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+
+---
+
+# KDD Cup 1999 Dataset
+
+## Overview
+
+The KDD Cup 1999 dataset is a benchmark dataset commonly used for evaluating intrusion detection systems (IDS). It was introduced as part of the Third International Knowledge Discovery and Data Mining Tools Competition, held alongside the Fifth International Conference on Knowledge Discovery and Data Mining (KDD-99).
+
+## Description
+
+The dataset comprises network connection records captured in a university department's local area network (LAN) over a period of several weeks. It is structured to represent various types of network traffic, including normal and malicious activities. Multiple types of attacks were simulated in the dataset, covering denial of service (DoS), user to root (U2R), remote to local (R2L), and probe attacks.
+
+## Files
+
+- **[kddcup.data_10_percent.gz](https://kdd.ics.uci.edu/databases/kddcup99/kddcup.data_10_percent.gz)**: This compressed file contains a 10% subset of the complete dataset. It serves as a manageable sample for initial exploration and experimentation.
+
+## Columns
+
+The dataset is composed of numerous features describing each network connection record. Some of the key columns include:
+
+- `duration`: The length of the connection in seconds.
+- `protocol_type`: The protocol used in the connection (e.g., TCP, UDP, ICMP).
+- `service`: The network service on the destination (e.g., http, smtp, telnet).
+- `flag`: The status of the connection (e.g., SF for normal, S0 for connection attempts, REJ for rejected connections).
+- `src_bytes`: The number of data bytes from source to destination.
+- `dst_bytes`: The number of data bytes from destination to source.
+- `target`: Binary label indicating whether the connection is normal or an attack.
+- `Attack Type`: Categorical label specifying the type of attack (DoS, U2R, R2L, probe).
+
+## Usage
+
+The KDD Cup 1999 dataset serves as valuable data for training and evaluating intrusion detection systems (IDS), anomaly detection algorithms, and machine learning models in general. Researchers and practitioners in the field of cybersecurity often utilize this dataset to benchmark their approaches and compare performance against existing methods.
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diff --git a/Intrusion Detection/Model/Intrusion_Detection_ann_dnn_cnn.ipynb b/Intrusion Detection/Model/Intrusion_Detection_ann_dnn_cnn.ipynb
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+++ b/Intrusion Detection/Model/Intrusion_Detection_ann_dnn_cnn.ipynb	
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+{
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "version": "3.6.4",
+      "file_extension": ".py",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "name": "python",
+      "mimetype": "text/x-python"
+    },
+    "kaggle": {
+      "accelerator": "none",
+      "dataSources": [
+        {
+          "sourceId": 208170,
+          "sourceType": "datasetVersion",
+          "datasetId": 90131
+        }
+      ],
+      "dockerImageVersionId": 30301,
+      "isInternetEnabled": true,
+      "language": "python",
+      "sourceType": "notebook",
+      "isGpuEnabled": false
+    },
+    "colab": {
+      "provenance": [],
+      "toc_visible": true,
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU"
+  },
+  "nbformat_minor": 0,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Importing Libraries"
+      ],
+      "metadata": {
+        "id": "1KhBNrb1fir1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns"
+      ],
+      "metadata": {
+        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:48.430767Z",
+          "iopub.execute_input": "2022-11-14T04:34:48.431246Z",
+          "iopub.status.idle": "2022-11-14T04:34:48.603521Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:48.431209Z",
+          "shell.execute_reply": "2022-11-14T04:34:48.601497Z"
+        },
+        "trusted": true,
+        "id": "e2n0bD3cfJnd"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Loading Dataset"
+      ],
+      "metadata": {
+        "id": "fRoHXJOYfm3n"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!unzip /content/kddcup.data_10_percent.gz.zip\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Y19C8zpBfqXo",
+        "outputId": "14d06c2b-11da-43b4-f00a-c0b215269f74"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Archive:  /content/kddcup.data_10_percent.gz.zip\n",
+            "  inflating: kddcup.data_10_percent.gz  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "path = \"/content/kddcup.data_10_percent.gz\"\n"
+      ],
+      "metadata": {
+        "id": "AwgYOgXefh28"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Prepocessing the data as taken dataset is subset"
+      ],
+      "metadata": {
+        "id": "cKkdwDJ-fJnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cols=\"\"\"duration,\n",
+        "protocol_type,\n",
+        "service,\n",
+        "flag,\n",
+        "src_bytes,\n",
+        "dst_bytes,\n",
+        "land,\n",
+        "wrong_fragment,\n",
+        "urgent,\n",
+        "hot,\n",
+        "num_failed_logins,\n",
+        "logged_in,\n",
+        "num_compromised,\n",
+        "root_shell,\n",
+        "su_attempted,\n",
+        "num_root,\n",
+        "num_file_creations,\n",
+        "num_shells,\n",
+        "num_access_files,\n",
+        "num_outbound_cmds,\n",
+        "is_host_login,\n",
+        "is_guest_login,\n",
+        "count,\n",
+        "srv_count,\n",
+        "serror_rate,\n",
+        "srv_serror_rate,\n",
+        "rerror_rate,\n",
+        "srv_rerror_rate,\n",
+        "same_srv_rate,\n",
+        "diff_srv_rate,\n",
+        "srv_diff_host_rate,\n",
+        "dst_host_count,\n",
+        "dst_host_srv_count,\n",
+        "dst_host_same_srv_rate,\n",
+        "dst_host_diff_srv_rate,\n",
+        "dst_host_same_src_port_rate,\n",
+        "dst_host_srv_diff_host_rate,\n",
+        "dst_host_serror_rate,\n",
+        "dst_host_srv_serror_rate,\n",
+        "dst_host_rerror_rate,\n",
+        "dst_host_srv_rerror_rate\"\"\"\n",
+        "\n",
+        "columns=[]\n",
+        "for c in cols.split(','):\n",
+        "    if(c.strip()):\n",
+        "       columns.append(c.strip())\n",
+        "\n",
+        "columns.append('target')\n",
+        "#print(columns)\n",
+        "print(len(columns))"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:48.778089Z",
+          "iopub.execute_input": "2022-11-14T04:34:48.778464Z",
+          "iopub.status.idle": "2022-11-14T04:34:48.785716Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:48.778433Z",
+          "shell.execute_reply": "2022-11-14T04:34:48.784648Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rypxjiVLfJng",
+        "outputId": "e01ee99c-8729-4802-bb4f-9fc98e4a5995"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "42\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "attacks_types = {\n",
+        "    'normal': 'normal',\n",
+        "'back': 'dos',\n",
+        "'buffer_overflow': 'u2r',\n",
+        "'ftp_write': 'r2l',\n",
+        "'guess_passwd': 'r2l',\n",
+        "'imap': 'r2l',\n",
+        "'ipsweep': 'probe',\n",
+        "'land': 'dos',\n",
+        "'loadmodule': 'u2r',\n",
+        "'multihop': 'r2l',\n",
+        "'neptune': 'dos',\n",
+        "'nmap': 'probe',\n",
+        "'perl': 'u2r',\n",
+        "'phf': 'r2l',\n",
+        "'pod': 'dos',\n",
+        "'portsweep': 'probe',\n",
+        "'rootkit': 'u2r',\n",
+        "'satan': 'probe',\n",
+        "'smurf': 'dos',\n",
+        "'spy': 'r2l',\n",
+        "'teardrop': 'dos',\n",
+        "'warezclient': 'r2l',\n",
+        "'warezmaster': 'r2l',\n",
+        "}"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:49.259933Z",
+          "iopub.execute_input": "2022-11-14T04:34:49.26304Z",
+          "iopub.status.idle": "2022-11-14T04:34:49.269676Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:49.262959Z",
+          "shell.execute_reply": "2022-11-14T04:34:49.268264Z"
+        },
+        "trusted": true,
+        "id": "DbYzs88BfJnh"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv(path,names=columns)\n",
+        "#Adding Attack Type column\n",
+        "df['Attack Type'] = df.target.apply(lambda r:attacks_types[r[:-1]])\n",
+        "\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:49.623447Z",
+          "iopub.execute_input": "2022-11-14T04:34:49.62418Z",
+          "iopub.status.idle": "2022-11-14T04:34:51.038923Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:49.62414Z",
+          "shell.execute_reply": "2022-11-14T04:34:51.037676Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "9bIMzxxQfJni",
+        "outputId": "b8e83e4b-be93-464b-c041-7f353fb1929d"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   duration protocol_type service flag  src_bytes  dst_bytes  land  \\\n",
+              "0         0           tcp    http   SF        181       5450     0   \n",
+              "1         0           tcp    http   SF        239        486     0   \n",
+              "2         0           tcp    http   SF        235       1337     0   \n",
+              "3         0           tcp    http   SF        219       1337     0   \n",
+              "4         0           tcp    http   SF        217       2032     0   \n",
+              "\n",
+              "   wrong_fragment  urgent  hot  ...  dst_host_same_srv_rate  \\\n",
+              "0               0       0    0  ...                     1.0   \n",
+              "1               0       0    0  ...                     1.0   \n",
+              "2               0       0    0  ...                     1.0   \n",
+              "3               0       0    0  ...                     1.0   \n",
+              "4               0       0    0  ...                     1.0   \n",
+              "\n",
+              "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
+              "0                     0.0                         0.11   \n",
+              "1                     0.0                         0.05   \n",
+              "2                     0.0                         0.03   \n",
+              "3                     0.0                         0.03   \n",
+              "4                     0.0                         0.02   \n",
+              "\n",
+              "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
+              "0                          0.0                   0.0   \n",
+              "1                          0.0                   0.0   \n",
+              "2                          0.0                   0.0   \n",
+              "3                          0.0                   0.0   \n",
+              "4                          0.0                   0.0   \n",
+              "\n",
+              "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
+              "0                       0.0                   0.0                       0.0   \n",
+              "1                       0.0                   0.0                       0.0   \n",
+              "2                       0.0                   0.0                       0.0   \n",
+              "3                       0.0                   0.0                       0.0   \n",
+              "4                       0.0                   0.0                       0.0   \n",
+              "\n",
+              "    target  Attack Type  \n",
+              "0  normal.       normal  \n",
+              "1  normal.       normal  \n",
+              "2  normal.       normal  \n",
+              "3  normal.       normal  \n",
+              "4  normal.       normal  \n",
+              "\n",
+              "[5 rows x 43 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9225c041-4254-4a7d-b272-fa05d0c85e16\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>duration</th>\n",
+              "      <th>protocol_type</th>\n",
+              "      <th>service</th>\n",
+              "      <th>flag</th>\n",
+              "      <th>src_bytes</th>\n",
+              "      <th>dst_bytes</th>\n",
+              "      <th>land</th>\n",
+              "      <th>wrong_fragment</th>\n",
+              "      <th>urgent</th>\n",
+              "      <th>hot</th>\n",
+              "      <th>...</th>\n",
+              "      <th>dst_host_same_srv_rate</th>\n",
+              "      <th>dst_host_diff_srv_rate</th>\n",
+              "      <th>dst_host_same_src_port_rate</th>\n",
+              "      <th>dst_host_srv_diff_host_rate</th>\n",
+              "      <th>dst_host_serror_rate</th>\n",
+              "      <th>dst_host_srv_serror_rate</th>\n",
+              "      <th>dst_host_rerror_rate</th>\n",
+              "      <th>dst_host_srv_rerror_rate</th>\n",
+              "      <th>target</th>\n",
+              "      <th>Attack Type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>tcp</td>\n",
+              "      <td>http</td>\n",
+              "      <td>SF</td>\n",
+              "      <td>181</td>\n",
+              "      <td>5450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.11</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>normal.</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>tcp</td>\n",
+              "      <td>http</td>\n",
+              "      <td>SF</td>\n",
+              "      <td>239</td>\n",
+              "      <td>486</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.05</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>normal.</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>tcp</td>\n",
+              "      <td>http</td>\n",
+              "      <td>SF</td>\n",
+              "      <td>235</td>\n",
+              "      <td>1337</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>normal.</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>tcp</td>\n",
+              "      <td>http</td>\n",
+              "      <td>SF</td>\n",
+              "      <td>219</td>\n",
+              "      <td>1337</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>normal.</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>tcp</td>\n",
+              "      <td>http</td>\n",
+              "      <td>SF</td>\n",
+              "      <td>217</td>\n",
+              "      <td>2032</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.02</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>normal.</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 43 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9225c041-4254-4a7d-b272-fa05d0c85e16')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9225c041-4254-4a7d-b272-fa05d0c85e16 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9225c041-4254-4a7d-b272-fa05d0c85e16');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-281dc0ce-f641-43c2-84b4-a0cc3968f48b\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-281dc0ce-f641-43c2-84b4-a0cc3968f48b')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-281dc0ce-f641-43c2-84b4-a0cc3968f48b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['Attack Type'].value_counts()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:51.040576Z",
+          "iopub.execute_input": "2022-11-14T04:34:51.041204Z",
+          "iopub.status.idle": "2022-11-14T04:34:51.071556Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:51.041175Z",
+          "shell.execute_reply": "2022-11-14T04:34:51.070486Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GCfcFVIHfJni",
+        "outputId": "9ae16dfe-2981-48dd-ec72-748de98a4b42"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Attack Type\n",
+              "dos       391458\n",
+              "normal     97278\n",
+              "probe       4107\n",
+              "r2l         1126\n",
+              "u2r           52\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# EDA"
+      ],
+      "metadata": {
+        "id": "Btki-mjCfJnj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Function to create and save bar graphs\n",
+        "def bar_graph(feature, filename):\n",
+        "    plt.figure(figsize=(10, 5))  # Adjust the figure size as needed\n",
+        "    df[feature].value_counts().plot(kind=\"bar\")\n",
+        "    plt.title(f\"Bar graph of {feature}\")\n",
+        "    plt.savefig(f\"/content/{filename}.png\")  # Save the plot as a .png file\n",
+        "    plt.show()\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "L9ltondDgO0D"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create and save bar graphs for the specified features\n",
+        "bar_graph('protocol_type', 'protocol_type')\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 507
+        },
+        "id": "34eRzFyxgVeR",
+        "outputId": "5d18077a-2adc-4795-e3da-563ff5440681"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "bar_graph('service', 'service')\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 562
+        },
+        "id": "Kaso4q0EgYu3",
+        "outputId": "d9cda079-a74d-472f-d29b-b526b7739895"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "bar_graph('flag', 'flag')\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 528
+        },
+        "id": "WFYR8mrZgoTJ",
+        "outputId": "ffd9afe2-6396-42bb-970b-d0751fa10326"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "bar_graph('logged_in', 'logged_in')\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 482
+        },
+        "id": "pmvp7dT1gqs3",
+        "outputId": "f2b0bfda-f9e0-4829-bb1f-0d602b0deb78"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "bar_graph('target', 'target')\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 585
+        },
+        "id": "P_8hB-K1gs1S",
+        "outputId": "89ce3bc0-e19f-45e8-9a02-e122b40c2d44"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "bar_graph('Attack Type', 'attack_type')\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 522
+        },
+        "id": "S4s85MD2gvQm",
+        "outputId": "6a8bf7df-79cb-4ce2-f291-be526afb6236"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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LFi1ShQoVVL58ebVp00YNGjRQnTp19Morr+inn36S1WrVhx9+WOSM4ptvvqmHH35YLVq00NChQ1W7dm0dP35ccXFxSklJKVTv4uKi9957Tz179tQzzzyjTz/9VB06dCj2d3AtX19fde7cWatXr5aPj0+hxwgUaNy4scLCwhyWcJekqVOnGjUzZszQ5s2b1aZNGw0ZMkRBQUE6e/asdu/erY0bN+rs2bN31CsA3DVOW9cQAFCkopZw9/T0tDdv3ty+cOFCh+W47Xa7ffHixfZ69erZPTw87A0aNLAvXbrUWKb7WpLsUVFRt9zH1atX7X/+85/t/v7+9rJly9o7dOhg//bbb+2VK1e2Dxs2rFC/O3fuLHSOc+fO2QcNGmSvUqWK3cvLyx4WFmY/ePCgvWbNmkUuA79161b70KFD7RUrVrR7eXnZ+/fvb//5558dzlmzZk17eHh4ofd67LHH7I899thNP9f58+ft0dHR9oCAAHuZMmXs9erVs7/++uuFvtdbWcI9PT3d7ubmZh8wYMCv1ly8eNFerlw5Y2nyCxcu2Pv162f38fGxS3JYzv2jjz6yBwUF2d3c3ByWc//mm2/soaGhdi8vL3uVKlXsQ4YMse/Zs6fIJd/3799vf+qpp+w+Pj52T09Pe/369e1//vOfjf3XLuF+bY+PPfaY3cvLy/7ll1/e8DMXuH4J92utWrXKLsk+dOjQIvcX/Bbfe+8947f74IMP2jdv3lyoNj093R4VFWUPDAy0lylTxu7v72/v2LGj/a233rqlPgHAGSx2u5PuEgYAlDiZmZmqWLGiXnvtNb366qumnTc2NlaDBg3Szp071bJlS9POC+f46KOP1LNnT23bts1hqf0CFotFUVFRxb78FABKCu7JAgAU6dKlS4XG5syZI0l6/PHH720zKFH+9a9/6YEHHtDDDz/s7FYAwCm4JwsAUKSVK1cqNjZWXbt2lZeXlz7//HO9//776tSpkx566CFnt4f70AcffKC9e/cqLi5Oc+fOveV79wCgtCFkAQCK1LRpU7m5uWnmzJmy2WzGYhivvfaas1vDferZZ5+Vl5eXIiMj9eKLLzq7HQBwGu7JAgAAAAATcU8WAAAAAJiIkAUAAAAAJuKerBvIz8/XyZMnVaFCBW7eBQAAAH7D7Ha7zp8/r4CAALm43HiuipB1AydPnlRgYKCz2wAAAABwnzhx4oSqV69+wxpC1g1UqFBB0i9fpNVqdXI3AAAAAJzFZrMpMDDQyAg3Qsi6gYJLBK1WKyELAAAAwC3dRsTCFwAAAABgIkIWAAAAAJiIkAUAAAAAJrqjkDVjxgxZLBaNGjXKGLt8+bKioqJUuXJleXl5qVevXkpPT3c4LjU1VeHh4SpXrpyqVq2qMWPG6OrVqw41W7ZsUYsWLeTh4aG6desqNja20PvPnz9ftWrVkqenp9q0aaMdO3Y47L+VXgAAAADATLcdsnbu3Kl//vOfatq0qcN4dHS0PvnkE61evVpbt27VyZMn9fTTTxv78/LyFB4ertzcXG3fvl3Lli1TbGysJk2aZNQcO3ZM4eHhat++vVJSUjRq1CgNHjxYGzZsMGpWrlypmJgYTZ48Wbt371azZs0UFham06dP33IvAAAAAGA2i91utxf3oAsXLqhFixZasGCBXnvtNTVv3lxz5sxRVlaWfH19tWLFCvXu3VuSdPDgQTVs2FBJSUlq27at1q9fr27duunkyZPy8/OTJC1atEjjxo1TRkaG3N3dNW7cOMXFxWn//v3Ge/bt21eZmZmKj4+XJLVp00atWrXSvHnzJP3y4ODAwECNHDlS48ePv6VebsZms8nb21tZWVmsLggAAAD8hhUnG9zWTFZUVJTCw8MVGhrqMJ6cnKwrV644jDdo0EA1atRQUlKSJCkpKUlNmjQxApYkhYWFyWaz6cCBA0bN9ecOCwszzpGbm6vk5GSHGhcXF4WGhho1t9LL9XJycmSz2Rw2AAAAACiOYj8n64MPPtDu3bu1c+fOQvvS0tLk7u4uHx8fh3E/Pz+lpaUZNdcGrIL9BftuVGOz2XTp0iWdO3dOeXl5RdYcPHjwlnu53vTp0zV16tQbfHoAAAAAuLFizWSdOHFCL7/8spYvXy5PT8+71ZPTTJgwQVlZWcZ24sQJZ7cEAAAAoIQpVshKTk7W6dOn1aJFC7m5ucnNzU1bt27Vm2++KTc3N/n5+Sk3N1eZmZkOx6Wnp8vf31+S5O/vX2iFv4LXN6uxWq0qW7asqlSpIldX1yJrrj3HzXq5noeHh6xWq8MGAAAAAMVRrJDVsWNH7du3TykpKcbWsmVL9e/f3/hzmTJllJiYaBxz6NAhpaamKiQkRJIUEhKiffv2OawCmJCQIKvVqqCgIKPm2nMU1BScw93dXcHBwQ41+fn5SkxMNGqCg4Nv2gsAAAAAmK1Y92RVqFBBjRs3dhgrX768KleubIxHRkYqJiZGlSpVktVq1ciRIxUSEmKs5tepUycFBQVpwIABmjlzptLS0jRx4kRFRUXJw8NDkjRs2DDNmzdPY8eO1QsvvKBNmzZp1apViouLM943JiZGERERatmypVq3bq05c+YoOztbgwYNkiR5e3vftBcAAAAAMFuxF764mdmzZ8vFxUW9evVSTk6OwsLCtGDBAmO/q6ur1q1bp+HDhyskJETly5dXRESEpk2bZtTUrl1bcXFxio6O1ty5c1W9enW9/fbbCgsLM2r69OmjjIwMTZo0SWlpaWrevLni4+MdFsO4WS8AAAAAYLbbek7WbwXPyQIAAAAg3YPnZAEAAAAAimb65YK4d2qNj7t5EQo5PiPc2S0AAACgFGMmCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExUrZC1cuFBNmzaV1WqV1WpVSEiI1q9fb+x//PHHZbFYHLZhw4Y5nCM1NVXh4eEqV66cqlatqjFjxujq1asONVu2bFGLFi3k4eGhunXrKjY2tlAv8+fPV61ateTp6ak2bdpox44dDvsvX76sqKgoVa5cWV5eXurVq5fS09OL83EBAAAAoNiKFbKqV6+uGTNmKDk5Wbt27VKHDh305JNP6sCBA0bNkCFDdOrUKWObOXOmsS8vL0/h4eHKzc3V9u3btWzZMsXGxmrSpElGzbFjxxQeHq727dsrJSVFo0aN0uDBg7VhwwajZuXKlYqJidHkyZO1e/duNWvWTGFhYTp9+rRREx0drU8++USrV6/W1q1bdfLkST399NO39SUBAAAAwK2y2O12+52coFKlSnr99dcVGRmpxx9/XM2bN9ecOXOKrF2/fr26deumkydPys/PT5K0aNEijRs3ThkZGXJ3d9e4ceMUFxen/fv3G8f17dtXmZmZio+PlyS1adNGrVq10rx58yRJ+fn5CgwM1MiRIzV+/HhlZWXJ19dXK1asUO/evSVJBw8eVMOGDZWUlKS2bdve0mez2Wzy9vZWVlaWrFbr7X5Fd02t8XHObqFEOj4j3NktAAAAoIQpTja47Xuy8vLy9MEHHyg7O1shISHG+PLly1WlShU1btxYEyZM0MWLF419SUlJatKkiRGwJCksLEw2m82YDUtKSlJoaKjDe4WFhSkpKUmSlJubq+TkZIcaFxcXhYaGGjXJycm6cuWKQ02DBg1Uo0YNo6YoOTk5stlsDhsAAAAAFIdbcQ/Yt2+fQkJCdPnyZXl5eWnNmjUKCgqSJPXr1081a9ZUQECA9u7dq3HjxunQoUP6z3/+I0lKS0tzCFiSjNdpaWk3rLHZbLp06ZLOnTunvLy8ImsOHjxonMPd3V0+Pj6FagrepyjTp0/X1KlTi/mNAAAAAMD/FDtk1a9fXykpKcrKytK///1vRUREaOvWrQoKCtLQoUONuiZNmqhatWrq2LGjjh49qjp16pja+N0wYcIExcTEGK9tNpsCAwOd2BEAAACAkqbYlwu6u7urbt26Cg4O1vTp09WsWTPNnTu3yNo2bdpIko4cOSJJ8vf3L7TCX8Frf3//G9ZYrVaVLVtWVapUkaura5E1154jNzdXmZmZv1pTFA8PD2PlxIINAAAAAIrjjp+TlZ+fr5ycnCL3paSkSJKqVasmSQoJCdG+ffscVgFMSEiQ1Wo1LjkMCQlRYmKiw3kSEhKM+77c3d0VHBzsUJOfn6/ExESjJjg4WGXKlHGoOXTokFJTUx3uHwMAAAAAsxXrcsEJEyaoS5cuqlGjhs6fP68VK1Zoy5Yt2rBhg44ePaoVK1aoa9euqly5svbu3avo6Gg9+uijatq0qSSpU6dOCgoK0oABAzRz5kylpaVp4sSJioqKkoeHhyRp2LBhmjdvnsaOHasXXnhBmzZt0qpVqxQX97+V9GJiYhQREaGWLVuqdevWmjNnjrKzszVo0CBJkre3tyIjIxUTE6NKlSrJarVq5MiRCgkJueWVBQEAAADgdhQrZJ0+fVoDBw7UqVOn5O3traZNm2rDhg164okndOLECW3cuNEIPIGBgerVq5cmTpxoHO/q6qp169Zp+PDhCgkJUfny5RUREaFp06YZNbVr11ZcXJyio6M1d+5cVa9eXW+//bbCwsKMmj59+igjI0OTJk1SWlqamjdvrvj4eIfFMGbPni0XFxf16tVLOTk5CgsL04IFC+7kuwIAAACAm7rj52SVZjwnq3TiOVkAAAAornvynCwAAAAAQGGELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATFStkLVy4UE2bNpXVapXValVISIjWr19v7L98+bKioqJUuXJleXl5qVevXkpPT3c4R2pqqsLDw1WuXDlVrVpVY8aM0dWrVx1qtmzZohYtWsjDw0N169ZVbGxsoV7mz5+vWrVqydPTU23atNGOHTsc9t9KLwAAAABgtmKFrOrVq2vGjBlKTk7Wrl271KFDBz355JM6cOCAJCk6OlqffPKJVq9era1bt+rkyZN6+umnjePz8vIUHh6u3Nxcbd++XcuWLVNsbKwmTZpk1Bw7dkzh4eFq3769UlJSNGrUKA0ePFgbNmwwalauXKmYmBhNnjxZu3fvVrNmzRQWFqbTp08bNTfrBQAAAADuBovdbrffyQkqVaqk119/Xb1795avr69WrFih3r17S5IOHjyohg0bKikpSW3bttX69evVrVs3nTx5Un5+fpKkRYsWady4ccrIyJC7u7vGjRunuLg47d+/33iPvn37KjMzU/Hx8ZKkNm3aqFWrVpo3b54kKT8/X4GBgRo5cqTGjx+vrKysm/ZyK2w2m7y9vZWVlSWr1XonX9NdUWt8nLNbKJGOzwh3dgsAAAAoYYqTDW77nqy8vDx98MEHys7OVkhIiJKTk3XlyhWFhoYaNQ0aNFCNGjWUlJQkSUpKSlKTJk2MgCVJYWFhstlsxmxYUlKSwzkKagrOkZubq+TkZIcaFxcXhYaGGjW30ktRcnJyZLPZHDYAAAAAKI5ih6x9+/bJy8tLHh4eGjZsmNasWaOgoCClpaXJ3d1dPj4+DvV+fn5KS0uTJKWlpTkErIL9BftuVGOz2XTp0iWdOXNGeXl5RdZce46b9VKU6dOny9vb29gCAwNv7UsBAAAAgP+v2CGrfv36SklJ0VdffaXhw4crIiJC33zzzd3o7Z6bMGGCsrKyjO3EiRPObgkAAABACeNW3APc3d1Vt25dSVJwcLB27typuXPnqk+fPsrNzVVmZqbDDFJ6err8/f0lSf7+/oVWASxY8e/amutXAUxPT5fValXZsmXl6uoqV1fXImuuPcfNeimKh4eHPDw8ivFtAAAAAICjO35OVn5+vnJychQcHKwyZcooMTHR2Hfo0CGlpqYqJCREkhQSEqJ9+/Y5rAKYkJAgq9WqoKAgo+bacxTUFJzD3d1dwcHBDjX5+flKTEw0am6lFwAAAAC4G4o1kzVhwgR16dJFNWrU0Pnz57VixQpt2bJFGzZskLe3tyIjIxUTE6NKlSrJarVq5MiRCgkJMVbz69Spk4KCgjRgwADNnDlTaWlpmjhxoqKioowZpGHDhmnevHkaO3asXnjhBW3atEmrVq1SXNz/VtKLiYlRRESEWrZsqdatW2vOnDnKzs7WoEGDJOmWegEAAACAu6FYIev06dMaOHCgTp06JW9vbzVt2lQbNmzQE088IUmaPXu2XFxc1KtXL+Xk5CgsLEwLFiwwjnd1ddW6des0fPhwhYSEqHz58oqIiNC0adOMmtq1aysuLk7R0dGaO3euqlevrrffflthYWFGTZ8+fZSRkaFJkyYpLS1NzZs3V3x8vMNiGDfrBQAAAADuhjt+TlZpxnOySieekwUAAIDiuifPyQIAAAAAFEbIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADBRsULW9OnT1apVK1WoUEFVq1ZVz549dejQIYeaxx9/XBaLxWEbNmyYQ01qaqrCw8NVrlw5Va1aVWPGjNHVq1cdarZs2aIWLVrIw8NDdevWVWxsbKF+5s+fr1q1asnT01Nt2rTRjh07HPZfvnxZUVFRqly5sry8vNSrVy+lp6cX5yMDAAAAQLEUK2Rt3bpVUVFR+vLLL5WQkKArV66oU6dOys7OdqgbMmSITp06ZWwzZ8409uXl5Sk8PFy5ubnavn27li1bptjYWE2aNMmoOXbsmMLDw9W+fXulpKRo1KhRGjx4sDZs2GDUrFy5UjExMZo8ebJ2796tZs2aKSwsTKdPnzZqoqOj9cknn2j16tXaunWrTp48qaeffrrYXxIAAAAA3CqL3W633+7BGRkZqlq1qrZu3apHH31U0i8zWc2bN9ecOXOKPGb9+vXq1q2bTp48KT8/P0nSokWLNG7cOGVkZMjd3V3jxo1TXFyc9u/fbxzXt29fZWZmKj4+XpLUpk0btWrVSvPmzZMk5efnKzAwUCNHjtT48eOVlZUlX19frVixQr1795YkHTx4UA0bNlRSUpLatm17089ns9nk7e2trKwsWa3W2/2a7ppa4+Oc3UKJdHxGuLNbAAAAQAlTnGxwR/dkZWVlSZIqVarkML58+XJVqVJFjRs31oQJE3Tx4kVjX1JSkpo0aWIELEkKCwuTzWbTgQMHjJrQ0FCHc4aFhSkpKUmSlJubq+TkZIcaFxcXhYaGGjXJycm6cuWKQ02DBg1Uo0YNo+Z6OTk5stlsDhsAAAAAFIfb7R6Yn5+vUaNG6aGHHlLjxo2N8X79+qlmzZoKCAjQ3r17NW7cOB06dEj/+c9/JElpaWkOAUuS8TotLe2GNTabTZcuXdK5c+eUl5dXZM3BgweNc7i7u8vHx6dQTcH7XG/69OmaOnVqMb8JAAAAAPif2w5ZUVFR2r9/vz7//HOH8aFDhxp/btKkiapVq6aOHTvq6NGjqlOnzu13eg9MmDBBMTExxmubzabAwEAndgQAAACgpLmtywVHjBihdevWafPmzapevfoNa9u0aSNJOnLkiCTJ39+/0Ap/Ba/9/f1vWGO1WlW2bFlVqVJFrq6uRdZce47c3FxlZmb+as31PDw8ZLVaHTYAAAAAKI5ihSy73a4RI0ZozZo12rRpk2rXrn3TY1JSUiRJ1apVkySFhIRo3759DqsAJiQkyGq1KigoyKhJTEx0OE9CQoJCQkIkSe7u7goODnaoyc/PV2JiolETHBysMmXKONQcOnRIqampRg0AAAAAmK1YlwtGRUVpxYoV+uijj1ShQgXj3iZvb2+VLVtWR48e1YoVK9S1a1dVrlxZe/fuVXR0tB599FE1bdpUktSpUycFBQVpwIABmjlzptLS0jRx4kRFRUXJw8NDkjRs2DDNmzdPY8eO1QsvvKBNmzZp1apViov732p6MTExioiIUMuWLdW6dWvNmTNH2dnZGjRokNFTZGSkYmJiVKlSJVmtVo0cOVIhISG3tLIgAAAAANyOYoWshQsXSvplmfZrLV26VM8//7zc3d21ceNGI/AEBgaqV69emjhxolHr6uqqdevWafjw4QoJCVH58uUVERGhadOmGTW1a9dWXFycoqOjNXfuXFWvXl1vv/22wsLCjJo+ffooIyNDkyZNUlpampo3b674+HiHxTBmz54tFxcX9erVSzk5OQoLC9OCBQuK9QUBAAAAQHHc0XOySjuek1U68ZwsAAAAFNc9e04WAAAAAMARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMVK2RNnz5drVq1UoUKFVS1alX17NlThw4dcqi5fPmyoqKiVLlyZXl5ealXr15KT093qElNTVV4eLjKlSunqlWrasyYMbp69apDzZYtW9SiRQt5eHiobt26io2NLdTP/PnzVatWLXl6eqpNmzbasWNHsXsBAAAAADMVK2Rt3bpVUVFR+vLLL5WQkKArV66oU6dOys7ONmqio6P1ySefaPXq1dq6datOnjypp59+2tifl5en8PBw5ebmavv27Vq2bJliY2M1adIko+bYsWMKDw9X+/btlZKSolGjRmnw4MHasGGDUbNy5UrFxMRo8uTJ2r17t5o1a6awsDCdPn36lnsBAAAAALNZ7Ha7/XYPzsjIUNWqVbV161Y9+uijysrKkq+vr1asWKHevXtLkg4ePKiGDRsqKSlJbdu21fr169WtWzedPHlSfn5+kqRFixZp3LhxysjIkLu7u8aNG6e4uDjt37/feK++ffsqMzNT8fHxkqQ2bdqoVatWmjdvniQpPz9fgYGBGjlypMaPH39LvdyMzWaTt7e3srKyZLVab/drumtqjY9zdgsl0vEZ4c5uAQAAACVMcbLBHd2TlZWVJUmqVKmSJCk5OVlXrlxRaGioUdOgQQPVqFFDSUlJkqSkpCQ1adLECFiSFBYWJpvNpgMHDhg1156joKbgHLm5uUpOTnaocXFxUWhoqFFzK70AAAAAgNncbvfA/Px8jRo1Sg899JAaN24sSUpLS5O7u7t8fHwcav38/JSWlmbUXBuwCvYX7LtRjc1m06VLl3Tu3Dnl5eUVWXPw4MFb7uV6OTk5ysnJMV7bbLabfQ0AAAAA4OC2Z7KioqK0f/9+ffDBB2b241TTp0+Xt7e3sQUGBjq7JQAAAAAlzG2FrBEjRmjdunXavHmzqlevboz7+/srNzdXmZmZDvXp6eny9/c3aq5f4a/g9c1qrFarypYtqypVqsjV1bXImmvPcbNerjdhwgRlZWUZ24kTJ27h2wAAAACA/ylWyLLb7RoxYoTWrFmjTZs2qXbt2g77g4ODVaZMGSUmJhpjhw4dUmpqqkJCQiRJISEh2rdvn8MqgAkJCbJarQoKCjJqrj1HQU3BOdzd3RUcHOxQk5+fr8TERKPmVnq5noeHh6xWq8MGAAAAAMVRrHuyoqKitGLFCn300UeqUKGCcW+Tt7e3ypYtK29vb0VGRiomJkaVKlWS1WrVyJEjFRISYqzm16lTJwUFBWnAgAGaOXOm0tLSNHHiREVFRcnDw0OSNGzYMM2bN09jx47VCy+8oE2bNmnVqlWKi/vfanoxMTGKiIhQy5Yt1bp1a82ZM0fZ2dkaNGiQ0dPNegEAAAAAsxUrZC1cuFCS9PjjjzuML126VM8//7wkafbs2XJxcVGvXr2Uk5OjsLAwLViwwKh1dXXVunXrNHz4cIWEhKh8+fKKiIjQtGnTjJratWsrLi5O0dHRmjt3rqpXr663335bYWFhRk2fPn2UkZGhSZMmKS0tTc2bN1d8fLzDYhg36wUAAAAAzHZHz8kq7XhOVunEc7IAAABQXPfsOVkAAAAAAEeELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATFTtkbdu2Td27d1dAQIAsFovWrl3rsP/555+XxWJx2Dp37uxQc/bsWfXv319Wq1U+Pj6KjIzUhQsXHGr27t2rRx55RJ6engoMDNTMmTML9bJ69Wo1aNBAnp6eatKkiT799FOH/Xa7XZMmTVK1atVUtmxZhYaG6vDhw8X9yAAAAABwy4odsrKzs9WsWTPNnz//V2s6d+6sU6dOGdv777/vsL9///46cOCAEhIStG7dOm3btk1Dhw419ttsNnXq1Ek1a9ZUcnKyXn/9dU2ZMkVvvfWWUbN9+3Y9++yzioyM1Ndff62ePXuqZ8+e2r9/v1Ezc+ZMvfnmm1q0aJG++uorlS9fXmFhYbp8+XJxPzYAAAAA3BKL3W633/bBFovWrFmjnj17GmPPP/+8MjMzC81wFfj2228VFBSknTt3qmXLlpKk+Ph4de3aVT/++KMCAgK0cOFCvfrqq0pLS5O7u7skafz48Vq7dq0OHjwoSerTp4+ys7O1bt0649xt27ZV8+bNtWjRItntdgUEBGj06NF65ZVXJElZWVny8/NTbGys+vbte9PPZ7PZ5O3traysLFmt1tv5iu6qWuPjnN1CiXR8RrizWwAAAEAJU5xscFfuydqyZYuqVq2q+vXra/jw4fr555+NfUlJSfLx8TECliSFhobKxcVFX331lVHz6KOPGgFLksLCwnTo0CGdO3fOqAkNDXV437CwMCUlJUmSjh07prS0NIcab29vtWnTxqi5Xk5Ojmw2m8MGAAAAAMVhesjq3Lmz3nnnHSUmJupvf/ubtm7dqi5duigvL0+SlJaWpqpVqzoc4+bmpkqVKiktLc2o8fPzc6gpeH2zmmv3X3tcUTXXmz59ury9vY0tMDCw2J8fAAAAwG+bm9knvPYyvCZNmqhp06aqU6eOtmzZoo4dO5r9dqaaMGGCYmJijNc2m42gBQAAAKBY7voS7g888ICqVKmiI0eOSJL8/f11+vRph5qrV6/q7Nmz8vf3N2rS09Mdagpe36zm2v3XHldUzfU8PDxktVodNgAAAAAojrsesn788Uf9/PPPqlatmiQpJCREmZmZSk5ONmo2bdqk/Px8tWnTxqjZtm2brly5YtQkJCSofv36qlixolGTmJjo8F4JCQkKCQmRJNWuXVv+/v4ONTabTV999ZVRAwAAAABmK3bIunDhglJSUpSSkiLplwUmUlJSlJqaqgsXLmjMmDH68ssvdfz4cSUmJurJJ59U3bp1FRYWJklq2LChOnfurCFDhmjHjh364osvNGLECPXt21cBAQGSpH79+snd3V2RkZE6cOCAVq5cqblz5zpcyvfyyy8rPj5eb7zxhg4ePKgpU6Zo165dGjFihKRfVj4cNWqUXnvtNX388cfat2+fBg4cqICAAIfVEAEAAADATMW+J2vXrl1q37698bog+ERERGjhwoXau3evli1bpszMTAUEBKhTp076y1/+Ig8PD+OY5cuXa8SIEerYsaNcXFzUq1cvvfnmm8Z+b29vffbZZ4qKilJwcLCqVKmiSZMmOTxLq127dlqxYoUmTpyoP/3pT6pXr57Wrl2rxo0bGzVjx45Vdna2hg4dqszMTD388MOKj4+Xp6dncT82AAAAANySO3pOVmnHc7JKJ56TBQAAgOJy+nOyAAAAAOC3ipAFAAAAACYiZAEAAACAiQhZAAAAAGAiQhYAAAAAmIiQBQAAAAAmImQBAAAAgIkIWQAAAABgIkIWAAAAAJiIkAUAAAAAJiJkAQAAAICJCFkAAAAAYCJCFgAAAACYiJAFAAAAACYiZAEAAACAiQhZAAAAAGAiQhYAAAAAmIiQBQAAAAAmImQBAAAAgIkIWQAAAABgIkIWAAAAAJiIkAUAAAAAJiJkAQAAAICJCFkAAAAAYCJCFgAAAACYiJAFAAAAACYiZAEAAACAiQhZAAAAAGAiQhYAAAAAmIiQBQAAAAAmImQBAAAAgIkIWQAAAABgIkIWAAAAAJiIkAUAAAAAJiJkAQAAAICJCFkAAAAAYKJih6xt27ape/fuCggIkMVi0dq1ax322+12TZo0SdWqVVPZsmUVGhqqw4cPO9ScPXtW/fv3l9VqlY+PjyIjI3XhwgWHmr179+qRRx6Rp6enAgMDNXPmzEK9rF69Wg0aNJCnp6eaNGmiTz/9tNi9AAAAAICZih2ysrOz1axZM82fP7/I/TNnztSbb76pRYsW6auvvlL58uUVFhamy5cvGzX9+/fXgQMHlJCQoHXr1mnbtm0aOnSosd9ms6lTp06qWbOmkpOT9frrr2vKlCl66623jJrt27fr2WefVWRkpL7++mv17NlTPXv21P79+4vVCwAAAACYyWK32+23fbDFojVr1qhnz56Sfpk5CggI0OjRo/XKK69IkrKysuTn56fY2Fj17dtX3377rYKCgrRz5061bNlSkhQfH6+uXbvqxx9/VEBAgBYuXKhXX31VaWlpcnd3lySNHz9ea9eu1cGDByVJffr0UXZ2ttatW2f007ZtWzVv3lyLFi26pV5uxmazydvbW1lZWbJarbf7Nd01tcbHObuFEun4jHBntwAAAIASpjjZwNR7so4dO6a0tDSFhoYaY97e3mrTpo2SkpIkSUlJSfLx8TECliSFhobKxcVFX331lVHz6KOPGgFLksLCwnTo0CGdO3fOqLn2fQpqCt7nVnq5Xk5Ojmw2m8MGAAAAAMVhashKS0uTJPn5+TmM+/n5GfvS0tJUtWpVh/1ubm6qVKmSQ01R57j2PX6t5tr9N+vletOnT5e3t7exBQYG3sKnBgAAAID/YXXBa0yYMEFZWVnGduLECWe3BAAAAKCEMTVk+fv7S5LS09MdxtPT0419/v7+On36tMP+q1ev6uzZsw41RZ3j2vf4tZpr99+sl+t5eHjIarU6bAAAAABQHG5mnqx27dry9/dXYmKimjdvLumXG8S++uorDR8+XJIUEhKizMxMJScnKzg4WJK0adMm5efnq02bNkbNq6++qitXrqhMmTKSpISEBNWvX18VK1Y0ahITEzVq1Cjj/RMSEhQSEnLLvQC4NSyycntYZAUAgN+mYs9kXbhwQSkpKUpJSZH0ywITKSkpSk1NlcVi0ahRo/Taa6/p448/1r59+zRw4EAFBAQYKxA2bNhQnTt31pAhQ7Rjxw598cUXGjFihPr27auAgABJUr9+/eTu7q7IyEgdOHBAK1eu1Ny5cxUTE2P08fLLLys+Pl5vvPGGDh48qClTpmjXrl0aMWKEJN1SLwAAAABgtmLPZO3atUvt27c3XhcEn4iICMXGxmrs2LHKzs7W0KFDlZmZqYcffljx8fHy9PQ0jlm+fLlGjBihjh07ysXFRb169dKbb75p7Pf29tZnn32mqKgoBQcHq0qVKpo0aZLDs7TatWunFStWaOLEifrTn/6kevXqae3atWrcuLFRcyu9AAAAAICZ7ug5WaUdz8kqnbiEq/j4rd0efmsAAJQeTntOFgAAAAD81hGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExkesiaMmWKLBaLw9agQQNj/+XLlxUVFaXKlSvLy8tLvXr1Unp6usM5UlNTFR4ernLlyqlq1aoaM2aMrl696lCzZcsWtWjRQh4eHqpbt65iY2ML9TJ//nzVqlVLnp6eatOmjXbs2GH2xwUAAAAAB3dlJqtRo0Y6deqUsX3++efGvujoaH3yySdavXq1tm7dqpMnT+rpp5829ufl5Sk8PFy5ubnavn27li1bptjYWE2aNMmoOXbsmMLDw9W+fXulpKRo1KhRGjx4sDZs2GDUrFy5UjExMZo8ebJ2796tZs2aKSwsTKdPn74bHxkAAAAAJN2lkOXm5iZ/f39jq1KliiQpKytLixcv1qxZs9ShQwcFBwdr6dKl2r59u7788ktJ0meffaZvvvlG7733npo3b64uXbroL3/5i+bPn6/c3FxJ0qJFi1S7dm298cYbatiwoUaMGKHevXtr9uzZRg+zZs3SkCFDNGjQIAUFBWnRokUqV66clixZcjc+MgAAAABIuksh6/DhwwoICNADDzyg/v37KzU1VZKUnJysK1euKDQ01Kht0KCBatSooaSkJElSUlKSmjRpIj8/P6MmLCxMNptNBw4cMGquPUdBTcE5cnNzlZyc7FDj4uKi0NBQo6YoOTk5stlsDhsAAAAAFIfpIatNmzaKjY1VfHy8Fi5cqGPHjumRRx7R+fPnlZaWJnd3d/n4+Dgc4+fnp7S0NElSWlqaQ8Aq2F+w70Y1NptNly5d0pkzZ5SXl1dkTcE5ijJ9+nR5e3sbW2Bg4G19BwAAAAB+u9zMPmGXLl2MPzdt2lRt2rRRzZo1tWrVKpUtW9bstzPVhAkTFBMTY7y22WwELQAAAADFcteXcPfx8dHvf/97HTlyRP7+/srNzVVmZqZDTXp6uvz9/SVJ/v7+hVYbLHh9sxqr1aqyZcuqSpUqcnV1LbKm4BxF8fDwkNVqddgAAAAAoDjuesi6cOGCjh49qmrVqik4OFhlypRRYmKisf/QoUNKTU1VSEiIJCkkJET79u1zWAUwISFBVqtVQUFBRs215yioKTiHu7u7goODHWry8/OVmJho1AAAAADA3WB6yHrllVe0detWHT9+XNu3b9dTTz0lV1dXPfvss/L29lZkZKRiYmK0efNmJScna9CgQQoJCVHbtm0lSZ06dVJQUJAGDBigPXv2aMOGDZo4caKioqLk4eEhSRo2bJi+//57jR07VgcPHtSCBQu0atUqRUdHG33ExMToX//6l5YtW6Zvv/1Ww4cPV3Z2tgYNGmT2RwYAAAAAg+n3ZP3444969tln9fPPP8vX11cPP/ywvvzyS/n6+kqSZs+eLRcXF/Xq1Us5OTkKCwvTggULjONdXV21bt06DR8+XCEhISpfvrwiIiI0bdo0o6Z27dqKi4tTdHS05s6dq+rVq+vtt99WWFiYUdOnTx9lZGRo0qRJSktLU/PmzRUfH19oMQwAAAAAMJPFbrfbnd3E/cpms8nb21tZWVn35f1ZtcbHObuFEun4jHBnt1Di8Fu7PfzWAAAoPYqTDe76PVkAAAAA8FtCyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBEhCwAAAAAMBEhCwAAAABMRMgCAAAAABMRsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwkZuzG7gX5s+fr9dff11paWlq1qyZ/vGPf6h169bObgsAcJ1a4+Oc3UKJdHxGuLNbAABco9TPZK1cuVIxMTGaPHmydu/erWbNmiksLEynT592dmsAAAAASqFSH7JmzZqlIUOGaNCgQQoKCtKiRYtUrlw5LVmyxNmtAQAAACiFSvXlgrm5uUpOTtaECROMMRcXF4WGhiopKalQfU5OjnJycozXWVlZkiSbzXb3m70N+TkXnd1CiXS//u95P+O3dnv4rRUfv7Xbw2+t+BpP3uDsFkqk/VPDnN0C4DQF/7/WbrfftLZUh6wzZ84oLy9Pfn5+DuN+fn46ePBgofrp06dr6tSphcYDAwPvWo+497znOLsD/FbwW8O9wm8N9wq/NUA6f/68vL29b1hTqkNWcU2YMEExMTHG6/z8fJ09e1aVK1eWxWJxYmcli81mU2BgoE6cOCGr1ersdlCK8VvDvcJvDfcKvzXcK/zWis9ut+v8+fMKCAi4aW2pDllVqlSRq6ur0tPTHcbT09Pl7+9fqN7Dw0MeHh4OYz4+PnezxVLNarXyf7S4J/it4V7ht4Z7hd8a7hV+a8VzsxmsAqV64Qt3d3cFBwcrMTHRGMvPz1diYqJCQkKc2BkAAACA0qpUz2RJUkxMjCIiItSyZUu1bt1ac+bMUXZ2tgYNGuTs1gAAAACUQqU+ZPXp00cZGRmaNGmS0tLS1Lx5c8XHxxdaDAPm8fDw0OTJkwtdegmYjd8a7hV+a7hX+K3hXuG3dndZ7LeyBiEAAAAA4JaU6nuyAAAAAOBeI2QBAAAAgIkIWQAAAABgIkIWAAAAAJiIkAUAAAAAJiJkAQAAAKVcXl6etm3bpszMTGe38ptAyIIp4uPj9fnnnxuv58+fr+bNm6tfv346d+6cEzsDgNuXmZmpt99+WxMmTNDZs2clSbt379ZPP/3k5M5Q0tlstlveADO4urqqU6dO/L3sHuE5WTBFkyZN9Le//U1du3bVvn371KpVK8XExGjz5s1q0KCBli5d6uwWUYK9+eabt1z70ksv3cVO8Fuyd+9ehYaGytvbW8ePH9ehQ4f0wAMPaOLEiUpNTdU777zj7BZRgrm4uMhisdywxm63y2KxKC8v7x51hdKuZcuW+tvf/qaOHTs6u5VSj5AFU3h5eWn//v2qVauWpkyZov379+vf//63du/era5duyotLc3ZLaIEq1279i3VWSwWff/993e5G/xWhIaGqkWLFpo5c6YqVKigPXv26IEHHtD27dvVr18/HT9+3NktogTbunXrLdc+9thjd7ET/JbEx8drwoQJ+stf/qLg4GCVL1/eYb/VanVSZ6WPm7MbQOng7u6uixcvSpI2btyogQMHSpIqVarEpQ64Y8eOHXN2C/gN2rlzp/75z38WGv/d737HPxzhjhGc4Axdu3aVJPXo0cNhJpVZU/MRsmCKhx9+WDExMXrooYe0Y8cOrVy5UpL03XffqXr16k7uDgCKz8PDo8h/JPruu+/k6+vrhI5QWuXn58vFpfBt8vn5+frxxx9Vo0YNJ3SF0mjz5s3ObuE3g8sFYYrU1FS9+OKLOnHihF566SVFRkZKkqKjo5WXl1ese2qAm/nxxx/18ccfKzU1Vbm5uQ77Zs2a5aSuUNoMHjxYP//8s1atWqVKlSpp7969cnV1Vc+ePfXoo49qzpw5zm4RJZzNZtPgwYP1ySefyGq16o9//KMmT54sV1dXSVJ6eroCAgKYXQBKIEIWgBIlMTFRPXr00AMPPKCDBw+qcePGOn78uOx2u1q0aKFNmzY5u0WUEllZWerdu7d27dql8+fPKyAgQGlpaQoJCdGnn35a6F4GoLhefvllxcfH669//asyMzP12muvqXHjxvrPf/4jd3d3paenq1q1asrPz3d2qyglmDW9dwhZME1eXp7Wrl2rb7/9VpLUqFEj9ejRw/gXOcAMrVu3VpcuXTR16lRjMYKqVauqf//+6ty5s4YPH+7sFlHKfP7559q7d68uXLigFi1aKDQ01NktoZSoWbOmli1bpscff1ySdObMGYWHh8vHx0cff/yxMjMzmcmCKZg1vfcIWTDFkSNH1LVrV/3000+qX7++JOnQoUMKDAxUXFyc6tSp4+QOUVpUqFBBKSkpqlOnjipWrKjPP/9cjRo10p49e/Tkk0+y4huAEqNcuXI6cOCAwwqq58+fV1hYmMqWLau3335bdevW5S++uGPMmt57PIwYpnjppZdUp04dnThxQrt379bu3buVmpqq2rVr89wimKp8+fLGfVjVqlXT0aNHjX1nzpxxVlsopRITE9WtWzfVqVNHderUUbdu3bRx40Znt4VSokaNGsbVHwUqVKigzz77TJcuXdJTTz3lpM5Q2qxdu1b//Oc/1bt3bw0ePFi7du1SRkaGunfvrpycHEm66XPbUDyELJhi69atmjlzpipVqmSMVa5cWTNmzCjWs0CAm2nbtq0+//xzSb8sRTt69Gj99a9/1QsvvKC2bds6uTuUJgsWLFDnzp1VoUIFvfzyy3r55ZdltVrVtWtXzZ8/39ntoRTo1KmTli5dWmjcy8tLGzZskKenpxO6QmmUkZGhmjVrGq+rVKmijRs36vz58+ratavxGB6Yh8sFYYpKlSpp3bp1ateuncP4F198oe7du+vs2bNO6gylzffff68LFy6oadOmys7O1ujRo7V9+3bVq1dPs2bNcviPCHAnqlevrvHjx2vEiBEO4/Pnz9f//d//6aeffnJSZygtzp07pxMnTig6OlqLFi1SvXr1HPafP39eu3fv5plauGMNGjTQrFmzjOdkFbhw4YI6deqkixcvat++fVyaaiJCFkwxcOBA7d69W4sXL1br1q0lSV999ZWGDBmi4OBgxcbGOrdBACgmLy8vpaSkqG7dug7jhw8f1oMPPqgLFy44qTOUNr6+vsY/FgF3w0svvaRTp05p9erVhfadP39eTzzxhHbu3EnIMhEhC6bIzMxURESEPvnkE5UpU0aSdOXKFT355JNaunSpfHx8nNsgSqULFy4UuknXarU6qRuUNv369dODDz6oMWPGOIz//e9/165du/TBBx84qTOUNtHR0fLw8NCMGTOc3QpKqXPnzunkyZNq1KhRkfuZNTWfm7MbQOng4+Ojjz76SEeOHDFu4m3YsGGhfwEG7tSxY8c0YsQIbdmyRZcvXzbG7Xa7LBYL/wqHO3Ltg9ODgoL017/+VVu2bFFISIgk6csvv9QXX3yh0aNHO6tFlEJXr17VkiVLtHHjRgUHBxd6BhsPWcedqlixoipWrKhp06bdsI6QZR5msnDbYmJibrmW/0DALA899JDsdrtefvll+fn5FVoNif9A4E5cu5T2jVgsFn3//fd3uRv8VrRv3/5X91ksFh6yDtM8+OCDDq+vXLmiY8eOyc3NTXXq1NHu3bud1FnpQ8jCbbv+Pwq7d+/W1atXjedkfffdd3J1dVVwcDD/gYBpvLy8lJycbPzOAADA7bPZbHr++ef11FNPacCAAc5up9TgckHcts2bNxt/njVrlipUqKBly5apYsWKkn65/nfQoEF65JFHnNUiSqFWrVrpxIkThCzcUwX/HslzZACUNlarVVOnTlX37t0JWSZiJgum+N3vfqfPPvus0A2V+/fvV6dOnXTy5EkndYbS5ujRoxo2bJiee+45NW7c2FhopUDTpk2d1BlKo3feeUevv/66Dh8+LEn6/e9/rzFjxvAXEQClyueff67u3bvr3Llzzm6l1GAmC6aw2WzKyMgoNJ6RkaHz5887oSOUVhkZGTp69KgGDRpkjFksFha+gOlmzZqlP//5zxoxYoQeeughSb/8RWTYsGE6c+aMoqOjndwhABTPtYv7SL/M0p86dUrvvvuuunTp4qSuSidmsmCKgQMH6r///a/eeOMNh+dkjRkzRo888oiWLVvm5A5RWgQFBalhw4YaO3ZskQtf8DBimKV27dqaOnWqBg4c6DC+bNkyTZkyRceOHXNSZwBwe65f3MfFxUW+vr7q0KGDJkyYoAoVKjips9KHkAVTXLx4Ua+88oqWLFmiK1euSJLc3NwUGRmp119/vdBytMDtKl++vPbs2cPjAXDXeXp6av/+/UU+jLhJkyYOjxAAAOBaLs5uAKVDuXLltGDBAv3888/6+uuv9fXXX+vs2bNasGABAQum6tChg/bs2ePsNvAbULduXa1atarQ+MqVK1WvXj0ndAQAKCm4JwumKl++PAsP4K7q3r27oqOjtW/fPjVp0qTQwhc9evRwUmcobaZOnao+ffpo27Ztxj1ZX3zxhRITE4sMXwAAFOByQQAliovLr0/As/AFzLZ7927NmjVL3377rSSpYcOGGj16dKEHegIAcC1CFgAA17ly5Yr++Mc/6s9//nOhG8UBALgZ7skCUGJcuXJFbm5u2r9/v7NbQSlXpkwZffjhh85uAwBQQhGyAJQYZcqUUY0aNbgkEPdEz549tXbtWme3AQAogVj4AkCJ8uqrr+pPf/qT3n33XVWqVMnZ7aAUq1evnqZNm6YvvvhCwcHBhVZKfemll5zUGQDgfsc9WQBKlAcffFBHjhzRlStXVLNmzUJ/8d29e7eTOkNpc6N7sSwWi77//vt72A0AoCRhJgtAidKzZ09nt4DfiGPHjhl/Lvj3SIvF4qx2AAAlCDNZAAD8isWLF2v27Nk6fPiwpF8uIRw1apQGDx7s5M4AAPczZrIAlEjJycnGs4saNWrEc4tgukmTJmnWrFkaOXKkQkJCJElJSUmKjo5Wamqqpk2b5uQOAQD3K2ayAJQop0+fVt++fbVlyxb5+PhIkjIzM9W+fXt98MEH8vX1dW6DKDV8fX315ptv6tlnn3UYf//99zVy5EidOXPGSZ0BAO53LOEOoEQZOXKkzp8/rwMHDujs2bM6e/as9u/fL5vNxmpvMNWVK1fUsmXLQuPBwcG6evWqEzoCAJQUzGQBKFG8vb21ceNGtWrVymF8x44d6tSpkzIzM53TGEqdkSNHqkyZMpo1a5bD+CuvvKJLly5p/vz5TuoMAHC/454sACVKfn6+ypQpU2i8TJkyys/Pd0JHKM0WL16szz77TG3btpUkffXVV0pNTdXAgQMVExNj1F0fxAAAv23MZAEoUZ588kllZmbq/fffV0BAgCTpp59+Uv/+/VWxYkWtWbPGyR2itGjfvv0t1VksFm3atOkudwMAKEkIWQBKlBMnTqhHjx46cOCAAgMDJUmpqalq0qSJPv74Y1WvXt3JHQIAgN86QhaAEsdutysxMdFYwr1hw4YKDQ11clcAAAC/IGQBKHESExOVmJio06dPF7oPa8mSJU7qCgAA4BcsfAGgRJk6daqmTZumli1bqlq1arJYLM5uCQAAwAEzWQBKlGrVqmnmzJkaMGCAs1sBAAAoEg8jBlCi5Obmql27ds5uAwAA4FcRsgCUKIMHD9aKFSuc3QYAAMCv4p4sACXK5cuX9dZbb2njxo1q2rRpoQcT81BYAADgbNyTBaBEudEDYnkoLAAAuB8QsgAAAADARNyTBQAAAAAmImQBAAAAgIkIWQAAAABgIkIWAABF2LJliywWizIzM53dCgCghCFkAQDuK0lJSXJ1dVV4eHihfVOmTFHz5s0LjVssFq1du/buN3cDFovlhtuUKVOc2h8A4N7hOVkAgPvK4sWLNXLkSC1evFgnT55UQECAs1u6JadOnTL+vHLlSk2aNEmHDh0yxry8vJzRFgDACZjJAgDcNy5cuKCVK1dq+PDhCg8PV2xsrLEvNjZWU6dO1Z49e4zZodjYWNWqVUuS9NRTT8lisRivjx49qieffFJ+fn7y8vJSq1attHHjRof3y8nJ0bhx4xQYGCgPDw/VrVtXixcvLrK3ixcvqkuXLnrooYeKvITQ39/f2Ly9vWWxWOTv768KFSro97//veLj4x3q165dq/Lly+v8+fM6fvy4LBaLPvjgA7Vr106enp5q3Lixtm7d6nDM/v371aVLF3l5ecnPz08DBgzQmTNnivclAwDuOkIWAOC+sWrVKjVo0ED169fXc889pyVLlqjgcY59+vTR6NGj1ahRI506dUqnTp1Snz59tHPnTknS0qVLderUKeP1hQsX1LVrVyUmJurrr79W586d1b17d6WmphrvN3DgQL3//vt688039e233+qf//xnkTNOmZmZeuKJJ5Sfn6+EhAT5+Pjc8mcqX768+vbtq6VLlzqML126VL1791aFChWMsTFjxmj06NH6+uuvFRISou7du+vnn382eujQoYMefPBB7dq1S/Hx8UpPT9czzzxzy70AAO4NLhcEANw3Fi9erOeee06S1LlzZ2VlZWnr1q16/PHHVbZsWXl5ecnNzU3+/v7GMWXLlpUk+fj4OIw3a9ZMzZo1M17/5S9/0Zo1a/Txxx9rxIgR+u6777Rq1SolJCQoNDRUkvTAAw8U6iktLU19+vRRvXr1tGLFCrm7uxf7cw0ePFjt2rXTqVOnVK1aNZ0+fVqffvppoZm1ESNGqFevXpKkhQsXKj4+XosXL9bYsWM1b948Pfjgg/q///s/o37JkiUKDAzUd999p9///vfF7gsAcHcwkwUAuC8cOnRIO3bs0LPPPitJcnNzU58+fX718r2buXDhgl555RU1bNhQPj4+8vLy0rfffmvMZKWkpMjV1VWPPfbYDc/zxBNPqG7dulq5cuVtBSxJat26tRo1aqRly5ZJkt577z3VrFlTjz76qENdSEiI8Wc3Nze1bNlS3377rSRpz5492rx5s7y8vIytQYMGkn65NBIAcP9gJgsAcF9YvHixrl696rDQhd1ul4eHh+bNmydvb+9ine+VV15RQkKC/v73v6tu3boqW7asevfurdzcXEn/mwG7mfDwcH344Yf65ptv1KRJk2L1cK3Bgwdr/vz5Gj9+vJYuXapBgwbJYrHc8vEXLlxQ9+7d9be//a3QvmrVqt12XwAA8zGTBQBwuqtXr+qdd97RG2+8oZSUFGPbs2ePAgIC9P7770uS3N3dlZeXV+j4MmXKFBr/4osv9Pzzz+upp55SkyZN5O/vr+PHjxv7mzRpovz8/EKLS1xvxowZioiIUMeOHfXNN9/c9md87rnn9MMPP+jNN9/UN998o4iIiEI1X375pfHnq1evKjk5WQ0bNpQktWjRQgcOHFCtWrVUt25dh618+fK33RcAwHyELACA061bt07nzp1TZGSkGjdu7LD16tXLuGSwVq1aOnbsmFJSUnTmzBnl5OQY44mJiUpLS9O5c+ckSfXq1dN//vMfI6z169dP+fn5xnvWqlVLEREReuGFF7R27VodO3ZMW7Zs0apVqwr19/e//139+/dXhw4ddPDgwdv6jBUrVtTTTz+tMWPGqFOnTqpevXqhmvnz52vNmjU6ePCgoqKidO7cOb3wwguSpKioKJ09e1bPPvusdu7cqaNHj2rDhg0aNGhQkcETAOA8hCwAgNMtXrxYoaGhRV4S2KtXL+3atUt79+5Vr1691LlzZ7Vv316+vr7GDNcbb7yhhIQEBQYG6sEHH5QkzZo1SxUrVlS7du3UvXt3hYWFqUWLFg7nXrhwoXr37q0XX3xRDRo00JAhQ5SdnV1kj7Nnz9YzzzyjDh066LvvvrutzxkZGanc3FwjOF1vxowZmjFjhpo1a6bPP/9cH3/8sapUqSJJCggI0BdffKG8vDx16tRJTZo00ahRo+Tj4yMXF/5zDgD3E4u9YG1cAABwV7377ruKjo7WyZMnHRbROH78uGrXrq2vv/5azZs3d16DAABTsPAFAAB32cWLF3Xq1CnNmDFDf/zjH297lUIAQMnA9QUAANxlM2fOVIMGDeTv768JEyY4ux0AwF3G5YIAAAAAYCJmsgAAAADARIQsAAAAADARIQsAAAAATETIAgAAAAATEbIAAAAAwESELAAAAAAwESELAAAAAExEyAIAAAAAExGyAAAAAMBE/w9MpsFLt99aZgAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Display the columns of the dataframe\n",
+        "print(df.columns)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "w6ZROcj4gxVa",
+        "outputId": "b16fde34-f497-4948-f25e-1761d5685ba5"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Index(['duration', 'protocol_type', 'service', 'flag', 'src_bytes',\n",
+            "       'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot',\n",
+            "       'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell',\n",
+            "       'su_attempted', 'num_root', 'num_file_creations', 'num_shells',\n",
+            "       'num_access_files', 'num_outbound_cmds', 'is_host_login',\n",
+            "       'is_guest_login', 'count', 'srv_count', 'serror_rate',\n",
+            "       'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate',\n",
+            "       'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count',\n",
+            "       'dst_host_srv_count', 'dst_host_same_srv_rate',\n",
+            "       'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate',\n",
+            "       'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
+            "       'dst_host_srv_serror_rate', 'dst_host_rerror_rate',\n",
+            "       'dst_host_srv_rerror_rate', 'target', 'Attack Type'],\n",
+            "      dtype='object')\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Display the columns of the dataframe\n",
+        "print(df.columns)\n",
+        "\n",
+        "# Drop columns with NaN\n",
+        "df = df.dropna(axis=1)\n",
+        "\n",
+        "# Keep columns where there are more than 1 unique values\n",
+        "df = df[[col for col in df if df[col].nunique() > 1]]\n",
+        "\n",
+        "# Select only numerical columns for correlation\n",
+        "df_numeric = df.select_dtypes(include=['number'])\n",
+        "\n",
+        "# Calculate the correlation matrix for numerical columns\n",
+        "corr = df_numeric.corr()\n",
+        "\n",
+        "plt.figure(figsize=(15,12))\n",
+        "\n",
+        "sns.heatmap(corr)\n",
+        "plt.savefig('correlation_heatmap.png')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:54.784787Z",
+          "iopub.execute_input": "2022-11-14T04:34:54.785347Z",
+          "iopub.status.idle": "2022-11-14T04:34:57.097796Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:54.785315Z",
+          "shell.execute_reply": "2022-11-14T04:34:57.096557Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "iNaNXenafJnm",
+        "outputId": "dd21552c-98be-4792-ae22-5637b65ac14e"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Index(['duration', 'protocol_type', 'service', 'flag', 'src_bytes',\n",
+            "       'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot',\n",
+            "       'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell',\n",
+            "       'su_attempted', 'num_root', 'num_file_creations', 'num_shells',\n",
+            "       'num_access_files', 'is_guest_login', 'count', 'srv_count',\n",
+            "       'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate',\n",
+            "       'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate',\n",
+            "       'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate',\n",
+            "       'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate',\n",
+            "       'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
+            "       'dst_host_srv_serror_rate', 'dst_host_rerror_rate',\n",
+            "       'dst_host_srv_rerror_rate', 'target', 'Attack Type'],\n",
+            "      dtype='object')\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x1200 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This variable is highly correlated with num_compromised and should be ignored for analysis.\n",
+        "#(Correlation = 0.9938277978738366)\n",
+        "df.drop('num_root',axis = 1,inplace = True)\n",
+        "\n",
+        "#This variable is highly correlated with serror_rate and should be ignored for analysis.\n",
+        "#(Correlation = 0.9983615072725952)\n",
+        "df.drop('srv_serror_rate',axis = 1,inplace = True)\n",
+        "\n",
+        "#This variable is highly correlated with rerror_rate and should be ignored for analysis.\n",
+        "#(Correlation = 0.9947309539817937)\n",
+        "df.drop('srv_rerror_rate',axis = 1, inplace=True)\n",
+        "\n",
+        "#This variable is highly correlated with srv_serror_rate and should be ignored for analysis.\n",
+        "#(Correlation = 0.9993041091850098)\n",
+        "df.drop('dst_host_srv_serror_rate',axis = 1, inplace=True)\n",
+        "\n",
+        "#This variable is highly correlated with rerror_rate and should be ignored for analysis.\n",
+        "#(Correlation = 0.9869947924956001)\n",
+        "df.drop('dst_host_serror_rate',axis = 1, inplace=True)\n",
+        "\n",
+        "#This variable is highly correlated with srv_rerror_rate and should be ignored for analysis.\n",
+        "#(Correlation = 0.9821663427308375)\n",
+        "df.drop('dst_host_rerror_rate',axis = 1, inplace=True)\n",
+        "\n",
+        "#This variable is highly correlated with rerror_rate and should be ignored for analysis.\n",
+        "#(Correlation = 0.9851995540751249)\n",
+        "df.drop('dst_host_srv_rerror_rate',axis = 1, inplace=True)\n",
+        "\n",
+        "#This variable is highly correlated with dst_host_srv_count and should be ignored for analysis.\n",
+        "#(Correlation = 0.9736854572953938)\n",
+        "df.drop('dst_host_same_srv_rate',axis = 1, inplace=True)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:57.099506Z",
+          "iopub.execute_input": "2022-11-14T04:34:57.09987Z",
+          "iopub.status.idle": "2022-11-14T04:34:57.510696Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:57.099838Z",
+          "shell.execute_reply": "2022-11-14T04:34:57.50944Z"
+        },
+        "trusted": true,
+        "id": "-VTRrgVhfJnn"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:57.512404Z",
+          "iopub.execute_input": "2022-11-14T04:34:57.513073Z",
+          "iopub.status.idle": "2022-11-14T04:34:57.536696Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:57.51304Z",
+          "shell.execute_reply": "2022-11-14T04:34:57.535669Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "xez6CK-bfJno",
+        "outputId": "d018dba0-b399-492f-e824-8ff1a507b3f2"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   duration protocol_type service flag  src_bytes  dst_bytes  land  \\\n",
+              "0         0           tcp    http   SF        181       5450     0   \n",
+              "1         0           tcp    http   SF        239        486     0   \n",
+              "2         0           tcp    http   SF        235       1337     0   \n",
+              "3         0           tcp    http   SF        219       1337     0   \n",
+              "4         0           tcp    http   SF        217       2032     0   \n",
+              "\n",
+              "   wrong_fragment  urgent  hot  ...  same_srv_rate  diff_srv_rate  \\\n",
+              "0               0       0    0  ...            1.0            0.0   \n",
+              "1               0       0    0  ...            1.0            0.0   \n",
+              "2               0       0    0  ...            1.0            0.0   \n",
+              "3               0       0    0  ...            1.0            0.0   \n",
+              "4               0       0    0  ...            1.0            0.0   \n",
+              "\n",
+              "   srv_diff_host_rate  dst_host_count  dst_host_srv_count  \\\n",
+              "0                 0.0               9                   9   \n",
+              "1                 0.0              19                  19   \n",
+              "2                 0.0              29                  29   \n",
+              "3                 0.0              39                  39   \n",
+              "4                 0.0              49                  49   \n",
+              "\n",
+              "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
+              "0                     0.0                         0.11   \n",
+              "1                     0.0                         0.05   \n",
+              "2                     0.0                         0.03   \n",
+              "3                     0.0                         0.03   \n",
+              "4                     0.0                         0.02   \n",
+              "\n",
+              "   dst_host_srv_diff_host_rate   target  Attack Type  \n",
+              "0                          0.0  normal.       normal  \n",
+              "1                          0.0  normal.       normal  \n",
+              "2                          0.0  normal.       normal  \n",
+              "3                          0.0  normal.       normal  \n",
+              "4                          0.0  normal.       normal  \n",
+              "\n",
+              "[5 rows x 33 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9019ea5a-cb9c-4f91-bfe6-ea1bff9af41f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>duration</th>\n",
+              "      <th>protocol_type</th>\n",
+              "      <th>service</th>\n",
+              "      <th>flag</th>\n",
+              "      <th>src_bytes</th>\n",
+              "      <th>dst_bytes</th>\n",
+              "      <th>land</th>\n",
+              "      <th>wrong_fragment</th>\n",
+              "      <th>urgent</th>\n",
+              "      <th>hot</th>\n",
+              "      <th>...</th>\n",
+              "      <th>same_srv_rate</th>\n",
+              "      <th>diff_srv_rate</th>\n",
+              "      <th>srv_diff_host_rate</th>\n",
+              "      <th>dst_host_count</th>\n",
+              "      <th>dst_host_srv_count</th>\n",
+              "      <th>dst_host_diff_srv_rate</th>\n",
+              "      <th>dst_host_same_src_port_rate</th>\n",
+              "      <th>dst_host_srv_diff_host_rate</th>\n",
+              "      <th>target</th>\n",
+              "      <th>Attack Type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>tcp</td>\n",
+              "      <td>http</td>\n",
+              "      <td>SF</td>\n",
+              "      <td>181</td>\n",
+              "      <td>5450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>9</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.11</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>normal.</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>tcp</td>\n",
+              "      <td>http</td>\n",
+              "      <td>SF</td>\n",
+              "      <td>239</td>\n",
+              "      <td>486</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>19</td>\n",
+              "      <td>19</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.05</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>normal.</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>tcp</td>\n",
+              "      <td>http</td>\n",
+              "      <td>SF</td>\n",
+              "      <td>235</td>\n",
+              "      <td>1337</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>29</td>\n",
+              "      <td>29</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>normal.</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>tcp</td>\n",
+              "      <td>http</td>\n",
+              "      <td>SF</td>\n",
+              "      <td>219</td>\n",
+              "      <td>1337</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>39</td>\n",
+              "      <td>39</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>normal.</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>tcp</td>\n",
+              "      <td>http</td>\n",
+              "      <td>SF</td>\n",
+              "      <td>217</td>\n",
+              "      <td>2032</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>49</td>\n",
+              "      <td>49</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.02</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>normal.</td>\n",
+              "      <td>normal</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 33 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9019ea5a-cb9c-4f91-bfe6-ea1bff9af41f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9019ea5a-cb9c-4f91-bfe6-ea1bff9af41f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9019ea5a-cb9c-4f91-bfe6-ea1bff9af41f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-3966f24a-96f2-4e76-884f-bd62ed883a89\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3966f24a-96f2-4e76-884f-bd62ed883a89')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-3966f24a-96f2-4e76-884f-bd62ed883a89 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Feature encoding"
+      ],
+      "metadata": {
+        "id": "ax_voBy5fJno"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#protocol_type feature mapping\n",
+        "pmap = {'icmp':0,'tcp':1,'udp':2}\n",
+        "df['protocol_type'] = df['protocol_type'].map(pmap)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:57.539043Z",
+          "iopub.execute_input": "2022-11-14T04:34:57.539627Z",
+          "iopub.status.idle": "2022-11-14T04:34:57.582924Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:57.539516Z",
+          "shell.execute_reply": "2022-11-14T04:34:57.581549Z"
+        },
+        "trusted": true,
+        "id": "IhJ8YcypfJnp"
+      },
+      "execution_count": 26,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#flag feature mapping\n",
+        "fmap = {'SF':0,'S0':1,'REJ':2,'RSTR':3,'RSTO':4,'SH':5 ,'S1':6 ,'S2':7,'RSTOS0':8,'S3':9 ,'OTH':10}\n",
+        "df['flag'] = df['flag'].map(fmap)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:58.165751Z",
+          "iopub.execute_input": "2022-11-14T04:34:58.166128Z",
+          "iopub.status.idle": "2022-11-14T04:34:58.208464Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:58.166098Z",
+          "shell.execute_reply": "2022-11-14T04:34:58.207739Z"
+        },
+        "trusted": true,
+        "id": "Puajj0WsfJnp"
+      },
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#attack type feature mapping\n",
+        "amap = {'dos':0,'normal':1,'probe':2,'r2l':3,'u2r':4}\n",
+        "df['Attack Type'] = df['Attack Type'].map(amap)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:58.769039Z",
+          "iopub.execute_input": "2022-11-14T04:34:58.769403Z",
+          "iopub.status.idle": "2022-11-14T04:34:58.802526Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:58.769378Z",
+          "shell.execute_reply": "2022-11-14T04:34:58.800772Z"
+        },
+        "trusted": true,
+        "id": "jUvm48P-fJnq"
+      },
+      "execution_count": 28,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.drop('service',axis = 1,inplace= True)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:34:59.794244Z",
+          "iopub.execute_input": "2022-11-14T04:34:59.794875Z",
+          "iopub.status.idle": "2022-11-14T04:34:59.857705Z",
+          "shell.execute_reply.started": "2022-11-14T04:34:59.794843Z",
+          "shell.execute_reply": "2022-11-14T04:34:59.856687Z"
+        },
+        "trusted": true,
+        "id": "08szUu2-fJnq"
+      },
+      "execution_count": 29,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Models"
+      ],
+      "metadata": {
+        "id": "oOrz_H99fJnr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from sklearn.metrics import accuracy_score"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:35:00.333238Z",
+          "iopub.execute_input": "2022-11-14T04:35:00.333657Z",
+          "iopub.status.idle": "2022-11-14T04:35:00.33976Z",
+          "shell.execute_reply.started": "2022-11-14T04:35:00.333626Z",
+          "shell.execute_reply": "2022-11-14T04:35:00.338333Z"
+        },
+        "trusted": true,
+        "id": "HdKTwtt2fJnr"
+      },
+      "execution_count": 30,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf\n",
+        "from keras.models import Sequential, Model\n",
+        "from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten, Dropout, Input, Concatenate, Add"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:35:01.062238Z",
+          "iopub.execute_input": "2022-11-14T04:35:01.062637Z",
+          "iopub.status.idle": "2022-11-14T04:35:07.263624Z",
+          "shell.execute_reply.started": "2022-11-14T04:35:01.062583Z",
+          "shell.execute_reply": "2022-11-14T04:35:07.262652Z"
+        },
+        "trusted": true,
+        "id": "dX08xr5EfJnr"
+      },
+      "execution_count": 31,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = df.drop(['target',], axis=1)\n",
+        "print(df.shape)\n",
+        "\n",
+        "# Target variable and train set\n",
+        "Y = df[['Attack Type']]\n",
+        "X = df.drop(['Attack Type',], axis=1)\n",
+        "\n",
+        "sc = MinMaxScaler()\n",
+        "X = sc.fit_transform(X)\n",
+        "\n",
+        "# Split test and train data\n",
+        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)\n",
+        "print(X_train.shape, X_test.shape)\n",
+        "print(Y_train.shape, Y_test.shape)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:35:07.265373Z",
+          "iopub.execute_input": "2022-11-14T04:35:07.265985Z",
+          "iopub.status.idle": "2022-11-14T04:35:07.654828Z",
+          "shell.execute_reply.started": "2022-11-14T04:35:07.265946Z",
+          "shell.execute_reply": "2022-11-14T04:35:07.653903Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AuJrNvMTfJnr",
+        "outputId": "f111fbe1-4772-4bb7-9312-77d9dd283e52"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(494021, 31)\n",
+            "(330994, 30) (163027, 30)\n",
+            "(330994, 1) (163027, 1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.to_csv(\"ids.csv\", index=False)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:37:55.966266Z",
+          "iopub.execute_input": "2022-11-14T04:37:55.966688Z",
+          "iopub.status.idle": "2022-11-14T04:37:59.294378Z",
+          "shell.execute_reply.started": "2022-11-14T04:37:55.966654Z",
+          "shell.execute_reply": "2022-11-14T04:37:59.292389Z"
+        },
+        "trusted": true,
+        "id": "2aDGMM_dfJns"
+      },
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.read_csv(\"ids.csv\")"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-14T04:37:59.296806Z",
+          "iopub.execute_input": "2022-11-14T04:37:59.297153Z",
+          "iopub.status.idle": "2022-11-14T04:37:59.945352Z",
+          "shell.execute_reply.started": "2022-11-14T04:37:59.297122Z",
+          "shell.execute_reply": "2022-11-14T04:37:59.944196Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 461
+        },
+        "id": "FB0UoN_xfJns",
+        "outputId": "586c2114-134a-40de-8ecc-865b7f19c118"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        duration  protocol_type  flag  src_bytes  dst_bytes  land  \\\n",
+              "0              0              1     0        181       5450     0   \n",
+              "1              0              1     0        239        486     0   \n",
+              "2              0              1     0        235       1337     0   \n",
+              "3              0              1     0        219       1337     0   \n",
+              "4              0              1     0        217       2032     0   \n",
+              "...          ...            ...   ...        ...        ...   ...   \n",
+              "494016         0              1     0        310       1881     0   \n",
+              "494017         0              1     0        282       2286     0   \n",
+              "494018         0              1     0        203       1200     0   \n",
+              "494019         0              1     0        291       1200     0   \n",
+              "494020         0              1     0        219       1234     0   \n",
+              "\n",
+              "        wrong_fragment  urgent  hot  num_failed_logins  ...  rerror_rate  \\\n",
+              "0                    0       0    0                  0  ...          0.0   \n",
+              "1                    0       0    0                  0  ...          0.0   \n",
+              "2                    0       0    0                  0  ...          0.0   \n",
+              "3                    0       0    0                  0  ...          0.0   \n",
+              "4                    0       0    0                  0  ...          0.0   \n",
+              "...                ...     ...  ...                ...  ...          ...   \n",
+              "494016               0       0    0                  0  ...          0.0   \n",
+              "494017               0       0    0                  0  ...          0.0   \n",
+              "494018               0       0    0                  0  ...          0.0   \n",
+              "494019               0       0    0                  0  ...          0.0   \n",
+              "494020               0       0    0                  0  ...          0.0   \n",
+              "\n",
+              "        same_srv_rate  diff_srv_rate  srv_diff_host_rate  dst_host_count  \\\n",
+              "0                 1.0            0.0                0.00               9   \n",
+              "1                 1.0            0.0                0.00              19   \n",
+              "2                 1.0            0.0                0.00              29   \n",
+              "3                 1.0            0.0                0.00              39   \n",
+              "4                 1.0            0.0                0.00              49   \n",
+              "...               ...            ...                 ...             ...   \n",
+              "494016            1.0            0.0                0.40              86   \n",
+              "494017            1.0            0.0                0.00               6   \n",
+              "494018            1.0            0.0                0.17              16   \n",
+              "494019            1.0            0.0                0.17              26   \n",
+              "494020            1.0            0.0                0.14               6   \n",
+              "\n",
+              "        dst_host_srv_count  dst_host_diff_srv_rate  \\\n",
+              "0                        9                     0.0   \n",
+              "1                       19                     0.0   \n",
+              "2                       29                     0.0   \n",
+              "3                       39                     0.0   \n",
+              "4                       49                     0.0   \n",
+              "...                    ...                     ...   \n",
+              "494016                 255                     0.0   \n",
+              "494017                 255                     0.0   \n",
+              "494018                 255                     0.0   \n",
+              "494019                 255                     0.0   \n",
+              "494020                 255                     0.0   \n",
+              "\n",
+              "        dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  Attack Type  \n",
+              "0                              0.11                         0.00            1  \n",
+              "1                              0.05                         0.00            1  \n",
+              "2                              0.03                         0.00            1  \n",
+              "3                              0.03                         0.00            1  \n",
+              "4                              0.02                         0.00            1  \n",
+              "...                             ...                          ...          ...  \n",
+              "494016                         0.01                         0.05            1  \n",
+              "494017                         0.17                         0.05            1  \n",
+              "494018                         0.06                         0.05            1  \n",
+              "494019                         0.04                         0.05            1  \n",
+              "494020                         0.17                         0.05            1  \n",
+              "\n",
+              "[494021 rows x 31 columns]"
+            ],
+            "text/html": [
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+              "    <div>\n",
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+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    .dataframe tbody tr th {\n",
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+              "        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>duration</th>\n",
+              "      <th>protocol_type</th>\n",
+              "      <th>flag</th>\n",
+              "      <th>src_bytes</th>\n",
+              "      <th>dst_bytes</th>\n",
+              "      <th>land</th>\n",
+              "      <th>wrong_fragment</th>\n",
+              "      <th>urgent</th>\n",
+              "      <th>hot</th>\n",
+              "      <th>num_failed_logins</th>\n",
+              "      <th>...</th>\n",
+              "      <th>rerror_rate</th>\n",
+              "      <th>same_srv_rate</th>\n",
+              "      <th>diff_srv_rate</th>\n",
+              "      <th>srv_diff_host_rate</th>\n",
+              "      <th>dst_host_count</th>\n",
+              "      <th>dst_host_srv_count</th>\n",
+              "      <th>dst_host_diff_srv_rate</th>\n",
+              "      <th>dst_host_same_src_port_rate</th>\n",
+              "      <th>dst_host_srv_diff_host_rate</th>\n",
+              "      <th>Attack Type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>181</td>\n",
+              "      <td>5450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>9</td>\n",
+              "      <td>9</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.11</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>239</td>\n",
+              "      <td>486</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>19</td>\n",
+              "      <td>19</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.05</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>235</td>\n",
+              "      <td>1337</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>29</td>\n",
+              "      <td>29</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>219</td>\n",
+              "      <td>1337</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>39</td>\n",
+              "      <td>39</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.03</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>217</td>\n",
+              "      <td>2032</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>49</td>\n",
+              "      <td>49</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.02</td>\n",
+              "      <td>0.00</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",
+              "      <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",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>494016</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>310</td>\n",
+              "      <td>1881</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.40</td>\n",
+              "      <td>86</td>\n",
+              "      <td>255</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.05</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>494017</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>282</td>\n",
+              "      <td>2286</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>6</td>\n",
+              "      <td>255</td>\n",
+              "      <td>0.0</td>\n",
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+              "      <td>0.05</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>494018</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>203</td>\n",
+              "      <td>1200</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.17</td>\n",
+              "      <td>16</td>\n",
+              "      <td>255</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.06</td>\n",
+              "      <td>0.05</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>494019</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>291</td>\n",
+              "      <td>1200</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.17</td>\n",
+              "      <td>26</td>\n",
+              "      <td>255</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>0.05</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>494020</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>219</td>\n",
+              "      <td>1234</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.14</td>\n",
+              "      <td>6</td>\n",
+              "      <td>255</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.17</td>\n",
+              "      <td>0.05</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>494021 rows × 31 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d63d4581-7f88-4314-91a4-f4a95669018e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d63d4581-7f88-4314-91a4-f4a95669018e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d63d4581-7f88-4314-91a4-f4a95669018e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-5aa39fc7-bfcd-41a4-b6da-4e40e3a9cb4d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Artificial Neural Network"
+      ],
+      "metadata": {
+        "id": "lXiMTVV2fJns"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1. **Input Layer (Dense Layer)**\n",
+        "    - `Dense(1024, input_dim=30, activation='relu')`\n",
+        "    - **Dense**: This is a fully connected layer with 1024 neurons.\n",
+        "    - **input_dim=30**: This specifies that the input layer expects 30 features (or input variables).\n",
+        "    - **activation='relu'**: The ReLU (Rectified Linear Unit) activation function is applied. ReLU is a popular activation function that introduces non-linearity into the model, allowing it to learn more complex patterns. It outputs 0 for any negative input and the input itself for any positive input.\n",
+        "\n",
+        "2. **Dropout Layer**\n",
+        "    - `Dropout(0.40)`\n",
+        "    - **Dropout**: This layer randomly sets 40% of the neurons to zero during each training step. Dropout is a regularization technique used to prevent overfitting by ensuring that the network doesn't rely too much on any individual neuron and instead learns a more robust representation of the data.\n",
+        "\n",
+        "3. **Output Layer (Dense Layer)**\n",
+        "    - `Dense(5, activation='sigmoid')`\n",
+        "    - **Dense**: This is another fully connected layer, but with only 5 neurons.\n",
+        "    - **activation='sigmoid'**: The Sigmoid activation function is used, which outputs values between 0 and 1. This is particularly useful for binary or multi-label classification tasks where each output can be interpreted as a probability of belonging to a certain class.\n",
+        "\n",
+        "### Architecture Summary\n",
+        "\n",
+        "- **Input Layer**: Takes in 30 features and passes them to the first Dense layer.\n",
+        "- **First Dense Layer**: 1024 neurons with ReLU activation function. It introduces non-linearity and allows the model to learn complex patterns.\n",
+        "- **Dropout Layer**: Regularizes the model by randomly turning off 40% of the neurons during training to prevent overfitting.\n",
+        "- **Output Layer**: 5 neurons with Sigmoid activation function. It produces 5 outputs, each representing the probability of a specific class.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "6aLon2EwpZpL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann_model = Sequential([\n",
+        "    Dense(1024, input_dim=30, activation='relu'),\n",
+        "    Dropout(0.40),\n",
+        "    Dense(5, activation='sigmoid')\n",
+        "])"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-07T14:04:43.132209Z",
+          "iopub.execute_input": "2022-11-07T14:04:43.132535Z",
+          "iopub.status.idle": "2022-11-07T14:04:43.163794Z",
+          "shell.execute_reply.started": "2022-11-07T14:04:43.132504Z",
+          "shell.execute_reply": "2022-11-07T14:04:43.162796Z"
+        },
+        "trusted": true,
+        "id": "F3vAYLiOfJnt"
+      },
+      "execution_count": 47,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann_model.compile(loss ='sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-07T14:04:43.165393Z",
+          "iopub.execute_input": "2022-11-07T14:04:43.165727Z",
+          "iopub.status.idle": "2022-11-07T14:04:43.176208Z",
+          "shell.execute_reply.started": "2022-11-07T14:04:43.165697Z",
+          "shell.execute_reply": "2022-11-07T14:04:43.174913Z"
+        },
+        "trusted": true,
+        "id": "umOrI3hAfJnt"
+      },
+      "execution_count": 48,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Train the model with early stopping\n",
+        "ann_model.fit(X_train, Y_train.values.ravel(), epochs=5, batch_size=32,validation_data=(X_test, Y_test))"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-07T14:04:43.292663Z",
+          "iopub.execute_input": "2022-11-07T14:04:43.293525Z",
+          "iopub.status.idle": "2022-11-07T14:09:05.699222Z",
+          "shell.execute_reply.started": "2022-11-07T14:04:43.293481Z",
+          "shell.execute_reply": "2022-11-07T14:09:05.697983Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DKqUMuDufJnu",
+        "outputId": "fea814d7-b774-43ee-ae83-9895cb231750"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "10344/10344 [==============================] - 39s 4ms/step - loss: 0.0047 - accuracy: 0.9988 - val_loss: 0.0042 - val_accuracy: 0.9990\n",
+            "Epoch 2/5\n",
+            "10344/10344 [==============================] - 40s 4ms/step - loss: 0.0044 - accuracy: 0.9989 - val_loss: 0.0048 - val_accuracy: 0.9988\n",
+            "Epoch 3/5\n",
+            "10344/10344 [==============================] - 54s 5ms/step - loss: 0.0042 - accuracy: 0.9989 - val_loss: 0.0043 - val_accuracy: 0.9991\n",
+            "Epoch 4/5\n",
+            "10344/10344 [==============================] - 54s 5ms/step - loss: 0.0044 - accuracy: 0.9989 - val_loss: 0.0040 - val_accuracy: 0.9991\n",
+            "Epoch 5/5\n",
+            "10344/10344 [==============================] - 40s 4ms/step - loss: 0.0042 - accuracy: 0.9990 - val_loss: 0.0041 - val_accuracy: 0.9990\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7b790048d4b0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 50
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get training and validation accuracy and loss values\n",
+        "history = ann_model.history\n",
+        "\n",
+        "# Plot the accuracy graph\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
+        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
+        "plt.title('Model Accuracy')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.legend()\n",
+        "plt.savefig('/content/ann_accuracy.png')\n",
+        "\n",
+        "\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "RKMDyNCOixEj",
+        "outputId": "4b3bb4a1-6add-49e2-82ba-0a2d427ce75e"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Plot the loss graph\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.plot(history.history['loss'], label='Training Loss')\n",
+        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+        "plt.title('Model Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.savefig('/content/ann_loss.png')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "IFHatzE5izni",
+        "outputId": "70db0133-4d7c-4611-f39a-651143da4de9"
+      },
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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9vQGIjY1l7969vP766wDMnDmTTz75JPP5ly9fJjAwkOXLl+Pv7w/AnTt3HmhNYjAYMjMKIYQQIu8kpqZTY8KmfH/dI5MCsbHIvdLonXfeYerUqVSqVInixYtz4cIF2rZty6effoqlpSWLFy+mffv2HD9+nPLlyz/0OB999BFTpkzhyy+/ZNasWbzyyiucO3eOEiVKZDv/zp07TJ06lSVLlqDX63n11VcZO3YsP//8MwBffPEFP//8MwsXLqR69erMmDGD1atX06JFixy/1759+3Ly5EnWrFmDg4MDb7/9Nm3btuXIkSOYm5szdOhQUlJS+Ouvv7C1teXIkSPY2dkB8MEHH3DkyBE2bNhAqVKlOHXqFImJiTnOklc03dxk9OjR9OnTB19fX+rXr8/06dNJSEigX79+APTu3ZuyZcsyefJkAEaOHEmzZs2YNm0a7dq1Y9myZYSEhDBv3jxA/W1s1KhRfPLJJ3h4eFCxYkU++OADXF1dMwvz/35TZvyDVa5cmXLlygHQrl07vv76ayZNmkTPnj2Ji4vj3XffpUKFCtStWzc//mqEEEIIUchNmjSJ559/PvPPJUqUwMvLK/PPH3/8Mb///jtr1qx56AlDUAvSnj17AvDZZ58xc+ZM9u3bR+vWrbOdn5qayty5c6lcuTIAw4YNY9KkSZmPz5o1i/Hjx/Pyyy8DMHv27Af2vXgaGcXy7t27adSoEQA///wzbm5urF69mq5du3L+/Hk6d+5M7dq1AahUqVLm88+fP0/dunUzrzVzd3fPcZa8pGnR3L17d65du8aECROIiorC29ubjRs3Zl7Id/78+SxnfBs1asTSpUt5//33effdd/Hw8GD16tXUqlUrc864ceNISEhg8ODB3L59m8aNG7Nx48an2v2lZcuWLF26lClTpjBlyhRsbGxo2LAhGzduxNraOvf+AoTQkqLAhX3g4gXmOdsdSQgh8oK1uYEjkwI1ed3c9N+GA/Hx8UycOJF169Zx5coV0tLSSExM5Pz58488Tp06dTLHtra2ODg4PHRPCwAbG5vMghnAxcUlc35MTAzR0dHUr18/83GDwYCPj0+OP00/evQoZmZmmZ/YA5QsWZJq1apx9OhRAEaMGMHrr7/O5s2bCQgIoHPnzpnv6/XXX6dz586EhYXxwgsv0LFjx8ziuyDRfBvtYcOGPfS3qx07djxwX9euXenatetDj6fT6Zg0aVKW36gexd3dPds1TD169KBHjx5PdAwhCqXwJbBmONToCN1+1DqNEEJk0ul0ubpMQiu2trZZ/jx27Fi2bNnC1KlTqVKlCtbW1nTp0oWUlJRHHue/7W51Ot0jC9zs5j/Neu28MHDgQAIDA1m3bh2bN29m8uTJTJs2jeHDh9OmTRvOnTvH+vXr2bJlC61atWLo0KFMnTpV08z/VeS6ZwghUM8yB3+jjo+shgv7NY0jhBBFwe7du+nbty8vv/wytWvXxtnZmbNnz+ZrBkdHR5ycnNi//97/99PT0wkLC8vxMatXr05aWlqWZgk3btzg+PHj1KhRI/M+Nzc3hgwZwm+//caYMWOYP39+5mOlS5emT58+/PTTT0yfPj1z6W1BUvh/jRNCPL3zwXDt6L0/b50IfdeCtCwSQog84+HhwW+//Ub79u3R6XR88MEHmjQYGD58OJMnT6ZKlSp4enoya9Ysbt269URt6w4ePIi9vX3mn3U6HV5eXnTo0IFBgwbx3XffYW9vzzvvvEPZsmXp0KEDAKNGjaJNmzZUrVqVW7dusX37dqpXrw7AhAkT8PHxoWbNmiQnJ7N27drMxwoSKZqFKIr2/6B+9QiEMzvg3N9wait4PP/IpwkhhMi5r776iv79+9OoUSNKlSrF22+/rcnuwW+//TZRUVH07t0bg8HA4MGDCQwMzOwU9ihNmzbN8meDwUBaWhoLFy5k5MiRvPjii6SkpNC0aVPWr1+fuVQkPT2doUOHcvHiRRwcHGjdujVff/01oHZUGz9+PGfPnsXa2pomTZqwbNmy3H/jz0inaL3IxYTFxsbi6OhITEyM9GwWBUf8NfiqOhhTYfBOOLgCgmeDU23431+gl1VbQoj8k5SURGRkJBUrVnyqi/ZF7jEajVSvXp1u3brx8ccfax0nTzzq++xJ6zX56ShEURO+RC2Yy/qAqzc0GQOWDhB9EA6t1DqdEEKIPHbu3Dnmz5/PiRMnOHjwIK+//jqRkZH06tVL62gFmhTNQhQlxnQIvbvLku8A9atNCXhupDre9jGkJWuTTQghRL7Q6/UsWrQIPz8/nnvuOQ4ePMjWrVsL5DrigkTWNAtRlJwKgtvnwaoY1Op07/4Gr8O++epjIQuhwRDNIgohhMhbbm5u7N69W+sYhY6caRaiKAm5ewGg9ytgft9GPRa20PxtdfzXl5Acl//ZhBBCiAJMimYhiorb5+HEJnXs2//Bx+u+BiUqw53rsGd2/mYTQgghCjgpmoUoKkIXAQpUbAalqjz4uMEcWn2gjoNnq102hBBCCAFI0SxE0ZCWAmGL1bHfgIfPq9ERXOtCSry6TEMIIYQQgBTNQhQNx/6EhGtg5wzV2j58nk4HARPVccgCuBmZL/GEEEKIgk6KZiGKgv0L1K8+fdRlGI9SqTlUaqH2ct7+WZ5HE0IIIQoDKZqFMHVXj6nbZOsMUK/Pkz0n42zzwRUQdTDPogkhRFHXvHlzRo0alflnd3d3pk+f/sjn6HQ6Vq9e/cyvnVvHKSqkaBbC1IXcPctcrQ04ln2y57h6Q81OgAJbP8qrZEIIUWi1b9+e1q1bZ/vYrl270Ol0/Pvvv0993P379zN48OBnjZfFxIkT8fb2fuD+K1eu0KZNm1x9rf9atGgRxYoVy9PXyC9SNAthylIS4MAv6ji7NnOP0vJ90JvBqS0QuSv3swkhRCE2YMAAtmzZwsWLFx94bOHChfj6+lKnTp2nPm7p0qWxsbHJjYiP5ezsjKWlZb68limQolkIU3ZwJSTHQvGK6jrlp1GyMvj0VcdbPwRFyfV4QgiRLUVRf+nP79tT/H/uxRdfpHTp0ixatCjL/fHx8axYsYIBAwZw48YNevbsSdmyZbGxsaF27dr88ssvjzzuf5dnnDx5kqZNm2JlZUWNGjXYsmXLA895++23qVq1KjY2NlSqVIkPPviA1NRUQD3T+9FHH3HgwAF0Oh06nS4z83+XZxw8eJCWLVtibW1NyZIlGTx4MPHx8ZmP9+3bl44dOzJ16lRcXFwoWbIkQ4cOzXytnDh//jwdOnTAzs4OBwcHunXrRnR0dObjBw4coEWLFtjb2+Pg4ICPjw8hISEAnDt3jvbt21O8eHFsbW2pWbMm69evz3GWx5FttIUwVYpybwdA3/6gz8HvyE3HQcRSuBQKR/+EGi/lbkYhhMhO6h34zDX/X/fdy+oOqU/AzMyM3r17s2jRIt577z10Oh0AK1asID09nZ49exIfH4+Pjw9vv/02Dg4OrFu3jtdee43KlStTv379x76G0WikU6dOODk5sXfvXmJiYrKsf85gb2/PokWLcHV15eDBgwwaNAh7e3vGjRtH9+7dOXToEBs3bmTr1q0AODo6PnCMhIQEAgMDadiwIfv37+fq1asMHDiQYcOGZfnFYPv27bi4uLB9+3ZOnTpF9+7d8fb2ZtCgQU/09/bf95dRMO/cuZO0tDSGDh1K9+7d2bFjBwCvvPIKdevW5dtvv8VgMBAREYG5uXpB+9ChQ0lJSeGvv/7C1taWI0eOYGdn99Q5npQUzUKYqkthcOUAGCyh7qs5O4a9EzQcqvZsDpqktqszyP82hBACoH///nz55Zfs3LmT5s2bA+rSjM6dO+Po6IijoyNjx47NnD98+HA2bdrEr7/++kRF89atWzl27BibNm3C1VX9JeKzzz57YB3y+++/nzl2d3dn7NixLFu2jHHjxmFtbY2dnR1mZmY4Ozs/9LWWLl1KUlISixcvxtZW/cVh9uzZtG/fni+++AInJycAihcvzuzZszEYDHh6etKuXTuCgoJyVDQHBQVx8OBBIiMjcXNzA2Dx4sXUrFmT/fv34+fnx/nz53nrrbfw9PQEwMPDI/P558+fp3PnztSuXRuASpUqPXWGpyE//YQwVRlnmWu+DDYlcn6cRiNg/w9w4yRE/Ky2rRNCiLxkbqOe9dXidZ+Cp6cnjRo1YsGCBTRv3pxTp06xa9cuJk2aBEB6ejqfffYZv/76K5cuXSIlJYXk5OQnXrN89OhR3NzcMgtmgIYNGz4wb/ny5cycOZPTp08THx9PWloaDg4OT/Vejh49ipeXV2bBDPDcc89hNBo5fvx4ZtFcs2ZNDAZD5hwXFxcOHsxZl6WM95dRMAPUqFGDYsWKcfToUfz8/Bg9ejQDBw5kyZIlBAQE0LVrVypXrgzAiBEjeP3119m8eTMBAQF07tw5R+vIn5SsaRbCFN25CYdWqeNH7QD4JKwcoOndMyU7PofUxGc7nhBCPI5Opy6TyO/b3SUWT2PAgAGsWrWKuLg4Fi5cSOXKlWnWrBkAX375JTNmzODtt99m+/btREREEBgYSEpKSq79VQUHB/PKK6/Qtm1b1q5dS3h4OO+9916uvsb9MpZGZNDpdBiNxjx5LVA7fxw+fJh27dqxbds2atSowe+//w7AwIEDOXPmDK+99hoHDx7E19eXWbNm5VkWKZqFMEUHfoG0JHCqDeX8nv14vgPA0Q3iLsPe7579eEIIYSK6deuGXq9n6dKlLF68mP79+2eub969ezcdOnTg1VdfxcvLi0qVKnHixIknPnb16tW5cOECV65cybzvn3/+yTJnz549VKhQgffeew9fX188PDw4d+5cljkWFhakp6c/9rUOHDhAQkJC5n27d+9Gr9dTrVq1J878NDLe34ULFzLvO3LkCLdv36ZGjRqZ91WtWpU333yTzZs306lTJxYuXJj5mJubG0OGDOG3335jzJgxzJ8/P0+yghTNQpgeRbnXm9mvf47OnDzA3ApavKuO//4KEm89+zGFEMIE2NnZ0b17d8aPH8+VK1fo27dv5mMeHh5s2bKFPXv2cPToUf73v/9l6QzxOAEBAVStWpU+ffpw4MABdu3axXvvvZdljoeHB+fPn2fZsmWcPn2amTNnZp6JzeDu7k5kZCQRERFcv36d5OTkB17rlVdewcrKij59+nDo0CG2b9/O8OHDee211zKXZuRUeno6ERERWW5Hjx4lICCA2rVr88orrxAWFsa+ffvo3bs3zZo1w9fXl8TERIYNG8aOHTs4d+4cu3fvZv/+/VSvXh2AUaNGsWnTJiIjIwkLC2P79u2Zj+UFKZqFMDWRO+HGKbCwh9rdcu+4dbpDmRqQFAN/T8+94wohRCE3YMAAbt26RWBgYJb1x++//z716tUjMDCQ5s2b4+zsTMeOHZ/4uHq9nt9//53ExETq16/PwIED+fTTT7PMeemll3jzzTcZNmwY3t7e7Nmzhw8++CDLnM6dO9O6dWtatGhB6dKls217Z2Njw6ZNm7h58yZ+fn506dKFVq1aMXv27Kf7y8hGfHw8devWzXJr3749Op2OP/74g+LFi9O0aVMCAgKoVKkSy5cvB8BgMHDjxg169+5N1apV6datG23atOGjj9RNt9LT0xk6dCjVq1endevWVK1alW+++eaZ8z6MTlGk+WpeiY2NxdHRkZiYmKdekC9Ejv3aG478AX4Dod203D328Q3wSw8ws4IR4eCgQUsoIYRJSUpKIjIykooVK2JlZaV1HGGiHvV99qT1mpxpFsKUxEXBsXXq2PcZLwDMTtXW4NZAXS+9Y3LuH18IIYQooKRoFsKUhC0GYxqUbwhONR4//2npdPC8+rEY4T/BtSe/oEUIIYQozKRoFsJUpKdB6CJ1nBdnmTOUb6BucqIYYdukvHsdIYQQogCRolkIU3FyE8ReAptSeb/ddasJoNOrW2tfDMnb1xJCCCEKACmahTAV++/uAFj3VTCzzNvXKlMdvHqq460T1TZ3QgjxDKQvgchLufH9JUWzEKbg5hk4HQTowLdf/rxm8/FgsISzu+BUUP68phDC5GTsMHfnzh2NkwhTlvH99d8dDZ+GWW6FEUJoKOTu7khVAqC4e/68ZjE3qD8IgmerZ5srtwS9/B4uhHg6BoOBYsWKcfXqVUDtF6zLjU2ZhEA9w3znzh2uXr1KsWLFMBgMOT6WFM1CFHapSWonCwC/PLwAMDtNxqgdO6IPwqFVUKdr/r6+EMIkODs7A2QWzkLktmLFimV+n+WUFM1CFHZH/oDEm+DoBh4v5O9r25SA50bAtk9g+ydQowOYWeRvBiFEoafT6XBxcaFMmTKkpqZqHUeYGHNz82c6w5xBimYhCruQuxcA+vQB/bP/T+GpNXgD9s2HW2fVlnf+g/M/gxDCJBgMhlwpboTIC7IAUYjCLOoQXNgLejOo21ubDBa20OxtdbzzC0iO0yaHEEIIkYekaBaiMMs4y1y9Pdg7aZejXm8oURnuXIfgOdrlEEIIIfKIFM1CFFbJcfDvr+o4L3cAfBIGc2j5vjreMwvir2mbRwghhMhlUjQLUVj9uxxS4qFUVXBvrHUaqNERXLzVTLumap1GCCGEyFVSNAtRGCkK7F+gjn37Q0HoaarXQ8BEdbz/B/XCQCGEEMJESNEsRGF0YS9cPQxm1ve2sy4IKreASs3BmArbP9M6jRBCCJFrpGgWojDaf/cCwNqdwbqYplEekHG2+d9f1e4eQgghhAmQolmIwibhOhxZrY61vgAwO651oebLgAJBH2mdRgghhMgVUjQLUdiE/wTpKWpxWrae1mmy1/IDtXf0yc1w9m+t0wghhBDPrEAUzXPmzMHd3R0rKyv8/f3Zt2/fI+evWLECT09PrKysqF27NuvXr8/yuKIoTJgwARcXF6ytrQkICODkyZPZHis5ORlvb290Oh0RERGZ90+cOBGdTvfAzdbW9pnfrxA5ZjRC6EJ1XBDPMmcoWVnt3Qyw5UP1wkUhhBCiENO8aF6+fDmjR4/mww8/JCwsDC8vLwIDA7l69Wq28/fs2UPPnj0ZMGAA4eHhdOzYkY4dO3Lo0L21k1OmTGHmzJnMnTuXvXv3YmtrS2BgIElJSQ8cb9y4cbi6uj5w/9ixY7ly5UqWW40aNejatWvuvXkhntbpbWpXCitHqNVZ6zSP1uxtMLeBSyFwbK3WaYQQQohnonnR/NVXXzFo0CD69etHjRo1mDt3LjY2NixYsCDb+TNmzKB169a89dZbVK9enY8//ph69eoxe/ZsQD3LPH36dN5//306dOhAnTp1WLx4MZcvX2b16tVZjrVhwwY2b97M1KkP9pS1s7PD2dk58xYdHc2RI0cYMKAAn90Tpi9jB0CvXmBho22Wx7F3hgZvqOOgSZCepm0eIYQQ4hloWjSnpKQQGhpKQEBA5n16vZ6AgACCg4OzfU5wcHCW+QCBgYGZ8yMjI4mKisoyx9HREX9//yzHjI6OZtCgQSxZsgQbm8cXH99//z1Vq1alSZMmD52TnJxMbGxslpsQueb2BTixUR379tc2y5N6bgRYl4DrJ+DAUq3TCCGEEDmmadF8/fp10tPTcXJyynK/k5MTUVFR2T4nKirqkfMzvj5qjqIo9O3blyFDhuDr6/vYnElJSfz888+PPcs8efJkHB0dM29ubm6PPXZuun0nBUXWjpqusB9BMYJ7EyhdVes0T8bKEZqMUcfbJ0NqorZ5hBBCiBzSfHmGFmbNmkVcXBzjx49/ovm///47cXFx9OnT55Hzxo8fT0xMTObtwoULuRH3iRy6FEObGbv4ZsfpfHtNkY/SUyFssTr2K2RLhPwGgkM5iLsM++ZpnUYIIYTIEU2L5lKlSmEwGIiOjs5yf3R0NM7Oztk+J2N98cPmZ3x91Jxt27YRHByMpaUlZmZmVKlSBQBfX99sC+Pvv/+eF1988YGz1/9laWmJg4NDllt++fdiDFdikvhy03E2HrqSb68r8smxtRAfDXZO4Pmi1mmejrkVtHhXHe/6ChJvaxpHCCGEyAlNi2YLCwt8fHwICgrKvM9oNBIUFETDhg2zfU7Dhg2zzAfYsmVL5vyKFSvi7OycZU5sbCx79+7NnDNz5kwOHDhAREQEERERmS3rli9fzqeffprl2JGRkWzfvr3AXwDYy788fRu5A/Dm8gMcuhSjbSCRuzJ2AKzXGwzm2mbJCa8eULo6JN2G3dO1TiOEEEI8Nc2XZ4wePZr58+fz448/cvToUV5//XUSEhLo168fAL17986yjGLkyJFs3LiRadOmcezYMSZOnEhISAjDhg0DQKfTMWrUKD755BPWrFnDwYMH6d27N66urnTs2BGA8uXLU6tWrcxb1arq+tDKlStTrly5LPkWLFiAi4sLbdq0yYe/jWfzfrvqNPEoRWJqOoMWh3A19sEWe6IQunYCzu4CnR58+mqdJmf0Bmg1QR3/Mxdi5dMQIYQQhYvmRXP37t2ZOnUqEyZMwNvbm4iICDZu3Ji5FOL8+fNcuXLvB2yjRo1YunQp8+bNw8vLi5UrV7J69Wpq1aqVOWfcuHEMHz6cwYMH4+fnR3x8PBs3bsTKyuqpshmNRhYtWkTfvn0xGAy584bzkJlBz+xe9ahU2pYrMUkMXhJKUmq61rHEswq5236xamtwLPfouQVZtTbg1gDSEmHn51qnEUIIIZ6KTpF2C3kmNjYWR0dHYmJi8nV9c+T1BDrO2U1MYiovebkyo4e646EohFLuwFeekBQDr6wCj4DHP6cgOxcMC1uDzgBD90IpD60TCSGEKOKetF7T/EyzyH0VS9ny7av1MNPrWHPgMnO2n9I6ksipQ6vUgrm4O1RuqXWaZ1ehIVRtA0q6uuGJEEIIUUhI0WyiGlUuxUcdagIwdfMJNhyUNaSFUsYOgD79QG8i/7m2mgDo4OgauBiqdRohhBDiiZjIT2GRnVf8K2R21Bj9q3TUKHQuhcHlcDBYQN1XtU6Te5xqgFdPdbz1Q5AVYkIIIQoBKZpN3PvtqtO0amkSU9MZ+KN01ChUMs4y1+gItqU0jZLrWoxXfxk4uwtOBz1+vhBCCKExKZpNnJlBz6yedalc2pao2CQGSUeNwiHxFhxcpY4L2w6AT6JYefAbpI63TgSjUdM4QgghxONI0VwEOFqb80MfP4rZmHPgwm3eWvkv0jSlgDuwTG3NVqYmuPlrnSZvNBkDlg4QdRAO/6Z1GiGEEOKRpGguItxL2fLNK2pHjT8PXGb2NumoUWApyr3ezH79wVTbBdqWhEYj1PG2jyEtRds8QgghxCNI0VyENKpcikkd1E1gpm2RjhoF1tldcP0EWNhBne5ap8lbDd8A2zJw6yyE/ah1GiGEEOKhpGguYnr5l6ffc+4AvPlrhHTUKIj2370AsE43sLTXNktes7CFZuPU8c4vIDle2zxCCCHEQ0jRXAS911btqJGUapSOGgVNXBQcW6uOfU3wAsDs+PSFEpUg4RoEz9E6jRBCCJEtKZqLIDODntm97uuosThEOmoUFGFLwJimXvznXEvrNPnDYA4t31fHe2ZCwnVt8wghhBDZkKK5iHKwuq+jxsUY6ahREBjTIXSROi4qZ5kz1HgZXLwgJR7+mqp1GiGEEOIBUjQXYe6lbPn2FZ/MjhqzpKOGtk5sgtiLYF0CanTQOk3+0ushYKI6DvkBbp3TNI4QQgjxX1I0F3ENK5fk447qMoCvtpxg3b/SUUMzGTsA1n0VzK20zaKFyi2hYjNIT4Htn2mdRgghhMhCimZBz/r3OmqMWRHBwYvSUSPf3YyEU3e3k/btp20WLWWcbf53OUQd0jSKEEIIcT8pmgWgdtRoltFRY/F+oqWjRv4KXQQoULmV2kmiqCpbD2p0BBQImqR1GiGEECKTFM0CUDtqzOpVlypl7IiOTWbQ4hASU6SjRr5IS4bwJerYr4hdAJidlh+AzgAnN8G5PVqnEUIIIQApmsV91I4avhSzMeffizG8tfKAdNTID0fWwJ0b4FAWPAK1TqO9UlWgXm91vOVDdVtxIYQQQmNSNIssKpS811Fj7b9XmBkkHTXyXMYFgD59wWCmaZQCo9nbYGYNF/fB8fVapxFCCCGkaBYPali5JJ/c7ajx9VbpqJGnog/D+WDQm907uyrAwQUavqGOt34E6Wna5hFCCFHkSdEsstWjfnn6P1cRkI4aeSpkgfrVsx3YO2ubpaB5biRYF4frx+HAL1qnEUIIUcRJ0Swe6t22njSvJh018kxyPBxYro6L2g6AT8LKEZqMUcc7JkNqorZ5hBBCFGlSNIuHMjPomdlTOmrkmYO/QkoclPSAik21TlMw+Q0Ch3IQewn2zdc6jRBCiCJMimbxSBkdNYrf7agxVjpq5A5Fgf13l2b49gedTts8BZW5FbQYr453TYPE25rGEUIIUXRJ0Sweq0JJW759Ve2ose7fK8wIOql1pMLv4n6IPqh2iPDuqXWags2rJ5T2hKTbsHuG1mmEEEIUUVI0iyfSoFJJPn1Z7agxfetJ1v57WeNEhdz+u23manVWL3YTD6c3QKsJ6vifbyFWurkIIYTIf1I0iyfW3a88Axrf7ajx6wH+vXhb20CF1Z2bcPh3dezXX9sshUW1tuDmD2mJsPMLrdMIIYQogqRoFk/l3bbVaVGtNMlpRgYtDiEqRjpqPLXwnyA9GVy8oayP1mkKB50OAiaq47DFcF023RFCCJG/pGgWT8Wg1zGzZ108pKNGzhiN93oz+8pZ5qdSoRFUbQ1KOmybpHUaIYQQRYwUzeKp2VuZ80MfP4rbmHPwUgxjVxzAaJSOGk/kzHa4FQmWjlC7i9ZpCp9WEwAdHPkDLoVqnUYIIUQRIkWzyJHyJW2Y+6oP5gYd6w5KR40nlnGW2asHWNhqm6Uwcqqp/t0BbJ2otu4TQggh8oEUzSLH/CuV5JOOakeNGUEn+fOAdNR4pJhLcHy9OpalGTnX4l0wWEDkX3B6m9ZphBBCFBFSNItn0t2vPAPvdtQYu+IABy7c1jZQQRb2IyhGqNAYynhqnabwKlYe/Aaq460T1XXiQgghRB6Tolk8s/HSUePx0lMh9Ed1LG3mnl2TsWBhD1H/wuHftE4jhBCiCJCiWTyzjI4aVZ3suBonHTWydXw9xEeBbRnwbK91msLPtiQ8N0Idb/sE0lK0zSOEEMLkSdEscoW9lTnf9/ajhK0FBy/FMGZFhHTUuF/GDoD1XgMzC22zmIoGb6i/hNyKVJe+CCGEEHlIimaRa+7vqLH+YBTTpaOG6vopiNwJ6MCnr9ZpTIelHTQbp453ToHkeG3zCCGEMGlSNItcVb9iCT7tWBuAmUEnWSMdNe61masaqF7EJnJPvT5Q3B0SrsI/32qdRgghhAmTolnkum5+bgxqonbUeGvFASKKckeN1ESI+Fkd+w7QNospMrOAlh+o490zIOG6tnmEEEKYLCmaRZ54p011WnqWITnNyODFIVyJSdQ6kjYO/QZJt9UzzFVaaZ3GNNXsBM51ICUOdk3TOo0QQggTJUWzyBMGvY4ZPbyzdNS4k5Kmdaz8F3L3AkCffqA3aJvFVOn1EDBRHe//Hm6f1zSOEEII0yRFs8gz9lbm/NBH7ahx6FIsY1ccKFodNS5HwKVQ0JtD3de0TmPaKreEik0hPQW2f6Z1GiGEECZIimaRp9xK2PDda/d11Nh6QutI+SfjLHONDmBXWtsspk6nu3e2+cAyiD6saRwhhBCmR4pmkef83Evw2ct3O2psO8UfEZc0TpQPkmLg4Ep17CcXAOaLsj7qLygoEDRJ6zRCCCFMjOZF85w5c3B3d8fKygp/f3/27dv3yPkrVqzA09MTKysrateuzfr167M8rigKEyZMwMXFBWtrawICAjh5Mvt+wcnJyXh7e6PT6YiIiHjgOFOnTqVq1apYWlpStmxZPv3002d6r0VZV183BjetBMBbK/81/Y4aB5ZB6h0oXR3KN9Q6TdHRcgLoDHBiI5wL1jqNEEIIE6Jp0bx8+XJGjx7Nhx9+SFhYGF5eXgQGBnL16tVs5+/Zs4eePXsyYMAAwsPD6dixIx07duTQoUOZc6ZMmcLMmTOZO3cue/fuxdbWlsDAQJKSkh443rhx43B1dc32tUaOHMn333/P1KlTOXbsGGvWrKF+/fq588aLqLdbe9LKswwpaUYGmXJHDUW5twOg3wB16YDIH6WqqLsuAmz9UP23EEIIIXKBTlG0+6ni7++Pn58fs2fPBsBoNOLm5sbw4cN55513HpjfvXt3EhISWLt2beZ9DRo0wNvbm7lz56IoCq6urowZM4axY8cCEBMTg5OTE4sWLaJHjx6Zz9uwYQOjR49m1apV1KxZk/DwcLy9vQE4evQoderU4dChQ1SrVi3H7y82NhZHR0diYmJwcHDI8XFMSXxyGp2/2cPx6DhqujqwYkhDbCzMtI6Vu87+DYvagbktjDkGVvJvn69ir8DMupCWCD1+Ac+2WicSQghRgD1pvabZmeaUlBRCQ0MJCAi4F0avJyAggODg7D9WDQ4OzjIfIDAwMHN+ZGQkUVFRWeY4Ojri7++f5ZjR0dEMGjSIJUuWYGNj88Dr/Pnnn1SqVIm1a9dSsWJF3N3dGThwIDdv3nzke0pOTiY2NjbLTWRlZ2nG9318KWlrweHLsYz51QQ7amScZa7TVQpmLTi4QIMh6jjoIzCma5tHCCGESdCsaL5+/Trp6ek4OTllud/JyYmoqKhsnxMVFfXI+RlfHzVHURT69u3LkCFD8PX1zfZ1zpw5w7lz51ixYgWLFy9m0aJFhIaG0qVLl0e+p8mTJ+Po6Jh5c3Nze+T8osqthA1z73bU2HAoiq9NqaNG/FU4+qc6lh0AtfPcKLAqBteOwYFftE4jhBDCBGh+IWB+mzVrFnFxcYwfP/6hc4xGI8nJySxevJgmTZrQvHlzfvjhB7Zv387x48cf+rzx48cTExOTebtw4UJevAWTcH9HjVmm1FEjbDEYU6GcH7jU0TpN0WVdDJqMUcfbJ0Pqg9c0CCGEEE9Ds6K5VKlSGAwGoqOjs9wfHR2Ns7Nzts9xdnZ+5PyMr4+as23bNoKDg7G0tMTMzIwqVaoA4OvrS58+fQBwcXHBzMyMqlWrZh6jevXqAJw///DdxiwtLXFwcMhyEw/X1deN/93XUSP8/C2NEz0jYzqELlLHcpZZe/UHg0NZiL0I++drnUYIIUQhp1nRbGFhgY+PD0FBQZn3GY1GgoKCaNgw+xZdDRs2zDIfYMuWLZnzK1asiLOzc5Y5sbGx7N27N3POzJkzOXDgABEREURERGS2rFu+fHlmS7nnnnuOtLQ0Tp8+nXmcEyfUJQQVKlR41rcu7jOutScB1dWOGoOXhHL5diHuqHFyC8RcAOviUPNlrdMIcytofvcTpV3T1N7ZQgghRA5pujxj9OjRzJ8/nx9//JGjR4/y+uuvk5CQQL9+/QDo3bt3lmUUI0eOZOPGjUybNo1jx44xceJEQkJCGDZsGAA6nY5Ro0bxySefsGbNGg4ePEjv3r1xdXWlY8eOAJQvX55atWpl3jLOJleuXJly5coBEBAQQL169ejfvz/h4eGEhobyv//9j+effz7L2Wfx7Ax6HdN71MXT2Z5rcckM/DGEOylpWsfKmYwdAL1fUQs2oT2vnlCqGiTegt0ztE4jhBCiENO0aO7evTtTp05lwoQJeHt7ExERwcaNGzMv5Dt//jxXrlzJnN+oUSOWLl3KvHnz8PLyYuXKlaxevZpatWplzhk3bhzDhw9n8ODB+Pn5ER8fz8aNG7GyevIiRq/X8+eff1KqVCmaNm1Ku3btqF69OsuWLcu9Ny8y2VmaMb+32lHjyJVYRi8vhB01bp1VzzQD+PbXNIq4j8EMWk1Qx8HfQFz2FxkLIYQQj6Npn2ZTJ32an07I2Zv0mr+XlHQjw1pUYWxgzntk57utE+Hvr6FSC+i9Wus04n6KAj+8ABf3qb/QvPi11omEEEIUIAW+T7MQ/+XrXoLPOqkdNWZvL0QdNdKSIWyJOvaTCwALHJ0OAiaq49Af4cbpR04XQgghsiNFsyhQuviU43/NCllHjaN/wp3rYO8KVdtonUZkx/058HgBlHTY9rHWaYQQQhRCUjSLAmdc4L2OGoMWF4KOGhk7APr0UdfQioKp1YeADg7/DpfCtE4jhBCikJGiWRQ493fUuB5fwDtqRB+B83tAZ4B6vbVOIx7FuRbU6a6Ot07UNIoQQojCR4pmUSDZWZrxfZ97HTXeXB5RMDtqhCxQv3q2BQdXbbOIx2vxLhgsIHInnN6mdRohhBCFiBTNosAqV9yG717zwcKgZ9PhaL7ackLrSFklx8OBu20IZQfAwqF4hXv/VlsngtGoaRwhhBCFhxTNokDzdS/B5Ps6aqwOL0AdNQ6thJQ4KFEZKjbTOo14Uk3HgoU9XDkAR37XOo0QQohCQopmUeB19inHkGaVARi36l/CCkJHDUW5dwGgb3/Qy39KhYZtKWg0XB1v+wTSU7XNI4QQolCQn/SiUBgXWI2A6k6kpBkZvDiUS1p31LgUClH/gpkVePfSNot4eg2Hgm1puHkGwn7UOo0QQohCQIpmUSjo9Tqm9/DO0lEjIVnDjhoZZ5lrdgKbEtrlEDljaQdNx6njnVMgJUHbPEIIIQo8KZpFoZHRUaOUnQVHteyocecmHP5NHcsOgIWXT18o7g7x0fDPN1qnEUIIUcBJ0SwKlfs7amw+Es20LcfzP0TEUkhLAuc6UNYn/19f5A4zC2jxvjrePRMSbmibRwghRIEmRbModHwqlODzzmpHjTnbT/N7+MX8e3Gj8V5vZr8BoNPl32uL3FerMzjXhuRY2DVN6zRCCCEKMCmaRaHUqV45Xm+udtR4e9VBQs/lU0eNyJ1w8zRYOkDtrvnzmiLv6PUQMFEd758Pt89rGkcIIUTBJUWzKLTeeqEaz9dQO2r8b0lI/nTUCLl7AaBXD7CwzfvXE3mvcitwbwLpKbB9stZphBBCFFBSNItCS6/XMb27N9VdHLgen5L3HTViL8Ox9erYt3/evY7IXzodBHykjg/8AtFHtM0jhBCiQJKiWRRqtvnZUSNsMSjpUOE5KFM9b15DaKOcD1R/CVAgaJLWaYQQQhRAUjSLQq9sMWu+e803s6PG1M150FEjPQ1C726CIWeZTVOrCaAzwIkNcC5Y6zRCCCEKGCmahUnwqVCcL7qoHTW+2XGa38JyuaPGiQ0QdxlsSkH19rl7bFEwlPKAuq+q460T1a3ShRBCiLukaBYm4+W65XjjbkeNd3K7o0bGDoD1XgMzy9w7rihYmr+jbo1+4R84sVHrNEIIIQoQKZqFSRn7QjVeqOFESrraUePirTvPftAbp+HMdkAHPv2e/Xii4HJwBf8h6njrR2BM1zaPEEKIAkOKZmFS9HodX+d2R42MzUw8nofiFZ49pCjYGo8CK0e4dhT+Xa51GiGEEAWEFM3C5NzrqGHJsag4Rj1LR43URIj4WR37Dsi9kKLgsi4OTcao422fQmqStnmEEEIUCFI0C5OkdtTwwcJMz5Yj0XyZ044ah1dD4i1wLK+eaRZFQ/3B4FAWYi/C/u+1TiOEEKIAkKJZmCyfCsWZ0rkOAN/uOM2q0Bx01MjYAdCnD+gNuZhOFGjm1upFgQC7pkJSjLZ5hBBCaE6KZmHSOtYty9AWakeN8b8dJPTczSd/8pV/4eJ+0JtDvd55lFAUWF69oFRV9ZOG3TO1TiOEEEJjUjQLkzfm+WoE1lQ7agxeHPrkHTUyzjJXbw92ZfIuoCiYDGbqhicA/3wDcVHa5hFCCKEpKZqFydPrdXzVzZsaLg7cSFA7asQ/rqNGUiz8u0Id+8kFgEWW54tQzg9S78DOKVqnEUIIoSEpmkWR8EBHjWWP6ajx73JITYDSnlDhufwLKgoWnQ4CJqrjsB/Vnt1CCCGKJCmaRZHhWsyaeb3Vjhpbj0YzZdNDOmooyr0dAH37q4WTKLrcG0OV58GYBts+0TqNEEIIjUjRLIqUeuWL82UXtaPG3J2nWZldR43zwerGFuY24NUjnxOKAingQ0AHh3+Dy+FapxFCCKEBKZpFkdPBuyzDWlQB4N3fDhJy9j8dNTLOMtfuou4MJ4RzbajTTR1v/UjbLEIIITQhRbMokkY/XzWzo8b/loRy4ebdjhrx1+DIH+pYdgAU92vxrtp+8Mx2OL1d6zRCCCHymRTNokjS63V83f1eR41Bi+921AhfAsZUKOsDrt5axxQFSXH3e51Utk4Eo1HLNEIIIfKZFM2iyLKxyNpR481fQlBCF6oPyllmkZ0mY8HCDq5EwJHVWqcRQgiRj6RoFkWaazFr5t/tqJF6Yiu62+fBqhjU6qR1NFEQ2ZWGRsPV8baPIT1V2zxCCCHyjRTNosire7ejxquGrQCccH0JzK01TiUKrIZDwaYU3DwDYYu1TiOEECKfSNEsBNDBPZ1WhggAhh33frCjhhAZLO2h2Th1vPMLSEnQNo8QQoh8IUWzEAChi9ChcMy6HifSnbN21BDiv3z6QbEKEB8N/3yrdRohhBD5QIpmIdJSMj9mr9hmODVd1Y4aA3+821FDiP8ys4CW76vj3TPgjnwyIYQQpk6KZiGO/QkJ18DOGcua7fm+jy+l7S05Hh3HyF/CSTcqWicUBVGtLuBUG5JjYdc0rdMIIYTIY1I0C7F/gfrVpw8YzHFxtGZ+b18szfQEHbvKlI3HtM0nCia9HgImquN98+D2BU3jCCGEyFtSNIui7eoxOPc36AxQr0/m3d5uxZjSpQ4A3/11hhUhUhCJbFRpBe5NID0FdkzWOo0QQog8JEWzKNpC7p5lrtYGHMtmeaiDd1lGtKwCwLu/H2S/dNQQ/6XT3TvbfOAXuHpU0zhCCCHyjhTNouhKSVALHQDf/tlOGRVQlTa1nElNV6SjhsheOV+o3h4UIwRN0jqNEEKIPFIgiuY5c+bg7u6OlZUV/v7+7Nu375HzV6xYgaenJ1ZWVtSuXZv169dneVxRFCZMmICLiwvW1tYEBARw8uTJbI+VnJyMt7c3Op2OiIiIzPvPnj2LTqd74PbPP/888/sVBcTBlepFXMUrQqUW2U7R63VM6+ZFrbIO3JSOGuJhWk4AnR6Or4fz8v8IIYQwRZoXzcuXL2f06NF8+OGHhIWF4eXlRWBgIFevXs12/p49e+jZsycDBgwgPDycjh070rFjRw4dOpQ5Z8qUKcycOZO5c+eyd+9ebG1tCQwMJCkp6YHjjRs3DldX14fm27p1K1euXMm8+fj4PPubFtpTFAj5QR379lcv6noIGwsz5vf2pYx01BAPU7oq1H1VHW+dqH5/CSGEMCmaF81fffUVgwYNol+/ftSoUYO5c+diY2PDggULsp0/Y8YMWrduzVtvvUX16tX5+OOPqVevHrNnzwbUs8zTp0/n/fffp0OHDtSpU4fFixdz+fJlVq9eneVYGzZsYPPmzUydOvWh+UqWLImzs3Pmzdzc/KFzk5OTiY2NzXITBdSlMLhyAAyW94qdR3BxtGbefR01vpCOGuK/mo8HMys4HwwnNmmdRgghRC7TtGhOSUkhNDSUgICAzPv0ej0BAQEEBwdn+5zg4OAs8wECAwMz50dGRhIVFZVljqOjI/7+/lmOGR0dzaBBg1iyZAk2NjYPzfjSSy9RpkwZGjduzJo1ax75fiZPnoyjo2Pmzc3N7ZHzhYYyzjLXfBlsSjzRU7zdivFlVy8A5v11hl+lo4a4n4Mr+P9PHQd9BMZ0bfMIIYTIVZoWzdevXyc9PR0nJ6cs9zs5OREVFZXtc6Kioh45P+Pro+YoikLfvn0ZMmQIvr6+2b6OnZ0d06ZNY8WKFaxbt47GjRvTsWPHRxbO48ePJyYmJvN24YIUVQXSnZtwaJU69hvwVE99ycs1s6PGe78fZF+kdNQQ92n8Jlg5wtUj8O+vWqcRQgiRi8y0DqCFWbNmERcXx/jx4x86p1SpUowePTrzz35+fly+fJkvv/ySl156KdvnWFpaYmlpmet5RS478AukJam7uZXze+qnjwqoyqlr8aw/GMWQn0L5Y+hzuJV4+KcVogixLq4WzlsnwvZP1U8yzK20TiWEECIXaHqmuVSpUhgMBqKjo7PcHx0djbOzc7bPcXZ2fuT8jK+PmrNt2zaCg4OxtLTEzMyMKlXUM4e+vr706dOHh/H39+fUqVNP8Q5FgaMo93oz+/VX++w+Jb1ex7Su3pkdNQb8uJ+4pNRcDioKLf8hYO8KMRfuLQMSQghR6GlaNFtYWODj40NQUFDmfUajkaCgIBo2bJjtcxo2bJhlPsCWLVsy51esWBFnZ+csc2JjY9m7d2/mnJkzZ3LgwAEiIiKIiIjIbFm3fPlyPv3004fmjYiIwMXFJWdvVhQMkX/BjVNgYQ+1u+X4MNYWhsyOGiei4xm5LEI6agiVuTU0f0cd/zUVkmK0zSOEECJXaL48Y/To0fTp0wdfX1/q16/P9OnTSUhIoF+/fgD07t2bsmXLMnmyukXtyJEjadasGdOmTaNdu3YsW7aMkJAQ5s2bB4BOp2PUqFF88skneHh4ULFiRT744ANcXV3p2LEjAOXLl8+Swc7ODoDKlStTrlw5AH788UcsLCyoW7cuAL/99hsLFizg+++/z/O/E5GHMs78eXUHS7tnOpSLozXze/vS7btgth27yucbjvJeuxq5EFIUet6vQPBsuH4C9syClu9rnUgIIcQz0rxo7t69O9euXWPChAlERUXh7e3Nxo0bMy/kO3/+PPr7eug2atSIpUuX8v777/Puu+/i4eHB6tWrqVWrVuaccePGkZCQwODBg7l9+zaNGzdm48aNWFk93drCjz/+mHPnzmFmZoanpyfLly+nS5cuufPGRf6Li4Jj69Sx79NdAPgwXm7FmNrVi+G/hDN/VyQeZezp5iddU4o8gxm0/AB+fQ2C54DfILB3evzzhBBCFFg6RZEu/HklNjYWR0dHYmJicHBw0DqO2DlFvTirfEPovzFXD/3VlhPMDDqJuUHHTwP88a9UMlePLwohRYHvA+BSCPgNhHbTtE4khBAiG09ar2m+uYkQ+SI9DUIXqeNcOst8v1GtPGhX24XUdIUhP4Vy/sadXH8NUcjodBAwUR2HLoIbp7VMI4QQ4hlJ0SyKhpObIPYS2JSCGtm3DHwWer2OqV29qF3WkVt3UqWjhlBVbAJVAsCYpn7KIYQQotCSolkUDfvvXgBY91Uwy5te2vd31Dh5NZ4Rv4RLRw0BrT5Uvx5aBZcjNI0ihBAi56RoFqbv5hk4HQTowLdfnr6Us6MV83v7YmmmZ/vxa0xefzRPX08UAi51oHZXdRz0kbZZhBBC5JgUzcL0hSxUv1YJgOLuef5yXm7FmNbNC4Dv/45k+f7zef6aooBr8R7ozeH0NjizQ+s0QgghckCKZmHaUpMg/Cd17Jf7FwA+zIt1XBnZygOA91cfYu+ZG/n22qIAKlERfPur460T1c4aQgghChUpmoVpO/IHJN4Eh3Lg8UK+vvTIVh60qyMdNcRdTd8CCzu4HA5HVmudRgghxFOSolmYtowdAH36gt6Qry+t1+uY2sWLOuWko4YA7EpDw2HqOOhjSJfvBSGEKEykaBamK+oQXNgLejOo11uTCNYWBua95ouTg9pRY7h01CjaGg1T2x7ePA3hS7ROI4QQ4ilI0SxMV8ZZZs8XNd3COKOjhpW5nh3Hr/GZdNQouizt1WUaADu+gBRZsiOEEIWFFM3CNCXHwb+/quN8vADwYeqUK8bUrmpHjR/+jmTZPumoUWT59oNi5SE+CvZ+q3UaIYQQT0iKZmGa/l0OKfFQqiq4N9E6DaB21BgVcK+jxj/SUaNoMrOEFu+r479nwJ2b2uYRQgjxRKRoFqZHUWD/AnXs2x90Om3z3GdkKw9erONCmlHh9Z9COXcjQetIQgu1u4JTLUiOgb+/0jqNEEKIJyBFszA9F/bC1cNgZg1ePbVOk4VOp2Nq1/s7aoQQKx01ih69/t722nvnQcxFbfMIIYR4LCmahenZf/cCwNqdwbqYplGyY2VuYH5vtaPGqavxjJCOGkWTx/NQoTGkJ8OOyVqnEUII8RhSNAvTknD93sYRvtpfAPgwTg5WfN/bTzpqFGU6HQRMVMcRS+HqMU3jCCGEeDQpmoVpCf8J0lPAtS6Urad1mkeqXc6RaV29AemoUWS5+aktERUjBE3SOo0QQohHkKJZmA6jEUIXquMCfJb5fu3quPBmQFVA7agRfFo6ahQ5rSaATg/H18H5vVqnEUII8RBSNAvTcXob3DoLVo5Qq7PWaZ7YiFZVaO/lqnbU+Fk6ahQ5pauB9yvqeOtEtfuLEEKIAkeKZmE6MnYA9OoFFjbaZnkKOp2OL7vUwaucI7elo0bR1Hw8mFnB+T1wcrPWaYQQQmRDimZhGm5fgBMb1bFvf22z5ICVuYF5vX1xdrDi1NV4hi8NJy3dqHUskV8cy0L9wep460dgTNc2jxBCiAdI0SxMQ9iP6sVU7k2gdFWt0+SIk4MV3/fxxcpcz84T1/hsvXRTKFIav6kuLbp6GA6u0DqNEEKI/5CiWRR+6akQtlgd+xWOCwAfplZZR77q5g3Agt2R/CIdNYoOmxLw3Ch1vO1TSEvWNI4QQoispGgWhd+xtRAfDXZOavuuQq5tbRdGP6+eLf9AOmoULf5DwN4FYs5DyAKt0wghhLiPFM2i8MvYAbBebzCYa5sllwxvmbWjxtnr0lGjSLCwgebvqOO/voSkWG3zCCGEyCRFsyjcrp2As7vUPrc+fbVOk2se7KixXzpqFBXer0JJD7hzA/bM0jqNEEKIu6RoFoVbxkfYVVuDYzlts+QyK3MD8+921Dh9LYFh0lGjaDCYQasP1HHwHIi/qm0eIYQQgBTNojBLuQMHlqrjQrID4NMqc19Hjb9OXOPT9Ue1jiTyQ/WXoKwPpCbAzilapxFCCIEUzaIwO7QKkmKguDtUbql1mjxTq6wjX9/tqLFw91mW7pWOGiZPp4OAieo4dCHcPKNpHCGEEFI0i8IsYwdAn36gN+1v5Ta1XRhzt6PGhD8Osef0dY0TiTxXsSlUbgXGNLUFnRBCCE2ZdqUhTNelMLgcDgYLqPuq1mnyxbCWVXgpo6PGT2HSUaMoCPhQ/XpoJVw5oG0WIYQo4qRoFoVTxlnmGh3BtpSmUfKLTqdjSpc6eLkVIyZR7agRkygdNUyaixfU6qKOt36kbRYhhCjiclQ0X7hwgYsXL2b+ed++fYwaNYp58+blWjAhHirxFhxcpY4L+Q6AT8vK3MD813xwcczoqBEmHTVMXcv3QG8Gp4Mg8i+t0wghRJGVo6K5V69ebN++HYCoqCief/559u3bx3vvvcekSZNyNaAQDziwDNISoUxNcPPXOk2+K+NgxfzevlibG9h18jqfrJOOGiatRCXw7a+Ot3wIiqJtHiGewpWYRJbtOy995oVJyFHRfOjQIerXrw/Ar7/+Sq1atdizZw8///wzixYtys18QmSlKPd6M/v1V7sMFEG1yjrydXcvABbtOcvPe89pnEjkqaZvgbktXA6DI39onUaIJ7LpcBStp+/ind8O0m1uMFdjk7SOJMQzyVHRnJqaiqWlJQBbt27lpZdeAsDT05MrV67kXjoh/uvsLrh+AizsoE53rdNoqnUtF8a+oHbU+PCPw+w5JR01TJZdGWg0TB1v+xjS07TNI8QjJKWmM3HNYf63JJSYxFR0OjgWFUeXucGcuyEXMIvCK0dFc82aNZk7dy67du1iy5YttG7dGoDLly9TsmTJXA0oRBb7714AWKcbWNprm6UAGNqiCh2873bU+DmMSOmoYboaDgObknDjFIQv0TqNENk6cy2eTt/sYdGeswAMblqJraObUaGkDedv3qHzt8EcvhyjbUghcihHRfMXX3zBd999R/PmzenZsydeXurHxGvWrMlctiFErouLgmNr1bGJ7gD4tHQ6HV90roO3dNQwfVYO6jINgB2fqztiClGA/B5+kRdn/c2RK7GUsLVgYT8/3m1bncql7VgxpCHVXRy4Hp9Mj+/+4Z8zN7SOK8RT0ylKzq4qSU9PJzY2luLFi2fed/bsWWxsbChTpkyuBSzMYmNjcXR0JCYmBgcHB63jFH47v4Ttn6gX/w3YrHWaAuVqXBIdZ+/mckwSTTxKsbCvH2YG6ShpctKSYbYv3D4PrT6EJqO1TiQECclpTPjjMKvC1K5aDSuVZHoPb5wcrLLMi01KZeCPIeyLvImFmZ7ZPevyQk1nLSILkcWT1ms5+qmamJhIcnJyZsF87tw5pk+fzvHjx6VgFnnDmA6hi9SxnGV+QBl7K+b3kY4aJs/MElq8p47/ng53bmoaR4gjl2NpP/tvVoVdRK+D0c9X5aeB/g8UzAAOVuYs7l+fgOpOpKQZGfJTKL+GXNAgtRA5k6OiuUOHDixevBiA27dv4+/vz7Rp0+jYsSPffvttrgYUAoATmyD2IliXgBodtE5TINV0zdpR46d/pKOGSardVW23mBwDf3+tdRpRRCmKwpLgs3T8ZjdnriXg7GDFL4MaMKKVBwb9w7saWZkbmPtqPbr6lMOowLiV/zJ35+l8TC5EzuWoaA4LC6NJkyYArFy5EicnJ86dO8fixYuZOXNmrgYUArjXZq7uq2D+4BkMoWpdy4W3AqsB8OEa6ahhkvSGe9tr75sHMZe0zSOKnJg7qbzxcxgf/HGYlDQjrTzLsGFkE/wrPVkjADODnild6vC/ZpUA+HzDMT5bf5QcrhYVIt/kqGi+c+cO9vZq54LNmzfTqVMn9Ho9DRo04Nw5Obslctmts3Bqqzr27adplMLgjeaV6ejtSrp01DBdHi9A+UaQlgQ7JmudRhQhoedu0XbmLjYcisLcoGPCizX4vo8vxW0tnuo4Op2O8W2q825bTwDm/XWGsSv+lR1ORYGWo6K5SpUqrF69mgsXLrBp0yZeeOEFAK5evSoXvIncF7IQUKByK3V3NPFIOp2Oz+/vqLFoPzF3pKOGSdHp4PmP1HHEz3DtuLZ5hMkzGhW+3XGabt8Fc+l2IhVK2vDb68/Rv3FFdM+wydTgppX5sksdDHodq8IuMuSnUJJS03MxuRC5J0dF84QJExg7dizu7u7Ur1+fhg0bAupZ57p16z718ebMmYO7uztWVlb4+/uzb9++R85fsWIFnp6eWFlZUbt2bdavX5/lcUVRmDBhAi4uLlhbWxMQEMDJkyezPVZycjLe3t7odDoiIiKynXPq1Cns7e0pVqzYU7838YzSku/1pPWTCwCflJW5gXm9fXB1tOLM9QSGLg2TMzimxq0+eL4IihGCJmmdRpiwa3HJ9Fm4jy82HiPdqPCSlytrhzemdjnHXDl+V1835r7qg6WZnq1Hr9L7h33SOlMUSDkqmrt06cL58+cJCQlh06ZNmfe3atWKr79+ugtTli9fzujRo/nwww8JCwvDy8uLwMBArl69mu38PXv20LNnTwYMGEB4eDgdO3akY8eOHDp0KHPOlClTmDlzJnPnzmXv3r3Y2toSGBhIUtKDW3iOGzcOV1fXh+ZLTU2lZ8+emWu4RT47sgbu3ACHsuARqHWaQuX+jhp/n7rOx2uPaB1J5LZWE0CnV/uXX3j0yQYhcmL3qeu0nbmLXSevY2WuZ0rnOszo4Y29lXmuvs7zNZxY3L8+9pZm7Dt7kx7z/uFqnGy7LQqWHPdpznDxotqXsVy5cjl6vr+/P35+fsyePRsAo9GIm5sbw4cP55133nlgfvfu3UlISGDt2rWZ9zVo0ABvb2/mzp2Loii4uroyZswYxo4dC0BMTAxOTk4sWrSIHj16ZD5vw4YNjB49mlWrVlGzZk3Cw8Px9vbO8npvv/02ly9fplWrVowaNYrbt28/9L0kJyeTnJyc+efY2Fjc3NykT/OzWNAazgerbbaajdM6TaG08VAUQ34KBeDjjrV4rUEFjROJXPXHUAj/CSo8B33XqUs3hHhGaelGpm89yZwdp1AUqOZkz+xedfFwytudWI9cjqX3gn1cj0+mfAkblgyoT4WStnn6mkLkaZ9mo9HIpEmTcHR0pEKFClSoUIFixYrx8ccfYzQ++UfAKSkphIaGEhAQcC+QXk9AQADBwcHZPic4ODjLfIDAwMDM+ZGRkURFRWWZ4+joiL+/f5ZjRkdHM2jQIJYsWYKNjU22r7Vt2zZWrFjBnDlznuj9TJ48GUdHx8ybm5vbEz1PPET0YbVg1ptBvd5apym0WtdyzuyoMXHNYf4+KR01TErz8WCwhHO74eQWrdMIE3DpdiI95v3D7O1qwdyzfnn+GPZcnhfMADVcHfjt9UaULyHbbouCJ0dF83vvvcfs2bP5/PPPCQ8PJzw8nM8++4xZs2bxwQcfPPFxrl+/Tnp6Ok5OTlnud3JyIioqKtvnREVFPXJ+xtdHzVEUhb59+zJkyBB8fX2zfZ0bN27Qt29fFi1a9MRnicePH09MTEzm7cIFadr+TDLazHm2A3vZNepZvNG8Mi/XLUu6UeGNn0M5cy1e60gitziWA//B6jjoI3iKExdC/Nfmw1G0nbGLkHO3sLc0Y3avukzuVBsrc0O+ZShf0oaVr2fddnuvbLstCoAcFc0//vgj33//Pa+//jp16tShTp06vPHGG8yfP59FixblcsTcN2vWLOLi4hg/fvxD5wwaNIhevXrRtGnTJz6upaUlDg4OWW4ih5Lj4cBydSw7AD4znU7H5E61qVu+GLFJaQz8MUQ6apiSxqPB0hGiD8HBFVqnEYVQclo6E9ccZvCSUGISU/Eq58i6EU14sc7Dr/nJS2XsrVg2uAH13UsQl5xG7wX72HIkWpMsQmTIUdF88+ZNPD09H7jf09OTmzeffFvXUqVKYTAYiI7O+h9CdHQ0zs7Zn1l0dnZ+5PyMr4+as23bNoKDg7G0tMTMzIwqVaoA4OvrS58+fTLnTJ06FTMzM8zMzBgwYAAxMTGYmZmxYMGCJ36PIocO/gopcVDSAyo++S8u4uGszA3Me803S0eNVOmoYRpsSkDjkep4+ydq1xkhntCZa/F0+mYPi/acBWBw00qsGNKI8iWzX7qYXxytzVk8QN12O/nuttsrZNttoaEcFc1eXl6ZF+7db/bs2dSpU+eJj2NhYYGPjw9BQUGZ9xmNRoKCgjLb2P1Xw4YNs8wH2LJlS+b8ihUr4uzsnGVObGwse/fuzZwzc+ZMDhw4QEREBBEREZkt65YvX86nn34KqGunMx6PiIhg0qRJ2NvbExERwcsvv/zE71HkgKLA/ru/mPj2lwubclFpe0u+7+OHjYV01DA5/q+DnTPcPn+3t7kQj/d7+EXaz/qbw5djKWFrwcK+frzbtjoWZjkqD3JdxrbbXXzKkW5UeGvlv3wn224LjZjl5ElTpkyhXbt2bN26NbMQDQ4O5sKFCw/0TH6c0aNH06dPH3x9falfvz7Tp08nISGBfv3Und969+5N2bJlmTxZ3fVq5MiRNGvWjGnTptGuXTuWLVtGSEgI8+bNA9SPoUeNGsUnn3yCh4cHFStW5IMPPsDV1ZWOHTsCUL58+SwZ7OzsAKhcuXJmF5Dq1atnmRMSEoJer6dWrVpP9f5EDlzcD9EHwcwKvHo8fr54KjVcHfi6uzf/WxLK4uBzWJsbGBVQFWuL/FuzKPKAhQ00fxvWvgl/fQl1XwHLvL9wSxROCclpTPjjMKvC1A5YDSqVYEaPujg5WGmc7EFmBj1fdqlDSVsLvvvrDJM3HONmQgrvtPF8po1VhHhaOfpVslmzZpw4cYKXX36Z27dvc/v2bTp16sThw4dZsmTJUx2re/fuTJ06lQkTJuDt7U1ERAQbN27MvJDv/PnzXLlyJXN+o0aNWLp0KfPmzcPLy4uVK1eyevXqLMXsuHHjGD58OIMHD8bPz4/4+Hg2btyIlVXB+5+ByMb+H9SvtTqrHzuLXBdY05m3W6tLrL776wzPf72TrbJesPCr+xqUrAJ3rsOeBz8NFALg6JVY2s/+m1VhF9HrYPTzVfl5YIMCWTBn0Ol0jG9bnfFt7v1/662Vsu22yF/P3Kf5fgcOHKBevXqkp8sWmPDkff/Efe7chGmekJ4MA7dBOR+tE5m0jYeuMOnPI1yOUTcRCKhehg/b18SthLZrGcUzOLwaVvQBc1sYGQF2ZbROJAoIRVH4ae95Pl57hJQ0I84OVszo4Y1/pZJaR3sqv4ZcYPxvB0k3KgRUd2J2r7r52t1DmJ487dMsRJ4J/0ktmF28oGw9rdOYvNa1XNg6phlDmlXGTK9j69GrBHy1k5lBJ0lKlV9+C6UaHcC1HqQmqMs0hABiElN54+cwPlh9iJQ0I608y7B+ZJNCVzADdLu77baFmZ6tR6PpvWAfsUnSDUjkPSmaRcFhNN7rzew7QC4AzCc2Fma808aTjaOa0LBSSZLTjHy15QStp//FzhPXtI4nnpZOBwET1XHIQrgZqWkcob2w87doO2MXGw5FYW7Q8cGLNfi+jy8lbC20jpZjz9dwYknGttuRN+n+nWy7LfKeFM2i4DizHW5Fqv1ma3fROk2RU6WMPUsH+TOjhzdl7C05e+MOfRbs4/WfQrl8O1HreOJpVGoGlVuCMRW2f6p1GqERo1Fh7s7TdJsbzKXbiVQoacOq1xsxoHFFk7iAzr9SSZb9rwGl7Cw5eiWWLt8Gc+5GgtaxhAl7qjXNnTp1euTjt2/fZufOnbKm+S5Z0/yUlr0Cx9ZC/f9B2ylapynS4pJSmb71JIv2nCXdqGBjYWBEKw/6P1exwLSiEo9x5QB8d7fH+f92gcuTtwMVhd/1+GRG/3qAv+5+WtTey5XPXq6FvZW5xsly37kbCbz2wz7O37xDKTtLFvevTw1X+ZkrntyT1mtPVTRntIF7nIULpUcoSNH8VGIuwfRaoBjhjb1Q5sHNc0T+O3ollgl/HGL/2VsAVCljx6QONWlUuZTGycQTWdkfDq2CKgHw6iqt04h8svvUdUYtj+BaXDJW5no+eqkm3XzdTOLs8sNcjU2i94J9HIuKw97KjB/6+FG/onRfEk8mT4pm8XSkaH4K2z+DnV9AhcbQb53WacR9FEVhVdglJq8/yo2EFABe8nLl/XbVKVOAW1QJ4MZpmFMfjGnQZy1UbKJ1IpGH0tKNTN96kjk7TqEoUNXJjtm96lHVqWj0645JTGXQjyHsO3sTSzM9c3rVI6CGk9axRCEg3TNE4ZGeCqE/qmO//tpmEQ/Q6XR08SnHtjHNea1BBXQ6WHPgMi2n7eSHvyOlT2pBVrIy+PRVx1s/VHfbFCbp8u1Eesz7h9nb1YK5Z/3y/DG0cZEpmOH+bbfLkJxm5H+y7bbIZVI0C+0dXw/xUWBbBjzba51GPISjjTkfd6zFmqGN8XIrRnxyGh+vPcKLs/4m5OxNreOJh2k6Tu3ZfCkUjv6pdRqRBzYfjqLNjF2EnLuFvaUZs3rWZXKn2kVyl091222fLNtuz/tLtt0WuUOKZqG9jB0A670GZoW3BVJRUbucI7+/3ojJnWpTzMacY1FxdJkbzJhfD3A9PlnreOK/7J2g4VB1HDQJ0tO0zSNyTXJaOhPXHGbwklBiElPxKufIuhFNaO/lqnU0TWVsuz24aSUAPlt/jMkbjiKrUcWzkqJZaOv6KYjcCejufYwsCjy9XkfP+uXZNqY5PfzcAFgVdpGWU3ewJFjtuCEKkEbDwaYk3DgJET9pnUbkgsjrCXT6Zg+L9pwFYFCTiqwY0ojyJWU3T1CXlb17/7bbO88wTrbdFs9IimahrYzNTKoGQrHy2mYRT62ErQWfd67Db280oqarA7FJaXzwx2E6ztlNxIXbWscTGawcoMlYdbzjc0i5o20e8UxWh1/ixZm7OHw5lhK2Fizs68d77WpIO8hs/K9ZZaZ0qYNeBytCL/L6z2Gy26nIMfkvTGgnNREiflbHvgO0zSKeSb3yxVkzrDGTOtTE3sqMg5diePmb3Yz/7SC37nbcEBrzGwCO5SHuCuz7Tus0IgfupKQxdsUBRi2PICElnQaVSrB+RBNaeJbROlqBdv+221uOyLbbIuekaBbaOfQbJN1WzzBXaaV1GvGMDHodvRu6s21MczrVK4uiwC/7ztNy2g6W7TuPUZZsaMvMElq8q47//hoSb2mbRzyVo1diaT/rb1aGXkSvgzcDqvLzwAY4O0rbxyfxQk1nFt+37XaP7/7hWpxcgyGejhTNQjshdy8A9OkH+qJ3lbepKm1vyVfdvPn1fw2p5mTPrTupvPPbQTrP3cOhSzFaxyva6nSDMjUgKUYtnEWBpygKS/45R4c5uzl9LQEnB0uWDmrAyAAPDHrT3awkLzS4b9vtI1di6TJ3D+dvyFIl8eRkc5M8JJubPMLlCJjXDPTmMPoo2JXWOpHIA6npRn7cc5avt5wgISUdvQ5ea1CB0S9Uw9Ha9LbzLRSOb4RfuoOZFQwPA8eyWicSDxGTmMo7q/5lw6EoAFp6lmFqVy9K2EqXoWdx7kYCr/6wlws3Eyltr267Xd1FfkYXZbK5iSjYMs4y1+ggBbMJMzfoGdikEkFjmvNiHReMCvwYfI5W03awKvSitIDSQtVAKN8Q0pJg5+dapxEPEXb+Fm1n7GLDoSjMDTreb1edH/r4SsGcCyqUtGXVkEZ4OttzLS6Zbt8Fsy9Ses2Lx5OiWeS/pBg4uFId+8kFgEWBs6MVs3vV4+eB/lQubcv1+BTGrDhA9+/+4XhUnNbxihadDgI+UsfhP8G1E9rmEVkYjQpzd56m29xgLt1OpHwJG1a93oiBTSqh08lyjNxSxsGK5f9riJ97ceKS0njth71sPRKtdSxRwEnRLPLfgWWQegdKV1fPeIki47kqpdgwsinjWlfD2tzAvrM3aTtzF5+sPUJ8smy6kW/K+0O1dqAYYdskrdOIu67HJ9N30X4+33CMNKPCi3VcWDuiMXXKFdM6mklytDZncX9/Wnne23Z7ZehFrWOJAkyKZpG/FOXeDoB+A9SzXqJIsTDT80bzKmwd04zAmk6kGxW+/zuSVtN28OeBy7JkI7+0mgA6vbq19oX9Wqcp8nafuk6bGbv468Q1rMz1fN6pNrN61sXBStb+5yVrCwNzX/Ohcz112+2xKw4w/68zWscSBZQUzSJ/ndsN14+DuS3U6a51GqGhssWs+e41Xxb286NCSRuiY5MZ/ks4r/6wl1NX47WOZ/rKeIJXL3W8daL6C63Id2npRqZuOs6rP+zlWlwyVZ3sWDOsMT3ql5flGPnE/O6224OaVATg0/VH+XzDMfkFXjxAimaRvzLOMtfpqu5SJoq8FtXKsGlUU94MqIqlmZ7dp27QZsZffLHxGHdSZMlGnmoxHgyWcO5vOLVV6zRFzuXbifSc/w+zt59CUaBnfTf+GNqYqk72WkcrcvR6He+1q8E7d7fdnrvzNG+vkm23RVZSNIv8E39V/SgYZAdAkYWVuYGRAR5sebMZLT3LkJqu8O2O0zz/1V9sPBQlZ3zyimM5qD9IHW/9CIxSIOSXLUeiaTtzF/vP3sLe0oxZPesyuVMdrC2kZ72WhjSrzJTO6rbbv4Zc5A3ZdlvcR4pmkX/CFoMxFcr5gUsdrdOIAqh8SRt+6OPLvNd8KFvMmku3ExnyUyj9Fu3n3I0EreOZpiZjwNIBog/CoZVapzF5yWnpTFxzmEGLQ7h9J5U65RxZN6IJ7b1ctY4m7urm58a3d7fd3nwkmj6y7ba4S4pmkT+M6RC6SB3LWWbxCDqdjhdqOrN1dDOGtqiMuUHHjuPXeP7rv/h6ywk565PbbErAcyPV8bZPIC1F2zwmLPJ6Ap2/3cOiPWcBGNi4IiuHNKJ8SRttg4kHBNZ05sd+9bGzNGOvbLst7pKiWeSPk1sg5gJYF4eaL2udRhQC1hYG3gr0ZNOopjTxKEVKmpEZQSd54eu/2HZM+qnmqgavg50T3D4HoQu1TmOSVodf4sWZuzh0KZbiNuYs6OvL+y/WwMJMfgwXVA0rl2TZ4AaUsrPgyJVYus7dw4Wbsu12USb/tYr8kbEDoPcrYG6lbRZRqFQqbcfi/vWZ06sezg5WnL95h/6LQhi0OISLt+QHWK6wsIVmb6vjnVMgWTacyS13UtJ4a8UBRi2PICElHf+KJdgwsiktPZ20jiaeQK2yjqwc0gi3EtacvXGHTt/u4eiVWK1jCY1I0Szy3q2z6plmAN/+mkYRhZNOp6NdHRe2jmnG4KaVMNPr2HIkmoCvdjJn+ymS02TJxjOr1xtKVIY71yF4jtZpTMLRK7G0n/U3K0IvotfBqAAPlg5qgLOjnDgoTNxLPbjt9v6zsu12USRFs8h7oYsABSq1gJKVtU4jCjE7SzPebVud9SOb4F+xBEmpRr7cdJw203fx98nrWscr3Azm0OoDdbxnFsRf0zZPIaYoCj/9c44Oc3Zz+loCTg6WLB3UgFEBVTHopfdyYVTGwYrlgxviW0HddvvV7/cSdFSWiRU1UjSLvJWWDGFL1LGfXAAockdVJ3uWDW7A9O7elLKz5Mz1BF79YS9Dfw7jSkyi1vEKrxodwbUupMTDX19qnaZQiklMZejSMN5ffYiUNCMtqpVm/YgmNKhUUuto4hk52pizZIA/Le9uuz14SSirZNvtIkWKZpG3jv6pftxr7wpV22idRpgQnU5Hx7pl2Ta2GX0buaPXwbqDV2g1bSfz/jpNqmxK8PR0OgiYqI5DFsDNSE3jFDbh52/RbuYu1h+Mwtyg4/121fmhjx8l7Sy1jiZyibWFge9e86FTvbKkGxXGrDjA97tk2+2iQopmkbcydgD06QMGM22zCJPkYGXOxJdq8ufwxtQrX4w7Kel8tv4Y7Wbu4p8zN7SOV/hUaq4upTKmwvbPtE5TKBiNCt/tPE3XucFcvJVI+RI2rBzSiIFNKqGX5Rgmx9ygZ2oXLwY2Vrfd/mTdUb7YKNtuFwU6Rf6V80xsbCyOjo7ExMTg4FAEt4yOPgLfNgSdAd48BA7SvF/kLaNRYWXYRT7fcIybCWq/4ZfrlmV8W0/K2MvFV0/scgTMawboYMgucK6tdaIC63p8MmN+PcDOE+oa8BfruPBZp9o4WJlrnEzkNUVRmLvzDF9sPAZAd183Pn25FmYGOR9Z2DxpvSb/siLvZPR79WwrBbPIF3q9jm6+bmwb04xX/Muj08Hv4ZdoNXUni3ZHkiZLNp6MqzfU7AQo6vbaIlt7Tl2nzYxd7DxxDStzPZ93qs2snnWlYC4idDodrzevzBeda6PXwfKQC7LttomTM815qEifaU6Oh6+qQ3IsvLYaKrfQOpEogg5cuM0Hfxzi34sxANRwceDjjrXwqVBc42SFwI3TMKc+GNOg7zpwb6x1ogIjLV3daGf29lMoCniUsWPOK/Wo6mSvdTShkY2HohixLJyUNCMNKpVgfm9f7OWXp0JDzjQLbR1aqRbMJSpDxWZapxFFlJdbMX5/4zk+fbkWjtbmHLkSS+dv9zBu5QFuxMuWuI9UsjLU66OOt3wIcn4FgMu3E+k1fy+ztqkFcw8/N9YMaywFcxHXuta9bbf/OXOTHvNk221TJEWzyH2Kcu8CQN/+oJdvM6Edg17HK/4V2DamGd18ywHwa8hFWk7byc97z5FulGLwoZq9DeY2cCkEjq3VOo3mthyJpu3MXew7exM7SzNm9qzL553rYG1h0DqaKADu33b78GXZdtsUSTUjct+lUIj6F8yswLuX1mmEAKCknSVTunix6vWGVHdxICYxlfd+P0Snb3bz78XbWscrmOydoOFQdRw0CdLTtM2jkeS0dD768zCDFodw+04qdco5sm5EY17ykms1RFa1yjqyYkgjyhVXt93u/O0ejkXJttumQopmkfsyzjLX7AQ2JbTNIsR/+FQowZ/DnuPD9jWwtzTjwMUYOszZzXu/H+T2nRSt4xU8jUaAdQm4fgIOLNU6Tb6LvJ5A52/3sHD3WQAGNq7IyiGNqFDSVttgosCqWMqWVa83opqTPVfjkuk2V7bdNhVSNIvcdecmHP5NHcsOgKKAMjPo6fdcRYLGNuPlumVRFPh573laTtvJryEXMMqSjXusHKDpWHW8fTKkFp0dF/+IuMSLM3dx6FIsxW3MWdDXl/dfrIGFmfzoFI/m5GDFr/9Tt92Ovbvt9rZjsu12YSf/5YvcFbEU0pLAuQ6U9dE6jRCPVMbeiq+7e7NscAM8ythxMyGFcSv/pet3wRy5LB+pZvIdAI5uEHcZ9n6ndZo8dycljXErDzByWQQJKenUr1iC9SOb0NLTSetoohD577bbgxaH8luYbLtdmEnRLHKP0ahuvQvqWWad7IQlCocGlUqyfmQT3m3riY2FgdBzt3hx1i4++vMwsUmpWsfTnrkVtHhXHf/9FSTe0jZPHjoWFctLs3fza8hF9DoY2cqDXwY1wMXRWutoohDK3Ha7rrrt9uhfZdvtwkyKZpF7InfCzdNg6QC1umidRoinYm7QM7hpZYLGNKNdHReMCizcfZZW03ayOvySbJFbpzuUqQFJMbDhbZNrQacoCj/vPUeH2bs5dTUeJwdLfh7YgDefr4pBtsIWz8DcoGdqVy8G3Lft9hTZdrtQkqJZ5J6QuxcA1ukOlnbaZhEih1wcrZnTqx5LBtSnUilbrsUlM2p5BD3n/8PJ6Dit42lHb4A2X4DOAP8uV7tpmIiYxFSGLQ3nvd8PkZxmpEW10qwf0YSGlUtqHU2YCL1ex/vtqjOudTUAvtlxmvG/HZRdSgsZ2REwDxWpHQFjL8PXtUBJhzf+gTLVtU4kxDNLTkvn+12RzNp2kqRUI2Z6HQMaV2REKw9sLc20jqeN8J/gj7tt6NpMAf//aZvnGYWfv8XwX8K5eCsRc4OOt1t70v+5iujl7LLII8v2nefd3w9iVCCwphMzetTFylx6fWupUO0IOGfOHNzd3bGyssLf3599+/Y9cv6KFSvw9PTEysqK2rVrs379+iyPK4rChAkTcHFxwdramoCAAE6ePJntsZKTk/H29kan0xEREZF5//Hjx2nRogVOTk5YWVlRqVIl3n//fVJTZX1jtsIWqwVz+UZSMAuTYWlmYGiLKmx5sxnP13Aizajw3V9nCPhqJ+sPXimaH6/WfRVafqCON7wNh3/XNk8OGY0K3+08Tde5wVy8lYhbCWtWDmnEwCaVpGAWeapH/fJ880o9LAx6Nh2Opt/C/cTJtROFguZF8/Llyxk9ejQffvghYWFheHl5ERgYyNWrV7Odv2fPHnr27MmAAQMIDw+nY8eOdOzYkUOHDmXOmTJlCjNnzmTu3Lns3bsXW1tbAgMDSUpKeuB448aNw9X1wQb15ubm9O7dm82bN3P8+HGmT5/O/Pnz+fDDD3PvzZuK9DQI/VEdS5s5YYLcStgwv7cvC/r64lbCmisxSbzxcxi9F+zjzLV4rePlvyZjwG8QoMBvgyFyl9aJnsr1+GT6LdrP5A3HSDMqtKvjwroRTfByK6Z1NFFEtK7lwqL+fthZmhF85gY95//D9XjZdrug03x5hr+/P35+fsyePRsAo9GIm5sbw4cP55133nlgfvfu3UlISGDt2ntbujZo0ABvb2/mzp2Loii4uroyZswYxo5Ve4vGxMTg5OTEokWL6NGjR+bzNmzYwOjRo1m1ahU1a9YkPDwcb2/vh2YdPXo0+/fvZ9euJ/sBUWSWZxz9E5a/CjalYPQRMLPUOpEQeSYpNZ1vd5zm252nSUkzYmHQM7hpJYa2qFK0tlM2psOKvnB0jXrxb78N4FxL61SPtefUdUYtj+BqXDKWZnomvlSTHn5u6KTbj9DAoUsx9FmwjxsJKbiXtGHJAH/cSthoHavIKRTLM1JSUggNDSUgICDzPr1eT0BAAMHBwdk+Jzg4OMt8gMDAwMz5kZGRREVFZZnj6OiIv79/lmNGR0czaNAglixZgo3N479BT506xcaNG2nWrNlD5yQnJxMbG5vlViRk7ABY7zUpmIXJszI38ObzVdk8qinNq5UmJd3I7O2nCPhqJ1uOFKHNC/QG6DQfKjwHybHwU2e4fV7rVA+Vlm7kq83HeeWHvVyNS8ajjB1rhjWmZ/3yUjALzdQq68jK12Xb7cJC06L5+vXrpKen4+SUtWG8k5MTUVFR2T4nKirqkfMzvj5qjqIo9O3blyFDhuDr6/vIjI0aNcLKygoPDw+aNGnCpEkPv2J88uTJODo6Zt7c3NweeWyTcOM0nNkO6MCnn9ZphMg37qVsWdjXj7mv+uDqaMWl24kMWhzCgEX7OX/jjtbx8oe5FfRYqraii4+CJZ3UXUELmCsxifSav5eZ206hKNDd1401wxpTzdle62hCZLvtdohsu10gab6mWQuzZs0iLi6O8ePHP3bu8uXLCQsLY+nSpaxbt46pU6c+dO748eOJiYnJvF24cCE3YxdMGZuZeDwPxStom0WIfKbT6Whdy5mtY5rxRvPKmBt0BB27yvNf72TG1pMkpaZrHTHvWReDV1eBQzm4cRKWdoOUgvNLw9Yj0bSZsYt9Z29iZ2nGjB7efNGlTtFaSiMKvIxtt30ytt3+QbbdLog0LZpLlSqFwWAgOjrrN0Z0dDTOzs7ZPsfZ2fmR8zO+PmrOtm3bCA4OxtLSEjMzM6pUqQKAr68vffr0yfI8Nzc3atSoQc+ePfn888+ZOHEi6enZ/yC0tLTEwcEhy82kpSZCxM/q2FcuABRFl42FGeNae7JhZFOeq1KS5DQjX289Qevpf7HjePYXNZsUB1d47TewKgYX98PKfuoFwhpKTktn0p9HGLg4hNt3Uqld1pG1wxvTwbusprmEeBhHG3N+GuBPi2qlSUpVt93+PVy23S5INC2aLSws8PHxISgoKPM+o9FIUFAQDRs2zPY5DRs2zDIfYMuWLZnzK1asiLOzc5Y5sbGx7N27N3POzJkzOXDgABEREURERGS2rFu+fDmffvrpQ/MajUZSU1MxGqUZOQCHV6vb6TqWV880C1HEVSljx08D/JnVsy5ODpacvXGHvgv3M2RJKJduJ2odL2+Vrga9fgUzKzixEdaO0mzXwLPXE+j87R4W7I4EYEDjiqx6vRHupWw1ySPEk7K2MDCvty8v3912+83lB/jh70itY4m7NO/OP3r0aPr06YOvry/169dn+vTpJCQk0K+fuj62d+/elC1blsmTJwMwcuRImjVrxrRp02jXrh3Lli0jJCSEefPmAerHpaNGjeKTTz7Bw8ODihUr8sEHH+Dq6krHjh0BKF++fJYMdnbq7nWVK1emXLlyAPz888+Ym5tTu3ZtLC0tCQkJYfz48XTv3h1zc/P8+Ksp+DJ2APTpo14UJIRAp9PR3suVFp5lmL7lBAv3nGXj4Sh2nrjG8FZVGNi4EhZmJroyrrw/dFmgdtMJXwL2LtDyvXyN8EfEJd77/RDxyWkUtzFnalcvWlV3evwThSggzA16pnX1oriNBQt2R/Lx2iPcTEhm7AvV5KJVjWleNHfv3p1r164xYcIEoqKi8Pb2ZuPGjZkX8p0/fx69/t4PmEaNGrF06VLef/993n33XTw8PFi9ejW1at1rdTRu3DgSEhIYPHgwt2/fpnHjxmzcuBErK6snzmVmZsYXX3zBiRMnUBSFChUqMGzYMN58883ce/OF2ZV/1Y9h9eZQr7fWaYQocOwszXj/xRp08S3HhNWH2Xf2JlM2HmdV6EU+7lCLRlVKaR0xb3i2g3ZfqWea/5oC9k7gNzDPX/ZOShoT1xzm1xD14+z6FUswo4c3Lo7Wef7aQuQ2vV7HBy9Wp6SdBV9uOs6c7ae5mZDCJx1rY5DNdzSjeZ9mU2bSfZr/HAmhi6BmJ+i6UOs0QhRoiqLwe/glPlt/lOvxKQC093Ll/XbVcXJ48l/mC5Udn8OOyYAOui2GGi/l2Usdi4pl2NJwTl2NR6eDES09GN6yCmYGEz2jL4qUX/ad57272263runM9B7esu12LnvSek2K5jxkskVzUixM84TUBOi7Dtwba51IiEIhJjGVrzYfZ8k/5zAq6tnoUQEe9GnkjrmpFXiKop5tDl0EBkvovRoqNMrll1BYuu88k/48QnKakTL2lszoUZeGlUvm6usIobWNh64w4pcIUtKNNKxUknm9fbC3kqWiuaVQbG4iCql/l6sFc2lPdWMDIcQTcbQ256MOtVgzrDHebsWIT07jk3VHaT/rb/ZFmlhfVp0O2k6Dau0gPRl+6QFXj+ba4WMSUxm2NJz3fj9EcpqR5tVKs2FkEymYhUlqXcuFRf38sLUwyLbbGpKiWTwdRbm3A6Bvf/UHoxDiqdQq68hvrzfii861KW5jzrGoOLp9F8zoXyO4FmdCPwgNZtDlB3Dzh6QYddfAmGdvoRVx4TbtZu5i3cErmOl1vNe2Ogv6+FHSTnYkFaarUZVSLBvckJK2Fhy6FEvXucFcuFlweqIXBbI8Iw+Z5PKMc3tgYRswt4Exx8DKUetEQhRqtxJSmLLpOMv2n0dRwN7KjLcCq/GKfwXTueDnzk1Y0BquH1c/oeq/EayLP/VhjEaF7/8+w5SNx0kzKriVsGZWz3p4uxXL/cxCFFBnrsXz2g/7uHQ7EScHSxb395fdLZ+RLM8QeSPjLHPtLlIwC5ELittaMLlTbX5/4zlqlXUgLimNCX8cpsOcvwk/f0vreLnDpoS6a6C9K1w7Br/0VDdHego34pPp/+N+Plt/jDSjQrvaLqwb0UQKZlHkVCptx6rXG1HVyY7o2GS6fRdM6DkTW95VQMmZ5jxkcmea46/BV9XBmAqDd4Krt9aJhDAp6UaFpXvP8eWm48QmqTvq9fBz4+3WnhS3tdA4XS6IPqKecU6OAc8X1a4aT9Djfc/p64xaFsHVuGQszfR82L4mPeu7Sc9aUaTdvpPCgB9DCD13CytzPd++4kMLzzJaxyqU5EyzyH3hS9SCuayPFMxC5AGDXsdrDd3ZNrY5XXzUjZaW7b9Ai2k7+GXfeYzGQn6Ow6kG9PxF7aZxbC2sG/PIXQPT0o18teUEr3y/l6txyVQpY8cfw56jl395KZhFkVfMxoKfBvjTPHPb7RBWh1/SOpZJk6JZPBljOoTe7cfsO0DbLEKYuFJ2lkzt6sWKIQ3xdLbn9p1Uxv92kE7f7uHQpRit4z0b9+eg8/eATv1/yl9fZjvtSkwivebvZWbQSRQFuvu6sWbYc3g6m8CndkLkEmsLA/N7+9LR25U0o8Ko5REskG2384wsz8hDJrU848RmWNoVrIqpFwCayy5bQuSHtHQjPwaf4+stJ4hPTkOvg1cbVGDMC9VwtC7EfVr3zYf1Y9Vx+5ng0yfzoa1Hohm78gC376RiZ2nGpy/XooN3WY2CClHwGY0KH687wsLdZwEY1qIKY16oKp/IPCFZniFyV8jdCwC9X5GCWYh8ZGbQM6BxRYLGNOMlL1eMCiwOPkeraTtYFXqRQnveo/4gaHK3aF47Co6tJzktnUl/HmHg4hBu30mldllH1g5vLAWzEI+h1+uY8GIN3gqsBsDs7ad49/dDpBf2JV0FjJxpzkMmc6b59nmYXgdQYFgolKqidSIhiqw9p68z4Y/DnLoaD4Cfe3E+7lircC5bUBRYMwzCf8JosOJt249ZcVUtkAc0rsi41tWwNJPtgoV4Gkv3nuf91eq2221qqdtuy39HjyZnmkXuCV0EKFCxmRTMQmisUeVSrB/RhHfaeGJtbmD/2Vu0m/k3H689QlxSqtbxno5OBy/OIMqpGfr0JN6N+Yi61tH80MeXD16sIT/ohciBXv7lmdOrHhYGPRsORdFv4X7ik9O0jmUSpGgWj5aWAmGL1bGfXAAoREFgYaZnSLPKBI1pRptazqQbFX74O5JW03ay5sDlQrNk405KGuN+P0yLc30IN1ahuC6eFXZTaVU2XetoQhRqbWrf23Z7z+kb9Jwn227nBimaxaMd+xMSroGdM1Rrq3UaIcR9XItZ8+2rPvzYvz7uJW24GpfMiF/CeeX7vZy6Gqd1vEc6HhXHS7N382vIRZJ0VgQ3+BalRBXM4i7BT10g8bbWEYUo1O7fdvvgpRi6zQ3m4i3ZdvtZSNEsHm3/AvWrTx8wFOIr9YUwYc2qlmbjqKaMfr4qlmZ69py+QZsZu/h8wzHupBSsj2UVRWHp3vO8NPtvTl2Np4y9JT8P9OeNtvXRvfYb2DnB1cOw7BVITdI6rhCFWu1yjqwY0pCyxaw5cz2Bzt/u4UR0wf6FuiCTCwHzUKG/EPDqMfjGH3QGGHUQHOUKdiEKugs37/DRn4fZevQqAK6OVkxoX4PAms6at5+KTUpl/KqDrDt4BVCL/WndvChlZ3lvUtRBWNgWkmOhRgfosvCJdg0UQjxcVEwSvRfs5UR0PI7W5izo64dPheJaxyow5EJA8exC7p5lrtZGCmYhCgm3EjZ838eP73v7Uq64NZdjkhjyUxh9F+7n7PUEzXJFXLhNu5m7WHfwCmZ6He+29WRhX7+sBTOAc23o8TMYLODIH7DxnUfuGiiEeDxnRyt+/V9D6pUvRkxiKq98/w/bj1/VOlahI0WzyF5KAhz4RR379tc2ixDiqQXUcGLLm80Y3rIKFgY9O09c44Xpf/HVlhMkpebfhXZGo8L8v87Q5ds9XLiZSLni1qwY0pDBTSuj1z/kzHfFpvDyd4AO9s2Dv7/Ot7xCmKpiNhb8NPC+bbd/lG23n5YUzSJ7B1eqH48WrwiVWmidRgiRA9YWBsa8UI1NbzaliUcpUtKMzAw6yfNf7yToaHSev/6N+GT6/7ifT9cfJc2o0K62C+tGNKFu+Sf4WLhWJ2j9uToO+gjCf87bsEIUATYWZszv7UuH+7bdXrhbtt1+UlI0iwcpyr0dAH37g16+TYQozCqWsmVx//p8+0o9XBytuHAzkQE/hjDwxxAu3Mybq+n3nL5Omxm72HH8GpZmej59uRaze9V9uq2/GwyB50ap4zXD4eSWPMkqRFFibtDzdTdv+jZyB+CjP48wbfPxQtOqUktyIWAeKrQXAl4Mhe9bgsESxhwDmxJaJxJC5JKE5DRmbjvJD7siSTMqWJnrGdaiCoOaVsqVzUTS0o3M3HaKWdtOoihQpYwds3vVzfmOhYoCvw+Bf5eBuQ30WQvlfJ45pxBFnaIozNl+iqmbTwDqpigfd6iF4WHLpkyYXAgoci7jLHPNl6VgFsLE2FqaMb5NdTaMbEKDSiVISjUydfMJ2kzfxa6T157p2FdiEun1/V5mBqkFc3dfN9YMe+7ZtvjW6aDDbKjcClLvwNKucP3UM+UUQoBOp2NYSw8+fbkWOp26/fbwX8JITpPNhR5GzjTnoUJ5pvnOTfiqOqQlwYAt4FZf60RCiDyiKAprDlzmk3VHuRan7hbWrrYL779YHRdH66c6VtDRaMauOMCtO6nYWhj4rFNtOnjnYted5Hj48UW4HA7FysOArWDvlHvHF6IIW3/wCqOWRZCSbqRR5ZLM6+2LnaWZ1rHyjZxpFjlzYJlaMDvVhnJ+WqcRQuQhnU5HB++ybBvTjP7PVcSg17Hu4BVaTdvJdztPk5pufOwxUtKMTPrzCAN+DOHWnVRqlXVg3YgmuVswA1jaQa8V6sXJt8/Dz50hKTZ3X0OIIqptbRcW/mfb7Ruy7fYDpGgW9yjKvd7Mfv3Vj0WFECbP3sqcCe1rsHZ4Y3wrFOdOSjqTNxyj7YxdBJ++8dDnnbuh7jC24O7V9/2fq8iq1xvhXso2b4LalYbXfgPb0uomKMtfhTT5wS5EbniuSil+GdyAEne33e4q224/QJZn5KFCtzzjzE5Y/BJY2KsXAFraaZ1ICJHPjEaFVWEX+XzDMW4kpADQ0duVd9tVp4y9Vea8NQcu8+5vB4lPTqOYjTlTu3gRUCOflktcDodFL0JKPNTqDJ2+ly4/QuSS09fi6f3DPi7dTsTZwYrFA+pT1cle61h5SpZniKeXcQGgV3cpmIUoovR6HV193dg2pjmvNaiATgerIy7TaupOFvwdSVxSKm+v/JcRv4QTn5yGn3tx1o9okn8FM4BrXei+BPRmcGgVbH5Pdg0UIpdULm3Hqtcb4VHGjqjYJLrODSb03C2tYxUIcqY5DxWqM81xUfB1TTD+v717j4uq2v8//hpA7oIayUXxkpKWF1BRxOpYSVGaRVniJUWzzI6Zfsnj0U5p/U597fStLNOj3dTyhrc0j6llmnZK8gpe8pKVpZWgHhMQE5TZvz+2DmcSQQzYMPN+Ph7zcFizZuYzy930drP2WufgsXQIvd7qikSkGtj1Uw5Pf7ibHYdPAuDj5UHBOTs2G4y4pTlPdIvCy9Oi8y87F8IHj5j3b/s73PCENXWIuKCTpwsZPGsLGYdO4lfLk2kPtufmFvWtLqtS6EyzlM/2983A3ChegVlEHNo0DGbpY13433vbUMe/FgXn7Fxd24e5Q+JIvb2FdYEZoG1vuP158/6aZ2DHAutqEXExdfy9mftwHF2vvZrfzhbx8Htb+TDTvbfd1pnmSlRjzjQXnYPX20Luz+bcwLYPWF2RiFRDJ/ILWbfvKLe0uJqrAn2sLqfYx3+D9CnmdI1+C6F5N6srEnEZhefsjF60g+U7fgHg2Z7XM+iGphZXVbF0plku34GPzcDsHwLX3211NSJSTdUL8Ob+Dg2rV2AGc2pG6/vN35YtHGheKCgiFcLby4PXkou33X72X3t41U233VZoFthy/gLAdg+CVzX7n6GISFk8PCBpGlxzs7mixtwH4MT3Vlcl4jI8PGxM6Hk9T952LQCT133L08t2U2R3r+Cs0OzuTnwP360FbNBhkNXViIhcGS9v6D0bwtpA/jGYfR+c+mPbgotIMZvNxohuUTyfZG67PXfTIZ6Yn+FW224rNLu7rTPNP5t3g3quNUdJRNyMbxD0XwJ1GsOvB2Hu/eb22yJSYR7s3JgpfdtTy9PcQfShWVs4VXDO6rKqhEKzOzt7BjLmmPdjh1hbi4hIRagdCg9+AP5XwZFMWDgAzhVaXZWIS+nRNpyZgzrh7+3Jl9/+h35vu8e22wrN7mzPh/DbCQhqCNcmWl2NiEjFCGkO/RZBLX/4bh0sfxzsdqurEnEpN0aFMP8Rc9vtnT/l8MCb6fx88jery6pUCs3u7MIOgB0GgYenpaWIiFSohh2g9/tg84SdC2Dts1ZXJOJyoiPrsPDReBrU8eP7Y/n0+udGDmTnWV1WpVFodldZu+HwJnNd0/YDra5GRKTiRd0G90wx73/5OqT/09p6RFxQ8/qBLH4svnjb7TfT2X7INbfdVmh2VxfOMre8y5wDKCLiimL6QbcJ5v2Px8GuxdbWI+KCwoP9WPhoPO0a1eHk6bP0f3sTG75xvdVrFJrdUUEe7Fxo3u+oCwBFxMXd+D/Q6VHz/tJh8P0Ga+sRcUF1A8xtt/90ftvtIbO2uNy22wrN7mjnAnMDgJBroclNVlcjIlK5bDa4YyJcnwT2s5DWH47stLoqEZfj7+3FOwNjuTs6gnN2g1ELMnlv4w9Wl1VhFJrdjWHAlhnm/diHzP+ZiIi4Og9PuPdN80RBYZ65hvOvP1hdlYjLubDtdkp8YwwDJiz/mlfXfOMS224rNLubw5vg6Nfg5QfRfa2uRkSk6tTyhT5zIbQ1nMo2dw3M/4/VVYm4HA8PG8/e3YrUC9turz3AMx/W/G23FZrdzZbzFwC26QV+dSwtRUSkyvkGQ//FEBwJJ76Deb2hMN/qqkRcjs1m44luUfz9/Lbbc746xBNpNXvb7WoRmqdOnUqTJk3w9fUlLi6OzZs3l9p/0aJFtGzZEl9fX9q0acPKlSudHjcMg/HjxxMeHo6fnx8JCQkcOHCgxNcqKCggJiYGm81GZmamo339+vXcc889hIeHExAQQExMDHPnzv3Dn9VS+cdhzzLzvnYAFBF3FRRu7hroVxd+3gqLBkHRWaurEnFJAzo35o2+7cxtt3ceYcisrTV2223LQ/OCBQtITU1lwoQJbN++nejoaBITEzl69GiJ/Tdu3Ejfvn0ZMmQIGRkZJCUlkZSUxO7dux19XnrpJSZPnsz06dPZtGkTAQEBJCYmcubMmYteb8yYMURERJT4Pm3btmXJkiXs3LmTwYMHM3DgQFasWFFxH76qZcyBokKIaAcN2ltdjYiIda6+FvotNKeqHfgE/jXKvOZDRCrcXW0jHNtuf/Htcfq//RUn8mve9vY2w+KZ2XFxcXTs2JEpU8wF6O12O5GRkYwYMYKxY8de1D85OZn8/Hyn8Nq5c2diYmKYPn06hmEQERHBk08+yejRowHIyckhNDSUWbNm0adPH8fzVq1aRWpqKkuWLKFVq1ZkZGQQExNzyVp79OhBaGgoM2bMuKzPlpubS3BwMDk5OQQFBV3WcyqN3Q5vtDMvfLl7CrQfYG09IiLVwf5VkNYPDDvcNBq6PWN1RSIua8fhkwyauZlfT5/lmqsDmD0kjgZ1/Kwu67LzmqVnmgsLC9m2bRsJCQmONg8PDxISEkhPTy/xOenp6U79ARITEx39Dx48SFZWllOf4OBg4uLinF4zOzubRx55hNmzZ+Pv739Z9ebk5FCvXr1LPl5QUEBubq7Trdr4bp0ZmH2DoXUvq6sREakeWtwJd71m3v/3y7D5bUvLEXFl0ZF1WDSsCxHBvnx/LJ/7p23k26M1Z9ttS0Pz8ePHKSoqIjTUeUe60NBQsrKySnxOVlZWqf0v/FlaH8MwGDRoEMOGDSM2Nvayal24cCFbtmxh8ODBl+wzceJEgoODHbfIyMjLeu0qcWEHwOh+4H15/0gQEXELHVLglr+Z91f+Bb5eZmk5Iq6sef1Alvy5C83rB3Ik5wz3T08no4Zsu235nGYrvPHGG+Tl5TFu3LjL6v/ZZ58xePBg3n77bVq1anXJfuPGjSMnJ8dxO3z4cEWV/MecPAzfrDbvxz5kbS0iItXRn/5y/vvRgA8egR++sLoiEZcVHuzHokfjiYk0t93uV0O23bY0NIeEhODp6Ul2drZTe3Z2NmFhYSU+JywsrNT+F/4src+6detIT0/Hx8cHLy8vmjdvDkBsbCwpKSlOz9uwYQM9e/Zk0qRJDBw4sNTP4+PjQ1BQkNOtWtj+njlfr8lN5sUvIiLizGaD7i9Dy7vMC6bn94Psr62uSsRlXdh2+6aoEH47W8TD721h+Y5frC6rVJaGZm9vbzp06MDatWsdbXa7nbVr1xIfH1/ic+Lj4536A6xZs8bRv2nTpoSFhTn1yc3NZdOmTY4+kydPZseOHWRmZpKZmelYsm7BggW88MILjuetX7+eHj168I9//IOhQ4dWzIeuakVnYfv75v2OWmZOROSSPDyh1zvQKB4KcmBOL/M3dSJSKQJ8vHg3pSM9oyM4W2QwMi2D99N/sLqsS/KyuoDU1FRSUlKIjY2lU6dOvPbaa+Tn5zvmDg8cOJAGDRowceJEAEaOHEnXrl155ZVX6NGjB2lpaWzdupW33noLMBfTHjVqFM8//zxRUVE0bdqUZ555hoiICJKSkgBo1KiRUw2BgYEANGvWjIYNGwLmlIy77rqLkSNH0qtXL8d8aG9v71IvBqx29q0wd74KDDXPoIiIyKXV8oO+82HGnXBsL8y5Dx76GPxr0Pe+SA3i7eXB68kx1POvxXvpPzL+w6/5z6lCRiVEYbPZrC7PieVzmpOTk3n55ZcZP348MTExZGZmsnr1aseFfIcOHeLIkSOO/l26dGHevHm89dZbREdHs3jxYpYtW0br1q0dfcaMGcOIESMYOnQoHTt25NSpU6xevRpfX9/Lruu9997j9OnTTJw4kfDwcMftvvvuq7gPXxUu7ADYfiB41rK2FhGRmsCvLjy4GIIawPFvYF4yFJ62uioRl3Vh2+3/STCnkL6+9gCTPi15UzorWb5OsyuzfJ3mY9/A1I5g84BRuyC4YdXXICJSUx3dCzMS4UwOtOgOvWeDp+W/oBVxabO/+pFJa75hwdDORIXWrpL3rBHrNEsl23p+E5Zr71BgFhEpr/rXQd8F4OUL+1fCR6naNVCkkg3o3JjPRt9cZYG5PBSaXVXhadgxz7wfqwsARUSuSON46PWu+Ru77e/B+hetrkjE5QX7Vc/ppArNrmr3EvNXinWbQLNbra5GRKTmuu4uczk6gA0vwtaZ1tYjIpZQaHZVF3YA7DAYPPTXLCLyh3QcAn8aY97/KBX2fWRtPSJS5ZSmXNHP2+GXDPD0hnYPWl2NiIhruOUpaDfA3Cxq8UNw6CurKxKRKqTQ7IounGW+PgkCQiwtRUTEZdhscNdr5sXV586YS9Ed3Wd1VSJSRRSaXc1vv8KuJeZ97QAoIlKxPL3g/pnQsCOcOWnuGpjzs9VViUgVUGh2NTvS4NxvUL8VRMZZXY2IiOvx9od+C+GqKMj9Cebeb56wEBGXptDsSgyjeG3mjg+Zv0oUEZGK518PBnwAgWFwdA/M7wdnz1hdlYhUIoVmV/LDv80tX70DoW2y1dWIiLi2Oo3gwSXgEwSHNsIHD4O9yOqqRKSSKDS7ki3nLwBs2xt8qt9OOiIiLiesNfSZZ65WtPdfsGqMdg0UcVEKza4iLwv2rTDvawdAEZGq0/QmuO8twAZb3oF/v2x1RSJSCRSaXUXmXLCfMy/+C2ttdTUiIu6l1b1w5z/M++ueh+2zra1HRCqcl9UFSAWJGwYB9aF2uNWViIi4p7hHzd/6ffEq/GskBFwNLe6wuioRqSA60+wqvAOg/QCISrC6EhER99VtPET3A6MIFg2Cw1usrkhEKohCs4iISEWx2eDuydA8wVwzf15vOH7A6qpEpAIoNIuIiFQkz1rwwHsQ0R5+OwGz74PcI1ZXJSJ/kEKziIhIRfMJhP6LoN41kHMI5j4AZ3KsrkpE/gCFZhERkcoQEAIPfmBepJ29C9L6w7kCq6sSkSuk0CwiIlJZ6jWFBxeDd21z19alj4LdbnVVInIFFJpFREQqU3g0JM8Gj1rw9VL4eJx2DRSpgRSaRUREKluzW+De6eb9TdPhy9etrUdEyk2hWUREpCq0uR8S/9e8/+kEyJxvbT0iUi4KzSIiIlUlfjh0GWHeX/44HPjU2npE5LIpNIuIiFSlhP8HbXqD/RwsHAg/b7O6IhG5DArNIiIiVcnDA+6ZCtfcAmfzYW5v+M93VlclImVQaBYREalqXt7mihrh0XD6OMy+F/Kyra5KREqh0CwiImIFn9rQfzHUbQInf4S590NBntVVicglKDSLiIhYJbC+uWugfwhk7YQFD8K5QqurEpESKDSLiIhY6apm0H8h1AqA79fDh3/WroEi1ZBCs4iIiNUadIDe74OHF+xaBJ+Ot7oiEfkdhWYREZHqICrBXFUDYOMbsHGKtfWIiBOFZhERkeoiug8kPGfe/+RvsGuxtfWIiINCs4iISHVyw0iIe8y8v3QYfPeZtfWICKDQLCIiUr3YbJD4v9DqXrCfNVfUOLLD6qpEqk7Oz9XyYliFZhERkerGwwPufROa3ASFp2DO/XDioNVViVSewtOwcyG8nwSTWsGhdKsruohCs4iISHXk5QN95kJoG8g/CnPug1PHrK5KpOIYBvyYDstHwMvXwgePwPefAQYc/srq6i7iZXUBIiIicgm+wfDgYnjnNjjxPczrDSn/Ap9AqysTuXInD8GONNgx3zyuL6jTCKL7mRfE1mtqXX2XoNAsIiJSndUOgwEfwLu3wy/bYVEK9E0Dz1pWVyZy+QrzYc9y2DEPDn5e3F4rAFolQUw/aNTFnJpUTSk0i4iIVHchUdBvIbzXE7791Px1dtI086JBkerKbodDGyFzPuxZZs7Pv6DJTRDTH67rWWN+c6LQLCIiUhNEdoQHZkFaP/PX2rXDIOFZq6sSudivP5jTLzLnwckfi9vrNjGDcttkqNvYququmEKziIhITdHiDuj5Oix/HL6YBIFh0HmY1VWJQMEp2POhGZR//KK43bv2+ekX/aFR5xr92xGFZhERkZqk/QA4lQXrnofVYyGwPrS+z+qqxB3Z7WZAzpxnBuazp88/YINruppBueVd4O1vaZkVRaFZRESkprlpNORlwZZ3YOmjEBACTf9kdVXiLk58b85T3pEGOYeK2+s1My/oi+4DwQ2tq6+SWH6J4tSpU2nSpAm+vr7ExcWxefPmUvsvWrSIli1b4uvrS5s2bVi5cqXT44ZhMH78eMLDw/Hz8yMhIYEDBw6U+FoFBQXExMRgs9nIzMx0tJ85c4ZBgwbRpk0bvLy8SEpK+qMfU0REpOLYbHDnS+ZFVEWFkNYfsnZbXZW4sjO5sP19mHEHTG4Hn79kBmafIOgwCB76BEZsgz+NdsnADBaH5gULFpCamsqECRPYvn070dHRJCYmcvTo0RL7b9y4kb59+zJkyBAyMjJISkoiKSmJ3buLvyheeuklJk+ezPTp09m0aRMBAQEkJiZy5syZi15vzJgxREREXNReVFSEn58fTzzxBAkJCRX3gUVERCqKhyfc9w40vgEKcmFOL3P9W5GKYi+C7z6DJY+Ym48sH2Hu1GfzgGbdoNe7MPobc559o7gaPV/5ctgMwzCsevO4uDg6duzIlClTALDb7URGRjJixAjGjh17Uf/k5GTy8/NZsWKFo61z587ExMQwffp0DMMgIiKCJ598ktGjRwOQk5NDaGgos2bNok+fPo7nrVq1itTUVJYsWUKrVq3IyMggJibmovccNGgQJ0+eZNmyZeX+fLm5uQQHB5OTk0NQUFC5ny8iIlKm307CzDvh6B64KgqGfAL+9ayuSmqy49+a6ynvSIPcn4vbQ641p1+0TYagi0861lSXm9csO9NcWFjItm3bnM7kenh4kJCQQHp6yfuNp6enX3TmNzEx0dH/4MGDZGVlOfUJDg4mLi7O6TWzs7N55JFHmD17Nv7+FTc5vaCggNzcXKebiIhIpfKrA/0XQ1BD+M8Bc9fAwtNlPk3EyW8nYetMc/fJKR3g36+Ygdk3GGKHwMNrYfhmuPF/XCowl4dlFwIeP36coqIiQkNDndpDQ0PZt29fic/JysoqsX9WVpbj8Qttl+pjGAaDBg1i2LBhxMbG8sMPP1TExwFg4sSJPPfccxX2eiIiIpcluAE8uARmJMJPW2DxYEieC5663l9KYS+C7z8zV7/Y9xGcOz+V1eYBzRMgui+06A61fK2ts5pwu/+a3njjDfLy8hg3blyFv/a4ceNITU11/Jybm0tkZGSFv4+IiMhF6reEfgvg/Xvgm9WwYhTc/YbLzzOVK3BsvxmUdy6AvCPF7Ve3LJ5+UTvMuvqqKctCc0hICJ6enmRnZzu1Z2dnExZW8l9UWFhYqf0v/JmdnU14eLhTnwvzldetW0d6ejo+Pj5OrxMbG0v//v157733rvgz+fj4XPS6IiIiVaZRZ7h/Bix4EDJmQ+1wuPVvVlcl1cFvv8LuJeZScT9vLW73qwut7zfDckQ7/SOrFJbNafb29qZDhw6sXbvW0Wa321m7di3x8fElPic+Pt6pP8CaNWsc/Zs2bUpYWJhTn9zcXDZt2uToM3nyZHbs2EFmZiaZmZmOJesWLFjACy+8UKGfUUREpMq17AE9XjXvf/6SuZazuKeic/DNJ7BoELzcAj560gzMNk+49g7o/T48uR96vAwN2iswl8HS6RmpqamkpKQQGxtLp06deO2118jPz2fw4MEADBw4kAYNGjBx4kQARo4cSdeuXXnllVfo0aMHaWlpbN26lbfeegsAm83GqFGjeP7554mKiqJp06Y888wzREREONZabtSokVMNgYGBADRr1oyGDYvXFdyzZw+FhYWcOHGCvLw8xzrOJa2wISIiUq3EDjY3P9nwInw0GgLqw/V3W12VVJWjeyFzLuxcCKf+6zf09Vudn37R29xJUsrF0tCcnJzMsWPHGD9+PFlZWcTExLB69WrHhXyHDh3Cw6P4ZHiXLl2YN28eTz/9NE899RRRUVEsW7aM1q1bO/qMGTOG/Px8hg4dysmTJ7nxxhtZvXo1vr7lm8TevXt3fvzxR8fP7dq1A8wLCUVERKq9m8ea81W3vwdLHoaAZdC4i9VVSWU5fQJ2LTaXivslo7jd/ypo84AZlsPa6mzyH2DpOs2uTus0i4iIpYrOwcIBsH+luXTY4NUQer3VVUlFKToL335qXtS3fxXYz5rtHl4QlWgG5ajbwcvb2jqrucvNa263eoaIiIjb8PQyd22bnQSHN5m7Bj68xmW3OXYbWbthx3xz9Yv8Y8XtYW0gpr95ZjkgxLr6XJRCs4iIiCvz9oe+aTDjDji+H2bfBw+t1q6BNU3+f2DXInOuctbO4nb/EHOJuJi+ZmiWSqPQLCIi4ur865mbn7x7mxmc5/eFgcuglp/VlUlpis7CgU/M6RfffPxf0y9qQYs7zLPKzRPAs5a1dboJhWYRERF3UCfy/K6Bd8Lhr8yLA3u/Dx6eVlcmv3dkpxmUdy2E0/8pbg+PMYNy614QcJVl5bkrhWYRERF3EdoK+s4zp2jsW2Gu23vXJK2oUB2cOmaG5Mx5kL27uD2gPkQnQ3Q/XcRpMYVmERERd9LkRuj1NixMgW0zISgCuo6xuir3dK7Q3PI8cx58uwbs58x2T29o0d1c/aJZN/OCTrGc/hZERETczfX3QPf/g5Wj4bMXIDAUOqRYXZV7MAw4knl++sVi+O1E8WMNOphBudV9ulCzGlJoFhERcUedHjF3Dfz3y7BiFARcDS27W12V68rLNpeI2zEfju4pbg8Mg+g+Zli+uoV19UmZFJpFRETc1a1Pm8E5cw4sHgwDl0OjOKurch1nz8A3qyBzvrkJiVFktnv6wHV3mfOUr7lZ0y9qCP0tiYiIuCubDXq+BvlHzaXN5ifDQx/rjOcfYRjw83ZzO+tdi+HMyeLHGnYy11NudR/41bGqQrlCCs0iIiLuzLMWPDAL3usJP28zdw0c8ol5gaBcvtxfzOkXmfPNtbAvqB1RPP0iJMq6+uQPU2gWERFxd94B0G8RzLgd/vMtzLkfBq/U2dCynP0N9n1kzlP+bh0YdrPdyxeu62kG5aZdtRa2i1BoFhEREXOzjAeXwLu3w9GvIa2/+XMtX6srq14MA37aYq5+sfsDKMgpfiyy8/nVL5LAN9iyEqVyKDSLiIiIqW4T6L8YZnaHH7+ApUPh/pk6UwqQ8zPsTDPD8n++LW4PamjOU47uC1c1s64+qXQKzSIiIlIsvC30mWvObd7zIaz6q7mmszvuGlh42px+kTkXvl8PGGZ7LX+47m7zrHKTm8DDw8oqpYooNIuIiIiza7rCfW/C4odgy9sQFA43PWl1VVXDMODwJjMo714KhXnFjzW+wQzK198DPrWtq1EsodAsIiIiF2vdC04dhdVjYe3/MzfhaNff6qoqz8nDsCPNXCruxPfF7XUamespR/eBek2tq08sp9AsIiIiJev8GOQdgS9fh+UjILA+RN1mdVUVpzAf9v7LPKt88N8UT78IMC/mi+5rnl3W9AtBoVlERERK0+3Z81tAp8HCgZCyAhp2sLqqK2e3w6F084K+Pcug8FTxY01uMqdfXHc3+ARaVqJUTwrNIiIicmkeHnDPFMg/Bt+thXkPwEOfQEhzqysrn19/MKdfZM6Dkz8Wt9dtAjH9oW0y1G1sVXVSAyg0i4iISOk8a0Hv92FWDziSCXPuhSGfQu1QqysrXcEpcwWQzHnmEnoXeNc2p1/E9ING8e65MoiUm0KziIiIlM0nEPovMjc/+fUgzO0Fg1aCb5DVlTmz282AnDkP9iyHs/nnH7CZq4JE94Pr7jJ3QRQpB4VmERERuTyB9WHAB2ZwztoFCx40g7SXj9WVmSteZM43p2DkHCpur9fM3HykbR+oE2ldfVLjKTSLiIjI5at3jRmUZ/aAgxtg2WNw3zvWrDBxJte8mC9zPhzaWNzuEwSt7jXnKkd20vQLqRAKzSIiIlI+Ee0geTbM6w27l0BgKCT+b9WEU3sRHPwcdsw3p1+c++38AzZodosZlFv2gFp+lV+LuBWFZhERESm/5t3gnn/C0qHw1T+hdjjc8ETlvd/xb82NR3YsgNyfituvijIv6GubDMENKu/9xe0pNIuIiMiViU6GU9mw5hnzFhhqtlWUMzmw+wPzrPLhTcXtPsHQppd5VrlBB02/kCqh0CwiIiJXrssIyMuCr6bCh3+GgBDzLPSVshfB95+Z85T3rYBzZ8x2mwc062aeVW7RHWr5Vkz9IpdJoVlERESunM0Gtz8Pp7LM+c0LBsCgFdCgffle59g3xdMv8n4pbr+6pRmU2/SGoPCKrV2kHBSaRURE5I/x8ICkaZB/3FxRY+4DMOQTuKpZ6c/77Vdz+kXmPPh5a3G7bx1o84AZliPaafqFVAsKzSIiIvLHeflA8hyY1d1cw3lOLzM4B9Z37ld07vz0i7mwbyUUFZjtNk+Ius0MytfeUT3Wfhb5LwrNIiIiUjF8g6D/Enj3tvO7Bj4Agz4ydxM8utc8o7xzgXnx4AX1rzcv6GvzQPXfllvcmkKziIiIVJzaofDgBzDjdjiSCbPvBftZ+CWjuI9fPWjbG6L7Qni0pl9IjaDQLCIiIhUrpDn0Wwjv9YSfNpttHl4QlWhuaR2VCF7e1tYoUk4KzSIiIlLxGsZC3zRz45OmXc3pF4FXW12VyBVTaBYREZHKcU1X8ybiAjysLkBEREREpLpTaBYRERERKYNCs4iIiIhIGRSaRURERETKoNAsIiIiIlIGhWYRERERkTIoNIuIiIiIlEGhWURERESkDNUiNE+dOpUmTZrg6+tLXFwcmzdvLrX/okWLaNmyJb6+vrRp04aVK1c6PW4YBuPHjyc8PBw/Pz8SEhI4cOBAia9VUFBATEwMNpuNzMxMp8d27tzJTTfdhK+vL5GRkbz00kt/6HOKiIiISM1keWhesGABqampTJgwge3btxMdHU1iYiJHjx4tsf/GjRvp27cvQ4YMISMjg6SkJJKSkti9e7ejz0svvcTkyZOZPn06mzZtIiAggMTERM6cOXPR640ZM4aIiIiL2nNzc7n99ttp3Lgx27Zt4//+7/949tlneeuttyruw4uIiIhIjWAzDMOwsoC4uDg6duzIlClTALDb7URGRjJixAjGjh17Uf/k5GTy8/NZsWKFo61z587ExMQwffp0DMMgIiKCJ598ktGjRwOQk5NDaGgos2bNok+fPo7nrVq1itTUVJYsWUKrVq3IyMggJiYGgGnTpvG3v/2NrKwsvL29ARg7dizLli1j3759l/XZcnNzCQ4OJicnh6CgoCsaHxERERGpPJeb1yw901xYWMi2bdtISEhwtHl4eJCQkEB6enqJz0lPT3fqD5CYmOjof/DgQbKyspz6BAcHExcX5/Sa2dnZPPLII8yePRt/f/8S3+dPf/qTIzBfeJ/9+/fz66+/llhbQUEBubm5TjcRERERqfksDc3Hjx+nqKiI0NBQp/bQ0FCysrJKfE5WVlap/S/8WVofwzAYNGgQw4YNIzY2tlzv89/v8XsTJ04kODjYcYuMjCyxn4iIiIjULJbPabbCG2+8QV5eHuPGjavQ1x03bhw5OTmO2+HDhyv09UVERETEGpaG5pCQEDw9PcnOznZqz87OJiwsrMTnhIWFldr/wp+l9Vm3bh3p6en4+Pjg5eVF8+bNAYiNjSUlJaXU9/nv9/g9Hx8fgoKCnG4iIiIiUvNZGpq9vb3p0KEDa9eudbTZ7XbWrl1LfHx8ic+Jj4936g+wZs0aR/+mTZsSFhbm1Cc3N5dNmzY5+kyePJkdO3aQmZlJZmamY8m6BQsW8MILLzje5/PPP+fs2bNO79OiRQvq1q1bAZ9eRERERGoKL6sLSE1NJSUlhdjYWDp16sRrr71Gfn4+gwcPBmDgwIE0aNCAiRMnAjBy5Ei6du3KK6+8Qo8ePUhLS2Pr1q2OpeBsNhujRo3i+eefJyoqiqZNm/LMM88QERFBUlISAI0aNXKqITAwEIBmzZrRsGFDAPr168dzzz3HkCFD+Otf/8ru3bt5/fXXmTRpUlUMi4iIiIhUI5aH5uTkZI4dO8b48ePJysoiJiaG1atXOy66O3ToEB4exSfEu3Tpwrx583j66ad56qmniIqKYtmyZbRu3drRZ8yYMeTn5zN06FBOnjzJjTfeyOrVq/H19b3suoKDg/nkk08YPnw4HTp0ICQkhPHjxzN06NCK+/AiIiIiUiNYvk6zK8vJyaFOnTocPnxY85tFREREqqHc3FwiIyM5efIkwcHBl+xn+ZlmV5aXlwegpedEREREqrm8vLxSQ7PONFciu93OL7/8Qu3atbHZbJX+fhf+paQz2840LpemsSmZxuXSNDYl07iUTONyaRqbklkxLoZhkJeXR0REhNOU4N/TmeZK5OHh4biwsCppubuSaVwuTWNTMo3LpWlsSqZxKZnG5dI0NiWr6nEp7QzzBW65uYmIiIiISHkoNIuIiIiIlEGh2YX4+PgwYcIEfHx8rC6lWtG4XJrGpmQal0vT2JRM41IyjculaWxKVp3HRRcCioiIiIiUQWeaRURERETKoNAsIiIiIlIGhWYRERERkTIoNIuIiIiIlEGhuYaZOnUqTZo0wdfXl7i4ODZv3lxq/0WLFtGyZUt8fX1p06YNK1eurKJKq1Z5xmXWrFnYbDanm6+vbxVWWzU+//xzevbsSUREBDabjWXLlpX5nPXr19O+fXt8fHxo3rw5s2bNqvQ6rVDesVm/fv1Fx4zNZiMrK6tqCq4iEydOpGPHjtSuXZv69euTlJTE/v37y3yeq3/PXMm4uMv3zLRp02jbtq1jI4r4+HhWrVpV6nNc/XiB8o+Luxwvv/fiiy9is9kYNWpUqf2qyzGj0FyDLFiwgNTUVCZMmMD27duJjo4mMTGRo0ePlth/48aN9O3blyFDhpCRkUFSUhJJSUns3r27iiuvXOUdFzB3Gjpy5Ijj9uOPP1ZhxVUjPz+f6Ohopk6deln9Dx48SI8ePbjlllvIzMxk1KhRPPzww3z88ceVXGnVK+/YXLB//36n46Z+/fqVVKE1NmzYwPDhw/nqq69Ys2YNZ8+e5fbbbyc/P/+Sz3GH75krGRdwj++Zhg0b8uKLL7Jt2za2bt3Krbfeyj333MPXX39dYn93OF6g/OMC7nG8/LctW7bw5ptv0rZt21L7VatjxpAao1OnTsbw4cMdPxcVFRkRERHGxIkTS+zfu3dvo0ePHk5tcXFxxqOPPlqpdVa18o7LzJkzjeDg4CqqrnoAjKVLl5baZ8yYMUarVq2c2pKTk43ExMRKrMx6lzM2n332mQEYv/76a5XUVF0cPXrUAIwNGzZcso+7fM/8t8sZF3f8nrmgbt26xjvvvFPiY+54vFxQ2ri42/GSl5dnREVFGWvWrDG6du1qjBw58pJ9q9MxozPNNURhYSHbtm0jISHB0ebh4UFCQgLp6eklPic9Pd2pP0BiYuIl+9dEVzIuAKdOnaJx48ZERkaW+a9/d+EOx8sfFRMTQ3h4OLfddhtffvml1eVUupycHADq1at3yT7ueNxczriA+33PFBUVkZaWRn5+PvHx8SX2ccfj5XLGBdzreBk+fDg9evS46FgoSXU6ZhSaa4jjx49TVFREaGioU3toaOgl51VmZWWVq39NdCXj0qJFC2bMmMGHH37InDlzsNvtdOnShZ9++qkqSq62LnW85Obm8ttvv1lUVfUQHh7O9OnTWbJkCUuWLCEyMpKbb76Z7du3W11apbHb7YwaNYobbriB1q1bX7KfO3zP/LfLHRd3+p7ZtWsXgYGB+Pj4MGzYMJYuXcr1119fYl93Ol7KMy7udLykpaWxfft2Jk6ceFn9q9Mx41Xl7yhisfj4eKd/7Xfp0oXrrruON998k7///e8WVibVVYsWLWjRooXj5y5duvDdd98xadIkZs+ebWFllWf48OHs3r2bL774wupSqpXLHRd3+p5p0aIFmZmZ5OTksHjxYlJSUtiwYcMlA6K7KM+4uMvxcvjwYUaOHMmaNWtq5IWOCs01REhICJ6enmRnZzu1Z2dnExYWVuJzwsLCytW/JrqScfm9WrVq0a5dO7799tvKKLHGuNTxEhQUhJ+fn0VVVV+dOnVy2UD5+OOPs2LFCj7//HMaNmxYal93+J65oDzj8nuu/D3j7e1N8+bNAejQoQNbtmzh9ddf580337yorzsdL+UZl99z1eNl27ZtHD16lPbt2zvaioqK+Pzzz5kyZQoFBQV4eno6Pac6HTOanlFDeHt706FDB9auXetos9vtrF279pJzpOLj4536A6xZs6bUOVU1zZWMy+8VFRWxa9cuwsPDK6vMGsEdjpeKlJmZ6XLHjGEYPP744yxdupR169bRtGnTMp/jDsfNlYzL77nT94zdbqegoKDEx9zheLmU0sbl91z1eOnWrRu7du0iMzPTcYuNjaV///5kZmZeFJihmh0zVX7poVyxtLQ0w8fHx5g1a5axZ88eY+jQoUadOnWMrKwswzAMY8CAAcbYsWMd/b/88kvDy8vLePnll429e/caEyZMMGrVqmXs2rXLqo9QKco7Ls8995zx8ccfG999952xbds2o0+fPoavr6/x9ddfW/URKkVeXp6RkZFhZGRkGIDx6quvGhkZGcaPP/5oGIZhjB071hgwYICj//fff2/4+/sbf/nLX4y9e/caU6dONTw9PY3Vq1db9REqTXnHZtKkScayZcuMAwcOGLt27TJGjhxpeHh4GJ9++qlVH6FSPPbYY0ZwcLCxfv1648iRI47b6dOnHX3c8XvmSsbFXb5nxo4da2zYsME4ePCgsXPnTmPs2LGGzWYzPvnkE8Mw3PN4MYzyj4u7HC8l+f3qGdX5mFFormHeeOMNo1GjRoa3t7fRqVMn46uvvnI81rVrVyMlJcWp/8KFC41rr73W8Pb2Nlq1amV89NFHVVxx1SjPuIwaNcrRNzQ01Ojevbuxfft2C6quXBeWSfv97cJYpKSkGF27dr3oOTExMYa3t7dxzTXXGDNnzqzyuqtCecfmH//4h9GsWTPD19fXqFevnnHzzTcb69ats6b4SlTSmABOx4E7fs9cybi4y/fMQw89ZDRu3Njw9vY2rr76aqNbt26OYGgY7nm8GEb5x8VdjpeS/D40V+djxmYYhlF157VFRERERGoezWkWERERESmDQrOIiIiISBkUmkVEREREyqDQLCIiIiJSBoVmEREREZEyKDSLiIiIiJRBoVlEREREpAwKzSIiIiIiZVBoFhGRSmez2Vi2bJnVZYiIXDGFZhERFzdo0CBsNttFtzvuuMPq0kREagwvqwsQEZHKd8cddzBz5kynNh8fH4uqERGpeXSmWUTEDfj4+BAWFuZ0q1u3LmBOnZg2bRp33nknfn5+XHPNNSxevNjp+bt27eLWW2/Fz8+Pq666iqFDh3Lq1CmnPjNmzKBVq1b4+PgQHh7O448/7vT48ePHuffee/H39ycqKorly5dX7ocWEalACs0iIsIzzzxDr1692LFjB/3796dPnz7s3bsXgPz8fBITE6lbty5btmxh0aJFfPrpp06heNq0aQwfPpyhQ4eya9culi9fTvPmzZ3e47nnnqN3797s3LmT7t27079/f06cOFGln1NE5ErZDMMwrC5CREQqz6BBg5gzZw6+vr5O7U899RRPPfUUNpuNYcOGMW3aNMdjnTt3pn379vzzn//k7bff5q9//SuHDx8mICAAgJUrV9KzZ09++eUXQkNDadCgAYMHD+b5558vsQabzcbTTz/N3//+d8AM4oGBgaxatUpzq0WkRtCcZhERN3DLLbc4hWKAevXqOe7Hx8c7PRYfH09mZiYAe/fuJTo62hGYAW644Qbsdjv79+/HZrPxyy+/0K1bt1JraNu2reN+QEAAQUFBHD169Eo/kohIlVJoFhFxAwEBARdNl6gofn5+l9WvVq1aTj/bbDbsdntllCQiUuE0p1lERPjqq68u+vm6664D4LrrrmPHjh3k5+c7Hv/yyy/x8PCgRYsW1K5dmyZNmrB27doqrVlEpCrpTLOIiBsoKCggKyvLqc3Ly4uQkBAAFi1aRGxsLDfeeCNz585l8+bNvPvuuwD079+fCRMmkJKSwrPPPsuxY8cYMWIEAwYMIDQ0FIBnn32WYcOGUb9+fe68807y8vL48ssvGTFiRNV+UBGRSqLQLCLiBlavXk14eLhTW4sWLdi3bx9grmyRlpbGn//8Z8LDw5k/fz7XX389AP7+/nz88ceMHDmSjh074u/vT69evXj11Vcdr5WSksKZM2eYNGkSo0ePJiQkhPvvv7/qPqCISCXT6hkiIm7OZrOxdOlSkpKSrC5FRKTa0pxmEREREZEyKDSLiIiIiJRBc5pFRNycZumJiJRNZ5pFRERERMqg0CwiIiIiUgaFZhERERGRMig0i4iIiIiUQaFZRERERKQMCs0iIiIiImVQaBYRERERKYNCs4iIiIhIGf4/zp6JX8DuzvkAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Deep Neural Network"
+      ],
+      "metadata": {
+        "id": "_HNTM3uKfJnu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1. **Input Layer (Dense Layer)**\n",
+        "    - `Dense(1024, input_dim=30, activation='relu')`\n",
+        "    - **Dense**: This is a fully connected layer with 1024 neurons.\n",
+        "    - **input_dim=30**: Indicates that the input layer expects 30 features (input variables).\n",
+        "    - **activation='relu'**: The ReLU (Rectified Linear Unit) activation function is used. ReLU introduces non-linearity and is commonly used in hidden layers of neural networks.\n",
+        "\n",
+        "2. **Dropout Layer**\n",
+        "    - `Dropout(0.10)`\n",
+        "    - **Dropout**: Randomly sets 10% of the neurons to zero during each training iteration. Dropout is a regularization technique used to prevent overfitting by reducing the interdependence of neurons.\n",
+        "\n",
+        "3. **Hidden Layers (Dense Layers)**\n",
+        "    - `Dense(768, activation='relu')`, `Dense(512, activation='relu')`, `Dense(256, activation='relu')`, `Dense(128, activation='relu')`\n",
+        "    - These layers are fully connected (dense) layers with decreasing numbers of neurons. They gradually reduce the dimensionality of the input representation.\n",
+        "    - Each layer applies the ReLU activation function to introduce non-linearity.\n",
+        "\n",
+        "4. **Output Layer (Dense Layer)**\n",
+        "    - `Dense(5, activation='softmax')`\n",
+        "    - **Dense**: This is the final fully connected layer with 5 neurons.\n",
+        "    - **activation='softmax'**: The Softmax activation function is used. Softmax converts raw output scores into probabilities, with each value representing the probability of a particular class. This is commonly used for multi-class classification tasks.\n",
+        "\n",
+        "### Architecture Summary\n",
+        "\n",
+        "- **Input Layer**: Takes in 30 features and passes them to the first Dense layer.\n",
+        "- **Hidden Layers**: Consist of several densely connected layers, each with ReLU activation, which progressively extract and transform features.\n",
+        "- **Dropout Layers**: Applied after each hidden layer to reduce overfitting.\n",
+        "- **Output Layer**: Produces probabilities for each class using the Softmax activation function, suitable for multi-class classification.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "0ciVwVraprjE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "deep_model = Sequential([\n",
+        "    Dense(1024, input_dim=30, activation='relu'),\n",
+        "    Dropout(0.10),\n",
+        "    Dense(768, activation='relu'),\n",
+        "    Dropout(0.10),\n",
+        "    Dense(512, activation='relu'),\n",
+        "    Dropout(0.10),\n",
+        "    Dense(256, activation='relu'),\n",
+        "    Dropout(0.10),\n",
+        "    Dense(128, activation='relu'),\n",
+        "    Dropout(0.10),\n",
+        "    Dense(5, activation='softmax')\n",
+        "])"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-07T14:09:05.700825Z",
+          "iopub.execute_input": "2022-11-07T14:09:05.703379Z",
+          "iopub.status.idle": "2022-11-07T14:09:05.779553Z",
+          "shell.execute_reply.started": "2022-11-07T14:09:05.703329Z",
+          "shell.execute_reply": "2022-11-07T14:09:05.778533Z"
+        },
+        "trusted": true,
+        "id": "nLKtLH2ofJnu"
+      },
+      "execution_count": 54,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "deep_model.compile(loss ='sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-07T14:09:05.781564Z",
+          "iopub.execute_input": "2022-11-07T14:09:05.782578Z",
+          "iopub.status.idle": "2022-11-07T14:09:05.793763Z",
+          "shell.execute_reply.started": "2022-11-07T14:09:05.782534Z",
+          "shell.execute_reply": "2022-11-07T14:09:05.792847Z"
+        },
+        "trusted": true,
+        "id": "94LbTJMifJnu"
+      },
+      "execution_count": 55,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "deep_model.fit(X_train, Y_train.values.ravel(), epochs=5, batch_size=32,validation_data=(X_test, Y_test))"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-07T14:09:05.971564Z",
+          "iopub.execute_input": "2022-11-07T14:09:05.971941Z",
+          "iopub.status.idle": "2022-11-07T14:24:38.665566Z",
+          "shell.execute_reply.started": "2022-11-07T14:09:05.971901Z",
+          "shell.execute_reply": "2022-11-07T14:24:38.664478Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "at-UuTj8fJn7",
+        "outputId": "974ae421-a414-42c0-a14d-c1d68ea48986"
+      },
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "10344/10344 [==============================] - 75s 7ms/step - loss: 0.0164 - accuracy: 0.9963 - val_loss: 0.0083 - val_accuracy: 0.9979\n",
+            "Epoch 2/5\n",
+            "10344/10344 [==============================] - 54s 5ms/step - loss: 0.0095 - accuracy: 0.9979 - val_loss: 0.0062 - val_accuracy: 0.9984\n",
+            "Epoch 3/5\n",
+            "10344/10344 [==============================] - 55s 5ms/step - loss: 0.0087 - accuracy: 0.9983 - val_loss: 0.0075 - val_accuracy: 0.9982\n",
+            "Epoch 4/5\n",
+            "10344/10344 [==============================] - 55s 5ms/step - loss: 0.0085 - accuracy: 0.9983 - val_loss: 0.0077 - val_accuracy: 0.9986\n",
+            "Epoch 5/5\n",
+            "10344/10344 [==============================] - 55s 5ms/step - loss: 0.0081 - accuracy: 0.9984 - val_loss: 0.0056 - val_accuracy: 0.9989\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7b790042ed10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get training and validation accuracy and loss values\n",
+        "history = deep_model.history\n",
+        "\n",
+        "# Plot the accuracy graph\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
+        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
+        "plt.title('Model Accuracy')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.legend()\n",
+        "plt.savefig('/content/deep_accuracy.png')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "0aboFk2RkOXR",
+        "outputId": "17b50423-1bdb-4eeb-928f-80431601b1bc"
+      },
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Plot the loss graph\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.plot(history.history['loss'], label='Training Loss')\n",
+        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+        "plt.title('Model Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.savefig('/content/deep_loss.png')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "JLCNP7mNkUFu",
+        "outputId": "80ec510a-8c2a-47a3-ed1a-76d178dadfad"
+      },
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Convolutional Neural Network"
+      ],
+      "metadata": {
+        "id": "PD9eyGO8fJn7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Architecture\n",
+        "\n",
+        "### Input Layer\n",
+        "- **Shape**: `(30, 1)`\n",
+        "- **Explanation**: This specifies that the input data has 30 features and 1 channel (assuming it's a 1D convolution for sequence data).\n",
+        "\n",
+        "### Convolutional Layers (Conv1D)\n",
+        "- **Convolutional Layers**: Three convolutional layers are defined.\n",
+        "  - **Convolution**: Each convolutional layer applies a convolution operation with a kernel size of 3 and \"same\" padding.\n",
+        "  - **Activation**: ReLU activation function is applied to introduce non-linearity.\n",
+        "  - **Pooling**: Each convolutional layer is followed by max-pooling with a pool size of 2.\n",
+        "  - **Dropout**: Dropout with a rate of 0.5 is applied after each convolutional layer to prevent overfitting.\n",
+        "\n",
+        "### Flatten Layers\n",
+        "- **Flatten**: After each max-pooling layer, the output is flattened to prepare for the fully connected layers.\n",
+        "\n",
+        "### Dense Layers\n",
+        "- **Dense Layers**: Two dense layers are defined.\n",
+        "  - **Dense**: Each dense layer has 256 neurons with ReLU activation.\n",
+        "  - **Dropout**: Dropout with a rate of 0.5 is applied after each dense layer.\n",
+        "\n",
+        "### Concatenation\n",
+        "- **Concatenate Layer**: The output of the second dense layer (prior to dropout) is concatenated with the outputs of the two Flatten layers.\n",
+        "\n",
+        "### Output Layer\n",
+        "- **Output Layer**: The concatenated output is passed through another dense layer with softmax activation to produce the final output with 5 classes.\n",
+        "\n",
+        "### Model Summary\n",
+        "- **Inputs**: A 1D tensor of shape (30, 1).\n",
+        "- **Outputs**: A softmax output layer producing probabilities for each of the 5 classes.\n",
+        "- **Model Type**: This model follows a convolutional neural network architecture followed by dense layers, incorporating dropout regularization and concatenation.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "2jZJMSjUp6bE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "inputs = Input(shape=(30, 1))\n",
+        "y = Conv1D(62, 3, padding=\"same\", activation=\"relu\", input_shape=(30,1))(inputs)\n",
+        "y = MaxPooling1D(pool_size=(2))(y)\n",
+        "y1 = Flatten()(y)\n",
+        "\n",
+        "y = Dropout(0.5)(y)\n",
+        "y = Conv1D(62, 3, padding=\"same\", activation=\"relu\", input_shape=(30,1))(inputs)\n",
+        "y = MaxPooling1D(pool_size=(2))(y)\n",
+        "y2 = Flatten()(y)\n",
+        "\n",
+        "y = Dropout(0.5)(y)\n",
+        "y = Conv1D(124, 3, padding=\"same\", activation=\"relu\", input_shape=(30,1))(inputs)\n",
+        "y = MaxPooling1D(pool_size=(2))(y)\n",
+        "y = Flatten()(y)\n",
+        "y = Dropout(0.5)(y)\n",
+        "y = Dense(256, activation=\"relu\")(y)\n",
+        "y = Dropout(0.5)(y)\n",
+        "y = Dense(5, activation='softmax')(y)\n",
+        "\n",
+        "y = Concatenate()([y, y1, y2])\n",
+        "\n",
+        "outputs = Dense(5, activation='softmax')(y)\n",
+        "cnn_model = Model(inputs=inputs, outputs=outputs)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-07T14:24:38.666823Z",
+          "iopub.execute_input": "2022-11-07T14:24:38.667181Z",
+          "iopub.status.idle": "2022-11-07T14:24:38.768606Z",
+          "shell.execute_reply.started": "2022-11-07T14:24:38.66715Z",
+          "shell.execute_reply": "2022-11-07T14:24:38.767675Z"
+        },
+        "trusted": true,
+        "id": "rSs8FFP3fJn8"
+      },
+      "execution_count": 59,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cnn_model.compile(loss ='sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-07T14:24:38.770158Z",
+          "iopub.execute_input": "2022-11-07T14:24:38.770592Z",
+          "iopub.status.idle": "2022-11-07T14:24:38.781538Z",
+          "shell.execute_reply.started": "2022-11-07T14:24:38.770549Z",
+          "shell.execute_reply": "2022-11-07T14:24:38.780374Z"
+        },
+        "trusted": true,
+        "id": "RU6NybUtfJn8"
+      },
+      "execution_count": 60,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cnn_model.fit(X_train.reshape((-1,30,1)), Y_train.values.ravel(), epochs=5, batch_size=32,validation_data=(X_test, Y_test))"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2022-11-07T14:24:39.040075Z",
+          "iopub.execute_input": "2022-11-07T14:24:39.040453Z",
+          "iopub.status.idle": "2022-11-07T14:37:06.1341Z",
+          "shell.execute_reply.started": "2022-11-07T14:24:39.040413Z",
+          "shell.execute_reply": "2022-11-07T14:37:06.13305Z"
+        },
+        "trusted": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LY7pGApOfJn9",
+        "outputId": "ca3ec03f-d8d2-4bf6-c2ea-ea988616b4fd"
+      },
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "10344/10344 [==============================] - 70s 6ms/step - loss: 0.0239 - accuracy: 0.9937 - val_loss: 0.0101 - val_accuracy: 0.9973\n",
+            "Epoch 2/5\n",
+            "10344/10344 [==============================] - 63s 6ms/step - loss: 0.0088 - accuracy: 0.9977 - val_loss: 0.0072 - val_accuracy: 0.9981\n",
+            "Epoch 3/5\n",
+            "10344/10344 [==============================] - 59s 6ms/step - loss: 0.0074 - accuracy: 0.9980 - val_loss: 0.0090 - val_accuracy: 0.9973\n",
+            "Epoch 4/5\n",
+            "10344/10344 [==============================] - 59s 6ms/step - loss: 0.0067 - accuracy: 0.9982 - val_loss: 0.0063 - val_accuracy: 0.9983\n",
+            "Epoch 5/5\n",
+            "10344/10344 [==============================] - 61s 6ms/step - loss: 0.0064 - accuracy: 0.9983 - val_loss: 0.0063 - val_accuracy: 0.9984\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7b790010a3e0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 61
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get training and validation accuracy and loss values\n",
+        "history = cnn_model.history\n",
+        "\n",
+        "# Plot the accuracy graph\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
+        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
+        "plt.title('Model Accuracy')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.legend()\n",
+        "plt.savefig('/content/cnn_accuracy.png')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "l0fECQN7lMqu",
+        "outputId": "1a7fe04b-dfed-4166-a5ae-87ba6727d08b"
+      },
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Plot the loss graph\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.plot(history.history['loss'], label='Training Loss')\n",
+        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+        "plt.title('Model Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.savefig('/content/cnn_loss.png')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "5Q10eDVBlWSk",
+        "outputId": "be4c875b-aa83-4ce8-80d5-6823394f0cbb"
+      },
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Comparision"
+      ],
+      "metadata": {
+        "id": "9ZyWxBOUneLR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "models = ['ANN', 'Deep NN', 'CNN']\n",
+        "accuracies = [\n",
+        "    ann_model.history.history['accuracy'][-1],\n",
+        "    deep_model.history.history['accuracy'][-1],\n",
+        "    cnn_model.history.history['accuracy'][-1]\n",
+        "]\n",
+        "\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "bars = plt.bar(models, accuracies, color=['red', 'green', 'blue'])\n",
+        "\n",
+        "# Annotate bars with accuracy scores\n",
+        "for bar, accuracy in zip(bars, accuracies):\n",
+        "    yval = bar.get_height()\n",
+        "    plt.text(bar.get_x() + bar.get_width()/2, yval + 0.01, round(accuracy, 6), ha='center', va='bottom')\n",
+        "\n",
+        "plt.xlabel('Models')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.title('Model Accuracy Comparison')\n",
+        "\n",
+        "# Save the plot as a PNG file\n",
+        "plt.savefig('/content/model_comparison.png')\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "m12JO1GWntP_",
+        "outputId": "c0cee97d-d97c-494e-b18f-878e5111ff65"
+      },
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Intrusion Detection/Model/README.md b/Intrusion Detection/Model/README.md
new file mode 100644
index 000000000..42cb329ff
--- /dev/null
+++ b/Intrusion Detection/Model/README.md	
@@ -0,0 +1,114 @@
+## **Intrusion Detection using Neural Network**
+
+### 🎯 **Goal**
+The primary objective of this project is to develop an Intrusion Detection System (IDS) capable of distinguishing between normal network traffic and various types of attacks using machine learning models. The dataset used for training and evaluation is the KDD Cup 1999 dataset, a widely used benchmark dataset for IDS research.
+
+### 🧵 **Dataset**
+The dataset, sourced from the KDD Cup 1999 competition, contains network connection records captured over several weeks from a university department's local area network (LAN). It includes both normal and malicious activities, simulated through different types of attacks such as denial of service (DoS), user to root (U2R), remote to local (R2L), and probe attacks.
+
+### 🧾 **Description**
+
+This project involves preprocessing the dataset, performing exploratory data analysis (EDA), training multiple machine learning models, and comparing their performance. The models used include Artificial Neural Networks (ANN), Deep Neural Networks (Deep NN), and Convolutional Neural Networks (CNN).
+
+### 🧮 **What I had done!**
+
+1. **Data Preprocessing**:
+   - Imported necessary libraries for data manipulation, visualization, and model training.
+   - Loaded the KDD Cup 1999 dataset and performed basic preprocessing steps.
+   - Mapped categorical features to numerical values and added attack type labels.
+
+2. **Exploratory Data Analysis (EDA)**:
+   - Visualized the distribution of protocol types, services, flags, and attack types.
+   - Investigated correlations between different features using heatmap visualization.
+
+3. **Feature Engineering**:
+   - Removed highly correlated features to improve model performance.
+
+4. **Model Training**:
+   - Trained three types of models: ANN, Deep NN, and CNN.
+   - Compiled and trained each model using the preprocessed dataset.
+
+5. **Model Evaluation**:
+   - Evaluated the performance of each model using accuracy metrics.
+   - Plotted training and validation accuracy/loss curves for visualization.
+
+6. **Comparison**:
+   - Compared the accuracy of ANN, Deep NN, and CNN models to determine the most effective approach for intrusion detection.
+
+### 🚀 **Models Implemented**
+
+#### Artificial Neural Network (ANN):
+- Configured with multiple dense layers and ReLU activation.
+- Trained with sparse categorical cross-entropy loss and Adam optimizer.
+
+#### Deep Neural Network (Deep NN):
+- Designed with several densely connected layers, gradually reducing dimensionality.
+- Utilized dropout layers for regularization to prevent overfitting.
+- Trained using sparse categorical cross-entropy loss and Adam optimizer.
+
+#### Convolutional Neural Network (CNN):
+- Constructed with convolutional and max-pooling layers to capture local patterns.
+- Incorporated dropout regularization to prevent overfitting.
+- Trained with sparse categorical cross-entropy loss and Adam optimizer.
+
+### 📚 **Libraries Needed**
+
+- TensorFlow and Keras: For building and training machine learning models.
+- NumPy and Pandas: For data manipulation and numerical operations.
+- Matplotlib and Seaborn: For data visualization.
+- Scikit-learn: For dataset splitting, evaluation, and feature engineering.
+
+### 📊 **Exploratory Data Analysis Results**
+
+- Attack Type Distribution
+![Attack Type Distribution](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/attack_type.png)
+- Flag Distribution
+![Flag Distribution](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/flag.png)
+- Logged-in Users Distribution
+![Logged-in Users Distribution](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/logged_in.png)
+- Protocol Type Distribution
+![Protocol Type Distribution](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/protocol_type.png)
+
+- Service Distribution
+![Service Distribution](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/service.png)
+
+- Target Distribution
+![Target Distribution](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/target.png)
+
+- Correlation Heatmap
+![Correlation Heatmap](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/correlation_heatmap.png)
+
+
+### 📈 **Performance of the Models**
+
+- **ANN Accuracy**: 99.89%
+- **Deep NN Accuracy**: 99.84%
+- **CNN Accuracy**: 99.83%
+
+<br>
+
+
+- ANN
+
+![ANN Accuracy](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/ann_accuracy.png)
+![ANN Loss](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/ann_loss.png)
+
+- DNN
+  
+![Deep NN Accuracy](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/deep_accuracy.png)
+![Deep NN Loss](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/deep_loss.png)
+
+- CNN
+  
+![CNN Accuracy](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/cnn_accuracy.png)
+![CNN Loss](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/cnn_loss.png)
+
+### 📢 **Conclusion**
+
+In conclusion, this project successfully developed an Intrusion Detection  using deep learning models trained on the KDD Cup 1999 dataset. 
+![Model Comparison](https://github.com/manishh12/DL-Simplified/blob/main/Intrusion%20Detection/Images/model_comparison.png)
+
+
+### ✒️ **Your Signature**
+
+[Manish Kumar Gupta]
diff --git a/Intrusion Detection/requirements.txt b/Intrusion Detection/requirements.txt
new file mode 100644
index 000000000..952193696
--- /dev/null
+++ b/Intrusion Detection/requirements.txt	
@@ -0,0 +1,7 @@
+Python (Python 3.x recommended)
+NumPy
+Pandas
+Seaborn
+Matplotlib
+TensorFlow
+Keras
\ No newline at end of file