Automobile_Dataset.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aruvi-B/Machine-Learning/blob/main/Automobile_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "xy7nnhbnEpuv"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "Q73Er2FsEzwh",
        "outputId": "a95b44dc-5656-4ec5-bfad-e60cf2dd5080"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     symboling normalized-losses         make fuel-type aspiration  \\\n",
              "0            3                 ?  alfa-romero       gas        std   \n",
              "1            3                 ?  alfa-romero       gas        std   \n",
              "2            1                 ?  alfa-romero       gas        std   \n",
              "3            2               164         audi       gas        std   \n",
              "4            2               164         audi       gas        std   \n",
              "..         ...               ...          ...       ...        ...   \n",
              "200         -1                95        volvo       gas        std   \n",
              "201         -1                95        volvo       gas      turbo   \n",
              "202         -1                95        volvo       gas        std   \n",
              "203         -1                95        volvo    diesel      turbo   \n",
              "204         -1                95        volvo       gas      turbo   \n",
              "\n",
              "    num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
              "0            two  convertible          rwd           front        88.6  ...   \n",
              "1            two  convertible          rwd           front        88.6  ...   \n",
              "2            two    hatchback          rwd           front        94.5  ...   \n",
              "3           four        sedan          fwd           front        99.8  ...   \n",
              "4           four        sedan          4wd           front        99.4  ...   \n",
              "..           ...          ...          ...             ...         ...  ...   \n",
              "200         four        sedan          rwd           front       109.1  ...   \n",
              "201         four        sedan          rwd           front       109.1  ...   \n",
              "202         four        sedan          rwd           front       109.1  ...   \n",
              "203         four        sedan          rwd           front       109.1  ...   \n",
              "204         four        sedan          rwd           front       109.1  ...   \n",
              "\n",
              "     engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
              "0            130         mpfi  3.47    2.68               9.0        111   \n",
              "1            130         mpfi  3.47    2.68               9.0        111   \n",
              "2            152         mpfi  2.68    3.47               9.0        154   \n",
              "3            109         mpfi  3.19     3.4              10.0        102   \n",
              "4            136         mpfi  3.19     3.4               8.0        115   \n",
              "..           ...          ...   ...     ...               ...        ...   \n",
              "200          141         mpfi  3.78    3.15               9.5        114   \n",
              "201          141         mpfi  3.78    3.15               8.7        160   \n",
              "202          173         mpfi  3.58    2.87               8.8        134   \n",
              "203          145          idi  3.01     3.4              23.0        106   \n",
              "204          141         mpfi  3.78    3.15               9.5        114   \n",
              "\n",
              "     peak-rpm city-mpg highway-mpg  price  \n",
              "0        5000       21          27  13495  \n",
              "1        5000       21          27  16500  \n",
              "2        5000       19          26  16500  \n",
              "3        5500       24          30  13950  \n",
              "4        5500       18          22  17450  \n",
              "..        ...      ...         ...    ...  \n",
              "200      5400       23          28  16845  \n",
              "201      5300       19          25  19045  \n",
              "202      5500       18          23  21485  \n",
              "203      4800       26          27  22470  \n",
              "204      5400       19          25  22625  \n",
              "\n",
              "[205 rows x 26 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>num-of-doors</th>\n",
              "      <th>body-style</th>\n",
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              "      <th>engine-location</th>\n",
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              "      <th>compression-ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak-rpm</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "      <th>price</th>\n",
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              "      <th>0</th>\n",
              "      <td>3</td>\n",
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              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>...</td>\n",
              "      <td>152</td>\n",
              "      <td>mpfi</td>\n",
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              "      <td>154</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
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              "      <td>mpfi</td>\n",
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              "      <td>volvo</td>\n",
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              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
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              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-51240553-981b-4b2d-8e4c-41e4f26b8b0b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_41b2d2cf-e2a5-48ea-8e5c-97600ce95a9b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_41b2d2cf-e2a5-48ea-8e5c-97600ce95a9b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "df = pd.read_csv(\"/content/Automobile_data.csv\")\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "CtWWMA21E3gc",
        "outputId": "58cc366b-8b2c-42b5-f264-801faacd9995"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        symboling  wheel-base      length       width      height  \\\n",
              "count  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
              "mean     0.834146   98.756585  174.049268   65.907805   53.724878   \n",
              "std      1.245307    6.021776   12.337289    2.145204    2.443522   \n",
              "min     -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
              "25%      0.000000   94.500000  166.300000   64.100000   52.000000   \n",
              "50%      1.000000   97.000000  173.200000   65.500000   54.100000   \n",
              "75%      2.000000  102.400000  183.100000   66.900000   55.500000   \n",
              "max      3.000000  120.900000  208.100000   72.300000   59.800000   \n",
              "\n",
              "       curb-weight  engine-size  compression-ratio    city-mpg  highway-mpg  \n",
              "count   205.000000   205.000000         205.000000  205.000000   205.000000  \n",
              "mean   2555.565854   126.907317          10.142537   25.219512    30.751220  \n",
              "std     520.680204    41.642693           3.972040    6.542142     6.886443  \n",
              "min    1488.000000    61.000000           7.000000   13.000000    16.000000  \n",
              "25%    2145.000000    97.000000           8.600000   19.000000    25.000000  \n",
              "50%    2414.000000   120.000000           9.000000   24.000000    30.000000  \n",
              "75%    2935.000000   141.000000           9.400000   30.000000    34.000000  \n",
              "max    4066.000000   326.000000          23.000000   49.000000    54.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-886fc1d9-3772-4378-9014-692ee9cd78f4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>symboling</th>\n",
              "      <th>wheel-base</th>\n",
              "      <th>length</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>curb-weight</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>compression-ratio</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\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>10.142537</td>\n",
              "      <td>25.219512</td>\n",
              "      <td>30.751220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\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>3.972040</td>\n",
              "      <td>6.542142</td>\n",
              "      <td>6.886443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\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>7.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\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>8.600000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\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>9.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\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>9.400000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>34.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\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>23.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>54.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-886fc1d9-3772-4378-9014-692ee9cd78f4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-886fc1d9-3772-4378-9014-692ee9cd78f4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-886fc1d9-3772-4378-9014-692ee9cd78f4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-72401b2d-652f-49b3-8291-04176e32d5f9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-72401b2d-652f-49b3-8291-04176e32d5f9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-72401b2d-652f-49b3-8291-04176e32d5f9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6qP6ShhE8em",
        "outputId": "23357cef-0509-4ead-d0f7-9228a670b315"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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   symboling          205 non-null    int64  \n",
            " 1   normalized-losses  205 non-null    object \n",
            " 2   make               205 non-null    object \n",
            " 3   fuel-type          205 non-null    object \n",
            " 4   aspiration         205 non-null    object \n",
            " 5   num-of-doors       205 non-null    object \n",
            " 6   body-style         205 non-null    object \n",
            " 7   drive-wheels       205 non-null    object \n",
            " 8   engine-location    205 non-null    object \n",
            " 9   wheel-base         205 non-null    float64\n",
            " 10  length             205 non-null    float64\n",
            " 11  width              205 non-null    float64\n",
            " 12  height             205 non-null    float64\n",
            " 13  curb-weight        205 non-null    int64  \n",
            " 14  engine-type        205 non-null    object \n",
            " 15  num-of-cylinders   205 non-null    object \n",
            " 16  engine-size        205 non-null    int64  \n",
            " 17  fuel-system        205 non-null    object \n",
            " 18  bore               205 non-null    object \n",
            " 19  stroke             205 non-null    object \n",
            " 20  compression-ratio  205 non-null    float64\n",
            " 21  horsepower         205 non-null    object \n",
            " 22  peak-rpm           205 non-null    object \n",
            " 23  city-mpg           205 non-null    int64  \n",
            " 24  highway-mpg        205 non-null    int64  \n",
            " 25  price              205 non-null    object \n",
            "dtypes: float64(5), int64(5), object(16)\n",
            "memory usage: 41.8+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PfTdZWwrE9Yx",
        "outputId": "f981e5d8-f126-47e5-8a78-bf6d3dcfe5bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "symboling            0\n",
              "normalized-losses    0\n",
              "make                 0\n",
              "fuel-type            0\n",
              "aspiration           0\n",
              "num-of-doors         0\n",
              "body-style           0\n",
              "drive-wheels         0\n",
              "engine-location      0\n",
              "wheel-base           0\n",
              "length               0\n",
              "width                0\n",
              "height               0\n",
              "curb-weight          0\n",
              "engine-type          0\n",
              "num-of-cylinders     0\n",
              "engine-size          0\n",
              "fuel-system          0\n",
              "bore                 0\n",
              "stroke               0\n",
              "compression-ratio    0\n",
              "horsepower           0\n",
              "peak-rpm             0\n",
              "city-mpg             0\n",
              "highway-mpg          0\n",
              "price                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "jrpb56ggFYoH"
      },
      "outputs": [],
      "source": [
        "# df['normalized-losses'] = df['normalized-losses'].replace('?', np.nan)\n",
        "# df['normalized-losses'] = df['normalized-losses'].fillna(df.mean())\n",
        "# df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VArzJMMMrV-W",
        "outputId": "6952d579-71bc-4d2b-f92d-db1e6229335b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(205, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CaJ9OL8dsIVc",
        "outputId": "f6798d0d-9e5d-4587-d576-1b120209e069"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The '?' is not present in the column.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-40-6785868fa00d>:1: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  if \"?\" in df['length'].values:\n"
          ]
        }
      ],
      "source": [
        "if \"?\" in df['length'].values:\n",
        "    print(\"The '?' is present in the column.\")\n",
        "else:\n",
        "    print(\"The '?' is not present in the column.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBwxr7xZquN8",
        "outputId": "2a61a4a8-4865-48fa-f9a7-fa0cb0d27b8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Price:  4\n",
            "Peak RPM 2\n",
            "Horsepower 2\n",
            "Stroke 4\n",
            "Number of doors 2\n",
            "Bore 4\n",
            "normalized-losses 41\n"
          ]
        }
      ],
      "source": [
        "# how many ? is present in the each columns before filtering\n",
        "print(\"Price: \", (df['price'] == '?').sum())\n",
        "print(\"Peak RPM\",(df[\"peak-rpm\"] == \"?\").sum())\n",
        "print(\"Horsepower\", (df[\"horsepower\"] == \"?\").sum())\n",
        "print(\"Stroke\", (df[\"stroke\"] == \"?\").sum())\n",
        "print(\"Number of doors\", (df['num-of-doors'] == \"?\").sum())\n",
        "print(\"Bore\", (df['bore'] == \"?\").sum())\n",
        "print(\"normalized-losses\", (df['normalized-losses'] == \"?\").sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "gEccBK-1ok7N"
      },
      "outputs": [],
      "source": [
        "df.replace(\"?\", np.nan, inplace = True)\n",
        "# df.dropna(axis = 0, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "for column in df.columns:\n",
        "  if df[column].dtype == np.float64 or df[column].dtype == np.int64:\n",
        "    df[column].fillna(df[column].mean(), inplace=True)\n",
        "  elif df[column].dtype == np.object:\n",
        "    df[column].fillna(df[column].mode()[0], inplace=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AIk3q8TMLCmf",
        "outputId": "98728fbc-8f16-448d-8519-a2001c241b5c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n",
            "<ipython-input-43-c7c387a74b31>:4: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  elif df[column].dtype == np.object:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afisoy-IXh_4",
        "outputId": "d2f6deaf-314a-4eda-b995-77c3a88e6c47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Price:  0\n",
            "Peak RPM 0\n",
            "Horsepower 0\n",
            "Stroke 0\n",
            "Number of doors 0\n",
            "Bore 0\n",
            "normalized-losses 0\n"
          ]
        }
      ],
      "source": [
        "# how many ? is present in the each columns after filtering\n",
        "print(\"Price: \", (df['price'] == '?').sum())\n",
        "print(\"Peak RPM\",(df[\"peak-rpm\"] == \"?\").sum())\n",
        "print(\"Horsepower\", (df[\"horsepower\"] == \"?\").sum())\n",
        "print(\"Stroke\", (df[\"stroke\"] == \"?\").sum())\n",
        "print(\"Number of doors\", (df['num-of-doors'] == \"?\").sum())\n",
        "print(\"Bore\", (df['bore'] == \"?\").sum())\n",
        "print(\"normalized-losses\", (df['normalized-losses'] == \"?\").sum())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYmd6sjGI_Vq",
        "outputId": "12d3f020-1f43-47ab-eb42-62c5ed835190"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(205, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "id": "p6zBAtNeirLk",
        "outputId": "301b19b4-39dd-4179-8c69-073f930ce5b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-46-972218f5ce85>:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  cm = df.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "cm = df.corr()\n",
        "plt.figure(figsize = (20, 10))\n",
        "sns.heatmap(cm, annot = True, cmap = \"coolwarm\", fmt = \".2f\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "KE-SJFExKiHe"
      },
      "outputs": [],
      "source": [
        "from sklearn import preprocessing\n",
        "label = preprocessing.LabelEncoder()\n",
        "df[\"make\"] = label.fit_transform(df[\"make\"])\n",
        "df[\"fuel-type\"] = label.fit_transform(df[\"fuel-type\"])\n",
        "df[\"engine-location\"] = label.fit_transform(df[\"engine-location\"])\n",
        "df[\"aspiration\"] = label.fit_transform(df[\"aspiration\"])\n",
        "df[\"num-of-doors\"] = label.fit_transform(df[\"num-of-doors\"])\n",
        "df[\"fuel-system\"] = label.fit_transform(df[\"fuel-system\"])\n",
        "df[\"body-style\"] = label.fit_transform(df[\"body-style\"])\n",
        "df[\"drive-wheels\"] = label.fit_transform(df[\"drive-wheels\"])\n",
        "df[\"engine-type\"] = label.fit_transform(df[\"engine-type\"])\n",
        "df[\"num-of-cylinders\"] = label.fit_transform(df[\"num-of-cylinders\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "du1Jlk29P06R",
        "outputId": "0c0abef9-20ea-49cd-c206-4c2e4bbba51f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of      symboling normalized-losses  make  fuel-type  aspiration  num-of-doors  \\\n",
              "0            3               161     0          1           0             1   \n",
              "1            3               161     0          1           0             1   \n",
              "2            1               161     0          1           0             1   \n",
              "3            2               164     1          1           0             0   \n",
              "4            2               164     1          1           0             0   \n",
              "..         ...               ...   ...        ...         ...           ...   \n",
              "200         -1                95    21          1           0             0   \n",
              "201         -1                95    21          1           1             0   \n",
              "202         -1                95    21          1           0             0   \n",
              "203         -1                95    21          0           1             0   \n",
              "204         -1                95    21          1           1             0   \n",
              "\n",
              "     body-style  drive-wheels  engine-location  wheel-base  ...  engine-size  \\\n",
              "0             0             2                0        88.6  ...          130   \n",
              "1             0             2                0        88.6  ...          130   \n",
              "2             2             2                0        94.5  ...          152   \n",
              "3             3             1                0        99.8  ...          109   \n",
              "4             3             0                0        99.4  ...          136   \n",
              "..          ...           ...              ...         ...  ...          ...   \n",
              "200           3             2                0       109.1  ...          141   \n",
              "201           3             2                0       109.1  ...          141   \n",
              "202           3             2                0       109.1  ...          173   \n",
              "203           3             2                0       109.1  ...          145   \n",
              "204           3             2                0       109.1  ...          141   \n",
              "\n",
              "     fuel-system  bore  stroke  compression-ratio  horsepower  peak-rpm  \\\n",
              "0              5  3.47    2.68                9.0         111      5000   \n",
              "1              5  3.47    2.68                9.0         111      5000   \n",
              "2              5  2.68    3.47                9.0         154      5000   \n",
              "3              5  3.19     3.4               10.0         102      5500   \n",
              "4              5  3.19     3.4                8.0         115      5500   \n",
              "..           ...   ...     ...                ...         ...       ...   \n",
              "200            5  3.78    3.15                9.5         114      5400   \n",
              "201            5  3.78    3.15                8.7         160      5300   \n",
              "202            5  3.58    2.87                8.8         134      5500   \n",
              "203            3  3.01     3.4               23.0         106      4800   \n",
              "204            5  3.78    3.15                9.5         114      5400   \n",
              "\n",
              "     city-mpg highway-mpg  price  \n",
              "0          21          27  13495  \n",
              "1          21          27  16500  \n",
              "2          19          26  16500  \n",
              "3          24          30  13950  \n",
              "4          18          22  17450  \n",
              "..        ...         ...    ...  \n",
              "200        23          28  16845  \n",
              "201        19          25  19045  \n",
              "202        18          23  21485  \n",
              "203        26          27  22470  \n",
              "204        19          25  22625  \n",
              "\n",
              "[205 rows x 26 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "df.info"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YtUcQA85RH_I",
        "outputId": "aa9c1906-0a23-4a2a-8c38-9fe4712f4476"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "length  width  height  curb-weight  engine-type  num-of-cylinders\n",
              "157.3   63.8   50.8    2128         3            2                   2\n",
              "               50.6    1989         3            2                   2\n",
              "186.7   68.4   56.7    3252         2            2                   2\n",
              "172.4   65.4   51.6    2403         3            2                   2\n",
              "177.8   66.5   55.5    2410         3            2                   2\n",
              "                                                                    ..\n",
              "170.2   63.8   53.5    2037         3            2                   1\n",
              "170.7   61.8   53.5    2337         3            2                   1\n",
              "        67.9   49.7    3071         5            3                   1\n",
              "                       3139         5            3                   1\n",
              "208.1   71.7   56.7    3900         5            0                   1\n",
              "Length: 189, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "df[['length', 'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders']].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "C6LzmxYVKiF2"
      },
      "outputs": [],
      "source": [
        "df['normalized-losses'] = df['normalized-losses'].astype(float)\n",
        "df['bore'] = df['bore'].astype(float)\n",
        "df['stroke'] = df['stroke'].astype(float)\n",
        "df['horsepower'] = df['horsepower'].astype(float)\n",
        "df['peak-rpm'] = df['peak-rpm'].astype(float)\n",
        "df['price'] = df['price'].astype(float)\n",
        "df['make'] = df['make'].astype(float)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "3OVK2MFISQpL"
      },
      "outputs": [],
      "source": [
        "for col in df.columns:\n",
        "    if df[col].dtype == 'object':\n",
        "        df.col = pd.to_numeric(df[col])\n",
        "        df.col = df[col].astype(float)\n",
        "        print(f\"Non-numerical column: {col}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MN5l_Ed_ePl4",
        "outputId": "a30af398-e796-4e8d-9c47-e2e51fbc851d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n",
              "       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n",
              "       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n",
              "       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n",
              "       'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n",
              "       'highway-mpg', 'price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[[\"make\", \"drive-wheels\"]]\n",
        "y = df['price']\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "labele = LabelEncoder()\n",
        "y = labele.fit_transform(y)\n"
      ],
      "metadata": {
        "id": "CzNZ_qbwQaON"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGWc57eQhDAu",
        "outputId": "3c1e5f1e-88b0-4d09-ba47-aa008957b305"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(205, 2)\n",
            "(205,)\n"
          ]
        }
      ],
      "source": [
        "print(X.shape)\n",
        "print(y.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "gwx5LWQY1MOB"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgmWfsmKQFI1",
        "outputId": "aed8d802-51ed-4bec-8d29-846555b387b4"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(164, 2)\n",
            "(164,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import mean_absolute_error"
      ],
      "metadata": {
        "id": "lwIpTigHT91V"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "tree_reg = DecisionTreeRegressor()\n",
        "tree_reg.fit(X_train, y_train)\n",
        "y_pred_tree = tree_reg.predict(X_test)\n",
        "mae_tree = mean_absolute_error(y_test, y_pred_tree)\n",
        "print(\"Mean Absolute Error:\", mae_tree)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07dWUZfcV_l5",
        "outputId": "3dcf935b-29da-4846-9b70-5e780db7353f"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 25.50081300813008\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scatter Plot"
      ],
      "metadata": {
        "id": "GS8Ha4UhWeE6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "4UHy7Une1sCV",
        "outputId": "39a43621-9d07-426c-aa68-bd5a9b39ef28"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.scatterplot(df, x=\"make\", y=\"horsepower\")\n",
        "plt.xlabel(\"Make\")\n",
        "plt.ylabel(\"Horse-Power\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Box Plot"
      ],
      "metadata": {
        "id": "-aDQmszBWqVr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "sFSX8E2fh5J8",
        "outputId": "7bc6cd2c-1f53-4528-d727-9cd808a341fb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(df, x=\"make\", y=\"horsepower\")\n",
        "plt.xlabel(\"Make\")\n",
        "plt.ylabel(\"Horse-Power\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bar plot"
      ],
      "metadata": {
        "id": "r9Wkui4lWtDr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "lrKbFqx8iCjr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "22a0129f-ed0e-4266-a99b-84a86d25b610"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(df, x=\"make\", y=\"horsepower\")\n",
        "plt.xlabel(\"Make\")\n",
        "plt.ylabel(\"Horse-Power\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Histogram"
      ],
      "metadata": {
        "id": "IRtPKBJ2Wvhc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "0PORMYXSiDVB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "5070f860-79ed-42cc-9fc8-2994b47b78eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(df, x=\"horsepower\")\n",
        "plt.ylabel(\"Horse-Power\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Line plot"
      ],
      "metadata": {
        "id": "6KM4Wz2iWxjI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "8skgNUk2vtW2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "3d7b4218-138d-4993-d5c4-b0a405945c1a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.lineplot(df, x=\"make\", y=\"horsepower\")\n",
        "plt.xlabel(\"Make\")\n",
        "plt.ylabel(\"Horse-Power\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pie chart"
      ],
      "metadata": {
        "id": "rmQpf_L4W1jV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_make_count = df['make'].value_counts()\n",
        "plt.pie(df_make_count, labels=df_make_count.index, autopct=\"%1.1f%%\")\n",
        "plt.xlabel(\"Make\")\n",
        "plt.ylabel(\"Horse-Power\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "23yVOxVAH-FH",
        "outputId": "49c427a1-8177-4423-8f5d-5be09c09c5f6"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mrqrUS-9uhc_"
      },
      "execution_count": 64,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPZzQdB6/9G9EYGMQ/ZzDIa",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}