Breast_Cancer_Prediction_using_Classification_Algorithms_.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMhXHkH7bSUBdxrrQqSZvPm",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/capricode-ui/AIML-Projects/blob/main/Breast_Cancer_Prediction_using_Classification_Algorithms_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "bcfxXohm3-hz"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Benign Tumor has class 2 and malignant has 4\n"
      ],
      "metadata": {
        "id": "OWQHx6-G8lI7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('Data (1).csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "lQvzQkXI4Jii"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "kIO63UNg4Lkl"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "Xk2yo4hB4Q8S"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "#decision tree classifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "KWQpp7zq4UKh",
        "outputId": "a76ce5e0-f771-4f07-d72d-2ea0066d98de"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 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-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 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-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 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-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 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-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=0)</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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "acc1=accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2hzPnmE4Wps",
        "outputId": "03c6bc41-e3c4-4b74-958e-650b72037bc3"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[103   4]\n",
            " [  3  61]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "classifier.fit(X_train, y_train)\n",
        "#KNN"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "wXBjx0SW4uZw",
        "outputId": "6c1f2147-08d7-43cb-abfb-00d6ef8e8de0"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 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-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 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-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 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-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 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-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</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-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "acc2=accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_6JYhEv4xB9",
        "outputId": "f4ae7b41-4ee0-4c15-e9d0-ca32ee96ea6d"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[103   4]\n",
            " [  5  59]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "r1MIFJen412s",
        "outputId": "b46123a3-81b2-4337-c767-9b63f15323da"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=0)</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-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "acc3=accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cZ8-onp4_N7",
        "outputId": "8d3e2ffd-d930-4233-e973-369c298f4430"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[102   5]\n",
            " [  3  61]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "#logistic regression"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "SMI5T0Mx56w9",
        "outputId": "e3c9c547-ab2f-4737-d588-b102b5a7aa42"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=0)</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-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "acc4=accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VZSgjJxL59gS",
        "outputId": "2ab86ca6-8713-4ea1-f398-240d448357c0"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[103   4]\n",
            " [  5  59]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(X_train, y_train)\n",
        "#naive bayes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "kTHGfFyX6DNn",
        "outputId": "2a8b717c-e0ed-4707-9c76-e77832e71d28"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 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-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 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-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 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-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 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-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "acc5=accuracy_score(y_test, y_pred)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MY9b0tZe6O9n",
        "outputId": "d1c00d67-93c6-4c46-9035-abcef424c41e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[99  8]\n",
            " [ 2 62]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "#RandomForestClassification"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "7spZDKox6aTb",
        "outputId": "dc4fe84e-a9c3-46d0-86b4-a9384a4f95d0"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 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-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 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-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 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-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 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-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=10, random_state=0)</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-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=10, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "acc6=accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "caYQGQY_6iip",
        "outputId": "52a762f1-4a40-4fb9-f52e-cc4b5c90c53f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[102   5]\n",
            " [  6  58]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'linear', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "#SVM"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "2paphsCG6q65",
        "outputId": "1bdc9bb0-9bc9-44d3-f567-d392877ead72"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear', random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 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-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 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-17 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-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 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-17 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-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 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-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 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-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 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-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</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-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "acc7=accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fu33etl86s9n",
        "outputId": "ca07631d-948f-4eb6-c999-1d44a1c4d213"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[102   5]\n",
            " [  5  59]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracies = [acc1, acc2, acc3, acc4, acc5, acc6,acc7]\n",
        "labels = ['Decision Tree', 'KNN', 'SVM', 'Logistic Regression', 'Naive Bayes', 'Random Forest', 'SVM (linear)']\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(labels, accuracies, color='blue', width=0.4)\n",
        "\n",
        "plt.xlabel(\"Classifiers\", fontsize=16)\n",
        "plt.ylabel(\"Accuracy\", fontsize=16)\n",
        "plt.title(\"Comparison of Classifier Accuracies\", fontsize=16)\n",
        "\n",
        "plt.xticks(rotation=45, fontsize=12)\n",
        "plt.yticks(fontsize=12)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "lQGNuMr2617r",
        "outputId": "a05217f3-c0cb-47b0-b545-d0959474dc08"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "OggdQS208itJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "0jmTTr1c8jwq"
      }
    }
  ]
}