Ensemble Learning Strategies for Enhanced Email Security.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# VotingClassifier"
      ],
      "metadata": {
        "id": "YmY61HSNyydL"
      },
      "id": "YmY61HSNyydL"
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Assuming 'Prediction' is the target column\n",
        "df = pd.read_csv('emails.csv')\n",
        "\n",
        "# Drop 'Email No.' column\n",
        "df = df.drop(['Email No.'], axis=1)\n",
        "\n",
        "# Selecting features (excluding 'Prediction' and other unwanted columns)\n",
        "X = df.drop(['Prediction'], axis=1)\n",
        "y = df['Prediction']\n",
        "\n",
        "# Ensure the data does not contain any missing values\n",
        "if X.isnull().values.any():\n",
        "    raise ValueError(\"Dataset contains missing values. Please handle them before proceeding.\")\n",
        "\n",
        "# Split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create models\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "nb_model = MultinomialNB()\n",
        "\n",
        "# Selecting only numeric columns for training\n",
        "numeric_columns = X_train.select_dtypes(include=['number']).columns\n",
        "X_train_numeric = X_train[numeric_columns]\n",
        "\n",
        "# Create an ensemble model\n",
        "ensemble_model = VotingClassifier(estimators=[('rf', rf_model), ('nb', nb_model)], voting='soft')\n",
        "ensemble_model.fit(X_train_numeric, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = ensemble_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "classification_rep = classification_report(y_test, y_pred)\n",
        "\n",
        "print(f'Accuracy VotingClassifier: {accuracy}')\n",
        "print(f'Classification Report VotingClassifier:\\n{classification_rep}')\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix1 = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Visualize the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix1, annot=True, fmt='d', cmap='Blues', xticklabels=ensemble_model.classes_, yticklabels=ensemble_model.classes_)\n",
        "plt.title('Confusion Matrix (VotingClassifier)')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "L7aste4fy2T4",
        "outputId": "dbe79a09-46a5-4a2e-9646-d271a7ec698b"
      },
      "id": "L7aste4fy2T4",
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy VotingClassifier: 0.9594202898550724\n",
            "Classification Report VotingClassifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.95      0.97       739\n",
            "           1       0.89      0.98      0.93       296\n",
            "\n",
            "    accuracy                           0.96      1035\n",
            "   macro avg       0.94      0.96      0.95      1035\n",
            "weighted avg       0.96      0.96      0.96      1035\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN"
      ],
      "metadata": {
        "id": "9rbxf0k7_rHz"
      },
      "id": "9rbxf0k7_rHz"
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming 'Prediction' is the target column\n",
        "df = pd.read_csv('emails.csv')\n",
        "\n",
        "# Drop 'Email No.' column\n",
        "df = df.drop(['Email No.'], axis=1)\n",
        "\n",
        "# Selecting features (excluding 'Prediction' and other unwanted columns)\n",
        "X = df.drop(['Prediction'], axis=1)\n",
        "y = df['Prediction']\n",
        "\n",
        "# Ensure the data does not contain any missing values\n",
        "if X.isnull().values.any():\n",
        "    raise ValueError(\"Dataset contains missing values. Please handle them before proceeding.\")\n",
        "\n",
        "# Split the data with a very small training set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.9, random_state=42)\n",
        "\n",
        "# Create a k-Nearest Neighbors model\n",
        "knn_model = KNeighborsClassifier(n_neighbors=3)  # You can adjust the number of neighbors as needed\n",
        "knn_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = knn_model.predict(X_test)\n",
        "\n",
        "# Evaluate the k-Nearest Neighbors model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "classification_rep = classification_report(y_test, y_pred)\n",
        "\n",
        "print(f'Accuracy (KNN): {accuracy}')\n",
        "print(f'Classification Report KNN:\\n{classification_rep}')\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix2 = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Visualize the confusion matrix for k-Nearest Neighbors\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix2, annot=True, fmt='d', cmap='Blues', xticklabels=knn_model.classes_, yticklabels=knn_model.classes_)\n",
        "plt.title('Confusion Matrix (KNN)')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "3OzFO4-i_vip",
        "outputId": "8df6cd77-deb8-4879-d436-d07bd0b78f1c"
      },
      "id": "3OzFO4-i_vip",
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (KNN): 0.8023630504833512\n",
            "Classification Report KNN:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.82      0.86      3321\n",
            "           1       0.63      0.75      0.69      1334\n",
            "\n",
            "    accuracy                           0.80      4655\n",
            "   macro avg       0.76      0.79      0.77      4655\n",
            "weighted avg       0.82      0.80      0.81      4655\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NB"
      ],
      "metadata": {
        "id": "au_96M8f_2oy"
      },
      "id": "au_96M8f_2oy"
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming 'Prediction' is the target column\n",
        "df = pd.read_csv('emails.csv')\n",
        "\n",
        "# Drop 'Email No.' column\n",
        "df = df.drop(['Email No.'], axis=1)\n",
        "\n",
        "# Selecting features (excluding 'Prediction' and other unwanted columns)\n",
        "X = df.drop(['Prediction'], axis=1)\n",
        "y = df['Prediction']\n",
        "\n",
        "# Ensure the data does not contain any missing values\n",
        "if X.isnull().values.any():\n",
        "    raise ValueError(\"Dataset contains missing values. Please handle them before proceeding.\")\n",
        "\n",
        "# Split the data with a very small training set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.9, random_state=42)\n",
        "\n",
        "# Create a Gaussian Naive Bayes model\n",
        "gnb_model = GaussianNB()\n",
        "gnb_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = gnb_model.predict(X_test)\n",
        "\n",
        "# Evaluate the Gaussian Naive Bayes model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "classification_rep = classification_report(y_test, y_pred)\n",
        "\n",
        "print(f'Accuracy (GaussianNB): {accuracy}')\n",
        "print(f'Classification Report GaussianNB:\\n{classification_rep}')\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix3 = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Visualize the confusion matrix for Gaussian Naive Bayes\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix3, annot=True, fmt='d', cmap='Blues', xticklabels=gnb_model.classes_, yticklabels=gnb_model.classes_)\n",
        "plt.title('Confusion Matrix (GaussianNB)')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "VOkUAg5G_4yz",
        "outputId": "0dece2c6-3a20-48e6-a079-911837af2214"
      },
      "id": "VOkUAg5G_4yz",
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (GaussianNB): 0.9364124597207304\n",
            "Classification Report GaussianNB:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.95      0.96      3321\n",
            "           1       0.87      0.91      0.89      1334\n",
            "\n",
            "    accuracy                           0.94      4655\n",
            "   macro avg       0.92      0.93      0.92      4655\n",
            "weighted avg       0.94      0.94      0.94      4655\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RF"
      ],
      "metadata": {
        "id": "YMqZWdzGA63X"
      },
      "id": "YMqZWdzGA63X"
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming 'Prediction' is the target column\n",
        "df = pd.read_csv('emails.csv')\n",
        "\n",
        "# Drop 'Email No.' column\n",
        "df = df.drop(['Email No.'], axis=1)\n",
        "\n",
        "# Selecting features (excluding 'Prediction' and other unwanted columns)\n",
        "X = df.drop(['Prediction'], axis=1)\n",
        "y = df['Prediction']\n",
        "\n",
        "# Ensure the data does not contain any missing values\n",
        "if X.isnull().values.any():\n",
        "    raise ValueError(\"Dataset contains missing values. Please handle them before proceeding.\")\n",
        "\n",
        "# Split the data with a very small training set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.9, random_state=42)\n",
        "\n",
        "# Create a Random Forest model\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = rf_model.predict(X_test)\n",
        "\n",
        "# Evaluate the Random Forest model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "classification_rep = classification_report(y_test, y_pred)\n",
        "\n",
        "print(f'Accuracy (Random Forest): {accuracy}')\n",
        "print(f'Classification Report Random Forest:\\n{classification_rep}')\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix4 = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Visualize the confusion matrix for Random Forest\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix4, annot=True, fmt='d', cmap='Blues', xticklabels=rf_model.classes_, yticklabels=rf_model.classes_)\n",
        "plt.title('Confusion Matrix (Random Forest)')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "gkLKljHCA5Bf",
        "outputId": "b66deaf2-2799-4fdf-b69a-c432c1aa4477"
      },
      "id": "gkLKljHCA5Bf",
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (Random Forest): 0.9368421052631579\n",
            "Classification Report Random Forest:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.96      0.96      3321\n",
            "           1       0.91      0.87      0.89      1334\n",
            "\n",
            "    accuracy                           0.94      4655\n",
            "   macro avg       0.93      0.92      0.92      4655\n",
            "weighted avg       0.94      0.94      0.94      4655\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# COMPARATIVE STUDY"
      ],
      "metadata": {
        "id": "khTXGIxvwq-2"
      },
      "id": "khTXGIxvwq-2"
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming conf_matrices is a list containing confusion matrices for different models\n",
        "conf_matrices = [conf_matrix1, conf_matrix2, conf_matrix3, conf_matrix4]\n",
        "\n",
        "# Create subplots\n",
        "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
        "\n",
        "# List of models for labels\n",
        "model_labels = ['VotingClassifier', 'KNN', 'GaussianNB', 'Random Forest']\n",
        "\n",
        "# Loop through each model's confusion matrix\n",
        "for i, conf_matrix in enumerate(conf_matrices):\n",
        "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', ax=axes.flatten()[i])\n",
        "    axes.flatten()[i].set_title(f'Confusion Matrix ({model_labels[i]})')\n",
        "    axes.flatten()[i].set_xlabel('Predicted')\n",
        "    axes.flatten()[i].set_ylabel('True')\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1oFmcZISwwRT",
        "outputId": "bec632c7-fb7f-46d7-85c3-a545beda083d"
      },
      "id": "1oFmcZISwwRT",
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 8 Axes>"
            ],
            "image/png": 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qdYsWLXTmzBnLZ2iLxx9/XJs2bVJCQoI2bNighISEXEvApSvzEnh4XPlVzszM1JkzZyzluFcP1boZs9ms/v3729S2ffv2+s9//qNJkyapW7duKl68uN5+++2bnpddeXP1Uzfpf9+va6uWkpOTVa5cOavt6lU2vLy8LH/+559/dPr0abVo0UIXLlzQoUOHbLqXq5UqVUp//fWXdu3adcM2O3bssKlK6GpX9/XcuXNKTk5WixYtcv0ZhYaGqnr16pbX9evXl6+v73V/H6pVq6bevXvrnXfeUXx8/A370bRpU8XExCgmJkYrVqzQq6++qv3796tz5865rspTunRpnT592tbbBIBCjXiIeOhqzo6HsqWmplrinho1amjkyJFq3ry5vv76a6shjlfHFhkZGTpz5oxq1KihUqVK5XrPTz/9tNX5LVq0UGZmpv744w9JV6rT0tPTNXToUKt2w4YNy3GtVatW6Z577tF9991n2eft7a2nn35av//+uw4cOGDVvk+fPlZzUjZt2lSGYejJJ5+0ate0aVP9+eefunz5cq6fzdWKFy9uiWmyt6lTp1rdy7BhwyzfC0l66qmn5Ovrm+N3L7ef+5IlS1SnTh3Vrl3bqjrq/vvvlyRt3LhRkmO/U7kh5kJBR3IJNvH19ZV05X/gbfHHH3/Iw8NDNWrUsNofGBioUqVKWf4Ry1a5cuUc1yhdurTOnTvnYI9zevTRR9W8eXMNHDhQAQEB6tmzpxYvXnzDfwSy+1mrVq0cx+rUqaPTp0/nKBO+9l6yAwd77uWBBx6Qj4+PPv/8c33yySe6++67c3yW2bKysjR9+nTdcccdMpvNKlu2rMqVK6eff/7ZqjT4Zm677Ta7Jqt888035e/vr7i4OM2cOVPly5e3+VzDMKxeZwfp58+ft9rv7e1tCRZGjRqV4zr79+/XQw89JD8/P/n6+qpcuXKWiR3tufdsY8aMkbe3t+655x7dcccdioiIyFGePHnyZO3bt0+VKlXSPffcowkTJtgU9K9YsULNmjVT8eLF5e/vr3LlymnevHm59tOR34exY8fq8uXLN517qWzZsgoNDVVoaKjCw8P13//+V++99562bduW64orhmHkmKsDANwV8RDx0LWcGQ9luzppsmDBAtWpU0cnT560SiZJV1YOHDdunCpVqmR1z0lJSTbFF9f+TLJ/zlcv7iFdSXJemwj7448/rvt9uPpa13tvPz8/SVKlSpVy7M/KyrLpZ1akSBFLTJO9NW7c2Or9r+2jp6enqlWrlqN/uf3cf/31V+3fvz/Hg86aNWtKkk6ePCnJsd+p3BBzoaAjuQSb+Pr6KigoSPv27bPrPFv/gixSpEiu+6/3j64t73HtpM5eXl7asmWL1q1bp969e+vnn3/Wo48+qnbt2uVo+2/8m3vJZjab1a1bNy1cuFBLly697lM6SXrttdc0YsQItWzZUh9//LHWrFmjmJgY1a1b165/1K4NWG7mp59+svyjunfvXpvOKVOmjKScgWXt2rUlKcf3q2jRopZgITg42OpYUlKSWrVqpT179mjSpElavny5YmJiLOP0r753W78jderU0eHDh/XZZ5/pvvvu05dffqn77rtP48ePt7R55JFHdPToUct8ClOmTFHdunX17bffXve+v/vuO3Xu3FnFixfX3LlztWrVKsXExOjxxx/P9XvhyHeoWrVq6tWrl03VS9dq27atJGnLli05jp07d05ly5a163oAUFgRD9mOeOj6rhcPZbs6adKvXz+tX79eCQkJ+s9//mPVbujQoXr11Vf1yCOPaPHixVq7dq1iYmJUpkyZXO/ZGT8TR13vvV3Zp6vl9nPPyspSvXr1clRHZW/PPPOM5Vxn/E4Rc6GgI7kEm3Xq1Em//fabYmNjb9q2SpUqysrK0q+//mq1PzExUUlJSZaVTpyhdOnSOVZ9kHI+MZEkDw8PtW3bVtOmTdOBAwf06quvasOGDZay1mtl9/Pw4cM5jh06dEhly5ZVyZIl/90NXMfjjz+un376Sf/880+uk35m++KLL9SmTRu9//776tmzp9q3b6/Q0NAcn4kzn4Skpqaqf//+Cg4O1tNPP63JkyffcChZtsqVK8vLy0vHjh2z2l+rVi3dcccdWrZsWY4nn9ezadMmnTlzRtHR0XruuefUqVMnhYaG5lpibs93pGTJknr00Ue1YMECHT9+XOHh4ZZJKrNVqFBBzzzzjJYtW6Zjx46pTJkyevXVV6/b1y+//FLFixfXmjVr9OSTT6pjx46WldicKbt6yZ6JMCVZSs+vrRyTriy5nf0UEgBAPHQt4iHnxUPXU6FCBQ0fPlzLly/X9u3bLfu/+OIL9e3bV1OnTrVMBH/ffffl+j2wRfbP+drv66lTp3IkwqpUqXLd78PV13KV631n09PTdezYMZv6V716dZ09e1Zt27bNUSEVGhpqVRV1s9+pm33vLl++rD///JOYCwUaySXYbPTo0SpZsqQGDhyoxMTEHMd/++03zZgxQ9KVMmZJOVYwmTZtmiQpPDzcaf2qXr26kpOT9fPPP1v2xcfH51ip4uzZsznObdiwoSRdd8nTChUqqGHDhlq4cKHVP9T79u3T2rVrLfeZF9q0aaOXX35Zs2fPtiwVn5siRYrkeLqzZMkS/f3331b7soM+RwOOq40ZM0bHjx/XwoULNW3aNN1+++3q27fvTZeOLVasmJo0aaIffvghx7EJEybo9OnTeuqpp3Ks4CHlfIKV/aTr6v3p6emaO3dujnOrV6+u7du3Kz093bJvxYoV+vPPP63aXbsam6enp4KDg2UYhjIyMpSZmZmjTLt8+fIKCgq64b0XKVJEJpPJ6unV77//rmXLll33HEdUr15dvXr10ttvv62EhASbz1u+fLkkqUGDBlb7k5OT9dtvv9m8IhIAuAPioSTLfuIh58dD1zN06FCVKFHCavh7bvc8a9YshyvQQkNDVaxYMc2aNcvqurmtSPjAAw9o586dVknW1NRUvfPOO7r99ttzVJznt9DQUHl6emrmzJlW9/L+++8rOTnZpt+9Rx55RH///bfefffdHMcuXrxoeSBqy+/Uzb53Bw4c0KVLl4i5UKAVdXUHUHBUr15dixYt0qOPPqo6deqoT58+uvPOO5Wenq5t27ZpyZIl6tevn6Qr/5Pat29fvfPOO5bhSzt37tTChQvVtWvX6y7r6oiePXtqzJgxeuihh/Tss8/qwoULmjdvnmrWrGk1meGkSZO0ZcsWhYeHq0qVKjp58qTmzp2rihUrWk1GeK0pU6aoY8eOCgkJ0YABA3Tx4kXNmjVLfn5+mjBhgtPu41oeHh4aO3bsTdt16tRJkyZNUv/+/XXvvfdq7969+uSTT1StWjWrdtWrV1epUqU0f/58+fj4qGTJkmratGmO5edvZsOGDZo7d67Gjx9vWUJ3wYIFat26tV566SVNnjz5hud36dJFL774olJSUixzV0hXnkzu27dPUVFR2rlzp3r27KmqVasqNTVV+/bt06effiofHx9LZdK9996r0qVLq2/fvnr22WdlMpn00Ucf5VpGPXDgQH3xxRfq0KGDHnnkEf3222/6+OOPrSbNlq5MzBkYGKjmzZsrICBABw8e1OzZsxUeHi4fHx8lJSWpYsWK6tGjhxo0aCBvb2+tW7dOu3btskwgmZvw8HBNmzZNHTp00OOPP66TJ09qzpw5qlGjhtX/BDjDiy++qI8++kiHDx9W3bp1cxz/+++/9fHHH0u6kozbs2eP3n77bZUtW1ZDhw61artu3ToZhqEuXbo4tY8AUJARDxEPSXkXD11PmTJl1L9/f82dO1cHDx5UnTp11KlTJ3300Ufy8/NTcHCwYmNjtW7dOsuwO3uVK1dOI0eOVFRUlDp16qQHHnhAP/30k7799tscw7VeeOEFffrpp+rYsaOeffZZ+fv7a+HChTp27Ji+/PJLq0m0XaFcuXKKjIzUxIkT1aFDB3Xu3FmHDx/W3Llzdffdd1vm6LyR3r17a/HixRo0aJA2btyo5s2bKzMzU4cOHdLixYu1Zs0aNWnSxKbfqZt972JiYlSiRAm1a9cuTz8XIE/l48p0KCR++eUX46mnnjJuv/12w9PT0/Dx8TGaN29uzJo1y2r594yMDGPixIlG1apVjWLFihmVKlUyIiMjrdoYxpWld8PDw3O8z7VLvl5v6V3DMIy1a9cad955p+Hp6WnUqlXL+Pjjj3Msvbt+/XqjS5cuRlBQkOHp6WkEBQUZjz32mPHLL7/keI9rlwldt26d0bx5c8PLy8vw9fU1HnzwQePAgQNWbbLf79plSLOXfz127Nh1P1PDsF5693qut/Tu888/b1SoUMHw8vIymjdvbsTGxua6ZO7XX39tBAcHG0WLFrW6z9yWrc929XVSUlKMKlWqGHfddZeRkZFh1W748OGGh4eHERsbe8N7SExMNIoWLWp89NFHuR7ftGmT0aNHD6NChQpGsWLFDF9fX6NJkybG+PHjjfj4eKu233//vdGsWTPDy8vLCAoKMkaPHm1ZFnjjxo1WbadOnWrcdttthtlsNpo3b2788MMPOT6jt99+22jZsqVRpkwZw2w2G9WrVzdGjRplJCcnG4ZhGGlpacaoUaOMBg0aGD4+PkbJkiWNBg0aGHPnzrV6r9yWg37//feNO+64wzCbzUbt2rWNBQsW5PiOGoZhSDIiIiJyfC5VqlSxWgI4+3u1a9euHG2zl+e99meavcx19ubh4WGUL1/eeOyxx4wjR47kuM6jjz5q3HfffTn2AwCIh4iH8iYeutH9//bbb0aRIkUs8cC5c+eM/v37G2XLljW8vb2NsLAw49ChQzbHDBs3bswRM2VmZhoTJ060fI6tW7c29u3bl+Oa2f3p0aOHUapUKaN48eLGPffcY6xYsSLX91iyZInV/uv16Xrfn2vZ8j0xDMOYPXu2Ubt2baNYsWJGQECAMXjwYOPcuXNWbW70c09PTzfeeOMNo27duobZbDZKly5tNG7c2Jg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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Assuming you have defined models\n",
        "models = [ensemble_model, knn_model, gnb_model, rf_model]\n",
        "\n",
        "# List of models for labels\n",
        "model_labels = ['VotingClassifier', 'KNN', 'GaussianNB', 'Random Forest']\n",
        "\n",
        "# Calculate cross-validation scores for each model\n",
        "cv_scores = [cross_val_score(model, X, y, cv=5, scoring='accuracy') for model in models]\n",
        "\n",
        "# Flatten the list of arrays\n",
        "flat_cv_scores = np.concatenate(cv_scores)\n",
        "\n",
        "# Create labels for boxplot\n",
        "labels = np.repeat(model_labels, [len(scores) for scores in cv_scores])\n",
        "\n",
        "# Box plot with strip plot\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.boxplot(x=labels, y=flat_cv_scores, showmeans=True, meanprops={\"marker\":\"o\"})\n",
        "sns.stripplot(x=labels, y=flat_cv_scores, jitter=True, color=\".3\", size=8)\n",
        "plt.title('Box Plot of Cross-Validation Scores')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "nCkZ5vMu2zPG",
        "outputId": "27ccc710-f7ec-43d3-d461-6309cb69ecd8"
      },
      "id": "nCkZ5vMu2zPG",
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}