Domain_Network_Security.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ArpanSM/Machine_Learning_Hackathons/blob/master/Domain_Network_Security.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sppiVf4v_mxU"
      },
      "source": [
        "!pip install pycaret[full]\n",
        "!pip install featurewiz\n",
        "!pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5H5wApeI92XT"
      },
      "source": [
        "!cp '/content/drive/MyDrive/Work/Datasets/NMIMS_Datathon/train.csv' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Datasets/NMIMS_Datathon/test.csv' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Datasets/NMIMS_Datathon/sample submission.csv' '/content/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJRGcWAr-d9y",
        "outputId": "b3f8b50e-355a-4ba4-d846-2634db44703f"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.read_csv(\"/content/train.csv\")\n",
        "dft = pd.read_csv(\"/content/test.csv\")\n",
        "sub = pd.read_csv(\"/content/sample submission.csv\")\n",
        "df.Action.replace(['allow', 'deny', 'drop', 'reset-both'], [0, 1, 2, 3], inplace=True)\n",
        "\n",
        "df[\"Bytes\"].fillna(df['Bytes Received']+ df['Bytes Sent'], inplace = True)\n",
        "df[\"Bytes Received\"].fillna(df['Bytes'] -  df['Bytes Sent'], inplace = True)\n",
        "df[\"Bytes Sent\"].fillna(df['Bytes'] -  df['Bytes Received'], inplace = True)\n",
        "df[\"Packets\"].fillna(df['pkts_sent']+ df['pkts_received'], inplace = True)\n",
        "df[\"pkts_received\"].fillna(df['Packets'] -  df['pkts_sent'], inplace = True)\n",
        "df[\"pkts_sent\"].fillna(df['Packets'] -  df['pkts_received'], inplace = True)\n",
        "\n",
        "dft[\"Bytes\"].fillna(dft['Bytes Received']+ dft['Bytes Sent'], inplace = True)\n",
        "dft[\"Bytes Received\"].fillna(dft['Bytes'] -  dft['Bytes Sent'], inplace = True)\n",
        "dft[\"Bytes Sent\"].fillna(dft['Bytes'] -  dft['Bytes Received'], inplace = True)\n",
        "dft[\"Packets\"].fillna(dft['pkts_sent']+ dft['pkts_received'], inplace = True)\n",
        "dft[\"pkts_received\"].fillna(dft['Packets'] -  dft['pkts_sent'], inplace = True)\n",
        "dft[\"pkts_sent\"].fillna(dft['Packets'] -  dft['pkts_received'], inplace = True)\n",
        "\n",
        "dfx = df.loc[df.isnull().mean(axis=1).lt(0.15)]\n",
        "dfx.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27208, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvk5pfzv6Koo"
      },
      "source": [
        "dfx[\"Source Port_Mean\"] = dfx.groupby('Source Port')[\"Bytes\"].transform('mean')\n",
        "Source_Port_Mean_mean_dict = dfx.groupby(['Source Port'])['Bytes'].mean().to_dict()\n",
        "dft['Source Port_Mean'] = dft['Source Port'].apply(lambda x:Source_Port_Mean_mean_dict.get(x,0))\n",
        "dfx[\"Source Port_min\"] = dfx.groupby('Source Port')[\"Bytes\"].transform('min')\n",
        "Source_Port_min_min_dict = dfx.groupby(['Source Port'])['Bytes'].min().to_dict()\n",
        "dft['Source Port_min'] = dft['Source Port'].apply(lambda x:Source_Port_min_min_dict.get(x,0))\n",
        "dfx[\"Source Port_max\"] = dfx.groupby('Source Port')[\"Bytes\"].transform('max')\n",
        "Source_Port_max_max_dict = dfx.groupby(['Source Port'])['Bytes'].max().to_dict()\n",
        "dft['Source Port_max'] = dft['Source Port'].apply(lambda x:Source_Port_max_max_dict.get(x,0))\n",
        "dfx[\"Source Port_count\"] = dfx.groupby('Source Port')[\"Bytes\"].transform('count')\n",
        "Source_Port_count_count_dict = dfx.groupby(['Source Port'])['Bytes'].count().to_dict()\n",
        "dft['Source Port_count'] = dft['Source Port'].apply(lambda x:Source_Port_count_count_dict.get(x,0))\n",
        "\n",
        "dfx[\"Destination Port_Mean\"] = dfx.groupby('Destination Port')[\"Bytes\"].transform('mean')\n",
        "Destination_Port_Mean_mean_dict = dfx.groupby(['Destination Port'])['Bytes'].mean().to_dict()\n",
        "dft['Destination Port_Mean'] = dft['Destination Port'].apply(lambda x:Destination_Port_Mean_mean_dict.get(x,0))\n",
        "dfx[\"Destination Port_min\"] = dfx.groupby('Destination Port')[\"Bytes\"].transform('min')\n",
        "Destination_Port_min_min_dict = dfx.groupby(['Destination Port'])['Bytes'].min().to_dict()\n",
        "dft['Destination Port_min'] = dft['Destination Port'].apply(lambda x:Destination_Port_min_min_dict.get(x,0))\n",
        "dfx[\"Destination Port_max\"] = dfx.groupby('Destination Port')[\"Bytes\"].transform('max')\n",
        "Destination_Port_max_max_dict = dfx.groupby(['Destination Port'])['Bytes'].max().to_dict()\n",
        "dft['Destination Port_max'] = dft['Destination Port'].apply(lambda x:Destination_Port_max_max_dict.get(x,0))\n",
        "dfx[\"Destination Port_count\"] = dfx.groupby('Destination Port')[\"Bytes\"].transform('count')\n",
        "Destination_Port_count_count_dict = dfx.groupby(['Destination Port'])['Bytes'].count().to_dict()\n",
        "dft['Destination Port_count'] = dft['Destination Port'].apply(lambda x:Destination_Port_count_count_dict.get(x,0))\n",
        "\n",
        "dfx[\"NAT Source Port_Mean\"] = dfx.groupby('NAT Source Port')[\"Bytes\"].transform('mean')\n",
        "NAT_Source_Port_Mean_mean_dict = dfx.groupby(['NAT Source Port'])['Bytes'].mean().to_dict()\n",
        "dft['NAT Source Port_Mean'] = dft['NAT Source Port'].apply(lambda x:NAT_Source_Port_Mean_mean_dict.get(x,0))\n",
        "dfx[\"NAT Source Port_min\"] = dfx.groupby('NAT Source Port')[\"Bytes\"].transform('min')\n",
        "NAT_Source_Port_min_min_dict = dfx.groupby(['NAT Source Port'])['Bytes'].min().to_dict()\n",
        "dft['NAT Source Port_min'] = dft['NAT Source Port'].apply(lambda x:NAT_Source_Port_min_min_dict.get(x,0))\n",
        "dfx[\"NAT Source Port_max\"] = dfx.groupby('NAT Source Port')[\"Bytes\"].transform('max')\n",
        "NAT_Source_Port_max_max_dict = dfx.groupby(['NAT Source Port'])['Bytes'].max().to_dict()\n",
        "dft['NAT Source Port_max'] = dft['NAT Source Port'].apply(lambda x:NAT_Source_Port_max_max_dict.get(x,0))\n",
        "dfx[\"NAT Source Port_count\"] = dfx.groupby('NAT Source Port')[\"Bytes\"].transform('count')\n",
        "NAT_Source_Port_count_count_dict = dfx.groupby(['NAT Source Port'])['Bytes'].count().to_dict()\n",
        "dft['NAT Source Port_count'] = dft['NAT Source Port'].apply(lambda x:NAT_Source_Port_count_count_dict.get(x,0))\n",
        " \n",
        "dfx[\"NAT Destination Port_Mean\"] = dfx.groupby('NAT Destination Port')[\"Bytes\"].transform('mean')\n",
        "NAT_Destination_Port_Mean_mean_dict = dfx.groupby(['NAT Destination Port'])['Bytes'].mean().to_dict()\n",
        "dft['NAT Destination Port_Mean'] = dft['NAT Destination Port'].apply(lambda x:NAT_Destination_Port_Mean_mean_dict.get(x,0))\n",
        "dfx[\"NAT Destination Port_min\"] = dfx.groupby('NAT Destination Port')[\"Bytes\"].transform('min')\n",
        "NAT_Destination_Port_min_min_dict = dfx.groupby(['NAT Destination Port'])['Bytes'].min().to_dict()\n",
        "dft['NAT Destination Port_min'] = dft['NAT Destination Port'].apply(lambda x:NAT_Destination_Port_min_min_dict.get(x,0))\n",
        "dfx[\"NAT Destination Port_max\"] = dfx.groupby('NAT Destination Port')[\"Bytes\"].transform('max')\n",
        "NAT_Destination_Port_max_max_dict = dfx.groupby(['NAT Destination Port'])['Bytes'].max().to_dict()\n",
        "dft['NAT Destination Port_max'] = dft['NAT Destination Port'].apply(lambda x:NAT_Destination_Port_max_max_dict.get(x,0))\n",
        "dfx[\"NAT Destination Port_count\"] = dfx.groupby('NAT Destination Port')[\"Bytes\"].transform('count')\n",
        "NAT_Destination_Port_count_count_dict = dfx.groupby(['NAT Destination Port'])['Bytes'].count().to_dict()\n",
        "dft['NAT Destination Port_count'] = dft['NAT Destination Port'].apply(lambda x:NAT_Destination_Port_count_count_dict.get(x,0))\n",
        "\n",
        "dfx[\"Packets_Mean\"] = dfx.groupby('Packets')[\"Bytes\"].transform('mean')\n",
        "Packets_Mean_mean_dict = dfx.groupby(['Packets'])['Bytes'].mean().to_dict()\n",
        "dft['Packets_Mean'] = dft['Packets'].apply(lambda x:Packets_Mean_mean_dict.get(x,0))\n",
        "dfx[\"Packets_min\"] = dfx.groupby('Packets')[\"Bytes\"].transform('min')\n",
        "Packets_min_min_dict = dfx.groupby(['Packets'])['Bytes'].min().to_dict()\n",
        "dft['Packets_min'] = dft['Packets'].apply(lambda x:Packets_min_min_dict.get(x,0))\n",
        "dfx[\"Packets_max\"] = dfx.groupby('Packets')[\"Bytes\"].transform('max')\n",
        "Packets_max_max_dict = dfx.groupby(['Packets'])['Bytes'].max().to_dict()\n",
        "dft['Packets_max'] = dft['Packets'].apply(lambda x:Packets_max_max_dict.get(x,0))\n",
        "dfx[\"Packets_count\"] = dfx.groupby('Packets')[\"Bytes\"].transform('count')\n",
        "Packets_count_count_dict = dfx.groupby(['Packets'])['Bytes'].count().to_dict()\n",
        "dft['Packets_count'] = dft['Packets'].apply(lambda x:Packets_count_count_dict.get(x,0))\n",
        "\n",
        "dfx[\"Source Port_Mean\"] = dfx.groupby('Source Port')[\"Elapsed Time (sec)\"].transform('mean')\n",
        "Source_Port_Mean_mean_dict = dfx.groupby(['Source Port'])['Elapsed Time (sec)'].mean().to_dict()\n",
        "dft['Source Port_Mean'] = dft['Source Port'].apply(lambda x:Source_Port_Mean_mean_dict.get(x,0))\n",
        "dfx[\"Source Port_min\"] = dfx.groupby('Source Port')[\"Elapsed Time (sec)\"].transform('min')\n",
        "Source_Port_min_min_dict = dfx.groupby(['Source Port'])['Elapsed Time (sec)'].min().to_dict()\n",
        "dft['Source Port_min'] = dft['Source Port'].apply(lambda x:Source_Port_min_min_dict.get(x,0))\n",
        "dfx[\"Source Port_max\"] = dfx.groupby('Source Port')[\"Elapsed Time (sec)\"].transform('max')\n",
        "Source_Port_max_max_dict = dfx.groupby(['Source Port'])['Elapsed Time (sec)'].max().to_dict()\n",
        "dft['Source Port_max'] = dft['Source Port'].apply(lambda x:Source_Port_max_max_dict.get(x,0))\n",
        "dfx[\"Source Port_count\"] = dfx.groupby('Source Port')[\"Elapsed Time (sec)\"].transform('count')\n",
        "Source_Port_count_count_dict = dfx.groupby(['Source Port'])['Elapsed Time (sec)'].count().to_dict()\n",
        "dft['Source Port_count'] = dft['Source Port'].apply(lambda x:Source_Port_count_count_dict.get(x,0))\n",
        " \n",
        "dfx[\"Destination Port_Mean\"] = dfx.groupby('Destination Port')[\"Elapsed Time (sec)\"].transform('mean')\n",
        "Destination_Port_Mean_mean_dict = dfx.groupby(['Destination Port'])['Elapsed Time (sec)'].mean().to_dict()\n",
        "dft['Destination Port_Mean'] = dft['Destination Port'].apply(lambda x:Destination_Port_Mean_mean_dict.get(x,0))\n",
        "dfx[\"Destination Port_min\"] = dfx.groupby('Destination Port')[\"Elapsed Time (sec)\"].transform('min')\n",
        "Destination_Port_min_min_dict = dfx.groupby(['Destination Port'])['Elapsed Time (sec)'].min().to_dict()\n",
        "dft['Destination Port_min'] = dft['Destination Port'].apply(lambda x:Destination_Port_min_min_dict.get(x,0))\n",
        "dfx[\"Destination Port_max\"] = dfx.groupby('Destination Port')[\"Elapsed Time (sec)\"].transform('max')\n",
        "Destination_Port_max_max_dict = dfx.groupby(['Destination Port'])['Elapsed Time (sec)'].max().to_dict()\n",
        "dft['Destination Port_max'] = dft['Destination Port'].apply(lambda x:Destination_Port_max_max_dict.get(x,0))\n",
        "dfx[\"Destination Port_count\"] = dfx.groupby('Destination Port')[\"Elapsed Time (sec)\"].transform('count')\n",
        "Destination_Port_count_count_dict = dfx.groupby(['Destination Port'])['Elapsed Time (sec)'].count().to_dict()\n",
        "dft['Destination Port_count'] = dft['Destination Port'].apply(lambda x:Destination_Port_count_count_dict.get(x,0))\n",
        " \n",
        "dfx[\"NAT Source Port_Mean\"] = dfx.groupby('NAT Source Port')[\"Elapsed Time (sec)\"].transform('mean')\n",
        "NAT_Source_Port_Mean_mean_dict = dfx.groupby(['NAT Source Port'])['Elapsed Time (sec)'].mean().to_dict()\n",
        "dft['NAT Source Port_Mean'] = dft['NAT Source Port'].apply(lambda x:NAT_Source_Port_Mean_mean_dict.get(x,0))\n",
        "dfx[\"NAT Source Port_min\"] = dfx.groupby('NAT Source Port')[\"Elapsed Time (sec)\"].transform('min')\n",
        "NAT_Source_Port_min_min_dict = dfx.groupby(['NAT Source Port'])['Elapsed Time (sec)'].min().to_dict()\n",
        "dft['NAT Source Port_min'] = dft['NAT Source Port'].apply(lambda x:NAT_Source_Port_min_min_dict.get(x,0))\n",
        "dfx[\"NAT Source Port_max\"] = dfx.groupby('NAT Source Port')[\"Elapsed Time (sec)\"].transform('max')\n",
        "NAT_Source_Port_max_max_dict = dfx.groupby(['NAT Source Port'])['Elapsed Time (sec)'].max().to_dict()\n",
        "dft['NAT Source Port_max'] = dft['NAT Source Port'].apply(lambda x:NAT_Source_Port_max_max_dict.get(x,0))\n",
        "dfx[\"NAT Source Port_count\"] = dfx.groupby('NAT Source Port')[\"Elapsed Time (sec)\"].transform('count')\n",
        "NAT_Source_Port_count_count_dict = dfx.groupby(['NAT Source Port'])['Elapsed Time (sec)'].count().to_dict()\n",
        "dft['NAT Source Port_count'] = dft['NAT Source Port'].apply(lambda x:NAT_Source_Port_count_count_dict.get(x,0))\n",
        "\n",
        "dfx[\"NAT Destination Port_Mean\"] = dfx.groupby('NAT Destination Port')[\"Elapsed Time (sec)\"].transform('mean')\n",
        "NAT_Destination_Port_Mean_mean_dict = dfx.groupby(['NAT Destination Port'])['Elapsed Time (sec)'].mean().to_dict()\n",
        "dft['NAT Destination Port_Mean'] = dft['NAT Destination Port'].apply(lambda x:NAT_Destination_Port_Mean_mean_dict.get(x,0))\n",
        "dfx[\"NAT Destination Port_min\"] = dfx.groupby('NAT Destination Port')[\"Elapsed Time (sec)\"].transform('min')\n",
        "NAT_Destination_Port_min_min_dict = dfx.groupby(['NAT Destination Port'])['Elapsed Time (sec)'].min().to_dict()\n",
        "dft['NAT Destination Port_min'] = dft['NAT Destination Port'].apply(lambda x:NAT_Destination_Port_min_min_dict.get(x,0))\n",
        "dfx[\"NAT Destination Port_max\"] = dfx.groupby('NAT Destination Port')[\"Elapsed Time (sec)\"].transform('max')\n",
        "NAT_Destination_Port_max_max_dict = dfx.groupby(['NAT Destination Port'])['Elapsed Time (sec)'].max().to_dict()\n",
        "dft['NAT Destination Port_max'] = dft['NAT Destination Port'].apply(lambda x:NAT_Destination_Port_max_max_dict.get(x,0))\n",
        "dfx[\"NAT Destination Port_count\"] = dfx.groupby('NAT Destination Port')[\"Elapsed Time (sec)\"].transform('count')\n",
        "NAT_Destination_Port_count_count_dict = dfx.groupby(['NAT Destination Port'])['Elapsed Time (sec)'].count().to_dict()\n",
        "dft['NAT Destination Port_count'] = dft['NAT Destination Port'].apply(lambda x:NAT_Destination_Port_count_count_dict.get(x,0))\n",
        " \n",
        "dfx[\"Packets_Mean\"] = dfx.groupby('Packets')[\"Elapsed Time (sec)\"].transform('mean')\n",
        "Packets_Mean_mean_dict = dfx.groupby(['Packets'])['Elapsed Time (sec)'].mean().to_dict()\n",
        "dft['Packets_Mean'] = dft['Packets'].apply(lambda x:Packets_Mean_mean_dict.get(x,0))\n",
        "dfx[\"Packets_min\"] = dfx.groupby('Packets')[\"Elapsed Time (sec)\"].transform('min')\n",
        "Packets_min_min_dict = dfx.groupby(['Packets'])['Elapsed Time (sec)'].min().to_dict()\n",
        "dft['Packets_min'] = dft['Packets'].apply(lambda x:Packets_min_min_dict.get(x,0))\n",
        "dfx[\"Packets_max\"] = dfx.groupby('Packets')[\"Elapsed Time (sec)\"].transform('max')\n",
        "Packets_max_max_dict = dfx.groupby(['Packets'])['Elapsed Time (sec)'].max().to_dict()\n",
        "dft['Packets_max'] = dft['Packets'].apply(lambda x:Packets_max_max_dict.get(x,0))\n",
        "dfx[\"Packets_count\"] = dfx.groupby('Packets')[\"Elapsed Time (sec)\"].transform('count')\n",
        "Packets_count_count_dict = dfx.groupby(['Packets'])['Elapsed Time (sec)'].count().to_dict()\n",
        "dft['Packets_count'] = dft['Packets'].apply(lambda x:Packets_count_count_dict.get(x,0))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "D5RZWXpC-seL",
        "outputId": "017df96a-5715-4848-fc53-d34deed2672e"
      },
      "source": [
        "dfx.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Source Port</th>\n",
              "      <th>Destination Port</th>\n",
              "      <th>NAT Source Port</th>\n",
              "      <th>NAT Destination Port</th>\n",
              "      <th>Bytes</th>\n",
              "      <th>Bytes Sent</th>\n",
              "      <th>Bytes Received</th>\n",
              "      <th>Packets</th>\n",
              "      <th>Elapsed Time (sec)</th>\n",
              "      <th>pkts_sent</th>\n",
              "      <th>pkts_received</th>\n",
              "      <th>Action</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>59276</td>\n",
              "      <td>49418.0</td>\n",
              "      <td>27015.0</td>\n",
              "      <td>54748.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>18986</td>\n",
              "      <td>58120.0</td>\n",
              "      <td>25174.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11131</td>\n",
              "      <td>46751.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>46751.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17229</td>\n",
              "      <td>50584.0</td>\n",
              "      <td>6881.0</td>\n",
              "      <td>29773.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>502.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>352.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>23612</td>\n",
              "      <td>7099.0</td>\n",
              "      <td>51505.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>62.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ID  Source Port  Destination Port  ...  pkts_sent  pkts_received  Action\n",
              "0  59276      49418.0           27015.0  ...        1.0            0.0       0\n",
              "1  18986      58120.0           25174.0  ...        1.0            0.0       1\n",
              "2  11131      46751.0              53.0  ...        1.0            1.0       0\n",
              "4  17229      50584.0            6881.0  ...        1.0            1.0       0\n",
              "6  23612       7099.0           51505.0  ...        1.0            0.0       1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "xC2p7twre_p5",
        "outputId": "da0b77b6-95b4-4200-d3d9-0f2a0bd4bd4f"
      },
      "source": [
        "dft.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Source Port</th>\n",
              "      <th>Destination Port</th>\n",
              "      <th>NAT Source Port</th>\n",
              "      <th>NAT Destination Port</th>\n",
              "      <th>Bytes</th>\n",
              "      <th>Bytes Sent</th>\n",
              "      <th>Bytes Received</th>\n",
              "      <th>Packets</th>\n",
              "      <th>Elapsed Time (sec)</th>\n",
              "      <th>pkts_sent</th>\n",
              "      <th>pkts_received</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23127</td>\n",
              "      <td>50142.0</td>\n",
              "      <td>445.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>61535</td>\n",
              "      <td>49496.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31457</td>\n",
              "      <td>49209.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>27106.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>199.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49294</td>\n",
              "      <td>49225.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.0</td>\n",
              "      <td>295.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8826</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ID  Source Port  ...  pkts_sent  pkts_received\n",
              "0  23127      50142.0  ...        1.0            0.0\n",
              "1  61535      49496.0  ...        1.0            1.0\n",
              "2  31457      49209.0  ...        NaN            1.0\n",
              "3  49294      49225.0  ...        2.0            2.0\n",
              "4   8826          NaN  ...        NaN            NaN\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IdGEOC8B-trq"
      },
      "source": [
        "from pandas_profiling import ProfileReport\n",
        "profile = ProfileReport(df, title='Pandas Profiling Report', html={'style':{'full_width':False}})\n",
        "profile.to_notebook_iframe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWIfYr1mC7T4"
      },
      "source": [
        "profile = ProfileReport(dft, title='Pandas Profiling Report', html={'style':{'full_width':False}})\n",
        "profile.to_notebook_iframe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lJ4z2MiEq1f1",
        "outputId": "a74d96cc-95bb-4f49-a2c0-24ae933e808f"
      },
      "source": [
        "from featurewiz import featurewiz\n",
        "features = featurewiz(dfx, target='Action', corr_limit=0.70, verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Imported featurewiz: advanced feature engg and selection library. Version=0.0.33\n",
            "output = featurewiz(dataname, target, corr_limit=0.70,\n",
            "                    verbose=2, sep=',', header=0, test_data='',\n",
            "                    feature_engg='', category_encoders='')\n",
            "Create new features via 'feature_engg' flag : ['interactions','groupby','target']\n",
            "                                \n",
            "Skipping feature engineering since no feature_engg input...\n",
            "Skipping category encoding since no category encoders specified in input...\n",
            "Shape of your Data Set loaded: (42643, 33)\n",
            "############## C L A S S I F Y I N G  V A R I A B L E S  ####################\n",
            "Classifying variables in data set...\n",
            "    32 Predictors classified...\n",
            "        1 variable(s) removed since they were ID or low-information variables\n",
            "Shape of your Data Set loaded: (42643, 33)\n",
            "Number of processors on machine = 1\n",
            "    GPU active on this device\n",
            "    Running XGBoost using GPU parameters\n",
            "############## C L A S S I F Y I N G  V A R I A B L E S  ####################\n",
            "Classifying variables in data set...\n",
            "    32 Predictors classified...\n",
            "        1 variable(s) removed since they were ID or low-information variables\n",
            "#### Single_Label Multi_Classification Feature Selection Started ####\n",
            "Searching for highly correlated variables from 31 variables using SULOV method\n",
            "#####  SULOV : Searching for Uncorrelated List Of Variables (takes time...) ############\n",
            "    Removing (13) highly correlated variables:\n",
            "    ['Bytes Sent', 'Bytes Received', 'Packets', 'Elapsed Time (sec)', 'pkts_sent', 'pkts_received', 'Source Port_min', 'Source Port_max', 'Destination Port_Mean', 'Destination Port_max', 'NAT Source Port_min', 'NAT Source Port_max', 'NAT Destination Port_min']\n",
            "    Following (18) vars selected: ['Source Port', 'Destination Port', 'NAT Source Port', 'NAT Destination Port', 'Source Port_count', 'Destination Port_count', 'NAT Source Port_count', 'NAT Destination Port_count', 'Packets_Mean', 'Packets_min', 'Packets_max', 'Packets_count', 'Bytes', 'NAT Destination Port_Mean', 'NAT Destination Port_max', 'NAT Source Port_Mean', 'Source Port_Mean', 'Destination Port_min']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "    Adding 0 categorical variables to reduced numeric variables  of 18\n",
            "############## F E A T U R E   S E L E C T I O N  ####################\n",
            "Current number of predictors = 18 \n",
            "    Finding Important Features using Boosted Trees algorithm...\n",
            "        using 18 variables...\n",
            "Warning: GPU exists but it is not turned on. Using CPU for predictions...\n",
            "        using 14 variables...\n",
            "        using 10 variables...\n",
            "        using 6 variables...\n",
            "        using 2 variables...\n",
            "Selected 16 important features from your dataset\n",
            "    Time taken (in seconds) = 54\n",
            "Returning list of 16 important features and dataframe.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYCSlMYot9RW",
        "outputId": "7bc41c14-254c-4faf-aaa2-50310084b1de"
      },
      "source": [
        "features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['Packets_count',\n",
              "  'Packets_Mean',\n",
              "  'NAT Source Port',\n",
              "  'Destination Port_min',\n",
              "  'Destination Port_count',\n",
              "  'NAT Destination Port',\n",
              "  'Destination Port',\n",
              "  'NAT Destination Port_count',\n",
              "  'Bytes',\n",
              "  'NAT Source Port_count',\n",
              "  'NAT Destination Port_Mean',\n",
              "  'NAT Source Port_Mean',\n",
              "  'NAT Destination Port_max',\n",
              "  'Packets_max',\n",
              "  'Packets_min',\n",
              "  'Source Port_Mean'],\n",
              "        Packets_count  Packets_Mean  ...  Source Port_Mean  Action\n",
              " 0            15045.0      6.772682  ...         29.663043       0\n",
              " 1            15045.0      6.772682  ...         10.000000       1\n",
              " 2             8252.0     58.398449  ...         30.000000       0\n",
              " 3            15045.0      6.772682  ...          0.000000       2\n",
              " 4             8252.0     58.398449  ...       1358.500000       0\n",
              " ...              ...           ...  ...               ...     ...\n",
              " 45864         8252.0     58.398449  ...         30.400000       0\n",
              " 45866         8252.0     58.398449  ...          0.000000       0\n",
              " 45867        15045.0      6.772682  ...         17.800000       2\n",
              " 45868          233.0    101.055794  ...         15.000000       0\n",
              " 45869        15045.0      6.772682  ...          0.000000       1\n",
              " \n",
              " [42643 rows x 17 columns])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D93F-AZsyxcG"
      },
      "source": [
        "df_sulov = dfx[['ID','Packets_count',\n",
        "  'Packets_Mean',\n",
        "  'NAT Source Port',\n",
        "  'Destination Port_min',\n",
        "  'Destination Port_count',\n",
        "  'NAT Destination Port',\n",
        "  'Destination Port',\n",
        "  'NAT Destination Port_count',\n",
        "  'Bytes',\n",
        "  'NAT Source Port_count',\n",
        "  'NAT Destination Port_Mean',\n",
        "  'NAT Source Port_Mean',\n",
        "  'NAT Destination Port_max',\n",
        "  'Packets_max',\n",
        "  'Packets_min',\n",
        "  'Source Port_Mean',\n",
        "  'Action',\n",
        "  'Bytes Sent', 'Bytes Received','Elapsed Time (sec)']]\n",
        "\n",
        "dft_sulov = dft[['ID','Packets_count',\n",
        "  'Packets_Mean',\n",
        "  'NAT Source Port',\n",
        "  'Destination Port_min',\n",
        "  'Destination Port_count',\n",
        "  'NAT Destination Port',\n",
        "  'Destination Port',\n",
        "  'NAT Destination Port_count',\n",
        "  'Bytes',\n",
        "  'NAT Source Port_count',\n",
        "  'NAT Destination Port_Mean',\n",
        "  'NAT Source Port_Mean',\n",
        "  'NAT Destination Port_max',\n",
        "  'Packets_max',\n",
        "  'Packets_min',\n",
        "  'Source Port_Mean',\n",
        "  'Bytes Sent', 'Bytes Received','Elapsed Time (sec)']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "AKNoYPM2zNHV",
        "outputId": "0a5694d5-443a-4cb9-ef79-63258681582f"
      },
      "source": [
        "df_sulov.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Packets_count</th>\n",
              "      <th>Packets_Mean</th>\n",
              "      <th>NAT Source Port</th>\n",
              "      <th>Destination Port_min</th>\n",
              "      <th>Destination Port_count</th>\n",
              "      <th>NAT Destination Port</th>\n",
              "      <th>Destination Port</th>\n",
              "      <th>NAT Destination Port_count</th>\n",
              "      <th>Bytes</th>\n",
              "      <th>NAT Source Port_count</th>\n",
              "      <th>NAT Destination Port_Mean</th>\n",
              "      <th>NAT Source Port_Mean</th>\n",
              "      <th>NAT Destination Port_max</th>\n",
              "      <th>Packets_max</th>\n",
              "      <th>Packets_min</th>\n",
              "      <th>Source Port_Mean</th>\n",
              "      <th>Action</th>\n",
              "      <th>Bytes Sent</th>\n",
              "      <th>Bytes Received</th>\n",
              "      <th>Elapsed Time (sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>59276</td>\n",
              "      <td>15045.0</td>\n",
              "      <td>6.772682</td>\n",
              "      <td>54748.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27015.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>142.333333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29.663043</td>\n",
              "      <td>0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18986</td>\n",
              "      <td>15045.0</td>\n",
              "      <td>6.772682</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>469.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25174.0</td>\n",
              "      <td>12300.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.066341</td>\n",
              "      <td>NaN</td>\n",
              "      <td>201.0</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11131</td>\n",
              "      <td>8252.0</td>\n",
              "      <td>58.398449</td>\n",
              "      <td>46751.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6645.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>6460.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31.374923</td>\n",
              "      <td>614.500000</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>57352</td>\n",
              "      <td>15045.0</td>\n",
              "      <td>6.772682</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12300.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>12322.0</td>\n",
              "      <td>1.066341</td>\n",
              "      <td>1.002678</td>\n",
              "      <td>201.0</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17229</td>\n",
              "      <td>8252.0</td>\n",
              "      <td>58.398449</td>\n",
              "      <td>29773.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6881.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>502.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1200.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1358.500000</td>\n",
              "      <td>0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>352.0</td>\n",
              "      <td>1200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ID  Packets_count  ...  Bytes Received  Elapsed Time (sec)\n",
              "0  59276        15045.0  ...             0.0                29.0\n",
              "1  18986        15045.0  ...             0.0                 0.0\n",
              "2  11131         8252.0  ...           130.0                30.0\n",
              "3  57352        15045.0  ...             0.0                 NaN\n",
              "4  17229         8252.0  ...           352.0              1200.0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_2dXFEXzQ1G",
        "outputId": "2efdc2eb-ea11-4c08-c0de-c0340aac089b"
      },
      "source": [
        "df_sulov.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Packets_count', 'Packets_Mean', 'NAT Source Port',\n",
              "       'Destination Port_min', 'Destination Port_count',\n",
              "       'NAT Destination Port', 'Destination Port',\n",
              "       'NAT Destination Port_count', 'Bytes', 'NAT Source Port_count',\n",
              "       'NAT Destination Port_Mean', 'NAT Source Port_Mean',\n",
              "       'NAT Destination Port_max', 'Packets_max', 'Packets_min',\n",
              "       'Source Port_Mean', 'Action', 'Bytes Sent', 'Bytes Received',\n",
              "       'Elapsed Time (sec)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "a9235eb7c15648c584f863ee00caaeb6",
            "79a2b4ec73994cffadac1c19ac11b07f",
            "f01f09fdad084bd59329012d7c6b85c5",
            "2ca69f320c584e1ca174846db64e0ae4",
            "5cd5bec56be04d77872645e49c039721",
            "01c9594ec52b4a729599b73117be15fa"
          ]
        },
        "id": "dUzvZU-RQxeI",
        "outputId": "2ea1cd96-fe61-4ec5-c4bf-171c35232d16"
      },
      "source": [
        "from pycaret.classification import *\n",
        "exp_clf = setup(dfx,\n",
        "                target='Action',\n",
        "                use_gpu = False,\n",
        "                numeric_imputation = 'mean', #median\n",
        "                categorical_imputation = 'mode', #constant\n",
        "                numeric_features = ['Bytes', 'Bytes Sent', 'Bytes Received','Elapsed Time (sec)'],\n",
        "                categorical_features = ['Source Port', 'Destination Port', 'NAT Source Port','NAT Destination Port', 'Packets', 'pkts_sent', 'pkts_received'],\n",
        "                #date_features = [],\n",
        "                ignore_features = ['ID'],\n",
        "                #ordinal_features = {'column_name' : [‘low’, ‘medium’, ‘high’] },\n",
        "                high_cardinality_features = ['Source Port', 'Destination Port', 'NAT Source Port','NAT Destination Port'],\n",
        "                #high_cardinality_method = 'frequency', #clustering,\n",
        "                #group_features = [],\n",
        "                handle_unknown_categorical = True,\n",
        "                #unknown_categorical_method = 'least_frequent', or 'most_frequent',\n",
        "                combine_rare_levels = True,\n",
        "                #bin_numeric_features = ['0','1'],\n",
        "                transformation = True,\n",
        "                #transformation_method = 'yeo-johnson',#'quantile',yeo-johnson,\n",
        "                #transform_target = True,\n",
        "                #transform_target_method = 'yeo-johnson', #box-cox\n",
        "                feature_selection = True,\n",
        "                normalize = True,\n",
        "                #normalize_method = 'zscore',#minmax ,maxabs ,robust, zscore,\n",
        "                #fix_imbalance = True,\n",
        "                #pca = True,\n",
        "                #pca_method = 'incremental', #kernel, incremental, linear\n",
        "                feature_interaction = False,\n",
        "                feature_ratio = False,\n",
        "                trigonometry_features=False,\n",
        "                polynomial_features = False,)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>3165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>Action</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Target Type</td>\n",
              "      <td>Multiclass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Label Encoded</td>\n",
              "      <td>0: 0, 1: 1, 2: 2, 3: 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(27208, 13)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>frequency</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(19045, 498)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(8163, 498)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>USI</td>\n",
              "      <td>e48b</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>zscore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>yeo-johnson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Fix Imbalance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Fix Imbalance Method</td>\n",
              "      <td>SMOTE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Description                   Value\n",
              "0                               session_id                    3165\n",
              "1                                   Target                  Action\n",
              "2                              Target Type              Multiclass\n",
              "3                            Label Encoded  0: 0, 1: 1, 2: 2, 3: 3\n",
              "4                            Original Data             (27208, 13)\n",
              "5                           Missing Values                    True\n",
              "6                         Numeric Features                       4\n",
              "7                     Categorical Features                       7\n",
              "8                         Ordinal Features                   False\n",
              "9                High Cardinality Features                    True\n",
              "10                 High Cardinality Method               frequency\n",
              "11                   Transformed Train Set            (19045, 498)\n",
              "12                    Transformed Test Set             (8163, 498)\n",
              "13                      Shuffle Train-Test                    True\n",
              "14                     Stratify Train-Test                   False\n",
              "15                          Fold Generator         StratifiedKFold\n",
              "16                             Fold Number                      10\n",
              "17                                CPU Jobs                      -1\n",
              "18                                 Use GPU                   False\n",
              "19                          Log Experiment                   False\n",
              "20                         Experiment Name        clf-default-name\n",
              "21                                     USI                    e48b\n",
              "22                         Imputation Type                  simple\n",
              "23          Iterative Imputation Iteration                    None\n",
              "24                         Numeric Imputer                    mean\n",
              "25      Iterative Imputation Numeric Model                    None\n",
              "26                     Categorical Imputer                    mode\n",
              "27  Iterative Imputation Categorical Model                    None\n",
              "28           Unknown Categoricals Handling          least_frequent\n",
              "29                               Normalize                    True\n",
              "30                        Normalize Method                  zscore\n",
              "31                          Transformation                    True\n",
              "32                   Transformation Method             yeo-johnson\n",
              "33                                     PCA                   False\n",
              "34                              PCA Method                    None\n",
              "35                          PCA Components                    None\n",
              "36                     Ignore Low Variance                   False\n",
              "37                     Combine Rare Levels                    True\n",
              "38                    Rare Level Threshold                     0.1\n",
              "39                         Numeric Binning                   False\n",
              "40                         Remove Outliers                   False\n",
              "41                      Outliers Threshold                    None\n",
              "42                Remove Multicollinearity                   False\n",
              "43             Multicollinearity Threshold                    None\n",
              "44                              Clustering                   False\n",
              "45                    Clustering Iteration                    None\n",
              "46                     Polynomial Features                   False\n",
              "47                       Polynomial Degree                    None\n",
              "48                    Trignometry Features                   False\n",
              "49                    Polynomial Threshold                    None\n",
              "50                          Group Features                   False\n",
              "51                       Feature Selection                    True\n",
              "52                Feature Selection Method                 classic\n",
              "53            Features Selection Threshold                     0.8\n",
              "54                     Feature Interaction                   False\n",
              "55                           Feature Ratio                   False\n",
              "56                   Interaction Threshold                    None\n",
              "57                           Fix Imbalance                   False\n",
              "58                    Fix Imbalance Method                   SMOTE"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 940,
          "referenced_widgets": [
            "0499aeea484c470bb9515ea11e8bc175",
            "f216cff8a2834e5db4356d5dce662714",
            "b4924639678441e6a2237ab0986bdce0"
          ]
        },
        "id": "b8sjikvrSACa",
        "outputId": "a870e4ed-e233-45d2-fca2-94a9cebba31b"
      },
      "source": [
        "lgbm = create_model('lightgbm')\n",
        "predict_model(lgbm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.9853</td>\n",
              "      <td>0.9916</td>\n",
              "      <td>0.7363</td>\n",
              "      <td>0.9854</td>\n",
              "      <td>0.9853</td>\n",
              "      <td>0.9747</td>\n",
              "      <td>0.9747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.9927</td>\n",
              "      <td>0.9996</td>\n",
              "      <td>0.7420</td>\n",
              "      <td>0.9921</td>\n",
              "      <td>0.9924</td>\n",
              "      <td>0.9873</td>\n",
              "      <td>0.9873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.9496</td>\n",
              "      <td>0.9582</td>\n",
              "      <td>0.7022</td>\n",
              "      <td>0.9488</td>\n",
              "      <td>0.9486</td>\n",
              "      <td>0.9125</td>\n",
              "      <td>0.9131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.9932</td>\n",
              "      <td>0.9999</td>\n",
              "      <td>0.7425</td>\n",
              "      <td>0.9937</td>\n",
              "      <td>0.9934</td>\n",
              "      <td>0.9882</td>\n",
              "      <td>0.9882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.9921</td>\n",
              "      <td>0.9994</td>\n",
              "      <td>0.7423</td>\n",
              "      <td>0.9911</td>\n",
              "      <td>0.9916</td>\n",
              "      <td>0.9864</td>\n",
              "      <td>0.9864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.9926</td>\n",
              "      <td>0.9997</td>\n",
              "      <td>0.7422</td>\n",
              "      <td>0.9932</td>\n",
              "      <td>0.9929</td>\n",
              "      <td>0.9873</td>\n",
              "      <td>0.9873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.9926</td>\n",
              "      <td>0.9992</td>\n",
              "      <td>0.7420</td>\n",
              "      <td>0.9926</td>\n",
              "      <td>0.9926</td>\n",
              "      <td>0.9873</td>\n",
              "      <td>0.9873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.9900</td>\n",
              "      <td>0.9974</td>\n",
              "      <td>0.7409</td>\n",
              "      <td>0.9906</td>\n",
              "      <td>0.9903</td>\n",
              "      <td>0.9828</td>\n",
              "      <td>0.9828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.9937</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.7435</td>\n",
              "      <td>0.9932</td>\n",
              "      <td>0.9934</td>\n",
              "      <td>0.9891</td>\n",
              "      <td>0.9892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.9953</td>\n",
              "      <td>0.9997</td>\n",
              "      <td>0.7452</td>\n",
              "      <td>0.9948</td>\n",
              "      <td>0.9950</td>\n",
              "      <td>0.9918</td>\n",
              "      <td>0.9919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.9877</td>\n",
              "      <td>0.9945</td>\n",
              "      <td>0.7379</td>\n",
              "      <td>0.9875</td>\n",
              "      <td>0.9876</td>\n",
              "      <td>0.9788</td>\n",
              "      <td>0.9788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0130</td>\n",
              "      <td>0.0123</td>\n",
              "      <td>0.0121</td>\n",
              "      <td>0.0131</td>\n",
              "      <td>0.0132</td>\n",
              "      <td>0.0225</td>\n",
              "      <td>0.0224</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "0       0.9853  0.9916  0.7363  0.9854  0.9853  0.9747  0.9747\n",
              "1       0.9927  0.9996  0.7420  0.9921  0.9924  0.9873  0.9873\n",
              "2       0.9496  0.9582  0.7022  0.9488  0.9486  0.9125  0.9131\n",
              "3       0.9932  0.9999  0.7425  0.9937  0.9934  0.9882  0.9882\n",
              "4       0.9921  0.9994  0.7423  0.9911  0.9916  0.9864  0.9864\n",
              "5       0.9926  0.9997  0.7422  0.9932  0.9929  0.9873  0.9873\n",
              "6       0.9926  0.9992  0.7420  0.9926  0.9926  0.9873  0.9873\n",
              "7       0.9900  0.9974  0.7409  0.9906  0.9903  0.9828  0.9828\n",
              "8       0.9937  1.0000  0.7435  0.9932  0.9934  0.9891  0.9892\n",
              "9       0.9953  0.9997  0.7452  0.9948  0.9950  0.9918  0.9919\n",
              "Mean    0.9877  0.9945  0.7379  0.9875  0.9876  0.9788  0.9788\n",
              "SD      0.0130  0.0123  0.0121  0.0131  0.0132  0.0225  0.0224"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
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          "data": {
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.9907</td>\n",
              "      <td>0.9997</td>\n",
              "      <td>0.7406</td>\n",
              "      <td>0.9907</td>\n",
              "      <td>0.9906</td>\n",
              "      <td>0.9839</td>\n",
              "      <td>0.984</td>\n",
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            "text/plain": [
              "                             Model  Accuracy     AUC  ...      F1   Kappa    MCC\n",
              "0  Light Gradient Boosting Machine    0.9907  0.9997  ...  0.9906  0.9839  0.984\n",
              "\n",
              "[1 rows x 8 columns]"
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              "      <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>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>0.0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>-0.973677</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>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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</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>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>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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>-0.973677</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>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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</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>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>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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>1.114982</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>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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</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>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>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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0000</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",
              "      <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",
              "      <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",
              "      <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>8158</th>\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>-0.973677</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>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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</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>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>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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8159</th>\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>1.114982</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>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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>8163 rows × 501 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      pkts_received_48.0  Packets_75.0  ...  Label   Score\n",
              "0                    0.0           0.0  ...      0  1.0000\n",
              "1                    0.0           0.0  ...      0  1.0000\n",
              "2                    0.0           0.0  ...      2  0.9933\n",
              "3                    0.0           0.0  ...      1  1.0000\n",
              "4                    0.0           0.0  ...      0  1.0000\n",
              "...                  ...           ...  ...    ...     ...\n",
              "8158                 0.0           0.0  ...      1  1.0000\n",
              "8159                 0.0           0.0  ...      0  1.0000\n",
              "8160                 0.0           0.0  ...      0  1.0000\n",
              "8161                 0.0           0.0  ...      0  1.0000\n",
              "8162                 0.0           0.0  ...      0  1.0000\n",
              "\n",
              "[8163 rows x 501 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 940,
          "referenced_widgets": [
            "8402a26d24474dd2ab7cd8465bda5e7c",
            "8ef8bfc159084067a3b6fd54fb3dfd09",
            "e33f56ad3bf14c60acc993b7e47bf740"
          ]
        },
        "id": "97afHccCU6Bg",
        "outputId": "bc49dbd9-2508-4339-b93c-3b4d4b6972bb"
      },
      "source": [
        "tuned_lgbm = tune_model(lgbm, optimize='F1')\n",
        "predict_model(tuned_lgbm)"
      ],
      "execution_count": null,
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
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              "      <td>0.9924</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.9921</td>\n",
              "      <td>0.9997</td>\n",
              "      <td>0.7418</td>\n",
              "      <td>0.9916</td>\n",
              "      <td>0.9919</td>\n",
              "      <td>0.9864</td>\n",
              "      <td>0.9864</td>\n",
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              "      <td>0.9906</td>\n",
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              "      <td>0.7401</td>\n",
              "      <td>0.9901</td>\n",
              "      <td>0.9903</td>\n",
              "      <td>0.9837</td>\n",
              "      <td>0.9837</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.9942</td>\n",
              "      <td>0.9999</td>\n",
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              "      <td>0.9937</td>\n",
              "      <td>0.9940</td>\n",
              "      <td>0.9900</td>\n",
              "      <td>0.9901</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0.7423</td>\n",
              "      <td>0.9911</td>\n",
              "      <td>0.9916</td>\n",
              "      <td>0.9864</td>\n",
              "      <td>0.9864</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.9926</td>\n",
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              "      <td>0.9927</td>\n",
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              "      <th>6</th>\n",
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              "      <td>0.9921</td>\n",
              "      <td>0.9924</td>\n",
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              "      <td>0.9873</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.9932</td>\n",
              "      <td>0.9998</td>\n",
              "      <td>0.7427</td>\n",
              "      <td>0.9927</td>\n",
              "      <td>0.9929</td>\n",
              "      <td>0.9882</td>\n",
              "      <td>0.9882</td>\n",
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              "      <th>8</th>\n",
              "      <td>0.9932</td>\n",
              "      <td>0.9999</td>\n",
              "      <td>0.7433</td>\n",
              "      <td>0.9927</td>\n",
              "      <td>0.9929</td>\n",
              "      <td>0.9882</td>\n",
              "      <td>0.9882</td>\n",
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              "      <th>9</th>\n",
              "      <td>0.9953</td>\n",
              "      <td>0.9996</td>\n",
              "      <td>0.7452</td>\n",
              "      <td>0.9948</td>\n",
              "      <td>0.9950</td>\n",
              "      <td>0.9918</td>\n",
              "      <td>0.9919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.9929</td>\n",
              "      <td>0.9997</td>\n",
              "      <td>0.7426</td>\n",
              "      <td>0.9924</td>\n",
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              "      <td>0.9877</td>\n",
              "      <td>0.9877</td>\n",
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              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0012</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>0.0013</td>\n",
              "      <td>0.0012</td>\n",
              "      <td>0.0012</td>\n",
              "      <td>0.0021</td>\n",
              "      <td>0.0021</td>\n",
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              "  </tbody>\n",
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              "</div>"
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              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "0       0.9927  0.9995  0.7422  0.9921  0.9924  0.9873  0.9873\n",
              "1       0.9921  0.9997  0.7418  0.9916  0.9919  0.9864  0.9864\n",
              "2       0.9906  0.9995  0.7401  0.9901  0.9903  0.9837  0.9837\n",
              "3       0.9942  0.9999  0.7436  0.9937  0.9940  0.9900  0.9901\n",
              "4       0.9921  0.9995  0.7423  0.9911  0.9916  0.9864  0.9864\n",
              "5       0.9926  0.9997  0.7425  0.9927  0.9926  0.9873  0.9873\n",
              "6       0.9926  0.9999  0.7423  0.9921  0.9924  0.9873  0.9873\n",
              "7       0.9932  0.9998  0.7427  0.9927  0.9929  0.9882  0.9882\n",
              "8       0.9932  0.9999  0.7433  0.9927  0.9929  0.9882  0.9882\n",
              "9       0.9953  0.9996  0.7452  0.9948  0.9950  0.9918  0.9919\n",
              "Mean    0.9929  0.9997  0.7426  0.9924  0.9926  0.9877  0.9877\n",
              "SD      0.0012  0.0002  0.0013  0.0012  0.0012  0.0021  0.0021"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.9909</td>\n",
              "      <td>0.9998</td>\n",
              "      <td>0.741</td>\n",
              "      <td>0.9903</td>\n",
              "      <td>0.9906</td>\n",
              "      <td>0.9843</td>\n",
              "      <td>0.9844</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Model  Accuracy     AUC  ...      F1   Kappa     MCC\n",
              "0  Light Gradient Boosting Machine    0.9909  0.9998  ...  0.9906  0.9843  0.9844\n",
              "\n",
              "[1 rows x 8 columns]"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
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              "      <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>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>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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8163 rows × 501 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      pkts_received_48.0  Packets_75.0  ...  Label   Score\n",
              "0                    0.0           0.0  ...      0  0.9998\n",
              "1                    0.0           0.0  ...      0  1.0000\n",
              "2                    0.0           0.0  ...      2  0.9872\n",
              "3                    0.0           0.0  ...      1  0.9992\n",
              "4                    0.0           0.0  ...      0  1.0000\n",
              "...                  ...           ...  ...    ...     ...\n",
              "8158                 0.0           0.0  ...      1  0.9999\n",
              "8159                 0.0           0.0  ...      0  1.0000\n",
              "8160                 0.0           0.0  ...      0  1.0000\n",
              "8161                 0.0           0.0  ...      0  1.0000\n",
              "8162                 0.0           0.0  ...      0  1.0000\n",
              "\n",
              "[8163 rows x 501 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OLHmFI_c0Tg"
      },
      "source": [
        "#traing on the whole dataset\n",
        "finalize_model(lgbm)\n",
        "#predictiing on test data\n",
        "test_predictions = predict_model(lgbm, data=dft)\n",
        "#storing the predictions in another dataframe and converting the dataframe to a csv file."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "_C24yzIQtaiJ",
        "outputId": "d904567e-7e54-4101-8354-9bcdd2216522"
      },
      "source": [
        "test_predictions.rename(columns = {'Label':'Action'}, inplace = True)\n",
        "subx = test_predictions[['ID','Action']]\n",
        "subx.Action.replace([0, 1, 2, 3],['allow', 'deny', 'drop', 'reset-both'], inplace=True)\n",
        "subx.to_csv(\"sub5.csv\",index=False)\n",
        "subx.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Action</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23127</td>\n",
              "      <td>drop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>61535</td>\n",
              "      <td>allow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31457</td>\n",
              "      <td>allow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49294</td>\n",
              "      <td>allow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8826</td>\n",
              "      <td>deny</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      ID Action\n",
              "0  23127   drop\n",
              "1  61535  allow\n",
              "2  31457  allow\n",
              "3  49294  allow\n",
              "4   8826   deny"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "tvsCheb7uS-1",
        "outputId": "2421b36a-e972-4a7a-8b8a-1bd0e92b0811"
      },
      "source": [
        "plot_model(tuned_lgbm, plot='error')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3Zhmpxa-iXm",
        "outputId": "867c564b-2d69-4dfb-a302-c12370583171"
      },
      "source": [
        "plot_model(tuned_lgbm, plot='feature')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+RURERCTvMZvN7IqM4avfTrIrMgaz2ZzbJd23fLldQG5o0aIFMTExGI2385m7uzv169enT58+eHp6PvD+U1JSWLJkCa+99hoAEyZMeOB9ptmzZw8FCxbEx8eHMmXKEBERkWP7vtP69esZMWIEDg4OlmXFihWjTZs2vPXWWzg7O2drv0eOHCEuLo5GjRrlVKkiIiIiAmyIOMPwTQeIvHzdsqxyURdCOvkR6OORi5Xdnyd2BmXkyJFERERw4MAB5s+fT5EiRejatSt79ux54H3/8ccfzJ8/PweqzGjRokUcPnz4oez739zd3YmIiCAiIoJDhw4xd+5cdu/ezeTJk7O9z3Xr1hEeHp6DVYqIiIjIhogzdF+8K104AYi8fJ3ui3exIeJMLlV2/57IGZQ72dvbU7lyZYYPH46dnR0jR45k27Zt2NnZcfbsWcaPH89vv/1GamoqzZs3Z/To0RQsWJCEhATGjh3Lrl27uHXrFl5eXowcOZLU1FR69OhBcnIyPj4+zJs3j40bN2IymZg2bRrr169n0aJF9OrVi+nTp3PlyhWaNWvGRx99hL29PSaTifHjx/PDDz8QHx9PtWrVGDt2LFWrVqV///788MMP7N69m2+++YaJEyfSsmVLtm7dSuXKlYmLi2PixImEh4dz8+ZN6taty+jRoylbtizR0dG0bNmShQsX8vHHH3Pq1CmqVavGlClTKFu2bJZ9MhgMeHp60rdvXyZNmsS4ceMA+PXXX/noo4/4+++/KVCgAF27dmXQoEEYjUZmzJjB4cOHcXJyYteuXQQGBrJixQqMRiPffvst33333cP+eLMlDgcuJKSQ35yU26XYrFu3UtQnK6hPWVOPrKM+WUd9so76lLW81iOz2cy7YftJvcvlXKlmM8GbDxBQvRwGg+ERV3f/nviAcqeePXsyb948jhw5go+PDwMHDsTPz49p06YRHx/PO++8Q0hICOPHj2fx4sVcunSJ7777DgcHB+bNm8eoUaPYsGED48ePZ+rUqfz8888AbNy4Md04Z8+e5fDhw2zevJmzZ88SFBTEd999R/v27Zk3bx4HDx5k8+bNODs7M27cOIKDg1m/fj2zZ8+mRYsW9O3blx49ehAdHZ1uvyNHjuTGjRuEhYXh4ODA+++/z+DBg1m7dq1lmyVLljBnzhwcHR155ZVXmD9/PmPHjrW6R6mpqdjZ2QFw6dIlevfuzbBhw+jWrRvHjx+nb9++FC9enBdffBGA33//nUGDBjF16lTs7Oz466+/qFmzJkOHDrV6TLPZTHx8vNXbP4iEhAT2GUux74wJMD2SMfMs9ck66lPW1CPrqE/WUZ+soz5lLQ/16GRMLKdjb9xzm+OXrrP9aBSNK7jn2LgJCQnA7XO1nAw+Cih3cHd3p1ChQpYT/7///puVK1fi5OSEk5MTb731Fr1792bcuHFcu3YNe3t78ufPT758+Rg4cCADBw60apybN28yePBgnJ2dqVKlCl5eXpw4cQKAfv360bNnTwoWLAhA27ZtWb9+PcnJyeTLd/eP6+rVq3z33XesWrUKNzc3AN5++206dOhAVFSU5ZemR48elChRAgB/f3+r72Exm81ERkayYMEC2rVrB8DmzZspXbq0JYz85z//oUuXLnz99deWZXZ2dvTo0eOBfmmTkpI4evRott9/34zlH91YIiIiIg/oesItq7bbf+w4bgkXc3z85OTkdPctPygFlH9JTk7GaDQSFRVFSkoK9evXT7c+JSWFK1eu8MILL9C7d2+efvppmjRpQqtWrWjZsqVVYxQpUsQSQACcnJy4dev2L1ZsbCwTJkxg37593Lx50zJmSkrKPQPKuXPnMJvNVK5c2bLMw+P2zVBnz561XMZ15+VcTk5OmEx3/6vApUuX8PHxsbwuVaoU7dq1swSx6OjodOMBlC9fnq+//tryumTJkg+cqO3t7XPk4QXWSEhIoN6pc5QqVQpHR8dHMmZeZDKZOH/+vPqUBfUpa+qRddQn66hP1lGfspbXelQBF1btznq72tU88c7hGZRTp07d8xw1OxRQ7nD69Gni4+OpVKkSZ86cwdnZmd9++y3Tbd3c3Ni6dSt79+5lx44djB49mrCwMKZPn57lOGlPD8vMkCFDcHR0JDQ0lJIlS7Jnzx569uyZ5T4TExPvuu7OgHA/YcHd3d1ymdr9jHnnGDnxC2swGLL91LDscCWR8oWdH+mYeU18vB3x59WnrKhPWVOPrKM+WUd9so76lLW81qOKbgUZ/fXBDDfI38nT3YVW3g/nHpSc3ucT+xSvzMyYMYOqVatStWpVPDw8iI+PJyoqyrL+xo0bXLlyBbh9mVZKSgqNGjVi5MiRrFmzhm+//dayPrsOHTpE9+7dKVmyJHD7sbzWKFeuHIDlUrE7f06bSclpHh4e6cZLGzOtFhERERF5+AwGAyGd/DDeJSgYDQYmd/TLEzfIgwIKADExMUyaNInvv/+eiRMnAlC1alV8fX2ZOHEisbGxXLt2jTFjxli+cPHtt98mJCSEGzdukJqaym+//UbhwoVxdXUlf/78XL9+nZiYGMulW9YqU6YMhw4dIikpiV27dllmMGJiYgBwdHTkzJkzXL+ePiEXLVoUf39/PvvsM65evUpcXByffvop9evXp1SpUg/aoky1a9eOqKgoVq1aRXJyMocOHWLDhg0EBgbe9T2Ojo5ER0cTFxf3UGoSEREReRIF+niw+tWmeLq7pFvu6e7C6leb6ntQ8oIJEybg4+ND9erV6dy5MzExMaxZs4YaNWpYtpk6dSpms5mWLVvyzDPPkJKSYvkOkPHjx3P69GmaNm1K3bp1WbZsGTNnzsRoNNKgQQPKli1Lq1at2LFjx33VNXr0aLZt20a9evVYu3Ytn3zyCTVr1iQoKIhLly7RvXt3VqxYwUsvvZThvSEhITg7O9OuXTvat29PwYIF+eyzzx6sUfdQpkwZPv/8c1atWkXdunV57733GDRoEAEBAXd9T1BQELt27aJ169akpKQ8tNpEREREnjSBPh4cC+7CzoGtWfFSE354ozXHgrvkqXACYDCb7/LAZBEbkPaUsTtv1n+Y4uPjOXr0KN7e3nnimtPcoj5ZR33KmnpkHfXJOuqTddSnrKlH1knrk729PQaDIcfO157YGRQREREREbE9CigiIiIiImIzFFBERERERMRmKKCIiIiIiIjNUEARERERERGboYAiIiIiIiI2QwFFRERERERshgKKiIiIiIjYDAUUERERERGxGQooIiIiIiJiMxRQRERERETEZiigiIiIiIiIzVBAERERERERm6GAIiIiIiIiNkMBRUREREREbIYCioiIiIiI2AwFFBERERERsRkKKCIiIiIiYjMUUERERERExGYooIiIiIiIiM1QQBEREREREZuhgCIiIiIiIjZDAUVERERERGyGAoqIiIiIiNgMBRQREREREbEZCigiIiIiImIzFFBERERERMRm5MvtAuTRa9GiBTExMRiNRgwGAy4uLjRo0IBhw4ZRokSJe773yJEjxMXF0ahRo0dUrYiIiIj8m9ls5qcT/3DuWjylCznTpFJxDAZDbpeVIxRQnlAjR46kR48eAMTExDBs2DBGjx7NnDlz7vm+devW4ezsrIAiIiIikks2RJxh+KYDRF6+bllWuagLIZ38CPTxyMXKcoYu8RJKlChB69atOXnyJDNnziQoKCjd+l9//ZUaNWowYsQIVqxYwcKFC3nmmWcAuHr1KkOHDsXf3x9fX18GDBhATEwMAKmpqUyePBl/f39q1apF586d+emnnx758YmIiIg8LjZEnKH74l3pwglA5OXrdF+8iw0RZ3KpspyjGZQnnNlsJjo6mtDQUDp27EiXLl2YMWMGkZGRVK5cGYBvv/2W5s2bM2nSJKKjo6lZsyZDhw4FIDg4mHz58rFlyxbs7OwYM2YMI0aMYOHChWzZsoXw8HDCwsJwdXVl48aNDB8+nB9//BF7e/vcPOx7isOBCwkp5Dcn5XYpNuvWrRT1yQrqU9bUI+uoT9ZRn6yjPmXNVntkNpt5N2w/qWZzputTzWaCNx8goHq5PH25lwLKE2rChAl8+OGHmM1mkpKSaNiwIS+++CJFixalTp06bNq0icGDBwOwfft2Pvjggwz7uHz5Mjt37mTr1q24uroCMHToUJo1a8bFixe5du0a+fLlw8nJCTs7O7p27UpgYCBG4/1N3JnNZuLj4x/8oK2QkJDAPmMp9p0xAaZHMmaepT5ZR33KmnpkHfXJOuqTddSnrNlgj07GxHI69sY9tzl+6Trbj0bRuIL7Q68nISEBuH2ulpOBSAHlCXXnPSjXrl1j6dKlBAQEEBYWRpcuXZgzZw6DBw8mIiKCmzdv0rRp0wz7iIqKAiAgICDdcjs7O86fP0+HDh0IDQ2ladOmNG7cmGbNmtGhQ4f7DihJSUkcPXo0m0eaDcbyj24sEREREStdT7hl1Xb7jx3HLeHiQ67mf5KTk3FwcMix/SmgCIUKFeKNN95g3bp1fP3113Tu3JkJEybw+++/s3PnTtq2bZvpL13+/PkB2LVrF0WKFMl036tXr+bAgQPs3LmT6dOns3LlSpYvX06+fNb/6tnb2+Pp6Zm9g7tPCQkJ1Dt1jlKlSuHo6PhIxsyLTCYT58+fV5+yoD5lTT2yjvpkHfXJOupT1my1RxVwYdXurLerXc0T70c0g3Lq1Kn7Oq+zhgKKpGMymShYsCAtW7bkm2++YceOHXz44YeZblumTBmMRiN//vknDRo0AG7PdsTGxlKiRAlMJhOpqan4+fnh5+fHgAEDaNy4MceOHaN69epW12QwGHB2ds6R47OGK4mUL+z8SMfMa+Lj7Yg/rz5lRX3KmnpkHfXJOuqTddSnrNlqjyq6FWT01wcz3CB/J093F1p5P9p7UHJ6LD3FSzCZTHz55ZdcuXKFli1bAtClSxfWrFlDUlIStWvXtmzr6OhIdHQ0cXFxuLi40L59e6ZMmcKFCxe4desWn3zyCb169cJsNjNx4kSGDx9ObGwsZrOZI0eOkJqaSunSpXPrUEVERETyLIPBQEgnP4x3CQRGg4HJHf3y9A3yoIDyxJowYQI+Pj74+PjQuHFjdu7cyfz58/HwuP3sbH9/f5ycnOjYsWO6X/KgoCB27dpF69atSUlJYdSoUZQvX54OHTrQpEkTjh8/zqxZszAYDLz77rsYjUbatGmDn58fEydOZOrUqbi5ueXWYYuIiIjkaYE+Hqx+tSme7i7plnu6u7D61aaPxfeg6BKvJ9COHTuy3CYhIYGEhASeffbZdMvbt29P+/btLa8LFy7M1KlTM92Hq6srn3766YMVKyIiIiLpBPp4EFC9HD+d+Ifz1xIo7eqEf0V9k7w8xkwmE+PGjcPf35/y5fVEKxERERFbYzAYaFq5RG6X8VDoEi9J59dff6Vu3bpcvnyZMWPG5HY5IiIiIvKE0QyKpFOnTh0OHTqU22WIiIiIyBNKMygiIiIiImIzFFBERERERMRmKKCIiIiIiIjNUEARERERERGboYAiIiIiIiI2QwFFRERERERshgKKiIiIiIjYDAUUERERERGxGQooIiIiIiJiMxRQRERERETEZiigiIiIiIiIzVBAERERERERm6GAIiIiIiIiNkMBRUREREREbIYCioiIiIiI2AwFFBERERERsRkKKCIiIiIiYjMUUERERERExGYooIiIiIiIiM1QQBEREREREZuhgCIiIiIiIjZDAUVERERERGyGAoqIiIiIiNgMBRQREREREbEZ+XK7gCdRixYtiImJwWg0YjAYcHFxoUGDBgwbNowSJUpk+f4jR44QFxdHo0aNcry2yMhIPvvsMw4cOMDVq1cpVKgQrVq1YujQoRQqVOiB9x8VFcWRI0do27ZtDlQrIiIitshsNvPTiX84dy2e0oWcaVKpOAaDIbfLkjxCMyi5ZOTIkURERHDo0CHWr1/PpUuXGD16tFXvXbduHeHh4Tle082bN3n11VcpU6YMmzZtIiIigpUrV3L8+HGGDBmSI2Ns27aNb7/9Nkf2JSIiIrZnQ8QZvCaF0nzWNl5ctpvms7bhNSmUDRFncrs0ySMUUGxAiRIlaN26NSdPngRg5syZBAUFpdvm119/pb7jrCcAACAASURBVEaNGowYMYIVK1awcOFCnnnmGQCuXr3K0KFD8ff3x9fXlwEDBhATEwNAamoqkydPxt/fn1q1atG5c2d++umnTOv4+++/uXjxIn369KFIkSIYDAbKly9PSEgIzz33HGazGYBjx47x6quvUqdOHRo0aMCECRNISkoCYP369XTu3JmNGzfSokULfH19GTJkCElJSSxYsIApU6bwzTff4OPjQ0pKykPpp4iIiOSODRFn6L54F5GXr6dbHnn5Ot0X71JIEavoEq9cZjabiY6OJjQ0lI4dOwLQpUsXZsyYQWRkJJUrVwbg22+/pXnz5kyaNIno6Ghq1qzJ0KFDAQgODiZfvnxs2bIFOzs7xowZw4gRI1i4cCFbtmwhPDycsLAwXF1d2bhxI8OHD+fHH3/E3t4+XS1ly5bF3t6ezz//nMGDB+Pq6gpAuXLlKFeuHAAJCQn06dOHl19+mXnz5hETE8PAgQNZsGAB/fv3B+Ds2bMcPnyYzZs3c/bsWYKCgvjuu+/o3bs3f//9NyaTiWnTpj2S/mZHHA5cSEghvzkpt0uxWbdupahPVlCfsqYeWUd9so76ZJ2H1Sez2cy7YftJ/f9/0Py3VLOZ4M0HCKheTpd7yT0poOSSCRMm8OGHH2I2m0lKSqJhw4a8+OKLwO2gUKdOHTZt2sTgwYMB2L59Ox988EGG/Vy+fJmdO3eydetWS6AYOnQozZo14+LFi1y7do18+fLh5OSEnZ0dXbt2JTAwEKMx4+SZu7s7ISEhjB8/ntWrV+Pt7U39+vVp06YNNWrUAOCHH37AbDbTr18/4HZ46d27N3PmzLEElJs3bzJ48GCcnZ2pUqUKXl5enDhxItu9MpvNxMfHZ/v99yMhIYF9xlLsO2MCTI9kzDxLfbKO+pQ19cg66pN11CfrPIQ+nYyJ5XTsjXtuc/zSdbYfjaJxBfccG/dhSEhISPdfyVxaf8xmc46GTgWUXDJy5Eh69OgBwLVr11i6dCkBAQGEhYVRpEgRunTpwpw5cxg8eDARERHcvHmTpk2bZthPVFQUAAEBAemW29nZcf78eTp06EBoaChNmzalcePGNGvWjA4dOmQaUAA6dOjAM888wy+//MK+ffv4+eefmT9/Ps899xzjxo0jKiqKy5cv4+PjY3mP2WzGwcHB8rpIkSIULFjQ8trJyYlbt25lu1dJSUkcPXo02++/b8byj24sERGRx8T1BOv+v37/seO4JVx8yNXkjFOnTuV2CXlCcnJyunPBB6WAYgMKFSrEG2+8wbp16/j666954YUXaNeuHRMmTOD3339n586dtG3bNtMPPn/+/ADs2rWLIkWKZLr/1atXc+DAAXbu3Mn06dNZuXIly5cvJ1++zD9+BwcHGjduTOPGjRkyZAhhYWG89957vPLKKzg6OlKlShU2bdp01+O5W/jJLnt7ezw9PXN0n3eTkJBAvVPnKFWqFI6Ojo9kzLzIZDJx/vx59SkL6lPW1CPrqE/WUZ+s87D6VAEXVu3Oerva1TzxzgMzKKdOnaJChQo4OTnldjk2K61PdzunzC4FFBtjMt2eai1YsCAtW7bkm2++YceOHXz44YeZbl+mTBmMRiN//vknDRo0AG7POMTGxlKiRAlMJhOpqan4+fnh5+fHgAEDaNy4MceOHaN69erp9rV9+3aio6Pp2bNnuuVNmjQBID4+Hg8PD6Kiorh58yYFChQA4MqVK9jb26ebNclJBoMBZ2fnh7LvzLiSSPnCzo90zLwmPt6O+PPqU1bUp6ypR9ZRn6yjPlnnYfWpoltBRn99MMMN8nfydHehlXfeuQfFyclJv0tWyOnPU0/xsgEmk4kvv/ySK1eu0LJlS8vyLl26sGbNGpKSkqhdu7ZluaOjI9HR0cTFxeHi4kL79u2ZMmUKFy5c4NatW3zyySf06tULs9nMxIkTGT58OLGxsZjNZo4cOUJqaiqlS5fOUIezszNTpkxh6dKlxMXFARATE8NHH31EmTJlqFatGv7+/ri5uRESEsKNGze4ePEigwYNYsqUKVYdq6OjI+fPn+fatWskJyc/YOdERETEVhgMBkI6+WG8y8mq0WBgcke/PBNOJPcooOSSCRMm4OPjg4+PD40bN2bnzp3Mnz8fDw8Pyzb+/v44OTnRsWPHdP9jDgoKYteuXbRu3ZqUlBRGjRpF+fLl6dChA02aNOH48ePMmjULg8HAu+++i9FopE2bNvj5+TFx4kSmTp2Km5tbhpoaNWrEzJkz2b59O23atKF69ep07dqV5ORkli1bhoODA/b29syaNYsTJ07QuHFjAgICqFChAsOHD7fquDt16sTJkydp3rw5//zzz4M3UkRERGxGoI8Hq19tiqe7S7rlnu4urH61KYE+Hnd5p8j/GMzmuzwLTnLdjRs3ePrpp1m/fj3lyz+ZN25HREQApLsp/2GKj4/n6NGjeHt7a0r3HtQn66hPWVOPrKM+WUd9ss6j6FPaN8mfv5ZAaVcn/CvmrW+S1++SddL6ZG9vj8FgyLHzNd2DYqNMJhPjxo3D39//iQ0nIiIikjcZDAaaVi6R22VIHqVLvGzQr7/+St26dbl8+TJjxozJ7XJERERERB4ZzaDYoDp16nDo0KHcLkNERERE5JHTDIqIiIiIiNgMBRQREREREbEZCigiIiIiImIzFFBERERERMRmKKCIiIiIiIjNUEARERERERGboYAiIiIiIiI2QwFFRERERERshgKKiIiIiIjYDAUUERERERGxGQooIiIiIiJiMxRQRERERETEZiigiIiIiIiIzVBAERERERERm6GAIiIiIiIiNkMBRUREREREbIYCioiIiIiI2AwFFBERERERsRkKKCIiIiIiYjMUUERERERExGYooIiIiIiIiM1QQBEREREREZuhgCIiIiIiIjbjsQkoLVq0YOXKlbldRgaNGzdm/fr19/2+X375BR8fHxITEx9CVZl78803+eyzzx7KvmNjY2natCm//vrrQ9m/iIiIiDwe8uV2AdZo0aIFMTExGI0Z89SkSZPo2LFjLlT1YNq0acO5c+cASE5OBiBfvv99HBEREURERDyyer766iuioqL49NNPH8r+3dzcGDVqFEOHDmXLli0UKFDgoYwjIiLyuDCbzfx04h/OXYundCFnmlQqjsFgyO2yRB66PBFQAEaOHEmPHj1yu4wc8+2331p+Dg4OxmQyMW3atFypJTExkZkzZzJixIh0ISmnPfPMM8yYMYPVq1fz2muvPbRxRERE8roNEWcYvukAkZevW5ZVLupCSCc/An08crEykYfvsbnE604mk4mRI0fi7++Pn58fL7zwAn/99ZdlvZeXF+vXr+fZZ5+lRo0aBAQEcOLECQBSU1OZPHky/v7+1KpVi86dO/PTTz9Z3rtnzx6ee+45fH19adKkCTNnzrSsS05OZvz48dSvX58mTZqwZs2abB/D3r178fLywmQyWWresmULQUFB1KhRg9dff50LFy7Qu3dvfH19CQoKIjo62vL+rVu30qVLF2rVqkXLli1ZtWrVXcfavn07JpOJNm3aWJbNnTuX5s2bU7NmTdq0aUNoaKhl3bFjx3j11VepU6cODRo0YMKECSQlJVnWh4aG0qZNG3x9fXn++ec5evSoZd1zzz3HV199le2+iIiIPO42RJyh++Jd6cIJQOTl63RfvIsNEWdyqTKRRyPPzKDcj3nz5nHw4EE2b96Ms7Mz48aNIzg4ON29IF9++SWffvopJUuWZMyYMbzzzjts3LiRLVu2EB4eTlhYGK6urmzcuJHhw4fz448/cvnyZQYOHMiYMWPo1KkTx48fp0+fPnh4eNCpUyfWrVvHN998w4oVKyhVqhQhISHExcXl2HF99dVXzJ49m/j4eDp16kTfvn0JCQnBw8ODF198kS+//JJRo0YRERHBBx98wIwZM2jYsCG//fYbffv2pUqVKvj5+WXY73//+1/q1KmDnZ0dAAcOHGDJkiWsXr2aUqVK8fPPP/PWW2/h7++Ps7Mzffr04eWXX2bevHnExMQwcOBAFixYQP/+/Tl8+DBjx47liy++oHbt2syZM4eBAweyfft27OzsqFevHuPGjePChQuULFkyx3qTk+Jw4EJCCvnNSVlv/IS6dStFfbKC+pQ19cg66pN1Hoc+mc1m3g3bT6rZnOn6VLOZ4M0HCKheTpd7yWMrzwSUCRMm8OGHH6Zb5uzszN69ezNs269fP3r27EnBggUBaNu2LevXryc5OdlyCVOXLl2oXLkyAH369KFLly7ExMRw7do18uXLh5OTE3Z2dnTt2pXAwECMRiObN2+mSpUqBAQEALdnNZ5//nlCQ0Pp1KkT3333HZ06dbLsd9CgQfecubhfHTp0oHjx4gBUqlSJp556iv/85z8A1KtXzzILtH79epo1a4a/vz8AderUoV27doSGhmYaUP7++2/q169veX39+nWMRiP58+fHYDDg7+/P/v37MRqNfP3115jNZvr16wdAuXLl6N27N3PmzKF///5s3LiRBg0a0KBBAwB69+5NxYoVMZlMODs7U7lyZYxGI3/99ZfVAcVsNhMfH5/Nrt2fhIQE9hlLse+MCTA9kjHzLPXJOupT1tQj66hP1snjfToZE8vp2Bv33Ob4petsPxpF4wru2RojISEh3X8lI/XIOmn9MZvNORqY80xAuZ97UGJjY5kwYQL79u3j5s2bAKSkpJCSkmIJKBUrVrRsX6ZMGQBiYmLo0KEDoaGhNG3alMaNG9OsWTM6dOiA0WjkzJkzRERE4OPjY3mv2Wy27CsmJoZmzZpZ1rm5ueHq6vpAx32nUqVKWX52dHSkRIkS6V6nPfHrzJkz7NmzJ0OdaYHl365evUrhwoUtrxs2bMh//vMfWrRoQcOGDWnatCldunTB2dmZqKgoLl++nGHfDg4OAERFReHh8b9rY52cnOjQoYPltdFoxNXVldjYWKuPOykpKd1lYg+dsfyjG0tEROQO1xNuWbXd/mPHcUu4+EBjnTp16oHe/yRQj6yTnJxsORfMCXkmoNyPIUOG4OjoSGhoKCVLlmTPnj307Nkz3TapqamWn83/fxrVYDBQuHBhVq9ezYEDB9i5cyfTp09n5cqVLF++nPz58/P0008ze/bsTMdNTEy0PJErs3Ee1L+TaWZPNQPInz8/PXr0YNSoUdkax8HBgdmzZ3Ps2DG+//57li9fzsKFC1m/fj2Ojo5UqVKFTZs23bVG812mpe92HFmxt7fH09Pzvt6TXQkJCdQ7dY5SpUrh6Oj4SMbMi0wmE+fPn1efsqA+ZU09so76ZJ3HoU8VcGHV7qy3q13NE+8HmEE5deoUFSpUwMnJKVv7eNypR9ZJ61NOP2TpsQwohw4d4uOPP7ZcQnTkyJEM25w5878bzNIe91uyZElMJhOpqan4+fnh5+fHgAEDaNy4MceOHcPDw4Pt27enm8a6ePEirq6uODg4ULx4cS5cuGDZ7z///MO1a9ce5qFmysPDI8Mjii9cuECxYsUs95ncqXDhwly9etXyOikpCZPJRLVq1ahWrRr9+vWjffv2hIeH4+HhQVRUFDdv3rQ8KvjKlSvY29tTsGBBypUrZ7nUDG6HtqVLlxIUFESRIkVITU0lLi6OIkWKWH08BoMBZ2fn+21DtrmSSPnCzo90zLwmPt6O+PPqU1bUp6ypR9ZRn6zzOPSpoltBRn99MMMN8nfydHehlfeD34Pi5OSUZ/v0qKhH1snp+6Eey6d4lSlThkOHDpGUlMSuXbv4+eefgduXYKUJDQ3l9OnT3Lx5k3nz5lG9enWKFSvGxIkTGT58OLGxsZjNZo4cOUJqaiqlS5emQ4cOXL16lVmzZnHr1i2ioqLo1asXixcvBqBJkyZs3ryZU6dOcePGDaZNm5Yrf8F59tlnOXDgAOvWrSMxMZGjR4/SrVu3dI82vlOVKlX4+++/La8XLlxI3759LWErMjKSuLg4PDw88Pf3x83NjZCQEG7cuMHFixcZNGgQU6ZMASAoKIi9e/eyc+dOkpKSWLRoEUuWLLHcD3TixAlSUlLw8vJ6yF0QERHJewwGAyGd/DDe5YTPaDAwuaOfbpCXx1qeCSgTJkzAx8cnw78RI0Zk2Hb06NFs27aNevXqsXbtWj755BNq1qxJUFAQly5dAm6fxL/77rs0bNiQ48ePM3XqVADeffddjEYjbdq0wc/Pj4kTJzJ16lTc3NwoUqQIs2bN4vvvv6du3bq89NJLNG/enF69egHQs2dPmjdvTvfu3Wnbti2+vr658qSqypUrM3XqVObPn0+dOnV466236N27N+3bt890+wYNGvDrr7+SkpICwGuvvUbVqlUJCAigVq1aDB48mKFDh+Lt7Y29vT2zZs3ixIkTNG7cmICAACpUqMDw4cMB8Pb2ZsqUKYwfP566deuyY8cOvvjiC+zt7YHbj0+uUKGCzT7BS0REJLcF+niw+tWmeLq7pFvu6e7C6leb6ntQ5LFnMGd1w8BjyMvLi3nz5tG0adPcLsUmJCYm0rJlS95//33atWv3UMcKCAigS5cuVn9RY9qlanfelP8wxcfHc/ToUby9vTWlew/qk3XUp6ypR9ZRn6zzuPUp7Zvkz19LoLSrE/4Vc+ab5B+3Pj0M6pF10vpkb2+PwWDIsfO1x/IeFLk/Dg4OvPHGG8yZM4dnnnnmoX2b/Pbt27l69Srdu3d/KPsXERF5nBgMBppWLpH1hiKPmTxziZc8XM8//zxlypRh5syZD2X/V65cYdy4cUyZMsVyc72IiIiIyL89kTMof/75Z26XYJMeVjgBKFKkCLt27Xpo+xcRERGRx4NmUERERERExGYooIiIiIiIiM1QQBEREREREZuhgCIiIiIiIjZDAUVERERERGyGAoqIiIiIiNgMBRQREREREbEZCigiIiIiImIzFFBERERERMRmKKCIiIiIiIjNUEARERERERGboYAiIiIiIiI2QwFFRERERERshgKKiIiIiIjYDAUUERERERGxGQooIiIiIiJiMxRQRERERETEZiigiIiIiIiIzVBAERERERERm6GAIiIiIiIiNkMBRUREREREbEa2A8ru3bstPx85coSJEyfy1Vdf5UhRIiIiIiLyZMpWQJkzZw7BwcEAxMbG0rNnT44dO8b8+fP5/PPPc7RAERERERF5cmQroKxZs4Y5c+YAEBYWRrly5Vi6dCnz588nLCwsRwsUEREREZEnR77svOny5cs89dRTAISHh9O2bVsAKlSowMWLF3OuOhEREXlimM1mfjrxD+euxVO6kDNNKhXHYDDkdlki8ohlK6C4uLgQGxuLg4MDv/zyC2+//TaAZZlAUlISX3zxBVu2bCEmJgaDwUD16tUZNGgQderUye3ysvTyyy+zf/9+7OzsAMiXLx8VK1akf//+tG7dOtv7Xbt2LS1atMDNzS2nShURkcfAhogzDN90gMjL1y3LKhd1IaSTH4E+HrlYmYg8atm6xKtVq1a89tprvPzyy5QvX57q1atjMpmYOHEi9evXz+ka86TJkyezY8cOpk+fzv79+/npp59o1KgRvXr1IioqKrfLs0qvXr2IiIggIiKCvXv30qtXL9555x0OHDiQrf2lpKQwefJkrly5ksOViohIXrYh4gzdF+9KF04AIi9fp/viXWyIOJNLlYlIbsjWDEpwcDCLFi3i+vXrvPjiiwCkpqZy5coVJk+enKMF5lU///wzXbt2xcvLC4CCBQsyYMAAypQpY5llMplMfPzxx2zfvp0rV65QvXp1Ro4cibe3N9HR0bRs2ZKtW7dSuXJlAKZMmcLBgwdZunQpe/fupX///gwaNIjp06ezYMECfH19WbhwIUuXLiUuLg5fX1/+7//+j7JlywKwbNkyli9fzrlz5yhbtixDhgyhVatWVh2Pg4MDHTt2ZN26dWzfvh0/Pz9SU1P54osv2LhxIzExMVSuXJlhw4bRsGFDAFq0aEG3bt1Yu3Yt/v7+bN68mRs3btClSxf69+/Pm2++mdNtzxFxOHAhIYX85qTcLsVm3bqVoj5ZQX3Kmnpknce5T2azmXfD9pNqNme6PtVsJnjzAQKql9PlXiJPiGwFFAcHB15//fV0y5ycnFi4cGGOFPU4qFixIhs2bMDf3x9vb2/L8s6dO1t+njZtGr/88gvLli3D3d2dqVOn0q9fP7Zv327VGElJSZw+fZrw8HAcHR3Zvn078+bN48svv6RSpUqMGzeOoUOH8tVXX7Ft2zY+//xz5s+fT7Vq1dixYweDBw9m27ZtlC5d2urjSklJsVz2tXz5cssDEypWrMiyZcsYOHAg27dvp2jRogBs2bKFhQsX4uHhQd++fWnZsiWhoaGW0GUNs9lMfHy81ds/iISEBPYZS7HvjAkwPZIx8yz1yTrqU9bUI+s8pn06GRPL6dgb99zm+KXrbD8aReMK7vfcLiEhId1/JXPqU9bUI+uk9cdsNufoHxCyFVAA1q1bx8aNGzl37hzff/89iYmJLFq0KENweVKNGjWKd955h4CAAMqUKUPt2rV5+umnad26tWUGZe3atYwbN84ywzF48GCWLVvGgQMHLMvuJSkpiRdeeIH8+fMDtz+TDh06UK1aNQCGDBnCvn37SE1NZe3atTz77LNUr14dgNatW1O7dm02b95s1WdmMpnYtm0bBw4c4L333rPU/8ILL1hmiXr16sX8+fP54Ycf6Nq1KwBNmjShfPny99O6TI/z6NGjD7SP+2J8sHpFRMR61xNuWbXd/mPHcUuw7kE8p06deoCKnhzqU9bUI+skJyfn6H3o2QooS5cuZdq0aQQGBnLw4EEArly5wooVKwAUUoDSpUvz1Vdfcfz4ccLDw/nll18YOXIkn332GcuWLSN//vxcv36dSpUqWd5ToEABihYtytmzZ60KKGnjpImKikp3D1DRokVp164dAGfOnOHnn39m8eLFlvVmsxlPT8+77nvhwoWW7e3t7fH09GTmzJn4+PgAEB0dnWEmxMPDg7Nnz1pelylTxqrjuJe0sR+FhIQE6p06R6lSpXB0dHwkY+ZFJpOJ8+fPq09ZUJ+yph5Z53HuUwVcWLU76+1qV/PE24oZlFOnTlGhQgWcnJxyqMLHj/qUNfXIOml9ypcv23MemcrW3pYtW8asWbNo0KABa9euBaBEiRLMmDGDQYMGKaDcwdPTE09PT1555RUuXrxIt27dWLx4Ma+99tpd33O3KbKUlJQMy+78hTAYDJjvcg1v/vz5effdd+nVq5fVtffq1YuhQ4fedX1iYmKmy++sP+1ysAdhMBhwdnZ+4P1Yy5VEyhd2fqRj5jXx8XbEn1efsqI+ZU09ss7j3KeKbgUZ/fXBDDfI38nT3YVW3tbfg+Lk5PTY9elhUJ+yph5ZJ6fvD8vWU7wuXLiQ6dO6nnrqKX0PCrf7M3bsWG7cSH9NbbFixahWrRoJCQkULVqUAgUKcOLECcv6uLg4Ll++jIeHh+UvZLdu/W/qO6unf5UrV46TJ09aXsfGxrJw4UKSkpLw8PDgzz//TLf9uXPn7hporOHh4ZGu/uTkZE6fPk25cuWyvU8REXmyGAwGQjr5YbzLCY7RYGByRz/dIC/yBMlWQClevDhnzmR85N/hw4dxdXV94KLyOjc3N8LDw3nvvfc4ceIEqampJCQksHnzZvbs2UOLFi0wGo107NiRuXPncuHCBeLj45kyZQrlypXD19cXNzc3XFxc2LZtGykpKezevZvff//9nuN27dqVLVu2cPDgQRITE5k5cybffPMN9vb2PPfcc2zdupUffviB5ORk/vvf/9KxY0fLJXrZ0aVLF1asWEFkZCSJiYnMnj2blJQUWrRoken2affKnDp1KkN4ExGRJ1egjwerX22Kp7tLuuWe7i6sfrWpvgdF5AmTrUu8WrVqxeDBgxk0aBBms5kjR45w+PBhZs2aRYcOHXK6xjzHwcGBpUuXMmPGDHr37k1sbCxGoxFvb2+mTp1KkyZNgNuPax4/fjzdunUjMTERX19fvvzyS8tlUWPGjOHjjz9myZIltGzZkhdffJHw8PC7jtuyZUuGDBnCG2+8QXx8PL6+vkydOhWAxo0bM3z4cMaNG8elS5coW7YsY8eOpVatWtk+zl69enHlyhX69u3LtWvX8Pb2ZsmSJRQqVCjT7d3d3WnTpg2DBg3i+eefZ+TIkdkeW0REHi+BPh4EVC/HTyf+4fy1BEq7OuFfUd8kL/IkMpizcY1PYmIio0aNYtOmTaSmpgK374Xo3r07wcHB+jZ5yTEREREAlhvzH7b4+HiOHj2Kt7e3rjm9B/XJOupT1tQj66hP1lGfrKM+ZU09sk5an+zt7TEYDDl2vpbt70EJCQnh/fff5/Tp0zg6OuLh4aGnHIiIiIiIyAPJ1j0oQUFBALi6ulKjRg28vLwUTkRERERE5IFlK6CYTCb++uuvnK5FRERERESecNm6xKt79+4MGTIEf39/ypUrh729vWWdwWCge/fuOVagiIiIiIg8ObIVUCZNmgRAZGRkhnUKKCIiIiIikl3ZCijHjh3L6TpERERERESydw+KiIiIiIjIw5CtGZRq1ard84uTjh49mu2CRERERETkyZWtgDJmzJh0ASUlJYWTJ0/y448/MnDgwBwrTkREREREnizZCig9evTIdHnr1q1ZtWoVgYGBD1SUiIiIiIg8mXL0HpS6devy448/5uQuRURERETkCZKjAeX7778nX75sTcqIiIiIiIhk7xKv/8fencdVUe9/HH8dEBByiyxDRTBJxUDFLRfE3RuuuKaWWtrNJUy7rpVp7pJ6c8sttbwt7laamGnq1dRyS0UlcyORzB2RVZb5/eGPcyVADshyjPfz8eBhfOd75vuZD9M58zkz3xlfX990bfHx8cTExGR6+ZeIiIiIiEhWclSg9OjRI12bg4MDlSpVonnz5g8dlIiIiIiIFE45KlBq165NgwYN0rXHx8ezy2sNzwAAIABJREFUefNm2rZt+9CBiYiIiIhI4ZOjOSgDBw7MsD0+Pp533333oQISEREREZHCK1tnUNauXcu6deu4e/duhpd5Xb16lRIlSuRacCIiIiIiUrhkq0Dx8/MjPj6ekJAQKlasmG55tWrV6NixY64FJyIiIiIihUu2CpQyZcrQu3dvLl++zKhRozLs89tvv+VKYCIiIiIiUvjkaA5KanGSkpLC3bt3zT9hYWG6zbCIiIiIiORYju7iFR4ezsiRIzlx4gTJyclplj377LO5EpiIiIiIiBQ+OTqDMmnSJJycnBg7diy2trZMmjSJLl264OPjw+eff57bMYqIiIiISCGRowLl2LFjzJkzhx49emBra0vXrl2ZPHkybdu2ZenSpbkdo4iIiIiIFBI5KlASEhIoXrz4vRXY2JCQkABAx44d2bBhQ+5FJyIiIiIihUqOCpTKlSuzfPlykpOTKV++PFu2bAHg5s2bxMXF5WqAIiIiIiJSeOSoQAkMDOTf//43MTEx9OjRg3feeYd27drRuXNnGjdunNsxioiIiIhIIZGju3j5+fmxc+dOSpQowUsvvUSxYsU4cuQIbm5uus1wBg4ePEi/fv04fPgw9vb2ubrufv36Ub16dYYNG5ar6xX5uzAMgz3nr/JHVCxlSzjR+JmnMJlMBR2WiIiIZCJHBQrAk08+CUBSUhIdO3bM0RPkmzdvTlJSEt999x1OTk7m9p9//pm3336bHTt2pOn/448/0r9/f3r16sX48eMBWLBgAQsXLgTuHYgkJiamKQImTZpEQEBAmvXMmzePjz76CDs7OwzDwNHREU9PT7p27UqHDh2yvR0Z+f7776lSpQpubm7UrVuXkJCQXFlvZGQk27Zto1u3bgAsX748V9abkSpVqmBnZ2c+mHNycsLLy4uRI0dStWrVHK0zOTmZ//znP7z66qu5GapIhr4KucjoTUc4d+OOua3SE8UJal+LTt4VCjAyERERyUyOLvFKSUlh7ty5NGvWjFq1agEQFxfH+PHjuXv3brbWdffuXRYsWGBR37Vr19K2bVs2b95snpg/ePBgQkJCCAkJYdmyZQAcOnTI3PbX4iRV9erVCQkJ4cSJE3z77bd0796dDz74gPfeey9b8Wdm7ty5/P7777myrvv99NNPrF27NtfXm5kFCxaYc7l9+3Y8PDzo378/d+7cyfrFGTh16pTu9Cb54quQi3RfsTtNcQJw7sYduq/YzVchFwsoMhEREXmQHJ1BmTdvHhs2bKBv377Mnj0bgNjYWI4ePcqcOXMYOXKkxesaMmQIM2fOpEuXLlSsWDHTfrdu3WLHjh1s2rSJkydPsm3bNtq1a5eT8NMpU6YM7dq1o3LlynTq1Im2bdtSv359UlJSmD9/Phs3buTatWt4eHjwzjvvULt2bQA2bNjA4sWLuXLlCo8//ji9e/emX79+dOjQgTNnzjB48GDat29PQEAAffr04fjx4zg4OFClShXmzZvHJ598QmhoKK6urgQFBVGtWjUANm7cyKJFi7h8+TKPP/44r732Gr169WLLli0MHz6clJQUvL29CQ4O5p133qFGjRqMGDECgFWrVrFixQr++OMPXFxcePPNN2nTpg0AvXv3plGjRpw7d44ffviBxx57jBEjRlh89qt48eKMGjWKL7/8kiNHjtCkSRNu377NlClT2LdvHzExMdStW5dx48ZRvnx5Ll26RIsWLRg/fjyzZ8+mT58+LFy4kKSkJLy9vfn444+pX79+rvwNc9Nt7PkzLpmiRmJBh2K14uOTrTpPhmEwfONhUgwjw+UphsGYb48Q4OWqy71ERESsTI4KlG+++YaFCxdSrVo15syZA8ATTzzBhx9+SJ8+fbJVoHh4eNC9e3cmT55sPgOS2Zienp64u7vTvn171q1bl2sFSqrKlSvTsGFDvvvuO+rXr8+KFSvYvHkzS5cupWzZsqxevZpBgwaxa9cuoqKimDhxIqtXr6ZKlSqcOHGC/v37U79+fTZu3EiVKlVYsGABfn5+/Pzzz+nGWrp0KdOnT8fFxYXAwEA+/PBDPv74Y8LDwxk9ejTLli2jQYMG/PTTT/Tr149atWrh7+/P2bNn2bNnD2vWrEm3zh07djBjxgwWL15MjRo12LZtGyNHjqRSpUpUqVIFgC+++IKpU6cydepUFi1axMSJE2nTpg12dnYW5SglJQXDMLCxuXfybezYsURHR7Nx40bs7e155513GDZsGOvWrTO/5sCBA+zYsYPHHnuMsmXLMmvWLPbu3Wvx38UwDGJjYy3u/zDi4uI4YOPCgYsJQEK+jPnIsuI8Xbhyk99vRj+wz9nrd9geGk4j99J5FkfqXQ11d8PMKUeWUZ4sozxZRnnKmnJkmdT8GIaRq1/45ahAuXnzpvnb/vu5ublx+/btbK9vyJAhvPDCC2zbto1WrVpl2GfdunXmCfgdO3bko48+4tKlS5QvXz7b4z1IxYoVOX/+vHnMV155BXd3d+DeGYgVK1awa9cuKleuTEpKinnujJeXF/v37zcfuGelY8eOPPPMM8C9uTipxVn58uX56aefKFmyJAANGjTgiSee4OTJk1nO+0gt2urUqQNAmzZtWL58OVu3bjUXKD4+PuY7rfn7+zN//nyuXr1KuXLlsow5KiqK+fPn88QTT+Dj42OeD7N69WqcnZ0BePPNN2nbti3h4eHmHTUgIIBixYpZlJeMJCYmEhoamuPXZ5uNW/6NJXniTly8Rf0O/3oW57hreRwNhIWF5fkYjzrlyDLKk2WUJ8soT1lTjiyTlJSUqzeCylGBUrZsWUJDQ/H09MS47xKKffv2mSfPZ0exYsUYMWIE06ZNy/A2xUePHiUsLAx/f38AXF1dqVmzJhs2bODNN9/MySZkKjk5GVtbWwAuXrzIlClTmDp1qnl5SkoKly9fxt/fn44dO+Lv70+9evXw9fWlU6dOPP744xaNc39h5ejoaJ5TYzKZWLlyJevWrePq1asYhsHdu3ctmttz6dKldJdMubm5ERERkeG4RYsWBSA+PvODucGDB5sLjWLFilGjRg2WL19OsWLFOHXqFIZhUKlSJXP/ChXuTTyOiIgwj1W2bNksY38QOzs7PDw8HmodloqLi6Ne2L3L4xwcHPJlzEdRQkICly9ftto8uVOc1T9m3a92VQ888/gMSlhYGO7u7jg6OubZOI8y5cgyypNllCfLKE9ZU44sk5qnIkVyfN+tDOVobR06dOCNN96gf//+GIbB999/z4kTJ1i5cmWO784UEBDA6tWrWbx4cbqD7LVr15KUlESLFi3MbYmJiVy5coXAwECLz1pY4tSpU9SsWRO4dwA/efJk/vGPf2TYd9KkSbz22mts376d7777jo8//pg1a9bg6uqa5TiZnQZbu3YtS5YsYcGCBdStWxdbW1uaNGliUeyZFTH3j5XdXKVeppad8f46ZmrBl1MmkynNXd7yWknu4lbKKV/HfNTExtoSe9l681TRuRjjthxLN0H+fh6li9PSM3/moDg6OlplnqyJcmQZ5ckyypNllKesKUeWye3P0hwd2Q8YMICAgADmzp1LYmIib775Jhs2bGDgwIEMHDgwx8GMGzeOTz/9lPDwcHNbTEwMwcHBTJgwga+//tr8k3qGYf/+/Tke76/27dvHkSNHaNu2LXDvTM3p06fT9Ll06RJw70xKVFQUbm5u9O/fnzVr1uDh4cG2bdseKoaQkBDq1KlD/fr1sbW15dq1a1y9etWi11aoUMF8eVqq8+fPW1Qw5UTqeu8fM/W/U8+kiBQEk8lEUPta2GTyhmljMjG9XS1NkBcREbFC2SpQ3nrrLeDeh/+bb77J/v37CQwM5NChQ+ZnlDzM2QxPT08CAgLMdwYDCA4OxsHBgU6dOuHm5mb+qVq1Ks2bN08zGTunEhISzHfI6tevH15eXgD06NGDL774gqNHj5KcnExwcDDt2rXjjz/+IDg4mG7dupkPyCMiIrhy5Yr5wNzBwYHff/+d6OgHT9T9q3LlynH+/Hlu375NREQEkydPpmzZsly5csW83mvXrhEZGZnuDEbHjh3ZtGkTR48eJTExkQ0bNnDmzBlzwZXbnnjiCXx9fZkzZw6RkZHcvn2b2bNn8/zzz+Pi4pLha4oWLcqdO3e4cuXKAy8tE3lYnbwrsKavHx6li6dp9yhdnDV9/fQcFBERESuVrUu8/vrgRBsbGz7++GMCAwNzLaBhw4axZcsW80Sb9evX0759+wwn3nTp0oXAwEAiIyMpVapUtsY5fvw43t7ewL1LkJ599llGjx6d5rkpXbt25fLlywQGBhIdHc0zzzzD/PnzKVu2LC4uLpw5c4a+ffsSFRVF6dKl6datGy1btgTuFTcffPAB+/bt45VXXrE4rp49e3LgwAGaNGlCuXLleP/99zlx4gSzZ8/mySefpGXLlnz55Zc0bdo03UMa27ZtS0REBKNGjeL69es888wzLF++3DzJPy8EBQUxYcIE/P39sbGxoUGDBkybNi3T/vXr16d8+fK0bNmSoKAg8y2QRfJCJ+8KBHi5suf8VS5HxVG2pCO+FfUkeREREWtmMoxMHhSQgerVq3P8+PEs20RyS0hICIC5mMxrsbGx5htA6JrTzClPllGesqYcWUZ5sozyZBnlKWvKkWVS82RnZ4fJZMq147VsXY+V0beO+iZSRERERERyS+7d/kpEREREROQhqUARERERERGrka1J8omJiQwfPjzLtlmzZj18ZCIiIiIiUuhkq0CpXbt2umdyZNQmIiIiIiKSE9kqUD777LO8ikNERERERERzUERERERExHqoQBEREREREauhAkVERERERKyGChQREREREbEaKlBERERERMRqqEARERERERGroQJFRERERESshgoUERERERGxGipQRERERETEaqhAERERERERq6ECRURERERErIYKFBERERERsRoqUERERERExGqoQBEREREREauhAkVERERERKyGChQREREREbEaKlBERERERMRqqEARERERERGroQJFRERERESshgoUERERERGxGkUKOgARkZwyDIM956/yR1QsZUs40fiZpzCZTAUdloiIiDwEFSiPiEuXLtGiRQuCg4OpVKlSQYcjUuC+CrnI6E1HOHfjjrmt0hPFCWpfi07eFQowMhEREXkYusQrlzVv3pznnnsOb29vvL29qV27Nr169eLAgQMFGtcnn3xCUlJSrqzr999/p3PnzjRq1CjLvsHBwbRv3x4fHx86d+7Mjz/+mCsxSOH2VchFuq/YnaY4ATh34w7dV+zmq5CLBRSZiIiIPCydQckDY8eOpWfPngDExcWxcuVKXn/9dTZt2oSrq2u+x3Pz5k2CgoLo1asXRYo83J98//79jBo1Ch8fH65cufLAvqGhoYwePZr58+dTv359tm7dSmBgIN999x1PP/30Q8WRl25jz59xyRQ1Egs6FKsVH59cYHkyDIPhGw+TYhgZLk8xDMZ8e4QAL1dd7iUiIvIIUoGSxxwdHenXrx+rVq1i9+7d+Pv78/7773Pw4EESExPx8fFh4sSJuLi4ABAeHs748eP55ZdfKFWqFK+++ip9+vRJt96IiAi6devG8OHD6dKlCxEREUyaNIlffvmFlJQUmjVrxrhx44iPj6dp06YYhkGdOnWYMGECfn5+jB8/nkOHDpGYmEj16tWZNGmSRcVTZGQkn376KceOHePw4cMP7Lt27VqaNGlCkyZNAOjQoQOff/45Gzdu5PXXX7c4h4ZhEBsba3H/hxEXF8cBGxcOXEwAEvJlzEdWAeXpwpWb/H4z+oF9zl6/w/bQcBq5l86nqDIWFxeX5l9JTzmyjPJkGeXJMspT1pQjy6TmxzCMXP1SUAVKPklOTsbW1pYZM2YQExPDDz/8gGEYDBs2jKlTpzJv3jwAAgMDqVevHh999BFhYWG89NJLVKpUCTc3N/O6YmJiGDhwIC+++CJdunTBMAwGDx5MrVq1+PDDD4mNjeVf//oXQUFBTJo0iWXLltGnTx8OHTqEg4MD7733HiVLlmT37t0kJyczffp0goKCmD9/fpbb4e/vD8CxY8ey7Hvy5ElzcZKqWrVqhISEZCd1JCYmEhoamq3XPBQbt6z7SIG5ExdvUb/Dv57FOe5aHkdjmbCwsIIOweopR5ZRniyjPFlGecqacmSZpKQk7O3tc219KlDyWExMDKtWreLmzZs0adKEzp07k5SUhJOTEwAtW7Zk0aJFAJw6dYrTp0+zYsUKHB0d8fT0ZP78+ZQpU8a8PsMwGDFiBFWrVmXo0KEAhISEcObMGVauXImjoyOOjo4MGTKE/v37M3HixHQxRUVFUapUKezt7TGZTLz//vvY2OT+dKTIyEhKliyZpq1kyZKcPXs2W+uxs7PDw8MjN0PLVFxcHPXC/sDFxQUHB4d8GfNRlJCQwOXLlwskT+4UZ7UFU5lqV/XA0wrOoISFheHu7o6jo2OBxmKtlCPLKE+WUZ4sozxlTTmyTGqeHnYKwV+pQMkDkydPZurUqQAULVoUT09PPv30U1xcXDhz5gzTp0/n+PHjxMfHk5KSQqlSpQC4ePEixYoVM/8O0LBhQ+DeXbwAZs+ezb59+9i7d6+5T3h4OMnJyTz//PNp4khOTubWrVvp4nvttdcYNGgQe/bswdfXF39/fxo0aJC7Sfh/RibzBLLDZDKZC7r8UJK7uJVyytcxHzWxsbbEXi6YPFV0Lsa4LcfSTZC/n0fp4rT0tJ45KI6OjtqfsqAcWUZ5sozyZBnlKWvKkWVy+/NWBUoeuH+S/P1SUlIYMGAAtWvXZuvWrTg7O7N27Vpmz54NgI2NDSkpKQ9c959//kmFChWYP38+Y8aMAcDBwQEnJyd++eUXi+Lz9vZmx44d7Nmzh127dhEYGEj37t0ZPXp0Nrf0wR5//HEiIyPTtEVGRuLs7Jyr40jhYjKZCGpfi+4rdmc4Ud7GZGJ6u1pWU5yIiIhI9ug2w/no+vXrRERE0Lt3b/NB+qlTp8zLXV1diYmJ4erVq+a27du3p7lF8bRp0/jggw/44osvOHjwIAAVKlQgNjaW8PBwc7/o6OgMz57AvSLBzs6OFi1aMGnSJBYuXMiqVatydVsBvLy8OHHiRJq2kJAQatSoketjSeHSybsCa/r64VG6eJp2j9LFWdPXT89BEREReYSpQMlHzs7OODk5cfToURISEti0aROhoaFER0cTExODp6cn1apVY/bs2cTExPDbb7/x7rvvEh//v0nBNjY2eHp6MnDgQEaPHk10dDSVK1fGx8eHKVOmcPPmTaKiohg/fjyjRo0C7l1mBnDhwgViY2Pp0aMHH3/8MQkJCSQmJnLs2LE0k/AfRt++fQkODgage/fu7Nu3j127dpGQkMC6desICwujQ4cOuTKWFG6dvCvw65iO7Bzcmi9fbsyuN1rz65iOKk5EREQecSpQ8lGRIkV4//33WbJkCQ0bNuTgwYPMmzePp59+mtatWwOwaNEiIiIiaNiwIQMHDmTw4MH4+fmlW9eAAQNwdnZm2rRpAMyaNQvDMGjRogWtWrUy350LwNPTEx8fH7p27crKlSuZPXs2O3fupH79+jRs2JD9+/czc+ZMi7ahX79+eHt7895773H9+nXzAylTz+aEh4dz+/ZtACpXrszMmTOZNm0atWvX5vPPP2fx4sU8+eSTD51LEbh3uZdfpTK86ONO42fK6LIuERGRvwGTkRuzmEXySOotib29vfNlvNjYWEJDQ/H09NSkuAdQniyjPGVNObKM8mQZ5ckyylPWlCPLpObJzs4Ok8mUa8drOoMiIiIiIiJWQ3fxErOBAwemuX3xX02aNImAgIB8jEhEREREChsVKGKW+sBIEREREZGCoku8RERERETEaqhAERERERERq6ECRURERERErIYKFBERERERsRoqUERERERExGqoQBEREREREauhAkVERERERKyGChQREREREbEaKlBERERERMRqqEARERERERGroQJFRERERESshgoUERERERGxGipQRERERETEaqhAERERERERq6ECRURERERErIYKFBERERERsRoqUERERERExGqoQBEREREREauhAkVERERERKyGChQREREREbEaKlBERERERMRqqEARERERERGrUaSgAxCRws0wDPacv8ofUbGULeFE42eewmQyFXRYIiIiUkD+9mdQmjdvjp+fH7GxsWnaf/75Z5o3b56u/48//kiVKlWYMGGCuW3BggV4e3vj7e2Nl5cXVapUMf/u7e3N119/neHY//3vf3n55ZepV68eXl5etGrViiVLlmAYRu5uZB7YsGFDuu1s3rw5QUFB6XKZHSdPnmTfvn25GKk8yr4KuUiVad/QbMH3vPT5jzRb8D1Vpn3DVyEXCzo0ERERKSB/+wIF4O7duyxYsMCivmvXrqVt27Zs3ryZhIQEAAYPHkxISAghISEsW7YMgEOHDpnbAgIC0q3n6NGjDBkyhBdffJH//ve/HDt2jGnTprFixQqWLFmSexuXh0qXLm3exuPHj7NkyRJ+/PFHpk+fnuN1rl+/XgWKAPeKk+4rdnPuxp007edu3KH7it0qUkRERAqpQnGJ15AhQ5g5cyZdunShYsWKmfa7desWO3bsYNOmTZw8eZJt27bRrl27HI154MABypcvT/v27c1tderUYe7cuWkuX9m+fTtz587l4sWLODs788orr9CnTx8AevfuTY0aNRgxYgQA586do02bNvzwww+UL1+e5s2b061bN9atW4evry8TJkzg5MmTTJw4kdOnT1OmTBmGDh1KmzZtAPj111+ZNm0aJ0+epEiRIrRr147Ro0djZ2eX5faYTCY8PDz45z//ybRp05g4cSJwr1D74IMPOHPmDI899hhdunRh6NCh2NjYMG/ePE6cOIGjoyO7d++mU6dOfPnll9jY2LB161a2bduWo9zmtdvY82dcMkWNxIIOxWrFxyc/VJ4Mw2D4xsOkZHI2McUwGPPtEQK8XHW5l4iISCFTKAoUDw8PunfvzuTJk81nQDLyzTff4Onpibu7O+3bt2fdunU5LlAqVqzIhQsXWLt2LR07dsTe3h6A2rVrm/v8+uuvDB06lDlz5tCkSRMOHTrEwIEDcXNzo0mTJhaNs3nzZpYvX06FChWIi4tjwIABvPrqq3z22WccPHiQgQMHUqVKFcqWLctrr71G7969+fjjj7ly5QqDBw9m2bJlDBw40OLtSklJwdbWFoDr16/Tv39/Ro0aRbdu3Th79iz//Oc/eeqpp3jppZeAe2eShg4dyqxZs7C1teW3335LU3RZwjCMh7qsLDvi4uI4YOPCgYsJQEK+jPnIeog8Xbhyk99vRj+wz9nrd9geGk4j99I5DLDgxcXFpflX0lOOLKM8WUZ5sozylDXlyDKp+TEMI1e/UCwUBQrcO4vywgsvsG3bNlq1apVhn3Xr1tGzZ08AOnbsyEcffcSlS5coX758tsdr2bIl/fr1Y8KECUydOpWaNWvSoEED2rZtS7ly5YB7lzs1aNCAli1bAtCgQQOaNm1KcHCwxQVK48aNcXNzA+7Nn0lMTOSVV17B1taWRo0aMXv2bIoWLcquXbswDIMBAwYA4OrqSv/+/Vm8eLFFBYphGJw7d45ly5bh7+8PwLfffkvZsmXNxUi1atXo2LEjW7ZsMbfZ2trSs2fPh9ppExMTCQ0NzfHrs83GLf/GKqTuxMVb1O/wr2dxjruWx9HkvbCwsIIOweopR5ZRniyjPFlGecqacmSZpKQk85fxuaHQFCjFihVjxIgRTJs2jcaNG6dbfvToUcLCwswH366urtSsWZMNGzbw5ptvZns8k8nEyJEjef3119m7dy8HDx5k1apVzJkzhylTphAQEMClS5eoVKlSmte5ublx5MgRi8dJLXYALl68yNNPP20+wwHQokUL4N6Zlhs3buDt7W1eZhjGA3em69evp+nv4uKCv78/gwcPBsg0/i1btph/f/rppx+6orazs8PDw+Oh1mGpuLg46oX9gYuLCw4ODvky5qMoISGBy5cv5zhP7hRn9Y9Z96td1QPPR/wMSlhYGO7u7jg6OhZ0OFZJObKM8mQZ5ckyylPWlCPLpOapSJHcLSkKTYECEBAQwOrVq1m8eDH169dPs2zt2rUkJSWZD+jh3jf3V65cITAwEBubnN1PoGTJkrRp04Y2bdpgGAbjxo0jKCiIgIAA7t69m+FrMjugT0lJSdd2fzFiY2OTYR8ABwcHnn32WTZt2mRx7KVLl2bv3r2ZLrck/tzYYU0mE05OTg+9HkuV5C5upZzydcxHTWysLbGXc56nis7FGLflWLoJ8vfzKF2clp5/jzkojo6O2p+yoBxZRnmyjPJkGeUpa8qRZXL7s7pQ3MXrfuPGjePTTz8lPDzc3BYTE0NwcDATJkzg66+/Nv+sW7eOq1evsn///myPs3TpUnbt2pWmzWQy4evrS3x8PIZhUKFCBc6fP5+mz/nz53F1dQXA3t6e+Pj/XQpz8eKD72rk6upKREREmsLh66+/JjQ0lAoVKhAeHk5MTIx52a1bt4iOfvA8gAfJKn6RzJhMJoLa18Imkzc0G5OJ6e1q/S2KExEREcmeQlegeHp6EhAQwOzZs81twcHBODg40KlTJ9zc3Mw/VatWpXnz5qxbty7b48TGxvLuu+/y3//+l/j4eFJSUjh9+jRLliyhefPmmEwmOnTowN69e9m5cydJSUns2bOHXbt2mW9b7O7uzv79+7l9+zbXrl1j1apVDxzTz88PJycnFi1aREJCAgcOHGD8+PHY2tri6+uLs7MzQUFBREdHc+3aNYYOHcrMmTOzvW2p/P39CQ8PZ/Xq1SQlJXH8+HG++uorOnXqlOlrHBwcuHTpErdv387xuPL30Mm7Amv6+uFRuniado/SxVnT149O3hUKKDIREREpSIXqEq9Uw4YNY8uWLeb5F+tRTNI0AAAgAElEQVTXr6d9+/YZzsfo0qULgYGBREZGUqpUKYvHGDJkCCVLluTDDz8kPDycu3fv8vTTT6eZw+Hj48OUKVOYNWsW//rXvyhfvjwzZ86kXr16APTv35+TJ0/i5+dHhQoVGD16NLt37850THt7ez755BPGjBnD0qVLcXFxYerUqVSuXBm498DJyZMn06hRI4oVK0aLFi0YPXq0xdv0V+XKlWP+/PnMmTOH6dOn89RTTzF06NAMnwuTqnPnzowdO5bWrVuzb9++NJeoSeHTybsCAV6u7Dl/lctRcZQt6YhvRT1JXkREpDAzGY/CY82l0AoJCQFIM1k/L8XGxhIaGoqnp6euOX0A5ckyylPWlCPLKE+WUZ4sozxlTTmyTGqe7OzsMJlMuXa8Vugu8RIREREREeulAkVERERERKyGChQREREREbEaKlBERERERMRqqEARERERERGroQJFRERERESshgoUERERERGxGipQRERERETEaqhAERERERERq6ECRURERERErIYKFBERERERsRoqUERERERExGqoQBEREREREauhAkVERERERKyGChQREREREbEaKlBERERERMRqqEARERERERGroQJFRERERESshgoUERERERGxGipQRERERETEaqhAERERERERq6ECRURERERErIYKFBERERERsRoqUERERERExGqoQBEREREREauhAkVERERERKxGkYIOQERyxjAM9py/yh9RsZQt4UTjZ57CZDIVdFgiIiIiD0UFyiPi0qVLtGjRguDgYCpVqlTQ4UgB+yrkIqM3HeHcjTvmtkpPFCeofS06eVcowMhEREREHo4u8cplzZs357nnnsPb2xtvb29q165Nr169OHDgQIHG9cknn5CUlPTQ64mPj2fKlCn4+flRp04dXn31VX777bdM+4eGhvLyyy9Tu3ZtWrduzfLlyx86hsLuq5CLdF+xO01xAnDuxh26r9jNVyEXCygyERERkYenMyh5YOzYsfTs2ROAuLg4Vq5cyeuvv86mTZtwdXXN93hu3rxJUFAQvXr1okiRh/uTz5gxg19++YVVq1ZRqlQppkyZQmBgIN9//326vvHx8QwYMIDu3buzZMkSLly4QL9+/ShfvjytW7d+qDjy0m3s+TMumaJGYkGHko5hGAzfeJgUw8hweYphMObbIwR4uepyLxEREXkkqUDJY46OjvTr149Vq1axe/du/P39ef/99zl48CCJiYn4+PgwceJEXFxcAAgPD2f8+PH88ssvlCpVildffZU+ffqkW29ERATdunVj+PDhdOnShYiICCZNmsQvv/xCSkoKzZo1Y9y4ccTHx9O0aVMMw6BOnTpMmDABPz8/xo8fz6FDh0hMTKR69epMmjTJouKpWLFijBo1irJlywLQt29f1q1bx5UrVyhTpkyavrt27SIxMZFBgwZha2vLc889R7du3Vi9enW2ChTDMIiNjbW4/8OIi4vjgI0LBy4mAAn5MmZ2XLhyk99vRj+wz9nrd9geGk4j99J5FkdcXFyafyVjylPWlCPLKE+WUZ4sozxlTTmyTGp+DMPI1S9GVaDkk+TkZGxtbZkxYwYxMTH88MMPGIbBsGHDmDp1KvPmzQMgMDCQevXq8dFHHxEWFsZLL71EpUqVcHNzM68rJiaGgQMH8uKLL9KlSxcMw2Dw4MHUqlWLDz/8kNjYWP71r38RFBTEpEmTWLZsGX369OHQoUM4ODjw3nvvUbJkSXbv3k1ycjLTp08nKCiI+fPnZ7kdb731VprfL1++jIODA6VKlUrX9+TJk1SpUgVbW1tzW7Vq1Vi7dm22cpeYmEhoaGi2XvNQbNyy7lNA7sTFW9Tv8K9ncY67lsfRQFhYWJ6P8XegPGVNObKM8mQZ5ckyylPWlCPLJCUlYW9vn2vrU4GSx2JiYli1ahU3b96kSZMmdO7cmaSkJJycnABo2bIlixYtAuDUqVOcPn2aFStW4OjoiKenJ/Pnz09zZsIwDEaMGEHVqlUZOnQoACEhIZw5c4aVK1fi6OiIo6MjQ4YMoX///kycODFdTFFRUZQqVQp7e3tMJhPvv/8+NjbZn450+/ZtpkyZQr9+/XBwcEi3PDIykhIlSqRpK1WqFJGRkaSkpFg8pp2dHR4eHtmOLyfi4uKoF/YHLi4uGW5TQXOnOKt/zLpf7aoeeObxGZSwsDDc3d1xdHTMs3EedcpT1pQjyyhPllGeLKM8ZU05skxqnh52CsFfqUDJA5MnT2bq1KkAFC1aFE9PTz799FNcXFw4c+YM06dP5/jx48THx5OSkmI++3Dx4kWKFSuW5mxEw4YNgXt38QKYPXs2+/btY+/eveY+4eHhJCcn8/zzz6eJIzk5mVu3bqWL77XXXmPQoEHs2bMHX19f/P39adCgQba28erVq7z22mt4enoyZMiQbL02u6cATSaTuaDLDyW5i1spp3wd01IVnYsxbsuxdBPk7+dRujgtPfNnDoqjo6NV5snaKE9ZU44sozxZRnmyjPKUNeXIMrl9zKG7eOWBsWPHEhISQkhICAcPHuQ///kPNWrUICUlhQEDBuDs7MzWrVsJCQnh/fffN7/OxsaGlJSUB677zz//pEKFCmkux3JwcMDJyck8ZurPqVOncHZ2TrcOb29vduzYwbvvvothGAQGBhIUFGTx9l28eJEePXpQu3Zt/v3vf6e5hOt+zs7O6QqkyMhISpUqlaMzNnLvDSCofS1sMnkjsDGZmN6ulibIi4iIyCNLR4n56Pr160RERNC7d29z4XDq1CnzcldXV2JiYrh69aq5bfv27WluUTxt2jQ++OADvvjiCw4ePAhAhQoViI2NJTw83NwvOjo6w7MncK9IsLOzo0WLFkyaNImFCxeyatUqi7bh5s2b9OvXj86dOzN+/PhMixMALy8vTp8+neb2xiEhIdSoUcOisSRjnbwrsKavHx6li6dp9yhdnDV9/fQcFBEREXmkqUDJR87Ozjg5OXH06FESEhLYtGkToaGhREdHExMTg6enJ9WqVWP27NnExMTw22+/8e677xIf/7+J0TY2Nnh6ejJw4EBGjx5NdHQ0lStXxsfHhylTpnDz5k2ioqIYP348o0aNAu5dZgZw4cIFYmNj6dGjBx9//DEJCQkkJiZy7NixNJPwH+Tf//43NWrUIDAwMMPls2bNYvr06QA0adKEYsWKsXDhQuLi4jh27Bjr1q0z34JZcq6TdwV+HdORnYNb8+XLjdn1Rmt+HdNRxYmIiIg88lSg5KMiRYrw/vvvs2TJEho2bMjBgweZN28eTz/9tPm2u4sWLSIiIoKGDRsycOBABg8ejJ+fX7p1pV4qNm3aNOBeYWAYBi1atKBVq1bmu3MBeHp64uPjQ9euXVm5ciWzZ89m586d1K9fn4YNG7J//35mzpxp0TasX7+erVu3mh9Emfrz9ddfA3Dt2jWuXLkCgL29PYsWLWLfvn3Uq1ePYcOG8dZbb9G0adOHTaVw73Ivv0pleNHHncbPlNFlXSIiIvK3YDKMTJ74JmIFQkJCgHvzZvJDbGwsoaGheHp6alLcAyhPllGesqYcWUZ5sozyZBnlKWvKkWVS82RnZ4fJZMq14zWdQREREREREauh2wyL2cCBA9PcvvivJk2aREBAQD5GJCIiIiKFjQoUMUt9YKSIiIiISEHRJV4iIiIiImI1VKCIiIiIiIjVUIEiIiIiIiJWQwWKiIiIiIhYDRUoIiIiIiJiNVSgiIiIiIiI1VCBIiIiIiIiVkMFioiIiIiIWA0VKCIiIiIiYjVUoIiIiIiIiNVQgSIiIiIiIlZDBYqIiIiIiFgNFSgiIiIiImI1VKCIiIiIiIjVUIEiIiIiIiJWQwWKiIiIiIhYDRUoIiIiIiJiNVSgiIiIiIiI1VCBIiIiIiIiVkMFioiIiIiIWA0VKCIiIiIiYjVUoIiIiIiIiNVQgSIZ8vb2Zu/evbm+3pUrV9K8efNcX6+IiIiI/D0UKegAClLz5s25cuUKNjY2mEwmihcvTv369Rk1ahRlypTJ8vUnT57k9u3bNGzYMFfj+vnnn+nTpw/29vbmtiJFilCxYkX++c9/4u/vn6vjZSQkJCTPxxAwDIM956/yR1QsZUs40fiZpzCZTAUdloiIiEiBKdQFCsDYsWPp2bMnAFeuXGHUqFGMGzeOxYsXZ/na9evX4+TklOsFSqpDhw7h4OAAwN27d9myZQsjRozgqaeeonbt2nkypuSfr0IuMnrTEc7duGNuq/REcYLa16KTd4UCjExERESk4OgSr/uUKVOG1q1bc+HCBQA++ugjOnfunKbPoUOHqF69Om+//TZffvkly5cvp1WrVgBERkYyYsQIfH198fHxYdCgQVy5cgWAlJQUpk+fjq+vLzVr1qRDhw7s2bPH4tjs7e3p2LEjdevW5YcffjC3BwcH07FjR2rWrEmLFi1YvXq1eVlycjIzZ86kUaNG1K1bl6FDhxIZGWmOZ+7cubRs2ZIaNWrQpUsXDh8+bH5tlSpV2L17NzNmzKB3795pYtm2bRt16tTh7t27xMfHM3HiRJo2bUrNmjXp3bs3Z8+eNfc9duwYHTp0oGbNmrz66qvcuHHD4m3+O/sq5CLdV+xOU5wAnLtxh+4rdvNVyMUCikxERESkYKlA+X+GYRAeHs4333xDu3btAOjYsSOnTp3i3Llz5n5bt26lWbNmTJs2jbp169KvXz+2bdsGwJgxY4iPj2fz5s3s2bMHJycn3n77bQA2b97Mvn372LhxI4cPH6Zv376MHj2axMTEbMV5f/+QkBDeffddRo4cyeHDhwkKCmL69OkcOXIEgM8++4xt27axevVqdu3aRVxcHJMmTQJgxYoVbN68maVLl3Lw4EECAgIYNGgQsbGxacZ74YUXOHz4sLmwgXsFSsuWLbG3t2fmzJmcOnWK1atX89NPP+Ht7U1gYCCGYZCcnMybb76Jr68vP//8M8OGDWPNmjXZ2t6CcBt7/oxL5o+YxDz5iYi+y/CNh0kxjAzHTzEMxnx7BCOT5SIiIiJ/Z4X+Eq/JkyczdepUDMMgMTGRBg0a8NJLLwFQvnx56tSpw6ZNmxg2bBgA27dv59133023nhs3brBz506Cg4MpWbIkACNGjKBp06Zcu3aNqKgoihQpgqOjI7a2tnTp0oVOnTphY2NZjRgXF8fmzZs5evSouejZsGEDTZs2xdfXF4A6derg7+/PN998Q61atdiwYQM9e/akfPnyALz33nvmYmvdunW88soruLu7A9C7d29WrFjBrl27aNOmjXlcb29vXFxc2LlzJ506dSIpKYldu3YxY8YMUlJS2LBhA7NnzzbP2Rk2bBiff/45x48fB+Dq1asMGjQIBwcHatSoQatWrdi5c6flfyDuFY9/LZzySlxcHAdsXDhwMQFIyJMxLly5ye83ox/Y5+z1O2wPDaeRe+k8ieFhxcXFpflXMqY8ZU05sozyZBnlyTLKU9aUI8uk5scwjFydQ1voC5T756BERUXx2WefERAQwMaNG3n88cfp2LEjixcvZtiwYYSEhBATE4Ofn1+69YSHhwMQEBCQpt3W1pbLly/Ttm1bvvnmG/z8/GjUqBFNmzalbdu2DyxQ6tSpY/7vxMREqlatysKFC/Hy8gLg4sWL7N+/H29vb3M/wzDMBUt4eLi5OAFwdXXF1dXV/NopU6YwdepU8/KUlBQuX76cLo4XXniB7du306lTJw4cOIDJZKJRo0bcuHGDmJgYBg8enGanTF2PyWSiRIkSFC9e3LwstSDKjsTEREJDQ7P9uhyzccvT1d+Ji7eo3+Ffz+Icdy1PY3lYYWFhBR3CI0F5yppyZBnlyTLKk2WUp6wpR5ZJSkpKc3Onh1XoC5T7lShRgjfeeIP169ezZcsWevXqhb+/P5MnT+bo0aPs3LmTF154IcM/QNGiRQHYvXs3jz/+eIbrX7NmDUeOHGHnzp3MnTuXlStX8sUXX1CkSMZ/hvsnyQ8fPpybN2+mKY6KFi1Kz549ee+99zJ8vclkIiUlJcNlRYsWZfLkyfzjH//IPCH/z9/fn5dffpn4+Hi+//57WrduTZEiRczbvGrVKnPRdL9NmzaRnJycpi2zeB7Ezs4ODw+PbL8uJ+Li4qgX9gcuLi7m3Oc2d4qz+ses+9Wu6oGnFZ9BCQsLw93dHUdHx4IOx2opT1lTjiyjPFlGebKM8pQ15cgyqXnK7Fg2p1SgZCIh4d7lPcWKFaNFixZ899137NixI80Zh/uVK1cOGxsbTp8+Tf369YF73/zfvHmTMmXKkJCQQEpKCrVq1aJWrVoMGjSIRo0a8euvv2Z4cP9Xb7/9Nv7+/qxfv54uXboAUKFChXS3A/7zzz958sknsbW1xdXV1TzhH+D333/nxx9/5KWXXsLV1ZXTp0+nKVAuXbqU5oxLKi8vL0qXLs2+ffvYvn07M2bMAKB48eKUKlWK06dPp9mG1PU89dRTREdHc+fOHfNZlPvn81jKZDLh5OSU7dflVEnu4lbKKc/GrOhcjHFbjqWbIH8/j9LFaenpavW3HHZ0dMzXv82jSnnKmnJkGeXJMsqTZZSnrClHlsnt4xVNkr9PQkICn3zyCbdu3aJFixbm9o4dO7J27VoSExPT3N7XwcGBS5cucfv2bYoXL06bNm2YOXMmf/75J/Hx8fz73/+mX79+GIbBlClTGD16NDdv3sQwDE6ePElKSgply5a1KLbSpUszfPhwgoKCuHbt3mU/Xbt25ciRI6xfv567d+8SGhpKt27d2Lp1KwBdunRh5cqVnD9/npiYGGbMmMGhQ4cA6NGjB1988QVHjx4lOTmZ4OBg2rVrxx9//JHh+C+88ALLli3DMAzq1atnbu/RowcLFy7k3LlzJCYm8umnn9K1a1fi4uKoUaMGJUuWZOnSpdy9e5dDhw5le/7J35HJZCKofS1sMvmf2cZkYnq7WlZfnIiIiIjkhUJfoEyePBlvb2+8vb1p1KgRO3fuZOnSpVSo8L/nUPj6+uLo6Ei7du3SHDR27tyZ3bt307p1a5KTk3nvvfdwc3Ojbdu2NG7cmLNnz7JgwQJMJhPDhw/HxsaGf/zjH9SqVYspU6Ywa9YsnJ2dLY71xRdf5JlnnmHixIkAVKpUiVmzZrF06VLq1KnDkCFD6N+/v3mSe+/evQkICKBnz540a9YMW1tb8+VgXbt2pVevXgQGBlK7dm2WLl3K/PnzMy2YXnjhBQ4dOsQLL7yAra2tuX3w4ME0btyYXr168fzzz7Nt2zY+/vhjHB0dKVq0KB999BE//PADdevWZf78+fTr18/yP87fWCfvCqzp64dH6eJp2j1KF2dNXz89B0VEREQKLZOhe5lmKTo6miZNmrBhwwbc3PJ2ArWklXoJ2/03AshLsbGxhIaG4unpmS+ndFOfJH85Ko6yJR3xrfhoPEk+v/P0qFKesqYcWUZ5sozyZBnlKWvKkWVS82RnZ4fJZMq14zXNQclCQkICEydOxNfXV8WJ5DqTyYRfpTIFHYaIiIiI1Sj0l3g9yKFDh6hbty43btxg/PjxBR2OiIiIiMjfns6gPECdOnXMDxwUEREREZG8pzMoIiIiIiJiNVSgiIiIiIiI1VCBIiIiIiIiVkMFioiIiIiIWA0VKCIiIiIiYjVUoIiIiIiIiNVQgSIiIiIiIlbDZBiGUdBBiGTmyJEjGIaBvb19voxnGAaJiYnY2dlhMpnyZcxHkfJkGeUpa8qRZZQnyyhPllGesqYcWSY1TwAmk4latWrlynr1oEaxavn9pmAymfKtGHqUKU+WUZ6yphxZRnmyjPJkGeUpa8qRZVLzlJiYmKvHbDqDIiIiIiIiVkNzUERERERExGqoQBEREREREauhAkVERERERKyGChQREREREbEaKlBERERERMRqqEARERERERGroQJFRERERESshgoUERERERGxGipQRERERETEaqhAEfl/ERERvP766zz//PM0a9aMGTNmkJKSUtBhFbiIiAjeeOMNnn/+eRo2bMiYMWOIiori0qVLVKlSBW9v7zQ/y5YtK+iQ812VKlXw8vJKk4dJkyYBsH//frp27UqtWrVo27YtGzduLOBoC8bBgwfT7SteXl5UqVKFn3/+OcN9acuWLQUddr7Ys2cPDRs25K233kq3LDg4mPbt2+Pj40Pnzp358ccfzctSUlL48MMPadGiBXXr1qV///6Eh4fnZ+j56kF5+v777+nQoQM+Pj784x//YM2aNeZl8+bNw9PTM93+df369fwMP99klqcNGzZQtWrVdHk4fvw4oP0p1dixY9PlqFq1arz99tsAjBkzhmrVqqVZXqdOnYLYhDyX2ec/QGhoKC+//DK1a9emdevWLF++PM1rH/TeZRFDRAzDMIxOnToZY8eONaKioowLFy4YrVu3NpYvX17QYRW4du3aGWPGjDGio6ONy5cvG507dzbeeecdIzw83KhcuXJBh2cVKleubISHh6drv3LlilGzZk1j7dq1Rnx8vLF3716jevXqxvHjxwsgSuuzcOFCY+jQocZPP/1kNGvWrKDDKRBLliwxWrdubfTo0cMYNmxYmmWnTp0yvLy8jF27dhnx8fHGN998Y9SoUcO4fPmyYRiG8Z///Mdo1qyZcfbsWePOnTvGxIkTjfbt2xspKSkFsSl56kF5OnbsmOHt7W1s27bNSExMNHbt2mU899xzxsGDBw3DMIy5c+cao0ePLoiw892D8rR+/Xrj5ZdfzvS12p8ylpiYaLRt29bYtWuXYRiGMXr0aGPu3Ln5EWaBy+zzPy4uzmjcuLExb948IyYmxjhx4oRRr149Y+vWrYZhZP3eZQmdQREBQkJC+PXXXxkxYgTFixfH3d2dV155hdWrVxd0aAUqKioKLy8vhg8fzmOPPcbTTz9Np06dOHToUEGH9kjYtGkT7u7udO3aFQcHBxo2bEjz5s1Zu3ZtQYdW4P744w8++eQTRo0aVdChFCgHBwfWrVuHm5tbumVr166lSZMmNGnSBAcHBzp06EDlypXNZ+FWr17NK6+8QqVKlShWrBhvvfUW586d49ixY/m9GXnuQXmKjIxkwIABtGzZkiJFitCkSRMqV65cKN+nHpSnrGh/ytiKFSsoW7YsTZo0yYfIrMeDPv937dpFYmIigwYNwsnJieeee45u3bqZj5myeu+yhAoUEeDkyZOUK1eOkiVLmtuee+45Lly4QHR0dAFGVrBKlCjBtGnTKF26tLnt8uXLPPXUU+bfR40aha+vL/Xr12fWrFkkJiYWRKgFbtasWTRt2pQ6derw3nvvERMTw8mTJ6lWrVqaftWqVePEiRMFFKX1mDNnDl26dKFs2bIAxMTEmC8laNy4MZ988gmGYRRwlHmvT58+FC9ePMNlme0/ISEhxMfHc/bs2TTLixUrhpubGyEhIXkac0F4UJ78/Px44403zL8nJSVx7do1ypQpY247ffo0PXr0MF9qme3LTR4RD8oT3Hv/fvXVV6lbty4tWrTgm2++AdD+lImoqCgWLVrEyJEj07T/9NNPBAQE4OPjQ9euXf+W7+kP+vw/efIkVapUwdbW1rzs/s+2B713WUoFigj3voErUaJEmrbUYuXWrVsFEZJVCgkJ4fPPP2fQoEHY29vj4+NDq1at2LlzJ0uWLGHjxo0sWLCgoMPMdzVr1qRhw4Z8//33rF69mqNHjzJhwoQM96tSpUoV+n3q0qVLfP/997z66qvAvQOhypUr07dvX/bs2cO0adOYP38+69evL+BIC1ZkZGSaL03g3vvSrVu3uH37NoZhZLq8MJs5cyZOTk60adMGgKeffhpXV1eCgoLYu3cv3bp1Y+DAgZw/f76AI81fzs7OuLu7M3LkSPbu3cu//vUv3nnnHfbv36/9KROff/45devW5dlnnzW3ubq64ubmxuLFi9mzZw916tShX79+f/s83f/5n9lnW2RkJCkpKQ9877KUChSR/1cYvq19GIcPH6Z///4MHz6chg0b8tRTT7Fq1SpatWqFnZ0d1atXZ8CAAWzYsKGgQ813q1evplu3btjb21OpUiVGjBjBt99+W2jPJmXliy++oHXr1jz55JPAvbOVn332GfXq1cPe3h5fX1969OhRKPelv8rqfUnvW/9jGAYzZszg22+/ZeHChTg4OADQrVs35s6di5ubG46Ojrzyyit4enoWuhtWNG3alKVLl1KtWjXs7e1p27YtrVq1SvP/mfan/0lOTuaLL76gT58+adrfeOMNpk6dSpkyZShWrBgjR47E3t6e7du3F1Ckee+vn/+ZMZlM5v9+2H1JBYoI975ZioyMTNMWGRmJyWTC2dm5gKKyHjt27OD111/nnXfeSfdmfb9y5cpx/fr1Qv8hV758eZKTk7GxsUm3X926davQ71Nbt26lefPmD+xTrlw5rl69mk8RWafHH388w/clZ2dnSpUqleH+FRkZyRNPPJGfYVqFlJQUxowZw44dO1i5ciXPPPPMA/tr/7onNQ/an9I7ePAgd+/ezfIOXba2tri4uPxt96eMPv+dnZ3TnQ2JjIw070cPeu+ylAoUEcDLy4vLly9z8+ZNc1tISAgeHh489thjBRhZwTty5AijR49mzpw5BAQEmNv379/PwoUL0/Q9f/485cqVS/Mtyt/dqVOnmD59epq2c+fOYW9vT5MmTdJdm3zixAlq1KiRnyFaldDQUCIiImjUqJG5bcuWLXz55Zdp+p0/fx5XV9f8Ds+qeHl5pdt/QkJCqFGjBg4ODjz77LOcPHnSvCwqKoqLFy9SvXr1/A61wE2dOpUzZ86wcuXKdPvNggUL2L9/f5q2c+fOFbr9a+XKlQQHB6dpS82D9qf0fvjhB+rXr0+RIkXMbYZhMG3aNH799Vdz2927d7l48eLfcn/K7PPfy8uL06dPk5SUZG5LfW9KXZ7Ze5elVKCIgPme5rNmzSI6Oppz587xySef0PP/2rv3oK5UD3cAAAysSURBVKjq/4/jT5SLqKSgTpaoGCTONNqsDmOKjmCoMWOKpigXG8pLilpW2sULgjKpI1hG6GhF3sEEVLAinKkkU2GVQs1Ck4BEYJwUFDQWgt8f/txpxQt++wn7+/J6/Lfn8znvz/t8Zmd33+d8ztmgoJZOrUXV1dWxdOlSFi5cyLBhwyzanJyciI+PZ//+/dTW1nLq1Ck+/fTTVjdnXbp0Yffu3WzevBmTycTvv//O+vXrmTJlCuPHj6ekpIQ9e/ZQU1PDoUOHOHToEIGBgS2ddos5c+YMnTt3pmPHjuZtdnZ2rFmzhsOHD1NbW8sPP/xASkpKq3sv3S4wMJAjR47w3XffUVNTQ3JyMoWFhYwbNw6AoKAgtm3bxvnz56mqqiImJsb8fx+tyYkTJ0hLS2Pz5s107ty5UXtFRQVRUVEUFBRQU1NDQkICxcXFTJgwoQWybTkmk4mVK1dy6tQpamtrOXDgAFlZWUydOhXQ++l2v/zyC66urhbbbGxsuHDhAlFRUZSXl1NdXU1MTAx2dnb4+fm1UKYPx72+/0eMGEHHjh3ZuHEjN27cIC8vj+TkZPNn9v0+u5rCpqG1r8UQ+V9lZWUsW7aMnJwcOnbsyNSpU5k3b16ruhpwu+PHjxMSEoK9vX2jtoyMDM6cOcNHH31EYWEhTk5OTJs2jZkzZ9KmTes692E0GomNjSU/Px97e3smTJjA66+/joODA0ajkejoaM6fP0+PHj148803GT16dEun3GI2bdpEeno6Bw4csNi+e/duEhISKC0tpWvXrsyZM4fJkye3UJbN59aPv1tnIm+drb31tJvMzExiY2MpKSnBw8ODJUuW4OXlBdw8mxsXF0dSUhLV1dUMHjyYFStW0L179xY4kofrXvO0ePFi9u7da3GmG8DLy4uEhARqamqIjY0lIyODiooKPDw8WLZsGQaDoXkPohnca54aGhrYuHEjycnJXLp0CVdXV9566y18fX0BvZ8Ai6dMjRkzhsDAQKZPn26xb0VFBWvWrCErK4uqqioGDBhAZGQk7u7uzZR987jf9391dTXLly/n9OnTdO3alZkzZxIcHGzuc6/PrqZQgSIiIiIiIlajdZ3mFBERERERq6YCRURERERErIYKFBERERERsRoqUERERERExGqoQBEREREREauhAkVERERERKyGChQREREREbEaKlBEROT/tX379tG/f39MJlOT+sfFxeHt7X3PPp6eniQmJv5fpCciIg9IBYqIiDx006dPJygo6K7tERER+Pr68vfffz9w7ICAAE6dOnXHfzxuKU0pglrK8ePHOXLkSEunISJyVypQRETkoQsNDSU3N5dff/21UVtVVRXp6ekEBQXRtm3bFsiuddm6dasKFBGxaipQRETkoRsxYgS9evVi165djdr2799PfX09gYGBFBYWMnv2bAYNGoTBYGDixIkcPnzY3DcuLo7x48cTFxfHwIEDycjIIDU1FU9PT2pqagDuG+OWr776itGjR2MwGJg6dSr5+fl3zX/37t2MGzcOg8GAt7c3K1as4MaNG00+/nfeeYc5c+aQkJCAt7c3BoOB6OhoysrKeOmllzAYDDz33HMYjUbzPp6enmzdupXw8HAMBgNeXl7ExsZSX19v7nPw4EEmTpzIwIEDGTx4MAsXLuTy5csAXLhwAU9PTz7//HNGjhxJeHg4kydPJjMzk4SEBPOyuOvXrxMZGcmQIUMYMGAAfn5+bNmyxTxGdnY2np6enDx5kuDgYAwGAyNHjmTfvn3mPnV1daxfvx4fHx8MBgNTpkwhOzvb3F5aWsqrr77KsGHDePrpp5k0aZKKJBG5KxUoIiLy0LVp04aQkBDS09OpqqqyaEtKSmLs2LF07tyZ+fPnY2dnR1ZWFtnZ2QwbNoz58+dz5coVc/+ysjIqKys5cuQIY8aMaTRWU2JcvXqVzMxMkpKSyMrKokuXLsycOZO6urpG8VJSUli7di3vvvsuJ06cYPv27RiNRiIiIh5oDnJzc6mvr+fbb79l+fLlbN++nQULFrB48WKys7Pp2bMnq1atstjn448/JiQkBKPRyLp169iyZQspKSkA5OTkMH/+fF588UWOHTtGSkoKBQUFLFiwoFH+27ZtIz4+nj179tCjRw9efvll87K42NhYDh8+zN69e8nLy2Pp0qWsWrWK77//3iLOBx98wHvvvYfRaGTUqFEsW7aMiooK4GbhmJaWxieffILRaGT06NG88sorlJSUYDKZCAsLw8HBgfT0dHJychg7diyzZs3i/PnzDzSHItI6qEAREZFm8cILLwBYnHk3Go2cPXuWadOmATeLlTVr1tChQwfs7e0JCAjg+vXrnD171rxPZWUlc+fOpV27dtjY2DQapykxTCYTixYtwsXFBScnJ8LDwykvLycvL69RvO3btzNp0iSGDBlCmzZteOKJJ5g7dy5ffvllk2/MB7C1tWX69OnY29ubC6uhQ4fy5JNPYm9vj4+PD7/99pvFPr6+vnh7e2Nra8vw4cPx9vbm66+/BmDHjh0MGTKEgIAA7O3tcXV1JTw8nOzsbC5evGiO4e/vj6ur6x3nCuDtt98mNTWV7t27Y2Njg4+PD926deOnn36y6BcSEoKbmxu2traMHTsWk8lEUVERDQ0NJCUlERoaioeHB7a2toSFhbFy5Uratm1LVlYWxcXFRERE4OzsjIODA2FhYbi5uXHgwIEmz5+ItB62LZ2AiIi0Dk5OTgQEBJh/zAIkJibi5eVFv379ADh58iTx8fHk5+dbLKG6tXwL4JFHHsHZ2fmu4zQ1xuOPP25+3bt3b+DmUqTbFRQUcO7cOXbu3GmxvaGhgdLSUvO+9/PYY4+ZiwRHR0cAixwcHR0tcgTw8PCweO3q6sqxY8cAKCoq4plnnrlj/+LiYlxdXQHo2bPnPfMqLy9n7dq1HD9+nGvXrgE3C7jbc/nncbZv3x6Av/76iytXrlBRUWExTtu2bXn++ecBSEtLo76+nqFDh1rEa2hooKSk5J65iUjrpAJFRESaTWhoKLt27SInJwd3d3cyMzOJjY0Fbv7gnjVrFlOmTOHDDz/ExcWF4uJiRo0aZRHDzs7urvGbGqNNmzsvIHBwcGi0rV27dsyaNYsZM2Y86OHed8y75XHLnZ5qdqvIub2AAMz3p/zzasm95qu+vp4ZM2bQtWtXEhMT6dWrFzY2NowYMeKu497u1oMN/nlvzD+1a9eO9u3b8+OPP941DxGRf9ISLxERaTbu7u54e3uTmppKWloa3bp1w8/PD4DTp09jMpmYM2cOLi4uAI2WGd1PU2NUVFRw6dIl8+uCggLg5lWO2/Xp04eff/7ZYltlZSWVlZUPlNt/orCw0OJ1cXGx+aqLm5tboxv7z507Z25rij///JPCwkJCQkLo3bs3NjY2lJaWUl5e3uQcO3XqhLOzc6P7SbZu3crZs2fp06cP169fb9T+xx9/0NDQ0ORxRKT1UIEiIiLNKjQ0lIMHD5KammrxaOFevXoBN2/+NplMZGVlkZGRAdx56dWdNDWGg4MDMTExVFZWcvXqVeLj43Fzc+Opp55qFDMsLIzMzEz279+PyWSirKyM1157jTfeeOM/n4Qm+uabbzh69Ci1tbVkZWVx9OhR/P39AQgKCuLYsWPs27eP2tpaioqKiI+Px9fXl0cfffSuMR0dHSkuLubatWt06tQJJycncnNzqaurIz8/n6ioKHr27NnkOQcIDg5m586dnD59mrq6OhITE1m3bh2Ojo54e3vTt29fIiMjuXjxInV1dXzxxRf4+/uTm5v7r+dIRP77aImXiIg0Kx8fH1xcXCgqKmLy5Mnm7f3792fevHlERUWxdOlShg4dSnR0NI6OjkRHRzcpdlNjdOvWjeHDhzNx4kQuX75Mv3792LBhwx2XMfn7+3P58mU2bNjAkiVL6NChA35+fixatOjfT8Z9hISEsGPHDsLDw7Gzs2PGjBmMHz8euPno5lWrVvHZZ58RFRWFs7Mzzz77bKOneN0uODiYmJgYfH192bt3L6tXr2b16tUkJyfTt29fIiIiyMvLY+3atSxatIhJkybdN8958+ZhY2PD7Nmzqa6uxsPDg02bNpnvS9m4cSOrV69m3Lhx1NTU4O7uzvvvv8+gQYP+/SSJyH8dmwZdXxUREbE6np6eREZGEhQU1NKpiIg0Ky3xEhERERERq6ECRURERERErIaWeImIiIiIiNXQFRQREREREbEaKlBERERERMRqqEARERERERGroQJFRERERESshgoUERERERGxGipQRERERETEaqhAERERERERq6ECRURERERErIYKFBERERERsRr/A5PaDEYZJLmvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKE8F9LW-pao"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}