Fine_Tuning_and_Validation.ipynb

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        "<a href=\"https://colab.research.google.com/github/darshanvjani/Kickstarter-Campaigns-Analysis/blob/main/Fine_Tuning_and_Validation.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": "code",
      "source": [
        "'''\n",
        "README\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "In this notebook we have again reduced the feature set from 47 to only 25 by training a decision tree.\n",
        "we are taking those 25 features and then training base model(Random Forest, SVC, Logistic Regression, XGBoost, Bagging(Decision Trees), Decision Tree) as well as fine tuning it using gridsearch CV and also validating the model results by\n",
        "using crossvalidation.\n",
        "Finally we are suign advance techniques like voterclassifier and SMOTE to again increase the overall classification performance.\n",
        "'''"
      ],
      "metadata": {
        "id": "sGkcI-Y2wknd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fq7zn_homiYf"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler,LabelEncoder,OneHotEncoder\n",
        "\n",
        "\n",
        "!pip install xgboost==1.3.3\n",
        "!pip install catboost\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(style = 'whitegrid')\n",
        "import os\n",
        "import re\n",
        "import string\n",
        "\n",
        "from sklearn.preprocessing        import LabelEncoder, StandardScaler\n",
        "from sklearn.model_selection      import train_test_split, StratifiedKFold, KFold\n",
        "from sklearn.linear_model         import LogisticRegression\n",
        "from sklearn.svm                  import SVC\n",
        "from sklearn.tree                 import DecisionTreeClassifier\n",
        "from sklearn.ensemble             import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier\n",
        "from lightgbm                     import LGBMClassifier\n",
        "# from xgboost                      import XGBClassifier\n",
        "# from catboost                     import CatBoostClassifier\n",
        "from sklearn.metrics              import accuracy_score,f1_score\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IvFEAyyp5zTS"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import StackingClassifier,VotingClassifier"
      ]
    },
    {
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      "execution_count": null,
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        "colab": {
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              "       goal  pledged  state  country  currency  currency_symbol  \\\n",
              "0    1500.0      0.0      1       20        12                0   \n",
              "1     500.0      0.0      1       20        12                0   \n",
              "2  100000.0    120.0      1       20        12                0   \n",
              "3    5000.0      0.0      1       20        12                0   \n",
              "4    3222.0    356.0      1        5         4                4   \n",
              "\n",
              "   backers_count  static_usd_rate  usd_pledged  \\\n",
              "0              0         1.000000     0.000000   \n",
              "1              0         1.000000     0.000000   \n",
              "2              5         1.000000   120.000000   \n",
              "3              0         1.000000     0.000000   \n",
              "4             17         1.114613   396.802395   \n",
              "\n",
              "                                             creator  ...  \\\n",
              "0  {\"urls\":{\"web\":{\"user\":\"https://www.kickstarte...  ...   \n",
              "1  {\"urls\":{\"web\":{\"user\":\"https://www.kickstarte...  ...   \n",
              "2  {\"urls\":{\"web\":{\"user\":\"https://www.kickstarte...  ...   \n",
              "3  {\"urls\":{\"web\":{\"user\":\"https://www.kickstarte...  ...   \n",
              "4  {\"urls\":{\"web\":{\"user\":\"https://www.kickstarte...  ...   \n",
              "\n",
              "   create_to_launch_days  launch_to_deadline_days  \\\n",
              "0                     17                       36   \n",
              "1                     10                       60   \n",
              "2                      1                       60   \n",
              "3                      0                       30   \n",
              "4                      0                       32   \n",
              "\n",
              "   launch_to_state_change_days  USorGB  TOPCOUNTRY  LaunchedTuesday  \\\n",
              "0                           36       1           1                0   \n",
              "1                           60       1           1                0   \n",
              "2                           60       1           1                0   \n",
              "3                           30       1           1                0   \n",
              "4                           32       0           0                0   \n",
              "\n",
              "   DeadlineWeekend  currency_trailing_code_b  disable_communication_b  \\\n",
              "0                0                         1                        0   \n",
              "1                0                         1                        0   \n",
              "2                0                         1                        0   \n",
              "3                0                         1                        0   \n",
              "4                0                         0                        0   \n",
              "\n",
              "   staff_pick_b  \n",
              "0             0  \n",
              "1             0  \n",
              "2             0  \n",
              "3             0  \n",
              "4             0  \n",
              "\n",
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          },
          "metadata": {},
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      ],
      "source": [
        "null_rm_cleaned_ds = pd.read_csv('/content/drive/MyDrive/257/ML Project/rm_column_encoded_2.csv')\n",
        "null_rm_cleaned_ds.drop(['Unnamed: 0','SuccessfulBool','spotlight_b'],axis=1,inplace=True)\n",
        "null_rm_cleaned_ds.head()"
      ]
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        "pd.set_option('max_colwidth', 20)"
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      "execution_count": null,
      "outputs": []
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    {
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        "def extract_name(element):\n",
        "    # Convert the string to a dictionary\n",
        "    element_dict = eval(element)\n",
        "    # Extract the name and return it\n",
        "    return element_dict[\"name\"]\n",
        "\n",
        "# Apply the function to the \"element\" column and put the result in a new \"name\" column\n",
        "null_rm_cleaned_ds[\"founder_name\"] = null_rm_cleaned_ds[\"creator\"].apply(extract_name)\n",
        "\n",
        "# Print the dataframe\n",
        "null_rm_cleaned_ds.head()"
      ],
      "metadata": {
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              "       goal  pledged  state  country  currency  currency_symbol  \\\n",
              "0    1500.0      0.0      1       20        12                0   \n",
              "1     500.0      0.0      1       20        12                0   \n",
              "2  100000.0    120.0      1       20        12                0   \n",
              "3    5000.0      0.0      1       20        12                0   \n",
              "4    3222.0    356.0      1        5         4                4   \n",
              "\n",
              "   backers_count  static_usd_rate  usd_pledged              creator  ...  \\\n",
              "0              0         1.000000     0.000000  {\"urls\":{\"web\":{...  ...   \n",
              "1              0         1.000000     0.000000  {\"urls\":{\"web\":{...  ...   \n",
              "2              5         1.000000   120.000000  {\"urls\":{\"web\":{...  ...   \n",
              "3              0         1.000000     0.000000  {\"urls\":{\"web\":{...  ...   \n",
              "4             17         1.114613   396.802395  {\"urls\":{\"web\":{...  ...   \n",
              "\n",
              "   launch_to_deadline_days  launch_to_state_change_days  USorGB  TOPCOUNTRY  \\\n",
              "0                   36                       36               1           1   \n",
              "1                   60                       60               1           1   \n",
              "2                   60                       60               1           1   \n",
              "3                   30                       30               1           1   \n",
              "4                   32                       32               0           0   \n",
              "\n",
              "   LaunchedTuesday  DeadlineWeekend  currency_trailing_code_b  \\\n",
              "0                0                0                    1        \n",
              "1                0                0                    1        \n",
              "2                0                0                    1        \n",
              "3                0                0                    1        \n",
              "4                0                0                    0        \n",
              "\n",
              "   disable_communication_b  staff_pick_b         founder_name  \n",
              "0                    0                 0       Diana L. Smith  \n",
              "1                    0                 0          Amy Planalp  \n",
              "2                    0                 0  Emmanuel Adeyefa...  \n",
              "3                    0                 0       Michael Felder  \n",
              "4                    0                 0      Werner Hülsmann  \n",
              "\n",
              "[5 rows x 46 columns]"
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              "      <th>launch_to_state_change_days</th>\n",
              "      <th>USorGB</th>\n",
              "      <th>TOPCOUNTRY</th>\n",
              "      <th>LaunchedTuesday</th>\n",
              "      <th>DeadlineWeekend</th>\n",
              "      <th>currency_trailing_code_b</th>\n",
              "      <th>disable_communication_b</th>\n",
              "      <th>staff_pick_b</th>\n",
              "      <th>founder_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>{\"urls\":{\"web\":{...</td>\n",
              "      <td>...</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Diana L. Smith</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>{\"urls\":{\"web\":{...</td>\n",
              "      <td>...</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Amy Planalp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100000.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>{\"urls\":{\"web\":{...</td>\n",
              "      <td>...</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Emmanuel Adeyefa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>{\"urls\":{\"web\":{...</td>\n",
              "      <td>...</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Michael Felder</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3222.0</td>\n",
              "      <td>356.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>1.114613</td>\n",
              "      <td>396.802395</td>\n",
              "      <td>{\"urls\":{\"web\":{...</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Werner Hülsmann</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 46 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a6af2bf-4ac4-4cce-b0dd-f550623f36fa')\"\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",
              "       width=\"24px\">\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",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-1a6af2bf-4ac4-4cce-b0dd-f550623f36fa 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-1a6af2bf-4ac4-4cce-b0dd-f550623f36fa');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "null_rm_cleaned_ds['founder_name'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uvj2zMBWU8t",
        "outputId": "3f62ba2b-54b3-4e20-a202-047aa51820fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Doug Gilliland             22\n",
              "Patrick Thomas Mitchell    19\n",
              "Michael                    18\n",
              "Isaac Porras               17\n",
              "MASIH VAHIDA               15\n",
              "                           ..\n",
              "Nathan Kennedy              1\n",
              "Christopher Allgood         1\n",
              "Finley                      1\n",
              "Bret Davis                  1\n",
              "Fiona O Doherty             1\n",
              "Name: founder_name, Length: 18504, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "null_rm_cleaned_ds.drop(['creator'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "wBeCknoRYsaE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vgYTqtaPtCuf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a305b60-104a-4413-dce5-2d10bced9424"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20627, 45)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "null_rm_cleaned_ds.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "null_rm_cleaned_ds['founder_name'] = LabelEncoder().fit_transform(null_rm_cleaned_ds['founder_name'])"
      ],
      "metadata": {
        "id": "rMdpmOerY0Fa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lFKyX0b_nEGW"
      },
      "outputs": [],
      "source": [
        "X = null_rm_cleaned_ds.drop(['state'],axis=1)\n",
        "y = null_rm_cleaned_ds['state']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "5B6ojdHNpOst",
        "outputId": "c8e4cdf0-510a-460c-b632-71fdb5b524a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "dt = DecisionTreeClassifier(random_state=1)\n",
        "dt.fit(X,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bovan0owpYf2",
        "outputId": "bf722d55-adc7-4c6e-9461-c5c270f16618"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('backers_count', 0.2397334317958416), ('launch_to_state_change_days', 0.20044771313034557), ('goal', 0.16572311535223253), ('pledged', 0.12457845779293684), ('launch_to_deadline_days', 0.10515809265090391), ('usd_pledged', 0.047651712014833106), ('launched_at_yr', 0.03943358909478405), ('disable_communication_b', 0.03166739439193931), ('blurb_len', 0.002603060171462587), ('deadline_month', 0.002561002807561282), ('blurb_len_clean', 0.0024761851015287417), ('launched_at_month', 0.0021851926242667376), ('state_changed_at_day', 0.00215635093880092), ('launched_at_day', 0.0021523281002555363), ('static_usd_rate', 0.0020761558639817933), ('created_at_day', 0.00200866041703674), ('created_at_hr', 0.001999746053419326), ('created_at_month', 0.0019850681158774842), ('create_to_launch_days', 0.001875291646856534), ('name_len', 0.0018362766438406888), ('state_changed_at_hr', 0.0018059628384713426), ('founder_name', 0.0018039709066515171), ('name_len_clean', 0.001617411409222453), ('state_changed_at_yr', 0.0014830590499729979), ('deadline_yr', 0.0013959227007521407)]\n"
          ]
        }
      ],
      "source": [
        "feat_imp = dict()\n",
        "importance = dt.feature_importances_\n",
        "# summarize feature importance\n",
        "for i,v in zip(X.columns,importance):\n",
        " feat_imp[i] = v\n",
        "\n",
        "\n",
        "def fun(dict_):\n",
        "  return dict_[1]\n",
        "sorted_arr = sorted(feat_imp.items(),reverse=True,key=fun)[:25]\n",
        "print(sorted_arr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQOD1EFvpSCw",
        "outputId": "e277871f-1943-425c-8bbe-e90a14303bf5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9884151516137759\n"
          ]
        }
      ],
      "source": [
        "sum_ = 0\n",
        "for y,z in sorted_arr:\n",
        "  sum_ = sum_ + z\n",
        "print(sum_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtE71cuCpfR9",
        "outputId": "5f43af34-01c7-4154-9055-10cfcbbf91d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['backers_count', 'launch_to_state_change_days', 'goal', 'pledged', 'launch_to_deadline_days', 'usd_pledged', 'launched_at_yr', 'disable_communication_b', 'blurb_len', 'deadline_month', 'blurb_len_clean', 'launched_at_month', 'state_changed_at_day', 'launched_at_day', 'static_usd_rate', 'created_at_day', 'created_at_hr', 'created_at_month', 'create_to_launch_days', 'name_len', 'state_changed_at_hr', 'founder_name', 'name_len_clean', 'state_changed_at_yr', 'deadline_yr']\n"
          ]
        }
      ],
      "source": [
        "important_colums = []\n",
        "for y,z in sorted_arr:\n",
        "  important_colums.append(y)\n",
        "\n",
        "print(important_colums)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h0rqRm0m52qw"
      },
      "outputs": [],
      "source": [
        "X_25 = X[important_colums]\n",
        "y = null_rm_cleaned_ds['state']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-qxbCpJtG-XI"
      },
      "outputs": [],
      "source": [
        "X_y_25 = pd.concat([X_25, y],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "m0y51OyxHiRO",
        "outputId": "53a87a56-867f-4e90-834e-962e62083c92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   backers_count  launch_to_state_change_days      goal  pledged  \\\n",
              "0              0                   36            1500.0      0.0   \n",
              "1              0                   60             500.0      0.0   \n",
              "2              5                   60          100000.0    120.0   \n",
              "3              0                   30            5000.0      0.0   \n",
              "4             17                   32            3222.0    356.0   \n",
              "\n",
              "   launch_to_deadline_days  usd_pledged  launched_at_yr  \\\n",
              "0                   36         0.000000            2014   \n",
              "1                   60         0.000000            2015   \n",
              "2                   60       120.000000            2015   \n",
              "3                   30         0.000000            2014   \n",
              "4                   32       396.802395            2016   \n",
              "\n",
              "   disable_communication_b  blurb_len  deadline_month  ...  created_at_hr  \\\n",
              "0                    0           21.0               1  ...             22   \n",
              "1                    0           23.0               5  ...              9   \n",
              "2                    0           18.0               3  ...              0   \n",
              "3                    0           21.0              10  ...             22   \n",
              "4                    0           18.0               6  ...             14   \n",
              "\n",
              "   created_at_month  create_to_launch_days  name_len  state_changed_at_hr  \\\n",
              "0                11                   17        11.0                   10   \n",
              "1                 2                   10         4.0                   16   \n",
              "2                 1                    1         8.0                    8   \n",
              "3                 9                    0         7.0                    0   \n",
              "4                 5                    0         7.0                   12   \n",
              "\n",
              "   founder_name  name_len_clean  state_changed_at_yr  deadline_yr  state  \n",
              "0          4504             9.0                 2015         2015      1  \n",
              "1           894             4.0                 2015         2015      1  \n",
              "2          5141             8.0                 2015         2015      1  \n",
              "3         11056             6.0                 2014         2014      1  \n",
              "4         17362             7.0                 2016         2016      1  \n",
              "\n",
              "[5 rows x 26 columns]"
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              "      <td>2016</td>\n",
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              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70fb484a-78e0-4a51-8816-175ac58badde')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-70fb484a-78e0-4a51-8816-175ac58badde');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "X_y_25.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Vwve5B49YAv",
        "outputId": "71e23ff8-7e99-4f9e-89ac-fd79368bdf53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20627 entries, 0 to 20626\n",
            "Data columns (total 26 columns):\n",
            " #   Column                       Non-Null Count  Dtype  \n",
            "---  ------                       --------------  -----  \n",
            " 0   backers_count                20627 non-null  int64  \n",
            " 1   launch_to_state_change_days  20627 non-null  int64  \n",
            " 2   goal                         20627 non-null  float64\n",
            " 3   pledged                      20627 non-null  float64\n",
            " 4   launch_to_deadline_days      20627 non-null  int64  \n",
            " 5   usd_pledged                  20627 non-null  float64\n",
            " 6   launched_at_yr               20627 non-null  int64  \n",
            " 7   disable_communication_b      20627 non-null  int64  \n",
            " 8   blurb_len                    20627 non-null  float64\n",
            " 9   deadline_month               20627 non-null  int64  \n",
            " 10  blurb_len_clean              20627 non-null  float64\n",
            " 11  launched_at_month            20627 non-null  int64  \n",
            " 12  state_changed_at_day         20627 non-null  int64  \n",
            " 13  launched_at_day              20627 non-null  int64  \n",
            " 14  static_usd_rate              20627 non-null  float64\n",
            " 15  created_at_day               20627 non-null  int64  \n",
            " 16  created_at_hr                20627 non-null  int64  \n",
            " 17  created_at_month             20627 non-null  int64  \n",
            " 18  create_to_launch_days        20627 non-null  int64  \n",
            " 19  name_len                     20627 non-null  float64\n",
            " 20  state_changed_at_hr          20627 non-null  int64  \n",
            " 21  founder_name                 20627 non-null  int64  \n",
            " 22  name_len_clean               20627 non-null  float64\n",
            " 23  state_changed_at_yr          20627 non-null  int64  \n",
            " 24  deadline_yr                  20627 non-null  int64  \n",
            " 25  state                        20627 non-null  int64  \n",
            "dtypes: float64(8), int64(18)\n",
            "memory usage: 4.1 MB\n"
          ]
        }
      ],
      "source": [
        "X_y_25.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_y_25.to_csv(\"XY25.csv\")"
      ],
      "metadata": {
        "id": "To92ymcgxLzA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix = X_y_25.corr()\n",
        "\n",
        "# Plot the heatmap\n",
        "plt.figure(figsize=(18, 16))\n",
        "sns.heatmap(corr_matrix,cmap='coolwarm', annot=True, fmt='.2f')\n",
        "plt.title('Heatmap of Top 25 Features')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "tcDIrMkkJ1Ms",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "931bd7f7-fe34-4f79-cc6a-57da68b47802"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UC0sWE8quZjO"
      },
      "outputs": [],
      "source": [
        "X_train, X_test,y_train, y_test = train_test_split(X_25, y, test_size = 0.2, shuffle=True, stratify=y, random_state=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_25_scaled = StandardScaler().fit_transform(X_25)\n",
        "\n",
        "X_train_s, X_test_s,y_train_s, y_test_s = train_test_split(X_25_scaled, y, test_size = 0.2, shuffle=True, stratify=y, random_state=1)"
      ],
      "metadata": {
        "id": "kMDVI25u7HoK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "98339cLx6GGD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a3d524a-f6cc-4c4f-b988-6a104509c484"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    9133\n",
              "3    4814\n",
              "0    1964\n",
              "2     406\n",
              "4     184\n",
              "Name: state, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "y_train.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0OZ0SPl06Ibm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4df7a56c-c129-4740-aab9-6aeabce09fa6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2283\n",
              "3    1204\n",
              "0     491\n",
              "2     102\n",
              "4      46\n",
              "Name: state, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "y_test.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gh2I2rQc9syv"
      },
      "source": [
        "Base Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SmSIJacN9ih3"
      },
      "outputs": [],
      "source": [
        "model_dict = {}\n",
        "\n",
        "model_dict['Logistic Regression'] = LogisticRegression()\n",
        "model_dict['Decision Tree'] = DecisionTreeClassifier(random_state = 1)\n",
        "model_dict['SVM'] = SVC(random_state = 1)\n",
        "# model_dict['Extra Trees'] = ExtraTreesClassifier(random_state = 1)\n",
        "model_dict['Random Forest'] = RandomForestClassifier(random_state = 1)\n",
        "model_dict['AdaBoost'] = AdaBoostClassifier(random_state = 1)\n",
        "model_dict['Bagging'] = BaggingClassifier(random_state = 1)\n",
        "model_dict['XGBoost'] = XGBClassifier(random_state = 1)\n",
        "# model_dict['LGBM'] = LGBMClassifier(random_state = 1)\n",
        "# model_dict['Cat'] = CatBoostClassifier(verbose=False, random_state =1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DmdyvUDM9oOm"
      },
      "outputs": [],
      "source": [
        "def model_test(X_train, X_test, y_train, y_test,model,model_name):\n",
        "    model.fit(X_train,y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test,y_pred)\n",
        "    f1 = f1_score(y_test,y_pred,average='macro')\n",
        "    print('======================================{}======================================='.format(model_name))\n",
        "    print('Accuracy Score is : {}'.format(accuracy))\n",
        "    print()\n",
        "    print('F1 Score is : {}'.format(f1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNM00l_K9pcM",
        "outputId": "d0f99a58-ad7a-47a3-ceda-b1e2ac753c69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "======================================Logistic Regression=======================================\n",
            "Accuracy Score is : 0.788899660688318\n",
            "\n",
            "F1 Score is : 0.34636214935621945\n",
            "======================================Decision Tree=======================================\n",
            "Accuracy Score is : 0.956858943286476\n",
            "\n",
            "F1 Score is : 0.9349592937418171\n",
            "======================================SVM=======================================\n",
            "Accuracy Score is : 0.6454192922927775\n",
            "\n",
            "F1 Score is : 0.2495997560776248\n",
            "======================================Random Forest=======================================\n",
            "Accuracy Score is : 0.9602520601066408\n",
            "\n",
            "F1 Score is : 0.9588794396979562\n",
            "======================================AdaBoost=======================================\n",
            "Accuracy Score is : 0.7198254968492487\n",
            "\n",
            "F1 Score is : 0.5346343651480557\n",
            "======================================Bagging=======================================\n",
            "Accuracy Score is : 0.9726126999515269\n",
            "\n",
            "F1 Score is : 0.9558337136972469\n",
            "[10:41:29] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
            "======================================XGBoost=======================================\n",
            "Accuracy Score is : 0.9861851672321862\n",
            "\n",
            "F1 Score is : 0.9841726453056925\n"
          ]
        }
      ],
      "source": [
        "for model_name,model in model_dict.items():\n",
        "    model_test(X_train, X_test, y_train, y_test, model, model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itTA81q59yVf"
      },
      "source": [
        "Stratified Cross Val Score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XflegT9M9076",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d097c9a-dbc6-430a-84c7-80f2127fdf90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression  Stratified Cross Val Accuracy 0.7717727961223871\n",
            "Decision Tree  Stratified Cross Val Accuracy 0.951639723499766\n",
            "SVM  Stratified Cross Val Accuracy 0.6462038500729805\n",
            "Random Forest  Stratified Cross Val Accuracy 0.9589719919583597\n",
            "AdaBoost  Stratified Cross Val Accuracy 0.7189865880862548\n",
            "Bagging  Stratified Cross Val Accuracy 0.9692745999834761\n",
            "XGBoost  Stratified Cross Val Accuracy 0.9854553532905548\n",
            "\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------\n",
            "\n",
            "Logistic Regression  Stratified Cross Val F1 0.3392306369483492\n",
            "Decision Tree  Stratified Cross Val F1 0.9298898057590331\n",
            "SVM  Stratified Cross Val F1 0.2513705923854753\n",
            "Random Forest  Stratified Cross Val F1 0.9550565100892918\n",
            "AdaBoost  Stratified Cross Val F1 0.5322804854057497\n",
            "Bagging  Stratified Cross Val F1 0.9548507266058037\n",
            "XGBoost  Stratified Cross Val F1 0.984385899910049\n"
          ]
        }
      ],
      "source": [
        "skf = StratifiedKFold(n_splits=5,shuffle=True, random_state=1)\n",
        "\n",
        "for model_name,model in model_dict.items():\n",
        "  print(model_name,\" Stratified Cross Val Accuracy\",sum(cross_val_score(model,X_train,y_train,cv=skf,n_jobs=-1,scoring='accuracy'))/5)\n",
        "print()\n",
        "print(\"-----------------------------------------------------------------------------------------------------------------------------------------------\")\n",
        "print()\n",
        "for model_name,model in model_dict.items():\n",
        "  print(model_name,\" Stratified Cross Val F1\",sum(cross_val_score(model,X_train,y_train,cv=skf,n_jobs=-1,scoring='f1_macro')/5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6HeZTNLHYYs"
      },
      "source": [
        "Ensemble of all the 5 classifiers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j0nIqpM-HbWt"
      },
      "outputs": [],
      "source": [
        "estimators = []\n",
        "for key,val in model_dict.items():\n",
        "  estimators.append((key,val))\n",
        "# estimators"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "estimators"
      ],
      "metadata": {
        "id": "Q8zitzSF-8FE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52941001-1ad7-4752-d391-3be02b8885bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Logistic Regression', LogisticRegression()),\n",
              " ('Decision Tree', DecisionTreeClassifier(random_state=1)),\n",
              " ('SVM', SVC(random_state=1)),\n",
              " ('Random Forest', RandomForestClassifier(random_state=1)),\n",
              " ('AdaBoost', AdaBoostClassifier(random_state=1)),\n",
              " ('Bagging', BaggingClassifier(random_state=1)),\n",
              " ('XGBoost',\n",
              "  XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,\n",
              "                colsample_bynode=None, colsample_bytree=None, gamma=None,\n",
              "                gpu_id=None, importance_type='gain', interaction_constraints=None,\n",
              "                learning_rate=None, max_delta_step=None, max_depth=None,\n",
              "                min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "                n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "                random_state=1, reg_alpha=None, reg_lambda=None,\n",
              "                scale_pos_weight=None, subsample=None, tree_method=None,\n",
              "                validate_parameters=None, verbosity=None))]"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9qwXX0pJHoEp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7e75475-01ea-4efe-83cb-291e51ea2da5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('AdaBoost', AdaBoostClassifier(random_state=1))"
            ]
          },
          "metadata": {},
          "execution_count": 216
        }
      ],
      "source": [
        "estimators.pop(0)\n",
        "estimators.pop(0)\n",
        "estimators.pop(0)\n",
        "estimators.pop(1)\n",
        "# estimators.pop(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VlipsAlRHepa"
      },
      "outputs": [],
      "source": [
        "# stacking_clf = StackingClassifier(estimators=estimators, final_estimator=CatBoostClassifier(verbose=False, random_state =1))\n",
        "# stacking_clf.fit(X_train, y_train)\n",
        "\n",
        "# # Make predictions on the test set\n",
        "# y_pred = stacking_clf.predict(X_test)\n",
        "\n",
        "# # Evaluate the accuracy of the model\n",
        "# accuracy = stacking_clf.score(X_test, y_test)\n",
        "# print(\"Accuracy:\", accuracy)\n",
        "# f1 = f1_score(y_test, y_pred,average=\"weighted\")\n",
        "# print(\"F1:\", f1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "icLk2OICHy_l"
      },
      "outputs": [],
      "source": [
        "# print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "voterclassifier"
      ],
      "metadata": {
        "id": "kam5ldACAyqQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# estimators = []\n",
        "# for key,val in model_dict.items():\n",
        "#   estimators.append((key,val))\n",
        "# estimators.pop(0)\n",
        "# estimators.pop(0)\n",
        "# estimators.pop(1)\n",
        "# # estimators.pop(2)"
      ],
      "metadata": {
        "id": "T98YCgX-E-27"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.base import BaseEstimator, ClassifierMixin\n",
        "\n",
        "# class ReshapePredictions(BaseEstimator, ClassifierMixin):\n",
        "#     def __init__(self, estimator):\n",
        "#         self.estimator = estimator\n",
        "\n",
        "#     def fit(self, X, y):\n",
        "#         self.estimator.fit(X, y)\n",
        "#         return self\n",
        "\n",
        "#     def predict(self, X):\n",
        "#         y_pred = self.estimator.predict(X)\n",
        "#         return y_pred.ravel()\n",
        "\n",
        "# estimators[2] = ('estimator_name', ReshapePredictions(estimators[2][1]))"
      ],
      "metadata": {
        "id": "U4w44XXDA09B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "voter = VotingClassifier(estimators=estimators)\n",
        "voter.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = voter.predict(X_test)\n",
        "\n",
        "# Evaluate the accuracy of the model\n",
        "accuracy = voter.score(X_test, y_test)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "f1 = f1_score(y_test, y_pred,average=\"weighted\")\n",
        "print(\"F1:\", f1)"
      ],
      "metadata": {
        "id": "abL2TDiGBFV2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ece028af-3633-4401-e89b-e0a77400023d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[15:02:54] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
            "Accuracy: 0.9793989335918565\n",
            "F1: 0.9789693469563725\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "A6ghsegGFEdP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20a323b8-1d59-477a-f5ce-5d100f4fc965"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.87      0.93       491\n",
            "           1       0.98      0.99      0.98      2283\n",
            "           2       0.99      1.00      1.00       102\n",
            "           3       0.98      1.00      0.99      1204\n",
            "           4       1.00      0.98      0.99        46\n",
            "\n",
            "    accuracy                           0.98      4126\n",
            "   macro avg       0.99      0.97      0.98      4126\n",
            "weighted avg       0.98      0.98      0.98      4126\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logit = LogisticRegression(random_state=1)\n",
        "logit.fit(X_train_s,y_train)\n",
        "y_pred = logit.predict(X_test_s)\n",
        "print(\"Accuracy: \",accuracy_score(y_test,y_pred))\n",
        "print(\"F1 Score: \",f1_score(y_test,y_pred,average='macro'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJhlqsy_Cp-M",
        "outputId": "94536489-8038-47e6-b5af-d7335af44c49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.8407658749394086\n",
            "F1 Score:  0.894136397791274\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC(random_state=1)\n",
        "svc.fit(X_train_s,y_train)\n",
        "y_pred = svc.predict(X_test_s)"
      ],
      "metadata": {
        "id": "U-XbYCVgCXBB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy: \",accuracy_score(y_test,y_pred))\n",
        "print(\"F1 Score: \",f1_score(y_test,y_pred,average='macro'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zfkjT9KIC9MF",
        "outputId": "7ec20f63-3814-4187-a5e0-e7b6e2d22528"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.7840523509452254\n",
            "F1 Score:  0.8392483462930211\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eDzPPip4H0t6"
      },
      "source": [
        "Hyperparameter tuning of each classification model and then doing the whole process again"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tYFe14nzNXFE"
      },
      "outputs": [],
      "source": [
        "param_grid = {'max_depth': [5,13,15,17,19,None], 'min_samples_leaf': [1, 2, 3, 4, 5], 'min_samples_split': [2, 5, 10], 'max_features': [None, 'sqrt', 'log2']}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vhKIWmmvMnoL"
      },
      "outputs": [],
      "source": [
        "clf = DecisionTreeClassifier(random_state=1)\n",
        "grid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=skf,verbose=True)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best hyperparameters:\", grid_search.best_params_)\n",
        "y_pred = grid_search.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test,y_pred)\n",
        "f1 = f1_score(y_test,y_pred,average='macro')\n",
        "print('Accuracy Score is : {}'.format(accuracy))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yh5ZkZuDOPUx"
      },
      "outputs": [],
      "source": [
        "dttuned = DecisionTreeClassifier(min_samples_leaf = 2 ,min_samples_split = 7,max_features = None,random_state=1,class_weight=\"balanced\",ccp_alpha=0.00015,criterion=\"gini\")\n",
        "dttuned.fit(X_train, y_train)\n",
        "y_pred_test = dttuned.predict(X_test)\n",
        "y_pred_train = dttuned.predict(X_train)\n",
        "accuracy_test = accuracy_score(y_test,y_pred_test)\n",
        "accuracy_train = accuracy_score(y_train,y_pred_train)\n",
        "f1 = f1_score(y_test,y_pred_test,average='macro')\n",
        "print('Test Accuracy Score is : {}'.format(accuracy_test))\n",
        "print('Train Accuracy Score is : {}'.format(accuracy_train))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Logistic Regression"
      ],
      "metadata": {
        "id": "BzJ8t9fP2jS4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#without scaling\n",
        "\n",
        "logreg = LogisticRegression(random_state=1,max_iter=78,multi_class='multinomial',penalty='l2')\n",
        "logreg.fit(X_train, y_train)\n",
        "y_pred_test = logreg.predict(X_test)\n",
        "y_pred_train = logreg.predict(X_train)\n",
        "accuracy_test = accuracy_score(y_test,y_pred_test)\n",
        "accuracy_train = accuracy_score(y_train,y_pred_train)\n",
        "f1 = f1_score(y_test,y_pred_test,average='macro')\n",
        "print('Test Accuracy Score is : {}'.format(accuracy_test))\n",
        "print('Train Accuracy Score is : {}'.format(accuracy_train))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "id": "4KT1R80n2lrD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#with scaling\n",
        "\n",
        "logreg = LogisticRegression(random_state=1,max_iter=78,multi_class='multinomial',penalty='l2')\n",
        "logreg.fit(X_train_s, y_train_s)\n",
        "y_pred_test = logreg.predict(X_test_s)\n",
        "y_pred_train = logreg.predict(X_train_s)\n",
        "accuracy_test = accuracy_score(y_test_s,y_pred_test)\n",
        "accuracy_train = accuracy_score(y_train_s,y_pred_train)\n",
        "f1 = f1_score(y_test_s,y_pred_test,average='macro')\n",
        "print('Test Accuracy Score is : {}'.format(accuracy_test))\n",
        "print('Train Accuracy Score is : {}'.format(accuracy_train))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "id": "KAvAIPv07ZUq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03c36ac9-a56b-4789-d2aa-afcbc580e455"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy Score is : 0.8378574890935531\n",
            "Train Accuracy Score is : 0.8360099387915884\n",
            "\n",
            "F1 Score is : 0.8935307498198671\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#SVM"
      ],
      "metadata": {
        "id": "vBrIHnw58zRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#without scaling\n",
        "\n",
        "svc = SVC(random_state=1,C=0.99)\n",
        "svc.fit(X_train, y_train)\n",
        "y_pred_test = svc.predict(X_test)\n",
        "y_pred_train = svc.predict(X_train)\n",
        "accuracy_test = accuracy_score(y_test,y_pred_test)\n",
        "accuracy_train = accuracy_score(y_train,y_pred_train)\n",
        "f1 = f1_score(y_test,y_pred_test,average='macro')\n",
        "print('Test Accuracy Score is : {}'.format(accuracy_test))\n",
        "print('Train Accuracy Score is : {}'.format(accuracy_train))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJqLsxF987cx",
        "outputId": "484262a9-59d4-477a-c45b-05c95e715ab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy Score is : 0.644449830344159\n",
            "Train Accuracy Score is : 0.6503848251621114\n",
            "\n",
            "F1 Score is : 0.24875411616336743\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC(random_state=1,C=0.99)\n",
        "svc.fit(X_train_s, y_train_s)\n",
        "y_pred_test = svc.predict(X_test_s)\n",
        "y_pred_train = svc.predict(X_train_s)\n",
        "accuracy_test = accuracy_score(y_test_s,y_pred_test)\n",
        "accuracy_train = accuracy_score(y_train_s,y_pred_train)\n",
        "f1 = f1_score(y_test_s,y_pred_test,average='macro')\n",
        "print('Test Accuracy Score is : {}'.format(accuracy_test))\n",
        "print('Train Accuracy Score is : {}'.format(accuracy_train))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GYDTcA6b80t6",
        "outputId": "172dd238-a871-4410-905e-da8131207ab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy Score is : 0.78429471643238\n",
            "Train Accuracy Score is : 0.7972850130295134\n",
            "\n",
            "F1 Score is : 0.8393553694114221\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mry51KhdN0WY"
      },
      "outputs": [],
      "source": [
        "param_grid = {\n",
        "    # 'n_estimators': [3000],\n",
        "    # 'max_depth': [4],\n",
        "    # 'min_samples_split': [2, 5, 10],\n",
        "    # 'min_samples_leaf': [1, 2, 4],\n",
        "    'max_features': [None],\n",
        "    # 'bootstrap': [True, False],\n",
        "    'class_weight': ['balanced'],\n",
        "    # 'criterion': ['gini', 'entropy']\n",
        "    'ccp_alpha':[0.0001]\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b4Nisl72OGnJ"
      },
      "outputs": [],
      "source": [
        "crf = RandomForestClassifier(random_state=1)\n",
        "grid_search = GridSearchCV(estimator=crf, param_grid=param_grid, cv=skf,verbose=True)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best hyperparameters:\", grid_search.best_params_)\n",
        "y_pred = grid_search.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test,y_pred)\n",
        "f1 = f1_score(y_test,y_pred,average='macro')\n",
        "print('Accuracy Score is : {}'.format(accuracy))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cACXSTl49Z-a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28afc10f-e9e8-4088-b4fe-ba5a2a66e941"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score is : 0.9777023751817742\n",
            "\n",
            "F1 Score is : 0.9795233100775971\n"
          ]
        }
      ],
      "source": [
        "rftuned = RandomForestClassifier(ccp_alpha = 0.0001,max_features = None, class_weight = 'balanced', random_state=1)\n",
        "rftuned.fit(X_train, y_train)\n",
        "y_pred = rftuned.predict(X_test)\n",
        "accuracy = accuracy_score(y_test,y_pred)\n",
        "f1 = f1_score(y_test,y_pred,average='macro')\n",
        "print('Accuracy Score is : {}'.format(accuracy))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rftuned = RandomForestClassifier(ccp_alpha = 0.0001,criterion='entropy',max_features = None, class_weight = 'balanced', random_state=1)\n",
        "rftuned.fit(X_train, y_train)\n",
        "y_pred_test = rftuned.predict(X_test)\n",
        "y_pred_train = rftuned.predict(X_train)\n",
        "accuracy_test = accuracy_score(y_test,y_pred_test)\n",
        "accuracy_train = accuracy_score(y_train,y_pred_train)\n",
        "f1 = f1_score(y_test,y_pred_test,average='macro')\n",
        "print('Test Accuracy Score is : {}'.format(accuracy_test))\n",
        "print('Train Accuracy Score is : {}'.format(accuracy_train))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4do3PrG7PrW2",
        "outputId": "35ca6b5c-1e8e-4444-bc0d-1cce88abb360"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy Score is : 0.9781871061560834\n",
            "Train Accuracy Score is : 0.9999393976122659\n",
            "\n",
            "F1 Score is : 0.979284480799117\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cyvakTFX93C7"
      },
      "source": [
        "this is the tuned random forest hyperparemeters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-HdwFaK-ULI"
      },
      "source": [
        "Bagging Classifier Hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "odHMANIL-k4H"
      },
      "outputs": [],
      "source": [
        "param_grid = {\n",
        "    'base_estimator': [DecisionTreeClassifier()],\n",
        "    'n_estimators': [34],\n",
        "    # 'max_samples': [0.5, 0.7, 0.9],\n",
        "    # 'max_features': [0.5, 0.7, 0.9],\n",
        "    # 'bootstrap': [True, False],\n",
        "    # 'bootstrap_features': [True, False],\n",
        "    'n_jobs': [-1]\n",
        "}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CcQoQ5ps-YBd"
      },
      "outputs": [],
      "source": [
        "crf = BaggingClassifier(random_state=1)\n",
        "grid_search = GridSearchCV(estimator=crf, param_grid=param_grid, cv=skf,verbose=2)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best hyperparameters:\", grid_search.best_params_)\n",
        "y_pred = grid_search.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test,y_pred)\n",
        "f1 = f1_score(y_test,y_pred,average='macro')\n",
        "print('Accuracy Score is : {}'.format(accuracy))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bgctuned = BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=1,class_weight=\"balanced\"),n_estimators=33,n_jobs=-1,random_state=1)\n",
        "bgctuned.fit(X_train, y_train)\n",
        "y_pred_test = bgctuned.predict(X_test)\n",
        "y_pred_train = bgctuned.predict(X_train)\n",
        "accuracy_test = accuracy_score(y_test,y_pred_test)\n",
        "accuracy_train = accuracy_score(y_train,y_pred_train)\n",
        "f1 = f1_score(y_test,y_pred_test,average='macro')\n",
        "print('Test Accuracy Score is : {}'.format(accuracy_test))\n",
        "print('Train Accuracy Score is : {}'.format(accuracy_train))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cr2Ff-26Rxo5",
        "outputId": "f391a983-95ad-4a11-e827-2d51cff20959"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy Score is : 0.9760058167716917\n",
            "Train Accuracy Score is : 0.9998181928367977\n",
            "\n",
            "F1 Score is : 0.978489723761314\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bgctuned = AdaBoostClassifier(estimator=DecisionTreeClassifier(random_state=1,class_weight=\"balanced\"),learning_rate=0.000001,random_state=1)\n",
        "bgctuned.fit(X_train, y_train)\n",
        "y_pred_test = bgctuned.predict(X_test)\n",
        "y_pred_train = bgctuned.predict(X_train)\n",
        "accuracy_test = accuracy_score(y_test,y_pred_test)\n",
        "accuracy_train = accuracy_score(y_train,y_pred_train)\n",
        "f1 = f1_score(y_test,y_pred_test,average='macro')\n",
        "print('Test Accuracy Score is : {}'.format(accuracy_test))\n",
        "print('Train Accuracy Score is : {}'.format(accuracy_train))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3UO_Cw7WgOC",
        "outputId": "c0ebea0f-3410-41e9-be20-12274605d6e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy Score is : 0.9602520601066408\n",
            "Train Accuracy Score is : 1.0\n",
            "\n",
            "F1 Score is : 0.964303940511169\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "fine tuned bagging model"
      ],
      "metadata": {
        "id": "VFfdeBa0NOlP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fine tuning XGBoost now"
      ],
      "metadata": {
        "id": "0ZcKcdvVNfIo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sZdttFMaMz_Y"
      },
      "outputs": [],
      "source": [
        "model_dict['XGBoost'].get_params()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    'scale_pos_weight':[1],\n",
        "    'n_estimators':[350],\n",
        "    'n_jobs':[-1],\n",
        "    'max_delta_step':[1],\n",
        "    'gamma': [0.025]\n",
        "}"
      ],
      "metadata": {
        "id": "ZAkvn6XtNiHS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Best hyperparameters: {'gamma': 0, 'max_delta_step': 1, 'n_estimators': 150, 'n_jobs': -1, 'scale_pos_weight': 1}"
      ],
      "metadata": {
        "id": "ZqIZg3svcV6j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crf = XGBClassifier(random_state = 1)\n",
        "grid_search = GridSearchCV(estimator=crf, param_grid=param_grid, cv=skf,verbose=2)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best hyperparameters:\", grid_search.best_params_)\n",
        "y_pred = grid_search.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test,y_pred)\n",
        "f1 = f1_score(y_test,y_pred,average='macro')\n",
        "print('Accuracy Score is : {}'.format(accuracy))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "id": "2sdllcEkO-sA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
        "#               colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
        "#               importance_type='gain', interaction_constraints='',\n",
        "#               learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
        "#               min_child_weight=1, missing=nan, monotone_constraints='()',\n",
        "#               n_estimators=100, n_jobs=12, num_parallel_tree=1,\n",
        "#               objective='multi:softprob', random_state=0, reg_alpha=0,\n",
        "#               reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
        "#               tree_method='exact', use_label_encoder=False,\n",
        "#               validate_parameters=1, verbosity=None)"
      ],
      "metadata": {
        "id": "M-bR9mxFbS0S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgbtuned = XGBClassifier(random_state=1,n_estimators=160,objective='multi:softprob',max_depth=4,learning_rate=0.20,n_jobs=-1)\n",
        "xgbtuned.fit(X_train, y_train)\n",
        "y_pred_test = xgbtuned.predict(X_test)\n",
        "y_pred_train = xgbtuned.predict(X_train)\n",
        "accuracy_test = accuracy_score(y_test,y_pred_test)\n",
        "accuracy_train = accuracy_score(y_train,y_pred_train)\n",
        "f1 = f1_score(y_test,y_pred_test,average='macro')\n",
        "print('Test Accuracy Score is : {}'.format(accuracy_test))\n",
        "print('Train Accuracy Score is : {}'.format(accuracy_train))\n",
        "print()\n",
        "print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qE1V1yAyY3Dt",
        "outputId": "1bb129bd-8bb5-4d2a-e08c-793ba046a60e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13:09:52] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
            "Test Accuracy Score is : 0.9873969946679593\n",
            "Train Accuracy Score is : 0.9987273498575844\n",
            "\n",
            "F1 Score is : 0.9874616054448246\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# (random_state=1,n_estimators=160,objective='multi:softprob',max_depth=4,learning_rate=0.20)\n",
        "\n",
        "# Test Accuracy Score is : 0.9873969946679593\n",
        "# Train Accuracy Score is : 0.9987273498575844\n",
        "\n",
        "# F1 Score is : 0.9874616054448246"
      ],
      "metadata": {
        "id": "kLD_WYeoc1mc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Best hyperparameters: {'gamma': 0.01, 'max_delta_step': 1, 'n_estimators': 170, 'n_jobs': -1, 'scale_pos_weight': 1}\n",
        "# Accuracy Score is : 0.9932137663596704\n",
        "\n",
        "# F1 Score is : 0.9919786671240878\n",
        "\n",
        "# Best hyperparameters: {'gamma': 0.025, 'max_delta_step': 1, 'n_estimators': 350, 'n_jobs': -1, 'scale_pos_weight': 1}\n",
        "# Accuracy Score is : 0.9932137663596704\n",
        "\n",
        "# F1 Score is : 0.9919786671240878\n",
        "\n",
        "# Best hyperparameters: {'gamma': 0.02, 'max_delta_step': 1, 'n_estimators': 250, 'n_jobs': -1, 'scale_pos_weight': 1}\n",
        "# Accuracy Score is : 0.993456131846825\n",
        "\n",
        "# F1 Score is : 0.9922375358009378"
      ],
      "metadata": {
        "id": "aE1UHkhXd2hd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model_dict['Cat'].get_all_params()"
      ],
      "metadata": {
        "id": "djB96jsld9eD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# param_grid = {\n",
        "#     'auto_class_weights': ['Balanced'],\n",
        "#     'l2_leaf_reg': [7],\n",
        "#     'eval_metric': ['TotalF1'],\n",
        "#     'iterations' :[3000],\n",
        "#     'verbose' :[False]\n",
        "\n",
        "# }"
      ],
      "metadata": {
        "id": "X47Q0U6IezjB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# crf = CatBoostClassifier(random_state = 1)\n",
        "# grid_search = GridSearchCV(estimator=crf, param_grid=param_grid, cv=skf,verbose=2)\n",
        "# # grid_search.fit(X_train, y_train)\n",
        "\n",
        "# print(\"Best hyperparameters:\", grid_search.best_params_)\n",
        "# y_pred = grid_search.predict(X_test)\n",
        "\n",
        "# accuracy = accuracy_score(y_test,y_pred)\n",
        "# f1 = f1_score(y_test,y_pred,average='macro')\n",
        "# print('Accuracy Score is : {}'.format(accuracy))\n",
        "# print()\n",
        "# print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "id": "DPL0eTC-eJU5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Best hyperparameters: {'auto_class_weights': 'Balanced', 'eval_metric': 'TotalF1', 'iterations': 1500, 'l2_leaf_reg': 6, 'verbose': False}\n",
        "Accuracy Score is : 0.9946679592825982\n",
        "\n",
        "F1 Score is : 0.9926026992944618\n",
        "\n",
        "\n",
        "Best hyperparameters: {'auto_class_weights': 'Balanced', 'eval_metric': 'TotalF1', 'iterations': 2000, 'l2_leaf_reg': 6, 'verbose': False}\n",
        "Accuracy Score is : 0.9951526902569074\n",
        "\n",
        "F1 Score is : 0.9940375965548187"
      ],
      "metadata": {
        "id": "9PQcqU7amXer"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# RandomForestClassifier(ccp_alpha = 0.0001,max_features = None, class_weight = 'balanced', random_state=1)\n",
        "# BaggingClassifier(base_estimator = DecisionTreeClassifier(),n_estimators = 34, n_jobs=-1, random_state=1)\n",
        "# XGBClassifier(gamma = 0.02, max_delta_step = 1,n_estimators = 250, n_jobs=-1, scale_pos_weight=1 ) Accuracy Score is : 0.993456131846825 F1 Score is : 0.9922375358009378\n",
        "# CatBoostClassifier(auto_class_weights='Balanced', eval_metric='TotalF1', iterations=2000, l2_leaf_reg=6, verbose=False, random_state =1)"
      ],
      "metadata": {
        "id": "QAjH8vWnFWSJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "FINE TUNED MODEL PERFORMANCE EVALUATION"
      ],
      "metadata": {
        "id": "kCiiFA6ZpqS6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model_dict_1 = {}\n",
        "\n",
        "# model_dict_1['Logistic Regression'] = LogisticRegression()\n",
        "# model_dict_1['Decision Tree'] = DecisionTreeClassifier(random_state = 1)\n",
        "# # model_dict['Extra Trees'] = ExtraTreesClassifier(random_state = 1)\n",
        "# model_dict_1['Random Forest'] = RandomForestClassifier(ccp_alpha = 0.0001,max_features = None, class_weight = 'balanced', random_state=1)\n",
        "# # model_dict['AdaBoost'] = AdaBoostClassifier(random_state = 1)\n",
        "# model_dict_1['Bagging'] = BaggingClassifier(base_estimator = DecisionTreeClassifier(),n_estimators = 34, n_jobs=-1, random_state=1)\n",
        "# model_dict_1['XGBoost'] = XGBClassifier(gamma = 0.02, max_delta_step = 1,n_estimators = 250, n_jobs=-1, scale_pos_weight=1)\n",
        "# # model_dict['LGBM'] = LGBMClassifier(random_state = 1)\n",
        "# model_dict_1['Cat'] = CatBoostClassifier(auto_class_weights='Balanced', eval_metric='TotalF1', iterations=2000, l2_leaf_reg=6, verbose=False, random_state =1)"
      ],
      "metadata": {
        "id": "H_iN3zBPpum9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_dict_1 = {}\n",
        "\n",
        "model_dict_1['Logistic Regression'] = LogisticRegression(random_state=1,max_iter=78,multi_class='multinomial',penalty='l2')\n",
        "model_dict_1['Decision Tree'] = DecisionTreeClassifier(min_samples_leaf = 2 ,min_samples_split = 7,max_features = None,random_state=1,class_weight=\"balanced\",ccp_alpha=0.00015,criterion=\"gini\")\n",
        "model_dict_1['SVM'] = SVC(random_state=1,C=0.99,probability=True)\n",
        "# model_dict['Extra Trees'] = ExtraTreesClassifier(random_state = 1)\n",
        "model_dict_1['Random Forest'] = RandomForestClassifier(ccp_alpha = 0.0001,criterion='entropy',max_features = None, class_weight = 'balanced', random_state=1)\n",
        "model_dict_1['AdaBoost'] = AdaBoostClassifier(estimator=DecisionTreeClassifier(random_state=1,class_weight=\"balanced\"),learning_rate=0.000001,random_state=1)\n",
        "model_dict_1['Bagging'] = BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=1,class_weight=\"balanced\"),n_estimators=33,n_jobs=-1,random_state=1)\n",
        "model_dict_1['XGBoost'] = XGBClassifier(random_state=1,n_estimators=160,objective='multi:softprob',max_depth=4,learning_rate=0.20,n_jobs=-1)"
      ],
      "metadata": {
        "id": "xbhFZ3kzo_9K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model_test(X_train, X_test, y_train, y_test,model,model_name):\n",
        "    model.fit(X_train,y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test,y_pred)\n",
        "    f1 = f1_score(y_test,y_pred,average='macro')\n",
        "    print('======================================{}======================================='.format(model_name))\n",
        "    print('Accuracy Score is : {}'.format(accuracy))\n",
        "    print()\n",
        "    print('F1 Score is : {}'.format(f1))"
      ],
      "metadata": {
        "id": "LtekAC5cqobM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for model_name,model in model_dict_1.items():\n",
        "    model_test(X_train, X_test, y_train, y_test, model, model_name)"
      ],
      "metadata": {
        "id": "k5AwhPapqq9F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8dac881-5149-40f3-9333-19804af11301"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "======================================Logistic Regression=======================================\n",
            "Accuracy Score is : 0.8005332040717402\n",
            "\n",
            "F1 Score is : 0.3560545520492499\n",
            "======================================Decision Tree=======================================\n",
            "Accuracy Score is : 0.9590402326708677\n",
            "\n",
            "F1 Score is : 0.9625363504512727\n",
            "======================================SVM=======================================\n",
            "Accuracy Score is : 0.644449830344159\n",
            "\n",
            "F1 Score is : 0.24875411616336743\n",
            "======================================Random Forest=======================================\n",
            "Accuracy Score is : 0.9781871061560834\n",
            "\n",
            "F1 Score is : 0.979284480799117\n",
            "======================================AdaBoost=======================================\n",
            "Accuracy Score is : 0.9602520601066408\n",
            "\n",
            "F1 Score is : 0.964303940511169\n",
            "======================================Bagging=======================================\n",
            "Accuracy Score is : 0.9760058167716917\n",
            "\n",
            "F1 Score is : 0.978489723761314\n",
            "[13:38:31] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
            "======================================XGBoost=======================================\n",
            "Accuracy Score is : 0.9873969946679593\n",
            "\n",
            "F1 Score is : 0.9874616054448246\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "skf = StratifiedKFold(n_splits=5,shuffle=True, random_state=1)\n",
        "\n",
        "for model_name,model in model_dict_1.items():\n",
        "  print(model_name,\" Stratified Cross Val Accuracy\",sum(cross_val_score(model,X_train,y_train,cv=skf,n_jobs=-1,scoring='accuracy'))/5)\n",
        "print()\n",
        "print(\"-----------------------------------------------------------------------------------------------------------------------------------------------\")\n",
        "print()\n",
        "for model_name,model in model_dict_1.items():\n",
        "  print(model_name,\" Stratified Cross Val F1\",sum(cross_val_score(model,X_train,y_train,cv=skf,n_jobs=-1,scoring='f1_macro')/5))"
      ],
      "metadata": {
        "id": "ldgZdPekqyqP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42decaa8-cde4-439e-adcb-44d52438643d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression  Stratified Cross Val Accuracy 0.7945587287598799\n",
            "Decision Tree  Stratified Cross Val Accuracy 0.9516999440022765\n",
            "SVM  Stratified Cross Val Accuracy 0.6457796260086475\n",
            "Random Forest  Stratified Cross Val Accuracy 0.9752133880458629\n",
            "AdaBoost  Stratified Cross Val Accuracy 0.9505484288507615\n",
            "Bagging  Stratified Cross Val Accuracy 0.9715772447284111\n",
            "XGBoost  Stratified Cross Val Accuracy 0.9850917720066462\n",
            "\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------\n",
            "\n",
            "Logistic Regression  Stratified Cross Val F1 0.3506750314380052\n",
            "Decision Tree  Stratified Cross Val F1 0.9559261729757529\n",
            "SVM  Stratified Cross Val F1 0.25099940131556375\n",
            "Random Forest  Stratified Cross Val F1 0.9759041230178913\n",
            "AdaBoost  Stratified Cross Val F1 0.9546995746328948\n",
            "Bagging  Stratified Cross Val F1 0.9730956324925933\n",
            "XGBoost  Stratified Cross Val F1 0.9845551304435213\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_estimators = []\n",
        "for key,val in model_dict_1.items():\n",
        "  tuned_estimators.append((key,val))\n",
        "tuned_estimators"
      ],
      "metadata": {
        "id": "PY6yr3Ju1wsR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d8555be-580e-47d8-b511-239489554473"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Logistic Regression',\n",
              "  LogisticRegression(max_iter=78, multi_class='multinomial', random_state=1)),\n",
              " ('Decision Tree',\n",
              "  DecisionTreeClassifier(ccp_alpha=0.00015, class_weight='balanced',\n",
              "                         min_samples_leaf=2, min_samples_split=7, random_state=1)),\n",
              " ('SVM', SVC(C=0.99, probability=True, random_state=1)),\n",
              " ('Random Forest',\n",
              "  RandomForestClassifier(ccp_alpha=0.0001, class_weight='balanced',\n",
              "                         criterion='entropy', max_features=None, random_state=1)),\n",
              " ('AdaBoost',\n",
              "  AdaBoostClassifier(estimator=DecisionTreeClassifier(class_weight='balanced',\n",
              "                                                      random_state=1),\n",
              "                     learning_rate=1e-06, random_state=1)),\n",
              " ('Bagging',\n",
              "  BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight='balanced',\n",
              "                                                          random_state=1),\n",
              "                    n_estimators=33, n_jobs=-1, random_state=1)),\n",
              " ('XGBoost',\n",
              "  XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "                colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
              "                importance_type='gain', interaction_constraints='',\n",
              "                learning_rate=0.2, max_delta_step=0, max_depth=4,\n",
              "                min_child_weight=1, missing=nan, monotone_constraints='()',\n",
              "                n_estimators=160, n_jobs=-1, num_parallel_tree=1,\n",
              "                objective='multi:softprob', random_state=1, reg_alpha=0,\n",
              "                reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
              "                tree_method='exact', validate_parameters=1, verbosity=None))]"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_estimators.pop(0)\n",
        "tuned_estimators.pop(0)\n",
        "tuned_estimators.pop(0)\n",
        "tuned_estimators.pop(1)"
      ],
      "metadata": {
        "id": "V0kZZhhW-L80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f6badf6-c291-4a2f-9e49-e4554c4d6e1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('AdaBoost',\n",
              " AdaBoostClassifier(estimator=DecisionTreeClassifier(class_weight='balanced',\n",
              "                                                     random_state=1),\n",
              "                    learning_rate=1e-06, random_state=1))"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_estimators"
      ],
      "metadata": {
        "id": "Sky8JuJk-UkN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03853ba4-a1f8-46ff-f663-f13b358d5006"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Random Forest',\n",
              "  RandomForestClassifier(ccp_alpha=0.0001, class_weight='balanced',\n",
              "                         criterion='entropy', max_features=None, random_state=1)),\n",
              " ('Bagging',\n",
              "  BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight='balanced',\n",
              "                                                          random_state=1),\n",
              "                    n_estimators=33, n_jobs=-1, random_state=1)),\n",
              " ('XGBoost',\n",
              "  XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "                colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
              "                importance_type='gain', interaction_constraints='',\n",
              "                learning_rate=0.2, max_delta_step=0, max_depth=4,\n",
              "                min_child_weight=1, missing=nan, monotone_constraints='()',\n",
              "                n_estimators=160, n_jobs=-1, num_parallel_tree=1,\n",
              "                objective='multi:softprob', random_state=1, reg_alpha=0,\n",
              "                reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
              "                tree_method='exact', validate_parameters=1, verbosity=None))]"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, est in enumerate(tuned_estimators):\n",
        "    y_pred = est[1].predict(X_test)\n",
        "    print(f\"Estimator {i}: {est[0]}, output shape: {y_pred.shape}\")\n"
      ],
      "metadata": {
        "id": "bAqh0ezp5Xfn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.base import BaseEstimator, ClassifierMixin\n",
        "\n",
        "class ReshapePredictions(BaseEstimator, ClassifierMixin):\n",
        "    def __init__(self, estimator):\n",
        "        self.estimator = estimator\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        self.estimator.fit(X, y)\n",
        "        return self\n",
        "\n",
        "    def predict(self, X):\n",
        "        y_pred = self.estimator.predict(X)\n",
        "        return y_pred.ravel()\n",
        "\n",
        "tuned_estimators[2] = ('estimator_name', ReshapePredictions(tuned_estimators[2][1]))"
      ],
      "metadata": {
        "id": "g0g8Ezq68Tkg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# tuned_estimators.pop()\n",
        "# tuned_estimators\n",
        "voter = VotingClassifier(estimators=tuned_estimators,voting ='soft')\n",
        "voter.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred_tuned = voter.predict(X_test)\n",
        "\n",
        "# Evaluate the accuracy of the model\n",
        "accuracy = voter.score(X_test, y_test)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "f1 = f1_score(y_test, y_pred_tuned,average=\"weighted\")\n",
        "print(\"F1:\", f1)"
      ],
      "metadata": {
        "id": "m3vG7XMVq38O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5ebc4ae-6c46-4584-bd6d-0ff84a72d408"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13:42:15] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
            "Accuracy: 0.9835191468734852\n",
            "F1: 0.983278399675893\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred_tuned))"
      ],
      "metadata": {
        "id": "U2yyCePIz12_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a7024be-96f6-4b15-c83c-1a4c5761537d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.90      0.95       491\n",
            "           1       0.98      0.99      0.99      2283\n",
            "           2       0.99      1.00      1.00       102\n",
            "           3       0.98      1.00      0.99      1204\n",
            "           4       1.00      1.00      1.00        46\n",
            "\n",
            "    accuracy                           0.98      4126\n",
            "   macro avg       0.99      0.98      0.98      4126\n",
            "weighted avg       0.98      0.98      0.98      4126\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##SMOTE"
      ],
      "metadata": {
        "id": "c3Zw04M1v-1j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "smote = SMOTE(random_state=1)\n",
        "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n",
        "\n",
        "voter.fit(X_train_resampled, y_train_resampled)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred_tuned = voter.predict(X_test)\n",
        "\n",
        "# Evaluate the accuracy of the model\n",
        "accuracy = voter.score(X_test, y_test)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "f1 = f1_score(y_test, y_pred_tuned,average=\"weighted\")\n",
        "print(\"F1:\", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbTxXmgZwAUE",
        "outputId": "35979163-f17e-41e3-f66c-d203b0aaeb59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13:47:05] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
            "Accuracy: 0.9832767813863306\n",
            "F1: 0.9830849559577659\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for model_name,model in model_dict_1.items():\n",
        "    model_test(X_train_resampled, X_test, y_train_resampled, y_test, model, model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qalklMwuxkJs",
        "outputId": "b96ce819-3ec8-4874-b32e-c7f4dd083792"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "======================================Logistic Regression=======================================\n",
            "Accuracy Score is : 0.6366941347552109\n",
            "\n",
            "F1 Score is : 0.3243829194595377\n",
            "======================================Decision Tree=======================================\n",
            "Accuracy Score is : 0.9602520601066408\n",
            "\n",
            "F1 Score is : 0.965363715821917\n",
            "======================================SVM=======================================\n",
            "Accuracy Score is : 0.6257876878332526\n",
            "\n",
            "F1 Score is : 0.311772824645417\n",
            "======================================Random Forest=======================================\n",
            "Accuracy Score is : 0.9796412990790111\n",
            "\n",
            "F1 Score is : 0.9803947938003692\n",
            "======================================AdaBoost=======================================\n",
            "Accuracy Score is : 0.9515269025690741\n",
            "\n",
            "F1 Score is : 0.9566039766674038\n",
            "======================================Bagging=======================================\n",
            "Accuracy Score is : 0.9760058167716917\n",
            "\n",
            "F1 Score is : 0.978823475865773\n",
            "[14:13:30] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
            "======================================XGBoost=======================================\n",
            "Accuracy Score is : 0.9844886088221038\n",
            "\n",
            "F1 Score is : 0.9836226291373545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred_tuned))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SP4smuenwy_o",
        "outputId": "66978f3d-b2a8-477e-ab26-ef3603be9996"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.91      0.95       491\n",
            "           1       0.98      0.99      0.99      2283\n",
            "           2       0.98      1.00      0.99       102\n",
            "           3       0.98      1.00      0.99      1204\n",
            "           4       1.00      1.00      1.00        46\n",
            "\n",
            "    accuracy                           0.98      4126\n",
            "   macro avg       0.99      0.98      0.98      4126\n",
            "weighted avg       0.98      0.98      0.98      4126\n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1_t4Kr2FCRduXo8CLBbFBHDL44aWxErXq",
      "authorship_tag": "ABX9TyOIJc2HALO/Hf/kJr+redEn",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}