diff --git "a/network-threat-detection-with-f1-99.ipynb" "b/network-threat-detection-with-f1-99.ipynb"
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+++ "b/network-threat-detection-with-f1-99.ipynb"
@@ -0,0 +1,3851 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2ad6ed82",
+   "metadata": {
+    "papermill": {
+     "duration": 0.01111,
+     "end_time": "2025-07-21T10:46:05.738808",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:05.727698",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This project focuses on detecting different types of network activity—specifically distinguishing between **Normal traffic**, **Bot Attacks**, and possibly **Scan Attacks**—based on structured network logs.\n",
+    "\n",
+    "Using the `Scan_Type` column as our **target label**, we aim to build a **multiclass classification model** that can identify the nature of each network request.\n",
+    "\n",
+    "The dataset includes features such as:\n",
+    "\n",
+    "* IP addresses\n",
+    "* Ports and protocols\n",
+    "* Request types\n",
+    "* Payload size\n",
+    "* User agent strings\n",
+    "* Request status (Success/Failure)\n",
+    "\n",
+    "Our goal is to preprocess the data, explore key patterns, and train models that can effectively classify each log entry into its correct scan type. This task simulates a real-world intrusion detection system (IDS) that goes beyond simple anomaly detection.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70f74f9b",
+   "metadata": {
+    "papermill": {
+     "duration": 0.009259,
+     "end_time": "2025-07-21T10:46:05.757866",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:05.748607",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "63694924",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:05.778335Z",
+     "iopub.status.busy": "2025-07-21T10:46:05.777866Z",
+     "iopub.status.idle": "2025-07-21T10:46:11.907529Z",
+     "shell.execute_reply": "2025-07-21T10:46:11.905915Z"
+    },
+    "papermill": {
+     "duration": 6.142828,
+     "end_time": "2025-07-21T10:46:11.909896",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:05.767068",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install shap -q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e28c0346",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:11.930206Z",
+     "iopub.status.busy": "2025-07-21T10:46:11.929800Z",
+     "iopub.status.idle": "2025-07-21T10:46:27.483002Z",
+     "shell.execute_reply": "2025-07-21T10:46:27.481717Z"
+    },
+    "papermill": {
+     "duration": 15.565334,
+     "end_time": "2025-07-21T10:46:27.485242",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:11.919908",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "import xgboost as xgb\n",
+    "import shap\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe046fef",
+   "metadata": {
+    "papermill": {
+     "duration": 0.008267,
+     "end_time": "2025-07-21T10:46:27.502264",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:27.493997",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c4f85558",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:27.522079Z",
+     "iopub.status.busy": "2025-07-21T10:46:27.520411Z",
+     "iopub.status.idle": "2025-07-21T10:46:27.586192Z",
+     "shell.execute_reply": "2025-07-21T10:46:27.584916Z"
+    },
+    "papermill": {
+     "duration": 0.077716,
+     "end_time": "2025-07-21T10:46:27.588319",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:27.510603",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"/kaggle/input/intrusion-detection-logs-normal-bot-scan/Network_logs.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dc9b650c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:27.607260Z",
+     "iopub.status.busy": "2025-07-21T10:46:27.606823Z",
+     "iopub.status.idle": "2025-07-21T10:46:27.642929Z",
+     "shell.execute_reply": "2025-07-21T10:46:27.641589Z"
+    },
+    "papermill": {
+     "duration": 0.047841,
+     "end_time": "2025-07-21T10:46:27.644754",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:27.596913",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (8846, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "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>Source_IP</th>\n",
+       "      <th>Destination_IP</th>\n",
+       "      <th>Port</th>\n",
+       "      <th>Request_Type</th>\n",
+       "      <th>Protocol</th>\n",
+       "      <th>Payload_Size</th>\n",
+       "      <th>User_Agent</th>\n",
+       "      <th>Status</th>\n",
+       "      <th>Intrusion</th>\n",
+       "      <th>Scan_Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>192.168.142.55</td>\n",
+       "      <td>42.156.67.167</td>\n",
+       "      <td>80</td>\n",
+       "      <td>FTP</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>2369</td>\n",
+       "      <td>curl/7.68.0</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>53.39.165.18</td>\n",
+       "      <td>94.60.242.119</td>\n",
+       "      <td>135</td>\n",
+       "      <td>SMTP</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>1536</td>\n",
+       "      <td>Wget/1.20.3</td>\n",
+       "      <td>Failure</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BotAttack</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>192.168.127.91</td>\n",
+       "      <td>7.10.192.3</td>\n",
+       "      <td>21</td>\n",
+       "      <td>SMTP</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>1183</td>\n",
+       "      <td>Wget/1.20.3</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>192.168.30.40</td>\n",
+       "      <td>130.169.82.211</td>\n",
+       "      <td>25</td>\n",
+       "      <td>HTTPS</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>666</td>\n",
+       "      <td>Mozilla/5.0</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>192.168.43.179</td>\n",
+       "      <td>35.140.102.220</td>\n",
+       "      <td>22</td>\n",
+       "      <td>FTP</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>2821</td>\n",
+       "      <td>Mozilla/5.0</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Source_IP  Destination_IP  Port Request_Type Protocol  Payload_Size  \\\n",
+       "0  192.168.142.55   42.156.67.167    80          FTP      UDP          2369   \n",
+       "1    53.39.165.18   94.60.242.119   135         SMTP      UDP          1536   \n",
+       "2  192.168.127.91      7.10.192.3    21         SMTP      TCP          1183   \n",
+       "3   192.168.30.40  130.169.82.211    25        HTTPS      TCP           666   \n",
+       "4  192.168.43.179  35.140.102.220    22          FTP      TCP          2821   \n",
+       "\n",
+       "    User_Agent   Status  Intrusion  Scan_Type  \n",
+       "0  curl/7.68.0  Success          0     Normal  \n",
+       "1  Wget/1.20.3  Failure          1  BotAttack  \n",
+       "2  Wget/1.20.3  Success          0     Normal  \n",
+       "3  Mozilla/5.0  Success          0     Normal  \n",
+       "4  Mozilla/5.0  Success          0     Normal  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"Shape:\", df.shape)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b13c57b8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:27.665200Z",
+     "iopub.status.busy": "2025-07-21T10:46:27.663795Z",
+     "iopub.status.idle": "2025-07-21T10:46:27.698798Z",
+     "shell.execute_reply": "2025-07-21T10:46:27.697344Z"
+    },
+    "papermill": {
+     "duration": 0.047312,
+     "end_time": "2025-07-21T10:46:27.700818",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:27.653506",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 8846 entries, 0 to 8845\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column          Non-Null Count  Dtype \n",
+      "---  ------          --------------  ----- \n",
+      " 0   Source_IP       8846 non-null   object\n",
+      " 1   Destination_IP  8846 non-null   object\n",
+      " 2   Port            8846 non-null   int64 \n",
+      " 3   Request_Type    8846 non-null   object\n",
+      " 4   Protocol        8846 non-null   object\n",
+      " 5   Payload_Size    8846 non-null   int64 \n",
+      " 6   User_Agent      8846 non-null   object\n",
+      " 7   Status          8846 non-null   object\n",
+      " 8   Intrusion       8846 non-null   int64 \n",
+      " 9   Scan_Type       8846 non-null   object\n",
+      "dtypes: int64(3), object(7)\n",
+      "memory usage: 691.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "182e4bf1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:27.723122Z",
+     "iopub.status.busy": "2025-07-21T10:46:27.722764Z",
+     "iopub.status.idle": "2025-07-21T10:46:27.735822Z",
+     "shell.execute_reply": "2025-07-21T10:46:27.734569Z"
+    },
+    "papermill": {
+     "duration": 0.026574,
+     "end_time": "2025-07-21T10:46:27.737916",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:27.711342",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Source_IP         0\n",
+       "Destination_IP    0\n",
+       "Port              0\n",
+       "Request_Type      0\n",
+       "Protocol          0\n",
+       "Payload_Size      0\n",
+       "User_Agent        0\n",
+       "Status            0\n",
+       "Intrusion         0\n",
+       "Scan_Type         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d3ecc5f3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:27.759333Z",
+     "iopub.status.busy": "2025-07-21T10:46:27.758921Z",
+     "iopub.status.idle": "2025-07-21T10:46:28.009558Z",
+     "shell.execute_reply": "2025-07-21T10:46:28.008264Z"
+    },
+    "papermill": {
+     "duration": 0.263507,
+     "end_time": "2025-07-21T10:46:28.011561",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:27.748054",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scan_Type\n",
+      "Normal       0.904\n",
+      "BotAttack    0.054\n",
+      "PortScan     0.042\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.countplot(x='Scan_Type', data=df, palette='viridis')\n",
+    "plt.title('Scan Type Distribution')\n",
+    "plt.xlabel('Scan Type')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()\n",
+    "\n",
+    "print(df['Scan_Type'].value_counts(normalize=True).round(3))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d138b91b",
+   "metadata": {
+    "papermill": {
+     "duration": 0.009456,
+     "end_time": "2025-07-21T10:46:28.031156",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:28.021700",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "There is a severe data imbalance in this dataset, we may consider using a certain metric data balancing techniques."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "35352a0a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:28.055082Z",
+     "iopub.status.busy": "2025-07-21T10:46:28.054742Z",
+     "iopub.status.idle": "2025-07-21T10:46:28.522366Z",
+     "shell.execute_reply": "2025-07-21T10:46:28.521094Z"
+    },
+    "papermill": {
+     "duration": 0.483546,
+     "end_time": "2025-07-21T10:46:28.524520",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:28.040974",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 4))\n",
+    "sns.countplot(x='Request_Type', hue='Scan_Type', data=df, palette='Set2')\n",
+    "plt.title('Request Type by Scan Type')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53b067b2",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011359,
+     "end_time": "2025-07-21T10:46:28.553830",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:28.542471",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "The scan_type distribution aginst request_type is semi-balanced, there is no certain request type that guarntee a bot attack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "78693433",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:28.580176Z",
+     "iopub.status.busy": "2025-07-21T10:46:28.579786Z",
+     "iopub.status.idle": "2025-07-21T10:46:28.792314Z",
+     "shell.execute_reply": "2025-07-21T10:46:28.791094Z"
+    },
+    "papermill": {
+     "duration": 0.227389,
+     "end_time": "2025-07-21T10:46:28.794226",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:28.566837",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='Protocol', hue='Scan_Type', data=df, palette='Set3')\n",
+    "plt.title('Protocol Distribution by Scan Type')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e107a7f8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011123,
+     "end_time": "2025-07-21T10:46:28.817290",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:28.806167",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Same Conclusion As Above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4b8afa07",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:28.914669Z",
+     "iopub.status.busy": "2025-07-21T10:46:28.914190Z",
+     "iopub.status.idle": "2025-07-21T10:46:29.118954Z",
+     "shell.execute_reply": "2025-07-21T10:46:29.117340Z"
+    },
+    "papermill": {
+     "duration": 0.219211,
+     "end_time": "2025-07-21T10:46:29.120610",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:28.901399",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='Status', hue='Scan_Type', data=df)\n",
+    "plt.title('Request Status by Scan Type')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cd8c145",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011714,
+     "end_time": "2025-07-21T10:46:29.144617",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:29.132903",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Most Normal requests are successful, while BotAttack and PortScan requests often fail. This suggests that the Status feature can help distinguish between normal and malicious traffic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "419810c6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:29.170600Z",
+     "iopub.status.busy": "2025-07-21T10:46:29.170144Z",
+     "iopub.status.idle": "2025-07-21T10:46:29.686854Z",
+     "shell.execute_reply": "2025-07-21T10:46:29.685727Z"
+    },
+    "papermill": {
+     "duration": 0.531874,
+     "end_time": "2025-07-21T10:46:29.688570",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:29.156696",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
+      "When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
+      "When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
+      "When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
+      "When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
+      "When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ss29qJpOJ/v37079/fzp37swdd9zBkiVLPIFJRaxWK9dffz1ms5mPP/4Yg+H0S9rdzscee6zSou2OHTvWqc3btm1Dr9dXGYw8/PDDXHnllSxdupQVK1bwzDPPMHPmTL7//nvOPffcap2nbFagPlQ210pNioPryl8jycaNG8eSJUtYs2YNvXr14osvvuCBBx6o0TQGmqbRuXNnOnfuzOjRo+nUqRMLFy7k7rvvrvYxbrzxRtasWcPjjz9Onz59CA0Nxel0ctlll1X4mqrs+XFnlGrr5ptv5pFHHmHhwoU89dRTfPjhh5x33nk1+oLi5m733/72N/r06VPhPg1peH5zJsGOaDA+/fRTLBYLK1as8BpmPW/evHL76vV6brnlFubPn8/LL7/M0qVLz9rVERcXR3BwMLt37y53265du9DpdLRu3RqATz75hOHDh3u+vbrl5OQQGxvrud6mTRu2bduGUsrrA7Sic9SEOyA8fvx4lfs99NBDbNmyhZ9++qlc4ay7q85oNNZbEFhWamoqP/74I4MHD640s+PWoUMHHn30UR599FH27t1Lnz59+Mc//sGHH34IVB581IY7o1X2mHv27AFco6vgdJYgJyfHq+vlzOxLTdrWpk0bwPW3PzNrsnv3bs/t9aU6jxPgsssuIy4ujoULFzJw4EAKCwurzICeTfv27YmKivL8b3bo0AFwBb6V/Z9lZ2ezcuVKnnvuOZ599lnPdndmxJ+io6MZPXo0Cxcu5NZbb2X16tW89tprFe57ZvuUUuzbt4/evXsDpx97eHi4T15jov5IN5ZoMPR6PZqmeX27PnjwIEuXLq1w/9tvv53s7Gzuvfde8vPzz1qDoNfrufTSS/nPf/7j1Z2Rnp7OokWLuOCCCzwjivR6fblvkEuWLClX43L55Zdz7Ngxr+HxhYWF/Otf/6rOQ2bVqlUVflN11wRU9W1z3rx5vP3228yZM6fCER/x8fEMGzaMt99+u8Kg6cSJE9VqY0WysrIYO3YsDoejygnbCgsLKS4u9trWoUMHwsLCvIbnh4SEkJOTU+v2lHXs2DE+//xzz/W8vDzef/99+vTpQ2JioqcNAD/99JNnv4KCAhYsWFDueNVt23nnnUd8fDxvvfWW12P76quv2Llzp2c0YX2pzuMEMBgMjB07lo8//pj58+fTq1cvz4d1VdavX1/hyLdffvmFzMxMz/9m3759adeuHa+99lq558n9v+3+EnLm/3plQYav3X777ezYsYPHH38cvV7PzTffXOF+77//vlc92ieffMLx48cZNWoUAP369aNDhw78/e9/r3BG6bq8xkT9ksyOaDBGjx7NK6+8wmWXXcYtt9xCRkYGc+bMoWPHjvz222/l9j/33HPp2bMnS5YsoVu3bvTt2/es53jhhRf49ttvueCCC3jggQcwGAy8/fbblJSUMGvWLM9+V1xxBTNmzOCOO+5gyJAh/P777yxcuNCrsBlchbP//Oc/GTduHJs2bSIpKYkPPvig2rNBT5o0icLCQq699lq6du2K1WplzZo1/Pvf/6Zt27bccccdFd7v5MmTPPDAA3Tv3h2z2ezJkLhde+21hISEMGfOHC644AJ69erFPffcQ/v27UlPT2ft2rUcOXKkwnmDzrRnzx4+/PBDlFLk5eWxdetWlixZQn5+vufvVdV9R4wYwY033kj37t0xGAx8/vnnpKene33A9OvXj7lz5/LCCy/QsWNH4uPjq1VTUpHOnTtz1113sWHDBhISEnjvvfdIT0/3yhBeeumlJCcnc9ddd3k+8N577z3i4uJITU31Ol5122Y0Gnn55Ze54447GDp0KGPHjvUMPW/btm254d91VZ3H6TZu3Dhmz57NqlWrqj10+4MPPmDhwoVce+219OvXD5PJxM6dO3nvvfewWCw89dRTgGtyxblz53LllVfSp08f7rjjDpKSkti1axfbt29nxYoVhIeHe+q1bDYbLVu25JtvvvHMr+Vvo0ePJiYmhiVLljBq1Khyc1q5RUdHc8EFF3DHHXeQnp7Oa6+9RseOHbnnnnsA12N/5513GDVqFD169OCOO+6gZcuWHD16lFWrVhEeHs5///tffz40UZmAjAETzY57SO+GDRuq3O/dd99VnTp1UmazWXXt2lXNmzev0mHCSp0ebv3iiy9WeDtnDD1XSqnNmzerlJQUFRoaqoKDg9Xw4cPVmjVrvPYpLi5Wjz76qEpKSlJBQUHq/PPPV2vXrlVDhw4tNwz50KFD6qqrrlLBwcEqNjZWTZ48WX399dfVGnr+1VdfqTvvvFN17dpVhYaGKpPJpDp27KgmTZqk0tPTvfYtO/TcPcS7skvZodN//PGHGjdunEpMTFRGo1G1bNlSXXHFFeqTTz6psm3u58990el0KjIyUp177rlq8uTJavv27eX2P3Po+cmTJ9XEiRNV165dVUhIiIqIiFADBw70GqqvlGuY8+jRo1VYWJgCPM9xVf83lQ09Hz16tFqxYoXq3bu35/9oyZIl5e6/adMmNXDgQGUymVRycrJ65ZVXKjxmZW07c+i527///W917rnnKrPZrKKjo9Wtt97qmVrBbfz48SokJKRcm6r6Xy+rJo/TrUePHkqn05VrS2V+++039fjjj6u+ffuq6OhoZTAYVFJSkrrhhhvU5s2by+3/v//9T11yySUqLCxMhYSEqN69e6s33njDc/uRI0fUtddeqyIjI1VERIS64YYb1LFjx8q9Rt3PwYkTJ7yOX9Hf5mwqmzZAKaUeeOABBahFixaVu839t/3oo4/U1KlTVXx8vAoKClKjR49Whw4dKrf/r7/+qq677joVExOjzGazatOmjbrxxhvVypUrq91W4VuaUnWs9hIigF5//XUeeeQRDh48WG70hhDitHPPPZfo6GhWrlwZ6KY0CI888gjvvvsuaWlp5TKxP/zwA8OHD2fJkiVe00qIxktqdkSjpZTi3XffZejQoRLoCFGFjRs3smXLFsaNGxfopjQIxcXFfPjhh4wZM6ZZLkDcHEnNjmh0CgoK+OKLL1i1ahW///47//nPfwLdJCEapG3btrFp0yb+8Y9/kJSUxE033RToJgVURkYG3333HZ988gmZmZmepSxE0yfBjmh0Tpw4wS233EJkZCRPPfUUV111VaCbJESD9MknnzBjxgy6dOnCRx991KRXaa+OHTt2cOuttxIfH8/s2bMrnRtHND1SsyOEEEKIJk1qdoQQQgjRpEmwI4QQQogmTWp2cK1vcuzYMcLCwup12nohhBBC+I5SilOnTtGiRYsq13uTYAfXtOvuNZGEEEII0bgcPnyYVq1aVXq7BDvgWcTw8OHDnrWRhBBCCNGw5eXl0bp167MuRizBDqdXNQ4PD5dgRwghhGhkzlaCIgXKQgghhGjSJNgRQgghRJMmwY4QQgghmjSp2RFCCNGkOBwObDZboJsh6oHRaESv19f5OBLsCCGEaBKUUqSlpZGTkxPopoh6FBkZSWJiYp3mwZNgRwghRJPgDnTi4+MJDg6WSWIbOaUUhYWFZGRkAJCUlFTrY0mwI4QQotFzOByeQCcmJibQzRH1JCgoCICMjAzi4+Nr3aUlBcpCCCEaPXeNTnBwcIBbIuqb+29alzosCXaEEEI0GdJ11fTUx99Ugh0hhBBCNGkS7AghhBCiSZNgRwghhKimEydOcP/995OcnIzZbCYxMZGUlBRWr14d0Hb98MMPaJpW5eWHH34IaBsDSUZjCSGEENU0ZswYrFYrCxYsoH379qSnp7Ny5UoyMzMD2q4hQ4Zw/Phxz/XJkyeTl5fHvHnzPNuio6MD0bQGQTI7QgghRDXk5OTw888/8/LLLzN8+HDatGnDgAEDmDp1KldddZVnn3vvvZeEhAQsFgs9e/Zk2bJlAGRmZjJ27FhatmxJcHAwvXr14qOPPvI6x7Bhw3jooYf485//THR0NImJiUyfPv2sbTOZTCQmJnouQUFBnszTnj17aN26NVlZWV73efjhh7nwwgsBmD9/PpGRkSxdupROnTphsVhISUnh8OHDXvf5z3/+Q9++fbFYLLRv357nnnsOu91e26fUbySzI4QQVUhNTeXkyZO1um9sbCzJycn13CIRKKGhoYSGhrJ06VIGDRqE2Wz2ut3pdDJq1ChOnTrFhx9+SIcOHdixY4dnbpji4mL69evHE088QXh4OF9++SW33347HTp0YMCAAZ7jLFiwgClTprB+/XrWrl3LhAkTOP/887nkkktq1e6LLrqI9u3b88EHH/D4448DrmHcCxcuZNasWZ79CgsL+etf/8r777+PyWTigQce4Oabb/Z00f3888+MGzeO2bNnc+GFF/LHH3/wpz/9CYBp06bVqm1+o4TKzc1VgMrNzQ10U4QQDcihQ4dUUHCwAmp1CQoOVocOHQr0w2gWioqK1I4dO1RRUZFPz/PJJ5+oqKgoZbFY1JAhQ9TUqVPV1q1blVJKrVixQul0OrV79+5qH2/06NHq0Ucf9VwfOnSouuCCC7z26d+/v3riiSdq1M7x48erq6++2nP95ZdfVt26dfNc//TTT1VoaKjKz89XSik1b948Bah169Z59tm5c6cC1Pr165VSSo0YMUK9+OKLXuf54IMPVFJSUo3aVlNV/W2r+/ktmR0hhKjEyZMnKSos5NYn/kZCcoca3Tc99Q8Wvvw4J0+elOxOEzJmzBhGjx7Nzz//zLp16/jqq6+YNWsW77zzDhkZGbRq1YrOnTtXeF+Hw8GLL77Ixx9/zNGjR7FarZSUlJSbCLF3795e15OSkjxLJtTWhAkTePrpp1m3bh2DBg1i/vz53HjjjYSEhHj2MRgM9O/f33O9a9euREZGsnPnTgYMGMDWrVtZvXo1f/3rX70eU3FxMYWFhQ16QkcJdoRohqRrpmYSkjvQqlOPQDdDNBAWi4VLLrmESy65hGeeeYa7776badOm8dhjj1V5v7/97W+8/vrrvPbaa/Tq1YuQkBAefvhhrFar135Go9HruqZpOJ3OOrU5Pj6eK6+8knnz5tGuXTu++uqrGo/Oys/P57nnnuO6664rd5vFYqlT+3xNgh0hmpnU1FS6dutGUWFhre4fFBzMrp07m13AI0RlunfvztKlS+nduzdHjhxhz549FWZ3Vq9ezdVXX81tt90GuGp89uzZQ/fu3f3SzrvvvpuxY8fSqlUrOnTowPnnn+91u91uZ+PGjZ76od27d5OTk0O3bt0A6Nu3L7t376Zjx45+aW99kmBHiGZGumaEqJ3MzExuuOEG7rzzTnr37k1YWBgbN25k1qxZXH311QwdOpSLLrqIMWPG8Morr9CxY0d27dqFpmlcdtlldOrUiU8++YQ1a9YQFRXFK6+8Qnp6ut+CnZSUFMLDw3nhhReYMWNGuduNRiOTJk1i9uzZGAwGHnzwQQYNGuQJfp599lmuuOIKkpOTuf7669HpdGzdupVt27bxwgsv+OUx1JYEO0I0U9I1I0TNhIaGMnDgQF599VX++OMPbDYbrVu35p577uGpp54C4NNPP+Wxxx5j7NixFBQU0LFjR1566SUAnn76afbv309KSgrBwcH86U9/4pprriE3N9cv7dfpdEyYMIEXX3yRcePGlbs9ODiYJ554gltuuYWjR49y4YUX8u6773puT0lJYdmyZcyYMYOXX34Zo9FI165dufvuu/3S/rqQYEcIIYSoBrPZzMyZM5k5c2al+0RHR/Pee+9VetvSpUurPEdFdTRnu09F5s+fX+H2o0ePcvnll5OUlFTh7dddd12FNTluKSkppKSk1Lg9gSbBjhBCCNHE5ebm8vvvv7No0SK++OKLQDfH72QGZSGEEKIRWLhwoWdiwzMvPXpU3SV99dVXc+mll3LffffVenLCxkwyO6JJkKHUQoim7qqrrmLgwIEV3nbmcPUznW2Y+YQJE5gwYUItW9bwSbAjGj0ZSi2EaA7CwsIICwsLdDMaJQl2RKMnQ6mFEEJURYId0WTIUGohhBAVkQJlIYQQQjRpktkRopkpsDoJ7nI+m7P0bPj1KIVWO04FoWYD4RYDraKCaRMTjMWoD3RT601tC9h37tzpg9YIIfxNgh0hGqmafoAfyLbxxZ4Cfj5USNw1UzmQD3C6qDurwLUY4bZjeWhA29gQ+rSOpHVUEJqm1W/j/aiuBezgWgBRCNF4SbAjRCNUkw9wQ0QCURffTXDnwaVbNKwnU+nUMp4uHdoQbNKjaRr5JXay8q0czCwgs8DKgZMFHDhZQFyYmfM7xJAcHezbB+UjNS1gVwqyrRpHC3UczswjNzuLn7PDOLozne4twkkMtzTq4K85qsvUFLXRHKaz+OGHHxg+fDjZ2dlERkYGujlnFdBgZ/r06Tz33HNe27p06cKuXbsAKC4u5tFHH2Xx4sWUlJSQkpLCm2++SUJCgmf/1NRU7r//flatWkVoaCjjx49n5syZGAwSx4mmqzof4E4Fu3L17M7T4URDQ9Eq2IkhdQM/vPsClzz3L7ol9Sp3vws6xZJdYGXLkRx2Hs/jxKkSlm45RuuoILqYG++H/NkK2JVS7M3IZ/3+LLIKXVkuTFGYEqIoVK6M17ZjebSMDOKK3klNqpuvKauPzF5N1XQ6iwkTJrBgwQJmzpzJk08+6dm+dOlSrr32WpRSvmpqsxHwiKBHjx589913nutlg5RHHnmEL7/8kiVLlhAREcGDDz7Iddddx+rVqwFwOByMHj2axMRE1qxZw/Hjxxk3bhxGo5EXX3zR749FiJqoy7dNdy1JZR/gJ06V8M2ONE7muz60W0cHMbRTHDGhZjYV7jnr8aNCTAzvEs+g9jFsOJDFb0dyOZxdxBEMRF18NwVWZ63a3VAdyyli1e4Mz/Nl1Gu0iwmBjN38/PHbXHzbZBzRbdmXkc/RnCI+23yUa85tQbAp4G+h4izqMjVFbdR2OguLxcLLL7/MvffeS1RUVL20xWq1YjKZ6uVYjV3AX6kGg4HExMRy23Nzc3n33XdZtGgRF198MQDz5s2jW7durFu3jkGDBvHNN9+wY8cOvvvuOxISEujTpw/PP/88TzzxBNOnT5c/ci0Fajbi5lREWl/fNs+sJXE6FRsPZbP+QCZOBUFGPcO6xNEpPrRWXS9BRj0XdY7jnNaR/Lz3BH+cKCC8/zVM+voEzxiPcO25LdHpGm+2p8TuYPW+TH4/6lp12qTX0Tc5kj7JkZgNejat3EDxgc3EGkro0yORvsklfP7rUU7kl/Dp5qPceF4rzAbJ8DQGDX1qipEjR7Jv3z5mzpzJrFmzKtzn008/5dlnn2Xfvn0kJSUxadIkHn30Uc/tbdu25a677mLv3r0sXbqU6667jmHDhvHwww/z4Ycf8uijj3L48GEuv/xy3n//fZYsWcK0adPIzc3l9ttv59VXX0Wvd/0/f/DBB7z++uvs3r2bkJAQLr74Yl577TXi4+P98nzUt4AHO3v37qVFixZYLBYGDx7MzJkzSU5OZtOmTdhsNkaOHOnZt2vXriQnJ7N27VoGDRrE2rVr6dWrl1e3VkpKCvfffz/bt2/n3HPPrfCcJSUllJSUeK7n5eX57gE2MoGajbi5FZHW9dvmzl9+5KsFr1NcXOzZllVg5ZsdaaTnuf63O8SFMLxLPCHmur/MI4KMXNG7BRt/28EPu9LJiWnFo0u2suiXVJ67qgc9W0bU+Rz+diyniBXb08grtgPQPSmcCzrFElRF91RcmJnr+7Xis1+PkFVg5ZcDWVzYKc5fTRZNmF6v58UXX+SWW27hoYceolWrVl63b9q0iRtvvJHp06dz0003sWbNGh544AFiYmK8lnn4+9//zrPPPsu0adMA+PnnnyksLGT27NksXryYU6dOcd1113HttdcSGRnJ8uXL2b9/P2PGjOH888/npptuAsBms/H888/TpUsXMjIymDJlChMmTGD58uV+e07qU0CDnYEDBzJ//ny6dOnC8ePHee6557jwwgvZtm0baWlpmEymcoVPCQkJpKWlAZCWluYV6Lhvd99WmZkzZ5arFRIugZqNuC7ndX/wFxUVk1dko8jmQClAgzCzwVOA2xDV9ttmeuofnt+dSrElNYc1+zNxOBUmg47hnePokhhW7487MUhx7L0HefqDlXy6q5BNh7K58p//49aByTx2aRcigxt+NtXhVPxyIIsNB7NQQLjFwCXdE2gVVb0C7OgQEyO6JvDF1mNsOZxDz5YRRDWCxy0avmuvvZY+ffowbdo03n33Xa/bXnnlFUaMGMEzzzwDQOfOndmxYwd/+9vfvIKdiy++2Cvb8/PPP2Oz2Zg7dy4dOrjeW6+//no++OAD0tPTCQ0NpXv37gwfPpxVq1Z5gp0777zTc4z27dsze/Zs+vfvT35+PqGhob56CnwmoMHOqFGjPL/37t2bgQMH0qZNGz7++GOCgoJ8dt6pU6cyZcoUz/W8vDxat27ts/M1RoFK+dbkvPkldg5lFpCZ2J+kO+fwc1ESP605WG4/vU4jKcJCm5hgOsaFNooP5Oo65TSwZOMR0vJcGZ42McGM7JpAqMWHL22nnWu7hvLA6P68uHwX/916jA/XpfLlb8d5LKULN53XGoO+Yc5XmlNoZcX2dM/z1S0xjKFd4mrcFdUuNoS2McEczCzkpz0nuLpPS180VzRDL7/8MhdffDGPPfaY1/adO3dy9dVXe207//zzee2113A4HJ7up/POO6/cMYODgz2BDriSAm3btvUKWhISEsjIyPBc37RpE9OnT2fr1q1kZ2fjdLrq9FJTU+nevXvdH6ifNah3pMjISDp37sy+fftITEzEarWSk5PjtU96erqnxicxMZH09PRyt7tvq4zZbCY8PNzrIho+pRQnTpXwy4EsFm9I5d3/HeC7nRmkE4kprg0KDZ12enK8ULMBDdc3+SPZRazel8mCtYf4YusxjmT7b2SGL1gxEH3JfWwujiMtrxijXmNE13iuPqeFbwOdMpIignhj7Ll8dM8gOieEkl1o4y+fb2PEKz/y8cbD2BwNq4j5YL6ORb+kkpZXjNmgY1TPRC7tkVjrmpuLOseh0+BgZiEHMwvqubWiubroootISUlh6tSptbp/SEhIuW1nroiuaVqF29wBTUFBASkpKYSHh7Nw4UI2bNjA559/DriKnhujgNfslJWfn88ff/zB7bffTr9+/TAajaxcuZIxY8YAsHv3blJTUxk82DVfyODBg/nrX/9KRkaGp2jq22+/JTw8vFFGnmeavPhX0vOKWXT3oEZdBFoXDqfiaE4RB04UsP9kvqe+wi0x3IIh7wibP32TK2+9lwFDzkdXpuvG4VTkFtk4nFXI/pMFpGYVeuaPaRcbQmfjmWds2E4V29hyOIctdCCsbycAOieEcmGnOELroTanNgZ3iOHLhy7kg7WH+OeqfRzKLOTPn/zGP7/fx4PDO3Jt35YYA5jpOVXiJPaaqWzKMgCKlpFBpPRIIMxStz9+VLCJ3q0i2XI4h9+O5NI2pvyHjBC18dJLL9GnTx+6dOni2datWzfPSGS31atX07lzZ09Wp77s2rWLzMxMXnrpJU+vx8aNG+v1HP4W0GDnscce48orr6RNmzYcO3aMadOmodfrGTt2LBEREdx1111MmTKF6OhowsPDmTRpEoMHD2bQoEEAXHrppXTv3p3bb7+dWbNmkZaWxtNPP83EiRMxm82BfGh1llNo5T9bjgFwPK+YlpG+69ZraApK7BzMdAUkh7OKsJbJEOh1GsnRwbSPC6FdTAghZgObVv7K6n2/YNHd7RXouPePDjERHWLinNaRZBda+TU1h+3HcjlwsoBDGAkfdAMOZ8Odx8LhVKRmFbLjWB5/nMx31SOho+ToLga0j2VYz06BbiJGvY47L2jHTf1b8+G6Q/zrp/2kZhXy509/49Xv9nDboDaMHZBMdIh/uxBX7zvJI9+cIKTL+WgohnSIpW+bqHL/J7XVu2UEWw7ncDCzgIISe70UgwvRq1cvbr31VmbPnu3Z9uijj9K/f3+ef/55brrpJtauXcs///lP3nzzzXo/f3JyMiaTiTfeeIP77ruPbdu28fzzz9f7efwpoK/MI0eOMHbsWDIzM4mLi+OCCy5g3bp1xMW5Rje8+uqr6HQ6xowZ4zWpoJter2fZsmXcf//9DB48mJCQEMaPH8+MGTMC9ZDqzb6M0yOLSmyOALbEt5xOxf6TBaw8UEj0ZZP49riBvNQDXvsEm/S0jQmhQ1wIraOD65QliAo2cXHXePq0juTHPSdIzSokauh4nl6Vyb/aFdCmgXw7tzucpGYXcuBEAX+cKKCozP9Aq8ggwnP28NWHjxH53L8C2MryQswG7h3agdsHt2HhulTe/mk/x3OL+duK3by+ci/X9GnB+CFt6dHCt6O3ThXb+NuK3by/9hAAtszDpPRIpFfb6Ho9T1SIiaQIC8dzi9mVdop+bepnfhRR/8oW9TeG88yYMYN///vfnut9+/bl448/5tlnn+X5558nKSmJGTNmeBUn15e4uDjmz5/PU089xezZs+nbty9///vfueqqq+r9XP4S0GBn8eLFVd5usViYM2cOc+bMqXSfNm3aNNqhcFXZWybYKbY1rNqH2soqsLIrLY+96fnsTj/F3vRT7E475emaCjsnhTyba9/4MDPtYkNoFxtCfJi53kcVRYeYuKZPC9Zs2cUvx63szgxm9Oz/8eJ1vbjqnBb1eq7qKrE5+KO0u+5QZiH2MtmmIKOezgmh9GwZQWyomU0rNwekjdUVbDJwz0XtGTekDV/+dpx5qw/y+9FcPt54hI83HqFjtJER7YK4oHUQIabqBa/VmcNJKcWXvx/nhWU7PUXIl7YP5p1/PEzU6x/V+XFVpFtSOMdzi9l5PI++yZENduRfcxUbG0tQcDALX37cb+cMCg4mNja22vvPnz+/3La2bdt6TZECMGbMGE9ZR0UOHjxYbtuECRPKBUTTp09n+vTpVbZh7NixjB071mtb2Zmchw0b1qhmdpacawPlldmxN77MzrGcIjYczGLH8Tx2HT/FrrQ8z/wvZzIbdHSINLB22SIuu+Jqenbr7JeZaTVNo02ok6XvTeSSZz5k50kbD330K2v2nWTalT0IMvl+sjiHcv2td6XlcTCz0Ks7LdRsoH1cCO1jQ2gdFdyg6rZqMpFjW2D6kCDW7Mnlr0tWY+4wgH1ZsC/Lxtx1GRTuWk3+tu8oObwdVOWBfVVzOCmlWPtHJi+v2M3Wwzmu88YE8+K1vbDkpfJ/9or/9+pD5/hQftxzgswCKxmnSkgIt/jsXKLmkpOT2bVzp6yN1cxJsNNANbbMjsOpSC/WiL7kPu7/MoP0guMV7tc6OoguCWF0Lr10SgilU3wY237bQr/H59Pixqv8PgW/I+8EM4bF8FNWKP9ctY/FGw6zOTWbf97Sl84JYT45Z1q+naiR9/LlESO2w6efq5gQEx3jQ+kQF0psqKnBZQnysk4AcNttt9X6GFc89CIkdedgvp5TWAjtNYLQXiOw6BQtgp0kBTmJMyvK9lZWNodTdulEivPXHGLncdfkoMEmPX+6qD33De2Axahn8+bUWre1OsxGPR3iQtiTns+O43kS7DRAycnJEnw0cxLsNFD70k95fm/ImZ2cQivbjuWx41geRTYjYX2vIL3AgV6n0bNFOL1bRdI1KYyuieF0SQwL2Iihs9HrNB69tAuD2sfw8L+3sCc9n6v++T+mX9mDm/q3rregY8vhHP710x98ve0E4f2uxKZcGZwuCWF0SQxrkAFOWUX5roBi9L1/oUvvfjW6r3vyx+iYWPr064ZSirS8YrYfy2NfRj7Fdif78/Xsz9ej12nEhZpJCDcTEWTEFqsR1P481h8tZrc9lX0Z+fx+JJeNh7JwJ8MsRh039GvNpBEdiQ/zb8DRNTGcPen57D9RwLDOyutvWNvlTCQ7IET9aZifPM1cfomdY7mnlwFoiJmdk/klbDiQxZ4yGSizTnHy12+YOfFmbrlkQIMNbKpyfsdYlj90IVM+3sLPe0/y5Ge/s/qPTF64picRQbUbqux0KlbuyuD/ft7PLweyPNuL9m/kkoHncG6vjvU2OshfYlq0qfGkk2cWbmqaRlJEEEkRQQzvEs+hrAIOnCjgYGYh+SV20vKKPXU3YCT+hum8vDobyPY6TtfEMK7u05KxA1oHbMLIVlFB6DTXazenyEZUsKnOWbDaLr0ihCiv8X0aNQN/ZHiv8dSQMjsFJXZW/3GSncdPZ57axATTq2UExuxDvDbzDUJuG8ie7TX7Zt2QFvOMCzOz4I4BvP3Tfv7+zW7+u/UYq/ed5PGULtx4Xmv01aydKbI6+GTzEd773wEOnHRNOmfUa1x1TksuiC3muhHTSRj2WaMLdHxBr9NoHxtK+9hQlFLkFNlIzysm41QJ+cV2snLyOHZoH7179iAxJpIOcaF0Tgjl/I6xtI6u3jIPvmTU60iKCOJoThGHswqJCjbVKQtWl6VXhBDlSbDTAO09M9hpAJkdpRRbDuewdn8mNoer36BTfCj920YTF+aa02jH/rrXczSUxTx1Oo37h3VgQLtonvj0N/Zl5DP1s9/510/7uW1QG8b0bVlhFsHpVGw5ksPX29JYsvEw2YWu4WXhFgO3DGzDhCFtSYywsHlzwx5NFUiaphEVbCIq2ETX0onQj+zN4pXnp/Dlpk307ds3sA2sROtod7BTRO9WkZ7ttcmCCSHqlwQ7DdDejFNe1wOd2cktsvHtjnSO5hQBrlmLh3aOIzHCO3tTH/UcZVfxbgj6tYniq8kX8v7aQ7z+3R4OnCzg+WU7eOHLHXSOD6N7i3AsRj2gOHCygN1ppzwBDrg+AO88vx03ntdaJpxr4pKjg1m3P4vD2YU4G9GQXCGaA3n3bYD2pXtnNwJZs7P/ZD4rtqVjdTgx6jUu6BhLr5YRVRbR1kc9R0Ni1Ou464J23Ny/NZ//epQP1x1iV9opdqe7LmcKNRu4uGs8o3snMbJbQrW7vUTjlhBmwaTXUWJ3cuKU74a6CyFqToKdBsjdjdUmJphDmYUByewoBesPZLJuv6ugNinCQkqPxFoX6TYFIWYDtw1qw22D2nDiVAmbU7PZf6IAm8OJUylaRwXTKSGULolhtV5cUjReOp1Gq6gg9p8s4HBWIRLiCtFwSLDTwBTbHBwuXZG7Z8sIDmUWBiCzo/Frtp4D+a5Ap1fLCIZ2jpMMRRlxYWZSeiQGuhmigWkdHewKdrKLkLLihiM1NVUmFWwgJkyYQE5ODkuXLvXreSXYaWD+OOFa6DEy2OhZ/NOfmR2HUxFz+UMcyHdlJoZ3ifMqthRCVK51lOs1ezSniFYBbotwSU1NpVu3rhQWFvntnMHBQezcuavaAc+ECRNYsGCB53p0dDT9+/dn1qxZ9O7du9rHqCqIuPfee3nnnXdYvHgxN9xwg9dtbdu25eGHH+bhhx/2bJs/fz4PP/wwOTk51Tp/QyfBTgNz8KQrq9MuNgSLwTWFrL8yO0op3vk1j9Bel6ChuLRHIl0Tw/1ybiGagugQE2aDq26nEHOgmyOAkydPUlhYxIdP3Ui35Difn29n6glue/HjGk8bcNlllzFv3jwA0tLSePrpp7niiitITa37DOCFhYUsXryYP//5z7z33nvlgp3mQIKdBiY1yxXstIkOxmx0ZVf8ldl5f+0hVvxRiFJOBsY5JNARooY0TSMuzMyR7CLyCQp0c0QZ3ZLj6Nu5ZaCbUSmz2UxioqtrPDExkSeffJILL7yQEydOEBcXx++//87kyZNZu3YtwcHBjBkzhldeeYXQ0FCmT5/uyQy5B4+sWrWKYcOGAbBkyRK6d+/Ok08+SYsWLTh8+DCtW7cGXAt6Hjp0iEceeYRHHnnEc9877rjD63jTpk1j+vTpfPDBB7z++uvs3r2bkJAQLr74Yl577TXi4+M9j2X79u088cQT/PTTTyil6NOnD/Pnz6dDhw7lHveGDRu4/PLLeeyxx3jiiSd88My6SLDTwLiDneToYMx+zOz8vPcEM5btACDnhwW0Gn+rz88pRE3VZvJJf09YmRBmKQ12ZI0sUTv5+fl8+OGHdOzYkZiYGAoKCkhJSWHw4MFs2LCBjIwM7r77bh588EHmz5/PY489xs6dO8nLy/Nkh6Kjoz3He/fdd7ntttuIiIhg1KhRzJ8/n2eeeQaAzz77jHPOOYc//elP3HPPPZ77vvbaazz77LPs3r0bgNDQUABsNhvPP/88Xbp0ISMjgylTpjBhwgSWL18OwNGjR7nooosYNmwY33//PeHh4axevRq73V7ucX7//fdcd911zJo1iz/96U++e0KRYKfBOVwa7LSODqbE7gpyfJ3ZySm08si/t+BwKoa3DWL+L5+CBDuiAamPBUj9NWFlfLir+0qCHVETy5Yt8wQUBQUFJCUlsWzZMnQ6HYsWLaK4uJj333+fkJAQAP75z39y5ZVX8vLLL5OQkEBQUBAlJSWe7JDb3r17WbduHZ999hngeg1NmTKFp59+Gk3TiI6ORq/XExYW5nXfiAjXFCNnHu/OO+/0/N6+fXtmz55N//79yc/PJzQ0lDlz5hAREcHixYsxGl2jdzt37lzu8X7++eeMGzeOd955h5tuuqkensGqSbDTwJTN7Lh/93Vm54Uvd3Iy30rH+FDu7RfKfJ+eTYiaa0wTVrpnFC/ADDqZgkBUz/Dhw5k7dy4A2dnZvPnmm4waNYpffvmFnTt3cs4553gCHYDzzz8fp9PJ7t27SUhIqPS47733HikpKcTGxgJw+eWXc9ddd/H9998zYsSIGrdz06ZNTJ8+na1bt5KdnY3T6fp8Sk1NpXv37mzZsoULL7zQE+hUZP369SxbtoxPPvmEa665psZtqA0JdhoQu8PpmaU4OSaYjNKJyXyZ2flpzwk+2XQETYOXx/RGyzzgs3MJUVeNYcLKyCAjJr0OqwOMMTL8WFRPSEgIHTt29Fx/5513iIiI4P/+7/9qfUyHw8GCBQtIS0vDYDB4bX/vvfdqHOy4u9NSUlJYuHAhcXFxpKamkpKSgtVqBSAo6Oy1ah06dCAmJob33nuP0aNHVxkY1RcJdhqQ47nFOJwKk15HQpjF5zU7JXYHT33+OwDjB7elX5soNjfTYKe2dR0yn4Y4k6ZpxIeZOZJThCmx49nvIEQFNE1Dp9NRVFREt27dmD9/PgUFBZ7szurVq9HpdHTp0gUAk8mEw+H9xXj58uWcOnWKX3/9Fb3+dJZx27Zt3HHHHeTk5BAZGVnhfSvatmvXLjIzM3nppZc8Bc4bN2702qd3794sWLAAm81WaRATGxvLZ599xrBhw7jxxhv5+OOPfR7wSLDTgLi7rVpFB6HTaaVrLuGp3alvH288wpHsIhLDLTye0sUn52jo6loLEhQczK6dOyXgEV7iw13Bjjmx/OgTISpSUlJCWloa4OrG+uc//0l+fj5XXnklAwYMYNq0aYwfP57p06dz4sQJJk2axO233+7pwmrbti0rVqxg9+7dxMTEEBERwbvvvsvo0aM555xzvM7VvXt3HnnkERYuXMjEiRNp27YtP/30EzfffDNms5nY2Fjatm1Lfn4+K1eu5JxzziE4OJjk5GRMJhNvvPEG9913H9u2beP555/3OvaDDz7IG2+8wc0338zUqVOJiIhg3bp1DBgwwBOYAcTHx/P9998zfPhwxo4dy+LFi72yT/VNgp0GpGy9DuDJ7JTY6r8by2p38tYPrvT+/cM6NNtFKutSC5Ke+gcLX368xvNpiKYvPsxVnCyZnYZjZ+qJBn2er7/+mqSkJADCwsLo2rUrS5Ys8QwfX7FiBZMnT6Z///5eQ8/d7rnnHn744QfOO+888vPzWbx4MV9++SWLFi0qdy6dTse1117Lu+++y8SJE5kxYwb33nsvHTp0oKSkBKUUQ4YM4b777uOmm24iMzPTM/R8/vz5PPXUU8yePZu+ffvy97//nauuuspz7JiYGL7//nsef/xxhg4dil6vp0+fPpx//vnl2pGYmMj333/PsGHDuPXWW1m0aJFXBqo+Nc9PuAbqzGAnJ9P1oskrLGbz5s01OtbZulc+23yEozlFxIWZual/61q2uOmoTS2IEJWJLy1SNsa1Q6nMALemeYuNjSU4OIjbXvzYb+cMDg7yFARXx/z585k/f36V+/Tq1Yvvv/++0tvj4uL45ptvvLZVNcrpzTff9Pw+aNAgtm7dWm6fuXPneoqm3caOHcvYsWO9timlvK737t2bFStWVHjeMx9nUlKSZ3i7L0mw04B4jcRKTeX6MdcQc8s/SD+RSb9+o2p0rKq6V2wOJ3N+2AfAvRe193SXidppDHO/CP+KDDaixwFGMwVK3mYDKTk5mZ07d8naWM2cvAobkLJz7Jw8eYziU64uFnNoBFPmfFbt45yte+Wb7ekczioiJsTELQPlBVlbjWnuF+FfmqYRQgl5BFPg9P1IE1G15ORkCT6aOQl2GpCymZ2i46AcrqF8TrR67WL5dPMRAG4e0Jpgk/wL1FZjmvtF+F9QabBT6JTXmBCBJq/CBiK3yEZOoQ1wZXb2HAdlKw12FDidCp1Oq/N5Tpwq4cc9rozEdX1lXeb60BjmfhH+F4zr9Vso3VhCBJwu0A0QLu4urJgQE6GlI6PcmR0Au1NVeL+a+s+Wozicij6tI+kQF1ovxxRClBeEa1LQIsnsCBFwEuw0EGXrddyU3eb53VFPwc6nm48CMKafZHWE8KWymR2nqp/XrxCiduQrRwNx5rBzAJQTDYVCw+50AjUbNXXmiJ8DOTZ2Hs/DoINkdYLNm8sPiZVRQkLUDzM2nLYSdEYzeUU2IoNNgW6SEM2WBDsNxB8nXKNy2sYEe23Xa2BXNevGqmyUUOSwO4gYOIbcnasZNnNmlceQUUJC1I0G2LOPYYpvR1ahVYIdIQJIgp0GYk+6K7jonBjmtd0T7DiqH+xUNkro2+MG8mwwfMhAWl9S8VB2GSUkRP2xZR7GFN+O7AIbVH+OOSFEPZNgpwFQSrE3/RQAXRLKBztQu5qdsqOEThXbyEs9iAb06dGZoEomEpRRQkLUH1uma5qH7ELrWfYUvpSamiqTCjZzEuw0AEdziiiwOjDqNdrGhnjd5h5t7qrZqb1DpTVBCeGWSgMdIUT9smW5gp2sAgl2AiU1NZWu3bpSVFjkt3MGBQexa+euagc8EyZMYMGCBQAYjUaSk5MZN24cTz31VK0Xx5wwYQI5OTksXbrUa/uPP/7Ic889x5YtWyguLqZly5YMGTKE//u//8NkarpdrRLsNAB7SrM67WNDMeq9B8jpNQVodR6NdSjTFey0OaMmSAjhO7bMwwBkF1hRSqFpdZ8rS9TMyZMnKSosYvxfx5PYLtHn50s7kMaCvyyo8QLBl112GfPmzaOkpITly5czceJEjEYjU6dOrdH5HQ5Hpf9nO3bs4LLLLmPSpEnMnj2boKAg9u7dy6efforDUf8LTjckEuw0AJXV68Dpbqy6zLPjdCrPaK+2MSFn2VsIUV/sWccARbHdSZHNITOWB1Biu0SSuzXcriWz2UxioisYu//++/n888/54osvuO+++5g8eTL//e9/KSkpYejQocyePZtOnToBroU1H374Yd5//32efPJJ9uzZw2233ebJFLkDn1WrVrFlyxYSExOZNWuW57wdOnTgsssu82rL6tWr+ctf/sIvv/yC2WxmwIABLF68mKioKL7++mteeOEFtm3bhl6vZ/Dgwbz++ut06NABgIMHD9KuXTs+/fRT3njjDdavX0+nTp146623GDx4sM+fx8rIPDsNwJ40V2anc3z5Sf48wU4NCpTPlJZXjNXuxGLQER9urvVxhBA1o+wlWDTXN+bsAttZ9hbitKCgIKxWKxMmTGDjxo188cUXrF27FqUUl19+OTbb6f+nwsJCXn75Zd555x22b9/O7NmzufHGG7nssss4fvw4x48fZ8iQISQmJnL8+HF++umnSs+7ZcsWRowYQffu3Vm7di3/+9//uPLKKz2Zn4KCAqZMmcLGjRtZuXIlOp2Oa6+9FucZpRZ/+ctfeOyxx9iyZQudO3dm7Nix2O123zxZ1SBfMxqAPRmlwU4FmZ36qNlxd2ElRwejkzS6EH4VpNkpVgayC620jAoKdHNEA6eUYuXKlaxYsYJRo0axdOlSVq9ezZAhQwBYuHAhrVu3ZunSpdxwww0A2Gw23nzzTc455xzPcYKCgigpKfFkiwBuuOEGVqxYwdChQ0lMTGTQoEGMGDGCcePGER4eDsCsWbM477zzePPNNz3369Hj9HI4Y8aM8Wrve++9R1xcHDt27KBnz56e7Y899hijR48G4LnnnqNHjx7s27ePrl271tdTVSOS2Qkwh1Ox192NlVB5N1ZdanYOZRUA0Ea6sITwu2Cd69tslozIElVYtmwZoaGhWCwWRo0axU033cSECRMwGAwMHDjQs19MTAxdunTxmgDWZDLRu3fvs55Dr9czb948jhw5wqxZs2jZsiUvvvgiPXr04Pjx48DpzE5l9u7dy9ixY2nfvj3h4eG0bdsWcBWCl1W2PUlJSQBkZGSc/YnwEQl2AuxwViEldidmg8579uRSrgLl2tfs2BxOTpxyrdHTSr5VCuF3QZor2Mkrkm4sUbnhw4ezZcsW9u7dS1FREQsWLKh2QXtQUFCNit9btmzJ7bffzj//+U+2b99OcXExb731ludYVbnyyivJysri//7v/1i/fj3r168HwGr1DuaNRqPnd3fbzuzq8icJdgJsd+lIrE4JoegrWNW8rgXKGadKcCoIMekJs0ivpRD+ZtG5ah1yJdgRVQgJCaFjx44kJyd7hpt369YNu93uCSgAMjMz2b17N927d6/yeCaTqVojrKKiokhKSqKgwNUD0Lt3b1auXFnhvu5zP/3004wYMYJu3bqRnZ1d3YcYUPLpF2DuyQQ7x5fvwoIy3Vi1LFBOy3XNhJwYYZFhr0IEgLtAOa/ILsPPAyjtQFqjO0+nTp24+uqrueeee3j77bcJCwvjySefpGXLllx99dVV3rdt27asWLGC3bt3ExMTQ0REBO+99x5btmzh2muvpUOHDhQXF/P++++zfft23njjDQCmTp1Kr169eOCBB7jvvvswmUysWrWKG264gejoaGJiYvjXv/5FUlISqampPPnkk/X2eH1Jgp0A213FsHOoe4Fy2WBHCOF/ltJuLKvDSbHdKZN6+llsbCxBwUEs+MsCv50zKDiI2Nj6WR9k3rx5TJ48mSuuuAKr1cpFF13E8uXLvbqJKnLPPffwww8/cN5555Gfn8+qVasYMGAA//vf/7jvvvs4duwYoaGh9OjRg6VLlzJ06FAAOnfuzDfffMNTTz3FgAEDCAoKYuDAgYwdOxadTsfixYt56KGH6NmzJ126dGH27NkMGzasXh6rL0mwE2D7SxcA7RhXftg51L0bKy2vNNgJl2BHiEDQa65u5AKrg7wimwQ7fpacnMyunbsa9HIR8+fPr/S2qKgo3n///UpvnzBhAhMmTCi3PS4ujm+++abc9g8++OCs7Rk6dCirV6+u8LaRI0eyY8cOr21Knf58atu2rdd1gMjIyHLb/E2CnQA7Xpp5qWxIqrtAuTajsUqcOvJL7Gi4lokQQgRGeJCRAquD3CKbvBYDIDk5WdaqauakQDmAim0Oz5o5LSIqC3ZcP2szqWCe07XOSWyoudwyFEII/wkPcnU5yIgsIQJDPgEDyJ3VCTLqCQ+qOMlWl5odd7Aj9TpCBFaExRXs5BZLsCNEIEiwE0DHc12r8CZFVj5Sqi41O3lO1xusBDtCBJb7y0xeUeCmyxeiOZNgJ4CO57gyO5V1YUEdZlDW6ckvzewkSY2AEAEVUdqNJXPt+F6gC2FF/auPv6kEOwHkyexUkXnxzKBcw5odY0xrnGiYDDoig6seoiiE8C13zc6pYhtO+TD2CfdQ7MLCwgC3RNQ399/0bMPtqyKjsQLIXbNTVbBT25odU1xbAGJDTDKJmRABFmo2oNPAqSC/xE64Rb6A1De9Xk9kZKRn/aXg4GB572vklFIUFhaSkZFBZGQken3tp22QYCeAPMFOZP13Yxlj2wAQE2quXeOEEPVGp2mEWYzkFtnIK7JJsOMj7hW+A7ngpKh/kZGRXqu314YEOwF0LKc63ViunzUtUDbGuYMdU+0aJ4SoVxFBrmAnt8hGq6hAt6Zp0jSNpKQk4uPjsdmkPqopMBqNdcrouEmwE0Cnu7HOntmpac2OqTTYiQ2RzI4QDYGMyPIfvV5fLx+QoumQAuUAKbTaPSMzkiKrqtmp+QzKdnQYIhIAyewI0VDIXDtCBI4EOwHizuqEmg1V9t/ra1GgXIgrm2PSHFhkHR4hGgSZRVmIwJFgJ0Dcc+ycbcI/d7DjVFR7yKo72AnR5E1ViIbC/aXmVLF0Ywnhbw0m2HnppZfQNI2HH37Ys624uJiJEycSExNDaGgoY8aMIT093et+qampjB49muDgYOLj43n88cex2xv+m8mxasyxA6eDHah+V1aBO9jRNfznQYjmItTiqtkpKLHjrMWM6EKI2msQwc6GDRt4++236d27t9f2Rx55hP/+978sWbKEH3/8kWPHjnHdddd5bnc4HIwePRqr1cqaNWtYsGAB8+fP59lnn/X3Q6ixtNyzz54M3sFOdYuUTwc7ktkRoqEINunRaaCAAqt8ERHCnwIe7OTn53Prrbfyf//3f0RFnR6PmZuby7vvvssrr7zCxRdfTL9+/Zg3bx5r1qxh3bp1AHzzzTfs2LGDDz/8kD59+jBq1Cief/555syZg9VqDdRDqpay62JVRdNqNrGgUup0N5ZkdoRoMHSaRojZld2Rriwh/Cvgwc7EiRMZPXo0I0eO9Nq+adMmbDab1/auXbuSnJzM2rVrAVi7di29evUiISHBs09KSgp5eXls37690nOWlJSQl5fndfG3Yzlnnz3ZzaBz/ZmqM9dOodWBHQPK6SBYanaEaFDCSoOd/BIJdoTwp4DOs7N48WI2b97Mhg0byt2WlpaGyWQiMjLSa3tCQgJpaWmefcoGOu7b3bdVZubMmTz33HN1bH3dnF4Xq+puLAC9TgNH9Wp2TuaXAGDPPoY+VEZiCdGQhFoMkCuZHSH8LWCZncOHDzN58mQWLlyIxeLfVbmnTp1Kbm6u53L48GG/nh9ODz1vcZZuLABDaeFOdWp2sgpc3XfWk6l1aJ0QwhfCSkdk5UuwI4RfBSzY2bRpExkZGfTt2xeDwYDBYODHH39k9uzZGAwGEhISsFqt5OTkeN0vPT3ds0ZGYmJiudFZ7utVraNhNpsJDw/3uvhTfond880usRqZHUNp0U51Mjs5pXN42LOO1aGFQghfcHdjnSqRLmYh/Clgwc6IESP4/fff2bJli+dy3nnnceutt3p+NxqNrFy50nOf3bt3k5qayuDBgwEYPHgwv//+u9eib99++y3h4eF0797d74+puo5mu7qwIoKMhJrP3pOoLw12qlOgnFNYGuzkSLAjREPjHn4u3VhC+FfAanbCwsLo2bOn17aQkBBiYmI82++66y6mTJlCdHQ04eHhTJo0icGDBzNo0CAALr30Urp3787tt9/OrFmzSEtL4+mnn2bixImYzQ13TaijOYUAtKxitfOyalKgnFPo6saySWZHiAZHCpSFCIwGvRDoq6++ik6nY8yYMZSUlJCSksKbb77puV2v17Ns2TLuv/9+Bg8eTEhICOPHj2fGjBkBbPXZuTM7LaOqG+xUr2bH4VSeb4z2bAl2hGho3JmdQqsDu9Pp+SIjhPCtBhXs/PDDD17XLRYLc+bMYc6cOZXep02bNixfvtzHLatfR3JKg51qZnb0+up1Y+UW2VCAHgeOguw6tVEIUf+CjHr0Og2HU1FQ4iAiSIIdIfxBXmkB4M7stKpmZsdYzW4sdxeWBSl+FKIh0jTNU6cnI7KE8B8JdgLgaA0zO9Udeu4eiRVEw549WojmLMwiI7KE8DcJdgKgxjU7nmCn6m4s90gsiwQ7QjRYYbJkhBB+J8GOn5XYHWSccs1yXN3Mjrsby1bNbizJ7AjRcLmLlKUbSwj/kWDHz46XrollMeqIDjFV6z7VzuxIN5YQDV6oZ2JBCXaE8BcJdvysbL2OpmnVuo9Bf/YCZbvD6UmLSzeWEA2XLBkhhP9JsONnp+t1gqt9H2PpPDu2KjI7uaVZHZNehxFHHVoohPClUFkyQgi/k2DHz2o6xw6czuxUtTaWuwsrMthI9fJFQohAcI/GKrY5q/wCI4SoPxLs+FlN59iB0zMo26oYeu4eiRUZZKxD64QQvmY26Dyv6QKp2xHCLyTY8bMj2TVbFwuqV6DsHokVGVy9omchRGBomkZIaVdWQYl0OQvhDxLs+JmnQLkGmZ3qzKCcV1rsGCGZHSEaPHfdToFVMjtC+IMEO37kcCrScl1Dz2uT2amqfz+vtGYnPKhBLXcmhKhAiFkPyOrnQviLBDt+lJ5XjN2pMOg0EsIt1b7f2YaeK3V6tfNwi2R2hGjoPJkdCXaE8AsJdvzI3YWVGGFBr6v+mCn30PPK1sYqtDpwKIUGnloAIUTD5X6dSmZHCP+QYMeP0vNcXVgtIqrfhQWnMzuVdWPlFbu6sEIthhoFUUKIwAgxSYGyEP4kwY4fZRe4RkxVd5kIN/cwVbtToVT57E5ekXRhCdGYSDeWEP4lwY4fZRW4MjBRNQ129KezNRVNLOjO7LgnKxNCNGzuAuUCq73CLzBCiPolwY4fZRW4VjuPDqlZBsagO/1nqqhI2R3sSGZHiMbBXbNjcyisMouyED4nwY4fZZXOchxVw4n/9DoNdylORXU7npFYMuxciEbBqNdhMrjefqVuRwjfk2DHj2pbswNVDz93z7ETJpkdIRqNUJPU7QjhLxLs+FFWabBT05odqHz4ufccO5LZEaKx8NTtSLAjhM9JsONH2aXrV8XUIbNzZjdWkc3hyfZIZkeIxsMz144sGSGEz0mw4ydKqdOZnVos1ulZDPSMbiz3sPNQs8yxI0RjIouBCuE/Euz4SZHNQYndlZWpTc2OZzHQMzI7p2TYuRCNksy1I4T/SLDjJ+6sjsmgI9ikr/H9y04sWFaeZySWdGEJ0ZiEmGQxUCH8RYIdP8kunVAwOtiEptW8u6mylc9Pz7EjmR0hGpMQyewI4TcS7PhJVmHtR2JBmaHnjjNrdmRCQSEaI083ltUhsygL4WMS7PjJ6Tl2aheUGCvpxnIPO5eaHSEal+DSoecOp/LU8wkhfEOCHT+py0gsqHzo+ek5diSzI0RjYtDpsBhdr2up2xHCtyTY8RP3HDu1GYkFFQ89L7E7POvqhEpmR4hGR+p2hPAPCXb8pK6ZnYqGnruzOhaDDqNe/pRCNDbuJSMksyOEb8knpJ/UV2bHVqZA2f0GKVkdIRond91OoVUmFhTClyTY8ZO6rIsFZefZOZ3ZyS8+PXuyEKLxCS7N7EiwI4RvSbDjJ+5gJ7qOBcplh56fHoklxclCNEbuiQULZX0sIXxKgh0/ySqdVDCqHoeee7qxJLMjRKPkyezI+lhC+JQEO36glKqHmp3yQ89Plci6WEI0Zu6lYwoksyOET0mw4wd5xXYcpRmZ2s+zU0FmR2p2hGjU3EPPpWZHCN+SYMcP3LMnh5j0WIw1XwQUyg89V0rJaCwhGjl3ZqfE7vSaVkIIUb8k2PGDuq6LBeWHnpfYnZ7fwySzI0SjZDbo0JcuDCzZHSF8R4IdPzi9LlYdgp0zhp67szoWo85TzyOEaFw0TZO5doTwA/mU9IO6zp4MeGZIdg899ww7N8uwcyEaMylSFsL3JNjxg7qOxALQlxl6rpQ6XZws9TpCNGohMvxcCJ+TYMcPPHPs1ENmB8DhVDLHjhBNhGR2hPA9CXb84FRx3efDcdfsANicSubYEaKJCJbh50L4nAQ7fmC1u4qKzcbaP906neYZtWF3OGWOHSGaiGBZMkIIn5Ngxw+spfNnmOo4asozsaBDcarEvS6WBDtCNGYhshioED4nwY4feDI7hvoJdmyS2RGiyfDU7JRIZkcIX5Fgxw/cwY6xosxO3nHY+221jmMonUU5v8TuWTZCgh0hGreyS0Yopc6ytxCiNiTY8QNPN9aZmR1rAcwbBQuvh13Lz3ocY2lmJ7N03p4go14mFBSikXNnduxO5XmvEELUL/mk9IMSeyXBznfTIfsAAGrT/LMex53ZSc0qBCA+3FxvbRRCBIZRr/PU80ndjhC+IcGOH7i7sbwKlA/8BL/86/T1fd+4urSq4K7ZOZZTBEDLyKD6bagQIiA8I7JkYkEhfEKCHT+wndmNZStG/WciAP8OC2Wz2YymFPy2uMrjuOfaKS3XkWBHiCbCvT6WTCwohG9IsOMH1jO7sX79AC0nlXS9ntkxsSwNCwHAvvl9qKJAsWyBs16nkRBu8V2jhRB+I8PPhfAtGcrjY1999RXpJ4oAMx99+AE/6nK53/F/RALvRIbTLrcXG8yHKNSyCc7az7wXHuCo1pJjx46VO5a7GwsgKcLiWS9LCNG4ycSCQviWBDs+lJqayujRo2lx33sYwuP411tvcmerQ0SODiJdr+fDEzp2vbCQqGFRfHt5MFfnFxC8dQHPfFrkOUZ+fr7nd3eBMkgXlhBNSXBpZqdAanaE8IladWO1b9+ezMzMcttzcnJo3759tY8zd+5cevfuTXh4OOHh4QwePJivvvrKc3txcTETJ04kJiaG0NBQxowZQ3p6utcx3AFFcHAw8fHxPP7449jtDePb0cmTJ1FKYQoJBmD49YN4blQEAO9GhtPS1p4r7r+CQT0H8X5YGE7gpp5GJj80lHNHngu4ngM3Y5nMjgQ7QjQd7podyewI4Ru1yuwcPHgQh6P8N5CSkhKOHj1a7eO0atWKl156iU6dOqGUYsGCBVx99dX8+uuv9OjRg0ceeYQvv/ySJUuWEBERwYMPPsh1113H6tWrAXA4HIwePZrExETWrFnD8ePHGTduHEajkRdffLE2D80n9EYzTifcOKwF8b9bydDrWZPYiesuvNazz4qDK1iel8cVBYVM6ZJPTkJvfv3uV6/juDM7Og0SI6ReR4imQmp2hPCtGgU7X3zxhef3FStWEBER4bnucDhYuXIlbdu2rfbxrrzySq/rf/3rX5k7dy7r1q2jVatWvPvuuyxatIiLL74YgHnz5tGtWzfWrVvHoEGD+Oabb9ixYwffffcdCQkJ9OnTh+eff54nnniC6dOnYzKZavLwfMahXEFKaIGrDmezxUzfFoO99ukZ25M5J/eQUlBIcsZeuhkSyh3HXbOTEG6peDZmIUSj5FkyQjI7QvhEjYKda665BgBN0xg/frzXbUajkbZt2/KPf/yjVg1xOBwsWbKEgoICBg8ezKZNm7DZbIwcOdKzT9euXUlOTmbt2rUMGjSItWvX0qtXLxISTgcGKSkp3H///Wzfvp1zzz23wnOVlJRQUlLiuZ6Xl1erNleXU7mCFEv+EQCygyJIDEn02qdlaEs2RiazJC+PW/LyudqxiyfPOE7rqGBCTDn0bBGBEKLpKJvZkSUjhKh/NQp2nE7XEOp27dqxYcMGYmNj69yA33//ncGDB1NcXExoaCiff/453bt3Z8uWLZhMJiIjI732T0hIIC0tDYC0tDSvQMd9u/u2ysycOZPnnnuuzm2vFp0ecAU7McVZAJwKjq5w1/6J/VmQk8otefl0UpmEnpGYSoywcPeF1a+JEkI0DkGlmR2loMgmXVlC1Lda9YUcOHCgXgIdgC5durBlyxbWr1/P/fffz/jx49mxY0e9HLsyU6dOJTc313M5fPiwz86l6Y2e3+NLcgHID42pcN8WoS3QotqQajCgR3FhsgyWE6I50Os0gozuImUJdoSob7X+NF25ciUrV64kIyPDk/Fxe++996p9HJPJRMeOHQHo168fGzZs4PXXX+emm27CarWSk5Pjld1JT08nMdHVBZSYmMgvv/zidTz3aC33PhUxm82Yzf5ZV6pssNOixNVdVhSWQGXlxf0S+vHLgd9IzrdzcXs9v/uhjUKIwAs26SmyOSgosaMPdGOEaGJqldl57rnnuPTSS1m5ciUnT54kOzvb61IXTqeTkpIS+vXrh9FoZOXKlZ7bdu/eTWpqKoMHu4p7Bw8ezO+//05GRoZnn2+//Zbw8HC6d+9ep3bUF83gCnY0FCE4cACOsKRK908KSWJTsGtG5RGdjZXuJ4RoWk4PP5fMjhD1rVaZnbfeeov58+dz++231+nkU6dOZdSoUSQnJ3Pq1CkWLVrEDz/84BnpdddddzFlyhSio6MJDw9n0qRJDB48mEGDBgFw6aWX0r17d26//XZmzZpFWloaTz/9NBMnTvRb5uZs3Jkdg86V/UrX6wm1RFW6v07TcTimLWRkcE60jhBV6I9mCiECrGyRcliA2yJEU1OrYMdqtTJkyJA6nzwjI4Nx48Zx/PhxIiIi6N27NytWrOCSSy4B4NVXX0Wn0zFmzBhKSkpISUnhzTff9Nxfr9ezbNky7r//fgYPHkxISAjjx49nxowZdW5bvXEHO5prSOkxowmzvupALCiqLX8YN9PBZqcrfyDf84Ro+mTJCCF8p1bBzt13382iRYt45pln6nTyd999t8rbLRYLc+bMYc6cOZXu06ZNG5YvX16ndviSpnc9xUZcb2AnzMFoWtVrWrUIbcEvFgsdbPl0U3vZ5vNWCiECzbNkhNUBDWOKMCGajFoFO8XFxfzrX//iu+++o3fv3hiN3rUlr7zySr00rilwd2OZsAGQFXT2BHVcUBwbzEGMPZVPV223BDtCNAMh7sxOiV2CHSHqWa2Cnd9++40+ffoAsG2b90fx2bIWzc3pYMcKQG5w5fU6bjpNx059DHCC1ioTsz2PEkO4L5sphAiwYLMsGSGEr9Qq2Fm1alV9t6PJco/GClKuYKcwNI7gatxP2aIo1jQsSmGyF0iwI0QTJ0tGCOE7ssCSr5VmdizK1Y1VHFZ+zauKhJWElXZ8gc5ZUuW+QojGzz0aq9jmxCkrRghRr2qV2Rk+fHiV3VXff/99rRvU1Li7sczYKNY0tNDKJzssK9gWjE3TQClsjkygre8aKYQIOItRh04Dp4Ji6ckSol7VKthx1+u42Ww2tmzZwrZt28otENrcubuxTJqNowY9YebqdUdpaFhL19TCWeSr5gkhGghN0wg2GcgvsVPikNpHIepTrYKdV199tcLt06dPJz8/v04Namo0nespNmHnuMmCQVf9p1y6sYRoXoJNevJL7BQ7z76vEKL66rVm57bbbqvRuljNgSezg42T5pAa3fd0sFNcz60SQjRE7iLlYsnsCFGv6jXYWbt2LRZLZUtcNk+na3bsFNcw2HF3Y2mS2RGiWQgpHX5eIjU7QtSrWnVjXXfddV7XlVIcP36cjRs31nlW5SanzKSCmiGoRne1lY7I0JzW+m6VEKIBksyOEL5Rq2AnIiLC67pOp6NLly7MmDGDSy+9tF4a1lR4lovQ7Dj1NVvF3J3Z0SvpxhKiOXAvGSHBjhD1q1bBzrx58+q7HU3W6ZodOw59zZ5uqyezI91YQjQH7iUjpEBZiPpVq2DHbdOmTezcuROAHj16cO6559ZLo5qSsmtjOXQ1y+zYFKCBTkk3lhDNgWR2hPCNWgU7GRkZ3Hzzzfzwww9ERkYCkJOTw/Dhw1m8eDFxcXH12cZG7XSwU4tuLHewIzU7QjQLwWZXZkcKlIWoX7UajTVp0iROnTrF9u3bycrKIisri23btpGXl8dDDz1U321s1Nw1OyZsNe/GKk1l6yXYEaJZcC8ZYVcamlFGtgpRX2qV2fn666/57rvv6Natm2db9+7dmTNnjhQon8k99Fyz49SbanRXqyodei7dWEI0C0a9hkGnYXcq9CGRgW6OEE1GrTI7TqcTo7F8l4zRaMTplMq6ssrW7KiadmNJZkeIZkXTNM9cO/qQqAC3Roimo1bBzsUXX8zkyZM5duyYZ9vRo0d55JFHGDFiRL01rilwj8YyUovMjifYsVW9oxCiyXDPtSPBjhD1p1bBzj//+U/y8vJo27YtHTp0oEOHDrRr1468vDzeeOON+m5jo+ZVoGyoXbAjo7GEaD7cwY5Ogh0h6k2tanZat27N5s2b+e6779i1axcA3bp1Y+TIkfXauKbAE+xoNtDVMNhxuCbaMUhmR4hmw12krA+VYEeI+lKjzM73339P9+7dycvLQ9M0LrnkEiZNmsSkSZPo378/PXr04Oeff/ZVWxul06Ox7KgaZnbcw091yl7fzRJCNFDu4efSjSVE/alRsPPaa69xzz33EB4eXu62iIgI7r33Xl555ZV6a1yTUKZA2ak31+iunpodJZkdIZoLT2ZHgh0h6k2Ngp2tW7dy2WWXVXr7pZdeyqZNm+rcqKZEZzi96jk1LVC2u7qxpEBZiObjdIFyZGAbIkQTUqNgJz09vcIh524Gg4ETJ07UuVFNic5QZui5sWaZnZLSzI5BurGEaDaCZei5EPWuRsFOy5Yt2bZtW6W3//bbbyQlJdW5UU2JJ9jRapPZcf3US7AjRLMRUiaz41QqwK0RommoUbBz+eWX88wzz1BcXFzutqKiIqZNm8YVV1xRb41rCtzz7GjY0dVwUsESu3s0lgQ7QjQXQaXBjqY3UmCVYEeI+lCjoedPP/00n332GZ07d+bBBx+kS5cuAOzatYs5c+bgcDj4y1/+4pOGNlbuoeeaZkdXw2mNrKWjsfRKVgUUorkw6HSYdAqrUyO7WF77QtSHGgU7CQkJrFmzhvvvv5+pU6eiSlOsmqaRkpLCnDlzSEhI8ElDGytNb0DhCnY0TavRfd0FygYJdoRoVsx6V7CTUyzL7whRH2o8qWCbNm1Yvnw52dnZ7Nu3D6UUnTp1IipKiukqpCvtutLVPGApKR2EJQXKQjQvFh2cArIl2BGiXtRqBmWAqKgo+vfvX59taZKUzt2NVfNg53RmR97whGhOLHrXaz9HurGEqBe1WhtLVI/DqUDnKjakFsFOiXRjCdEsWUrfNqQbS4j6IcGOD9nLvk/pa/6mVWyTzI4QzZHZk9mR174Q9UGCHR+yOcsMG9Vq/qZltUlmR4jmyJ3ZkdFYQtQPCXZ8yOY4Hew4dTWfL6OkdI4No2R2hGhWLDrJ7AhRnyTY8SF3ZseEFbu7dqcGSmwS7AjRHEnNjhD1S4IdH3LX7JixY9fV/Kl2Z3YMMmW8EM2KezRWXokTm0MCHiHqSoIdHzqd2bFJZkcIUW0mHSinq14nM98a4NYI0fhJsOND9tLaQhN27LqaT2lUUuIOdiSzI0RzomngKMgB4MSpksA2RogmQIIdH/JkdjRbnbqx9IAmI7KEaFYcBdkAnMgvv/CyEKJmJNjxIXewY6xtZqfMisc6p63e2iWEaPg8wY5kdoSoMwl2fMg99Ly23VhFJadrdfRO6bcXojlxSrAjRL2RYMeH3KOxTNhw6Gse7FjLZHY0p6SyhWhOHPkS7AhRXyTY8SG783Rmx1GL0VjKrvB0XkmwI0Sz4u7GOimjsYSoMwl2fMhds2PWbNj1xhrfX9kVVk0DQKcK67VtQoiGTWp2hKg/Euz4kK3M0HNHLWp2cIKtNNjBKW94QjQnnqHn+fLaF6KuJNjxobKTCjprkdmB08GOziHdWEI0J5LZEaL+SLDjQ/Yyo7GcutoFO1ZcwY6miuqtXUKIhs8d7OSX2Cm02gPcGiEaNwl2fMhWOhrLqNlxGGoX7NhLMzuadGMJ0awoaxFmvev1f/KUFCkLURcS7PiQVzdWLTM7ttI/kU5JsCNEcxNpcb3+ZRZlIepGgh0fKjv0vK41O5rU7AjR7HiCHanbEaJOJNjxIfdoLDM2lN5Uu2NopZkd6cYSotmJsrjm55JgR4i6kWDHh2z1kNmxl/6JNCV99kI0N5LZEaJ+SLDjQ/Yyq54rQ90yO3rJ7AjR7Jyu2ZHXvxB1IcGOD5WdVFDpzbU7hjuzIwuBCtHsSDeWEPVDgh0fcndjGbHjrGVmx+4ZjSXBjhDNTYR0YwlRLwIa7MycOZP+/fsTFhZGfHw811xzDbt37/bap7i4mIkTJxITE0NoaChjxowhPT3da5/U1FRGjx5NcHAw8fHxPP7449jtgZ+Ey+ZwTbRjwgZ1LFCWbiwhmp8oCXaEqBcBDXZ+/PFHJk6cyLp16/j222+x2WxceumlFBQUePZ55JFH+O9//8uSJUv48ccfOXbsGNddd53ndofDwejRo7FaraxZs4YFCxYwf/58nn322UA8JC92T7BT+24sO640tk7ZzrKnEKKpiXR3Y+WXoJQKcGuEaLxqsTpl/fn666+9rs+fP5/4+Hg2bdrERRddRG5uLu+++y6LFi3i4osvBmDevHl069aNdevWMWjQIL755ht27NjBd999R0JCAn369OH555/niSeeYPr06ZhMtcuo1Aebw1W0Y9ZsYKhbzY5eanaEaHbcBco2hyK3yEZkcODez4RozBpUzU5ubi4A0dHRAGzatAmbzcbIkSM9+3Tt2pXk5GTWrl0LwNq1a+nVqxcJCQmefVJSUsjLy2P79u0VnqekpIS8vDyviy+4u7EM2NDVshvLrrniUb1kdoRodox6jYgg17QV0pUlRO01mGDH6XTy8MMPc/7559OzZ08A0tLSMJlMREZGeu2bkJBAWlqaZ5+ygY77dvdtFZk5cyYRERGeS+vWrev50bi4gx1Nc6DTavdUewqUnRLsCNEcxYW5ssIS7AhRew0m2Jk4cSLbtm1j8eLFPj/X1KlTyc3N9VwOHz7sk/PYnO5gx45WuuxDjY9RWrNjkNFYQjRLcaGlwY7MtSNErQW0ZsftwQcfZNmyZfz000+0atXKsz0xMRGr1UpOTo5Xdic9PZ3ExETPPr/88ovX8dyjtdz7nMlsNmM2166Gpibcq55rmqPWx7CX/ol0zsCPLhNC+F98uOu9Kj1P1scTorYCmtlRSvHggw/y+eef8/3339OuXTuv2/v164fRaGTlypWebbt37yY1NZXBgwcDMHjwYH7//XcyMjI8+3z77beEh4fTvXt3/zyQSnQKK+B83TaCtfxaH8PdjSU1O0I0TwnhFgDS8ySzI0RtBTSzM3HiRBYtWsR//vMfwsLCPDU2ERERBAUFERERwV133cWUKVOIjo4mPDycSZMmMXjwYAYNGgTApZdeSvfu3bn99tuZNWsWaWlpPP3000ycONEv2Zuq3NlhP8Ny/85+g4lttTyGO7NjUJLZEaI5ig+TzI4QdRXQYGfu3LkADBs2zGv7vHnzmDBhAgCvvvoqOp2OMWPGUFJSQkpKCm+++aZnX71ez7Jly7j//vsZPHgwISEhjB8/nhkzZvjrYVRKlU4EaKtlvQ6cnmdHL91YQjRL7sxOhmR2hKi1gAY71Zkky2KxMGfOHObMmVPpPm3atGH58uX12bT6YXd9E7PVciQWgAP30HMJdoRojhIjSruxTklmR4jaajCjsZoi5XS9OVnrEOzYpBtLiGYtIcxds1MssygLUUsS7PiSw92NVffMjkHVfkSXEKLxco/GKrY5ySuSLz1C1IYEO75UL8GOa/ZUvQQ7QjRLFqPeM4uydGUJUTsS7PiQ5qx7sOPuxjI6JdgRorlKkLl2hKgTCXZ8yeGa9dim09f+EMpdoCzBjhDNlcy1I0TdSLDjQ7rSlcrt9dCNZVDOemmTEKLxOR3sSGZHiNqQYMeHNHdmR6t9ZsfTjSWZHSGaLenGEqJuJNjxIc1Z92DHqSSzI0RzJ5kdIepGgh0f0jtd61k56hDsuLuxjBLsCNFsxYdJzY4QdSHBjg/pSoOdumR27BLsCNHsubuxMiSzI0StSLDjQzqHK9ix1yXY8XRjycypQjRX7iUjMk6V4HTKe4EQNSXBjg+517OqS7Dj9GR25A1OiOYqNtSMpoHdqcgssAa6OUI0OhLs+JB7pXK7rvbrrdqVCQAjCqQrS4hmyajXERMiI7KEqC0JdnzIUBrsOLTaBzvuAmWQlc+FaM48dTuyZIQQNSbBjg95Mjt1GY1VmtmB0wXPQojmR2ZRFqL2JNjxIfdEgA7NeJY9K+f0CnbkG50QzZU72EnLlfcBIWpKgh0fMpQu3umoQ80OGPB0Xil5kxOiuZJuLCFqT4IdH3LPemyvQ82OpvTYNM31u0Pe5IRoriSzI0Tt1SXlIM7is6gE9CXZZBtCiajlMTT02DQIUqA5i+q1fUKIxiOpdK6d4xLsCFFjktnxoV0tz+fvx/UUGCNrfQwNDRulmR3pxhKi2WoRGQTAsRz50iNETUmw40N3t7qbg7MOEqYLq9NxrJrrz6RzyigMIZord2Ynr9hOQYlMQyFETUiw0wjYPTU78o1OiOYqzGIkzOyqPDieK+8FQtSEBDuNgK00s6MpyewI0ZwlRbqyO8dypEtbiJqQYKcRsEk3lhACSIpw1e1IZkeImpFgpxGwe4Id+TYnRHPWQjI7QtSKBDuNgF26sYQQQAvJ7AhRKxLsNAJ2nWttLZ3TGuCWCCECKSnSHexIZkeImpBgpxGw4w52JLMjRHPWIsLdjSWZHSFqQoKdRsAmmR0hBN6ZHaVUgFsjROMhwU4j4F5bSy+ZHSGaNffEgoVWB7lFtgC3RojGQ4KdRsCuuTI7eiWZHSGaM4tRT3SICZARWULUhAQ7jYBdJ5kdIYTL6QVBpW5HiOqSYKcRON2NJZkdIZo798SCx2RElhDVJsFOI2DXjADondJHL0Rz555Y8LiMyBKi2iTYaQQcutJgR2p2hGj2Ti8ZIZkdIapLgp1GQIIdIYTb6SUjJLMjRHVJsNMI2DXX6AuD0x7glgghAk0yO0LUnAQ7jYA7s2NQUrMjRHPnqdnJLcLplIkFhagOCXYaAYfOldnRS2ZHiGYvKSIIg07D5lCkn5LsjhDVIcFOI+DUmQEwKAl2hGju9DqNllGurqzUzMIAt0aIxkGCnUZAanaEEGW1jgoG4HC2FCkLUR0S7DQCktkRQpTVOtqV2TmcJZkdIapDgp1GwB3sGJUjwC0RQjQEraNLMzsS7AhRLRLsNAJOzZ3ZkWBHCFG2G0uCHSGqQ4KdRsCpcw01lcyOEALKZnakZkeI6pBgpxFw6l3BjsnpDHBLhBANQXJpsJOWV0yxTb4ECXE2Euw0AlKzI4QoKyrYSIhJD8BRWTZCiLOSYKcRcGqukRdGJZkdIQRomiZFykLUgAQ7jYDSu4MdmRpeCOHSKkqCHSGqS4KdRsCpk8yOEMKbu25HJhYU4uwk2GkElOYqUDYAmtTtCCGQiQWFqAkJdhoBpQ/2/K53ysrnQojTc+2kSrAjxFlJsNMY6MoEO8oawIYIIRqK5Bip2RGiuiTYaQx0FtzVOpqjOKBNEUI0DK1KVz7PK7aTWygZXyGqIsFOI6DXjFg1DQDNKd/ihBAQbDIQG2oCpCtLiLMJaLDz008/ceWVV9KiRQs0TWPp0qVetyulePbZZ0lKSiIoKIiRI0eyd+9er32ysrK49dZbCQ8PJzIykrvuuov8/Hw/Pgrf02smrK5YR4IdIYRHu9gQAPafbFrveULUt4AGOwUFBZxzzjnMmTOnwttnzZrF7Nmzeeutt1i/fj0hISGkpKRQXHy6K+fWW29l+/btfPvttyxbtoyffvqJP/3pT/56CH6h0/TYcGd2pBtLCOHSPjYUgP0nCgLcEiEaNkMgTz5q1ChGjRpV4W1KKV577TWefvpprr76agDef/99EhISWLp0KTfffDM7d+7k66+/ZsOGDZx33nkAvPHGG1x++eX8/e9/p0WLFn57LL5m1XSAE51DMjtCCJcO8a7Mzh8nJLMjRFUabM3OgQMHSEtLY+TIkZ5tERERDBw4kLVr1wKwdu1aIiMjPYEOwMiRI9HpdKxfv97vbfYlW2nNjk7JBGJCCBfJ7AhRPQHN7FQlLS0NgISEBK/tCQkJntvS0tKIj4/3ut1gMBAdHe3ZpyIlJSWUlJR4rufl5dVXs33GqtMDNhmNJYTw6BBfGuyczMfpVOh0WoBbJETD1GAzO740c+ZMIiIiPJfWrVsHuklnZdNcfyqp2RFCuLWOCsKo1yi2OTmeJ+8NQlSmwQY7iYmJAKSnp3ttT09P99yWmJhIRkaG1+12u52srCzPPhWZOnUqubm5nsvhw4frufX1zx3s6CTYEUKUMuh1tIkprdvJkLodISrTYIOddu3akZiYyMqVKz3b8vLyWL9+PYMHDwZg8ODB5OTksGnTJs8+33//PU6nk4EDB1Z6bLPZTHh4uNelobNregB0SoIdIcRp7d3Dz6VIWYhKBbRmJz8/n3379nmuHzhwgC1bthAdHU1ycjIPP/wwL7zwAp06daJdu3Y888wztGjRgmuuuQaAbt26cdlll3HPPffw1ltvYbPZePDBB7n55pub1EgsAJs72HGWnGVPIURz0j4uFEjnDylSFqJSAQ12Nm7cyPDhwz3Xp0yZAsD48eOZP38+f/7znykoKOBPf/oTOTk5XHDBBXz99ddYLBbPfRYuXMiDDz7IiBEj0Ol0jBkzhtmzZ/v9sfiaXYIdIUQFOsTJxIJCnE1Ag51hw4ahlKr0dk3TmDFjBjNmzKh0n+joaBYtWuSL5jUoNp3rT6WXYEcIUYYrswN/ZEhmR4jKNNiaHeHNndnRO2XVcyHEae7MTlpeMfkl9gC3RoiGSYKdRsJemtmRbiwhRFmRwSZiQlwLgh6Quh0hKiTBTiPh0IwA6JUtwC0RQjQ0HeJOTy4ohChPgp1Gwq65a3akG0sI4c29Rtae9FMBbokQDZMEO42EXefK7Bgk2BFCnKFbkmuusB3HGv7SN0IEggQ7jYRd5+qTl24sIcSZuruDneMS7AhREQl2Ggl3zY7BKcGOEMJb16RwNA3S80o4mS+DGIQ4kwQ7jYRDJwXKQoiKhZoNtC1dI2unZHeEKEeCnUbCUdqNZXDKPBpCiPLcXVnbpW5HiHIk2GkknJoZAIOSYEcIUV73FlKkLERlJNhpJDyZHQl2hBAV8AQ70o0lRDkS7DQS0o0lhKhKj9JurP0n8imyOgLcGiEaFgl2GgmnztWNZVTyJiaEKC8+3EJsqBmngl1pkt0RoiwJdhoJp2YBwCDBjhCiEtKVJUTFJNhpJNyZHQl2hBCVkRFZQlRMgp1GwqlzZXakG0sIUZmeLV3Bzm9HcgLbECEaGAl2GglPsON0BrglQoiGql+bKMA1/PxUsUxAKoSbBDuNhFMXBIBJSbAjhKhYUkQQraODcCrYnJoT6OYI0WBIsNNIuIMdowQ7Qogq9G8bDcCGA1kBbokQDYch0A0QVdu+eT3ZOTmEBh0CoyvYWfXVf6q8z5Gdm/3UOiFEQzOgbTSfbT7KLwcl2BHCTYKdBsphcxUib/rPu2wCWrQzwzgzRmDZq39GVecYTilmFqK56d/OldnZcjiHErsDs0Ef4BYJEXgS7DRQeqPrDarv6L60aNMCg7kY+AWAax8YhZXK38CO7jnKr9/9il4nb3JCNDftY0OIDTVxMt/K70dyOa+0W0uI5kyCnQbu3JRz6XthXwpLcuErV7AzavwIrKagSu/zy/Jf+PW7X/3VRCFEA6JpGv3bRvPVtjR+OZglwY4QSIFyo6HpjJ7fdQ4ZUiqEqJy7SPkXKVIWApBgp9HQ6wyUaKVXHNaAtkUI0bANKK3b2XQwG7tDRnAKIcFOI6HX9FhxRTuaBDtCiCp0SwonOsTEqRK7jMoSAgl2Gg2dpsOqSbAjhDg7vU5jRNd4AL7Znh7g1ggReBLsNBKapmErDXakG0sIcTaX9kgE4Nsd6ShVnckqhGi6JNhpRGya688lmR0hxNlc2CmWIKOeozlFsgq6aPYk2GlErDpXZkdnl9FYQoiqWYx6LuocC8A3O6QrSzRvEuw0Iu7Mjgw9F0JUx6XdXV1Z32xPC3BLhAgsCXYaEXfNjuaUYEcIcXYXd41Hr9PYlXaKQ5kFgW6OEAEjwU4jYtNJZkcIUX1RISYGt48B4N8bDge4NUIEjgQ7jYhdc611JcGOEKK6bhuUDMDiDYcptsniwKJ5kmCnEbGXZnakG0sIUV0juyWQFGEhq8DKl78dD3RzhAgICXYaEXtpgbLeYQ9wS4QQjYVBr+O2QW0AeH/twcA2RogAkWCnEbHrSruxJLMjhKiBm/q3xqTXsfVILlsO5wS6OUL4nQQ7jYgn2JHMjhCiBmJDzVzROwmAV7/dIzMqi2ZHgp1GxB3s6J0S7AghambSiE4Y9Ro/7jnB97syAt0cIfxKgp1GxOHpxpJgRwhRM+1iQ7jrgvYAzFi2Q0ZmiWZFgp1GxCaZHSFEHUy6uCMJ4WYOZRbyfz/tD3RzhPAbCXYaEYcn2JFvZEKImgsxG5g6qhsAr63cy097TgS4RUL4hyHQDRDV59C5/lxGhwQ7QjQHO3furNX9YmNjSU5OrvC2q/u04Oe9J/l08xEmLtzMZw8MoVNCWF2aKUSDJ8FOI1JgtAAQYS3y+bkO7t5W4/sc2lu7N2YRGPI39o/aPM9Hdv8GwG233Varc1qCgti9a1eFAY+mabx4XU8OZxXyy8EsJszbwLsTzqNrYnitziVEYyDBTiNyLDgSgNZFeT47hypdkuKnRbP5qZbHcEg3W4Mmf2P/qI/nubaKi4o5evRopdkds0HPW7f3Y8zcNRw4WcB1b67hb9efw+jS4elCNDUS7DQiaSGRAETbSrCUFFBsDqn3cxiCQgG4/6qBnN+zTY3uu3rbIeZ+sR59aW2RvzXGTEVt2gxweO8OggyQuXcTB1UuBuwYSy86FDqcaDjL/K48v7c8eYDLOxm4YmBnureJQ+P0nCsK7YyfQJk9th3M4Is1u+ip7adVTjhK01y3a66jl/3pPGNbgpZJ+yiN4j/Wkqqyy7TS3dKKfmootHr5O/n7/6M+XktP3DCIXp0qDlgqcyAtm2fe+xaz2VzlftEhJj67fwgPfrSZ1fsymbhoM5//Gs/jKV3pkijdWqJpkWCnMTGFcthgoLXdTnReGsfiOvjsVOf3bMOtI/vU+H5zv1hf/405C79mKpRCr6wYnFbCySc5QsO+fzWZ6hhG7J6gw/XThqncNjtGZSP+1BH6X2bGnPsvLJvBYtAw60t/GsBiqHqbsZcGvcKBj6Cm88O1BG4JBo6UXqrv4o7wUMcQUG/D9hqe1wz/eCgM+LTmbe4Fjh5hKOcTsEbvCaCcmg40nSs40nRQGqS5KTQmmIuZNSUUpd6DHPf2Mvuosvufsb01MDkUU8k0DKtN5ZqlvM6nldkON1itPDMplPioQ4QHp1XwoEr31zTQ6UEzgM51ufCcAi4wBHFRj1xaJaWfvk3v3kcPOmPpNqPrd73r+vbDJt7+RENvzQNbMRjMrnNUICrExII7BvC3b3bzzs8H+G5nBit3ZXBBx1iu7N2Ci7vFExtaddDU2G3YsIE9e/bU6r6dO3emf//+9dwi4QsS7DQiUZYo9pqMtLbbifFxsFMX/v4GbbZYiLLA/aP7MqhLIkbNjgEHBhzocaDHiV4r/Ynz9DacnMjO5cDRDLqykNgd/0XvtGJwlmBwWjE4i12BjaMEg3JvLzl9YgvwcBjwec0/vOOAuPr5EHEqSh+RDkdpRkRB6c/TFwAnGvlFVtKzC+nUKprIsFC8AwRV5tNfeV9Xiuz8Io5k5BASGYPZbEIDdDih9KdWmq9xt6Ls7zjsKIcVk0GHXnOdVUNV9jlcjl6nAQ5Qjpo93xpEhdV14GlB6aUGTEC0DiiEosIa3TVZB7f0MoI6CseO1ui+PYDDj4TB11fD1wAaGIPBGOT6aSrzuzEIgzGIqcYQbjgnhleOdGH5iVh+3nuSn/eeBKBNqJMeMRqtwg20jDDRIjKIFlGhRIQGExoSSkhICEZzMOga3+De1NRUhgwZgt1eu+k8dDodr7zyCjExse5Xi+v/WpX+X1fxz2232zEYavcRXJcgqy7BXaDaXB8k2GlEYiwx7DEZubiwiMjcY4FuTjnW0jeMmmZYLAZICNHom6Qj6tDXGNVWQigkhCJClOtnMEWEUEQQxZgpwYLV89OAg/eeCAf2lV6qr2ssXBhrAn6D7BrdFYAim0JpejS9AUfpR7sDPQ6l8wQf7osrzNLhUDoOZ+azfvdxbhzak05tWpb5tm4443f3N37v25b8vJMJf1vK58/ewKXnn1ujOSS++m4Lt73zMV+/cAUp555To8e79OuN3PnWZ9T4g7+Mf/9lDDeO6Hd6gyobVClQTu9tSvHpT78zafYX3PjYy/TsO8DVwaVKO8BKf2rKSdkoSCsN0nb/tollbz3PX++8lMsHdj5L67zTPF9t2MOz875l/IhedExOOOtjK9sl+Pv+ND77eQev/mkEA3t1rOI8CpwOcNpKf9pZv+MAi77dzMTLe9O5dZxne7mLw1b60+q5brdZcdhKMBu00+eyFbguVegIvAkcMsXzX+dgvnQMZKdqy6F8HYfyARxAUekly+u+JmyEUEyIzkqIzopBA4NOYdTAoAOjDgyaKt3m+mnQwKhTaJ6AQOcKDjTt9N/f81R5X1co7ErDqTTsitKfGg5F6UUrvZT93XWxK1f3qF1p2J2Kcx/+F5pe7/nC4O46dZaG486z/P7qcR1UsJi8rvQLla70oseJXjk81zU0DMqKERvG0myxvvSic1jRHFbX39VuRdmtKHsJylqIs6QQh7WIifc/QOuWrQgLCyMsPILQ8CiCg0PQqgg66xrc1YXBYOCPP/6otI7M5+cPyFlFrZgNZlKDwiAnj6icmn3b8wdTacTvqjNojQUrwVoxwRQTpJVgKQ1OLJqVIEqwaFYslGDUynYhfVfzLEkpu9JhMJlBb3JddIbTqX6dvjTN707/u35uPXCCD1b+Tu+UsSR26IFdZ8ahM2HXmbFrZX4vvbivO3QmNq1bw/sv3suHT93IrcP61KitG7/bwtPff8x5F3ekU3LNAg4AKyYKbZR23fiP99+4Zm9a7jqUiKAz3nZK636oIsNTjJnj+YpsLZJTlpoV0aZqaWxJc5JNOIQl1ui+WSqdjcecvNClKylDavZ3Oqm2sPaj38nRRUFEqxrdd982B7PXr+PyKzvQuW3NzvvbnqP0u28OL8x4jo5tWriyksrmyloqa+nPEgxOm6dL1v3ToKzoHMVcxgmucH5GoUNjjy2OQ/Yo0p3hpDvDSXNGkuaM4hQWrLi69qwYsWIk2wmlib7Gw0clhu4wx8uZ/+Nlr9ewHQ+tA9c3tNPf0vQ4iNQKidIXEW2wEmVyEmXRiAo2EB1swlFSyICBfbnsnFa0jjBiwFkm2KxafdSRnTx5UoIdUT0nw+Lh+FHi8k+6vgH7+cPOw2GFohwozoGSPCg+xfn6VH6aEEy/NgcI1na52lfdwymN46ccGExB6EzBlGDEigGrMnreSK3KiA0DNvSuChilx4aB/+04xuz/bOTL52+q8QfStj+28I+1mxh32fn0SRhas+eguv0vTdDwc9rU+LmGwNR0NTfHs04B8PSz03x+Lp1eT3BwEMFBwQQFB2EOCsYSZMZsNGAyGTEZ9ZgMBvQGd3ZSj+auPdL0KJ0BXWkyR6eBhnY6sYNy5XPKJHiUKvNdyOlEUw5Qrqxen3N6EWQ2u7pPS4+no8yxtdJ4QtPQawoNjZzsLL76+mseu34IbRMi0etc++o11xB9TQMdGjqd5srnaBo6neu4327ax/QFK3n93pEMPqdL6Tk0lKa5MrsKHE5wKIXTqXAohcPpuvz420Hm/nc9j44ZTId2yZQ4oNihUWJXFDugxKFRXLqt2KmjxKlR5NCT7zSSbdWTVgjKHE4hFgqUhVMEebLKmSqMTHsY2IFiwGvwbgxc8CzvAjhd2acYLY8E3SniDYUkmIqIM9lIsNhJCFLEB+tJDDUQG+IKFeZ+sb5Wr/3Ne47yzHvf1uI/rP5IsNPIFEe0oFjbgsXpILwgi7zQWB+dSUHJqdKAJrv0Z64ruCnKBlv5GoS2OmjbxgAUlb4jaWAKBXMYmEPBGFJaL1D60xTi+X3xDzu57dUlfP3C6Bq/kIrYgmPpxro/ZNHg1aYerLYj3hqrnPxioG7ZN//d11HH8+r4dV8a//j4f/z3s6U1am9Zz93Yn56to2t0H52mY/+xbIx6HTHhwTW670bNxpZt+4i7uR+Dk2syqtbJl+t2cMVf3/faqgGhwWYiI0IJDQ8nNCyC4PBwgkLDMYZEoAsKR5nDsZvCKDJEcMoQRSbhONFxQkVywhHp+lOUVHROMGElUUtk1MN9+KKwmJ0bi2kZZKNlqEbLMD2J4WbMhoZdsyXBTiMTGRTLfqOB7lYbMXnpdQp2DI5iIkqOElF0xPWz+BjDnb/x5AMhdDZ8C+tWnOUAFrBEgiUCzGFsSj3Fy5/9yqM3X8zA8/q6ApxqZ54CmyWRD9GGrbb1YGV5d5c2fXXJvjW2+0Ldgrvi4uJanTMQah7M2oBMINPzeL9+4SZGDupNZqGdjHwb6QWKjCJIL9aTYTWRYTOTYQ8h3RHGCRWBFROpKgHMCexwlB6uDA0ncVoeLfW5tDDl08pcTKsgG61CFK3DdVgdtaxNqEcS7DQyriJlE92tNqLzjnOgRY/Kd1aKEOtJIoqPEFF81HOJLL0eYsus+H5xelwd75orkLFEQlBk+Z8Gi9fddh3cwpIdG7hLFwWWxjEbq3yINg4+qRUSTUpz61qt6+PV6zTiQ43EhxrpWW4vG655GnKw2Q+Slm/jPxsPsXTTUc7v3xurJYYjJcEctYdzzBlJMWYyVCQZ9kh+tQOFlBvw0efPQzh+eD/07VvjNtcHefU3Mq7h5yaggLjMQ4QWZmN02AgvyCS8IIvwgiyG2Pbz+P0hdFRPYd5gq/J4xfpQci2tyLW0JNfSkm3HClm4YAGTxo7i6hGDAlcT5CfyIdq4NLcPNCECzWjQ0zpSTxRFrFzxHY9fGEPKkFjcI/OUM42sQivH8mwczVccKdRxtMjEEWswR2xhHHFEc4pgsrVIQkICM+EsNKFgZ86cOfztb38jLS2Nc845hzfeeIMBAwYEuln1zqAzsD8kArKyaX9iH+2/mVXxjvF6wIYTHafMieRaWpQGNKWBjbkFOZZWlBgjvO625fiPrDzwHncQ3OQDnbLkQ1QIIWpO02nEhJqJCTXTy+sWG5CFcmayZucR7pv7LaHXLwpMI2kiwc6///1vpkyZwltvvcXAgQN57bXXSElJYffu3cTHxwe6efXuSFQrNp44Tje7wux04tDpyQuJJi84mryQaH47cIqF81dzzrjnaTXoapy6JvFnFkII0choOo0go45tO/ahC9BSQtBEgp1XXnmFe+65hzvuuAOAt956iy+//JL33nuPJ598MsCtq39hwfHckZRA1+iuDG89vNztv6T+wrf7fyJJi6WFBDpCCCGauUb/SWi1Wtm0aRNTp071bNPpdIwcOZK1a9cGsGW+E21xDZE8mHeQn4/8TKgp1Ov2tNA0YkfFkha+id9O1Wxa4LTw/cSOimVd1ElK8nbU6L6/Rp0kdlQsq0JPclTu67P7BvLccl+5r9xX7lvT+x7V5xI7KpZ8e36N7lefNKVU4MeE1cGxY8do2bIla9asYfDgwZ7tf/7zn/nxxx9Zv758TUVJSQklJacnFMjNzSU5OZnDhw8THl5/o4i2bNnC0KFDGfvMWBLanH2q+bJ2rtvJindWcPlDl9O5t/cU90WqiJ/tP5dOuSWEEEI0fDM6zuDSAZfW6zHz8vJo3bo1OTk5REREVLpfo8/s1MbMmTN57rnnym1v3bq1T8730fMf1fq+y2cvZznL67E1QgghhP/dwA0+O/apU6eadrATGxuLXq8nPT3da3t6ejqJiRWvgzN16lSmTJniue50OsnKyiImJqba64RUhzvirO+MkfAmz7P/yHPtH/I8+4c8z/7hy+dZKcWpU6do0aJFlfs1+mDHZDLRr18/Vq5cyTXXXAO4gpeVK1fy4IMPVngfs9mM2Wz22hYZGemzNoaHh8sLyQ/kefYfea79Q55n/5Dn2T989TxXldFxa/TBDsCUKVMYP3485513HgMGDOC1116joKDAMzpLCCGEEM1Xkwh2brrpJk6cOMGzzz5LWloaffr04euvvyYhoWZFwUIIIYRoeppEsAPw4IMPVtptFShms5lp06aV6zIT9UueZ/+R59o/5Hn2D3me/aMhPM+Nfui5EEIIIURVms/iR0IIIYRoliTYEUIIIUSTJsGOEEIIIZo0CXaEEEII0aRJsONDc+bMoW3btlgsFgYOHMgvv/wS6CY1WD/99BNXXnklLVq0QNM0li5d6nW7Uopnn32WpKQkgoKCGDlyJHv37vXaJysri1tvvZXw8HAiIyO56667yM/3Xnjut99+48ILL8RisdC6dWtmzZrl64fWoMycOZP+/fsTFhZGfHw811xzDbt37/bap7i4mIkTJxITE0NoaChjxowpN0N5amoqo0ePJjg4mPj4eB5//HHsdrvXPj/88AN9+/bFbDbTsWNH5s+f7+uH12DMnTuX3r17eyZRGzx4MF999ZXndnmOfeOll15C0zQefvhhzzZ5ruvH9OnT0TTN69K1a1fP7Q3+eVbCJxYvXqxMJpN677331Pbt29U999yjIiMjVXp6eqCb1iAtX75c/eUvf1GfffaZAtTnn3/udftLL72kIiIi1NKlS9XWrVvVVVddpdq1a6eKioo8+1x22WXqnHPOUevWrVM///yz6tixoxo7dqzn9tzcXJWQkKBuvfVWtW3bNvXRRx+poKAg9fbbb/vrYQZcSkqKmjdvntq2bZvasmWLuvzyy1VycrLKz8/37HPfffep1q1bq5UrV6qNGzeqQYMGqSFDhnhut9vtqmfPnmrkyJHq119/VcuXL1exsbFq6tSpnn3279+vgoOD1ZQpU9SOHTvUG2+8ofR6vfr666/9+ngD5YsvvlBffvml2rNnj9q9e7d66qmnlNFoVNu2bVNKyXPsC7/88otq27at6t27t5o8ebJnuzzX9WPatGmqR48e6vjx457LiRMnPLc39OdZgh0fGTBggJo4caLnusPhUC1atFAzZ84MYKsahzODHafTqRITE9Xf/vY3z7acnBxlNpvVRx99pJRSaseOHQpQGzZs8Ozz1VdfKU3T1NGjR5VSSr355psqKipKlZSUePZ54oknVJcuXXz8iBqujIwMBagff/xRKeV6Xo1Go1qyZIlnn507dypArV27VinlCkx1Op1KS0vz7DN37lwVHh7ueW7//Oc/qx49enid66abblIpKSm+fkgNVlRUlHrnnXfkOfaBU6dOqU6dOqlvv/1WDR061BPsyHNdf6ZNm6bOOeecCm9rDM+zdGP5gNVqZdOmTYwcOdKzTafTMXLkSNauXRvAljVOBw4cIC0tzev5/P/27j0oqvKNA/h3EXdBlhUEZRECJRJCFBEEd7w0hUEYZjRNaEaEUiHYhNyGzEamy6BdaMJhyMwBBpsh0yHTHJK4NTC4xrogF2VAMSm5CSEsoCD7/P5wPD9XsNBAYH0+M2cG933Oe973mR3m4T3n9cycORPe3t5CPsvKymBmZgZPT08hZs2aNTAwMIBSqRRiVq9eDbFYLMT4+fmhrq4Of//990OazeRy7do1AMCsWbMAACqVCoODgzq5dnZ2hp2dnU6uFy1apPM/lPv5+aG7uxs1NTVCzJ193I55FL//Q0NDyM7ORm9vLxQKBed4HERGRuL5558flg/O9diqr6/H3Llz4eDggE2bNuHy5csApkaeudgZB1evXsXQ0NCw11VYWVmhpaVlgkY1dd3O2T/ls6WlBXPmzNFpNzQ0xKxZs3RiRurjzms8SrRaLaKiorBixQq4uroCuJUHsVg87MW4d+f63/J4r5ju7m709/ePx3QmnaqqKkilUkgkEoSHhyMnJwcuLi6c4zGWnZ2NM2fOICkpaVgb53rseHt7IyMjA7m5uUhLS0NjYyNWrVqFnp6eKZFnvXldBGPs/kRGRqK6uholJSUTPRS95OTkhIqKCly7dg2HDx9GSEgIiouLJ3pYeqWpqQnvvvsu8vLyYGRkNNHD0Wv+/v7Cz4sXL4a3tzfs7e1x6NAhGBsbT+DIRodXdsaBpaUlpk2bNuxJ9NbWVsjl8gka1dR1O2f/lE+5XI62tjad9ps3b6Kzs1MnZqQ+7rzGo2Lbtm04fvw4CgsLYWtrK3wul8sxMDCArq4unfi7c/1vebxXjEwmmxK/GMeCWCyGo6MjPDw8kJSUBDc3N3z11Vec4zGkUqnQ1taGpUuXwtDQEIaGhiguLkZKSgoMDQ1hZWXFuR4nZmZmWLBgARoaGqbEd5qLnXEgFovh4eGB/Px84TOtVov8/HwoFIoJHNnUNH/+fMjlcp18dnd3Q6lUCvlUKBTo6uqCSqUSYgoKCqDVauHt7S3E/PbbbxgcHBRi8vLy4OTkBHNz84c0m4lFRNi2bRtycnJQUFCA+fPn67R7eHhg+vTpOrmuq6vD5cuXdXJdVVWlU1zm5eVBJpPBxcVFiLmzj9sxj/L3X6vV4saNG5zjMeTj44OqqipUVFQIh6enJzZt2iT8zLkeHxqNBhcuXIC1tfXU+E7/50ec2Yiys7NJIpFQRkYG1dbW0ltvvUVmZmY6T6Kz/+vp6SG1Wk1qtZoAUHJyMqnVavrjjz+I6NbWczMzMzp69CidPXuW1q9fP+LWc3d3d1IqlVRSUkJPPPGEztbzrq4usrKyouDgYKqurqbs7GyaMWPGI7X1fOvWrTRz5kwqKirS2ULa19cnxISHh5OdnR0VFBRQeXk5KRQKUigUQvvtLaS+vr5UUVFBubm5NHv27BG3kMbFxdG5c+coNTX1kdqqm5CQQMXFxdTY2Ehnz56lhIQEEolEdPLkSSLiHI+nO3djEXGux0pMTAwVFRVRY2MjlZaW0po1a8jS0pLa2tqIaPLnmYudcbR3716ys7MjsVhMXl5edOrUqYke0qRVWFhIAIYdISEhRHRr+/kHH3xAVlZWJJFIyMfHh+rq6nT66OjooI0bN5JUKiWZTEahoaHU09OjE1NZWUkrV64kiURCNjY2tHv37oc1xUlhpBwDoPT0dCGmv7+fIiIiyNzcnGbMmEGBgYHU3Nys08+lS5fI39+fjI2NydLSkmJiYmhwcFAnprCwkJYsWUJisZgcHBx0rqHvNm/eTPb29iQWi2n27Nnk4+MjFDpEnOPxdHexw7keG0FBQWRtbU1isZhsbGwoKCiIGhoahPbJnmcREdF/Xx9ijDHGGJuc+JkdxhhjjOk1LnYYY4wxpte42GGMMcaYXuNihzHGGGN6jYsdxhhjjOk1LnYYY4wxpte42GGMMcaYXuNihzE2qVy6dAkikQgVFRVT6jqJiYlYsmTJmPTFGBtbXOwwxu7bG2+8AZFIBJFIJLzw8sMPP8TNmzcnemjjJicnB8uXL8fMmTNhamqKhQsXIioqSmiPjY0d9l4fxtjkYDjRA2CMTU3PPfcc0tPTcePGDZw4cQKRkZGYPn063nvvvYke2pjLz89HUFAQPvnkE7zwwgsQiUSora1FXl6eECOVSiGVSidwlIyxe+GVHcbYA5FIJJDL5bC3t8fWrVuxZs0a/PTTT0hOTsaiRYtgYmKCxx57DBEREdBoNACA3t5eyGQyHD58WKevH3/8ESYmJujp6RnxWsXFxfDy8oJEIoG1tTUSEhJ0VpFyc3OxcuVKmJmZwcLCAgEBAbhw4YJOH6dPn4a7uzuMjIzg6ekJtVo96rkeO3YMK1asQFxcHJycnLBgwQK8+OKLSE1NFWLuvo11e+XrzmPevHlCe3V1Nfz9/SGVSmFlZYXg4GBcvXp11GNijI0eFzuMsTFhbGyMgYEBGBgYICUlBTU1NcjMzERBQQHi4+MBACYmJtiwYQPS09N1zk1PT8fLL78MU1PTYf3+9ddfWLt2LZYtW4bKykqkpaXhwIED+Pjjj4WY3t5eREdHo7y8HPn5+TAwMEBgYCC0Wi0AQKPRICAgAC4uLlCpVEhMTERsbOyo5yaXy1FTU4Pq6upRn9Pc3CwcDQ0NcHR0xOrVqwEAXV1deOaZZ+Du7o7y8nLk5uaitbUVr7zyyqj7Z4zdhzF5nShj7JESEhJC69evJ6Jbb6TPy8sjiURCsbGxw2J/+OEHsrCwEP6tVCpp2rRpdOXKFSIiam1tJUNDQyoqKiIiosbGRgJAarWaiIh27NhBTk5OpNVqhT5SU1NJKpXS0NDQiONrb28nAFRVVUVERPv27SMLCwvq7+8XYtLS0nSu8080Gg2tXbuWAJC9vT0FBQXRgQMH6Pr160LMrl27yM3Nbdi5Wq2WAgMDycPDg/r6+oiI6KOPPiJfX1+duKamJgJAdXV1/zoextj94ZUdxtgDOX78OKRSKYyMjODv74+goCAkJibi119/hY+PD2xsbGBqaorg4GB0dHSgr68PAODl5YWFCxciMzMTAHDw4EHY29sLqx53O3fuHBQKBUQikfDZihUroNFo8OeffwIA6uvrsXHjRjg4OEAmkwm3iy5fviz0sXjxYhgZGQl9KBSKUc/VxMQEP//8MxoaGrBz505IpVLExMTAy8tLmNe97NixA2VlZTh69CiMjY0BAJWVlSgsLBSe85FKpXB2dgaAYbffGGP/HRc7jLEH8vTTT6OiogL19fXo7+9HZmYm2tvbERAQgMWLF+PIkSNQqVTCcy0DAwPCuWFhYcjIyABw6xZWaGioTjFzv9atW4fOzk7s378fSqUSSqVy2DXHwuOPP46wsDB8++23OHPmDGpra/H999/fM/7gwYP48ssvkZOTAxsbG+FzjUaDdevWoaKiQueor6+/Z9HHGHtwvBuLMfZATExM4OjoqPOZSqWCVqvFF198AQODW39LHTp0aNi5r732GuLj45GSkoLa2lqEhITc8zpPPvkkjhw5AiISCqLS0lKYmprC1tYWHR0dqKurw/79+7Fq1SoAQElJybA+srKycP36dWF159SpUw8+eQDz5s3DjBkz0NvbO2J7WVkZwsLCsG/fPixfvlynbenSpThy5AjmzZsHQ0P+NczYeOOVHcbYmHF0dMTg4CD27t2LixcvIisrC19//fWwOHNzc7z00kuIi4uDr68vbG1t79lnREQEmpqa8M477+D8+fM4evQodu3ahejoaBgYGMDc3BwWFhb45ptv0NDQgIKCAkRHR+v08eqrr0IkEuHNN99EbW0tTpw4gc8//3zU80pMTER8fDyKiorQ2NgItVqNzZs3Y3BwEM8+++yw+JaWFgQGBmLDhg3w8/NDS0sLWlpa0N7eDgCIjIxEZ2cnNm7ciN9//x0XLlzAL7/8gtDQUAwNDY16XIyx0eFihzE2Ztzc3JCcnIw9e/bA1dUV3333HZKSkkaM3bJlCwYGBrB58+Z/7NPGxgYnTpzA6dOn4ebmhvDwcGzZsgU7d+4EABgYGCA7OxsqlQqurq7Yvn07PvvsM50+pFIpjh07hqqqKri7u+P999/Hnj17Rj2vp556ChcvXsTrr78OZ2dn+Pv7o6WlBSdPnoSTk9Ow+PPnz6O1tRWZmZmwtrYWjmXLlgEA5s6di9LSUgwNDcHX1xeLFi1CVFQUzMzMhBUxxtjYERERTfQgGGOPnqysLGzfvh1XrlyBWCye6OEwxvQY3yxmjD1UfX19aG5uxu7du/H2229zocMYG3e8XsoYe6g+/fRTODs7Qy6XT5pXS4SHh+tsA7/zCA8Pn+jhMcb+I76NxRh75LW1taG7u3vENplMhjlz5jzkETHGxhIXO4wxxhjTa3wbizHGGGN6jYsdxhhjjOk1LnYYY4wxpte42GGMMcaYXuNihzHGGGN6jYsdxhhjjOk1LnYYY4wxpte42GGMMcaYXvsfFdR+JuWkKfUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=df, x='Payload_Size', hue='Scan_Type', bins=30, kde=True, multiple='stack')\n",
+    "plt.title('Payload Size Distribution by Scan Type')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "275aa782",
+   "metadata": {
+    "papermill": {
+     "duration": 0.013906,
+     "end_time": "2025-07-21T10:46:29.716113",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:29.702207",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Normal traffic has a wide range of payload sizes, while BotAttack are mostly  big payloads. and PortScan payloads are limited to samll sizes, This suggests that payload size is a strong feature for distinguishing malicious activity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "aaffcaa2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:29.746840Z",
+     "iopub.status.busy": "2025-07-21T10:46:29.746415Z",
+     "iopub.status.idle": "2025-07-21T10:46:29.926793Z",
+     "shell.execute_reply": "2025-07-21T10:46:29.925584Z"
+    },
+    "papermill": {
+     "duration": 0.198608,
+     "end_time": "2025-07-21T10:46:29.928556",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:29.729948",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.boxplot(x='Scan_Type', y='Payload_Size', data=df)\n",
+    "plt.title('Payload Size by Scan Type')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "029287a8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.013712,
+     "end_time": "2025-07-21T10:46:29.956000",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:29.942288",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Same conclusions as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "49384298",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:29.985076Z",
+     "iopub.status.busy": "2025-07-21T10:46:29.984748Z",
+     "iopub.status.idle": "2025-07-21T10:46:30.000821Z",
+     "shell.execute_reply": "2025-07-21T10:46:29.999368Z"
+    },
+    "papermill": {
+     "duration": 0.032805,
+     "end_time": "2025-07-21T10:46:30.002754",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:29.969949",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 10 Source IPs:\n",
+      "Source_IP\n",
+      "33.5.52.172        58\n",
+      "95.139.88.51       57\n",
+      "154.113.2.100      57\n",
+      "78.199.98.120      56\n",
+      "197.134.233.238    54\n",
+      "94.40.34.8         48\n",
+      "216.181.134.4      46\n",
+      "201.232.85.16      46\n",
+      "161.77.134.239     45\n",
+      "219.110.174.163    44\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Top 10 Destination IPs:\n",
+      "Destination_IP\n",
+      "42.156.67.167      1\n",
+      "221.131.211.130    1\n",
+      "192.58.191.114     1\n",
+      "174.11.100.96      1\n",
+      "70.172.217.52      1\n",
+      "217.57.35.68       1\n",
+      "7.241.191.145      1\n",
+      "199.169.101.163    1\n",
+      "166.155.7.212      1\n",
+      "154.88.151.127     1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "top_sources = df['Source_IP'].value_counts().head(10)\n",
+    "top_dest = df['Destination_IP'].value_counts().head(10)\n",
+    "\n",
+    "print(\"Top 10 Source IPs:\")\n",
+    "print(top_sources)\n",
+    "\n",
+    "print(\"\\nTop 10 Destination IPs:\")\n",
+    "print(top_dest)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e2087b8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.013606,
+     "end_time": "2025-07-21T10:46:30.030426",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.016820",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We won't take into account the ip sources, since it is not a very refelctive and can not be generalized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a0a795cb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:30.061298Z",
+     "iopub.status.busy": "2025-07-21T10:46:30.061007Z",
+     "iopub.status.idle": "2025-07-21T10:46:30.467982Z",
+     "shell.execute_reply": "2025-07-21T10:46:30.466443Z"
+    },
+    "papermill": {
+     "duration": 0.425645,
+     "end_time": "2025-07-21T10:46:30.469984",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.044339",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_encoded = df.copy()\n",
+    "\n",
+    "# Encode categoricals\n",
+    "for col in ['Request_Type', 'Protocol', 'User_Agent', 'Status', 'Scan_Type']:\n",
+    "    df_encoded[col] = df_encoded[col].astype('category').cat.codes\n",
+    "\n",
+    "# Drop IPs for now\n",
+    "df_encoded = df_encoded.drop(['Source_IP', 'Destination_IP'], axis=1)\n",
+    "\n",
+    "# Correlation heatmap\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.heatmap(df_encoded.corr(), annot=True, cmap='coolwarm', fmt=\".2f\")\n",
+    "plt.title('Feature Correlation Heatmap')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6c49e90",
+   "metadata": {
+    "papermill": {
+     "duration": 0.016031,
+     "end_time": "2025-07-21T10:46:30.502900",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.486869",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "There is no any strong colinearity between any two features, so we can proceed to the next step without droping any features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcabc96f",
+   "metadata": {
+    "papermill": {
+     "duration": 0.01629,
+     "end_time": "2025-07-21T10:46:30.535403",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.519113",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Data preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "311978ea",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:30.568693Z",
+     "iopub.status.busy": "2025-07-21T10:46:30.568248Z",
+     "iopub.status.idle": "2025-07-21T10:46:30.584635Z",
+     "shell.execute_reply": "2025-07-21T10:46:30.583555Z"
+    },
+    "papermill": {
+     "duration": 0.035487,
+     "end_time": "2025-07-21T10:46:30.586612",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.551125",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "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>Source_IP</th>\n",
+       "      <th>Destination_IP</th>\n",
+       "      <th>Port</th>\n",
+       "      <th>Request_Type</th>\n",
+       "      <th>Protocol</th>\n",
+       "      <th>Payload_Size</th>\n",
+       "      <th>User_Agent</th>\n",
+       "      <th>Status</th>\n",
+       "      <th>Intrusion</th>\n",
+       "      <th>Scan_Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>192.168.142.55</td>\n",
+       "      <td>42.156.67.167</td>\n",
+       "      <td>80</td>\n",
+       "      <td>FTP</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>2369</td>\n",
+       "      <td>curl/7.68.0</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>53.39.165.18</td>\n",
+       "      <td>94.60.242.119</td>\n",
+       "      <td>135</td>\n",
+       "      <td>SMTP</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>1536</td>\n",
+       "      <td>Wget/1.20.3</td>\n",
+       "      <td>Failure</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BotAttack</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>192.168.127.91</td>\n",
+       "      <td>7.10.192.3</td>\n",
+       "      <td>21</td>\n",
+       "      <td>SMTP</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>1183</td>\n",
+       "      <td>Wget/1.20.3</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>192.168.30.40</td>\n",
+       "      <td>130.169.82.211</td>\n",
+       "      <td>25</td>\n",
+       "      <td>HTTPS</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>666</td>\n",
+       "      <td>Mozilla/5.0</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>192.168.43.179</td>\n",
+       "      <td>35.140.102.220</td>\n",
+       "      <td>22</td>\n",
+       "      <td>FTP</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>2821</td>\n",
+       "      <td>Mozilla/5.0</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8841</th>\n",
+       "      <td>192.168.62.111</td>\n",
+       "      <td>217.201.54.87</td>\n",
+       "      <td>22</td>\n",
+       "      <td>DNS</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>651</td>\n",
+       "      <td>python-requests/2.25.1</td>\n",
+       "      <td>Failure</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8842</th>\n",
+       "      <td>192.168.154.70</td>\n",
+       "      <td>11.93.131.139</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Telnet</td>\n",
+       "      <td>ICMP</td>\n",
+       "      <td>592</td>\n",
+       "      <td>Nikto/2.1.6</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8843</th>\n",
+       "      <td>192.168.199.211</td>\n",
+       "      <td>213.172.225.26</td>\n",
+       "      <td>443</td>\n",
+       "      <td>DNS</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>518</td>\n",
+       "      <td>nmap/7.80</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8844</th>\n",
+       "      <td>192.168.107.115</td>\n",
+       "      <td>194.38.119.179</td>\n",
+       "      <td>53</td>\n",
+       "      <td>SSH</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>1149</td>\n",
+       "      <td>Mozilla/5.0</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8845</th>\n",
+       "      <td>201.232.85.16</td>\n",
+       "      <td>168.101.218.238</td>\n",
+       "      <td>31337</td>\n",
+       "      <td>FTP</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>12</td>\n",
+       "      <td>python-requests/2.25.1</td>\n",
+       "      <td>Failure</td>\n",
+       "      <td>1</td>\n",
+       "      <td>PortScan</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8846 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Source_IP   Destination_IP   Port Request_Type Protocol  \\\n",
+       "0      192.168.142.55    42.156.67.167     80          FTP      UDP   \n",
+       "1        53.39.165.18    94.60.242.119    135         SMTP      UDP   \n",
+       "2      192.168.127.91       7.10.192.3     21         SMTP      TCP   \n",
+       "3       192.168.30.40   130.169.82.211     25        HTTPS      TCP   \n",
+       "4      192.168.43.179   35.140.102.220     22          FTP      TCP   \n",
+       "...               ...              ...    ...          ...      ...   \n",
+       "8841   192.168.62.111    217.201.54.87     22          DNS      UDP   \n",
+       "8842   192.168.154.70    11.93.131.139     22       Telnet     ICMP   \n",
+       "8843  192.168.199.211   213.172.225.26    443          DNS      TCP   \n",
+       "8844  192.168.107.115   194.38.119.179     53          SSH      TCP   \n",
+       "8845    201.232.85.16  168.101.218.238  31337          FTP      TCP   \n",
+       "\n",
+       "      Payload_Size              User_Agent   Status  Intrusion  Scan_Type  \n",
+       "0             2369             curl/7.68.0  Success          0     Normal  \n",
+       "1             1536             Wget/1.20.3  Failure          1  BotAttack  \n",
+       "2             1183             Wget/1.20.3  Success          0     Normal  \n",
+       "3              666             Mozilla/5.0  Success          0     Normal  \n",
+       "4             2821             Mozilla/5.0  Success          0     Normal  \n",
+       "...            ...                     ...      ...        ...        ...  \n",
+       "8841           651  python-requests/2.25.1  Failure          0     Normal  \n",
+       "8842           592             Nikto/2.1.6  Success          0     Normal  \n",
+       "8843           518               nmap/7.80  Success          0     Normal  \n",
+       "8844          1149             Mozilla/5.0  Success          0     Normal  \n",
+       "8845            12  python-requests/2.25.1  Failure          1   PortScan  \n",
+       "\n",
+       "[8846 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4598ddd7",
+   "metadata": {
+    "papermill": {
+     "duration": 0.017213,
+     "end_time": "2025-07-21T10:46:30.622958",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.605745",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Drop irrelevant or high-cardinality features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "44cf0a1c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:30.660356Z",
+     "iopub.status.busy": "2025-07-21T10:46:30.660028Z",
+     "iopub.status.idle": "2025-07-21T10:46:30.666635Z",
+     "shell.execute_reply": "2025-07-21T10:46:30.665550Z"
+    },
+    "papermill": {
+     "duration": 0.027407,
+     "end_time": "2025-07-21T10:46:30.668378",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.640971",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df.drop(['Source_IP', 'Destination_IP'], axis=1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b9ba335",
+   "metadata": {
+    "papermill": {
+     "duration": 0.016016,
+     "end_time": "2025-07-21T10:46:30.701612",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.685596",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Convert categorical_cols into int columns using LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "72ff7535",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:30.736120Z",
+     "iopub.status.busy": "2025-07-21T10:46:30.735016Z",
+     "iopub.status.idle": "2025-07-21T10:46:30.747633Z",
+     "shell.execute_reply": "2025-07-21T10:46:30.746541Z"
+    },
+    "papermill": {
+     "duration": 0.031859,
+     "end_time": "2025-07-21T10:46:30.749323",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.717464",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "categorical_cols = ['Request_Type', 'Protocol', 'User_Agent', 'Status', 'Port']\n",
+    "for col in categorical_cols:\n",
+    "    df[col] = df[col].astype('category')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91c2621f",
+   "metadata": {
+    "papermill": {
+     "duration": 0.015926,
+     "end_time": "2025-07-21T10:46:30.781301",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.765375",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Show encoded category values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ab697dc6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:30.815574Z",
+     "iopub.status.busy": "2025-07-21T10:46:30.814605Z",
+     "iopub.status.idle": "2025-07-21T10:46:30.821350Z",
+     "shell.execute_reply": "2025-07-21T10:46:30.820015Z"
+    },
+    "papermill": {
+     "duration": 0.026246,
+     "end_time": "2025-07-21T10:46:30.823517",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.797271",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Request_Type categories: ['DNS', 'FTP', 'HTTP', 'HTTPS', 'SMTP', 'SSH', 'Telnet']\n",
+      "Protocol categories: ['ICMP', 'TCP', 'UDP']\n",
+      "User_Agent categories: ['Mozilla/5.0', 'Nikto/2.1.6', 'Wget/1.20.3', 'curl/7.68.0', 'nmap/7.80', 'python-requests/2.25.1']\n",
+      "Status categories: ['Failure', 'Success']\n",
+      "Port categories: [21, 22, 23, 25, 53, 80, 135, 443, 4444, 6667, 8080, 31337]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in categorical_cols:\n",
+    "    print(f\"{col} categories: {df[col].cat.categories.tolist()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "109e0745",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:30.858025Z",
+     "iopub.status.busy": "2025-07-21T10:46:30.857615Z",
+     "iopub.status.idle": "2025-07-21T10:46:30.864175Z",
+     "shell.execute_reply": "2025-07-21T10:46:30.863023Z"
+    },
+    "papermill": {
+     "duration": 0.026073,
+     "end_time": "2025-07-21T10:46:30.865836",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.839763",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "for col in categorical_cols:\n",
+    "    df[col] = df[col].cat.codes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf33c58a",
+   "metadata": {
+    "papermill": {
+     "duration": 0.016189,
+     "end_time": "2025-07-21T10:46:30.898523",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.882334",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Encode target label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e40274df",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:30.935148Z",
+     "iopub.status.busy": "2025-07-21T10:46:30.934735Z",
+     "iopub.status.idle": "2025-07-21T10:46:30.943810Z",
+     "shell.execute_reply": "2025-07-21T10:46:30.942690Z"
+    },
+    "papermill": {
+     "duration": 0.029689,
+     "end_time": "2025-07-21T10:46:30.945617",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.915928",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "target_encoder = LabelEncoder()\n",
+    "df['Scan_Type_Label'] = target_encoder.fit_transform(df['Scan_Type'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3631318b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:30.982027Z",
+     "iopub.status.busy": "2025-07-21T10:46:30.981625Z",
+     "iopub.status.idle": "2025-07-21T10:46:30.988513Z",
+     "shell.execute_reply": "2025-07-21T10:46:30.987181Z"
+    },
+    "papermill": {
+     "duration": 0.027812,
+     "end_time": "2025-07-21T10:46:30.990163",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:30.962351",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Label Mapping: {'BotAttack': 0, 'Normal': 1, 'PortScan': 2}\n"
+     ]
+    }
+   ],
+   "source": [
+    "label_mapping = dict(zip(target_encoder.classes_, target_encoder.transform(target_encoder.classes_)))\n",
+    "print(\"Label Mapping:\", label_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "65e24f28",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:31.027535Z",
+     "iopub.status.busy": "2025-07-21T10:46:31.027088Z",
+     "iopub.status.idle": "2025-07-21T10:46:31.034682Z",
+     "shell.execute_reply": "2025-07-21T10:46:31.033500Z"
+    },
+    "papermill": {
+     "duration": 0.028598,
+     "end_time": "2025-07-21T10:46:31.036513",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.007915",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df.drop(['Scan_Type'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c6bd3edb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:31.074468Z",
+     "iopub.status.busy": "2025-07-21T10:46:31.074090Z",
+     "iopub.status.idle": "2025-07-21T10:46:31.089680Z",
+     "shell.execute_reply": "2025-07-21T10:46:31.088522Z"
+    },
+    "papermill": {
+     "duration": 0.038339,
+     "end_time": "2025-07-21T10:46:31.091625",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.053286",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "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>Port</th>\n",
+       "      <th>Request_Type</th>\n",
+       "      <th>Protocol</th>\n",
+       "      <th>Payload_Size</th>\n",
+       "      <th>User_Agent</th>\n",
+       "      <th>Status</th>\n",
+       "      <th>Intrusion</th>\n",
+       "      <th>Scan_Type_Label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2369</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1536</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1183</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>666</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2821</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8841</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>651</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8842</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>592</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8843</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>518</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8844</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8845</th>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8846 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Port  Request_Type  Protocol  Payload_Size  User_Agent  Status  \\\n",
+       "0        5             1         2          2369           3       1   \n",
+       "1        6             4         2          1536           2       0   \n",
+       "2        0             4         1          1183           2       1   \n",
+       "3        3             3         1           666           0       1   \n",
+       "4        1             1         1          2821           0       1   \n",
+       "...    ...           ...       ...           ...         ...     ...   \n",
+       "8841     1             0         2           651           5       0   \n",
+       "8842     1             6         0           592           1       1   \n",
+       "8843     7             0         1           518           4       1   \n",
+       "8844     4             5         1          1149           0       1   \n",
+       "8845    11             1         1            12           5       0   \n",
+       "\n",
+       "      Intrusion  Scan_Type_Label  \n",
+       "0             0                1  \n",
+       "1             1                0  \n",
+       "2             0                1  \n",
+       "3             0                1  \n",
+       "4             0                1  \n",
+       "...         ...              ...  \n",
+       "8841          0                1  \n",
+       "8842          0                1  \n",
+       "8843          0                1  \n",
+       "8844          0                1  \n",
+       "8845          1                2  \n",
+       "\n",
+       "[8846 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b679beae",
+   "metadata": {
+    "papermill": {
+     "duration": 0.017448,
+     "end_time": "2025-07-21T10:46:31.127800",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.110352",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Scale the payload size feautre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "53d7a4a0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:31.164627Z",
+     "iopub.status.busy": "2025-07-21T10:46:31.164162Z",
+     "iopub.status.idle": "2025-07-21T10:46:31.175858Z",
+     "shell.execute_reply": "2025-07-21T10:46:31.174673Z"
+    },
+    "papermill": {
+     "duration": 0.032657,
+     "end_time": "2025-07-21T10:46:31.177863",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.145206",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "df['Payload_Size'] = scaler.fit_transform(df[['Payload_Size']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "35dee998",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:31.214452Z",
+     "iopub.status.busy": "2025-07-21T10:46:31.213991Z",
+     "iopub.status.idle": "2025-07-21T10:46:31.229387Z",
+     "shell.execute_reply": "2025-07-21T10:46:31.228354Z"
+    },
+    "papermill": {
+     "duration": 0.036103,
+     "end_time": "2025-07-21T10:46:31.231081",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.194978",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "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>Port</th>\n",
+       "      <th>Request_Type</th>\n",
+       "      <th>Protocol</th>\n",
+       "      <th>Payload_Size</th>\n",
+       "      <th>User_Agent</th>\n",
+       "      <th>Status</th>\n",
+       "      <th>Intrusion</th>\n",
+       "      <th>Scan_Type_Label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.841271</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-0.068551</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.454106</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.018786</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.334957</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8841</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>-1.035169</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8842</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1.099610</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8843</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.180435</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8844</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.491242</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8845</th>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.733100</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8846 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Port  Request_Type  Protocol  Payload_Size  User_Agent  Status  \\\n",
+       "0        5             1         2      0.841271           3       1   \n",
+       "1        6             4         2     -0.068551           2       0   \n",
+       "2        0             4         1     -0.454106           2       1   \n",
+       "3        3             3         1     -1.018786           0       1   \n",
+       "4        1             1         1      1.334957           0       1   \n",
+       "...    ...           ...       ...           ...         ...     ...   \n",
+       "8841     1             0         2     -1.035169           5       0   \n",
+       "8842     1             6         0     -1.099610           1       1   \n",
+       "8843     7             0         1     -1.180435           4       1   \n",
+       "8844     4             5         1     -0.491242           0       1   \n",
+       "8845    11             1         1     -1.733100           5       0   \n",
+       "\n",
+       "      Intrusion  Scan_Type_Label  \n",
+       "0             0                1  \n",
+       "1             1                0  \n",
+       "2             0                1  \n",
+       "3             0                1  \n",
+       "4             0                1  \n",
+       "...         ...              ...  \n",
+       "8841          0                1  \n",
+       "8842          0                1  \n",
+       "8843          0                1  \n",
+       "8844          0                1  \n",
+       "8845          1                2  \n",
+       "\n",
+       "[8846 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e756f35e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.018098,
+     "end_time": "2025-07-21T10:46:31.267301",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.249203",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "I previously tested the model and found that the Intrusion feature had a very high SHAP value. The model also achieved 100% accuracy, which may indicate overfitting. To encourage better generalization, we could consider dropping this feature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "706dfc53",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:31.306619Z",
+     "iopub.status.busy": "2025-07-21T10:46:31.305367Z",
+     "iopub.status.idle": "2025-07-21T10:46:31.313950Z",
+     "shell.execute_reply": "2025-07-21T10:46:31.312439Z"
+    },
+    "papermill": {
+     "duration": 0.030839,
+     "end_time": "2025-07-21T10:46:31.316048",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.285209",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X = df.drop(['Scan_Type_Label', 'Intrusion'], axis=1)\n",
+    "y = df['Scan_Type_Label']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9453c260",
+   "metadata": {
+    "papermill": {
+     "duration": 0.017029,
+     "end_time": "2025-07-21T10:46:31.351301",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.334272",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Split the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d9814684",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:31.387819Z",
+     "iopub.status.busy": "2025-07-21T10:46:31.387058Z",
+     "iopub.status.idle": "2025-07-21T10:46:31.400246Z",
+     "shell.execute_reply": "2025-07-21T10:46:31.399038Z"
+    },
+    "papermill": {
+     "duration": 0.033945,
+     "end_time": "2025-07-21T10:46:31.402275",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.368330",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.3, random_state=42, stratify=y\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a6252340",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:31.438604Z",
+     "iopub.status.busy": "2025-07-21T10:46:31.438018Z",
+     "iopub.status.idle": "2025-07-21T10:46:31.444322Z",
+     "shell.execute_reply": "2025-07-21T10:46:31.442919Z"
+    },
+    "papermill": {
+     "duration": 0.026994,
+     "end_time": "2025-07-21T10:46:31.446733",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.419739",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train shape: (6192, 6)\n",
+      "Test shape: (2654, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Train shape:\", X_train.shape)\n",
+    "print(\"Test shape:\", X_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7136bc2f",
+   "metadata": {
+    "papermill": {
+     "duration": 0.017695,
+     "end_time": "2025-07-21T10:46:31.483506",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.465811",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Model Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "2f818469",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:31.519198Z",
+     "iopub.status.busy": "2025-07-21T10:46:31.518811Z",
+     "iopub.status.idle": "2025-07-21T10:46:31.524801Z",
+     "shell.execute_reply": "2025-07-21T10:46:31.523776Z"
+    },
+    "papermill": {
+     "duration": 0.026278,
+     "end_time": "2025-07-21T10:46:31.526567",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.500289",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "models = {\n",
+    "    'Logistic Regression': LogisticRegression(max_iter=1000),\n",
+    "    'KNN': KNeighborsClassifier(),\n",
+    "    'Decision Tree': DecisionTreeClassifier(),\n",
+    "    'Random Forest': RandomForestClassifier(),\n",
+    "    'XGBoost': xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss'),\n",
+    "    'MLP': MLPClassifier(max_iter=500)\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb17e9ef",
+   "metadata": {
+    "papermill": {
+     "duration": 0.017239,
+     "end_time": "2025-07-21T10:46:31.560555",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.543316",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Test all the models and show SHAP graph for each model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "316abb23",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:31.596262Z",
+     "iopub.status.busy": "2025-07-21T10:46:31.595886Z",
+     "iopub.status.idle": "2025-07-21T10:46:31.604915Z",
+     "shell.execute_reply": "2025-07-21T10:46:31.603607Z"
+    },
+    "papermill": {
+     "duration": 0.029005,
+     "end_time": "2025-07-21T10:46:31.606767",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.577762",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "sample_idx = np.random.choice(X_test.index, size=min(200, len(X_test)), replace=False)\n",
+    "X_sample = X_test.loc[sample_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "11e7b6e7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:46:31.642182Z",
+     "iopub.status.busy": "2025-07-21T10:46:31.641772Z",
+     "iopub.status.idle": "2025-07-21T10:47:22.755163Z",
+     "shell.execute_reply": "2025-07-21T10:47:22.753758Z"
+    },
+    "papermill": {
+     "duration": 51.133473,
+     "end_time": "2025-07-21T10:47:22.757080",
+     "exception": false,
+     "start_time": "2025-07-21T10:46:31.623607",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔍 Training Logistic Regression...\n",
+      "✅ Accuracy: 0.9748\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.89      0.70      0.78       144\n",
+      "           1       0.98      0.99      0.99      2400\n",
+      "           2       0.90      1.00      0.95       110\n",
+      "\n",
+      "    accuracy                           0.97      2654\n",
+      "   macro avg       0.92      0.90      0.91      2654\n",
+      "weighted avg       0.97      0.97      0.97      2654\n",
+      "\n",
+      "📊 SHAP Explanation for Logistic Regression\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de82255756d348bdb3f30fbee7762117",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/200 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x390 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔍 Training KNN...\n",
+      "✅ Accuracy: 0.9744\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.98      0.58      0.73       144\n",
+      "           1       0.98      1.00      0.99      2400\n",
+      "           2       0.90      0.94      0.92       110\n",
+      "\n",
+      "    accuracy                           0.97      2654\n",
+      "   macro avg       0.95      0.84      0.88      2654\n",
+      "weighted avg       0.97      0.97      0.97      2654\n",
+      "\n",
+      "📊 SHAP Explanation for KNN\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71f78dfa78cd4cd5b31fb432e17ba3d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/200 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x390 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔍 Training Decision Tree...\n",
+      "✅ Accuracy: 0.9992\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.99      0.99       144\n",
+      "           1       1.00      1.00      1.00      2400\n",
+      "           2       1.00      1.00      1.00       110\n",
+      "\n",
+      "    accuracy                           1.00      2654\n",
+      "   macro avg       1.00      1.00      1.00      2654\n",
+      "weighted avg       1.00      1.00      1.00      2654\n",
+      "\n",
+      "📊 SHAP Explanation for Decision Tree\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x390 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔍 Training Random Forest...\n",
+      "✅ Accuracy: 0.9981\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.97      0.99       144\n",
+      "           1       1.00      1.00      1.00      2400\n",
+      "           2       0.99      1.00      1.00       110\n",
+      "\n",
+      "    accuracy                           1.00      2654\n",
+      "   macro avg       1.00      0.99      0.99      2654\n",
+      "weighted avg       1.00      1.00      1.00      2654\n",
+      "\n",
+      "📊 SHAP Explanation for Random Forest\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x390 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔍 Training XGBoost...\n",
+      "✅ Accuracy: 0.9992\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.99      1.00       144\n",
+      "           1       1.00      1.00      1.00      2400\n",
+      "           2       0.99      1.00      1.00       110\n",
+      "\n",
+      "    accuracy                           1.00      2654\n",
+      "   macro avg       1.00      1.00      1.00      2654\n",
+      "weighted avg       1.00      1.00      1.00      2654\n",
+      "\n",
+      "📊 SHAP Explanation for XGBoost\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x390 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔍 Training MLP...\n",
+      "✅ Accuracy: 0.9853\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.93      0.86      0.89       144\n",
+      "           1       0.99      0.99      0.99      2400\n",
+      "           2       0.92      1.00      0.96       110\n",
+      "\n",
+      "    accuracy                           0.99      2654\n",
+      "   macro avg       0.95      0.95      0.95      2654\n",
+      "weighted avg       0.99      0.99      0.99      2654\n",
+      "\n",
+      "📊 SHAP Explanation for MLP\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5aae460f17c04ed38ef3fff3c8d82b54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/200 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x390 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for name, model in models.items():\n",
+    "    print(f\"\\n🔍 Training {name}...\")\n",
+    "    model.fit(X_train, y_train)\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    \n",
+    "    acc = accuracy_score(y_test, y_pred)\n",
+    "    print(f\"✅ Accuracy: {acc:.4f}\")\n",
+    "    print(classification_report(y_test, y_pred))\n",
+    "\n",
+    "    # SHAP explanation\n",
+    "    print(f\"📊 SHAP Explanation for {name}\")\n",
+    "    \n",
+    "    # Tree-based models use TreeExplainer\n",
+    "    if name in ['Random Forest', 'XGBoost', 'Decision Tree']:\n",
+    "        explainer = shap.TreeExplainer(model)\n",
+    "        shap_values = explainer.shap_values(X_sample)\n",
+    "        shap.summary_plot(shap_values, X_sample, plot_type=\"bar\", show=True)\n",
+    "    \n",
+    "    # Model-agnostic SHAP (slow, so sample used)\n",
+    "    else:\n",
+    "        # Convert model to function for KernelExplainer\n",
+    "        explainer = shap.KernelExplainer(model.predict_proba, X_train.iloc[:100])\n",
+    "        shap_values = explainer.shap_values(X_sample)\n",
+    "        shap.summary_plot(shap_values, X_sample, plot_type=\"bar\", show=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4bdd95b",
+   "metadata": {
+    "papermill": {
+     "duration": 0.02522,
+     "end_time": "2025-07-21T10:47:22.807243",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:22.782023",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Since all models performed exceptionally well on the test dataset, I'll proceed with the Decision Tree model for the final evaluation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7de9e075",
+   "metadata": {
+    "papermill": {
+     "duration": 0.025495,
+     "end_time": "2025-07-21T10:47:22.859679",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:22.834184",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Model Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "72abb833",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:47:22.912841Z",
+     "iopub.status.busy": "2025-07-21T10:47:22.912409Z",
+     "iopub.status.idle": "2025-07-21T10:47:22.939301Z",
+     "shell.execute_reply": "2025-07-21T10:47:22.937996Z"
+    },
+    "papermill": {
+     "duration": 0.054882,
+     "end_time": "2025-07-21T10:47:22.941201",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:22.886319",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(random_state=42)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt_model = DecisionTreeClassifier(random_state=42)\n",
+    "dt_model.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03f59973",
+   "metadata": {
+    "papermill": {
+     "duration": 0.025253,
+     "end_time": "2025-07-21T10:47:22.992629",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:22.967376",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Model Testing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b07da5f9",
+   "metadata": {
+    "papermill": {
+     "duration": 0.024551,
+     "end_time": "2025-07-21T10:47:23.042236",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:23.017685",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Predict on the test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "d529403a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:47:23.093621Z",
+     "iopub.status.busy": "2025-07-21T10:47:23.093251Z",
+     "iopub.status.idle": "2025-07-21T10:47:23.101619Z",
+     "shell.execute_reply": "2025-07-21T10:47:23.100357Z"
+    },
+    "papermill": {
+     "duration": 0.036439,
+     "end_time": "2025-07-21T10:47:23.103505",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:23.067066",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = dt_model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "2cb85be8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:47:23.155714Z",
+     "iopub.status.busy": "2025-07-21T10:47:23.155238Z",
+     "iopub.status.idle": "2025-07-21T10:47:23.171540Z",
+     "shell.execute_reply": "2025-07-21T10:47:23.170046Z"
+    },
+    "papermill": {
+     "duration": 0.047741,
+     "end_time": "2025-07-21T10:47:23.176681",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:23.128940",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.99      0.99       144\n",
+      "           1       1.00      1.00      1.00      2400\n",
+      "           2       1.00      1.00      1.00       110\n",
+      "\n",
+      "    accuracy                           1.00      2654\n",
+      "   macro avg       1.00      1.00      1.00      2654\n",
+      "weighted avg       1.00      1.00      1.00      2654\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = classification_report(y_test, y_pred, output_dict=False)\n",
+    "print(report)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "bc3295ab",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:47:23.232218Z",
+     "iopub.status.busy": "2025-07-21T10:47:23.231877Z",
+     "iopub.status.idle": "2025-07-21T10:47:23.466573Z",
+     "shell.execute_reply": "2025-07-21T10:47:23.465455Z"
+    },
+    "papermill": {
+     "duration": 0.260988,
+     "end_time": "2025-07-21T10:47:23.468604",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:23.207616",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7abf8439a910>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=dt_model.classes_)\n",
+    "disp.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea0af267",
+   "metadata": {
+    "papermill": {
+     "duration": 0.025453,
+     "end_time": "2025-07-21T10:47:23.520552",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:23.495099",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Save the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "ace2c2ad",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-21T10:47:23.575299Z",
+     "iopub.status.busy": "2025-07-21T10:47:23.574960Z",
+     "iopub.status.idle": "2025-07-21T10:47:23.584944Z",
+     "shell.execute_reply": "2025-07-21T10:47:23.583686Z"
+    },
+    "papermill": {
+     "duration": 0.039514,
+     "end_time": "2025-07-21T10:47:23.586828",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:23.547314",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['network_logs_decision_tree_model.joblib']"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "joblib.dump(dt_model, 'network_logs_decision_tree_model.joblib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58ba7bf6",
+   "metadata": {
+    "papermill": {
+     "duration": 0.026342,
+     "end_time": "2025-07-21T10:47:23.641051",
+     "exception": false,
+     "start_time": "2025-07-21T10:47:23.614709",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  }
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