\"]\n",
    "scaler_transformer = make_column_transformer((StandardScaler(), scaled_cols), remainder=\"passthrough\")\n",
    "\n",
    "scaled = scaler_transformer.fit_transform(data[scaled_cols])\n",
    "scaled_df = pd.DataFrame(scaled, index=data.index, columns=scaled_cols)\n",
    "scaled_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "246a1c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([scaled_df, transformed_df], axis=1)\n",
    "y = data.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "991e85e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>0</th>\n",
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       "      <th>0</th>\n",
       "      <td>1.498191</td>\n",
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       "      <td>0.630870</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.503514</td>\n",
       "      <td>1.589958</td>\n",
       "      <td>0.854333</td>\n",
       "      <td>-1.477038</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.196232</td>\n",
       "      <td>1.197027</td>\n",
       "      <td>0.550470</td>\n",
       "      <td>-1.658577</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.061796</td>\n",
       "      <td>1.197027</td>\n",
       "      <td>0.546923</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.042591</td>\n",
       "      <td>0.935072</td>\n",
       "      <td>0.565841</td>\n",
       "      <td>-1.840117</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cylinders  displacement  horsepower    weight  acceleration  model_year  \\\n",
       "0   1.498191      1.090604    0.673118  0.630870     -1.295498   -1.627426   \n",
       "1   1.498191      1.503514    1.589958  0.854333     -1.477038   -1.627426   \n",
       "2   1.498191      1.196232    1.197027  0.550470     -1.658577   -1.627426   \n",
       "3   1.498191      1.061796    1.197027  0.546923     -1.295498   -1.627426   \n",
       "4   1.498191      1.042591    0.935072  0.565841     -1.840117   -1.627426   \n",
       "\n",
       "     0    1    2  \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  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns = X.columns.astype(str)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f92b55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    18.0\n",
       "1    15.0\n",
       "2    18.0\n",
       "3    16.0\n",
       "4    17.0\n",
       "Name: mpg, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a7ba41b",
   "metadata": {},
   "source": [
    "### Linear Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "06c447ff-8814-4e3b-a652-ed7abc5d9856",
   "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>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\"><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\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "558dee0e-f838-4a21-b851-a511d7b7919e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 82.74\n"
     ]
    }
   ],
   "source": [
    "y_pred = reg.predict(X_test)\n",
    "acc = r2_score(y_test, y_pred)\n",
    "print('Accuracy: %.2f' % (acc*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ca3587",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c20ab872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 12))\n",
    "sns.heatmap(X.corr(), annot=True, cmap='coolwarm', \n",
    "            xticklabels=X.columns,\n",
    "            yticklabels=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "668afc91",
   "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>cylinders</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>-1.477038</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>-1.658577</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>-1.840117</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cylinders  acceleration  model_year    0    1    2\n",
       "0   1.498191     -1.295498   -1.627426  1.0  0.0  0.0\n",
       "1   1.498191     -1.477038   -1.627426  1.0  0.0  0.0\n",
       "2   1.498191     -1.658577   -1.627426  1.0  0.0  0.0\n",
       "3   1.498191     -1.295498   -1.627426  1.0  0.0  0.0\n",
       "4   1.498191     -1.840117   -1.627426  1.0  0.0  0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we should drop the most correlated features\n",
    "# drop `displacement`, `horsepower` and `weight`\n",
    "X.drop(columns=['displacement', 'horsepower', 'weight'], axis=1, inplace=True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e1ccea92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6, 6))\n",
    "sns.heatmap(X.corr(), annot=True, cmap='coolwarm', \n",
    "            xticklabels=X.columns,\n",
    "            yticklabels=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "228ac17a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 73.43\n"
     ]
    }
   ],
   "source": [
    "# applying linear regression again \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(X_train, y_train)\n",
    "\n",
    "y_pred = reg.predict(X_test)\n",
    "acc = r2_score(y_test, y_pred)\n",
    "print('Accuracy: %.2f' % (acc*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b12913c9",
   "metadata": {},
   "source": [
    "### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "23d021d9",
   "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>PC0</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.559020</td>\n",
       "      <td>0.474630</td>\n",
       "      <td>-0.010944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.662745</td>\n",
       "      <td>0.412303</td>\n",
       "      <td>-0.143910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.766471</td>\n",
       "      <td>0.349975</td>\n",
       "      <td>-0.276875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.559020</td>\n",
       "      <td>0.474630</td>\n",
       "      <td>-0.010944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.870196</td>\n",
       "      <td>0.287648</td>\n",
       "      <td>-0.409840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC0       PC1       PC3\n",
       "0  2.559020  0.474630 -0.010944\n",
       "1  2.662745  0.412303 -0.143910\n",
       "2  2.766471  0.349975 -0.276875\n",
       "3  2.559020  0.474630 -0.010944\n",
       "4  2.870196  0.287648 -0.409840"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=3)\n",
    "X_p = pd.DataFrame(pca.fit_transform(X), columns=['PC0', 'PC1', 'PC3'])\n",
    "X_p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "712165ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 74.65\n"
     ]
    }
   ],
   "source": [
    "# applying linear regression again \n",
    "X_train, X_test, y_train, y_test = train_test_split(X_p, y, test_size=0.2, random_state=0)\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(X_train, y_train)\n",
    "\n",
    "y_pred = reg.predict(X_test)\n",
    "acc = r2_score(y_test, y_pred)\n",
    "print('Accuracy: %.2f' % (acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d86dff08",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}