{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# ACFA7003 MSc Dissertation Technical Notebook\n",
        "\n",
        "## Project Title\n",
        "AI Governance and Predictive Risk Analytics in Banking Cybersecurity: A Supervised Machine-Learning Decision-Support System Using the UNSW-NB15 Dataset\n",
        "\n",
        "## Notebook Purpose\n",
        "This notebook develops the technical artefact for the MSc dissertation. It uses the UNSW-NB15 intrusion-detection dataset to build supervised machine-learning models for cyber-risk classification and prepares outputs for a Streamlit-based AI governance decision-support dashboard.\n",
        "\n",
        "## Research Context\n",
        "The project focuses on banking cybersecurity decision support. The aim is not only to classify network traffic as normal or attack, but also to support explainable, auditable and human-reviewed AI use in cyber-risk decision-making."
      ],
      "metadata": {
        "id": "ssO64ZQjPdje"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score,\n",
        "    confusion_matrix,\n",
        "    classification_report,\n",
        "    roc_auc_score,\n",
        "    roc_curve\n",
        ")\n",
        "\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "pd.set_option(\"display.width\", 120)\n",
        "\n",
        "print(\"Libraries imported successfully.\")"
      ],
      "metadata": {
        "id": "9-psVmyRPgJy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3cb6dc3f-10d7-49b1-ca1c-2d2ad12e5ce3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Libraries imported successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "faYLchh4VtNZ",
        "outputId": "588c2aec-4ed2-4249-90a4-34a62899ecf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_path = \"/content/drive/MyDrive/MSc _Dissertation /UNSW_NB15/Training_and_Testing Sets/UNSW_NB15_training-set.csv\"\n",
        "test_path = \"/content/drive/MyDrive/MSc _Dissertation /UNSW_NB15/Training_and_Testing Sets/UNSW_NB15_testing-set.csv\"\n",
        "\n",
        "train_df = pd.read_csv(train_path)\n",
        "test_df = pd.read_csv(test_path)\n",
        "\n",
        "print(\"Training dataset loaded successfully.\")\n",
        "print(\"Testing dataset loaded successfully.\")\n",
        "\n",
        "print(\"\\nTraining shape:\", train_df.shape)\n",
        "print(\"Testing shape:\", test_df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L7xmzgkgX7j-",
        "outputId": "c0147ba2-90f5-48ad-be18-d3f13d5440f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training dataset loaded successfully.\n",
            "Testing dataset loaded successfully.\n",
            "\n",
            "Training shape: (175341, 45)\n",
            "Testing shape: (82332, 45)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preview first five rows of training data\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "equQu6-dZ8M6",
        "outputId": "cf51f517-fec4-4849-ec97-e163c979ccfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id       dur proto service state  spkts  dpkts  sbytes  dbytes       rate  sttl  dttl         sload          dload  \\\n",
              "0   1  0.121478   tcp       -   FIN      6      4     258     172  74.087490   252   254  14158.942380    8495.365234   \n",
              "1   2  0.649902   tcp       -   FIN     14     38     734   42014  78.473372    62   252   8395.112305  503571.312500   \n",
              "2   3  1.623129   tcp       -   FIN      8     16     364   13186  14.170161    62   252   1572.271851   60929.230470   \n",
              "3   4  1.681642   tcp     ftp   FIN     12     12     628     770  13.677108    62   252   2740.178955    3358.622070   \n",
              "4   5  0.449454   tcp       -   FIN     10      6     534     268  33.373826   254   252   8561.499023    3987.059814   \n",
              "\n",
              "   sloss  dloss      sinpkt      dinpkt          sjit          djit  swin       stcpb       dtcpb  dwin    tcprtt  \\\n",
              "0      0      0   24.295600    8.375000     30.177547     11.830604   255   621772692  2202533631   255  0.000000   \n",
              "1      2     17   49.915000   15.432865     61.426934   1387.778330   255  1417884146  3077387971   255  0.000000   \n",
              "2      1      6  231.875571  102.737203  17179.586860  11420.926230   255  2116150707  2963114973   255  0.111897   \n",
              "3      1      3  152.876547   90.235726    259.080172   4991.784669   255  1107119177  1047442890   255  0.000000   \n",
              "4      2      1   47.750333   75.659602   2415.837634    115.807000   255  2436137549  1977154190   255  0.128381   \n",
              "\n",
              "     synack    ackdat  smean  dmean  trans_depth  response_body_len  ct_srv_src  ct_state_ttl  ct_dst_ltm  \\\n",
              "0  0.000000  0.000000     43     43            0                  0           1             0           1   \n",
              "1  0.000000  0.000000     52   1106            0                  0          43             1           1   \n",
              "2  0.061458  0.050439     46    824            0                  0           7             1           2   \n",
              "3  0.000000  0.000000     52     64            0                  0           1             1           2   \n",
              "4  0.071147  0.057234     53     45            0                  0          43             1           2   \n",
              "\n",
              "   ct_src_dport_ltm  ct_dst_sport_ltm  ct_dst_src_ltm  is_ftp_login  ct_ftp_cmd  ct_flw_http_mthd  ct_src_ltm  \\\n",
              "0                 1                 1               1             0           0                 0           1   \n",
              "1                 1                 1               2             0           0                 0           1   \n",
              "2                 1                 1               3             0           0                 0           2   \n",
              "3                 1                 1               3             1           1                 0           2   \n",
              "4                 2                 1              40             0           0                 0           2   \n",
              "\n",
              "   ct_srv_dst  is_sm_ips_ports attack_cat  label  \n",
              "0           1                0     Normal      0  \n",
              "1           6                0     Normal      0  \n",
              "2           6                0     Normal      0  \n",
              "3           1                0     Normal      0  \n",
              "4          39                0     Normal      0  "
            ],
            "text/html": [
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              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>dur</th>\n",
              "      <th>proto</th>\n",
              "      <th>service</th>\n",
              "      <th>state</th>\n",
              "      <th>spkts</th>\n",
              "      <th>dpkts</th>\n",
              "      <th>sbytes</th>\n",
              "      <th>dbytes</th>\n",
              "      <th>rate</th>\n",
              "      <th>sttl</th>\n",
              "      <th>dttl</th>\n",
              "      <th>sload</th>\n",
              "      <th>dload</th>\n",
              "      <th>sloss</th>\n",
              "      <th>dloss</th>\n",
              "      <th>sinpkt</th>\n",
              "      <th>dinpkt</th>\n",
              "      <th>sjit</th>\n",
              "      <th>djit</th>\n",
              "      <th>swin</th>\n",
              "      <th>stcpb</th>\n",
              "      <th>dtcpb</th>\n",
              "      <th>dwin</th>\n",
              "      <th>tcprtt</th>\n",
              "      <th>synack</th>\n",
              "      <th>ackdat</th>\n",
              "      <th>smean</th>\n",
              "      <th>dmean</th>\n",
              "      <th>trans_depth</th>\n",
              "      <th>response_body_len</th>\n",
              "      <th>ct_srv_src</th>\n",
              "      <th>ct_state_ttl</th>\n",
              "      <th>ct_dst_ltm</th>\n",
              "      <th>ct_src_dport_ltm</th>\n",
              "      <th>ct_dst_sport_ltm</th>\n",
              "      <th>ct_dst_src_ltm</th>\n",
              "      <th>is_ftp_login</th>\n",
              "      <th>ct_ftp_cmd</th>\n",
              "      <th>ct_flw_http_mthd</th>\n",
              "      <th>ct_src_ltm</th>\n",
              "      <th>ct_srv_dst</th>\n",
              "      <th>is_sm_ips_ports</th>\n",
              "      <th>attack_cat</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.121478</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>FIN</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>258</td>\n",
              "      <td>172</td>\n",
              "      <td>74.087490</td>\n",
              "      <td>252</td>\n",
              "      <td>254</td>\n",
              "      <td>14158.942380</td>\n",
              "      <td>8495.365234</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24.295600</td>\n",
              "      <td>8.375000</td>\n",
              "      <td>30.177547</td>\n",
              "      <td>11.830604</td>\n",
              "      <td>255</td>\n",
              "      <td>621772692</td>\n",
              "      <td>2202533631</td>\n",
              "      <td>255</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.649902</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>FIN</td>\n",
              "      <td>14</td>\n",
              "      <td>38</td>\n",
              "      <td>734</td>\n",
              "      <td>42014</td>\n",
              "      <td>78.473372</td>\n",
              "      <td>62</td>\n",
              "      <td>252</td>\n",
              "      <td>8395.112305</td>\n",
              "      <td>503571.312500</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>49.915000</td>\n",
              "      <td>15.432865</td>\n",
              "      <td>61.426934</td>\n",
              "      <td>1387.778330</td>\n",
              "      <td>255</td>\n",
              "      <td>1417884146</td>\n",
              "      <td>3077387971</td>\n",
              "      <td>255</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52</td>\n",
              "      <td>1106</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1.623129</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>FIN</td>\n",
              "      <td>8</td>\n",
              "      <td>16</td>\n",
              "      <td>364</td>\n",
              "      <td>13186</td>\n",
              "      <td>14.170161</td>\n",
              "      <td>62</td>\n",
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              "      <td>1572.271851</td>\n",
              "      <td>60929.230470</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>231.875571</td>\n",
              "      <td>102.737203</td>\n",
              "      <td>17179.586860</td>\n",
              "      <td>11420.926230</td>\n",
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              "      <td>2116150707</td>\n",
              "      <td>2963114973</td>\n",
              "      <td>255</td>\n",
              "      <td>0.111897</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>0.050439</td>\n",
              "      <td>46</td>\n",
              "      <td>824</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1.681642</td>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp</td>\n",
              "      <td>FIN</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>628</td>\n",
              "      <td>770</td>\n",
              "      <td>13.677108</td>\n",
              "      <td>62</td>\n",
              "      <td>252</td>\n",
              "      <td>2740.178955</td>\n",
              "      <td>3358.622070</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>152.876547</td>\n",
              "      <td>90.235726</td>\n",
              "      <td>259.080172</td>\n",
              "      <td>4991.784669</td>\n",
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              "      <td>1107119177</td>\n",
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              "      <td>64</td>\n",
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              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.449454</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>FIN</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>534</td>\n",
              "      <td>268</td>\n",
              "      <td>33.373826</td>\n",
              "      <td>254</td>\n",
              "      <td>252</td>\n",
              "      <td>8561.499023</td>\n",
              "      <td>3987.059814</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>47.750333</td>\n",
              "      <td>75.659602</td>\n",
              "      <td>2415.837634</td>\n",
              "      <td>115.807000</td>\n",
              "      <td>255</td>\n",
              "      <td>2436137549</td>\n",
              "      <td>1977154190</td>\n",
              "      <td>255</td>\n",
              "      <td>0.128381</td>\n",
              "      <td>0.071147</td>\n",
              "      <td>0.057234</td>\n",
              "      <td>53</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>0</td>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8674482e-17f2-4e25-bc19-5d34f5fea9cb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8674482e-17f2-4e25-bc19-5d34f5fea9cb');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display column names\n",
        "print(\"Columns in the training dataset:\")\n",
        "print(train_df.columns.tolist())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ra3cCaDMaQhk",
        "outputId": "f842b103-0ffc-4037-bbf0-7319a4b76b3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns in the training dataset:\n",
            "['id', 'dur', 'proto', 'service', 'state', 'spkts', 'dpkts', 'sbytes', 'dbytes', 'rate', 'sttl', 'dttl', 'sload', 'dload', 'sloss', 'dloss', 'sinpkt', 'dinpkt', 'sjit', 'djit', 'swin', 'stcpb', 'dtcpb', 'dwin', 'tcprtt', 'synack', 'ackdat', 'smean', 'dmean', 'trans_depth', 'response_body_len', 'ct_srv_src', 'ct_state_ttl', 'ct_dst_ltm', 'ct_src_dport_ltm', 'ct_dst_sport_ltm', 'ct_dst_src_ltm', 'is_ftp_login', 'ct_ftp_cmd', 'ct_flw_http_mthd', 'ct_src_ltm', 'ct_srv_dst', 'is_sm_ips_ports', 'attack_cat', 'label']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset information\n",
        "print(\"Training Dataset Information:\")\n",
        "train_df.info()\n",
        "\n",
        "print(\"\\nTesting Dataset Information:\")\n",
        "test_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XO1cYEf7bBW9",
        "outputId": "f30e6f6f-e904-4f12-f597-cf64cb3b34e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Dataset Information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 175341 entries, 0 to 175340\n",
            "Data columns (total 45 columns):\n",
            " #   Column             Non-Null Count   Dtype  \n",
            "---  ------             --------------   -----  \n",
            " 0   id                 175341 non-null  int64  \n",
            " 1   dur                175341 non-null  float64\n",
            " 2   proto              175341 non-null  object \n",
            " 3   service            175341 non-null  object \n",
            " 4   state              175341 non-null  object \n",
            " 5   spkts              175341 non-null  int64  \n",
            " 6   dpkts              175341 non-null  int64  \n",
            " 7   sbytes             175341 non-null  int64  \n",
            " 8   dbytes             175341 non-null  int64  \n",
            " 9   rate               175341 non-null  float64\n",
            " 10  sttl               175341 non-null  int64  \n",
            " 11  dttl               175341 non-null  int64  \n",
            " 12  sload              175341 non-null  float64\n",
            " 13  dload              175341 non-null  float64\n",
            " 14  sloss              175341 non-null  int64  \n",
            " 15  dloss              175341 non-null  int64  \n",
            " 16  sinpkt             175341 non-null  float64\n",
            " 17  dinpkt             175341 non-null  float64\n",
            " 18  sjit               175341 non-null  float64\n",
            " 19  djit               175341 non-null  float64\n",
            " 20  swin               175341 non-null  int64  \n",
            " 21  stcpb              175341 non-null  int64  \n",
            " 22  dtcpb              175341 non-null  int64  \n",
            " 23  dwin               175341 non-null  int64  \n",
            " 24  tcprtt             175341 non-null  float64\n",
            " 25  synack             175341 non-null  float64\n",
            " 26  ackdat             175341 non-null  float64\n",
            " 27  smean              175341 non-null  int64  \n",
            " 28  dmean              175341 non-null  int64  \n",
            " 29  trans_depth        175341 non-null  int64  \n",
            " 30  response_body_len  175341 non-null  int64  \n",
            " 31  ct_srv_src         175341 non-null  int64  \n",
            " 32  ct_state_ttl       175341 non-null  int64  \n",
            " 33  ct_dst_ltm         175341 non-null  int64  \n",
            " 34  ct_src_dport_ltm   175341 non-null  int64  \n",
            " 35  ct_dst_sport_ltm   175341 non-null  int64  \n",
            " 36  ct_dst_src_ltm     175341 non-null  int64  \n",
            " 37  is_ftp_login       175341 non-null  int64  \n",
            " 38  ct_ftp_cmd         175341 non-null  int64  \n",
            " 39  ct_flw_http_mthd   175341 non-null  int64  \n",
            " 40  ct_src_ltm         175341 non-null  int64  \n",
            " 41  ct_srv_dst         175341 non-null  int64  \n",
            " 42  is_sm_ips_ports    175341 non-null  int64  \n",
            " 43  attack_cat         175341 non-null  object \n",
            " 44  label              175341 non-null  int64  \n",
            "dtypes: float64(11), int64(30), object(4)\n",
            "memory usage: 60.2+ MB\n",
            "\n",
            "Testing Dataset Information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 82332 entries, 0 to 82331\n",
            "Data columns (total 45 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   id                 82332 non-null  int64  \n",
            " 1   dur                82332 non-null  float64\n",
            " 2   proto              82332 non-null  object \n",
            " 3   service            82332 non-null  object \n",
            " 4   state              82332 non-null  object \n",
            " 5   spkts              82332 non-null  int64  \n",
            " 6   dpkts              82332 non-null  int64  \n",
            " 7   sbytes             82332 non-null  int64  \n",
            " 8   dbytes             82332 non-null  int64  \n",
            " 9   rate               82332 non-null  float64\n",
            " 10  sttl               82332 non-null  int64  \n",
            " 11  dttl               82332 non-null  int64  \n",
            " 12  sload              82332 non-null  float64\n",
            " 13  dload              82332 non-null  float64\n",
            " 14  sloss              82332 non-null  int64  \n",
            " 15  dloss              82332 non-null  int64  \n",
            " 16  sinpkt             82332 non-null  float64\n",
            " 17  dinpkt             82332 non-null  float64\n",
            " 18  sjit               82332 non-null  float64\n",
            " 19  djit               82332 non-null  float64\n",
            " 20  swin               82332 non-null  int64  \n",
            " 21  stcpb              82332 non-null  int64  \n",
            " 22  dtcpb              82332 non-null  int64  \n",
            " 23  dwin               82332 non-null  int64  \n",
            " 24  tcprtt             82332 non-null  float64\n",
            " 25  synack             82332 non-null  float64\n",
            " 26  ackdat             82332 non-null  float64\n",
            " 27  smean              82332 non-null  int64  \n",
            " 28  dmean              82332 non-null  int64  \n",
            " 29  trans_depth        82332 non-null  int64  \n",
            " 30  response_body_len  82332 non-null  int64  \n",
            " 31  ct_srv_src         82332 non-null  int64  \n",
            " 32  ct_state_ttl       82332 non-null  int64  \n",
            " 33  ct_dst_ltm         82332 non-null  int64  \n",
            " 34  ct_src_dport_ltm   82332 non-null  int64  \n",
            " 35  ct_dst_sport_ltm   82332 non-null  int64  \n",
            " 36  ct_dst_src_ltm     82332 non-null  int64  \n",
            " 37  is_ftp_login       82332 non-null  int64  \n",
            " 38  ct_ftp_cmd         82332 non-null  int64  \n",
            " 39  ct_flw_http_mthd   82332 non-null  int64  \n",
            " 40  ct_src_ltm         82332 non-null  int64  \n",
            " 41  ct_srv_dst         82332 non-null  int64  \n",
            " 42  is_sm_ips_ports    82332 non-null  int64  \n",
            " 43  attack_cat         82332 non-null  object \n",
            " 44  label              82332 non-null  int64  \n",
            "dtypes: float64(11), int64(30), object(4)\n",
            "memory usage: 28.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check missing values in training and testing datasets\n",
        "missing_train = train_df.isnull().sum().sort_values(ascending=False)\n",
        "missing_test = test_df.isnull().sum().sort_values(ascending=False)\n",
        "\n",
        "print(\"Top missing values in training dataset:\")\n",
        "display(missing_train.head(10))\n",
        "\n",
        "print(\"\\nTop missing values in testing dataset:\")\n",
        "display(missing_test.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "E7nhYiqBb1uK",
        "outputId": "37d268a0-f553-4962-8819-80765adcea75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top missing values in training dataset:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "id         0\n",
              "dur        0\n",
              "proto      0\n",
              "service    0\n",
              "state      0\n",
              "spkts      0\n",
              "dpkts      0\n",
              "sbytes     0\n",
              "dbytes     0\n",
              "rate       0\n",
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            "\n",
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        {
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              "id         0\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# Confirm total missing values across the full datasets\n",
        "total_missing_train = train_df.isnull().sum().sum()\n",
        "total_missing_test = test_df.isnull().sum().sum()\n",
        "\n",
        "print(\"Total missing values in training dataset:\", total_missing_train)\n",
        "print(\"Total missing values in testing dataset:\", total_missing_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E19pjDzpc2Qh",
        "outputId": "ce8f6ea3-4d9f-49ed-d681-b913cebcc789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total missing values in training dataset: 0\n",
            "Total missing values in testing dataset: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check duplicate records\n",
        "train_duplicates = train_df.duplicated().sum()\n",
        "test_duplicates = test_df.duplicated().sum()\n",
        "\n",
        "print(\"Duplicate rows in training dataset:\", train_duplicates)\n",
        "print(\"Duplicate rows in testing dataset:\", test_duplicates)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UbjBtlHSeUH1",
        "outputId": "e9b64440-6705-4e4c-b658-6abf8288359e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicate rows in training dataset: 0\n",
            "Duplicate rows in testing dataset: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Target variable distribution\n",
        "print(\"Training label distribution:\")\n",
        "display(train_df[\"label\"].value_counts())\n",
        "\n",
        "print(\"\\nTraining label distribution (%):\")\n",
        "display(train_df[\"label\"].value_counts(normalize=True) * 100)\n",
        "\n",
        "print(\"\\nTesting label distribution:\")\n",
        "display(test_df[\"label\"].value_counts())\n",
        "\n",
        "print(\"\\nTesting label distribution (%):\")\n",
        "display(test_df[\"label\"].value_counts(normalize=True) * 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "id": "FZHiULMuerSG",
        "outputId": "6d786367-8794-4ac5-905b-7dc477d0a8a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training label distribution:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68.062233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31.937767</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Testing label distribution:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "label\n",
              "1    45332\n",
              "0    37000\n",
              "Name: count, dtype: int64"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>37000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Testing label distribution (%):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "label\n",
              "1    55.060001\n",
              "0    44.939999\n",
              "Name: proportion, dtype: float64"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55.060001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>44.939999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot class distribution for the training dataset\n",
        "label_counts = train_df[\"label\"].value_counts().sort_index()\n",
        "\n",
        "plt.figure(figsize=(6, 4))\n",
        "plt.bar([\"Normal (0)\", \"Attack (1)\"], label_counts.values)\n",
        "plt.title(\"Training Dataset Class Distribution\")\n",
        "plt.xlabel(\"Class Label\")\n",
        "plt.ylabel(\"Number of Records\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "CbEZRvvff8_5",
        "outputId": "497f3180-1797-4630-ff15-9abe88cd538f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Attack category distribution\n",
        "print(\"Attack category distribution in training dataset:\")\n",
        "display(train_df[\"attack_cat\"].value_counts())\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "train_df[\"attack_cat\"].value_counts().plot(kind=\"bar\")\n",
        "plt.title(\"Attack Category Distribution in Training Dataset\")\n",
        "plt.xlabel(\"Attack Category\")\n",
        "plt.ylabel(\"Number of Records\")\n",
        "plt.xticks(rotation=45, ha=\"right\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 936
        },
        "id": "GpLKqCzUhx4P",
        "outputId": "55c4db65-ca3f-435a-890a-821386a4fbc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Attack category distribution in training dataset:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "attack_cat\n",
              "Normal            56000\n",
              "Generic           40000\n",
              "Exploits          33393\n",
              "Fuzzers           18184\n",
              "DoS               12264\n",
              "Reconnaissance    10491\n",
              "Analysis           2000\n",
              "Backdoor           1746\n",
              "Shellcode          1133\n",
              "Worms               130\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>attack_cat</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Normal</th>\n",
              "      <td>56000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Generic</th>\n",
              "      <td>40000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Exploits</th>\n",
              "      <td>33393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuzzers</th>\n",
              "      <td>18184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DoS</th>\n",
              "      <td>12264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Reconnaissance</th>\n",
              "      <td>10491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analysis</th>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Backdoor</th>\n",
              "      <td>1746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shellcode</th>\n",
              "      <td>1133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worms</th>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Feature and Target Separation\n",
        "# ============================================================\n",
        "\n",
        "# Target variable\n",
        "target_column = \"label\"\n",
        "\n",
        "# Columns excluded from model inputs\n",
        "# id is a record identifier and does not provide useful predictive meaning.\n",
        "# attack_cat is excluded to prevent target leakage because it directly describes the attack category.\n",
        "excluded_columns = [\"id\", \"attack_cat\", target_column]\n",
        "\n",
        "# Separate input features and target labels\n",
        "X_train = train_df.drop(columns=excluded_columns)\n",
        "y_train = train_df[target_column]\n",
        "\n",
        "X_test = test_df.drop(columns=excluded_columns)\n",
        "y_test = test_df[target_column]\n",
        "\n",
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"y_train shape:\", y_train.shape)\n",
        "print(\"X_test shape:\", X_test.shape)\n",
        "print(\"y_test shape:\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mT6SdxL3jJCR",
        "outputId": "86c8a6b2-f0b3-4a29-9413-108adb476a1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (175341, 42)\n",
            "y_train shape: (175341,)\n",
            "X_test shape: (82332, 42)\n",
            "y_test shape: (82332,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify categorical and numerical features\n",
        "categorical_features = X_train.select_dtypes(include=[\"object\"]).columns.tolist()\n",
        "numerical_features = X_train.select_dtypes(exclude=[\"object\"]).columns.tolist()\n",
        "\n",
        "print(\"Categorical features:\", categorical_features)\n",
        "print(\"Number of categorical features:\", len(categorical_features))\n",
        "\n",
        "print(\"\\nNumber of numerical features:\", len(numerical_features))\n",
        "print(\"Numerical features:\")\n",
        "print(numerical_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xEYvWVUQjbud",
        "outputId": "ed9783b7-bdd7-4d7e-ccc2-5be9707bd1ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Categorical features: ['proto', 'service', 'state']\n",
            "Number of categorical features: 3\n",
            "\n",
            "Number of numerical features: 39\n",
            "Numerical features:\n",
            "['dur', 'spkts', 'dpkts', 'sbytes', 'dbytes', 'rate', 'sttl', 'dttl', 'sload', 'dload', 'sloss', 'dloss', 'sinpkt', 'dinpkt', 'sjit', 'djit', 'swin', 'stcpb', 'dtcpb', 'dwin', 'tcprtt', 'synack', 'ackdat', 'smean', 'dmean', 'trans_depth', 'response_body_len', 'ct_srv_src', 'ct_state_ttl', 'ct_dst_ltm', 'ct_src_dport_ltm', 'ct_dst_sport_ltm', 'ct_dst_src_ltm', 'is_ftp_login', 'ct_ftp_cmd', 'ct_flw_http_mthd', 'ct_src_ltm', 'ct_srv_dst', 'is_sm_ips_ports']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Preprocessing Pipeline\n",
        "# ============================================================\n",
        "\n",
        "# OneHotEncoder converts categorical variables into machine-readable binary columns.\n",
        "# StandardScaler standardises numerical variables so that Logistic Regression is not dominated by large-scale features.\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), categorical_features),\n",
        "        (\"num\", StandardScaler(), numerical_features)\n",
        "    ]\n",
        ")\n",
        "\n",
        "print(\"Preprocessing pipeline created successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IGwbuiG5jhXo",
        "outputId": "45996cd1-16c2-408e-8cf1-06531840f629"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Preprocessing pipeline created successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test 1: Logistic Regression Model"
      ],
      "metadata": {
        "id": "1dT5o0mF7axg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Baseline Model: Logistic Regression\n",
        "# ============================================================\n",
        "\n",
        "logistic_regression_model = Pipeline(\n",
        "    steps=[\n",
        "        (\"preprocessor\", preprocessor),\n",
        "        (\"classifier\", LogisticRegression(\n",
        "            max_iter=1000,\n",
        "            class_weight=\"balanced\",\n",
        "            random_state=42,\n",
        "            n_jobs=-1\n",
        "        ))\n",
        "    ]\n",
        ")\n",
        "\n",
        "print(\"Logistic Regression pipeline created successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OmAjvrGNseI7",
        "outputId": "74ffa01e-3082-4ac6-b636-ee7170bb4f09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression pipeline created successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Logistic Regression baseline model\n",
        "logistic_regression_model.fit(X_train, y_train)\n",
        "\n",
        "print(\"Logistic Regression model trained successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCITDhwLtMeO",
        "outputId": "bcd3ffce-bbd1-41c6-863f-dfa18c996468"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression model trained successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate predictions\n",
        "y_pred_lr = logistic_regression_model.predict(X_test)\n",
        "\n",
        "# Generate prediction probabilities for ROC-AUC\n",
        "y_proba_lr = logistic_regression_model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "print(\"Predictions generated successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_4jwbBJuMiu",
        "outputId": "9205da29-0c32-466d-cc0d-388f2fcb62d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions generated successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Logistic Regression Evaluation\n",
        "# ============================================================\n",
        "\n",
        "accuracy_lr = accuracy_score(y_test, y_pred_lr)\n",
        "precision_lr = precision_score(y_test, y_pred_lr)\n",
        "recall_lr = recall_score(y_test, y_pred_lr)\n",
        "f1_lr = f1_score(y_test, y_pred_lr)\n",
        "roc_auc_lr = roc_auc_score(y_test, y_proba_lr)\n",
        "\n",
        "print(\"Logistic Regression Performance\")\n",
        "print(\"--------------------------------\")\n",
        "print(f\"Accuracy:  {accuracy_lr:.4f}\")\n",
        "print(f\"Precision: {precision_lr:.4f}\")\n",
        "print(f\"Recall:    {recall_lr:.4f}\")\n",
        "print(f\"F1-score:  {f1_lr:.4f}\")\n",
        "print(f\"ROC-AUC:   {roc_auc_lr:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STvMgoweurX7",
        "outputId": "2f195424-4317-447f-8fcb-eb29a8bebdeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Performance\n",
            "--------------------------------\n",
            "Accuracy:  0.8354\n",
            "Precision: 0.8023\n",
            "Recall:    0.9303\n",
            "F1-score:  0.8616\n",
            "ROC-AUC:   0.9558\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Detailed classification report\n",
        "print(\"Classification Report: Logistic Regression\")\n",
        "print(classification_report(y_test, y_pred_lr, target_names=[\"Normal\", \"Attack\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuqCHgFKwQK_",
        "outputId": "dd14f0bf-425a-48c7-9a7a-94ec03792c99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report: Logistic Regression\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Normal       0.89      0.72      0.80     37000\n",
            "      Attack       0.80      0.93      0.86     45332\n",
            "\n",
            "    accuracy                           0.84     82332\n",
            "   macro avg       0.85      0.82      0.83     82332\n",
            "weighted avg       0.84      0.84      0.83     82332\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "cm_lr = confusion_matrix(y_test, y_pred_lr)\n",
        "\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm_lr)\n",
        "\n",
        "plt.figure(figsize=(6, 5))\n",
        "plt.imshow(cm_lr)\n",
        "plt.title(\"Confusion Matrix - Logistic Regression\")\n",
        "plt.xlabel(\"Predicted Label\")\n",
        "plt.ylabel(\"True Label\")\n",
        "plt.xticks([0, 1], [\"Normal\", \"Attack\"])\n",
        "plt.yticks([0, 1], [\"Normal\", \"Attack\"])\n",
        "\n",
        "# Add values inside the matrix\n",
        "for i in range(cm_lr.shape[0]):\n",
        "    for j in range(cm_lr.shape[1]):\n",
        "        plt.text(j, i, cm_lr[i, j], ha=\"center\", va=\"center\")\n",
        "\n",
        "plt.colorbar()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "GnqfTEhgxLDN",
        "outputId": "1b09914e-ec73-4194-82c5-0845f2ea5e8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[26608 10392]\n",
            " [ 3160 42172]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ROC curve\n",
        "fpr_lr, tpr_lr, thresholds_lr = roc_curve(y_test, y_proba_lr)\n",
        "\n",
        "plt.figure(figsize=(6, 5))\n",
        "plt.plot(fpr_lr, tpr_lr, label=f\"Logistic Regression ROC-AUC = {roc_auc_lr:.4f}\")\n",
        "plt.plot([0, 1], [0, 1], linestyle=\"--\")\n",
        "plt.title(\"ROC Curve - Logistic Regression\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "RFbi43aBxnCf",
        "outputId": "e0c89644-68a8-477c-c9b3-df951aabd3dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Store model performance for later comparison\n",
        "model_results = pd.DataFrame({\n",
        "    \"Model\": [\"Logistic Regression\"],\n",
        "    \"Accuracy\": [accuracy_lr],\n",
        "    \"Precision\": [precision_lr],\n",
        "    \"Recall\": [recall_lr],\n",
        "    \"F1-score\": [f1_lr],\n",
        "    \"ROC-AUC\": [roc_auc_lr]\n",
        "})\n",
        "\n",
        "model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "zxHbsBrIyDj7",
        "outputId": "9ac8ba79-2652-4cce-fb00-7a941648bc92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Accuracy  Precision    Recall  F1-score   ROC-AUC\n",
              "0  Logistic Regression  0.835398   0.802298  0.930292  0.861567  0.955842"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43f80d9a-bab1-4bca-9748-625461fbdd3d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1-score</th>\n",
              "      <th>ROC-AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.835398</td>\n",
              "      <td>0.802298</td>\n",
              "      <td>0.930292</td>\n",
              "      <td>0.861567</td>\n",
              "      <td>0.955842</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43f80d9a-bab1-4bca-9748-625461fbdd3d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "type": "dataframe",
              "variable_name": "model_results",
              "summary": "{\n  \"name\": \"model_results\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8353981440994996,\n        \"max\": 0.8353981440994996,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8353981440994996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8022981508256601,\n        \"max\": 0.8022981508256601,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8022981508256601\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.9302920674137475,\n        \"max\": 0.9302920674137475,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9302920674137475\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8615673776252349,\n        \"max\": 0.8615673776252349,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8615673776252349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.9558424721156347,\n        \"max\": 0.9558424721156347,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9558424721156347\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initial Interpretation: Logistic Regression Baseline\n",
        "\n",
        "Logistic Regression was used as the baseline supervised machine-learning model because it is simple, efficient and interpretable. In the context of banking cybersecurity, a baseline model is useful because it provides an initial benchmark against which more complex models, such as Decision Tree and Random Forest, can be compared.\n",
        "\n",
        "The Logistic Regression baseline achieved an accuracy of 0.8354, precision of 0.8023, recall of 0.9303, F1-score of 0.8616 and ROC-AUC of 0.9558. The high recall indicates that the model was effective at identifying attack traffic, which is important in cybersecurity because missed attacks can create serious operational and security risks.\n",
        "\n",
        "The confusion matrix showed 42,172 true attack predictions and 3,160 false negatives. This means that although most attacks were detected, some attack records were still incorrectly classified as normal. The model also produced 10,392 false positives, where normal traffic was incorrectly classified as attack traffic. In a banking cybersecurity context, false negatives may increase security risk, while false positives may increase analyst workload.\n",
        "\n",
        "Therefore, the model should not be judged by accuracy alone. Precision, recall, F1-score, ROC-AUC and confusion matrix analysis provide a more complete evaluation. These findings also support the need for the proposed AI governance layer, including explainability, confidence scores, risk thresholds, audit logging and human review."
      ],
      "metadata": {
        "id": "jKG97tVGz845"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test 2: Decision Tree Model"
      ],
      "metadata": {
        "id": "5d9JoBdd0Rq1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "print(\"Decision Tree library imported successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ArR1p_zW0YrW",
        "outputId": "c0c58ed5-1451-4052-9b31-90ded6c0d264"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree library imported successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Model 2: Decision Tree Classifier\n",
        "# ============================================================\n",
        "\n",
        "decision_tree_model = Pipeline(\n",
        "    steps=[\n",
        "        (\"preprocessor\", preprocessor),\n",
        "        (\"classifier\", DecisionTreeClassifier(\n",
        "            criterion=\"gini\",\n",
        "            max_depth=12,\n",
        "            min_samples_split=50,\n",
        "            min_samples_leaf=25,\n",
        "            class_weight=\"balanced\",\n",
        "            random_state=42\n",
        "        ))\n",
        "    ]\n",
        ")\n",
        "\n",
        "print(\"Decision Tree pipeline created successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tcq3U5Cp0gxS",
        "outputId": "e774a12a-6625-4dea-8269-88aa9e749182"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree pipeline created successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Decision Tree model\n",
        "decision_tree_model.fit(X_train, y_train)\n",
        "\n",
        "print(\"Decision Tree model trained successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fWGaWMkH0ql9",
        "outputId": "815323ed-4324-4800-ecff-4ebbb675dac5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree model trained successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate predictions\n",
        "y_pred_dt = decision_tree_model.predict(X_test)\n",
        "\n",
        "# Generate prediction probabilities for ROC-AUC\n",
        "y_proba_dt = decision_tree_model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "print(\"Decision Tree predictions generated successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Po7UoV6s0yah",
        "outputId": "2e00f30e-c60c-4970-8c43-40692cab147c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree predictions generated successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Decision Tree Evaluation\n",
        "# ============================================================\n",
        "\n",
        "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
        "precision_dt = precision_score(y_test, y_pred_dt)\n",
        "recall_dt = recall_score(y_test, y_pred_dt)\n",
        "f1_dt = f1_score(y_test, y_pred_dt)\n",
        "roc_auc_dt = roc_auc_score(y_test, y_proba_dt)\n",
        "\n",
        "print(\"Decision Tree Performance\")\n",
        "print(\"-------------------------\")\n",
        "print(f\"Accuracy:  {accuracy_dt:.4f}\")\n",
        "print(f\"Precision: {precision_dt:.4f}\")\n",
        "print(f\"Recall:    {recall_dt:.4f}\")\n",
        "print(f\"F1-score:  {f1_dt:.4f}\")\n",
        "print(f\"ROC-AUC:   {roc_auc_dt:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySuBuW1O3K4U",
        "outputId": "07828adc-819b-45f4-ec3d-0411fcaf1f7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Performance\n",
            "-------------------------\n",
            "Accuracy:  0.9211\n",
            "Precision: 0.9043\n",
            "Recall:    0.9581\n",
            "F1-score:  0.9304\n",
            "ROC-AUC:   0.9756\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Detailed classification report\n",
        "print(\"Classification Report: Decision Tree\")\n",
        "print(classification_report(y_test, y_pred_dt, target_names=[\"Normal\", \"Attack\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_iZBrvZ3S4e",
        "outputId": "c03eae8c-781f-49aa-fa04-969cf7c51724"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report: Decision Tree\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Normal       0.94      0.88      0.91     37000\n",
            "      Attack       0.90      0.96      0.93     45332\n",
            "\n",
            "    accuracy                           0.92     82332\n",
            "   macro avg       0.92      0.92      0.92     82332\n",
            "weighted avg       0.92      0.92      0.92     82332\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "cm_dt = confusion_matrix(y_test, y_pred_dt)\n",
        "\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm_dt)\n",
        "\n",
        "plt.figure(figsize=(6, 5))\n",
        "plt.imshow(cm_dt)\n",
        "plt.title(\"Confusion Matrix - Decision Tree\")\n",
        "plt.xlabel(\"Predicted Label\")\n",
        "plt.ylabel(\"True Label\")\n",
        "plt.xticks([0, 1], [\"Normal\", \"Attack\"])\n",
        "plt.yticks([0, 1], [\"Normal\", \"Attack\"])\n",
        "\n",
        "# Add values inside the matrix\n",
        "for i in range(cm_dt.shape[0]):\n",
        "    for j in range(cm_dt.shape[1]):\n",
        "        plt.text(j, i, cm_dt[i, j], ha=\"center\", va=\"center\")\n",
        "\n",
        "plt.colorbar()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "IFu0fbsS3oKh",
        "outputId": "2940e51f-3731-4775-d099-de97350b0880"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[32403  4597]\n",
            " [ 1900 43432]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ROC curve\n",
        "fpr_dt, tpr_dt, thresholds_dt = roc_curve(y_test, y_proba_dt)\n",
        "\n",
        "plt.figure(figsize=(6, 5))\n",
        "plt.plot(fpr_dt, tpr_dt, label=f\"Decision Tree ROC-AUC = {roc_auc_dt:.4f}\")\n",
        "plt.plot([0, 1], [0, 1], linestyle=\"--\")\n",
        "plt.title(\"ROC Curve - Decision Tree\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "RGoY5i0w36nH",
        "outputId": "aed06782-db6d-4be6-e526-7bd4425e1e34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add Decision Tree results to model comparison table\n",
        "decision_tree_results = pd.DataFrame({\n",
        "    \"Model\": [\"Decision Tree\"],\n",
        "    \"Accuracy\": [accuracy_dt],\n",
        "    \"Precision\": [precision_dt],\n",
        "    \"Recall\": [recall_dt],\n",
        "    \"F1-score\": [f1_dt],\n",
        "    \"ROC-AUC\": [roc_auc_dt]\n",
        "})\n",
        "\n",
        "model_results = pd.concat([model_results, decision_tree_results], ignore_index=True)\n",
        "\n",
        "model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "m_FfIvbM4PNo",
        "outputId": "b3b1e81b-e24f-4e5f-ae25-db5b08a68a28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Accuracy  Precision    Recall  F1-score   ROC-AUC\n",
              "0  Logistic Regression  0.835398   0.802298  0.930292  0.861567  0.955842\n",
              "1        Decision Tree  0.921088   0.904287  0.958087  0.930410  0.975614"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5eebf5d3-74ce-4281-a77f-5f302b45d6a4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1-score</th>\n",
              "      <th>ROC-AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.835398</td>\n",
              "      <td>0.802298</td>\n",
              "      <td>0.930292</td>\n",
              "      <td>0.861567</td>\n",
              "      <td>0.955842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.921088</td>\n",
              "      <td>0.904287</td>\n",
              "      <td>0.958087</td>\n",
              "      <td>0.930410</td>\n",
              "      <td>0.975614</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5eebf5d3-74ce-4281-a77f-5f302b45d6a4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5eebf5d3-74ce-4281-a77f-5f302b45d6a4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5eebf5d3-74ce-4281-a77f-5f302b45d6a4');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
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              "type": "dataframe",
              "variable_name": "model_results",
              "summary": "{\n  \"name\": \"model_results\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Decision Tree\",\n          \"Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06059173032686067,\n        \"min\": 0.8353981440994996,\n        \"max\": 0.9210877908953992,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9210877908953992,\n          0.8353981440994996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07211700210122135,\n        \"min\": 0.8022981508256601,\n        \"max\": 0.9042869932748964,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9042869932748964,\n          0.8022981508256601\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01965398712377678,\n        \"min\": 0.9302920674137475,\n        \"max\": 0.9580870025588988,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9580870025588988,\n          0.9302920674137475\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04867902444893038,\n        \"min\": 0.8615673776252349,\n        \"max\": 0.9304099142040038,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9304099142040038,\n          0.8615673776252349\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013980559360860905,\n        \"min\": 0.9558424721156347,\n        \"max\": 0.9756139687733263,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9756139687733263,\n          0.9558424721156347\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interpretation: Decision Tree Model\n",
        "\n",
        "The Decision Tree model improved on the Logistic Regression baseline across all evaluation metrics. It achieved an accuracy of 0.9211, precision of 0.9043, recall of 0.9581, F1-score of 0.9304 and ROC-AUC of 0.9756. This indicates that the Decision Tree was more effective at classifying normal and attack traffic in the UNSW-NB15 testing set.\n",
        "\n",
        "The confusion matrix showed 43,432 correctly detected attack records and 1,900 false negatives. This is important in a banking cybersecurity context because false negatives represent attack traffic incorrectly classified as normal. Compared with Logistic Regression, the Decision Tree reduced false negatives and false positives, meaning it improved both cyber-risk detection and analyst workload management.\n",
        "\n",
        "The results suggest that a tree-based approach is more suitable than the linear baseline for this dataset, likely because network traffic patterns contain non-linear relationships. However, a single Decision Tree can still be vulnerable to overfitting, so the next stage will evaluate Random Forest as an ensemble model. Random Forest is expected to provide stronger generalisation by combining multiple decision trees."
      ],
      "metadata": {
        "id": "f_yDNObX5t2T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test 3: Random Forest Model"
      ],
      "metadata": {
        "id": "hsLNsO0B6CHz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import time\n",
        "\n",
        "print(\"Random Forest library imported successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZO9LQoY-6F5x",
        "outputId": "2fad89ac-cd26-4f87-d992-bfdb3ea12b35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest library imported successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Model 3: Random Forest Classifier\n",
        "# ============================================================\n",
        "\n",
        "random_forest_model = Pipeline(\n",
        "    steps=[\n",
        "        (\"preprocessor\", preprocessor),\n",
        "        (\"classifier\", RandomForestClassifier(\n",
        "            n_estimators=200,\n",
        "            criterion=\"gini\",\n",
        "            max_depth=None,\n",
        "            min_samples_split=10,\n",
        "            min_samples_leaf=5,\n",
        "            max_features=\"sqrt\",\n",
        "            class_weight=\"balanced\",\n",
        "            random_state=42,\n",
        "            n_jobs=-1\n",
        "        ))\n",
        "    ]\n",
        ")\n",
        "\n",
        "print(\"Random Forest pipeline created successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZWpGWZxW6NxE",
        "outputId": "88e0160f-857f-49d0-9826-3a77a537cff7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest pipeline created successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Random Forest model and record training time\n",
        "start_time_rf = time.time()\n",
        "\n",
        "random_forest_model.fit(X_train, y_train)\n",
        "\n",
        "end_time_rf = time.time()\n",
        "training_time_rf = end_time_rf - start_time_rf\n",
        "\n",
        "print(\"Random Forest model trained successfully.\")\n",
        "print(f\"Training time: {training_time_rf:.2f} seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDN_WPam76Am",
        "outputId": "fab2eb6f-27cb-46cd-e21e-1889f41b50ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest model trained successfully.\n",
            "Training time: 242.98 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate predictions\n",
        "y_pred_rf = random_forest_model.predict(X_test)\n",
        "\n",
        "# Generate prediction probabilities for ROC-AUC\n",
        "y_proba_rf = random_forest_model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "print(\"Random Forest predictions generated successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zW9jrjHp8sfD",
        "outputId": "98c5f1b0-a50f-4376-dd4a-442517d63c24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest predictions generated successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Random Forest Evaluation\n",
        "# ============================================================\n",
        "\n",
        "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
        "precision_rf = precision_score(y_test, y_pred_rf)\n",
        "recall_rf = recall_score(y_test, y_pred_rf)\n",
        "f1_rf = f1_score(y_test, y_pred_rf)\n",
        "roc_auc_rf = roc_auc_score(y_test, y_proba_rf)\n",
        "\n",
        "print(\"Random Forest Performance\")\n",
        "print(\"-------------------------\")\n",
        "print(f\"Accuracy:  {accuracy_rf:.4f}\")\n",
        "print(f\"Precision: {precision_rf:.4f}\")\n",
        "print(f\"Recall:    {recall_rf:.4f}\")\n",
        "print(f\"F1-score:  {f1_rf:.4f}\")\n",
        "print(f\"ROC-AUC:   {roc_auc_rf:.4f}\")\n",
        "print(f\"Training time: {training_time_rf:.2f} seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPf6wGXC99f2",
        "outputId": "eb336cae-39f8-4635-a4ac-317c855568ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Performance\n",
            "-------------------------\n",
            "Accuracy:  0.9006\n",
            "Precision: 0.8636\n",
            "Recall:    0.9733\n",
            "F1-score:  0.9151\n",
            "ROC-AUC:   0.9845\n",
            "Training time: 242.98 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Detailed classification report\n",
        "print(\"Classification Report: Random Forest\")\n",
        "print(classification_report(y_test, y_pred_rf, target_names=[\"Normal\", \"Attack\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4t76837-8Ul",
        "outputId": "c6fbf566-3921-47df-f9a5-5503dc23b97e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report: Random Forest\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Normal       0.96      0.81      0.88     37000\n",
            "      Attack       0.86      0.97      0.92     45332\n",
            "\n",
            "    accuracy                           0.90     82332\n",
            "   macro avg       0.91      0.89      0.90     82332\n",
            "weighted avg       0.91      0.90      0.90     82332\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
        "\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm_rf)\n",
        "\n",
        "plt.figure(figsize=(6, 5))\n",
        "plt.imshow(cm_rf)\n",
        "plt.title(\"Confusion Matrix - Random Forest\")\n",
        "plt.xlabel(\"Predicted Label\")\n",
        "plt.ylabel(\"True Label\")\n",
        "plt.xticks([0, 1], [\"Normal\", \"Attack\"])\n",
        "plt.yticks([0, 1], [\"Normal\", \"Attack\"])\n",
        "\n",
        "# Add values inside the matrix\n",
        "for i in range(cm_rf.shape[0]):\n",
        "    for j in range(cm_rf.shape[1]):\n",
        "        plt.text(j, i, cm_rf[i, j], ha=\"center\", va=\"center\")\n",
        "\n",
        "plt.colorbar()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "xVT4zZy4--M6",
        "outputId": "ee5bb919-579e-4a50-83af-ef8d90b6eb13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[30030  6970]\n",
            " [ 1212 44120]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ROC curve\n",
        "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, y_proba_rf)\n",
        "\n",
        "plt.figure(figsize=(6, 5))\n",
        "plt.plot(fpr_rf, tpr_rf, label=f\"Random Forest ROC-AUC = {roc_auc_rf:.4f}\")\n",
        "plt.plot([0, 1], [0, 1], linestyle=\"--\")\n",
        "plt.title(\"ROC Curve - Random Forest\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "weT7Mr8H_P7M",
        "outputId": "cf72c409-a5a8-4a88-9661-9b6b8179e752"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add Random Forest results to model comparison table\n",
        "random_forest_results = pd.DataFrame({\n",
        "    \"Model\": [\"Random Forest\"],\n",
        "    \"Accuracy\": [accuracy_rf],\n",
        "    \"Precision\": [precision_rf],\n",
        "    \"Recall\": [recall_rf],\n",
        "    \"F1-score\": [f1_rf],\n",
        "    \"ROC-AUC\": [roc_auc_rf]\n",
        "})\n",
        "\n",
        "model_results = pd.concat([model_results, random_forest_results], ignore_index=True)\n",
        "\n",
        "model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "amb2PwEV_f6H",
        "outputId": "592619da-d20f-4f86-d801-6ecfe0469f4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Accuracy  Precision    Recall  F1-score   ROC-AUC\n",
              "0  Logistic Regression  0.835398   0.802298  0.930292  0.861567  0.955842\n",
              "1        Decision Tree  0.921088   0.904287  0.958087  0.930410  0.975614\n",
              "2        Random Forest  0.900622   0.863574  0.973264  0.915144  0.984536"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dbf02593-58c7-4c80-8716-a530ea9a277a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1-score</th>\n",
              "      <th>ROC-AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.835398</td>\n",
              "      <td>0.802298</td>\n",
              "      <td>0.930292</td>\n",
              "      <td>0.861567</td>\n",
              "      <td>0.955842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.921088</td>\n",
              "      <td>0.904287</td>\n",
              "      <td>0.958087</td>\n",
              "      <td>0.930410</td>\n",
              "      <td>0.975614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.900622</td>\n",
              "      <td>0.863574</td>\n",
              "      <td>0.973264</td>\n",
              "      <td>0.915144</td>\n",
              "      <td>0.984536</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbf02593-58c7-4c80-8716-a530ea9a277a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-dbf02593-58c7-4c80-8716-a530ea9a277a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dbf02593-58c7-4c80-8716-a530ea9a277a');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interpretation: Random Forest Model\n",
        "\n",
        "The Random Forest model achieved an accuracy of 0.9006, precision of 0.8636, recall of 0.9733, F1-score of 0.9151 and ROC-AUC of 0.9845. Although its accuracy and F1-score were lower than the Decision Tree model, it achieved the highest recall and ROC-AUC of all three models.\n",
        "\n",
        "This is important in a banking cybersecurity context because recall measures the proportion of actual attacks detected by the model. The Random Forest model produced only 1,212 false negatives, meaning it missed fewer attacks than both Logistic Regression and Decision Tree. However, it produced 6,970 false positives, which means it incorrectly flagged more normal records as attacks than the Decision Tree model.\n",
        "\n",
        "These results show a practical cybersecurity trade-off. Random Forest is stronger for reducing missed attacks, while Decision Tree currently offers a better balance between attack detection and analyst workload. Therefore, the final model should not be selected only by accuracy. The decision should consider recall, false negatives, false positives, ROC-AUC, explainability and suitability for the proposed AI governance dashboard."
      ],
      "metadata": {
        "id": "Z51E7IHLDUQ3"
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    {
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      "source": [
        "# ============================================================\n",
        "# Detailed Model Comparison: Confusion Matrix Outcomes\n",
        "# ============================================================\n",
        "\n",
        "def extract_confusion_values(cm):\n",
        "    tn, fp, fn, tp = cm.ravel()\n",
        "    return tn, fp, fn, tp\n",
        "\n",
        "tn_lr, fp_lr, fn_lr, tp_lr = extract_confusion_values(cm_lr)\n",
        "tn_dt, fp_dt, fn_dt, tp_dt = extract_confusion_values(cm_dt)\n",
        "tn_rf, fp_rf, fn_rf, tp_rf = extract_confusion_values(cm_rf)\n",
        "\n",
        "detailed_model_comparison = pd.DataFrame({\n",
        "    \"Model\": [\"Logistic Regression\", \"Decision Tree\", \"Random Forest\"],\n",
        "    \"Accuracy\": [accuracy_lr, accuracy_dt, accuracy_rf],\n",
        "    \"Precision\": [precision_lr, precision_dt, precision_rf],\n",
        "    \"Recall\": [recall_lr, recall_dt, recall_rf],\n",
        "    \"F1-score\": [f1_lr, f1_dt, f1_rf],\n",
        "    \"ROC-AUC\": [roc_auc_lr, roc_auc_dt, roc_auc_rf],\n",
        "    \"True Normal\": [tn_lr, tn_dt, tn_rf],\n",
        "    \"False Positives\": [fp_lr, fp_dt, fp_rf],\n",
        "    \"False Negatives\": [fn_lr, fn_dt, fn_rf],\n",
        "    \"True Attacks\": [tp_lr, tp_dt, tp_rf]\n",
        "})\n",
        "\n",
        "detailed_model_comparison"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "R0D-7qypDWZB",
        "outputId": "043e0b08-562a-4070-9b1e-2eeb2e5ed375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Accuracy  Precision    Recall  F1-score   ROC-AUC  True Normal  False Positives  \\\n",
              "0  Logistic Regression  0.835398   0.802298  0.930292  0.861567  0.955842        26608            10392   \n",
              "1        Decision Tree  0.921088   0.904287  0.958087  0.930410  0.975614        32403             4597   \n",
              "2        Random Forest  0.900622   0.863574  0.973264  0.915144  0.984536        30030             6970   \n",
              "\n",
              "   False Negatives  True Attacks  \n",
              "0             3160         42172  \n",
              "1             1900         43432  \n",
              "2             1212         44120  "
            ],
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              "      <th>Model</th>\n",
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              "summary": "{\n  \"name\": \"detailed_model_comparison\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Logistic Regression\",\n          \"Decision Tree\",\n          \"Random Forest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04475061288776466,\n        \"min\": 0.8353981440994996,\n        \"max\": 0.9210877908953992,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8353981440994996,\n          0.9210877908953992,\n          0.9006218724189865\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.051338752405208986,\n        \"min\": 0.8022981508256601,\n        \"max\": 0.9042869932748964,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8022981508256601,\n          0.9042869932748964,\n          0.8635740849481307\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.021792496041941736,\n        \"min\": 0.9302920674137475,\n        \"max\": 0.9732639195270449,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9302920674137475,\n          0.9580870025588988,\n          0.9732639195270449\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.036154266020478,\n        \"min\": 0.8615673776252349,\n        \"max\": 0.9304099142040038,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8615673776252349,\n          0.9304099142040038,\n          0.9151438468399328\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01468473606720578,\n        \"min\": 0.9558424721156347,\n        \"max\": 0.9845362070466304,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9558424721156347,\n          0.9756139687733263,\n          0.9845362070466304\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"True Normal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2913,\n        \"min\": 26608,\n        \"max\": 32403,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          26608,\n          32403,\n          30030\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"False Positives\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2913,\n        \"min\": 4597,\n        \"max\": 10392,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10392,\n          4597,\n          6970\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"False Negatives\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 987,\n        \"min\": 1212,\n        \"max\": 3160,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3160,\n          1900,\n          1212\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"True Attacks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 987,\n        \"min\": 42172,\n        \"max\": 44120,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          42172,\n          43432,\n          44120\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analysis: Random Forest"
      ],
      "metadata": {
        "id": "SBzVKP4QExQx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Model Analysis: Random Forest\n",
        "# ============================================================\n",
        "\n",
        "thresholds_to_test = [0.30, 0.40, 0.50, 0.60, 0.70]\n",
        "\n",
        "threshold_results = []\n",
        "\n",
        "for threshold in thresholds_to_test:\n",
        "    # Convert probabilities into binary predictions using custom threshold\n",
        "    y_pred_threshold = (y_proba_rf >= threshold).astype(int)\n",
        "\n",
        "    # Evaluation metrics\n",
        "    accuracy = accuracy_score(y_test, y_pred_threshold)\n",
        "    precision = precision_score(y_test, y_pred_threshold)\n",
        "    recall = recall_score(y_test, y_pred_threshold)\n",
        "    f1 = f1_score(y_test, y_pred_threshold)\n",
        "\n",
        "    # Confusion matrix values\n",
        "    tn, fp, fn, tp = confusion_matrix(y_test, y_pred_threshold).ravel()\n",
        "\n",
        "    threshold_results.append({\n",
        "        \"Threshold\": threshold,\n",
        "        \"Accuracy\": accuracy,\n",
        "        \"Precision\": precision,\n",
        "        \"Recall\": recall,\n",
        "        \"F1-score\": f1,\n",
        "        \"True Normal\": tn,\n",
        "        \"False Positives\": fp,\n",
        "        \"False Negatives\": fn,\n",
        "        \"True Attacks\": tp\n",
        "    })\n",
        "\n",
        "threshold_results_df = pd.DataFrame(threshold_results)\n",
        "\n",
        "threshold_results_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PdLHmglSE0fe",
        "outputId": "f85444b3-2946-435e-f7ae-35daafbdf290"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Threshold  Accuracy  Precision    Recall  F1-score  True Normal  False Positives  False Negatives  True Attacks\n",
              "0        0.3  0.837366   0.773410  0.996603  0.870935        23764            13236              154         45178\n",
              "1        0.4  0.872492   0.817991  0.988331  0.895129        27031             9969              529         44803\n",
              "2        0.5  0.900622   0.863574  0.973264  0.915144        30030             6970             1212         44120\n",
              "3        0.6  0.917808   0.902885  0.953256  0.927387        32352             4648             2119         43213\n",
              "4        0.7  0.927076   0.943102  0.923255  0.933073        34475             2525             3479         41853"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Threshold</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1-score</th>\n",
              "      <th>True Normal</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.3</td>\n",
              "      <td>0.837366</td>\n",
              "      <td>0.773410</td>\n",
              "      <td>0.996603</td>\n",
              "      <td>0.870935</td>\n",
              "      <td>23764</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.4</td>\n",
              "      <td>0.872492</td>\n",
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              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.6</td>\n",
              "      <td>0.917808</td>\n",
              "      <td>0.902885</td>\n",
              "      <td>0.953256</td>\n",
              "      <td>0.927387</td>\n",
              "      <td>32352</td>\n",
              "      <td>4648</td>\n",
              "      <td>2119</td>\n",
              "      <td>43213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.927076</td>\n",
              "      <td>0.943102</td>\n",
              "      <td>0.923255</td>\n",
              "      <td>0.933073</td>\n",
              "      <td>34475</td>\n",
              "      <td>2525</td>\n",
              "      <td>3479</td>\n",
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              "type": "dataframe",
              "variable_name": "threshold_results_df",
              "summary": "{\n  \"name\": \"threshold_results_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15811388300841894,\n        \"min\": 0.3,\n        \"max\": 0.7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.4,\n          0.7,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0365098706601306,\n        \"min\": 0.8373657873001992,\n        \"max\": 0.9270757421172813,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8724918622163922,\n          0.9270757421172813,\n          0.9006218724189865\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06711979896161781,\n        \"min\": 0.7734104837881329,\n        \"max\": 0.9431024381450268,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8179909442780983,\n          0.9431024381450268,\n          0.8635740849481307\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.029466313713567328,\n        \"min\": 0.9232550957381099,\n        \"max\": 0.9966028412600371,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9883305391335039,\n          0.9232550957381099,\n          0.9732639195270449\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0254681264936943,\n        \"min\": 0.8709347830277794,\n        \"max\": 0.9330732359826106,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8951290657715976,\n          0.9330732359826106,\n          0.9151438468399328\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"True Normal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4247,\n        \"min\": 23764,\n        \"max\": 34475,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          27031,\n          34475,\n          30030\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"False Positives\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4247,\n        \"min\": 2525,\n        \"max\": 13236,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9969,\n          2525,\n          6970\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"False Negatives\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1335,\n        \"min\": 154,\n        \"max\": 3479,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          529,\n          3479,\n          1212\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"True Attacks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1335,\n        \"min\": 41853,\n        \"max\": 45178,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          44803,\n          41853,\n          44120\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot threshold impact on precision, recall and F1-score\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(threshold_results_df[\"Threshold\"], threshold_results_df[\"Precision\"], marker=\"o\", label=\"Precision\")\n",
        "plt.plot(threshold_results_df[\"Threshold\"], threshold_results_df[\"Recall\"], marker=\"o\", label=\"Recall\")\n",
        "plt.plot(threshold_results_df[\"Threshold\"], threshold_results_df[\"F1-score\"], marker=\"o\", label=\"F1-score\")\n",
        "\n",
        "plt.title(\"Random Forest Threshold Impact on Precision, Recall and F1-score\")\n",
        "plt.xlabel(\"Classification Threshold\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "5-rjBsU0FqBO",
        "outputId": "811356b8-a482-4f89-e58b-55b05f351253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot threshold impact on false positives and false negatives\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(threshold_results_df[\"Threshold\"], threshold_results_df[\"False Positives\"], marker=\"o\", label=\"False Positives\")\n",
        "plt.plot(threshold_results_df[\"Threshold\"], threshold_results_df[\"False Negatives\"], marker=\"o\", label=\"False Negatives\")\n",
        "\n",
        "plt.title(\"Random Forest Threshold Impact on False Positives and False Negatives\")\n",
        "plt.xlabel(\"Classification Threshold\")\n",
        "plt.ylabel(\"Number of Records\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "neJRSXe6F4uE",
        "outputId": "31b5ea32-1bd3-41f3-aee3-96c536008e00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test 4: Feature Importance and Explainability"
      ],
      "metadata": {
        "id": "Rwm-RhyAJT8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Extract Feature Names After Preprocessing\n",
        "# ============================================================\n",
        "\n",
        "# Check whether the Decision Tree preprocessor has been fitted.\n",
        "# This prevents errors if the notebook runtime has been restarted\n",
        "# or cells have been executed out of order.\n",
        "\n",
        "if not hasattr(decision_tree_model.named_steps[\"preprocessor\"], \"transformers_\"):\n",
        "    print(\"Decision Tree preprocessor not fitted. Re-fitting the Decision Tree model...\")\n",
        "    decision_tree_model.fit(X_train, y_train)\n",
        "\n",
        "# Get fitted preprocessor from the Decision Tree pipeline\n",
        "fitted_preprocessor_dt = decision_tree_model.named_steps[\"preprocessor\"]\n",
        "\n",
        "# Get encoded categorical feature names\n",
        "categorical_encoded_features = fitted_preprocessor_dt.named_transformers_[\"cat\"].get_feature_names_out(categorical_features)\n",
        "\n",
        "# Combine encoded categorical feature names with numerical feature names\n",
        "all_feature_names = list(categorical_encoded_features) + numerical_features\n",
        "\n",
        "print(\"Total transformed features:\", len(all_feature_names))\n",
        "print(\"First 20 transformed feature names:\")\n",
        "print(all_feature_names[:20])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISbMCvcxGWtj",
        "outputId": "39f72156-24e9-4107-de5f-dd04a19f1ef9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total transformed features: 194\n",
            "First 20 transformed feature names:\n",
            "['proto_3pc', 'proto_a/n', 'proto_aes-sp3-d', 'proto_any', 'proto_argus', 'proto_aris', 'proto_arp', 'proto_ax.25', 'proto_bbn-rcc', 'proto_bna', 'proto_br-sat-mon', 'proto_cbt', 'proto_cftp', 'proto_chaos', 'proto_compaq-peer', 'proto_cphb', 'proto_cpnx', 'proto_crtp', 'proto_crudp', 'proto_dcn']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Check Feature Name and Model Importance Alignment\n",
        "# ============================================================\n",
        "\n",
        "decision_tree_classifier = decision_tree_model.named_steps[\"classifier\"]\n",
        "\n",
        "print(\"Number of transformed feature names:\", len(all_feature_names))\n",
        "print(\"Number of Decision Tree importance values:\", len(decision_tree_classifier.feature_importances_))\n",
        "\n",
        "if len(all_feature_names) == len(decision_tree_classifier.feature_importances_):\n",
        "    print(\"Feature names and Decision Tree importance values are correctly aligned.\")\n",
        "else:\n",
        "    print(\"Warning: Feature names and importance values do not match.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "siBjtUz1Hsdv",
        "outputId": "b1fdc741-1d5a-4a54-9abc-39b1db5c78f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of transformed feature names: 194\n",
            "Number of Decision Tree importance values: 194\n",
            "Feature names and Decision Tree importance values are correctly aligned.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Decision Tree Feature Importance\n",
        "# ============================================================\n",
        "\n",
        "dt_feature_importance = pd.DataFrame({\n",
        "    \"Feature\": all_feature_names,\n",
        "    \"Importance\": decision_tree_classifier.feature_importances_\n",
        "}).sort_values(by=\"Importance\", ascending=False)\n",
        "\n",
        "dt_feature_importance.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "1gmlB9dsIXCb",
        "outputId": "fd39d20d-a170-4008-8e9d-3f445badd8ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Feature  Importance\n",
              "161              sttl    0.712735\n",
              "192        ct_srv_dst    0.088950\n",
              "158            sbytes    0.072349\n",
              "178             smean    0.050548\n",
              "187    ct_dst_src_ltm    0.018096\n",
              "179             dmean    0.008652\n",
              "165             sloss    0.006537\n",
              "175            tcprtt    0.004679\n",
              "155               dur    0.004632\n",
              "159            dbytes    0.004534\n",
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              "167            sinpkt    0.002738\n",
              "160              rate    0.002706\n",
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              "185  ct_src_dport_ltm    0.001895\n",
              "180       trans_depth    0.001734\n",
              "169              sjit    0.001501\n",
              "168            dinpkt    0.001454\n",
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              "type": "dataframe",
              "variable_name": "dt_feature_importance",
              "summary": "{\n  \"name\": \"dt_feature_importance\",\n  \"rows\": 194,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"proto_tp++\",\n          \"sjit\",\n          \"proto_sep\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.051861428830551864,\n        \"min\": 0.0,\n        \"max\": 0.7127346411513632,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          4.111631613569305e-05,\n          0.0007217848304313027,\n          0.0005532974795242282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot top 15 Decision Tree feature importances\n",
        "top_dt_features = dt_feature_importance.head(15)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(top_dt_features[\"Feature\"][::-1], top_dt_features[\"Importance\"][::-1])\n",
        "plt.title(\"Top 15 Feature Importances - Decision Tree\")\n",
        "plt.xlabel(\"Importance Score\")\n",
        "plt.ylabel(\"Feature\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "aFt1PWVcIfBU",
        "outputId": "6a53ce09-d029-43b7-cf10-1bd46dba4bf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Random Forest Feature Importance\n",
        "# ============================================================\n",
        "\n",
        "random_forest_classifier = random_forest_model.named_steps[\"classifier\"]\n",
        "\n",
        "print(\"Number of transformed feature names:\", len(all_feature_names))\n",
        "print(\"Number of Random Forest importance values:\", len(random_forest_classifier.feature_importances_))\n",
        "\n",
        "rf_feature_importance = pd.DataFrame({\n",
        "    \"Feature\": all_feature_names,\n",
        "    \"Importance\": random_forest_classifier.feature_importances_\n",
        "}).sort_values(by=\"Importance\", ascending=False)\n",
        "\n",
        "rf_feature_importance.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "ih_V6snBIqc8",
        "outputId": "733b6c8e-3375-43fe-c6b1-24387359f743"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of transformed feature names: 194\n",
            "Number of Random Forest importance values: 194\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Feature  Importance\n",
              "161            sttl    0.105474\n",
              "183    ct_state_ttl    0.082797\n",
              "163           sload    0.063320\n",
              "160            rate    0.044347\n",
              "164           dload    0.042001\n",
              "162            dttl    0.040953\n",
              "159          dbytes    0.040839\n",
              "168          dinpkt    0.033731\n",
              "192      ct_srv_dst    0.032158\n",
              "158          sbytes    0.031772\n",
              "155             dur    0.030933\n",
              "179           dmean    0.030380\n",
              "149       state_INT    0.029438\n",
              "175          tcprtt    0.029176\n",
              "177          ackdat    0.028020\n",
              "178           smean    0.027925\n",
              "167          sinpkt    0.027742\n",
              "187  ct_dst_src_ltm    0.027243\n",
              "182      ct_srv_src    0.024016\n",
              "176          synack    0.023642"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>sttl</td>\n",
              "      <td>0.105474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>ct_state_ttl</td>\n",
              "      <td>0.082797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>sload</td>\n",
              "      <td>0.063320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>rate</td>\n",
              "      <td>0.044347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>dload</td>\n",
              "      <td>0.042001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>dttl</td>\n",
              "      <td>0.040953</td>\n",
              "    </tr>\n",
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              "      <th>159</th>\n",
              "      <td>dbytes</td>\n",
              "      <td>0.040839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>dinpkt</td>\n",
              "      <td>0.033731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>ct_srv_dst</td>\n",
              "      <td>0.032158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>sbytes</td>\n",
              "      <td>0.031772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>dur</td>\n",
              "      <td>0.030933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>dmean</td>\n",
              "      <td>0.030380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>state_INT</td>\n",
              "      <td>0.029438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>tcprtt</td>\n",
              "      <td>0.029176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>ackdat</td>\n",
              "      <td>0.028020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>smean</td>\n",
              "      <td>0.027925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>sinpkt</td>\n",
              "      <td>0.027742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>ct_dst_src_ltm</td>\n",
              "      <td>0.027243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>ct_srv_src</td>\n",
              "      <td>0.024016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>synack</td>\n",
              "      <td>0.023642</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "rf_feature_importance",
              "summary": "{\n  \"name\": \"rf_feature_importance\",\n  \"rows\": 194,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"proto_pvp\",\n          \"sinpkt\",\n          \"proto_isis\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013843332881466193,\n        \"min\": 0.0,\n        \"max\": 0.10547417298874437,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          2.948788533575405e-07,\n          6.08957269639921e-06,\n          2.671592325221299e-07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot top 15 Random Forest feature importances\n",
        "top_rf_features = rf_feature_importance.head(15)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(top_rf_features[\"Feature\"][::-1], top_rf_features[\"Importance\"][::-1])\n",
        "plt.title(\"Top 15 Feature Importances - Random Forest\")\n",
        "plt.xlabel(\"Importance Score\")\n",
        "plt.ylabel(\"Feature\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "n-c9W2IYI6by",
        "outputId": "aa87cc18-0bec-4b3e-fc41-2343f8f9f623"
      },
      "execution_count": null,
      "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": [
        "# Test 5 SHAP Explainability: Random Forest"
      ],
      "metadata": {
        "id": "kshjd47oN3kA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# SHAP Explainability Setup\n",
        "# ============================================================\n",
        "\n",
        "!pip -q install shap\n",
        "\n",
        "import shap\n",
        "\n",
        "print(\"SHAP imported successfully.\")\n",
        "print(\"SHAP version:\", shap.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7QZ8j25ZN5N4",
        "outputId": "2f2d8087-6cdf-4566-c89b-4d0d2b901c74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SHAP imported successfully.\n",
            "SHAP version: 0.52.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Reusable SHAP Function for Random Forest Explainability\n",
        "# ============================================================\n",
        "\n",
        "def run_random_forest_shap_analysis(X_test, y_test, sample_size, random_state=42):\n",
        "    \"\"\"\n",
        "    Runs SHAP explainability for the trained Random Forest model\n",
        "    using a fixed sample from the test set.\n",
        "\n",
        "    The sample is used only for explainability, not for model training\n",
        "    or model performance evaluation.\n",
        "    \"\"\"\n",
        "\n",
        "    print(f\"Running SHAP analysis with sample size: {sample_size}\")\n",
        "\n",
        "    # Sample test records for SHAP explainability\n",
        "    X_test_shap = X_test.sample(n=sample_size, random_state=random_state)\n",
        "    y_test_shap = y_test.loc[X_test_shap.index]\n",
        "\n",
        "    # Get fitted Random Forest preprocessor and classifier\n",
        "    fitted_preprocessor_rf = random_forest_model.named_steps[\"preprocessor\"]\n",
        "    random_forest_classifier = random_forest_model.named_steps[\"classifier\"]\n",
        "\n",
        "    # Transform sampled test data\n",
        "    X_test_shap_transformed = fitted_preprocessor_rf.transform(X_test_shap)\n",
        "\n",
        "    # Convert sparse matrix to dense array if needed\n",
        "    if hasattr(X_test_shap_transformed, \"toarray\"):\n",
        "        X_test_shap_transformed = X_test_shap_transformed.toarray()\n",
        "\n",
        "    # Create DataFrame with transformed feature names\n",
        "    X_test_shap_df = pd.DataFrame(\n",
        "        X_test_shap_transformed,\n",
        "        columns=all_feature_names,\n",
        "        index=X_test_shap.index\n",
        "    )\n",
        "\n",
        "    print(\"Transformed SHAP sample shape:\", X_test_shap_df.shape)\n",
        "\n",
        "    # Create SHAP TreeExplainer\n",
        "    explainer_rf = shap.TreeExplainer(random_forest_classifier)\n",
        "\n",
        "    # Calculate SHAP values\n",
        "    shap_values_rf = explainer_rf.shap_values(X_test_shap_df, check_additivity=False)\n",
        "\n",
        "    # Extract SHAP values for attack class\n",
        "    if isinstance(shap_values_rf, list):\n",
        "        shap_values_rf_attack = shap_values_rf[1]\n",
        "    else:\n",
        "        shap_values_array = np.array(shap_values_rf)\n",
        "\n",
        "        if shap_values_array.ndim == 3 and shap_values_array.shape[2] == 2:\n",
        "            shap_values_rf_attack = shap_values_array[:, :, 1]\n",
        "        elif shap_values_array.ndim == 3 and shap_values_array.shape[0] == 2:\n",
        "            shap_values_rf_attack = shap_values_array[1]\n",
        "        else:\n",
        "            shap_values_rf_attack = shap_values_array\n",
        "\n",
        "    print(\"SHAP values calculated successfully.\")\n",
        "    print(\"SHAP values shape for attack class:\", shap_values_rf_attack.shape)\n",
        "\n",
        "    # Create global SHAP importance table\n",
        "    shap_importance_rf = pd.DataFrame({\n",
        "        \"Feature\": all_feature_names,\n",
        "        \"Mean_ABS_SHAP\": np.abs(shap_values_rf_attack).mean(axis=0)\n",
        "    }).sort_values(by=\"Mean_ABS_SHAP\", ascending=False)\n",
        "\n",
        "    return X_test_shap, y_test_shap, X_test_shap_df, shap_values_rf_attack, shap_importance_rf"
      ],
      "metadata": {
        "id": "Sf2zek8gPXNt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# SHAP Run 1: 500-Record Sample\n",
        "# ============================================================\n",
        "\n",
        "X_test_shap_500, y_test_shap_500, X_test_shap_df_500, shap_values_rf_attack_500, shap_importance_rf_500 = run_random_forest_shap_analysis(\n",
        "    X_test, y_test, sample_size=500,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "shap_importance_rf_500.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "4oKwKS8qQn4B",
        "outputId": "35ecf0a3-ad6c-424b-de37-1b5bf460bafe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running SHAP analysis with sample size: 500\n",
            "Transformed SHAP sample shape: (500, 194)\n",
            "SHAP values calculated successfully.\n",
            "SHAP values shape for attack class: (500, 194)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Feature  Mean_ABS_SHAP\n",
              "161              sttl       0.076102\n",
              "183      ct_state_ttl       0.045698\n",
              "162              dttl       0.027586\n",
              "149         state_INT       0.027059\n",
              "163             sload       0.022224\n",
              "159            dbytes       0.019304\n",
              "192        ct_srv_dst       0.017386\n",
              "171              swin       0.015465\n",
              "187    ct_dst_src_ltm       0.015199\n",
              "164             dload       0.015140\n",
              "186  ct_dst_sport_ltm       0.014664\n",
              "177            ackdat       0.014609\n",
              "160              rate       0.014592\n",
              "133         service_-       0.014413\n",
              "178             smean       0.014360\n",
              "165             sloss       0.014005\n",
              "113         proto_tcp       0.013852\n",
              "168            dinpkt       0.013462\n",
              "182        ct_srv_src       0.012657\n",
              "158            sbytes       0.012588"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Mean_ABS_SHAP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>sttl</td>\n",
              "      <td>0.076102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>ct_state_ttl</td>\n",
              "      <td>0.045698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>dttl</td>\n",
              "      <td>0.027586</td>\n",
              "    </tr>\n",
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              "      <th>149</th>\n",
              "      <td>state_INT</td>\n",
              "      <td>0.027059</td>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>ct_dst_sport_ltm</td>\n",
              "      <td>0.014664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>ackdat</td>\n",
              "      <td>0.014609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>rate</td>\n",
              "      <td>0.014592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>service_-</td>\n",
              "      <td>0.014413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>smean</td>\n",
              "      <td>0.014360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>sloss</td>\n",
              "      <td>0.014005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>proto_tcp</td>\n",
              "      <td>0.013852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>dinpkt</td>\n",
              "      <td>0.013462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>ct_srv_src</td>\n",
              "      <td>0.012657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>sbytes</td>\n",
              "      <td>0.012588</td>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "shap_importance_rf_500",
              "summary": "{\n  \"name\": \"shap_importance_rf_500\",\n  \"rows\": 194,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"proto_mfe-nsp\",\n          \"proto_tcp\",\n          \"proto_aris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean_ABS_SHAP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008069143027704756,\n        \"min\": 0.0,\n        \"max\": 0.07610207181081273,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          1.1001919066724492e-06,\n          6.562415520116553e-06,\n          9.684080981224817e-07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# SHAP Global Bar Plot: 500-Record Sample\n",
        "# ============================================================\n",
        "\n",
        "shap.summary_plot(\n",
        "    shap_values_rf_attack_500,\n",
        "    X_test_shap_df_500,\n",
        "    plot_type=\"bar\",\n",
        "    max_display=15,\n",
        "    show=False\n",
        ")\n",
        "\n",
        "plt.title(\"Top SHAP Feature Importance - Random Forest (500 Records)\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "46-2Cn8xWpR2",
        "outputId": "7ea319a1-075a-4a9b-f6ac-9afcdf179a0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x750 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# SHAP Summary Plot: 500-Record Sample\n",
        "# ============================================================\n",
        "\n",
        "shap.summary_plot(\n",
        "    shap_values_rf_attack_500,\n",
        "    X_test_shap_df_500,\n",
        "    max_display=15\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "SIIf5k1kXEQV",
        "outputId": "8503a55d-29d6-40b2-b82f-7b47b476c21e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x750 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Local SHAP Explanation for One High-Risk Attack Prediction\n",
        "# ============================================================\n",
        "\n",
        "# Convert Random Forest predictions and probabilities to Series using original X_test index\n",
        "rf_pred_series = pd.Series(y_pred_rf, index=X_test.index)\n",
        "rf_proba_series = pd.Series(y_proba_rf, index=X_test.index)\n",
        "\n",
        "# Select correctly detected attack cases within the 500-record SHAP sample\n",
        "candidate_indices = X_test_shap_500.index[\n",
        "    (y_test_shap_500 == 1) & (rf_pred_series.loc[X_test_shap_500.index] == 1)\n",
        "]\n",
        "\n",
        "# Select the correctly detected attack case with the highest predicted attack probability\n",
        "selected_case_index = rf_proba_series.loc[candidate_indices].idxmax()\n",
        "selected_case_position = list(X_test_shap_df_500.index).index(selected_case_index)\n",
        "\n",
        "print(\"Selected case index:\", selected_case_index)\n",
        "print(\"True label:\", y_test.loc[selected_case_index])\n",
        "print(\"Predicted label:\", rf_pred_series.loc[selected_case_index])\n",
        "print(\"Predicted attack probability:\", rf_proba_series.loc[selected_case_index])\n",
        "\n",
        "# Create local SHAP explanation table\n",
        "local_shap_rf_500 = pd.DataFrame({\n",
        "    \"Feature\": all_feature_names,\n",
        "    \"Feature_Value\": X_test_shap_df_500.iloc[selected_case_position].values,\n",
        "    \"SHAP_Value\": shap_values_rf_attack_500[selected_case_position]\n",
        "})\n",
        "\n",
        "local_shap_rf_500[\"Absolute_SHAP_Value\"] = local_shap_rf_500[\"SHAP_Value\"].abs()\n",
        "\n",
        "local_shap_rf_500 = local_shap_rf_500.sort_values(\n",
        "    by=\"Absolute_SHAP_Value\",\n",
        "    ascending=False\n",
        ")\n",
        "\n",
        "local_shap_rf_500.head(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "H3VZ0BBpXzQE",
        "outputId": "0b39f832-5ea9-4073-9824-8424b55c904f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected case index: 8317\n",
            "True label: 1\n",
            "Predicted label: 1\n",
            "Predicted attack probability: 1.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Feature  Feature_Value  SHAP_Value  Absolute_SHAP_Value\n",
              "161              sttl       0.723268    0.068289             0.068289\n",
              "163             sload       0.081945    0.047188             0.047188\n",
              "183      ct_state_ttl       0.729064    0.040433             0.040433\n",
              "149         state_INT       1.000000    0.035871             0.035871\n",
              "159            dbytes      -0.103923    0.029775             0.029775\n",
              "186  ct_dst_sport_ltm      -0.208566    0.026228             0.026228\n",
              "168            dinpkt      -0.089370    0.023522             0.023522\n",
              "160              rate       0.094951    0.021768             0.021768\n",
              "178             smean      -0.179560    0.020932             0.020932\n",
              "155               dur      -0.209773    0.019537             0.019537\n",
              "164             dload      -0.277208    0.019082             0.019082\n",
              "119         proto_udp       0.000000    0.017112             0.017112\n",
              "171              swin      -0.915407    0.016114             0.016114\n",
              "165             sloss      -0.075040    0.015538             0.015538\n",
              "167            sinpkt      -0.136142    0.014417             0.014417"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-15b3d99d-5b31-4cc7-9673-6931c4d47ac2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Feature_Value</th>\n",
              "      <th>SHAP_Value</th>\n",
              "      <th>Absolute_SHAP_Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>sttl</td>\n",
              "      <td>0.723268</td>\n",
              "      <td>0.068289</td>\n",
              "      <td>0.068289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>sload</td>\n",
              "      <td>0.081945</td>\n",
              "      <td>0.047188</td>\n",
              "      <td>0.047188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>ct_state_ttl</td>\n",
              "      <td>0.729064</td>\n",
              "      <td>0.040433</td>\n",
              "      <td>0.040433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>state_INT</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.035871</td>\n",
              "      <td>0.035871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>dbytes</td>\n",
              "      <td>-0.103923</td>\n",
              "      <td>0.029775</td>\n",
              "      <td>0.029775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>ct_dst_sport_ltm</td>\n",
              "      <td>-0.208566</td>\n",
              "      <td>0.026228</td>\n",
              "      <td>0.026228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>dinpkt</td>\n",
              "      <td>-0.089370</td>\n",
              "      <td>0.023522</td>\n",
              "      <td>0.023522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>rate</td>\n",
              "      <td>0.094951</td>\n",
              "      <td>0.021768</td>\n",
              "      <td>0.021768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>smean</td>\n",
              "      <td>-0.179560</td>\n",
              "      <td>0.020932</td>\n",
              "      <td>0.020932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>dur</td>\n",
              "      <td>-0.209773</td>\n",
              "      <td>0.019537</td>\n",
              "      <td>0.019537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>dload</td>\n",
              "      <td>-0.277208</td>\n",
              "      <td>0.019082</td>\n",
              "      <td>0.019082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>proto_udp</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017112</td>\n",
              "      <td>0.017112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>swin</td>\n",
              "      <td>-0.915407</td>\n",
              "      <td>0.016114</td>\n",
              "      <td>0.016114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>sloss</td>\n",
              "      <td>-0.075040</td>\n",
              "      <td>0.015538</td>\n",
              "      <td>0.015538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>sinpkt</td>\n",
              "      <td>-0.136142</td>\n",
              "      <td>0.014417</td>\n",
              "      <td>0.014417</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-15b3d99d-5b31-4cc7-9673-6931c4d47ac2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-15b3d99d-5b31-4cc7-9673-6931c4d47ac2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-15b3d99d-5b31-4cc7-9673-6931c4d47ac2');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "local_shap_rf_500",
              "summary": "{\n  \"name\": \"local_shap_rf_500\",\n  \"rows\": 194,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"proto_larp\",\n          \"sbytes\",\n          \"proto_pnni\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Feature_Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2172652082623646,\n        \"min\": -0.9154073429658452,\n        \"max\": 1.0,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          -0.484345974086776,\n          -0.4807028160415566,\n          -0.04946549146719891\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SHAP_Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008716218302209247,\n        \"min\": -0.007648517642695351,\n        \"max\": 0.06828872971684645,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          -6.205321506014303e-07,\n          -7.216755080000743e-06,\n          -5.564227255829532e-07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Absolute_SHAP_Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008563706718716127,\n        \"min\": 0.0,\n        \"max\": 0.06828872971684645,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          6.205321506014303e-07,\n          7.216755080000743e-06,\n          5.564227255829532e-07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Local SHAP Chart for Selected High-Risk Prediction\n",
        "# ============================================================\n",
        "\n",
        "top_local_shap_rf_500 = local_shap_rf_500.head(15).sort_values(by=\"SHAP_Value\")\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(top_local_shap_rf_500[\"Feature\"], top_local_shap_rf_500[\"SHAP_Value\"])\n",
        "plt.title(\"Local SHAP Explanation - Selected Random Forest Prediction\")\n",
        "plt.xlabel(\"SHAP Value\")\n",
        "plt.ylabel(\"Feature\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "2qe5pqonYR8a",
        "outputId": "7f4eb571-82b7-4d20-d0ee-ff06b6340ecd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# SHAP Run 2: 1,000-Record Robustness Check\n",
        "# ============================================================\n",
        "\n",
        "X_test_shap_1000, y_test_shap_1000, X_test_shap_df_1000, shap_values_rf_attack_1000, shap_importance_rf_1000 = run_random_forest_shap_analysis(\n",
        "    X_test, y_test, sample_size=1000,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "shap_importance_rf_1000.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "r00TxNomY8SD",
        "outputId": "fdbcbac4-426f-4ba7-9cf7-8c7d6fe62f3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running SHAP analysis with sample size: 1000\n",
            "Transformed SHAP sample shape: (1000, 194)\n",
            "SHAP values calculated successfully.\n",
            "SHAP values shape for attack class: (1000, 194)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Feature  Mean_ABS_SHAP\n",
              "161              sttl       0.075750\n",
              "183      ct_state_ttl       0.045500\n",
              "162              dttl       0.027637\n",
              "149         state_INT       0.026934\n",
              "163             sload       0.022050\n",
              "159            dbytes       0.019164\n",
              "192        ct_srv_dst       0.017427\n",
              "171              swin       0.015507\n",
              "164             dload       0.015046\n",
              "187    ct_dst_src_ltm       0.015041\n",
              "186  ct_dst_sport_ltm       0.014696\n",
              "177            ackdat       0.014619\n",
              "178             smean       0.014561\n",
              "160              rate       0.014427\n",
              "133         service_-       0.014333\n",
              "165             sloss       0.013990\n",
              "113         proto_tcp       0.013882\n",
              "168            dinpkt       0.013406\n",
              "158            sbytes       0.012943\n",
              "182        ct_srv_src       0.012642"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d02981f-0b34-431d-9cb6-7da65853450c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Mean_ABS_SHAP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>sttl</td>\n",
              "      <td>0.075750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>ct_state_ttl</td>\n",
              "      <td>0.045500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>dttl</td>\n",
              "      <td>0.027637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>state_INT</td>\n",
              "      <td>0.026934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>sload</td>\n",
              "      <td>0.022050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>dbytes</td>\n",
              "      <td>0.019164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>ct_srv_dst</td>\n",
              "      <td>0.017427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>swin</td>\n",
              "      <td>0.015507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>dload</td>\n",
              "      <td>0.015046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>ct_dst_src_ltm</td>\n",
              "      <td>0.015041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>ct_dst_sport_ltm</td>\n",
              "      <td>0.014696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>ackdat</td>\n",
              "      <td>0.014619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>smean</td>\n",
              "      <td>0.014561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>rate</td>\n",
              "      <td>0.014427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>service_-</td>\n",
              "      <td>0.014333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>sloss</td>\n",
              "      <td>0.013990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>proto_tcp</td>\n",
              "      <td>0.013882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>dinpkt</td>\n",
              "      <td>0.013406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>sbytes</td>\n",
              "      <td>0.012943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>ct_srv_src</td>\n",
              "      <td>0.012642</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d02981f-0b34-431d-9cb6-7da65853450c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d02981f-0b34-431d-9cb6-7da65853450c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6d02981f-0b34-431d-9cb6-7da65853450c');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "shap_importance_rf_1000",
              "summary": "{\n  \"name\": \"shap_importance_rf_1000\",\n  \"rows\": 194,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"proto_sccopmce\",\n          \"proto_tcp\",\n          \"proto_aris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean_ABS_SHAP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008043841410329725,\n        \"min\": 0.0,\n        \"max\": 0.0757502534878365,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          1.0997234814922547e-06,\n          6.51838279336895e-06,\n          9.78203177592491e-07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# SHAP Global Bar Plot: 1,000-Record Sample\n",
        "# ============================================================\n",
        "\n",
        "shap.summary_plot(\n",
        "    shap_values_rf_attack_1000,\n",
        "    X_test_shap_df_1000,\n",
        "    plot_type=\"bar\",\n",
        "    max_display=15,\n",
        "    show=False\n",
        ")\n",
        "\n",
        "plt.title(\"Top SHAP Feature Importance - Random Forest (1,000 Records)\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "rqbDvriNZkjL",
        "outputId": "664832a7-fce3-47ce-8194-6c8d5314b647"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x750 with 1 Axes>"
            ],
            "image/png": 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GyJEjrW0pKSmGJOPuu+82zp8/b23/8MMPDUnGL7/8YhiGYVy8eNGoXr26UbVqVePkyZM24+bm5lr/3apVK6NevXrGuXPnbJY3a9bMqF27tl2dZlStWtV45JFHrN8Lv/zyi/Hkk0/m+z2W3+v1P//5j2GxWGwuWcn7+l9+jAzDMBo0aGA0bNjQ+v+89553333X2nbx4kXjoYcesntd169f3yhfvrxx/Phxa9tPP/1kODg4GL1797a25b3OY2NjbcYMDg42LBaLMWrUKGt73muyT58+hTpO+X3P571ui/o+6uDgYPz66682fZ9++mkjMDDQOHbsmE17jx49DB8fH+vxf/TRR+0u37xSUS9/evDBB22+Nvm5nsufIiMjjRo1ati1Z2VlGZKMV155xTAMw9iwYYMhyZg9e7Zd35dfftmQZP1+KOyYBcl7jfz+++/G0aNHjd27dxszZsww3NzcjHLlylkvi8zNzTVq165ttG3b1ub7Mzs726hevbrRpk0ba1vv3r0NBweHfC8DzVv3xRdfNCQZa9assS7LzMw0qlevblSrVs36XpP3flKjRg2777n69esbgYGBNj83v/rqK0OSzeVPL7zwguHt7X3NS8EMwzDeeecdQ5Jx5MiRa/a9HXH5E3ALLVu2TI6Ojnr++edt2l966SUZhqHly5fbtN9///1q2LCh9f9VqlTRo48+qpUrV9pd2nC5ChUq6KefftKAAQN08uRJTZkyRY8//rjKly+vN9980+4SAVdXV61atSrfP/lZvny5jh8/rp49e1rbevbsqZ9++snusiPp0uUl5cqVU7ly5XT33XdrwoQJioyMzPdSg/xMmjQp37pWrVqlU6dOqWfPnjp27Jj1j6Ojo5o0aaKUlBTrGG5ubtZ/nzt3TseOHbNeHrZp06ZC1VFUAwYMsPn/559/rtzcXHXr1s2m3ooVK6p27do29RbV5ZPkfX19FRISIg8PD3Xr1s3aHhISIl9fX+3cudNu/djYWJszDQMHDpSTk5P1U8uvv/5af//9t1588UU5OPzvR0e/fv3k7e1td1mBi4uLYmJiCl2/i4uLddycnBwdP35cnp6eCgkJyffrExMTYzOZNO9yirx927x5s3bt2qUXX3zR7uxP3pmXEydO6Ntvv1W3bt2UmZlp/XocP35cbdu21R9//KEDBw4Ueh8K46uvvrJ+L9SrV09z5sxRTEyM3nvvPZt+l79es7KydOzYMTVr1kyGYWjz5s124175WnvooYdsvs7Lli2Tk5OT9cyFJDk6Ouq5556zWe/QoUP673//q6eeekr+/v7W9nvvvVdt2rSx+RQ7z+WvPUdHR4WFhckwDD399NPW9rzXZH6vvfw0adLE7nu+d+/e1n0pyvto8+bNdc8991j/bxiGFi9erKioKBmGYfO92LZtW2VkZFhfc76+vtq/f782bNhQqLoL4/jx43Znpm6Es2fPysXFxa4973LXvEvn8v4ubN/C9LuWkJAQlStXTtWqVVPfvn1Vq1YtLV++XO7u7pKk//73v/rjjz/0+OOP6/jx49avR1ZWllq1aqXvvvtOubm5ys3N1ZdffqmoqKh8z7TkfW8vW7ZMjRs3tjmL4unpqdjYWO3evVtbt261Wa9Pnz4233N53wd9+vSRj4+Ptb1NmzY2ryXp0mskKyurwJ+Xl8v7uhf1yoDbBZc/AbfQnj17VKlSJbs7P+SdWt6zZ49Ne+3ate3GuOuuu5Sdna2jR4+qYsWKBW4rMDBQ8fHxmjx5sv744w+tXLlSo0eP1tChQxUYGGj3i0B+1/cWZO7cuapevbpcXFys17HXrFlT7u7umjdvnt555x2b/tWqVdO0adOst5+tXbu2ypcvX+jtNW7cON8fIH/88Yck6eGHH853PW9vb+u/T5w4oREjRuiTTz7RX3/9ZdMvIyOj0LUUxZWXGP3xxx8yDCPfr6skm1/qi8LV1dXucgsfHx8FBwdbf8he3p7fXIkra/L09FRgYKD1MoW812ZISIhNvzJlyqhGjRp2r92goKAi3aknNzdXH374oSZPnqxdu3bZhOayZcva9b/8WnDpfz+s8/Yt71rtq10muGPHDhmGoX//+9/697//nW+fv/76S0FBQfkuO3r0qE2dnp6e17yrWpMmTfTWW28pJydHW7Zs0VtvvaWTJ0/aHau9e/dq6NChSkpKsvt6Xfl6ze/r7+fnZ7Penj17FBgYaFfflV/Pgr7O0qX3qZUrVyorK0seHh7W9iu/Fj4+PnJ1dbVeCnR5++VzmK4mICCgwPekor6PXvl9ePToUZ06dUpTp07V1KlT891G3nvEv/71L3399ddq3LixatWqpUceeUSPP/64HnjggULtR0Gu/GDnRnBzc9P58+ft2vNuw5v3S3Pe34XtW5h+17J48WJ5e3vr6NGjGj9+vHbt2mWzbt57+dUu183IyNDff/+t06dPX/Puf3v27FGTJk3s2i9/jVw+xpWvkbzXUH7v1Vd+0PHss89q4cKFat++vYKCgvTII4+oW7duateund26eV/3K9+XSwtCBVDKWSwW3XXXXbrrrrsUGRmp2rVra968eQXe/vVaTp8+reTkZJ07dy7fN9z58+fr7bfftnnT9PDwKFJoKay851zMmTMn34Dl5PS/t7hu3brp+++/18svv6z69evL09NTubm5ateu3TWflyEV/EPgameMrvyBm5ubK4vFouXLl8vR0dGu//Xe5je/sa7WfjN+oblSYX/ZyPPOO+/o3//+t/r27as333xT/v7+cnBw0Isvvpjv1+dG7FveuHFxcWrbtm2+fa685eflGjVqZPML7LBhw675XIHLf1lu27at6tSpow4dOujDDz/UkCFDJF16TbVp00YnTpzQv/71L9WpU0ceHh46cOCAnnrqKbvjUdCxuFXy235xvvaulN/3oSQ98cQTBf4Se++990q69Evo77//riVLlmjFihVavHixJk+erKFDh2rEiBHXVU/ZsmXzDfZmBQYGKiUlRYZh2LxfHTp0SJKs85ICAwNt2i936NAh+fv7W89OFHbMawkPD7eGzKioKNWrV0+9evVSenq6HBwcrF+T9957T/Xr1893DE9PT504caJQ2yuqor5fXa58+fL673//q5UrV2r58uVavny5Pv74Y/Xu3VuzZs2y6Zv3db8ycJcWhArgFqpataq+/vprZWZm2nzK9ttvv1mXXy7v05vLbd++Xe7u7ledCFiQGjVqyM/PL98fJoX1+eef69y5c4qPj7d7Y/z999/1xhtvaN26ddc1ea+oatasKenSm/rVQsvJkyf1zTffaMSIERo6dKi1Pb/jW1B4yPsk/Mq75Vz5qei16jUMQ9WrV9ddd91V6PVuhT/++EMtW7a0/v/MmTM6dOiQIiIiJP3vtfn777+rRo0a1n5///23du3aVejQWNDxXbRokVq2bKmPPvrIpv3UqVPX9QM477WxZcuWAmvL2w9nZ+frCr3z5s2zufzj8uNSWJGRkWrevLneeecd9e/fXx4eHvrll1+0fft2zZo1y3rJj6RCXV5RkKpVq+qbb77RmTNnbMLr77//btcvv3bp0vtUQECAzVmK4lDU99ErlStXTl5eXsrJySnU193Dw0Pdu3dX9+7d9ffff6tTp056++239eqrr8rV1bXInzrXqVNHixcvLtI6hVG/fn1Nnz5d27Zts7lEJ+/mBXm/rAcFBalcuXL5PkQzLS3N5pf6wo5ZFJ6enho2bJhiYmK0cOFC9ejRw/r96u3tfdWvSbly5eTt7a0tW7ZcdRtVq1Yt8DWct/xa60v5/4zIb9wyZcooKipKUVFRys3N1bPPPquEhAT9+9//tvlgYteuXQoICLiun9+3A+ZUALdQRESEcnJyNHHiRJv2Dz74QBaLRe3bt7dp/+GHH2xOs+7bt0+JiYl65JFHrvrp5Pr165WVlWXXnpaWpuPHj+d7aUNhzZ07VzVq1NCAAQPUpUsXmz9xcXHy9PTUvHnzrnv8omjbtq28vb31zjvv6MKFC3bL8+7YlHesrvyUdNy4cXbr5P3CdGV48Pb2VkBAgN0tfSdPnlzoejt16iRHR0eNGDHCrhbDMAp9acjNMHXqVJtjGB8fr4sXL1pfk61bt1aZMmU0fvx4m9o/+ugjZWRkKDIyslDb8fDwyPc2po6OjnbH5LPPPrvuOQ333XefqlevrnHjxtltL2875cuXV4sWLZSQkJBv0L7WHb8eeOABtW7d2vrnekKFdOkSm+PHj2vatGmS8n+9GoZhd4vKooiIiNDFixcVHx9vbcvJydGECRNs+gUGBqp+/fqaNWuWzXHbsmWLvvrqK2vILE5FfR+9kqOjozp37qzFixfn+8vp5V/3K78ny5Qpo3vuuUeGYVi/Xwp6zyjI/fffr5MnTxZ6fklhPfroo3J2drZ5TzIMQ1OmTFFQUJCaNWtmbe/cubOWLFlicyvrb775Rtu3b1fXrl2va8yi6NWrl4KDg613S2rYsKFq1qypMWPG6MyZM3b9874mDg4Oeuyxx5ScnJxvKMr7nomIiFBaWprNLW+zsrI0depUVatWzW5exJUu/z64/HLDVatW2c3HuPI14uDgYD3TdeWlY+np6br//vuvuu3bGWcqgFsoKipKLVu21Ouvv67du3fr//7v//TVV18pMTFRL774ovXTmjyhoaFq27atzS1lJV3ztPucOXM0b948dezYUQ0bNlSZMmW0bds2zZgxQ66urnrttdeuq/6DBw8qJSXFboJkHhcXF7Vt21afffaZxo8ff91zBArL29tb8fHxevLJJ3XfffepR48eKleunPbu3aulS5fqgQce0MSJE+Xt7W293eqFCxcUFBSkr776Srt27bIbM29i/Ouvv64ePXrI2dlZUVFR8vDw0DPPPKNRo0bpmWeeUVhYmL777jtt37690PXWrFlTb731ll599VXt3r1bjz32mLy8vLRr1y598cUXio2NVVxc3A07PkXx999/q1WrVurWrZt+//13TZ48WQ8++KCio6MlXfqE8NVXX9WIESPUrl07RUdHW/s1atRITzzxRKG207BhQ8XHx+utt95SrVq1VL58eT388MPq0KGDRo4cqZiYGDVr1ky//PKL5s2bd92/qDs4OCg+Pl5RUVGqX7++YmJiFBgYqN9++02//vqr9WnFkyZN0oMPPqh69eqpX79+qlGjho4cOaIffvhB+/fvt3tOxs3Qvn17hYaGauzYsRo0aJDq1KmjmjVrKi4uTgcOHJC3t7cWL15s6pKZqKgoPfDAA3rllVe0e/du3XPPPfr888/znU/03nvvqX379rr//vv19NNPW28p6+Pjc83Lu26For6P5mfUqFFKSUlRkyZN1K9fP91zzz06ceKENm3apK+//tp6mc0jjzyiihUr6oEHHlCFChW0bds2TZw4UZGRkdazJFd7z8hPZGSknJyc9PXXXys2NtZm2Zw5c7Rnzx5lZ2dLkr777ju99dZbkqQnn3zS+gl6amqqWrZsaXPJXXBwsF588UW99957unDhgho1aqQvv/xSa9as0bx582w+iHrttdf02WefqWXLlnrhhRd05swZvffee6pXr57NDRaKMmZRODs764UXXtDLL7+sFStWqF27dpo+fbrat2+vunXrKiYmRkFBQTpw4IBSUlLk7e2t5ORkSZculfzqq6/UvHlzxcbG6u6779ahQ4f02Wefae3atfL19dUrr7yiBQsWqH379nr++efl7++vWbNmadeuXVq8eLHNzSYK8p///EeRkZF68MEH1bdvX504ccL6zJLLg88zzzyjEydO6OGHH1ZwcLD27NmjCRMmqH79+ja34v3rr7/0888/a9CgQdd1zG4Lt+QeU8AdKr8namdmZhr/+Mc/jEqVKhnOzs5G7dq1jffee8/mNnqG8b8nWc+dO9eoXbu24eLiYjRo0MBISUm55nZ//vln4+WXXzbuu+8+w9/f33BycjICAwONrl27Gps2bbLpW5Qnar///vuGJOObb74psP/MmTNtbkWa3xO1C6uwT5BOSUkx2rZta/j4+Biurq5GzZo1jaeeesrmdrz79+83OnbsaPj6+ho+Pj5G165djYMHD+Z728Y333zTCAoKMhwcHGxuFZmdnW08/fTTho+Pj+Hl5WV069bN+Ouvvwq8peyVT5HNs3jxYuPBBx80PDw8DA8PD6NOnTrGoEGDjN9//73Ix6Ogr19Bx/3KpxXnjbl69WojNjbW8PPzMzw9PY1evXrZ3FI0z8SJE406deoYzs7ORoUKFYyBAwfa3bL1al/zw4cPG5GRkYaXl5chyXp72XPnzhkvvfSSERgYaLi5uRkPPPCA8cMPPxjNmze3uQVt3i0gP/vsM5txC7rl79q1a402bdoYXl5ehoeHh3HvvfcaEyZMsOnz559/Gr179zYqVqxoODs7G0FBQUaHDh2MRYsW5bsP1+tqT4rO+77Jq3/r1q1G69atDU9PTyMgIMDo16+f8dNPP9ntY0Ff/7zX4OWOHz9uPPnkk4a3t7fh4+NjPPnkk8bmzZvzPW5ff/218cADDxhubm6Gt7e3ERUVZWzdujXfbVz5Oi/qa/JKhXmidlHfR/Nz5MgRY9CgQUblypUNZ2dno2LFikarVq2MqVOnWvskJCQY4eHhRtmyZQ0XFxejZs2axssvv2xkZGTYjFXQe0ZBoqOjjVatWtm1592OOb8/l7/3Jycn53vb5pycHOOdd94xqlatapQpU8aoW7euMXfu3Hxr2LJli/HII48Y7u7uhq+vr9GrVy/j8OHDdv2KMuaVrvZemJGRYfj4+Nh8f2/evNno1KmT9XhXrVrV6Natm93PnD179hi9e/c2ypUrZ7i4uBg1atQwBg0aZHOb6T///NPo0qWL4evra7i6uhqNGzc2lixZYjNOQe8neRYvXmzcfffdhouLi3HPPfcYn3/+ud0TtRctWmQ88sgjRvny5Y0yZcoYVapUMfr3728cOnTIZqz4+HjD3d3dOH36dKGO3e3IYhjFMGsKwDVZLBYNGjTI7hQ/cCPNnDlTMTEx2rBhQ7532AJw461Zs0YtWrTQb7/9VuDd4K7mn//8pxYsWKAdO3bke8tXlDwNGjRQixYt7B7OWJowpwIAAOAWeuihh/TII4/o3Xffva71U1JS9O9//5tAcZtYsWKF/vjjD7366qvFXcpNxZwKAACAW+zKh/QVxY18GB9uvnbt2uU7Ab204UwFAAAAAFOYUwEAAADAFM5UAAAAADCFUAEAAADAFEIFSrQrn/ILAACAkodQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBUAAAAATCFUAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBUAAAAATCFUAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUyyGYRjFXQRQEMuYi8VdAgAAQIlhxDkVdwn54kwFAAAAAFMIFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVCBWyIqKkqxsbHFXQYAAABuAkIFiiQhIUGpqalFXgYAAIDSi1CBIpk2bVqBweFqywAAAFB6ESoAAAAAmGIxDMMo7iJQMpw/f14zZ87UypUrdeTIETk7O6tChQpq1qyZunbtqujo6HzXS0pKKnDZxo0bJV2aUxEYGKipU6cWqSbLmItF2wkAAIBSzIhzKu4S8lUyq0KxGD16tJKSkhQZGalevXopJydH+/bt04YNGxQbG6uRI0dq6NChatCggTp27Ghdz8/Pr8BlAAAAKP0IFbBKTU1Vs2bNNGLEiHyXR0REaOjQoQoKClJEREShlwEAAKB0Y04FrDw9PbVz507t2LGjuEsBAADAbYRQAashQ4YoMzNTPXr00KOPPqo333xTqampys3NLe7SAAAAUIJx+ROsWrRooaSkJK1bt06bNm1SWlqaEhMT1aBBA02ePFnOzs7FXSIAAABKIM5UwIaPj48iIiL0xhtvKDExUb1799bmzZu1evXq4i4NAAAAJRShApKknJwcZWZm2rRZLBaFhIRIkjIyMiRJ7u7u1n9f6WrLAAAAUHpx+RMkSdnZ2WrXrp3Cw8MVEhIiPz8/HTx4UIsWLZK3t7fCw8MlSaGhoUpLS9PMmTNVsWJFWSwWtW3b9prLAAAAUHrx8DtIki5cuKCEhASlpaXpwIEDys7OVkBAgMLCwhQTE6MqVapIkvbu3avRo0dry5YtysrKkvS/B9xdbRkPvwMAADCvpD78jlCBEo1QAQAA8D8lNVQwpwIAAACAKYQKAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIApJXP6OPD/JXjPUExMjJydnYu7FAAAABSAMxUAAAAATCFUAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBSLYRhGcRcBFMQy5mJxlwAAdxQjzqm4SwBwG+JMBQAAAABTCBUAAAAATCFUAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMIVQAQAAAMAUQgVuiYSEBIWFhengwYPFXQoAAABuMEJFPjZu3KiEhARlZmbe1O0kJCQoNTXV9Djz589XcnKy+YIKITU1VQkJCUVeBgAAgNKLUJGP9PR0TZs27aaHimnTpt2QULFgwYJbGiqmTZtW5GUAAAAovQgVAAAAAEyxGIZhFHcRt9KFCxc0f/58rVy5Unv27JGTk5OqVKmiDh06qHv37ho+fLiWLFlit16/fv3Uv3//Qm3j/PnzmjlzplauXKkjR47I2dlZFSpUULNmzfTCCy/o4MGDio6OznfdjRs3SpK++uorLV++XNu3b9eJEyfk7u6u+vXra8CAAapdu7a1f1hYWL7jJCUlqVKlSpKkrVu3asaMGdq8ebOys7MVGBioyMhI9enTR05OToXaJ0mKjY3Vpk2b7NqHDRum5OTkApdFRUUpISFB06ZNs6mrMCxjLha6LwDAPCOu8D8XACDPHfXOceHCBQ0ePFjp6elq2rSp2rdvrzJlymjHjh1KSUlR9+7d1alTJ2VlZSklJUVDhgyRr6+vJNn8In8to0ePVlJSkiIjI9WrVy/l5ORo37592rBhgyTJz89PI0eO1NChQ9WgQQN17NjRboyFCxfKx8dHHTt2VEBAgPbv368vvvhCTz/9tObOnasqVapIkkaOHKmxY8fK19dXffv2ta7v5+cnSVq7dq1efvllVa5cWU888YS8vb31yy+/KCEhQdu3b9fo0aMLvV99+/aVYRjavHmzRo4caW2/9957Va5cuQKXAQAAoHS7o0LF/PnzlZ6erpiYGA0aNMhmWW5urqRLvwTXqlVLKSkpatGiRZE+Vc+TmpqqZs2aacSIEfkud3NzU0REhIYOHaqgoCBFRETY9ZkwYYLc3Nxs2iIjI/X4449r/vz5euWVVyRJERERio+Pl7+/v90458+f15tvvqnQ0FDFx8dbz0p07txZtWvX1gcffKCNGzcWeLbjSk2bNtWKFSu0efNmu20FBwcXuAwAAACl2x01p2LFihXy9vbWM888Y7fMweHGHQpPT0/t3LlTO3bsuO4x8gKFYRg6c+aMTp06JT8/P1WtWlVbtmwp1Bjr16/X8ePHFRUVZR0j788DDzxg7QMAAACYcUedqdi7d69CQkLk4uJyU7czZMgQDRs2TD169FBQUJDCwsL00EMPKTw8vNDh5bffftOUKVOUnp6us2fP2iwLCgoq1Bi7du2SJJvLka50/PjxQo0FAAAAFOSOChW3SosWLZSUlKR169Zp06ZNSktLU2Jioho0aKDJkyfL2dn5qusfPnxYsbGx8vDw0NNPP61q1arJ1dVVFotF77//vl3IKEjeHPwXXnhBd911V759ypUrV7SdAwAAAK5wR4WKqlWravfu3fr7779VpkyZAvtZLBbT2/Lx8VFERIQiIiJkGIYmTJig2bNna/Xq1WrduvVV101JSVF2drbGjh1rN98hIyPDrvaC6s2bzO3m5qYmTZqY2Jtrb+taywAAAFB63VFzKtq1a6fTp0/ro48+slt2+Z113d3dJUmnT58u8jZycnLsHppnsVgUEhIi6VIouHw7l/8/T94lUlfe7feLL77I93IlNze3fGu9//775e/vr5kzZ+a7nXPnzikrK6sQe2W7rSv3ozDLAAAAUHrdUWcqevbsqTVr1uijjz7S1q1b1aRJE7m4uGjnzp3as2ePJk+eLEkKDQ2VJI0fP95629maNWuqVq1a19xGdna22rVrp/DwcIWEhMjPz08HDx7UokWL5O3trfDwcGvf0NBQpaWlaebMmapYsaIsFovatm2rBx54QBMmTNDQoUPVrVs3eXl56aefftL333+v4OBg5eTk2GyzXr16SkxMVHx8vKpXry6LxaLw8HC5ublpxIgRiouLU+fOnRUdHa3KlSsrMzNTu3fvVkpKit57771C3/0pb1sLFy7UqFGj9OCDD8rJyUmhoaEKCgq66jIAAACUXnfcw+/Onz+vuXPnauXKldq/f7/KlCmjKlWqKCoqSl27drX2mzVrlj7//HMdPnxYOTk5hX743YULF5SQkKC0tDQdOHBA2dnZCggIUFhYmGJiYqyXJEmXJo6PHj1aW7ZssZ4xyHv43aZNmzRp0iT98ccfcnBw0P/93//pueee07vvvqtDhw4pOTnZOs6JEyf0n//8R+np6crMzJRhGDYPmduxY4dmzZqljRs36uTJk/L29lZwcLCaNWumrl27ysfHp9DHLzc3V+PHj9dXX32lY8eOKTc31/qAu6st4+F3AHB74OF3AK7HHRcqcHshVADArUWoAHA97qg5FQAAAABuPD6OKKQLFy4UagKyn5+fHB0db0FFN052drays7Ov2sfR0VF+fn63qCIAAADcTggVhfTTTz9pwIAB1+xX1DkDJcGcOXM0bdq0q/YJDAy0mccBAAAA5CFUFNJdd92lSZMmXbNf2bJlb0E1N1ZkZKTq169/1T43+ynkAAAAuH0xURslGhO1AeDWYqI2gOvBOwdKtATvGYqJiZGzs3NxlwIAAIACcPcnAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJhCqAAAAABgCqECAAAAgCmECgAAAACmECoAAAAAmEKoAAAAAGCKxTAMo7iLAApiGXOxuEsASg0jzqm4SwAAlFKcqQAAAABgCqECAAAAgCmECgAAAACmECoAAAAAmEKoAAAAAGAKoQIAAACAKYQKAAAAAKYQKlCgqKgoxcbGlrixAAAAULIQKlAkCQkJSk1NLfIyAAAAlF6EChTJtGnTCgwOV1sGAACA0otQAQAAAMAUp+IuAMXv8OHDGjdunH744QdJ0n333aeXXnrJps/BgwcVHR0tSVqyZImWLFliXZaUlFTgso0bN97s8gEAAFDMCBV3uMzMTMXGxurIkSPq1KmTatSooU2bNql///46f/68tZ+fn59GjhypoUOHqkGDBurYsWOhlgEAAKD0I1Tc4WbPnq2DBw9q6NCh1rMNXbt21fvvv68FCxZY+7m5uSkiIkJDhw5VUFCQIiIibMa52jIAAACUbsypuMOlpqaqbNmyioyMtGnv06dPMVUEAACA2w2h4g534MABVa5cWY6OjjbtAQEB8vLyKqaqAAAAcDshVAAAAAAwhVBxhwsKCtK+ffuUk5Nj037s2DFlZmYWU1UAAAC4nRAq7nDNmzfX8ePHtXTpUpv2WbNm5dvf3d1dGRkZRV4GAACA0ou7P93hevfurRUrVujtt9/Wtm3bVLNmTaWnp+vnn3+Wr6+vXf/Q0FClpaVp5syZqlixoiwWi9q2bXvNZQAAACi9LIZhGMVdBIrX4cOHNXbsWK1fv17S/x5+N3DgQAUGBmrq1KnWvnv37tXo0aO1ZcsWZWVlSfrfA+6utiwqKspurMKwjLloev8AXGLE8TkSAODmIFSgRCNUADcOoQIAcLMwpwIAAACAKYQKAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIAp3AoEJVqC9wzFxMTI2dm5uEsBAABAAThTAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBUAAAAATCFUAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMsRiGYRR3EUBBLGMuFncJwG3JiHMq7hIAAHcQzlQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBUAAAAATCFUAAAAADCFUHGDJSQkKDU11fQ48+fPV3JysvmCCiEhIUFhYWHaunWrtW3jxo0KCwtTWFiYvvjii3zXCwsL04svvmgzRmH+DB8+/BbsFQAAAG4Vp+IuoLSZNm2aOnTooBYtWpgaZ8GCBQoMDFRUVNSNKcyEqVOnqn379nJ1dS2wz8MPP6zKlSvbtI0dO1aSNGTIEJv24ODgG18kAAAAig2hAld1zz33aOvWrVqwYIFiYmIK7Fe7dm3Vrl3bpi0+Pl6SFBERcVNrBAAAQPEiVBTB+fPnNXPmTK1cuVJHjhyRs7OzKlSooGbNmqlr166Kjo6WJC1ZskRLliyxrrdx40ZJ0ldffaXly5dr+/btOnHihNzd3VW/fn0NGDDA5hfysLAwSdKhQ4es/5akpKQkVapUSZK0detWzZgxQ5s3b1Z2drYCAwMVGRmpPn36yMnpxn1ZW7duLcMwNGvWLHXs2FG+vr43bGwAAACUDoSKIhg9erSSkpIUGRmpXr16KScnR/v27dOGDRsUGxurkSNHaujQoWrQoIE6duxot/7ChQvl4+Ojjh07KiAgQPv379cXX3yhp59+WnPnzlWVKlUkSSNHjtTYsWPl6+urvn37Wtf38/OTJK1du1Yvv/yyKleurCeeeELe3t765ZdflJCQoO3bt2v06NE3bJ8tFosGDx6sQYMGacaMGXaXMgEAAACEiiJITU1Vs2bNNGLEiHyXR0REaOjQoQoKCsr3kp8JEybIzc3Npi0yMlKPP/645s+fr1deecU6Tnx8vPz9/e3GOX/+vN58802FhoYqPj7eelaic+fOql27tj744APrJOsbpUmTJmrSpIkWLVqknj17KjAw8IaNDQAAgNsfd38qAk9PT+3cuVM7duy4rvXzAoVhGDpz5oxOnTolPz8/Va1aVVu2bCnUGOvXr9fx48cVFRVlHSPvzwMPPGDtc6M999xzunDhgnWeBAAAAJCHMxVFMGTIEA0bNkw9evRQUFCQwsLC9NBDDyk8PFwODtfOZ7/99pumTJmi9PR0nT171mZZUFBQoWrYtWuXpEuXSBXk+PHjhRqrKOrUqaO2bdtqxYoVevLJJ+0mZQMAAODORagoghYtWigpKUnr1q3Tpk2blJaWpsTERDVo0ECTJ0+Ws7NzgesePnxYsbGx8vDw0NNPP61q1arJ1dVVFotF77//vl3IKIhhGJKkF154QXfddVe+fcqVK1f0nSuEgQMH6ptvvtGECRM0fvz4m7INAAAA3H4IFUXk4+OjiIgIRUREyDAMTZgwQbNnz9bq1avVunXrAtdLSUlRdna2xo4dazffISMjQ2XKlLFps1gs+Y6TN5nbzc1NTZo0Mbk3RRMUFKQuXbpowYIF1jtaAQAAAMypKKScnBxlZmbatFksFoWEhEi6FAwkyd3d3frvy+VdHpV3piHPF198ke/lSm5ubjp9+rRd+/333y9/f3/NnDkz3+2cO3dOWVlZhdyronv66afl4eHBmQoAAABYcaaikLKzs9WuXTuFh4crJCREfn5+OnjwoBYtWiRvb2+Fh4dLkkJDQ5WWlqaZM2eqYsWKslgsatu2rR544AFNmDBBQ4cOVbdu3eTl5aWffvpJ33//vYKDg5WTk2OzvXr16ikxMVHx8fGqXr26LBaLwsPD5ebmphEjRiguLk6dO3dWdHS0KleurMzMTO3evVspKSl67733bujdny7n6+urJ598UlOmTLkp4wMAAOD2Q6goJFdXV/Xs2VNpaWlKS0tTdna2AgICFB4erpiYGOs8hldeeUWjR4/Wxx9/bD1j0LZtWwUHB2v8+PGaNGmSPv74Yzk4OOj//u//lJCQoHfffVeHDh2y2d6zzz6rjIwMffbZZ8rMzJRhGEpKSpKbm5vuv/9+zZo1S7NmzdLy5ct18uRJeXt7Kzg4WL169brpk6ifeOIJLVq0SMeOHbup2wEAAMDtwWJceT0OUIJYxlws7hKA25IRx2dGAIBbhzkVAAAAAEzho6xSKjs7W9nZ2Vft4+joKD8/v1tUEQAAAEorQkUpNWfOHE2bNu2qfQIDA5WcnHyLKgIAAEBpRagopSIjI1W/fv2r9nFxcbk1xQAAAKBUI1SUUsHBwQoODi7uMgAAAHAHIFSgREvwnqGYmBg5OzsXdykAAAAoAHd/AgAAAGAKoQIAAACAKYQKAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJhCqAAAAABgCqECAAAAgCmECgAAAACmWAzDMIq7CKAgljEXi7sE4KqMOKfiLgEAgGLHmQoAAAAAphAqAAAAAJhCqAAAAABgCqECAAAAgCmECgAAAACmECoAAAAAmEKoAAAAAGAKoeIOEBUVpdjY2GKtYePGjQoLC1NycnKx1gEAAIAbj1ABAAAAwBRCBQAAAABTCBUAAAAATHEq7gJgzvnz5zVz5kytXLlSR44ckbOzsypUqKBmzZrphRdeuOq6qampmj17trZv3y6LxaLatWurd+/eatGihU2/H3/8UYmJidq6dauOHTsmZ2dn1a1bV3379lXDhg3zHXfq1KnavXu3/Pz81KFDBzVo0OBG7jYAAABKEELFbW706NFKSkpSZGSkevXqpZycHO3bt08bNmy46nqfffaZRo8erWrVqumZZ56RJC1ZskRxcXF67bXX1KlTJ2vf5ORkZWRkKCIiQhUqVNBff/2lxMREPfvss5oyZYpNYEhJSdE///lPVapUSc8884wcHR2VnJystWvX3pwDAAAAgGJnMQzDKO4icP0efvhhhYaGavz48QX2iYqKUmBgoKZOnSpJOn36tCIjI1W2bFnNnTtXnp6ekqQzZ86oV69eOnnypJYuXSovLy9J0tmzZ+Xm5mYz5vHjx9WtWzfVrVvXuu2cnBxFR0fr3LlzWrx4sXx9fa3j9ujRQ4cPH9awYcMUFRVV6P2zjLlY6L5AcTDi+GwGAADmVNzmPD09tXPnTu3YsaPQ66xfv15nz55Vjx49rIEib6wePXooOztb69evt7ZfHiiys7N16tQpOTo6KjQ0VL/++qt12bZt23TkyBFFR0dbA0XeuJ07d77OPQQAAEBJx0dst7khQ4Zo2LBh6tGjh4KCghQWFqaHHnpI4eHhcnDIPzMeOHBAklSjRg27ZXlteX0kaf/+/Zo0aZJ+/PFHZWZm2vS3WCx241atWtVu3OrVqxdxzwAAAHC7IFTc5lq0aKGkpCStW7dOmzZtUlpamhITE9WgQQNNnjxZzs7OpsbPzs5Wv379dPbsWfXs2VO1atWSh4eHLBaLZs6cec25GwAAACj9CBWlgI+PjyIiIhQRESHDMDRhwgTNnj1bq1evVuvWre36BwcHS5J27typxo0b2yzbtWuXJCkoKEiSlJaWpqNHj2ro0KGKjo626RsfH2/z/7x19uzZY7fNvHEBAABQ+jCn4jaWk5OT7+VIISEhkqSMjIx812vSpInc3Nz06aefKisry9qelZWlTz/9VO7u7mratKkkydHRUZJ05Xz+H3/8UVu2bLFpu/vuu1WhQgUlJSXp1KlT1vYzZ85o8eLF17eTAAAAKPE4U3Eby87OVrt27RQeHq6QkBD5+fnp4MGDWrRokby9vRUeHp7vel5eXnr++ec1evRoPfXUU+rQoYOkS7eU3bdvn1577TXrBO769eurbNmyGjdunA4dOqTy5ctr+/btWrZsmWrVqmUzQdzR0VH/+Mc/9Oqrr6pPnz567LHH5OjoqKSkJPn4+Ojw4cM3/6AAAADgliNU3MZcXV3Vs2dPpaWlKS0tTdnZ2QoICFB4eLhiYmJUrly5Atft2rWrAgICNGfOHE2bNk2SdNddd2nMmDE2D7/z8vLSxIkTNX78eH366afKyclRnTp19OGHHyoxMdHurlOtW7eWg4ODpk+frqlTp8rf39/68LvBgwfflOMAAACA4sVzKlCi8ZwKlHQ8pwIAAOZUAAAAADCJUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMIXblqBES/CeoZiYGDk7Oxd3KQAAACgAZyoAAAAAmEKoAAAAAGAKoQIAAACAKYQKAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJhCqAAAAABgCqECAAAAgCmECgAAAACmWAzDMIq7CKAgljEXi7sElBJGnFNxlwAAQKnFmQoAAAAAphAqAAAAAJhCqAAAAABgCqECAAAAgCmECgAAAACmECoAAAAAmEKoAAAAAGAKoeI2EBUVpdjYWJu2sLAwDR8+vHgKAgAAAC5DqICN+fPnKzk5ubjLAAAAwG2EUAEbCxYsIFQAAACgSAgVAAAAAExxKu4C8D+HDx/WuHHj9MMPP0iS7rvvPr300ktXXWf9+vWKj4/XH3/8IU9PT7Vp00bPPvus3N3dJUnz5s3TBx98oIkTJ6pp06Y26/79999q3769ateurSlTpigsLEySdOjQIeu/JSkpKUmVKlWSJG3dulUzZszQ5s2blZ2drcDAQEVGRqpPnz5ycvrfy+nPP//U1KlT9fPPP+vUqVPy9vZWtWrV9OSTT+rBBx80f7AAAABQYhAqSojMzEzFxsbqyJEj6tSpk2rUqKFNmzapf//+On/+fL7r/Pbbb/rmm2/02GOPKTIyUhs3btQnn3yiP//8U5MmTZKDg4MiIyM1adIkJSUl2YWKlJQUZWRk6LHHHpMkjRw5UmPHjpWvr6/69u1r7efn5ydJWrt2rV5++WVVrlxZTzzxhLy9vfXLL78oISFB27dv1+jRoyVJp06d0sCBAyVJnTt3VsWKFXXq1Clt27ZNW7ZsIVQAAACUMoSKEmL27Nk6ePCghg4dqujoaElS165d9f7772vBggX5rrNjxw6NGTNGLVq0sPYfM2aMPvnkE61atUpt27aVr6+vWrZsaQ0QPj4+1vUTExPl7e2tli1bSpIiIiIUHx8vf39/RURE2Gzr/PnzevPNNxUaGqr4+HjrWYnOnTurdu3a+uCDD7Rx40aFhYXpp59+0okTJ/Sf//xHbdq0udGHCgAAACUMcypKiNTUVJUtW1aRkZE27X369ClwnapVq1oDRZ6nnnrKOl6ejh076u+//9by5cutbQcPHtSGDRvUrl07ubi4XLO+9evX6/jx44qKitKZM2d06tQp658HHnjA2keSPD09JUnff/+9zpw5c82xAQAAcHvjTEUJceDAAd1zzz1ydHS0aQ8ICJCXl1e+61SvXt2uLa//gQMHrG1hYWGqUqWKkpKS1KNHD0lScnKyDMOwXvp0Lbt27ZJ06RKpghw/flyS1LBhQ0VGRio5OVnLly/XPffcoyZNmqhNmzaqUaNGobYHAACA2weh4g7RsWNHffjhh9q2bZtCQkKUnJyse+65R3fddVeh1jcMQ5L0wgsvFLhOuXLlrP8eMWKEnnzySX3//ffavHmz5s6dqxkzZmjIkCHq3r27+R0CAABAiUGoKCGCgoK0b98+5eTk2JytOHbsmDIzM/NdJ+/sweXy+gcFBdm0R0VFafLkyUpMTFTz5s11+PBh66VSl7NYLPluq0qVKpIkNzc3NWnSpFD7VKtWLdWqVUu9e/dWZmam+vTpo4kTJ6pbt24FbgcAAAC3H+ZUlBDNmzfX8ePHtXTpUpv2WbNmFbjOnj17bOZOXN6/efPmNu2+vr5q0aKFVqxYoYULF8rV1VXt2rWzG9PNzU2nT5+2a7///vvl7++vmTNnKiMjw275uXPnlJWVJUnKyMhQbm6uzXIvLy8FBQXp3LlzBd7NCgAAALcnzlSUEL1799aKFSv09ttva9u2bapZs6bS09P1888/y9fXN991atWqpX//+9967LHHVKVKFW3cuFHffPON7rvvPj3yyCN2/Tt27KhVq1ZpzZo16tChg3VC9eXq1aunxMRExcfHq3r16rJYLAoPD5ebm5tGjBihuLg4de7cWdHR0apcubIyMzO1e/dupaSk6L333lNYWJiWLl2q+fPnq2XLlgoODpaTk5M2bdqkH374QW3atJGrq+uNPnwAAAAoRoSKEsLb21vTp0/X2LFjtWzZMkmXHn6XkJBgfebDlerUqaN//OMfmjx5sj7//HN5eHioW7duGjRokBwc7E9CNWrUSJUrV9a+ffv06KOP5jvms88+q4yMDH322WfKzMyUYRhKSkqSm5ub7r//fs2aNUuzZs3S8uXLdfLkSXl7eys4OFi9evVS7dq1JV2aqP37779rzZo1OnbsmBwdHVWpUiW9+OKL6tat2w06YgAAACgpLEbeDFzcEbp166acnBwtXry4uEspFMuYi8VdAkoJI47PUAAAuFmYU3EH2bBhg3bu3KmOHTsWdykAAAAoRfjo7g6wYcMG7d+/XzNnzpSfn1+hn00BAAAAFAah4g4wbdo0/fTTT6pevbqGDx+e7wRtAAAA4HoxpwIlGnMqcKMwpwIAgJuHORUAAAAATOGjO5RoCd4zFBMTI2dn5+IuBQAAAAXgTAUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBUAAAAATCFUAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMMViGIZR3EUABbGMuVjcJaAEMuKcirsEAABwGc5UAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVKBQNm7cqLCwMCUnJxd3KQAAAChhCBUmbNy4UQkJCcrMzCzuUkq0hIQEpaamFncZAAAAuEkIFSakp6dr2rRphIprmDZtGqECAACgFCNU3KaysrKKuwQAAABAkuRU3AWUVBcuXND8+fO1cuVK7dmzR05OTqpSpYo6dOig7t27a/jw4VqyZIkkKTo62rpev3791L9//0Jt4/z585o5c6ZWrlypI0eOyNnZWRUqVFCzZs30wgsvWPuFhYWpQ4cOioiIUEJCgrZv3667775bjzzyiEaNGqX3339fzZs3txk7NzdXHTp0kK+vr+bPn1+kfU9NTdXUqVO1e/du+fn5qUOHDmrQoEGR6z948KD12CxZssR6vKRLl44BAACgdCBU5OPChQsaPHiw0tPT1bRpU7Vv315lypTRjh07lJKSou7du6tTp07KyspSSkqKhgwZIl9fX0lS7dq1C72d0aNHKykpSZGRkerVq5dycnK0b98+bdiwwa7v1q1b9e233+qxxx5Thw4dJEmtWrXS2LFjtXTpUrtQkZaWpr/++ku9evUq0r6npKTon//8pypVqqRnnnlGjo6OSk5O1tq1a4tcv5+fn0aOHKmhQ4eqQYMG6tixY5FqAQAAwO2BUJGP+fPnKz09XTExMRo0aJDNstzcXEnSvffeq1q1aiklJUUtWrRQpUqViryd1NRUNWvWTCNGjLhm3507d2rSpElq0qSJTftDDz2kNWvW6PTp0/L29ra2L126VI6Ojmrfvn2h68nJydGYMWPk7e2tWbNmWYNS586d1aNHjyLX7+bmpoiICA0dOlRBQUGKiIgodC0AAAC4fTCnIh8rVqyQt7e3nnnmGbtlDg437pB5enpq586d2rFjxzX73nXXXXaBQpI6dOigv//+W1999ZW1LTs72/oLv7+/f6Hr2bZtm44cOaLo6GhroMirs3PnzqbqBwAAQOlFqMjH3r17Va1aNbm4uNzU7QwZMkSZmZnq0aOHHn30Ub355ptKTU21ng25XJUqVfId4/7775e/v7+WLVtmbfv222919uxZRUZGFqmeAwcOSJKqVq1qt6x69eqm6gcAAEDpxeVPxahFixZKSkrSunXrtGnTJqWlpSkxMVENGjTQ5MmT5ezsbO3r6uqa7xhOTk5q27atFixYoH379qly5cpaunSpvL29FR4eXmLqBwAAQOnFmYp8VK1aVbt379bff/991X4Wi8X0tnx8fBQREaE33nhDiYmJ6t27tzZv3qzVq1cXeoy8idtLly7V4cOHlZ6erjZt2qhMmTJFqiUoKEiStGfPHrtlu3btumn1AwAA4PZGqMhHu3btdPr0aX300Ud2ywzDsP7b3d1dknT69OkibyMnJ8fuoXkWi0UhISGSpIyMjEKPFRISotq1a2v58uVatmyZ9XayRXX33XerQoUKSkpK0qlTp6ztZ86c0eLFi6+7fnd39yLtDwAAAG4vXP6Uj549e2rNmjX66KOPtHXrVjVp0kQuLi7auXOn9uzZo8mTJ0uSQkNDJUnjx4+33na2Zs2aqlWr1jW3kZ2drXbt2ik8PFwhISHy8/PTwYMHtWjRouu6dCkyMlLjxo3TrFmzVKVKFdWrV6/I++3o6Kh//OMfevXVV9WnTx899thjcnR0VFJSknx8fHT48OHrqj80NFRpaWmaOXOmKlasKIvForZt2xa5PgAAAJRMhIp8ODs7a+LEiZo7d65WrlypyZMnq0yZMqpSpYqioqKs/erXr6/nnntOn3/+ud566y3l5OSoX79+hQoVrq6u6tmzp9LS0pSWlqbs7GwFBAQoPDxcMTExKleuXJFqbt++vSZMmKCsrCz17t27yPucp3Xr1nJwcND06dM1depU+fv7Wx9+N3jw4Ouq/5VXXtHo0aP18ccfW58ETqgAAAAoPSzG5dfzACWMZczF4i4BJZARx+chAACUJMypAAAAAGAKH/fdYBcuXCjUpGQ/Pz85OjregoouTbQ+d+7cVfs4OzvLx8fnltQDAACA0oVQcYP99NNPGjBgwDX7JSUlqVKlSregImnMmDFasmTJVfvcd999mjp16i2pBwAAAKULcypusNOnT2vbtm3X7Fe/fv2b/sTuPDt37tTRo0ev2sfb21t33333LamnKJhTgfwwpwIAgJKFUIESjVCB/BAqAAAoWfjJjBItwXuGYmJi5OzsXNylAAAAoADc/QkAAACAKYQKAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJhCqAAAAABgCqECAAAAgCmECgAAAACmECoAAAAAmGIxDMMo7iKAgljGXCzuEnCTGHFOxV0CAAC4QThTAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBUAAAAATCFU4LocPHhQYWFhSkhIKO5SAAAAUMwIFQAAAABM4ZG2uC6BgYFat26dHB0di7sUAAAAFDNCBa6LxWKRi4tLcZcBAACAEoDLn0qx8+fPKyEhQZ06ddIDDzygFi1aqHv37vrwww8lSf3791dUVJTNOitWrFBYWJh69uxp075o0SKFhYVpy5YtkvKfU3F525o1a9S7d281a9ZMbdu21YcffqiLFy/e5D0GAABAceBMRSk2evRoJSUlKTIyUr169VJOTo727dunDRs2SJIaNWqkKVOmaP/+/QoODpYkbdiwQQ4ODtqxY4dOnTolX19fa7uHh4fuvvvua2533bp1WrRokTp37qzo6GitXr1ac+bMkZeXl/r27XvT9hcAAADFg1BRiqWmpqpZs2YaMWJEvsvDwsIkXQoMeaFi48aNatu2rZYvX64NGzaoTZs2MgxD6enpuu+++wo1h2Lnzp1auHChKlWqJEnq3Lmzunfvrk8//ZRQAQAAUApx+VMp5unpqZ07d2rHjh35Lg8NDZW7u7v1zMWhQ4d04MABtW3bVjVr1lRaWpokWc9a5IWQa2nRooU1UEiX5l+EhYXp+PHjys7ONrlXAAAAKGkIFaXYkCFDlJmZqR49eujRRx/Vm2++qdTUVOXm5kqSnJycVL9+faWnp0uS0tLS5OjoqPvuu0+NGjXSxo0bJcnmcqnCCAoKsmvz8fGRJGVkZJjeLwAAAJQshIpSrEWLFkpKStLIkSPVqFEjbdiwQXFxcerfv78uXLggSdYzCH/++ac2btyounXryt3dXWFhYdq3b58OHz6sDRs2yNfXV7Vr1y7Udh0cCn5ZGYZxQ/YNAAAAJQehopTz8fFRRESE3njjDSUmJqp3797avHmzVq9eLel/Zx82bNigDRs2WP8fFhYmR0dH/fjjj9q8ebMaNmwoi8VSbPsBAACAkotQUUrl5OQoMzPTps1isSgkJETS/y5DCgkJkbe3txYvXqxjx45ZQ4Wnp6dCQkI0f/58nTlzptCXPgEAAODOw92fSqns7Gy1a9dO4eHhCgkJkZ+fnw4ePKhFixbJ29tb4eHhki5dqnTfffcpNTVVLi4uuvfee61jNGrUSLNmzZKkQk/SBgAAwJ2HUFFKubq6qmfPnkpLS1NaWpqys7MVEBCg8PBwxcTEqFy5cta+YWFhSk1N1b333qsyZcpY2/NCRfny5VWtWrVi2AsAAADcDiwGM2dRglnG8BTu0sqI4zMNAABKC+ZUAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMIXbr6BES/CeoZiYGDk7Oxd3KQAAACgAZyoAAAAAmEKoAAAAAGAKoQIAAACAKYQKAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJhCqAAAAABgCqECAAAAgCkWwzCM4i4CKIhlzMXiLgE3kBHnVNwlAACAm4AzFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBWlTFRUlGJjY6/Zdqtt3LhRYWFhSk5OLtY6AAAAcOMRKgAAAACYQqgAAAAAYAqhAgAAAIApTsVdAK7P4cOHNW7cOP3www+SpPvuu08vvfRSkcZITU3V7NmztX37dlksFtWuXVu9e/dWixYtbPr9+OOPSkxM1NatW3Xs2DE5Ozurbt266tu3rxo2bJjvuFOnTtXu3bvl5+enDh06qEGDBte9rwAAACjZCBW3oczMTMXGxurIkSPq1KmTatSooU2bNql///46f/58ocb47LPPNHr0aFWrVk3PPPOMJGnJkiWKi4vTa6+9pk6dOln7JicnKyMjQxEREapQoYL++usvJSYm6tlnn9WUKVNsAkNKSor++c9/qlKlSnrmmWfk6Oio5ORkrV279sYeBAAAAJQYhIrb0OzZs3Xw4EENHTpU0dHRkqSuXbvq/fff14IFC665/unTpzV+/HgFBwdr5syZ8vT0lCR16dJFvXr10rhx49SmTRt5eXlJkt544w25ubnZjNG5c2d169ZNH3/8sTVU5OTkaMyYMfL29tasWbPk6+tr7dujR48btfsAAAAoYZhTcRtKTU1V2bJlFRkZadPep0+fQq2/fv16nT17Vj169LAGCkny9PRUjx49lJ2drfXr11vbLw8U2dnZOnXqlBwdHRUaGqpff/3Vumzbtm06cuSIoqOjrYEib9zOnTsXdTcBAABwm+BMxW3owIEDuueee+To6GjTHhAQYD27cK31JalGjRp2y/La8vpI0v79+zVp0iT9+OOPyszMtOlvsVjsxq1atarduNWrV79mXQAAALg9ESpwVdnZ2erXr5/Onj2rnj17qlatWvLw8JDFYtHMmTO1YcOG4i4RAAAAxYxQcRsKCgrSvn37lJOTY3O24tixY3ZnEvITHBwsSdq5c6caN25ss2zXrl3WbUhSWlqajh49ajN/I098fLxdXZK0Z88eu23mjQsAAIDShzkVt6HmzZvr+PHjWrp0qU37rFmzCrV+kyZN5Obmpk8//VRZWVnW9qysLH366adyd3dX06ZNJckaWgzDsBnjxx9/1JYtW2za7r77blWoUEFJSUk6deqUtf3MmTNavHhxofcPAAAAtxfOVNyGevfurRUrVujtt9/Wtm3bVLNmTaWnp+vnn3+2mSBdEC8vLz3//PMaPXq0nnrqKXXo0EHSpVvK7tu3T6+99pp1Anf9+vVVtmxZjRs3TocOHVL58uW1fft2LVu2TLVq1dKOHTus4zo6Ouof//iHXn31VfXp00ePPfaYHB0dlZSUJB8fHx0+fPimHA8AAAAUL0LFbcjb21vTp0/X2LFjtWzZMkmXHn6XkJCggQMHFmqMrl27KiAgQHPmzNG0adMkSXfddZfGjBlj8/A7Ly8vTZw4UePHj9enn36qnJwc1alTRx9++KESExNtQoUktW7dWg4ODpo+fbqmTp0qf39/68PvBg8efGMOAAAAAEoUi3HldS1ACWIZc7G4S8ANZMTxOQYAAKURcyoAAAAAmEKoAAAAAGAKoQIAAACAKYQKAAAAAKYQKgAAAACYwq1YUKIleM9QTEyMnJ2di7sUAAAAFIAzFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBUAAAAATCFUAAAAADCFUAEAAADAFIthGEZxFwEUxDLmYnGXgBvIiHMq7hIAAMBNwJkKAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJhCqAAAAABgyh0fKqKiohQbG1vcZRQ7jgMAAACu120RKjZu3KiEhARlZmYWdylWqampSkhIKO4ybqr58+crOTm5uMsAAABACXdbhIr09HRNmzatxIWKadOmFXcZN9WCBQsIFQAAALim2yJUwFZWVlZxlwAAAABYORV3ARcuXND8+fO1cuVK7dmzR05OTqpSpYo6dOig7t27a/jw4VqyZIkkKTo62rpev3791L9//0Jv5/Dhwxo3bpx++OEHSdJ9992nl156Kd++a9eu1ezZs/Xnn3/q3Llz8vX11T333KPBgweratWqio2N1aZNmyRJYWFh1vWGDRumqKioQtWTkZGh6dOn67vvvtPRo0fl5uamwMBAPfLII+rdu7ck6eDBg4qOjla/fv1UvXp1zZ49W7t27VKbNm00fPhwSZcuDZszZ462bNmis2fPqly5cmrYsKGef/55+fr6Fvr4XClvvw4dOmSzj0lJSapUqZKioqIUGBiouLg4jRs3Tr/88otcXV0VERGh5557Tjk5OYqPj9fKlSuVkZGhunXr6rXXXlP16tWvuyYAAACUTMUaKi5cuKDBgwcrPT1dTZs2Vfv27VWmTBnt2LFDKSkp6t69uzp16qSsrCylpKRoyJAh1l+Ua9euXejtZGZmKjY2VkeOHFGnTp1Uo0YNbdq0Sf3799f58+dt+qanp2vIkCGqWbOmYmJi5OnpqWPHjiktLU379u1T1apV1bdvXxmGoc2bN2vkyJHWde+9995C1/TKK69o06ZN6ty5s2rXrq3z589r165dSk9Pt4aKPKtXr9ann36qzp07q3PnzvLw8JAkLV68WKNGjVL58uXVuXNnBQYG6vDhw1qzZo2OHDliKlSMHDlSY8eOla+vr/r27Wtt9/Pzs/77r7/+0qBBg9SmTRs9/PDDWr9+vebNmydHR0ft3LlT58+fV58+fZSRkaE5c+bopZde0qJFi+TgwAkyAACA0qRYQ8X8+fOVnp6umJgYDRo0yGZZbm6upEu/qNeqVUspKSlq0aKFKlWqVOTtzJ49WwcPHtTQoUOtZzu6du2q999/XwsWLLDpu3r1auXm5mrSpEny9/e3tj/zzDPWfzdt2lQrVqzQ5s2bFRERUeR6zpw5ow0bNqhLly765z//ec3+f/75pz755BObT/mPHDmiMWPGqFq1apoxY4a8vLysywYOHGg9ftcrIiJC8fHx8vf3L3Af9+/fr1GjRql169aSpC5duuiJJ57QnDlz9NBDD2ny5MmyWCySJB8fH40ZM0br16/X/fffb6o2AAAAlCzF+pHxihUr5O3tbfMLe54b+Wl2amqqypYtq8jISJv2Pn362PX19PSUJH377be6ePHiDavhci4uLipTpoy2bNmigwcPXrP/gw8+aHfZ0Ndff60LFy6oX79+NoEiz604G1C+fHlroMhTv359GYah7t27WwNFXrsk7d2796bXBQAAgFurWEPF3r17Va1aNbm4uNzU7Rw4cECVK1eWo6OjTXtAQIDdL+TdunVTSEiIRo0apVatWun555/XJ598opMnT96wepydnTVkyBD9+eefio6OVrdu3fTuu+8qLS0t3/5VqlSxa9u3b58kKSQk5IbVVVT5nTXKO55BQUE27d7e3pIuzSUBAABA6cLF7Vfw9fXV7NmzNWXKFHXr1k3Z2dkaO3asOnXqpJ9//vmGbadLly5KTk7WG2+8oZCQEH3zzTd69tln9eqrr9r1dXV1vWHbvZGudjakoGWGYdyscgAAAFBMijVUVK1aVbt379bff/991X6XX0ZzPYKCgrRv3z7l5OTYtB87dizfZ184OjoqLCxMgwYN0vTp0zVv3jxlZ2fro48+umE1SZfOlDz22GN68803tWzZMrVt21arVq3Sr7/+es11885ebN++3XQdBbkR+wgAAIDSr1hDRbt27XT69GmbX9bzXP6Jtru7uyTp9OnT17Wd5s2b6/jx41q6dKlN+6xZs+z6njp1yq6tWrVqcnV1tdm+m5ubpOu7nOfcuXM6d+6cTZujo6P1jlaF2c9WrVrJ2dlZ06ZN05kzZ+yW34gzAm5ubtd9zAEAAHDnKNa7P/Xs2VNr1qzRRx99pK1bt6pJkyZycXHRzp07tWfPHk2ePFmSFBoaKkkaP3689bazNWvWVK1atQq1nd69e2vFihV6++23tW3bNtWsWVPp6en6+eef7W67+tZbb+mvv/5SkyZNFBgYqPPnz2vVqlXKysqymehdr149LVy4UKNGjdKDDz4oJycnhYaG2s0lyM+ePXsUGxurli1bqmbNmvLy8tLu3bu1aNEiBQUFqUGDBtcco0KFCnrppZc0evRo9ejRQ5GRkQoMDNRff/2l1atXa+jQoabnW9SrV0+JiYmKj49X9erVZbFYFB4ebg1UAAAAgFTMocLZ2VkTJ07U3LlztXLlSk2ePFllypRRlSpVbB4iV79+fT333HP6/PPP9dZbbyknJ0f9+vUrdKjw9vbW9OnTNXbsWC1btkzSpYffJSQkaODAgTZ9IyIilJycrKVLl+rkyZPy8PBQjRo1NHr0aLVq1crar23btvr999/11Vdf6ZtvvlFubq6GDRtWqFBRoUIFRUdHKz09Xampqbpw4YLKlSunjh07qk+fPoWeQ9GlSxcFBwdr9uzZ+uSTT6zjNGrUSBUqVCjUGFfz7LPPKiMjQ5999pkyMzNlGIaSkpIIFQAAALBhMZg5ixLMMubm3NYXxcOIK9bPMQAAwE3C3Z8AAAAAmHLbfmx44cKFQk2S9vPzs3s+xc1y7ty5fCdNXykgIOAWVCOdPHnS7o5XV3J3d7dOhAcAAACux20bKn766ScNGDDgmv2SkpLyfUjbzbBq1SqNGDHimv02btx4C6q5NEH90KFDV+3Tr18/9e/f/5bUAwAAgNLptp1Tcfr0aW3btu2a/erXr3/Tn9id59ixY/rzzz+v2a9Jkya3oBrpv//9r86fP3/VPkFBQQoODr4l9VwP5lSULsypAACgdLptQwXuDISK0oVQAQBA6cRPeJRoCd4zFBMTI2dn5+IuBQAAAAXg7k8AAAAATCFUAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTLIZhGMVdBFAQy5iLxV0CTDLinIq7BAAAcJNxpgIAAACAKYQKAAAAAKYQKgAAAACYQqgAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJhy24WKqKgoxcbGFncZpQ7HFQAAANfrpoSKjRs3KiEhQZmZmTdj+OuSmpqqhISE4i7jpsjMzFRCQoI2btx4Q8edP3++kpOTb+iYAAAAKH1uSqhIT0/XtGnTSlyomDZtWnGXcVNkZmZq2rRpSk9Pv6HjLliwgFABAACAa7rtLn/C/5w7d04XL14s7jIAAABwh3Mq6goXLlzQ/PnztXLlSu3Zs0dOTk6qUqWKOnTooO7du2v48OFasmSJJCk6Otq6Xr9+/dS/f/9Cb+fw4cMaN26cfvjhB0nSfffdp5deeinfvmvXrtXs2bP1559/6ty5c/L19dU999yjwYMHq2rVqoqNjdWmTZskSWFhYdb1hg0bpqioqELVk5GRoenTp+u7777T0aNH5ebmpsDAQD3yyCPq3bu3JOngwYOKjo5Wv379VLVqVc2cOVN79+6Vn5+foqOj9fTTT8vJyfaQ//HHH0pISNDmzZt19uxZBQUFqUOHDnriiSfk6Oho7Zd3XFetWqXx48dr3bp1OnnypIYOHaoRI0ZIkqZNm2Y9GxMYGGjqLEPecTp06JDNMUtKSlKlSpUUFRWlwMBAxcXFady4cfrll1/k6uqqiIgIPffcc8rJyVF8fLxWrlypjIwM1a1bV6+99pqqV69+3TUBAACgZCpSqLhw4YIGDx6s9PR0NW3aVO3bt1eZMmW0Y8cOpaSkqHv37urUqZOysrKUkpKiIUOGyNfXV5JUu3btQm8nMzNTsbGxOnLkiDp16qQaNWpo06ZN6t+/v86fP2/TNz09XUOGDFHNmjUVExMjT09PHTt2TGlpadq3b5+qVq2qvn37yjAMbd68WSNHjrSue++99xa6pldeeUWbNm1S586dVbt2bZ0/f167du1Senq6NVTk+e6773TgwAF17dpVZcuW1Xfffadp06bp8OHDGjZsmLXf1q1bFRsbKycnJ2vfNWvWaMKECfrjjz/01ltv2dUxaNAglS1bVk8//bTOnj2rZs2aaciQIRo7dqxatmypli1bSpLc3d0LvW/5GTlypMaOHStfX1/17dvX2u7n52f9919//aVBgwapTZs2evjhh7V+/XrNmzdPjo6O2rlzp86fP68+ffooIyNDc+bM0UsvvaRFixbJwYETZAAAAKVJkULF/PnzlZ6erpiYGA0aNMhmWW5urqRLv6jXqlVLKSkpatGihSpVqlTkombPnq2DBw9q6NCh1rMdXbt21fvvv68FCxbY9F29erVyc3M1adIk+fv7W9ufeeYZ67+bNm2qFStWaPPmzYqIiChyPWfOnNGGDRvUpUsX/fOf/7xm/z/++EOzZ89WnTp1JEndu3fXyy+/rOTkZHXq1En16tWTJI0ZM0YXLlzQxx9/bA1d3bt316uvvqoVK1YoOjpajRs3thm7Zs2aevPNN23aWrRoobFjx6pWrVrXtX/5iYiIUHx8vPz9/Qscc//+/Ro1apRat24tSerSpYueeOIJzZkzRw899JAmT54si8UiSfLx8dGYMWO0fv163X///TekRgAAAJQMRfrIeMWKFfL29rb5hd060A389Dk1NVVly5ZVZGSkTXufPn3s+np6ekqSvv3225s2v8DFxUVlypTRli1bdPDgwWv2b9KkiTVQSJLFYrGezUhJSZEknThxQj///LPCw8NtzuJYLBbrmYG8vpd74oknTO3LjVS+fHlroMhTv359GYah7t27WwNFXrsk7d2791aWCAAAgFugSElg7969qlatmlxcXG5WPZKkAwcOqHLlyjZzCiQpICBAXl5eNm3dunVTSEiIRo0apVatWun555/XJ598opMnT96wepydnTVkyBD9+eefio6OVrdu3fTuu+8qLS0t3/7VqlWza6tRo4akS/smyRpO8tovV716dTk4OFj7Xq5q1arXuxs3XH5nofK+PkFBQTbt3t7eki7NTQEAAEDpcttf3O7r66vZs2drypQp6tatm7KzszV27Fh16tRJP//88w3bTpcuXZScnKw33nhDISEh+uabb/Tss8/q1VdfvWHbKAxXV9dbur2rudrZqYKWGYZxs8oBAABAMSlSqKhatap2796tv//++6r9Lr/s5XoEBQVp3759ysnJsWk/duxYvs++cHR0VFhYmAYNGqTp06dr3rx5ys7O1kcffXTDapIunSl57LHH9Oabb2rZsmVq27atVq1apV9//dWm3+7du+3W3blzp3XfpP99yp/XfuX6ubm5dp/2F+RG7NutHBcAAAClS5FCRbt27XT69GmbX9bzXP4JdN6dh06fPn1dRTVv3lzHjx/X0qVLbdpnzZpl1/fUqVN2bdWqVZOrq6vN9t3c3CRd3+U3586d07lz52zaHB0drXMhrtzP9evX67fffrP+3zAMzZ49W9KlSdWS5O/vr3vvvVffffedduzYYdP3448/liTrnZyuJW/frvd4X23cGz0mAAAASp8i3f2pZ8+eWrNmjT766CNt3bpVTZo0kYuLi3bu3Kk9e/Zo8uTJkqTQ0FBJ0vjx4623na1Zs6Zq1apVqO307t1bK1as0Ntvv61t27apZs2aSk9P188//2y9RW2et956S3/99ZeaNGmiwMBAnT9/XqtWrVJWVpbNRO969epp4cKFGjVqlB588EE5OTkpNDS0UGcD9uzZo9jYWLVs2VI1a9aUl5eXdu/erUWLFikoKEgNGjSw6V+7dm0NGDBAXbt2VUBAgFavXq20tDRFRETY3MY2Li5OsbGx6tevn/WWsmvXrtUPP/ygdu3a2d35qSC+vr6qXLmyvvrqKwUHB8vf319ubm4KDw8v1PoFqVevnhITExUfH6/q1avLYrEoPDzcGmIAAAAAqYihwtnZWRMnTtTcuXO1cuVKTZ48WWXKlFGVKlVsHiJXv359Pffcc/r888/11ltvKScnR/369St0qPD29tb06dM1duxYLVu2TNKlh98lJCRo4MCBNn0jIiKUnJyspUuX6uTJk/Lw8FCNGjU0evRotWrVytqvbdu2+v333/XVV1/pm2++UW5uroYNG1aoUFGhQgVFR0crPT1dqampunDhgsqVK6eOHTuqT58+dvMcwsPDrQ+/27Nnj/z9/fXMM8/Y3TXrnnvu0YwZM5SQkKBFixZZH3733HPPFfkuT2+++abGjh2rSZMm6dy5cwoMDDQdKp599lllZGTos88+U2ZmpgzDUFJSEqECAAAANiwGM2dvmMufqF2Up4ejYJYxN+c2wbh1jLgifXYBAABuQ7f93Z8AAAAAFK9b9hHihQsXCjVJ2s/Pz+75FDfLuXPndObMmWv2CwgIuAXV3HgnT560u4PWldzd3a0T6wEAAIDrcctCxU8//aQBAwZcs19SUlK+D1W7GVatWqURI0Zcs9/GjRtvQTU3Xu/evXXo0KGr9uFSLQAAAJh1y+ZUnD59Wtu2bbtmv/r169/0J3bnOXbsmP78889r9mvSpMktqObG++9//6vz589ftU9QUJCCg4NvUUVFx5yK2x9zKgAAKP2YqI0SjVBx+yNUAABQ+vHTHiVagvcMxcTEyNnZubhLAQAAQAG4+xMAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBUAAAAATCFUAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMMViGIZR3EUABbGMuVjcJcAEI86puEsAAAC3AGcqAAAAAJhCqAAAAABgCqECAAAAgCmECgAAAACmECoAAAAAmEKoAAAAAGAKoQIAAACAKYSKUio5OVlhYWHauHHjdY+RkJCgsLAwHTx48AZWBgAAgNKGUIGbZuPGjUpISFBmZmZxlwIAAICbiFCBmyY9PV3Tpk0jVAAAAJRyhAoAAAAApjgVdwG4JCsrS7NmzdL69eu1f/9+ZWdnq0KFCmrVqpX69esnV1dXa1/DMPTll1/qyy+/1M6dOyVJlSpVUsuWLTVgwICrbuejjz5SfHy8unXrpri4ODk4OCg3N1ezZs3SF198oWPHjik4OFgxMTH5rr9792598skn2rRpkw4fPqycnBxVr15dXbp00WOPPWbtN3z4cC1ZskSSFB0dbW3v16+f+vfvf72HCQAAACUQoaKEOHr0qBITE/Xwww+rXbt2cnR01KZNmzR79mz9/vvvmjhxorXv0KFDtXz5coWGhqpv377y8vLS7t279c033xQYKnJycvTuu+9q8eLFGjx4sJ566inrsg8++EALFizQfffdp8cff1wnTpzQ6NGjFRQUZDfOxo0btWnTJj344IOqVKmSzp07p6+//lpvvfWWTp48aQ0jnTp1UlZWllJSUjRkyBD5+vpKkmrXrn3jDhoAAABKBEJFCREUFKSlS5fKyel/X5Ju3bopPj5eH330kbZs2aLQ0FCtWrVKy5cvV/v27TVixAg5OPzvCrbc3Nx8xz537pzeeOMNrV27VsOHD1eHDh2sy/LOPDRq1EgTJ06Uo6OjJOnhhx/Wk08+aTdWZGSkunTpYtP2+OOPa8CAAZo5c6aefPJJOTk56d5771WtWrWUkpKiFi1aqFKlSqaODwAAAEou5lSUEM7OztZAcfHiRZ0+fVqnTp1S48aNJUlbtmyRJC1fvlyS9OKLL9oECkl2/5ek06dPa9CgQUpLS9MHH3xgEygkafXq1TIMQ7169bIGCkmqU6eOmjRpYjeem5ub9d/nz5/XqVOndPr0aTVt2lRZWVnavXv3dew9AAAAbmecqShBPvvsMy1evFg7d+60O+uQdwelffv2KSAgQGXLli3UmCNGjFB2dramTZum+vXr2y0/cOCAJKlatWp2y6pXr64ff/zRpi07O1tTp07VqlWrdOTIEbt1Tp8+Xai6AAAAUHoQKkqIuXPnaty4cWratKl69OihgIAAOTs76+jRoxo+fHiBlzZdS5s2bZScnKzp06drzJgxNhO+r8frr7+utWvXqmPHjrrvvvvk4+MjBwcHrVu3TvPnz7/uOgEAAHD7IlSUEMuWLVOlSpU0fvx4m8uYvv/+e5t+VapU0erVq3X8+PFCna1o166dGjVqpKFDh+of//iHPvjgA5tgkTcZe/fu3QoODrZZd9euXTb/z8zM1Nq1axUREaHXXnvNZllaWprdti0WyzXrAwAAwO2PORUlhKOjoywWiwzDsLZdvHhRM2fOtOnXvn17SdL48ePtzgpcvu7l2rZtq7ffflubN2/W888/r+zsbOuy5s2by2KxaN68ecrJybG2//bbb3ZBIS/sXLmdY8eO6csvv7Tbrru7uyQuiQIAACjtOFNRQrRq1UoTJ07U888/r5YtWyorK0srV660uRuUJLVu3Vpt2rTR0qVLtW/fPoWHh8vLy0t79+7VDz/8oIULF+Y7fuvWreXk5KRXX31VgwcP1vjx4+Xp6alq1aqpa9euWrhwoQYOHKiHH35YJ06c0MKFC1W7dm39/vvv1jE8PDzUtGlTLV++XC4uLqpbt64OHTqkzz//XEFBQcrIyLDZZmhoqKRLAah9+/YqU6aMatasqVq1at3gowcAAIDiRKgoIZ588kkZhqHExES9//77Klu2rNq0aaPo6Gh17drVpu/bb7+tBg0aKDExUdOmTZOjo6MqVaqk1q1bX3UbLVq00Hvvvad//vOfGjx4sCZOnChPT0/FxcWpbNmy+uKLL/Thhx+qcuXK+te//qW9e/fahApJevPNNzVhwgStWbNGS5cuVeXKlfXss8/KyclJI0aMsOlbv359Pffcc/r888/11ltvKScnR/369SNUAAAAlDIWo6BrZoASwDLmYnGXABOMOD63AADgTsCcCgAAAACmECoAAAAAmEKoAAAAAGAKoQIAAACAKYQKAAAAAKYQKgAAAACYwv0eUaIleM9QTEyMnJ2di7sUAAAAFIAzFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTCBUAAAAATCFUAAAAADCFUAEAAADAFIthGEZxFwEUxDLmYnGXABOMOKfiLgEAANwCnKkAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJhCqAAAAABgCqECAAAAgCmECgAAAACmECoAAAAAmEKoAAAAAGAKoQIAAACAKYQKAAAAAKY4FXcB+J/z589r5syZWrlypY4cOSJnZ2dVqFBBzZo10wsvvCBJCgsLU4cOHRQZGanJkydr+/bt8vHxUbdu3fTUU0/p9OnTGjdunNasWaPs7Gw1atRIr7/+usqVK2ezrTNnzmjGjBn69ttvdeTIEXl4eKhx48Z69tlnFRwcbO2XlZWlWbNmaf369dq/f7+ys7NVoUIFtWrVSv369ZOrq6u178aNGzVgwAANGzZMhmFo7ty52rdvn8qWLauuXbuqT58+t+ZAAgAA4JYiVJQgo0ePVlJSkiIjI9WrVy/l5ORo37592rBhg02/33//XWvWrFHHjh0VGRmpVatWaeLEiXJxcdGSJUtUqVIlxcbGat++ffr00081bNgwTZ482br+mTNn1LdvXx0+fFjR0dGqUaOGjh07pkWLFumpp57SnDlzFBgYKEk6evSoEhMT9fDDD6tdu3ZydHTUpk2bNHv2bP3++++aOHGi3X4sXrxYJ06cUHR0tLy8vLR8+XJNmDBBFSpUULt27W7uQQQAAMAtZzEMwyjuInDJww8/rNDQUI0fP77APmFhYbJYLPr4448VGhoqSbpw4YI6dOigEydOqFu3bnr55Zet/ceOHav58+dr0aJFqlatmiRpzJgx+uKLL/Txxx/rrrvusvY9dOiQevTooZYtW2r48OHWsS0Wi5ycbPNnfHy8PvroI82cOdNaR96ZioCAAC1atEienp6SpHPnzqlDhw6qXLmyPv744yIdE8uYi0Xqj5LFiONzCwAA7gTMqShBPD09tXPnTu3YseOq/erVq2f9RV6SnJ2dVbduXRmGoR49etj0bdCggSRp3759kiTDMLR8+XI1aNBA5cuX16lTp6x/3NzcFBoaqh9//NFm7LxAcfHiRZ0+fVqnTp1S48aNJUlbtmyxqy8qKsoaKCTJ1dVV9erV0969e4tyOAAAAHCb4GPEEmTIkCEaNmyYevTooaCgIIWFhemhhx5SeHi4HBz+l/+CgoLs1vX29pYkVapUyabdy8tLkpSRkSFJOnnypDIyMvTjjz+qdevW+dZx+bYk6bPPPtPixYu1c+dO5ebm2izLzMy0Wz+/+nx8fKw1AAAAoHQhVJQgLVq0UFJSktatW6dNmzYpLS1NiYmJatCggSZPnixnZ2dJkqOjY4FjFLQs7yq3vL8bN25cqInTc+fO1bhx49S0aVP16NFDAQEBcnZ21tGjRzV8+HC7kHGt+gAAAFD6ECpKGB8fH0VERCgiIkKGYWjChAmaPXu2Vq9eXeCZhaLw8/OTl5eXsrKy1KRJk2v2X7ZsmSpVqqTx48fbnMH4/vvvTdcCAACA0oE5FSVETk6O3aVEFotFISEhknTDLh1ycHBQu3bt9Ouvv+rrr7/Ot8+JEyes/3Z0dJTFYtHl8/kvXryomTNn3pB6AAAAcPvjTEUJkZ2drXbt2ik8PFwhISHy8/PTwYMHtWjRInl7eys8PPyGbWvQoEH66aef9Oqrr+qbb75RvXr15OzsrEOHDmndunW6++67rXd/atWqlSZOnKjnn39eLVu2VFZWllauXGl3NygAAADcufjNsIRwdXVVz549lZaWprS0NGVnZysgIEDh4eGKiYmxe3idGZ6enpoxY4bmzp2rVatW6bvvvpOjo6PKly+v+vXr67HHHrP2ffLJJ2UYhhITE/X++++rbNmyatOmjaKjo9W1a9cbVhMAAABuXzynAiUaz6m4vfGcCgAA7gzMqQAAAABgCqECAAAAgCmECgAAAACmECoAAAAAmEKoAAAAAGAKt2ZBiZbgPUMxMTFydnYu7lIAAABQAM5UAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhVABAAAAwBRCBQAAAABTLIZhGMVdBFAQy5iLxV0CrpMR51TcJQAAgFuEMxUAAAAATCFUAAAAADCFUAEAAADAFEIFAAAAAFMIFQAAAABMIVQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVKDIkpOTNX/+/OIuAwAAACUEoQJFlpycrAULFhR3GQAAACghCBV3uJycHJ07d664ywAAAMBtzKm4C8Ctk5ycrBEjRmjSpEn65ZdflJycrMOHD+uNN95QuXLllJiYqK1bt+rYsWNydnZW3bp11bdvXzVs2NA6RlRUlA4dOiRJCgsLs7ZPmTLF+v+9e/dq2rRpSktLU0ZGhsqVK6fWrVsrNjZWbm5ut3anAQAAcNMRKu5AH374oS5evKiOHTvKw8NDVatW1aeffqqMjAxFRESoQoUK+uuvv5SYmKhnn31WU6ZMUYMGDSRJL730kiZOnKhTp05pyJAh1jGrV68uSdq2bZsGDBggLy8vderUSeXLl9f27dv1ySef6KefftLUqVPl5MTLDgAAoDTht7s70Llz5zR//ny5urpa22rXrm13FqFz587q1q2bPv74Y2uoaNGihebPn6/z588rIiLCbuyRI0cqICBAs2fPloeHh7W9cePGevnll7V8+XJFRUXdpD0DAABAcWBOxR2oS5cuNoFCkk2gyM7O1qlTp+To6KjQ0FD9+uuvhRp3x44d+uOPP9SuXTtduHBBp06dsv6pX7++3Nzc9OOPP97QfQEAAEDx40zFHahKlSp2bfv379ekSZP0448/KjMz02aZxWIp1Li7du2SJCUkJCghISHfPidOnChitQAAACjpCBV3oCvPUmRnZ6tfv346e/asevbsqVq1asnDw0MWi0UzZ87Uhg0bCjWuYRiSpCeeeEL3339/vn28vb3NFQ8AAIASh1ABpaWl6ejRoxo6dKiio6NtlsXHx9v1L+jMRd4ZEAcHBzVp0uTGFwoAAIASiTkVkKOjo6T/nWnI8+OPP2rLli12/d3d3XX69Gm7/iEhIapZs6YWL16s/fv326138eJFZWRk3MDKAQAAUBJwpgKqX7++ypYtq3HjxunQoUPW28AuW7ZMtWrV0o4dO2z6h4aGas2aNXr33Xd17733ysHBQY0aNZK/v79GjhypgQMHqmfPnoqOjlaNGjV07tw57d+/X99++60GDx7M3Z8AAABKGUIF5OXlpYkTJ2r8+PH69NNPlZOTozp16ujDDz9UYmKiXajo1auXDhw4oG+++UaLFy9Wbm6upkyZIn9/f4WEhGjevHn6+OOP9d1332nx4sXy8PBQYGCgoqKi1KhRo2LaSwAAANwsFuPKa1iAEsQy5mJxl4DrZMTxmQUAAHcK5lQAAAAAMIVQAQAAAMAUQgUAAAAAUwgVAAAAAEwhVAAAAAAwhduzoERL8J6hmJgYOTs7F3cpAAAAKABnKgAAAACYQqgAAAAAYAqhAgAAAIAphAoAAAAAphAqAAAAAJhCqAAAAABgCqECAAAAgCmECgAAAACmECoAAAAAmEKoAAAAAGAKoeL/tXfncTXl/x/AX7eu277QQomKVJasUULUkCRrjHVKDCN87dvMGCrrREzE0FCUzBgjmrFkGYxhLMlEGVlGYYgWWmm9n98fHvf8Ou69qW63xPv5eHjM9Lmfc87n8z6fc+75nPM5n0sIIYQQQghRiIAxxuq7EITII9hQVt9FIDXAFgrruwiEEEIIqUP0pIIQQgghhBCiEOpUEEIIIYQQQhRCnQpCCCGEEEKIQqhTQQghhBBCCFEIdSoIIYQQQgghCqFOBSGEEEIIIUQh1KkghBBCCCGEKIQ6FR8of39/2Nvb13cxCCGEEELIR4A6FYQQQgghhBCF0C9qf6DKyspQXl4ONTW1+i6KQugXtRsm+kVtQggh5ONCTyoagMLCwmovIxQKG3yHghBCCCGENAx0O1FBxcXF2L17N06cOIHnz5+jUaNGaNq0KZycnDBnzhwu35UrVxAZGYlbt26hpKQELVu2xKhRozBq1Cje+oYMGQITExPMnz8foaGhSEpKgp6eHpYsWYI5c+Zg4cKFGDt2rFQ5fH198fjxY8TFxUEoFMLf3x9HjhzBtWvXePmysrIQERGBCxcuICMjA9ra2mjTpg28vb3h6OjI5Xv06BF++OEHXL16Fbm5uTAyMkL//v0xbdo0aGho1HIUCSGEEEJIQ0adCgV9++23+PXXXzF48GBMmDAB5eXlePz4MeLj47k8MTExWLt2Lezs7DB58mRoaGjgypUrWLduHZ48ecLrfADA8+fP4efnh/79+8PV1RWvXr2Co6MjDAwMcPToUalOxaNHj5CUlISxY8dCKJS/S58+fYopU6bgxYsX8PDwQLt27fD69WskJSXh6tWrXKfi9u3bmD59OnR0dDBy5EgYGxvj7t27+Omnn3Djxg2EhYVVuh1CCCGEEPJxoStDBZ07dw5OTk4ICAiQ+XlWVhY2bNgANzc3rF69mksfPXo0NmzYgOjoaHh5ecHMzIz77MmTJ1i2bBmGDx/OW5eHhweioqLw4MEDtGrViks/evQoAMDT07PSsq5btw6ZmZnYsmULevbsyftMLBZz/x8YGAhDQ0NERkZCS0uLS+/RowcWLVqE48ePY8iQIZVuixBCCCGEfDzonQoFaWtr48GDB7h//77Mz0+fPo2SkhIMGzYMOTk5vH99+vSBWCzG1atXecvo6enJvGgfPHgwgP/vRAAAYwzHjx9H69atYWtrK7ecubm5uHTpEpycnKQ6FACgovKmKdy/fx/37t2Du7s7SktLeeXt3LkzNDQ0cPny5XcHhhBCCCGEfDToSYWC5s+fjxUrVmDs2LFo3rw57O3t0adPHzg7O0NFRQVpaWkAgBkzZshdx4sXL3h/N2/eHKqqqlL5rKysYGtri7i4OMycORMqKiq4fv06nj59itmzZ1dazsePH4MxBhsbm0rzpaamAgB27NiBHTt2VKm8lSktLUVubi4vTVNTE5qamlVeByGEEEIIeb9Rp0JB/fr1w6+//oqLFy/i+vXruHr1KmJjY9GlSxds27YNkhl7AwICYGhoKHMdzZs35/2trq4ud3uDBw9GcHAw4uPj4eDggKNHj0JVVRUeHh61Uh9JeSdOnCjziQYA6OrqVnl9N27cwPTp03lpU6dOxRdffFHzQhJCCCGEkPcKdSpqgZ6eHjw8PODh4QHGGLZs2YLIyEj88ccfaNGiBQBAX18fDg4OCm/L3d0dISEhOHr0KDp16oTff/8dDg4OcjssEi1atIBAIMCdO3cqzdeyZUsAb4ZD1UZ5ra2tsXXrVl7a250oQgghhBDSsNE7FQooLy9Hfn4+L00gEHBDjHJzczFgwACIRCLs2LEDRUVFUusoKChASUlJlbfZuHFjODk54ezZs4iLi0NhYSH3rkVl9PT04OTkhL/++gtXrlyR+lzyhMLGxgatW7fGwYMH8d9//0nlKysrkxrOVBldXV04ODjw/lV8KZ0QQgghhDR89KRCAa9evYK7uzucnZ1hY2ODxo0b4+nTp/jll1+gq6sLZ2dnGBkZYenSpVi1ahVGjx4NDw8PmJiY4OXLl7h//z7OnTuHAwcOwNTUtMrb9fT0xPnz57Fp0yZoa2ujX79+VVpu8eLFmDx5MmbPng1PT0+0bdsWRUVFuHXrFkxMTDB79mwIBAIEBgbCz88P48aNw9ChQ9GqVSsUFRXhv//+w5kzZzBr1iya/YkQQgghhHCoU6EAdXV1jBs3DlevXsXVq1fx6tUrGBoawtnZGb6+vjAyMgIADB06FC1btsTevXsRExOD/Px86Ovrw9zcHH5+fjAwMKjWdvv06QM9PT3k5uZi+PDhVf7l7ObNmyMqKgo7d+7ExYsXcfToUejq6qJNmzYYMWIEl8/GxgbR0dGIiIjA+fPncfDgQWhpacHExARDhgxB9+7dq1VeQgghhBDyYRMwybgXQt5Dgg1l9V0EUgNsId2vIIQQQj4m9E4FIYQQQgghRCF0O5HUSFFREQoKCt6Z712zUhFCCCGEkIaPOhWkRk6dOoWAgIB35rt27VodlIYQQgghhNQn6lSQGunZs6fU708QQgghhJCPE3UqSI0YGhrS0CZCCCGEEAKAOhXkPbdDNxy+vr5o1KhRfReFEEIIIYTIQbM/EUIIIYQQQhRCnQpCCCGEEEKIQqhTQQghhBBCCFEIdSoIIYQQQgghCqFOBSGEEEIIIUQh1KkghBBCCCGEKIQ6FYQQQgghhBCFUKeCEEIIIYQQohDqVBBCCCGEEEIUQp0KQgghhBBCiEKoU0EIIYQQQghRCHUqCCGEEEIIIQqhTgUhhBBCCCFEIdSpIIQQQgghhCiEOhWEEEIIIYQQhVCnghBCCCGEEKIQ6lQQQgghhBBCFEKdCkIIIYQQQohCqFNBCCGEEEIIUQh1KgghhBBCCCEKoU4FIYQQQgghRCHUqSCEEEIIIYQohDoVhBBCCCGEEIVQp4IQQgghhBCiEOpUEEIIIYQQQhQirO8CECIPYwyvX79GXl4eGjVqVN/FIYQQQgj5aOno6EAgEMj9XMAYY3VYHkKqLCsrC0ZGRvVdDEIIIYSQj15ubi50dXXlfk5PKsh7S01NDZ07d8bRo0ehra1d38VpEAoKCjB48GCKWTVQzKqPYlZ9FLPqo5hVH8Ws+ihmVaejo1Pp59SpIO8tgUAAVVVV6Orq0oFeRSoqKhSzaqKYVR/FrPooZtVHMas+iln1UcxqD72oTQghhBBCCFEIdSoIIYQQQgghCqFOBXlviUQiTJ06FSKRqL6L0mBQzKqPYlZ9FLPqo5hVH8Ws+ihm1Ucxqz00+xMhhBBCCCFEIfSkghBCCCGEEKIQ6lQQQgghhBBCFEKdCkIIIYQQQohC6HcqiNKlpaUhKCgIN2/ehJaWFjw8PDBjxgw0atSo0uUYY9izZw8OHDiAnJwcWFtbY/78+bCzs+Ply8zMRFBQEK5cuQKhUAgXFxfMmzevQc83rcyYvXz5Ert27UJSUhLu3r0LoVCIP//8U9lVUjplxuzKlSs4fPgwkpOT8eLFC5iammLIkCEYP348hMKGexpVZsxu3bqFrVu34t9//0VeXh6aNGkCBwcH+Pn5wcjISNlVUxpln88kxGIxvL29kZKSgnXr1qF///7KqE6dUGbMrl27hunTp0stO2DAAKxdu7bW61JX6qKdXbhwAeHh4bh79y4aNWoEa2trBAYGomnTpsqqllIpM2b+/v44cuSIzOVnzZqFSZMm1WZVGqyG+21IGoS8vDxMnz4dLVu2xPr165GRkYFNmzahqKgIS5YsqXTZPXv2YMeOHZg1axbatGmDAwcOYNasWYiOjoaZmRkAoKysDLNmzQIArFq1CkVFRQgJCcGyZcvw3XffKbt6SqHsmGVkZODkyZNo37492rZti3v37tVFtZRK2TGLiYlBUVERvvjiCzRr1gzJycnYsWMHUlNTsWLFirqoYq1Tdszy8/NhYWGB4cOHo0mTJnjy5Al27tyJf/75B5GRkQ1yphVlx6yimJgYZGZmKqsqdaauYrZixQpYWFhwf+vr6yuhNnWjLmJ27NgxrFy5EhMnTsSMGTNQWFiIxMRElJSUKLt6SqHsmH3++efw8vLiLXfy5En8+OOPcHJyUlq9GhxGiBKFh4ez3r17s5ycHC7t4MGDrEePHiwjI0PuckVFRczZ2ZmFhoZyaSUlJczT05OtXbuWSzt+/Dizt7dnqampXNqlS5dYt27dWFJSUu1Wpo4oO2bl5eXc/2/fvp317t27lmtQ95Qds5cvX0otu2vXLmZvby/zs4ZA2TGTRXJsJiYmKl6BelBXMXv58iVzdXVlsbGxrFu3buzUqVO1W5E6pOyYxcfHs27durFbt24ppwL1QNkxy8nJYc7OzuzAgQPKqUA9qI/z2dSpU9no0aMVL/wHhN6pIEr1119/oUePHtDT0+PSBgwYALFYjMuXL8td7ubNmygsLOQ98m/UqBFcXFxw8eJF3vrbtGnDu0Pl4OAAPT09Xr6GRNkxU1H58A57ZcdM1l1PGxsbMMaQlZVVO5WoY8qOmSySOJaWlipW+HpSVzELDQ1Ft27dYG9vX7sVqAf10c4aOmXH7NSpUxCLxRg2bJhyKlAP6rqdZWRkIDExEe7u7rVTgQ/Eh3d1Qd4raWlpvAt+ANDR0YGhoSHS0tIqXQ6A1LKWlpZ49uwZioqKuHzm5ua8PAKBAObm5pWu/32m7Jh9iOojZomJiRCJRDA1Na1hqetXXcWsvLwcpaWlSEtLw+bNm2Fra4vOnTsrXoF6UBcxS05ORlxcHObOnVs7ha5nddXO5syZgx49esDDwwMhISEN+nyn7JglJyfDwsICR44cgaenJxwcHDB+/PgG3Vmr6++AEydOQCwWY+DAgQqU+sND71QQpcrLy4OOjo5Uuo6ODvLy8ipdTiQSQU1NTWo5xhjy8/Ohrq6O/Px8mevX1dWtdP3vM2XH7ENU1zF79OgRfvrpJ3h5eUFTU1PxCtSDuorZtGnTcOPGDQBAu3btEBIS0mBfbld2zMRiMYKCgjBx4kSYmpri6dOntV6HuqbsmGlra8Pb2xtdu3aFmpoa4uPjsXfvXqSmpjbo9+qUGbPs7Gw8fPgQ27dvx//+9z8YGhriwIEDmD9/Pvbt24fWrVvXep2Ura6/A+Li4tCxY0c0b95c8cJ/QBrmmZ0QQupJQUEBFi1aBFNTU8yYMaO+i/Pe++abb1BQUIDHjx9jz549mDFjBnbt2tWgZ2dTlsOHDyM7O5tmkqkGW1tb2Nracn93794dhoaGCAoKQnJyMjp06FCPpXs/icVivHr1CitXrkTfvn0BAPb29hg5ciT27NmDwMDAei7h+y0tLQ137tzBokWL6rso7x0a/kSUSldXFwUFBVLp+fn50NXVrXS5kpISFBcXSy0nEAi4OxI6Ojoy15+Xl1fp+t9nyo7Zh6iuYlZaWopFixYhPz8fISEh0NDQqJ0K1IO6ipmFhQU6dOiAQYMGYevWrXj8+DEOHTpUO5WoY8qM2atXr7B161ZMnjwZpaWlyM/PR2FhIQCgqKhI5nYbgvo4nw0YMAAAkJKSUsNS1y9lx0yyju7du3N5hEIhunTpggcPHtRGFepcXbaz48ePQ1VVFW5ubooX/ANDnQqiVBYWFlLjGQsKCpCVlSU1hvHt5QDg4cOHvPS0tDQ0a9aMexwpa/2MMTx8+LDS9b/PlB2zD1FdxEwsFmPZsmW4ffs2Nm/ejGbNmtVW8etFfbQzAwMDGBsb4/HjxzUtdr1SZsxycnKQm5uLtWvXwsXFBS4uLhg3bhyAN3Pkvz2dZUNB57PqU3bMWrVqJXcdDXVK2bpsZydOnICDgwMaN26saLE/ONSpIErl5OSEq1evIj8/n0s7ffo0VFRU4OjoKHe5jh07QktLC6dPn+bSysrKcPbsWfTq1Yu3/nv37uHRo0dc2tWrV5Gbm8vL15AoO2YforqI2bfffos///wTwcHBsLKyqv1K1LH6aGfPnj1Denp6gx2HrMyYGRgYYPv27bx/q1evBvDmvZSgoCAl1Uq56qOdnThxAsCbd3gaImXHrE+fPgDefFdKlJaW4vr167yhZA1JXbWz5ORk/Pfff/SCthz0TgVRKi8vL+zfvx8LFizA5MmTkZGRgZCQEIwcOZL3q7p+fn5IT0/H4cOHAQBqamrw9fVFWFgYGjduDCsrKxw4cAC5ubmYOHEit1z//v0RERGBxYsXY+bMmSgqKsJ3332H3r17N9ixtMqOGQDuBJqamgqxWMz93b59e5iYmNRNRWuRsmMWHh6OgwcP4rPPPoNIJEJSUhL3maWlZYN8P0DZMVuzZg309fXRrl07aGtrIy0tDdHR0TAwMGiwU1kqM2ZqampSU8hKXtRu1aoVOnXqVDeVrGXKbmfffPMNzMzMYGtry72ovW/fPvTr16/BdiqUHTNbW1u4urpi9erVyM3N5V7UfvHiBby9veu6urWiLr43gTcvaKupqcHFxaWuqtagUKeCKJWuri6+//57rF+/HgsWLICWlhaGDx8u9YJreXk5ysvLeWk+Pj5gjGHv3r14+fIlrK2tsWXLFt6vggqFQmzZsgXr16/H119/DVVVVbi4uGD+/Pl1Uj9lUHbMAGDp0qUy/16xYgWGDBmihFopl7JjJpnnPCoqClFRUbzlt2/f3iB/T0DZMWvfvj0OHTqEAwcOoKSkBM2aNUOvXr3g6+vbYH/tuC6OzQ+NsmPWqlUrHD9+HNHR0SgpKYGpqSl8fX3h6+tbJ/VThrpoZwEBAQgNDUVoaCgKCwtha2uLrVu3NtinsHURs/Lycpw6dQrOzs4NdtY/ZRMwxlh9F4IQQgghhBDScNE7FYQQQgghhBCFUKeCEEIIIYQQohDqVBBCCCGEEEIUQp0KQgghhBBCiEKoU0EIIYQQQghRCHUqCCGEEEIIIQqhTgUhhBBCCCFEIdSpIIQQQgghhCiEOhWEvEcyMjKgp6eHH374gZc+adIkWFhY1E+hPhD+/v4QCARIS0urk+3t3r1banuvX7+GqakpAgICqr0+eW2D1JxkH507d66+i0LqmaLnB2pLH6+0tDQIBAL4+/vX6XbPnTsHgUCA3bt312j5xMREqKio4I8//qi1MlGngpD3yLJly2BkZARfX98q5X/27BkWLlyIDh06QEdHB7q6umjTpg3Gjh2LmJgYXt5+/fpBW1tb7rokX6rXrl2T+fnLly+hoaEBgUCAqKgoueuxsLCAQCDg/olEIlhYWODzzz/H48ePq1SvD5WGhgaWLl2K9evXIz09vVrLVrdtkI9bYmIi/P3966wTTepfWloa/P39kZiYWKfbpbYmLScnB/7+/u91J7Nz584YPnw4FixYAMZYrayTOhWEvCf+++8/hIeH43//+x+EQuE78z98+BCdOnXC1q1b4ejoiHXr1mHt2rXw9PRESkoKIiIiarV80dHRKC4uhqWlJcLDwyvNa2ZmhqioKERFRSEkJAQODg4IDw+Hg4MDsrKyarVcDc2UKVMgEAiwcePGKi9T3bZBquazzz7D69ev4ezsXN9FqXWJiYkICAigC72PSFpaGgICAuqlU/ExtzVzc3O8fv0ay5Yt49JycnIQEBDwXncqAGDu3LlISEjAsWPHamV99O1EyHtix44dEAgEGDduXJXyb9iwARkZGTh8+DCGDRsm9fmzZ89qtXy7du2Ci4sLhg0bhrlz5+LBgwdo1aqVzLx6enqYOHEi97efnx+MjY0RGhqKiIgILFq0qFbL1pBoaWlh5MiR2L17N1atWgU1NbV3LlPdtlHfysvLUVxcDE1NzfouSqVUVVWhqqpa38UghDRgAoEA6urq9V2MGunTpw8sLCywfft2DB48WOH10ZMK0mBJxrD+/vvvCAwMhLm5OTQ0NODg4IDLly8DAP744w/07t0bWlpaMDExwcqVK2Wu69q1axgxYgQMDQ2hpqYGGxsbrF69GmVlZbx8V69exaRJk2BtbQ1NTU3o6OigV69eOHTokNQ6J02aBIFAgNzcXO6iWl1dHb169cKVK1ek8h84cAD29vYwNjauUv3v3bsHAPjkk09kft6sWbMqracqrl+/jsTERPj4+GD8+PEQCoXvfFrxtoEDBwIA7t+/LzfP8ePHIRAIsHnzZpmf9+zZE0ZGRigtLQVQvf0hi2QfySIQCDBp0iSp9P3796N3797Q0dGBpqYmHBwc8Msvv1RpexKDBg1CVlYWzp49W6X88tqGWCzG6tWr4ezsjGbNmkEkEqFly5bw8/NDdnY2ly8nJwfq6uoYOXKkzPV/+eWXEAgEvDucubm5WLJkCaysrKCmpgYjIyOMGzcODx484C0rOQ5Pnz6NlStXonXr1lBXV8fPP/8MADh58iTGjBmDVq1aQUNDA/r6+nBzc5M7jvfgwYPo1KkT1NXV0bJlSwQEBOD06dMyxw4XFxdjzZo1aN++PdTV1aGvr48hQ4bg77//rlJcZY2Dr63zioWFBfr164fr16/D1dUV2traaNKkCXx8fJCRkcHLm5+fj2XLlsHBwYE7B1lZWWHp0qV49eqV1LoZY/jhhx/g4OAAbW1taGtrw87ODsuXLwfwZiijZJici4sLNxRRVnt+282bNzFixAgYGBhAXV0d7dq1Q1BQEMrLy3n5qnt+k0Uy5PKff/7B3LlzYWJiAk1NTXzyySe4c+cOACAmJgZdu3aFhoYGLCwsEBYWJnNdO3fu5PLp6enBzc0NFy5ckMonFouxdu1aWFpaQl1dHR06dEB0dLTcMqanp8PPzw8tW7aESCSCqakppk2bJrUPq6uqce7Xr5/M9+neHse/e/duuLi4AAB8fX25fd6vXz8A/PH3W7ZsgbW1NdTV1WFtbY0tW7ZIrV/Sft/29jj+mrY1SfvJzs7GpEmTYGhoCB0dHQwfPpy7IRYWFoa2bdtCXV0dtra2iI2NlVrPtm3b4ObmhubNm0MkEsHExAQTJ06U+dSkvLwcK1euhLm5OdTV1dGxY0fs379f5vs01Wnfb++Lc+fOwdLSEgAQEBDAxUSyHyt7F0Led1JsbCy6dOkCdXV1tGjRAt988w33Pfi26pwXBQIBBg4ciLi4OBQUFMhcX3XQkwrS4C1duhTl5eWYM2cOSkpKEBwcDDc3N0RGRmLKlCmYNm0aJkyYgJ9//hnLly+HpaUl7y760aNHMXLkSFhZWWHBggVo0qQJLl26hOXLlyMxMREHDhzg8h46dAgpKSn49NNPYW5ujuzsbOzZswcjR45EdHQ0xo8fL1W+gQMHwsjICMuXL0d2djY2btyIwYMHIzU1FTo6OgCA58+f486dO5g9e3aV6926dWsAwA8//IC5c+fKvTh+m7zhR7IuXiR27doFbW1teHl5QUtLC56entizZw8CAwOholK1exOSTpChoaHcPG5ubmjWrBkiIyOlYnHv3j1cvnwZs2fPRqNGjQDUbH8oYtmyZVi9ejXc3d2xcuVKqKio4NChQxg9ejRCQ0Mxc+bMKq2nZ8+eAN58ubi7u1eat7K2UVJSgvXr18PLywvDhg2DlpYW4uPjsWvXLly4cAEJCQkQiUTQ19fH0KFDERsbixcvXqBJkybcOsRiMaKjo9GxY0d07twZwJsOhZOTEx49eoTJkyejffv2SE9Px7Zt2+Dg4IBr167B3NycV5aFCxeitLQUU6dOha6uLmxsbAC8udh58eIFvL29YWZmhidPnmDnzp345JNPcPbsWfTp04dbx/79+zFu3Di0bt0aK1asgFAoxJ49e/Dbb79J1b20tBTu7u7466+/8Nlnn2HWrFnIzc3FDz/8gF69euH8+fOwt7ev0v6QRdHzCvBm2Nonn3wCLy8vjBo1CtevX0d4eDiuXbuG+Ph47kmOJCZeXl5cp/2PP/5AUFAQ/v77b5w4cYK33s8++wzR0dFwcHDA119/DX19faSkpOCXX35BYGAgRo4cifT0dISFheGrr75C27ZtAfz/OUOea9euoW/fvmjUqBFmzpyJZs2a4bfffsOSJUtw48YNmRffVTm/vYuPjw+0tbXx1VdfITMzE8HBwRg4cCBWrlyJxYsXw8/PD5MnT8auXbvwxRdfoF27dujduze3/JIlSxAUFIQePXpgzZo1yM/PR1hYGFxcXBAbGwsPDw8u7/z58xESEgJnZ2fMmzcPGRkZmDlzpsynro8ePULPnj1RUlKCKVOmoHXr1rh//z6+//57nD17FteuXYOenl6V6qhonN/F2dkZX331FdasWYNp06Zxx1XTpk15+bZs2YJnz57hiy++gI6ODn788UfMnj0bL168wIoVK6q93Zq2NQl3d3eYmZkhMDAQ9+/fx+bNmzFixAiMHDkSYWFhmDJlCtTV1bF582aMGjUKd+/e5S7YgTdP7B0dHTF79mw0adIEycnJ2LlzJ86cOYOkpCQYGBhweWfNmoXt27fDxcUFCxcuRGZmJmbMmMFb39tq0r7btm2LTZs2Yd68eVxdAFT6TmNlDh06BC8vL1hYWGD58uUQCoWIiIjA0aNHpfLW5LzYs2dP7NixAxcuXHjn99E7MUIaqIiICAaAdenShRUXF3PpsbGxDAATCoUsPj6eSy8uLmbNmjVjjo6OXNrr169Z06ZNWZ8+fVhpaSlv/Rs3bmQA2NmzZ7m0goICqXIUFhYya2tr1rZtW166j48PA8D8/Px46T///DMDwLZv386lnTlzhgFgISEhMuvq4+PDzM3NeWn//vsv09XVZQBYixYt2Pjx49mmTZvYtWvXZK6jb9++DMA7/1WMmSRG+vr6zMfHh0s7fPgwA8COHTsmtR1zc3Nma2vLMjMzWWZmJnvw4AELDw9nenp6TCgUsqSkJJnlk1i4cCEDwG7dusVLX7ZsGQPAEhISuLTq7I8VK1YwACw1NZVLk+wjWQDw6pyQkMAAsC+//FIq77Bhw5iOjg7Ly8vj0iTts+L2KhIKhczT01PmZxVV1jbEYjF79eqVVPrOnTsZALZ//34u7ciRIwwA27p1Ky/v6dOnGQAWHBzMpc2ePZupq6uzxMREXt60tDSmo6PDi4ukntbW1qywsFCqLLL20bNnz5iBgQEbNGgQl1ZaWspMTU2ZsbExe/HiBZeen5/PLC0tGQAWERHBpUuOz7i4ON66c3NzWYsWLVjfvn2ltvs2SdkrHuO1cV5h7M1xAIBt2rSJly4p99q1a3nrKCkpkSqfpM1fuXKFS9u/fz8DwCZOnMjKy8t5+Sv+Latu7+Lk5MRUVVXZjRs3uDSxWMxGjx7NALDTp09z6dU5v8kjOSY9PT2ZWCzm0kNCQhgApqOjwx49esSlZ2RkMDU1NTZ27FguLSUlhQkEAtarVy/e/nry5AnT09Nj5ubmrKysjJfX1dWVS2PszbEtEAikjtehQ4cyIyMj9vjxY1654+PjmaqqKluxYgWXVp14VyfOffv2lTr3M8ZYamoqA8Arw9mzZ6WOk7c/09bW5tWnuLiYde/enQmFQl66ubm5zGNI1jZq0tYk7WfGjBm89Hnz5nHfabm5uVz6jRs3GAC2dOlSXn5Z5xfJOe3bb7/l0pKTkxkANnDgQN5xcvPmTaaioiL3u6Eq7VvWvpCVJlHZfnr7O6msrIy1aNGCGRgYsMzMTC49JyeHtWzZslbOi3/++ScDwDZs2CD1WXXR8CfS4Pn5+UEkEnF/S+7QODg48HrkIpEIPXr04O6YA8CpU6fw/Plz+Pr6IicnB1lZWdw/yd2tkydPcvm1tLS4/3/16hWys7Px6tUruLq64vbt28jLy5Mq37x583h/u7q6AgCvHJmZmQDAu4P8Lq1atcKNGze4u+P79u3DvHnzYG9vj44dOyIhIUFqGXV1dZw6dUrmv88++0zmdmJiYpCTkwMfHx8uzcPDA0ZGRnKHQKWkpMDIyAhGRkZo1aoVJk+eDENDQ8TGxqJDhw6V1kuyncjISC6NMYa9e/eiQ4cO6Nq1K5dek/1RU9HR0RAIBPDx8eG1k6ysLAwdOhT5+fm4dOlSldfXpEmTKg2hqKxtCAQCaGhoAHjzaF/ShiVtrOJj+oEDB6Jp06a8uAJv4iwUCjFhwgQAb2IdHR0NZ2dnNG/enFdPLS0tODo68o4JCT8/P5nvUFTcRwUFBcjOzoaqqiocHBx45UtISMDTp08xadIkNG7cmEvX1tbG9OnTpda7d+9e2Nraolu3brwylpSUYMCAAbhw4QJev34tI6JVo8h5RUJXVxczZszgpc2YMQO6urq8IXoikYh7+lZWVoaXL18iKysL/fv3B8Dfj5K72Bs2bJB6SljVp4ayZGRk4K+//sLQoUPRsWNHLl0gEODrr78GAJnDCqtyfnuX2bNn8560SmI9dOhQtGjRgks3MjKCjY0Nb92xsbFgjGHx4sW8/WVqagpfX188fPiQG/YhyTt//nzeuzRdu3bFgAEDeGXKzc3FkSNHMHToUKirq/PamIWFBaysrGQeB+9S0zjXlgkTJsDMzIz7WyQSYd68eSgrK5P5RFDZ5s6dy/tbsu+9vb2hq6vLpXfs2BG6urpS7UpyfhGLxcjNzUVWVhY6deoEPT093nFz5MgRAMCcOXN4x4mdnR03NFeW2mjfikhISMDjx4/h6+vLe8qvp6dXa+dFydMcRYf0ATT8iXwA3n5sLbkgkfVIs3Hjxryx5rdv3wYATJ48We76nz9/zv1/RkYGli1bhtjYWJkHYE5ODu9EKKt8kgO4YjkkX6ismtO6WVhYIDQ0FKGhoUhPT8eFCxcQFRWF3377DZ6enrh16xbvYlRVVZW7UHmbrPHHwJuhT0ZGRjAzM+O9D+Hm5oYDBw4gKytLakiThYUF93sKknHIVlZWVaqTpOMQHR2NNWvWQEVFBefPn0daWhqCgoJ4eWuyP2rq9u3bYIzB1tZWbp6KbeVdGGNVGrL2rrbx888/Izg4GH///bfUGNuXL19y/y/pOGzcuBF3796FtbU1CgsLERMTAzc3N26YRGZmJrKzs3Hy5EkYGRnJ3Kasi1dra2uZef/99198/fXXOHHiBHJycmTWDQBSU1MBgBs2VZGstNu3b+P169dyywi8GepX8aK0OhQ5r1RcR8ULXQBQU1NDq1atpN5N2bZtG7Zv345bt25BLBbzPqu4H+/duwcTExOpYS2KksS/ffv2Up+1bdsWKioqUmUGqnZ+e5fqxvrhw4dVKrck7cGDB7C3t+fKL+sYbteuHa+TcOfOHYjFYuzatQu7du2qUrmroqZxri2S4UkVtWvXDgCUul15FD3Ozpw5g8DAQFy5cgVFRUW8zyoeN+86vxw/frxK5atJ+1bEu9rs22pyXpR8t1R1CHVlqFNBGjx5s7dUZVYXycG0fv16bjz520xNTbm8bm5uuH37NubMmQN7e3vo6elBVVUVERER2Ldvn9TFQGXlqHiRKDkBvHjx4p1llsfExASjR4/G6NGjMWHCBOzbtw/Hjh2TGuddHampqTh79iwYY3IvGvfu3St1t0lLS0tu56UqvL29MXfuXJw5cwb9+/dHZGQkVFVVeXWp6f6oSN5J9O0X9CXbEwgEOH78uNx9KutCQZ6XL19WeuKXqKxtxMTEYMyYMejRowdCQkLQokULqKuro7y8HO7u7lL19/b2xsaNGxEZGYlVq1YhJiYGBQUFvKdQknbZv39/LFmypMr1kfWUoqCgAM7OzigsLMTcuXNhZ2cHHR0dqKioYO3atThz5kyV1/82xhjs7OwqnZq3KvGVR5HzSnVt3LgRCxYsgJubG2bPng1TU1OIRCI8efIEkyZNemc7rk9VOb/VdB21se6akmxj4sSJvOOjIslTQmWqzjmqIW5XkX0fHx8PNzc3WFlZYd26dbC0tOR+S2ns2LG1ctwoow1WdvGuaHxrcl6UfLcocr6UoE4F+ai1adMGQNUugm/evIkbN25g+fLlUr+IvHPnToXKIbkYra1Hqo6Ojti3bx+ePHmi0HoiIiK4mWb09fWlPl+2bBnCw8OlOhWKGj9+PBYtWoTIyEj06tULv/zyCwYMGAATExMuT23sD8lTnLdfXpZ1x65NmzaIi4tDy5YtZd7tq460tDSUlZW9cygYUHnbiIqKgrq6Os6ePcu7qE9JSZG5rk6dOqFTp07Yu3cvVq5cicjISO4lbgkjIyPo6+sjLy9PoY4hAPz+++94+vQpwsPDpX60r+Kc7gC4mVEks/5UJCutTZs2yMzMhKurq0LDfpTpwYMHKCkp4T2tKC4uxoMHD3h3HqOiomBhYYHjx4/z6hIXFye1Tmtra8TGxuL58+eVPq2o7l1HyZ3hW7duSX2WkpICsVhcozvzyiYp061bt6ReDv7nn394eST/TUlJkZtXwsrKCgKBACUlJQofBxVVN85NmjSROZRV1jmqKvtc8nS+orfjJNmurBsZNd2uMuzbtw/l5eU4fvw478lGYWEh7ykFwD+/vN2OZZ1fFFVZTCp+77zt7fhWbLNve7vNAjU7L0pGIFTl++hd3s8zMSF1ZODAgTA2Nsa6detkHuCvX79Gfn4+gP+/Y/H2HYrk5GSFx8AaGRmhffv23JSVVXHu3DmZY8bFYjE3NlbW49GqEovF2L17N+zs7PD5559j1KhRUv/GjRuHpKQkxMfH13g7shgZGWHQoEGIiYlBdHQ08vLypO4W1sb+kDx9OX36NC89ODhYKq/knZOvvvpKatpHoHpDnyT7uW/fvu/MW1nbUFVVhUAg4N2RY4xh1apVctfn4+ODhw8fYt++fThz5gzGjBnDm2NdRUUFEyZMwNWrV+VOlVvVsbfy9tHJkyelpmW0t7eHiYkJdu/ezbsgKCgowPbt26XW7e3tjWfPnsm9I1ed/aEseXl52LZtGy9t27ZtyMvLw/Dhw7k0yX6sGKeysjKsW7dOap2Sd18WL14sdSe24vKSmWaq+vTT2NgYTk5O+O2335CcnMxb59q1awEAI0aMqNK66tLQoUMhEAiwfv163vC/9PR0REREwNzcHF26dOHl3bhxI+8Yvn79utQ5wMDAAB4eHoiJiZF57DHGuPedqqO6cba2tkZ+fj6uXr3KpYnFYmzatElq3VXZ59HR0fjvv/+4v0tKSrBp0yaoqqrC09OTt92UlBTejani4mJs3bq1RttVBnnnlzVr1kgdG0OGDAEAhISE8D5LSkqSml2tNlQWE0tLSwiFQqk299dff0m1tW7dusHMzAwRERG8mRvz8vJq7bx4+fJlCIVC9OrV690Vewd6UkE+alpaWoiMjMTw4cNhY2ODyZMnw8rKCjk5OUhJSUFMTAwOHTqEfv36oW3btmjfvj2CgoLw6tUr2NjY4O7du9ixYwfs7Oxk3k2qjtGjR2PlypVIT0/n3ZGXZ8OGDbh48SKGDBmCrl27Qk9PD8+ePcPBgweRkJAAFxcXhX7M5uTJk3j8+DGmTJkiN4+Xlxf8/f2xa9cudO/evcbbksXHxwe//vorFixYAD09Pd5FGIBa2R/jxo3DV199hWnTpiElJQVNmjRBXFyczGl3u3fvDn9/f/j7+6Nz584YPXo0TE1NkZ6ezv0iaUlJSZXqduzYMRgaGnLzyr+LvLYxatQoHDx4EK6urvD29kZpaSkOHz5c6fTAEyZMwOLFizFjxgyIxWKZQztWr16Nixcv4tNPP8Wnn34KR0dHiEQiPHz4EMeOHUO3bt1kzrH+tt69e6NZs2ZYsGAB0tLSYGZmhsTERERFRcHOzg5JSUlcXqFQiA0bNmDChAno0aMHpkyZAqFQiN27d8PAwACpqam8u39z5szBqVOnsGjRIpw5cwaurq7Q1dXFo0eP8Pvvv3NPcOpT69atERAQgOTkZHTr1g0JCQkIDw+Hra0tb4rgUaNG4csvv8SgQYMwcuRI5OXlYd++fdzL2xWNHj0aY8aMQWRkJO7du4ehQ4eicePGuHv3Lk6cOMFdqHbv3h0qKipYvXo1Xr58CS0tLVhaWsLBwUFueUNCQtC3b1/06dOHm+r0yJEjOHHiBMaPHy/3N3Hqk42NDRYtWoSgoCA4OztjzJgx3JSyBQUFiI6O5i4+bW1tMXPmTISGhsLV1RVeXl7IyMhAaGgoOnXqJDWP//fff4/evXvD2dkZ3t7e6NKlC8RiMR48eIDY2Fh4e3tzv01QHdWJ87Rp0xAcHIwRI0Zgzpw5EIlE+OWXX2QOk2nXrh10dHSwbds2aGpqQl9fH8bGxtzLxcCbzoKDgwOmT58OHR0d7Nu3D/Hx8fjmm2944+xnzZqFn376Cf3798f06dNRUlKCqKgomcMca9LWasOIESOwadMmeHh4YNq0aRCJRDh16hRu3rwp9Z5f+/btMW3aNISFhaF///4YMWIEMjMzsXXrVnTp0gUJCQm1+sTFwMAAVlZW+Omnn9C6dWs0bdoUWlpaGDJkCLS1tTFp0iTs3LkT48aNQ79+/XDv3j1ERESgY8eOuHHjBrceVVVVbNq0CZ9++il69OiBqVOncr8TZWBggEePHvG2W93zImMMcXFxcHd3r/GUtzwKzx9FSD2pbBo7vDUdqIS8KUSTkpLYhAkTmKmpKWvUqBEzNjZmPXv2ZIGBgSw7O5vLl5aWxkaNGsUMDQ2ZhoYG6969O4uJiVF4ulLG3kyBKBQKZU7rJmtK2UuXLrH58+cze3t7ZmxszIRCIdPT02OOjo4sODiYFRUV8fL37duXaWlpySwPY/8/vaNkusxRo0YxAOzmzZtyl2GMMWtra6anp8dNbWpubs7at29f6TJVUVxczJo0acIAsM8//1xmnursD1lpjDF2+fJl5uTkxNTU1JiBgQGbOnUqe/nypdw2dOTIEebm5sYaN27MRCIRMzMzY+7u7uz777/n5ZM3pWxBQQHT0tJiCxcurHIsKmsbYWFhrG3btkxNTY01a9aMTZ06lWVnZ8stP2OMeXp6MgCsTZs2crdZWFjIAgMDWYcOHZi6ujrT1tZmtra27PPPP2eXL1+Wqqe86SRv3LjBBg4cyPT19Zm2tjbr27cvO3/+vNzj4+eff2Z2dnZMJBKxFi1aMH9/fxYTEyM1RS5jb6ahDQkJYfb29kxTU5NpamoyKysrNn78eHbixAm5daus7LV1XpFMyZmQkMBcXFyYpqYm09fXZxMnTmTPnj3j5S0rK2Nr1qxhrVu3ZiKRiLVs2ZItWrSI/fPPPzKnpSwvL2ehoaGsS5cuTENDg2lrazM7Ozvm7+/Py7d7927Wtm1b1qhRo0rbQ0WJiYls2LBhXPu2tbVl3377LW8KVnl1flec3ibvmKxsOk55U6yGhYWxzp07MzU1Naajo8P69+/Pzp8/L5WvvLycrVq1irVs2ZKJRCLWvn17tnfvXrllyczMZAsXLmRt2rRhampqTE9Pj3Xo0IHNnj2bN+11dadVrWqcGWPs6NGjrFOnTkwkEjETExO2ePFilpKSIjNGR48eZV26dGFqamoMADeFaMVpTENCQpiVlRUTiUTMysqKfffddzLLuHv3bmZtbc0aNWrELCws2Lfffst+//13mdOhVretyWs/lU23Kmua20OHDrGuXbsyTU1NZmBgwMaMGcMePnwoM29ZWRnz9/dnLVq0YCKRiNnZ2bH9+/ezBQsWMADs+fPn7ywfY9LtW157vXLlCnNycmKampoMAK/d5ufnsylTprAmTZowDQ0N1rt3b3bx4kW52z148CDXBszMzNiyZcvYyZMnZcaqOufFc+fOMQDsyJEjMutaXQLG6uCNJ0JIlUyfPh0nT57EnTt3eHcpJ02ahHPnzsn8lVDyftq9ezd8fX2RmprK+0XckJAQfP3119wsPlUlr218DIKDg7Fw4UJcunQJjo6O9V2cKrGwsICFhQXv17oJqS/nzp2Di4sLIiIiqvTL6h+TIUOG4MyZM8jLy1PKRAzvsxEjRuDx48eIj4+vlSc19E4FIe+RwMBAZGdnIyIior6LQpTg9evXWLduHRYtWlStDgXwcbSNkpISqfdVCgoKsHXrVhgYGPB+o4QQQqpD1juIN2/exPHjx+Hq6vrRdSj+/vtvxMbGIjg4uNaGftE7FYS8R4yNjZGbm1vfxSBKoqGhgfT09Bot+zG0jQcPHmDQoEEYO3YsLC0tkZ6ejj179iA1NRXff/+91G8+EEJIVe3ZsweRkZEYPHgwjIyMkJKSgrCwMIhEIgQGBtZ38eqc5B2h2kSdCkIIIe8FIyMjODo6Ijo6GhkZGRAKhbCzs8O6devw6aef1nfxCCENWNeuXXHo0CFs3rwZL168gI6ODlxdXbFixQpuhjCiGHqnghBCCCGEEKIQeqeCEEIIIYQQohDqVBBCCCGEEEIUQp0KQgghhBBCiEKoU0EIIYQQQghRCHUqCCGEEEIIIQqhTgUhhBBCCCFEIdSpIIQQQgghhCiEOhWEEEIIIYQQhfwfnNoQRu5b5LQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# SHAP Summary Plot: 1,000-Record Sample\n",
        "# ============================================================\n",
        "\n",
        "shap.summary_plot(\n",
        "    shap_values_rf_attack_1000,\n",
        "    X_test_shap_df_1000,\n",
        "    max_display=15\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "-H-6iGwFcBru",
        "outputId": "fcede0ac-aaf4-49ba-edff-c488d0325225"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x750 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# SHAP Stability Comparison: 500 vs 1,000 Records\n",
        "# ============================================================\n",
        "\n",
        "top_15_shap_500 = shap_importance_rf_500.head(15)[\"Feature\"].tolist()\n",
        "top_15_shap_1000 = shap_importance_rf_1000.head(15)[\"Feature\"].tolist()\n",
        "\n",
        "common_top_features = list(set(top_15_shap_500).intersection(set(top_15_shap_1000)))\n",
        "overlap_count = len(common_top_features)\n",
        "overlap_percentage = (overlap_count / 15) * 100\n",
        "\n",
        "print(\"Top 15 SHAP features from 500-record sample:\")\n",
        "print(top_15_shap_500)\n",
        "\n",
        "print(\"\\nTop 15 SHAP features from 1,000-record sample:\")\n",
        "print(top_15_shap_1000)\n",
        "\n",
        "print(\"\\nCommon features in both Top 15 lists:\")\n",
        "print(common_top_features)\n",
        "\n",
        "print(f\"\\nTop 15 feature overlap: {overlap_count}/15\")\n",
        "print(f\"Top 15 feature overlap percentage: {overlap_percentage:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTwa5W8lcfFc",
        "outputId": "ca9b1725-4f24-4dc8-fef4-e2b117a3c008"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 15 SHAP features from 500-record sample:\n",
            "['sttl', 'ct_state_ttl', 'dttl', 'state_INT', 'sload', 'dbytes', 'ct_srv_dst', 'swin', 'ct_dst_src_ltm', 'dload', 'ct_dst_sport_ltm', 'ackdat', 'rate', 'service_-', 'smean']\n",
            "\n",
            "Top 15 SHAP features from 1,000-record sample:\n",
            "['sttl', 'ct_state_ttl', 'dttl', 'state_INT', 'sload', 'dbytes', 'ct_srv_dst', 'swin', 'dload', 'ct_dst_src_ltm', 'ct_dst_sport_ltm', 'ackdat', 'smean', 'rate', 'service_-']\n",
            "\n",
            "Common features in both Top 15 lists:\n",
            "['dbytes', 'ct_dst_sport_ltm', 'ct_srv_dst', 'sload', 'ackdat', 'state_INT', 'rate', 'sttl', 'swin', 'smean', 'ct_state_ttl', 'dttl', 'ct_dst_src_ltm', 'service_-', 'dload']\n",
            "\n",
            "Top 15 feature overlap: 15/15\n",
            "Top 15 feature overlap percentage: 100.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# SHAP Stability Table: 500 vs 1,000 Records\n",
        "# ============================================================\n",
        "\n",
        "shap_500_ranked = shap_importance_rf_500.reset_index(drop=True).copy()\n",
        "shap_500_ranked[\"Rank_500\"] = shap_500_ranked.index + 1\n",
        "\n",
        "shap_1000_ranked = shap_importance_rf_1000.reset_index(drop=True).copy()\n",
        "shap_1000_ranked[\"Rank_1000\"] = shap_1000_ranked.index + 1\n",
        "\n",
        "shap_stability_table = pd.merge(\n",
        "    shap_500_ranked[[\"Feature\", \"Rank_500\", \"Mean_ABS_SHAP\"]],\n",
        "    shap_1000_ranked[[\"Feature\", \"Rank_1000\", \"Mean_ABS_SHAP\"]],\n",
        "    on=\"Feature\",\n",
        "    how=\"inner\",\n",
        "    suffixes=(\"_500\", \"_1000\")\n",
        ")\n",
        "\n",
        "shap_stability_table[\"Rank_Difference\"] = (\n",
        "    shap_stability_table[\"Rank_500\"] - shap_stability_table[\"Rank_1000\"]\n",
        ").abs()\n",
        "\n",
        "shap_stability_table = shap_stability_table.sort_values(by=\"Rank_500\")\n",
        "\n",
        "shap_stability_table.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "ldjuAhcjcvZ5",
        "outputId": "481f01f8-82cb-438c-a899-e37bcd4a2985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Feature  Rank_500  Mean_ABS_SHAP_500  Rank_1000  Mean_ABS_SHAP_1000  Rank_Difference\n",
              "0               sttl         1           0.076102          1            0.075750                0\n",
              "1       ct_state_ttl         2           0.045698          2            0.045500                0\n",
              "2               dttl         3           0.027586          3            0.027637                0\n",
              "3          state_INT         4           0.027059          4            0.026934                0\n",
              "4              sload         5           0.022224          5            0.022050                0\n",
              "5             dbytes         6           0.019304          6            0.019164                0\n",
              "6         ct_srv_dst         7           0.017386          7            0.017427                0\n",
              "7               swin         8           0.015465          8            0.015507                0\n",
              "8     ct_dst_src_ltm         9           0.015199         10            0.015041                1\n",
              "9              dload        10           0.015140          9            0.015046                1\n",
              "10  ct_dst_sport_ltm        11           0.014664         11            0.014696                0\n",
              "11            ackdat        12           0.014609         12            0.014619                0\n",
              "12              rate        13           0.014592         14            0.014427                1\n",
              "13         service_-        14           0.014413         15            0.014333                1\n",
              "14             smean        15           0.014360         13            0.014561                2\n",
              "15             sloss        16           0.014005         16            0.013990                0\n",
              "16         proto_tcp        17           0.013852         17            0.013882                0\n",
              "17            dinpkt        18           0.013462         18            0.013406                0\n",
              "18        ct_srv_src        19           0.012657         20            0.012642                1\n",
              "19            sbytes        20           0.012588         19            0.012943                1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-51df69d4-e37b-4a8d-9ccd-087d64dd972c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Rank_500</th>\n",
              "      <th>Mean_ABS_SHAP_500</th>\n",
              "      <th>Rank_1000</th>\n",
              "      <th>Mean_ABS_SHAP_1000</th>\n",
              "      <th>Rank_Difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sttl</td>\n",
              "      <td>1</td>\n",
              "      <td>0.076102</td>\n",
              "      <td>1</td>\n",
              "      <td>0.075750</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ct_state_ttl</td>\n",
              "      <td>2</td>\n",
              "      <td>0.045698</td>\n",
              "      <td>2</td>\n",
              "      <td>0.045500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>dttl</td>\n",
              "      <td>3</td>\n",
              "      <td>0.027586</td>\n",
              "      <td>3</td>\n",
              "      <td>0.027637</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>state_INT</td>\n",
              "      <td>4</td>\n",
              "      <td>0.027059</td>\n",
              "      <td>4</td>\n",
              "      <td>0.026934</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>sload</td>\n",
              "      <td>5</td>\n",
              "      <td>0.022224</td>\n",
              "      <td>5</td>\n",
              "      <td>0.022050</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>dbytes</td>\n",
              "      <td>6</td>\n",
              "      <td>0.019304</td>\n",
              "      <td>6</td>\n",
              "      <td>0.019164</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ct_srv_dst</td>\n",
              "      <td>7</td>\n",
              "      <td>0.017386</td>\n",
              "      <td>7</td>\n",
              "      <td>0.017427</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>swin</td>\n",
              "      <td>8</td>\n",
              "      <td>0.015465</td>\n",
              "      <td>8</td>\n",
              "      <td>0.015507</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ct_dst_src_ltm</td>\n",
              "      <td>9</td>\n",
              "      <td>0.015199</td>\n",
              "      <td>10</td>\n",
              "      <td>0.015041</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>dload</td>\n",
              "      <td>10</td>\n",
              "      <td>0.015140</td>\n",
              "      <td>9</td>\n",
              "      <td>0.015046</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>ct_dst_sport_ltm</td>\n",
              "      <td>11</td>\n",
              "      <td>0.014664</td>\n",
              "      <td>11</td>\n",
              "      <td>0.014696</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ackdat</td>\n",
              "      <td>12</td>\n",
              "      <td>0.014609</td>\n",
              "      <td>12</td>\n",
              "      <td>0.014619</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>rate</td>\n",
              "      <td>13</td>\n",
              "      <td>0.014592</td>\n",
              "      <td>14</td>\n",
              "      <td>0.014427</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>service_-</td>\n",
              "      <td>14</td>\n",
              "      <td>0.014413</td>\n",
              "      <td>15</td>\n",
              "      <td>0.014333</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>smean</td>\n",
              "      <td>15</td>\n",
              "      <td>0.014360</td>\n",
              "      <td>13</td>\n",
              "      <td>0.014561</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>sloss</td>\n",
              "      <td>16</td>\n",
              "      <td>0.014005</td>\n",
              "      <td>16</td>\n",
              "      <td>0.013990</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>proto_tcp</td>\n",
              "      <td>17</td>\n",
              "      <td>0.013852</td>\n",
              "      <td>17</td>\n",
              "      <td>0.013882</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>dinpkt</td>\n",
              "      <td>18</td>\n",
              "      <td>0.013462</td>\n",
              "      <td>18</td>\n",
              "      <td>0.013406</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>ct_srv_src</td>\n",
              "      <td>19</td>\n",
              "      <td>0.012657</td>\n",
              "      <td>20</td>\n",
              "      <td>0.012642</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>sbytes</td>\n",
              "      <td>20</td>\n",
              "      <td>0.012588</td>\n",
              "      <td>19</td>\n",
              "      <td>0.012943</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "shap_stability_table",
              "summary": "{\n  \"name\": \"shap_stability_table\",\n  \"rows\": 194,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"proto_mfe-nsp\",\n          \"proto_tcp\",\n          \"proto_aris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank_500\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 1,\n        \"max\": 194,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          139,\n          17,\n          156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean_ABS_SHAP_500\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008069143027704756,\n        \"min\": 0.0,\n        \"max\": 0.07610207181081273,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          1.1001919066724492e-06,\n          6.562415520116553e-06,\n          9.684080981224817e-07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank_1000\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 1,\n        \"max\": 194,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          141,\n          17,\n          156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean_ABS_SHAP_1000\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008043841410329725,\n        \"min\": 0.0,\n        \"max\": 0.0757502534878365,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          1.0997234814922547e-06,\n          6.51838279336895e-06,\n          1.0007079653004923e-06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank_Difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 28,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0,\n          8,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SHAP Explainability Interpretation\n",
        "\n",
        "SHAP explainability was applied to the Random Forest model to examine which transformed network traffic features contributed most strongly to attack prediction. The Random Forest model had already been trained and evaluated using the full UNSW-NB15 training and testing sets. SHAP was not used for model training or model performance measurement; it was used only to support model interpretation.\n",
        "\n",
        "The first SHAP run used a 500-record sample from the test set to confirm that the explainability workflow operated correctly. A second SHAP run used a 1,000-record sample as a robustness check. This helped assess whether the most important explanatory features remained broadly stable when the explanation sample size increased.\n",
        "\n",
        "This explainability stage supports the AI governance aim of the project by making the model less opaque. Instead of only reporting that the model predicted attack traffic, SHAP provides evidence of which features contributed most to the prediction. This supports transparency, auditability and human review in a banking cybersecurity decision-support context."
      ],
      "metadata": {
        "id": "ibirbIYRdC2I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Milestone 6: Export Model for Streamlit Dashboard\n",
        "\n",
        "This section exports the trained machine-learning pipelines, model evaluation tables, threshold analysis outputs, feature-importance results, SHAP explainability outputs, sample dashboard records, feature schema, and AI governance risk policy. These programs will be used to build the Streamlit AI governance dashboard."
      ],
      "metadata": {
        "id": "vbg70KB95CD0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Milestone 6: Export Model for Streamlit Dashboard\n",
        "# ============================================================\n",
        "\n",
        "import os\n",
        "import json\n",
        "import joblib\n",
        "import shutil\n",
        "from datetime import datetime\n",
        "\n",
        "# Main export folder in Google Drive\n",
        "export_dir = \"/content/drive/MyDrive/MSc_Dissertation/Streamlit_Artefacts\"\n",
        "\n",
        "# Create folder if it does not already exist\n",
        "os.makedirs(export_dir, exist_ok=True)\n",
        "\n",
        "print(\"Export folder ready:\")\n",
        "print(export_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHVHNTJE_PKV",
        "outputId": "c5facd2c-e4a1-47a6-b966-eed0169c6d14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Export folder ready:\n",
            "/content/drive/MyDrive/MSc_Dissertation/Streamlit_Artefacts\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Save Trained Model Pipelines\n",
        "# ============================================================\n",
        "\n",
        "joblib.dump(logistic_regression_model, os.path.join(export_dir, \"logistic_regression_pipeline.joblib\"))\n",
        "joblib.dump(decision_tree_model, os.path.join(export_dir, \"decision_tree_pipeline.joblib\"))\n",
        "joblib.dump(random_forest_model, os.path.join(export_dir, \"random_forest_pipeline.joblib\"))\n",
        "\n",
        "print(\"Model pipelines saved successfully:\")\n",
        "print(\"1. logistic_regression_pipeline.joblib\")\n",
        "print(\"2. decision_tree_pipeline.joblib\")\n",
        "print(\"3. random_forest_pipeline.joblib\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kCs5oQEhCQTW",
        "outputId": "297dbf87-07f3-4c9d-b49b-12c761b6683f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model pipelines saved successfully:\n",
            "1. logistic_regression_pipeline.joblib\n",
            "2. decision_tree_pipeline.joblib\n",
            "3. random_forest_pipeline.joblib\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Save Model Evaluation Tables\n",
        "# ============================================================\n",
        "\n",
        "model_results.to_csv(os.path.join(export_dir, \"model_results_summary.csv\"), index=False)\n",
        "\n",
        "detailed_model_comparison.to_csv(\n",
        "    os.path.join(export_dir, \"detailed_model_comparison.csv\"),\n",
        "    index=False\n",
        ")\n",
        "\n",
        "threshold_results_df.to_csv(\n",
        "    os.path.join(export_dir, \"random_forest_threshold_analysis.csv\"),\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"Model evaluation tables saved successfully:\")\n",
        "print(\"1. model_results_summary.csv\")\n",
        "print(\"2. detailed_model_comparison.csv\")\n",
        "print(\"3. random_forest_threshold_analysis.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xHLbk8TkCh1K",
        "outputId": "e2b3d7a6-6114-432a-e076-a26f03d4d301"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model evaluation tables saved successfully:\n",
            "1. model_results_summary.csv\n",
            "2. detailed_model_comparison.csv\n",
            "3. random_forest_threshold_analysis.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Save Feature Importance Tables\n",
        "# ============================================================\n",
        "\n",
        "dt_feature_importance.to_csv(\n",
        "    os.path.join(export_dir, \"decision_tree_feature_importance.csv\"),\n",
        "    index=False\n",
        ")\n",
        "\n",
        "rf_feature_importance.to_csv(\n",
        "    os.path.join(export_dir, \"random_forest_feature_importance.csv\"),\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"Feature importance tables saved successfully:\")\n",
        "print(\"1. decision_tree_feature_importance.csv\")\n",
        "print(\"2. random_forest_feature_importance.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "so-VY_FuC7iP",
        "outputId": "0053b65c-fd26-4fe1-a4b3-09ce4ad0e878"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature importance tables saved successfully:\n",
            "1. decision_tree_feature_importance.csv\n",
            "2. random_forest_feature_importance.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Save SHAP Explainability Outputs\n",
        "# ============================================================\n",
        "\n",
        "# Save 500-record SHAP importance table\n",
        "shap_importance_rf_500.to_csv(\n",
        "    os.path.join(export_dir, \"shap_importance_random_forest_500.csv\"),\n",
        "    index=False\n",
        ")\n",
        "\n",
        "# Save 1,000-record SHAP importance table\n",
        "shap_importance_rf_1000.to_csv(\n",
        "    os.path.join(export_dir, \"shap_importance_random_forest_1000.csv\"),\n",
        "    index=False\n",
        ")\n",
        "\n",
        "# Save SHAP stability table\n",
        "shap_stability_table.to_csv(\n",
        "    os.path.join(export_dir, \"shap_stability_table_500_vs_1000.csv\"),\n",
        "    index=False\n",
        ")\n",
        "\n",
        "# Save local SHAP explanation if it exists\n",
        "if \"local_shap_rf_500\" in globals():\n",
        "    local_shap_rf_500.to_csv(\n",
        "        os.path.join(export_dir, \"local_shap_high_risk_prediction.csv\"),\n",
        "        index=False\n",
        "    )\n",
        "    print(\"Local SHAP explanation saved.\")\n",
        "\n",
        "print(\"SHAP explainability outputs saved successfully:\")\n",
        "print(\"1. shap_importance_random_forest_500.csv\")\n",
        "print(\"2. shap_importance_random_forest_1000.csv\")\n",
        "print(\"3. shap_stability_table_500_vs_1000.csv\")\n",
        "print(\"4. local_shap_high_risk_prediction.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHXi_4PeDHHK",
        "outputId": "eff59733-258b-42a9-f24b-d86009d91481"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Local SHAP explanation saved.\n",
            "SHAP explainability outputs saved successfully:\n",
            "1. shap_importance_random_forest_500.csv\n",
            "2. shap_importance_random_forest_1000.csv\n",
            "3. shap_stability_table_500_vs_1000.csv\n",
            "4. local_shap_high_risk_prediction.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Save Sample Records for Streamlit Dashboard Demo\n",
        "# ============================================================\n",
        "\n",
        "dashboard_sample_size = 100\n",
        "\n",
        "# Sample from original test dataframe\n",
        "dashboard_sample_full = test_df.sample(n=dashboard_sample_size, random_state=42).copy()\n",
        "\n",
        "# Model-input version: remove id, attack_cat and label\n",
        "dashboard_sample_inputs = dashboard_sample_full.drop(columns=[\"id\", \"attack_cat\", \"label\"])\n",
        "\n",
        "# Save both versions\n",
        "dashboard_sample_full.to_csv(\n",
        "    os.path.join(export_dir, \"dashboard_sample_records_full.csv\"),\n",
        "    index=False\n",
        ")\n",
        "\n",
        "dashboard_sample_inputs.to_csv(\n",
        "    os.path.join(export_dir, \"dashboard_sample_records_inputs.csv\"),\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"Dashboard sample records saved successfully:\")\n",
        "print(\"1. dashboard_sample_records_full.csv\")\n",
        "print(\"2. dashboard_sample_records_inputs.csv\")\n",
        "\n",
        "print(\"\\nFull sample shape:\", dashboard_sample_full.shape)\n",
        "print(\"Input sample shape:\", dashboard_sample_inputs.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ARguqWTDRpB",
        "outputId": "b738b97d-9f17-489c-faac-fa630c385ba7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dashboard sample records saved successfully:\n",
            "1. dashboard_sample_records_full.csv\n",
            "2. dashboard_sample_records_inputs.csv\n",
            "\n",
            "Full sample shape: (100, 45)\n",
            "Input sample shape: (100, 42)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Save Feature Schema and Project Metadata\n",
        "# ============================================================\n",
        "\n",
        "feature_schema = {\n",
        "    \"project_title\": \"AI Governance and Predictive Risk Analytics in Banking Cybersecurity\",\n",
        "    \"dataset\": \"UNSW-NB15\",\n",
        "    \"target_column\": \"label\",\n",
        "    \"excluded_columns\": [\"id\", \"attack_cat\", \"label\"],\n",
        "    \"categorical_features\": categorical_features,\n",
        "    \"numerical_features\": numerical_features,\n",
        "    \"model_input_features\": X_train.columns.tolist(),\n",
        "    \"transformed_feature_count\": len(all_feature_names),\n",
        "    \"transformed_feature_names\": all_feature_names,\n",
        "    \"main_dashboard_model\": \"random_forest_pipeline.joblib\",\n",
        "    \"reason_for_main_model\": \"Random Forest achieved the highest attack recall and ROC-AUC, making it suitable for cyber-risk detection and governance-focused decision support.\",\n",
        "    \"created_at\": datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
        "}\n",
        "\n",
        "with open(os.path.join(export_dir, \"feature_schema_and_metadata.json\"), \"w\") as f:\n",
        "    json.dump(feature_schema, f, indent=4)\n",
        "\n",
        "print(\"Feature schema and metadata saved successfully:\")\n",
        "print(\"feature_schema_and_metadata.json\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-lt-d4JDd9o",
        "outputId": "c9a767c8-1c3b-40af-f066-56a198ca820b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature schema and metadata saved successfully:\n",
            "feature_schema_and_metadata.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Save AI Governance Risk Threshold Policy\n",
        "# ============================================================\n",
        "\n",
        "risk_policy = {\n",
        "    \"classification_threshold_default\": 0.50,\n",
        "    \"risk_levels\": {\n",
        "        \"Low\": {\n",
        "            \"attack_probability_range\": \"0.00 - 0.39\",\n",
        "            \"recommendation\": \"Monitor only. No immediate escalation required.\"\n",
        "        },\n",
        "        \"Medium\": {\n",
        "            \"attack_probability_range\": \"0.40 - 0.59\",\n",
        "            \"recommendation\": \"Review event context and monitor for repeated suspicious behaviour.\"\n",
        "        },\n",
        "        \"High\": {\n",
        "            \"attack_probability_range\": \"0.60 - 0.79\",\n",
        "            \"recommendation\": \"Investigate as a likely cyber-risk event and record analyst decision.\"\n",
        "        },\n",
        "        \"Critical\": {\n",
        "            \"attack_probability_range\": \"0.80 - 1.00\",\n",
        "            \"recommendation\": \"Escalate immediately for human review and incident-response consideration.\"\n",
        "        }\n",
        "    },\n",
        "    \"governance_note\": \"The dashboard is designed as a decision-support tool. It does not make autonomous final cybersecurity decisions. A human analyst or risk officer should review high-risk and critical outputs.\"\n",
        "}\n",
        "\n",
        "with open(os.path.join(export_dir, \"ai_governance_risk_policy.json\"), \"w\") as f:\n",
        "    json.dump(risk_policy, f, indent=4)\n",
        "\n",
        "print(\"AI governance risk policy saved successfully:\")\n",
        "print(\"ai_governance_risk_policy.json\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-j_Aq5eCDm3I",
        "outputId": "66c6f6b5-6d88-4ed2-a2b2-fec6b0c2997a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AI governance risk policy saved successfully:\n",
            "ai_governance_risk_policy.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Validate Exported Random Forest Pipeline\n",
        "# ============================================================\n",
        "\n",
        "loaded_rf_model = joblib.load(os.path.join(export_dir, \"random_forest_pipeline.joblib\"))\n",
        "\n",
        "# Test prediction using the first five dashboard sample input records\n",
        "test_predictions = loaded_rf_model.predict(dashboard_sample_inputs.head(5))\n",
        "test_probabilities = loaded_rf_model.predict_proba(dashboard_sample_inputs.head(5))[:, 1]\n",
        "\n",
        "validation_output = pd.DataFrame({\n",
        "    \"Prediction\": test_predictions,\n",
        "    \"Attack_Probability\": test_probabilities\n",
        "})\n",
        "\n",
        "print(\"Exported Random Forest model loaded and tested successfully.\")\n",
        "display(validation_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "ByaePiAlD8fZ",
        "outputId": "fe26f840-ce1b-4d0a-e96d-14d8c73313db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Exported Random Forest model loaded and tested successfully.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Prediction  Attack_Probability\n",
              "0           0            0.006725\n",
              "1           1            0.980058\n",
              "2           1            0.993862\n",
              "3           0            0.006233\n",
              "4           1            0.985245"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e8a5a3e-73f5-44b3-984c-ce7bf19b8c26\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Prediction</th>\n",
              "      <th>Attack_Probability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-5e8a5a3e-73f5-44b3-984c-ce7bf19b8c26 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5e8a5a3e-73f5-44b3-984c-ce7bf19b8c26');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_d6a6e197-3ecd-4b84-b274-1b953c4d6fb5\">\n",
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              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('validation_output')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "       width=\"24px\">\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d6a6e197-3ecd-4b84-b274-1b953c4d6fb5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('validation_output');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "validation_output",
              "summary": "{\n  \"name\": \"validation_output\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Prediction\",\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\": \"Attack_Probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5367409480900852,\n        \"min\": 0.006233499026896021,\n        \"max\": 0.9938617806903545,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.980057721484257,\n          0.9852451919835616\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================================================\n",
        "# Create Zipped Streamlit Package\n",
        "# ============================================================\n",
        "\n",
        "zip_base_path = \"/content/drive/MyDrive/MSc_Dissertation/Streamlit_Artefacts_Package\"\n",
        "\n",
        "# Remove old zip if it exists\n",
        "if os.path.exists(zip_base_path + \".zip\"):\n",
        "    os.remove(zip_base_path + \".zip\")\n",
        "\n",
        "# Create zip archive\n",
        "shutil.make_archive(zip_base_path, \"zip\", export_dir)\n",
        "\n",
        "print(\"Streamlit artefact package created successfully:\")\n",
        "print(zip_base_path + \".zip\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-zTNnRUEGGz",
        "outputId": "344c32de-9c9e-483c-9406-778a39c1fa95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Streamlit artefact package created successfully:\n",
            "/content/drive/MyDrive/MSc_Dissertation/Streamlit_Artefacts_Package.zip\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "print(sklearn.__version__)"
      ],
      "metadata": {
        "id": "HDWeO08qcGp4",
        "outputId": "36c392c6-20b7-4174-b82d-ea9266d6f2bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.6.1\n"
          ]
        }
      ]
    }
  ]
}