malware_ditaction_insa_1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "S2wsDnyw2cdK",
      "metadata": {
        "id": "S2wsDnyw2cdK"
      },
      "source": [
        "#### Importing the dataset\n",
        "#### Source :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "0a97afd4",
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-10-23T20:35:42.482824Z",
          "iopub.status.busy": "2024-10-23T20:35:42.482394Z",
          "iopub.status.idle": "2024-10-23T20:35:43.458612Z",
          "shell.execute_reply": "2024-10-23T20:35:43.457163Z"
        },
        "id": "0a97afd4",
        "outputId": "c03ad516-ce09-4ffc-afb8-82246fa4fe43",
        "papermill": {
          "duration": 0.985069,
          "end_time": "2024-10-23T20:35:43.461497",
          "exception": false,
          "start_time": "2024-10-23T20:35:42.476428",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Setup complete\n"
          ]
        }
      ],
      "source": [
        "import joblib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn.ensemble as ek\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import display\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split, learning_curve,  cross_val_score\n",
        "\n",
        "# Load the dataset (adjust the file path if necessary)\n",
        "dataset = pd.read_csv('content/dataset_malwares.csv')\n",
        "print(\"\\nSetup complete\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "ae18012a",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total number of samples: 19611\n"
          ]
        }
      ],
      "source": [
        "total_size = dataset.shape[0]\n",
        "print(\"Total number of samples:\", total_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QrKiKu-g320y",
      "metadata": {
        "id": "QrKiKu-g320y"
      },
      "source": [
        "#### About the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "QKpADr80y2nD",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "QKpADr80y2nD",
        "outputId": "262ed4b6-479b-4049-fcd0-759fe31b7ed1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(Malware\n",
              " 1    14599\n",
              " 0     5012\n",
              " Name: count, dtype: int64,\n",
              " np.int64(0))"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['Malware'].value_counts(), dataset.isnull().sum().sum()  # The dataset contains: 14,599 malware samples (labeled as 1) and\n",
        "                                                                 # 5,012 benign samples (labeled as 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "i23Z2mKh4srK",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "i23Z2mKh4srK",
        "outputId": "b5ff4333-0e9c-40d6-cd53-d1af8da333a0"
      },
      "outputs": [
        {
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              "\n",
              "   DirectoryEntryExport  ImageDirectoryEntryExport  ImageDirectoryEntryImport  \\\n",
              "0                     0                          0                      54440   \n",
              "1                     0                          0                     262276   \n",
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              "4                     0                          0                      61440   \n",
              "\n",
              "   ImageDirectoryEntryResource  ImageDirectoryEntryException  \\\n",
              "0                        77824                         73728   \n",
              "1                       294912                             0   \n",
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              "[5 rows x 79 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.head()    #Top 5 row of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "UZxvoqJF4kOv",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "UZxvoqJF4kOv",
        "outputId": "c4e63289-332e-422a-8a9f-97759f292963"
      },
      "outputs": [
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              "                                          Name  e_magic  e_cblp  e_cp  e_crlc  \\\n",
              "19606                                 clip.exe    23117     144     3       0   \n",
              "19607             VNC-Server-6.2.0-Windows.exe    23117     144     3       0   \n",
              "19608  Microsoft.GroupPolicy.Management.ni.dll    23117       0     0       0   \n",
              "19609                        cryptuiwizard.dll    23117     144     3       0   \n",
              "19610                              winhttp.dll    23117     144     3       0   \n",
              "\n",
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              "19606          4           0       65535     0   184  ...      3221225536   \n",
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              "\n",
              "       SectionMainChar  DirectoryEntryImport  DirectoryEntryImportSize  \\\n",
              "19606                0                     8                        85   \n",
              "19607                0                    10                       391   \n",
              "19608                0                     0                         0   \n",
              "19609                0                    12                       162   \n",
              "19610                0                    35                       226   \n",
              "\n",
              "       DirectoryEntryExport  ImageDirectoryEntryExport  \\\n",
              "19606                     0                          0   \n",
              "19607                     0                          0   \n",
              "19608                     0                          0   \n",
              "19609                     8                      89008   \n",
              "19610                    58                       8348   \n",
              "\n",
              "       ImageDirectoryEntryImport  ImageDirectoryEntryResource  \\\n",
              "19606                      24948                        28672   \n",
              "19607                    1413420                      1462272   \n",
              "19608                          0                        61440   \n",
              "19609                      94904                       102400   \n",
              "19610                     545812                       557056   \n",
              "\n",
              "       ImageDirectoryEntryException  ImageDirectoryEntrySecurity  \n",
              "19606                             0                            0  \n",
              "19607                             0                     18855424  \n",
              "19608                             0                            0  \n",
              "19609                             0                            0  \n",
              "19610                             0                            0  \n",
              "\n",
              "[5 rows x 79 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.tail()     #Last 5 row of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "8YxUjDU50et5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "8YxUjDU50et5",
        "outputId": "e1586609-65cb-4da1-e0b5-948cfd5c1bc6"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>e_magic</th>\n",
              "      <th>e_cblp</th>\n",
              "      <th>e_cp</th>\n",
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              "      <th>...</th>\n",
              "      <th>SectionMaxChar</th>\n",
              "      <th>SectionMainChar</th>\n",
              "      <th>DirectoryEntryImport</th>\n",
              "      <th>DirectoryEntryImportSize</th>\n",
              "      <th>DirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryImport</th>\n",
              "      <th>ImageDirectoryEntryResource</th>\n",
              "      <th>ImageDirectoryEntryException</th>\n",
              "      <th>ImageDirectoryEntrySecurity</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>19611</td>\n",
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              "      <td>19611.000000</td>\n",
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              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.00000</td>\n",
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              "      <td>19611.000000</td>\n",
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              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
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              "      <th>unique</th>\n",
              "      <td>19611</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>178.615726</td>\n",
              "      <td>71.660752</td>\n",
              "      <td>49.146958</td>\n",
              "      <td>37.370710</td>\n",
              "      <td>37.032635</td>\n",
              "      <td>64178.739687</td>\n",
              "      <td>10.418490</td>\n",
              "      <td>226.46530</td>\n",
              "      <td>...</td>\n",
              "      <td>3.163632e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.112437</td>\n",
              "      <td>101.912804</td>\n",
              "      <td>14.131865</td>\n",
              "      <td>3.368566e+05</td>\n",
              "      <td>4.047213e+05</td>\n",
              "      <td>5.555810e+05</td>\n",
              "      <td>1.238834e+06</td>\n",
              "      <td>8.814868e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>987.200729</td>\n",
              "      <td>1445.192977</td>\n",
              "      <td>1212.201919</td>\n",
              "      <td>864.515405</td>\n",
              "      <td>915.833139</td>\n",
              "      <td>9110.755873</td>\n",
              "      <td>637.116265</td>\n",
              "      <td>1249.68033</td>\n",
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              "      <td>5.860332e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.525158</td>\n",
              "      <td>127.076767</td>\n",
              "      <td>154.958102</td>\n",
              "      <td>2.181191e+07</td>\n",
              "      <td>4.704601e+06</td>\n",
              "      <td>6.772167e+06</td>\n",
              "      <td>5.868961e+07</td>\n",
              "      <td>2.167579e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>23117.0</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>184.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.221226e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.960400e+04</td>\n",
              "      <td>4.096000e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>3.000000</td>\n",
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              "      <td>65535.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>184.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.221226e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>8.222400e+04</td>\n",
              "      <td>1.310720e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>23117.0</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>184.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.221226e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.477080e+05</td>\n",
              "      <td>3.870720e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.184640e+05</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>59448.000000</td>\n",
              "      <td>63200.000000</td>\n",
              "      <td>64613.000000</td>\n",
              "      <td>43690.000000</td>\n",
              "      <td>43690.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>61436.000000</td>\n",
              "      <td>65464.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>4.294967e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>588.000000</td>\n",
              "      <td>4016.000000</td>\n",
              "      <td>7319.000000</td>\n",
              "      <td>2.147484e+09</td>\n",
              "      <td>5.368914e+08</td>\n",
              "      <td>8.304108e+08</td>\n",
              "      <td>2.906159e+09</td>\n",
              "      <td>2.415919e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11 rows × 79 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               Name  e_magic        e_cblp          e_cp        e_crlc  \\\n",
              "count         19611  19611.0  19611.000000  19611.000000  19611.000000   \n",
              "unique        19611      NaN           NaN           NaN           NaN   \n",
              "top     winhttp.dll      NaN           NaN           NaN           NaN   \n",
              "freq              1      NaN           NaN           NaN           NaN   \n",
              "mean            NaN  23117.0    178.615726     71.660752     49.146958   \n",
              "std             NaN      0.0    987.200729   1445.192977   1212.201919   \n",
              "min             NaN  23117.0      0.000000      0.000000      0.000000   \n",
              "25%             NaN  23117.0    144.000000      3.000000      0.000000   \n",
              "50%             NaN  23117.0    144.000000      3.000000      0.000000   \n",
              "75%             NaN  23117.0    144.000000      3.000000      0.000000   \n",
              "max             NaN  23117.0  59448.000000  63200.000000  64613.000000   \n",
              "\n",
              "           e_cparhdr    e_minalloc    e_maxalloc          e_ss         e_sp  \\\n",
              "count   19611.000000  19611.000000  19611.000000  19611.000000  19611.00000   \n",
              "unique           NaN           NaN           NaN           NaN          NaN   \n",
              "top              NaN           NaN           NaN           NaN          NaN   \n",
              "freq             NaN           NaN           NaN           NaN          NaN   \n",
              "mean       37.370710     37.032635  64178.739687     10.418490    226.46530   \n",
              "std       864.515405    915.833139   9110.755873    637.116265   1249.68033   \n",
              "min         0.000000      0.000000      0.000000      0.000000      0.00000   \n",
              "25%         4.000000      0.000000  65535.000000      0.000000    184.00000   \n",
              "50%         4.000000      0.000000  65535.000000      0.000000    184.00000   \n",
              "75%         4.000000      0.000000  65535.000000      0.000000    184.00000   \n",
              "max     43690.000000  43690.000000  65535.000000  61436.000000  65464.00000   \n",
              "\n",
              "        ...  SectionMaxChar  SectionMainChar  DirectoryEntryImport  \\\n",
              "count   ...    1.961100e+04          19611.0          19611.000000   \n",
              "unique  ...             NaN              NaN                   NaN   \n",
              "top     ...             NaN              NaN                   NaN   \n",
              "freq    ...             NaN              NaN                   NaN   \n",
              "mean    ...    3.163632e+09              0.0              6.112437   \n",
              "std     ...    5.860332e+08              0.0              7.525158   \n",
              "min     ...    1.073742e+09              0.0              0.000000   \n",
              "25%     ...    3.221226e+09              0.0              2.000000   \n",
              "50%     ...    3.221226e+09              0.0              4.000000   \n",
              "75%     ...    3.221226e+09              0.0              8.000000   \n",
              "max     ...    4.294967e+09              0.0            588.000000   \n",
              "\n",
              "        DirectoryEntryImportSize  DirectoryEntryExport  \\\n",
              "count               19611.000000          19611.000000   \n",
              "unique                       NaN                   NaN   \n",
              "top                          NaN                   NaN   \n",
              "freq                         NaN                   NaN   \n",
              "mean                  101.912804             14.131865   \n",
              "std                   127.076767            154.958102   \n",
              "min                     0.000000              0.000000   \n",
              "25%                    17.000000              0.000000   \n",
              "50%                    80.000000              0.000000   \n",
              "75%                   135.000000              0.000000   \n",
              "max                  4016.000000           7319.000000   \n",
              "\n",
              "        ImageDirectoryEntryExport  ImageDirectoryEntryImport  \\\n",
              "count                1.961100e+04               1.961100e+04   \n",
              "unique                        NaN                        NaN   \n",
              "top                           NaN                        NaN   \n",
              "freq                          NaN                        NaN   \n",
              "mean                 3.368566e+05               4.047213e+05   \n",
              "std                  2.181191e+07               4.704601e+06   \n",
              "min                  0.000000e+00               0.000000e+00   \n",
              "25%                  0.000000e+00               2.960400e+04   \n",
              "50%                  0.000000e+00               8.222400e+04   \n",
              "75%                  0.000000e+00               2.477080e+05   \n",
              "max                  2.147484e+09               5.368914e+08   \n",
              "\n",
              "        ImageDirectoryEntryResource  ImageDirectoryEntryException  \\\n",
              "count                  1.961100e+04                  1.961100e+04   \n",
              "unique                          NaN                           NaN   \n",
              "top                             NaN                           NaN   \n",
              "freq                            NaN                           NaN   \n",
              "mean                   5.555810e+05                  1.238834e+06   \n",
              "std                    6.772167e+06                  5.868961e+07   \n",
              "min                    0.000000e+00                  0.000000e+00   \n",
              "25%                    4.096000e+04                  0.000000e+00   \n",
              "50%                    1.310720e+05                  0.000000e+00   \n",
              "75%                    3.870720e+05                  0.000000e+00   \n",
              "max                    8.304108e+08                  2.906159e+09   \n",
              "\n",
              "        ImageDirectoryEntrySecurity  \n",
              "count                  1.961100e+04  \n",
              "unique                          NaN  \n",
              "top                             NaN  \n",
              "freq                            NaN  \n",
              "mean                   8.814868e+05  \n",
              "std                    2.167579e+07  \n",
              "min                    0.000000e+00  \n",
              "25%                    0.000000e+00  \n",
              "50%                    0.000000e+00  \n",
              "75%                    3.184640e+05  \n",
              "max                    2.415919e+09  \n",
              "\n",
              "[11 rows x 79 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# summary of numeric attributes\n",
        "dataset.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "RLU6lTstPqha",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "RLU6lTstPqha",
        "outputId": "f1777d86-572b-4b19-9f03-d50926dd08d5"
      },
      "outputs": [
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>e_magic</th>\n",
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              "      <th>DirectoryEntryImportSize</th>\n",
              "      <th>DirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryImport</th>\n",
              "      <th>ImageDirectoryEntryResource</th>\n",
              "      <th>ImageDirectoryEntryException</th>\n",
              "      <th>ImageDirectoryEntrySecurity</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3758096608</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>152</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>54440</td>\n",
              "      <td>77824</td>\n",
              "      <td>73728</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>VirusShare_ef9130570fddc174b312b2047f5f4cf0</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3791650880</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>311</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>262276</td>\n",
              "      <td>294912</td>\n",
              "      <td>0</td>\n",
              "      <td>346112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>VirusShare_ef84cdeba22be72a69b198213dada81a</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3221225536</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>176</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36864</td>\n",
              "      <td>40960</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VirusShare_6bf3608e60ebc16cbcff6ed5467d469e</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3224371328</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>155</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>356352</td>\n",
              "      <td>1003520</td>\n",
              "      <td>0</td>\n",
              "      <td>14109472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>VirusShare_2cc94d952b2efb13c7d6bbe0dd59d3fb</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3227516992</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>61440</td>\n",
              "      <td>73728</td>\n",
              "      <td>0</td>\n",
              "      <td>90624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19606</th>\n",
              "      <td>clip.exe</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3221225536</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>85</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24948</td>\n",
              "      <td>28672</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19607</th>\n",
              "      <td>VNC-Server-6.2.0-Windows.exe</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3221225536</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>391</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1413420</td>\n",
              "      <td>1462272</td>\n",
              "      <td>0</td>\n",
              "      <td>18855424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19608</th>\n",
              "      <td>Microsoft.GroupPolicy.Management.ni.dll</td>\n",
              "      <td>23117</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3758096448</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>61440</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19609</th>\n",
              "      <td>cryptuiwizard.dll</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3221225536</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>162</td>\n",
              "      <td>8</td>\n",
              "      <td>89008</td>\n",
              "      <td>94904</td>\n",
              "      <td>102400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19610</th>\n",
              "      <td>winhttp.dll</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3221225536</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>226</td>\n",
              "      <td>58</td>\n",
              "      <td>8348</td>\n",
              "      <td>545812</td>\n",
              "      <td>557056</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19611 rows × 79 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              Name  e_magic  e_cblp  e_cp  \\\n",
              "0      VirusShare_a878ba26000edaac5c98eff4432723b3    23117     144     3   \n",
              "1      VirusShare_ef9130570fddc174b312b2047f5f4cf0    23117     144     3   \n",
              "2      VirusShare_ef84cdeba22be72a69b198213dada81a    23117     144     3   \n",
              "3      VirusShare_6bf3608e60ebc16cbcff6ed5467d469e    23117     144     3   \n",
              "4      VirusShare_2cc94d952b2efb13c7d6bbe0dd59d3fb    23117     144     3   \n",
              "...                                            ...      ...     ...   ...   \n",
              "19606                                     clip.exe    23117     144     3   \n",
              "19607                 VNC-Server-6.2.0-Windows.exe    23117     144     3   \n",
              "19608      Microsoft.GroupPolicy.Management.ni.dll    23117       0     0   \n",
              "19609                            cryptuiwizard.dll    23117     144     3   \n",
              "19610                                  winhttp.dll    23117     144     3   \n",
              "\n",
              "       e_crlc  e_cparhdr  e_minalloc  e_maxalloc  e_ss  e_sp  ...  \\\n",
              "0           0          4           0       65535     0   184  ...   \n",
              "1           0          4           0       65535     0   184  ...   \n",
              "2           0          4           0       65535     0   184  ...   \n",
              "3           0          4           0       65535     0   184  ...   \n",
              "4           0          4           0       65535     0   184  ...   \n",
              "...       ...        ...         ...         ...   ...   ...  ...   \n",
              "19606       0          4           0       65535     0   184  ...   \n",
              "19607       0          4           0       65535     0   184  ...   \n",
              "19608       0          0           0           0     0     0  ...   \n",
              "19609       0          4           0       65535     0   184  ...   \n",
              "19610       0          4           0       65535     0   184  ...   \n",
              "\n",
              "       SectionMaxChar  SectionMainChar  DirectoryEntryImport  \\\n",
              "0          3758096608                0                     7   \n",
              "1          3791650880                0                    16   \n",
              "2          3221225536                0                     6   \n",
              "3          3224371328                0                     8   \n",
              "4          3227516992                0                     2   \n",
              "...               ...              ...                   ...   \n",
              "19606      3221225536                0                     8   \n",
              "19607      3221225536                0                    10   \n",
              "19608      3758096448                0                     0   \n",
              "19609      3221225536                0                    12   \n",
              "19610      3221225536                0                    35   \n",
              "\n",
              "       DirectoryEntryImportSize  DirectoryEntryExport  \\\n",
              "0                           152                     0   \n",
              "1                           311                     0   \n",
              "2                           176                     0   \n",
              "3                           155                     0   \n",
              "4                            43                     0   \n",
              "...                         ...                   ...   \n",
              "19606                        85                     0   \n",
              "19607                       391                     0   \n",
              "19608                         0                     0   \n",
              "19609                       162                     8   \n",
              "19610                       226                    58   \n",
              "\n",
              "       ImageDirectoryEntryExport  ImageDirectoryEntryImport  \\\n",
              "0                              0                      54440   \n",
              "1                              0                     262276   \n",
              "2                              0                      36864   \n",
              "3                              0                     356352   \n",
              "4                              0                      61440   \n",
              "...                          ...                        ...   \n",
              "19606                          0                      24948   \n",
              "19607                          0                    1413420   \n",
              "19608                          0                          0   \n",
              "19609                      89008                      94904   \n",
              "19610                       8348                     545812   \n",
              "\n",
              "       ImageDirectoryEntryResource  ImageDirectoryEntryException  \\\n",
              "0                            77824                         73728   \n",
              "1                           294912                             0   \n",
              "2                            40960                             0   \n",
              "3                          1003520                             0   \n",
              "4                            73728                             0   \n",
              "...                            ...                           ...   \n",
              "19606                        28672                             0   \n",
              "19607                      1462272                             0   \n",
              "19608                        61440                             0   \n",
              "19609                       102400                             0   \n",
              "19610                       557056                             0   \n",
              "\n",
              "       ImageDirectoryEntrySecurity  \n",
              "0                                0  \n",
              "1                           346112  \n",
              "2                                0  \n",
              "3                         14109472  \n",
              "4                            90624  \n",
              "...                            ...  \n",
              "19606                            0  \n",
              "19607                     18855424  \n",
              "19608                            0  \n",
              "19609                            0  \n",
              "19610                            0  \n",
              "\n",
              "[19611 rows x 79 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Display the DataFrame in a spreadsheet-like format\n",
        "display(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "hwl4j5NxwVf_",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "hwl4j5NxwVf_",
        "outputId": "bdd37ae9-076b-4383-f6a8-e526b8d81819"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['Name', 'e_magic', 'e_cblp', 'e_cp', 'e_crlc', 'e_cparhdr',\n",
            "       'e_minalloc', 'e_maxalloc', 'e_ss', 'e_sp', 'e_csum', 'e_ip', 'e_cs',\n",
            "       'e_lfarlc', 'e_ovno', 'e_oemid', 'e_oeminfo', 'e_lfanew', 'Machine',\n",
            "       'NumberOfSections', 'TimeDateStamp', 'PointerToSymbolTable',\n",
            "       'NumberOfSymbols', 'SizeOfOptionalHeader', 'Characteristics', 'Magic',\n",
            "       'MajorLinkerVersion', 'MinorLinkerVersion', 'SizeOfCode',\n",
            "       'SizeOfInitializedData', 'SizeOfUninitializedData',\n",
            "       'AddressOfEntryPoint', 'BaseOfCode', 'ImageBase', 'SectionAlignment',\n",
            "       'FileAlignment', 'MajorOperatingSystemVersion',\n",
            "       'MinorOperatingSystemVersion', 'MajorImageVersion', 'MinorImageVersion',\n",
            "       'MajorSubsystemVersion', 'MinorSubsystemVersion', 'SizeOfHeaders',\n",
            "       'CheckSum', 'SizeOfImage', 'Subsystem', 'DllCharacteristics',\n",
            "       'SizeOfStackReserve', 'SizeOfStackCommit', 'SizeOfHeapReserve',\n",
            "       'SizeOfHeapCommit', 'LoaderFlags', 'NumberOfRvaAndSizes',\n",
            "       'SuspiciousImportFunctions', 'SuspiciousNameSection', 'SectionsLength',\n",
            "       'SectionMinEntropy', 'SectionMaxEntropy', 'SectionMinRawsize',\n",
            "       'SectionMaxRawsize', 'SectionMinVirtualsize', 'SectionMaxVirtualsize',\n",
            "       'SectionMaxPhysical', 'SectionMinPhysical', 'SectionMaxVirtual',\n",
            "       'SectionMinVirtual', 'SectionMaxPointerData', 'SectionMinPointerData',\n",
            "       'SectionMaxChar', 'SectionMainChar', 'DirectoryEntryImport',\n",
            "       'DirectoryEntryImportSize', 'DirectoryEntryExport',\n",
            "       'ImageDirectoryEntryExport', 'ImageDirectoryEntryImport',\n",
            "       'ImageDirectoryEntryResource', 'ImageDirectoryEntryException',\n",
            "       'ImageDirectoryEntrySecurity'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "X = dataset.drop(columns=['Malware'])\n",
        "feature_names = X.columns  # Save the feature names\n",
        "print(feature_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "q2pPfa1s5y0P",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2pPfa1s5y0P",
        "outputId": "bb825426-d6e7-4516-9151-452cc0d7fec6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19611 entries, 0 to 19610\n",
            "Data columns (total 79 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   Name                          19611 non-null  object \n",
            " 1   e_magic                       19611 non-null  int64  \n",
            " 2   e_cblp                        19611 non-null  int64  \n",
            " 3   e_cp                          19611 non-null  int64  \n",
            " 4   e_crlc                        19611 non-null  int64  \n",
            " 5   e_cparhdr                     19611 non-null  int64  \n",
            " 6   e_minalloc                    19611 non-null  int64  \n",
            " 7   e_maxalloc                    19611 non-null  int64  \n",
            " 8   e_ss                          19611 non-null  int64  \n",
            " 9   e_sp                          19611 non-null  int64  \n",
            " 10  e_csum                        19611 non-null  int64  \n",
            " 11  e_ip                          19611 non-null  int64  \n",
            " 12  e_cs                          19611 non-null  int64  \n",
            " 13  e_lfarlc                      19611 non-null  int64  \n",
            " 14  e_ovno                        19611 non-null  int64  \n",
            " 15  e_oemid                       19611 non-null  int64  \n",
            " 16  e_oeminfo                     19611 non-null  int64  \n",
            " 17  e_lfanew                      19611 non-null  int64  \n",
            " 18  Machine                       19611 non-null  int64  \n",
            " 19  NumberOfSections              19611 non-null  int64  \n",
            " 20  TimeDateStamp                 19611 non-null  int64  \n",
            " 21  PointerToSymbolTable          19611 non-null  int64  \n",
            " 22  NumberOfSymbols               19611 non-null  int64  \n",
            " 23  SizeOfOptionalHeader          19611 non-null  int64  \n",
            " 24  Characteristics               19611 non-null  int64  \n",
            " 25  Magic                         19611 non-null  int64  \n",
            " 26  MajorLinkerVersion            19611 non-null  int64  \n",
            " 27  MinorLinkerVersion            19611 non-null  int64  \n",
            " 28  SizeOfCode                    19611 non-null  int64  \n",
            " 29  SizeOfInitializedData         19611 non-null  int64  \n",
            " 30  SizeOfUninitializedData       19611 non-null  int64  \n",
            " 31  AddressOfEntryPoint           19611 non-null  int64  \n",
            " 32  BaseOfCode                    19611 non-null  int64  \n",
            " 33  ImageBase                     19611 non-null  int64  \n",
            " 34  SectionAlignment              19611 non-null  int64  \n",
            " 35  FileAlignment                 19611 non-null  int64  \n",
            " 36  MajorOperatingSystemVersion   19611 non-null  int64  \n",
            " 37  MinorOperatingSystemVersion   19611 non-null  int64  \n",
            " 38  MajorImageVersion             19611 non-null  int64  \n",
            " 39  MinorImageVersion             19611 non-null  int64  \n",
            " 40  MajorSubsystemVersion         19611 non-null  int64  \n",
            " 41  MinorSubsystemVersion         19611 non-null  int64  \n",
            " 42  SizeOfHeaders                 19611 non-null  int64  \n",
            " 43  CheckSum                      19611 non-null  int64  \n",
            " 44  SizeOfImage                   19611 non-null  int64  \n",
            " 45  Subsystem                     19611 non-null  int64  \n",
            " 46  DllCharacteristics            19611 non-null  int64  \n",
            " 47  SizeOfStackReserve            19611 non-null  int64  \n",
            " 48  SizeOfStackCommit             19611 non-null  int64  \n",
            " 49  SizeOfHeapReserve             19611 non-null  int64  \n",
            " 50  SizeOfHeapCommit              19611 non-null  int64  \n",
            " 51  LoaderFlags                   19611 non-null  int64  \n",
            " 52  NumberOfRvaAndSizes           19611 non-null  int64  \n",
            " 53  Malware                       19611 non-null  int64  \n",
            " 54  SuspiciousImportFunctions     19611 non-null  int64  \n",
            " 55  SuspiciousNameSection         19611 non-null  int64  \n",
            " 56  SectionsLength                19611 non-null  int64  \n",
            " 57  SectionMinEntropy             19611 non-null  float64\n",
            " 58  SectionMaxEntropy             19611 non-null  int64  \n",
            " 59  SectionMinRawsize             19611 non-null  int64  \n",
            " 60  SectionMaxRawsize             19611 non-null  int64  \n",
            " 61  SectionMinVirtualsize         19611 non-null  int64  \n",
            " 62  SectionMaxVirtualsize         19611 non-null  int64  \n",
            " 63  SectionMaxPhysical            19611 non-null  int64  \n",
            " 64  SectionMinPhysical            19611 non-null  int64  \n",
            " 65  SectionMaxVirtual             19611 non-null  int64  \n",
            " 66  SectionMinVirtual             19611 non-null  int64  \n",
            " 67  SectionMaxPointerData         19611 non-null  int64  \n",
            " 68  SectionMinPointerData         19611 non-null  int64  \n",
            " 69  SectionMaxChar                19611 non-null  int64  \n",
            " 70  SectionMainChar               19611 non-null  int64  \n",
            " 71  DirectoryEntryImport          19611 non-null  int64  \n",
            " 72  DirectoryEntryImportSize      19611 non-null  int64  \n",
            " 73  DirectoryEntryExport          19611 non-null  int64  \n",
            " 74  ImageDirectoryEntryExport     19611 non-null  int64  \n",
            " 75  ImageDirectoryEntryImport     19611 non-null  int64  \n",
            " 76  ImageDirectoryEntryResource   19611 non-null  int64  \n",
            " 77  ImageDirectoryEntryException  19611 non-null  int64  \n",
            " 78  ImageDirectoryEntrySecurity   19611 non-null  int64  \n",
            "dtypes: float64(1), int64(77), object(1)\n",
            "memory usage: 11.8+ MB\n"
          ]
        }
      ],
      "source": [
        "dataset.info()    # info about the whole dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "F26YHxM36Rvz",
      "metadata": {
        "id": "F26YHxM36Rvz"
      },
      "source": [
        "#### Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "Y6RB84Bs6UR4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "Y6RB84Bs6UR4",
        "outputId": "7ebe491a-dd84-44f5-8da8-81829accc86b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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KMdc+2jIaOp3BZEbMlTOQ8pt3kTTtVdhS+qFy1bsIH3Y9XMVH0HhwE5LueR0hyb1RueJtWb43okAL1tEMRzId9NGWo6h1ekTHkJ2nugTO/CzE3fTHlm3O/Cx4Kotw7LUprR5buvh5hKT0ReIvXzjjeZqO/Qipvhon3rzn1EafhMrV76Jm25dImTHvjK9x5u+GuzwfMdc8jMrV82DvdhGMVhscvUej+KOl8n2TRAG0an8J9hfVoldiuOgoAcWS6YAmjxfvfp8rOoYi6vZ8B5MjEvbumS3bIi+5FWGDrmz1uMJ5DyFq/K9gz7j4rM8T2n8cbF0HtdpW8unTCO03HmEDJpzxeJ/HhYrv3kTs9b+DwWgCfBJ8P81ESl74fPqblqTg4PM1nyD06pTBoqMEFKfLOuCLHSdQUtskOobsfD4JdXtWILT/5c1v9CeZwqJgjeva6g8AmCPiYOmU2PK4E3N/g4YDG5u/xh5x5tcYzTCFRsESk3LGa1dt/Bj2bhfBmtAdABDSuS8aDmyEqyQXtTuWwta5j3LfOJHClmQV4Hhlg+gYAcWS8ZMk+fD2uiOiYyjCmbcL3ppShA28wq+v91Qch9TU/n9IrtI8NOSsR+To21u2OXqPgr17JooWzIKrNBdRE37tVyYiNfBIPryzXp+zH+di8Pl8XJDeD8t2F+LBj3aIjkFEGmO3mLDh9+MRHWoVHSUgOJLx09z1+hzFEJGyGt1efLBJn2ekng1Lxg85RTXYdaxKdAwi0qhPtx1DsEwisWT88OkP+ru6n4gC50RVIzYcCo4bG7Jk2sntlbB41wnRMYhI4z7bfkx0hIBgybTTyuxizS/nT0TiLd9bhOpGt+gYimPJtNOnOlwIk4gCr8kj4ausAtExFMeSaYeSGifWHigVHYOIdOKzbfqfMmPJtMPCHSfglYLjjBAiUt7u49XIKaoRHUNRLJl2CJYDdUQUOHo/W5Ul00bb8ipwpLRedAwi0pnFu07A7dXvwq8smTZauEPfextEJEZFvQurc0pEx1AMS6YNfD4fvtun318CIhLru33FoiMohiXTBruOVaGsTn9L+hOROqzeXwJJpycVsWTaYJWOh7JEJF5ZnQu7jleJjqEIlkwbrMhmyRCRslZm63PKjCVzASeqGpFdqO/z2IlIvJU63ZllyVzAKp3uXRCRuuQU1ery1swsmQvgVBkRBYoeRzMsmfNocHmw6Uhw3POBiMRbocOZE5bMeaw/WAaXR79X4hKRumw5UoG6Jo/oGLJiyZyHXs/2ICJ1cnklrNfZSu8smfMIltujEpF6bMmtEB1BViyZcyisbsSJqkbRMYgoyOw4Wik6gqxYMuewVWd7E0SkDdmFNXC6vaJjyIYlcw7b8vS1N0FE2uD2+rD7eLXoGLJhyZzDD3kcyRCRGHqaMmPJnEVdkwcHimtFxyCiILU9nyWja3uOV0Onq24TkQbs5EhG37J0uuQ2EWlDWZ0LR8v1sY4ZS+YsdrNkiEgwvRyXYcmcRdYx/ZzZQUTaxJLRqYp6Fy/CJCLhsnRyGjNL5jSHS+tERyAiQq5O3otYMqfJLasXHYGICDVOD8rrmkTH6DCWzGlYMkSkFnp4P2LJnCZPB3+pRKQPR3TwfsSSOY0e9hyISB/08H7EkvkZn8+HfJ1cAEVE2pdbypLRlaIaJxp1tMQ2EWlbXjlLRlf0MDQlIv3IK6+Hz6fthRRZMj/DkiEiNXG6JRRUO0XH6BC/Smb8+PGoqqo6Y3tNTQ3Gjx/f0UzC8MwyIlIbrR+X8atk1qxZA5fLdcZ2p9OJ9evXdziUKFxOhojUpqhG2yMZc3sevHv37pb/37dvH4qKilo+9nq9WL58OTp37ixfugCrqD+zOImIRKpq0Pb7UrtKZvDgwTAYDDAYDGedFrPb7Xj99ddlCxdoVQ1u0RGIiFqpDKaSyc3Nhc/nQ7du3bB161bExcW1fM5qtSI+Ph4mk0n2kIFS3ciSISJ1qdT4zm+7SqZLly4AAEmSFAkjmtb3GIhIf6qDqWR+7uDBg1i9ejVKSkrOKJ2nn366w8ECzen2wunWZ3kSkXZpfefXr5KZO3cuZsyYgdjYWCQmJsJgMLR8zmAwaLJkeDyGiNQoqKbLfvK3v/0Nzz33HGbNmiV3HmGqGrW9t0BE+qT1s8v8uk6msrISt956q9xZhOJIhojUSOvTZX6VzK233opvv/1W7ixCaX1vgYj0yemW4NTwwr1+TZdlZGTgqaeewubNmzFgwABYLJZWn3/kkUdkCRdIHMkQkVpVNbiRGKnNy0MMPj+W+ExPTz/3ExoMOHLkSIdCiTB/Qy7+vGSf6BhERGdY9/g4pMU4RMfwi18jmdzcXLlzEBHROXg1vNw/l/o/6eenYRMRqYlXwxfA+zWSmT59+nk/P2/ePL/CEBHRmbza7Rj/SqaysrLVx263G3v37kVVVZWm7ydDRKRGXkm702V+lcyiRYvO2CZJEmbMmIHu3bt3OJQInC0jOX3R81v0qd8mOgbphNE4D0CE6Bh+8XvtstMZjUbMnDkTY8eOxRNPPCHX0xJpUornGOzle0XHIL0waPc6GVkP/B8+fBgej0fOpyTSJIuk7bsZksqYLBd+jEr5NZKZOXNmq499Ph8KCwuxbNkyTJs2TZZggcbZMpKTWWoSHYH0xKjNCzEBP0tm586drT42Go2Ii4vDyy+/fMEzz4iCgYklQ3IyynZkI+D8Sr569Wq5cwjH62RITiYvS4ZkFGwl85PS0lLs378fANCrV69Wt2PWmrAQ7f4lkvoYWTIkJ6N2j8n4deC/vr4e06dPR1JSEsaMGYMxY8YgOTkZ9957LxoaGuTOGBCdHNr9SyT1MXp54J9kpOED/36VzMyZM7F27VosWbIEVVVVqKqqwpdffom1a9fisccekztjQEQ5rKIjkI4YPSwZkonJCtgiRafwm19zRAsXLsTnn3+OsWPHtmy79tprYbfbMXnyZLz55pty5QsYlgzJiiVDcglL0PTV4n6NZBoaGpCQkHDG9vj4eO1Ol4VqdzhK6mIySDDwmAzJJTxRdIIO8atkRowYgWeeeQZO56m9tcbGRjz77LMYMWKEbOECKcJmgdmo3b0FUo8Is4ZXMyT1CTtzh15L/Joue+2113D11VcjJSUFgwYNAgBkZWUhJCRE07dl7uSwoKyOt2Gmjok0ewDtrmdIahOeJDpBh/hVMgMGDMDBgwexYMEC5OTkAACmTp2K22+/HXa7XdaAgdTJYWXJUIdFWDwAf41ILhqfLvOrZJ5//nkkJCTgvvvua7V93rx5KC0txaxZs2QJF2hRPI2ZZBBh9rJkSD4aH8n4dUzmrbfeQu/evc/Y3q9fP8yZM6fDoUTpxDPMSAbhJrfoCKQnGh/J+FUyRUVFSEo6s13j4uJQWFjY4VCixISyZKjjwkxciZxkFIwlk5qaig0bNpyxfcOGDUhOTu5wKFHSYhyiI5AOcCRDstL4dJlfx2Tuu+8+PProo3C73S23W165ciWeeOIJzV7xDwDdYkNFRyAdCOVIhuRiCgEc0aJTdIhfJfP444+jvLwcDzzwAFyu5iOcNpsNs2bNwh/+8AdZAwZSemyY6AikA6EGjmRIJuHavkYG8LNkDAYDXnzxRTz11FPIzs6G3W5Hjx49EBISIne+gOoS44DRAEi8xoE6INTIkiGZaHyqDOjgUv9hYWHIzMyUK4twNosJSZF2nKhqFB2FNMzBkiG5aPxqf8DPA/961i2Ox2WoY+ycLiO5dEoTnaDDWDKn6RrDkqGOsRl4JSbJJGmQ6AQdxpI5TTrPMKMOsoMjGZJJ8hDRCTqMJXOadE6XUQeFcE0ZkkNIBBCTITpFh7FkTsNrZaijbOC9ZEgGSYM0fbOyn7BkTpMa5UCo1SQ6BmmYlSMZkkPyYNEJZMGSOY3RaMCg1E6iY5CGWX0sGZJB8lDRCWTBkjmLoWlRoiOQhll9nC4jGejgoD/AkjmroV06iY5AGmaRWDLUQfYoIDpddApZsGTOYkhqlB6Ot5EgZpYMdVTSYNEJZMOSOYuoUCuvlyG/mSQek6EO0slUGcCSOScelyF/mTiSoY5iyegfS4b8ZfI6RUcgrWPJ6B8P/pO/jCwZ6ojQOKBTqugUsmHJnEPP+HCEh3ToTggUpIweTpdRB+jooD/Akjkno9GAwWmdRMcgDTJ4eT8i6oDu40QnkBVL5jwu6xknOgJpkZvTZdQBva4VnUBWLJnzmNBH+3elo8Cym7ww+LyiY5BWxffVzUWYP2HJnEfX2FB059L/1A6RZhYMdUDviaITyI4lcwEczVB7RLBkqCN0NlUGsGQu6HKWDLVDhNkjOgJpVURnoLM+Vl7+OZbMBQzrEoUoh0V0DNIIlgz5rdc1ohMogiVzASajAeN6xYuOQRoRbnaLjkBapcPjMQBLpk04ZUZtFWbkSIb8EBIJdL1UdApFsGTaYEzPWFhN/FHRhYWZWDLkhx4TAJM+p+X5ztkG4TYLhneLFh2DNCDMxOky8oNOp8oAlkybXTsgSXQE0gCHkSVD7WSyAhlXiE6hGJZMG00cmIQQM39cdH4OA0uG2qnrpYAtQnQKxfBds40ibBZc0ZcnAND5sWSo3Xrr7wLMn2PJtMMtQ1NERyCVsxt462VqB5MV6Huj6BSKYsm0w5iecYgLDxEdg1TMxpEMtUef64HQWNEpFMWSaQeT0YCbh3YWHYNUzAaOZKgdht0jOoHiWDLtdFtmGgwG0SlIrVgy1GaxPYF0fV6A+XMsmXZKjw3FJekxomOQSoWwZKitgmAUA7Bk/HLbxamiI5BKWX1NoiOQFphtwOCpolMEBEvGD1f3T0R0qFV0DFIhC3jgn9qg302APUp0ioBgyfghxGzClEyOZuhMFokjGWqD4feLThAwZtEBtGr6qHTM+z4XTR5JdBRSEYuGpsueX9+EL3LcyCmTYDcbMDLVhBcnhKBXrKnlMWPn12Ntfuu7fd4/zII519nP+9zZpV7MWtGEtfkeeCSgb5wRCyc7kBbZvF878xsn5u9yIdRqwAuX23D7wFOLQ372oxvv73ZjyVSHjN+tiqSNBJKHiE4RMCwZP8WFh+AXw1KwYMtR0VFIRcwaGsmszffgwUwrMpNN8EjAH1c14coPG7DvgTCEWk+dQnnfUAv+Mu7U9WEOy/lPrzxcIWH0fxpw7xALnh0biogQA34s9cJ28t1myX43Ptrjxrd3huJguYTpXzXiqgwTYh1GVDt9eHJVE1bcpdOCAYARD4pOEFAsmQ64f0x3fPzDMXgln+gopBJmr3ZKZvkdoa0+nj/Jhvh/1mF7oRdjupx6a3BYDEgMa/vM+pOrnLi2hxn/uMLWsq179Kmvzy6TMLarCRclN/959Bsncit9iHUAT3znxIyLLC0jHt2JSgd66XsZmdPp9G8yMNJiHJjI1ZnpZ0ySU3QEv1Wf7Mdoe+uRyoI9bsT+oxb9/68Of1jhRIP73DtVks+HZQc96BltxFUf1iP+pVoMf6cOi3NOnRAxKMGEbQVeVDb6sL3Ai0a3DxnRRnx/1IMdRV48MlzHJ9VcMgMwBtfbbnB9twqYMba76AikIkYNjWR+TvL58OhyJ0almtA//tQxmV8OsODDm+xYPc2BP4wOwQe73bjji8ZzPk9JvQ91LuCFDU24ursZ397pwE29Lbj5k0aszWu+odtVGWbcMdCCzLl1uPvLRrx3ox2hVmDGMifmTLTjzW1u9HqjDqPm1ePHEu85X0tzbJHAkDtEpwg4Tpd1UJ+kCIzrFYfV+0tFRyEVMHq0OZJ5cJkTe0u8+H566ym0Xw87NaoYkGBCUrgBl7/fgMMVUqspsJ/8NHM8qZcZ/29E83GcwYkmbDzmxZztLlzWtfkt589jbfjz2FPTac+uacKEdDMsJuBv65qwZ0Yolh7w4K7Fjdj+6zC5v10xht0NWEMv+DC94UhGBg+MyxAdgVTC4NVeyTz0dSOWHvRg9bRQpESc/y1heOfmUc6hirOfVRnrMMBsBPrGmVpt7xNrxNHqs0+z5ZR58eEeN/46PgRr8jwY08WEuFAjJvezYEehhNomHRzzDIkERj0qOoUQLBkZZHaNRmbX4Liwis7PoKGRjM/nw0NfN2JRjger7nIgPerCbwe7ipqnr5LCz36GmdVkQGayCfvLW5fQgQoJXSLP/Bqfz4f7lzrxypUhCLMa4JUA98kv/em/Xh10DEY/CjiC8xbuLBmZ8NgMGQw+TZXMg1878eFuNz662Y7wEAOK6iQU1UloPHlg/3CFhL+ubcL2Ai/yqiR8td+NuxY3YkwXEwYmnBqp9H6jDouyTx3Yf3ykFZ/sdWPudhcOVUh4Y6sLS/Z78EDmmQf039nhRpzDgOt7NV8nMyrNjFW5Hmw+7sGrm5rQN86ITjaNr0gbkQJc8oDoFMLwmIxMxvdOwNC0TthxtEp0FBIkwqytg9RvbmsuhrHvNbTa/p9JNtw92AqrCViR68FrW1yod/mQGmnELX0s+NOY1vdU2l8uofpnU1o39bFgznU+PP+9C48sd6JXjBELJ9sxOq31201xnYTn1jdh472njlNc3NmEx0aEYOJHjYgPNeC9G89/0acmjH8SsNgu/DidMvh8Pj0MRlVh59FK3PzmRvAnGpzS7E6s800XHYPUJGEAcP+6oDtt+eeC9ztXwJC0KFw3MFl0DBIkUmMjGQqAK54N6oIBWDKym3V1L4SY+WMNRhFmj+gIpCbdxwMZl4tOIRzfDWWWEuXA9NHpomOQAOEsGfqJwQhc8RfRKVSBJaOAB8dlIDYs5MIPJF0JM7Fk6KSBtwGJA0SnUAWWjALCQsyYeUVP0TEowMKMvGEZofmul+P/JDqFarBkFDIlMxW9E8NFx6AA4kiGADQvghnZWXQK1WDJKMRkNODJiX1Ex6AACuVIhhwxwOiZolOoCktGQZf2iMOEPgmiY1CAsGQIl80CbBGiU6gKS0Zhz93UHxE2LqwQDBwGlkxQS70EyLxPdArVYckoLCHChqeu6ys6BgWA3egSHYFEsYQCN70Z9Bdeng1/IgFw60WpGNcrTnQMUpidI5ngdeVfgehuolOoEksmQJ6/eSDCOW2mazZwJBOUMiYAmfeKTqFaLJkASYy04amJnDbTsxCWTPCxRwE3vCE6haqxZAJocmYqLuvJaTO9CgGny4LOtf8EIpJEp1A1lkyAvXDLAISHcNpMjziSCTL9bgYG/EJ0CtVjyQRYUqSdF2nqlNXXJDoCBUp4EjDxZdEpNIElI8BtF6fh8t7xomOQzCw+jmSCxg1vAI5o0Sk0gSUjyCuTByMlSge3lqUWFo5kgsOwe4AeE0Sn0AyWjCCRDgvm3DEMVt7gTDcsEktG96LSgaueE51CU/gOJ1D/zpF49oZ+omOQTMwsGX0zGIGb5gDWUNFJNIUlI9jUi9Nw67AU0TFIBiYvS0bXrvgLkHaJ6BSaw5JRgb/e2B99k7hyq9aZvE7REUgpQ6cBIx8WnUKTWDIqYLOYMOeOYVytWeOMLBl9Sr8MmPiK6BSaxZJRibQYB16ZPBgGg+gk5C+WjA7F9AAmvw+YuAPoL5aMikzom4AHxnYXHYP8ZPCwZHTFHg3c/ilg7yQ6iaaxZFTmsSt64doBiaJjkD9YMvphsgK3LeDy/TJgyaiM0WjAq1MGY3g6rybWkhCjBIPkER2D5HL9v4AuI0Wn0AWWjAqFmE2YO+0i9E4MFx2F2ijS4hUdgeQyeiYw+JeiU+gGS0alImwWvDf9YnTuxKVntCDSzFGMLvSdBFz+tOgUusKSUbGECBvem34xOjksoqPQBYSzZLQveQhw01vgKZ7yYsmoXEZ8GN6dlgmbhX9VahZhYsloWkQKMPVjwMKZA7nxnUsDhnWJwhtTh8Jk5B6WWnEko2E/naoczrM6lcCS0YgJfRPw3I39RcegcwjnSEab7NHAtCVAAheqVQpLRkNuuzgNf5nUj1PGKhRmcouOQO3liGkumETuvCmJJaMxd43oihdvHgjOnKlLqJEjGU1xxAB3fcWCCQCWjAZNzkzFq1MGw8ymUY1QI0cymsERTECxZDRq0uDOeOOXQ2A18a9QDRxGl+gI1BahcTwGE2B8h9Kwq/sn4a07hyGEt3AWzsGRjPpFpgLTv2HBBBjfnTRuXO94zLs7E3aLSXSUoGYDS0bVYnoA05cDMVzlPNBYMjowKiMW7997McJCeM8LUWwGloxqJQ5sLphI3uZcBJaMTmR2jcaCXw1HbFiI6ChByc6RjDqljQTuXgqExopOErRYMjoyKLUTvnpoFPomRYiOEnRC0CQ6Ap2u5zXAnV8AtkjRSYIaS0ZnkjvZ8fmMEbi6H5fICKQQ8Owy9TAAl80Cpv5XtrXIZs+eja5du8Jms2H48OHYunWrLM8bDFgyOuSwmvHmHUPx8PgM0VGCBktGJWyRzQtdjvujbKspf/LJJ5g5cyaeeeYZ7NixA4MGDcJVV12FkpISWZ5f7ww+n88nOgQp56usAjz+WRaaPJLoKLq2PmMBUo8vEx0juMX3A6Z8IPsZZMOHD0dmZibeeOMNAIAkSUhNTcXDDz+M3//+97K+lh5xJKNzNwxKxqf3j0B8OE8IUJJF4khGqP6/AH61QvaCcblc2L59OyZMmNCyzWg0YsKECdi0aZOsr6VXLJkg0HxCwGgMTOEBUKVYfDzwL4TRDFz1d+AX7wJWh+xPX1ZWBq/Xi4SEhFbbExISUFRUJPvr6RFLJkgkRtrw6f0jcMtQXiugBIvEkgm40Hjgri+BEQ+KTkLnwZIJIjaLCS9PHoTXpgzmhZsyM7FkAislE7h/LdB1tKIvExsbC5PJhOLi4lbbi4uLkZjIMzjbgiUThG4c0hlLHx6NAZ05fSYXk9cpOkLwuGg6cPfXQESy4i9ltVoxbNgwrFy5smWbJElYuXIlRowYofjr6wFLJkh1jQ3Fwhkjcd+l6bwJmgxMXo5kFGe2A5NmA9e9CpitAXvZmTNnYu7cuXjvvfeQnZ2NGTNmoL6+Hvfcc0/AMmgZ50yCmNVsxJMT+2Jc73j87tMsFFRzb9xfRo5klJV+GXD9a0B0t4C/9JQpU1BaWoqnn34aRUVFGDx4MJYvX37GyQB0drxOhgAANU43nl68F4t3FYiOoklHOj0Io7NSdAz9sUc1nz02+Jeik5CfWDLUyrLdhXj6y70or+d1H+2RG3YvDJ5G0TH0ZcCtwNUvcHFLjWPJ0BmqG9z4xzc5+O/Wo5D429EmubbbYQB/WLKITAOuewXocYXoJCQDlgydU9axKjz15V7sPl4tOoqqhZs92GO+S3QM7TOYgOG/AcY/CVhDRachmbBk6LwkyYcFW/Lx0jf7UeP0iI6jSim2JnwPnmnUIQkDgBv+DXQeKjoJyYwlQ21SVteEvy/Lxhc7T4iOojr9wuuxzH2f6BjaZLYDY2cBIx4GTDzZVY9YMtQuW46U46kv9+JAcZ3oKKoxIqoa/22cITqG9nQbB0x8WfZFLUldWDLUbh6vhA8352P2msMoreVFiFfGVuDtuodEx9COLqOAsX8A0i8VnYQCgONTajezyYi7R6XjtovT8MGmfMxZezioT3kON7lFR9CGtBHN5dLtMtFJKIBYMuQ3m8WE+8Z0wy+Hp2H+xjy8ve4IqhuD7w03zMQTIs4rdXhzuXQfJzoJCcCSoQ4LDTHjwXEZuGtEF7z7fS7e/T4XtUF0JloYRzJnl5LZXC4Zl4tOQgKxZEg24TYLHp3QE/eMTMfb6w9j/oY81Lu8omMpzmFkybTSeRgw9o9AjwkXfizpHkuGZBfpsODxq3rj3tHdsGBzPhZsOYqiGv0uIOkwBs+o7byShzSXS88rRSchFeHZZaQ4j1fCt/uKMX9jHrbmVoiOI7sXu+3GlIIXRMcQxNB8IH/4DKDX1aLDkApxJEOKM5uMuHZAEq4dkIScohq8tzEfi3eeQKNbH1NpQTld5ogFhtwODJ3G61zovDiSISGqG934bNsxfLA5H/nlDaLjdMg7PTZhwrHXRccIjK6XAsPuBvrcENAbh5F2sWRIKJ/PhzUHSrFoxwmszC7W5IkCH/ZYi9HH3hIdQzmRqcDAycCgqUBsD9FpSGM4XUZCGQwGjOsVj3G94uF0e7EqpwRLdxdgVU4JnG5JdLw2sRl0OF1mDQf6TgIG3QZ0HQ3eo5v8xZIh1bBZTC3HbuqbPFiRXYyluwux9kApXB71Fk6ITydL65htQPoYYOAUoPdEwGIXnYh0gCVDqhQaYsakwZ0xaXBn1Djd+O7HYizdXYANh8tVVzgh0PCSOgn9m6/E7zaueU0xi010ItIZHpMhTXG6vdieX4kNh8qw8XA59pyohlfw7TvXZHyCrse/FJqhzcISmgvlp2IJTxCdiHSOIxnSFJvFhFEZsRiV0Xzf91qnG1uOVGDD4TJsPFSOAyW1CPRuk1XN02VmO9BlBNB9fHOpJPYXnYiCDEcypCtldU3YeLgcW3PLsa+gBvuLahU/Y+2Hbu8grmCVoq/RZvZoIK43kHJR82glbSSnwEgolgzpms/nQ355A7ILa5BdWIN9hbXILqzBiapG2V5jZ9fZiCraINvztYk9CojrA8T1AuL7NBdLfB8gLD6wOYgugCVDQam60Y2ck8WTW1aPwmonimqcKKp2oqyuCe05zLMn7RWEl2xTJqitU+sSievVXC48lkIawWMyFJQi7RYM7xaD4d1izvicxyuhuLYJRdWNzeVT7WwpoZpGN+qbPGhweVHv8qChyQuj0QCYQgDJDfjOceabxQHYIptLw96p9X9tkWffFhrLkQlpHkcyRHKSJEDyNBeO92TpWMO4BAsFLZYMEREpxig6ABER6RdLhoiIFMOSISIixbBkiIhIMSwZIiJSDEuGiIgUw5IhIiLFsGSIiEgxLBkiIlIMS4aIiBTDkiEiIsWwZIiISDEsGSIiUgxLhoiIFMOSISIixbBkiIhIMSwZIiJSDEuGiIgUw5IhIiLFsGSIiEgxLBkiIlIMS4aIiBTDkiEiIsWwZIiISDEsGSIiUgxLhoiIFMOSISIixbBkiIhIMSwZIiJSDEuGiIgUw5IhIiLFsGSIiEgxLBkiIlIMS4aIiBTDkiEiIsWwZIiISDEsGSIiUgxLhoiIFMOSISIixbBkiIhIMSwZIiJSDEuGiIgUw5IhIiLFsGSIiEgxLBkiIlLM/wfrbGGHcmO+oAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dataset[\"Malware\"].value_counts().plot(kind=\"pie\",autopct=\"%1.1f%%\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "788ba617",
      "metadata": {},
      "source": [
        "#### Selecting Key Features Based on Importance Scores using randomforest "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "286c5737",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-10-23T20:35:43.879863Z",
          "iopub.status.busy": "2024-10-23T20:35:43.879467Z",
          "iopub.status.idle": "2024-10-23T20:35:49.023550Z",
          "shell.execute_reply": "2024-10-23T20:35:49.021646Z"
        },
        "id": "286c5737",
        "papermill": {
          "duration": 5.152872,
          "end_time": "2024-10-23T20:35:49.026813",
          "exception": false,
          "start_time": "2024-10-23T20:35:43.873941",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "X = dataset.drop(columns=['Name', 'Malware'])  #Droping this because classification model will not accept object type elements (float and int only)\n",
        "y = dataset['Malware']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9dba6d86",
      "metadata": {},
      "source": [
        "Isolates features by dropping the \"Malware\" target column, then stores feature names in feature_names for consistent reference in training and predictions.\n",
        "\n",
        "The \"Malware\" column is dropped because it’s the target label that we’re trying to predict, not a feature. When training a model, only the features (input data) should be used to help the model learn, while the target column is used separately to evaluate its predictions.\n",
        "\n",
        "In this case:\n",
        "\n",
        "    Features (input data): The characteristics and attributes of the software file.\n",
        "    Target (output label): The \"Malware\" column, which indicates whether the file is malware or benign.\n",
        "\n",
        "Dropping \"Malware\" ensures that only the relevant inputs are used for training without including the label that the model is meant to predict."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "f7a3bcea",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Spliting the data (80% train, 20% test)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "7c4df5c3",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                        Feature  Importance\n",
            "46           SizeOfStackReserve    0.084371\n",
            "25           MajorLinkerVersion    0.080592\n",
            "39        MajorSubsystemVersion    0.078010\n",
            "36  MinorOperatingSystemVersion    0.071939\n",
            "35  MajorOperatingSystemVersion    0.068704\n",
            "..                          ...         ...\n",
            "58            SectionMaxRawsize    0.000000\n",
            "68              SectionMainChar    0.000000\n",
            "66        SectionMinPointerData    0.000000\n",
            "62           SectionMinPhysical    0.000000\n",
            "64            SectionMinVirtual    0.000000\n",
            "\n",
            "[77 rows x 2 columns]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Initialize and train the Random Forest Classifier\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_model.fit(X_train, y_train)\n",
        "# Feature Importance\n",
        "importances = rf_model.feature_importances_\n",
        "feature_names = X.columns\n",
        "\n",
        "# Create a DataFrame for better visualization\n",
        "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})\n",
        "\n",
        "# Sort the DataFrame by importance\n",
        "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "# Print the feature importances\n",
        "print(importance_df)\n",
        "\n",
        "# Visualize Feature Importances\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.barh(importance_df['Feature'], importance_df['Importance'], color='skyblue')\n",
        "plt.xlabel('Importance')\n",
        "plt.title('Feature Importances from Random Forest Classifier')\n",
        "plt.gca().invert_yaxis()  # Invert y axis to show the most important features at the top\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "8a11716e",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Features selected (importance > 0.01):\n",
            "['SizeOfStackReserve' 'MajorLinkerVersion' 'MajorSubsystemVersion'\n",
            " 'MinorOperatingSystemVersion' 'MajorOperatingSystemVersion'\n",
            " 'TimeDateStamp' 'Characteristics' 'MinorImageVersion' 'ImageBase'\n",
            " 'Subsystem' 'MinorSubsystemVersion' 'DllCharacteristics'\n",
            " 'SizeOfInitializedData' 'DirectoryEntryImportSize' 'SectionMaxChar'\n",
            " 'MajorImageVersion' 'DirectoryEntryExport' 'ImageDirectoryEntryExport'\n",
            " 'AddressOfEntryPoint' 'SizeOfHeaders' 'CheckSum' 'e_lfanew'\n",
            " 'SectionMinEntropy']\n"
          ]
        },
        {
          "data": {
            "image/png": 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4eKMMsNjY2FgOHz6cbSx3KlGihKVTp06Zyu/3ty2rvzdZ9bGTk5OlV69emdoPDQ21FC1a1PL7779blXfp0sXi6upqXJuMfvPx8bG6Xunp6ZayZcta/P39rb4vr127ZilVqpSlVatWRllOP7MZ/ve//1kAy6RJk7LqMhF5zDS1TURE/jU++eQTYmJirB5w+3+qL1++TNeuXfn999+Nh62tLXXr1mXz5s1GG46OjsbzlJQUfv/9d5555hkA9u7d+1jifvnll61eR0dHk56eTlBQkFW8Hh4elC1b1ireB9W3b1/juZubG+XLl8fJyYmgoCCjvHz58ri5uXHy5MlM+/fv39/qf81feeUV8uTJw9dffw3Axo0bSUtLY8iQIVYjNfr164eLiwvr1q2zas/e3p7evXvnOH57e3uj3Vu3bnHx4kXMZjPly5fP8vr07t3baq2TjKlXGef2448/curUKYYMGZJplFfGCKtLly6xadMmgoKCjJElv//+OxcvXsTf359jx47x888/A7f79PDhwxw7dizH55QTd79HMvp76NChVuVvvPEGQKZ+rlixIvXq1TNeZ4xyad68OV5eXpnKs7r2WenTpw/u7u4UK1aMNm3akJSUxKJFizItLH3n5+qPP/4gKSmJRo0aZXnNWrZsaYyYAKhSpQouLi5GTLdu3WLDhg0EBgZaxe7r64u/v79VW09LP40aNSrTd5OHhwe3bt3i22+/JTAwEB8fH6N+0aJF6datG999950xfShDv379sLW1NV7n9PvN0dEROzs74uLispyO+jAyYnN2dn7oNu58b2SMvGnSpAknT54kKSkJ+L/1i9auXcuNGzeybMfNzY2rV6/ecxrpX7F8+XJ8fX2pUKGCVT83b94cINP3cpMmTahYsWKO2r548WKmUXx3yu5v219hsVhYuXIlAQEBWCwWq3Py9/cnKSkp0+ezV69eVtdr3759HDt2jG7dunHx4kVj/6tXr9KiRQu2bt1Kenr6A31mM2T0x90jsUTkydDUNhER+deoU6dOlguSZvyoz/gH/91cXFyM55cuXSIsLIyoqChjEdYMGT9qHrW7p48dO3YMi8VC2bJls6x/vzv0ZMfBwQF3d3erMldXV0qUKGEkTe4sz+rH5t0xmc1mihYtakxpOnPmDHA7GXUnOzs7fHx8jO0Zihcv/kCL2mbcXWnmzJmcOnWKW7duGdsypj3d6c4fLfB/P04yzi1jLZ973d3v+PHjWCwWRo4cyciRI7Osc+HCBYoXL857771Hhw4dKFeuHJUrV6ZNmzb06NEj2+kyOXX3e+TMmTPY2NhY3WUKwMPDAzc3t0z9fHc/ZNwZytPTM8vynCYaRo0aRaNGjUhOTmbVqlVERUVlOdVr7dq1jBs3jn379lmtJ3P3+y6rWOH2dcuI6bfffuP69etZfj7Kly9vJI/g6eknPz8/WrZsman8l19+4dq1a5k+L3D7R3Z6ejrnzp2jUqVKRnlW3xdw/+83e3t7Jk2axBtvvEGRIkV45plnaN++PT179nzoO+xltP3nn38+9N0r4+PjGT16NDt27ODatWtW25KSknB1daVJkyZ06tSJsLAwpk6dStOmTQkMDKRbt27Y29sDt6f8LVu2jLZt21K8eHFat25NUFAQbdq0eai47nbs2DESEhIyfYdmuPvvxd3X6X4s/3/Kalay+9v2V/z2229cvnyZTz/9lE8//TTLOvc7p4z3Xq9evbI9TlJSEqmpqTn+zGbI6I+sviNE5PFTIklERP710tPTgdtrRWT1gylPnv/7cxkUFMT27dt58803qVatGmazmfT0dNq0aWO0cy/Z/aP3zoTH3e78H96MeE0mE998843VyIMMd6+fk1NZtXWv8nv9sHlU7j73+xk/fjwjR46kT58+jB071lhnZsiQIVlen0dxbhntDhs2LNv/Pc9IVDRu3JgTJ07w5Zdf8u233zJv3jymTp3K7NmzrUaDPajs+imnP7Ie17W/M0ESGBjItWvX6NevHw0bNjSSL9u2beO5556jcePGzJw5k6JFi5I3b14iIiIyLer+KGLKSm7306OU1fcF5Oz7bciQIQQEBLB69Wo2bNjAyJEjmTBhAps2baJ69eoPHEuFChWA2+vfZIz2exAnTpygRYsWVKhQgQ8//BBPT0/s7Oz4+uuvmTp1qnFuJpOJFStW8P333/PVV1+xYcMG+vTpw5QpU/j+++8xm80ULlyYffv2sWHDBr755hu++eYbIiIi6NmzJwsWLHjg2O6Wnp6On58fH374YZbb7042Psh3W8GCBR/ZKLGcyujb7t27Z5sIujsBnt177/3336datWpZtmE2mzMtRp4TGf2R07WgROTRUiJJRET+9TKmyRQuXDjLUQEZ/vjjD2JjYwkLC2PUqFFGeVbTlLL7YZox4uXuO5TdPfLhfvFaLBZKlSpFuXLlcrzfk3Ds2DGaNWtmvE5OTub8+fM8++yzAMbdxxITE62m6qSlpXHq1Kl79v+dsuvfFStW0KxZMz777DOr8suXLz/UD46M98ahQ4eyjS3jPPLmzZuj+AsUKEDv3r3p3bs3ycnJNG7cmDFjxvylRNLdSpYsSXp6OseOHcPX19co//XXX7l8+bJxHZ60iRMnsmrVKsLDw5k9ezYAK1euxMHBgQ0bNhijR+D23boehru7O46Ojll+LhMTE61eP639lMHd3Z18+fJlihtu3yXQxsYmU4Libjn9fruz/htvvMEbb7zBsWPHqFatGlOmTOHzzz8HHmwESEBAABMmTODzzz9/qETSV199RWpqKmvWrLEaDZbd9N1nnnmGZ555hvDwcJYsWUJwcDBRUVHGZ8vOzo6AgAACAgJIT09nwIABzJkzh5EjR2YalfagSpcuzf79+2nRosUjHyVToUIFTp069UjbvFNW8bq7u+Ps7MytW7dy/L18t4z3nouLyz3beJDPbIaM/rjzcysiT47WSBIRkX89f39/XFxcGD9+fJbra2TcaS1j1MHdowymTZuWaR8nJycgc8LIxcWFQoUKsXXrVqvymTNn5jjejh07YmtrS1hYWKZYLBYLFy9ezHFbj9qnn35q1YezZs3i5s2btG3bFri9vo2dnR3Tp0+3iv2zzz4jKSkp0x26suPk5JSpb+H2Nbq7T5YvX26sUfSgatSoQalSpZg2bVqm42Ucp3DhwjRt2pQ5c+Zw/vz5TG3ceae+u6+N2WymTJkyD/U/8veSkbi7+72ZMVoip/38qJUuXZpOnToRGRnJL7/8Aty+ZiaTyWpU3unTpzPd5SqnbG1t8ff3Z/Xq1Zw9e9YoT0hIYMOGDVZ1n9Z+ymBra0vr1q358ssvjemhcDvRtWTJEho2bGg19TYrOf1+u3btGikpKVbbSpcujbOzs9X7M7vPXlbq1atHmzZtmDdvXpbXMy0tjWHDhmW7f1bfuUlJSZmSjH/88Uemz33GCJiM2O/+7NnY2Bgjah7F5y8oKIiff/6ZuXPnZtp2/fp14w56D6NevXocOnTokX9PZMjqmtra2tKpUydWrlzJoUOHMu2TkzuQ1qxZk9KlS/PBBx+QnJycbRsP8pnNsGfPHkwmk9WaZSLy5GhEkoiI/Ou5uLgwa9YsevToQY0aNejSpQvu7u6cPXuWdevW0aBBA2bMmIGLiwuNGzdm8uTJ3Lhxg+LFi/Ptt99m+T/FNWvWBOCdd96hS5cu5M2bl4CAAJycnOjbty8TJ06kb9++1KpVi61bt3L06NEcx1u6dGnGjRvHiBEjOH36NIGBgTg7O3Pq1ClWrVpF//797/nj7HFKS0ujRYsWxq3PZ86cScOGDXnuueeA2//zPGLECMLCwmjTpg3PPfecUa927dp07949R8epWbMms2bNYty4cZQpU4bChQvTvHlz2rdvz3vvvUfv3r2pX78+Bw8eZPHixVajnx6EjY0Ns2bNIiAggGrVqtG7d2+KFi3KTz/9xOHDh40fOZ988gkNGzbEz8+Pfv364ePjw6+//sqOHTv473//y/79+4HbizU3bdqUmjVrUqBAAXbv3s2KFSsYNGjQQ8WXnapVq9KrVy8+/fRTLl++TJMmTdi5cycLFiwgMDDQatTYk/bmm2+ybNkypk2bxsSJE2nXrh0ffvghbdq0oVu3bly4cIFPPvmEMmXKcODAgYc6RlhYGOvXr6dRo0YMGDCAmzdv8vHHH1OpUiWrNp/mfsowbtw4YmJiaNiwIQMGDCBPnjzMmTOH1NRUJk+efN/9c/r9dvToUeOzW7FiRfLkycOqVav49ddf6dKli9Fedp+97CxcuJDWrVvTsWNHAgICaNGiBU5OThw7doyoqCjOnz/PBx98kOW+rVu3NkYRvfTSSyQnJzN37lwKFy5slbRdsGABM2fO5Pnnn6d06dL8+eefzJ07FxcXFyNZ2LdvXy5dukTz5s0pUaIEZ86c4eOPP6ZatWqPZFRLjx49WLZsGS+//DKbN2+mQYMG3Lp1i59++olly5axYcOGh17HqEOHDowdO5YtW7bQunXrvxzr3WrWrMnGjRv58MMPKVasGKVKlaJu3bpMnDiRzZs3U7duXfr160fFihW5dOkSe/fuZePGjVy6dOme7drY2DBv3jzatm1LpUqV6N27N8WLF+fnn39m8+bNuLi48NVXXwE5/8xmiImJoUGDBlmufSciT8CTvUmciIjIk5fV7e6zsnnzZou/v7/F1dXV4uDgYCldurQlJCTEsnv3bqPOf//7X8vzzz9vcXNzs7i6ulo6d+5s3IZ49OjRVu2NHTvWUrx4cYuNjY3V7byvXbtmCQ0Ntbi6ulqcnZ0tQUFBlgsXLmRqI7vbTmdYuXKlpWHDhhYnJyeLk5OTpUKFCpaBAwdaEhMTH7g/evXqZXFycspUt0mTJpZKlSplKi9ZsqTVbewz2tyyZYulf//+lvz581vMZrMlODjYcvHixUz7z5gxw1KhQgVL3rx5LUWKFLG88sormW4Rnt2xLZbbty5v166dxdnZ2QIYt+FOSUmxvPHGG5aiRYtaHB0dLQ0aNLDs2LEj0626M25XvXz5cqt2s7pdtsVisXz33XeWVq1aWZydnS1OTk6WKlWqWN1u3mKxWE6cOGHp2bOnxcPDw5I3b15L8eLFLe3bt7esWLHCqDNu3DhLnTp1LG5ubhZHR0dLhQoVLOHh4Za0tLQsz/PuuO6+Nfu93iM3btywhIWFWUqVKmXJmzevxdPT0zJixAhLSkqKVb27r2UGwDJw4MAcxXG37Po3Q9OmTS0uLi6Wy5cvWywWi+Wzzz6zlC1b1mJvb2+pUKGCJSIiIsvblGcVU8Y53H378i1btlhq1qxpsbOzs/j4+Fhmz56dZZtPcz9l2Lt3r8Xf399iNpst+fLlszRr1syyfft2qzr3+5673/fb77//bhk4cKClQoUKFicnJ4urq6ulbt26lmXLllm1k91n716uXbtm+eCDDyy1a9e2mM1mi52dnaVs2bKWwYMHW44fP27Uy+r6rFmzxlKlShWLg4ODxdvb2zJp0iTL/Pnzrb5T9+7da+natavFy8vLYm9vbylcuLClffv2Vt/dK1assLRu3dpSuHBhi52dncXLy8vy0ksvWc6fP2/VR4Bl8+bNmWK6+zN293eKxWKxpKWlWSZNmmSpVKmSxd7e3pI/f35LzZo1LWFhYZakpCSjXnbv43upUqWKJTQ01Krsftc8q++zrPr4p59+sjRu3Nji6OhoAaw+S7/++qtl4MCBFk9PT0vevHktHh4elhYtWlg+/fRTo8793sc//vijpWPHjpaCBQta7O3tLSVLlrQEBQVZYmNjrerl9DN7+fJli52dnWXevHnZ9peIPF4miyUXVgEUERGRf5TIyEh69+7Nrl27Hvndg0RE/u0WLVrEwIEDOXv27EPfAe+fYtq0aUyePJkTJ0488A0ZROTR0BpJIiIiIiIiT7Hg4GC8vLz45JNPcjuUXHXjxg0+/PBD3n33XSWRRHKR1kgSERERERF5itnY2GS56PW/Td68ea0W5BaR3KERSSIiIiIiIiIikiNaI0lERERERERERHJEI5JERERERERERCRHlEgSEREREREREZEc0WLbIk+h9PR0/ve//+Hs7IzJZMrtcEREREREROQfzmKx8Oeff1KsWDFsbLIfd6REkshT6H//+x+enp65HYaIiIiIiIj8y5w7d44SJUpku12JJJGnkLOzM3D7A+zi4pLL0YiIiIiIiMg/3ZUrV/D09DR+j2ZHiSSRp1DGdDYXFxclkkREREREROSJud/yKlpsW0REREREREREckSJJBERERERERERyRElkkREREREREREJEeUSBIRERERERERkRxRIklERERERERERHJEiSQREREREREREckRJZJERERERERERCRHlEgSEREREREREZEcUSJJRERERERERERyRIkkERERERERERHJESWSREREREREREQkR5RIEhERERERERGRHFEiSUREREREREREckSJJBERERERERERyRElkkREREREREREJEeUSBIRERERERERkRxRIklERERERERERHJEiSQREREREREREckRJZJERERERERERCRHlEgSEREREREREZEcUSJJRERERERERERyRIkkERERERERERHJESWSREREREREREQkR5RIEhERERERERGRHFEiSUREREREREREciRPbgcgItn7cP9FHMxpuR2GiIiIiIiIPKC3qxfK7RAeC41IEhERERERERGRHFEiSUREREREREREckSJJBERERERERERyRElkkREREREREREJEeUSHrCTCYTq1evzpVjr169mjJlymBra8uQIUNyJYYM3t7eTJs2LVdjEBEREREREZEHo0TSI/bbb7/xyiuv4OXlhb29PR4eHvj7+xMfHw/A+fPnadu27SM95oIFC6hduzb58uXD2dmZJk2asHbt2kz1XnrpJV544QXOnTvH2LFjAZg7dy5Vq1bFbDbj5uZG9erVmTBhgrFPSEgIgYGBjzTenAgJCcFkMmEymcibNy+lSpXirbfeIiUl5YnHIiIiIiIiIiK35cntAP5pOnXqRFpaGgsWLMDHx4dff/2V2NhYLl68CICHh8cjPd6wYcOYMWMG48aNIzAwkBs3bvD555/ToUMHPvroIwYNGgRAcnIyFy5cwN/fn2LFigEwf/58hgwZwvTp02nSpAmpqakcOHCAQ4cOPdIYH1abNm2IiIjgxo0b7Nmzh169emEymZg0aVKuxJOWloadnV2uHFtERERERETkaaARSY/Q5cuX2bZtG5MmTaJZs2aULFmSOnXqMGLECJ577jnAemrbmDFjjFE3dz4iIyMBSE9PZ8KECZQqVQpHR0eqVq3KihUrjON9//33TJkyhffff59hw4ZRpkwZfH19CQ8PZ8iQIQwdOpRz584RFxeHs7MzAM2bN8dkMhEXF8eaNWsICgoiNDSUMmXKUKlSJbp27Up4eLgR34IFC/jyyy+N2OLi4gAYPnw45cqVI1++fPj4+DBy5Ehu3Lhh1R9fffUVtWvXxsHBgUKFCvH8889n23fz5s3Dzc2N2NhYoyxjRJenpyeBgYG0bNmSmJgYY/v9+uePP/4gODgYd3d3HB0dKVu2LBEREcb2c+fOERQUhJubGwUKFKBDhw6cPn3a2J4xGis8PJxixYpRvnx5/vOf/1C3bt1M8VetWpX33nvP6nx8fX1xcHCgQoUKzJw5M9tzFxEREREREfm7UCLpETKbzZjNZlavXk1qaup96w8bNozz588bjw8++IB8+fJRq1YtACZMmMDChQuZPXs2hw8f5vXXX6d79+5s2bIFgKVLl2I2m3nppZcytf3GG29w48YNVq5cSf369UlMTARg5cqVnD9/nvr16+Ph4cH333/PmTNnso0vKCiINm3aGDHWr18fAGdnZyIjIzly5AgfffQRc+fOZerUqca+69at4/nnn+fZZ5/lxx9/JDY2ljp16mR5nMmTJ/P222/z7bff0qJFiyzrHDp0iO3bt1uNCLpf/4wcOZIjR47wzTffkJCQwKxZsyhUqBAAN27cwN/fH2dnZ7Zt20Z8fDxms5k2bdqQlpZmHCM2NpbExERiYmJYu3YtwcHB7Ny5kxMnThh1Dh8+zIEDB+jWrRsAixcvZtSoUYSHh5OQkMD48eMZOXIkCxYsyPLcAFJTU7ly5YrVQ0RERERERORpo6ltj1CePHmIjIykX79+zJ49mxo1atCkSRO6dOlClSpVMtXPSDzB7dFF7777LgsWLKBy5cqkpqYyfvx4Nm7cSL169QDw8fHhu+++Y86cOTRp0oSjR49SunTpLKdbFStWDBcXF44ePYqdnR2FCxcGoECBAsb0utGjR9OxY0e8vb0pV64c9erV49lnn+WFF17AxsYGs9mMo6Mjqampmabkvfvuu8Zzb29vhg0bRlRUFG+99RYA4eHhdOnShbCwMKNe1apVM8U5fPhwFi1axJYtW6hUqZLVtrVr12I2m7l58yapqanY2NgwY8YMgBz1z9mzZ6levbqRmPP29jba/uKLL0hPT2fevHmYTCYAIiIicHNzIy4ujtatWwPg5OTEvHnzrPq4atWqLFmyhJEjRwK3E0d169alTJkyRr9OmTKFjh07AlCqVCmOHDnCnDlz6NWrV6Y+gNtJsTv7SkRERERERORppBFJj1inTp343//+x5o1a2jTpg1xcXHUqFHDmK6WlbNnzxIYGGiMAAI4fvw4165do1WrVkbCyWw2s3DhQqvRMBaL5aFjLVq0KDt27ODgwYO89tpr3Lx5k169etGmTRvS09Pvue8XX3xBgwYN8PDwwGw28+6773L27Flj+759+7IdXZRhypQpzJ07l++++y5TEgmgWbNm7Nu3jx9++IFevXrRu3dvOnXqBOSsf1555RWioqKoVq0ab731Ftu3bzfa3r9/P8ePH8fZ2dnYt0CBAqSkpFj1r5+fX6ZEXXBwMEuWLAFu9//SpUsJDg4G4OrVq5w4cYLQ0FCruMaNG2fV7t1GjBhBUlKS8Th37tw9+05EREREREQkN2hE0mPg4OBAq1ataNWqFSNHjqRv376MHj2akJCQTHWvXr3Kc889R7169azW2ElOTgZuTxErXry41T729vYAlCtXju+++y7LRaD/97//ceXKFcqVK3ffeCtXrkzlypUZMGAAL7/8Mo0aNWLLli00a9Ysy/o7duwgODiYsLAw/P39cXV1JSoqiilTphh1HB0d73vcRo0asW7dOpYtW8bbb7+dabuTk5Mxymf+/PlUrVqVzz77jNDQ0Bz1T9u2bTlz5gxff/01MTExtGjRgoEDB/LBBx+QnJxMzZo1Wbx4cabjuru7W8Vwt65duzJ8+HD27t3L9evXOXfuHC+++CLwf9dt7ty5mdZSsrW1zbYv7O3tjbhFREREREREnlZKJD0BFStWNBbYvpPFYqF79+6kp6ezaNEiY4pVxj729vacPXuWJk2aZNluly5dmD59OnPmzGHw4MFW2z744APy5s1rjOB5kFjhdoILwM7Ojlu3blnV2b59OyVLluSdd94xyu5eZ6lKlSrExsbSu3fvbI9Vp04dBg0aRJs2bciTJw/Dhg3Ltq6NjQ3/+c9/GDp0KN26dctR/8DtpFCvXr3o1asXjRo14s033+SDDz6gRo0afPHFFxQuXBgXF5fsOyQLJUqUoEmTJixevJjr16/TqlUrY+pgkSJFKFasGCdPnjRGKYmIiIiIiIj8UyiR9AhdvHiRzp0706dPH6pUqYKzszO7d+9m8uTJdOjQIVP9MWPGsHHjRr799luSk5ON0Syurq44OzszbNgwXn/9ddLT02nYsCFJSUnEx8fj4uJCr169qFevHq+99hpvvvkmaWlpBAYGcuPGDT7//HM++ugjpk2bhqenZ7bxvvLKKxQrVozmzZtTokQJzp8/z7hx43B3dzfWHfL29mbDhg0kJiZSsGBBXF1dKVu2LGfPniUqKoratWuzbt06Vq1aZdX26NGjadGiBaVLl6ZLly7cvHmTr7/+muHDh1vVq1+/Pl9//TVt27YlT548DBkyJNt4O3fuzJtvvsknn3zCsGHD7ts/o0aNombNmlSqVInU1FTWrl2Lr68vcHt62vvvv0+HDh147733KFGiBGfOnCE6Opq33nqLEiVK3PNaBwcHM3r0aNLS0qwWGQcICwvj1VdfxdXVlTZt2pCamsru3bv5448/GDp06D3bFREREREREXmaKZH0CJnNZurWrcvUqVM5ceIEN27cwNPTk379+vGf//wnU/0tW7aQnJxs3AktQ0REBCEhIYwdOxZ3d3cmTJjAyZMncXNzo0aNGlZtTZs2jSpVqjBz5kzeffddbG1tqVGjBqtXryYgIOCe8bZs2ZL58+cza9YsLl68SKFChahXrx6xsbEULFgQgH79+hEXF0etWrVITk5m8+bNPPfcc7z++usMGjSI1NRU2rVrx8iRIxkzZozRdtOmTVm+fDljx45l4sSJuLi40Lhx4yzjaNiwIevWrePZZ5/F1tY20+iqDHny5GHQoEFMnjyZV1555b79Y2dnx4gRIzh9+jSOjo40atSIqKgoAPLly8fWrVsZPnw4HTt25M8//6R48eK0aNEiRyOUXnjhBQYNGoStrS2BgYFW2/r27Uu+fPl4//33efPNN3FycsLPz++eSTIRERERERGRvwOT5a+s1iwij8WVK1dwdXVl9NaTOJidczscEREREREReUBvVy+U2yE8kIzfoUlJSfccYKG7tomIiIiIiIiISI4okSQiIiIiIiIiIjmiNZJEnmJDqxZ84LvKiYiIiIiIiDwuGpEkIiIiIiIiIiI5okSSiIiIiIiIiIjkiBJJIiIiIiIiIiKSI0okiYiIiIiIiIhIjmixbZGn2If7L+JgTsvtMERERERERJ5Kb1cvlNsh/OtoRJKIiIiIiIiIiOSIEkkiIiIiIiIiIpIjSiSJiIiIiIiIiEiOKJEkIiIiIiIiIiI5okTS35TJZGL16tWP9RinT5/GZDKxb9++HO8zZswYqlWr9thietK8vb2ZNm1abochIiIiIiIi8lRQIukJCQkJwWQy8fLLL2faNnDgQEwmEyEhITlu7/z587Rt2/Yvx9W0aVOGDBmS5TZPT0/Onz9P5cqV//Jx/orBgwfj6+ub5bazZ89ia2vLmjVrHsuxd+3aRf/+/R9L2yIiIiIiIiJ/N0okPUGenp5ERUVx/fp1oywlJYUlS5bg5eX1QG15eHhgb2//0LGkpd3/lvK2trZ4eHiQJ0+ehz7OX3Xr1i169+7NTz/9xPbt2zNtj4yMpHDhwjz77LMP3HZO+sDd3Z18+fI9cNsiIiIiIiIi/0RKJD1BNWrUwNPTk+joaKMsOjoaLy8vqlevbpStX7+ehg0b4ubmRsGCBWnfvj0nTpywauvuqW0HDx6kefPmODo6UrBgQfr3709ycrKxPSQkhMDAQMLDwylWrBjly5e/b7x3T22Li4vDZDIRGxtLrVq1yJcvH/Xr1ycxMTHbNk6cOIGPjw+DBg3CYrGQmprKsGHDKF68OE5OTtStW5e4uDijfmRkJG5ubqxZs4aKFStib29PgQIFqFGjBvPnz7dq22KxEBkZSa9evciTJw+HDh2ibdu2mM1mihQpQo8ePfj999+N+k2bNmXQoEEMGTKEQoUK4e/vj8ViYcyYMXh5eWFvb0+xYsV49dVXjX3untp29uxZOnTogNlsxsXFhaCgIH799Vdje8bUvkWLFuHt7Y2rqytdunThzz//vG9/i4iIiIiIiDztlEh6wvr06UNERITxev78+fTu3duqztWrVxk6dCi7d+8mNjYWGxsbnn/+edLT07Ns8+rVq/j7+5M/f3527drF8uXL2bhxI4MGDbKqFxsbS2JiIjExMaxdu/ahz+Gdd95hypQp7N69mzx58tCnT58s6x04cICGDRvSrVs3ZsyYgclkYtCgQezYsYOoqCgOHDhA586dadOmDceOHTP2u3btGpMmTWLevHkcPnyYwoULExoayrJly7h69apRLy4ujlOnTtGnTx8uX75M8+bNqV69Ort372b9+vX8+uuvBAUFWcW0YMEC7OzsiI+PZ/bs2axcuZKpU6cyZ84cjh07xurVq/Hz88vyfNLT0+nQoQOXLl1iy5YtxMTEcPLkSV588UWreidOnGD16tWsXbuWtWvXsmXLFiZOnHjPPk1NTeXKlStWDxEREREREZGnTe7NWfqX6t69OyNGjODMmTMAxMfHExUVZTUqp1OnTlb7zJ8/H3d3d44cOZLlekVLliwhJSWFhQsX4uTkBMCMGTMICAhg0qRJFClSBAAnJyfmzZuHnZ3dXzqH8PBwmjRpAsDbb79Nu3btSElJwcHBwaizfft22rdvzzvvvMMbb7wB3B7NExERwdmzZylWrBgAw4YNY/369URERDB+/HgAbty4wcyZM6latarRXrdu3XjjjTdYvny5sZZUREQEDRs2pFy5cowbN47q1asbbWT0m6enJ0ePHqVcuXIAlC1blsmTJxt11q1bh4eHBy1btiRv3rx4eXlRp06dLM87NjaWgwcPcurUKTw9PQFYuHAhlSpVYteuXdSuXRu4nXCKjIzE2dkZgB49ehAbG0t4eHi2fTphwgTCwsLu1/UiIiIiIiIiuUojkp4wd3d32rVrR2RkJBEREbRr145ChQpZ1Tl27Bhdu3bFx8cHFxcXvL29gduJmKwkJCRQtWpVI4kE0KBBA9LT062mnfn5+f3lJBJAlSpVjOdFixYF4MKFC0bZ2bNnadWqFaNGjTKSSHB7+t2tW7coV64cZrPZeGzZssVq6p6dnZ3VMQDc3Nzo2LGjMb3typUrrFy5ktDQUAD279/P5s2brdqtUKECgFXbNWvWtGq3c+fOXL9+HR8fH/r168eqVau4efNmluedkJCAp6enkUQCqFixIm5ubiQkJBhl3t7eRhIpo4/u7J+sjBgxgqSkJONx7ty5e9YXERERERERyQ0akZQL+vTpY0w7++STTzJtDwgIoGTJksydO5dixYqRnp5O5cqVc7Q49L3cmWj6K/LmzWs8N5lMAFbT7tzd3SlWrBhLly6lT58+uLi4AJCcnIytrS179uzB1tbWqk2z2Ww8d3R0NNq9U2hoKC1atOD48eNs3rwZW1tbOnfubLSdMQLrbhnJLsjcB56eniQmJrJx40ZiYmIYMGAA77//Plu2bLE6zwdx934mkynbaYkZ7O3t/9Li6SIiIiIiIiJPghJJuaBNmzakpaVhMpnw9/e32nbx4kUSExOZO3cujRo1AuC77767Z3u+vr5ERkZy9epVI1ESHx+PjY1NjhbVftQcHR1Zu3Ytzz77LP7+/nz77bc4OztTvXp1bt26xYULF4xzexDNmjWjVKlSREREsHnzZrp06WKcb40aNVi5ciXe3t4PfJc5R0dHAgICCAgIYODAgVSoUIGDBw9So0YNq3q+vr6cO3eOc+fOGaOSjhw5wuXLl6lYseIDn4+IiIiIiIjI342mtuUCW1tbEhISOHLkSKaROfnz56dgwYJ8+umnHD9+nE2bNjF06NB7thccHIyDgwO9evXi0KFDbN68mcGDB9OjRw9jfaR7+e2339i3b5/V4847kT0MJycn1q1bR548eWjbti3JycmUK1eO4OBgevbsSXR0NKdOnWLnzp1MmDCBdevW3bdNk8lEnz59mDVrFjt27DCmtQEMHDiQS5cu0bVrV3bt2sWJEyfYsGEDvXv35tatW9m2GRkZyWeffcahQ4c4efIkn3/+OY6OjpQsWTJT3ZYtW+Ln50dwcDB79+5l586d9OzZkyZNmlCrVq2H6ygRERERERGRvxElknKJi4uLMeXrTjY2NkRFRbFnzx4qV67M66+/zvvvv3/PtvLly8eGDRu4dOkStWvX5oUXXqBFixbMmDEjR7EsWbKE6tWrWz3mzp37UOd1J7PZzDfffIPFYqFdu3ZcvXqViIgIevbsyRtvvEH58uUJDAxk165deHl55ajNkJAQkpKSqFSpEnXr1jXKixUrRnx8PLdu3aJ169b4+fkxZMgQ3NzcsLHJ/m3u5ubG3LlzadCgAVWqVGHjxo189dVXFCxYMFNdk8nEl19+Sf78+WncuDEtW7bEx8eHL7744sE7R0RERERERORvyGSxWCy5HYQ8mNTUVBwcHIiJiaFly5a5HY48BleuXMHV1ZXRW0/iYHa+/w4iIiIiIiL/Qm9XL3T/SpIjGb9Dk5KSshz4kkFrJP3NXLlyhejoaGxsbIy7komIiIiIiIiIPAlKJP3NjB49miVLljBp0iRKlCiR2+GIiIiIiIiIyL+IEkl/M1OnTmXq1Km5HYaIiIiIiIiI/AspkSTyFBtateA956aKiIiIiIiIPEm6a5uIiIiIiIiIiOSIEkkiIiIiIiIiIpIjSiSJiIiIiIiIiEiOKJEkIiIiIiIiIiI5osW2RZ5iH+6/iIM5LbfDEBERERERybG3qxfK7RDkMdKIJBERERERERERyRElkkREREREREREJEeUSBIRERERERERkRxRIklERERERERERHJEiaQHZDKZWL16dW6HAUBkZCRubm65HcY/WtOmTRkyZEhuhyEiIiIiIiLyVPjHJ5JCQkIwmUy8/PLLmbYNHDgQk8lESEhIjts7f/48bdu2/ctxnTp1im7dulGsWDEcHBwoUaIEHTp04KeffvrLbT8pTyqpNmXKFPLnz09KSkqmbdeuXcPFxYXp06c/lmNHR0czduzYx9K2iIiIiIiIyN/NPz6RBODp6UlUVBTXr183ylJSUliyZAleXl4P1JaHhwf29vYPHUtaWho3btygVatWJCUlER0dTWJiIl988QV+fn5cvnz5odv+p+rRowdXr14lOjo607YVK1aQlpZG9+7dH7jdtLS0+9YpUKAAzs7OD9y2iIiIiIiIyD/RvyKRVKNGDTw9Pa0SEdHR0Xh5eVG9enWjbP369TRs2BA3NzcKFixI+/btOXHihFVbd4/COXjwIM2bN8fR0ZGCBQvSv39/kpOTje0hISEEBgYSHh5OsWLFKF++PIcPH+bEiRPMnDmTZ555hpIlS9KgQQPGjRvHM888A0BcXBwmk8kqsbRv3z5MJhOnT5+2imn16tWULVsWBwcH/P39OXfunLFt//79NGvWDGdnZ1xcXKhZsya7d+/m6tWruLi4sGLFikxtOTk58eeff5KWlsagQYMoWrQoDg4OlCxZkgkTJgDg7e0NwPPPP4/JZDJeA3z55ZfUqFEDBwcHfHx8CAsL4+bNm1Z9OGfOHNq3b0++fPnw9fVlx44dHD9+nKZNm+Lk5ET9+vWNvi9cuDABAQHMnz8/07WdP38+gYGBFChQgHPnzhEUFISbmxsFChSgQ4cOVn2V1bUAmDlzptF/RYoU4YUXXjD2uXtq2x9//EHPnj3Jnz8/+fLlo23bthw7dszYnjHdcMOGDfj6+mI2m2nTpg3nz5/PFLuIiIiIiIjI382/IpEE0KdPHyIiIozX8+fPp3fv3lZ1rl69ytChQ9m9ezexsbHY2Njw/PPPk56enmWbV69exd/fn/z587Nr1y6WL1/Oxo0bGTRokFW92NhYEhMTiYmJYe3atbi7u2NjY8OKFSu4devWXzqva9euER4ezsKFC4mPj+fy5ct06dLF2B4cHEyJEiXYtWsXe/bs4e233yZv3rw4OTnRpUsXqz4BiIiI4IUXXsDZ2Znp06ezZs0ali1bRmJiIosXLzYSRrt27TLqnz9/3ni9bds2evbsyWuvvcaRI0eYM2cOkZGRhIeHWx1n7Nix9OzZk3379lGhQgW6devGSy+9xIgRI9i9ezcWi8WqH0NDQ9m0aRNnzpwxyk6ePMnWrVsJDQ3lxo0b+Pv74+zszLZt24iPjzeSOHeOPLr7WuzevZtXX32V9957j8TERNavX0/jxo2z7e+QkBB2797NmjVr2LFjBxaLhWeffZYbN25YXZMPPviARYsWsXXrVs6ePcuwYcPueR1TU1O5cuWK1UNERERERETkaZMntwN4Urp3786IESOMRER8fDxRUVHExcUZdTp16mS1z/z583F3d+fIkSNUrlw5U5tLliwhJSWFhQsX4uTkBMCMGTMICAhg0qRJFClSBAAnJyfmzZuHnZ2dse/06dN56623CAsLo1atWjRr1ozg4GB8fHwe6Lxu3LjBjBkzqFu3LgALFizA19eXnTt3UqdOHc6ePcubb75JhQoVAChbtqyxb9++falfvz7nz5+naNGiXLhwga+//pqNGzcCcPbsWcqWLUvDhg0xmUyULFnS2Nfd3R0ANzc3PDw8jPKwsDDefvttevXqBYCPjw9jx47lrbfeYvTo0Ua93r17ExQUBMDw4cOpV68eI0eOxN/fH4DXXnvNKtHn7+9PsWLFiIiIYMyYMcDt0T+enp60aNGCJUuWkJ6ezrx58zCZTMDtJJebmxtxcXG0bt06y2sRHR2Nk5MT7du3x9nZmZIlS1qNUrvTsWPHWLNmDfHx8dSvXx+AxYsX4+npyerVq+ncubNxTWbPnk3p0qUBGDRoEO+99949r+OECRMICwu7Zx0RERERERGR3PavGZHk7u5Ou3btiIyMJCIignbt2lGoUCGrOseOHaNr1674+Pjg4uJijL45e/Zslm0mJCRQtWpVI4kE0KBBA9LT00lMTDTK/Pz8rJJIcHuh719++YXFixdTr149li9fTqVKlYiJiXmg88qTJw+1a9c2XleoUAE3NzcSEhIAGDp0KH379qVly5ZMnDjRaqpenTp1qFSpEgsWLADg888/p2TJksaInJCQEPbt20f58uV59dVX+fbbb+8bz/79+3nvvfcwm83Go1+/fpw/f55r164Z9apUqWI8z0i4+fn5WZWlpKQYI3NsbW3p1asXkZGRWCwW0tPTWbBgAb1798bGxob9+/dz/PhxnJ2djeMWKFCAlJQUq3O++1q0atWKkiVL4uPjQ48ePVi8eLFVnHdKSEggT548RtIOoGDBgpQvX97ob4B8+fIZSSTASNLdy4gRI0hKSjIed05PFBEREREREXla/GsSSXB7eltkZCQLFiygT58+mbYHBARw6dIl5s6dyw8//MAPP/wA5GxR5nu5M9F0J2dnZwICAggPD2f//v00atSIcePGAWBjc/vSWCwWo/6d06dyasyYMRw+fJh27dqxadMmKlasyKpVq4ztffv2JTIyErg9gqd3797GiJ4aNWpw6tQpxo4dy/Xr1wkKCrJaPygrycnJhIWFsW/fPuNx8OBBjh07hoODg1Evb968xvOM42VVdue0wj59+nD27Fk2bdpEbGws586dM0YtJScnU7NmTavj7tu3j6NHj9KtWzejjbuvhbOzM3v37mXp0qUULVqUUaNGUbVq1b+06Pmd55FxLndex6zY29vj4uJi9RARERERERF52vyrEkkZ6+VkrKdzp4sXL5KYmMi7775LixYt8PX15Y8//rhne76+vuzfv5+rV68aZfHx8djY2BgLOeeUyWSiQoUKRlsZU8fuXKR53759mfa7efMmu3fvNl4nJiZy+fJlfH19jbJy5crx+uuv8+2339KxY0erdZG6d+/OmTNnmD59OkeOHDGmpGVwcXHhxRdfZO7cuXzxxResXLmSS5cuAbcTJnev8VSjRg0SExMpU6ZMpkdGcuxhlS5dmiZNmjB//nwiIiJo2bKlMd2uRo0aHDt2jMKFC2c6rqur6z3bzZMnDy1btmTy5MkcOHCA06dPs2nTpkz1fH19uXnzppFghP9731SsWPEvnZuIiIiIiIjI38G/KpFka2tLQkICR44cwdbW1mpb/vz5KViwIJ9++inHjx9n06ZNDB069J7tBQcH4+DgQK9evTh06BCbN29m8ODB9OjRw5iulZV9+/bRoUMHVqxYwZEjRzh+/DifffYZ8+fPp0OHDgCUKVMGT09PxowZw7Fjx1i3bh1TpkzJ1FbevHkZPHgwP/zwA3v27CEkJIRnnnmGOnXqcP36dQYNGkRcXBxnzpwhPj6eXbt2WSWZ8ufPT8eOHXnzzTdp3bo1JUqUMLZ9+OGHLF26lJ9++omjR4+yfPlyPDw8cHNzA27fuS02NpZffvnFSLqNGjWKhQsXEhYWxuHDh0lISCAqKop333333hcnh0JDQ4mOjmbVqlWEhoYa5cHBwRQqVIgOHTqwbds2Tp06RVxcHK+++ir//e9/s21v7dq1TJ8+nX379nHmzBkWLlxIenp6lonAsmXL0qFDB/r168d3333H/v376d69O8WLFzeum4iIiIiIiMg/2b8qkQRkO23IxsaGqKgo9uzZQ+XKlXn99dd5//3379lWvnz52LBhA5cuXaJ27dq88MILtGjRghkzZtxzvxIlSuDt7U1YWBh169alRo0afPTRR4SFhfHOO+8AtxNEGUmcKlWqMGnSJGPa290xDB8+nG7dutGgQQPMZjNffPEFcDtxdvHiRXr27Em5cuUICgqibdu2mRZ1Dg0NJS0tLdN0P2dnZyZPnkytWrWoXbs2p0+f5uuvvzZGFk2ZMoWYmBg8PT2NBar9/f1Zu3Yt3377LbVr1+aZZ55h6tSpVgt1/xWdOnXC3t6efPnyERgYaNUPW7duxcvLi44dO+Lr60toaCgpKSn3nCbm5uZGdHQ0zZs3x9fXl9mzZ7N06VIqVaqUZf2IiAhq1qxJ+/btqVevHhaLha+//jrTdDYRERERERGRfyKT5X6Lt4ghNTUVBwcHYmJiaNmyZW6H88gsWrSI119/nf/973+ZFgWX3HHlyhVcXV0ZvfUkDmbn3A5HREREREQkx96uXuj+leSpk/E7NCkp6Z4DMvI8wZj+1q5cuUJ0dDQ2NjZUqFAht8N5JK5du8b58+eZOHEiL730kpJIIiIiIiIiInJP/7qpbQ9r9OjRDB8+nEmTJlmtI/R3NnnyZCpUqICHhwcjRozI7XBERERERERE5CmnqW0iT6GcDikUEREREREReRRy+jtUI5JERERERERERCRHlEgSEREREREREZEcUSJJRERERERERERyRIkkERERERERERHJkTy5HYCIZO/D/RdxMKfldhgiIiIiIvIA3q5eKLdDEHlsNCJJRERERERERERyRIkkERERERERERHJESWSREREREREREQkR5RIEhERERERERGRHHlsiaSmTZsyZMiQx9X8P4r66unl7e3NtGnTcjsMERERERERkafCAyWSQkJCMJlMvPzyy5m2DRw4EJPJREhICADR0dGMHTv2kQSZE4cPHyYoKAh3d3fs7e0pV64co0aN4tq1a08shvuJi4vDZDJx+fJlq/LH1VerVq3imWeewdXVFWdnZypVqvTIElanT5/GZDKxb9++R9LevQwePBhfX98st509exZbW1vWrFnzWI69a9cu+vfv/1jaFhEREREREfm7eeARSZ6enkRFRXH9+nWjLCUlhSVLluDl5WWUFShQAGdn50cTZTbS0m7fFv3777+nbt26pKWlsW7dOo4ePUp4eDiRkZG0atXKqPe443hYj6OvYmNjefHFF+nUqRM7d+5kz549hIeHc+PGjUd6nCchNDSUn376ie3bt2faFhkZSeHChXn22WcfuN2cXDd3d3fy5cv3wG2LiIiIiIiI/BM9cCKpRo0aeHp6Eh0dbZRFR0fj5eVF9erVjbK7p2t5e3szfvx4+vTpg7OzM15eXnz66adWbR88eJDmzZvj6OhIwYIF6d+/P8nJycb2kJAQAgMDCQ8Pp1ixYpQvXx6LxUJoaCi+vr5ER0dTp04dSpYsSefOnfnqq6/YsWMHU6dONdowmUzMmjWLtm3b4ujoiI+PDytWrLCK49y5cwQFBeHm5kaBAgXo0KEDp0+fvmccAIsWLaJWrVo4Ozvj4eFBt27duHDhAnB7BE+zZs0AyJ8/v9XorYfpq+3bt1OtWjUcHByoVasWq1evthoh9NVXX9GgQQPefPNNypcvT7ly5QgMDOSTTz4x4rGxsWH37t1W7U6bNo2SJUuSnp7OH3/8QXBwMO7u7jg6OlK2bFkiIiIAKFWqFADVq1fHZDLRtGlTo4158+bh6+uLg4MDFSpUYObMmca2jJFMy5Yto1GjRjg6OlK7dm2OHj3Krl27qFWrFmazmbZt2/Lbb78BUK1aNWrUqMH8+fOtYrVYLERGRtKrVy/y5MnDoUOHaNu2LWazmSJFitCjRw9+//13o37Tpk0ZNGgQQ4YMoVChQvj7+2OxWBgzZgxeXl7Y29tTrFgxXn31VatrcefUtrNnz9KhQwfMZjMuLi4EBQXx66+/GtvHjBlDtWrVWLRoEd7e3ri6utKlSxf+/PNPRERERERERP7uHmqNpD59+hgJBYD58+fTu3fv++43ZcoUatWqxY8//siAAQN45ZVXSExMBODq1av4+/uTP39+du3axfLly9m4cSODBg2yaiM2NpbExERiYmJYu3Yt+/bt48iRIwwdOhQbG+vTqVq1Ki1btmTp0qVW5SNHjqRTp07s37+f4OBgunTpQkJCAgA3btzA398fZ2dntm3bRnx8PGazmTZt2liNYLk7jox9x44dy/79+1m9ejWnT582kkWenp6sXLkSgMTERM6fP89HH330UH115coVAgIC8PPzY+/evYwdO5bhw4db7e/h4cHhw4c5dOhQlu17e3vTsmVLq+sIEBERQUhICDY2NowcOZIjR47wzTffkJCQwKxZsyhUqBAAO3fuBGDjxo2cP3/eSCwuXryYUaNGER4eTkJCAuPHj2fkyJEsWLDA6jijR4/m3XffZe/eveTJk4du3brx1ltv8dFHH7Ft2zaOHz/OqFGjjPqhoaEsW7aMq1evGmVxcXGcOnWKPn36cPnyZZo3b0716tXZvXs369ev59dffyUoKMjquAsWLMDOzo74+Hhmz57NypUrmTp1KnPmzOHYsWOsXr0aPz+/LPssPT2dDh06cOnSJbZs2UJMTAwnT57kxRdftKp34sQJVq9ezdq1a1m7di1btmxh4sSJWbaZITU1lStXrlg9RERERERERJ42eR5mp+7duzNixAjOnDkDQHx8PFFRUcTFxd1zv2effZYBAwYAMHz4cKZOncrmzZspX748S5YsISUlhYULF+Lk5ATAjBkzCAgIYNKkSRQpUgQAJycn5s2bh52dHQBffPEFQLZr6Pj6+vLdd99ZlXXu3Jm+ffsCMHbsWGJiYvj444+ZOXMmX3zxBenp6cybNw+TyQTcTq64ubkRFxdH69ats4wDbifYMvj4+DB9+nRq165NcnIyZrOZAgUKAFC4cGHc3Nz+Ul+ZTCbmzp2Lg4MDFStW5Oeff6Zfv37G/oMHD2bbtm34+flRsmRJnnnmGVq3bk1wcDD29vYA9O3bl5dffpkPP/wQe3t79u7dy8GDB/nyyy+B26NvqlevTq1atYDbyacM7u7uABQsWBAPDw+jfPTo0UyZMoWOHTsCt0cuHTlyhDlz5tCrVy+j3rBhw/D39wfgtddeo2vXrsTGxtKgQQPgduIoMjLSqN+tWzfeeOMNli9fbiTnIiIiaNiwIeXKlWPcuHFUr16d8ePHG/vMnz8fT09Pjh49Srly5QAoW7YskydPNuqsW7cODw8PWrZsSd68efHy8qJOnTpZXpPY2FgOHjzIqVOn8PT0BGDhwoVUqlSJXbt2Ubt2beB2wikyMtKYrtijRw9iY2MJDw/Psl2ACRMmEBYWlu12ERERERERkafBQ41Icnd3p127dkRGRhIREUG7du2MkSr3UqVKFeO5yWTCw8PDmPqVkJBA1apVjSQSQIMGDUhPTzdG4gD4+flZJW8yWCyWHMdfr169TK8zRiTt37+f48eP4+zsjNlsNhJAKSkpnDhx4p5x7Nmzh4CAALy8vHB2dqZJkybA7YTMg7pXXyUmJlKlShUcHByMOncnP5ycnFi3bh3Hjx/n3XffxWw288Ybb1CnTh1jAfLAwEBsbW1ZtWoVcHu9oWbNmhkJo1deeYWoqCiqVavGW2+9leUaRXe6evUqJ06cIDQ01Og7s9nMuHHjrPru7vPLSBLeORKoSJEixvkCuLm50bFjR2N625UrV1i5ciWhoaHA7eu2efNmq+NWqFABwOrYNWvWtIqjc+fOXL9+HR8fH/r168eqVau4efNmlueXkJCAp6enkUQCqFixIm5ubsb7B24n3O5c86po0aJW55KVESNGkJSUZDzOnTt3z/oiIiIiIiIiueGhRiTB7dE3GdPOMtbduZ+8efNavTaZTKSnpz/Qce9MNAHGSJOEhASrNZoyJCQkGHVyIjk5mZo1a7J48eJM2zJG4WQVR8bUPH9/fxYvXoy7uztnz57F39//oRbjfhR9BVC6dGlKly5N3759eeeddyhXrhxffPEFvXv3xs7Ojp49exIREUHHjh1ZsmSJ1XS7tm3bcubMGb7++mtiYmJo0aIFAwcO5IMPPsjyWBnrWc2dO5e6detabbO1tc32/DJGft1ddvf5hoaG0qJFC44fP87mzZuxtbWlc+fOxrEzRq/drWjRosbzu6+bp6cniYmJbNy4kZiYGAYMGMD777/Pli1bMl2DnHqYa2dvb2+MFBMRERERERF5Wj3UiCTAWDMoY02hv8rX15f9+/dbrYETHx+PjY2NsZh1VqpVq0aFChWYOnVqph/r+/fvZ+PGjXTt2tWq/Pvvv8/0OmNqXI0aNTh27BiFCxemTJkyVg9XV9ds4/jpp5+4ePEiEydOpFGjRlSoUCHTKJSMEUy3bt26R0/cX/ny5Tl48CCpqalG2a5du+67n7e3N/ny5bPq4759+7Jx40ZmzpzJzZs3jSlpGdzd3enVqxeff/4506ZNMxb9zupcihQpQrFixTh58mSmvstYnPuvaNasGaVKlSIiIoKIiAi6dOliJIZq1KjB4cOH8fb2znTsu5NHd3N0dCQgIIDp06cTFxfHjh07OHjwYKZ6vr6+nDt3zmq00JEjR7h8+TIVK1b8y+cnIiIiIiIi8rR76ESSra0tCQkJHDlyJNNok4cRHByMg4MDvXr14tChQ2zevJnBgwfTo0cPY+pTVkwmE5999hlHjhwxbnV/9uxZli9fTkBAAPXq1bO6IxrA8uXLmT9/PkePHmX06NHs3LnTGF0VHBxMoUKF6NChA9u2bePUqVPExcXx6quv8t///jfbOLy8vLCzs+Pjjz/m5MmTrFmzhrFjx1rVKVmyJCaTibVr1/Lbb79Z3ZHuQXTr1o309HT69+9PQkICGzZsMEYJZYzuGTNmDG+99ZaxIPWPP/5Inz59uHHjBq1atTLa8vX15ZlnnmH48OF07doVR0dHY9uoUaP48ssvOX78OIcPH2bt2rVGwq1w4cI4Ojoai1onJSUBEBYWxoQJE5g+fTpHjx7l4MGDRERE8OGHHz7Uud7JZDLRp08fZs2axY4dO4xpbQADBw7k0qVLdO3alV27dnHixAk2bNhA796975m4i4yM5LPPPuPQoUOcPHmSzz//HEdHR0qWLJmpbsuWLfHz8yM4OJi9e/eyc+dOevbsSZMmTYx1pERERERERET+yR46kQTg4uKCi4vLIwkkX758bNiwgUuXLlG7dm1eeOEFWrRowYwZM+67b/369fn++++xtbWlbdu2lClThhEjRtCrVy9iYmIyTRkKCwsjKiqKKlWqsHDhQpYuXWqMKMmXLx9bt27Fy8uLjh074uvrS2hoKCkpKfc8V3d3dyIjI1m+fDkVK1Zk4sSJmaaAFS9enLCwMN5++22KFCmS6Y50OeXi4sJXX33Fvn37qFatGu+8845xh7OMdZOaNGnCyZMn6dmzJxUqVKBt27b88ssvfPvtt5lGeIWGhpKWlma1WDjcHnU0YsQIqlSpQuPGjbG1tSUqKgqAPHnyMH36dObMmUOxYsXo0KEDcHuE07x584iIiMDPz48mTZoQGRn5SEYkAYSEhJCUlESlSpWsps8VK1aM+Ph4bt26RevWrfHz82PIkCG4ubllupvfndzc3Jg7dy4NGjSgSpUqbNy4ka+++oqCBQtmqmsymfjyyy/Jnz8/jRs3pmXLlvj4+BgLvouIiIiIiIj805ksD7JK9T+AyWRi1apVBAYG5nYoj9TixYvp3bs3SUlJVqOKcmLs2LEsX76cAwcOPKbo5EFduXIFV1dXRm89iYPZ+f47iIiIiIjIU+Pt6ve/GZXI0ybjd2hSUtI9B9I89GLbkrsWLlyIj48PxYsXZ//+/QwfPpygoKAHSiIlJydz+vRpZsyYwbhx4x5jtCIiIiIiIiLyT/CXprZJ7vnll1/o3r07vr6+vP7663Tu3NlYCDunBg0aRM2aNWnatGmmaW0iIiIiIiIiInf7101tE/k70NQ2EREREZG/L01tk78jTW0T+QcYWrXgI1vQXkREREREROSv0tQ2ERERERERERHJESWSREREREREREQkR5RIEhERERERERGRHFEiSUREREREREREckSLbYs8xT7cfxEHc1puhyEiIiIi8regu6WJPH4akSQiIiIiIiIiIjmiRJKIiIiIiIiIiOSIEkkiIiIiIiIiIpIjSiSJiIiIiIiIiEiO5GoiyWQysXr16twM4anQtGlThgwZktthSBa8vb2ZNm1abochIiIiIiIi8lR4oERSSEgIJpOJl19+OdO2gQMHYjKZCAkJyXF758+fp23btg8SQrYOHz5MUFAQ7u7u2NvbU65cOUaNGsW1a9ceSfuPQlxcHCaTicuXL1uVR0dHM3bs2Ed+vFWrVvHMM8/g6uqKs7MzlSpVemQJq9OnT2Mymdi3b98jae9eBg8ejK+vb5bbzp49i62tLWvWrHksx961axf9+/d/LG2LiIiIiIiI/N088IgkT09PoqKiuH79ulGWkpLCkiVL8PLyeqC2PDw8sLe3f9AQDGlpt2+L/v3331O3bl3S0tJYt24dR48eJTw8nMjISFq1amXUe1z+avsFChTA2dn5EUVzW2xsLC+++CKdOnVi586d7Nmzh/DwcG7cuPFIj/MkhIaG8tNPP7F9+/ZM2yIjIylcuDDPPvvsA7ebk+vm7u5Ovnz5HrhtERERERERkX+iB04k1ahRA09PT6Kjo42y6OhovLy8qF69ulG2fv16GjZsiJubGwULFqR9+/acOHHCqq27p7YdPHiQ5s2b4+joSMGCBenfvz/JycnG9pCQEAIDAwkPD6dYsWKUL18ei8VCaGgovr6+REdHU6dOHUqWLEnnzp356quv2LFjB1OnTrU65qxZs2jbti2Ojo74+PiwYsUKq7jOnTtHUFAQbm5uFChQgA4dOnD69Ol7xgGwaNEiatWqhbOzMx4eHnTr1o0LFy4At0fwNGvWDID8+fNbjd66e2qbt7c348ePp0+fPjg7O+Pl5cWnn35qFeP27dupVq0aDg4O1KpVi9WrV1uNEPrqq69o0KABb775JuXLl6dcuXIEBgbyySefGPHY2Niwe/duq3anTZtGyZIlSU9P548//iA4OBh3d3ccHR0pW7YsERERAJQqVQqA6tWrYzKZaNq0qdHGvHnz8PX1xcHBgQoVKjBz5kxjW8ZIpmXLltGoUSMcHR2pXbs2R48eZdeuXdSqVQuz2Uzbtm357bffAKhWrRo1atRg/vz5VrFaLBYiIyPp1asXefLk4dChQ7Rt2xaz2UyRIkXo0aMHv//+u1G/adOmDBo0iCFDhlCoUCH8/f2xWCyMGTMGLy8v7O3tKVasGK+++qrVtbhzatvZs2fp0KEDZrMZFxcXgoKC+PXXX43tY8aMoVq1aixatAhvb29cXV3p0qULf/75JyIiIiIiIiJ/dw+1RlKfPn2MhALA/Pnz6d27t1Wdq1evMnToUHbv3k1sbCw2NjY8//zzpKenZ9nm1atX8ff3J3/+/OzatYvly5ezceNGBg0aZFUvNjaWxMREYmJiWLt2Lfv27ePIkSMMHToUGxvr06latSotW7Zk6dKlVuUjR46kU6dO7N+/n+DgYLp06UJCQgIAN27cwN/fH2dnZ7Zt20Z8fDxms5k2bdpYjWC5O46MfceOHcv+/ftZvXo1p0+fNpJFnp6erFy5EoDExETOnz/PRx99lG0fT5kyhVq1avHjjz8yYMAAXnnlFRITEwG4cuUKAQEB+Pn5sXfvXsaOHcvw4cOt9vfw8ODw4cMcOnQoy/a9vb1p2bKl1XUEiIiIICQkBBsbG0aOHMmRI0f45ptvSEhIYNasWRQqVAiAnTt3ArBx40bOnz9vJBYXL17MqFGjCA8PJyEhgfHjxzNy5EgWLFhgdZzRo0fz7rvvsnfvXvLkyUO3bt146623+Oijj9i2bRvHjx9n1KhRRv3Q0FCWLVvG1atXjbK4uDhOnTpFnz59uHz5Ms2bN6d69ers3r2b9evX8+uvvxIUFGR13AULFmBnZ0d8fDyzZ89m5cqVTJ06lTlz5nDs2DFWr16Nn59fln2Wnp5Ohw4duHTpElu2bCEmJoaTJ0/y4osvWtU7ceIEq1evZu3ataxdu5YtW7YwceLELNvMkJqaypUrV6weIiIiIiIiIk+bPA+zU/fu3RkxYgRnzpwBID4+nqioKOLi4ow6nTp1stpn/vz5uLu7c+TIESpXrpypzSVLlpCSksLChQtxcnICYMaMGQQEBDBp0iSKFCkCgJOTE/PmzcPOzg6AL774AiDbNXR8fX357rvvrMo6d+5M3759ARg7diwxMTF8/PHHzJw5ky+++IL09HTmzZuHyWQCbidX3NzciIuLo3Xr1lnGAbcTbBl8fHyYPn06tWvXJjk5GbPZTIECBQAoXLgwbm5u2XUvAM8++ywDBgwAYPjw4UydOpXNmzdTvnx5lixZgslkYu7cuTg4OFCxYkV+/vln+vXrZ+w/ePBgtm3bhp+fHyVLluSZZ56hdevWBAcHG9MJ+/bty8svv8yHH36Ivb09e/fu5eDBg3z55ZfA7dE31atXp1atWsDt5FMGd3d3AAoWLIiHh4dRPnr0aKZMmULHjh2B2yOXjhw5wpw5c+jVq5dRb9iwYfj7+wPw2muv0bVrV2JjY2nQoAFwO3EUGRlp1O/WrRtvvPEGy5cvN5JzERERNGzYkHLlyjFu3DiqV6/O+PHjjX3mz5+Pp6cnR48epVy5cgCULVuWyZMnG3XWrVuHh4cHLVu2JG/evHh5eVGnTp0sr0lsbCwHDx7k1KlTeHp6ArBw4UIqVarErl27qF27NnA74RQZGWlMV+zRowexsbGEh4dn2S7AhAkTCAsLy3a7iIiIiIiIyNPgoUYkubu7065dOyIjI4mIiKBdu3bGSJUMx44do2vXrvj4+ODi4mIkIc6ePZtlmwkJCVStWtVIIgE0aNCA9PR0YyQOgJ+fn1XyJoPFYslx/PXq1cv0OmNE0v79+zl+/DjOzs6YzWYjAZSSkmI1NS+rOPbs2UNAQABeXl44OzvTpEmTe57zvVSpUsV4bjKZ8PDwMKbJJSYmUqVKFRwcHIw6dyc/nJycWLduHcePH+fdd9/FbDbzxhtvUKdOHWMB8sDAQGxtbVm1ahVwe72hZs2aGdfqlVdeISoqimrVqvHWW29luUbRna5evcqJEycIDQ01+s5sNjNu3LhM0xrvPL+MJOGdI4GKFClinC+Am5sbHTt2NKa3XblyhZUrVxIaGgrcvm6bN2+2Om6FChUArI5ds2ZNqzg6d+7M9evX8fHxoV+/fqxatYqbN29meX4JCQl4enoaSSSAihUr4ubmZrx/4HbC7c41r4oWLWp1LlkZMWIESUlJxuPcuXP3rC8iIiIiIiKSGx5qRBLcHn2TMe0sY92dOwUEBFCyZEnmzp1LsWLFSE9Pp3Llyn95Yeo7E02AMdIkISHBao2mDAkJCUadnEhOTqZmzZosXrw407aMUThZxZExNc/f35/Fixfj7u7O2bNn8ff3f6hzzps3r9Vrk8mU7bTAeyldujSlS5emb9++vPPOO5QrV44vvviC3r17Y2dnR8+ePYmIiKBjx44sWbLEarpd27ZtOXPmDF9//TUxMTG0aNGCgQMH8sEHH2R5rIz1rObOnUvdunWtttna2mZ7fhkjv+4uu/t8Q0NDadGiBcePH2fz5s3Y2trSuXNn49gZo9fuVrRoUeP53dfN09OTxMRENm7cSExMDAMGDOD9999ny5Ytma5BTj3MtbO3t/9LC8+LiIiIiIiIPAkPNSIJMNYMylhT6E4XL14kMTGRd999lxYtWuDr68sff/xxz/Z8fX3Zv3+/1Ro48fHx2NjYGItZZ6VatWpUqFCBqVOnZvqxvn//fjZu3EjXrl2tyr///vtMrzOmxtWoUYNjx45RuHBhypQpY/VwdXXNNo6ffvqJixcvMnHiRBo1akSFChUyjULJGMF069ate/TE/ZUvX56DBw+SmppqlO3ateu++3l7e5MvXz6rPu7bty8bN25k5syZ3Lx505iSlsHd3Z1evXrx+eefM23aNGPR76zOpUiRIhQrVoyTJ09m6ruMxbn/imbNmlGqVCkiIiKIiIigS5cuRmKoRo0aHD58GG9v70zHvjt5dDdHR0cCAgKYPn06cXFx7Nixg4MHD2aq5+vry7lz56xGCx05coTLly9TsWLFv3x+IiIiIiIiIk+7h04k2drakpCQwJEjRzKNNsmfPz8FCxbk008/5fjx42zatImhQ4fes73g4GAcHBzo1asXhw4dYvPmzQwePJgePXoYU5+yYjKZ+Oyzzzhy5Ihxq/uzZ8+yfPlyAgICqFevntUd0QCWL1/O/PnzOXr0KKNHj2bnzp3G6Krg4GAKFSpEhw4d2LZtG6dOnSIuLo5XX32V//73v9nG4eXlhZ2dHR9//DEnT55kzZo1jB071qpOyZIlMZlMrF27lt9++83qjnQPolu3bqSnp9O/f38SEhLYsGGDMUooY3TPmDFjeOutt4wFqX/88Uf69OnDjRs3aNWqldGWr68vzzzzDMOHD6dr1644Ojoa20aNGsWXX37J8ePHOXz4MGvXrjUSboULF8bR0dFY1DopKQmAsLAwJkyYwPTp0zl69CgHDx4kIiKCDz/88KHO9U4mk4k+ffowa9YsduzYYUxrAxg4cCCXLl2ia9eu7Nq1ixMnTrBhwwZ69+59z8RdZGQkn332GYcOHeLkyZN8/vnnODo6UrJkyUx1W7ZsiZ+fH8HBwezdu5edO3fSs2dPmjRpYqwjJSIiIiIiIvJP9tCJJAAXFxdcXFwyN2pjQ1RUFHv27KFy5cq8/vrrvP/++/dsK1++fGzYsIFLly5Ru3ZtXnjhBVq0aMGMGTPuG0f9+vX5/vvvsbW1pW3btpQpU4YRI0bQq1cvYmJiMk0ZCgsLIyoqiipVqrBw4UKWLl1qjCjJly8fW7duxcvLi44dO+Lr60toaCgpKSlZnmsGd3d3IiMjWb58ORUrVmTixImZpoAVL16csLAw3n77bYoUKZLpjnQ55eLiwldffcW+ffuoVq0a77zzjnGHs4x1k5o0acLJkyfp2bMnFSpUoG3btvzyyy98++23mUZ4hYaGkpaWZrVYONwedTRixAiqVKlC48aNsbW1JSoqCoA8efIwffp05syZQ7FixejQoQNwe4TTvHnziIiIwM/PjyZNmhAZGflIRiQBhISEkJSURKVKlaymzxUrVoz4+Hhu3bpF69at8fPzY8iQIbi5uWW6m9+d3NzcmDt3Lg0aNKBKlSps3LiRr776ioIFC2aqazKZ+PLLL8mfPz+NGzemZcuW+Pj4GAu+i4iIiIiIiPzTmSwPskr1I5SamoqDgwMxMTG0bNnyiR3XZDKxatUqAgMDn9gxn4TFixfTu3dvkpKSrEYV5cTYsWNZvnw5Bw4ceEzRyYO6cuUKrq6ujN56Egez8/13EBERERER3q5e6P6VRCRLGb9Dk5KS7jmQ5qEX2/4rrly5QnR0NDY2NsadteTBLFy4EB8fH4oXL87+/fsZPnw4QUFBD5RESk5O5vTp08yYMYNx48Y9xmhFRERERERE5J8gVxJJo0ePZsmSJUyaNIkSJUrkRgh/e7/88gujRo3il19+oWjRonTu3Jnw8PAHamPQoEEsXbqUwMDATNPaRERERERERETulmtT20Qke5raJiIiIiLy4DS1TeThPdVT20QkZ4ZWLXjPD7CIiIiIiIjIk/SX7tomIiIiIiIiIiL/HkokiYiIiIiIiIhIjiiRJCIiIiIiIiIiOaJEkoiIiIiIiIiI5IgW2xZ5in24/yIO5rTcDkNERERE/gV0xzMRyQmNSBIRERERERERkRxRIklERERERERERHJEiSQREREREREREckRJZL+gUJCQggMDMztMERERERERETkH0aJpL8Zk8l0z8eYMWP46KOPiIyMfOyxhISEGMfNmzcvRYoUoVWrVsyfP5/09PQHaisyMhI3N7eHimPu3LlUrVoVs9mMm5sb1atXZ8KECVZxKrEmIiIiIiIi8tfprm1/M+fPnzeef/HFF4waNYrExESjzGw2Yzabn1g8bdq0ISIiglu3bvHrr7+yfv16XnvtNVasWMGaNWvIk+fxvsXmz5/PkCFDmD59Ok2aNCE1NZUDBw5w6NChx3pcERERERERkX8jjUj6m/Hw8DAerq6umEwmqzKz2ZxpBE7Tpk0ZPHgwQ4YMIX/+/BQpUoS5c+dy9epVevfujbOzM2XKlOGbb76xOtahQ4do27YtZrOZIkWK0KNHD37//XerOvb29nh4eFC8eHFq1KjBf/7zH7788ku++eYbq1FRH374IX5+fjg5OeHp6cmAAQNITk4GIC4ujt69e5OUlGQ1sgogNTWVYcOGUbx4cZycnKhbty5xcXFGu2vWrCEoKIjQ0FDKlClDpUqV6Nq1K+Hh4QCMGTOGBQsW8OWXXxptZ+w/fPhwypUrR758+fDx8WHkyJHcuHHDaHvMmDFUq1aN+fPn4+XlhdlsZsCAAdy6dYvJkyfj4eFB4cKFjWNlMJlMzJo1i7Zt2+Lo6IiPjw8rVqx4mMstIiIiIiIi8lRRIulfYsGCBRQqVIidO3cyePBgXnnlFTp37kz9+vXZu3cvrVu3pkePHly7dg2Ay5cv07x5c6pXr87u3btZv349v/76K0FBQfc9VvPmzalatSrR0dFGmY2NDdOnT+fw4cMsWLCATZs28dZbbwFQv359pk2bhouLC+fPn+f8+fMMGzYMgEGDBrFjxw6ioqI4cOAAnTt3pk2bNhw7dgy4nVj7/vvvOXPmTJaxDBs2jKCgINq0aWO0Xb9+fQCcnZ2JjIzkyJEjfPTRR8ydO5epU6da7X/ixAm++eYb1q9fz9KlS/nss89o164d//3vf9myZQuTJk3i3Xff5YcffrDab+TIkXTq1In9+/cTHBxMly5dSEhIyLbPUlNTuXLlitVDRERERERE5GmjRNK/RNWqVXn33XcpW7YsI0aMwMHBgUKFCtGvXz/Kli3LqFGjuHjxIgcOHABgxowZVK9enfHjx1OhQgWqV6/O/Pnz2bx5M0ePHr3v8SpUqMDp06eN10OGDKFZs2Z4e3vTvHlzxo0bx7JlywCws7PLNLrKbDZz9uxZIiIiWL58OY0aNaJ06dIMGzaMhg0bEhERAcDo0aNxc3PD29ub8uXLExISwrJly4w1msxmM46OjsbIKQ8PD+zs7AB49913qV+/Pt7e3gQEBDBs2DAjpgzp6enMnz+fihUrEhAQQLNmzUhMTGTatGmUL1+e3r17U758eTZv3my1X+fOnenbty/lypVj7Nix1KpVi48//jjb/powYQKurq7Gw9PT8759LCIiIiIiIvKkaY2kf4kqVaoYz21tbSlYsCB+fn5GWZEiRQC4cOECAPv372fz5s1Zrrd04sQJypUrd8/jWSwWTCaT8Xrjxo1MmDCBn376iStXrnDz5k1SUlK4du0a+fLly7KNgwcPcuvWrUzHSk1NpWDBggAULVqUHTt2cOjQIbZu3cr27dvp1asX8+bNY/369djYZJ8r/eKLL5g+fTonTpwgOTmZmzdv4uLiYlXH29sbZ2dn43WRIkWwtbW1ardIkSJGv2WoV69eptf79u3LNpYRI0YwdOhQ4/WVK1eUTBIREREREZGnjhJJ/xJ58+a1ep1xp7U7XwPGSJ7k5GQCAgKYNGlSpraKFi163+MlJCRQqlQpAE6fPk379u155ZVXCA8Pp0CBAnz33XeEhoaSlpaWbSIpOTkZW1tb9uzZg62trdW2uxNclStXpnLlygwYMICXX36ZRo0asWXLFpo1a5Zl2zt27CA4OJiwsDD8/f1xdXUlKiqKKVOmWNW7X79llD3oXeruZm9vj729/V9qQ0RERERERORxUyJJslSjRg1WrlyJt7f3A995bdOmTRw8eJDXX38dgD179pCens6UKVOMkTx3TyGzs7Pj1q1bVmXVq1fn1q1bXLhwgUaNGuX4+BUrVgTg6tWr2ba9fft2SpYsyTvvvGOUZbfO0sP4/vvv6dmzp9Xr6tWrP7L2RURERERERHKD1kiSLA0cOJBLly7RtWtXdu3axYkTJ9iwYQO9e/e2Ssqkpqbyyy+/8PPPP7N3717Gjx9Phw4daN++vZFIKVOmDDdu3ODjjz/m5MmTLFq0iNmzZ1sdz9vbm+TkZGJjY/n999+5du0a5cqVIzg4mJ49exIdHc2pU6fYuXMnEyZMYN26dQC88sorjB07lvj4eM6cOWMkcNzd3Y3pZd7e3hw4cIDExER+//13bty4QdmyZTl79ixRUVGcOHGC6dOns2rVqkfWf8uXL2f+/PkcPXqU0aNHs3PnTgYNGvTI2hcRERERERHJDUokSZaKFStGfHw8t27donXr1vj5+TFkyBDc3Nys1gdav349RYsWxdvbmzZt2rB582amT5/Ol19+aUxHq1q1Kh9++CGTJk2icuXKLF68mAkTJlgdr379+rz88su8+OKLuLu7M3nyZAAiIiLo2bMnb7zxBuXLlycwMJBdu3bh5eUFQMuWLfn+++/p3Lkz5cqVo1OnTjg4OBAbG2uso9SvXz/Kly9PrVq1cHd3Jz4+nueee47XX3+dQYMGUa1aNbZv387IkSMfWf+FhYURFRVFlSpVWLhwIUuXLjVGSomIiIiIiIj8XZksFoslt4MQ+ScxmUysWrWKwMDAh27jypUruLq6MnrrSRzMzvffQURERETkL3q7eqHcDkFEclHG79CkpKRMN6K6k0YkiYiIiIiIiIhIjiiRJCIiIiIiIiIiOaK7tok8YpotKiIiIiIiIv9USiSJPMWGVi14z7mpIiIiIiIiIk+SpraJiIiIiIiIiEiOKJEkIiIiIiIiIiI5okSSiIiIiIiIiIjkiBJJIiIiIiIiIiKSI1psW+Qp9uH+iziY03I7DBEREfkXebt6odwOQUREnmIakSQiIiIiIiIiIjmiRJKIiIiIiIiIiOSIEkkiIiIiIiIiIpIjSiSJiIiIiIiIiEiOKJH0L2cymVi9enVuh/FEeHt7M23atIfePzIyEjc3t0cWj4iIiIiIiMjfjRJJ/3C//PILgwcPxsfHB3t7ezw9PQkICCA2Nja3Q7uvkJAQAgMDH1l7u3bton///jmqm1XS6cUXX+To0aOPLB4RERERERGRv5s8uR2APD6nT5+mQYMGuLm58f777+Pn58eNGzfYsGEDAwcO5Keffnosx01LS8POzu6xtP0wMuJxd3f/S+04Ojri6Oj4iKISERERERER+fvRiKR/sAEDBmAymdi5cyedOnWiXLlyVKpUiaFDh/L9998b9X7//Xeef/558uXLR9myZVmzZo2x7datW4SGhlKqVCkcHR0pX748H330kdVxMkYOhYeHU6xYMcqXLw/AokWLqFWrFs7Oznh4eNCtWzcuXLhgte/hw4dp3749Li4uODs706hRI06cOMGYMWNYsGABX375JSaTCZPJRFxcHADnzp0jKCgINzc3ChQoQIcOHTh9+vR947lzlJHFYmHMmDF4eXlhb29PsWLFePXVVwFo2rQpZ86c4fXXXzeODVlPbfvqq6+oXbs2Dg4OFCpUiOeff97YNnPmTMqWLYuDgwNFihThhRdeeMArKCIiIiIiIvJ00Yikf6hLly6xfv16wsPDcXJyyrT9zoRIWFgYkydP5v333+fjjz8mODiYM2fOUKBAAdLT0ylRogTLly+nYMGCbN++nf79+1O0aFGCgoKMNmJjY3FxcSEmJsYou3HjBmPHjqV8+fJcuHCBoUOHEhISwtdffw3Azz//TOPGjWnatCmbNm3CxcWF+Ph4bt68ybBhw0hISODKlStEREQAUKBAAW7cuIG/vz/16tVj27Zt5MmTh3HjxtGmTRsOHDhgjITKKp47rVy5kqlTpxIVFUWlSpX45Zdf2L9/PwDR0dFUrVqV/v37069fv2z7eN26dTz//PO88847LFy4kLS0NOPcdu/ezauvvsqiRYuoX78+ly5dYtu2bdm2lZqaSmpqqvH6ypUr2dYVERERERERyS1KJP1DHT9+HIvFQoUKFe5bNyQkhK5duwIwfvx4pk+fzs6dO2nTpg158+YlLCzMqFuqVCl27NjBsmXLrBJJTk5OzJs3z2pKW58+fYznPj4+TJ8+ndq1a5OcnIzZbOaTTz7B1dWVqKgo8ubNC0C5cuWMfRwdHUlNTcXDw8Mo+/zzz0lPT2fevHnGSKGIiAjc3NyIi4ujdevW2cZzp7Nnz+Lh4UHLli3JmzcvXl5e1KlTB7idsLK1tTVGUmUnPDycLl26WPVP1apVjfadnJxo3749zs7OlCxZkurVq2fb1oQJE6zaEREREREREXkaaWrbP5TFYslx3SpVqhjPnZyccHFxsZqC9sknn1CzZk3c3d0xm818+umnnD171qoNPz+/TEmbPXv2EBAQgJeXF87OzjRp0gTA2Hffvn00atTISCLlxP79+zl+/DjOzs6YzWbMZjMFChQgJSWFEydO3DOeO3Xu3Jnr16/j4+NDv379WLVqFTdv3sxxHBnxt2jRIsttrVq1omTJkvj4+NCjRw8WL17MtWvXsm1rxIgRJCUlGY9z5849UCwiIiIiIiIiT4ISSf9QZcuWxWQy5WhB7bsTOSaTifT0dACioqIYNmwYoaGhfPvtt+zbt4/evXuTlpZmtc/d0+euXr2Kv78/Li4uLF68mF27drFq1SoAY9+HWbg6OTmZmjVrsm/fPqvH0aNH6datW7bx3M3T05PExERmzpyJo6MjAwYMoHHjxty4cSPHsdwrfmdnZ/bu3cvSpUspWrQoo0aNomrVqly+fDnL+vb29ri4uFg9RERERERERJ42SiT9QxUoUAB/f38++eQTrl69mml7dgmNu8XHx1O/fn0GDBhA9erVKVOmjNXIn+z89NNPXLx4kYkTJ9KoUSMqVKiQaaHtKlWqsG3btmyTN3Z2dty6dcuqrEaNGhw7dozChQtTpkwZq4erq2uOzimDo6MjAQEBTJ8+nbi4OHbs2MHBgwezPfbdqlSpQmxsbLbb8+TJQ8uWLZk8eTIHDhzg9OnTbNq06YFiFBEREREREXmaKJH0D/bJJ59w69Yt6tSpw8qVKzl27BgJCQlMnz6devXq5aiNsmXLsnv3bjZs2MDRo0cZOXIku3btuu9+Xl5e2NnZ8fHHH3Py5EnWrFnD2LFjreoMGjSIK1eu0KVLF3bv3s2xY8dYtGgRiYmJwO27rB04cIDExER+//13bty4QXBwMIUKFaJDhw5s27aNU6dOERcXx6uvvsp///vfHPdNZGQkn332GYcOHeLkyZN8/vnnODo6UrJkSePYW7du5eeff+b333/Pso3Ro0ezdOlSRo8eTUJCAgcPHmTSpEkArF27lunTp7Nv3z7OnDnDwoULSU9PN+4gJyIiIiIiIvJ3pETSP5iPjw979+6lWbNmvPHGG1SuXJlWrVoRGxvLrFmzctTGSy+9RMeOHXnxxRepW7cuFy9eZMCAAffdz93dncjISJYvX07FihWZOHEiH3zwgVWdggULsmnTJpKTk2nSpAk1a9Zk7ty5xlS7fv36Ub58eWrVqoW7uzvx8fHky5ePrVu34uXlRceOHfH19SU0NJSUlJQHmg7m5ubG3LlzadCgAVWqVGHjxo189dVXFCxYEID33nuP06dPU7p0adzd3bNso2nTpixfvpw1a9ZQrVo1mjdvzs6dO432o6Ojad68Ob6+vsyePZulS5dSqVKlHMcoIiIiIiIi8rQxWR5kVWYReSKuXLmCq6sro7eexMHsnNvhiIiIyL/I29UL5XYIIiKSCzJ+hyYlJd1zoIZGJImIiIiIiIiISI4okSQiIiIiIiIiIjmiRJKIiIiIiIiIiORIntwOQESyN7RqwQdaRFxERERERETkcdKIJBERERERERERyRElkkREREREREREJEeUSBIRERERERERkRxRIklERERERERERHJEi22LPMU+3H8RB3NabochIiIiT4G3qxfK7RBEREQ0IklERERERERERHJGiSQREREREREREckRJZJERERERERERCRHlEgSEREREREREZEcUSLpKdO0aVOGDBmS22HI/xcSEkJgYGBuhyEiIiIiIiLyVFAi6QkICQnBZDLx8ssvZ9o2cOBATCYTISEhAERHRzN27NgnEtffJWm1cuVKbG1t+fnnn7PcXrZsWYYOHfpYjv3RRx8RGRn5WNoWERERERER+btRIukJ8fT0JCoqiuvXrxtlKSkpLFmyBC8vL6OsQIECODs7P9ZY0tL+XreTf+655yhYsCALFizItG3r1q0cP36c0NDQB243J/3g6uqKm5vbA7ctIiIiIiIi8k+kRNITUqNGDTw9PYmOjjbKoqOj8fLyonr16kbZ3aOEvL29GT9+PH369MHZ2RkvLy8+/fRTq7YPHjxI8+bNcXR0pGDBgvTv35/k5GRje8b0rPDwcIoVK0b58uWzjNHb25tx48bRs2dPzGYzJUuWZM2aNfz222906NABs9lMlSpV2L17t7HPxYsX6dq1K8WLFydfvnz4+fmxdOlSq3b//PNPgoODcXJyomjRokydOjXTeaampjJs2DCKFy+Ok5MTdevWJS4uDoC8efPSo0ePLEcGzZ8/n7p161KpUiUuX75M3759cXd3x8XFhebNm7N//36j7pgxY6hWrRrz5s2jVKlSODg4ALBixQr8/PyM/mvZsiVXr1616rs743z11VcpXLgwDg4ONGzYkF27dhnb4+LiMJlMxMbGUqtWLfLly0f9+vVJTEzMss/vbPfKlStWDxEREREREZGnjRJJT1CfPn2IiIgwXs+fP5/evXvfd78pU6ZQq1YtfvzxRwYMGMArr7xiJCauXr2Kv78/+fPnZ9euXSxfvpyNGzcyaNAgqzZiY2NJTEwkJiaGtWvXZnusqVOn0qBBA3788UfatWtHjx496NmzJ927d2fv3r2ULl2anj17YrFYgNujqmrWrMm6des4dOgQ/fv3p0ePHuzcudNoc+jQocTHx7NmzRpiYmLYtm0be/futTruoEGD2LFjB1FRURw4cIDOnTvTpk0bjh07BkBoaCjHjh1j69atxj7JycmsWLHCGI3UuXNnLly4wDfffMOePXuoUaMGLVq04NKlS8Y+x48fZ+XKlURHR7Nv3z7Onz9P165d6dOnDwkJCcTFxdGxY0fj/O721ltvsXLlShYsWMDevXspU6YM/v7+VscAeOedd5gyZQq7d+8mT5489OnTJ9s+B5gwYQKurq7Gw9PT8571RURERERERHKDEklPUPfu3fnuu+84c+YMZ86cIT4+nu7du993v2effZYBAwZQpkwZhg8fTqFChdi8eTMAS5YsISUlhYULF1K5cmWaN2/OjBkzWLRoEb/++qvRhpOTE/PmzaNSpUpUqlTpnsd66aWXKFu2LKNGjeLKlSvUrl2bzp07U65cOYYPH05CQoLRdvHixRk2bBjVqlXDx8eHwYMH06ZNG5YtWwbcHo20YMECPvjgA1q0aEHlypWJiIjg1q1bxjHPnj1LREQEy5cvp1GjRpQuXZphw4bRsGFDI/FWsWJFnnnmGebPn2/st2zZMiwWC126dOG7775j586dLF++nFq1alG2bFk++OAD3NzcWLFihbFPWloaCxcupHr16lSpUoXz589z8+ZNOnbsiLe3N35+fgwYMACz2Zypb65evcqsWbN4//33adu2LRUrVmTu3Lk4Ojry2WefWdUNDw+nSZMmVKxYkbfffpvt27eTkpKSbb+PGDGCpKQk43Hu3Lls64qIiIiIiIjkljy5HcC/ibu7O+3atSMyMhKLxUK7du0oVKjQfferUqWK8dxkMuHh4cGFCxcASEhIoGrVqjg5ORl1GjRoQHp6OomJiRQpUgQAPz8/7OzsHuhYd+57d9mFCxfw8PDg1q1bjB8/nmXLlvHzzz+TlpZGamoq+fLlA+DkyZPcuHGDOnXqGG24/j/27jusq/L/4/jzI8r8MBQH4ELFgQaGe+U2nGGaKxfuNHfOSsWRKzE1s0wT1Fw5MsvUlCQNy5GCmoR7ZDi+phCagMDvDy/Pz4+goqlgvR7Xda6Lc8597vt97o/90ft63/dxdrZYXnfo0CFSUlIoVaqURSyJiYm4uroa5927d2fIkCF8+OGHODo6smjRItq0aYOjoyNRUVEkJCRYtAf4+++/OXHihHFetGhR8uXLZ5yXL1+eBg0a4OPjg7+/Py+//DKvvfYauXPnTjc3J06cIDk5mZo1axrXcuXKRZUqVYiOjr7vPLq7uxtzdvd+WHezsbHBxsYmw3siIiIiIiIi2YUSSc9Y9+7djWVnH330UaaeyZUrl8W5yWQiNTX1kca9O9GU2bFMJtN9r90Z//3332f27NnMmjULHx8fHBwcGDx48CNt6J2QkICVlRW//PILVlZWFvfurgxq3749Q4YM4YsvvqB27dpEREQwZcoUow93d3djX6W73b1Z9r3zYGVlxdatW9m1axffffcdH374Ie+88w67d++mWLFimX6Hez1ozkRERERERESeV1ra9ow1btyYpKQkkpOT8ff3/8f9eXt7ExUVZWwODRAREUGOHDnuu6n2kxQREUFAQACdOnWifPnyFC9enKNHjxr3ixcvTq5cuSw2pI6Li7No4+fnR0pKCpcuXcLLy8vicHNzM9o5OjrSpk0bFi1aREhICKVKleKll14Cbm9mfuHCBXLmzJmuj4dVfZlMJmrWrMn48eM5cOAA1tbWfPnll+nalShRAmtrayIiIoxrycnJ7N27l7Jlyz765ImIiIiIiIg8Z5RIesasrKyIjo7myJEj6apvHkfHjh2xtbWla9euHD58mO3btzNgwAA6d+5sLEN7mkqWLGlU9ERHR9OnTx+LvZkcHR3p2rUrw4cPZ/v27fz666/06NGDHDlyGJU6pUqVomPHjnTp0oV169Zx6tQp9uzZw5QpU9i4caPFeD169GDXrl188sknFhtYN2zYkOrVq9OyZUu+++47Tp8+za5du3jnnXcsvjJ3r927dzN58mT27dvH2bNnWbduHZcvX8bb2ztdWwcHB/r27cvw4cPZvHkzR44coVevXty4ccPY8FtERERERETk30xL27KAk5PTE+vL3t6eLVu2MGjQICpXroy9vT2tW7dm5syZT2yMB3n33Xc5efIk/v7+2Nvb07t3b1q2bElcXJzRZubMmbzxxhs0b94cJycnRowYwblz57C1tTXahISEMGnSJN566y3Onz9P3rx5qVatGs2bN7cYr1atWpQuXZrjx4/TpUsX47rJZOLbb7/lnXfeoVu3bly+fBk3Nzdq1679wISak5MTO3bsYNasWcTHx1O0aFGCg4Np0qRJhu2nTp1KamoqnTt35q+//qJSpUps2bIlwz2VRERERERERP5tTGn3+865yFNy/fp1ChYsSHBwsCp57iM+Ph5nZ2fG7TiJrdkxq8MRERGRbGCU38M/0iIiIvK47vx/aFxc3AMLYFSRJE/dgQMH+O2336hSpQpxcXFMmDABgICAgCyOTEREREREREQehRJJ8kzMmDGDmJgYrK2tqVixIjt37nzoJtgiIiIiIiIikr1oaZtINpTZkkIRERERERGRJyGz/x+qr7aJiIiIiIiIiEimKJEkIiIiIiIiIiKZokSSiIiIiIiIiIhkihJJIiIiIiIiIiKSKfpqm0g2NjPqCrbmpKwOQ0RERB7DKD99oVZERP59VJEkIiIiIiIiIiKZokSSiIiIiIiIiIhkihJJIiIiIiIiIiKSKUokiYiIiIiIiIhIpiiRJCIiIiIiIiIimaJEkvxjgYGBtGzZMqvDeKjTp09jMpmMw9raGi8vLyZNmkRaWlpWhyciIiIiIiKS7eXM6gBEnrVt27ZRrlw5EhMT+fHHH+nZsyfu7u706NEjq0MTERERERERydZUkSRPVN26dRkwYACDBw8md+7cFChQgAULFnD9+nW6deuGo6MjXl5ebNq0yXgmJSWFHj16UKxYMezs7ChdujSzZ8+26PfWrVsMHDgQFxcXXF1dGTlyJF27drWohEpNTWXKlClGP+XLl2fNmjXpYnR1dcXNzY2iRYvSsWNHatasyf79+437e/fupVGjRuTNmxdnZ2fq1KljcT8tLY2goCCKFCmCjY0NHh4eDBw40LifmJjIsGHDKFiwIA4ODlStWpXw8PAnMLsiIiIiIiIiWUuJJHniFi9eTN68edmzZw8DBgygb9++tGnThho1arB//35efvllOnfuzI0bN4DbCaBChQqxevVqjhw5wtixY3n77bf54osvjD6nTZvGsmXLCAkJISIigvj4eNavX28x7pQpU1iyZAmffPIJv/76K0OGDKFTp0788MMP94113759/PLLL1StWtW49tdff9G1a1d+/PFHfv75Z0qWLEnTpk3566+/AFi7di0ffPAB8+fP59ixY6xfvx4fHx/j+f79+/PTTz+xcuVKDh48SJs2bWjcuDHHjh27bxyJiYnEx8dbHCIiIiIiIiLZjSlNm8PIPxQYGMi1a9dYv349devWJSUlhZ07dwK3q42cnZ1p1aoVS5YsAeDChQu4u7vz008/Ua1atQz77N+/PxcuXDAqitzc3Bg2bBjDhg0z+i1evDh+fn6sX7+exMRE8uTJw7Zt26hevbrRT8+ePblx4wbLly/n9OnTRrVSjhw5SEpKIjk5md69ezN//vz7vl9qaiouLi4sX76c5s2bM3PmTObPn8/hw4fJlSuXRduzZ89SvHhxzp49i4eHh3G9YcOGVKlShcmTJ2c4RlBQEOPHj093fdyOk9iaHe8bm4iIiGRfo/zyZnUIIiIimRYfH4+zszNxcXE4OTndt532SJInztfX1/jbysoKV1dXi4qdAgUKAHDp0iXj2kcffcSiRYs4e/Ysf//9N0lJSbz44osAxMXFcfHiRapUqWLRb8WKFUlNTQXg+PHj3Lhxg0aNGlnEkpSUhJ+fn8W1VatW4e3tTXJyMocPH2bAgAHkzp2bqVOnAnDx4kXeffddwsPDuXTpEikpKdy4cYOzZ88C0KZNG2bNmkXx4sVp3LgxTZs2pUWLFuTMmZNDhw6RkpJCqVKlLMZMTEzE1dX1vnM2evRohg4dapzHx8dTuHDh+7YXERERERERyQpKJMkTd2+VjslksrhmMpkAjCTQypUrGTZsGMHBwVSvXh1HR0fef/99du/enekxExISANi4cSMFCxa0uGdjY2NxXrhwYby8vADw9vbmxIkTjBkzhqCgIGxtbenatStXrlxh9uzZFC1aFBsbG6pXr05SUpLxfExMDNu2bWPr1q3069eP999/nx9++IGEhASsrKz45ZdfsLKyshjXbDbfN34bG5t0cYqIiIiIiIhkN0okSZaLiIigRo0a9OvXz7h24sQJ429nZ2cKFCjA3r17qV27NnB7adv+/fuNqqWyZctiY2PD2bNnqVOnziONb2Vlxa1bt0hKSsLW1paIiAjmzZtH06ZNATh37hz/+9//LJ6xs7OjRYsWtGjRgjfffJMyZcpw6NAh/Pz8SElJ4dKlS7z00kuPMx0iIiIiIiIi2ZYSSZLlSpYsyZIlS9iyZQvFihVj6dKl7N27l2LFihltBgwYwJQpU/Dy8qJMmTJ8+OGHXL161ahucnR0ZNiwYQwZMoTU1FRq1apFXFwcERERODk50bVrV6OvK1eucOHCBW7dusWhQ4eYPXs29erVM9aAlixZkqVLl1KpUiXi4+MZPnw4dnZ2xvOhoaGkpKRQtWpV7O3t+fzzz7Gzs6No0aK4urrSsWNHunTpQnBwMH5+fly+fJmwsDB8fX1p1qzZM5pVERERERERkSdPiSTJcn369OHAgQO0a9cOk8lEhw4d6NevH5s2bTLajBw5kgsXLtClSxesrKzo3bs3/v7+FsvHJk6cSL58+ZgyZQonT57ExcWFChUq8Pbbb1uM17BhQ+B2JZK7uztNmzblvffeM+5/9tln9O7dmwoVKlC4cGEmT55sbPIN4OLiwtSpUxk6dCgpKSn4+Pjw9ddfG3sghYSEMGnSJN566y3Onz9P3rx5qVatGs2bN38q8yciIiIiIiLyrOirbfJcSk1Nxdvbm7Zt2zJx4sSsDueJu7Nbvr7aJiIi8vzSV9tEROR5oq+2yb/KmTNn+O6776hTpw6JiYnMnTuXU6dO8frrr2d1aCIiIiIiIiL/GTmyOgCRzMiRIwehoaFUrlyZmjVrcujQIbZt24a3t3dWhyYiIiIiIiLyn6GKJHkuFC5cmIiIiKwOQ0REREREROQ/TYkkkWxsaHnXB65NFREREREREXmWtLRNREREREREREQyRYkkERERERERERHJFCWSREREREREREQkU5RIEhERERERERGRTNFm2yLZ2MyoK9iak7I6DBERkf+UUX55szoEERGRbEsVSSIiIiIiIiIikilKJImIiIiIiIiISKYokSQiIiIiIiIiIpmiRJKIiIiIiIiIiGSKEkmSbYSHh2Mymbh27VpWhyIiIiIiIiIiGVAiSZ6Yy5cv07dvX4oUKYKNjQ1ubm74+/sTERGR1aFliqenJ7NmzcrqMERERERERESyrZxZHYD8e7Ru3ZqkpCQWL15M8eLFuXjxImFhYVy5ciWrQxMRERERERGRJ0AVSfJEXLt2jZ07dzJt2jTq1atH0aJFqVKlCqNHj+aVV17h9OnTmEwmIiMjLZ4xmUyEh4db9BUREYGvry+2trZUq1aNw4cPG/fOnDlDixYtyJ07Nw4ODpQrV45vv/2WtLQ0vLy8mDFjhkVfkZGRmEwmjh8/TlpaGkFBQUbFlIeHBwMHDgSgbt26nDlzhiFDhmAymTCZTEYfP/74Iy+99BJ2dnYULlyYgQMHcv36deO+p6cnkyZNokuXLpjNZooWLcqGDRu4fPkyAQEBmM1mfH192bdv3xOccREREREREZFnT4kkeSLMZjNms5n169eTmJj4j/oaPnw4wcHB7N27l3z58tGiRQuSk5MBePPNN0lMTGTHjh0cOnSIadOmYTabMZlMdO/enZCQEIu+QkJCqF27Nl5eXqxdu5YPPviA+fPnc+zYMdavX4+Pjw8A69ato1ChQkyYMIHY2FhiY2MBOHHiBI0bN6Z169YcPHiQVatW8eOPP9K/f3+LcT744ANq1qzJgQMHaNasGZ07d6ZLly506tSJ/fv3U6JECbp06UJaWlqG75yYmEh8fLzFISIiIiIiIpLdKJEkT0TOnDkJDQ1l8eLFuLi4ULNmTd5++20OHjz4yH2NGzeORo0a4ePjw+LFi7l48SJffvklAGfPnqVmzZr4+PhQvHhxmjdvTu3atQEIDAwkJiaGPXv2AJCcnMzy5cvp3r278aybmxsNGzakSJEiVKlShV69egGQJ08erKyscHR0xM3NDTc3NwCmTJlCx44dGTx4MCVLlqRGjRrMmTOHJUuWcPPmTSPmpk2b0qdPH0qWLMnYsWOJj4+ncuXKtGnThlKlSjFy5Eiio6O5ePFihu88ZcoUnJ2djaNw4cKPPG8iIiIiIiIiT5sSSfLEtG7dmj/++IMNGzbQuHFjwsPDqVChAqGhoY/UT/Xq1Y2/8+TJQ+nSpYmOjgZg4MCBTJo0iZo1azJu3DiLRJWHhwfNmjVj0aJFAHz99dckJibSpk0bANq0acPff/9N8eLF6dWrF19++SW3bt16YCxRUVGEhoYaFVdmsxl/f39SU1M5deqU0c7X19f4u0CBAgBGtdPd1y5dupThOKNHjyYuLs44zp079/CJEhEREREREXnGlEiSJ8rW1pZGjRoxZswYdu3aRWBgIOPGjSNHjtv/1O5e2nVnudqj6NmzJydPnqRz584cOnSISpUq8eGHH1rcX7lyJX///TchISG0a9cOe3t7AAoXLkxMTAzz5s3Dzs6Ofv36Ubt27QfGkZCQQJ8+fYiMjDSOqKgojh07RokSJYx2uXLlMv6+s79SRtdSU1MzHMfGxgYnJyeLQ0RERERERCS7USJJnqqyZcty/fp18uXLB2DsPQRYbLx9t59//tn4++rVqxw9ehRvb2/jWuHChXnjjTdYt24db731FgsWLDDuNW3aFAcHBz7++GM2b95sLGu7w87OjhYtWjBnzhzCw8P56aefOHToEADW1takpKRYtK9QoQJHjhzBy8sr3WFtbf14kyIiIiIiIiLynMqZ1QHIv8OVK1do06YN3bt3x9fXF0dHR/bt28f06dMJCAjAzs6OatWqMXXqVIoVK8alS5d49913M+xrwoQJuLq6UqBAAd555x3y5s1Ly5YtARg8eDBNmjShVKlSXL16le3bt1skmaysrAgMDGT06NGULFnSYplcaGgoKSkpVK1aFXt7ez7//HPs7OwoWrQocPvrazt27KB9+/bY2NiQN29eRo4cSbVq1ejfvz89e/bEwcGBI0eOsHXrVubOnfv0JlREREREREQkG1JFkjwRZrOZqlWr8sEHH1C7dm1eeOEFxowZQ69evYyEy6JFi7h16xYVK1Zk8ODBTJo0KcO+pk6dyqBBg6hYsSIXLlzg66+/Nqp/UlJSePPNN/H29qZx48aUKlWKefPmWTzfo0cPkpKS6Natm8V1FxcXFixYQM2aNfH19WXbtm18/fXXuLq6ArcTWKdPn6ZEiRJGBZWvry8//PADR48e5aWXXsLPz4+xY8fi4eHxROdPRERERERE5HlgSrvf98hFnlM7d+6kQYMGnDt3ztjk+nkTHx+Ps7Mz43acxNbsmNXhiIiI/KeM8sub1SGIiIg8c3f+PzQuLu6B+/ZqaZv8ayQmJnL58mWCgoJo06bNc5tEEhEREREREcmutLRN/jVWrFhB0aJFuXbtGtOnT8/qcERERERERET+dbS0TSQbymxJoYiIiIiIiMiTkNn/D1VFkoiIiIiIiIiIZIoSSSIiIiIiIiIikilKJImIiIiIiIiISKYokSQiIiIiIiIiIpmSM6sDEJH7mxl1BVtzUlaHISLynzDKL29WhyAiIiKS7akiSUREREREREREMkWJJBERERERERERyRQlkkREREREREREJFOUSBIRERERERERkUxRIukudevWZfDgwVkdRqaFhobi4uKS1WH8qz1v/yZEREREREREnqZ/fSIpMDAQk8nEG2+8ke7em2++iclkIjAwEIB169YxceLEZxLXqVOneP311/Hw8MDW1pZChQoREBDAb7/99kzGfxJMJhPr169/6uMEBweTO3dubt68me7ejRs3cHJyYs6cOU9l7Gf5b0JEREREREQku/vXJ5IAChcuzMqVK/n777+Nazdv3mT58uUUKVLEuJYnTx4cHR2faixJSUkkJyfTqFEj4uLiWLduHTExMaxatQofHx+uXbv2VMd/HnXu3Jnr16+zbt26dPfWrFlDUlISnTp1euR+k5KSHtrmWfybEBEREREREXle/CcSSRUqVKBw4cIWiYh169ZRpEgR/Pz8jGv3LmPy9PRk8uTJdO/eHUdHR4oUKcKnn35q0fehQ4eoX78+dnZ2uLq60rt3bxISEoz7gYGBtGzZkvfeew8PDw9Kly7Nr7/+yokTJ5g3bx7VqlWjaNGi1KxZk0mTJlGtWjUAwsPDMZlMFomlyMhITCYTp0+ftohh/fr1lCxZEltbW/z9/Tl37pxxLyoqinr16uHo6IiTkxMVK1Zk3759XL9+HScnJ9asWZOuLwcHB/766y+SkpLo378/7u7u2NraUrRoUaZMmWLMDcCrr76KyWQyzgG++uorKlSogK2tLcWLF2f8+PHcunXLuG8ymZg/fz7NmzfH3t4eb29vfvrpJ44fP07dunVxcHCgRo0anDhxAoD8+fPTokULFi1alO63XbRoES1btiRPnjycO3eOtm3b4uLiQp48eQgICLCYq4x+C4B58+YZ81egQAFee+21+/6buHr1Kl26dCF37tzY29vTpEkTjh07Zty/s9xwy5YteHt7Yzabady4MbGxseliFxEREREREXne/CcSSQDdu3cnJCTEOF+0aBHdunV76HPBwcFUqlSJAwcO0K9fP/r27UtMTAwA169fx9/fn9y5c7N3715Wr17Ntm3b6N+/v0UfYWFhxMTEsHXrVr755hvy5ctHjhw5WLNmDSkpKf/ovW7cuMF7773HkiVLiIiI4Nq1a7Rv396437FjRwoVKsTevXv55ZdfGDVqFLly5cLBwYH27dtbzAlASEgIr732Go6OjsyZM4cNGzbwxRdfEBMTw7Jly4yE0d69e432sbGxxvnOnTvp0qULgwYN4siRI8yfP5/Q0FDee+89i3EmTpxIly5diIyMpEyZMrz++uv06dOH0aNHs2/fPtLS0izmsUePHnz//fecOXPGuHby5El27NhBjx49SE5Oxt/fH0dHR3bu3ElERISRxLm78uje32Lfvn0MHDiQCRMmEBMTw+bNm6ldu/Z95zswMJB9+/axYcMGfvrpJ9LS0mjatCnJyckWv8mMGTNYunQpO3bs4OzZswwbNuyBv2NiYiLx8fEWh4iIiIiIiEh2859JJHXq1Ikff/yRM2fOcObMGSIiIjK1HKpp06b069cPLy8vRo4cSd68edm+fTsAy5cv5+bNmyxZsoQXXniB+vXrM3fuXJYuXcrFixeNPhwcHFi4cCHlypWjXLlyFCxYkDlz5jB27Fhy585N/fr1mThxIidPnnzk90pOTmbu3LlUr16dihUrsnjxYnbt2sWePXsAOHv2LA0bNqRMmTKULFmSNm3aUL58eQB69uzJli1bjGqZS5cu8e2339K9e3fj2ZIlS1KrVi2KFi1KrVq16NChAwD58uUDwMXFBTc3N+N8/PjxjBo1iq5du1K8eHEaNWrExIkTmT9/vkXc3bp1o23btpQqVYqRI0dy+vRpOnbsiL+/P97e3gwaNIjw8HCjvb+/Px4eHhaJr9DQUAoXLkyDBg1YtWoVqampLFy4EB8fH7y9vQkJCeHs2bMW/dz7W5w9exYHBweaN29O0aJF8fPzY+DAgRnO9bFjx9iwYQMLFy7kpZdeonz58ixbtozz589b7BWVnJzMJ598QqVKlahQoQL9+/cnLCzsgb/jlClTcHZ2No7ChQs/sL2IiIiIiIhIVvjPJJLy5ctHs2bNCA0NJSQkhGbNmpE3b96HPufr62v8bTKZcHNz49KlSwBER0dTvnx5HBwcjDY1a9YkNTXVqFoC8PHxwdra2qLfN998kwsXLrBs2TKqV6/O6tWrKVeuHFu3bn2k98qZMyeVK1c2zsuUKYOLiwvR0dEADB06lJ49e9KwYUOmTp1qLBcDqFKlCuXKlWPx4sUAfP755xQtWtSoyAkMDCQyMpLSpUszcOBAvvvuu4fGExUVxYQJEzCbzcbRq1cvYmNjuXHjhtHu7nktUKCAMU93X7t586ZRmWNlZUXXrl0JDQ0lLS2N1NRUFi9eTLdu3ciRIwdRUVEcP34cR0dHY9w8efJw8+ZNi3e+97do1KgRRYsWpXjx4nTu3Jlly5ZZxHm36OhocubMSdWqVY1rrq6ulC5d2phvAHt7e0qUKGGcu7u7G/9m7mf06NHExcUZx93LE0VERERERESyi/9MIgluL28LDQ1l8eLFRtXNw+TKlcvi3GQykZqa+kjj3p1oupujoyMtWrTgvffeIyoqipdeeolJkyYBkCPH7Z8mLS3NaH/38qnMCgoK4tdff6VZs2Z8//33lC1bli+//NK437NnT0JDQ4Hby9S6deuGyWQCbu8tderUKSZOnMjff/9N27ZtLfYPykhCQgLjx48nMjLSOA4dOsSxY8ewtbU12t09r3fGy+ja3XPdvXt3zp49y/fff09YWBjnzp0zlicmJCRQsWJFi3EjIyM5evQor7/+utHHvb+Fo6Mj+/fvZ8WKFbi7uzN27FjKly//jzY9z+jfzN2/Y0ZsbGxwcnKyOERERERERESym/9UIunOfjl39tP5p7y9vYmKiuL69evGtYiICHLkyGFs5JxZJpOJMmXKGH3dWSp29ybNkZGR6Z67desW+/btM85jYmK4du0a3t7exrVSpUoxZMgQvvvuO1q1amWxPKxTp06cOXOGOXPmcOTIEbp27WrRv5OTE+3atWPBggWsWrWKtWvX8ueffwK3Eyb37vFUoUIFYmJi8PLySnfcSY49rhIlSlCnTh0WLVpESEgIDRs2pGjRosa4x44dI3/+/OnGdXZ2fmC/OXPmpGHDhkyfPp2DBw9y+vRpvv/++3TtvL29uXXrFrt37zauXblyhZiYGMqWLfuP3k1ERERERETkefCfSiRZWVkRHR3NkSNHsLKy+sf9dezYEVtbW7p27crhw4fZvn07AwYMoHPnzsZyrYxERkYSEBDAmjVrOHLkCMePH+ezzz5j0aJFBAQEAODl5UXhwoUJCgri2LFjbNy4keDg4HR95cqViwEDBrB7925++eUXAgMDqVatGlWqVOHvv/+mf//+hIeHG/tC7d271yLJlDt3blq1asXw4cN5+eWXKVSokHFv5syZrFixgt9++42jR4+yevVq3NzccHFxAW5/uS0sLIwLFy5w9epVAMaOHcuSJUsYP348v/76K9HR0axcuZJ33333H8833N50e926dXz55Zf06NHDuN6xY0fy5s1LQEAAO3fu5NSpU4SHhzNw4EB+//33+/b3zTffMGfOHCIjIzlz5gxLliwhNTU1w0RgyZIlCQgIoFevXvz4449ERUXRqVMnChYsaPxuIiIiIiIiIv9m/6lEEvBElw3Z29uzZcsW/vzzTypXrsxrr71GgwYNmDt37gOfK1SoEJ6enowfP56qVatSoUIFZs+ezfjx43nnnXeA2wmiO0kcX19fpk2bZix7uzeGkSNH8vrrr1OzZk3MZjOrVq0CbifOrly5QpcuXShVqhRt27alSZMmjB8/3qKPHj16kJSUlG65n6OjI9OnT6dSpUpUrlyZ06dP8+233xqVRcHBwWzdupXChQvj5+cH3N4U+5tvvuG7776jcuXKVKtWjQ8++MCoHPqnWrdujY2NDfb29rRs2dJiHnbs2EGRIkVo1aoV3t7e9OjRg5s3bz7w93ZxcWHdunXUr18fb29vPvnkE1asWEG5cuUybB8SEkLFihVp3rw51atXJy0tjW+//TbdcjYRERERERGRfyNT2sM2b5F/vaVLlzJkyBD++OOPdJuCS9aIj4/H2dmZcTtOYmt2zOpwRET+E0b5PfwjHCIiIiL/Vnf+PzQuLu6BBRk5n2FMks3cuHGD2NhYpk6dSp8+fZREEhEREREREZEH+s8tbZP/N336dMqUKYObmxujR4/O6nBEREREREREJJtTIuk/LCgoiOTkZMLCwjCbzVkdjoiIiIiIiIhkc1raJpKNDS3v+sQ2hxcRERERERH5p1SRJCIiIiIiIiIimaJEkoiIiIiIiIiIZIoSSSIiIiIiIiIikilKJImIiIiIiIiISKZos22RbGxm1BVszUlZHYaIPCOj/PJmdQgiIiIiIg+kiiQREREREREREckUJZJERERERERERCRTlEgSEREREREREZFMUSJJREREREREREQyRYmk55TJZGL9+vUAnD59GpPJRGRk5CP1ERQUxIsvvvjEY8uOQkNDcXFx+Ud9eHp6MmvWrCcSj4iIiIiIiMjzSImkbCYwMBCTyYTJZCJXrlwUKFCARo0asWjRIlJTUx+pr7Vr11K3bl2cnZ0xm834+voyYcIE/vzzz6cU/ZPzuMmx+2nXrh1Hjx7NVNv7JZ327t1L7969n0g8IiIiIiIiIs8jJZKyocaNGxMbG8vp06fZtGkT9erVY9CgQTRv3pxbt25lqo933nmHdu3aUblyZTZt2sThw4cJDg4mKiqKpUuXPtX4k5OTn2r/jyo5ORk7Ozvy58//j/rJly8f9vb2TygqERERERERkeePEknZkI2NDW5ubhQsWJAKFSrw9ttv89VXX7Fp0yZCQ0Mf+vyePXuYPHkywcHBvP/++9SoUQNPT08aNWrE2rVr6dq1q0X7pUuX4unpibOzM+3bt+evv/4y7m3evJlatWrh4uKCq6srzZs358SJE8b9O5VDq1atok6dOtja2rJs2TKuXLlChw4dKFiwIPb29vj4+LBixQqLcVNTU5k+fTpeXl7Y2NhQpEgR3nvvPQCKFSsGgJ+fHyaTibp16xrPLVy4EG9vb2xtbSlTpgzz5s17aDz3VhlFRUVRr149HB0dcXJyomLFiuzbt4/w8HC6detGXFycURkWFBQEpF/adu3aNfr06UOBAgWwtbXlhRde4JtvvgHgzJkztGjRgty5c+Pg4EC5cuX49ttvH/rbiYiIiIiIiGRnObM6AMmc+vXrU758edatW0fPnj0f2HbZsmWYzWb69euX4f27EyonTpxg/fr1fPPNN1y9epW2bdsydepUI6Fz/fp1hg4diq+vLwkJCYwdO5ZXX32VyMhIcuT4/zzkqFGjCA4Oxs/PD1tbW27evEnFihUZOXIkTk5ObNy4kc6dO1OiRAmqVKkCwOjRo1mwYAEffPABtWrVIjY2lt9++w24nQyrUqUK27Zto1y5clhbWxvvNnbsWObOnYufnx8HDhygV69eODg4WCTI7o1ny5YtFnPQsWNH/Pz8+Pjjj7GysiIyMpJcuXJRo0YNZs2axdixY4mJiQHAbDanm8PU1FSaNGnCX3/9xeeff06JEiU4cuQIVlZWALz55pskJSWxY8cOHBwcOHLkSIb93JGYmEhiYqJxHh8ff9+2IiIiIiIiIllFiaTnSJkyZTh48OBD2x07dozixYuTK1euh7ZNTU0lNDQUR0dHADp37kxYWJiRSGrdurVF+0WLFpEvXz6OHDnCCy+8YFwfPHgwrVq1smg7bNgw4+8BAwawZcsWvvjiC6pUqcJff/3F7NmzmTt3rpEAKlGiBLVq1QJuLyMDcHV1xc3Nzehn3LhxBAcHG2MVK1aMI0eOMH/+fItEUkbx3O3s2bMMHz6cMmXKAFCyZEnjnrOzMyaTyWLce23bto09e/YQHR1NqVKlAChevLhF/61bt8bHxyfdvYxMmTKF8ePHP7CNiIiIiIiISFbT0rbnSFpaGiaTKVPtMsvT09NIIgG4u7tz6dIl4/zYsWN06NCB4sWL4+TkhKenJ3A7UXK3SpUqWZynpKQwceJEfHx8yJMnD2azmS1bthjPRUdHk5iYSIMGDTId6/Xr1zlx4gQ9evTAbDYbx6RJkyyW22UUz72GDh1Kz549adiwIVOnTk33/MNERkZSqFAhI4l0r4EDBzJp0iRq1qzJuHHjHpoAHD16NHFxccZx7ty5R4pHRERERERE5FlQIuk5Eh0dbewd9CClSpXi5MmTmdr0+t6qJZPJZPF1uBYtWvDnn3+yYMECdu/eze7duwFISkqyeM7BwcHi/P3332f27NmMHDmS7du3ExkZib+/v/GcnZ3dQ2O7V0JCAgALFiwgMjLSOA4fPszPP//8wHjuFRQUxK+//kqzZs34/vvvKVu2LF9++WWmY3lY/D179uTkyZN07tyZQ4cOUalSJT788MP7trexscHJycniEBEREREREclulEh6Tnz//fccOnQo3VKzjLz++uskJCRYbEJ9t2vXrmVqzCtXrhATE8O7775LgwYN8Pb25urVq5l6NiIigoCAADp16kT58uUpXrw4R48eNe6XLFkSOzs7wsLCMnz+zp5IKSkpxrUCBQrg4eHByZMn8fLysjgyk2C7V6lSpRgyZAjfffcdrVq1IiQkxBj77nEz4uvry++//27xTvcqXLgwb7zxBuvWreOtt95iwYIFjxyjiIiIiIiISHaiPZKyocTERC5cuEBKSgoXL15k8+bNTJkyhebNm9OlS5eHPl+1alVGjBjBW2+9xfnz53n11Vfx8PDg+PHjfPLJJ9SqVYtBgwY9tJ/cuXPj6urKp59+iru7O2fPnmXUqFGZeoeSJUuyZs0adu3aRe7cuZk5cyYXL16kbNmyANja2jJy5EhGjBiBtbU1NWvW5PLly/z666/06NGD/PnzY2dnx+bNmylUqBC2trY4Ozszfvx4Bg4ciLOzM40bNyYxMZF9+/Zx9epVhg4dmqnY/v77b4YPH85rr71GsWLF+P3339m7d6+RpPP09CQhIYGwsDDKly+Pvb099vb2Fn3UqVOH2rVr07p1a2bOnImXlxe//fYbJpOJxo0bM3jwYJo0aUKpUqW4evUq27dvx9vbO1PxiYiIiIiIiGRXqkjKhjZv3oy7uzuenp40btyY7du3M2fOHL766ivjq2APM23aNJYvX87u3bvx9/enXLlyxtfX7t6U+kFy5MjBypUr+eWXX3jhhRcYMmQI77//fqaefffdd6lQoQL+/v7UrVsXNzc3WrZsadFmzJgxvPXWW4wdOxZvb2/atWtn7M+UM2dO5syZw/z58/Hw8CAgIAC4vWRs4cKFhISE4OPjQ506dQgNDX2kiiQrKyuuXLlCly5dKFWqFG3btqVJkybGZtc1atTgjTfeoF27duTLl4/p06dn2M/atWupXLkyHTp0oGzZsowYMcKoZEpJSeHNN9/E29ubxo0bU6pUqftWiImIiIiIiIg8L0xpj7Izs4g8E/Hx8Tg7OzNux0lszY4Pf0BE/hVG+eXN6hBERERE5D/qzv+HxsXFPXDfXlUkiYiIiIiIiIhIpiiRJCIiIiIiIiIimaJEkoiIiIiIiIiIZIq+2iaSjQ0t7/rAtakiIiIiIiIiz5IqkkREREREREREJFOUSBIRERERERERkUxRIklERERERERERDJFeySJZGMzo65ga07K6jBE5BGN8sub1SGIiIiIiDwVqkgSEREREREREZFMUSJJREREREREREQyRYkkERERERERERHJFCWSREREREREREQkU5RIekpMJhPr16/PkrHXr1+Pl5cXVlZWDB48+LH6CAwMpGXLlg9sEx4ejslk4tq1a5nut27duhYxeXp6MmvWrMeKMTNOnz6NyWQiMjLyqY0hIiIiIiIi8l+hRNJjunz5Mn379qVIkSLY2Njg5uaGv78/ERERAMTGxtKkSZMnOubixYupXLky9vb2ODo6UqdOHb755pt07fr06cNrr73GuXPnmDhx4mMlfGbPnk1oaKhxfm8CCKBGjRrExsbi7Oz8mG8Ee/fupXfv3o/9/KO6k1i6czg6OlKuXDnefPNNjh079sj9Pe1EmIiIiIiIiEh2okTSY2rdujUHDhxg8eLFHD16lA0bNlC3bl2uXLkCgJubGzY2Nk9svGHDhtGnTx/atWvHwYMH2bNnD7Vq1SIgIIC5c+ca7RISErh06RL+/v54eHjg6Oj4WOM5Ozvj4uLywDbW1ta4ublhMpkeawyAfPnyYW9v/9jPP65t27YRGxtLVFQUkydPJjo6mvLlyxMWFvbMYxERERERERF5XiiR9BiuXbvGzp07mTZtGvXq1aNo0aJUqVKF0aNH88orrwCWS9uCgoIsqmDuHHcqflJTU5kyZQrFihXDzs6O8uXLs2bNGmO8n3/+meDgYN5//32GDRuGl5cX3t7evPfeewwePJihQ4dy7tw5wsPDjcRR/fr1MZlMhIeHp4s/NDQUFxcXtmzZgre3N2azmcaNGxMbG2u0uXtpW2BgID/88AOzZ882Yj99+nS6SqcrV67QoUMHChYsiL29PT4+PqxYseKBc3l3RU9oaGiG8xQUFGS0X7hwId7e3tja2lKmTBnmzZtn0d+ePXvw8/PD1taWSpUqceDAgQzHdXV1xc3NjeLFixMQEMC2bduoWrUqPXr0ICUlBYATJ04QEBBAgQIFMJvNVK5cmW3bthl91K1blzNnzjBkyBAj1sedBxEREREREZHngRJJj8FsNmM2m1m/fj2JiYkPbT9s2DBiY2ONY8aMGdjb21OpUiUApkyZwpIlS/jkk0/49ddfGTJkCJ06deKHH34AYMWKFZjNZvr06ZOu77feeovk5GTWrl1LjRo1iImJAWDt2rXExsZSo0aNDGO6ceMGM2bMYOnSpezYsYOzZ88ybNiwDNvOnj2b6tWr06tXL+MdChcunK7dzZs3qVixIhs3buTw4cP07t2bzp07s2fPnofOEUC7du0s5mnFihXkzJmTmjVrArBs2TLGjh3Le++9R3R0NJMnT2bMmDEsXrwYuF2N1bx5c8qWLcsvv/xCUFDQfd/pXjly5GDQoEGcOXOGX375xeivadOmhIWFceDAARo3bkyLFi04e/YsAOvWraNQoUJMmDDBiPlx5yExMZH4+HiLQ0RERERERCS7yZnVATyPcubMSWhoKL169eKTTz6hQoUK1KlTh/bt2+Pr65uu/Z3EE9yuLnr33XdZvHgxL7zwAomJiUyePJlt27ZRvXp1AIoXL86PP/7I/PnzqVOnDkePHqVEiRJYW1un69vDwwMnJyeOHj2KtbU1+fPnByBPnjy4ubnd9x2Sk5P55JNPKFGiBAD9+/dnwoQJGbZ1dnbG2toae3v7B/ZZsGBBi8TNgAED2LJlC1988QVVqlS573N32NnZYWdnB9yuBnrzzTeZPHkyjRo1AmDcuHEEBwfTqlUrAIoVK8aRI0eYP38+Xbt2Zfny5aSmpvLZZ59ha2tLuXLl+P333+nbt+9DxwYoU6YMcHsfpSpVqlC+fHnKly9v3J84cSJffvklGzZsoH///uTJkwcrKyscHR0t5uVx5mHKlCmMHz8+U3GKiIiIiIiIZBVVJD2m1q1b88cff7BhwwYaN25MeHg4FSpUsNig+l5nz56lZcuWDBs2jLZt2wJw/Phxbty4QaNGjYyEk9lsZsmSJZw4ccJ4Ni0t7YnGb29vbySRANzd3bl06dI/6jMlJYWJEyfi4+NDnjx5MJvNbNmyxajgyay4uDiaN29Os2bNGD58OADXr1/nxIkT9OjRw2KeJk2aZMxTdHQ0vr6+2NraGn3dSc5lxp05vrNELSEhgWHDhuHt7Y2Liwtms5no6OiHvs/jzMPo0aOJi4szjnPnzmU6bhEREREREZFnRRVJ/4CtrS2NGjWiUaNGjBkzhp49ezJu3DgCAwPTtb1+/TqvvPIK1atXt6j8SUhIAGDjxo0ULFjQ4pk7m3WXKlWKH3/8kaSkpHRVSX/88Qfx8fGUKlXqkWLPlSuXxbnJZPrHyar333+f2bNnM2vWLHx8fHBwcGDw4MEkJSVluo+UlBTatWuHk5MTn376qXH9zjwtWLCAqlWrWjxjZWX1j+K+Izo6Grhd6QS3lyRu3bqVGTNm4OXlhZ2dHa+99tpD3+dx5sHGxuaJbs4uIiIiIiIi8jQokfQElS1b1thg+25paWl06tSJ1NRUli5davGVs7Jly2JjY8PZs2epU6dOhv22b9+eOXPmMH/+fAYMGGBxb8aMGeTKlYvWrVs/0Xe5l7W1tbEJ9f1EREQQEBBAp06dgNubiB89epSyZctmepwhQ4Zw6NAh9u3bZ1FZVKBAATw8PDh58iQdO3bM8Flvb2+WLl3KzZs3jWd//vnnTI2bmprKnDlzKFasGH5+fsb7BAYG8uqrrwK3k1mnT5+2eC6jeXkS8yAiIiIiIiKSHSmR9BiuXLlCmzZt6N69O76+vjg6OrJv3z6mT59OQEBAuvZBQUFs27aN7777joSEBKO6xtnZGUdHR4YNG8aQIUNITU2lVq1axMXFERERgZOTE127dqV69eoMGjSI4cOHk5SURMuWLUlOTubzzz83Kl8y2vz6SfL09GT37t2cPn0as9lMnjx50rUpWbIka9asYdeuXeTOnZuZM2dy8eLFTCdQQkJCmDdvHl9++SUmk4kLFy4A/7/H1Pjx4xk4cCDOzs40btyYxMRE9u3bx9WrVxk6dCivv/4677zzDr169WL06NGcPn2aGTNmZDjWlStXuHDhAjdu3ODw4cPMmjWLPXv2sHHjRqPCqWTJkqxbt44WLVpgMpkYM2YMqamp6eZlx44dtG/fHhsbG/LmzfuP50FEREREREQku1Ii6TGYzWaqVq3KBx98wIkTJ0hOTqZw4cL06tWLt99+O137H374gYSEhHRfUAsJCSEwMJCJEyeSL18+pkyZwsmTJ3FxcaFChQoWfc2aNQtfX1/mzZvHu+++i5WVFRUqVGD9+vW0aNHiqb/zsGHD6Nq1K2XLluXvv//m1KlT6dq8++67nDx5En9/f+zt7enduzctW7YkLi4uU2P88MMPpKSk8Morr1hcHzduHEFBQfTs2RN7e3vef/99hg8fjoODAz4+PgwePBi4/bt8/fXXvPHGG/j5+VG2bFmmTZuWYbVWw4YNgdt7RRUtWpR69erx6aef4uXlZbSZOXMm3bt3p0aNGuTNm5eRI0em+5rahAkT6NOnDyVKlCAxMZG0tLR/PA8iIiIiIiIi2ZUp7Unv4iwi/1h8fDzOzs6M23ESW7NjVocjIo9olF/erA5BREREROSR3Pn/0Li4OJycnO7bTl9tExERERERERGRTFEiSUREREREREREMkWJJBERERERERERyRRtti2SjQ0t7/rAtakiIiIiIiIiz5IqkkREREREREREJFOUSBIRERERERERkUxRIklERERERERERDJFiSQREREREREREckUbbYtko3NjLqCrTkpq8MQ+c8a5Zc3q0MQEREREclWVJEkIiIiIiIiIiKZokSSiIiIiIiIiIhkihJJIiIiIiIiIiKSKUokiYiIiIiIiIhIpvzrE0kmk4n169dndRjyHAsMDKRly5ZZHYaIiIiIiIhIlntuE0mBgYGYTCZMJhO5cuWiQIECNGrUiEWLFpGammq0i42NpUmTJk81lqCgIF588cWnOsbdQkNDjXe/+7C1tX2kfp5kki00NBQXF5cn0tfT4unpyaxZs9JdX7BgAeXLl8dsNuPi4oKfnx9Tpkwx7s+ePZvQ0NBnF6iIiIiIiIhINpUzqwP4Jxo3bkxISAgpKSlcvHiRzZs3M2jQINasWcOGDRvImTMnbm5uD+wjOTmZXLlyPaOIHywpKQlra+tMtXVyciImJsbimslkytKYsqsHvcOiRYsYPHgwc+bMoU6dOiQmJnLw4EEOHz5stHF2dn5WoYqIiIiIiIhka89tRRKAjY0Nbm5uFCxYkAoVKvD222/z1VdfsWnTJqOC5O6qm9OnT2MymVi1ahV16tTB1taWZcuWAbBw4UK8vb2xtbWlTJkyzJs3z2Ks33//nQ4dOpAnTx4cHByoVKkSu3fvJjQ0lPHjxxMVFWVUBt0Z++zZswQEBGA2m3FycqJt27ZcvHjR6PNOJdPChQspVqwYtra2LFmyBFdXVxITEy3Gb9myJZ07dzbOTSYTbm5uFkeBAgWM+3Xr1mXgwIGMGDGCPHny4ObmRlBQkHHf09MTgFdffRWTyWSc/5OY7nann0WLFlGkSBHMZjP9+vUjJSWF6dOn4+bmRv78+XnvvfcsnjOZTHz88cc0adIEOzs7ihcvzpo1ayzaHDp0iPr162NnZ4erqyu9e/cmISHBuH9nKdp7772Hh4cHpUuXpm7dupw5c4YhQ4YYvxPAhg0baNu2LT169MDLy4ty5crRoUMHi7juXtp259/QvUfdunWN9j/++CMvvfQSdnZ2FC5cmIEDB3L9+vUM50lERERERETkefJcJ5IyUr9+fcqXL8+6devu22bUqFEMGjSI6Oho/P39WbZsGWPHjuW9994jOjqayZMnM2bMGBYvXgxAQkICderU4fz582zYsIGoqChGjBhBamoq7dq146233qJcuXLExsYSGxtLu3btSE1NJSAggD///JMffviBrVu3cvLkSdq1a2cRy/Hjx1m7di3r1q0jMjKSNm3akJKSwoYNG4w2ly5dYuPGjXTv3v2R5mLx4sU4ODiwe/dupk+fzoQJE9i6dSsAe/fuBSAkJITY2Fjj/EnGdOLECTZt2sTmzZtZsWIFn332Gc2aNeP333/nhx9+YNq0abz77rvs3r3b4rkxY8bQunVroqKi6NixI+3btyc6OhqA69ev4+/vT+7cudm7dy+rV69m27Zt9O/f36KPsLAwYmJi2Lp1K9988w3r1q2jUKFCTJgwwfidANzc3Pj55585c+ZMpua0cOHCxvOxsbEcOHAAV1dXateubbxz48aNad26NQcPHmTVqlX8+OOP6eK7V2JiIvHx8RaHiIiIiIiISHbzXC9tu58yZcpw8ODB+94fPHgwrVq1Ms7HjRtHcHCwca1YsWIcOXKE+fPn07VrV5YvX87ly5fZu3cvefLkAcDLy8t43mw2p1tGt3XrVg4dOsSpU6coXLgwAEuWLKFcuXLs3buXypUrA7eXXS1ZsoR8+fIZz77++uuEhITQpk0bAD7//HOKFCliUfUSFxeH2Wy2eK+XXnqJTZs2Gee+vr6MGzcOgJIlSzJ37lzCwsJo1KiRMZ6Li0u65X+PG9O9UlNTWbRoEY6OjpQtW5Z69eoRExPDt99+S44cOShdujTTpk1j+/btVK1a1XiuTZs29OzZE4CJEyeydetWPvzwQ+bNm8fy5cu5efMmS5YswcHBAYC5c+fSokULpk2bZlRlOTg4sHDhQoslbVZWVjg6Olq877hx42jVqhWenp6UKlWK6tWr07RpU1577TVy5EifZ7WysjKev3nzJi1btqR69epGtdeUKVPo2LEjgwcPNub9zrK5jz/++L77WE2ZMoXx48ffdy5FREREREREsoN/ZSIpLS3tgfsFVapUyfj7+vXrnDhxgh49etCrVy/j+q1bt4y9cSIjI/Hz8zOSSJkRHR1N4cKFjSQSQNmyZXFxcSE6OtpIJBUtWtQiYQPQq1cvKleuzPnz5ylYsCChoaHG5uJ3ODo6sn//fovn7OzsLM59fX0tzt3d3bl06dJDY3/cmO7l6emJo6OjcV6gQAGsrKwsEjQFChRIF1P16tXTnUdGRgK357V8+fJGEgmgZs2apKamEhMTYySSfHx8MrW3k7u7Oz/99BOHDx9mx44d7Nq1i65du7Jw4UI2b96cYTLpju7du/PXX3+xdetWo11UVBQHDx40lkzC7X+PqampnDp1Cm9v7wz7Gj16NEOHDjXO4+PjLf7tiIiIiIiIiGQH/8pEUnR0NMWKFbvv/buTEHf21lmwYIFFVQzcrj6B9AmaJ+nuWO7w8/OjfPnyLFmyhJdffplff/2VjRs3WrTJkSOHRVVURu7dRNxkMll80e5Jx5SZ8R83pkeV0Ts8yAsvvMALL7xAv379eOONN3jppZf44YcfqFevXobtJ02axJYtW9izZ49FsiwhIYE+ffowcODAdM8UKVLkvuPb2NhgY2PzSDGLiIiIiIiIPGv/ukTS999/z6FDhxgyZEim2hcoUAAPDw9OnjxJx44dM2zj6+vLwoUL+fPPPzOsSrK2tiYlJcXimre3N+fOnePcuXNGZcmRI0e4du0aZcuWfWhcPXv2ZNasWZw/f56GDRs+leqUXLlypYs7q2MC+Pnnn+nSpYvFuZ+fH3B7XkNDQ7l+/bqRLIqIiDCWyj1IRr9TRu78PvfbIHvt2rVMmDCBTZs2UaJECYt7FSpU4MiRIw9N8omIiIiIiIg8j57rzbYTExO5cOEC58+fZ//+/UyePJmAgACaN29ukYh4mPHjxzNlyhTmzJnD0aNHOXToECEhIcycOROADh064ObmRsuWLYmIiODkyZOsXbuWn376Cbi9hOvUqVNERkbyv//9j8TERBo2bIiPjw8dO3Zk//797Nmzhy5dulCnTh2LpXX38/rrr/P777+zYMGCDDe0TktL48KFC+mOR6nu8fT0JCwsjAsXLnD16tV/HNOTsnr1ahYtWsTRo0cZN24ce/bsMTar7tixI7a2tnTt2pXDhw+zfft2BgwYQOfOnS2+WpcRT09PduzYwfnz5/nf//4HQN++fZk4cSIRERGcOXPGSGLly5cv3RI7gMOHD9OlSxdGjhxJuXLljHn/888/ARg5ciS7du2if//+REZGcuzYMb766quHbrYtIiIiIiIi8jx4rhNJmzdvxt3dHU9PTxo3bsz27duZM2cOX331lbEsLTN69uzJwoULCQkJwcfHhzp16hAaGmosj7O2tua7774jf/78NG3aFB8fH6ZOnWqM0bp1axo3bky9evXIly8fK1aswGQy8dVXX5E7d25q165Nw4YNKV68OKtWrcpUTM7OzrRu3Rqz2Wx8ev5u8fHxuLu7pzsyswfSHcHBwWzdupXChQsbFT//JKYnZfz48axcuRJfX1+WLFnCihUrjCohe3t7tmzZwp9//knlypV57bXXaNCgAXPnzn1ovxMmTOD06dOUKFHC2AOqYcOG/Pzzz7Rp04ZSpUrRunVrbG1tCQsLw9XVNV0f+/bt48aNG0yaNMli3u9s1O7r68sPP/zA0aNHeemll/Dz82Ps2LF4eHg8wRkSERERERERyRqmtLS0tKwOQjLWoEEDypUrx5w5c7I6FMPTjslkMvHll18+1UTV8yA+Ph5nZ2fG7TiJrdnx4Q+IyFMxyi9vVocgIiIiIvJM3Pn/0Li4OJycnO7b7l+3R9K/wdWrVwkPDyc8PJx58+ZldThA9oxJRERERERERJ4tJZKyIT8/P65evcq0adMeuoH0s5IdYxIRERERERGRZ0uJpGzo9OnTWR1COs8qJq20FBEREREREcm+lEgSycaGlnd94NpUERERERERkWfpuf5qm4iIiIiIiIiIPDtKJImIiIiIiIiISKYokSQiIiIiIiIiIpmiRJKIiIiIiIiIiGSKNtsWycZmRl3B1pyU1WGIZEuj/PJmdQgiIiIiIv85qkgSEREREREREZFMUSJJREREREREREQyRYkkERERERERERHJFCWSREREREREREQkU5RIkkwLDQ3FxcUlq8N4ZgIDA2nZsmVWhyEiIiIiIiKSbSiR9Jy6fPkyffv2pUiRItjY2ODm5oa/vz8RERFPpH9PT09mzZplca1du3YcPXr0ifR/x+nTpzGZTFhZWXH+/HmLe7GxseTMmROTycTp06ef6LhpaWl8+umnVK1aFbPZjIuLC5UqVWLWrFncuHHjiY4lIiIiIiIi8m+hRNJzqnXr1hw4cIDFixdz9OhRNmzYQN26dbly5cpTG9POzo78+fM/lb4LFizIkiVLLK4tXryYggULPpXxOnfuzODBgwkICGD79u1ERkYyZswYvvrqK7777runMibcTmDdunXrqfUvIiIiIiIi8jQpkfQcunbtGjt37mTatGnUq1ePokWLUqVKFUaPHs0rr7xitOnZsyf58uXDycmJ+vXrExUVZdHP119/TeXKlbG1tSVv3ry8+uqrANStW5czZ84wZMgQTCYTJpMJyHhp28cff0yJEiWwtramdOnSLF261OK+yWRi4cKFvPrqq9jb21OyZEk2bNiQ7p26du1KSEiIxbWQkBC6du1qcS0lJYUePXpQrFgx7OzsKF26NLNnzzbu37x5k3LlytG7d2/j2okTJ3B0dGTRokUAfPHFFyxbtowVK1bw9ttvU7lyZTw9PQkICOD777+nXr16FmPOmDEDd3d3XF1defPNN0lOTjbuLV26lEqVKuHo6Iibmxuvv/46ly5dMu6Hh4djMpnYtGkTFStWxMbGhh9//DHd+ycmJhIfH29xiIiIiIiIiGQ3SiQ9h8xmM2azmfXr15OYmJhhmzZt2nDp0iU2bdrEL7/8QoUKFWjQoAF//vknABs3buTVV1+ladOmHDhwgLCwMKpUqQLAunXrKFSoEBMmTCA2NpbY2NgMx/jyyy8ZNGgQb731FocPH6ZPnz5069aN7du3W7QbP348bdu25eDBgzRt2pSOHTsacdzxyiuvcPXqVSPJ8uOPP3L16lVatGhh0S41NZVChQqxevVqjhw5wtixY3n77bf54osvALC1tWXZsmUsXryYr776ipSUFDp16kSjRo3o3r07AMuWLaN06dIEBASkeyeTyYSzs7Nxvn37dk6cOMH27dtZvHgxoaGhhIaGGveTk5OZOHEiUVFRrF+/ntOnTxMYGJiu31GjRjF16lSio6Px9fVNd3/KlCk4OzsbR+HChTOccxEREREREZGsZEpLS0vL6iDk0a1du5ZevXrx999/U6FCBerUqUP79u3x9fXlxx9/pFmzZly6dAkbGxvjGS8vL0aMGEHv3r2pUaMGxYsX5/PPP8+wf09PTwYPHszgwYONa6GhoQwePJhr164BULNmTcqVK8enn35qtGnbti3Xr19n48aNwO3EzLvvvsvEiRMBuH79OmazmU2bNtG4cWNOnz5NsWLFjGV6cXFxLFq0iO7du+Pi4kKXLl3w8/Pj1KlTeHp6Zhhr//79uXDhAmvWrDGuvf/++0yfPp327duzdu1aDh06hKurKwBly5alZMmSfPXVVw+c48DAQMLDwzlx4gRWVlbG++XIkYOVK1dm+My+ffuoXLkyf/31F2azmfDwcOrVq8f69eszTFzdkZiYaJEUjI+Pp3DhwozbcRJbs+MD4xT5rxrllzerQxARERER+deIj4/H2dmZuLg4nJyc7ttOFUnPqdatW/PHH3+wYcMGGjduTHh4OBUqVCA0NJSoqCgSEhJwdXU1qpfMZjOnTp3ixIkTAERGRtKgQYN/FEN0dDQ1a9a0uFazZk2io6Mtrt1dgePg4ICTk5PF8q87unfvzurVq7lw4QKrV682Koju9dFHH1GxYkXy5cuH2Wzm008/5ezZsxZt3nrrLUqVKsXcuXNZtGiRkUSC2/sUZVa5cuWMJBKAu7u7Rey//PILLVq0oEiRIjg6OlKnTh2AdPFUqlTpgePY2Njg5ORkcYiIiIiIiIhkN0okPcdsbW1p1KgRY8aMYdeuXQQGBjJu3DgSEhJwd3cnMjLS4oiJiWH48OHA7Y2zn5VcuXJZnJtMJlJTU9O18/HxoUyZMnTo0AFvb29eeOGFdG1WrlzJsGHD6NGjB9999x2RkZF069aNpKQki3aXLl3i6NGjWFlZcezYMYt7pUqV4rfffvvHsV+/fh1/f3+cnJxYtmwZe/fu5csvvwRIF4+Dg0OmxhMRERERERHJzpRI+hcpW7Ys169fp0KFCly4cIGcOXPi5eVlceTNe3spiK+vL2FhYffty9rampSUlAeO5+3tTUREhMW1iIgIypYt+9jv0L17d8LDw+9bjRQREUGNGjXo168ffn5+eHl5GVVW9/bj4+PD4sWLGTlypEWV1Ouvv87Ro0czXNqWlpZGXFxcpmL97bffuHLlClOnTuWll16iTJkyGVZaiYiIiIiIiPxbKJH0HLpy5Qr169fn888/5+DBg5w6dYrVq1czffp0AgICaNiwIdWrV6dly5Z89913nD59ml27dvHOO++wb98+AMaNG8eKFSsYN24c0dHRHDp0iGnTphljeHp6smPHDs6fP8///ve/DOMYPnw4oaGhfPzxxxw7doyZM2eybt06hg0b9tjv1qtXLy5fvkzPnj0zvF+yZEn27dvHli1bOHr0KGPGjGHv3r0WbT766CN++uknFi9eTMeOHWnZsiUdO3Y0qoTatm1Lu3bt6NChA5MnT2bfvn2cOXOGb775hoYNG6bbLPx+ihQpgrW1NR9++CEnT55kw4YNxl5QIiIiIiIiIv9GSiQ9h8xmM1WrVuWDDz6gdu3avPDCC4wZM4ZevXoxd+5cTCYT3377LbVr16Zbt26UKlWK9u3bc+bMGQoUKABA3bp1Wb16NRs2bODFF1+kfv367NmzxxhjwoQJnD59mhIlSpAvX74M42jZsiWzZ89mxowZlCtXjvnz5xMSEkLdunUf+91y5sxJ3rx5yZkzZ4b3+/TpQ6tWrWjXrh1Vq1blypUr9OvXz7j/22+/MXz4cObNm2d8+WzevHn873//Y8yYMcDt5WnLly9n5syZrF+/njp16uDr60tQUBABAQH4+/tnKtZ8+fIRGhrK6tWrKVu2LFOnTmXGjBmP/e4iIiIiIiIi2Z2+2iaSDd3ZLV9fbRO5P321TURERETkydFX20RERERERERE5IlSIklERERERERERDIl441oRCRbGFre9YElhSIiIiIiIiLPkiqSREREREREREQkU5RIEhERERERERGRTFEiSUREREREREREMkWJJBERERERERERyRRtti2Sjc2MuoKtOSmrwxB5Kkb55c3qEERERERE5BGpIklERERERERERDJFiSQREREREREREckUJZJERERERERERCRTlEgSEREREREREZFMUSLpOWQymVi/fn1Wh/GfEBgYSMuWLbM6DBEREREREZFsQYmkZyAwMBCTycQbb7yR7t6bb76JyWQiMDAw0/3FxsbSpEmTfxxX3bp1GTx48D/u52lbu3YtVlZWnD9/PsP7JUuWZOjQoU9l7NmzZxMaGvpU+hYRERERERF53iiR9IwULlyYlStX8vfffxvXbt68yfLlyylSpMgj9eXm5oaNjc1jx5KU9Hx9Tv6VV17B1dWVxYsXp7u3Y8cOjh8/To8ePR6538zMg7OzMy4uLo/ct4iIiIiIiMi/kRJJz0iFChUoXLgw69atM66tW7eOIkWK4OfnZ1zbvHkztWrVwsXFBVdXV5o3b86JEycs+rp3aduhQ4eoX78+dnZ2uLq60rt3bxISEoz7d5Znvffee3h4eFC6dOkMY/T09GTSpEl06dIFs9lM0aJF2bBhA5cvXyYgIACz2Yyvry/79u0znrly5QodOnSgYMGC2Nvb4+Pjw4oVKyz6/euvv+jYsSMODg64u7vzwQcfpKuGSkxMZNiwYRQsWBAHBweqVq1KeHg4ALly5aJz584ZVgYtWrSIqlWrUq5cOa5du0bPnj3Jly8fTk5O1K9fn6ioKKNtUFAQL774IgsXLqRYsWLY2toCsGbNGnx8fIz5a9iwIdevX7eYu7vjHDhwIPnz58fW1pZatWqxd+9e4354eDgmk4mwsDAqVaqEvb09NWrUICYmJsM5FxEREREREXmeKJH0DHXv3p2QkBDjfNGiRXTr1s2izfXr1xk6dCj79u0jLCyMHDly8Oqrr5Kampphn9evX8ff35/cuXOzd+9eVq9ezbZt2+jfv79Fu7CwMGJiYti6dSvffPPNfWP84IMPqFmzJgcOHKBZs2Z07tyZLl260KlTJ/bv30+JEiXo0qULaWlpwO2qqooVK7Jx40YOHz5M79696dy5M3v27DH6HDp0KBEREWzYsIGtW7eyc+dO9u/fbzFu//79+emnn1i5ciUHDx6kTZs2NG7cmGPHjgHQo0cPjh07xo4dO4xnEhISWLNmjVGN1KZNGy5dusSmTZv45ZdfqFChAg0aNODPP/80njl+/Dhr165l3bp1REZGEhsbS4cOHejevTvR0dGEh4fTqlUr4/3uNWLECNauXcvixYvZv38/Xl5e+Pv7W4wB8M477xAcHMy+ffvImTMn3bt3v++cw+0EVXx8vMUhIiIiIiIikt3kzOoA/ks6derE6NGjOXPmDAARERGsXLnSqLwBaN26tcUzixYtIl++fBw5coQXXnghXZ/Lly/n5s2bLFmyBAcHBwDmzp1LixYtmDZtGgUKFADAwcGBhQsXYm1t/cAYmzZtSp8+fQAYO3YsH3/8MZUrV6ZNmzYAjBw5kurVq3Px4kXc3NwoWLAgw4YNM54fMGAAW7Zs4YsvvqBKlSr89ddfLF68mOXLl9OgQQMAQkJC8PDwMJ45e/YsISEhnD171rg+bNgwNm/eTEhICJMnT6Zs2bJUq1aNRYsWUbt2bQC++OIL0tLSaN++PT/++CN79uzh0qVLxrK/GTNmsH79etasWUPv3r2B28vZlixZQr58+QDYv38/t27dolWrVhQtWhQAHx+fDOfm+vXrfPzxx4SGhhp7VC1YsICtW7fy2WefMXz4cKPte++9R506dQAYNWoUzZo14+bNm0YV1L2mTJnC+PHjH/jbiIiIiIiIiGQ1VSQ9Q/ny5aNZs2aEhoYSEhJCs2bNyJs3r0WbY8eO0aFDB4oXL46TkxOenp7A7WRLRqKjoylfvryRRAKoWbMmqampFsupfHx8HppEAvD19TX+vpOEujuxcufapUuXAEhJSWHixIn4+PiQJ08ezGYzW7ZsMeI9efIkycnJVKlSxejD2dnZYnndoUOHSElJoVSpUpjNZuP44YcfLJb1de/enTVr1vDXX38Bt5Nsbdq0wdHRkaioKBISEnB1dbXo49SpUxZ9FC1a1EgiAZQvX54GDRrg4+NDmzZtWLBgAVevXs1wbk6cOEFycjI1a9Y0ruXKlYsqVaoQHR1933l0d3e3mLOMjB49mri4OOM4d+7cfduKiIiIiIiIZBVVJD1j3bt3N5adffTRR+nut2jRgqJFi7JgwQI8PDxITU3lhRde+McbZN+daHqQXLlyGX+bTKb7Xruz1O79999n9uzZzJo1Cx8fHxwcHBg8ePAjxZuQkICVlRW//PILVlZWFvfMZrPxd/v27RkyZAhffPEFtWvXJiIigilTphh9uLu7W1R33XH3Ztn3zoOVlRVbt25l165dfPfdd3z44Ye888477N69m2LFimX6He71oDnLiI2NzT/aQF1ERERERETkWVBF0jPWuHFjkpKSSE5Oxt/f3+LelStXiImJ4d1336VBgwZ4e3vftzrmDm9vb6KioozNoeH2krkcOXLcd1PtJykiIoKAgAA6depE+fLlKV68OEePHjXuFy9enFy5cllsSB0XF2fRxs/Pj5SUFC5duoSXl5fF4ebmZrRzdHSkTZs2LFq0iJCQEEqVKsVLL70E3N7M/MKFC+TMmTNdH/dWfd3LZDJRs2ZNxo8fz4EDB7C2tubLL79M165EiRJYW1sTERFhXEtOTmbv3r2ULVv20SdPRERERERE5DmjiqRnzMrKylgGdW/1Te7cuXF1deXTTz/F3d2ds2fPMmrUqAf217FjR8aNG0fXrl0JCgri8uXLDBgwgM6dOxvL0J6mkiVLsmbNGnbt2kXu3LmZOXMmFy9eNBIrjo6OdO3aleHDh5MnTx7y58/PuHHjyJEjh1GpU6pUKTp27EiXLl0IDg7Gz8+Py5cvExYWhq+vL82aNTPG69GjBy+99BLR0dGMHDnSuN6wYUOqV69Oy5YtmT59OqVKleKPP/5g48aNvPrqq1SqVCnD+Hfv3k1YWBgvv/wy+fPnZ/fu3Vy+fBlvb+90bR0cHOjbt6/xLkWKFGH69OncuHHD2PBbRERERERE5N9MiaQs4OTklOH1HDlysHLlSgYOHMgLL7xA6dKlmTNnDnXr1r1vX/b29mzZsoVBgwZRuXJl7O3tad26NTNnznxK0Vt69913OXnyJP7+/tjb29O7d29atmxJXFyc0WbmzJm88cYbNG/eHCcnJ0aMGMG5c+csNp4OCQlh0qRJvPXWW5w/f568efNSrVo1mjdvbjFerVq1KF26NMePH6dLly7GdZPJxLfffss777xDt27duHz5Mm5ubtSuXfuBCTUnJyd27NjBrFmziI+Pp2jRogQHBxubad9r6tSppKam0rlzZ/766y8qVarEli1byJ079+NOoYiIiIiIiMhzw5R2v++cS7aUmJiIra0tW7dupWHDhlkdzmO5fv06BQsWJDg4WJU89xEfH4+zszPjdpzE1uyY1eGIPBWj/B687FRERERERJ6dO/8fGhcXd98CGFBF0nMlPj6edevWkSNHDsqUKZPV4WTagQMH+O2336hSpQpxcXFMmDABgICAgCyOTEREREREREQehRJJz5Fx48axfPlypk2bRqFChbI6nEcyY8YMYmJisLa2pmLFiuzcufOhm2CLiIiIiIiISPaipW0i2ZCWtsl/gZa2iYiIiIhkH1raJvIvMLS86wP/AxYRERERERF5lnJkdQAiIiIiIiIiIvJ8UCJJREREREREREQyRYkkERERERERERHJFCWSREREREREREQkU7TZtkg2NjPqCrbmpKwOQ8SgL62JiIiIiPy3qSJJREREREREREQyRYkkERERERERERHJFCWSREREREREREQkU5RIEhERERERERGRTFEi6SkxmUysX78+q8MQEREREREREXlilEh6RIGBgZhMJkwmE7ly5aJAgQI0atSIRYsWkZqaarSLjY2lSZMmTzWWoKAgXnzxxac6xt1CQ0ONd7/7sLW1faR+nmSS7UnF9LSEhobi4uKS1WGIiIiIiIiIPBE5szqA51Hjxo0JCQkhJSWFixcvsnnzZgYNGsSaNWvYsGEDOXPmxM3N7YF9JCcnkytXrmcU8YMlJSVhbW2dqbZOTk7ExMRYXDOZTP+JmB5VcnJyVocgIiIiIiIi8kSpIukx2NjY4ObmRsGCBalQoQJvv/02X331FZs2bSI0NBSwrLo5ffo0JpOJVatWUadOHWxtbVm2bBkACxcuxNvbG1tbW8qUKcO8efMsxvr999/p0KEDefLkwcHBgUqVKrF7925CQ0MZP348UVFRRhXOnbHPnj1LQEAAZrMZJycn2rZty8WLF40+71QyLVy4kGLFimFra8uSJUtwdXUlMTHRYvyWLVvSuXNn49xkMuHm5mZxFChQwLhft25dBg4cyIgRI8iTJw9ubm4EBQUZ9z09PQF49dVXMZlMxvnTiuny5cu4ubkxefJko/2uXbuwtrYmLCzMYuz58+dTuHBh7O3tadu2LXFxccYzqampTJgwgUKFCmFjY8OLL77I5s2bjfv3+427detGXFyc8RvdPRciIiIiIiIizxslkp6Q+vXrU758edatW3ffNqNGjWLQoEFER0fj7+/PsmXLGDt2LO+99x7R0dFMnjyZMWPGsHjxYgASEhKoU6cO58+fZ8OGDURFRTFixAhSU1Np164db731FuXKlSM2NpbY2FjatWtHamoqAQEB/Pnnn/zwww9s3bqVkydP0q5dO4tYjh8/ztq1a1m3bh2RkZG0adOGlJQUNmzYYLS5dOkSGzdupHv37o80F4sXL8bBwYHdu3czffp0JkyYwNatWwHYu3cvACEhIcTGxhrnTyumfPnysWjRIoKCgti3bx9//fUXnTt3pn///jRo0MBi7C+++IKvv/6azZs3c+DAAfr162fcnz17NsHBwcyYMYODBw/i7+/PK6+8wrFjxyzGu/s3rlevHrNmzcLJycn4jYYNG5ZhnImJicTHx1scIiIiIiIiItmNlrY9QWXKlOHgwYP3vT948GBatWplnI8bN47g4GDjWrFixThy5Ajz58+na9euLF++nMuXL7N3717y5MkDgJeXl/G82WxOt4xu69atHDp0iFOnTlG4cGEAlixZQrly5di7dy+VK1cGbi8dW7JkCfny5TOeff311wkJCaFNmzYAfP755xQpUoS6desabeLi4jCbzRbv9dJLL7Fp0ybj3NfXl3HjxgFQsmRJ5s6dS1hYGI0aNTLGc3FxSbf872nF1LRpU3r16kXHjh2pVKkSDg4OTJkyxaL9zZs3WbJkCQULFgTgww8/pFmzZgQHB+Pm5saMGTMYOXIk7du3B2DatGls376dWbNm8dFHHxn93PsbOzs7GxVTDzJlyhTGjx//wDYiIiIiIiIiWU2JpCcoLS3tgXvzVKpUyfj7+vXrnDhxgh49etCrVy/j+q1bt3B2dgYgMjISPz8/I4mUGdHR0RQuXNhIIgGULVsWFxcXoqOjjURS0aJFLRI2AL169aJy5cqcP3+eggULEhoaamwufoejoyP79++3eM7Ozs7i3NfX1+Lc3d2dS5cuPTT2pxnTjBkzeOGFF1i9ejW//PILNjY2FveLFCliJJEAqlevTmpqKjExMdjb2/PHH39Qs2ZNi2dq1qxJVFSUxbW7f+NHMXr0aIYOHWqcx8fHW/yGIiIiIiIiItmBEklPUHR0NMWKFbvvfQcHB+PvhIQEABYsWEDVqlUt2llZWQHpkyFP0t2x3OHn50f58uVZsmQJL7/8Mr/++isbN260aJMjRw6LqqiM3LuJuMlksviiXVbEdOLECf744w9SU1M5ffo0Pj4+D43ncWT0DplhY2OTLrklIiIiIiIikt0okfSEfP/99xw6dIghQ4Zkqn2BAgXw8PDg5MmTdOzYMcM2vr6+LFy4kD///DPDqiRra2tSUlIsrnl7e3Pu3DnOnTtnVLQcOXKEa9euUbZs2YfG1bNnT2bNmsX58+dp2LDhU6mKyZUrV7q4n2ZMSUlJdOrUiXbt2lG6dGl69uzJoUOHyJ8/v9Hm7Nmz/PHHH3h4eADw888/kyNHDkqXLo2TkxMeHh5ERERQp04d45mIiAiqVKnywLEz+o1EREREREREnlfabPsxJCYmcuHCBc6fP8/+/fuZPHkyAQEBNG/enC5dumS6n/HjxzNlyhTmzJnD0aNHOXToECEhIcycOROADh064ObmRsuWLYmIiODkyZOsXbuWn376Cbj9BbRTp04RGRnJ//73PxITE2nYsCE+Pj507NiR/fv3s2fPHrp06UKdOnUytezq9ddf5/fff2fBggUZbmidlpbGhQsX0h2ZqTi6w9PTk7CwMC5cuMDVq1efekzvvPMOcXFxzJkzh5EjR1KqVKl0/dja2tK1a1eioqLYuXMnAwcOpG3btsbeRsOHD2fatGmsWrWKmJgYRo0aRWRkJIMGDXrouyYkJBAWFsb//vc/bty4kdlpEhEREREREcl2lEh6DJs3b8bd3R1PT08aN27M9u3bmTNnDl999ZWxLC0zevbsycKFCwkJCcHHx4c6deoQGhpqLI+ztrbmu+++I3/+/DRt2hQfHx+mTp1qjNG6dWsaN25MvXr1yJcvHytWrMBkMvHVV1+RO3duateuTcOGDSlevDirVq3KVEzOzs60bt0as9lMy5Yt092Pj4/H3d093ZGZPZDuCA4OZuvWrRQuXBg/P7+nGlN4eDizZs1i6dKlODk5kSNHDpYuXcrOnTv5+OOPjT68vLxo1aoVTZs25eWXX8bX15d58+YZ9wcOHMjQoUN566238PHxYfPmzWzYsIGSJUs+MPYaNWrwxhtv0K5dO/Lly8f06dMzPU8iIiIiIiIi2Y0pLS0tLauDkOylQYMGlCtXjjlz5mR1KIanGVNQUBDr168nMjLyiff9uOLj43F2dmbcjpPYmh2zOhwRwyi/vFkdgoiIiIiIPAV3/j80Li4OJyen+7bTHkliuHr1KuHh4YSHh1tU42Sl7BiTiIiIiIiIyH+VEkli8PPz4+rVq0ybNo3SpUtndThA9oxJRERERERE5L9KS9tEsqHMlhSKiIiIiIiIPAmZ/f9QbbYtIiIiIiIiIiKZokSSiIiIiIiIiIhkihJJIiIiIiIiIiKSKUokiYiIiIiIiIhIpuirbSLZ2MyoK9iak7I6DPkXGeWXN6tDEBERERGR55gqkkREREREREREJFOUSBIRERERERERkUxRIklERERERERERDJFiSQREREREREREckUJZKegqCgIF588cWsDkNERERERERE5InK8kRSYGAgLVu2zOowHur06dOYTCbjcHR0pFy5crz55pscO3bMou2wYcMICwt76jGZTCbWr1//1Me5w9PT02IO7hxTp07NdB9POsn2JGJ6mjw9PZk1a1ZWhyEiIiIiIiLyROTM6gCeN9u2baNcuXLcuHGDQ4cOMXv2bMqXL8/XX39NgwYNADCbzZjN5vv2kZSUhLW19bMK+aGSk5PJlStXptpOmDCBXr16WVxzdHT8T8T0KLLbbywiIiIiIiLyJGR5RdLd6taty4ABAxg8eDC5c+emQIECLFiwgOvXr9OtWzccHR3x8vJi06ZNxjMpKSn06NGDYsWKYWdnR+nSpZk9e7ZFv7du3WLgwIG4uLjg6urKyJEj6dq1q0UlVGpqKlOmTDH6KV++PGvWrEkXo6urK25ubhQvXpyAgAC2bdtG1apV6dGjBykpKUD6qps7VVfvvfceHh4elC5dGoBz587Rtm1bXFxcyJMnDwEBAZw+fdpivEWLFlGuXDlsbGxwd3enf//+wO1KF4BXX30Vk8lknAN8/PHHlChRAmtra0qXLs3SpUst+jSZTHz88ce88sorODg4MGnSJLy8vJgxY4ZFu8jISEwmE8ePHzeuOTo64ubmZnE4ODgAEB4ejslkIiwsjEqVKmFvb0+NGjWIiYkBIDQ0lPHjxxMVFWVUDoWGhj7VmCZMmICHhwdXrlwx2jdr1ox69eqRmppqMXaTJk2ws7OjePHi6X77Q4cOUb9+fezs7HB1daV3794kJCQ88DeuW7cuZ86cYciQIcb7ioiIiIiIiDzPslUiCWDx4sXkzZuXPXv2MGDAAPr27UubNm2oUaMG+/fv5+WXX6Zz587cuHEDuJ0AKlSoEKtXr+bIkSOMHTuWt99+my+++MLoc9q0aSxbtoyQkBAiIiKIj49PtyRsypQpLFmyhE8++YRff/2VIUOG0KlTJ3744YcHxpsjRw4GDRrEmTNn+OWXX+7bLiwsjJiYGLZu3co333xDcnIy/v7+ODo6snPnTiIiIjCbzTRu3JikpCTgdkLozTffpHfv3hw6dIgNGzbg5eUFwN69ewEICQkhNjbWOP/yyy8ZNGgQb731FocPH6ZPnz5069aN7du3W8QTFBTEq6++yqFDh+jRowfdu3cnJCTEok1ISAi1a9c2xsysd955h+DgYPbt20fOnDnp3r07AO3ateOtt96iXLlyxMbGEhsbS7t27Z5qTO+88w6enp707NkTgI8++ohdu3axePFicuT4/3/+Y8aMoXXr1kRFRdGxY0fat29PdHQ0ANevX8ff35/cuXOzd+9eVq9ezbZt24yk3h33/sbr1q2jUKFCTJgwwXjf+0lMTCQ+Pt7iEBEREREREcluTGlpaWlZGUBgYCDXrl1j/fr11K1bl5SUFHbu3AncrjZydnamVatWLFmyBIALFy7g7u7OTz/9RLVq1TLss3///ly4cMGoKnFzc2PYsGEMGzbM6Ld48eL4+fmxfv16EhMTyZMnD9u2baN69epGPz179uTGjRssX76c06dPU6xYMQ4cOJBuj5/ffvsNb29vVq1aRdu2bQkKCmL9+vVERkYa77h582bOnj1rLHf6/PPPmTRpEtHR0UalSlJSEi4uLqxfv56XX36ZggUL0q1bNyZNmpThe5pMJr788kuLyqqaNWtSrlw5Pv30U+Na27ZtuX79Ohs3bjSeGzx4MB988IHR5o8//qBIkSLs2rWLKlWqkJycjIeHBzNmzKBr167A7Sqo2NjYdEvONm3axEsvvUR4eDj16tVj27ZtxjK/b7/9lmbNmvH3339ja2ubbm7ufpenERPAyZMnefHFF+nXrx9z5sxh4cKFvP766xZjv/HGG3z88cfGtWrVqlGhQgXmzZvHggULGDlyJOfOnTMqnb799ltatGjBH3/8QYECBTL8je/EN3jwYAYPHpzhb3hHUFAQ48ePT3d93I6T2Jqzdpme/LuM8sub1SGIiIiIiEg2FB8fj7OzM3FxcTg5Od23XbbbI8nX19f428rKCldXV3x8fIxrBQoUAODSpUvGtY8++ohFixZx9uxZ/v77b5KSkoxkT1xcHBcvXqRKlSoW/VasWNFY2nT8+HFu3LhBo0aNLGJJSkrCz8/voTHfycU9aOmSj4+PRYIhKiqK48ePp9vL5+bNm5w4cYJLly7xxx9/GAmZzIqOjqZ3794W12rWrJluuV+lSpUszj08PGjWrBmLFi2iSpUqfP311yQmJtKmTRuLdsOHDycwMNDiWsGCBS3O7/4N3d3dgdu/V5EiRR4Y+9OKqXjx4syYMYM+ffrQrl07iyTSHXcnEO+c30l2RUdHU758eSOJBLfnNDU1lZiYGOPf5L2/8aMYPXo0Q4cONc7j4+MpXLjwY/UlIiIiIiIi8rRku0TSvZUlJpPJ4tqdZM2dJNDKlSsZNmwYwcHBVK9eHUdHR95//312796d6THv7HWzcePGdEkRGxubhz5/ZwlUsWLF7tvm7iTEnTErVqzIsmXL0rXNly+fxbKrp+HeeOB2BVbnzp354IMPCAkJoV27dtjb21u0yZs370OXlT3o98qqmHbs2IGVlRWnT5/m1q1b5Mz55P/pZxR/ZtnY2GTq35qIiIiIiIhIVsp2eyQ9qoiICGrUqEG/fv3w8/PDy8uLEydOGPednZ0pUKCAsYcQ3F7atn//fuO8bNmy2NjYcPbsWby8vCyOh1WFpKamMmfOHIoVK5ap6qU7KlSowLFjx8ifP3+6MZ2dnXF0dMTT05OwsLD79pErVy5jg+87vL29iYiIsLgWERFB2bJlHxpT06ZNcXBw4OOPP2bz5s3G3kZPkrW1dbqYn3ZMq1atYt26dYSHh3P27FkmTpyYrs3PP/+c7tzb2xu4PadRUVFcv37duB8REUGOHDmMjdPv51HfV0RERERERCQ7y3YVSY+qZMmSLFmyhC1btlCsWDGWLl3K3r17LaqDBgwYwJQpU/Dy8qJMmTJ8+OGHXL161aiWcXR0ZNiwYQwZMoTU1FRq1apFXFwcERERODk5GfvxAFy5coULFy5w48YNDh8+zKxZs9izZw8bN27Eysoq03F37NiR999/n4CAACZMmEChQoU4c+YM69atY8SIERQqVIigoCDeeOMN8ufPT5MmTfjrr7+IiIhgwIABAEaiqWbNmtjY2JA7d26GDx9O27Zt8fPzo2HDhnz99desW7eObdu2PTQmKysrAgMDGT16NCVLlky33Avgr7/+4sKFCxbX7O3tH7h+8m6enp6cOnWKyMhIChUqhKOj4wMrcf5pTL///jt9+/Zl2rRp1KpVi5CQEJo3b06TJk0s9thavXo1lSpVolatWixbtow9e/bw2WefAbd/q3HjxtG1a1eCgoK4fPkyAwYMoHPnzsaytge9744dO2jfvj02Njbkzav9aUREREREROT59dxXJPXp04dWrVrRrl07qlatypUrV+jXr59Fm5EjR9KhQwe6dOlC9erVMZvN+Pv7Y2tra7SZOHEiY8aMYcqUKXh7e9O4cWM2btyYbrlaw4YNcXd3x8fHh1GjRuHt7c3BgwepV6/eI8Vtb2/Pjh07KFKkCK1atcLb25sePXpw8+ZNIynTtWtXZs2axbx58yhXrhzNmzfn2LFjRh/BwcFs3bqVwoULG9VQLVu2ZPbs2cyYMYNy5coxf/58QkJCqFu3bqbi6tGjB0lJSXTr1i3D+2PHjsXd3d3iGDFiRKbfu3Xr1jRu3Jh69eqRL18+VqxY8dRiSktLIzAwkCpVqhhfWPP396dv37506tTJWNIIMH78eFauXImvry9LlixhxYoVRhWXvb09W7Zs4c8//6Ry5cq89tprNGjQgLlz5z409gkTJnD69GlKlChBvnz5MjNFIiIiIiIiItlWln+1LSukpqbi7e1N27ZtM1zm9F+2c+dOGjRowLlz5x5abfOsPO2YMvr6XVa7s1u+vtomT5q+2iYiIiIiIhl5br/a9jScOXOG7777jjp16pCYmMjcuXM5depUhl/v+q9KTEzk8uXLBAUF0aZNm2yRRMqOMYmIiIiIiIj8lz33S9syI0eOHISGhlK5cmVq1qzJoUOH2LZtm7GZssCKFSsoWrQo165dY/r06VkdDpA9YxIRERERERH5L/tPLm0Tye60tE2eFi1tExERERGRjGhpm8i/wNDyrpn+Ip6IiIiIiIjI0/afWNomIiIiIiIiIiL/nBJJIiIiIiIiIiKSKUokiYiIiIiIiIhIpiiRJCIiIiIiIiIimaLNtkWysZlRV7A1J2V1GPKc0hfaRERERETkSVNFkoiIiIiIiIiIZIoSSSIiIiIiIiIikilKJImIiIiIiIiISKYokSQiIiIiIiIiIpmiRNIzFhQUxIsvvvjANoGBgbRs2fKZxPMkBAUFUaBAAUwmE+vXr8/qcJ6K8PBwTCYT165dy+pQRERERERERLKMEklPwE8//YSVlRXNmjXL6lCemL///ptx48ZRqlQpbGxsyJs3L23atOHXX3+1aBcdHc348eOZP38+sbGxNGnShKCgIEwmU7qjTJkymR7/9OnTmEwmIiMjn8j73B1Tzpw58fT0ZMiQISQkJGTq+Ro1ahAbG4uzs3Omx3zeEoIiIiIiIiIiD5MzqwP4N/jss88YMGAAn332GX/88QceHh5PdbykpCSsra2fWv+JiYk0bNiQs2fPEhwcTNWqVbl48SJTpkyhatWqbNu2jWrVqgFw4sQJAAICAjCZTEYf5cqVY9u2bRb95sz55P+5Pcpc3Inp1q1bRERE0L17d27cuMH8+fMf+qy1tTVubm7/NFwRERERERGR55oqkv6hhIQEVq1aRd++fWnWrBmhoaEW96dOnUqBAgVwdHSkR48e3Lx50+J+SkoKQ4cOxcXFBVdXV0aMGEFaWppFm7p169K/f38GDx5M3rx58ff3B+Dw4cM0adIEs9lMgQIF6Ny5M//73/+M59asWYOPjw92dna4urrSsGFDrl+/DtxeqlWlShUcHBxwcXGhZs2anDlzBoBZs2bx008/8c0339C2bVuKFi1KlSpVWLt2Ld7e3vTo0YO0tDSCgoJo0aIFADly5LBIJOXMmRM3NzeLI2/evMZ9T09PJk+eTPfu3XF0dKRIkSJ8+umnxv1ixYoB4Ofnh8lkom7dusD/V/m89957eHh4ULp0aSZMmMALL7yQ7rd58cUXGTNmTLqYChUqRLt27ejYsSMbNmwAbifPBg4cSP78+bG1taVWrVrs3bvXePbepW2hoaG4uLiwZcsWvL29MZvNNG7cmNjYWOB2BdTixYv56quvjEqo8PDwdDGKiIiIiIiIPE+USPqHvvjiC8qUKUPp0qXp1KkTixYtMhJBX3zxBUFBQUyePJl9+/bh7u7OvHnzLJ4PDg4mNDSURYsW8eOPP/Lnn3/y5Zdfphtn8eLFWFtbExERwSeffMK1a9eoX78+fn5+7Nu3j82bN3Px4kXatm0LQGxsLB06dKB79+5ER0cTHh5Oq1atSEtL49atW7Rs2ZI6depw8OBBfvrpJ3r37m0kgpYvX06jRo0oX768RQw5cuRgyJAhHDlyhKioKIYNG0ZISIgx3p0kSmYFBwdTqVIlDhw4QL9+/ejbty8xMTEA7NmzB4Bt27YRGxvLunXrjOfCwsKIiYlh69atfPPNN8Y73p34OXDgAAcPHqRbt273Hd/Ozo6kpCQARowYwdq1a1m8eDH79+/Hy8sLf39//vzzz/s+f+PGDWbMmMHSpUvZsWMHZ8+eZdiwYQAMGzaMtm3bGsml2NhYatSocd++EhMTiY+PtzhEREREREREshstbfuHPvvsMzp16gRA48aNiYuL44cffqBu3brMmjWLHj160KNHDwAmTZrEtm3bLKqSZs2axejRo2nVqhUAn3zyCVu2bEk3TsmSJZk+fbpxPmnSJPz8/Jg8ebJxbdGiRRQuXJijR4+SkJDArVu3aNWqFUWLFgXAx8cHgD///JO4uDiaN29OiRIlAPD29jb6OXr0KPXq1cvwfe+0O3r0KC+++CIuLi4A6ZZ9HTp0CLPZbHGtU6dOfPLJJ8Z506ZN6devHwAjR47kgw8+YPv27ZQuXZp8+fIB4Orqmq5vBwcHFi5caLGkzd/fn5CQECpXrgxASEgIderUoXjx4hm+xy+//MLy5cupX78+169f5+OPPyY0NJQmTZoAsGDBArZu3cpnn33G8OHDM+wjOTmZTz75xJjD/v37M2HCBADMZjN2dnYkJiZmaknclClTGD9+/EPbiYiIiIiIiGQlVST9AzExMezZs4cOHToAt5dOtWvXjs8++wy4vRF11apVLZ6pXr268XdcXByxsbEWbXLmzEmlSpXSjVWxYkWL86ioKLZv347ZbDaOO5tZnzhxgvLly9OgQQN8fHxo06YNCxYs4OrVqwDkyZOHwMBA/P39adGiBbNnz05XTXTv8rpHVbp0aSIjIy2OO0mWO3x9fY2/TSYTbm5uXLp06aF9+/j4pNsXqVevXqxYsYKbN2+SlJTE8uXL6d69u0WbO8ktOzs7qlSpQvXq1Zk7dy4nTpwgOTmZmjVrGm1z5cpFlSpViI6Ovm8c9vb2RhIJwN3dPVPxZ2T06NHExcUZx7lz5x6rHxEREREREZGnSRVJ/8Bnn33GrVu3LDbXTktLw8bGhrlz5z7RsRwcHCzOExISaNGiBdOmTUvX1t3dHSsrK7Zu3cquXbv47rvv+PDDD3nnnXfYvXs3xYoVIyQkhIEDB7J582ZWrVrFu+++y9atW6lWrRqlSpW6bwLlzvVSpUo9MF5ra2u8vLwe2CZXrlwW5yaTidTU1Ac+A+nnAqBFixbY2Njw5ZdfYm1tTXJyMq+99ppFm9KlS7NhwwZy5syJh4eHkYy6ePHiQ8fMbPyPm4CzsbHBxsbmsZ4VEREREREReVZUkfSYbt26xZIlSwgODraouomKisLDw4MVK1bg7e3N7t27LZ77+eefjb+dnZ1xd3e3aHPr1i1++eWXh45foUIFfv31Vzw9PfHy8rI47iRaTCYTNWvWZPz48Rw4cABra2uL/Zf8/PwYPXo0u3bt4oUXXmD58uUAtG/fnm3bthEVFWUxZmpqKh988AFly5ZNt3/Sk3YnyZOSkpKp9jlz5qRr166EhIQQEhJC+/btsbOzS9enl5cXnp6eFhVNJUqUMPafuiM5OZm9e/dStmzZf/QOmY1fRERERERE5HmgiqTH9M0333D16lV69OiBs7Ozxb3WrVvz2WefMWzYMAIDA6lUqRI1a9Zk2bJl/Prrrxb79gwaNIipU6dSsmRJypQpw8yZM40vgz3Im2++yYIFC+jQoQMjRowgT548HD9+nJUrV7Jw4UL27dtHWFgYL7/8Mvnz52f37t1cvnwZb29vTp06xaeffsorr7yCh4cHMTExHDt2jC5dugAwZMgQvvrqK1q0aEFwcDBVq1bl4sWLTJ48mejoaLZt22bxhbaM3Lp1iwsXLlhcM5lMFChQIFPzmz9/fuzs7Ni8eTOFChXC1tY23Tzfq2fPnsYeTncnhR7GwcGBvn37Mnz4cPLkyUORIkWYPn06N27cMPa3ehyenp5s2bKFmJgYXF1dcXZ2TlfFJCIiIiIiIvI8UUXSY/rss89o2LBhhsmN1q1bs2/fPry9vRkzZgwjRoygYsWKnDlzhr59+1q0feutt+jcuTNdu3alevXqODo68uqrrz50fA8PDyIiIkhJSeHll1/Gx8eHwYMH4+LiQo4cOXBycmLHjh00bdqUUqVK8e677xIcHEyTJk2wt7fnt99+o3Xr1pQqVYrevXvz5ptv0qdPHwBsbW35/vvv6dKlC2+//TZeXl40btwYKysrfv75Z6pVq/bQ+H799Vfc3d0tjjubfmdGzpw5mTNnDvPnz8fDw4OAgICHPlOyZElq1KhBmTJl0u1N9TBTp06ldevWdO7cmQoVKnD8+HG2bNlC7ty5H6mfu/Xq1YvSpUtTqVIl8uXL90jJLREREREREZHsyJT2T3dVFskm0tLSKFmyJP369WPo0KFZHc4/Eh8fj7OzM+N2nMTW7JjV4chzapRf3qwOQUREREREnhN3/j80Li4OJyen+7bT0jb5V7h8+TIrV67kwoULdOvWLavDEREREREREflXUiJJ/hXy589P3rx5+fTTT//RcjQRERERERERuT8lkuRf4d+6QnNoedcHlhSKiIiIiIiIPEvabFtERERERERERDJFiSQREREREREREckUJZJERERERERERCRTlEgSEREREREREZFM0WbbItnYzKgr2JqTsjoMeQ6M8sub1SGIiIiIiMh/gCqSREREREREREQkU5RIEhERERERERGRTFEiSUREREREREREMkWJJBERERERERERyRQlkgSTycT69euzZOz169fj5eWFlZUVgwcPzpIY7uXp6cmsWbOyOgwRERERERGRbEeJpP+Ay5cv07dvX4oUKYKNjQ1ubm74+/sTEREBQGxsLE2aNHmiYy5evJjKlStjb2+Po6MjderU4ZtvvknXrk+fPrz22mucO3eOiRMnEh4ejslk4tq1a+naKsEjIiIiIiIikrWUSPoPaN26NQcOHGDx4sUcPXqUDRs2ULduXa5cuQKAm5sbNjY2T2y8YcOG0adPH9q1a8fBgwfZs2cPtWrVIiAggLlz5xrtEhISuHTpEv7+/nh4eODo6PjEYshKSUlJWR2CiIiIiIiIyFOhRNK/3LVr19i5cyfTpk2jXr16FC1alCpVqvB/7d15XFXV/v/x9xFkkDFBQBxRUJEAwSn1ftXEgSynyik10bSsbNbKypxyjEatNCe08WaZeUNL5YoDqZSKU1wHkrQCKQcQMzDYvz98cH6dQDvmOUDwej4e+yFn77XX/qzz6UDn81h77UmTJqlv376SLG9tmzp1qkwmU6ktISFBklRcXKzZs2crKChIrq6uioyM1Mcff2y+3s6dO/XSSy/pxRdf1IQJExQcHKzQ0FDNnDlTjz76qB5//HGdPHlSycnJ5sJRt27dZDKZlJycfM1jGzNmjOrUqSNPT09169ZN+/btMx/PyMhQv3795O/vL3d3d7Vt21abNm2y6CMnJ0d9+vSRq6urgoKC9N57713zdaZOnapWrVppyZIlCgoKkouLiyTp448/Vnh4uFxdXeXj46Pu3bvrwoUL1zRGAAAAAAAqEwpJVZy7u7vc3d21Zs0aFRQU/GX7CRMmKCsry7zFx8erVq1aatOmjSRp9uzZWrlypRYuXKhDhw7pscce0/Dhw7VlyxZJ0gcffCB3d3fdd999pfp+4okndOnSJX3yySfq2LGjDh8+LEn65JNPlJWVpY4dO17T2AYOHKicnBytX79eu3fvVnR0tGJiYnTmzBlJl2c89e7dW0lJSdq7d69iY2PVp08fnThxwtxHXFycTp48qc2bN+vjjz/Wm2++qZycnGu6jiQdO3ZMn3zyiVavXq20tDRlZWVp6NChGj16tNLT05WcnKzbb79dhmGUOZaCggLl5eVZbAAAAAAAVDaOFR0A7MvR0VEJCQkaO3asFi5cqOjoaHXp0kVDhgxRREREqfYlhSfp8uyi5557TitWrNCNN96ogoICzZo1S5s2bVKHDh0kSU2aNNH27du1aNEidenSRUeOHFHTpk3l5ORUqu/AwEB5enrqyJEjcnJykp+fnySpdu3aCggIsGhbv379Uuf/+uuv5p+3b9+u1NRU5eTkmG/Li4+P15o1a/Txxx/r3nvvVWRkpCIjI83nzJgxQ59++qnWrl2r8ePH68iRI1q/fr1SU1PVtm1bSdLSpUsVGhp6TdeRLt/OtnLlStWpU0eStGfPHv3++++6/fbb1ahRI0lSeHj4FfM0e/ZsTZs27YrHAQAAAACoDCgkVQN33HGHbr31Vm3btk07d+7U+vXrNW/ePC1ZskRxcXFlnnPixAn1799fEyZM0KBBgyRdnnXz66+/qkePHhZtCwsLFRUVZX59pVk312Lbtm2l1kzq2rWr+ed9+/YpPz9fPj4+Fm0uXryojIwMSZdnJE2dOlWJiYnKysrS77//rosXL5pnJKWnp8vR0VGtW7c2n9+iRQt5e3tf03UkqVGjRuYikiRFRkYqJiZG4eHh6tWrl3r27Kk777xTN9xwQ5njnTRpkh5//HHz67y8PDVo0OBqbxEAAAAAAOWOQlI14eLioh49eqhHjx6aPHmyxowZoylTppRZSLpw4YL69u2rDh06aPr06eb9+fn5kqTExETVq1fP4pyS2TrNmjXT9u3bVVhYWGpW0k8//aS8vDw1a9bsL+MNCgqyKOhIl2dX/TGWunXrlrmuUsl5EyZM0MaNGxUfH6/g4GC5urrqzjvvvKbFsK25jiS5ublZHHNwcNDGjRv11VdfacOGDZo/f76effZZ7dq1S0FBQaX6cnZ2tumC5wAAAAAA2ANrJFVTLVu2LHPhZ8MwNHz4cBUXF+udd96RyWSyOMfZ2VknTpxQcHCwxVYye2bIkCHKz8/XokWLSvUdHx+vmjVr6o477rju+KOjo5WdnS1HR8dSsfj6+kqSUlJSFBcXpwEDBig8PFwBAQHKzMw099GiRQv9/vvv2r17t3nf4cOHde7cuWu6zpWYTCZ16tRJ06ZN0969e+Xk5KRPP/30uscOAAAAAEBFYUZSFXf69GkNHDhQo0ePVkREhDw8PPTNN99o3rx56tevX6n2U6dO1aZNm7Rhwwbl5+ebZyF5eXnJw8NDEyZM0GOPPabi4mL961//Um5urlJSUuTp6amRI0eqQ4cOeuSRRzRx4kQVFhaqf//+unTpkt5991299tprevXVV21yy1b37t3VoUMH9e/fX/PmzVOzZs30008/KTExUQMGDFCbNm0UEhKi1atXq0+f3irlwAAAKttJREFUPjKZTJo8ebKKi4vNfTRv3lyxsbG677779NZbb8nR0VGPPvqoXF1dr+k6Zdm1a5eSkpLUs2dP+fn5adeuXfr5558t1l8CAAAAAOCfhkJSFefu7q727dvrlVdeUUZGhi5duqQGDRpo7NixeuaZZ0q137Jli/Lz80s9QW358uWKi4vTjBkzVKdOHc2ePVvfffedvL29FR0dbdHXq6++qoiICL355pt67rnn5ODgoOjoaK1Zs0Z9+vSxybhMJpPWrVunZ599VqNGjdLPP/+sgIAAde7cWf7+/pKkl19+WaNHj1bHjh3l6+urp556qtTT0JYvX64xY8aoS5cu8vf31wsvvKDJkydf03XK4unpqa1bt+rVV19VXl6eGjVqpJdeekm33HKLTcYPAAAAAEBFMBm2WBkZgE3l5eXJy8tLU7Z+Jxd3j78+AdXe01FXv9USAAAAAK6m5Htobm6uPD09r9iONZIAAAAAAABgFQpJAAAAAAAAsAqFJAAAAAAAAFiFxbaBSuzxSJ+r3psKAAAAAEB5YkYSAAAAAAAArEIhCQAAAAAAAFahkAQAAAAAAACrUEgCAAAAAACAVVhsG6jEXt53Wi7uhRUdBiqJp6N8KzoEAAAAANUcM5IAAAAAAABgFQpJAAAAAAAAsAqFJAAAAAAAAFiFQhIAAAAAAACsQiEJFcJkMmnNmjV26z8zM1Mmk0lpaWl2uwYAAAAAANUNhSTYRXZ2th566CE1adJEzs7OatCggfr06aOkpKSKDs1s37596tu3r/z8/OTi4qLGjRtr8ODBysnJqejQAAAAAAColBwrOgBUPZmZmerUqZO8vb314osvKjw8XJcuXdKXX36pBx98UP/73/8qOkT9/PPPiomJ0W233aYvv/xS3t7eyszM1Nq1a3XhwoWKDg8AAAAAgEqJGUmwuQceeEAmk0mpqam644471KxZM4WFhenxxx/Xzp07ze1++eUXDRgwQLVq1VJISIjWrl1r0c/Bgwd1yy23yN3dXf7+/hoxYoR++eUX8/Hi4mLNmzdPwcHBcnZ2VsOGDTVz5swyYyoqKtLo0aPVokULnThxQikpKcrNzdWSJUsUFRWloKAg3XzzzXrllVcUFBQkSUpISJC3t7dFP2vWrJHJZDK/njp1qlq1aqVly5apYcOGcnd31wMPPKCioiLNmzdPAQEB8vPzu2JcAAAAAAD8k1BIgk2dOXNGX3zxhR588EG5ubmVOv7Hwsy0adM0aNAg7d+/X71799awYcN05swZSdK5c+fUrVs3RUVF6ZtvvtEXX3yhU6dOadCgQebzJ02apDlz5mjy5Mn69ttv9f7778vf37/UNQsKCjRw4EClpaVp27ZtatiwoQICAvT777/r008/lWEY1zXmjIwMrV+/Xl988YU++OADLV26VLfeeqt++OEHbdmyRXPnztVzzz2nXbt2XbGPgoIC5eXlWWwAAAAAAFQ23NoGmzp27JgMw1CLFi3+sm1cXJyGDh0qSZo1a5Zef/11paamKjY2VgsWLFBUVJRmzZplbr9s2TI1aNBAR44cUd26dfXaa69pwYIFGjlypCSpadOm+te//mVxjfz8fN16660qKCjQ5s2b5eXlJUm66aab9Mwzz+iuu+7SuHHj1K5dO3Xr1k133313mcWoqykuLtayZcvk4eGhli1b6uabb9bhw4e1bt061ahRQ82bN9fcuXO1efNmtW/fvsw+Zs+erWnTpl3TdQEAAAAAKG/MSIJNXcvsnoiICPPPbm5u8vT0NC90vW/fPm3evFnu7u7mraQ4lZGRofT0dBUUFCgmJuaq1xg6dKguXLigDRs2mItIJWbOnKns7GwtXLhQYWFhWrhwoVq0aKEDBw5YPQZJaty4sTw8PMyv/f391bJlS9WoUcNi39UW8Z40aZJyc3PN28mTJ68pBgAAAAAAygOFJNhUSEiITCaTVQtq16xZ0+K1yWRScXGxpMszifr06aO0tDSL7ejRo+rcubNcXV2tiqd3797av3+/duzYUeZxHx8fDRw4UPHx8UpPT1dgYKDi4+MlSTVq1ChVGLt06ZJV47ja2Mri7OwsT09Piw0AAAAAgMqGQhJsqnbt2urVq5feeOONMp9+du7cOav6iY6O1qFDh9S4cWMFBwdbbG5ubgoJCZGrq6uSkpKu2s/999+vOXPmqG/fvtqyZctV2zo5Oalp06bmuOvUqaPz589bjCMtLc2q+AEAAAAAqIooJMHm3njjDRUVFaldu3b65JNPdPToUaWnp+v1119Xhw4drOrjwQcf1JkzZzR06FB9/fXXysjI0JdffqlRo0apqKhILi4ueuqpp/Tkk09q5cqVysjI0M6dO7V06dJSfT300EN64YUXdNttt2n79u2SpM8//1zDhw/X559/riNHjujw4cOKj4/XunXr1K9fP0lS+/btVat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            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Set the importance threshold\n",
        "importance_threshold = 0.01\n",
        "\n",
        "# Filter features based on the importance threshold\n",
        "important_features = importance_df[importance_df['Importance'] > importance_threshold]['Feature']\n",
        "print(f\"Features selected (importance > {importance_threshold}):\\n{important_features.values}\")\n",
        "\n",
        "# Visualize Feature Importances of Important Features\n",
        "plt.figure(figsize=(12, 8))\n",
        "plt.barh(importance_df['Feature'][importance_df['Importance'] > importance_threshold],\n",
        "         importance_df['Importance'][importance_df['Importance'] > importance_threshold],\n",
        "         color='skyblue')\n",
        "plt.xlabel('Importance')\n",
        "plt.title('Feature Importances from Random Forest Classifier (Filtered)')\n",
        "plt.gca().invert_yaxis()  # Invert y axis to show the most important features at the top\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "681450ec",
      "metadata": {},
      "source": [
        "save this filtered dataset to a CSV file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "6b2eef4c",
      "metadata": {},
      "outputs": [],
      "source": [
        "# To see the dataset with only the important features\n",
        "# Create a new dataset with only the important features\n",
        "important_features_list = important_features.tolist()  # Convert to list\n",
        "important_features_dataset = dataset[important_features_list]  # Filter the dataset\n",
        "\n",
        "# save this filtered dataset to a CSV file\n",
        "important_features_dataset.to_csv('filtered_important_features_dataset.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "dba276be",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Selected Important Features (importance > 0.01):\n",
            "SizeOfStackReserve\n",
            "MajorLinkerVersion\n",
            "MajorSubsystemVersion\n",
            "MinorOperatingSystemVersion\n",
            "MajorOperatingSystemVersion\n",
            "TimeDateStamp\n",
            "Characteristics\n",
            "MinorImageVersion\n",
            "ImageBase\n",
            "Subsystem\n",
            "MinorSubsystemVersion\n",
            "DllCharacteristics\n",
            "SizeOfInitializedData\n",
            "DirectoryEntryImportSize\n",
            "SectionMaxChar\n",
            "MajorImageVersion\n",
            "DirectoryEntryExport\n",
            "ImageDirectoryEntryExport\n",
            "AddressOfEntryPoint\n",
            "SizeOfHeaders\n",
            "CheckSum\n",
            "e_lfanew\n",
            "SectionMinEntropy\n",
            "23\n"
          ]
        }
      ],
      "source": [
        "# Display the list of selected important features, one per line\n",
        "print(\"Selected Important Features (importance > 0.01):\")\n",
        "for feature in important_features:\n",
        "    print(feature) \n",
        "print(len(important_features))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "I_K6cyuTMbED",
      "metadata": {
        "id": "I_K6cyuTMbED"
      },
      "source": [
        "#### Testing which Classifiers will give better result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "1dac90a2",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create training and testing datasets with only the important features\n",
        "X_train_important = X_train[important_features]\n",
        "X_test_important = X_test[important_features]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "cOQ_D8uBLA_x",
      "metadata": {
        "id": "cOQ_D8uBLA_x"
      },
      "outputs": [],
      "source": [
        "model = { \"DecisionTree\": DecisionTreeClassifier(max_depth=10),\n",
        "         \"RandomForest\":ek.RandomForestClassifier(n_estimators=50)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "Bm6QFyA5MI4U",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bm6QFyA5MI4U",
        "outputId": "4a4f375b-b192-4349-ec85-7f3d2f1ebdb3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DecisionTree : 0.9867448381340811 \n",
            "RandomForest : 0.9913331633953607 \n"
          ]
        }
      ],
      "source": [
        "results = {}\n",
        "for algo in model:\n",
        "    clf = model[algo]\n",
        "    clf.fit(X_train_important,y_train)\n",
        "    score = clf.score(X_test_important,y_test)\n",
        "    print (\"%s : %s \" %(algo, score))\n",
        "    results[algo] = score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "62CqVWQ1Mjkj",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62CqVWQ1Mjkj",
        "outputId": "da5a3ef0-55ef-4786-a3f5-06dc9c01e015"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using RandomForest for classification, with 23 features.\n"
          ]
        }
      ],
      "source": [
        "winner = max(results, key=results.get)# Selecting the classifier with good result\n",
        "print(\"Using\", winner, \"for classification, with\",len(important_features), 'features.')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "iHRe4e7WMv2s",
      "metadata": {
        "id": "iHRe4e7WMv2s"
      },
      "source": [
        "😌Now it is clear that we should use RandomForest classification to our model since it gives a better result."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6ee2b6d",
      "metadata": {},
      "source": [
        "#### Training New Model Based on the important features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "f33c777b",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Benign       1.00      0.97      0.98      1003\n",
            "     Malware       0.99      1.00      0.99      2920\n",
            "\n",
            "    accuracy                           0.99      3923\n",
            "   macro avg       0.99      0.98      0.99      3923\n",
            "weighted avg       0.99      0.99      0.99      3923\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Train a new model with only the important features\n",
        "rf_model_filtered = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_model_filtered.fit(X_train_important, y_train)\n",
        "\n",
        "# Predictions and Evaluation with important features\n",
        "y_pred_filtered = rf_model_filtered.predict(X_test_important)\n",
        "print(classification_report(y_test, y_pred_filtered, target_names=['Benign', 'Malware']))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QJvkc6ooNksQ",
      "metadata": {
        "id": "QJvkc6ooNksQ"
      },
      "source": [
        "#### Saving the model as malwareclassifier.pkl and features to be extracted as features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "11cf252f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-10-23T20:35:49.058755Z",
          "iopub.status.busy": "2024-10-23T20:35:49.058336Z",
          "iopub.status.idle": "2024-10-23T20:35:49.149405Z",
          "shell.execute_reply": "2024-10-23T20:35:49.148128Z"
        },
        "id": "11cf252f",
        "outputId": "43f7c42a-bef7-47e2-86a0-81a07b841fe8",
        "papermill": {
          "duration": 0.099509,
          "end_time": "2024-10-23T20:35:49.152133",
          "exception": false,
          "start_time": "2024-10-23T20:35:49.052624",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['malwareclassifier-V2.pkl']"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Save the New trained model to a file\n",
        "joblib.dump(rf_model_filtered, 'malwareclassifier-V2.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c115d15c",
      "metadata": {},
      "source": [
        "#### Using Cross-Validation to Assess Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "9e7440d0",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Split training data further into train and validation sets\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "74b9ebfa",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training scores: [0.99203187 0.99243028 0.99282869 0.99003984 0.98924303]\n",
            "Average Training Score: 0.9913147410358565\n",
            "Validation scores: [0.98566879 0.99203822 0.99203822 0.98245614 0.97926635]\n",
            "Average Validation Score: 0.9862935421936427\n"
          ]
        }
      ],
      "source": [
        "# Evaluate training performance\n",
        "train_scores = cross_val_score(rf_model, X_train, y_train, cv=5)\n",
        "print(\"Training scores:\", train_scores)\n",
        "print(\"Average Training Score:\", np.mean(train_scores))\n",
        "\n",
        "# Evaluate validation performance through cross-validation\n",
        "val_scores = cross_val_score(rf_model, X_val, y_val, cv=5)  \n",
        "print(\"Validation scores:\", val_scores)\n",
        "print(\"Average Validation Score:\", np.mean(val_scores))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "965d4eb8",
      "metadata": {},
      "source": [
        "Results show that both the training and validation scores are quite high, with the following insights:\n",
        "\n",
        "    Training Scores: The model performs very well on the training set, averaging around 99.13%.\n",
        "\n",
        "    Validation Scores: The validation performance is also high, averaging around 98.63%.\n",
        "    \n",
        "    Off by only 0.51% or 0.051....\n",
        "\n",
        "Analysis\n",
        "\n",
        "    No Overfitting: The training and validation scores are close, indicating that our model is likely not overfitting. If the training score were significantly higher than the validation score, it would suggest overfitting.\n",
        "    \n",
        "    Slight Underfitting Possibility: Since the validation score is a bit lower than the training score, it could indicate a small underfitting, but it’s minimal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "b84ccc14",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "train_sizes, train_scores, val_scores = learning_curve(rf_model, X_train, y_train, cv=5, n_jobs=-1)\n",
        "\n",
        "# Compute the average and standard deviation of train/validation scores\n",
        "train_mean = np.mean(train_scores, axis=1)\n",
        "train_std = np.std(train_scores, axis=1)\n",
        "val_mean = np.mean(val_scores, axis=1)\n",
        "val_std = np.std(val_scores, axis=1)\n",
        "\n",
        "# Plot learning curves\n",
        "plt.plot(train_sizes, train_mean, label='Training Score')\n",
        "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.1, color=\"r\")\n",
        "\n",
        "plt.plot(train_sizes, val_mean, label='Validation Score')\n",
        "plt.fill_between(train_sizes, val_mean - val_std, val_mean + val_std, alpha=0.1, color=\"g\")\n",
        "\n",
        "plt.xlabel('Training Set Size')\n",
        "plt.ylabel('Score')\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "87416033",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Set bar width and positions\n",
        "bar_width = 0.40\n",
        "train_positions = np.arange(len(train_sizes))\n",
        "val_positions = train_positions + bar_width\n",
        "\n",
        "# Plot the bar chart with error bars\n",
        "plt.bar(train_positions, train_mean, yerr=train_std, width=bar_width, label='Training Score', color='skyblue', capsize=5)\n",
        "plt.bar(val_positions, val_mean, yerr=val_std, width=bar_width, label='Validation Score', color='lightgreen', capsize=5)\n",
        "\n",
        "# Add labels and legend\n",
        "plt.xlabel('Training Set Size')\n",
        "plt.ylabel('Score')\n",
        "plt.xticks(train_positions + bar_width / 2, train_sizes)\n",
        "plt.legend(loc='best')\n",
        "plt.title('Training and Validation Scores by Training Set Size')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kaggle": {
      "accelerator": "none",
      "dataSources": [
        {
          "datasetId": 5935585,
          "sourceId": 9705223,
          "sourceType": "datasetVersion"
        }
      ],
      "dockerImageVersionId": 30786,
      "isGpuEnabled": false,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.6"
    },
    "papermill": {
      "default_parameters": {},
      "duration": 10.54853,
      "end_time": "2024-10-23T20:35:49.886580",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2024-10-23T20:35:39.338050",
      "version": "2.6.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}