LR,NB,KNN,DT,RF,SVM,XGB.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MasrukHabib/Practice_of_Machine_Learning/blob/main/LR%2CNB%2CKNN%2CDT%2CRF%2CSVM%2CXGB.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USsDNIDieiQ8"
      },
      "source": [
        "#Mount with Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hsz0nt2ESkHt"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pe3VO1xSen0J"
      },
      "source": [
        "#Loade the dataset and check the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "6pKkBKZiTiL9",
        "outputId": "944601cc-38ae-4fb4-8952-c62c923c5cb7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n",
              "0         1         1         0         0         1         1         0   \n",
              "1         1         1         0         0         1         1         0   \n",
              "2         1         1         0         0         0         1         1   \n",
              "3         0         1         0         0         1         1         0   \n",
              "4         1         1         1         1         1         1         1   \n",
              "\n",
              "   A8_Score  A9_Score  A10_Score  gender  jundice  austi1  result  Class/ASD  \n",
              "0         1         0          0       1        0       0       5          0  \n",
              "1         1         0          0       1        0       0       5          0  \n",
              "2         1         0          0       1        0       0       5          0  \n",
              "3         0         0          1       0        1       0       4          0  \n",
              "4         1         1          1       1        1       0      10          1  "
            ],
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        element.innerHTML = '';\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 292,\n  \"fields\": [\n    {\n      \"column\": \"A1_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A2_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A3_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A4_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A5_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A6_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": 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      \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A10_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"jundice\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"austi1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"result\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class/ASD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        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            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"/content/Autism_Child_Data.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nUNZa-dKAtL9"
      },
      "outputs": [],
      "source": [
        "# Define feature matrix X and target vector y\n",
        "X = df.drop('Class/ASD', axis=1)\n",
        "y = df['Class/ASD']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MQCGY9WQAs-E"
      },
      "outputs": [],
      "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=0.4, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EioGQ6eCBDwI",
        "outputId": "2c94e338-e586-47cc-eb6d-8b25bdff1087"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class Distribution:\n",
            "Class/ASD\n",
            "0    151\n",
            "1    141\n",
            "Name: count, dtype: int64\n",
            "The data is balanced.\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Calculate class distribution\n",
        "class_distribution = df['Class/ASD'].value_counts()\n",
        "\n",
        "# Print class distribution\n",
        "print(\"Class Distribution:\")\n",
        "print(class_distribution)\n",
        "\n",
        "# Check if data is balanced\n",
        "balanced = class_distribution.min() / class_distribution.max() >= 0.5\n",
        "# Print the result\n",
        "if balanced:\n",
        "    print(\"The data is balanced.\")\n",
        "else:\n",
        "    print(\"The data is imbalanced.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "DUOMvAw_AqEQ",
        "outputId": "0b4f9752-5db1-4b50-b13b-c3ef0cabdfac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plotting the balance of the target variable\n",
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "# Pie chart\n",
        "plt.subplot(1, 2, 2)\n",
        "y.value_counts().plot.pie(autopct='%1.1f%%', colors=['skyblue', 'lightgreen'])\n",
        "plt.title('Class Distribution')\n",
        "plt.ylabel('')  # Hide y-label for the pie chart\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using Logistic Regression"
      ],
      "metadata": {
        "id": "XqINRj7rIYPl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_QHtTsDBJKf",
        "outputId": "6cc2a787-fa54-4387-bf84-6d4bc7fd5911"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression - Testing Evaluation\n",
            "[[63  0]\n",
            " [ 0 54]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        63\n",
            "           1       1.00      1.00      1.00        54\n",
            "\n",
            "    accuracy                           1.00       117\n",
            "   macro avg       1.00      1.00      1.00       117\n",
            "weighted avg       1.00      1.00      1.00       117\n",
            "\n",
            "LogisticRegression - Traing Evaluation\n",
            "[[88  0]\n",
            " [ 0 87]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        88\n",
            "           1       1.00      1.00      1.00        87\n",
            "\n",
            "    accuracy                           1.00       175\n",
            "   macro avg       1.00      1.00      1.00       175\n",
            "weighted avg       1.00      1.00      1.00       175\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Initialize and train the classifier\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "print('LogisticRegression - Testing Evaluation')\n",
        "\n",
        "# Predict the labels for the test set\n",
        "y_pred_test = classifier.predict(X_test)\n",
        "\n",
        "# Calculate confusion matrix for Tesing\n",
        "cm_LR_Test = confusion_matrix(y_test, y_pred_test)\n",
        "print(cm_LR_Test)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred_test))\n",
        "\n",
        "\n",
        "print('LogisticRegression - Traing Evaluation')\n",
        "# Predict the labels for the train\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "\n",
        "# Calculate confusion matrix for Train\n",
        "cm_LR_Train = confusion_matrix(y_train, y_pred_train)\n",
        "print(cm_LR_Train)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_train, y_pred_train))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using Naive Bayes"
      ],
      "metadata": {
        "id": "7kRc8l9craZF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Initialize the Naive Bayes model\n",
        "nb_model = GaussianNB()\n",
        "\n",
        "# Train the model\n",
        "nb_model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "print('GaussianNB - Testing Evaluation')\n",
        "# Make predictions\n",
        "y_pred_test = nb_model.predict(X_test)\n",
        "\n",
        "# Calculate confusion matrix for Tesing\n",
        "cm_NB_Test = confusion_matrix(y_test, y_pred_test)\n",
        "print(cm_NB_Test)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred_test))\n",
        "\n",
        "\n",
        "print('GaussianNB - Traing Evaluation')\n",
        "# Predict the labels for the train set\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "\n",
        "# Calculate confusion matrix for Train\n",
        "cm_NB_Train = confusion_matrix(y_train, y_pred_train)\n",
        "print(cm_NB_Train)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_train, y_pred_train))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YfYcKXvFquYa",
        "outputId": "489982f0-13ff-46a0-cfad-c29457b5ec18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GaussianNB - Testing Evaluation\n",
            "[[61  2]\n",
            " [ 5 49]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.97      0.95        63\n",
            "           1       0.96      0.91      0.93        54\n",
            "\n",
            "    accuracy                           0.94       117\n",
            "   macro avg       0.94      0.94      0.94       117\n",
            "weighted avg       0.94      0.94      0.94       117\n",
            "\n",
            "GaussianNB - Traing Evaluation\n",
            "[[88  0]\n",
            " [ 0 87]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        88\n",
            "           1       1.00      1.00      1.00        87\n",
            "\n",
            "    accuracy                           1.00       175\n",
            "   macro avg       1.00      1.00      1.00       175\n",
            "weighted avg       1.00      1.00      1.00       175\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using KNN"
      ],
      "metadata": {
        "id": "M7EnI53gsajS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "# Initialize the KNN model\n",
        "knn_model = KNeighborsClassifier(n_neighbors=8)\n",
        "\n",
        "# Train the model\n",
        "knn_model.fit(X_train, y_train)\n",
        "\n",
        "print('KNN - Testing Evaluation')\n",
        "# Make predictions\n",
        "y_pred_test = knn_model.predict(X_test)\n",
        "\n",
        "# Calculate confusion matrix for Tesing\n",
        "cm_KNN_Test = confusion_matrix(y_test, y_pred_test)\n",
        "print(cm_KNN_Test)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred_test))\n",
        "\n",
        "\n",
        "print('KNN - Traing Evaluation')\n",
        "# Predict the labels for the train set\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "\n",
        "# Calculate confusion matrix for Train\n",
        "cm_KNN_Train = confusion_matrix(y_train, y_pred_train)\n",
        "print(cm_KNN_Train)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_train, y_pred_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E7umnOdbquSU",
        "outputId": "e5ed0096-6e3f-4e63-ffaa-0ef023c115dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN - Testing Evaluation\n",
            "[[62  1]\n",
            " [ 0 54]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99        63\n",
            "           1       0.98      1.00      0.99        54\n",
            "\n",
            "    accuracy                           0.99       117\n",
            "   macro avg       0.99      0.99      0.99       117\n",
            "weighted avg       0.99      0.99      0.99       117\n",
            "\n",
            "KNN - Traing Evaluation\n",
            "[[88  0]\n",
            " [ 0 87]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        88\n",
            "           1       1.00      1.00      1.00        87\n",
            "\n",
            "    accuracy                           1.00       175\n",
            "   macro avg       1.00      1.00      1.00       175\n",
            "weighted avg       1.00      1.00      1.00       175\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using DT"
      ],
      "metadata": {
        "id": "-4YctCiws83Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Initialize the Decision Tree model\n",
        "dt_model = DecisionTreeClassifier(random_state=42)\n",
        "\n",
        "# Train the model\n",
        "dt_model.fit(X_train, y_train)\n",
        "\n",
        "print('DecisionTree - Testing Evaluation')\n",
        "# Make predictions\n",
        "y_pred_test = dt_model.predict(X_test)\n",
        "\n",
        "# Calculate confusion matrix for Tesing\n",
        "cm_DT_Test = confusion_matrix(y_test, y_pred_test)\n",
        "print(cm_DT_Test)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred_test))\n",
        "\n",
        "\n",
        "print('DecisionTree - Traing Evaluation')\n",
        "# Predict the labels for the train set\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "\n",
        "# Calculate confusion matrix for Train\n",
        "cm_DT_Train = confusion_matrix(y_train, y_pred_train)\n",
        "print(cm_DT_Train)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_train, y_pred_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQQtModpryck",
        "outputId": "5fb3deae-bcfd-4f9c-88c6-0727a546d4bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTree - Testing Evaluation\n",
            "[[63  0]\n",
            " [ 0 54]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        63\n",
            "           1       1.00      1.00      1.00        54\n",
            "\n",
            "    accuracy                           1.00       117\n",
            "   macro avg       1.00      1.00      1.00       117\n",
            "weighted avg       1.00      1.00      1.00       117\n",
            "\n",
            "DecisionTree - Traing Evaluation\n",
            "[[88  0]\n",
            " [ 0 87]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        88\n",
            "           1       1.00      1.00      1.00        87\n",
            "\n",
            "    accuracy                           1.00       175\n",
            "   macro avg       1.00      1.00      1.00       175\n",
            "weighted avg       1.00      1.00      1.00       175\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using RF"
      ],
      "metadata": {
        "id": "08A-fNPztUhR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "# Initialize the Random Forest model\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the model\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "print('RandomForest - Testing Evaluation')\n",
        "# Make predictions\n",
        "y_pred_test = rf_model.predict(X_test)\n",
        "\n",
        "# Calculate confusion matrix for Tesing\n",
        "cm_rf_Test = confusion_matrix(y_test, y_pred_test)\n",
        "print(cm_rf_Test)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred_test))\n",
        "\n",
        "\n",
        "print('RandomForest - Traing Evaluation')\n",
        "# Predict the labels for the train set\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "\n",
        "# Calculate confusion matrix for Train\n",
        "cm_rf_Train = confusion_matrix(y_train, y_pred_train)\n",
        "print(cm_rf_Train)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_train, y_pred_train))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFoiDw_qryMp",
        "outputId": "ff438fc5-b85a-42de-9515-aec37cd1a2f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForest - Testing Evaluation\n",
            "[[63  0]\n",
            " [ 0 54]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        63\n",
            "           1       1.00      1.00      1.00        54\n",
            "\n",
            "    accuracy                           1.00       117\n",
            "   macro avg       1.00      1.00      1.00       117\n",
            "weighted avg       1.00      1.00      1.00       117\n",
            "\n",
            "RandomForest - Traing Evaluation\n",
            "[[88  0]\n",
            " [ 0 87]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        88\n",
            "           1       1.00      1.00      1.00        87\n",
            "\n",
            "    accuracy                           1.00       175\n",
            "   macro avg       1.00      1.00      1.00       175\n",
            "weighted avg       1.00      1.00      1.00       175\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using SVM"
      ],
      "metadata": {
        "id": "bUOxZyLeHVAv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Initialize the SVM model\n",
        "svm_model = SVC(kernel='linear', random_state=42)\n",
        "\n",
        "# Train the model\n",
        "svm_model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "print('SVM - Testing Evaluation')\n",
        "# Make predictions\n",
        "y_pred_test = svm_model.predict(X_test)\n",
        "\n",
        "# Calculate confusion matrix for Tesing\n",
        "cm_svm_Test = confusion_matrix(y_test, y_pred_test)\n",
        "print(cm_svm_Test)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred_test))\n",
        "\n",
        "\n",
        "print('SVM - Traing Evaluation')\n",
        "# Predict the labels for the train set\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "\n",
        "# Calculate confusion matrix for Train\n",
        "cm_svm_Train = confusion_matrix(y_train, y_pred_train)\n",
        "print(cm_svm_Train)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_train, y_pred_train))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LXIZaWVFthvn",
        "outputId": "3cca6963-3b5f-4ba2-cc76-d4f5bb43e9c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM - Testing Evaluation\n",
            "[[63  0]\n",
            " [ 0 54]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        63\n",
            "           1       1.00      1.00      1.00        54\n",
            "\n",
            "    accuracy                           1.00       117\n",
            "   macro avg       1.00      1.00      1.00       117\n",
            "weighted avg       1.00      1.00      1.00       117\n",
            "\n",
            "SVM - Traing Evaluation\n",
            "[[88  0]\n",
            " [ 0 87]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        88\n",
            "           1       1.00      1.00      1.00        87\n",
            "\n",
            "    accuracy                           1.00       175\n",
            "   macro avg       1.00      1.00      1.00       175\n",
            "weighted avg       1.00      1.00      1.00       175\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using XGBoost"
      ],
      "metadata": {
        "id": "55Ffi0jnHsrp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Initialize the XGBoost model\n",
        "xgb_model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss', random_state=42)\n",
        "\n",
        "# Train the model\n",
        "xgb_model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "print('XGBoost - Testing Evaluation')\n",
        "# Make predictions\n",
        "y_pred_test = xgb_model.predict(X_test)\n",
        "\n",
        "# Calculate confusion matrix for Tesing\n",
        "cm_xgb_Test = confusion_matrix(y_test, y_pred_test)\n",
        "print(cm_xgb_Test)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred_test))\n",
        "\n",
        "\n",
        "print('XGBoost - Traing Evaluation')\n",
        "# Predict the labels for the train set\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "\n",
        "# Calculate confusion matrix for Train\n",
        "cm_xgb_Train = confusion_matrix(y_train, y_pred_train)\n",
        "print(cm_xgb_Train)\n",
        "\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_train, y_pred_train))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPeXraEh1vl4",
        "outputId": "d5bd2ff2-0d81-46b8-d794-ff0603da320e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost - Testing Evaluation\n",
            "[[63  0]\n",
            " [ 0 54]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        63\n",
            "           1       1.00      1.00      1.00        54\n",
            "\n",
            "    accuracy                           1.00       117\n",
            "   macro avg       1.00      1.00      1.00       117\n",
            "weighted avg       1.00      1.00      1.00       117\n",
            "\n",
            "XGBoost - Traing Evaluation\n",
            "[[88  0]\n",
            " [ 0 87]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        88\n",
            "           1       1.00      1.00      1.00        87\n",
            "\n",
            "    accuracy                           1.00       175\n",
            "   macro avg       1.00      1.00      1.00       175\n",
            "weighted avg       1.00      1.00      1.00       175\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [02:32:49] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}