{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ZZzI3hGVHutJ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "from sklearn import datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading the Dataset:\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data\n",
        "Y = iris.target\n"
      ],
      "metadata": {
        "id": "Q7Ox6qxSHyVc"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualizing the Dataset:\n",
        "iris.target_names\n",
        "iris.feature_names\n",
        "iris.data[0:5]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQhaDQc6H1vy",
        "outputId": "b11bc3a3-bfcb-4279-c5bc-eaff4db16029"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.1, 3.5, 1.4, 0.2],\n",
              "       [4.9, 3. , 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.3, 0.2],\n",
              "       [4.6, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.6, 1.4, 0.2]])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting the Dataset:\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)"
      ],
      "metadata": {
        "id": "NhMqEiZdH45K"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Building the AdaBoost Model:\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "adb = AdaBoostClassifier()\n",
        "model = adb.fit(X_train, Y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KtRFVN1KH788",
        "outputId": "5be24e5e-abb8-4ead-d9d0-09cb6c496ed8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 0, 2, 0, 0, 0, 1, 1, 0, 0, 1, 2, 1, 0, 1, 1, 0, 2, 1, 1, 1, 1,\n",
              "       1, 0, 0, 1, 0, 2, 1, 0, 0, 0, 1, 1, 1, 2, 1, 1, 1, 2, 1, 0, 2, 0,\n",
              "       2])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluating the Model\n",
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
        "print(confusion_matrix(Y_test, y_pred))\n",
        "print(classification_report(Y_test, y_pred))\n",
        "print(accuracy_score(Y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qTKfesVH-7t",
        "outputId": "5a2524db-2654-4cda-c338-f378e1b83694"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[16  0  0]\n",
            " [ 0 19  1]\n",
            " [ 0  1  8]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        16\n",
            "           1       0.95      0.95      0.95        20\n",
            "           2       0.89      0.89      0.89         9\n",
            "\n",
            "    accuracy                           0.96        45\n",
            "   macro avg       0.95      0.95      0.95        45\n",
            "weighted avg       0.96      0.96      0.96        45\n",
            "\n",
            "0.9555555555555556\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"Using SVM as a base estimator\"\"\"\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "svc = SVC(probability = True, kernel='linear')\n",
        "adb = AdaBoostClassifier(n_estimators=100,base_estimator = svc, learning_rate= 0.01)"
      ],
      "metadata": {
        "id": "6dWkRlsnID2d"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Training and Predicting the AdaBoost Classifier\n",
        "model = adb.fit(X_train, Y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKAL9CRiIJDC",
        "outputId": "d2f2590c-fada-40e6-8528-c7cbe5f60883"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 0, 2, 0, 0, 0, 1, 2, 0, 0, 1, 2, 1, 0, 2, 1, 0, 2, 1, 1, 1, 1,\n",
              "       1, 0, 0, 1, 0, 2, 1, 0, 0, 0, 1, 1, 1, 2, 1, 1, 1, 2, 1, 0, 2, 0,\n",
              "       2])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluating the Model:\n",
        "print(confusion_matrix(Y_test, y_pred))\n",
        "print(classification_report(Y_test, y_pred))\n",
        "print(accuracy_score(Y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18Kfv9LXIN3H",
        "outputId": "10c7f8c6-1ab3-4763-ea59-ba293f7c17a2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[16  0  0]\n",
            " [ 0 18  2]\n",
            " [ 0  0  9]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        16\n",
            "           1       1.00      0.90      0.95        20\n",
            "           2       0.82      1.00      0.90         9\n",
            "\n",
            "    accuracy                           0.96        45\n",
            "   macro avg       0.94      0.97      0.95        45\n",
            "weighted avg       0.96      0.96      0.96        45\n",
            "\n",
            "0.9555555555555556\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jxe86GSrIRzh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}