CAR PRICE PREDICTION WITH MACHINE LEARNING.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/kaggle/input/carprice/CarPrice.csv')\n"
   ]
  },
  {
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   "execution_count": 20,
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   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',None)\n",
    "pd.set_option('display.max_rows',None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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    {
     "data": {
      "text/plain": [
       "(205, 26)"
      ]
     },
     "execution_count": 21,
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    }
   ],
   "source": [
    "df.shape"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
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       "      <th>drivewheel</th>\n",
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       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
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       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>chevrolet impala</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.4</td>\n",
       "      <td>141.1</td>\n",
       "      <td>60.3</td>\n",
       "      <td>53.2</td>\n",
       "      <td>1488</td>\n",
       "      <td>l</td>\n",
       "      <td>three</td>\n",
       "      <td>61</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.03</td>\n",
       "      <td>9.5</td>\n",
       "      <td>48</td>\n",
       "      <td>5100</td>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>5151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>subaru trezia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>65.4</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2290</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.0</td>\n",
       "      <td>82</td>\n",
       "      <td>4800</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>7463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>169</td>\n",
       "      <td>2</td>\n",
       "      <td>toyota corolla</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>98.4</td>\n",
       "      <td>176.2</td>\n",
       "      <td>65.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2536</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>146</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.50</td>\n",
       "      <td>9.3</td>\n",
       "      <td>116</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>9639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 264gl</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>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3062</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</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>22625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>honda accord</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>96.5</td>\n",
       "      <td>167.5</td>\n",
       "      <td>65.2</td>\n",
       "      <td>53.3</td>\n",
       "      <td>2236</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>110</td>\n",
       "      <td>1bbl</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.58</td>\n",
       "      <td>9.0</td>\n",
       "      <td>86</td>\n",
       "      <td>5800</td>\n",
       "      <td>27</td>\n",
       "      <td>33</td>\n",
       "      <td>7895.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     car_ID  symboling           CarName fueltype aspiration doornumber  \\\n",
       "18       19          2  chevrolet impala      gas        std        two   \n",
       "146     147          0     subaru trezia      gas        std       four   \n",
       "168     169          2    toyota corolla      gas        std        two   \n",
       "204     205         -1       volvo 264gl      gas      turbo       four   \n",
       "37       38          0      honda accord      gas        std        two   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  carlength  carwidth  \\\n",
       "18   hatchback        fwd          front       88.4      141.1      60.3   \n",
       "146      wagon        fwd          front       97.0      173.5      65.4   \n",
       "168    hardtop        rwd          front       98.4      176.2      65.6   \n",
       "204      sedan        rwd          front      109.1      188.8      68.9   \n",
       "37   hatchback        fwd          front       96.5      167.5      65.2   \n",
       "\n",
       "     carheight  curbweight enginetype cylindernumber  enginesize fuelsystem  \\\n",
       "18        53.2        1488          l          three          61       2bbl   \n",
       "146       53.0        2290       ohcf           four         108       2bbl   \n",
       "168       52.0        2536        ohc           four         146       mpfi   \n",
       "204       55.5        3062        ohc           four         141       mpfi   \n",
       "37        53.3        2236        ohc           four         110       1bbl   \n",
       "\n",
       "     boreratio  stroke  compressionratio  horsepower  peakrpm  citympg  \\\n",
       "18        2.91    3.03               9.5          48     5100       47   \n",
       "146       3.62    2.64               9.0          82     4800       28   \n",
       "168       3.62    3.50               9.3         116     4800       24   \n",
       "204       3.78    3.15               9.5         114     5400       19   \n",
       "37        3.15    3.58               9.0          86     5800       27   \n",
       "\n",
       "     highwaympg    price  \n",
       "18           53   5151.0  \n",
       "146          32   7463.0  \n",
       "168          30   9639.0  \n",
       "204          25  22625.0  \n",
       "37           33   7895.0  "
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   "source": [
    "df.sample(5)"
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       "Empty DataFrame\n",
       "Columns: [car_ID, symboling, CarName, fueltype, aspiration, doornumber, carbody, drivewheel, enginelocation, wheelbase, carlength, carwidth, carheight, curbweight, enginetype, cylindernumber, enginesize, fuelsystem, boreratio, stroke, compressionratio, horsepower, peakrpm, citympg, highwaympg, price]\n",
       "Index: []"
      ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   car_ID            205 non-null    int64  \n",
      " 1   symboling         205 non-null    int64  \n",
      " 2   CarName           205 non-null    object \n",
      " 3   fueltype          205 non-null    object \n",
      " 4   aspiration        205 non-null    object \n",
      " 5   doornumber        205 non-null    object \n",
      " 6   carbody           205 non-null    object \n",
      " 7   drivewheel        205 non-null    object \n",
      " 8   enginelocation    205 non-null    object \n",
      " 9   wheelbase         205 non-null    float64\n",
      " 10  carlength         205 non-null    float64\n",
      " 11  carwidth          205 non-null    float64\n",
      " 12  carheight         205 non-null    float64\n",
      " 13  curbweight        205 non-null    int64  \n",
      " 14  enginetype        205 non-null    object \n",
      " 15  cylindernumber    205 non-null    object \n",
      " 16  enginesize        205 non-null    int64  \n",
      " 17  fuelsystem        205 non-null    object \n",
      " 18  boreratio         205 non-null    float64\n",
      " 19  stroke            205 non-null    float64\n",
      " 20  compressionratio  205 non-null    float64\n",
      " 21  horsepower        205 non-null    int64  \n",
      " 22  peakrpm           205 non-null    int64  \n",
      " 23  citympg           205 non-null    int64  \n",
      " 24  highwaympg        205 non-null    int64  \n",
      " 25  price             205 non-null    float64\n",
      "dtypes: float64(8), int64(8), object(10)\n",
      "memory usage: 41.8+ KB\n"
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      "text/plain": [
       "car_ID              0\n",
       "symboling           0\n",
       "CarName             0\n",
       "fueltype            0\n",
       "aspiration          0\n",
       "doornumber          0\n",
       "carbody             0\n",
       "drivewheel          0\n",
       "enginelocation      0\n",
       "wheelbase           0\n",
       "carlength           0\n",
       "carwidth            0\n",
       "carheight           0\n",
       "curbweight          0\n",
       "enginetype          0\n",
       "cylindernumber      0\n",
       "enginesize          0\n",
       "fuelsystem          0\n",
       "boreratio           0\n",
       "stroke              0\n",
       "compressionratio    0\n",
       "horsepower          0\n",
       "peakrpm             0\n",
       "citympg             0\n",
       "highwaympg          0\n",
       "price               0\n",
       "dtype: int64"
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       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>0.834146</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>3.329756</td>\n",
       "      <td>3.255415</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>104.117073</td>\n",
       "      <td>5125.121951</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>13276.710571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>59.322565</td>\n",
       "      <td>1.245307</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>0.270844</td>\n",
       "      <td>0.313597</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>39.544167</td>\n",
       "      <td>476.985643</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>7988.852332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>166.300000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2145.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>173.200000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>183.100000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16503.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>208.100000</td>\n",
       "      <td>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           car_ID   symboling   wheelbase   carlength    carwidth   carheight  \\\n",
       "count  205.000000  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
       "mean   103.000000    0.834146   98.756585  174.049268   65.907805   53.724878   \n",
       "std     59.322565    1.245307    6.021776   12.337289    2.145204    2.443522   \n",
       "min      1.000000   -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
       "25%     52.000000    0.000000   94.500000  166.300000   64.100000   52.000000   \n",
       "50%    103.000000    1.000000   97.000000  173.200000   65.500000   54.100000   \n",
       "75%    154.000000    2.000000  102.400000  183.100000   66.900000   55.500000   \n",
       "max    205.000000    3.000000  120.900000  208.100000   72.300000   59.800000   \n",
       "\n",
       "        curbweight  enginesize   boreratio      stroke  compressionratio  \\\n",
       "count   205.000000  205.000000  205.000000  205.000000        205.000000   \n",
       "mean   2555.565854  126.907317    3.329756    3.255415         10.142537   \n",
       "std     520.680204   41.642693    0.270844    0.313597          3.972040   \n",
       "min    1488.000000   61.000000    2.540000    2.070000          7.000000   \n",
       "25%    2145.000000   97.000000    3.150000    3.110000          8.600000   \n",
       "50%    2414.000000  120.000000    3.310000    3.290000          9.000000   \n",
       "75%    2935.000000  141.000000    3.580000    3.410000          9.400000   \n",
       "max    4066.000000  326.000000    3.940000    4.170000         23.000000   \n",
       "\n",
       "       horsepower      peakrpm     citympg  highwaympg         price  \n",
       "count  205.000000   205.000000  205.000000  205.000000    205.000000  \n",
       "mean   104.117073  5125.121951   25.219512   30.751220  13276.710571  \n",
       "std     39.544167   476.985643    6.542142    6.886443   7988.852332  \n",
       "min     48.000000  4150.000000   13.000000   16.000000   5118.000000  \n",
       "25%     70.000000  4800.000000   19.000000   25.000000   7788.000000  \n",
       "50%     95.000000  5200.000000   24.000000   30.000000  10295.000000  \n",
       "75%    116.000000  5500.000000   30.000000   34.000000  16503.000000  \n",
       "max    288.000000  6600.000000   49.000000   54.000000  45400.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:06:20.395115Z",
     "iopub.status.busy": "2023-02-09T16:06:20.394653Z",
     "iopub.status.idle": "2023-02-09T16:06:20.406449Z",
     "shell.execute_reply": "2023-02-09T16:06:20.405276Z",
     "shell.execute_reply.started": "2023-02-09T16:06:20.395077Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "car_ID             -0.109093\n",
       "symboling          -0.079978\n",
       "wheelbase           0.577816\n",
       "carlength           0.682920\n",
       "carwidth            0.759325\n",
       "carheight           0.119336\n",
       "curbweight          0.835305\n",
       "enginesize          0.874145\n",
       "boreratio           0.553173\n",
       "stroke              0.079443\n",
       "compressionratio    0.067984\n",
       "horsepower          0.808139\n",
       "peakrpm            -0.085267\n",
       "citympg            -0.685751\n",
       "highwaympg         -0.697599\n",
       "price               1.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:06:47.926623Z",
     "iopub.status.busy": "2023-02-09T16:06:47.925625Z",
     "iopub.status.idle": "2023-02-09T16:06:47.954939Z",
     "shell.execute_reply": "2023-02-09T16:06:47.953795Z",
     "shell.execute_reply.started": "2023-02-09T16:06:47.926584Z"
    }
   },
   "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>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>-1</td>\n",
       "      <td>volvo 144ea</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>188.8</td>\n",
       "      <td>68.8</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3049</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</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>19045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>honda civic</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>150.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>52.6</td>\n",
       "      <td>1837</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>79</td>\n",
       "      <td>1bbl</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.07</td>\n",
       "      <td>10.1</td>\n",
       "      <td>60</td>\n",
       "      <td>5500</td>\n",
       "      <td>38</td>\n",
       "      <td>42</td>\n",
       "      <td>5399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>129</td>\n",
       "      <td>3</td>\n",
       "      <td>porsche boxter</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>rear</td>\n",
       "      <td>89.5</td>\n",
       "      <td>168.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>51.6</td>\n",
       "      <td>2800</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>six</td>\n",
       "      <td>194</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.74</td>\n",
       "      <td>2.90</td>\n",
       "      <td>9.5</td>\n",
       "      <td>207</td>\n",
       "      <td>5900</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>37028.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>bmw 320i</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2395</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>108</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.8</td>\n",
       "      <td>101</td>\n",
       "      <td>5800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>16430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>subaru dl</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>96.9</td>\n",
       "      <td>173.6</td>\n",
       "      <td>65.4</td>\n",
       "      <td>54.9</td>\n",
       "      <td>2420</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.0</td>\n",
       "      <td>82</td>\n",
       "      <td>4800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>8013.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     car_ID  symboling         CarName fueltype aspiration doornumber  \\\n",
       "201     202         -1     volvo 144ea      gas      turbo       four   \n",
       "32       33          1     honda civic      gas        std        two   \n",
       "128     129          3  porsche boxter      gas        std        two   \n",
       "10       11          2        bmw 320i      gas        std        two   \n",
       "148     149          0       subaru dl      gas        std       four   \n",
       "\n",
       "         carbody drivewheel enginelocation  wheelbase  carlength  carwidth  \\\n",
       "201        sedan        rwd          front      109.1      188.8      68.8   \n",
       "32     hatchback        fwd          front       93.7      150.0      64.0   \n",
       "128  convertible        rwd           rear       89.5      168.9      65.0   \n",
       "10         sedan        rwd          front      101.2      176.8      64.8   \n",
       "148        wagon        4wd          front       96.9      173.6      65.4   \n",
       "\n",
       "     carheight  curbweight enginetype cylindernumber  enginesize fuelsystem  \\\n",
       "201       55.5        3049        ohc           four         141       mpfi   \n",
       "32        52.6        1837        ohc           four          79       1bbl   \n",
       "128       51.6        2800       ohcf            six         194       mpfi   \n",
       "10        54.3        2395        ohc           four         108       mpfi   \n",
       "148       54.9        2420       ohcf           four         108       2bbl   \n",
       "\n",
       "     boreratio  stroke  compressionratio  horsepower  peakrpm  citympg  \\\n",
       "201       3.78    3.15               8.7         160     5300       19   \n",
       "32        2.91    3.07              10.1          60     5500       38   \n",
       "128       3.74    2.90               9.5         207     5900       17   \n",
       "10        3.50    2.80               8.8         101     5800       23   \n",
       "148       3.62    2.64               9.0          82     4800       23   \n",
       "\n",
       "     highwaympg    price  \n",
       "201          25  19045.0  \n",
       "32           42   5399.0  \n",
       "128          25  37028.0  \n",
       "10           29  16430.0  \n",
       "148          29   8013.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Cleaning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:08:05.854980Z",
     "iopub.status.busy": "2023-02-09T16:08:05.854538Z",
     "iopub.status.idle": "2023-02-09T16:08:05.862213Z",
     "shell.execute_reply": "2023-02-09T16:08:05.861101Z",
     "shell.execute_reply.started": "2023-02-09T16:08:05.854944Z"
    }
   },
   "outputs": [],
   "source": [
    "df['Company Name']  = df['CarName'].apply(lambda x : x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:08:20.087907Z",
     "iopub.status.busy": "2023-02-09T16:08:20.087432Z",
     "iopub.status.idle": "2023-02-09T16:08:20.096295Z",
     "shell.execute_reply": "2023-02-09T16:08:20.094928Z",
     "shell.execute_reply.started": "2023-02-09T16:08:20.087869Z"
    }
   },
   "outputs": [],
   "source": [
    "df.drop('CarName',axis = 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:08:33.515867Z",
     "iopub.status.busy": "2023-02-09T16:08:33.515088Z",
     "iopub.status.idle": "2023-02-09T16:08:33.543331Z",
     "shell.execute_reply": "2023-02-09T16:08:33.542130Z",
     "shell.execute_reply.started": "2023-02-09T16:08:33.515827Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "      <th>Company Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</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>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</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>13495.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</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>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</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>16500.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</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>16500.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</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>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</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>13950.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</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>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</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>17450.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling fueltype aspiration doornumber      carbody drivewheel  \\\n",
       "0       1          3      gas        std        two  convertible        rwd   \n",
       "1       2          3      gas        std        two  convertible        rwd   \n",
       "2       3          1      gas        std        two    hatchback        rwd   \n",
       "3       4          2      gas        std       four        sedan        fwd   \n",
       "4       5          2      gas        std       four        sedan        4wd   \n",
       "\n",
       "  enginelocation  wheelbase  carlength  carwidth  carheight  curbweight  \\\n",
       "0          front       88.6      168.8      64.1       48.8        2548   \n",
       "1          front       88.6      168.8      64.1       48.8        2548   \n",
       "2          front       94.5      171.2      65.5       52.4        2823   \n",
       "3          front       99.8      176.6      66.2       54.3        2337   \n",
       "4          front       99.4      176.6      66.4       54.3        2824   \n",
       "\n",
       "  enginetype cylindernumber  enginesize fuelsystem  boreratio  stroke  \\\n",
       "0       dohc           four         130       mpfi       3.47    2.68   \n",
       "1       dohc           four         130       mpfi       3.47    2.68   \n",
       "2       ohcv            six         152       mpfi       2.68    3.47   \n",
       "3        ohc           four         109       mpfi       3.19    3.40   \n",
       "4        ohc           five         136       mpfi       3.19    3.40   \n",
       "\n",
       "   compressionratio  horsepower  peakrpm  citympg  highwaympg    price  \\\n",
       "0               9.0         111     5000       21          27  13495.0   \n",
       "1               9.0         111     5000       21          27  16500.0   \n",
       "2               9.0         154     5000       19          26  16500.0   \n",
       "3              10.0         102     5500       24          30  13950.0   \n",
       "4               8.0         115     5500       18          22  17450.0   \n",
       "\n",
       "  Company Name  \n",
       "0  alfa-romero  \n",
       "1  alfa-romero  \n",
       "2  alfa-romero  \n",
       "3         audi  \n",
       "4         audi  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:09:20.159887Z",
     "iopub.status.busy": "2023-02-09T16:09:20.159162Z",
     "iopub.status.idle": "2023-02-09T16:09:20.167012Z",
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     "shell.execute_reply.started": "2023-02-09T16:09:20.159849Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alfa-romero', 'audi', 'bmw', 'chevrolet', 'dodge', 'honda',\n",
       "       'isuzu', 'jaguar', 'maxda', 'mazda', 'buick', 'mercury',\n",
       "       'mitsubishi', 'Nissan', 'nissan', 'peugeot', 'plymouth', 'porsche',\n",
       "       'porcshce', 'renault', 'saab', 'subaru', 'toyota', 'toyouta',\n",
       "       'vokswagen', 'volkswagen', 'vw', 'volvo'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Company Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:09:27.251815Z",
     "iopub.status.busy": "2023-02-09T16:09:27.250928Z",
     "iopub.status.idle": "2023-02-09T16:09:27.256673Z",
     "shell.execute_reply": "2023-02-09T16:09:27.255623Z",
     "shell.execute_reply.started": "2023-02-09T16:09:27.251778Z"
    }
   },
   "outputs": [],
   "source": [
    "def replace(error,correct):\n",
    "    df['Company Name'] = df['Company Name'].replace(to_replace=error,value=correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:09:48.090193Z",
     "iopub.status.busy": "2023-02-09T16:09:48.089775Z",
     "iopub.status.idle": "2023-02-09T16:09:48.098702Z",
     "shell.execute_reply": "2023-02-09T16:09:48.097435Z",
     "shell.execute_reply.started": "2023-02-09T16:09:48.090160Z"
    }
   },
   "outputs": [],
   "source": [
    "replace('maxda','mazda')\n",
    "replace('porcshce','porsche')\n",
    "replace('Nissan','nissan')\n",
    "replace('vw','volkswagen')\n",
    "replace('vokswagen','volkswagen')\n",
    "replace('toyouta','toyota')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:10:01.356889Z",
     "iopub.status.busy": "2023-02-09T16:10:01.356490Z",
     "iopub.status.idle": "2023-02-09T16:10:01.363647Z",
     "shell.execute_reply": "2023-02-09T16:10:01.362812Z",
     "shell.execute_reply.started": "2023-02-09T16:10:01.356858Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alfa-romero', 'audi', 'bmw', 'chevrolet', 'dodge', 'honda',\n",
       "       'isuzu', 'jaguar', 'mazda', 'buick', 'mercury', 'mitsubishi',\n",
       "       'nissan', 'peugeot', 'plymouth', 'porsche', 'renault', 'saab',\n",
       "       'subaru', 'toyota', 'volkswagen', 'volvo'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Company Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:11:28.045921Z",
     "iopub.status.busy": "2023-02-09T16:11:28.045520Z",
     "iopub.status.idle": "2023-02-09T16:11:28.053802Z",
     "shell.execute_reply": "2023-02-09T16:11:28.052592Z",
     "shell.execute_reply.started": "2023-02-09T16:11:28.045890Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'fueltype', 'aspiration', 'doornumber', 'carbody',\n",
       "       'drivewheel', 'enginelocation', 'wheelbase', 'carlength', 'carwidth',\n",
       "       'carheight', 'curbweight', 'enginetype', 'cylindernumber', 'enginesize',\n",
       "       'fuelsystem', 'boreratio', 'stroke', 'compressionratio', 'horsepower',\n",
       "       'peakrpm', 'citympg', 'highwaympg', 'price', 'Company Name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:11:40.466072Z",
     "iopub.status.busy": "2023-02-09T16:11:40.465389Z",
     "iopub.status.idle": "2023-02-09T16:11:40.495382Z",
     "shell.execute_reply": "2023-02-09T16:11:40.494207Z",
     "shell.execute_reply.started": "2023-02-09T16:11:40.466033Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "      <th>Company Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>114.2</td>\n",
       "      <td>198.9</td>\n",
       "      <td>68.4</td>\n",
       "      <td>58.7</td>\n",
       "      <td>3230</td>\n",
       "      <td>l</td>\n",
       "      <td>four</td>\n",
       "      <td>120</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.19</td>\n",
       "      <td>8.4</td>\n",
       "      <td>97</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>12440.0</td>\n",
       "      <td>peugeot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0</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>97.2</td>\n",
       "      <td>172.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>52.5</td>\n",
       "      <td>2145</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.5</td>\n",
       "      <td>82</td>\n",
       "      <td>4800</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>7126.0</td>\n",
       "      <td>subaru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>3</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>159.3</td>\n",
       "      <td>64.2</td>\n",
       "      <td>55.6</td>\n",
       "      <td>2254</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.5</td>\n",
       "      <td>90</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>11595.0</td>\n",
       "      <td>volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1</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>99.2</td>\n",
       "      <td>178.5</td>\n",
       "      <td>67.9</td>\n",
       "      <td>49.7</td>\n",
       "      <td>3139</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>181</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.27</td>\n",
       "      <td>9.0</td>\n",
       "      <td>160</td>\n",
       "      <td>5200</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>18399.0</td>\n",
       "      <td>nissan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>1</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>95.7</td>\n",
       "      <td>158.7</td>\n",
       "      <td>63.6</td>\n",
       "      <td>54.5</td>\n",
       "      <td>2040</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>92</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.03</td>\n",
       "      <td>9.0</td>\n",
       "      <td>62</td>\n",
       "      <td>4800</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>6338.0</td>\n",
       "      <td>toyota</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling fueltype aspiration doornumber      carbody drivewheel  \\\n",
       "109          0      gas        std       four        wagon        rwd   \n",
       "141          0      gas        std       four        sedan        fwd   \n",
       "189          3      gas        std        two  convertible        fwd   \n",
       "106          1      gas        std        two    hatchback        rwd   \n",
       "151          1      gas        std        two    hatchback        fwd   \n",
       "\n",
       "    enginelocation  wheelbase  carlength  carwidth  carheight  curbweight  \\\n",
       "109          front      114.2      198.9      68.4       58.7        3230   \n",
       "141          front       97.2      172.0      65.4       52.5        2145   \n",
       "189          front       94.5      159.3      64.2       55.6        2254   \n",
       "106          front       99.2      178.5      67.9       49.7        3139   \n",
       "151          front       95.7      158.7      63.6       54.5        2040   \n",
       "\n",
       "    enginetype cylindernumber  enginesize fuelsystem  boreratio  stroke  \\\n",
       "109          l           four         120       mpfi       3.46    3.19   \n",
       "141       ohcf           four         108       2bbl       3.62    2.64   \n",
       "189        ohc           four         109       mpfi       3.19    3.40   \n",
       "106       ohcv            six         181       mpfi       3.43    3.27   \n",
       "151        ohc           four          92       2bbl       3.05    3.03   \n",
       "\n",
       "     compressionratio  horsepower  peakrpm  citympg  highwaympg    price  \\\n",
       "109               8.4          97     5000       19          24  12440.0   \n",
       "141               9.5          82     4800       32          37   7126.0   \n",
       "189               8.5          90     5500       24          29  11595.0   \n",
       "106               9.0         160     5200       19          25  18399.0   \n",
       "151               9.0          62     4800       31          38   6338.0   \n",
       "\n",
       "    Company Name  \n",
       "109      peugeot  \n",
       "141       subaru  \n",
       "189   volkswagen  \n",
       "106       nissan  \n",
       "151       toyota  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:12:54.268711Z",
     "iopub.status.busy": "2023-02-09T16:12:54.268216Z",
     "iopub.status.idle": "2023-02-09T16:12:54.672668Z",
     "shell.execute_reply": "2023-02-09T16:12:54.671201Z",
     "shell.execute_reply.started": "2023-02-09T16:12:54.268658Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt1 = sns.countplot(x='Company Name',data = df)\n",
    "plt.title('Companies Countplot')\n",
    "ax = plt.gca()\n",
    "ax.tick_params(axis='x', labelrotation = 45)\n",
    "plt.subplot(2,2,2)\n",
    "plt2 = sns.countplot(x='fueltype',data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:13:10.648877Z",
     "iopub.status.busy": "2023-02-09T16:13:10.647804Z",
     "iopub.status.idle": "2023-02-09T16:13:11.073110Z",
     "shell.execute_reply": "2023-02-09T16:13:11.072203Z",
     "shell.execute_reply.started": "2023-02-09T16:13:10.648835Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,5))\n",
    "plt.subplot(1,2,1)\n",
    "df.groupby('Company Name').mean()['price'].sort_values().plot(kind = 'bar')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.boxplot(x='price',data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:14:46.693411Z",
     "iopub.status.busy": "2023-02-09T16:14:46.692903Z",
     "iopub.status.idle": "2023-02-09T16:14:47.460238Z",
     "shell.execute_reply": "2023-02-09T16:14:47.458776Z",
     "shell.execute_reply.started": "2023-02-09T16:14:46.693374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "sns.countplot(x='symboling',data = df)\n",
    "plt.title('Symboling Countplot')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.countplot(x='aspiration',data = df)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(axis='x', labelrotation = 45)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt1 = sns.boxplot(x='symboling',y='price',data = df)\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt2 = sns.boxplot(x='aspiration',y='price',data = df)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(axis='x', labelrotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:15:09.831266Z",
     "iopub.status.busy": "2023-02-09T16:15:09.830865Z",
     "iopub.status.idle": "2023-02-09T16:15:10.307194Z",
     "shell.execute_reply": "2023-02-09T16:15:10.306159Z",
     "shell.execute_reply.started": "2023-02-09T16:15:09.831234Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Company Name', ylabel='price'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,5))\n",
    "sns.boxplot(x='Company Name',y = 'price',data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:16:11.695787Z",
     "iopub.status.busy": "2023-02-09T16:16:11.695304Z",
     "iopub.status.idle": "2023-02-09T16:16:11.708036Z",
     "shell.execute_reply": "2023-02-09T16:16:11.706632Z",
     "shell.execute_reply.started": "2023-02-09T16:16:11.695723Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      205.000000\n",
       "mean     13276.710571\n",
       "std       7988.852332\n",
       "min       5118.000000\n",
       "25%       7788.000000\n",
       "50%      10295.000000\n",
       "75%      16503.000000\n",
       "max      45400.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:16:42.099287Z",
     "iopub.status.busy": "2023-02-09T16:16:42.098892Z",
     "iopub.status.idle": "2023-02-09T16:16:42.892912Z",
     "shell.execute_reply": "2023-02-09T16:16:42.891794Z",
     "shell.execute_reply.started": "2023-02-09T16:16:42.099256Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13, 13))\n",
    "\n",
    "plt.subplot(4,2,1)\n",
    "sns.countplot(x='drivewheel',data = df)\n",
    "plt.subplot(4,2,2)\n",
    "sns.boxplot(x='drivewheel',y='price',data = df)\n",
    "plt.subplot(4,2,3)\n",
    "sns.countplot(x='enginetype',data = df)\n",
    "\n",
    "plt.subplot(4,2,4)\n",
    "sns.boxplot(x='enginetype',y='price',data = df)\n",
    "\n",
    "plt.subplot(4,2,5)\n",
    "sns.countplot(x='fuelsystem',data = df)\n",
    "plt.subplot(4,2,6)\n",
    "plt1 = sns.boxplot(x='fuelsystem',y='price',data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:17:11.768944Z",
     "iopub.status.busy": "2023-02-09T16:17:11.767719Z",
     "iopub.status.idle": "2023-02-09T16:17:12.071962Z",
     "shell.execute_reply": "2023-02-09T16:17:12.070789Z",
     "shell.execute_reply.started": "2023-02-09T16:17:11.768900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='enginelocation', ylabel='price'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,5))\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(x='enginelocation',data = df)\n",
    "plt.subplot(1,2,2)\n",
    "sns.boxplot(x='enginelocation',y='price',data = df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:17:42.018179Z",
     "iopub.status.busy": "2023-02-09T16:17:42.017776Z",
     "iopub.status.idle": "2023-02-09T16:17:42.028168Z",
     "shell.execute_reply": "2023-02-09T16:17:42.027073Z",
     "shell.execute_reply.started": "2023-02-09T16:17:42.018146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'wheelbase', 'carlength', 'carwidth', 'carheight',\n",
       "       'curbweight', 'enginesize', 'boreratio', 'stroke', 'compressionratio',\n",
       "       'horsepower', 'peakrpm', 'citympg', 'highwaympg', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['price'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:18:02.007850Z",
     "iopub.status.busy": "2023-02-09T16:18:02.005307Z",
     "iopub.status.idle": "2023-02-09T16:18:02.018128Z",
     "shell.execute_reply": "2023-02-09T16:18:02.016781Z",
     "shell.execute_reply.started": "2023-02-09T16:18:02.007806Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling          -0.079978\n",
       "wheelbase           0.577816\n",
       "carlength           0.682920\n",
       "carwidth            0.759325\n",
       "carheight           0.119336\n",
       "curbweight          0.835305\n",
       "enginesize          0.874145\n",
       "boreratio           0.553173\n",
       "stroke              0.079443\n",
       "compressionratio    0.067984\n",
       "horsepower          0.808139\n",
       "peakrpm            -0.085267\n",
       "citympg            -0.685751\n",
       "highwaympg         -0.697599\n",
       "price               1.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:18:21.437030Z",
     "iopub.status.busy": "2023-02-09T16:18:21.436613Z",
     "iopub.status.idle": "2023-02-09T16:18:23.651818Z",
     "shell.execute_reply": "2023-02-09T16:18:23.650667Z",
     "shell.execute_reply.started": "2023-02-09T16:18:21.436990Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f68d7549850>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(x_vars = ['wheelbase','carwidth','carlength','horsepower'],y_vars = ['price'],data = df)\n",
    "sns.pairplot(x_vars = ['carheight','curbweight','enginesize','peakrpm'],y_vars = ['price'],data = df)\n",
    "sns.pairplot(x_vars = ['boreratio','stroke','compressionratio','citympg'],y_vars = ['price'],data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:22:43.142485Z",
     "iopub.status.busy": "2023-02-09T16:22:43.141865Z",
     "iopub.status.idle": "2023-02-09T16:22:44.389952Z",
     "shell.execute_reply": "2023-02-09T16:22:44.388816Z",
     "shell.execute_reply.started": "2023-02-09T16:22:43.142451Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,5))\n",
    "sns.heatmap(df.corr(),annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:22:54.600075Z",
     "iopub.status.busy": "2023-02-09T16:22:54.599366Z",
     "iopub.status.idle": "2023-02-09T16:22:54.606415Z",
     "shell.execute_reply": "2023-02-09T16:22:54.605283Z",
     "shell.execute_reply.started": "2023-02-09T16:22:54.600038Z"
    }
   },
   "outputs": [],
   "source": [
    "imp = df[['carwidth','curbweight','enginesize','boreratio','horsepower','highwaympg','citympg','wheelbase']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:23:06.233889Z",
     "iopub.status.busy": "2023-02-09T16:23:06.233457Z",
     "iopub.status.idle": "2023-02-09T16:23:06.251061Z",
     "shell.execute_reply": "2023-02-09T16:23:06.249804Z",
     "shell.execute_reply.started": "2023-02-09T16:23:06.233852Z"
    }
   },
   "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>carwidth</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>citympg</th>\n",
       "      <th>wheelbase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>carwidth</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.867032</td>\n",
       "      <td>0.735433</td>\n",
       "      <td>0.559150</td>\n",
       "      <td>0.640732</td>\n",
       "      <td>-0.677218</td>\n",
       "      <td>-0.642704</td>\n",
       "      <td>0.795144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curbweight</th>\n",
       "      <td>0.867032</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850594</td>\n",
       "      <td>0.648480</td>\n",
       "      <td>0.750739</td>\n",
       "      <td>-0.797465</td>\n",
       "      <td>-0.757414</td>\n",
       "      <td>0.776386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enginesize</th>\n",
       "      <td>0.735433</td>\n",
       "      <td>0.850594</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.583774</td>\n",
       "      <td>0.809769</td>\n",
       "      <td>-0.677470</td>\n",
       "      <td>-0.653658</td>\n",
       "      <td>0.569329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boreratio</th>\n",
       "      <td>0.559150</td>\n",
       "      <td>0.648480</td>\n",
       "      <td>0.583774</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.573677</td>\n",
       "      <td>-0.587012</td>\n",
       "      <td>-0.584532</td>\n",
       "      <td>0.488750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>0.640732</td>\n",
       "      <td>0.750739</td>\n",
       "      <td>0.809769</td>\n",
       "      <td>0.573677</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.770544</td>\n",
       "      <td>-0.801456</td>\n",
       "      <td>0.353294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highwaympg</th>\n",
       "      <td>-0.677218</td>\n",
       "      <td>-0.797465</td>\n",
       "      <td>-0.677470</td>\n",
       "      <td>-0.587012</td>\n",
       "      <td>-0.770544</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971337</td>\n",
       "      <td>-0.544082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citympg</th>\n",
       "      <td>-0.642704</td>\n",
       "      <td>-0.757414</td>\n",
       "      <td>-0.653658</td>\n",
       "      <td>-0.584532</td>\n",
       "      <td>-0.801456</td>\n",
       "      <td>0.971337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.470414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheelbase</th>\n",
       "      <td>0.795144</td>\n",
       "      <td>0.776386</td>\n",
       "      <td>0.569329</td>\n",
       "      <td>0.488750</td>\n",
       "      <td>0.353294</td>\n",
       "      <td>-0.544082</td>\n",
       "      <td>-0.470414</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            carwidth  curbweight  enginesize  boreratio  horsepower  \\\n",
       "carwidth    1.000000    0.867032    0.735433   0.559150    0.640732   \n",
       "curbweight  0.867032    1.000000    0.850594   0.648480    0.750739   \n",
       "enginesize  0.735433    0.850594    1.000000   0.583774    0.809769   \n",
       "boreratio   0.559150    0.648480    0.583774   1.000000    0.573677   \n",
       "horsepower  0.640732    0.750739    0.809769   0.573677    1.000000   \n",
       "highwaympg -0.677218   -0.797465   -0.677470  -0.587012   -0.770544   \n",
       "citympg    -0.642704   -0.757414   -0.653658  -0.584532   -0.801456   \n",
       "wheelbase   0.795144    0.776386    0.569329   0.488750    0.353294   \n",
       "\n",
       "            highwaympg   citympg  wheelbase  \n",
       "carwidth     -0.677218 -0.642704   0.795144  \n",
       "curbweight   -0.797465 -0.757414   0.776386  \n",
       "enginesize   -0.677470 -0.653658   0.569329  \n",
       "boreratio    -0.587012 -0.584532   0.488750  \n",
       "horsepower   -0.770544 -0.801456   0.353294  \n",
       "highwaympg    1.000000  0.971337  -0.544082  \n",
       "citympg       0.971337  1.000000  -0.470414  \n",
       "wheelbase    -0.544082 -0.470414   1.000000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:23:36.927756Z",
     "iopub.status.busy": "2023-02-09T16:23:36.927004Z",
     "iopub.status.idle": "2023-02-09T16:23:36.936983Z",
     "shell.execute_reply": "2023-02-09T16:23:36.935728Z",
     "shell.execute_reply.started": "2023-02-09T16:23:36.927704Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['carwidth', 'curbweight', 'enginesize', 'boreratio', 'horsepower',\n",
       "       'highwaympg', 'wheelbase'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = imp.drop('citympg',axis = 1)\n",
    "num_imp_cols = imp.columns\n",
    "num_imp_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:25:31.454138Z",
     "iopub.status.busy": "2023-02-09T16:25:31.453698Z",
     "iopub.status.idle": "2023-02-09T16:25:31.459448Z",
     "shell.execute_reply": "2023-02-09T16:25:31.458058Z",
     "shell.execute_reply.started": "2023-02-09T16:25:31.454098Z"
    }
   },
   "outputs": [],
   "source": [
    "num_imp_cols = list(num_imp_cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:32:26.607869Z",
     "iopub.status.busy": "2023-02-09T16:32:26.606765Z",
     "iopub.status.idle": "2023-02-09T16:32:26.637157Z",
     "shell.execute_reply": "2023-02-09T16:32:26.635973Z",
     "shell.execute_reply.started": "2023-02-09T16:32:26.607825Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "      <th>Company Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</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>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</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>13495.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</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>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</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>16500.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</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>16500.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</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>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</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>13950.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</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>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</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>17450.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling fueltype aspiration doornumber      carbody drivewheel  \\\n",
       "0          3      gas        std        two  convertible        rwd   \n",
       "1          3      gas        std        two  convertible        rwd   \n",
       "2          1      gas        std        two    hatchback        rwd   \n",
       "3          2      gas        std       four        sedan        fwd   \n",
       "4          2      gas        std       four        sedan        4wd   \n",
       "\n",
       "  enginelocation  wheelbase  carlength  carwidth  carheight  curbweight  \\\n",
       "0          front       88.6      168.8      64.1       48.8        2548   \n",
       "1          front       88.6      168.8      64.1       48.8        2548   \n",
       "2          front       94.5      171.2      65.5       52.4        2823   \n",
       "3          front       99.8      176.6      66.2       54.3        2337   \n",
       "4          front       99.4      176.6      66.4       54.3        2824   \n",
       "\n",
       "  enginetype cylindernumber  enginesize fuelsystem  boreratio  stroke  \\\n",
       "0       dohc           four         130       mpfi       3.47    2.68   \n",
       "1       dohc           four         130       mpfi       3.47    2.68   \n",
       "2       ohcv            six         152       mpfi       2.68    3.47   \n",
       "3        ohc           four         109       mpfi       3.19    3.40   \n",
       "4        ohc           five         136       mpfi       3.19    3.40   \n",
       "\n",
       "   compressionratio  horsepower  peakrpm  citympg  highwaympg    price  \\\n",
       "0               9.0         111     5000       21          27  13495.0   \n",
       "1               9.0         111     5000       21          27  16500.0   \n",
       "2               9.0         154     5000       19          26  16500.0   \n",
       "3              10.0         102     5500       24          30  13950.0   \n",
       "4               8.0         115     5500       18          22  17450.0   \n",
       "\n",
       "  Company Name  \n",
       "0  alfa-romero  \n",
       "1  alfa-romero  \n",
       "2  alfa-romero  \n",
       "3         audi  \n",
       "4         audi  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:32:45.088751Z",
     "iopub.status.busy": "2023-02-09T16:32:45.087386Z",
     "iopub.status.idle": "2023-02-09T16:32:45.099137Z",
     "shell.execute_reply": "2023-02-09T16:32:45.097868Z",
     "shell.execute_reply.started": "2023-02-09T16:32:45.088690Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling          -0.079978\n",
       "wheelbase           0.577816\n",
       "carlength           0.682920\n",
       "carwidth            0.759325\n",
       "carheight           0.119336\n",
       "curbweight          0.835305\n",
       "enginesize          0.874145\n",
       "boreratio           0.553173\n",
       "stroke              0.079443\n",
       "compressionratio    0.067984\n",
       "horsepower          0.808139\n",
       "peakrpm            -0.085267\n",
       "citympg            -0.685751\n",
       "highwaympg         -0.697599\n",
       "price               1.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:33:12.527794Z",
     "iopub.status.busy": "2023-02-09T16:33:12.527391Z",
     "iopub.status.idle": "2023-02-09T16:33:12.533876Z",
     "shell.execute_reply": "2023-02-09T16:33:12.532357Z",
     "shell.execute_reply.started": "2023-02-09T16:33:12.527760Z"
    }
   },
   "outputs": [],
   "source": [
    "num_imp_cols.append('cylindernumber')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:33:35.048433Z",
     "iopub.status.busy": "2023-02-09T16:33:35.047724Z",
     "iopub.status.idle": "2023-02-09T16:33:35.053083Z",
     "shell.execute_reply": "2023-02-09T16:33:35.051877Z",
     "shell.execute_reply.started": "2023-02-09T16:33:35.048394Z"
    }
   },
   "outputs": [],
   "source": [
    "num_imp_cols.append('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dummy Variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:34:38.882119Z",
     "iopub.status.busy": "2023-02-09T16:34:38.881711Z",
     "iopub.status.idle": "2023-02-09T16:34:38.895898Z",
     "shell.execute_reply": "2023-02-09T16:34:38.894587Z",
     "shell.execute_reply.started": "2023-02-09T16:34:38.882085Z"
    }
   },
   "outputs": [],
   "source": [
    "fueltype = pd.get_dummies(df['fueltype'],drop_first=True)\n",
    "aspiration = pd.get_dummies(df['aspiration'],drop_first=True)\n",
    "carbody = pd.get_dummies(df['carbody'],drop_first=True)\n",
    "drivewheel = pd.get_dummies(df['drivewheel'],drop_first=True)\n",
    "enginelocation = pd.get_dummies(df['enginelocation'],drop_first=True)\n",
    "fuelsystem = pd.get_dummies(df['fuelsystem'],drop_first=True)\n",
    "CompanyName = pd.get_dummies(df['Company Name'],drop_first=True)\n",
    "enginetype = pd.get_dummies(df['enginetype'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:34:49.444577Z",
     "iopub.status.busy": "2023-02-09T16:34:49.444171Z",
     "iopub.status.idle": "2023-02-09T16:34:49.450644Z",
     "shell.execute_reply": "2023-02-09T16:34:49.449647Z",
     "shell.execute_reply.started": "2023-02-09T16:34:49.444542Z"
    }
   },
   "outputs": [],
   "source": [
    "df= df[num_imp_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:35:04.043547Z",
     "iopub.status.busy": "2023-02-09T16:35:04.042828Z",
     "iopub.status.idle": "2023-02-09T16:35:04.062240Z",
     "shell.execute_reply": "2023-02-09T16:35:04.060833Z",
     "shell.execute_reply.started": "2023-02-09T16:35:04.043498Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carwidth</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64.1</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>111</td>\n",
       "      <td>27</td>\n",
       "      <td>88.6</td>\n",
       "      <td>four</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64.1</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>111</td>\n",
       "      <td>27</td>\n",
       "      <td>88.6</td>\n",
       "      <td>four</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.5</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>2.68</td>\n",
       "      <td>154</td>\n",
       "      <td>26</td>\n",
       "      <td>94.5</td>\n",
       "      <td>six</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66.2</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>102</td>\n",
       "      <td>30</td>\n",
       "      <td>99.8</td>\n",
       "      <td>four</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.4</td>\n",
       "      <td>2824</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>115</td>\n",
       "      <td>22</td>\n",
       "      <td>99.4</td>\n",
       "      <td>five</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carwidth  curbweight  enginesize  boreratio  horsepower  highwaympg  \\\n",
       "0      64.1        2548         130       3.47         111          27   \n",
       "1      64.1        2548         130       3.47         111          27   \n",
       "2      65.5        2823         152       2.68         154          26   \n",
       "3      66.2        2337         109       3.19         102          30   \n",
       "4      66.4        2824         136       3.19         115          22   \n",
       "\n",
       "   wheelbase cylindernumber    price    price  \n",
       "0       88.6           four  13495.0  13495.0  \n",
       "1       88.6           four  16500.0  16500.0  \n",
       "2       94.5            six  16500.0  16500.0  \n",
       "3       99.8           four  13950.0  13950.0  \n",
       "4       99.4           five  17450.0  17450.0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:35:23.094893Z",
     "iopub.status.busy": "2023-02-09T16:35:23.093666Z",
     "iopub.status.idle": "2023-02-09T16:35:23.104536Z",
     "shell.execute_reply": "2023-02-09T16:35:23.102826Z",
     "shell.execute_reply.started": "2023-02-09T16:35:23.094834Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df,fueltype,aspiration,carbody,drivewheel,enginelocation,fuelsystem,enginetype,CompanyName],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:35:36.244562Z",
     "iopub.status.busy": "2023-02-09T16:35:36.244150Z",
     "iopub.status.idle": "2023-02-09T16:35:36.278564Z",
     "shell.execute_reply": "2023-02-09T16:35:36.277506Z",
     "shell.execute_reply.started": "2023-02-09T16:35:36.244527Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carwidth</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
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       "      <th>hatchback</th>\n",
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       "      <th>subaru</th>\n",
       "      <th>toyota</th>\n",
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       "      <th>volvo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carwidth  curbweight  enginesize  boreratio  horsepower  highwaympg  \\\n",
       "0      64.1        2548         130       3.47         111          27   \n",
       "1      64.1        2548         130       3.47         111          27   \n",
       "2      65.5        2823         152       2.68         154          26   \n",
       "3      66.2        2337         109       3.19         102          30   \n",
       "4      66.4        2824         136       3.19         115          22   \n",
       "\n",
       "   wheelbase cylindernumber    price    price  gas  turbo  hardtop  hatchback  \\\n",
       "0       88.6           four  13495.0  13495.0    1      0        0          0   \n",
       "1       88.6           four  16500.0  16500.0    1      0        0          0   \n",
       "2       94.5            six  16500.0  16500.0    1      0        0          1   \n",
       "3       99.8           four  13950.0  13950.0    1      0        0          0   \n",
       "4       99.4           five  17450.0  17450.0    1      0        0          0   \n",
       "\n",
       "   sedan  wagon  fwd  rwd  rear  2bbl  4bbl  idi  mfi  mpfi  spdi  spfi  \\\n",
       "0      0      0    0    1     0     0     0    0    0     1     0     0   \n",
       "1      0      0    0    1     0     0     0    0    0     1     0     0   \n",
       "2      0      0    0    1     0     0     0    0    0     1     0     0   \n",
       "3      1      0    1    0     0     0     0    0    0     1     0     0   \n",
       "4      1      0    0    0     0     0     0    0    0     1     0     0   \n",
       "\n",
       "   dohcv  l  ohc  ohcf  ohcv  rotor  audi  bmw  buick  chevrolet  dodge  \\\n",
       "0      0  0    0     0     0      0     0    0      0          0      0   \n",
       "1      0  0    0     0     0      0     0    0      0          0      0   \n",
       "2      0  0    0     0     1      0     0    0      0          0      0   \n",
       "3      0  0    1     0     0      0     1    0      0          0      0   \n",
       "4      0  0    1     0     0      0     1    0      0          0      0   \n",
       "\n",
       "   honda  isuzu  jaguar  mazda  mercury  mitsubishi  nissan  peugeot  \\\n",
       "0      0      0       0      0        0           0       0        0   \n",
       "1      0      0       0      0        0           0       0        0   \n",
       "2      0      0       0      0        0           0       0        0   \n",
       "3      0      0       0      0        0           0       0        0   \n",
       "4      0      0       0      0        0           0       0        0   \n",
       "\n",
       "   plymouth  porsche  renault  saab  subaru  toyota  volkswagen  volvo  \n",
       "0         0        0        0     0       0       0           0      0  \n",
       "1         0        0        0     0       0       0           0      0  \n",
       "2         0        0        0     0       0       0           0      0  \n",
       "3         0        0        0     0       0       0           0      0  \n",
       "4         0        0        0     0       0       0           0      0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:36:05.663004Z",
     "iopub.status.busy": "2023-02-09T16:36:05.662484Z",
     "iopub.status.idle": "2023-02-09T16:36:05.669868Z",
     "shell.execute_reply": "2023-02-09T16:36:05.669061Z",
     "shell.execute_reply.started": "2023-02-09T16:36:05.662955Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:36:10.728289Z",
     "iopub.status.busy": "2023-02-09T16:36:10.727850Z",
     "iopub.status.idle": "2023-02-09T16:36:10.741355Z",
     "shell.execute_reply": "2023-02-09T16:36:10.740397Z",
     "shell.execute_reply.started": "2023-02-09T16:36:10.728255Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df.drop('price',axis = 1)\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:36:28.070379Z",
     "iopub.status.busy": "2023-02-09T16:36:28.069342Z",
     "iopub.status.idle": "2023-02-09T16:36:28.078456Z",
     "shell.execute_reply": "2023-02-09T16:36:28.077402Z",
     "shell.execute_reply.started": "2023-02-09T16:36:28.070330Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "np.random.seed(0)\n",
    "df_train, df_test = train_test_split(df, train_size = 0.7, test_size = 0.3, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:36:41.743618Z",
     "iopub.status.busy": "2023-02-09T16:36:41.743227Z",
     "iopub.status.idle": "2023-02-09T16:36:41.752438Z",
     "shell.execute_reply": "2023-02-09T16:36:41.750864Z",
     "shell.execute_reply.started": "2023-02-09T16:36:41.743588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143, 53)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:36:56.465591Z",
     "iopub.status.busy": "2023-02-09T16:36:56.465188Z",
     "iopub.status.idle": "2023-02-09T16:36:56.473318Z",
     "shell.execute_reply": "2023-02-09T16:36:56.471955Z",
     "shell.execute_reply.started": "2023-02-09T16:36:56.465554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62, 53)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:37:53.753639Z",
     "iopub.status.busy": "2023-02-09T16:37:53.753245Z",
     "iopub.status.idle": "2023-02-09T16:37:53.759540Z",
     "shell.execute_reply": "2023-02-09T16:37:53.758650Z",
     "shell.execute_reply.started": "2023-02-09T16:37:53.753609Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:38:12.730408Z",
     "iopub.status.busy": "2023-02-09T16:38:12.729991Z",
     "iopub.status.idle": "2023-02-09T16:38:12.735206Z",
     "shell.execute_reply": "2023-02-09T16:38:12.733948Z",
     "shell.execute_reply.started": "2023-02-09T16:38:12.730375Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:39:19.926389Z",
     "iopub.status.busy": "2023-02-09T16:39:19.925954Z",
     "iopub.status.idle": "2023-02-09T16:39:19.963651Z",
     "shell.execute_reply": "2023-02-09T16:39:19.962572Z",
     "shell.execute_reply.started": "2023-02-09T16:39:19.926351Z"
    }
   },
   "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>carwidth</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
       "      <th>gas</th>\n",
       "      <th>turbo</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "      <th>fwd</th>\n",
       "      <th>rwd</th>\n",
       "      <th>rear</th>\n",
       "      <th>2bbl</th>\n",
       "      <th>4bbl</th>\n",
       "      <th>idi</th>\n",
       "      <th>mfi</th>\n",
       "      <th>mpfi</th>\n",
       "      <th>spdi</th>\n",
       "      <th>spfi</th>\n",
       "      <th>dohcv</th>\n",
       "      <th>l</th>\n",
       "      <th>ohc</th>\n",
       "      <th>ohcf</th>\n",
       "      <th>ohcv</th>\n",
       "      <th>rotor</th>\n",
       "      <th>audi</th>\n",
       "      <th>bmw</th>\n",
       "      <th>buick</th>\n",
       "      <th>chevrolet</th>\n",
       "      <th>dodge</th>\n",
       "      <th>honda</th>\n",
       "      <th>isuzu</th>\n",
       "      <th>jaguar</th>\n",
       "      <th>mazda</th>\n",
       "      <th>mercury</th>\n",
       "      <th>mitsubishi</th>\n",
       "      <th>nissan</th>\n",
       "      <th>peugeot</th>\n",
       "      <th>plymouth</th>\n",
       "      <th>porsche</th>\n",
       "      <th>renault</th>\n",
       "      <th>saab</th>\n",
       "      <th>subaru</th>\n",
       "      <th>toyota</th>\n",
       "      <th>volkswagen</th>\n",
       "      <th>volvo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>63.8</td>\n",
       "      <td>2191</td>\n",
       "      <td>98</td>\n",
       "      <td>2.97</td>\n",
       "      <td>68</td>\n",
       "      <td>38</td>\n",
       "      <td>93.7</td>\n",
       "      <td>four</td>\n",
       "      <td>7609.0</td>\n",
       "      <td>7609.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>68.3</td>\n",
       "      <td>2778</td>\n",
       "      <td>151</td>\n",
       "      <td>3.94</td>\n",
       "      <td>143</td>\n",
       "      <td>27</td>\n",
       "      <td>94.5</td>\n",
       "      <td>four</td>\n",
       "      <td>22018.0</td>\n",
       "      <td>22018.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3.47</td>\n",
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       "      <td>27</td>\n",
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       "      <td>3157</td>\n",
       "      <td>130</td>\n",
       "      <td>3.62</td>\n",
       "      <td>162</td>\n",
       "      <td>22</td>\n",
       "      <td>104.3</td>\n",
       "      <td>four</td>\n",
       "      <td>18950.0</td>\n",
       "      <td>18950.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     carwidth  curbweight  enginesize  boreratio  horsepower  highwaympg  \\\n",
       "122      63.8        2191          98       2.97          68          38   \n",
       "125      68.3        2778         151       3.94         143          27   \n",
       "166      64.0        2300          98       3.24         112          29   \n",
       "1        64.1        2548         130       3.47         111          27   \n",
       "199      67.2        3157         130       3.62         162          22   \n",
       "\n",
       "     wheelbase cylindernumber    price    price  gas  turbo  hardtop  \\\n",
       "122       93.7           four   7609.0   7609.0    1      0        0   \n",
       "125       94.5           four  22018.0  22018.0    1      0        0   \n",
       "166       94.5           four   9538.0   9538.0    1      0        0   \n",
       "1         88.6           four  16500.0  16500.0    1      0        0   \n",
       "199      104.3           four  18950.0  18950.0    1      1        0   \n",
       "\n",
       "     hatchback  sedan  wagon  fwd  rwd  rear  2bbl  4bbl  idi  mfi  mpfi  \\\n",
       "122          0      1      0    1    0     0     1     0    0    0     0   \n",
       "125          1      0      0    0    1     0     0     0    0    0     1   \n",
       "166          1      0      0    0    1     0     0     0    0    0     1   \n",
       "1            0      0      0    0    1     0     0     0    0    0     1   \n",
       "199          0      0      1    0    1     0     0     0    0    0     1   \n",
       "\n",
       "     spdi  spfi  dohcv  l  ohc  ohcf  ohcv  rotor  audi  bmw  buick  \\\n",
       "122     0     0      0  0    1     0     0      0     0    0      0   \n",
       "125     0     0      0  0    1     0     0      0     0    0      0   \n",
       "166     0     0      0  0    0     0     0      0     0    0      0   \n",
       "1       0     0      0  0    0     0     0      0     0    0      0   \n",
       "199     0     0      0  0    1     0     0      0     0    0      0   \n",
       "\n",
       "     chevrolet  dodge  honda  isuzu  jaguar  mazda  mercury  mitsubishi  \\\n",
       "122          0      0      0      0       0      0        0           0   \n",
       "125          0      0      0      0       0      0        0           0   \n",
       "166          0      0      0      0       0      0        0           0   \n",
       "1            0      0      0      0       0      0        0           0   \n",
       "199          0      0      0      0       0      0        0           0   \n",
       "\n",
       "     nissan  peugeot  plymouth  porsche  renault  saab  subaru  toyota  \\\n",
       "122       0        0         1        0        0     0       0       0   \n",
       "125       0        0         0        1        0     0       0       0   \n",
       "166       0        0         0        0        0     0       0       1   \n",
       "1         0        0         0        0        0     0       0       0   \n",
       "199       0        0         0        0        0     0       0       0   \n",
       "\n",
       "     volkswagen  volvo  \n",
       "122           0      0  \n",
       "125           0      0  \n",
       "166           0      0  \n",
       "1             0      0  \n",
       "199           0      1  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:39:58.478090Z",
     "iopub.status.busy": "2023-02-09T16:39:58.477632Z",
     "iopub.status.idle": "2023-02-09T16:39:58.485300Z",
     "shell.execute_reply": "2023-02-09T16:39:58.484016Z",
     "shell.execute_reply.started": "2023-02-09T16:39:58.478053Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train,y_train = df_train.drop('price',axis = 1),df_train['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:40:26.541113Z",
     "iopub.status.busy": "2023-02-09T16:40:26.540657Z",
     "iopub.status.idle": "2023-02-09T16:40:26.575970Z",
     "shell.execute_reply": "2023-02-09T16:40:26.574825Z",
     "shell.execute_reply.started": "2023-02-09T16:40:26.541077Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carwidth</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
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       "      <th>hardtop</th>\n",
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       "      <th>dodge</th>\n",
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       "      <th>mitsubishi</th>\n",
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       "      <th>renault</th>\n",
       "      <th>saab</th>\n",
       "      <th>subaru</th>\n",
       "      <th>toyota</th>\n",
       "      <th>volkswagen</th>\n",
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       "  </thead>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     carwidth  curbweight  enginesize  boreratio  horsepower  highwaympg  \\\n",
       "160      64.4        2094          98       3.19          70          47   \n",
       "186      65.5        2275         109       3.19          85          34   \n",
       "59       66.5        2385         122       3.39          84          32   \n",
       "165      64.0        2265          98       3.24         112          29   \n",
       "140      63.8        2240         108       3.62          73          31   \n",
       "\n",
       "     wheelbase cylindernumber   price   price  gas  turbo  hardtop  hatchback  \\\n",
       "160       95.7           four  7738.0  7738.0    1      0        0          0   \n",
       "186       97.3           four  8495.0  8495.0    1      0        0          0   \n",
       "59        98.8           four  8845.0  8845.0    1      0        0          1   \n",
       "165       94.5           four  9298.0  9298.0    1      0        0          0   \n",
       "140       93.3           four  7603.0  7603.0    1      0        0          1   \n",
       "\n",
       "     sedan  wagon  fwd  rwd  rear  2bbl  4bbl  idi  mfi  mpfi  spdi  spfi  \\\n",
       "160      1      0    1    0     0     1     0    0    0     0     0     0   \n",
       "186      1      0    1    0     0     0     0    0    0     1     0     0   \n",
       "59       0      0    1    0     0     1     0    0    0     0     0     0   \n",
       "165      1      0    0    1     0     0     0    0    0     1     0     0   \n",
       "140      0      0    0    0     0     1     0    0    0     0     0     0   \n",
       "\n",
       "     dohcv  l  ohc  ohcf  ohcv  rotor  audi  bmw  buick  chevrolet  dodge  \\\n",
       "160      0  0    1     0     0      0     0    0      0          0      0   \n",
       "186      0  0    1     0     0      0     0    0      0          0      0   \n",
       "59       0  0    1     0     0      0     0    0      0          0      0   \n",
       "165      0  0    0     0     0      0     0    0      0          0      0   \n",
       "140      0  0    0     1     0      0     0    0      0          0      0   \n",
       "\n",
       "     honda  isuzu  jaguar  mazda  mercury  mitsubishi  nissan  peugeot  \\\n",
       "160      0      0       0      0        0           0       0        0   \n",
       "186      0      0       0      0        0           0       0        0   \n",
       "59       0      0       0      1        0           0       0        0   \n",
       "165      0      0       0      0        0           0       0        0   \n",
       "140      0      0       0      0        0           0       0        0   \n",
       "\n",
       "     plymouth  porsche  renault  saab  subaru  toyota  volkswagen  volvo  \n",
       "160         0        0        0     0       0       1           0      0  \n",
       "186         0        0        0     0       0       0           1      0  \n",
       "59          0        0        0     0       0       0           0      0  \n",
       "165         0        0        0     0       0       1           0      0  \n",
       "140         0        0        0     0       1       0           0      0  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:40:47.575269Z",
     "iopub.status.busy": "2023-02-09T16:40:47.574088Z",
     "iopub.status.idle": "2023-02-09T16:40:47.582278Z",
     "shell.execute_reply": "2023-02-09T16:40:47.580870Z",
     "shell.execute_reply.started": "2023-02-09T16:40:47.575230Z"
    }
   },
   "outputs": [],
   "source": [
    "X_test = df_test.drop('price',axis = 1)\n",
    "y_test = df_test['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tuning the svm model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:44:04.251441Z",
     "iopub.status.busy": "2023-02-09T16:44:04.251047Z",
     "iopub.status.idle": "2023-02-09T16:44:04.257089Z",
     "shell.execute_reply": "2023-02-09T16:44:04.255960Z",
     "shell.execute_reply.started": "2023-02-09T16:44:04.251410Z"
    }
   },
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.1,1, 10, 100, 1000], 'gamma': [1,0.1,0.01,0.001,0.0001], 'kernel': ['rbf']} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-09T16:48:08.359860Z",
     "iopub.status.busy": "2023-02-09T16:48:08.358865Z",
     "iopub.status.idle": "2023-02-09T16:48:08.365192Z",
     "shell.execute_reply": "2023-02-09T16:48:08.363835Z",
     "shell.execute_reply.started": "2023-02-09T16:48:08.359817Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "grid = GridSearchCV(SVR(),param_grid,refit=True,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Residual Analysis\n",
    "fig = plt.figure()\n",
    "sns.displot((y_test - y_pred),bins = 30,kde= True)\n",
    "fig.suptitle('Error Terms')       \n",
    "plt.xlabel('Errors')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EVALUATION OF THE MODEL\n",
    "fig = plt.figure()\n",
    "plt.scatter(y_test,y_pred)\n",
    "fig.suptitle('y_test vs y_pred', fontsize=20)          \n",
    "plt.xlabel('y_test')                        \n",
    "plt.ylabel('y_pred') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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