Column_Transform.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import OrdinalEncoder"
      ],
      "metadata": {
        "id": "X0oon55FLjeX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sz3K8-YnLBsH",
        "outputId": "7a8c06c3-e289-4340-a305-4850a7dd3c92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/100-days-of-machine-learning/day28-column-transformer/covid_toy.csv')"
      ],
      "metadata": {
        "id": "qwNsiNTzLFbZ"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df.head()"
      ],
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      "outputs": [
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            "text/plain": [
              "   age  gender  fever cough     city has_covid\n",
              "0   60    Male  103.0  Mild  Kolkata        No\n",
              "1   27    Male  100.0  Mild    Delhi       Yes\n",
              "2   42    Male  101.0  Mild    Delhi        No\n",
              "3   31  Female   98.0  Mild  Kolkata        No\n",
              "4   65  Female  101.0  Mild   Mumbai        No"
            ],
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      "source": [
        "df[\"city\"].value_counts()"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Kolkata      32\n",
              "Bangalore    30\n",
              "Delhi        22\n",
              "Mumbai       16\n",
              "Name: city, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
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      "cell_type": "code",
      "source": [
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mild      62\n",
              "Strong    38\n",
              "Name: cough, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"gender\"].value_counts()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "HSjTF_qZMNLk",
        "outputId": "d052a35c-1d9a-4cc3-9ffb-0ef815c18050"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female    59\n",
              "Male      41\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLYn7LXqMTlN",
        "outputId": "0f14eef9-13b6-4e48-8283-2c8e2bca9d5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age           0\n",
              "gender        0\n",
              "fever        10\n",
              "cough         0\n",
              "city          0\n",
              "has_covid     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(df.drop(columns=[\"has_covid\"]),df[\"has_covid\"],test_size=0.2)\n",
        "# df.drop(columns=[\"has_covid\"])---------include all columns except has_covid column"
      ],
      "metadata": {
        "id": "lW5d9pnAMdlS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
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      "outputs": [
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          "data": {
            "text/plain": [
              "    age  gender  fever   cough       city\n",
              "57   49  Female   99.0  Strong  Bangalore\n",
              "61   81  Female   98.0  Strong     Mumbai\n",
              "22   71  Female   98.0  Strong    Kolkata\n",
              "69   73  Female  103.0    Mild      Delhi\n",
              "73   34    Male   98.0  Strong    Kolkata\n",
              "..  ...     ...    ...     ...        ...\n",
              "48   66    Male   99.0  Strong  Bangalore\n",
              "45   72    Male   99.0    Mild  Bangalore\n",
              "62   56  Female  104.0  Strong  Bangalore\n",
              "96   51  Female  101.0  Strong    Kolkata\n",
              "26   19  Female  100.0    Mild    Kolkata\n",
              "\n",
              "[80 rows x 5 columns]"
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              "      <th>61</th>\n",
              "      <td>81</td>\n",
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              "      <th>48</th>\n",
              "      <td>66</td>\n",
              "      <td>Male</td>\n",
              "      <td>99.0</td>\n",
              "      <td>Strong</td>\n",
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              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>72</td>\n",
              "      <td>Male</td>\n",
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              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>19</td>\n",
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              "      <td>100.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Kolkata</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ad03898e-5a5b-4f58-b3f0-8914c17b22a9');\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": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "gPm_V0N7O1RI",
        "outputId": "402d2b8a-179b-46cc-d720-b7f4cf006532"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  gender  fever   cough       city\n",
              "14   51    Male  104.0    Mild  Bangalore\n",
              "65   69  Female  102.0    Mild  Bangalore\n",
              "36   38  Female  101.0    Mild  Bangalore\n",
              "99   10  Female   98.0  Strong    Kolkata\n",
              "97   20  Female  101.0    Mild  Bangalore\n",
              "86   25    Male  104.0    Mild  Bangalore\n",
              "93   27    Male  100.0    Mild    Kolkata\n",
              "84   69  Female   98.0  Strong     Mumbai\n",
              "75    5    Male  102.0    Mild    Kolkata\n",
              "77    8  Female  101.0    Mild    Kolkata\n",
              "49   44    Male  104.0    Mild     Mumbai\n",
              "95   12  Female  104.0    Mild  Bangalore\n",
              "16   69  Female  103.0    Mild    Kolkata\n",
              "5    84  Female    NaN    Mild  Bangalore\n",
              "38   49  Female  101.0    Mild      Delhi\n",
              "94   79    Male    NaN  Strong    Kolkata\n",
              "56   71    Male    NaN  Strong    Kolkata\n",
              "88    5  Female  100.0    Mild    Kolkata\n",
              "54   60  Female   99.0    Mild     Mumbai\n",
              "63   10    Male  100.0    Mild  Bangalore"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b526dad-c8f4-402b-b788-f175256c168c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>fever</th>\n",
              "      <th>cough</th>\n",
              "      <th>city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>51</td>\n",
              "      <td>Male</td>\n",
              "      <td>104.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Bangalore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>69</td>\n",
              "      <td>Female</td>\n",
              "      <td>102.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Bangalore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>38</td>\n",
              "      <td>Female</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Bangalore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "      <td>98.0</td>\n",
              "      <td>Strong</td>\n",
              "      <td>Kolkata</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>20</td>\n",
              "      <td>Female</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Bangalore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>25</td>\n",
              "      <td>Male</td>\n",
              "      <td>104.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Bangalore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>27</td>\n",
              "      <td>Male</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Kolkata</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>69</td>\n",
              "      <td>Female</td>\n",
              "      <td>98.0</td>\n",
              "      <td>Strong</td>\n",
              "      <td>Mumbai</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>5</td>\n",
              "      <td>Male</td>\n",
              "      <td>102.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Kolkata</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>8</td>\n",
              "      <td>Female</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Kolkata</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>44</td>\n",
              "      <td>Male</td>\n",
              "      <td>104.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Mumbai</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>104.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Bangalore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>69</td>\n",
              "      <td>Female</td>\n",
              "      <td>103.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Kolkata</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>84</td>\n",
              "      <td>Female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Bangalore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>49</td>\n",
              "      <td>Female</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Delhi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>79</td>\n",
              "      <td>Male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Strong</td>\n",
              "      <td>Kolkata</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>71</td>\n",
              "      <td>Male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Strong</td>\n",
              "      <td>Kolkata</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Kolkata</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>60</td>\n",
              "      <td>Female</td>\n",
              "      <td>99.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Mumbai</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>10</td>\n",
              "      <td>Male</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Bangalore</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b526dad-c8f4-402b-b788-f175256c168c')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .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-9b526dad-c8f4-402b-b788-f175256c168c 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-9b526dad-c8f4-402b-b788-f175256c168c');\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": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_DjO00LO4Pc",
        "outputId": "d62b43c7-5fb5-46d2-f8ad-3e23f2c9d476"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "57     No\n",
              "61     No\n",
              "22    Yes\n",
              "69     No\n",
              "73    Yes\n",
              "     ... \n",
              "48     No\n",
              "45     No\n",
              "62    Yes\n",
              "96    Yes\n",
              "26    Yes\n",
              "Name: has_covid, Length: 80, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1MpU1DAUO7Wo",
        "outputId": "896eb836-3fe2-48f7-a3f2-fedeba887f64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14     No\n",
              "65     No\n",
              "36     No\n",
              "99    Yes\n",
              "97     No\n",
              "86    Yes\n",
              "93    Yes\n",
              "84     No\n",
              "75    Yes\n",
              "77     No\n",
              "49     No\n",
              "95     No\n",
              "16    Yes\n",
              "5     Yes\n",
              "38    Yes\n",
              "94    Yes\n",
              "56     No\n",
              "88     No\n",
              "54    Yes\n",
              "63     No\n",
              "Name: has_covid, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# adding simple imputer to fever column\n",
        "si=SimpleImputer()\n",
        "x_train_fever = si.fit_transform(x_train[[\"fever\"]])\n",
        "# missing values are being replaced by mean here above"
      ],
      "metadata": {
        "id": "u8HVoEfdO81l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_fever = si.fit_transform(x_test[[\"fever\"]])\n"
      ],
      "metadata": {
        "id": "4rZJp0MQPVWh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_fever"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJqsbtbtPt0Q",
        "outputId": "6ac87c8f-17fd-472f-c6b8-07c929f9e009"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[104.        ],\n",
              "       [102.        ],\n",
              "       [101.        ],\n",
              "       [ 98.        ],\n",
              "       [101.        ],\n",
              "       [104.        ],\n",
              "       [100.        ],\n",
              "       [ 98.        ],\n",
              "       [102.        ],\n",
              "       [101.        ],\n",
              "       [104.        ],\n",
              "       [104.        ],\n",
              "       [103.        ],\n",
              "       [101.29411765],\n",
              "       [101.        ],\n",
              "       [101.29411765],\n",
              "       [101.29411765],\n",
              "       [100.        ],\n",
              "       [ 99.        ],\n",
              "       [100.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_fever.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aFO8C-4cPvKk",
        "outputId": "ffc7d3c5-eb11-4b15-c587-72d380ee40bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# OrdinalEncoding -->cough"
      ],
      "metadata": {
        "id": "i0TESgOKP0MC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "oe = OrdinalEncoder(categories = [[\"Mild\",\"Strong\"]])\n",
        "x_train_cough = oe.fit_transform(x_train[[\"cough\"]])\n",
        "\n",
        "# also for test data\n",
        "x_test_cough = oe.fit_transform(x_test[[\"cough\"]])\n",
        "\n",
        "# x_train_cough\n",
        "x_train_cough.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CxTZ7-_nQJ1Z",
        "outputId": "1f449807-c48c-4bae-fe9c-9f775399a640"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# OneHotCoding -> gender , city\n",
        "ohe = OneHotEncoder(drop = \"first\", sparse = False)\n",
        "\n",
        "x_train_gender_city = ohe.fit_transform(x_train[[\"gender\",\"city\"]])\n",
        "\n",
        "# also the test data\n",
        "\n",
        "x_test_gender_city = ohe.fit_transform(x_test[[\"gender\",\"city\"]])\n",
        "\n",
        "x_train_gender_city\n",
        "# x_train_gender_city.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ecdW_eSQvDJ",
        "outputId": "205db457-5d95-4b0e-f2e9-962c9df44d1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1.],\n",
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              "       [1., 0., 0., 0.],\n",
              "       [1., 0., 0., 1.],\n",
              "       [0., 0., 0., 0.],\n",
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              "       [1., 1., 0., 0.],\n",
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              "       [0., 1., 0., 0.],\n",
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              "       [0., 0., 1., 0.],\n",
              "       [0., 0., 1., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extracting Age\n",
        "x_train_age = x_train.drop(columns=[\"gender\",\"fever\",\"cough\",\"city\"]).values\n",
        "\n",
        "# also for the test data\n",
        "\n",
        "x_test_age = x_test.drop(columns=[\"gender\",\"fever\",\"cough\",\"city\"]).values\n",
        "x_train_age"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvUpI3GhRkiP",
        "outputId": "081faf84-2f4d-4240-89e9-c46fd569b04d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[49],\n",
              "       [81],\n",
              "       [71],\n",
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              "       [42],\n",
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              "       [72],\n",
              "       [56],\n",
              "       [51],\n",
              "       [19]])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_age.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxngdfDFSNcS",
        "outputId": "c18f6d61-fec8-4f9f-ee0d-8c38814a50c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_transformed = np.concatenate((x_train_age,x_train_fever,x_train_gender_city,x_train_cough),axis=1)\n",
        "x_test_transformed = np.concatenate((x_test_age,x_test_fever,x_test_gender_city,x_test_cough),axis=1)"
      ],
      "metadata": {
        "id": "vom-Epc7SUCG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# x_train_transformed\n",
        "x_train_transformed.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQKrla8XTAGw",
        "outputId": "4a840de2-116f-4e2e-9701-cb0b8606c66a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ColumnTransformer\n",
        "from sklearn.compose import ColumnTransformer"
      ],
      "metadata": {
        "id": "8BOM5t2MTBFU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transformer = ColumnTransformer(transformers = [\n",
        "    ('tnf1',SimpleImputer(),[\"fever\"]),\n",
        "    ('tnf2',OrdinalEncoder(categories=[[\"Mild\",\"Strong\"]]),[\"cough\"]),\n",
        "    ('tnf3',OneHotEncoder(sparse=False,drop=\"first\"),[\"gender\",\"city\"]),\n",
        "],remainder=\"passthrough\")"
      ],
      "metadata": {
        "id": "c3ZZMGB3UX3a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transformer.fit_transform(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1FS7SgwVU9z",
        "outputId": "4784ca09-466b-45b5-a43e-89aff96aeba3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          1.        ,   0.        ,  19.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transformer.fit_transform(x_train).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ls5EWwIBVeKO",
        "outputId": "aafe6573-8c99-41c6-d94d-6d918408b756"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transformer.fit_transform(x_test).shape"
      ],
      "metadata": {
        "id": "zJQyg0pmVvZw",
        "outputId": "9503cb6f-60f8-43b5-a5f1-4f98e6940f83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BQ0Yi0CoV1VE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}