{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "importing all necessary libraries and the dataset"
      ],
      "metadata": {
        "id": "uCCi_sxuj6LA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "w3PM3P2WaPnH"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "cancer_dataset = load_breast_cancer()\n",
        "X = cancer_dataset.data\n",
        "y = cancer_dataset.target\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BaggingClassifier"
      ],
      "metadata": {
        "id": "g_OJ1OT4hzJg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = DecisionTreeClassifier(max_depth=5)\n",
        "\n",
        "bagging_classifier_model = BaggingClassifier(base_model, max_samples=0.8, max_features=0.8)\n",
        "\n",
        "bagging_classifier_model.fit(X_train, y_train)\n",
        "\n",
        "accuracy = bagging_classifier_model.score(X_test, y_test)\n",
        "print(\"Accuracy: \", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFxb1KwshxFB",
        "outputId": "f65e4d1d-ff70-4c0a-bf96-ca2094af049f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.9649122807017544\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BoostingClassifier"
      ],
      "metadata": {
        "id": "dTVe1-jLh74o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = DecisionTreeClassifier(max_depth=5)\n",
        "\n",
        "boosting_classifier_model = AdaBoostClassifier(base_model)\n",
        "\n",
        "boosting_classifier_model.fit(X_train, y_train)\n",
        "\n",
        "accuracy = boosting_classifier_model.score(X_test, y_test)\n",
        "print(\"Accuracy: \", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rasckig1ckCi",
        "outputId": "400ef180-a5d7-4739-87c1-7eca59cf7a1b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.9649122807017544\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# StackingClassifier"
      ],
      "metadata": {
        "id": "HwgqJ7D_h8p_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_model = LogisticRegression(max_iter=5000)\n",
        "decision_tree_model = DecisionTreeClassifier()\n",
        "svc_model = SVC()\n",
        "\n",
        "stack = StackingClassifier(\n",
        "    estimators=[('lr', logistic_model), ('dt', decision_tree_model), ('svc', svc_model)],\n",
        "    final_estimator=LogisticRegression(random_state=1)\n",
        ")\n",
        "\n",
        "stack.fit(X_train, y_train)\n",
        "\n",
        "accuracy = stack.score(X_test, y_test)\n",
        "print('Accuracy: %.2f' % accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74Sdd84xdJ7E",
        "outputId": "9629ef74-bcb1-46c4-dc7d-0a70f21f4ea1"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.96\n"
          ]
        }
      ]
    }
  ]
}