Handling_Categorical_Data_with_Ordinal_Encoder.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings \n",
        "warnings.filterwarnings (\"ignore\")\n"
      ]
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      "source": [
        "df=pd.read_csv(\"cars.csv\")\n",
        "df.head()"
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              "   symboling normalized-losses         make fuel-type   body-style  \\\n",
              "0          3                 ?  alfa-romero       gas  convertible   \n",
              "1          3                 ?  alfa-romero       gas  convertible   \n",
              "2          1                 ?  alfa-romero       gas    hatchback   \n",
              "3          2               164         audi       gas        sedan   \n",
              "4          2               164         audi       gas        sedan   \n",
              "\n",
              "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
              "0          rwd           front   64.1    48.8        dohc          130   \n",
              "1          rwd           front   64.1    48.8        dohc          130   \n",
              "2          rwd           front   65.5    52.4        ohcv          152   \n",
              "3          fwd           front   66.2    54.3         ohc          109   \n",
              "4          4wd           front   66.4    54.3         ohc          136   \n",
              "\n",
              "  horsepower  city-mpg  highway-mpg  price  \n",
              "0        111        21           27  13495  \n",
              "1        111        21           27  16500  \n",
              "2        154        19           26  16500  \n",
              "3        102        24           30  13950  \n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 205 entries, 0 to 204\n",
            "Data columns (total 15 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   body-style         205 non-null    object \n",
            " 5   drive-wheels       205 non-null    object \n",
            " 6   engine-location    205 non-null    object \n",
            " 7   width              205 non-null    float64\n",
            " 8   height             205 non-null    float64\n",
            " 9   engine-type        205 non-null    object \n",
            " 10  engine-size        205 non-null    int64  \n",
            " 11  horsepower         205 non-null    object \n",
            " 12  city-mpg           205 non-null    int64  \n",
            " 13  highway-mpg        205 non-null    int64  \n",
            " 14  price              205 non-null    int64  \n",
            "dtypes: float64(2), int64(5), object(8)\n",
            "memory usage: 24.1+ KB\n"
          ]
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        "df[\"normalized-losses\"].replace(\"?\",np.nan,inplace=True)"
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              "   symboling normalized-losses         make fuel-type   body-style  \\\n",
              "0          3               NaN  alfa-romero       gas  convertible   \n",
              "1          3               NaN  alfa-romero       gas  convertible   \n",
              "2          1               NaN  alfa-romero       gas    hatchback   \n",
              "3          2               164         audi       gas        sedan   \n",
              "4          2               164         audi       gas        sedan   \n",
              "\n",
              "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
              "0          rwd           front   64.1    48.8        dohc          130   \n",
              "1          rwd           front   64.1    48.8        dohc          130   \n",
              "2          rwd           front   65.5    52.4        ohcv          152   \n",
              "3          fwd           front   66.2    54.3         ohc          109   \n",
              "4          4wd           front   66.4    54.3         ohc          136   \n",
              "\n",
              "  horsepower  city-mpg  highway-mpg  price  \n",
              "0        111        21           27  13495  \n",
              "1        111        21           27  16500  \n",
              "2        154        19           26  16500  \n",
              "3        102        24           30  13950  \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-24ad40e5-9cc3-4653-af3e-43baf7689026 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-24ad40e5-9cc3-4653-af3e-43baf7689026');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "si=SimpleImputer(missing_values=np.nan,strategy=\"mean\")\n",
        "df[[\"normalized-losses\",\"horsepower\"]]=si.fit_transform(df[[\"normalized-losses\",\"horsepower\"]])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "_7CbBkMQg4OH",
        "outputId": "21483b48-69d9-4d0c-a02e-81c6a84dd038"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   symboling  normalized-losses         make fuel-type   body-style  \\\n",
              "0          3              122.0  alfa-romero       gas  convertible   \n",
              "1          3              122.0  alfa-romero       gas  convertible   \n",
              "2          1              122.0  alfa-romero       gas    hatchback   \n",
              "3          2              164.0         audi       gas        sedan   \n",
              "4          2              164.0         audi       gas        sedan   \n",
              "\n",
              "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
              "0          rwd           front   64.1    48.8        dohc          130   \n",
              "1          rwd           front   64.1    48.8        dohc          130   \n",
              "2          rwd           front   65.5    52.4        ohcv          152   \n",
              "3          fwd           front   66.2    54.3         ohc          109   \n",
              "4          4wd           front   66.4    54.3         ohc          136   \n",
              "\n",
              "   horsepower  city-mpg  highway-mpg  price  \n",
              "0       111.0        21           27  13495  \n",
              "1       111.0        21           27  16500  \n",
              "2       154.0        19           26  16500  \n",
              "3       102.0        24           30  13950  \n",
              "4       115.0        18           22  17450  "
            ],
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              "      <th></th>\n",
              "      <th>symboling</th>\n",
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>122.0</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
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              "      <td>front</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>152</td>\n",
              "      <td>154.0</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
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              "      <td>sedan</td>\n",
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              "      <td>front</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>ohc</td>\n",
              "      <td>109</td>\n",
              "      <td>102.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>ohc</td>\n",
              "      <td>136</td>\n",
              "      <td>115.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e5302f6-ef93-45ba-8961-5d4bad36f0c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6e5302f6-ef93-45ba-8961-5d4bad36f0c4 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-6e5302f6-ef93-45ba-8961-5d4bad36f0c4');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,:-1]\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "jsVgo0I-ibJo",
        "outputId": "5c2af6e2-ccfa-4b2c-d780-7b65ded3f0c9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     symboling  normalized-losses         make fuel-type   body-style  \\\n",
              "0            3              122.0  alfa-romero       gas  convertible   \n",
              "1            3              122.0  alfa-romero       gas  convertible   \n",
              "2            1              122.0  alfa-romero       gas    hatchback   \n",
              "3            2              164.0         audi       gas        sedan   \n",
              "4            2              164.0         audi       gas        sedan   \n",
              "..         ...                ...          ...       ...          ...   \n",
              "200         -1               95.0        volvo       gas        sedan   \n",
              "201         -1               95.0        volvo       gas        sedan   \n",
              "202         -1               95.0        volvo       gas        sedan   \n",
              "203         -1               95.0        volvo    diesel        sedan   \n",
              "204         -1               95.0        volvo       gas        sedan   \n",
              "\n",
              "    drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
              "0            rwd           front   64.1    48.8        dohc          130   \n",
              "1            rwd           front   64.1    48.8        dohc          130   \n",
              "2            rwd           front   65.5    52.4        ohcv          152   \n",
              "3            fwd           front   66.2    54.3         ohc          109   \n",
              "4            4wd           front   66.4    54.3         ohc          136   \n",
              "..           ...             ...    ...     ...         ...          ...   \n",
              "200          rwd           front   68.9    55.5         ohc          141   \n",
              "201          rwd           front   68.8    55.5         ohc          141   \n",
              "202          rwd           front   68.9    55.5        ohcv          173   \n",
              "203          rwd           front   68.9    55.5         ohc          145   \n",
              "204          rwd           front   68.9    55.5         ohc          141   \n",
              "\n",
              "     horsepower  city-mpg  highway-mpg  \n",
              "0         111.0        21           27  \n",
              "1         111.0        21           27  \n",
              "2         154.0        19           26  \n",
              "3         102.0        24           30  \n",
              "4         115.0        18           22  \n",
              "..          ...       ...          ...  \n",
              "200       114.0        23           28  \n",
              "201       160.0        19           25  \n",
              "202       134.0        18           23  \n",
              "203       106.0        26           27  \n",
              "204       114.0        19           25  \n",
              "\n",
              "[205 rows x 14 columns]"
            ],
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>-1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>volvo</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>ohc</td>\n",
              "      <td>141</td>\n",
              "      <td>114.0</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>-1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>volvo</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>68.8</td>\n",
              "      <td>55.5</td>\n",
              "      <td>ohc</td>\n",
              "      <td>141</td>\n",
              "      <td>160.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>-1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>volvo</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>173</td>\n",
              "      <td>134.0</td>\n",
              "      <td>18</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>-1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>volvo</td>\n",
              "      <td>diesel</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>ohc</td>\n",
              "      <td>145</td>\n",
              "      <td>106.0</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>-1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>volvo</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>ohc</td>\n",
              "      <td>141</td>\n",
              "      <td>114.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a86855d-cec8-40a5-9ba2-fe195b3c984b')\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df.iloc[:,-1]\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixw3CSM6ihxA",
        "outputId": "afc574fe-dac0-4523-f646-29ba88cb41f5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      13495\n",
              "1      16500\n",
              "2      16500\n",
              "3      13950\n",
              "4      17450\n",
              "       ...  \n",
              "200    16845\n",
              "201    19045\n",
              "202    21485\n",
              "203    22470\n",
              "204    22625\n",
              "Name: price, Length: 205, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now For creating the ML Model we need encode all the catergorical data.\n",
        "\n",
        "This is done by:\n",
        "\n",
        "1.Label Encoder (implemented to y i.e target/output only)\n",
        "\n",
        "2.Ordinal Encoder (implemented to x i.e.features/Input only)\n",
        "\n",
        "Here y is already numeric in nature.\n",
        "\n",
        "but x has columns which are catergorical in nature. So we will use them for encoding."
      ],
      "metadata": {
        "id": "6H-Rz1jEiIMF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.select_dtypes(object).columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RyNfloZThxET",
        "outputId": "8e60ef19-81fb-4485-f83e-c9588407732b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['make', 'fuel-type', 'body-style', 'drive-wheels', 'engine-location',\n",
              "       'engine-type'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "catcol=df.select_dtypes(object).columns\n",
        "catcol"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKvfdopCjcXc",
        "outputId": "7f750fcc-c135-46c2-e2da-32b93ce2e6c4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['make', 'fuel-type', 'body-style', 'drive-wheels', 'engine-location',\n",
              "       'engine-type'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Ordinal Encoding**"
      ],
      "metadata": {
        "id": "ha0jo9u3jpEU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "oe=OrdinalEncoder()\n",
        "df[catcol]=oe.fit_transform(df[catcol])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "IllnYdGKjkbN",
        "outputId": "058f581e-3802-4b8d-d20a-46861c0cfcef"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     symboling  normalized-losses  make  fuel-type  body-style  drive-wheels  \\\n",
              "0            3              122.0   0.0        1.0         0.0           2.0   \n",
              "1            3              122.0   0.0        1.0         0.0           2.0   \n",
              "2            1              122.0   0.0        1.0         2.0           2.0   \n",
              "3            2              164.0   1.0        1.0         3.0           1.0   \n",
              "4            2              164.0   1.0        1.0         3.0           0.0   \n",
              "..         ...                ...   ...        ...         ...           ...   \n",
              "200         -1               95.0  21.0        1.0         3.0           2.0   \n",
              "201         -1               95.0  21.0        1.0         3.0           2.0   \n",
              "202         -1               95.0  21.0        1.0         3.0           2.0   \n",
              "203         -1               95.0  21.0        0.0         3.0           2.0   \n",
              "204         -1               95.0  21.0        1.0         3.0           2.0   \n",
              "\n",
              "     engine-location  width  height  engine-type  engine-size  horsepower  \\\n",
              "0                0.0   64.1    48.8          0.0          130       111.0   \n",
              "1                0.0   64.1    48.8          0.0          130       111.0   \n",
              "2                0.0   65.5    52.4          5.0          152       154.0   \n",
              "3                0.0   66.2    54.3          3.0          109       102.0   \n",
              "4                0.0   66.4    54.3          3.0          136       115.0   \n",
              "..               ...    ...     ...          ...          ...         ...   \n",
              "200              0.0   68.9    55.5          3.0          141       114.0   \n",
              "201              0.0   68.8    55.5          3.0          141       160.0   \n",
              "202              0.0   68.9    55.5          5.0          173       134.0   \n",
              "203              0.0   68.9    55.5          3.0          145       106.0   \n",
              "204              0.0   68.9    55.5          3.0          141       114.0   \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 15 columns]"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>body-style</th>\n",
              "      <th>drive-wheels</th>\n",
              "      <th>engine-location</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>engine-size</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130</td>\n",
              "      <td>111.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>122.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130</td>\n",
              "      <td>111.0</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>122.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>152</td>\n",
              "      <td>154.0</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>109</td>\n",
              "      <td>102.0</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>136</td>\n",
              "      <td>115.0</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>-1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>141</td>\n",
              "      <td>114.0</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "      <td>16845</td>\n",
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              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>-1</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>68.8</td>\n",
              "      <td>55.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>141</td>\n",
              "      <td>160.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>19045</td>\n",
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              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>-1</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>173</td>\n",
              "      <td>134.0</td>\n",
              "      <td>18</td>\n",
              "      <td>23</td>\n",
              "      <td>21485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>-1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>145</td>\n",
              "      <td>106.0</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>22470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>-1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>141</td>\n",
              "      <td>114.0</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>22625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8435d40-c34b-41ea-a2c3-d7640d416024')\"\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here the target variable is **contineous** i.e it becomes the **Regression problem.**\n",
        "\n",
        "So we are using **Ordinal Encoding.**\n",
        "\n",
        "In classification tasks where **target variable is categorical** we will bw using **label Encoding.**"
      ],
      "metadata": {
        "id": "aF8XJMRLkdF5"
      }
    }
  ]
}