STEG_Model_Selection.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/AbdulrahmanElshafie/EDA-Project-on-STEG-Dataset/blob/main/STEG_Model_Selection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
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      "execution_count": null,
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        "id": "kN29bkZB-rxy",
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        "outputId": "1a66eb0e-8f3a-46bf-df57-545e261e4672"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-1.2.3-cp310-cp310-manylinux2014_x86_64.whl (98.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.5/98.5 MB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.25.2)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.4)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (24.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.2)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.3)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.2.3\n"
          ]
        }
      ],
      "source": [
        "pip install catboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "623SUr4h9pa_"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib as mlp\n",
        "import matplotlib.pyplot as plt\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier, AdaBoostClassifier, VotingClassifier\n",
        "from sklearn.ensemble import HistGradientBoostingClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from catboost import CatBoostClassifier\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import precision_score, recall_score, accuracy_score\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "mlp.rcParams[\"figure.facecolor\"] = \"white\"\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "e0W389OIjITf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRnCThbSmhsl",
        "outputId": "70e779ce-eb31-49ef-a6be-810a7d7199f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hQEw59tCZZ3o"
      },
      "outputs": [],
      "source": [
        "# test_set = pd.read_csv('/content/drive/MyDrive/Selected Project1/Copy of test set.csv')\n",
        "# train_set = pd.read_csv('/content/drive/MyDrive/Selected Project1/Copy of training set.csv')\n",
        "test_set = pd.read_csv('/content/drive/MyDrive/GP Projects/Project01 - EDA/test set.csv').dropna()\n",
        "training_set = pd.read_csv('/content/drive/MyDrive/GP Projects/Project01 - EDA/training set.csv').dropna()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_set.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYtPMZ-rKh5Z",
        "outputId": "bea98392-7a28-45ee-a15c-3e289f023a25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2120068, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95j6H5U2Kj6W",
        "outputId": "5567608e-ae8c-4195-da09-f70dc48aeef6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(530020, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = training_set\n",
        "x_train = x_train.drop(columns=['target'])\n",
        "\n",
        "x_test = test_set\n",
        "x_test = x_test.drop(columns=['target'])\n",
        "\n",
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kxz3m5qmZjPx",
        "outputId": "5558ae43-8877-40a2-dd29-33c358231184"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2120068, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = training_set['target']\n",
        "y_test = test_set['target']\n",
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fg2Z-f9LaAuu",
        "outputId": "8faf4952-8d83-4654-8b78-d03a5ed98d5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          0.0\n",
              "1          1.0\n",
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              "          ... \n",
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          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gradient Boosting Classifier"
      ],
      "metadata": {
        "id": "ycxbBq1eEPZD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HGB_clf = GradientBoostingClassifier()\n",
        "HGB_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "516d6de2-74d2-45af-a064-7c44c6e04a0b",
        "id": "0-pNbYgbENHK"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_HGB = HGB_clf.predict(x_test)\n",
        "\n",
        "# Calculate precision and recall\n",
        "precision_HGB = precision_score(y_test, y_pred_HGB)\n",
        "recall_HGB = recall_score(y_test, y_pred_HGB)\n",
        "\n",
        "print(precision_HGB)\n",
        "print(recall_HGB)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d366c305-3a51-4f40-8277-abde4bc4b980",
        "id": "JHCVNZG1ENHS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7913018785670577\n",
            "0.7451696734586268\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HGB_clf.score(x_test, y_test )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "255eeb8b-6955-451d-edf8-6050b4cc3cd0",
        "id": "DPan8SoQENHS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7738934379834723"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Tree Classifier"
      ],
      "metadata": {
        "id": "xO4xYyukEUxh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HGB_clf = DecisionTreeClassifier()\n",
        "HGB_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "SEMmyqHt33qS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "cc07122c-8999-4362-c4e4-5ae017275847"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "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()</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()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_HGB = HGB_clf.predict(x_test)\n",
        "\n",
        "# Calculate precision and recall\n",
        "precision_HGB = precision_score(y_test, y_pred_HGB)\n",
        "recall_HGB = recall_score(y_test, y_pred_HGB)\n",
        "\n",
        "print(precision_HGB)\n",
        "print(recall_HGB)"
      ],
      "metadata": {
        "id": "eb_WLUsG6nF9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e75d791c-4f17-4c99-92e2-febecfb9bdb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8497446488922268\n",
            "0.8522729840683967\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HGB_clf.score(x_test, y_test )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sed-VNo5_iSj",
        "outputId": "f5d8eb77-279b-42c6-e7d0-97944d8195a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.850503754575299"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression"
      ],
      "metadata": {
        "id": "vV3vaMdYEbT8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HGB_clf = LogisticRegression()\n",
        "HGB_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "6cdf8efc-a89b-4021-a159-327f149d90d9",
        "id": "DPkLyY1RDHXg"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_HGB = HGB_clf.predict(x_test)\n",
        "\n",
        "# Calculate precision and recall\n",
        "precision_HGB = precision_score(y_test, y_pred_HGB)\n",
        "recall_HGB = recall_score(y_test, y_pred_HGB)\n",
        "\n",
        "print(precision_HGB)\n",
        "print(recall_HGB)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db4b7bd6-f8a3-4b46-cfb6-21697bd478f7",
        "id": "F9bViUuLDHXh"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5295771288992632\n",
            "0.5519980415050281\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HGB_clf.score(x_test, y_test )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f760f4d6-886b-44cd-d443-cde6b449acb4",
        "id": "MeT-noTWDHXh"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5299441530508283"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cat Boost Classifier"
      ],
      "metadata": {
        "id": "nhBSJBYiEfHx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HGB_clf = CatBoostClassifier()\n",
        "HGB_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7505d24-0afd-4e7e-b008-151b7e74900c",
        "id": "l62qGPLQDHuJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate set to 0.271197\n",
            "0:\tlearn: 0.6124819\ttotal: 1.19s\tremaining: 19m 47s\n",
            "1:\tlearn: 0.5717774\ttotal: 3.05s\tremaining: 25m 21s\n",
            "2:\tlearn: 0.5500209\ttotal: 5.45s\tremaining: 30m 11s\n",
            "3:\tlearn: 0.5386812\ttotal: 7.01s\tremaining: 29m 4s\n",
            "4:\tlearn: 0.5319343\ttotal: 8.77s\tremaining: 29m 6s\n",
            "5:\tlearn: 0.5279976\ttotal: 10s\tremaining: 27m 37s\n",
            "6:\tlearn: 0.5257139\ttotal: 11.7s\tremaining: 27m 39s\n",
            "7:\tlearn: 0.5238181\ttotal: 13.2s\tremaining: 27m 10s\n",
            "8:\tlearn: 0.5229542\ttotal: 14.6s\tremaining: 26m 47s\n",
            "9:\tlearn: 0.5221882\ttotal: 16.1s\tremaining: 26m 31s\n",
            "10:\tlearn: 0.5216227\ttotal: 17.7s\tremaining: 26m 30s\n",
            "11:\tlearn: 0.5211951\ttotal: 20.4s\tremaining: 28m\n",
            "12:\tlearn: 0.5207563\ttotal: 22s\tremaining: 27m 49s\n",
            "13:\tlearn: 0.5203914\ttotal: 23.8s\tremaining: 27m 54s\n",
            "14:\tlearn: 0.5201620\ttotal: 25.2s\tremaining: 27m 37s\n",
            "15:\tlearn: 0.5197135\ttotal: 26.9s\tremaining: 27m 36s\n",
            "16:\tlearn: 0.5192718\ttotal: 28.6s\tremaining: 27m 33s\n",
            "17:\tlearn: 0.5190183\ttotal: 30.1s\tremaining: 27m 20s\n",
            "18:\tlearn: 0.5186916\ttotal: 31.9s\tremaining: 27m 29s\n",
            "19:\tlearn: 0.5185518\ttotal: 33.4s\tremaining: 27m 18s\n",
            "20:\tlearn: 0.5183353\ttotal: 35.7s\tremaining: 27m 45s\n",
            "21:\tlearn: 0.5180130\ttotal: 38s\tremaining: 28m 8s\n",
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            "28:\tlearn: 0.5166002\ttotal: 46.3s\tremaining: 25m 50s\n",
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            "999:\tlearn: 0.4665830\ttotal: 15m 45s\tremaining: 0us\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x786b20419a80>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_HGB = HGB_clf.predict(x_test)\n",
        "\n",
        "# Calculate precision and recall\n",
        "precision_HGB = precision_score(y_test, y_pred_HGB)\n",
        "recall_HGB = recall_score(y_test, y_pred_HGB)\n",
        "\n",
        "print(precision_HGB)\n",
        "print(recall_HGB)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e30af0ea-211e-43e1-fd6c-7ebd75c87bd8",
        "id": "j2g4dvsPDHuJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7983233900516028\n",
            "0.7836955293585929\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HGB_clf.score(x_test, y_test )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adabf454-9750-44b1-ab5a-8d4c510c235a",
        "id": "k4bZEEK6DHuJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.792466322025584"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ada Boost Classifier"
      ],
      "metadata": {
        "id": "IBnQBhWiEyq6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HGB_clf = AdaBoostClassifier()\n",
        "HGB_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "9a60b924-1f34-4123-fa24-d6731176dd33",
        "id": "AONH_4BaLrjB"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier()</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_HGB = HGB_clf.predict(x_test)\n",
        "\n",
        "# Calculate precision and recall\n",
        "precision_HGB = precision_score(y_test, y_pred_HGB)\n",
        "recall_HGB = recall_score(y_test, y_pred_HGB)\n",
        "\n",
        "print(precision_HGB)\n",
        "print(recall_HGB)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d79c51d-b92b-42f7-fc8c-c5f45a3f0f03",
        "id": "CSWXQ99mLrjI"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7920454453629407\n",
            "0.7383375390757411\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HGB_clf.score(x_test, y_test )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f79abb86-702c-4b21-81b8-049c4408a8eb",
        "id": "0RadKWvbLrjI"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7718123844383231"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TZ2MkK_8DOj9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}