ManjuMachineLearning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "064df396",
   "metadata": {},
   "outputs": [],
   "source": [
    "# all regessions Import\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "# Modelling\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.linear_model import LinearRegression, Ridge,Lasso\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from catboost import CatBoostRegressor\n",
    "from xgboost import XGBRegressor\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bfaf1bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Automobilepricedata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "89f44228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling normalized-losses         make fuel-type aspiration  \\\n",
       "0            3                 ?  alfa-romero       gas        std   \n",
       "1            3                 ?  alfa-romero       gas        std   \n",
       "2            1                 ?  alfa-romero       gas        std   \n",
       "3            2               164         audi       gas        std   \n",
       "4            2               164         audi       gas        std   \n",
       "..         ...               ...          ...       ...        ...   \n",
       "200         -1                95        volvo       gas        std   \n",
       "201         -1                95        volvo       gas      turbo   \n",
       "202         -1                95        volvo       gas        std   \n",
       "203         -1                95        volvo    diesel      turbo   \n",
       "204         -1                95        volvo       gas      turbo   \n",
       "\n",
       "    num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "0            two  convertible          rwd           front        88.6  ...   \n",
       "1            two  convertible          rwd           front        88.6  ...   \n",
       "2            two    hatchback          rwd           front        94.5  ...   \n",
       "3           four        sedan          fwd           front        99.8  ...   \n",
       "4           four        sedan          4wd           front        99.4  ...   \n",
       "..           ...          ...          ...             ...         ...  ...   \n",
       "200         four        sedan          rwd           front       109.1  ...   \n",
       "201         four        sedan          rwd           front       109.1  ...   \n",
       "202         four        sedan          rwd           front       109.1  ...   \n",
       "203         four        sedan          rwd           front       109.1  ...   \n",
       "204         four        sedan          rwd           front       109.1  ...   \n",
       "\n",
       "     engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "0            130         mpfi  3.47    2.68               9.0        111   \n",
       "1            130         mpfi  3.47    2.68               9.0        111   \n",
       "2            152         mpfi  2.68    3.47               9.0        154   \n",
       "3            109         mpfi  3.19    3.40              10.0        102   \n",
       "4            136         mpfi  3.19    3.40               8.0        115   \n",
       "..           ...          ...   ...     ...               ...        ...   \n",
       "200          141         mpfi  3.78    3.15               9.5        114   \n",
       "201          141         mpfi  3.78    3.15               8.7        160   \n",
       "202          173         mpfi  3.58    2.87               8.8        134   \n",
       "203          145          idi  3.01    3.40              23.0        106   \n",
       "204          141         mpfi  3.78    3.15               9.5        114   \n",
       "\n",
       "     peak-rpm city-mpg highway-mpg  price  \n",
       "0        5000       21          27  13495  \n",
       "1        5000       21          27  16500  \n",
       "2        5000       19          26  16500  \n",
       "3        5500       24          30  13950  \n",
       "4        5500       18          22  17450  \n",
       "..        ...      ...         ...    ...  \n",
       "200      5400       23          28  16845  \n",
       "201      5300       19          25  19045  \n",
       "202      5500       18          23  21485  \n",
       "203      4800       26          27  22470  \n",
       "204      5400       19          25  22625  \n",
       "\n",
       "[205 rows x 26 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2c1ced5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 26)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "0ef3ae17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses     object\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                  object\n",
       "stroke                object\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm              object\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1f845596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([dtype('int64'), dtype('O'), dtype('float64')], dtype=object)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0af32d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price\n",
       "?        4\n",
       "9279     2\n",
       "5572     2\n",
       "6229     2\n",
       "6692     2\n",
       "        ..\n",
       "18280    1\n",
       "18344    1\n",
       "18399    1\n",
       "18420    1\n",
       "35550    1\n",
       "Name: count, Length: 187, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.value_counts('price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ca950452",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      13495\n",
      "1      16500\n",
      "2      16500\n",
      "3      13950\n",
      "4      17450\n",
      "       ...  \n",
      "200    16845\n",
      "201    19045\n",
      "202    21485\n",
      "203    22470\n",
      "204    22625\n",
      "Name: price, Length: 205, dtype: object\n"
     ]
    }
   ],
   "source": [
    "correlated_cols = []\n",
    "for i in df.price:\n",
    "    if i == '?':\n",
    "        correlated_cols= df.price.replace('?',1000)\n",
    "print(correlated_cols)   \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "63be01ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price\n",
       "1000     4\n",
       "8921     2\n",
       "18150    2\n",
       "8845     2\n",
       "8495     2\n",
       "        ..\n",
       "45400    1\n",
       "16503    1\n",
       "5389     1\n",
       "6189     1\n",
       "22625    1\n",
       "Name: count, Length: 187, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlated_cols.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "88c7a1c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Newprice']=correlated_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "a40422ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>Newprice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845</td>\n",
       "      <td>16845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045</td>\n",
       "      <td>19045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485</td>\n",
       "      <td>21485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470</td>\n",
       "      <td>22470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625</td>\n",
       "      <td>22625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling normalized-losses         make fuel-type aspiration  \\\n",
       "0            3                 ?  alfa-romero       gas        std   \n",
       "1            3                 ?  alfa-romero       gas        std   \n",
       "2            1                 ?  alfa-romero       gas        std   \n",
       "3            2               164         audi       gas        std   \n",
       "4            2               164         audi       gas        std   \n",
       "..         ...               ...          ...       ...        ...   \n",
       "200         -1                95        volvo       gas        std   \n",
       "201         -1                95        volvo       gas      turbo   \n",
       "202         -1                95        volvo       gas        std   \n",
       "203         -1                95        volvo    diesel      turbo   \n",
       "204         -1                95        volvo       gas      turbo   \n",
       "\n",
       "    num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "0            two  convertible          rwd           front        88.6  ...   \n",
       "1            two  convertible          rwd           front        88.6  ...   \n",
       "2            two    hatchback          rwd           front        94.5  ...   \n",
       "3           four        sedan          fwd           front        99.8  ...   \n",
       "4           four        sedan          4wd           front        99.4  ...   \n",
       "..           ...          ...          ...             ...         ...  ...   \n",
       "200         four        sedan          rwd           front       109.1  ...   \n",
       "201         four        sedan          rwd           front       109.1  ...   \n",
       "202         four        sedan          rwd           front       109.1  ...   \n",
       "203         four        sedan          rwd           front       109.1  ...   \n",
       "204         four        sedan          rwd           front       109.1  ...   \n",
       "\n",
       "     fuel-system  bore  stroke  compression-ratio horsepower peak-rpm  \\\n",
       "0           mpfi  3.47    2.68                9.0        111     5000   \n",
       "1           mpfi  3.47    2.68                9.0        111     5000   \n",
       "2           mpfi  2.68    3.47                9.0        154     5000   \n",
       "3           mpfi  3.19    3.40               10.0        102     5500   \n",
       "4           mpfi  3.19    3.40                8.0        115     5500   \n",
       "..           ...   ...     ...                ...        ...      ...   \n",
       "200         mpfi  3.78    3.15                9.5        114     5400   \n",
       "201         mpfi  3.78    3.15                8.7        160     5300   \n",
       "202         mpfi  3.58    2.87                8.8        134     5500   \n",
       "203          idi  3.01    3.40               23.0        106     4800   \n",
       "204         mpfi  3.78    3.15                9.5        114     5400   \n",
       "\n",
       "     city-mpg highway-mpg  price Newprice  \n",
       "0          21          27  13495    13495  \n",
       "1          21          27  16500    16500  \n",
       "2          19          26  16500    16500  \n",
       "3          24          30  13950    13950  \n",
       "4          18          22  17450    17450  \n",
       "..        ...         ...    ...      ...  \n",
       "200        23          28  16845    16845  \n",
       "201        19          25  19045    19045  \n",
       "202        18          23  21485    21485  \n",
       "203        26          27  22470    22470  \n",
       "204        19          25  22625    22625  \n",
       "\n",
       "[205 rows x 27 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "a6c32eee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Newprice = pd.to_numeric(df.Newprice, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "bc1a6905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([dtype('int64'), dtype('O'), dtype('float64')], dtype=object)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "25267221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses     object\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                  object\n",
       "stroke                object\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm              object\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                 object\n",
       "Newprice               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "37844c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "num = ['int64', 'float64','int32']\n",
    "num_vars = list(df.select_dtypes(include=num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "575c5096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['symboling',\n",
       " 'wheel-base',\n",
       " 'length',\n",
       " 'width',\n",
       " 'height',\n",
       " 'curb-weight',\n",
       " 'engine-size',\n",
       " 'compression-ratio',\n",
       " 'city-mpg',\n",
       " 'highway-mpg',\n",
       " 'Newprice']"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "41e00c23",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[num_vars]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "b208bc48",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>symboling</th>\n",
       "      <td>205.0</td>\n",
       "      <td>0.834146</td>\n",
       "      <td>1.245307</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheel-base</th>\n",
       "      <td>205.0</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>86.6</td>\n",
       "      <td>94.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>102.4</td>\n",
       "      <td>120.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>205.0</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>141.1</td>\n",
       "      <td>166.3</td>\n",
       "      <td>173.2</td>\n",
       "      <td>183.1</td>\n",
       "      <td>208.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>205.0</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>60.3</td>\n",
       "      <td>64.1</td>\n",
       "      <td>65.5</td>\n",
       "      <td>66.9</td>\n",
       "      <td>72.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>205.0</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>47.8</td>\n",
       "      <td>52.0</td>\n",
       "      <td>54.1</td>\n",
       "      <td>55.5</td>\n",
       "      <td>59.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curb-weight</th>\n",
       "      <td>205.0</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>1488.0</td>\n",
       "      <td>2145.0</td>\n",
       "      <td>2414.0</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>4066.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>205.0</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>61.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>205.0</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-mpg</th>\n",
       "      <td>205.0</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>205.0</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>16.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newprice</th>\n",
       "      <td>205.0</td>\n",
       "      <td>12968.941463</td>\n",
       "      <td>8048.748032</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>7689.0</td>\n",
       "      <td>10198.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>45400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count          mean          std     min     25%      50%  \\\n",
       "symboling          205.0      0.834146     1.245307    -2.0     0.0      1.0   \n",
       "wheel-base         205.0     98.756585     6.021776    86.6    94.5     97.0   \n",
       "length             205.0    174.049268    12.337289   141.1   166.3    173.2   \n",
       "width              205.0     65.907805     2.145204    60.3    64.1     65.5   \n",
       "height             205.0     53.724878     2.443522    47.8    52.0     54.1   \n",
       "curb-weight        205.0   2555.565854   520.680204  1488.0  2145.0   2414.0   \n",
       "engine-size        205.0    126.907317    41.642693    61.0    97.0    120.0   \n",
       "compression-ratio  205.0     10.142537     3.972040     7.0     8.6      9.0   \n",
       "city-mpg           205.0     25.219512     6.542142    13.0    19.0     24.0   \n",
       "highway-mpg        205.0     30.751220     6.886443    16.0    25.0     30.0   \n",
       "Newprice           205.0  12968.941463  8048.748032  1000.0  7689.0  10198.0   \n",
       "\n",
       "                       75%      max  \n",
       "symboling              2.0      3.0  \n",
       "wheel-base           102.4    120.9  \n",
       "length               183.1    208.1  \n",
       "width                 66.9     72.3  \n",
       "height                55.5     59.8  \n",
       "curb-weight         2935.0   4066.0  \n",
       "engine-size          141.0    326.0  \n",
       "compression-ratio      9.4     23.0  \n",
       "city-mpg              30.0     49.0  \n",
       "highway-mpg           34.0     54.0  \n",
       "Newprice           16500.0  45400.0  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "e843f36c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column symboling has duplicates count 199\n",
      "Column wheel-base has duplicates count 152\n",
      "Column length has duplicates count 130\n",
      "Column width has duplicates count 161\n",
      "Column height has duplicates count 156\n",
      "Column curb-weight has duplicates count 34\n",
      "Column engine-size has duplicates count 161\n",
      "Column compression-ratio has duplicates count 173\n",
      "Column city-mpg has duplicates count 176\n",
      "Column highway-mpg has duplicates count 175\n",
      "Column Newprice has duplicates count 18\n"
     ]
    }
   ],
   "source": [
    "for cols in df.columns:\n",
    "    sum = df[cols].duplicated().sum()\n",
    "    if sum > 0:\n",
    "        print(f\"Column {cols} has duplicates count {sum}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "fb921e58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling            0\n",
       "wheel-base           0\n",
       "length               0\n",
       "width                0\n",
       "height               0\n",
       "curb-weight          0\n",
       "engine-size          0\n",
       "compression-ratio    0\n",
       "city-mpg             0\n",
       "highway-mpg          0\n",
       "Newprice             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "5c88be77",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_23208\\1379821321.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.dropna(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "1d3ace70",
   "metadata": {},
   "outputs": [
    {
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       "     symboling  wheel-base  length  width  height  curb-weight  engine-size  \\\n",
       "0            3        88.6   168.8   64.1    48.8         2548          130   \n",
       "1            3        88.6   168.8   64.1    48.8         2548          130   \n",
       "2            1        94.5   171.2   65.5    52.4         2823          152   \n",
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       "203         -1       109.1   188.8   68.9    55.5         3217          145   \n",
       "204         -1       109.1   188.8   68.9    55.5         3062          141   \n",
       "\n",
       "     compression-ratio  city-mpg  highway-mpg  Newprice  \n",
       "0                  9.0        21           27     13495  \n",
       "1                  9.0        21           27     16500  \n",
       "2                  9.0        19           26     16500  \n",
       "3                 10.0        24           30     13950  \n",
       "4                  8.0        18           22     17450  \n",
       "..                 ...       ...          ...       ...  \n",
       "200                9.5        23           28     16845  \n",
       "201                8.7        19           25     19045  \n",
       "202                8.8        18           23     21485  \n",
       "203               23.0        26           27     22470  \n",
       "204                9.5        19           25     22625  \n",
       "\n",
       "[205 rows x 11 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "1765ac8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,:10]\n",
    "y = df.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "72a8ab1d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3217</td>\n",
       "      <td>145</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-1</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3062</td>\n",
       "      <td>141</td>\n",
       "      <td>9.5</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  wheel-base  length  width  height  curb-weight  engine-size  \\\n",
       "0            3        88.6   168.8   64.1    48.8         2548          130   \n",
       "1            3        88.6   168.8   64.1    48.8         2548          130   \n",
       "2            1        94.5   171.2   65.5    52.4         2823          152   \n",
       "3            2        99.8   176.6   66.2    54.3         2337          109   \n",
       "4            2        99.4   176.6   66.4    54.3         2824          136   \n",
       "..         ...         ...     ...    ...     ...          ...          ...   \n",
       "200         -1       109.1   188.8   68.9    55.5         2952          141   \n",
       "201         -1       109.1   188.8   68.8    55.5         3049          141   \n",
       "202         -1       109.1   188.8   68.9    55.5         3012          173   \n",
       "203         -1       109.1   188.8   68.9    55.5         3217          145   \n",
       "204         -1       109.1   188.8   68.9    55.5         3062          141   \n",
       "\n",
       "     compression-ratio  city-mpg  highway-mpg  \n",
       "0                  9.0        21           27  \n",
       "1                  9.0        21           27  \n",
       "2                  9.0        19           26  \n",
       "3                 10.0        24           30  \n",
       "4                  8.0        18           22  \n",
       "..                 ...       ...          ...  \n",
       "200                9.5        23           28  \n",
       "201                8.7        19           25  \n",
       "202                8.8        18           23  \n",
       "203               23.0        26           27  \n",
       "204                9.5        19           25  \n",
       "\n",
       "[205 rows x 10 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ad497696",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "46f1d657",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "b40fded4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Newprice             1.000000\n",
      "engine-size          0.841345\n",
      "curb-weight          0.802759\n",
      "width                0.699343\n",
      "length               0.686092\n",
      "wheel-base           0.580161\n",
      "height               0.156938\n",
      "compression-ratio    0.077583\n",
      "symboling           -0.072384\n",
      "city-mpg            -0.661754\n",
      "highway-mpg         -0.689114\n",
      "Name: Newprice, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "sorted_corr = corr[\"Newprice\"].sort_values(ascending=False)\n",
    "print(sorted_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "63c8110f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7153929982658673\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import skew\n",
    "skewness = skew(df[\"Newprice\"])\n",
    "print(skewness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "4df76bc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_23208\\3646314542.py:6: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df[\"Newprice\"],fit=norm)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 12968.94 and sigma = 8029.09\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from scipy.stats import skew\n",
    "from scipy.stats import norm\n",
    "from scipy import stats\n",
    "from scipy.special import boxcox1p\n",
    "sns.distplot(df[\"Newprice\"],fit=norm)\n",
    "(mu,sigma) = norm.fit(df[\"Newprice\"])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Price Per Area distribution')\n",
    "\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(df[\"Newprice\"],plot = plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf43cbe0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "96c4ed0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "corrmatrix = X_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c7f5a2a0",
   "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>symboling</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>symboling</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.569144</td>\n",
       "      <td>-0.377214</td>\n",
       "      <td>-0.283471</td>\n",
       "      <td>-0.547891</td>\n",
       "      <td>-0.281102</td>\n",
       "      <td>-0.151574</td>\n",
       "      <td>-0.203207</td>\n",
       "      <td>0.012613</td>\n",
       "      <td>0.080801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheel-base</th>\n",
       "      <td>-0.569144</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.865246</td>\n",
       "      <td>0.807593</td>\n",
       "      <td>0.563051</td>\n",
       "      <td>0.770854</td>\n",
       "      <td>0.552954</td>\n",
       "      <td>0.219713</td>\n",
       "      <td>-0.495869</td>\n",
       "      <td>-0.566079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>-0.377214</td>\n",
       "      <td>0.865246</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.847764</td>\n",
       "      <td>0.447282</td>\n",
       "      <td>0.877701</td>\n",
       "      <td>0.672723</td>\n",
       "      <td>0.136416</td>\n",
       "      <td>-0.707380</td>\n",
       "      <td>-0.739691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>-0.283471</td>\n",
       "      <td>0.807593</td>\n",
       "      <td>0.847764</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.265830</td>\n",
       "      <td>0.845633</td>\n",
       "      <td>0.688241</td>\n",
       "      <td>0.208793</td>\n",
       "      <td>-0.633781</td>\n",
       "      <td>-0.673286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>-0.547891</td>\n",
       "      <td>0.563051</td>\n",
       "      <td>0.447282</td>\n",
       "      <td>0.265830</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274192</td>\n",
       "      <td>0.045701</td>\n",
       "      <td>0.212764</td>\n",
       "      <td>-0.057867</td>\n",
       "      <td>-0.103373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curb-weight</th>\n",
       "      <td>-0.281102</td>\n",
       "      <td>0.770854</td>\n",
       "      <td>0.877701</td>\n",
       "      <td>0.845633</td>\n",
       "      <td>0.274192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.834501</td>\n",
       "      <td>0.139383</td>\n",
       "      <td>-0.775215</td>\n",
       "      <td>-0.815647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>-0.151574</td>\n",
       "      <td>0.552954</td>\n",
       "      <td>0.672723</td>\n",
       "      <td>0.688241</td>\n",
       "      <td>0.045701</td>\n",
       "      <td>0.834501</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026752</td>\n",
       "      <td>-0.644604</td>\n",
       "      <td>-0.670615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>-0.203207</td>\n",
       "      <td>0.219713</td>\n",
       "      <td>0.136416</td>\n",
       "      <td>0.208793</td>\n",
       "      <td>0.212764</td>\n",
       "      <td>0.139383</td>\n",
       "      <td>0.026752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.280140</td>\n",
       "      <td>0.228769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-mpg</th>\n",
       "      <td>0.012613</td>\n",
       "      <td>-0.495869</td>\n",
       "      <td>-0.707380</td>\n",
       "      <td>-0.633781</td>\n",
       "      <td>-0.057867</td>\n",
       "      <td>-0.775215</td>\n",
       "      <td>-0.644604</td>\n",
       "      <td>0.280140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>0.080801</td>\n",
       "      <td>-0.566079</td>\n",
       "      <td>-0.739691</td>\n",
       "      <td>-0.673286</td>\n",
       "      <td>-0.103373</td>\n",
       "      <td>-0.815647</td>\n",
       "      <td>-0.670615</td>\n",
       "      <td>0.228769</td>\n",
       "      <td>0.976643</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   symboling  wheel-base    length     width    height  \\\n",
       "symboling           1.000000   -0.569144 -0.377214 -0.283471 -0.547891   \n",
       "wheel-base         -0.569144    1.000000  0.865246  0.807593  0.563051   \n",
       "length             -0.377214    0.865246  1.000000  0.847764  0.447282   \n",
       "width              -0.283471    0.807593  0.847764  1.000000  0.265830   \n",
       "height             -0.547891    0.563051  0.447282  0.265830  1.000000   \n",
       "curb-weight        -0.281102    0.770854  0.877701  0.845633  0.274192   \n",
       "engine-size        -0.151574    0.552954  0.672723  0.688241  0.045701   \n",
       "compression-ratio  -0.203207    0.219713  0.136416  0.208793  0.212764   \n",
       "city-mpg            0.012613   -0.495869 -0.707380 -0.633781 -0.057867   \n",
       "highway-mpg         0.080801   -0.566079 -0.739691 -0.673286 -0.103373   \n",
       "\n",
       "                   curb-weight  engine-size  compression-ratio  city-mpg  \\\n",
       "symboling            -0.281102    -0.151574          -0.203207  0.012613   \n",
       "wheel-base            0.770854     0.552954           0.219713 -0.495869   \n",
       "length                0.877701     0.672723           0.136416 -0.707380   \n",
       "width                 0.845633     0.688241           0.208793 -0.633781   \n",
       "height                0.274192     0.045701           0.212764 -0.057867   \n",
       "curb-weight           1.000000     0.834501           0.139383 -0.775215   \n",
       "engine-size           0.834501     1.000000           0.026752 -0.644604   \n",
       "compression-ratio     0.139383     0.026752           1.000000  0.280140   \n",
       "city-mpg             -0.775215    -0.644604           0.280140  1.000000   \n",
       "highway-mpg          -0.815647    -0.670615           0.228769  0.976643   \n",
       "\n",
       "                   highway-mpg  \n",
       "symboling             0.080801  \n",
       "wheel-base           -0.566079  \n",
       "length               -0.739691  \n",
       "width                -0.673286  \n",
       "height               -0.103373  \n",
       "curb-weight          -0.815647  \n",
       "engine-size          -0.670615  \n",
       "compression-ratio     0.228769  \n",
       "city-mpg              0.976643  \n",
       "highway-mpg           1.000000  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrmatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "f9912d27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corrmatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "09eb8fb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,18))\n",
    "sns.heatmap(corrmatrix.corr(), cmap= \"BuGn\", annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d3aaa83c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation(df, threshold):\n",
    "    correlated_cols = set()\n",
    "    corr_matrix = df.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corr_matrix.iloc[i,j]) > threshold:\n",
    "                colname = corr_matrix.columns[i]\n",
    "                correlated_cols.add(colname)\n",
    "    return correlated_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "cd5d112a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Newprice',\n",
       " 'city-mpg',\n",
       " 'curb-weight',\n",
       " 'engine-size',\n",
       " 'highway-mpg',\n",
       " 'length',\n",
       " 'width'}"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation(df,0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "a3bb6436",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "3d3d1bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sel = SelectFromModel(LogisticRegression(penalty=\"l2\", C=7, solver=\"liblinear\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5294ca8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>SelectFromModel(estimator=LogisticRegression(C=7, solver=&#x27;liblinear&#x27;))</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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectFromModel</label><div class=\"sk-toggleable__content\"><pre>SelectFromModel(estimator=LogisticRegression(C=7, solver=&#x27;liblinear&#x27;))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=7, solver=&#x27;liblinear&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=7, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectFromModel(estimator=LogisticRegression(C=7, solver='liblinear'))"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "d7426dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train1 = sel.transform(X_train)\n",
    "X_test1 = sel.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "f858a495",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glm = LogisticRegression()\n",
    "glm.fit(X_train1, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "4fdd3c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = glm.predict(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "824e6a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1000, 23875, 22470, 10795,  7129,  7775,  8495,  7499,  7499,\n",
       "        5399,  6692, 14489,  6095,  5118, 11694,  8499,  8495, 11048,\n",
       "        1000,  5399, 18150, 11245,  8778,  5389, 17950, 16500,  9295,\n",
       "        7689, 36880,  1000,  1000,  9988,  7295,  9988, 16925,  8495,\n",
       "        7499,  1000, 10198, 13415,  6095, 13415, 17950,  7999, 11549,\n",
       "        7775, 16558,  8845,  1000,  9988, 13415,  8495,  1000, 22470,\n",
       "       16500,  7129,  7689, 15510,  9298,  9298, 15750,  5151],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "babab3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "531b6a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "333d321b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88      9279\n",
       "72     35056\n",
       "114    17075\n",
       "158     7898\n",
       "163     8058\n",
       "       ...  \n",
       "133    12170\n",
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       "180    15690\n",
       "90      7099\n",
       "Name: Newprice, Length: 62, dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "879d712e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(true, predicted):\n",
    "    mae = mean_absolute_error(true, predicted)\n",
    "    mse = mean_squared_error(true, predicted)\n",
    "    rmse = np.sqrt(mean_squared_error(true, predicted))\n",
    "    r2_square = r2_score(true, predicted)\n",
    "    return mae, rmse, r2_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "fd3b8a30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 4263.6389\n",
      "- Mean Absolute Error: 2998.2303\n",
      "- R2 Score: 0.7596\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 3586.9996\n",
      "- Mean Absolute Error: 2650.4306\n",
      "- R2 Score: 0.6070\n",
      "===================================\n",
      "\n",
      "\n",
      "Lasso\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 4263.6396\n",
      "- Mean Absolute Error: 2997.7379\n",
      "- R2 Score: 0.7596\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 3584.9741\n",
      "- Mean Absolute Error: 2649.0127\n",
      "- R2 Score: 0.6074\n",
      "===================================\n",
      "\n",
      "\n",
      "Ridge\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 4263.6513\n",
      "- Mean Absolute Error: 2996.7722\n",
      "- R2 Score: 0.7596\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 3579.7634\n",
      "- Mean Absolute Error: 2645.0542\n",
      "- R2 Score: 0.6085\n",
      "===================================\n",
      "\n",
      "\n",
      "K-Neighbors Regressor\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 3842.9853\n",
      "- Mean Absolute Error: 2402.4364\n",
      "- R2 Score: 0.8047\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 2750.6801\n",
      "- Mean Absolute Error: 1759.3935\n",
      "- R2 Score: 0.7689\n",
      "===================================\n",
      "\n",
      "\n",
      "Decision Tree\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 308.0790\n",
      "- Mean Absolute Error: 80.4545\n",
      "- R2 Score: 0.9987\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 2738.1398\n",
      "- Mean Absolute Error: 1929.4516\n",
      "- R2 Score: 0.7710\n",
      "===================================\n",
      "\n",
      "\n",
      "Random Forest Regressor\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 1423.0381\n",
      "- Mean Absolute Error: 806.6587\n",
      "- R2 Score: 0.9732\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 1943.3398\n",
      "- Mean Absolute Error: 1301.0551\n",
      "- R2 Score: 0.8846\n",
      "===================================\n",
      "\n",
      "\n",
      "XGBRegressor\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 308.0915\n",
      "- Mean Absolute Error: 82.2101\n",
      "- R2 Score: 0.9987\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 2061.3519\n",
      "- Mean Absolute Error: 1403.3272\n",
      "- R2 Score: 0.8702\n",
      "===================================\n",
      "\n",
      "\n",
      "CatBoosting Regressor\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 443.0381\n",
      "- Mean Absolute Error: 308.5344\n",
      "- R2 Score: 0.9974\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 2521.1091\n",
      "- Mean Absolute Error: 1518.5978\n",
      "- R2 Score: 0.8058\n",
      "===================================\n",
      "\n",
      "\n",
      "AdaBoost Regressor\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 2023.3566\n",
      "- Mean Absolute Error: 1657.8361\n",
      "- R2 Score: 0.9459\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 2066.5405\n",
      "- Mean Absolute Error: 1644.4697\n",
      "- R2 Score: 0.8695\n",
      "===================================\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#All regression models are avaiable in the below code\n",
    "models = {\n",
    "    \"Linear Regression\": LinearRegression(),\n",
    "    \"Lasso\": Lasso(),\n",
    "    \"Ridge\": Ridge(),\n",
    "    \"K-Neighbors Regressor\": KNeighborsRegressor(),\n",
    "    \"Decision Tree\": DecisionTreeRegressor(),\n",
    "    \"Random Forest Regressor\": RandomForestRegressor(),\n",
    "    \"XGBRegressor\": XGBRegressor(), \n",
    "    \"CatBoosting Regressor\": CatBoostRegressor(verbose=False),\n",
    "    \"AdaBoost Regressor\": AdaBoostRegressor()\n",
    "}\n",
    "model_list = []\n",
    "r2_list =[]\n",
    "\n",
    "for i in range(len(list(models))):\n",
    "    model = list(models.values())[i]\n",
    "    model.fit(X_train, y_train) # Train model\n",
    "\n",
    "    # Make predictions\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "    \n",
    "    # Evaluate Train and Test dataset\n",
    "    model_train_mae , model_train_rmse, model_train_r2 = evaluate_model(y_train, y_train_pred)\n",
    "\n",
    "    model_test_mae , model_test_rmse, model_test_r2 = evaluate_model(y_test, y_test_pred)\n",
    "\n",
    "    \n",
    "    print(list(models.keys())[i])\n",
    "    model_list.append(list(models.keys())[i])\n",
    "    \n",
    "    print('Model performance for Training set')\n",
    "    print(\"- Root Mean Squared Error: {:.4f}\".format(model_train_rmse))\n",
    "    print(\"- Mean Absolute Error: {:.4f}\".format(model_train_mae))\n",
    "    print(\"- R2 Score: {:.4f}\".format(model_train_r2))\n",
    "\n",
    "    print('----------------------------------')\n",
    "    \n",
    "    print('Model performance for Test set')\n",
    "    print(\"- Root Mean Squared Error: {:.4f}\".format(model_test_rmse))\n",
    "    print(\"- Mean Absolute Error: {:.4f}\".format(model_test_mae))\n",
    "    print(\"- R2 Score: {:.4f}\".format(model_test_r2))\n",
    "    r2_list.append(model_test_r2)\n",
    "    \n",
    "    print('='*35)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "5d74678f",
   "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>Model Name</th>\n",
       "      <th>R2_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest Regressor</td>\n",
       "      <td>0.884636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>0.870199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AdaBoost Regressor</td>\n",
       "      <td>0.869545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CatBoosting Regressor</td>\n",
       "      <td>0.805841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.770974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K-Neighbors Regressor</td>\n",
       "      <td>0.768871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>0.608545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.607404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.606960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model Name  R2_Score\n",
       "5  Random Forest Regressor  0.884636\n",
       "6             XGBRegressor  0.870199\n",
       "8       AdaBoost Regressor  0.869545\n",
       "7    CatBoosting Regressor  0.805841\n",
       "4            Decision Tree  0.770974\n",
       "3    K-Neighbors Regressor  0.768871\n",
       "2                    Ridge  0.608545\n",
       "1                    Lasso  0.607404\n",
       "0        Linear Regression  0.606960"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(model_list, r2_list)), columns=['Model Name', 'R2_Score']).sort_values(by=[\"R2_Score\"],ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "59b27bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.030111\n",
      "0:\tlearn: 8553.1353972\ttotal: 1.37ms\tremaining: 1.37s\n",
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      "4:\tlearn: 8000.5364650\ttotal: 5.87ms\tremaining: 1.17s\n",
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      "19:\tlearn: 6263.5858715\ttotal: 27.1ms\tremaining: 1.33s\n",
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     ]
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "cata=CatBoostRegressor()\n",
    "model=cata.fit(X_train, y_train)\n",
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "9525764f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual Value</th>\n",
       "      <th>Predicted Value</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>88</th>\n",
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       "      <th>180</th>\n",
       "      <td>15690</td>\n",
       "      <td>17918.124496</td>\n",
       "      <td>-2228.124496</td>\n",
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       "    <tr>\n",
       "      <th>90</th>\n",
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       "<p>62 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual Value  Predicted Value    Difference\n",
       "88           9279      9100.078114    178.921886\n",
       "72          35056     23357.479514  11698.520486\n",
       "114         17075     20312.367461  -3237.367461\n",
       "158          7898      8946.470780  -1048.470780\n",
       "163          8058      8036.315232     21.684768\n",
       "..            ...              ...           ...\n",
       "133         12170     14370.313799  -2200.313799\n",
       "166          9538      9587.685283    -49.685283\n",
       "41          12945     10574.358931   2370.641069\n",
       "180         15690     17918.124496  -2228.124496\n",
       "90           7099      7028.412581     70.587419\n",
       "\n",
       "[62 rows x 3 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df=pd.DataFrame({'Actual Value':y_test,'Predicted Value':y_pred,'Difference':y_test-y_pred})\n",
    "pred_df.head(150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "4f792ffc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy of the model is 80.58\n"
     ]
    }
   ],
   "source": [
    "score = r2_score(y_test, y_pred)*100\n",
    "print(\" Accuracy of the model is %.2f\" %score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "90eac39c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               Newprice   R-squared:                       0.758\n",
      "Model:                            OLS   Adj. R-squared:                  0.746\n",
      "Method:                 Least Squares   F-statistic:                     60.90\n",
      "Date:                Tue, 21 May 2024   Prob (F-statistic):           1.98e-54\n",
      "Time:                        01:02:41   Log-Likelihood:                -1988.4\n",
      "No. Observations:                 205   AIC:                             3999.\n",
      "Df Residuals:                     194   BIC:                             4035.\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const             -1.396e+04   1.81e+04     -0.773      0.441   -4.96e+04    2.17e+04\n",
      "symboling           620.2607    308.221      2.012      0.046      12.366    1228.156\n",
      "wheel-base          113.3456    130.873      0.866      0.388    -144.771     371.462\n",
      "length                9.4706     69.617      0.136      0.892    -127.832     146.774\n",
      "width              -110.2880    308.476     -0.358      0.721    -718.686     498.110\n",
      "height              238.5592    173.218      1.377      0.170    -103.073     580.191\n",
      "curb-weight          -0.6203      2.108     -0.294      0.769      -4.779       3.538\n",
      "engine-size         128.1611     14.515      8.829      0.000      99.533     156.789\n",
      "compression-ratio   242.6694    103.829      2.337      0.020      37.891     447.448\n",
      "city-mpg           -102.2251    212.846     -0.480      0.632    -522.014     317.564\n",
      "highway-mpg        -212.7568    197.992     -1.075      0.284    -603.250     177.736\n",
      "==============================================================================\n",
      "Omnibus:                       34.817   Durbin-Watson:                   1.269\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              259.482\n",
      "Skew:                          -0.251   Prob(JB):                     4.51e-57\n",
      "Kurtosis:                       8.489   Cond. No.                     1.67e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.67e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "# Add a constant to X for intercept\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Print the summary of the regression results\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "e094a40e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:               Newprice   R-squared (uncentered):                   0.932\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.927\n",
      "Method:                 Least Squares   F-statistic:                              182.5\n",
      "Date:                Tue, 21 May 2024   Prob (F-statistic):                    1.53e-72\n",
      "Time:                        01:05:16   Log-Likelihood:                         -1398.1\n",
      "No. Observations:                 143   AIC:                                      2816.\n",
      "Df Residuals:                     133   BIC:                                      2846.\n",
      "Df Model:                          10                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "symboling           988.2639    400.506      2.468      0.015     196.079    1780.449\n",
      "wheel-base          199.0306    164.758      1.208      0.229    -126.854     524.916\n",
      "length               66.1686     88.747      0.746      0.457    -109.370     241.707\n",
      "width              -554.1786    240.576     -2.304      0.023   -1030.029     -78.328\n",
      "height              273.1614    194.664      1.403      0.163    -111.877     658.200\n",
      "curb-weight          -2.0007      2.520     -0.794      0.429      -6.986       2.984\n",
      "engine-size         131.8297     16.552      7.964      0.000      99.090     164.570\n",
      "compression-ratio   438.2139    126.902      3.453      0.001     187.206     689.221\n",
      "city-mpg            113.6739    298.097      0.381      0.704    -475.949     703.297\n",
      "highway-mpg        -516.3185    289.069     -1.786      0.076   -1088.086      55.449\n",
      "==============================================================================\n",
      "Omnibus:                       16.193   Durbin-Watson:                   1.867\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               55.700\n",
      "Skew:                           0.182   Prob(JB):                     8.03e-13\n",
      "Kurtosis:                       6.036   Cond. No.                     3.17e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[3] The condition number is large, 3.17e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "X = sm.add_constant(X_train)\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y_train, X_train).fit()\n",
    "\n",
    "# Print the summary of the regression results\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "33cce724",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Residuals\n",
    "residuals=y_test-y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "faeeb41d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x249cade4ed0>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot this residuals \n",
    "\n",
    "sns.displot(residuals,kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "9469a467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x249cae6fc90>"
      ]
     },
     "execution_count": 188,
     "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_pred,residuals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "43b0dfae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x249cd25ffd0>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.displot(y_pred,kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "fa5fff11",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_23208\\520176394.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_test-y_pred)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Newprice', ylabel='Density'>"
      ]
     },
     "execution_count": 191,
     "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": [
    "sns.distplot(y_test-y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "267ee238",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_23208\\476133659.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Newprice', ylabel='Density'>"
      ]
     },
     "execution_count": 195,
     "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": [
    "sns.distplot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "6aaa0af3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x249cd5b8b90>"
      ]
     },
     "execution_count": 196,
     "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,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "995d7547",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "from math import sqrt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
    "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import xgboost as xgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "508d6128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  2650.4305817742697\n",
      "Mean Squared Error:  12866565.911384381\n",
      "Root Mean Squared Error:  3586.999569470894\n",
      "R-squared:  0.6069603511989793\n"
     ]
    }
   ],
   "source": [
    "df\n",
    "#Without scaleing\n",
    "reg = LinearRegression()\n",
    "def train_and_evaluate_model(X_train, y_train, X_test, y_test):\n",
    "    # Initialize the Linear Regression model\n",
    "    reg = LinearRegression()\n",
    "\n",
    "    # Train the model on the training data\n",
    "    model = reg.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions on the testing data\n",
    "    predictions = model.predict(X_test)\n",
    "\n",
    "    # Calculate Mean Absolute Error (MAE)\n",
    "    mae = mean_absolute_error(y_test, predictions)\n",
    "    print(\"Mean Absolute Error: \", mae)\n",
    "\n",
    "    # Calculate Mean Squared Error (MSE)\n",
    "    mse = mean_squared_error(y_test, predictions)\n",
    "    print(\"Mean Squared Error: \", mse)\n",
    "\n",
    "    # Calculate Root Mean Squared Error (RMSE)\n",
    "    rmse = sqrt(mse)\n",
    "    print(\"Root Mean Squared Error: \", rmse)\n",
    "\n",
    "    # Calculate R-squared\n",
    "    r2 = r2_score(y_test, predictions)\n",
    "    print(\"R-squared: \", r2)\n",
    "\n",
    "train_and_evaluate_model(X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "f404f493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  2650.430581774276\n",
      "Mean Squared Error:  12866565.911384404\n",
      "Root Mean Squared Error:  3586.9995694708973\n",
      "R-squared:  0.6069603511989785\n"
     ]
    }
   ],
   "source": [
    "#with_scaleing _technique\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "train_and_evaluate_model(X_train_scaled,y_train,X_test_scaled,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "51a96851",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.71771706e+00, -1.56078180e+00, -4.88454002e-01, ...,\n",
       "        -1.22351516e-01, -1.11502506e+00, -7.31998185e-01],\n",
       "       [ 1.71771706e+00, -4.60996131e-01, -1.60336929e-01, ...,\n",
       "        -3.87808453e-01,  9.75116976e-02,  2.92799274e-01],\n",
       "       [ 9.12361191e-01, -8.81502415e-01, -1.41678523e+00, ...,\n",
       "        -1.48897209e-01,  1.00691427e+00,  1.17119710e+00],\n",
       "       ...,\n",
       "       [-6.98350541e-01,  9.29909271e-01, -2.79820120e-04, ...,\n",
       "         3.19586020e+00,  1.00691427e+00,  1.31759673e+00],\n",
       "       [ 1.71771706e+00, -1.56078180e+00, -4.88454002e-01, ...,\n",
       "        -1.22351516e-01, -1.11502506e+00, -7.31998185e-01],\n",
       "       [-6.98350541e-01,  2.43402790e+00,  1.91240263e+00, ...,\n",
       "        -4.14354147e-01, -8.11890873e-01, -8.78397822e-01]])"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "bd30ed72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.50370641, -0.46099613, -0.20835406, -0.2988112 , -0.87110057,\n",
       "        -0.39117897, -0.45585422, -0.65326539, -0.20562249,  0.        ],\n",
       "       [ 1.71771706, -0.41247618,  0.42387152,  1.96650073, -1.19266851,\n",
       "         2.0358305 ,  2.31331572, -0.44089984, -1.26659216, -1.75679564],\n",
       "       [-0.69835054,  2.4340279 ,  1.91240263,  1.03372523,  1.98281492,\n",
       "         1.65720188,  0.48209044,  2.93040327,  0.0975117 , -0.73199819],\n",
       "       [-0.69835054, -0.55803604, -0.69652824, -0.74299001, -0.30835667,\n",
       "        -0.63350129, -0.45585422,  3.32858867,  1.46161555,  0.87839782],\n",
       "       [ 0.10700532, -0.75211587, -0.50445971, -0.92066153, -0.46914064,\n",
       "        -0.83417446, -0.72383841, -0.25507998,  0.70378008,  0.58559855],\n",
       "       [-0.69835054, -0.55803604, -0.69652824, -0.74299001, -0.38874866,\n",
       "        -0.63350129, -0.45585422,  3.32858867,  2.06788393,  2.48879383],\n",
       "       [ 0.91236119, -0.29926294, -0.26437405, -0.25439332,  0.77693513,\n",
       "        -0.75276931, -0.47818624, -0.25507998,  0.40064589,  0.58559855],\n",
       "       [ 0.10700532, -0.75211587, -0.38441688, -1.0094973 , -0.10737671,\n",
       "        -1.10868021, -0.74617043, -0.14889721,  1.00691427,  1.02479746],\n",
       "       [ 0.10700532, -0.75211587, -0.7765568 , -1.0094973 ,  0.29458322,\n",
       "        -1.30935338, -0.74617043, -0.14889721,  1.00691427,  1.02479746],\n",
       "       [ 0.10700532, -0.75211587, -1.52882521, -1.09833306, -0.7103166 ,\n",
       "        -1.39265168, -0.90249454, -0.09580582,  2.06788393,  1.90319528],\n",
       "       [-0.69835054, -0.55803604, -0.69652824, -0.74299001, -0.30835667,\n",
       "        -0.97616019, -0.72383841, -0.25507998,  2.06788393,  2.48879383],\n",
       "       [ 1.71771706, -0.52568941, -0.14433122,  0.10094973, -1.43384447,\n",
       "         0.42287257,  0.5714185 , -0.78599386, -0.81189087, -0.87839782],\n",
       "       [ 0.10700532, -0.97854233, -1.27273383, -0.83182577,  0.13379925,\n",
       "        -1.33396424, -0.88016252, -0.25507998,  1.00691427,  1.1711971 ],\n",
       "       [ 0.10700532, -0.55803604, -1.30474526, -1.09833306,  0.29458322,\n",
       "        -1.18251279, -0.8578305 , -0.25507998,  1.61318265,  1.31759673],\n",
       "       [-0.69835054, -0.3477829 , -0.24036548, -0.2988112 ,  0.21419123,\n",
       "        -0.42525555, -0.50051825, -0.25507998, -0.0540554 , -0.73199819],\n",
       "       [ 1.71771706, -0.46099613, -0.16033693, -0.2988112 , -1.75541241,\n",
       "        -0.45365269, -0.45585422, -0.65326539, -0.20562249,  0.        ],\n",
       "       [-0.69835054, -0.05666317,  0.22380013,  0.18978549,  0.69654315,\n",
       "        -0.37792697, -0.18787003, -0.36126276,  0.24907879,  0.29279927],\n",
       "       [ 1.71771706, -0.75211587, -0.74454538, -0.92066153, -0.95149256,\n",
       "        -0.73573102, -0.47818624, -0.38780845, -0.0540554 , -0.14639964],\n",
       "       [ 0.10700532, -0.88150242, -0.61649969, -1.0094973 , -1.19266851,\n",
       "        -0.79252531, -0.72383841, -0.14889721,  1.00691427,  1.1711971 ],\n",
       "       [-0.69835054, -0.75211587, -1.2967424 , -1.09833306, -0.7103166 ,\n",
       "        -1.32639167, -0.90249454, -0.09580582,  2.06788393,  1.90319528],\n",
       "       [ 0.91236119,  0.04037675,  0.12776587,  0.14536761,  0.21419123,\n",
       "         0.40583428,  0.12477819, -0.52053692, -0.96345797, -1.1711971 ],\n",
       "       [-0.69835054, -0.3477829 , -0.24036548, -0.2988112 ,  0.21419123,\n",
       "        -0.18861266, -0.50051825, -0.600174  , -0.0540554 , -0.14639964],\n",
       "       [-0.69835054, -0.55803604, -0.42443116, -1.09833306,  2.14359889,\n",
       "        -0.60510414, -0.8578305 , -0.25507998,  0.40064589,  0.29279927],\n",
       "       [ 0.10700532, -0.88150242, -1.41678523, -1.0094973 , -1.19266851,\n",
       "        -1.38886539, -0.90249454, -0.14624264,  1.91631684,  1.61039601],\n",
       "       [-0.69835054,  1.41510883,  0.93605427,  1.03372523,  1.17889506,\n",
       "         1.11197666,  0.48209044,  2.93040327,  0.55221298,  0.43919891],\n",
       "       [ 1.71771706, -1.26966206, -0.3444026 ,  0.81163583, -1.63482444,\n",
       "         0.87344063,  1.12971889, -0.25507998, -0.81189087, -0.73199819],\n",
       "       [-0.69835054, -0.36395622, -0.1123198 , -0.2988112 ,  0.45536719,\n",
       "        -0.35899554, -0.50051825, -0.25507998, -0.20562249, -0.14639964],\n",
       "       [ 0.10700532, -0.88150242, -1.41678523, -1.0094973 , -1.19266851,\n",
       "        -0.91179333, -0.72383841, -0.6267197 , -0.0540554 ,  0.        ],\n",
       "       [-1.50370641,  2.66045436,  2.20850828,  2.49951531,  1.09850308,\n",
       "         2.13995338,  2.31331572, -0.44089984, -1.26659216, -1.75679564],\n",
       "       [ 0.91236119, -2.02980804, -2.43314787, -0.96507942, -1.19266851,\n",
       "        -1.49677455, -0.8578305 , -0.2019886 ,  1.00691427,  1.1711971 ],\n",
       "       [ 0.10700532, -0.42864949, -0.47244829, -0.03230391, -1.11227653,\n",
       "        -0.59942471, -0.45585422, -0.22853429,  0.0975117 ,  0.14639964],\n",
       "       [-1.50370641,  0.52557631,  0.04773731,  0.18978549,  0.45536719,\n",
       "        -0.3703544 , -0.18787003, -0.33471707,  0.40064589,  0.29279927],\n",
       "       [ 0.10700532, -0.97854233, -1.27273383, -0.83182577,  0.13379925,\n",
       "        -1.36236139, -0.88016252, -0.25507998,  0.85534717,  0.14639964],\n",
       "       [-1.50370641,  0.52557631,  0.04773731,  0.18978549,  0.05340726,\n",
       "        -0.2870561 , -0.18787003, -0.33471707,  0.40064589,  0.29279927],\n",
       "       [ 0.91236119,  0.33149648,  0.14377158, -0.56531849,  0.21419123,\n",
       "        -0.40632412, -0.50051825, -0.30817137, -0.20562249, -0.14639964],\n",
       "       [ 0.91236119, -0.29926294, -0.26437405, -0.25439332,  0.77693513,\n",
       "        -0.58617271, -0.47818624,  0.01037695,  0.24907879,  0.29279927],\n",
       "       [ 0.10700532, -0.75211587, -0.7765568 , -1.0094973 ,  0.29458322,\n",
       "        -1.36425453, -0.74617043, -0.14889721,  1.00691427,  1.02479746],\n",
       "       [ 0.10700532, -0.88150242, -1.41678523, -1.0094973 , -1.2730605 ,\n",
       "        -1.17494022, -0.90249454, -0.14889721,  1.00691427,  1.1711971 ],\n",
       "       [-0.69835054,  0.20210993,  0.64795147,  0.36745702,  0.53575918,\n",
       "        -0.08827607, -0.47818624, -0.25507998,  0.0975117 ,  0.14639964],\n",
       "       [-2.30906227,  0.83286936,  1.10411423,  0.50071066,  0.9779151 ,\n",
       "         0.57243088,  0.23643826, -0.12235152, -0.20562249, -0.29279927],\n",
       "       [ 0.10700532, -0.97854233, -0.65651397, -0.83182577,  0.13379925,\n",
       "        -1.25823852, -0.88016252, -0.25507998,  1.00691427,  1.1711971 ],\n",
       "       [-1.50370641,  0.83286936,  1.10411423,  0.50071066,  1.50046301,\n",
       "         0.81853948,  0.23643826, -0.12235152, -0.0540554 , -0.29279927],\n",
       "       [-0.69835054,  1.41510883,  0.93605427,  1.03372523,  1.17889506,\n",
       "         1.21609953,  0.48209044,  2.93040327,  0.55221298,  0.43919891],\n",
       "       [ 0.10700532, -0.75211587, -0.75254823, -1.0094973 , -0.18776869,\n",
       "        -1.10110764, -0.74617043, -0.14889721,  1.00691427,  1.02479746],\n",
       "       [ 0.91236119, -0.12135644,  0.09575445, -0.20997544, -0.7103166 ,\n",
       "        -0.11099379,  0.34809834, -0.1754429 , -0.0540554 ,  0.        ],\n",
       "       [ 0.91236119, -0.29926294, -0.26437405, -0.25439332,  0.77693513,\n",
       "        -0.65432586, -0.74617043,  3.46131714,  1.91631684,  2.34239419],\n",
       "       [ 1.71771706,  0.6064429 ,  0.67996289,  0.72280006, -0.7103166 ,\n",
       "         0.76931776,  0.90639873, -0.1754429 , -0.81189087, -0.87839782],\n",
       "       [-0.69835054, -0.42864949, -0.60049398, -0.38764696, -0.18776869,\n",
       "        -0.70733387, -0.45585422, -0.25507998,  0.40064589,  0.43919891],\n",
       "       [ 0.10700532, -0.46099613, -0.20835406, -0.2988112 , -0.87110057,\n",
       "        -0.46311841, -0.18787003, -0.38780845,  0.0975117 ,  0.29279927],\n",
       "       [-1.50370641,  0.52557631,  0.04773731,  0.18978549,  0.45536719,\n",
       "        -0.53695099, -0.18787003, -0.33471707,  0.70378008,  0.58559855],\n",
       "       [-2.30906227,  0.83286936,  1.10411423,  0.50071066,  0.9779151 ,\n",
       "         0.61597317,  0.23643826, -0.12235152, -0.0540554 , -0.29279927],\n",
       "       [-0.69835054, -0.31543626, -0.12832551, -0.38764696,  0.37497521,\n",
       "        -0.58238643, -0.23253407, -0.38780845,  0.40064589,  0.58559855],\n",
       "       [ 0.10700532, -0.88150242, -1.41678523, -1.0094973 , -1.2730605 ,\n",
       "        -1.21658937, -0.90249454, -0.14889721,  1.00691427,  1.1711971 ],\n",
       "       [-0.69835054,  2.4340279 ,  1.91240263,  1.03372523,  1.98281492,\n",
       "         1.55307901,  0.48209044,  2.93040327,  0.0975117 , -0.73199819],\n",
       "       [ 1.71771706, -0.75211587, -0.488454  ,  0.98930735, -1.43384447,\n",
       "         0.3187497 ,  0.45975842, -0.12235152, -0.81189087, -0.43919891],\n",
       "       [ 0.10700532, -0.88150242, -2.00099368, -0.92066153, -0.46914064,\n",
       "        -1.26770423, -0.8578305 , -0.2019886 ,  0.85534717,  0.58559855],\n",
       "       [ 0.10700532, -0.88150242, -1.41678523, -1.0094973 , -1.19266851,\n",
       "        -0.91179333, -0.72383841, -0.6267197 , -0.0540554 ,  0.        ],\n",
       "       [ 0.91236119, -0.00814321,  0.92805141,  0.18978549,  0.93771911,\n",
       "         0.16161882, -0.21020205, -0.1754429 , -0.50875668, -0.29279927],\n",
       "       [ 0.10700532, -0.75211587, -0.50445971, -0.92066153, -0.46914064,\n",
       "        -0.58617271, -0.72383841, -0.14889721,  0.24907879, -0.14639964],\n",
       "       [-0.69835054, -0.42864949,  0.0317316 , -0.38764696,  0.13379925,\n",
       "        -0.2738041 , -0.45585422, -0.25507998, -0.0540554 , -0.29279927],\n",
       "       [-1.50370641,  0.865216  ,  1.02408568,  0.18978549,  0.13379925,\n",
       "         0.98702922,  0.90639873, -0.2019886 , -0.66032378, -0.87839782],\n",
       "       [ 0.10700532, -0.75211587, -0.7765568 , -1.0094973 ,  0.29458322,\n",
       "        -1.12193221, -0.61217833,  3.16931451,  3.1288536 ,  2.92799274]])"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "794b370c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=1, tol=1e-7, max_iter=20000))\n",
    "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=0.9, random_state=3, max_iter=10000, tol=1e-5))\n",
    "KRR = KernelRidge(alpha=0.6,kernel=\"polynomial\",degree=2,coef0=2.5)\n",
    "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
    "                                   max_depth=4, max_features='sqrt',\n",
    "                                   min_samples_leaf=15, min_samples_split=10, \n",
    "                                   loss='huber', random_state =5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "ad413be3",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AverageModels(BaseEstimator, RegressorMixin,TransformerMixin):\n",
    "    \n",
    "    def __init__(self,models):\n",
    "        self.models = models\n",
    "    \n",
    "    def fit(self,X,y):\n",
    "        self.models_ = [clone(x) for x in self.models]\n",
    "        \n",
    "        for model in self.models_:\n",
    "            model.fit(X,y)\n",
    "            \n",
    "        return self\n",
    "    \n",
    "    def predict(self,X):\n",
    "        predictions = np.column_stack([model.predict(X) for model in self.models_])\n",
    "        \n",
    "        return np.mean(predictions,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "9e55ec24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Means Squared Error:  8057372.813198579\n",
      "Root Mean Squared Error:  2838.55118206429\n",
      "R2 Score:  0.7538685145228683\n"
     ]
    }
   ],
   "source": [
    "averaged_models = AverageModels(models=(ENet, GBoost, KRR, lasso))\n",
    "averaged_models.fit(X_train_scaled,y_train)\n",
    "predictions = averaged_models.predict(X_test_scaled)  # Corrected line: Removed y_train\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "rmse = sqrt(mse)\n",
    "r2 = r2_score(y_test,predictions)\n",
    "print(\"Means Squared Error: \",mse)\n",
    "print(\"Root Mean Squared Error: \", rmse)\n",
    "print(\"R2 Score: \",r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "ec85b947",
   "metadata": {},
   "outputs": [],
   "source": [
    "class StackingAveragedModels(BaseEstimator,RegressorMixin,TransformerMixin):\n",
    "    \n",
    "    def __init__(self,base_models,meta_models,n_folds = 5):\n",
    "        \n",
    "        self.base_models = base_models\n",
    "        self.meta_models = meta_models\n",
    "        self.n_folds = n_folds\n",
    "    \n",
    "    \n",
    "    def fit(self,X,y):\n",
    "        self.base_models_ = [list() for x in self.base_models]\n",
    "        self.meta_model_ = clone(self.meta_models)\n",
    "        kfold = KFold(n_splits = self.n_folds,shuffle=True,random_state = 42)\n",
    "    \n",
    "        out_of_fold_predictions = np.zeros((X.shape[0],len(self.base_models)))\n",
    "        \n",
    "        for i,model in enumerate(self.base_models):\n",
    "            for train_index,holdout_index in kfold.split(X,y):\n",
    "                instance = clone(model)\n",
    "                self.base_models_[i].append(instance)\n",
    "                instance.fit(X.iloc[train_index], y.iloc[train_index])\n",
    "                y_pred = instance.predict(X.iloc[holdout_index])\n",
    "                out_of_fold_predictions[holdout_index,i] = y_pred\n",
    "        self.meta_model_.fit(out_of_fold_predictions,y)\n",
    "        return self\n",
    "    \n",
    "    def predict(self,X):\n",
    "        meta_features = np.column_stack([np.column_stack([model.predict(X) for model in base_models]).mean(axis=1) for base_models in self.base_models_])\n",
    "        return self.meta_model_.predict(meta_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "936fc2c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Means Squared Error:  2514353.1980962832\n",
      "Root Mean Squared Error:  1585.6712137439727\n",
      "R2 Score:  0.9667483318955076\n"
     ]
    }
   ],
   "source": [
    "stacked_averaged_models = StackingAveragedModels(base_models = (ENet,GBoost,KRR),meta_models=lasso)\n",
    "stacked_averaged_models.fit(X_train,y_train)\n",
    "stacked_pred = stacked_averaged_models.predict(X_train)\n",
    "mse = mean_squared_error(y_train, stacked_pred)\n",
    "rmse = sqrt(mse)\n",
    "r2 = r2_score(y_train,stacked_pred)\n",
    "print(\"Means Squared Error: \",mse)\n",
    "print(\"Root Mean Squared Error: \", rmse)\n",
    "print(\"R2 Score: \",r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "65456a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,  6))  # Optional: Set the figure size\n",
    "plt.scatter(y_train, stacked_pred, alpha=0.5)  # Alpha makes the points semi-transparent\n",
    "plt.plot([min(y_train), max(y_train)], [min(y_train), max(y_train)], color='red', linestyle='--')  # Add a diagonal line for reference\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('Actual vs Predicted Values')\n",
    "plt.grid(True)  # Optional: Add grid lines\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "a254bc44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  1744.9032258064517\n",
      "Mean Squared Error:  6720556.354838709\n",
      "Root Mean Squared Error:  2592.403586411404\n",
      "R-squared:  0.7947047310334675\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'squared_error',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dtree=DecisionTreeRegressor()\n",
    "def train_and_evaluate_model_1(X_train, y_train, X_test, y_test):\n",
    "    # Initialize the Linear Regression model\n",
    "    dtree = DecisionTreeRegressor()\n",
    "\n",
    "    # Train the model on the training data\n",
    "    model = dtree.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions on the testing data\n",
    "    tree_predictions = model.predict(X_test)\n",
    "\n",
    "    # Calculate Mean Absolute Error (MAE)\n",
    "    mae = mean_absolute_error(y_test, tree_predictions)\n",
    "    print(\"Mean Absolute Error: \", mae)\n",
    "\n",
    "    # Calculate Mean Squared Error (MSE)\n",
    "    mse = mean_squared_error(y_test, tree_predictions)\n",
    "    print(\"Mean Squared Error: \", mse)\n",
    "\n",
    "    # Calculate Root Mean Squared Error (RMSE)\n",
    "    rmse = sqrt(mse)\n",
    "    print(\"Root Mean Squared Error: \", rmse)\n",
    "\n",
    "    # Calculate R-squared\n",
    "    r2 = r2_score(y_test, tree_predictions)\n",
    "    print(\"R-squared: \", r2)\n",
    "\n",
    "train_and_evaluate_model_1(X_train,y_train,X_test,y_test)\n",
    "\n",
    "dtree.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "2377325c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'squared_error',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "ecac7980",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error:  1286.4171774193549\n",
      "Mean Squared Error:  3724794.9532409054\n",
      "Root Mean Squared Error:  1929.9727856218349\n",
      "R-squared:  0.8862173395480549\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "def train_and_evaluate_model_1(X_train, y_train, X_test, y_test):\n",
    "    # Initialize the Linear Regression model\n",
    "    dforest = RandomForestRegressor()\n",
    "    # Train the model on the training data\n",
    "    model = dforest.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions on the testing data\n",
    "    random_predictions = model.predict(X_test)\n",
    "\n",
    "    # Calculate Mean Absolute Error (MAE)\n",
    "    mae = mean_absolute_error(y_test, random_predictions)\n",
    "    print(\"Mean Absolute Error: \", mae)\n",
    "\n",
    "    # Calculate Mean Squared Error (MSE)\n",
    "    mse = mean_squared_error(y_test, random_predictions)\n",
    "    print(\"Mean Squared Error: \", mse)\n",
    "\n",
    "    # Calculate Root Mean Squared Error (RMSE)\n",
    "    rmse = sqrt(mse)\n",
    "    print(\"Root Mean Squared Error: \", rmse)\n",
    "\n",
    "    # Calculate R-squared\n",
    "    r2 = r2_score(y_test, random_predictions)\n",
    "    print(\"R-squared: \", r2)\n",
    "\n",
    "train_and_evaluate_model_1(X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "9931f8ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00420036 0.02778628 0.05447032 0.03624288 0.01367085 0.22254343\n",
      " 0.4655844  0.01578539 0.03675651 0.12295958]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "dforest = RandomForestRegressor()\n",
    "    # Train the model on the training data\n",
    "model = dforest.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions on the testing data\n",
    "random_predictions = model.predict(X_test)\n",
    "\n",
    "print(model.feature_importances_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "56f822b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1= df.iloc[:,:10]\n",
    "feat_importances = pd.Series(model.feature_importances_,index=df1.columns)\n",
    "feat_importances.nlargest(5).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "d015bd25",
   "metadata": {},
   "outputs": [
    {
     "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,random_predictions)\n",
    "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='black', linestyle='--')  # Add a diagonal line for reference\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('Actual vs Predicted Values')\n",
    "plt.grid(True)  # Optional: Add grid lines\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "960c49a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00420036 0.02778628 0.05447032 0.03624288 0.01367085 0.22254343\n",
      " 0.4655844  0.01578539 0.03675651 0.12295958]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6216eac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33688a5e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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