CTR_GT.ipynb

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        "<a href=\"https://colab.research.google.com/github/kiarashrahmani/CTR-Prediction/blob/main/CTR_GT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "**Load Data**"
      ],
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        "id": "BsuGsgmP3GEf"
      }
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      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "6UKJ5IV53Tsd"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/train.csv')"
      ],
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        "id": "0iXAtwNr3VrW"
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      "execution_count": 3,
      "outputs": []
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        "df.head()"
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      "execution_count": 4,
      "outputs": [
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              "             id  click      hour    C1  banner_pos   site_id site_domain  \\\n",
              "0  3.190000e+18      0  14102101  1005           0  85f751fd    c4e18dd6   \n",
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              "4  3.190000e+18      0  14102101  1005           0  1fbe01fe    f3845767   \n",
              "\n",
              "  site_category    app_id app_domain  ... device_type device_conn_type    C14  \\\n",
              "0      50e219e0  c0c5eb49   f2f777fb  ...           1                0  21611   \n",
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              "   C15  C16   C17  C18  C19     C20  C21  \n",
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              "3  320   50  2480    3  297  100111   61  \n",
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          "metadata": {},
          "execution_count": 4
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      "source": [
        "df.set_index('id',inplace=True)"
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        "id": "fsx2o9dS4QH-"
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      "outputs": []
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        "y = df['click']"
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      "outputs": []
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      "source": [
        "x = df.loc[:,df.columns!='click']"
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      "execution_count": 7,
      "outputs": []
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        "outputId": "93d7bd26-b5f5-4845-dafe-9b8b7b6f76b7"
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      "execution_count": 8,
      "outputs": [
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "Float64Index: 100001 entries, 3.19e+18 to 1.35e+19\n",
            "Data columns (total 22 columns):\n",
            " #   Column            Non-Null Count   Dtype \n",
            "---  ------            --------------   ----- \n",
            " 0   hour              100001 non-null  int64 \n",
            " 1   C1                100001 non-null  int64 \n",
            " 2   banner_pos        100001 non-null  int64 \n",
            " 3   site_id           100001 non-null  object\n",
            " 4   site_domain       100001 non-null  object\n",
            " 5   site_category     100001 non-null  object\n",
            " 6   app_id            100001 non-null  object\n",
            " 7   app_domain        100001 non-null  object\n",
            " 8   app_category      100001 non-null  object\n",
            " 9   device_id         100001 non-null  object\n",
            " 10  device_ip         100001 non-null  object\n",
            " 11  device_model      100001 non-null  object\n",
            " 12  device_type       100001 non-null  int64 \n",
            " 13  device_conn_type  100001 non-null  int64 \n",
            " 14  C14               100001 non-null  int64 \n",
            " 15  C15               100001 non-null  int64 \n",
            " 16  C16               100001 non-null  int64 \n",
            " 17  C17               100001 non-null  int64 \n",
            " 18  C18               100001 non-null  int64 \n",
            " 19  C19               100001 non-null  int64 \n",
            " 20  C20               100001 non-null  int64 \n",
            " 21  C21               100001 non-null  int64 \n",
            "dtypes: int64(13), object(9)\n",
            "memory usage: 17.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Transformation**"
      ],
      "metadata": {
        "id": "vTGaOfHR6Prn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
        "numeric_columns = x.select_dtypes(include=numerics)\n",
        "non_numeric_columns = list(set(x.columns) - set(numeric_columns)) + list(\n",
        "    set(numeric_columns) - set(x.columns))\n",
        "le = LabelEncoder()\n",
        "x[non_numeric_columns] = x[non_numeric_columns].apply(le.fit_transform)"
      ],
      "metadata": {
        "id": "LepwEalG4ye2"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "1dOhS81d6moC",
        "outputId": "3754a73e-4800-46a2-bebc-61bf9add9023"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  hour    C1  banner_pos  site_id  site_domain  site_category  \\\n",
              "id                                                                              \n",
              "3.190000e+18  14102101  1005           0      572          699              4   \n",
              "3.190000e+18  14102101  1005           0      398          409              3   \n",
              "3.190000e+18  14102101  1005           0      398          409              3   \n",
              "3.190000e+18  14102101  1005           0      572          699              4   \n",
              "3.190000e+18  14102101  1005           0      126          856              1   \n",
              "\n",
              "              app_id  app_domain  app_category  device_id  ...  device_type  \\\n",
              "id                                                         ...                \n",
              "3.190000e+18     682          66             2       7607  ...            1   \n",
              "3.190000e+18     853          30             0       7607  ...            1   \n",
              "3.190000e+18     853          30             0       7607  ...            1   \n",
              "3.190000e+18     374           8             2       7607  ...            1   \n",
              "3.190000e+18     853          30             0       7607  ...            1   \n",
              "\n",
              "              device_conn_type    C14  C15  C16   C17  C18  C19     C20  C21  \n",
              "id                                                                            \n",
              "3.190000e+18                 0  21611  320   50  2480    3  297  100111   61  \n",
              "3.190000e+18                 0  17653  300  250  1994    2   39      -1   33  \n",
              "3.190000e+18                 0  17653  300  250  1994    2   39      -1   33  \n",
              "3.190000e+18                 0  21611  320   50  2480    3  297  100111   61  \n",
              "3.190000e+18                 0  15706  320   50  1722    0   35  100084   79  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>hour</th>\n",
              "      <th>C1</th>\n",
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              "      <th>device_conn_type</th>\n",
              "      <th>C14</th>\n",
              "      <th>C15</th>\n",
              "      <th>C16</th>\n",
              "      <th>C17</th>\n",
              "      <th>C18</th>\n",
              "      <th>C19</th>\n",
              "      <th>C20</th>\n",
              "      <th>C21</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3.190000e+18</th>\n",
              "      <td>14102101</td>\n",
              "      <td>1005</td>\n",
              "      <td>0</td>\n",
              "      <td>572</td>\n",
              "      <td>699</td>\n",
              "      <td>4</td>\n",
              "      <td>682</td>\n",
              "      <td>66</td>\n",
              "      <td>2</td>\n",
              "      <td>7607</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>21611</td>\n",
              "      <td>320</td>\n",
              "      <td>50</td>\n",
              "      <td>2480</td>\n",
              "      <td>3</td>\n",
              "      <td>297</td>\n",
              "      <td>100111</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.190000e+18</th>\n",
              "      <td>14102101</td>\n",
              "      <td>1005</td>\n",
              "      <td>0</td>\n",
              "      <td>398</td>\n",
              "      <td>409</td>\n",
              "      <td>3</td>\n",
              "      <td>853</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>7607</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>17653</td>\n",
              "      <td>300</td>\n",
              "      <td>250</td>\n",
              "      <td>1994</td>\n",
              "      <td>2</td>\n",
              "      <td>39</td>\n",
              "      <td>-1</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.190000e+18</th>\n",
              "      <td>14102101</td>\n",
              "      <td>1005</td>\n",
              "      <td>0</td>\n",
              "      <td>398</td>\n",
              "      <td>409</td>\n",
              "      <td>3</td>\n",
              "      <td>853</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>7607</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
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              "      <td>17653</td>\n",
              "      <td>300</td>\n",
              "      <td>250</td>\n",
              "      <td>1994</td>\n",
              "      <td>2</td>\n",
              "      <td>39</td>\n",
              "      <td>-1</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.190000e+18</th>\n",
              "      <td>14102101</td>\n",
              "      <td>1005</td>\n",
              "      <td>0</td>\n",
              "      <td>572</td>\n",
              "      <td>699</td>\n",
              "      <td>4</td>\n",
              "      <td>374</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>7607</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>21611</td>\n",
              "      <td>320</td>\n",
              "      <td>50</td>\n",
              "      <td>2480</td>\n",
              "      <td>3</td>\n",
              "      <td>297</td>\n",
              "      <td>100111</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.190000e+18</th>\n",
              "      <td>14102101</td>\n",
              "      <td>1005</td>\n",
              "      <td>0</td>\n",
              "      <td>126</td>\n",
              "      <td>856</td>\n",
              "      <td>1</td>\n",
              "      <td>853</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>7607</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>15706</td>\n",
              "      <td>320</td>\n",
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              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>100084</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc7d7eb9-0c2a-4200-a18e-4768d0276ada')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </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-bc7d7eb9-0c2a-4200-a18e-4768d0276ada 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-bc7d7eb9-0c2a-4200-a18e-4768d0276ada');\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-81c18b27-b6c0-49b8-9534-16dc83613462\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-81c18b27-b6c0-49b8-9534-16dc83613462')\"\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",
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              "    </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-81c18b27-b6c0-49b8-9534-16dc83613462 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Training**"
      ],
      "metadata": {
        "id": "24GB7E6y88tv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33)"
      ],
      "metadata": {
        "id": "BN9LDH9O6F4Q"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, precision_score\n",
        "\n",
        "\n",
        "# Define the parameter grid to search\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200],\n",
        "    'max_depth': [None, 10, 20],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}\n",
        "\n",
        "rf_model = RandomForestClassifier(random_state=42)\n",
        "\n",
        "grid_search = GridSearchCV(estimator=rf_model, param_grid=param_grid, scoring='accuracy', cv=3)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "best_params = grid_search.best_params_\n",
        "best_model = grid_search.best_estimator_\n"
      ],
      "metadata": {
        "id": "4e0TMVG17FlG"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict = best_model.predict(X_test)"
      ],
      "metadata": {
        "id": "iRGIwUB88Qu-"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluation**"
      ],
      "metadata": {
        "id": "sEMm3cYZ9ATA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score\n",
        "print(accuracy_score(y_pred=y_predict,y_true=y_test))\n",
        "print(precision_score(y_pred=y_predict,y_true=y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "irEzJZk-8YTm",
        "outputId": "ab2a60e7-93ef-4aac-b9e1-e6ecaa77b023"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8394897124329566\n",
            "0.5422661870503597\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create final file**"
      ],
      "metadata": {
        "id": "cQp_B4vg6018"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv('/content/test.csv')"
      ],
      "metadata": {
        "id": "keWcVQ0G8Yl8"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.set_index('id',inplace=True)"
      ],
      "metadata": {
        "id": "5Jc4JDSW8gbt"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
        "numeric_columns = df_test.select_dtypes(include=numerics)\n",
        "non_numeric_columns = list(set(df_test.columns) - set(numeric_columns)) + list(\n",
        "    set(numeric_columns) - set(df_test.columns))\n",
        "df_test[non_numeric_columns] = df_test[non_numeric_columns].apply(le.fit_transform)"
      ],
      "metadata": {
        "id": "pK4MK2BZ8kA0"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict = best_model.predict(df_test)"
      ],
      "metadata": {
        "id": "5vYLNoIX811s"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLBiSzDO-GV2",
        "outputId": "df5d7576-6fea-49ef-dc07-ae3f0ff4c00f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_final= pd.DataFrame()"
      ],
      "metadata": {
        "id": "k-ysYOnM771L"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_final['id'] = df_test.index\n",
        "df_final['Click'] = y_predict"
      ],
      "metadata": {
        "id": "rhy3ABIP8rCw"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_final.to_csv('submission.csv')"
      ],
      "metadata": {
        "id": "Vxmnp-JR7Vxb"
      },
      "execution_count": 23,
      "outputs": []
    }
  ]
}