LInear_Regeression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cb203729",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2afd0925",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"auto-mpg.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "344647b5",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model year  \\\n",
       "0  18.0          8         307.0        130    3504          12.0          70   \n",
       "1  15.0          8         350.0        165    3693          11.5          70   \n",
       "2  18.0          8         318.0        150    3436          11.0          70   \n",
       "3  16.0          8         304.0        150    3433          12.0          70   \n",
       "4  17.0          8         302.0        140    3449          10.5          70   \n",
       "\n",
       "   origin                   car name  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a446146c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(\"car name\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f5ea485",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
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       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
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       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
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       "      <td>150</td>\n",
       "      <td>3433</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model year  \\\n",
       "0  18.0          8         307.0        130    3504          12.0          70   \n",
       "1  15.0          8         350.0        165    3693          11.5          70   \n",
       "2  18.0          8         318.0        150    3436          11.0          70   \n",
       "3  16.0          8         304.0        150    3433          12.0          70   \n",
       "4  17.0          8         302.0        140    3449          10.5          70   \n",
       "\n",
       "   origin  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e48cf186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             float64\n",
       "cylinders         int64\n",
       "displacement    float64\n",
       "horsepower       object\n",
       "weight            int64\n",
       "acceleration    float64\n",
       "model year        int64\n",
       "origin            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6c92339d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0"
      ]
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     "execution_count": 40,
     "metadata": {},
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   ],
   "source": [
    "duplicate=df.duplicated()\n",
    "duplicate.sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b338b730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             0\n",
       "cylinders       0\n",
       "displacement    0\n",
       "horsepower      0\n",
       "weight          0\n",
       "acceleration    0\n",
       "model year      0\n",
       "origin          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0e66abd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           398 non-null    float64\n",
      " 1   cylinders     398 non-null    int64  \n",
      " 2   displacement  398 non-null    float64\n",
      " 3   horsepower    398 non-null    object \n",
      " 4   weight        398 non-null    int64  \n",
      " 5   acceleration  398 non-null    float64\n",
      " 6   model year    398 non-null    int64  \n",
      " 7   origin        398 non-null    int64  \n",
      "dtypes: float64(3), int64(4), object(1)\n",
      "memory usage: 25.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dfbf3311",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"origin\"]=df[\"origin\"].map({1:'america',2:'europe',3:'asia'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ba139f2f",
   "metadata": {},
   "outputs": [
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       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>america</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model year  \\\n",
       "0  18.0          8         307.0        130    3504          12.0          70   \n",
       "1  15.0          8         350.0        165    3693          11.5          70   \n",
       "2  18.0          8         318.0        150    3436          11.0          70   \n",
       "3  16.0          8         304.0        150    3433          12.0          70   \n",
       "4  17.0          8         302.0        140    3449          10.5          70   \n",
       "\n",
       "    origin  \n",
       "0  america  \n",
       "1  america  \n",
       "2  america  \n",
       "3  america  \n",
       "4  america  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ee2e64f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             float64\n",
       "cylinders         int64\n",
       "displacement    float64\n",
       "horsepower       object\n",
       "weight            int64\n",
       "acceleration    float64\n",
       "model year        int64\n",
       "origin           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cd3a9be2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df=pd.get_dummies(df,columns=[\"origin\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "13f6699d",
   "metadata": {},
   "outputs": [
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       "      <td>150</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>454.0</td>\n",
       "      <td>220</td>\n",
       "      <td>4354</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>440.0</td>\n",
       "      <td>215</td>\n",
       "      <td>4312</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455.0</td>\n",
       "      <td>225</td>\n",
       "      <td>4425</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>390.0</td>\n",
       "      <td>190</td>\n",
       "      <td>3850</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>383.0</td>\n",
       "      <td>170</td>\n",
       "      <td>3563</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>340.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3609</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3761</td>\n",
       "      <td>9.5</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455.0</td>\n",
       "      <td>225</td>\n",
       "      <td>3086</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>113.0</td>\n",
       "      <td>95</td>\n",
       "      <td>2372</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22.0</td>\n",
       "      <td>6</td>\n",
       "      <td>198.0</td>\n",
       "      <td>95</td>\n",
       "      <td>2833</td>\n",
       "      <td>15.5</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "      <td>199.0</td>\n",
       "      <td>97</td>\n",
       "      <td>2774</td>\n",
       "      <td>15.5</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85</td>\n",
       "      <td>2587</td>\n",
       "      <td>16.0</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>88</td>\n",
       "      <td>2130</td>\n",
       "      <td>14.5</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1835</td>\n",
       "      <td>20.5</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mpg  cylinders  displacement horsepower  weight  acceleration  \\\n",
       "0   18.0          8         307.0        130    3504          12.0   \n",
       "1   15.0          8         350.0        165    3693          11.5   \n",
       "2   18.0          8         318.0        150    3436          11.0   \n",
       "3   16.0          8         304.0        150    3433          12.0   \n",
       "4   17.0          8         302.0        140    3449          10.5   \n",
       "5   15.0          8         429.0        198    4341          10.0   \n",
       "6   14.0          8         454.0        220    4354           9.0   \n",
       "7   14.0          8         440.0        215    4312           8.5   \n",
       "8   14.0          8         455.0        225    4425          10.0   \n",
       "9   15.0          8         390.0        190    3850           8.5   \n",
       "10  15.0          8         383.0        170    3563          10.0   \n",
       "11  14.0          8         340.0        160    3609           8.0   \n",
       "12  15.0          8         400.0        150    3761           9.5   \n",
       "13  14.0          8         455.0        225    3086          10.0   \n",
       "14  24.0          4         113.0         95    2372          15.0   \n",
       "15  22.0          6         198.0         95    2833          15.5   \n",
       "16  18.0          6         199.0         97    2774          15.5   \n",
       "17  21.0          6         200.0         85    2587          16.0   \n",
       "18  27.0          4          97.0         88    2130          14.5   \n",
       "19  26.0          4          97.0         46    1835          20.5   \n",
       "\n",
       "    model year  origin_america  origin_asia  origin_europe  \n",
       "0           70            True        False          False  \n",
       "1           70            True        False          False  \n",
       "2           70            True        False          False  \n",
       "3           70            True        False          False  \n",
       "4           70            True        False          False  \n",
       "5           70            True        False          False  \n",
       "6           70            True        False          False  \n",
       "7           70            True        False          False  \n",
       "8           70            True        False          False  \n",
       "9           70            True        False          False  \n",
       "10          70            True        False          False  \n",
       "11          70            True        False          False  \n",
       "12          70            True        False          False  \n",
       "13          70            True        False          False  \n",
       "14          70           False         True          False  \n",
       "15          70            True        False          False  \n",
       "16          70            True        False          False  \n",
       "17          70            True        False          False  \n",
       "18          70           False         True          False  \n",
       "19          70           False        False           True  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b5acaaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['origin_america', 'origin_asia', 'origin_europe']] = df[['origin_america', 'origin_asia', 'origin_europe']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "856c2e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin_america</th>\n",
       "      <th>origin_asia</th>\n",
       "      <th>origin_europe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.514573</td>\n",
       "      <td>5.454774</td>\n",
       "      <td>193.425879</td>\n",
       "      <td>2970.424623</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>76.010050</td>\n",
       "      <td>0.625628</td>\n",
       "      <td>0.198492</td>\n",
       "      <td>0.175879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.815984</td>\n",
       "      <td>1.701004</td>\n",
       "      <td>104.269838</td>\n",
       "      <td>846.841774</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>3.697627</td>\n",
       "      <td>0.484569</td>\n",
       "      <td>0.399367</td>\n",
       "      <td>0.381197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>2223.750000</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>17.175000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement       weight  acceleration  \\\n",
       "count  398.000000  398.000000    398.000000   398.000000    398.000000   \n",
       "mean    23.514573    5.454774    193.425879  2970.424623     15.568090   \n",
       "std      7.815984    1.701004    104.269838   846.841774      2.757689   \n",
       "min      9.000000    3.000000     68.000000  1613.000000      8.000000   \n",
       "25%     17.500000    4.000000    104.250000  2223.750000     13.825000   \n",
       "50%     23.000000    4.000000    148.500000  2803.500000     15.500000   \n",
       "75%     29.000000    8.000000    262.000000  3608.000000     17.175000   \n",
       "max     46.600000    8.000000    455.000000  5140.000000     24.800000   \n",
       "\n",
       "       model year  origin_america  origin_asia  origin_europe  \n",
       "count  398.000000      398.000000   398.000000     398.000000  \n",
       "mean    76.010050        0.625628     0.198492       0.175879  \n",
       "std      3.697627        0.484569     0.399367       0.381197  \n",
       "min     70.000000        0.000000     0.000000       0.000000  \n",
       "25%     73.000000        0.000000     0.000000       0.000000  \n",
       "50%     76.000000        1.000000     0.000000       0.000000  \n",
       "75%     79.000000        1.000000     0.000000       0.000000  \n",
       "max     82.000000        1.000000     1.000000       1.000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ea0884e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['horsepower'] = df['horsepower'].replace(to_replace='?',value=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "677a61df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg               0\n",
       "cylinders         0\n",
       "displacement      0\n",
       "horsepower        0\n",
       "weight            0\n",
       "acceleration      0\n",
       "model year        0\n",
       "origin_america    0\n",
       "origin_asia       0\n",
       "origin_europe     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3f7e609a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93.5"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "defa7a46",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['horsepower'] = df['horsepower'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "00fb791a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>429.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>4341</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>454.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>4354</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>440.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4312</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>4425</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>390.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3850</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0  18.0          8         307.0       130.0    3504          12.0   \n",
       "1  15.0          8         350.0       165.0    3693          11.5   \n",
       "2  18.0          8         318.0       150.0    3436          11.0   \n",
       "3  16.0          8         304.0       150.0    3433          12.0   \n",
       "4  17.0          8         302.0       140.0    3449          10.5   \n",
       "5  15.0          8         429.0       198.0    4341          10.0   \n",
       "6  14.0          8         454.0       220.0    4354           9.0   \n",
       "7  14.0          8         440.0       215.0    4312           8.5   \n",
       "8  14.0          8         455.0       225.0    4425          10.0   \n",
       "9  15.0          8         390.0       190.0    3850           8.5   \n",
       "\n",
       "   model year  origin_america  origin_asia  origin_europe  \n",
       "0          70               1            0              0  \n",
       "1          70               1            0              0  \n",
       "2          70               1            0              0  \n",
       "3          70               1            0              0  \n",
       "4          70               1            0              0  \n",
       "5          70               1            0              0  \n",
       "6          70               1            0              0  \n",
       "7          70               1            0              0  \n",
       "8          70               1            0              0  \n",
       "9          70               1            0              0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "59b1354e",
   "metadata": {},
   "outputs": [],
   "source": [
    "median=df[\"horsepower\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "60ed67fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"horsepower\"]=df[\"horsepower\"].replace(np.nan,median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9410295c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outlier(col):\n",
    "    sorted(col)\n",
    "    Q1,Q3=col.quantile([0.25,0.75])\n",
    "    IQR=Q3-Q1\n",
    "    lower_range=Q1-(1.5*IQR)\n",
    "    upper_range=Q3+(1.5*IQR)\n",
    "    return lower_range,upper_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f89bd2a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='horsepower'>"
      ]
     },
     "execution_count": 64,
     "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.boxplot(x=\"horsepower\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "65836dfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "lowAge,upAge=remove_outlier(df[\"horsepower\"])\n",
    "df[\"horsepower\"]=np.where(df[\"horsepower\"]>upAge,upAge,df[\"horsepower\"])\n",
    "df[\"horsepower\"]=np.where(df[\"horsepower\"]<lowAge,lowAge,df[\"horsepower\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b6b8aa8",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "786d2acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0b6ec50e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df[[\"mpg\"]]\n",
    "X=df.drop([\"mpg\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "842251db",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.15,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "693a0fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8e5bcdf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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