AI_Phase5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**IBM Naan Mudhalvan - Artificial Intelligence**\n",
        "\n",
        "**Market Basket Analysis**"
      ],
      "metadata": {
        "id": "8ANFZaKfPhba"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "UuA6V3GP400y"
      },
      "outputs": [],
      "source": [
        "#packages import\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import r2_score, mean_absolute_error,mean_squared_error\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.svm import SVR\n",
        "from mlxtend.frequent_patterns import apriori"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Read the Data\n",
        "dataset = pd.read_excel(\"Market_basket_analysis.xlsx\")\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EWiBLUiD46Aa",
        "outputId": "5f8acc26-f6e4-46c4-b7ed-625aa67bce5a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BillNo                             Itemname  Quantity                Date  \\\n",
              "0  536365   WHITE HANGING HEART T-LIGHT HOLDER         6 2010-12-01 08:26:00   \n",
              "1  536365                  WHITE METAL LANTERN         6 2010-12-01 08:26:00   \n",
              "2  536365       CREAM CUPID HEARTS COAT HANGER         8 2010-12-01 08:26:00   \n",
              "3  536365  KNITTED UNION FLAG HOT WATER BOTTLE         6 2010-12-01 08:26:00   \n",
              "4  536365       RED WOOLLY HOTTIE WHITE HEART.         6 2010-12-01 08:26:00   \n",
              "\n",
              "   Price  CustomerID         Country  \n",
              "0   2.55     17850.0  United Kingdom  \n",
              "1   3.39     17850.0  United Kingdom  \n",
              "2   2.75     17850.0  United Kingdom  \n",
              "3   3.39     17850.0  United Kingdom  \n",
              "4   3.39     17850.0  United Kingdom  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-74d323f0-33e7-45c0-b5ec-b6bc80a7e7db\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BillNo</th>\n",
              "      <th>Itemname</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Date</th>\n",
              "      <th>Price</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-74d323f0-33e7-45c0-b5ec-b6bc80a7e7db')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-74d323f0-33e7-45c0-b5ec-b6bc80a7e7db button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-74d323f0-33e7-45c0-b5ec-b6bc80a7e7db');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-79dea5a3-5528-4be3-972e-7742e26dd8b4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79dea5a3-5528-4be3-972e-7742e26dd8b4')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-79dea5a3-5528-4be3-972e-7742e26dd8b4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "KoHw-Ft723aQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Preparation**"
      ],
      "metadata": {
        "id": "HHLZ7PR3Fc4Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Cleaning\n",
        "dataset['Description'] = dataset['Itemname'].str.strip()\n",
        "dataset.dropna(axis=0, subset=['BillNo'], inplace=True)\n",
        "dataset['BillNo'] = dataset['BillNo'].astype('str')\n",
        "dataset=dataset[~dataset['BillNo'].str.contains('C')]\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "opqL5xjXFlnt",
        "outputId": "ae72d700-7d2a-49aa-868a-f48bb0ffbc1c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BillNo                             Itemname  Quantity                Date  \\\n",
              "0  536365   WHITE HANGING HEART T-LIGHT HOLDER         6 2010-12-01 08:26:00   \n",
              "1  536365                  WHITE METAL LANTERN         6 2010-12-01 08:26:00   \n",
              "2  536365       CREAM CUPID HEARTS COAT HANGER         8 2010-12-01 08:26:00   \n",
              "3  536365  KNITTED UNION FLAG HOT WATER BOTTLE         6 2010-12-01 08:26:00   \n",
              "4  536365       RED WOOLLY HOTTIE WHITE HEART.         6 2010-12-01 08:26:00   \n",
              "\n",
              "   Price  CustomerID         Country                          Description  \n",
              "0   2.55     17850.0  United Kingdom   WHITE HANGING HEART T-LIGHT HOLDER  \n",
              "1   3.39     17850.0  United Kingdom                  WHITE METAL LANTERN  \n",
              "2   2.75     17850.0  United Kingdom       CREAM CUPID HEARTS COAT HANGER  \n",
              "3   3.39     17850.0  United Kingdom  KNITTED UNION FLAG HOT WATER BOTTLE  \n",
              "4   3.39     17850.0  United Kingdom       RED WOOLLY HOTTIE WHITE HEART.  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c9ef50f4-26d1-4d4e-8bfe-15674b4b7544\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BillNo</th>\n",
              "      <th>Itemname</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Date</th>\n",
              "      <th>Price</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "      <th>Description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9ef50f4-26d1-4d4e-8bfe-15674b4b7544')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c9ef50f4-26d1-4d4e-8bfe-15674b4b7544 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c9ef50f4-26d1-4d4e-8bfe-15674b4b7544');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-eef02fab-00f9-4f45-8de9-65572c50ec4b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eef02fab-00f9-4f45-8de9-65572c50ec4b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eef02fab-00f9-4f45-8de9-65572c50ec4b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Country'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "He6PIwvO7wJU",
        "outputId": "5681988b-6d87-4452-948b-16334844e3af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "United Kingdom          487622\n",
              "Germany                   9042\n",
              "France                    8408\n",
              "Spain                     2485\n",
              "Netherlands               2363\n",
              "Belgium                   2031\n",
              "Switzerland               1967\n",
              "Portugal                  1501\n",
              "Australia                 1185\n",
              "Norway                    1072\n",
              "Italy                      758\n",
              "Sweden                     451\n",
              "Unspecified                446\n",
              "Austria                    398\n",
              "Poland                     330\n",
              "Japan                      321\n",
              "Israel                     295\n",
              "Hong Kong                  284\n",
              "Singapore                  222\n",
              "Iceland                    182\n",
              "USA                        179\n",
              "Greece                     145\n",
              "Malta                      112\n",
              "United Arab Emirates        68\n",
              "RSA                         58\n",
              "Lebanon                     45\n",
              "Lithuania                   35\n",
              "Brazil                      32\n",
              "Bahrain                     18\n",
              "Saudi Arabia                 9\n",
              "Name: Country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjofGEAo8TnJ",
        "outputId": "e0d467dc-3805-4746-932f-90712d1a5e63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(522064, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Viewing transaction basket\n",
        "mybasket = (dataset[dataset['Country'] == \"Germany\"]).groupby(['BillNo', 'Description'])['Quantity'].sum().unstack().reset_index().fillna(0).set_index('BillNo')"
      ],
      "metadata": {
        "id": "UzN5bFzI8bu9"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mybasket.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "QseihFYT-TEU",
        "outputId": "3db95e0a-8684-4f72-d7d5-11650e56bc1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Description  10 COLOUR SPACEBOY PEN  12 COLOURED PARTY BALLOONS  \\\n",
              "BillNo                                                            \n",
              "536527                          0.0                         0.0   \n",
              "536840                          0.0                         0.0   \n",
              "536861                          0.0                         0.0   \n",
              "536967                          0.0                         0.0   \n",
              "536983                          0.0                         0.0   \n",
              "\n",
              "Description  12 IVORY ROSE PEG PLACE SETTINGS  \\\n",
              "BillNo                                          \n",
              "536527                                    0.0   \n",
              "536840                                    0.0   \n",
              "536861                                    0.0   \n",
              "536967                                    0.0   \n",
              "536983                                    0.0   \n",
              "\n",
              "Description  12 MESSAGE CARDS WITH ENVELOPES  12 PENCIL SMALL TUBE WOODLAND  \\\n",
              "BillNo                                                                        \n",
              "536527                                   0.0                            0.0   \n",
              "536840                                   0.0                            0.0   \n",
              "536861                                   0.0                            0.0   \n",
              "536967                                   0.0                            0.0   \n",
              "536983                                   0.0                            0.0   \n",
              "\n",
              "Description  12 PENCILS SMALL TUBE RED RETROSPOT  12 PENCILS SMALL TUBE SKULL  \\\n",
              "BillNo                                                                          \n",
              "536527                                       0.0                          0.0   \n",
              "536840                                       0.0                          0.0   \n",
              "536861                                       0.0                          0.0   \n",
              "536967                                       0.0                          0.0   \n",
              "536983                                       0.0                          0.0   \n",
              "\n",
              "Description  12 PENCILS TALL TUBE POSY  12 PENCILS TALL TUBE RED RETROSPOT  \\\n",
              "BillNo                                                                       \n",
              "536527                             0.0                                 0.0   \n",
              "536840                             0.0                                 0.0   \n",
              "536861                             0.0                                 0.0   \n",
              "536967                             0.0                                 0.0   \n",
              "536983                             0.0                                 0.0   \n",
              "\n",
              "Description  12 PENCILS TALL TUBE SKULLS  ...  YULETIDE IMAGES GIFT WRAP SET  \\\n",
              "BillNo                                    ...                                  \n",
              "536527                               0.0  ...                            0.0   \n",
              "536840                               0.0  ...                            0.0   \n",
              "536861                               0.0  ...                            0.0   \n",
              "536967                               0.0  ...                            0.0   \n",
              "536983                               0.0  ...                            0.0   \n",
              "\n",
              "Description  ZINC  HEART T-LIGHT HOLDER  ZINC  STAR T-LIGHT HOLDER  \\\n",
              "BillNo                                                               \n",
              "536527                              0.0                        0.0   \n",
              "536840                              0.0                        0.0   \n",
              "536861                              0.0                        0.0   \n",
              "536967                              0.0                        0.0   \n",
              "536983                              0.0                        0.0   \n",
              "\n",
              "Description  ZINC BOX SIGN HOME  ZINC FOLKART SLEIGH BELLS  \\\n",
              "BillNo                                                       \n",
              "536527                      0.0                        0.0   \n",
              "536840                      0.0                        0.0   \n",
              "536861                      0.0                        0.0   \n",
              "536967                      0.0                        0.0   \n",
              "536983                      0.0                        0.0   \n",
              "\n",
              "Description  ZINC HEART LATTICE T-LIGHT HOLDER  ZINC METAL HEART DECORATION  \\\n",
              "BillNo                                                                        \n",
              "536527                                     0.0                          0.0   \n",
              "536840                                     0.0                          0.0   \n",
              "536861                                     0.0                          0.0   \n",
              "536967                                     0.0                          0.0   \n",
              "536983                                     0.0                          0.0   \n",
              "\n",
              "Description  ZINC T-LIGHT HOLDER STAR LARGE  ZINC T-LIGHT HOLDER STARS SMALL  \\\n",
              "BillNo                                                                         \n",
              "536527                                  0.0                              0.0   \n",
              "536840                                  0.0                              0.0   \n",
              "536861                                  0.0                              0.0   \n",
              "536967                                  0.0                              0.0   \n",
              "536983                                  0.0                              0.0   \n",
              "\n",
              "Description  ZINC WILLIE WINKIE  CANDLE STICK  \n",
              "BillNo                                         \n",
              "536527                                    0.0  \n",
              "536840                                    0.0  \n",
              "536861                                    0.0  \n",
              "536967                                    0.0  \n",
              "536983                                    0.0  \n",
              "\n",
              "[5 rows x 1695 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-560eeb3a-e4b0-4df8-80f2-08765aaac537\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Description</th>\n",
              "      <th>10 COLOUR SPACEBOY PEN</th>\n",
              "      <th>12 COLOURED PARTY BALLOONS</th>\n",
              "      <th>12 IVORY ROSE PEG PLACE SETTINGS</th>\n",
              "      <th>12 MESSAGE CARDS WITH ENVELOPES</th>\n",
              "      <th>12 PENCIL SMALL TUBE WOODLAND</th>\n",
              "      <th>12 PENCILS SMALL TUBE RED RETROSPOT</th>\n",
              "      <th>12 PENCILS SMALL TUBE SKULL</th>\n",
              "      <th>12 PENCILS TALL TUBE POSY</th>\n",
              "      <th>12 PENCILS TALL TUBE RED RETROSPOT</th>\n",
              "      <th>12 PENCILS TALL TUBE SKULLS</th>\n",
              "      <th>...</th>\n",
              "      <th>YULETIDE IMAGES GIFT WRAP SET</th>\n",
              "      <th>ZINC  HEART T-LIGHT HOLDER</th>\n",
              "      <th>ZINC  STAR T-LIGHT HOLDER</th>\n",
              "      <th>ZINC BOX SIGN HOME</th>\n",
              "      <th>ZINC FOLKART SLEIGH BELLS</th>\n",
              "      <th>ZINC HEART LATTICE T-LIGHT HOLDER</th>\n",
              "      <th>ZINC METAL HEART DECORATION</th>\n",
              "      <th>ZINC T-LIGHT HOLDER STAR LARGE</th>\n",
              "      <th>ZINC T-LIGHT HOLDER STARS SMALL</th>\n",
              "      <th>ZINC WILLIE WINKIE  CANDLE STICK</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BillNo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>536527</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>536840</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>536861</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>536967</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>536983</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1695 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-560eeb3a-e4b0-4df8-80f2-08765aaac537')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-560eeb3a-e4b0-4df8-80f2-08765aaac537 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-560eeb3a-e4b0-4df8-80f2-08765aaac537');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-393ea06f-87d3-4f7c-8986-8e44c392883f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-393ea06f-87d3-4f7c-8986-8e44c392883f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-393ea06f-87d3-4f7c-8986-8e44c392883f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting all +ve values to 1 & everything else to 0\n",
        "def my_encode_units(x):\n",
        "  if x <=0:\n",
        "    return 0\n",
        "  if x >=1:\n",
        "    return 1\n",
        "\n",
        "mybasket_sets = mybasket.applymap(my_encode_units)\n",
        "mybasket_sets.drop('POSTAGE', inplace=True, axis=1)\n"
      ],
      "metadata": {
        "id": "8_gEkvAo-oco"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training Model**"
      ],
      "metadata": {
        "id": "HkiLB0qAGEfT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "my_frequent_items = apriori(mybasket_sets, min_support=0.07, use_colnames=True)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "iJpX0-60_mIT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "38ead9a2-0850-4c1b-d0d4-d18ec1225c05"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-ad54dcdba792>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# Filter columns based on the selected itemsets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mencoded_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmybasket_sets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselected_itemsets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Create a heatmap for the selected frequent itemsets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3811\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3812\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3813\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3814\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3815\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   6068\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6069\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6070\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_missing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6072\u001b[0m         \u001b[0mkeyarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   6128\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6129\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6130\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6132\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"None of [Index([         ('6 RIBBONS RUSTIC CHARM'),\\n             ('ALARM CLOCK BAKELIKE PINK'),\\n                     ('GUMBALL COAT RACK'),\\n                ('JAM MAKING SET PRINTED'),\\n               ('JUMBO BAG RED RETROSPOT'),\\n            ('JUMBO BAG WOODLAND ANIMALS'),\\n                    ('LUNCH BAG WOODLAND'),\\n       ('PACK OF 72 RETROSPOT CAKE CASES'),\\n         ('PLASTERS IN TIN CIRCUS PARADE'),\\n              ('PLASTERS IN TIN SPACEBOY')],\\n      dtype='object', name='Description')] are in the [columns]\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#generating rules\n",
        "from mlxtend.frequent_patterns.association_rules import association_rules\n",
        "my_rules = association_rules(my_frequent_items, metric='lift', min_threshold=1)"
      ],
      "metadata": {
        "id": "H2cCc8sAAMmq"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#viewing top 100 rules\n",
        "my_rules.head(100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "4eA290pZAkbI",
        "outputId": "6d580121-3a38-4ee7-dd7d-9ddbc7e3201f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            antecedents                           consequents  \\\n",
              "0    (PLASTERS IN TIN WOODLAND ANIMALS)  (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "1  (ROUND SNACK BOXES SET OF4 WOODLAND)    (PLASTERS IN TIN WOODLAND ANIMALS)   \n",
              "2   (ROUND SNACK BOXES SET OF 4 FRUITS)  (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "3  (ROUND SNACK BOXES SET OF4 WOODLAND)   (ROUND SNACK BOXES SET OF 4 FRUITS)   \n",
              "4                  (SPACEBOY LUNCH BOX)  (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "5  (ROUND SNACK BOXES SET OF4 WOODLAND)                  (SPACEBOY LUNCH BOX)   \n",
              "\n",
              "   antecedent support  consequent support   support  confidence      lift  \\\n",
              "0            0.137856            0.245077  0.074398    0.539683  2.202098   \n",
              "1            0.245077            0.137856  0.074398    0.303571  2.202098   \n",
              "2            0.157549            0.245077  0.131291    0.833333  3.400298   \n",
              "3            0.245077            0.157549  0.131291    0.535714  3.400298   \n",
              "4            0.102845            0.245077  0.070022    0.680851  2.778116   \n",
              "5            0.245077            0.102845  0.070022    0.285714  2.778116   \n",
              "\n",
              "   leverage  conviction  zhangs_metric  \n",
              "0  0.040613    1.640006       0.633174  \n",
              "1  0.040613    1.237951       0.723103  \n",
              "2  0.092679    4.529540       0.837922  \n",
              "3  0.092679    1.814509       0.935072  \n",
              "4  0.044817    2.365427       0.713415  \n",
              "5  0.044817    1.256018       0.847826  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6faee30f-67ee-4b70-91b4-658e9bde95b9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "      <th>zhangs_metric</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.137856</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.539683</td>\n",
              "      <td>2.202098</td>\n",
              "      <td>0.040613</td>\n",
              "      <td>1.640006</td>\n",
              "      <td>0.633174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.137856</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.303571</td>\n",
              "      <td>2.202098</td>\n",
              "      <td>0.040613</td>\n",
              "      <td>1.237951</td>\n",
              "      <td>0.723103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.131291</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>3.400298</td>\n",
              "      <td>0.092679</td>\n",
              "      <td>4.529540</td>\n",
              "      <td>0.837922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.131291</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>3.400298</td>\n",
              "      <td>0.092679</td>\n",
              "      <td>1.814509</td>\n",
              "      <td>0.935072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(SPACEBOY LUNCH BOX)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.102845</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.070022</td>\n",
              "      <td>0.680851</td>\n",
              "      <td>2.778116</td>\n",
              "      <td>0.044817</td>\n",
              "      <td>2.365427</td>\n",
              "      <td>0.713415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(SPACEBOY LUNCH BOX)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.102845</td>\n",
              "      <td>0.070022</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>2.778116</td>\n",
              "      <td>0.044817</td>\n",
              "      <td>1.256018</td>\n",
              "      <td>0.847826</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6faee30f-67ee-4b70-91b4-658e9bde95b9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6faee30f-67ee-4b70-91b4-658e9bde95b9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6faee30f-67ee-4b70-91b4-658e9bde95b9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b63e2751-2832-45fd-9df8-30f0af8d168a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b63e2751-2832-45fd-9df8-30f0af8d168a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b63e2751-2832-45fd-9df8-30f0af8d168a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Marketing Recommendations**"
      ],
      "metadata": {
        "id": "QwlNjH_IGicO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Assuming my_frequent_items contains the frequent itemsets and mybasket_sets contains the encoded transactions\n",
        "\n",
        "# Select a subset of frequent itemsets (e.g., top N itemsets)\n",
        "top_n_itemsets = my_frequent_items.head(10)  # Change 10 to the desired number of itemsets to display\n",
        "\n",
        "# Extract the selected frequent itemsets and their support values\n",
        "selected_itemsets = top_n_itemsets['itemsets']\n",
        "support_values = top_n_itemsets['support']\n",
        "\n",
        "# Convert frozensets to strings for display\n",
        "itemset_strings = selected_itemsets.apply(lambda x: ', '.join(list(x)))\n",
        "\n",
        "# Create a horizontal bar chart for the selected frequent itemsets\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=support_values, y=itemset_strings, palette='viridis')\n",
        "plt.xlabel('Support')\n",
        "plt.ylabel('Selected Top N Frequent Itemsets')\n",
        "plt.title('Top 10 Frequent Itemsets')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "Dic9JxrwA4Ag",
        "outputId": "6baaff09-77ff-4bab-85d5-e7777faedefa"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mybasket_sets['SPACEBOY LUNCH BOX'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FgvlU7ZrBna4",
        "outputId": "63e99b7d-1f46-4ad5-cfad-c8b3ed8231b6"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#condition based rules filteration\n",
        "my_rules[(my_rules['lift'] >= 3) & (my_rules['confidence'] >= 0.3)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Xfez7scUBx_R",
        "outputId": "2f87d1ec-047d-41ef-8cdd-8f103cc76917"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            antecedents                           consequents  \\\n",
              "2   (ROUND SNACK BOXES SET OF 4 FRUITS)  (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
              "3  (ROUND SNACK BOXES SET OF4 WOODLAND)   (ROUND SNACK BOXES SET OF 4 FRUITS)   \n",
              "\n",
              "   antecedent support  consequent support   support  confidence      lift  \\\n",
              "2            0.157549            0.245077  0.131291    0.833333  3.400298   \n",
              "3            0.245077            0.157549  0.131291    0.535714  3.400298   \n",
              "\n",
              "   leverage  conviction  zhangs_metric  target  Cluster  \n",
              "2  0.092679    4.529540       0.837922       1        1  \n",
              "3  0.092679    1.814509       0.935072       1        3  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-00c08472-8b34-4d68-a7f2-2a3e68103a1b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "      <th>zhangs_metric</th>\n",
              "      <th>target</th>\n",
              "      <th>Cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.131291</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>3.400298</td>\n",
              "      <td>0.092679</td>\n",
              "      <td>4.529540</td>\n",
              "      <td>0.837922</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
              "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
              "      <td>0.245077</td>\n",
              "      <td>0.157549</td>\n",
              "      <td>0.131291</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>3.400298</td>\n",
              "      <td>0.092679</td>\n",
              "      <td>1.814509</td>\n",
              "      <td>0.935072</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00c08472-8b34-4d68-a7f2-2a3e68103a1b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-00c08472-8b34-4d68-a7f2-2a3e68103a1b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-00c08472-8b34-4d68-a7f2-2a3e68103a1b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e4cdfa5a-88b7-4b36-bcf1-6f6dca82f34f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e4cdfa5a-88b7-4b36-bcf1-6f6dca82f34f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e4cdfa5a-88b7-4b36-bcf1-6f6dca82f34f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualization of Dataset**"
      ],
      "metadata": {
        "id": "gDg_5dWEG4ML"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(dataset, x='Price', y='Quantity', bins=50, color='y')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ihs-b-JhHATx",
        "outputId": "6ca2bd60-4c7c-4922-cdfa-02c861472a4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Price', ylabel='Quantity'>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(dataset, x='Price',  palette='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "varDtftMH7eS",
        "outputId": "bd0c184a-3ea9-4c03-f6fe-fd89c389f6e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Price'>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.pairplot(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "id": "bi8BTkrwIA1K",
        "outputId": "e92f2eeb-62d9-4cf4-d70b-14ecf10c844e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x78e81ecf8c40>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 750x750 with 12 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.hist(figsize=(10,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "id": "bSZ_uj9kJ2k0",
        "outputId": "8b069842-d3f9-4c1b-d5dd-8fae030b0734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Quantity'}>,\n",
              "        <Axes: title={'center': 'Date'}>],\n",
              "       [<Axes: title={'center': 'Price'}>,\n",
              "        <Axes: title={'center': 'CustomerID'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualizing Correlation**"
      ],
      "metadata": {
        "id": "bjjJ6F8mNSKO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "Xfa3GOT7NdPa",
        "outputId": "b6c92345-1a39-4c70-a701-616f3790cb9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-48-c187c74d1e71>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  dataset.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Quantity     Price  CustomerID\n",
              "Quantity    1.000000 -0.002803   -0.005990\n",
              "Price      -0.002803  1.000000   -0.010245\n",
              "CustomerID -0.005990 -0.010245    1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-23f60701-7a66-458c-b71d-f98aeeee53e7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Price</th>\n",
              "      <th>CustomerID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Quantity</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.002803</td>\n",
              "      <td>-0.005990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>-0.002803</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.010245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>-0.005990</td>\n",
              "      <td>-0.010245</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23f60701-7a66-458c-b71d-f98aeeee53e7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-23f60701-7a66-458c-b71d-f98aeeee53e7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-23f60701-7a66-458c-b71d-f98aeeee53e7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7e784df8-8f42-4a44-b67d-676d81c943a9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7e784df8-8f42-4a44-b67d-676d81c943a9')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7e784df8-8f42-4a44-b67d-676d81c943a9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.heatmap(dataset.corr(), annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "OIFzaMj8Nolk",
        "outputId": "a1ac69cd-d359-48b1-f02a-362c1f23c2ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-49-1afac6b5cf53>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(dataset.corr(), annot=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = [20, 4] , dpi = 150)\n",
        "plt.scatter (dataset[\"Quantity\"] ,dataset[\"Price\"] , color = \"red\")\n",
        "plt.title (\"The relationship between Quantity and Price\" , weight='bold', fontsize = 25)\n",
        "plt.xticks (range (0 , 205 , 10), fontsize = 20)\n",
        "plt.yticks (fontsize = 20)\n",
        "plt.xlabel ('Quantity', fontsize = 20 )\n",
        "plt.ylabel ('Price' , fontsize = 20)\n",
        "plt.grid ()\n",
        "plt.show ()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "PCNm4kU0bQlS",
        "outputId": "afd635a2-d201-444f-fe82-a196b7c16d72"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = [20, 4] , dpi = 150)\n",
        "plt.scatter (dataset[\"Quantity\"] , dataset[\"Country\"] , color = \"red\")\n",
        "plt.title (\"The relationship between Quantity and Country\" , weight='bold', fontsize = 25)\n",
        "plt.xticks (range (10 , 85 , 5), fontsize = 20)\n",
        "plt.yticks (fontsize = 5)\n",
        "plt.xlabel ('Quantity', fontsize = 20 )\n",
        "plt.ylabel ('Country' , fontsize = 20)\n",
        "plt.grid ()\n",
        "plt.show ()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "OsNtjRCbdiiT",
        "outputId": "3e77b0e1-94b0-4289-defa-5969bd04ea48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAACeMAAAKKCAYAAAC0p9JyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAABcSAAAXEgFnn9JSAAEAAElEQVR4nOzdd3gU1dvG8XtTaCH0EnqLgARIIDFUIYIiUkUQRaSjAlIUu4gFERUVEAuK9AAiAgJSlB6kSAsgIL13EEJJIKTN+we/5KUku7ObTdnk+7muXLCz55x5ZjKZmd199jkWwzAMAQAAAAAAAAAAAAAAAAAAh7lldAAAAAAAAAAAAAAAAAAAALg6kvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglkvEAAAAAAAAAAAAAAAAAAEglj4wOAAAAAHDUsWPHVKFCBZvtDMNIh2iQ1Xz44Yf66KOPrLbp1q2bpkyZkj4BuQj2W/aQGX/PU6ZMUY8ePay2ady4sdasWZM+AWVSFovFZpujR4+qfPnyaR8MgCztxo0b2rt3r44fP66zZ8/qxo0bio6OVs6cOZU7d24VL15cZcqU0YMPPqgCBQpkdLjIJjLjPQyQFkJCQhQWFma1zeTJk9W9e/f0CcgBhmHo0KFDOnr0qE6dOqWrV6/q5s2bslgs8vb2lre3t4oXL64HH3xQZcuWNXWfCwAAAKQHkvEAAABglZk3cNPL6tWrFRISktFhAAAAAEjG33//rd9++01//PGH9uzZo/j4eJt9LBaLKlWqpKZNm6p169Z6/PHH5eHB29YAkB0dOHBACxYs0NKlS7V161Zdv37dVL+8efMqMDBQjz/+uB5//HHVqlWL5DwAAABkGN7VAAAAAIBsZP78+dqxY4fVNiEhISS+Asi21qxZY7OCYkBAgJ588sl0iQfI7BISEhQaGqqvvvpKu3btsrt/YuWjQ4cO6ccff1SpUqXUr18/DRo0SF5eXmkQMTIzV7pXHTNmjK5cuWK1Tffu3ak2C5iwcOFCjRo1yuEvg0ZGRiosLExhYWF699135evrq5deekk9evRQ4cKFnRwtMgNXul4AAIDsh2Q8AAAAAMhG5s+fr6lTp9psxxvWALKrNWvWmJrCkGQ8QNqyZYt69+6tf/75x2ljnj59WkOGDNG3336rMWPGqGPHjk4bG5mfK92rjhkzRsePH7faJiQkhGQ8wIqdO3eqb9++2rhxo1PHPXTokN544w3NnDlT4eHhTh0bmYMrXS8AAED245bRAQAAAAAAAAAAXMtXX32l+vXrOzUR705nz57VM888o169eik2NjZN1gEAyDiff/65HnroIacn4t0pISEhzcYGAAAAUkJlPAAAAAAAAACAaQMHDtQ333yTLuuaNGmSjh8/roULFypPnjzpsk4AQNqJi4tTz549FRoamtGhAAAAAGmCyngAAAAAAAAAAFPeeecdhxPx3Nwcezt65cqV6tixo+Lj4x3qDwDIHBISEtSlS5dUJeI5ei0BAAAA0gt3rAAAAAAAp/nwww9lGIbVnylTpmR0mMiCunfvbvPYW7NmTUaHCQAu7ZdfftFnn31mun2FChX0/vvva+vWrbp48aLi4uJ0+fJl/fPPP/rss8/k5+dneqzFixfrgw8+cCRsIAn3qkDGeuuttzRr1iy7+oSEhOjLL79UeHi4zp07p5iYGMXExOjMmTPauXOnJk6cqK5du6p06dJpFDUAAABgH6apBQAAgFX2Ji4cO3ZMFSpUsNmuW7dufMgBAAAAuIizZ8+qT58+ptq6ubnp9ddf17Bhw5QzZ867nitYsKAKFiyoGjVq6PXXX9fo0aM1dOhQRUdH2xz3s88+U+vWrVWnTh2HtgEAkHEWL16sL7/80nT74OBgjR49WvXr17/vOXd3d5UoUUIlSpRQzZo11bNnTxmGoRUrVujLL7/UsmXLnBk6AAAAYBcq4wEAAAAAAAAArHrvvfd05coVU22///57ff755/cl4t3L3d1dr7/+un799Vd5eNj+3nh8fLxee+01UzEAADKPmzdv6uWXXzbdvnv37tqwYUOyiXgpsVgseuyxx/Tnn3/qr7/+Uu3atR0JFQAAAEg1KuMBAAAg2zpy5IjCw8N14sQJ3bx5UwULFlSxYsVUp04dlSlTJk3Xff78ee3cuVOXL1/W5cuXdeXKFeXJk0dFihRR4cKFVbNmTZUqVSpNY0gvN27c0JYtW7R//35FREQoOjpanp6eqlevnh555BG7x4uIiND27dv133//6fLly4qIiFDOnDlVuHBhFSlSRFWrVlWlSpXSYEucKyIiQidPntSZM2d09uxZRUZG6ubNm7p165Y8PDyUO3duFS5cWKVKlVLlypWZcicZUVFRCg8P1/nz5xUREaHLly/LYrGoUKFCKlSokHx8fFSrVi3lzp07Q+O8du2aNm3apIMHD+rKlSvKlSuXihYtqipVqigoKEhubs79ntyFCxe0f/9+HTt2TNevX1dUVJRu3bqlXLlyycvLS0WKFFHZsmVVrlw5+fj4OHXdZsTFxWnr1q3as2eP/vvvP0lS0aJFVaZMGTVo0EB58uRJ95icJTo6Wlu2bNG+fft0+fJlJSQkqFChQqpatarq1KmjXLlyZXSIqXLs2DGFh4fr+PHjioqKUu7cuVW2bFkFBgaqYsWKabru6Ohobd++XWfPnk36e09ISEg695crV07+/v5O/3uC4w4cOKCDBw8mXasjIyOVP39+FSlSRMWKFVNgYKDy5cuXLrG4wr1DbGystm7dqn///TdTnhuPHj1quqr1q6++qpdeesmu8Vu1aqWvvvpKgwYNstl2/fr1Wr58uR577DG71pGZXbx4UadOndLp06d1/vx5RUVFJd0X5siRI+l1QunSpVW1alUVK1YsQ+PN7McrXIurvS5KSEjQjh07tGvXLp0/f17x8fEqUqSISpYsqYYNGyp//vxOX2dkZKQ2btyogwcPKiIiQp6enipSpIj8/PwUGBhoKpk5o33zzTc6fvy4qbY9e/bUhAkTZLFYHF5fw4YNtXnzZv32228Oj5Ho1q1bSfehie/hxMfHq1ChQknvI9WuXVve3t6pXldWk12vF85+HwwAALggAwAAAHCio0ePGpJs/nTr1i3d1nWnW7duGd98841RrVo1q32qVatmTJo0yYiLi0t1nIlWrlxpdO/e3fD19TUVt6+vr/Hiiy8a27dvd1oMjvrggw/s/p2Gh4cbzzzzjJErV65UHwNbtmwx+vbta/j5+RkWi8VmLGXKlDG6dOlihIWFpes2p+T48ePGrFmzjEGDBhmPPPKIUbx4cVPHwJ0/xYsXNzp27GjMnj3biImJMbVes38jjvyUK1cuzfdbcg4cOGC8/vrrRlBQkOHh4WFzXTly5DDq1q1rvPvuu8aJEyccXm/jxo1trmvy5Ml39Vm1apXRqlUrq3EWLFjQePnll41z5845HJth3D6/vPjii0apUqXs+j0WKlTIaNy4sTFw4EDjl19+MU6fPm1qfY78no8cOWL07dvXyJ8/f4p9cubMabRp08ah897kyZNtxtS4ceM02bZ//vnHeP755408efKk2CdPnjxG165djb1799q9bc5k5rg4evRoUvuYmBhj3LhxRvXq1a32qV69uvHDDz8YsbGxTov15MmTxtChQ4369esbOXLksBl3/vz5jVatWhmhoaF2xZFW50np7nuQhx56yGpbNzc34/Llyzbjfemll6yOs3z5cptj/PTTTzZjHzlypOl9aBi377GmTp1qtGvXzihWrJjN8d3d3Y2goCDjvffeM86ePWvXusxwlXuHgwcPGi+++KKRL1++NDk3Osvrr79u6pj38fExrl275tA64uLijBo1aphaT9u2ba2O5cjrBGu6detmc6wPPvjA1FgHDhwwpkyZYvTp08do2LChUahQIbvPLWXKlDG6d+9uLFmyxEhISDC9HXfKDMerK92rmrnPcPQncX3r1q2z2dbX19eh3/nMmTNtjt2oUSO7x7VXRr0uupcj941nz541XnvtNaNo0aIp9nF3dzdCQkKMNWvWOGFvGcbmzZuN9u3bG56enimus0CBAkb//v2N48eP39XXkdcvaSUmJsYoWbKkqd9v5cqVjaioqHSJy5rTp08b77//vtGgQQMjZ86cNuN2d3c3atWqZbzyyiuput8vV66czXWtXr3a1FgZ+foou14vzLwPNn36dJvjNm3a1OY+Sc6IESNsjt21a1eHxgYAAPYhGQ8AAABOlZmT8dauXWtUrFjRrjflgoODjfPnz6cqzlmzZhn+/v6penOwRYsWxo4dO1K7yxxmz5uQ8fHxxltvvWW4ubml+hhYtmyZ0ahRo1TtuwYNGjj0YYizksrWr1/vlDeI7/wpWbKkMWnSJJvrdpU3rM3Ytm2b0bZtW5vHlbUfDw8P47nnnjP2799v9/rt+TDr4sWLxlNPPWVXbHnz5jV+//13u+P6559/jIYNGzr1d9u5c2eb67Xn9xwXF2cMGzbMVDJV4o/FYjHef/99u/ZFRnzYFBMTY7z++uuGu7u7Xcfh+++/79Rkb3uYiTExGW/Hjh02k9fv/alevbrxzz//pCrGffv2Gc8995yphNuUfipUqGD89NNPTtsnjv7c6d1337XZfsGCBTbjrVKlitUxhgwZYnOM5557zmYsZhO/oqOjjREjRhglSpRweD/lzJnT6NOnj3HhwgVT67TGVe4dYmNjjaFDh1pNsLj3x5FzozMkJCSYTvYeNWpUqtY1d+5cU+vJkSOH1aS/zJqMN2PGDKefZypXrmwsXLjQ9LYkygzHqyvdq6ZHMp5hGEZwcLDN9suWLbP7992qVSub406bNs3uce2Rka+L7mXvfeM333xjeHl52RVb7969Hf6Swo0bN4y+ffuaSiZP/MmTJ48xduzYpDEyUzLewoULTW/HypUr0yWmlBw8eNDo0qWLXee7e38sFovRqlUrY/PmzXav39WT8bLr9cKe98FiYmJs3ldZLBaH3rew9QUqScbatWvtHhcAANiPuTsAAACQLYwbN06PPPKIjhw5Yle/zZs3q0GDBoqIiLB7ndeuXdMzzzyjZ599Vjt37rS7/52WLFmiOnXqaPz48akaJ63Fx8erY8eO+vzzz5WQkODwODExMRo0aJCaNWumtWvXpiqm9evXq0mTJho2bFiqYnJUWqzzzJkz6tmzp5555hnFx8c7ffzMxDAMjRo1SnXr1tWCBQtStT/j4uI0c+ZM1a5dW9OmTXNilP9v9+7dql27tubNm2dXv8jISLVr107z58833WfFihVq0KCB1q1bZ2eU1iVOH+QM169fV4sWLfT+++8rJibGdD/DMDRs2DC9/vrrTovF2SIjI/X444/ryy+/tOvvMC4uTsOGDVP79u3t2ifpbcmSJapXr57+/fdfu/rt3r1b9evX18qVKx1a76RJk1S7dm3NnDlTcXFxDo0h3Z5S84UXXtBTTz2lq1evOjyOM5mZUnPNmjVWnz937pz279+fqjEkKSwszOrzRYsWlb+/v81x9u/frzp16ujdd9/V2bNnbbZPya1bt/TDDz+odu3a2rBhg0NjuNK9w5UrV9SsWTN9/PHHio2NNd0vo86N4eHhOn36tM12bm5uevbZZ1O1rpYtW5qa4jEmJkbLli1L1boyQlocTwcOHFCbNm302muvOX1syfWOV1c3ePBgm23GjRtn15iXL1/Wn3/+abVNgQIF1KFDB7vGtZcrvi66deuWOnXqpAEDBigqKsquvhMmTHDonHj16lU1bdpU48aNk2EYpvvduHFDAwcOVK9evTLkda81c+bMMdWuRo0aatKkSRpHk7IZM2aodu3aCg0Ntet8dy/DMLRo0SLVr19fI0eOtOv36Mqy6/XC3vfBPD09NWDAAKttDMPQDz/8YFccu3fv1u7du622qVKlih5++GG7xgUAAI4hGQ8AAABZ3qhRo9SvXz+H35w/dOiQXn75Zbv6HD58WLVq1dLs2bMdWmdybt26pZdeekkDBw502pjONnDgQM2dOzdVY/z333+qX7++xo4d66Sobn/w88EHH6h9+/ZZKnlt9uzZ6tevX0aHkWZiYmLUvn17vfbaa6n6MOReUVFR6tatm/r16+fUD0b++ecfNW7cWCdPnnSof1xcnHr16qVz587ZbHv+/Hk9++yzun79ukPrSg+JyWqpSZb46quvMmWyRWxsrNq0aaPVq1c7PMaCBQvUvXt35wXlRKtXr1a7du108+ZNh/pHRkaqbdu2Cg8PN90nISFBvXr1Uq9evXTjxg2H1puc3377TUFBQTp//rzTxnRU/fr1lSdPHqttbCXJmUm027Jli9V9ePDgQZuJVU2bNpXFYrHaZtGiRQoMDEz1Fw7udOrUKYWEhOiXX36xq58r3TtcvXpVjz76aKrOH+l9bjSb9F2/fn2VKFEiVevKmTOnWrVqZaqto4mbWdWoUaP06aefOnVMVzxeXV2HDh1UtmxZq21+//13UwmyiebMmWPzXrpz587KnTu36TEzm7R4XRQTE6N27dpp1qxZDo8xd+5cu75QFxcXp9atW2vjxo0Or3PSpEnq27evw/3TgtlzyIsvvpjGkSTPMAwNGDBAzz//vFNfX8XFxemtt97Sk08+mam/hOMM2fl64cj7YC+99JK8vLystpkyZYpdr8dmzpxps03v3r1NjwcAAFKHZDwAAABkec6oEvHzzz9r06ZNptqeP39eTZo0sbsKn1nffPONvvrqqzQZOzVWrVql77//PlVj3LhxQ82aNdO2bducFNXd5s+fr1deeSVNxs4o48ePT9UHRJlZ165d9dtvv6XZ+OPGjdNbb73ltPFGjx6ty5cvp2qMy5cv66OPPrLZbvjw4bp06VKq1pXW5s6dm6oPEhNlxr/Z2bNnp+qDpkQ///yzpk6d6oSInKtPnz6p/sAwKipKzz//vKKjo02179evnyZNmpSqdabk0KFDatOmjcPJhc6SI0cONWrUyGqbHTt2WK3kZyYZLyYmRuvXr0/xeTPH7qOPPmr1+bCwMHXo0MHuKkFmxMbGqlu3bla34U6udu8wf/58p8SanudGs4m1gYGBTlmf2XHS6nfuyoYMGeKUa28iVzxeXZ27u7vNL1/FxcXZleD1888/22yTFRI0nP26aOPGjVq6dGmqx3n77bdN3w8NHz5cf/31V6rXOX78eKeeC1LjzJkzpr+s9Mgjj6RxNMl755139O2336bZ+AsXLlSXLl3SbPzMILteLxx9H6xAgQLq0aOH1TYRERF2ndNstfX09FS3bt1MjwcAAFKHZDwAAABkW7YqvtzL7BQRnTt31okTJ+wa28PDw672b775plMSQZzJ0WpgdxowYIC2b99uVx93d3e72n/77beZMvnF3d1dbm6OvUT7+OOPs9zUN99//73dlZEsFovdf9dffPGFFi9ebFcfe9n7e50xY4YiIyNTfD4hIUG//vqrXWN6eHjYvW/Sir37Y+/evTarhaW3lKZPdeRveNCgQXZVt0kPKSXiOfK7Gzp0qM12oaGh+vHHH+0a283Nza54Nm/enCkqidqaqjYhIcHqh/BmkvFstTMzhrU4//vvPz3zzDO6deuWqVgS2XOvc+vWLT355JOmko6zyr1DZj43Hjp0yFS7mjVrOmV9NWrUMNXu8OHDTllfZuTodTtxqr+0lpmP16zghRdekLe3t9U2EyZMMDWd++nTp21O3R0YGKiAgAB7QkxTmf11kb2xRUREmHpds337dn3yySdOiyezVGI7cOCAqXb58uXTgw8+mMbR3G/x4sX6/PPP7e5n7zl69uzZqf7yoivK6teL1LwP9sorr9jcP2anJd+4caOOHj1qtU2bNm1UtGhR0/EBAIDUse8TPwAAAMCFubu7q0+fPurRo4f8/Pzk7u6uHTt2aPjw4Vq4cKHN/r/99psmTZpk9U3X0NBQrVy50uZYJUqUUP/+/fXEE0+oatWqyp07t2JiYrR//37Nnz9fo0ePVkRERIr9ExIS9Oabb2rz5s2ZJsEmOQ888IDq16+v4sWLS7r9rfitW7dq375997X966+/TFVFKlCggPr27as2bdrIz89P3t7eiouL05EjR7RkyRJ9+eWXNhNbhgwZomeeeUa5cuVybMMcVKhQIT388MNq2LChHnjgAfn6+qpEiRLy8vJSzpw5Jd1OQDh79qz27t2rFStWaMqUKTarrf3777/6888/1bx587uWFyxY8L7pyn799VebFW6aNm1qsypS/vz5rT6fGhcvXtS7775rqm2xYsX0zjvvqHXr1ipbtqwMw9CRI0c0d+5cjRw5UteuXbM5Rv/+/fXYY48pR44cqQ09SZ06dfTmm28qJCREBQsW1JkzZzR16lR9/PHHNitjXL9+XcuXL1e7du2Sff7UqVM2p9wsV66c3nzzTTVp0kTly5dXrly5lJCQoOvXr+vSpUvav3+/9u7dq/DwcIWFhenUqVMOb6sZJUuW1NChQ9WqVSuVLFlS169f1+LFi/XWW2+ZWve8efPUuHHjNI3RUdWrV9c777yjpk2bqlixYoqMjNS2bdv03Xffac6cOTb7X716VWPGjNEXX3yRDtHax8PDQy+//LK6du2qatWqydPTU6dOndLChQv1ySefmJr69dtvv9Xbb7+twoULJ/t8RESEBg8ebCqWzp0769lnn1VgYKCKFCkiSTp79qzCwsI0ZswYbd682eoYU6dO1auvvnpfwtC958kVK1bYvJbXrl1bTz/9tM2472UrGU+6nSyX3DSd58+f1/79+02tJzXJeA888IDVKRLfeustU7/72rVrq2/fvmrcuLEqVKggDw8PRUZGavv27QoNDdWkSZOsTv/633//acSIEVarAbv6vYOrnBvNftBcoUIFp6yvYsWKptqdPXtW8fHxdidXZhY+Pj5q3Lix6tevL19fX/n6+qpo0aLy8vJKuie5efOmzpw5o127dumPP/5QaGiozam8//jjD+3du9fpSS1pcby60r1qYGDgfbF+/vnnunLlitV+L7zwgs1j+t4E1Hz58qlXr14aM2ZMin3OnDmjBQsWqH379lbH/uWXX5SQkGAzxvSW3q+LUqtAgQJ655131L59e5UrV04xMTFauXKl3n77bf377782+8+bN89mRarhw4ebSrB0c3NTz5491bt3b9WsWVM5c+bUuXPntHTpUg0fPlzHjh0zu1np4vjx46baValSxeEkTEfdunVL/fv3N9XW29tbb7zxhtq3b69KlSrJ3d1dJ0+e1KJFi0zfF7/77rt6+umns3xCVHa/Xph9H6xSpUpq06aN5s+fn+JYW7Zs0bZt22xWDTZTATUjzvUAAGRrBgAAAOBER48eNSTZ/OnWrVu6rUuSkStXLmP58uUpjtW1a1dT4+zfvz/FMeLj443KlSvbHKN169bGtWvXrG7bqVOnjCpVqtgca86cOQ7vP3t88MEHpve1JCMoKMjYsGFDiuMdOnTIWLp06V3LHn30UZvj1qlTxzh37pzVWK9evWo0aNDA5lhffvllqrfZzHG8detWo2vXrsaqVauMhIQEm+3vdf78eaN69eo2Y3n99ddNjdetWzebY33wwQd2x5nIGftt6NChpo6zgIAA47///ktxnKNHjxply5Y1NdbEiROtxtS4cWPTx/+LL75oxMfHJzvO8uXLDTc3N5tjvPPOOynGsnnzZqt9vb29bf6d3Ovw4cPG6NGjjfr16xtPPPGEzfb2nBOCgoKMiIiIZMc5ffq04ePjY3OMevXq2Yxp8uTJNsdp3LixU7ftqaeeMmJiYlIc67vvvjM1TsGCBY2oqCibsTmD2W3LkyePsXbt2hTHuXDhglGtWjVTY40YMSLFcYYPH26zf7FixYzNmzdb3a6EhARj4MCBNsdq1aqVzX3krPN/SooXL27zbyY5s2bNMv378/T0NCIjI+8bY9++fTb79uvXL8XYjx07Znh4eNgc46OPPrJ5zQsLCzPy5MljdZxcuXIZp06dSnEMV7x3uPP3nB7nRmfw9vY2tU1btmxxyvouXLhgej+mtA/Nvk4wy1n3TwsXLjT69evn8L46ePCgUbJkSZuxfPvttzbHyqzHqyvcqyYqV66czbFWr17tUJxHjx413N3drY7dtGlTm+MEBgZaHcPLy8u4evWqQzHaKzO9LjJz35j4U6FCBePEiRPJjnP9+nVT90MlSpSwGs/x48dt/r4lGR4eHsb8+fNTHCcyMtJo2LCh6W2bPHmyzX2VWqNGjTIVS7NmzdI8lntNnDjRVGzlypUzjhw5kuI4ly5dMmrVqmVqrKFDh1qNyZnnlYx4fZTdrhf3bru974OtXbvW5ri9evWyGmdcXJzN1xflypVL8T0KAACQNpimFgAAANnCqFGjrH4b9tNPPzVVYW7nzp0pPhcWFmZzChZfX1/Nnj3b5rRDpUqVMlXpZcaMGTbbpLfmzZtr7dq1qlevXoptKlWqdFe1gqNHj2rFihVWxy1QoIB+//33pG8XpyRfvnz6+eefbVZJSa99FxgYqKlTp+qRRx5xqIphsWLF7vsGeHJsTT/lKgzD0OTJk2228/Ly0sKFC1OstiVJ5cuX19y5c01VWJg4caJdcaakbt26GjduXIrrfPTRR01VxrJ2rrF1HFWuXNnm38m9KlasqFdeeUXr16+3ewpca/Lnz6/ff/9dBQoUSPb5kiVLatCgQTbHsbY/MkqFChU0Y8YMeXp6ptimX79+6tKli82xIiIi9PvvvzszvFQbNWqUHn744RSfL1q0qObNm2d1+xNNmzYtxed++uknm/1nzJihhx56yGobi8WiUaNGqWrVqlbbLV261Grl2fRgqzrH9u3bdf369fuWp1TRLrnrXWxsrNavX296jDtZO0dNmjTJZtWe559/Xu+//77Nc1WjRo00ZMgQq22io6M1d+7cZJ9z5XsHVzs33rx501S7fPnyOWV9tu6T72Q2tsyidevW+u677xQUFORQf19fX73zzjs22znzvtDVjtesonz58ilWSU60atUqq68/Dxw4oG3btlkd4+mnn3ba364trvi6yMPDQwsWLFCZMmWSfT5v3rwaOnSozXHOnj2rixcvpvj8zJkzrVaLTfTuu++qbdu2KT7v5eWlefPmpWkVc3vZquaZqGDBgmkcyf3MvAZ0c3PTnDlzrFZ/LVSokBYsWCAvLy+b402ePDnNp1LOKNn5euHI+2CS9PDDD9t8nfPzzz9brcK6cuVKm5UZe/Toke6VJwEAyO648gIAACDLe+CBB/TSSy9ZbVOyZElTU2tZmxbH2tQSifr37296erP69evbfEN65cqVpqaySS8+Pj6aMWOGcufObVc/M/uue/fupqdzKVOmzH3TPd1rx44dpqaSyQzMTDOW3NS/rig8PNzU1DUvv/xyih+K3SkoKMjm9F2StHHjRv3333+mYrRm5MiRNt/kbtCggc1xrJ1rbCWV7Nq1y+Z0PdaY+RDJrNdee00+Pj5W25jZHzdu3NCtW7ecFZZTDBkyxNT5/MMPPzQ13rp161IZkfOULVvW1DRGVapUUadOnWy227dvX7J/X9u3b7c5dVlAQIDN5LVE7u7uNqeli4+P1/Lly02Nl1ZsJeTGx8frr7/+um95Sol0zz//fLLLk2tvKxnP3d1djzzySIrPm7lev/766zbbJGrZsqXNNn/88YfDsWTWewdXOzeaTRpwJLkmOfZ8WJxVExqsSe/7Qlc7XrMSW9O4G4ahcePGpfh8Vpy2ML2P/65du9q8Npg5/iXr9/dm7gO9vLz05ptv2mxXtGhRDRgwwFRMmYmzriFmXbx4URs3brTZrkOHDqYSqMuUKaN+/frZbHfq1Clt377dVIyuJrteLxx9HyzRq6++avX5GzduaOrUqSk+b+tcnzi1NQAASF8k4wEAACDLe/755019qFeuXDmbbax9G3XDhg02+7/yyiuyWCymf2xV77l27ZrNagfpafDgwSpUqJDd/czsuzFjxti173bs2GF1PMMwTFUIcra4uDiFhYXpk08+Uffu3dWgQQNVqFBBxYsXV968eeXm5nbftvj6+toc9/r166aqKWR2Zo4FSaYSgBI999xzNtsYhqFNmzaZHjM5FStWtFpJLFFqzzVlypSxmogYExOjBg0aqEuXLpo4caI2btyoCxcu2FxnWujWrZvNNmb2h2R9n6Q3d3d3dejQwVTbihUrmvoAL7kqZhnl6aefNp0M07FjR1PtkvvbNvP3vmPHDrvO/WPGjLE55urVq03FnFbMJBeGhYXd9fj8+fPJJheULl1avXv3TnaM5LbT1nXvoYceSrGazvXr17V7926r/aXbCZRmf18BAQE2xwsLC0s24cqV7x1c7dxo9sPl5Co6OuLq1aum22am6k+OiI6O1tKlS/Xhhx+qc+fOCg4OVvny5VW0aFHlyZMn2ePUzDnEWtKPvVzteM1K6tWrp7p161ptM3Xq1BQrRNpK0KhWrZrq16/vcHyp5Qqvi8wc/6VKlbJZWVVK+fg3DMNUUliLFi1Mf2nmmWeeMdUuPZi9hqR35eK///7bVEK3s193SjL1+3ZF2fV64ej7YImefvppm180/OGHH5JdHh0drd9++81q38cff9zUFxkBAIBzeWR0AAAAAEBaa9iwoal2Zj7Mi46OTvG5vXv3mo7JmU6dOqU6depkyLrvZWY6xuRk5L5LLwcPHtSIESP022+/2fUhs1mGYSgiIkJFihRx+tjpyUwli9y5c9usUHGn4OBg0+s2U6UpJWarYqT2XCNJzz77rL744gur/adPn67p06cnLcuXL598fX3l6+uratWqKSAgQHXq1LFZvcBRZcqUUdmyZW22M5tIYWufpKcHHnjArgSQoKAgbd261WqbQ4cOpTYsp7E1VdKdzE61mNz2ZYdzf3JKlSqlBx980Or235vwlVICWOPGjRUcHKw8efLcNw3c1q1bFRUVlfTB/b59+3Tu3DmrsVlL8tm/f78SEhKs9k8LN27c0OXLl++bltxVjx9XPDfmz59fkZGRNts5KxnP7Dju7u5OreaansLDwzVixAgtWbIkTabadVZSiyser1nN4MGDrSa+R0REaNasWerRo8ddy8PDw7V//36rY/fq1cspMdrLVV4XeXp6mnqd7ebmJm9vb5vJQykd/5cuXTKVQGvP9NbVqlVL9t4gI5hNUkrvZDyzFRTtea+lRo0aypUrl81zXVapan+n7Hy9cPR9sEQeHh4aMGCA1cqX+/bt0+rVq++rYL1kyRKb59GUvrgDAADSFpXxAAAAkOWZmcpGknLmzGmzTUrfnL5y5YqioqLsistZMqri1b0qVKjgcFJPRiVGpMe+S0hI0FtvvaUHH3xQU6ZMSZMPnBJl1DHoTKdPn7bZpmLFiqaqTyQqWbKkqYoMZtZtjdlzjZmpTW1VaXjnnXdsTld7r2vXrik8PFyzZ8/Whx9+qCeffFIlSpRQ9erVNXToUB05csSu8Wxx5rlXylxTEVaqVMmu9hUrVrTZ5vr165lm2nF7tq948eLKkyePzXbJfciclc/9ttiaqjY8PPyu5Kd7K+Ulaty4sXLkyKF69erd91xcXNxd096ZqehmLa6MTGJM7nfmqsePM68VUvqcG0uXLm2q3cmTJ52yvmPHjplql1bJ5Gnp1q1b6tatmwIDAzV37tw0ScSTnHdP6IrHa1bz1FNPqXz58lbbfP/99/ctmzlzptU+OXLkUNeuXVMTmt1c7XVRhQoVTN+npub+3mwlS3vuz9zc3GweN+nFbCW0AwcOpOs5wsxrPy8vL5UoUcL0mO7u7qpQoYJT1u1qsuv1IjXvg93pxRdfVN68ea22ceRcX7x4cbVu3TpVsQEAAMeQjAcAAIAsr0CBAqba5ciRw+F1ZGQS1KVLlzJs3XeqXLmyw30zav+l9b4zDEPPPPOMRo4cmS5TyLrKG9bWmDkW8uXLZ/e4Zr6Bb6bqjzVmzzWenp6pWo8kFSxYUMuXL1exYsVSPdaePXs0fPhwVa5cWb1799a1a9dSPaaUPufejGLvMWi2vTOnFUyNtNi+5LYtq577zbCVjGc2kS4kJOSuf+9151S1tqbn9fLySjapL1Fmu9dx1eMnPa8VzmI2AWTXrl1OWd8///xjql3NmjWdsr70cvPmTTVt2lTTpk3L6FBMc8XjNatxd3fXwIEDrbbZunXrXRV4DcPQL7/8YrXPk08+ma4VtV3xdZHZ419K3d+A2Ypw3t7edo3ryGumtFClShVT7a5cuZKuVW9d+XVnZpRdrxepeR/sTvnz51fPnj2ttpk/f77Onj2b9Pj69etavHix1T7dunXLcvscAABXQTIeAAAAsjwzFbGk298ed0WZpZKSPR9WZBZpve++++47zZkzJ03Xgcwjvc81NWrU0L///qsXXnjBdNUOa+Lj4zVx4kQ1a9bMKVMNZvVzL1xXZrhuNm7cWB4eHlbbJFbDu3DhQrIfTpcsWVIPPPCApJST8e5M4kuput6dMWXWD+syw+8sUWpjccVzY2BgoKl227dvd8r6wsPDTbVzRjKe2YQdZ1SwGzp0qNavX5/qcdKTKx6vWVHv3r1tJgWNGzcu6f9r1661WT00vactdMXXRWaPf4m/AWtKlixpusKqmSq+SN9rl1nZ9XrhzPfBBg0aZHX/xMXFacKECUmP582bZ3M6X6aoBQAg42Stux4AAAAgg9iaTiI7MDvdSHKy4v6Ljo7WkCFDbLZzd3dX165dtWjRIp04cUI3b96UYRh3/Rw9ejQdIs4cvLy8bLZxpHKbmWmwXPE4LFy4sMaPH68zZ87o22+/VZs2bVL9gcCmTZv03nvvOSfALMreY9Bs+0KFCjkSjtOlxfYlt22u+DfnLN7e3qpbt67VNonJcyl9MN24ceOk/wcHByc7XfC2bdsUGRmpvXv36vz581bXZ6taX2b7fWW2eLKyBg0amGoXFhaW6mknY2Ji9Pvvv5tqm1ISqj1iY2NNtUvtdp08eVKjR4+22S5Xrlx6+eWXtWLFCp09e1a3bt26777QVpVLZD3e3t42EypmzZqlK1euSJJ+/vlnq23Lly+vRx991Fnh2cTrIusKFixoqp29X5ZxVrVrZ2jSpImpduPHj0/jSP6fK7/uTK9rF2xLzftg96pYsaKefPJJq21++umnpOqits71jRo1SvriDgAASH8k4wEAAABOkD9/flNv5i5fvvy+DxRS+/Phhx+m/QamsVKlStls89NPPzl9302ZMiXNtmnp0qU237x3d3fXokWLNHXqVLVs2VJlypRJ9s3crDiNTUrMHAtHjx5VQkKC6THPnDljqiqAmXVnVoUKFdLLL7+sBQsW6NKlSzp48KAWLVqkr776Sv3791ebNm3k5+dnsxJXonHjxum///5L46hd15EjR5ze3tvb2/TvJ63Zs33nz5/XjRs3bLZLLhnPzN9c586dnX7uP3bsmOntS0u2kt+2bt2qqKgoU8l4OXLkSHaK2bi4OP3111+mkndsJWaY+X15eHgkmzyR2p/kkq6y4r1DZvXQQw+pePHiNtvFxMTot99+S9W6lixZkpRQZE2BAgXUtGnTVK1Lkqnzl6RUT504Z84cm/cuXl5e2rBhg7799ls1bdpUPj4+yU7lnp3uC/H/Bg4cKHd39xSfv3HjhqZOnarY2FibFeh69eoli8Xi7BBTxOsi68x+GePw4cOmx0xISMg09zuS1KFDB1Ptdu7cmW4Jx2buI6Kiou6aFtSW+Ph4U/s9ta870+vahfQ3ePBgq8+fPHlSixYt0oULF7Ry5UqrbV944QVnhgYAAOxEMh4AAADgJFWrVrXZZsOGDekQievJivvO1nSAktSmTRs1b97cZrsDBw44IySXYOZYuHHjhnbt2mV6zM2bNztt3a7Azc1Nvr6+atmypQYPHqxvvvlGCxYs0O7du3Xt2jXNnz9fFSpUsDpGbGysVqxYkU4Ru54DBw7YVWli69atNtv4+vqmJiSnMvs3I5nbNin57TPzN7dx40bTsbgaW8lvsbGx2rBhQ4rJePcmqFmbqtbWtG8lSpRQ9erVrbapXLmyzeSNuLg4u46f1MiK9w6ZlZubmzp27Giq7WeffWa6Ys+9EhISTH/J5Mknn7Q6rbLZSjVmzuUnT57UiRMnTI2XEjP3hS+++KJq1apls112ui/E/ytXrpzat29vtc0PP/ygP//8U5cuXUqxjbu7u3r06OHs8KzidZF1hQsXNlUdz+w9lyT9+++/phO20kPz5s1VsmRJU2379u2bLrGbfe1nz33Nrl27TH0JzNq6zVy/zL4OcbWp0XG7GnFwcLDVNt9//71mz56tuLi4FNsUKFDAdBIsAABIGyTjAQAAAE6SXEWae02dOtXqG2b2iI2NdfjDzszGzL6bM2eO06ZZSUhIMPUmeWqcOXPGZhuzHwDMnj07teEkMVN5K3Hak4xg5liQbE/JcqeZM2fabGOxWFSnTh3TY7qq3Llzq23btqaOqT179qRDRK4pPj5ec+fONdX2yJEjpj48NTsNZHowU8EpkdnzU/369e9bZubv/ciRI1q1apWpdZgRFRVls016nSeDg4OVL18+q21mz56dbFUTHx8fValS5a5l1pLxbCVCmKkwli9fPvn5+dlsN2HCBJttzLL2+8qK9w6Z2csvv2yqktb+/fv19ddfO7SO77//Xjt37rTZzs3NTa+//rrVNvnz5ze1TjOVpqZNm2ZqLGsy631hZpTZ71XvlN6x2qqYtG/fPpttmjdvnu7VoDn+rbNYLMneJ91ryZIlpu5jpMy3nzw9PTVo0CBTbffv368BAwbIMIxUr9faPXvdunVNXdec/bpTsn4PY+b6ZebatX79eruqKboiV7pe2MPWeXz58uU277U6d+7s1Cl0AQCA/UjGAwAAAJykbdu2NtscOXJEw4cPT9V6rly5os8//1zly5fX6dOnUzVWZmFm312/ft30G/gpuXnzpsaNG6eqVatq06ZNqRrLFjNTrJ06dcpmm7/++supH6Z4e3vbbHPy5Emnrc9egYGBpj4g/O6770wd/+Hh4aaSpurVq6ciRYqYijEz+Pfff1PV/8EHH7TZhmlqrRsxYoRu3bpls53ZKk8NGzZMZUTOc/z4cU2aNMlmu/3795v6gLJq1arJ/n3Vrl1bZcqUsdl/wIABpj98To5hGFq0aJFCQkL05Zdf2myfXudJDw8PPfLII1bbTJ06Ndnld05Rmyg4OFh58uS5b/nmzZt14cIFq+uxNWVuIjPX6+nTp6c6gfLw4cPq37+/1cogWfHeITOrUqWKnnnmGVNt3377bS1cuNCu8ZcuXapXX33VVNsOHTrYTAzNnTu3cufObXOstWvXWn3+/Pnzps4btjjrvnDGjBlZ/jjM7Peqd0rvWOvUqWMzaevgwYNWn8+IaQsz6+uizMTMfWBUVJRGjhxps93Fixf1zTffOCMspxowYIDKlStnqu2kSZPUq1evVCVSrV+/XnXq1NHHH3+c7PNFixZV3bp1bY4zZ84chYeH22x3+vRpjRs3zma7UqVKWa2CWrhwYZtj2Lp2GYahN9980+Y4rs6Vrhf26NChg8qWLZvi84Zh6NChQ1bHYIpaAAAyHsl4AAAAgJM0adJElSpVstnuo48+0ocffmhXVbvY2FgtXbpUPXv2VJkyZfT222+bqjDgKnx9fVOs6HOnqVOn6qWXXrIrKSMhIUFr167VgAEDVLZsWfXr18/mh1TOUKhQIZtt5s6da3WqpU2bNumpp55ySlWARAUKFLDZZuHChRl2fFksFlNTZ0VGRqpt27aKiIhIsc2JEyfUvn17UxW+evbsaVecGe25557TAw88oE8++cShCnZmqvyYSWLIzg4fPqwuXbpYrXb6/fffKzQ01OZYBQoUUKtWrZwZXqq9+uqrVqe2+u+//9S+fXtT17IuXboku9xisahXr142+//7779q1qyZjh8/brPtnfbu3athw4bpwQcfVOvWrRUWFmbqfGrmPLl+/Xq7pstOia0kuJT2b3LXzBw5cpiuLnovW1PmJurZs6fc3d2ttjEMQ23bttVvv/1mVwxXrlzRtGnT1KpVK1WuXFnfffed1et9Vrx3yOw+//xz5c2b12a7+Ph4PfPMM/rqq69sJlIkJCRo9OjRat++vanq0d7e3vrss89MxVuzZk2bbb7//vsUp6C9du2a2rZtayqRyBYz94UTJkzQxYsXU3x+0aJF2eLD9cx+r3onM7FOnDjRqdXMbVVMsqZEiRJq2bKl02IxK7O+LspMnnvuOZvXV+n2l0GsJTtHRUWpffv2TjlvOVvu3Ln13XffmW4/efJkNWjQQBs3bjTdxzAMrVixQk888YQaNmyobdu2WW1v5j40Pj5eHTp0sHofGhERobZt2yoyMtLmeD169LBakc/MtSssLExLlixJ9rmEhAT1799fGzZssDmOq3Ol64U93N3dNXDgQIf7BwUFyd/f34kRAQAAR9iu4QsAAADAFDc3Nw0ZMsRUUs9HH32kGTNmqEePHnrkkUdUrVo15cuXT4Zh6OrVq7p8+bIOHTqkbdu2aevWrVq9enWmfEPdmYYOHao1a9bYbDd+/HgtWrRI3bt3V7NmzVS9enUVLFhQFotF165dU0REhI4dO6atW7dq27ZtWrNmjc6dO5f2G3APM4mZN27cUIMGDfTmm2/qqaeeUtmyZWUYhnbt2qVp06bphx9+UExMjFPjMlMRLSIiQn5+fmrRooV8fX2TTcoaMGCAvLy8nBrbnWOPHTtW165ds9pu27Ztqlatmt555x21atUqaf8dOXJE8+bN0+eff25qesJy5crp+eefd1b46ebQoUN677339N5776lEiRJq2rSpAgICVKVKFVWpUkWFCxeWt7e3PD09ZRiGLl68qH/++Uc///yzpkyZYnP89J7CzBX9+uuv2rdvn9599101bdpURYoUUVRUlLZu3arvvvtOc+bMMTVOr1690uzvyVGRkZF65JFH1L9/f3Xt2lUPPvigPDw8dPr0aS1cuFDDhw/X+fPnbY6TK1cuvfjiiyk+379/f3399ddWE2slacOGDapWrZo6deqkJ598UgEBASpevLg8PT1148YNXb58WWfPnlV4eLi2bdumdevWJTu9qxlmzpNxcXGqU6eOWrRooapVq8rLy+u+D1a7du2qkiVLWh3HbBLcvZKrjCfdTtJbuXKlXWNVq1bNZpyJKlasqM6dO9tM6I2MjNRTTz2lhx9+WF26dFHdunVVqVIl5cmTR3FxcYqIiNClS5e0e/dubdu2TVu2bNFff/1l9zUvq907ZHZly5bV2LFjTd3rRkdH6/XXX9ekSZPUpUsXtWjRQmXKlFHBggV19epVnTx5UkuXLlVoaKhdia3ffPONKlSoYKptnTp1bFaRu3jxoho0aKCRI0eqRYsWypcvn86ePavFixfr448/dlpFnUqVKtmM5ezZswoKCtJ7772nZs2aqXTp0oqJidG2bdv0008/afr06aanEHdlrnCvmujBBx+0eQ5at26dqlSpombNmqlkyZLKkSPHXc8XLlzYriTLdu3aqUKFCjp69Kjd8Xbr1s3UtI7OlllfF2UmZcuWVZs2bWwmssfFxaldu3bq1auXevXqpZo1aypnzpw6d+6c/vjjDw0fPtyhYyO9tGzZUq+//rrpiqObNm1S/fr1FRISotatW6tJkyYqWbKkihQpovj4eF26dEkXL17U1q1bFRYWppUrV5qqspjo+eef18cff2zzCx9Hjx5VzZo19cYbb6h9+/aqVKmS3N3ddfLkSS1atEgjRozQ2bNnba4vX758GjBggNU2derUMRV7+/bt9cEHH6hLly4qVaqUrly5orVr1+rzzz/PFol4kmtdL+z1wgsv6KOPPtL169ft7tu7d+80iAgAANjNAAAAAJzo6NGjhiSbP926dUu3dZnVrVs3m2N98MEHVsdISEgwGjVqZCque3/c3NwMNzc3u/ocPXo0dTvRpA8++CBdfqddunRxaN9ZLBbD3d3drj6rV69O021eu3atQ8eAI9tvz/Fg9u/G1s/Zs2fTZL8l+uabbxw6DiwWi939Fi5caDOexo0b2xxn8uTJNscxDMNYvXq1zbHKlStndQx/f3/T2+fp6enQftm2bVuK63f2OcEZx/fkyZNtjtG4cWObsZjZtpR+HPkbzpcvn3Hy5EnT+yq1HN02R7dv8ODBNmOaOHGiwzF5eHjY1d7WddwwDCM6OtrIkSNHqvaVJGPjxo2mfidlypSxa9zixYunONZff/1ld5yDBg0yFWeic+fOGUWKFHFon9h7rbZ1LjSMrHXvcC8zMaXXveCdBg4c6PDfRWrudfr27WtXnGaut86Mz9r5Zdq0aekai2T7NVBmPV5d5V7VMMzde9j6qVKliul9nGjMmDF2r8disRiHDh2ye13OkBlfFznrvjFRuXLlbI5n6xqydetWu+9rUruvzL5+cab4+HijY8eOqfr92rPN/v7+VuNZuHBhmseQ+PPNN9/Y3D/Xrl0z8ubNm27HgLNeH3G9cL5XXnnF7m3x8vIyrl27lmYxAQAA85imFgAAAHAii8WiWbNmqXTp0nb3TUhIyBbVLqwZN26cQ9NpGIZhcxq09NagQQPVqFHDrj4p/f4DAwOdEZIkqXz58qa/bZ+R+vfvrw4dOtjVxzAMu6euGjx4sFq3bm1XH1cTGxtr937x9/dX7dq10ygi13dvVZtEjpzDx4wZ49A1Iy2lNE2avdtXpUoVDR8+3Ga7nj17mpomLDlmprW0V86cOdWuXTunj5sSW1PV3iulqniSFBwcrDx58tg1nr3V+YoXL65ffvlFnp6edvWTlCbX6qx07+AqxowZ4/B0qY7e6/bo0cOu6Q2l25UizVTNuVNa3Yu3a9dOxYsXd0oszrwvzIxc5V5Vktq2bZtslaW01qtXL1PTM94pJCTEVIW6tJBZXxdlNoGBgXr77bft7pfSvjIz7W1GcHNz0/Tp09WlSxeHx3Dmubp169Z6/fXX0zyG9u3bq3///jbbeXt72121Pbu+j+RK1wtHDBo0yO6/444dO8rb2zuNIgIAAPYgGQ8AAABwshIlSmj16tWmp9DC//Py8tLy5cuzRBKQm5ubxowZIze31L3sqlChgsaPH++kqG576623nDpeWpk+fXqaJsq98MILpqdIyk48PT01ceLEjA4jU3v66afVvHnzVI/TqVMn9ejRwwkROdfXX3+d6g9wvb29NWPGDNNJCj/++KO6d++eqnU602uvvZZuH2LbmwxnLRkvR44cqlevnumxPD09FRISYtf6JalJkyaaM2eO3Yl/aSEr3Tu4CovFovHjx2vEiBFp/nfi5uam999/XxMmTLhvKmgzxowZ41C/O/n7+6f6fiRv3rz67LPPUjWGdDvh9pNPPkn1OJmdq9yrFixY0OHE1NTImzev3evNyGkLM/Proszm/fffV6NGjVI9zrPPPqsGDRo4IaK04enpqWnTpumzzz5zKLnf2UaOHKk+ffqk2fgtW7bUjBkzTLf/4IMPVLhw4VSts0CBAqaS/1ydq1wvHFG+fHk99dRTdvVhiloAADIPkvEAAACANODr66sdO3bY/Y1me7i7u8vDwyPNxs8oRYsW1YYNGzR48OBUf3hqTc6cOdNs7ERNmjTR6NGjHe5funRpLVu2TIUKFXJiVLers/Tt29epY6aFnDlzasGCBfrss8+ceqznyZNHEyZM0Pjx49P0GHNFefLk0c8//5ylq444g4eHh+bNm6fHH3/c4TE6deqk0NBQJ0blPC1bttSsWbNSrABoS/78+bVs2TK7jiN3d3dNnjxZU6ZMSdNqDrly5TLV7qGHHkq3hJemTZvadS6ylTxnT3JdnTp1lDdvXtPt79SmTRtt375dQUFBDvU3w+zvKyvdO7iSd955R+vXr1f16tXTZPyyZctq2bJl+uijjxxO4mnWrJneeOMNh2N4+OGHtWrVKqfci3Xv3l2vvvqqw/2rV6+uxYsXZ4vj0FXuVSXps88+y5Bk4IEDB5q+Py5UqJDat2+fxhFZl1lfF2U2np6eWrJkiR555BGHx2jfvr2mTZvmEq9z3nrrLW3ZssWuLxLYy8z1w2KxaNy4cZoyZYq8vLyctm4PDw998sknWrhwoV3nbh8fH02aNMnh18ClSpXS6tWrs8VrOle6Xjhi8ODBpttWq1ZN9evXT8NoAACAPUjGAwAAANJIvnz5FBoaqvnz56tu3bpOGzcoKEijR4/WqVOnMt3Uhs6SM2dOffXVV1qzZo2aNWvmtA8Sqlatqo8//lhHjhxJ0zf87zRw4EBNnz7d7mSHhg0batOmTfL19U2TuL7//nuNGTNG+fLlS5PxncViseitt97Sxo0b1bJly1QdC+7u7urYsaO2bdvm8JSYmUXjxo1VsGBBp4752GOPaePGjRn+Ya2ryJ07txYtWqS33nrLrspQuXLl0scff6zp06dn2unDJKlDhw5au3at3VPaBQcH66+//nL4utetWzft3LlTPXv2NJ2IZUuxYsU0YMAA/f3333ZN//bWW2/p559/VrFixZwSR0qKFStmeprVokWLqlq1albb2JOMZ+8UufeqXLmyNmzYoFGjRjmtIrCHh4datGihmTNnKjw83HS/rHTv4Erq1KmjnTt3asqUKapZs6ZTxixUqJC++OILHThwQE2bNk31eJ9//rmGDRtmV0Kfh4eH3n77ba1cudKpyT9fffWVvvzyS7srQbVr107r169XkSJFnBZLZucq96q5c+fW2rVr9cILL6TrF6VKly6tp59+2lTb559/PlMkcWbW10WZjZeXlxYvXqxXXnnFrntFT09PDRs2TLNnz84U1ebM8vf314YNGzR//nynVAVMVKlSJY0cOVLLli0z3adbt24KDw/Xc889l6q/Z4vFoubNm2vdunV69913HUoob9OmjRYuXGj3l1TatGmj8PBwBQQE2L1OV+Uq1wtH1K1b1/T9J1XxAADIXEjGAwAAANJY27ZttXHjRm3dulUvv/yy/P39Tb+p7ubmpkqVKqlr166aMmWKTp48qS1btuiVV16Rj49PGkee8Ro1aqQ///xTe/fu1RtvvKE6deqYrtRksVhUpkwZdezYUT/88IMOHDigvXv36r333kv3KYQ7d+6svXv36pVXXlGBAgWstg0KClJoaKjWrl2rkiVLpmlcgwYN0pkzZzRlyhT17NlTtWrVUvHixU1PK5megoKCtGjRIu3du1eDBw9W7dq1Tf0deXp6Kjg4WO+8844OHz6sX375RVWrVk2HiNPW119/rf/++09btmzRl19+qWeffVaVK1e2+4OeBx98UK+99po2bdqkZcuWOS2RIrvw8PDQZ599pu3bt+u5556z+rdTqFAh9erVS7t379Z7772X6qna0kOdOnW0Z88ejR49WlWqVEmxncViUZ06dfTTTz9p48aNqlGjRqrWW6FCBU2cOFGnTp3SyJEj9dhjj9n14VqhQoXUrFkzffrpp9q0aZPOnDmjsWPHqk6dOnbH8uyzz+rUqVP69ddf1adPH9WpU0clSpSQl5eXUyvOmE2KszZFbaLg4GDT08faO0Vucjw9PfXqq6/q0KFDmj9/vp577jm7rrM5c+ZUQECABg0apPnz5+u///7T4sWL1alTJ4emwc0q9w6uxM3NLSmRdvPmzXr33XcVGBjocDJI4cKF1a1bN6cmDw0dOlQ7duxQu3btrCb65s2bVz169NDu3bv16aefOj2hxWKx6LXXXtOuXbvUs2dPq8e4m5ubQkJCtGjRIs2bNy9LJhnY4ir3ql5eXho/frxOnDihL7/8Uk8//bSqVq2qIkWKOFxl1gyzFZMyU4JGZn1dlNnkzp1bo0eP1tatW9WpUyerCYx3nreGDh3qEveYyWnbtq3CwsK0f/9+ff755woJCbErcTN37tyqX7++hg0bpi1btujgwYN644037E5irly5smbMmKGjR49q6NChqlevnqm/Yzc3N/n7+2vQoEHavXu3li5d6tC9552eeOIJHTp0SIMGDbKaGO7u7q7mzZtr2bJlWrBgQZp/kSQzcpXrhSPMnOtz5Mihrl27pkM0AADALIthGEZGBwEAAABkN1FRUdq+fbvOnz+viIgIRUREKCYmRnnz5pW3t7cKFSokX19fPfDAA5miikFmcuvWLe3YsUNnzpxJ2nc3b96Ul5eXvL29VaBAAVWqVEmVK1d26hQzzhIfH6/t27dr9+7dunTpkqKjo+Xt7a3y5csrKCgo233QlBqRkZHatm2bLly4oMuXLysiIkIWi0WFChVSwYIF5ePjo8DAwCzxBrxZ0dHROn78uI4fP67Tp08rMjJSUVFRiouLU548eeTl5aUCBQrI19dXlStXTtPpQF3Vhx9+qI8++shqm27dumnKlCn3LY+OjtbmzZu1d+9eXb58WR4eHipRooTKlSununXrulSVkuQcPnxY4eHhOnnypG7evKlChQqpZMmSqlWrlsqWLZum605ISNDevXt15MgRRURE6MqVK7p27Zpy5colb29v5cuXT2XLllWVKlWyVeWozOzChQvauXNn0vn5ypUrMgxD3t7e8vb2VrFixVSlShWVL18+zRMHXP3ewVXdunVL+/bt0/Hjx3XmzBlFRkbq5s2bOnPmjH744QerfR966CGtWrXK4WmUrbl586bWr1+v48eP69KlS4qPj1eRIkVUrVo1PfTQQ2maPHWvmJgYbdmyRfv379elS5cUGxurfPnyqVKlSgoODlbhwoXTLRa4pipVqujAgQMpPh8cHKxNmzalY0Tm8brIvOjoaG3YsEGnTp3S2bNnZRiGihQpogcffDDdz1vpKSEhQYcOHdLRo0d1+vRpXblyRTdv3pTFYrnrfqJq1appej8RHR2t8PBwnT17VhEREbp8+bLi4+NVsGBBFSpUSMWKFVPt2rXTNGk6ISFB27Zt0969e/Xff//p5s2byp8/v3x9fVW3bl2bya1wXfHx8cqXL59u3LiRYpuOHTvql19+SceoAACALSTjAQAAAAAA/E9qkvEAAOYMGTJEI0aMsNrm8ccf1++//+7yicxAWjl16pTKlSunhISEFNv89NNPmaoyHgDAPhs3blT9+vWttlm+fLlTKl4DAADncc161QAAAAAAAAAAl/TJJ5+oW7duVtv8+eef6tGjh/guOZC8sWPHWk3E8/b21rPPPpuOEQEAnG306NFWn69YsaKaNm2aTtEAAACzSMYDAAAAAAAAAKSrn376SY8//rjVNjNmzNDrr7+eThEBrmPOnDkaNWqU1Tbdu3dPk6meAQDp4+uvv9avv/5qtc3LL78si8WSThEBAACzPDI6AAAAAAAAAABA9uLp6ak5c+aocePGCg8PT7HdqFGj5OPjozfeeCMdowMyh3Hjxunq1auSpNjYWF24cEEbNmyw+jcjSW5ubho4cGB6hAgASKXPPvss6f8xMTE6c+aMwsLCtG/fPqv9vL291atXr7QODwAAOIBkPAAAAAAAAABAusubN68WL16sevXq6dixYym2e+utt+Tj46MuXbqkX3BAJvD555/r+PHjdvfr0aOHfH190yAiAICzvfPOOw71e/PNN5U/f34nRwMAAJyBaWoBAAAAAAAAABnCx8dHf/zxhwoXLpxiG8Mw1LNnT/3xxx/pGBngmgoUKKDhw4dndBgAgDRUoUIFvfbaaxkdBgAASAGV8QAAAAAAAAAAGaZKlSr6448/tGjRIqvtdu/erUcffVQeHrytDaRk0qRJ8vHxyegwAABpxNPTU7NmzVLu3LkzOhQAAJAC3rUAAAAAAAAAAGSooKAgBQUFZXQYgMuyWCwaM2aM2rVrl9GhAADSSI4cOTRz5kwFBwdndCgAAMAKpqkFAAAAAAAAAABwUWXLltXvv/+ugQMHZnQoAIA0Ur16dYWFhal9+/YZHQoAALCByngAAAAAAAAAAAAuwM3NTfny5VPJkiUVFBSkNm3aqE2bNvL09Mzo0AAATuLh4aF8+fKpbNmyeuihh9S+fXs99thjcnOjzg4AAK7AYhiGkdFBABnBx8dHUVFRKlu2bEaHAgAAAAAAAAAAAAAAACATOHHihLy8vHTu3Dm7+1IZD9lWVFSUYmNjMzoMAMiUoqKilJCQkPTY29s7A6MBAAAAAAAAAAAAACB9xMbGKioqyqG+JOMh20qsiLdnz54MjgQAMp9Vq1bp+vXrSY/btm2bgdEAAAAAAAAAAAAAAJA+/Pz8HO7LxPIAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAAAAAAAAAAAAAAAAAKQSyXgAAOD/WSy3f/bskQzj/38sloyODAAAAAAAAAAAAACATI1kPAAAIBUrZjvhzmKRSpdOn3gAAAAAAAAAAAAAAHAxJOMBAJDdFSsmXbxoru3p07fbAwAAAAAAAAAAAACAu5CMBwBAdmc2Ec/R9gAAAAAAAAAAAAAAZAMk4yHdWSwWBQQEqEaNGqpXr552796d9FxoaKj8/f3l7++vBx98UF999dVdfTdv3iyLxaKFCxemd9gAkDXZmprW2f0AAAAAAAAAAAAAAMiiSMZDunN3d9eOHTu0a9cuNW3aVK+99pok6fTp0xoyZIhWrVqlnTt3aufOnWrRosVdfUNDQ9WoUSNNnz49I0IHAAAAAAAAAAAAAAAAgGR5ZHQAyN4aNmyoefPmSZLOnz+vPHnyKF++fJKkHDly6MEHH0xqGxcXp7lz52rjxo0KDg7W1atXlT9/fpvr8PPzS3b54cOH5ePjo1WrVjlhSwDARY0dm+ziKB+fux6vSq4d508AAAAAAAAAAAAAQBYTFRUlLy8vh/pSGQ8ZasmSJWrXrp0kyd/fX+XLl1fZsmXVqVMnTZkyRbGxsUlt//jjDwUEBKhcuXJq0aKF5syZk1FhAwAAAAAAAAAAAAAAAMBdLIZhGBkdBLIXi8Uif39/XbhwQZIUHh4unzsqMG3btk2rV6/WtGnT5OPjo2XLlkmSnn32WbVu3VqdO3fWihUrNHz4cK1Zs8bhOBIr5u3Zs8fxjQEAV2exJLt41dixul6mTNLjtv9LnL4LtxAAAAAAAAAAAAAAgCwmNTlFJOMh3Xl4eCguLk5xcXHq1auXoqKikq1yd/HiRRUrVkyXLl2Sh4eHSpYsqUKFCsnNzU2GYejcuXM6ePCgypYt61AcJOMBgEjGAwAAAAAAAAAAAADgDqnJKWKaWmQYDw8Pff3111q9erV2796tM2fOaMuWLUnPb9++XQUKFFD+/Pk1d+5ctW7dWidOnNCxY8d0/PhxDRgwQDNmzMjALQAAAAAAAAAAAAAAAACA20jGQ4YqUKCAXnvtNY0YMUKxsbF65513VLVqVfn7+2vIkCGaO3eu3N3dFRoaqqeffvquvk8//bSmT5+eQZEDQBbhaHU7quIBAAAAAAAAAAAAAHAXj4wOANlPXFzcXY/ffffdpP+vWLEi2T6rVq26b1mdOnWYYhYAnKFoUeniRfPtS5VKu1gAAAAAAAAAAAAAAHBRVMYDACC7u3DhdkKeGaVKSadOpW08AAAAAAAAAAAAAAC4ICrjAQCA2wl5kmSxpNyGqWkBAAAAAAAAAAAAAEgRlfEAAIA5Tz6Z0REAAAAAAAAAAAAAAJBpkYwHAABuV8SzVhVPkhYsuN3m11/TJyYAAAAAAAAAAAAAAFwIyXgAAGR3tpLw7tWxozRtWtrEAgAAAAAAAAAAAACAiyIZD05nsVjUtm3bu5YFBgaqfPnyNvuGhIRo3bp1kqQpU6boxIkTaREiACC1unXL6AgAAAAAAAAAAAAAAMhUSMaD07m7u+vkyZOKiIiQJO3bt08JCQl2j0MyHgCkA3ur4t3pySedFgYAAAAAAAAAAAAAAK6OZDykiQ4dOmjOnDmSpBkzZui5555Leu7mzZt67LHHFBgYKD8/P33xxRf39Z8xY4a2bt2qHj16KCAgQMeOHdPkyZMVHBysWrVqKSQkREePHk237QEAJGPBgoyOAAAAAAAAAAAAAACATMNiGIaR0UEga/Hw8NDBgwfVq1cvrVq1Sv7+/vr111/VrFkzHTt2TPHx8bp+/boKFCigmJgYNWjQQKGhoapatapCQkI0fPhwNWzY8K7/S9KlS5dUuHBhSdK8efP066+/6ueff7YZj5+fX7LLDx8+LB8fH02aNMl5Gw8ArmbPnmQXR/n4KMHTM+mx98mTyfdP4RwLAAAAAAAAAAAAAIAr6tmzp7y8vLQnhc/TrfFIg3gAVahQQbGxsfr1119VvXp15cmTJ+k5wzA0bNgwrVy5UoZh6NSpU9q9e7eqVq1qdcz9+/dryJAh+u+//xQfHy83Nwo7AgAAAAAAAAAAAAAAAMgcSMZDmnnmmWfUp08fTZs27a7lM2bM0OHDh7V582blzJlT7du3V3R0tM3xOnfurJkzZ6pevXratWuX2rVrZyqOlLJUEyvmNWnSxNQ4AJAlNW2a7OJVY8fqepkySY+bDByYfH8K7AIAAAAAAAAAAAAAshAvLy+H+5KMhzTTqVMnRUVFqVmzZjp//nzS8qtXr6pIkSLKmTOnjh49quXLlyebWOft7a1r164lPb527ZpKlSolSRo/fnzabwAAAAAAAAAAAAAAAAAAmMQ8n0gzhQsX1ltvvSVPT8+7lnfp0kUHDx6Un5+f+vfvr8aNGyfbv2fPnnr11VcVEBCgY8eOaeTIkWrcuLECAwNVsGDB9NgEAIA1bdtmdAQAAAAAAAAAAAAAAGQaFsNgfjlkT4nT1KY0jS0AZBsWy32L7p2mtm1yU4NzCwEAAAAAAAAAAAAAyGJSk1NEZTwAAGC/2bMzOgIAAAAAAAAAAAAAADIVkvEAAMju7K1wN3u29PTTaRMLAAAAAAAAAAAAAAAuyiOjAwAAAJlAYkJeMlPW3mXXLql69bSPBwAAAAAAAAAAAAAAF0NlPAAAcDsJz1YiniTVqCF5eEhXrqR5SAAAAAAAAAAAAAAAuBKS8QAAyO7MJOHdKT5eKlhQunw5beIBAAAAAAAAAAAAAMAFkYyHdHXs2DH5+vretWz69Onq3r27Dh06pEcffVQBAQGqVq2aHnnkESUkJNzVtlq1anrqqafSM2QAQEoaNMjoCAAAAAAAAAAAAAAAyDQ8MjoAIFH//v3Vq1cvderUSZL0zz//yHJHtaZt27bJw8NDGzZsUEREhAoWLJhRoQJA1mFvVbw77dsn7d4tVa/uvHgAAAAAAAAAAAAAAHBRVMZDpnHmzBmVLl066XHNmjXvSsZLrKDXunVrzZ49OyNCBADca/DgjI4AAAAAAAAAAAAAAIBMgcp4yDReffVVtWjRQsHBwQoJCVGXLl1Uvnx5SVJ8fLxmz56trVu36sCBA3rvvff00ksvmRrXz88v2eWHDx+Wj4+PVq1a5axNAADXM3ZssoujfHzuerwqhXZyd5c4jwIAAAAAAAAAAAAAsoioqCh5eXk51JfKeEhXlhSmQ7RYLOrRo4cOHjyo7t27a+/evapZs6YOHDggSVq+fLmqVaumEiVKqFGjRjpx4oSOHj2anqEDAJJjGBkdAQAAAAAAAAAAAAAAmYLFMPgUHeknMjJSZcuW1eXLl5OWff311zp9+rRGjhx5V9uWLVuqadOmGjx4sDp37qxly5YlZZ1euXJFr7/+ut577z2HY0msmLdnzx6HxwAAl5dCkvSqsWN1vUyZpMdt27VLvn+hQtKlS2kRGQAAAAAAAAAAAAAA6S41OUVUxkO6yps3rypVqqSFCxdKkm7cuKFZs2apSZMmWrp0qWJiYiRJ165d0+HDh1WuXDlFRkbqjz/+0OHDh3Xs2DEdO3ZM69at0/Tp0zNyUwAAkhQYmNERAAAAAAAAAAAAAACQKZCMh3QXGhqqsWPHKiAgQHXr1lXbtm3VvHlzrVy5Uv7+/vL391edOnXUoUMHPfXUU5o3b54aNGigfPnyJY1RvXp1ubm5acuWLRm4JQAAjRqV0REAAAAAAAAAAAAAAJApME0tsi2mqQWA/0lmqlpT09RWrSrt3ZuWkQEAAAAAAAAAAAAAkK6YphYAAKS/jRszOgIAAAAAAAAAAAAAADINkvEAAMju7C2S6+4uRURIBQqkSTgAAAAAAAAAAAAAALgikvEAAMDthDwzSXm7dklxcSTiAQAAAAAAAAAAAABwD5LxAADA//PxSfm5fv2k6tXTLxYAAAAAAAAAAAAAAFwIyXgAAEDq21eyWKRz51Ju8/33t9usXJl+cQEAAAAAAAAAAAAA4CJIxgMAILvr21f64Qfz7R99VFqyJO3iAQAAAAAAAAAAAADABZGMB6ezWCwKCAhQQECAqlevrl9//dVpY7///vta8r8EkO7du2v69OlOGxsAsi17EvEStWzp/DgAAAAAAAAAAAAAAHBhHhkdALIed3d37dixQ5J08OBB1atXT08//fR97eLi4uThYd8hOGzYMGeECABIVKyY431ffln67jvnxQIAAAAAAAAAAAAAgAujMh7S1LVr11SgQIGkx+XLl9fbb7+toKAgffPNN1q6dKnq1q2rWrVqqU6dOgoPD5ck/fDDD0nV9apUqaKCBQtKohoeADjdxYuO9/3+e+fFAQAAAAAAAAAAAACAi6MyHpwuPj5eAQEBio2N1ZEjRzRt2rS7ns+RI4e2bt0qSYqIiNCGDRvk5uam8PBwvfzyy9q4caP69OmjPn36yDAMdezYUcHBwQ7H4+fnl+zyw4cPy8fHR6tWrXJ4bABweWPHJrs4ysfnrserUmgnzqEAAAAAAAAAAAAAgCwkKipKXl5eDvUlGQ9Od+c0tQcOHFDDhg1Vr149lS5dWpLUuXPnpLbnzp3T888/r+PHj8vDw0OHDh26a6yPPvpIuXPn1htvvJFu8QMAAAAAAAAAAAAAAACAvUjGQ5qqXLmyKlSooI0bN+rpp5+WpLsyR/v166cXX3xRnTp10vXr15Omo5WkOXPmaNmyZVq9enWqYtizZ0+yyxMr5jVp0iRV4wOAS2vaNNnFq8aO1fUyZZIeNxk4MPn+hpEWUQEAAAAAAAAAAAAAkCEcrYonkYyHNHb+/HkdOHBAvr6+yT5/9erVpIp5P/74Y9LyHTt2aMiQIQoLC1POnDnTJVYAAAAAAAAAAAAAAAAAcBTJeHC6+Ph4BQQEyDAM3bp1S++9955q1aqVbNvhw4erR48eypcvn9q3b5+0/Ouvv9a1a9fUvHlzSZK3t7f++uuvdIkfALKVokWlixcd69uvn3NjAQAAAAAAAAAAAADAhVkMg/nlkD0lTlOb0jS2AJBtWCz3Lbp3mtq27drd349bCAAAAAAAAAAAAABAFpOanCI3ZwcDAABcTJ8+9vdZscL5cQAAAAAAAAAAAAAA4MJIxgMAILsbN86+hLwVK6SmTdMuHgAAAAAAAAAAAAAAXBDJeAAA4HZCnplpZw2DRDwAAAAAAAAAAAAAAJJBMh4AAJAslts/Ztrly5f28QAAAAAAAAAAAAAA4GJIxgMAILszk4R3p+vXpVy50iYWAAAAAAAAAAAAAABcFMl4WdCxY8fk6+t717Lp06ere/fuGRPQ/7z//vtasmSJJOnAgQOqVauWatWqpU2bNqlFixa6cOGCXeOFhIRo3bp1aREqAMCWW7cyOgIAAAAAAAAAAAAAADIVj4wOANnHsGHDkv4/b948tWzZUsOHD5ekpCQ9AEA6s7cq3r19DcN5sQAAAAAAAAAAAAAA4MKojJfNfPjhh+rWrZuaNm0qX19fvfvuu5KkhIQEvfjii/Lz81PNmjWTquhNmTJFrVu31qOPPqoqVaqoW7duiomJkSQdP35crVq1UlBQkIKCghQWFpY01pAhQ1SjRg35+/tr8ODBkqTu3btr+vTp+u233zRmzBhNnDhRAQEBio+PV/ny5XXq1ClJ0j///KMmTZooMDBQDRs21K5duyRJly9fVsuWLVWtWjV16NBB0dHR6bnrAAAAAAAAAAAAAAAAACBFVMbLhnbt2qX169crISFBvr6+6tevny5cuKDjx49rz549kqSIiIik9uvWrdPu3btVsmRJdejQQePHj1f//v3Vs2dPjR07Vn5+fjpx4oRCQkJ0+PBhTZw4Udu3b9e2bduUI0cOXbp06a71t2vXTjt37pSHh4fee++9u56LjY3Viy++qLlz56pUqVLasmWLevfurU2bNumjjz5SzZo1tXjxYm3fvl1BQUGmttfPzy/Z5YcPH5aPj49WrVplz+4DgKxl7NhkF0f5+Nz1eFUK7cQ5FAAAAAAAAAAAAACQhURFRcnLy8uhviTjZUGWFKYcTFzesmVL5c6dW5JUtWpVHTt2TNWrV9fx48fVp08fNWvWTC1atEjq9+ijj6pUqVKSpK5du2r69Onq3r271q1bp86dOye1i4mJ0YULF/Tnn3+qb9++ypEjhySpcOHCpmPfv3+/9uzZo5YtWyYtu3z5siRp7dq1mjFjhiSpVq1aqlmzpulxAQAAAAAAAAAAAAAAACAtkYyXBRUuXDgpgS3RpUuXVLRoUUlSzpw5k5a7u7srLi5OBQoU0Pbt27Vy5UotXrxYH330kbZv3y4p+eS+hIQE5cmTRzt27LjvOcMwHI7dMAxVqlQp2XEdlVjt716JFfOaNGnitHUBgMtp2jTZxavGjtX1MmWSHjcZODD5/qk45wMAAAAAAAAAAAAAkNk4WhVPktycGAcyibx586pSpUpauHChJOnGjRuaNWuW1aSzixcv6tatW2rVqpVGjRqlEydO6MaNG5KkFStW6OzZszIMQ9OnT1fjxo2VL18++fn5adKkSUljhIeHS5KaN2+ucePGKSYmRpLum6bWmqpVq+r69etauXKlpNvJeYlJgY0bN06qjLdjxw7t2rXL9LgAAAAAAAAAAAAAAAAAkJZIxsuiQkNDNXbsWAUEBKhu3bpq27atmjdvnmL7kydPqkmTJvL391dwcLA+/PBD5c2bV5LUoEEDdenSRVWrVlXu3Ln1wgsvSJJmzJih3377Tf7+/qpWrZq+/fZbSVKvXr0UEBCgWrVqKSAgQJ9++qnpuD09PTV//nwNHz5c/v7+8vPz09y5cyVJ77//vnbs2KFq1arpk08+UVBQkKO7BwAAAAAAAAAAAAAAAACcymKkZk5RZHlTpkzRunXrNGHChIwOxekSp6lNaRpbAMg2kpmO/N5patu2a3d/P24hAAAAAAAAAAAAAABZTGpyiqiMBwAA7OftndERAAAAAAAAAAAAAACQqXhkdADI3Lp3767u3btndBgAgLRkGMlWx0uRt7d07VraxQMAAAAAAAAAAAAAgAsiGQ8AAPz/lLO2kvKYmhYAAAAAAAAAAAAAgGQxTS0AAJCWLDFXHW/lyrSPBQAAAAAAAAAAAAAAF0QyHgAA2d2SJVLLlubaPvro7fYAAAAAAAAAAAAAAOAuJOMBAJDdmU3Ec7Q9AAAAAAAAAAAAAADZAMl4Lu7LL79U9erV5e/vr+rVq2vmzJlOG7t79+6aPn26JKl3797asWNHim1btGih2rVrO2Vd9woKCnJ4XACADS+95Fi/l192bhwAAAAAAAAAAAAAALg4j4wOAI7btGmTZsyYoS1btih37tyKiorS2bNn02RdEyZMSPG5CxcuaOfOncqXL5/+/fdfVatW7b42cXFx8vBw7HDbunWrQ/0AACaMH+9Yv++/l777zrmxAAAAAAAAAAAAAADgwkjGc2GnT59W4cKFlStXLkmSl5eXfH19JUnbtm3TwIEDdePGDRmGoZEjR6pZs2Y6duyYHn30UR06dEiSNH36dK1YsUJTpkxRdHS0XnjhBW3ZskXlypW7K3kuJCREw4cPV8OGDe+L4+eff1b79u1VvHhxhYaG6tNPP5UkTZkyRXPmzFFsbKyOHz+uffv2qUOHDjp27Jiio6P12GOPafTo0UnjrFu3TmPHjlVERIT69u2rwYMHS5I8PDwUFxcnSVb7p8TPzy/Z5YcPH5aPj49WrVplcwwAyLLGjk12cZSPz12PVyXXjvMnAAAAAAAAAAAAACCLiYqKkpeXl0N9mabWhTVr1kyXL19WxYoV1aNHD82ZM0eGYUiSHnjgAa1Zs0bbt2/X4sWL1bdvX5vjjRs3TvHx8dq7d68mTJigdevWmYojNDRUzz//vDp37qyff/45KQbpdvW+adOmad++fZKkH3/8UVu3btU///yjo0eP6s8//0xqu3v3bq1bt05btmzRd999p127dt23Lmv9AQAAAAAAAAAAAAAAACCjUBnPheXNm1dbtmzRxo0btWbNGr355ptatmyZxo8fr8jISPXu3Vv//vuvPDw8dPLkSf33339Wx1u7dq1eeOEFWSwWlSlTRk2aNLEZw759+3Tt2jUFBwdLkkqXLq2wsDCFhIRIkh599FEVL148qf348eM1e/ZsxcfH68KFC2rYsKEef/xxSVKnTp2UI0cO5ciRQ23atNHatWtVo0aNu9ZnrX9K9uzZk+zyxIp5ZrYTALKspk2TXbxq7FhdL1Mm6XGTgQPvb3RH8jUAAAAAAAAAAAAAAFmBo1XxJJLxXJ67u7saNmyYlJTWtGlTjR8/XkOGDFHt2rX1yy+/yGKxqHDhwoqOjpaHh4cSEhKS+kdHR6dq/aGhobp48aLKly8vSYqMjNT06dOTkvHuPDjDwsI0d+5crV27Vt7e3nrttdfuWr/FYrG6Llv9AQAAAAAAAAAAAAAAACCjME2tC9u/f3/S9K+StH37dpUrV06SdPXqVZUqVUoWi0Vz5szR5cuXJUnFixdXRESEzp8/r/j4eM2fPz+pf+PGjTVz5kxJ0unTp7V69Wqr6zcMQzNmzNDff/+tY8eO6dixYzp48KAWLFiQbJLc1atXVaBAAXl7e+vSpUuaO3fuXc/PmjVLsbGxunr1qn7//Xc1atTIrv4AAAe8+KJj/fr1c24cAAAAAAAAAAAAAAC4OJLxXFjiVLTVqlVTzZo1NX36dIWGhkqS3nnnHY0YMUIBAQEKCwtT2bJlJUmenp765JNPVK9ePTVq1Cipop0k9enTRxaLRVWrVlXPnj3VoEEDq+v/66+/lC9fPlWpUiVpWcGCBRUUFKTff//9vvbNmzdX3rx5VaVKFXXo0OG+ZLtq1aqpQYMGCgoKUr9+/e6botZWfwCAA3780bF+333n3DgAAAAAAAAAAAAAAHBxFsMwjIwOAsgIfn5+kqQ9e/ZkcCQAkMGWLJFatrxr0aqxY3W9TJmkx23btfv/J1eskJo2Ta/oAAAAAAAAAAAAAABIN6nJKaIyHgAA2V2LFtLixebakogHAAAAAAAAAAAAAECySMYDAAC3E/IMQ+rXL/nn+/W7/TyJeAAAAAAAAAAAAAAAJItkPAAA8P++/z7l5YUKSbt3p288AAAAAAAAAAAAAAC4CJLxAACAVKqUZLFYbxMRIdWoIfn4SFeupEtYAAAAAAAAAAAAAAC4CpLxAADI7kqVks6cMd/+/HmpSBHp8uW0iwkAAAAAAAAAAAAAABdDMp6kY8eOydfX965l06dPV/fu3W32bdGihS5cuCBJGjNmjK5du2b3+rt3767p06dbXUft2rXtHtfs+IlCQkL0wAMPKCAgIOnHHmfOnFGbNm3sjm/Hjh1auHCh3f0AAE5iTyJeovh4qUED58cCAAAAAAAAAAAAAICLIhkvlZYsWaJixYpJcjwZz5oLFy5o586dunnzpv79999k28TFxTltfZMnT9aOHTuSfuxRsmTJFJPq4uPjU+xHMh4AZCBbU9Nas2+ftHu382IBAAAAAAAAAAAAAMCFkYxnwocffqhu3bqpadOm8vX11bvvvpv0XPny5XXq1Cl98cUXOnPmjB5//HEFBAQoJiZGx48fV6tWrRQUFKSgoCCFhYVJkqKjo9WlSxdVrVpVjz/+uC5evJjiun/++We1b99ezz//vEJDQ5OWT5kyRa1atdLjjz+u6tWrS5I6dOigoKAgVa9eXa+++upd46xbt07BwcF64IEHNGrUKLu2f82aNapfv746duyoypUrq2PHjtqwYYMaNWqk8uXLa/bs2ZLurjB47NgxVahQQS+++KL8/f21adMmjRgxQg899JD8/f3VqlUrXbp0SdevX9f777+v3377TQEBARo7dqwk6ZtvvlFwcLD8/f3Vu3dvxcbGKiEhQS+++KL8/PxUs2ZNU5ULAQBpbPDgjI4AAAAAAAAAAAAAAIBMwSOjA3AVu3bt0vr165WQkCBfX1/169dPpUuXTnr+jTfe0Hfffac///wzaXnPnj01duxY+fn56cSJEwoJCdHhw4c1btw4xcfHa+/evTp16pSqV6+uTp06Jbve0NBQff/99ypWrJhCQkI0YsQIWf5XxWjTpk3avXu3ihcvLkn68ccfVbhwYSUkJOipp57Sn3/+qccff1yStHv3bq1bt043btxQYGCgHnvsMdWoUeO+9fXo0UNeXl6SJF9fX82ZM0eS9M8//2jmzJkqW7as6tSpo9GjR2v16tXav3+/WrZsqY4dO9431rFjx/TMM89o/PjxkqQqVaokJTKOGjVKX375pT799FMNGzZM69at04QJEyRJq1at0ubNm/X333/Lzc1N/fv314QJE1SnTh0dP35ce/bskSRFRESY+t35+fklu/zw4cPy8fHRqlWrTI0DAFnS/5Kg7xXl43PX41UptJO7u8R5FAAAAAAAAAAAAACQRURFRSXlT9mLZDwpKbnN2vKWLVsqd+7ckqSqVavq2LFjdyXj3SsyMlLr1q1T586dk5bFxMTowoULWrt2rV544QVZLBaVKVNGTZo0SXaMffv26dq1awoODpYklS5dWmFhYQoJCZEkPfroo0mJeJI0fvx4zZ49W/Hx8bpw4YIaNmyYlIzXqVMn5ciRQzly5FCbNm20du3aZJPxJk+erIYNG963vFatWipfvrwkqUaNGqpTp47c3d1VrVo1nT17VgkJCff18fHxUdOmTZMeb9iwQZ9++qmuX7+umzdvqmrVqslu95IlS7R27VrVrl1b0u1Kgrlz51anTp10/Phx9enTR82aNVOLFi2S7Q8ASEeGkdERAAAAAAAAAAAAAACQKZCMJ6lw4cK6fPnyXcsuXbqkokWLJj3OmTNn0v/d3d0VFxdndcyEhATlyZNHO3bscDiu0NBQXbx4MSkJLjIyUtOnT09KxrszAzMsLExz587V2rVr5e3trddee03R0dFJz6eUcGjWndvv5uZ212NJySbj3RnfrVu31L17d23evFmVKlXS77//rq+//jrZdRmGoVdffVWvvPLKfc9t375dK1eu1OLFi/XRRx9p+/btcnOzPttyYiW9eyVWzEspGRIAsoU7kqbvtGrsWF0vUybpcZOBA5PvX6iQdOlSWkQGAAAAAAAAAAAAAEC6c7QqniRZz2LKJvLmzatKlSpp4cKFkqQbN25o1qxZdidpeXt769q1a5KkfPnyyc/PT5MmTUp6Pjw8XJLUuHFjzZw5U5J0+vRprV69+r6xDMPQjBkz9Pfff+vYsWM6duyYDh48qAULFtyVZJfo6tWrKlCggLy9vXXp0iXNnTv3rudnzZql2NhYXb16Vb///rsaNWpk17alVnR0tBISElSsWDHFx8dr4sSJSc/dud8k6YknntDkyZN15coVSbenoz169KguXryoW7duqVWrVho1apROnDihGzdupOt2AADuERiY0REAAAAAAAAAAAAAAJApkIz3P6GhoRo7dqwCAgJUt25dtW3bVs2bN7drjL59+6pdu3YKCAhQTEyMZsyYod9++03+/v6qVq2avv32W0lSnz59ZLFYVLVqVfXs2VMNGjS4b6y//vpL+fLlU5UqVZKWFSxYUEFBQfr999/va9+8eXPlzZtXVapUUYcOHe5LtqtWrZoaNGigoKAg9evXL9kpaiWpR48eCggISPpJTIhLrfz582vw4MGqWbOm6tatq8qVKyc916RJEx07dkwBAQEaO3asHn30UfXp00eNGjVSzZo11bRpU506dUonT55UkyZN5O/vr+DgYH344YfKmzevU+IDADho1KiMjgAAAAAAAAAAAAAAgEzBYhiGkdFBABkhcZralKaxBYBsI5mpzO+dprZtu3b396taVdq7Ny0jAwAAAAAAAAAAAAAgXaUmp4jKeAAAZHclS9rfx8ND2rjR+bEAAAAAAAAAAAAAAOCiSMYDACC7O33avoS84sWlixelAgXSLCQAAAAAAAAAAAAAAFwNyXgAAOB2Qp6tmeu9vaVdu6Rz50jEAwAAAAAAAAAAAADgHiTjAQAAqUYNyWKx3ub69dvtrlxJl5AAAAAAAAAAAAAAAHAlJOMBAJDd1agh7d5tvn3BgtLly2kXDwAAAAAAAAAAAAAALohkPAAAsjt7EvESNWjg/DgAAAAAAAAAAAAAAHBhJOPhPh4eHmnaHgCQidiamjYl+/Y5lsQHAAAAAAAAAAAAAEAWRTIeAABwzODBGR0BAAAAAAAAAAAAAACZBiXNYEpCQoKGDh2q5cuXKzo6Wq1atdKIESOSnn/33Xf1+++/y9PTU1OnTlWNGjW0bds2DRw4UDdu3JBhGBo5cqSaNWsm6XY1vffff1/z5s1TQkKC5syZo8qVKysmJkaDBg3SX3/9JUl65ZVX1Lt3b6t9bPHz80t2+eHDh+Xj46NVq1aldvcAgOsaOzbZxVE+Pnc9XpVcO3d3iXMoAAAAAAAAAAAAACALiYqKkpeXl0N9qYwHU6ZMmSJJ2rx5s3bs2KHdu3dr6dKlkqT4+HiVLl1au3bt0gcffKAePXpIkh544AGtWbNG27dv1+LFi9W3b9+k8eLj41W5cmXt2LFDXbt21RdffCFJGjdunC5evKh//vlHa9eu1YgRI7Rnzx6rfQAAGcQwMjoCAAAAAAAAAAAAAAAyDSrjwZQlS5Zo586dWrx4saTbGaAHDx7UE088IUnq1q2bJKlt27bq0aOHoqKiFBkZqd69e+vff/+Vh4eHTp48qf/++09FihSRxWJR+/btJUkPPfSQ/vjjD0nSmjVr1Lt3b7m5ualQoUJq06aNwsLC5Ofnl2IfWxKT+e6VWDGvSZMmDu4VAMgCmjZNdvGqsWN1vUyZpMdNBg68v1GhQtKlS2kVGQAAAAAAAAAAAAAA6c7RqngSyXgwyTAMffHFF3ryySdN9xkyZIhq166tX375RRaLRYULF1Z0dLQkyc3NTZ6enpIkd3d3xcXFSZIsFstdY9z5OKU+AIAMEhiY0REAAAAAAAAAAAAAAJBpME0tTHniiSc0bty4pGS6M2fO6Ny5c0nPh4aGSpIWLVqkihUrysvLS1evXlWpUqVksVg0Z84cXb582eZ6QkJCNHXqVBmGoYiICP3+++9q3Lhx2mwUACB1Ro3K6AgAAAAAAAAAAAAAAMg0qIyH+8THx6t06dJJj1u2bKkffvhBp06dUlBQkCwWi7y8vDRlyhT5+PjI3d1dJ0+eVM2aNeXh4aEpU6ZIkt555x117dpVX331lR5++GGVLVvW5rr79OmjvXv3qkaNGpKkt99+O2k6WQBAGjAM6Z6qpKZUrSpVr+78eAAAAAAAAAAAAAAAcFEWwzCMjA4CyAiJSX579uzJ4EgAIIPVqCHt3n3XolVjx+p6mTJJj9u2a3d3n4gIqUCBdAgOAAAAAAAAAAAAAID0k5qcIqapBQAgu9u1y74qdyTiAQAAAAAAAAAAAABwH5LxAADA7YQ8W8VyE9uQiAcAAAAAAAAAAAAAwH08MjoAAADgImrUkNzdpYAAydNT8vaWQkKkXr2k4sUzOjoAAAAAAAAAAAAAADIUlfEAAIBksdz+sSU+Xtq2Tfr7b2n5cmnIEKlMGemll6To6LSPEwAAAAAAAAAAAACATIpkPAAAsjszSXjWxMZK48dLzZtLN286JyYAAAAAAAAAAAAAAFwMyXjZ1Jdffqnq1avL399f1atX18yZM7Vw4UINGzYso0MDALiqsDDplVcyOgoAAAAAAAAAAAAAADKER0YHgPS3adMmzZgxQ1u2bFHu3LkVFRWls2fPytfXV23atMno8JIVFxcnDw8OVwBwutRWxbvX5MnSsGFS8eLOHRcAAAAAAAAAAAAAgEyOynjZ0OnTp1W4cGHlypVLkuTl5SVfX19NmTJFvXv3liRNmTJFbdu2VatWrVS5cmV17do1qf+OHTtUq1Yt1axZU0OHDk1Kkrt586Yee+wxBQYGys/PT1988UVSn5CQEL366quqXbu2HnjgAc2aNSvpuXHjxql69eqqUaOGBgwYoLi4uLv6BAcH6+2339bly5f17LPPKjg4WAEBAZozZ06a7ysAgJ1iY6WJEzM6CgAAAAAAAAAAAAAA0h2lxrKhZs2aafjw4apYsaJCQkLUsmVLtW/f/r52W7du1T///KP8+fMrKChI69evV4MGDdStWzd9+eWXeuyxx/Ttt98mtc+RI4d+/fVXFShQQDExMWrQoIFat26tqlWrSpLOnDmjbdu26dSpU3rooYfUpEkTnTlzRl999ZW2bNmi/Pnz66mnntJPP/2kvn37SpIuX76sTZs2yWKxqGvXrurZs6eaNWumK1euJI1RqFAhq9vr5+eX7PLDhw/Lx8dHq1atcnRXAoDrGzs22cVRPj53PV6VQrtk5c0rcW4FAAAAAAAAAAAAALigqKgoeXl5OdSXynjZUN68ebVlyxaFhoaqUqVKevPNN/XSSy/d165JkyYqXLiwPDw8VKtWLR09elRXr17V2bNn9dhjj0mSunTpktTeMAwNGzZM/v7+CgoK0uHDh7V79+6k57t27SqLxaIyZcqoXr162rJli8LCwtSuXTsVLFhQbm5u6t69u1avXp3U57nnnpPlf1MoLl26VG+++aYCAgIUEhKimJgYHTlyJK12EwDAUfHxGR0BAAAAAAAAAAAAAADpjsp42ZS7u7saNmyohg0b6vHHH1fTpk1Vv379u9rkzJnzrvaJ08feKTFRTpJmzJihw4cPa/PmzcqZM6fat2+v6OjoZNumtOzex3dmmSYkJGjNmjUqUKCAuY38nz179iS7PLFiXpMmTewaDwCylKZNk128auxYXS9TJulxk4EDzY/52GPSsmWpjQwAAAAAAAAAAAAAgHTnaFU8icp42dL+/fu1b9++pMfbt29XuXLlTPXNnz+/SpQooZUrV0qSpk+fnvTc1atXVaRIEeXMmVNHjx7V8uXL7+obGhoqwzB0+vRp/f333woODlajRo00f/58XblyRQkJCZo6dWqKyXFPPPGERo8efVfchmGY3m4AQDoJCcnoCAAAAAAAAAAAAAAASHdUxsuGIiMjNWjQIF2+fFkeHh4qUKCAQkNDtWPHDlP9J0+erJ49e8owDDVv3lz58+eXdHvK2jlz5sjPz0/ly5dX48aN7+rn4+OjwMBARUZGatSoUSpatKiKFi2qwYMHq2HDhpKkkJAQ9e7dO9n1jh07VgMHDlSNGjWUkJCgMmXKaMmSJclW3AMAZBBPT6lXr4yOAgAAAAAAAAAAAACAdGcxKC0GO0VGRipv3rySbk9NO336dC1dutRqn5CQEA0fPjwp6S4zSJymNqVpbAEg20gmqfneaWrbtmtnbqwXX5R+/NFZkQEAAAAAAAAAAAAAkK5Sk1NEZTzYbcWKFfrggw+UkJCgggULasKECRkdEgAgM2jcWPr664yOAgAAAAAAAAAAAACADEEyHuz25JNP6sknn7Srz5o1a9IkFgCAExhGstXxTPP0lHr0uJ2IlyuX8+ICAAAAAAAAAAAAAMCFkIwHAABuJ+RJ5pLyiheXSpeWChWSQkKkXr1uLwMAAAAAAAAAAAAAIBtzy+gAAABAJmCxmK+Od/68FB4ulS0rDR5MIh4AAAAAAAAAAAAAACIZDwAAODJFrWFIEydKzZpJN286PyYAAAAAAAAAAAAAAFwMyXgAAMBxf/0lvfJKRkcBAAAAAAAAAAAAAECGIxkvG/Dw8Ljr8bp16xQSEpLm650yZYp69+6d9PjDDz9Uo0aNFBkZmebrBgCY5EhVvHtNnnx76loAAAAAAAAAAAAAALIxkvGQLj744AOtXr1aS5cuVd68eTM6HACAM8XG3p6yFgAAAAAAAAAAAACAbMzDdhNkde+//77mzZsnSerYsaPef/99SVL58uXVtWtXLVmyRNeuXdOUKVNUv359GYahwYMHa8mSJSpatKjKli2ratWq6b333ktx/LCwMC1ZskReXl6SpHHjxum7776TxWJRSEiIRo8eLQ8PD4WEhCg4OFhr167VhQsX9NVXX6ldu3aSpJEjR+qnn35SoUKF5O/vr4SEBE2YMMHm9vn5+SW7/PDhw/Lx8dGqVavs3mcAkGWMHZvs4igfn7ser0qhXZK8eSXOpwAAAAAAAAAAAAAAFxcVFZWU42QvkvGygfj4eAUEBCQ9joqKUqlSpSRJCxYs0IoVK7R161ZJUqNGjfTQQw/piSeekCTlyZNHW7du1Zw5c/TBBx9o+fLlmj9/vrZv367du3frxo0bql27tqpVq5bsuufNm6fSpUtr48aNSQfpjh079NVXX2nLli3Knz+/nnrqKf3000/q27evJOnatWv6+++/tXXrVnXq1Ent2rXT9u3bNXnyZG3fvl05cuRQSEhIiusEAGSA+PiMjgAAAAAAAAAAAAAAgAxFMl424O7urh07diQ9XrduXVIVuzVr1qhz587KlSuXJOm5557T6tWrk5Lxnn76aUnSQw89pLfffluStHbtWnXs2FGenp7Knz+/2rRpk+K6AwICdPDgQa1atUqtW7eWJIWFhaldu3YqWLCgJKl79+6aOXNmUjJe4joDAwN1/PjxpHW2bt06aYrbZ555Rrt27TK1/Xv27El2eWLFvCZNmpgaBwCypKZNk128auxYXS9TJulxk4EDrY/z2GPSsmXOjAwAAAAAAAAAAAAAgHTnaFU8SXJzYhxWGYaRXquCHSwWi9XHOXPmlHQ7oS8uLs7UGHeqWLGiFi9erJdeeilpOliz67RYLEpISLB7nQCADBASktERAAAAAAAAAAAAAACQodItGa9s2bL68MMPdeLEifRaJUwICQnRzJkzdevWLUVHR+vnn3+2WSmuUaNGmjNnjuLi4nTt2jUtXLjQavuaNWtq3rx5eu655/T333+rUaNGmj9/vq5cuaKEhARNnTrV5joffvhhLVq0SFFRUYqNjdXs2bPt3lYAQBrx9JR69croKAAAAAAAAAAAAAAAyFDplox3+vRpffzxx6pYsaKeeOIJ/fbbb4qPj0+v1SMFbdq0UdOmTRUYGKigoCC1aNFCzZs3t9rnySefVI0aNVStWjW1bNlS/v7+yp8/v9U+devW1fTp09WuXTtZLBYNHjxYDRs2VM2aNVWyZEn17t3bav/atWura9euCggIUMOGDVWpUiWb6wQAmOCMyrU9ekjFi6d+HAAAAAAAAAAAAAAAXJjFSKf5Y93c3GSxWGQYRtIUo8WKFVP37t3Vq1cv+fr6pkcYcJLIyEjlzZtXUVFRevjhhzVu3DjVqVMnXdYZGxurJ598Ul27dtUzzzzj8Hh+fn6SpD179jgrRABwTclM/b1q7FhdL1Mm6XHbdu2S7/vww9KyZVKuXGkVHQAAAAAAAAAAAAAA6SY1OUXpVhnP09PzrkQ8wzB0/vx5jRw5UlWqVFGTJk00a9YsxcTEpFdISIX27dsrICBAgYGBat++fZon4knSgAEDVKtWLdWoUUMVK1bU008/nebrBIBswZG8fIvl9tS0JOIBAAAAAAAAAAAAACBJ8kivFZ05c0ZTpkzRxIkTtW/fPkm6KzEvLCxMYWFhKliwoLp27arevXurWrVq6RUe7PTnn3+m+zonT56c7usEgGwjMSEvmSp5dylZUurWTRo0iKlpAQAAAAAAAAAAAAC4Q7pVxitcuLBee+01/fvvv1q7dq26dOmiXLly3Vct7/Lly/r6669Vo0YNNWjQQNOmTdPNmzfTK0wAAJCSwoWl06elESNIxAMAAAAAAAAAAAAA4B7plox3p4YNG2rq1Kk6c+aMvvnmG/n7+8v4X0WeOxPz/v77b/Xo0UMlS5ZU//79tWPHjowIFwCArM9isV0V79Kl223690+fmAAAAAAAAAAAAAAAcCEZkoyXKH/+/Hr55ZcVHh6uLVu26IUXXlDevHmTEvOk20l5V69e1bhx4xQYGKiHHnpIP/30kyIjIzMwcgAAshBbSXj3+u47qUePtIkFAAAAAAAAAAAA/8fenUdVVe//H38dBgERcSjlqjiXXEFARTTlChcsTUuj1G5p5ayZOdS1utWvL/k1rWxQrLzmRDl0G/xqw9XSIiFyQg0HylISh5y6ooAoyrB/f3A5iR4EDodzBJ6Ptc7q7M/en/fnvc/ZHdfS93p/AADVlEOL8a7UtWtXLVy4UCdOnNDixYvVo0cPi93ydu7cqQkTJuhPf/qTxo4dq23btjky7Vrp4sWLmjZtmtq2baugoCAFBwfrqaeeUkFBgaNTAwDYS1ycozMAAAAAAAAAAAAAAOCGcsMU4xWrW7euRo0apc2bN2vfvn2aPHmyGjZseE23vJycHC1dulQ9e/ZUYGCg3nrrLZ07d85xidci48eP1++//659+/Zp9+7d2rp1q7y8vHTp0qUS1+Xn5zsoQwBAuVW0K96VbrrJdnkAAAAAAAAAAAAAAFDN3XDFeFfq2LGj5s6dq+PHj2vVqlX661//KqmoU57JZJJhGDIMQ/v27dOUKVPUokULjRs3TqmpqQ7OvOY6dOiQ1qxZo3feeUd169aVJLm7u+v//b//p7p166p169Z65plnFBISovnz5+vw4cO66667FBISopCQECUkJEiSCgsL9dxzzyk0NFSBgYF69tlnzWvEx8crNDRUQUFB6tatmzIyMiRJ8+fPN4+PGTNGeXl59v8AAAB/OHPG0RkAAAAAAAAAAAAAAHDDcHF0AuVx+fJlZWVlKTMzs8S46YpuPoZh6MKFC1qyZImWLl2qoUOH6vXXX9ef/vQne6dbo+3bt0/t27dX/fr1S72mTp062rFjhyQpKipKsbGx8vf315EjRxQREaG0tDTF/Xd7w+3bt6uwsFD33HOP1q9fr27duumhhx7SN998Iz8/P2VnZ8vNzU3x8fHavn27tm7dKicnJ02aNEmLFy/Wo48+WmbO/v7+FsfT0tLk4+Oj+Pj4in8QAFBTxMZaHM7x8SlxHF/KdeI3FAAAAAAAAAAAAABQg+Tk5MjT09OquTd0Md62bdu0aNEiffTRR8rJyZH0RwFe8ba19evXV3Z2dolzhYWF+vDDD/Xll19qzZo1Cg8Pd0D2tcOHH36o2bNnKyMjQ5988okkadiwYZKk8+fPKykpyXwsFRVWnj59WuvWrdPu3bv173//W1LRQ3zgwAEVFBSoR48e8vPzkyR5eXlJktatW6fExER16dJFkpSbmysPDw+73ScAAAAAAAAAAAAAAAAAXM8NV4x37tw5LV++XIsWLTJvN1tceFe8Na0k9e7dW+PHj9fgwYN19OhRLV68WMuWLdOpU6fM1507d04DBw7U3r171bJlS4fdU03i7++vgwcPKjs7W15eXrr//vt1//33KyIiQpcvX5Ykc2VoYWGh6tatq5SUlGviGIahOXPm6J577ikx/vnnn1tc1zAMTZs2TVOnTq1wzqVtW1zcMS8yMrLCMQGgxoiKsjgcHxurbF9f83Hk5MmW5//3z2UAAAAAAAAAAAAAAGoCa7viSZKTDfOolISEBA0fPlzNmjXT1KlTtW/fPnPhXbEGDRpoypQp+vHHH7Vp0yY98MADcnV1Vdu2bTVr1iwdPXpU7777rpo0aWLuknf+/Hm98cYbjrilGqlt27YaNGiQJk2apIsXL0oqKrrLzc295tr69evL399fS5cuNY/t2rVLknTnnXdqwYIF5nnHjx/XyZMnddttt2nr1q3av3+/JCk7O1uXL1/WnXfeqWXLluncuXOSpLNnz+rQoUNVeasAAAAAAAAAAAAAAAAAUG4O7Yz3+++/Ky4uTosXL9bBgwclXdsFzzAM9ejRQ+PHj9f9998vd3f3UuO5uLhozJgxuvvuu9W5c2edOnVKhmHoq6++ssv91BYLFy7Us88+q44dO8rb21t169ZVZGSkgoKCrrl25cqVmjRpkubNm6e8vDz16NFDS5cu1ejRo3Xs2DGFhITIZDLJ09NTcXFx8vPz0/LlyzV8+HDl5eXJ3d1d69evV58+fTRhwgT17t1bUtF3PW/ePLVp08betw8AKNa4saMzAAAAAAAAAAAAAADghmEyrm4/ZwcbNmzQokWL9PnnnysvL69EAZ5UVJDn5eWl4cOHa/z48QoMDKzwGrNnz9Zzzz0nSXJ3d9eFCxdsdwOoEYq3qS1tG1sAqDX+++fvla7epnZQdPS189iiFgAAAAAAAAAAAABQw1SmpshunfGOHz+upUuXaunSpTp8+LAky13wunTpogkTJuiBBx6o1P67AQEB5veXLl2qXPIAAKCkxx5zdAYAAAAAAAAAAAAAANxQ7FaM17JlS3PBnVRUgFdchFe3bl397W9/04QJE9S1a1ebrFe3bl3zOgAA4DoMw2J3vFI99pj01ltVlw8AAAAAAAAAAAAAANWQ3YrxCgsLSxTgGYahTp06afz48Ro+fLjq169fJesahkFBHgAAZSnecrasPzNvuUWaMKHq8wEAAAAAAAAAAAAAoJqxWzGeVFQY5+7uriFDhmjChAm67bbbqmytqKgoFRYWVll8AABqlPIWrh84IHXqJLVsKe3eLTVoUKVpAQAAAAAAAAAAAABQXditGK9Dhw4aP368HnnkETVs2NBeywIAgLJY00H2yBGpeXPp6FGpUSPb5wQAAAAAAAAAAAAAQDVjt2K8n376yV5LAQAAe7hwQerVS+LPeAAAAAAAAAAAAAAA5GSPRRITE3XvvfeaX0lJSfZYFg7i4lL1NZ5xcXEaM2ZMla8DADWeNV3xrrR/v7Rvn21yAQAAAAAAAAAAAACgGrNLZ7zk5GStXbtWklSnTh3FxcXZY1lUA/n5+XYp3gMAVKEnnpA2bHB0FgAAAAAAAAAAAAAAOJRdqqAKCgrM71u2bKn69evbY1k42IULFzRs2DClpaWpsLBQgwYN0ksvvaSYmBgdOHBAR48elbOzs9atW6eBAwcqIyNDubm5GjFihKZPny5JOnz4sB577DGdPHlSkvT6668rPDy8Qnn4+/tbHE9LS5OPj4/i4+Mrd6MAUJ3FxloczvHxKXEcX8p1kiRnZ4nfUgAAAAAAAAAAAABADZCTkyNPT0+r5tqlGO9Pf/qTJMlkMqlhw4b2WBI3gC+//FJNmjTRmjVrJElnz541n0tJSdG2bdtUr149FRQU6OOPP1aDBg10+fJl9erVS3fffbf8/Pw0atQoxcbGyt/fX0eOHFFERITS0tIcdUsAAEsMw9EZAAAAAAAAAAAAAADgcHYpxmvRooX5/e+//26PJXEDCAoK0vTp0/XEE0/o9ttv1x133GE+N3DgQNWrV0+SZBiGZsyYoW+++UaGYejYsWPat2+fWrRooaSkJA0bNsw87/Llyzp9+nSF8khNTbU4XtwxLzIysqK3BgA1R1SUxeH42Fhl+/qajyMnTy49RqNG0pkzts4MAAAAAAAAAAAAAAC7s7YrnmSnYrxevXqpQYMGOnfunA4fPqwTJ06Yu+Wh5mrXrp127dqljRs36r333tNbb72lf//735JKPrQrV65UWlqatm/fLjc3N913333Kzc1VYWGh6tatq5SUFAfdAQCgXLp2dXQGAAAAAAAAAAAAAAA4nJM9FqlTp46GDBliPl64cKE9loWDHTt2TK6urho8eLDeeOMNbd++3eJ1mZmZuummm+Tm5qZDhw5p48aNkqT69evL399fS5cuNV+7a9cuu+QOAKiAN95wdAYAAAAAAAAAAAAAADicXYrxJOmFF16Qt7e3JGnOnDnasmWLvZaGg+zdu1e33XabgoODdeedd2r+/PkWr3vooYd04MAB+fv7a9KkSQoPDzefW7lypdasWaOgoCB17NhRb731lr3SB4DawTAqN9/PTwoIsE0uAAAAAAAAAAAAAABUYybDqOy/wpff119/rXvuuUcXLlyQl5eXXn/9dY0ZM8ZeywMl+Pv7S5JSU1MdnAkAOJjJdM1QfGyssn19zceDoqOvnVe3rvTbb1KDBlWYHAAAAAAAAAAAAAAA9lOZmiIXWydTmiNHjujWW2/VihUr9Oijj+rUqVMaP368XnrpJd1///0KDQ1VmzZtVL9+fbm6ulYodsuWLasoawAAagHDsFiQd10tW0q7d1OIBwAAAAAAAAAAAADAf9mtGK9169YyXfEP/SaTSYZh6PDhw5ozZ47VcU0mk/Lz822RIgAAtVdxo9yyivJuuUX6v/9ja1oAAAAAAAAAAAAAAK7iZO8Fr9wV12QymYvyKvMCAAA2EhFR+rnbbqMQDwAAAAAAAAAAAACAUti9GE8SxXQAANxo3nmnqCvepk2lX7Nli9Spk9Shg3TunL0yAwAAAAAAAAAAAACgWrDbNrWPPPKIvZYCAAAV8c470mOPlf/6X36RWrWSDh2SGjWqurwAAAAAAAAAAAAAAKhG7FaMt2zZMnstBRszmUwKCgqSJOXn5+t//ud/NGTIkOvOiYmJkYuLi55//nmr1z1+/LgmTJigzz77zOoYAIByqEghXrGsLKlXL+mnn2yfDwAAAAAAAAAAAAAA1ZDdivFQfTk7OyslJUWSdODAAd12221lFuOVV35+vlxcLD+GzZo1oxAPAKraX/5i/dz9+6V9+6SAANvlAwAAAAAAAAAAAABANeXk6ARQvWRlZalBgwbm48cff1zdunVTp06dNHz4cF26dMl87sCBA4qKilL79u317LPPmsdbt26tZ555RiEhIZo/f77Wr1+vHj16qHPnzurevbt27dolSUpPT1f79u3N79u2batJkyapU6dO6tmzp06fPm2fmwaAmiwpqXLzn3jCNnkAAAAAAAAAAAAAAFDN0RkPZSooKFBwcLDy8vL066+/6v333zefi4mJUePGjSVJkydPVlxcnMaPHy9J2rt3r77//nsVFhaqffv2mjhxolq0aCFJqlOnjnbs2CFJOnv2rDZv3iwnJyft2rVLjz32mLZs2XJNHunp6XrwwQf11ltvadKkSVq0aJGee+65MvP39/e3OJ6WliYfHx/Fx8dX7AMBgJokNtbicI6PT4nj+FKuk7OzxO8oAAAAAAAAAAAAAKCGyMnJkaenp1Vz7VaMN2rUKPP7p556Sn5+flbH+umnnzRnzhxJkslk0pIlSyqdH0p35Ta1v/zyi8LCwnTbbbepRYsW+vTTT7VgwQLl5uYqMzNThYWF5nkDBgyQh4eHJMnPz0/p6enmYrxhw4aZrzt58qSGDx+uw4cPy8XFRQcPHrSYR/PmzdWzZ09JUrdu3fTdd99Vxe0CACrCMBydAQAAAAAAAAAAAAAANwS7FePFxcXJZDJJkoYPH16pYrzjx4+XiEcxnv3ceuutatOmjbZs2aJu3bopJiZGO3fu1M0336z58+dr9+7d5mvd3NzM752dnZWfn28+vrJ6dOLEiRo3bpweeOABZWdnq2HDhhbXvl6860lNTbU4XtwxLzIyslxxAKBGioqyOBwfG6tsX1/zceTkyZbnN2oknTlTFZkBAAAAAAAAAAAAAGB31nbFkyQnG+ZRJsPG3XNsHQ9lO3XqlH755Re1b99eWVlZ8vDwUMOGDXXhwgUtX77cqpiZmZnmjnkLFy60ZboAgKrWtaujMwAAAAAAAAAAAAAA4IZgt854ksyd7FC9FBQUKDg4WIZh6NKlS3r++efVuXNnSVJERIT8/Px08803KzQ0VLm5uRWOP3PmTI0cOVL169fXfffdZ+v0AQDXExYmJSVZP/+NN2yXCwAAAAAAAAAAAAAA1ZjJsFN7OScnJ3Mx3saNGyu1NeiGDRvUr18/SZKLi4suX75skxxRuxRvU1vaNrYAUGtYKJa/epvaQdHR187z85N++qkqMwMAAAAAAAAAAAAAwK4qU1Nk121qbSUjI8P8vl69eg7MBACAGuDttys+p359acsW2+cCAAAAAAAAAAAAAEA1VS2L8TZv3iypaNvbpk2bOjgbAACquYkTK1aQd+ut0uHDUoMGVZYSAAAAAAAAAAAAAADVTbUqxsvJydH777+vxYsXm7e8DQwMdHBWAADUABMnSuXduf7XXyVfX+mWW6Rnn5VOnara3AAAAAAAAAAAAAAAqAZcbBmsbdu25bpu+PDhcnd3L3dcwzB04cIFnTlzRoZhyPhvsYDJZNKAAQOsyhUAAFzhv0Xu5ZKfL50/Lx08KM2eLb3yijRypPTWW1IF/nwHAAAAAAAAAAAAAKAmsWkxXnp6ukwmk7lY7mrF4ydPnqzUOsVrtGvXTkOHDq1ULAAAar2KFOJZUlgoLVki/fKL9NVXkoeHbfICAAAAAAAAAAAAAKAaqZJtak0m0zWvss6X51XMMAx16NBBa9eurVCHPQAAUIW++06aOtXRWQAAAAAAAAAAAAAA4BA27YzXsmXLawrvih0+fNh8rkmTJhUqonNycpKnp6caNWokf39/3XHHHbrrrrvk7Oxsk7xRcdu3b1f37t316aefauDAgVbFiIuLU2RkpFq2bGnx/I4dO7R48WL985//rEyqAIDrqWxXvKstWybNmCE1bWrbuAAAAAAAAAAAAAAA3OBsvk1taZyc/mjCt3LlSkVGRtpyadjZ8uXL1bt3b61YsaJSxXjt27e3WIyXn5+vkJAQhYSEVDZVAIA95eUVbVn77LOOzgQAAAAAAAAAAAAAALuyaTFeWQzDKLVzHqqP/Px8rV69Wlu2bFFoaKgyMzPl7e0tFxcX5efnS5KSkpL0/PPPa9OmTdq/f79Gjhyp3NxcXb58WS+99JJycnK0Y8cOjRw5Up6enlq7dq1iYmLk5uamPXv2qG3btho7dqxmzpypr7/+WkePHtXw4cOVnZ2tvLw8Pf300xo+fHi58vX397c4npaWJh8fH8XHx9vsswGAaic21uJwjo9PieP4Uq6zqF49id9WAAAAAAAAAAAAAEA1lJOTI09PT6vm2q0Y75FHHjG/b9asmb2WRRX48ssvFRwcrFatWql///765JNPNHr06FKvf+eddzR58mQ98MADMgxDWVlZ8vb21qJFizRz5kyFhYWZrz148KASExPl6uqqTZs2mcdvuukmrV+/XnXr1lVWVpa6du2qu+66Sw0aNKjCOwUAWKWgwNEZAAAAAAAAAAAAAABgd3Yrxlu2bJm9lkIVW7FihYYNGyZJGjZsmGbOnHndYrywsDDNmDFDBw4c0IABA9S1a9dSrx06dKhcXV2vGc/Pz9eUKVOUnJwsJycnnThxQgcPHizXNrapqakWx4s75rFlMoBaLSrK4nB8bKyyfX3Nx5GTJ5c/5u23Sxs2VDYzAAAAAAAAAAAAAADsztqueJKdt6lF9ZeVlaUvvvhCmzdv1nPPPSfDMHTy5EkdOXJETk5O5q2Ic3NzzXOGDh2qHj166KuvvtLEiRM1ePBgTZ8+3WL80h7mN954Qx4eHkpJSZGzs7O6du1aYg0AwA0kIsLRGQAAAAAAAAAAAAAAYHcU46FCVq9erbvvvlsffPCBeezvf/+7Vq5cqVatWiklJUWdO3fWJ598Yj5/8OBBtWvXTmPHjpWnp6fWrFkjSfLy8lJWVla51s3MzJSPj4+cnZ21bds27d6927Y3BgCwDVdX6TrdUgEAAAAAAAAAAAAAqKmcHJ0Aqpfly5dryJAhJcaGDBmiFStW6NVXX9XgwYMVGhqq+vXrm89/9NFHCggIUOfOnTV//nw9//zzkqRRo0Zp2rRpCg4OVnp6+nXXnTRpktasWaPAwEDNmzdP3bp1s/m9AUCtZBi2jTdypNS0qW1jAgAAAAAAAAAAAABQDZgMw9b/Cl8+Z86c0TfffKNdu3bp6NGjyszM1MWLF1XRdEwmk7755psqyhI1mb+/vyQpNTXVwZkAgIOZTNcMxcfGKtvX13w8KDq67Dh/+Yu0YYPk7m7L7AAAAAAAAAAAAAAAsJvK1BTZfZvao0eP6qmnntKaNWuUl5dXqViGYchkoYAAAABUgGFYLMgrNyenoo54b71FIR4AAAAAAAAAAAAAoNayazHehg0bNGTIEJ0/f97cAY9iOgAAbgDFnWnL+nPZyano5e4u+fhIQ4ZIU6awNS0AAAAAAAAAAAAAoNZzstdCqampuvfee5WdnV2io51hGFa/AACAnRUWSvn50vnz0sGD0uzZRUV5JlPpL1dXqXHjoqK9U6ccfQcAAAAAAAAAAAAAAFQJuxXj/f3vf9eFCxdkMpnMr/vvv1//93//p19//VXnz59XYWFhhV8FBQX2ugUAAGqu4sK5qpCfL2VkSLGxRYV7jzwi5eZWzVoAAAAAAAAAAAAAADiIXbapPXXqlDZs2CCTySTDMOTt7a3PP/9cYWFh9lgeAABcj723jH//fenAAembbyQPD/uuDQAAAAAAAAAAAABAFbFLZ7zExETz1rImk0lz586lEK8aee211xQQEKCgoCAFBARo1apVNo0fERGhpKQkm8YEANzgtmyRpk51dBYAAAAAAAAAAAAAANiMXTrjnThxwvze09NTDz74oD2WhQ1s27ZNK1euVHJysjw8PJSTk1Pi+wQAVHP27op3pSVLpBkzpKZNHZcDAAAAAAAAAAAAAAA2YpfOeDk5OZIkk8mkdu3aydXV1R7LwgZ+++03NW7cWO7u7pKKiinbt2+v4OBgHT58WJIUHBys6dOnS5Li4uL097//XZK0Z88eRUZGqmvXrgoLC9PevXslSRkZGRowYIA6duyowYMHKzc317xeaXNiYmL0yCOPKCoqSu3bt9ezzz5rt88AAFBFCgqKCvIAAAAAAAAAAAAAAKgB7NIZr3Hjxn8s6GKXJWEjd9xxh2bOnKm2bdsqIiJCAwYM0H333afw8HAlJCTo7rvvlouLizZv3ixJ2rRpkwYPHqy8vDyNGzdOq1evVvPmzZWcnKwxY8Zo27ZtevHFFxUYGKh///vf+uGHHxQSEiJJ150jSXv37tX333+vwsJCtW/fXhMnTlSLFi3KvAd/f3+L42lpafLx8VF8fLyNPi0AqIZiYy0O5/j4lDiOL+W6SqtXT+J3GAAAAAAAAAAAAABwg8jJyZGnp6dVc+1SGRcUFCRJMgxDx44ds8eSsJF69eopOTlZW7Zs0aZNm/TUU09pw4YN6tevn/7973+rQYMG6tevnxITE5Wdna3vvvtOc+fO1c8//6zU1FQNGDDAHCsjI0OSlJiYqJUrV0qSOnfurMDAQEm67hxJGjBggDw8PCRJfn5+Sk9PL1cxHgDgBlZQ4OgMAAAAAAAAAAAAAACwCbsU44WGhqpFixY6duyYTp8+rdTU1FK7leHG4+zsrLCwMIWFhalv376KiorS7Nmz9fTTT6tBgwa68847VVBQoJUrV8rb21sNGjTQ0aNH1a5dO6WkpJR7HcMwrjvHzc2tRE75+fnlipuammpxvPgZjIyMLHeOAFDjREVZHI6PjVW2r6/5OHLy5KpZ//bbpQ0bqiY2AAAAAAAAAAAAAAAVZG1XPElysmEepTKZTHr66afNx6+++qo9loUN/Pzzz9q/f7/5+IcfflCrVq3UuHFjubu769NPP1XPnj0VERGh2bNnKyIiQlJR57rs7Gx98803kooK7X744QdJUnh4uLkzXkpKivbu3VvmHABADfXfPzcAAAAAAAAAAAAAAKju7FKMJ0kTJ05UZGSkDMPQihUrtGzZMnstjUo4f/68xowZo44dOyowMFArVqzQ8uXLJRUV1fn4+Khu3brq1auXjh8/rvDwcEmSq6ur1q5dq5kzZyooKEj+/v5avXq1JOmFF15QSkqKOnbsqJdeekkhISFlzgEA1EDOztLo0Y7OAgAAAAAAAAAAAAAAmzAZhmHYa7Hs7GwNGDBASUlJcnJy0pQpU/T888+rYcOG9koBMCvepra0bWwBoNYwma4Zunqb2kHR0bZfd9w4aeFC28cFAAAAAAAAAAAAAMBKlakpcrF1MqV5//33JUkPP/ywjh8/rl9//VVz587VwoUL1adPH4WEhKhJkyZyd3evcOyHH37Y1ukCAICq1KuXNG+eo7MAAAAAAAAAAAAAAMBm7FaMN2LECJmu6LxjMplkGIYuXLigzz//XJ9//rnVsSnGAwCgEgzDYne8KvPww0Ud8awowAcAAAAAAAAAAAAA4EZlt2K8YoZhmIvyTFf9w39FdswtLua7OgYAALBC8Z/BVfHnqouLVL++NHy49OyzUtOmtl8DAAAAAAAAAAAAAAAHs2sxXnGxXUWK7sqKBQAAbMDaIjxXV2nkyKItZ+l0BwAAAAAAAAAAAACoxexWjLds2TJ7LQUAACqiMt3w8vKkd9+Vfv5ZWr9e8vCwXV4AAAAAAAAAAAAAAFQjdivGe+SRR+y1FAAAsLeEBGnqVGnhQkdnAgAAAAAAAAAAAACAQzg5OgFULy4u1tdvtm7dWseOHbNJHunp6Wrfvr1NYgFArVaZrnhXW7ZMOnXKdvEAAAAAAAAAAAAAAKhGKMYDAAC2kZcnLVni6CwAAAAAAAAAAAAAAHAIu21Ti5onPj5ezzzzjC5duqQ6deroq6++UqNGjTR//nwtX75cly5dUrdu3bRgwQK5urqWmPv4449r69atys3NVVBQkJYsWSI3NzfFxMTo0KFDOnbsmA4fPqyhQ4dq1qxZkqSvv/5akydPVp06ddSvX79y5+nv729xPC0tTT4+PoqPj7f+QwCA6i421uJwjo9PieP4Uq67Rr16Er+rAAAAAAAAAAAAAIBqKicnR56enlbNpTMerPKf//xHDz30kN5//33t3r1b8fHxqlevnuLj47V9+3Zt3bpVu3fvlru7uxYvXnzN/JiYGCUnJ2vv3r1q1KiR4uLizOf27t2rL774Qrt379ayZct07Ngx5ebmasSIEfr444+VkpKiS5cu2fFuAQDlVlDg6AwAAAAAAAAAAAAAAHAIOuPBKlu3blWPHj3k5+cnSfLy8pIkrVu3TomJierSpYskKTc3Vx4eHtfM//TTT7VgwQLl5uYqMzNThYWF5nMDBgwwz/Hz81N6errOnDmjZs2ambvcjRgxQp9//nm5ck1NTbU4XhwrMjKyXHEAoEaKirI4HB8bq2xfX/Nx5OTJ5Yt3++3Shg22yAwAAAAAAAAAAAAAALuztiueZMdivCNHjlRZ7JYtW1ZZbFhmGEap49OmTdPUqVNLnZuenq6YmBjt3LlTN998s+bPn6/du3ebz7u5uZnfOzs7Kz8/32Z5AwCqWESEozMAAAAAAAAAAAAAAMAh7FaM17p1a5lMJpvHNZlMFGs5wG233aYJEyZo//798vPzU3Z2ttzc3HTnnXfqySef1IgRI9SgQQOdPXtW586dU5s2bcxzs7Ky5OHhoYYNG+rChQtavny5AgMDr7uen5+fjh8/rh9//FEdO3bU8uXLq/oWAQAV5eoqjR7t6CwAAAAAAAAAAAAAAHAIu29TW1pHNVQvN910k5YvX67hw4crLy9P7u7uWr9+vfr06aMJEyaod+/ekiQXFxfNmzevRDFeYGCgIiIi5Ofnp5tvvlmhoaHKzc297npubm6Ki4vT4MGD5ebmpn79+lXp/QFArWEYkq2K5UeOlJo2tU0sAAAAAAAAAAAAAACqGZNhp+o4JycnmUwmq4rxru6od2UMk8mkgoKCSueH2sff31+SlJqa6uBMAMDBLBTjxcfGKtvX13w8KDr6+jHCw6Uvv5Tc3W2dHQAAAAAAAAAAAAAAdlOZmiK7dcZ75JFHKjznwoUL+v3337Vr1y5lZWVJKiq+a9u2rf7yl7/YOkUAAGqnynTHc3Ut6og3bx6FeAAAAAAAAAAAAACAWs1uxXjLli2zeq5hGFq3bp1iYmK0c+dOHTp0SPfff79eeuklG2YIAEAtVtx1tqyivNtvl7KzJS8vKSJCGj2arWkBAAAAAAAAAAAAAJAdi/Eqw2QyacCAAerbt6/Gjh2r9957Ty+//LIKCgr08ssvOzo9AABqj40bS75/7rnrXz9xovT220XvT52SFi+WEhLKV9BX0eutnQMAAAAAAAAAAAAAgA2YDKO4FU71UFBQoLCwMG3btk0mk0nr16/XHXfc4ei0UA1VZn9nAKhxruqIFx8bq2xfX/PxoOjoysXv37+oeC8v79pzV291e/GiNGWKFBdXvusl6+YAAAAAAAAAAAAAAHCVytQUOdk6marm7OysGTNmmI//8Y9/ODAbAABqgLK2prWFdessF8lJRePvviv16ydlZEh33iktWlS+6y9eLHpVdA4AAAAAAAAAAAAAADZW7YrxJCkyMlJeXl4yDEMpKSnav3+/o1PCFVxcqsXuxwCAG01CgtSrV9F/y3v91KlFHfEqOgcAAAAAAAAAAAAAABurlsV4zs7Oat26tfl4+/btjksGAIDqzB5d8SqiogX2S5cWbU1bEcuWSadOVWwOAAAAAAAAAAAAAABlqJbFeJLk5uZmfn/ixAkHZoLSDB48WCEhIQoICNC0adPM4yNGjNCECRMUGhqqW265RW+88Ua55kyaNElhYWFq06aN3n77bbveCwDgBpWfX/rWtKXJy5OWLKmafAAAAAAAAAAAAAAAtVa13U/0yJEj5vdsi3pjWrhwoRo3bqzCwkLde++9+uqrr9S3b19J0r59+5SUlKQLFy6oa9euuv3229WpU6frzklPT1dCQoJOnTqljh07aty4cXJ1dS0zD39/f4vjaWlp8vHxUXx8vO1uGgCqm9hYi8M5Pj4ljuNLua7aqldP4vcfAAAAAAAAAAAAAHCVnJwceXp6WjW3WnbGS0hI0OnTp83HTZo0cWA2KM27776rzp07Kzg4WFu3btXevXvN5x544AHVqVNHDRo00MCBA5WYmFjmnOjoaDk7O6tZs2Zq2LChTrHFIADAWgUFjs4AAAAAAAAAAAAAAFDDVLuWcmfOnNHEiRNlMplkGIYkKTQ01MFZ4WoJCQlavXq1EhMT5eXlpSeffFK5ubnm8yaTqcJzrtya2NnZWfn5+eXKJTU11eJ4cce8yMjIcsUBgBopKsricHxsrLJ9fc3HkZMn2ysj+7j9dmnDBkdnAQAAAAAAAAAAAAC4wVjbFU+qRp3xzp07p0WLFik4OFj79++XVFTQ5e/vrw4dOjg4O1wtMzNTDRo0kJeXl86cOaPVq1eXOP+vf/1LeXl5yszM1Oeff67evXuXOQcAAJuJiHB0BgAAAAAAAAAAAACAGsZunfGs7T528eJFnTp1SkeOHJFhGDIMw9wVz2Qy6eWXX7ZxpqiM/Px8ubm5qV+/flq6dKk6dOigZs2aqXfv3iWu69ixo3r16qWzZ89q4sSJ6tSpkzp06HDdOQAAXMPFRTKZpLy88s9xdZVGj666nAAAAAAAAAAAAAAAtZLdivE2bdpkcWvS8ijejlZSie1pX3zxRfXv398m+cE29uzZo/bt26tOnTpau3ZtqdeFhYXpn//8Z4mx682Ji4srcXzw4MFKZgoAkCQZRlEx243Cz0/6bwfcchk1qugeFi0q/5yRI6WmTSueGwAAAAAAAAAAAAAA11Ettqk1mUzmQj7DMNS8eXN98sknev755x2cGa40Z84cDR8+nG6FAADrhIdLmzcX/be818+bV/Sq6BwAAAAAAAAAAAAAAGzMZFzZdq4KOTlZV/fn7u6u+vXrq1WrVurSpYv69++v/v37y9nZ2cYZorbx9/eXJKWmpjo4EwC4AVzVHS8+NlbZvr7m40HR0ZWLP2CAtGGD5e1kXV2LutXNmye5u0sXL0pTp0rLlpXvesm6OQAAAAAAAAAAAAAAXKUyNUV226a2sLDQXksBAICKKq7Nt/WWtRMnSm+/XfT+1ClpyRJp0yYpO1vy8pIiIqTRo0tuG+vhIS1cKM2YUb7rrZ0DAAAAAAAAAAAAAIAN2a0zHnCjoTMeAFzhnXekxx4zH163M95zz0kzZ9ozOwAAAAAAAAAAAAAA7KIyNUXW7R0LAABqjqsK8cr00kvStGlVlw8AAAAAAAAAAAAAANUQxXgAANR2FSnEKzZ3rs3TAAAAAAAAAAAAAACgOqMYD3JxcblmbMeOHZowYYIkKT09Xe+//775XHp6utq3b2+X3Pr376/Tp0/bZS0AqJX+8hfr57ZqZbs8AAAAAAAAAAAAAACo5q6twnKQ/Px8nTp1SmfPnlV2dra8vLzUsGFDNW3a1GKxGKpWSEiIQkJCJP1RjPfwww/bPY9169bZfU0AqFWSkqyfe+SI7fIAAAAAAAAAAAAAAKCaMxmGYThq8bS0NC1evFiJiYn64YcfdOnSpWuucXNzU5cuXRQeHq4xY8aoTZs2Dsi0ZnNxcVF+fn6JsU2bNmnmzJn6+uuv1blzZx08eFDt2rVTv379NGHCBEVGRqp///5KSEiQl5eX1q5dqyZNmmjEiBHq06ePhg8fLklq3769vv76a7Vu3VqPP/64tm7dqtzcXAUFBWnJkiVyc3NTTEyMDh06pGPHjunw4cMaOnSoZs2aJUlq3bq1kpKS1KJFi1Lnl8Xf39/ieFpamnx8fLR06dJKfoIAUI2lploczvHxUaGrq/nY6+hRy/NL+Y0FAAAAAAAAAAAAAKA6GjVqlDw9PZVayr+nX49Dtqk9efKk7r33XnXo0EGvvvqqucDKMIxrXrm5udqyZYtefvll3XLLLRo8eLBOnjzpiLRrrTfffFPdu3dXSkqKXn75ZUlF3fIefPBB7d27V126dNGiRYvKjBMTE6Pk5GTt3btXjRo1UlxcnPnc3r179cUXX2j37t1atmyZjh07VqH5AAAAAAAAAAAAAAAAAOBIdt//dePGjXrwwQeVkZGh4qZ8JpNJJpNJknRlo77iseJxwzC0Zs0aJSQk6IMPPlCfPn3smzzMmjdvrp49e0qSunXrpu+++67MOZ9++qkWLFig3NxcZWZmqrCw0HxuwIAB8vDwkCT5+fkpPT1dLVq0KPf86ymtSrW4Y15kZGS54gBAjRQVZXE4PjZW2b6+5uPIyZMtz3dcg10AAAAAAAAAAAAAAGzO09PT6rl2Lcb7/vvvdc899+jixYuSiortiovsXFxc5Ofnp5tuukmenp7KycnRf/7zH/3888/Ky8srcf2ZM2d0zz33aOPGjbrtttvseQv4ryu3h3V2djZvc+vi4lKiSC43N1dSUSe9mJgY7dy5UzfffLPmz5+v3bt3lxmvWFnzAQAAAAAAAAAAAAAAAMCR7LZNbU5OjoYMGaKLFy+W6Hj3t7/9TRs2bND58+e1Z88excfH6/PPP1d8fLz27Nmj7Oxsbdy4UX/7298k/dFF78KFCxoyZIguXLhgr1uotby8vJSVlVWua9u0aaOdO3dKkjZv3qzjx49LkrKysuTh4aGGDRvqwoULWr58eYVyqOx8AEApwsKsn9uype3yAAAAAAAAAAAAAACgmrNbMd6rr76qkydPmrvbNW/eXN9//71WrVqlPn36qE6dOhbn1alTR1FRUVq1apW2bNlSYuvSEydOaM6cOfa6hRqroKBALVq0ML/Gjx9f4nxQUJC8vb0VGBioZ5555rqxxo4dq+TkZHXq1EkrVqxQ69atJUmBgYGKiIiQn5+foqKiFBoaWqEcKzsfAFCKcmwzXqrDh22XBwAAAAAAAAAAAAAA1ZzJMAzDHgv5+vrq+PHjMgxDjRs31s6dO9XSio46R44cUUhIiM6cOWMu6jt69GgVZIyazt/fX5KUmprq4EwAwMHeeUd67LESQ/Gxscr29TUfD4qOLjnnueekmTPtkR0AAAAAAAAAAAAAAHZTmZoiu3TG27Nnj3777TdJRdvMvvLKK1YV4klSy5YtNXv2bBXXEB4/flx79uyxWa4AANQ6EydKb79d/uspxAMAAAAAAAAAAAAA4Bp2KcYrrhI0DEPu7u7629/+Vql4DzzwgDw8PK6JDwAArDRxomQYUkRE6de0bFl0DYV4AAAAAAAAAAAAAABcwy7FeKdPn5ZU1BWvTZs2qlu3bqXi1a1bV23atLkmPgAAqKRNm0o/d+SIZDJJHh5SUJB0xx3SrFnSqVN2Sw8AAAAAAAAAAAAAgBuVXYrxcnNzze+v7GhXGe7u7ub3ly5dsklMAABqLZOp6FUeubnSnj3Sxo1FW9b6+krjxxeNAwAAAAAAAAAAAABQS9mlGO/mm2+WVLRN7ZEjR2wS8+jRo+b3N910k01iAgBQK5W3CK80eXnSu+9K/fpJFy/aJicAAAAAAAAAAAAAAKoZuxTj+fr6mt//5z//0bZt2yoVb9u2bfr999/Nxy1btqxUPFhmMpkUHBys4OBgBQQE6OOPPy5zTkxMjGbOnFnmdf3792d7YQCoaRISpKlTHZ0FAAAAAAAAAAAAAAAOYZdivLCwMLm7u8v03847zzzzTKXi/eMf/zC/d3NzU1hYWKXiwTJnZ2elpKQoJSVFa9as0aOPPmqz2OvWrVOTJk1sFg8AYKXKdsW72rJl0qlTto0JAAAAAAAAAAAAAEA1YJdiPA8PD91xxx0yDEOGYSgxMVGjR49WYWFhheIYhqHx48dr06ZNMplMMplM6tu3r9zd3asocxTLyspSgwYNzMd79uxRZGSkunbtqrCwMO3du/eaORkZGerfv7/8/f113333qXv37kpKSpIktW7dWseOHZMkubi4mOckJSUpIiJCkrRp0yb17NlTQ4cO1a233qqhQ4dq8+bN6t27t1q3bq2PPvqo6m4YAGCdvDxpyRJHZwEAAAAAAAAAAAAAgN25lH2Jbbz44ov64osvzAV5cXFxSklJ0Wuvvaa//vWvZc7ftGmTpk+frl27dslkMskwDDk7O2vGjBl2yL52KigoUHBwsPLy8vTrr7/q/ffflyTl5eVp3LhxWr16tZo3b67k5GSNGTPmmu2HX3zxRXXu3Fnr1q3T7t271bVr1wrnsGfPHq1atUotW7ZU9+7d9eabb+rbb7/Vzz//rAEDBmjo0KFlxvD397c4npaWJh8fH8XHx1c4LwCoMWJjLQ7n+PiUOI4v5TqL6tWT+G0FAAAAAAAAAAAAAFRDOTk58vT0tGqu3YrxgoKC9OSTT2rOnDnmYroffvhBffr0UcuWLfXXv/5VgYGBuummm+Tp6amcnBydOXNGu3fv1qZNm3T48GFJRd3xirviPfnkk+rUqZO9bqHWKd6mVpJ++eUXhYWF6bbbbtO5c+eUmpqqAQMGmK/NyMi4Zn5iYqJWrlwpqej7t+a76ty5s1q3bi1J6tSpk7p37y5nZ2d17NhRJ06cUGFhoZyc7NLgEQBQXgUFjs4AAAAAAAAAAAAAAAC7s1sxniS98sorOnnypJYvXy6TySSpqLju8OHDeu+990qdZxiGJJmL8AzD0MMPP6yXX37ZLnlDuvXWW9WmTRtt2bJFfn5+ateunblQr7yKv/OrOTk5mYssc3NzS5xzc3Mrcd2Vx5LKVYyXmppqcby4Y15kZGSZuQNAjRUVZXE4PjZW2b6+5uPIyZPLH/P226UNGyqbGQAAAAAAAAAAAAAAdmdtVzxJsntLsffee0/z5s2Tu7t7iS53ksxb2F75kkoW4bm7u2v+/PmKi4uzd+q12qlTp/TLL7+offv28vPzU3Z2tr755htJMnc5vFrv3r31wQcfSJL27t2rPXv2WIzdqlUrc2HfJ598UjU3AACwn4gIR2cAAAAAAAAAAAAAAIDdOWR/z8cff1y//PKLnnnmGd18880lCu+uVnyuSZMmevbZZ/XLL7/oscces3PGtVNBQYGCg4MVFBSk8PBwPf/88+rcubNcXV21du1azZw5U0FBQfL399fq1auvmf/CCy8oOTlZ/v7+evHFFxUQECBvb+9rrnv11Vc1ePBghYaGqn79+va4NQBAVXF1lUaPdnQWAAAAAAAAAAAAAADYnckorQrOjvbv369t27bp8OHDOnv2rM6fP6969eqpYcOGatWqlXr06KEOHTo4Ok1U0OXLl2UymeTq6qoDBw4oMjJSv/zyizw8PBydmqQ/tqktbRtbAKg1LGwjfvU2tYOio8sXa9w4aeFCW2UGAAAAAAAAAAAAAIBdVaamyMXWyVjDz89Pfn5+jk4DNnbixAlFR0eroKBAhmHon//85w1TiAcAqALh4dK8eY7OAgAAAAAAAAAAAAAAh7ghivFQM7Vq1Uq7du1ydBoAgLIYhsXueOXm6iqNHFlUiOfubru8AAAAAAAAAAAAAACoRijGAwAARQV5UvmL8m6+Wfrzn6W+faXRo6WmTasuNwAAAAAAAAAAAAAAqgEnWwa7dOmSunfvrrZt25pfa9eutUns9evXq127dua4YWFhKigosElsAABqPZOpYt3xfv9d+u47KS1N8vauurwAAAAAAAAAAAAAAKgmbFqM9+abbyo5OVnp6ek6fPiwoqKidM8999gk9p133qno6Gilp6crPT1dW7Zs0TvvvGOT2AAA1GrWblFrGNLSpdLtt0sXL9o2JwAAAAAAAAAAAAAAqhmbFeNduHBBL7/8skz//Qf9Dh06aMGCBbYKL0l6+eWX1alTJ5lMJhmGof/93/9VXl6eTdcAAAAVlJQkTZ3q6CwAAAAAAAAAAAAAAHAomxXjrV69WllZWTIMQyaTSbNnz5aLi4utwkuSXFxcFBsbK8MwJElnzpzRp59+atM1UHWufB5mzJhRrjkxMTGaOXNmVaUEALC2K97Vli6VTp2yTSwAAAAAAAAAAAAAAKohmxXjrVixQpJkMpnUtWtXDRo0yFahSwgPD9dtt91mPo6Li6uSdVC1yluMBwCoJvLzpSVLHJ0FAAAAAAAAAAAAAAAOY5PWdYWFhfr+++/NW9QOHTrUFmFLNXjwYG3ZskWGYSgxMdHcjQ/Vw7Rp01RQUKDg4GDddNNN+vrrr/X4449r69atys3NVVBQkJYsWSI3NzfznJycHPn5+Wn//v3y9PSUJPn5+WndunVq27btddfz9/e3OJ6WliYfHx/Fx8fb7uYAoLqJjbU4nOPjU+I4vpTrSqhXT+I3FQAAAAAAAAAAAABQjeXk5JjrkyrKJp3x9u3bpwsXLpi3j62qrnjF7r77bvP7nJwcpaamVul6sK0333xTzs7OSklJ0ddffy2paDva5ORk7d27V40aNbqm46Gnp6cGDhyoDz/8UJKUkJCgFi1alFmIBwCwo4ICR2cAAAAAAAAAAAAAAIDD2KQz3k8//WR+X7duXd1yyy22CFuq9u3bq27durpw4YIk6ccff1RAQECVromq9emnn2rBggXKzc1VZmamCgsLr7lm/PjxGj9+vEaNGqXFixdrzJgx5YpdWrFmcce8yMhI6xMHgOouKsricHxsrLJ9fc3HkZMnlx3r9tulDRtslRkAAAAAAAAAAAAAAHZnbVc8yUad8c6ePStJMplMatq0qS1ClulPf/qT+X1GRoZd1kTVSE9PV0xMjNatW6e9e/dq+vTpys3Nvea6wMBAmUwmJSUladOmTYqOjnZAtgCAUkVEODoDAAAAAAAAAAAAAAAcxqbFeJJ000032SJkmRo3bmx+f+7cObusCdupW7eucnJyJElZWVny8PBQw4YNdeHCBS1fvrzUeePHj9f999+vwYMHy83NzV7pAgDK4uIijR7t6CwAAAAAAAAAAAAAAHAYmxTjOTn9ESYzM9MWIct05Tomk8kua8J2Jk2apK5du6pPnz4KDAxURESE/Pz8FBUVpdDQ0FLnDR06VGfOnNHYsWPtmC0A1GCGYZs4o0ZJduqOCwAAAAAAAAAAAADAjcjFFkHq168vSTIMQ7///rstQpbpynW8vLzssiYqJz8/3/x+1qxZmjVrlvl44cKFFufExMSUON6+fbtCQ0PVsWPHKskRAGCFv/xFmjfP0VkAAAAAAAAAAAAAAOBQNumM5+vra35/9uxZHT161BZhS3X06FFlZGSYO+JduT5qrr///e965JFHNGfOHEenAgA1i7Xd8Uymoo54GzZI7u62zQkAAAAAAAAAAAAAgGrGJsV4AQEBkv7YLnbdunW2CFuq9evXSyrqxHfl+qjZXnvtNaWnp6t79+6OTgUAah7DKF9RnrOz1Lq19I9/SCdOSEuWUIgHAAAAAAAAAAAAAIBsVIzXunVrNW/e3Hy8ZMkSW4Qt1ZXxfXx81KZNmypdDwAA/JeTk/T//p80a5bUtKmjswEAAAAAAAAAAAAA4IZhk2I8SbrnnntkGIYMw9DOnTv1wQcf2Cp0Cf/617+UnJwsk8kkk8mke++9t0rWAQCgVjGZil5lycuTRo8uKsr7+eeqzwsAAAAAAAAAAAAAgGrCZsV448ePl1S0Va1hGHrssce0d+9eW4WXJO3bt0+PPfaYeQ1JGjt2rE3XAACg1ilPEd7VDEPy85P27bN9PgAAAAAAAAAAAAAAVEM2K8YLCAhQdHS0DMOQyWTSuXPndMcdd2j79u02iZ+cnKy+ffvq7Nmz5jUGDRqkwMBAm8THtUwmk4KDgxUQEKDw8HClpaVVaH5KSoo+++yzKspOGjFihFasWFFl8QEA5RAU5OgMAAAAAAAAAAAAAAC4IdisGE+S5s6dK29vb0lFhVynTp1Sr1699NRTTykjI8OqmGfPntUzzzyjXr166cSJEzL9t3tP/fr1NXfuXFulDgucnZ2VkpKiffv2qUePHnryySfLPTc/P7/Ki/EAADZgTVe8KxUWSkuX2iYXAAAAAAAAAAAAAACqMZsW4/n6+mr58uVycioKazKZVFBQoNdff13NmzfXgw8+qA8//LDMDmu//vqrPvroIw0fPlzNmzfXnDlzlJ+fb96e1tnZWe+9955atmxpy/RxHRERETpw4IAkacGCBQoICFCnTp30+OOPKz8/33zNtGnTFBoaqqefflovvPCC1qxZo+DgYMXGxiouLk5jxowxxxwzZozi4uIkSYcPH1ZYWJgCAgI0duxYtWrVSseOHZMkDR48WCEhIQoICNC0adPse+MAgLJNmuToDAAAAAAAAAAAAAAAcDgXWwe86667FBcXp9GjRysvL89cQHfp0iV9+OGH+vDDDyVJ9erV080336wGDRrI09NTOTk5yszM1O+//67s7GxzPMMwJMkcx9XVVYsWLdLAgQNtnTpKYRiGPvvsMwUFBSklJUWvv/66kpOT5e3trXvvvVeLFi3So48+KknKyMjQtm3bZDKZ1KlTJyUlJWnx4sWSZC68s2Ty5Ml65JFHNHbsWH3xxRfmOZK0cOFCNW7cWIWFhbr33nv11VdfqW/fvuXO39/f3+J4WlqafHx8FB8fX+5YAFDjxMZaHM7x8SlxHF/KdX9cwG8pAAAAAAAAAAAAAKD6y8nJkaenp1VzbV6MJ0nDhg1Thw4ddP/99+vQoUPmrWWLC+skKTs721x0V1xoZ8mVc1u3bq0PP/xQ3bp1q4q0cZWCggIFBwfLMAz5+flp7ty5+uijjxQdHa2GDRtKkkaMGKFVq1aZi/EefPBB83dWEYmJifrggw8kFRV0FseXpHfffVcfffSRCgoKdPr0aYWFhVWoGA8AAAAAAAAAAAAAAAAAqlqVFONJUkhIiFJSUvQ///M/Wrx4sc6fPy9JpRZqWRo3DEOGYahevXoaPXq0YmJi5O3tXVUp4yrOzs5KSUkpMXb193T18fWqQl1cXFRYWGg+zs3NLTOHhIQErV69WomJifLy8tKTTz5ZrnlXSk1NtThe3DEvMjKyQvEAoEaJirI4HB8bq2xfX/Nx5OTJ149TSlE9AAAAAAAAAAAAAADVibVd8STJyYZ5XMPLy0tvvPGGjh49qtmzZys0NFTOzs7mIrvrvZydnRUaGqrZs2fryJEjevPNNynEuwH07t1ba9eu1blz51RYWKj33nuv1GI2Ly8vZWVlmY/btGmj3bt3q6CgQGfOnNG3335bIm5xZ7x169bp7NmzkqTMzEw1aNBAXl5eOnPmjFavXl2FdwcAsIqHh6MzAAAAAAAAAAAAAADA4aqsM96VvL299fTTT+vpp5/WhQsXtHXrVu3fv18ZGRnKyMhQdna2vLy81KhRIzVq1Eh+fn7q0aOH6tata4/0UAHBwcF64oknFBYWJkmKiIjQmDFjLF4bGRmpV155RcHBwRo1apQmT56sgIAA/fnPf1b79u0VEhJivnbu3LkaPny43nzzTfXs2VNNmzaVt7e3+vXrp6VLl6pDhw5q1qyZevfubZf7BABUwFtvOToDAAAAAAAAAAAAAAAczmQY7CsHx7tw4YLc3d3l5OSkpKQkjRs3Tj/++GOVrlm8TW1p29gCQK1hYav4q7epHRQdbXmuk5NUUFBVmQEAAAAAAAAAAAAAYFeVqSmyS2c8oCx79+7V+PHjVVhYqDp16mjZsmWOTgkAUB5VXDgNAAAAAAAAAAAAAEB1QTEebgjdu3dXSkqKo9MAgNrJMCx2x7suk0n66SepQ4eqyQkAAAAAAAAAAAAAgGrGydEJAACAG4BhFL3K4uIiLVkiFRZSiAcAAAAAAAAAAAAAwBUoxgMAAEWd7srTHS8/Xxo/XsrNrfqcAAAAAAAAAAAAAACoRijGAwCgtqvoFrX5+ZKHh3TxYtXkAwAAAAAAAAAAAABANUQxHgAAsM7UqY7OAAAAAAAAAAAAAACAGwbFeLCayWRScHCwAgICFB4errS0tOteHxERoaSkJJut7+LiYrNYAFBrVbQr3pXefVc6dcp2uQAAAAAAAAAAAAAAUI1RjAerOTs7KyUlRfv27VOPHj305JNPOjolAIC9LVni6AwAAAAAAAAAAAAAALgh0FoMNhEREaEvvvhCkrRgwQK9/fbbMplMioiI0JtvvnlNF7tZs2ZpzZo1unz5snx9ffXee++pcePGiouL05o1a1RQUKBffvlFPXr00Pvvvy9JSklJ0ciRI1VQUKDo6Ohy5+bv729xPC0tTT4+PoqPj7fyrgGgBoiNtTic4+NT4ji+lOtUr57E7ygAAAAAAAAAAAAAoIbIycmRp6enVXPpjIdKMwxDn332mYKCgpSSkqLXX39d3333nXbv3q2jR49q0aJF18wZP368kpOTtXv3bkVGRuq1114zn9uxY4fee+89/fjjj9qzZ4++//57SdIjjzyil19+WXv27FGjRo3sdn8AgOsoKHB0BgAAAAAAAAAAAAAA3BDojAerFRQUKDg4WIZhyM/PT3PnztVHH32k6OhoNWzYUJI0YsQIrVq1So8++miJuZs3b9bs2bOVnZ2tixcvys/Pz3wuMjJSjRs3liR17txZhw4dUkBAgE6cOKG+ffua45Z3W9zU1FSL48Ud8yIjIyt24wBQk0RFWRyOj41Vtq+v+Thy8mTL82+/XdqwoSoyAwAAAAAAAAAAAADA7qztiidRjIdKcHZ2VkpKSokxk8l03WNJunTpkkaMGKHt27erXbt2+vzzzzVv3jzzeTc3txJr5Ofn2zZxAIDtREQ4OgMAAAAAAAAAAAAAAG4IbFMLm+rdu7fWrl2rc+fOqbCwUO+99941nedyc3NVWFioJk2aqKCgQEuWLCkzrre3t5o1a6aNGzdKkpYvX14l+QMAKmj0aEdnAAAAAAAAAAAAAADADYFiPNhUcHCwnnjiCYWFhSkwMFDNmjXTmDFjSlzj7e2tJ554QoGBgerRo4duvfXWcsWOi4vTU089pcDAQP3nP/+pivQBoPYxDOvnjhsnNW1qu1wAAAAAAAAAAAAAAKjGTIZRmX+FB6ovf39/SVJqaqqDMwEAB7OwpXh8bKyyfX3Nx4Oio6+dd/Gi5O5elZkBAAAAAAAAAAAAAGBXlakpojMeAAC1XUXr8l1cKMQDAAAAAAAAAAAAAOAqFOMBAICigrzyFOWdPCnl5VGIBwAAAAAAAAAAAADAVSjGAwAA5efjU7St7dWvunWlpUsdnR0AAAAAAAAAAAAAAA5DMR4AAPijqM5aFy9Ko0dLzs7Szz/bLi8AAAAAAAAAAAAAAKoJivEAAKjtKlOEd7XCQsnPT9q3z3YxAQAAAAAAAAAAAACoBijGg8299tprCggIUFBQkAICArRq1Sqr4nz22WeaMWOGjbMDANhFUJCjMwAAAAAAAAAAAAAAwK5cHJ0AapZt27Zp5cqVSk5OloeHh3JycnTixAmrYg0cOFADBw60cYYAgBJs2RXvSoWF0tKl0qhRVRMfAAAAAAAAAAAAAIAbDJ3xYFO//fabGjduLHd3d0mSp6en2rdvr7i4ON19993q06ePOnTooEceeUSXL1+WJM2aNUvdunVTUFCQ7rrrLp05c0aSFBcXpzFjxpjfDxo0SHfddZduvfVWPfzww465QQBA+U2a5OgMAAAAAAAAAAAAAACwGzrjwabuuOMOzZw5U23btlVERIQGDBig++67T5KUlJSkffv2qVmzZho8eLDeffddTZo0SePHj9ezzz4rSXrjjTf02muvafbs2dfE3rFjh/bs2SNvb2+FhITo+++/V69evcrMyd/f3+J4WlqafHx8FB8fX4k7BoBqLjbW4nCOj0+J4/hSrisTv7EAAAAAAAAAAAAAgGokJydHnp6eVs2lMx5sql69ekpOTtby5cvVrl07PfXUUxo/frwkqU+fPmrevLlMJpMefvhhJSQkSJI2b96snj17qlOnTnrnnXe0d+9ei7EjIyPVuHFjubi4qHPnzjp06JDd7gsAAAAAAAAAAAAAAAAArofOeLA5Z2dnhYWFKSwsTH379lVUVJR69uwpk8l0zbWXLl3SiBEjtH37drVr106ff/655s2bZzGum5tbiTXy8/PLlU9qaqrF8eKOeZGRkeWKAwA1UlSUxeH42Fhl+/qajyMnT7YuvmFYNw8AAAAAAAAAAAAAAAewtiueRGc82NjPP/+s/fv3m49/+OEHtWrVSpL09ddf68SJEzIMQytWrFB4eLhyc3NVWFioJk2aqKCgQEuWLHFU6gAAW/PwcHQGAAAAAAAAAAAAAADYDZ3xYFPnz5/XlClTlJGRIRcXFzVo0EDLly9XSkqKevXqpYceekhHjx5V9+7dNXbsWLm5uemJJ55QYGCgbrrpJv31r3/Vrl27HH0bAABbeOstR2cAAAAAAAAAAAAAAIDdmAyD/eNQ9eLi4pSUlKTFixc7OhWz4m1qS9vGFgBqDQvbiF+9Te2g6OiKxXRykgoKKpsZAAAAAAAAAAAAAAB2VZmaIrapBQAAtvfjj47OAAAAAAAAAAAAAAAAu2KbWtjFiBEjNGLECEenAQCwxDAsdsezipNTUSFehw62iQcAAAAAAAAAAAAAQDVBZzwAAFBUkFeZnes9PKQlS4q2pqUQDwAAAAAAAAAAAABQC9EZDwAAVK4znqur9NBD0oMP2i4fAAAAAAAAAAAAAACqGTrjAQBQ21V2i9q8POndd6V+/aSLF22TEwAAAAAAAAAAAAAA1QzFeAAAwDYSEqSpUx2dBQAAAAAAAAAAAAAADkExXg302muvKSAgQEFBQQoICNCqVavKNa9///46ffq0JGnu3LnKysqyWU4xMTGaOXOmzeJFREQoKSnJZvEAoNaqbFe8qy1bJp06ZduYAAAAAAAAAAAAAABUAxTj1TDbtm3TypUrlZycrN27d2vbtm0KDQ0t19x169apSZMmkmxbjJefn2+TOACAaiAvT1qyxNFZAAAAAAAAAAAAAABgdy6OTgC29dtvv6lx48Zyd3eXJHl6eqp9+/YKDg7Wp59+qlatWik4OFi333675syZo7i4OO3bt0+vvfaaWrduraSkJH3wwQc6fvy4+vbtK1dXV3322WcaOHCgeY2ff/5Z//rXvzRo0CDNnz9fy5cv16VLl9StWzctWLBArq6uat26tf72t7/p66+/1rBhw0rkuH79er344ou6dOmS6tSpowULFqhLly5KT09XZGSk+vfvr4SEBHl5eWnt2rVq0qSJMjIy9NBDD+nQoUPq2LGjcnNzy/2Z+Pv7WxxPS0uTj4+P4uPjrfikAaCGiI21OJzj41PiOL6U6yyqV0/itxUAAAAAAAAAAAAAUA3l5OTI09PTqrl0xqth7rjjDmVkZKht27YaOXKkPvnkExmGofDwcCUkJOjs2bNycXHR5s2bJUmbNm1SREREiRjTp09Xs2bN9NVXXyklJUUtW7ZUSkqKUlJS9OKLL6pTp0664447FB8fr+3bt2vr1q3avXu33N3dtXjxYnOcOnXqaMeOHZo2bVqJ+D169NDmzZv1ww8/aMGCBXrsscfM59LT0/Xggw9q79696tKlixYtWiRJevHFFxUYGKgff/xRzz33nJKTk6voEwQAVFpBgaMzAAAAAAAAAAAAAADA7uiMV8PUq1dPycnJ2rJlizZt2qSnnnpKGzZsUL9+/fTvf/9bDRo0UL9+/ZSYmKjs7Gx99913mjt3brli7927V9OnT9e3334rDw8PrVu3TomJierSpYskKTc3Vx4eHubrr+6IV+zkyZMaPny4Dh8+LBcXFx08eNB8rnnz5urZs6ckqVu3bvruu+8kSYmJiVq5cqUkqXPnzgoMDCz3Z5KammpxvLhjXmRkZLljAUCNExVlcTg+NlbZvr7m48jJk8sf8/bbpQ0bKpsZAAAAAAAAAAAAAAB2Z21XPIlivBrJ2dlZYWFhCgsLU9++fRUVFaXZs2fr6aefVoMGDXTnnXeqoKBAK1eulLe3txo0aFBmzP/85z+6//77tXz5cjVv3lySZBiGpk2bpqlTp1qcU9qDOXHiRI0bN04PPPCAsrOz1bBhQ/M5Nze3EveRn59f/hsHANwYruq4CgAAAAAAAAAAAABAbcA2tTXMzz//rP3795uPf/jhB7Vq1UqNGzeWu7u7Pv30U/Xs2VMRERGaPXv2NVvUFvPy8lJWVpYkKS8vT4MHD9Y//vEPde/e3XzNnXfeqWXLluncuXOSpLNnz+rQoUNl5piZmakWLVpIkhYuXFiu+woPDzd3xktJSdHevXvLNQ8AYGeurtLo0Y7OAgAAAAAAAAAAAAAAu6MzXg1z/vx5TZkyRRkZGXJxcVGDBg20fPlySUUFbSkpKapbt6569eql48ePKzw83GKcRx99VNHR0fLw8NC8efO0ZcsWnTt3Tq+//rok6c0331SfPn00YcIE9e7dW5Lk4uKiefPmqU2bNtfNcebMmRo5cqTq16+v++67r1z39cILL+ihhx5Sx44d5e/vr5CQkPJ+JACA6zEMyWSyXbyRI6WmTW0XDwAAAAAAAAAAAACAasJkGIbh6CQAR/D395ckpaamOjgTAHAwC8V48bGxyvb1NR8Pio4uO054uPTll5K7uy2zAwAAAAAAAAAAAADAbipTU8Q2tQAA1HaVrct3dZXGjaMQDwAAAAAAAAAAAABQq7FNLQAA+KMgr7xb1gYGFm1HGxEhjR7N1rQAAAAAAAAAAAAAgFqPYjwAAFBxqamSp6c0cCCFeAAAAAAAAAAAAAAAiG1qAQCAVNQRr7xd8SSpoEDaskXq1Em69Vbp3LkqSw0AAAAAAAAAAAAAgOqAYjwAAGq7ihThWXLggNSqlZSRYZt8AAAAAAAAAAAAAACohijGQ7mYTCaNGDHCfLxp0yb16dPHcQkBAG4sWVlSr16OzgIAAAAAAAAAAAAAAIehGA/l4uTkpE2bNunAgQNWx8jPz7dhRgAAm6hsV7wr7d8v7dtnu3gAAAAAAAAAAAAAAFQjFOOhXEwmk6ZPn67//d//tXj+hRdeUEBAgAICAjRjxgzzeOvWrfXMM88oJCRE8+bNU7NmzXTp0iUVFhaqUaNGevvttyVJMTExeuuttyRJgwcPVkhIiAICAjRt2jRJ0uHDh3XrrbfKMAxJ0oULF9SiRQtlZ2dX5W0DACrqiSccnQEAAAAAAAAAAAAAAA7h4ugEUH2MHTtWr732mn755ZcS459++qm+/vpr7dixQ5LUu3dvdevWTXfeeackqU6dOuZziYmJ2rZtm7y9vXXLLbcoISFBjz32mDZt2mQuxlu4cKEaN26swsJC3Xvvvfrqq6/Ut29f3XLLLfr2228VGRmpjz/+WP369ZOXl1eZefv7+1scT0tLk4+Pj+Lj463+TACg2ouNtTic4+NT4ji+lOuu4ews8bsKAAAAAAAAAAAAAKimcnJy5OnpadVcOuOh3OrUqaOnn366ROc7Sdq0aZOGDRsmd3d3ubu768EHH9S3335rPj9s2DDz+/DwcCUkJCghIUFjx47VgQMHlJubq4MHD5qL5t5991117txZwcHB2rp1q/bu3StJGj9+vBYtWiRJWrx4scaMGVPVtwwAqKj/djAFAAAAAAAAAAAAAKC2oTMeKmTUqFF69dVXFRYWZh4zmUwlrrn6+MpK0YiICD311FPy9vbWK6+8om+++UZxcXEKDQ2VyWRSQkKCVq9ercTERHl5eenJJ59Ubm6uJGnAgAF64okn9P333yszM1M9evQoV86pqakWx4uL/yIjI8sVBwBqpKgoi8PxsbHK9vU1H0dOnly+eI0aSWfO2CIzAAAAAAAAAAAAAADsztqueBKd8VBBderU0TPPPKPZs2ebxyIiIrRq1SpdunRJubm5+uCDD0otcAsODtaPP/6otLQ0tW/fXhEREZo9e7YiIiIkSZmZmWrQoIG8vLx05swZrV692jzX2dlZDz30kIYOHarRo0dX6X0CAKzUtaujMwAAAAAAAAAAAAAAwCEoxkOFjRw5Uk5Ofzw6AwcOVFRUlLp27aqQkBD1799f/fr1szjXyclJXbp0UUBAgKSibWuPHDmi8PBwSVK/fv1Ur149dejQQYMHD1bv3r1LzH/44Yd15swZPfTQQ1V0dwCASnnjDUdnAAAAAAAAAAAAAACAQ5gMwzAcnQRQXu+9956++eYbvf/++5WOVbxNbWnb2AJArXHV9uLStdvUDoqOLjuOn5/000+2zAwAAAAAAAAAAAAAALuqTE2Ri62TAarK3/72N+3atUtffvmlo1MBAFytfn1pyxZHZwEAAAAAAAAAAAAAgMNQjIdq41//+pejUwCAmskwLHbHK7dbbpG2b5caNLBZSgAAAAAAAAAAAAAAVDcU4wEAgKKCPMm6oryCAumeeyRXV+ny5aKXl5cUESGNHi01bWrLTAEAAAAAAAAAAAAAuCFRjAcAACrXGe/XX4teV9u4UYqJkUaOlObNk9zdrV8DAAAAAAAAAAAAAIAbnJOjEwAAAA5WmUK8suTlSe++K/XrJ128WHXrAAAAAAAAAAAAAADgYBTjAQCAqpeQIE2d6ugsAAAAAAAAAAAAAACoMhTjwezixYuaNm2a2rZtq6CgIAUHB+upp55SQUGBo1MDAFSVquyKd7Vly6RTp+y3HgAAAAAAAAAAAAAAdkQxHszGjx+v33//Xfv27dPu3bu1detWeXl56dKlS+Wan5+fX8UZAgCqtbw8ackSR2cBAAAAAAAAAAAAAECVMBmGYTg6CTjeoUOHFBgYqN9++03169e/5vyePXs0depUZWZmysPDQwsWLFCnTp0UExOjAwcO6OjRo3J2dtb//M//6Nlnn1WLFi2UkpKi4OBgTZ06Vc8884yOHDmiV199VUOHDtXFixc1cOBAZWRkKDc3VyNGjND06dMlSREREQoNDVViYqJOnz6t119/XdHR0RozZox69+6thx9+WJI0a9YsXbp0SS+++OJ1783f39/ieFpamnx8fLR06dJKfnoAUI2lploczvHxUaGrq/nY6+hR26xXr57UqpVtYgEAAAAAAAAAAAAAYGOjRo2Sp6enUkv59/TroTMeJEn79u1T+/btLRbi5eXlady4cVq+fLl27typN998U2PGjDGfT0lJ0bp16/Ttt99KKirce/XVV7V//34dOnRIb775pr799lutW7dOTz/9tCSpTp06+vjjj7Vz50798MMP+uijj7R//35zzKysLG3dulUfffSRnnrqKUlFnfsWL14sSTIMQ8uWLdOoUaOq7DMBAFQBtj4HAAAAAAAAAAAAANRQLo5OADemDz/8ULNnz1ZGRoZWrFih1NRUDRgwwHw+IyPD/H7gwIGqV6+e+bhz585q3bq1JKlTp07q3r27nJ2d1bFjR504cUKFhYUyDEMzZszQN998I8MwdOzYMe3bt09+fn6SpCFDhkiSunbtqsOHD0uSunXrppycHO3fv1+//fab2rVrp1bl6K5UWpVqcce8yMjICnwyAFDDREVZHI6PjVW2r6/5OHLyZNusd/vt0oYNtokFAAAAAAAAAAAAAICNeXp6Wj2XYjxIKipMO3jwoLKzs+Xl5aX7779f999/vyIiImQymdSuXTulpKRYnHv1A+jm5mZ+7+TkVOJYkgoLC7Vy5UqlpaVp+/btcnNz03333afc3NxrYphMJhUWFprHi7vj/fbbbxo7dmxlbxsAYG8REY7OAAAAAAAAAAAAAACAKsE2tZAktW3bVoMGDdKkSZN08eJFSUVFc8UFctnZ2frmm28kFW0R+8MPP1RqvczMTN10001yc3PToUOHtHHjxnLNe/DBB7V69Wpt3rxZAwcOrFQOAAA7c3WVRo92dBYAAAAAAAAAAAAAAFQJOuPBbOHChXr22WfVsWNHeXt7q27duoqMjFRwcLDWrl2ryZMn64knnlBeXp7uvfdede7c2eq1HnroIX3yySfy9/dX69atFR4eXq559erVU+/evdW0aVO5urpavT4A4L8MQzKZ7LPWyJFS06b2WQsAAAAAAAAAAAAAADszGYZhODoJoLzy8vIUGBiozz//XO3bt69ULH9/f0lSamqqLVIDgOrLQjFefGyssn19zceDoqMrt0Z4uPTll5K7e+XiAAAAAAAAAAAAAABQhSpTU8Q2tag2vvvuO91yyy2Kjo6udCEeAOAKVVmX7+oqjRtHIR4AAAAAAAAAAAAAoMZjm1pUG3/5y1+Unp7u6DQAoGYqLsir6Ja1Tk5S69aSr69Up4506ZJ0+bLk5SVFREijR7M1LQAAAAAAAAAAAACgVqAYDwAAWK+wUPr116LX1b75Rlq9WnrvPSkgQDp1Slq8WEpIkLKziwr2QkKKCgF37vxjLCJC6tlTeukladeuouK+OnWkrl2lN96Qbr752jjlLfyzlANFgwAAAAAAAAAAAAAAGzAZRlXuTQfcuCqzvzMA1DhXdcSLj41Vtq+v+XhQdHTl4terV9Q1Ly+vcnGux9VVGjlSmjfv2i1xL16UpkyR4uIs53C9uQAAAAAAAAAAAACAWqMyNUVOtk4GAABUMxXdmtYa589XbSGeVBT/3Xelfv2Kiu+KXbwo3XmntGhR6TmUNhcAAAAAAAAAAAAAgHKiGK+GMJlMGjFihPl406ZN6tOnT5nz5s6dq6ysLPNxRESEkpKSKpVLXFycxowZU6kYVxoxYoRWrFhhs3gAgBouIUGaOvWP4ylTisasmQsAAAAAAAAAAAAAQDlRjFdDODk5adOmTTpw4ECF5l1djFdZ+fn5NosFALADe3TFc4Rly6RTp6STJ4u2prVmLgAAAAAAAAAAAAAAFUAxXg1hMpk0ffp0/e///q/F8/Pnz1doaKiCgoI0ZswY5eXlac6cOTp+/Lj69u2r4OBgXb58WZL02WefqUePHmrbtq3WrFljjvHxxx+re/fu6ty5s+677z5lZmZKKuqmN23aNIWGhuqZZ54pse7OnTvVq1cvde7cWcHBwdqwYYP5nIuLi2bMmKHg4GAFBgbql19+kSTl5ubqoYcekp+fn/r27avff//dPOeFF16Qv7+/AgMDdfvtt9vmwwMA1Dx5edKSJUWvim6PWzwXAAAAAAAAAAAAAIAKcHF0ArCdsWPH6rXXXjMXtRWLj4/X9u3btXXrVjk5OWnSpElavHixpk+frrfffltfffWVWrRoYb4+KytLW7du1Y4dO/TAAw8oOjpaP//8sxYtWqTExES5ublpzpw5mjVrll555RVJUkZGhrZt2yaTyaS4KzoQ3XLLLdq0aZNcXV3122+/qXfv3kpLS5MkFRQU6NZbb1VKSopee+01zZkzR4sWLdKCBQtUUFCgn376SceOHVNAQIAeeOABZWRk6JNPPtG+ffvk5OSks2fPlutz8ff3tzielpYmHx8fxcfHV+RjBoCaJTbW4nCOj0+J4/hSrruh1atX9F9rcq9XT+LPBwAAAAAAAAAAAACodXJycuTp6WnVXIrxapA6dero6aef1owZMzRmzBjz+Lp165SYmKguXbpIKuo85+HhUWqcIUOGSJK6du2qw4cPS5I2btyovXv3qnv37pKkvLw8derUyTznwQcflMnCVofnz5/XmDFj9OOPP8rFxUVHjx7Vf/7zH910000ymUy67777JEndunXTl19+KUlKTEzU2LFjZTKZ5Ovrq8jISEmSt7e3PD09NWLECPXt21d333231Z8VAKAWKChwzFwAAAAAAAAAAAAAQK1EMV4NM2rUKL366qsKCwszjxmGoWnTpmnq1KnliuHm5iapaOvbwsJCc4z7779fc+fOtTintGrQ5557Tl26dNGHH34ok8mkxo0bKzc3V5Lk5OQkV1dXSZKzs7Py8/Ovm5ezs7M2b96sxMREffXVV3r++eeVkpIib2/v685LTU21OF7cMa+42A8AaqWoKIvD8bGxyvb1NR9HTp5sr4xsp3g7840brZt7xdbqAAAAAAAAAAAAAIDawdqueJLkZMM8cAOoU6eOnnnmGc2ePds8duedd2rZsmU6d+6cJOns2bM6dOiQJMnLy0tZWVllxu3Tp4/WrFmjY8eOSZIuXLig/fv3lzkvMzNTzZs3l8lk0ieffKKMjIwy54SHh2vVqlWSpN9++03ffvutJCk7O1tnzpxRVFSUXn75Zbm7u5vzAQDgGhERUni49XMBAAAAAAAAAAAAAKgAivFqoJEjR8rJ6Y+vtk+fPpowYYJ69+6twMBARUVFmYvYHn30UUVHRys4OFiXL18uNeaf//xnvfHGGxo4cKCCgoLUo0ePUjvOXekf//iHZs2apeDgYCUkJKhly5ZlzpkwYYJMJpP8/Pw0atQo9erVS1JRYd+gQYMUGBiowMBADRo0yNzdDgCAElxdpdGji17/7cJa4bkAAAAAAAAAAAAAAFSAyTAMw9FJAI5QXMhXnqJCAKjRTKZrhq7epnZQdLQ9M6q8ceOkhQv/eL9okXVzAQAAAAAAAAAAAAC1SmVqiuiMBwAAapbwcGnevD+O580r/3a1V88FAAAAAAAAAAAAAKCcKMYDAKC2s0eTXC+vim8XW1GurkVd7b78UnJ3/2Pcw0Nav77oXGk5lDYXAAAAAAAAAAAAAIBycnF0AgAA4AZQXJBnYcvaSnFxkbp1k+66S+rZU3rpJWnXLuny5aJzDRpIBQVSVlbR9TffLA0ZIt1xhzRrlrRzZ9G1depIXbtKb7xRdM2SJdKmTVJ2dlGhX0SENHq01LSp5Tw8PIq2np0xo+JzAQAAAAAAAAAAAAAoB5Nh2KMdDnDjqcz+zgBQ41xVhBcfG6tsX1/z8aDoaPvl4uoqjRxZtF0sXeoAAAAAAAAAAAAAAHZUmZoitqkFAKC2s3U3vMrKy5PefVfq10+6eNHR2QAAAAAAAAAAAAAAUC4U4wEAgBtTQoI0daqjswAAAAAAAAAAAAAAoFwoxsM1tm/fLpPJpM8++8zqGHFxcTpy5IhVc1u3bq1jx45Jkvr376/Tp09bnQcAoAw3Wle8qy1bJp065egsAAAAAAAAAAAAAAAoE8V4uMby5cvVu3dvrVixwuoY1yvGy8/PL3ecdevWqUmTJlbnAQCo5vLypCVLHJ0FAAAAAAAAAAAAAABlcnF0Arix5Ofna/Xq1dqyZYtCQ0OVmZkpb29vubi4mIvokpKS9Pzzz2vTpk3av3+/Ro4cqdzcXF2+fFkvvfSScnJytGPHDo0cOVKenp5au3atYmJi5Obmpj179qht27Z6+eWXNXz4cGVnZysvL09PP/20hg8ffk0+rVu3VlJSklq0aKHHH39cW7duVW5uroKCgrRkyRK5ubmVeU/+/v4Wx9PS0uTj46P4+PjKfWgAUJ3FxloczvHxKXEcX8p1dlGvnsRvNQAAAAAAAAAAAADADnJycuTp6WnVXIrxUMKXX36p4OBgtWrVSv3799cnn3yi0aNHl3r9O++8o8mTJ+uBBx6QYRjKysqSt7e3Fi1apJkzZyosLMx87cGDB5WYmChXV1ddvHhR69evV926dZWVlaWuXbvqrrvuUoMGDUpdKyYmRo0bN5YkTZ48WXFxcRo/frzN7h0AcIMqKHB0BgAAAAAAAAAAAAAAlIliPJSwYsUKDRs2TJI0bNgwzZw587rFeGFhYZoxY4YOHDigAQMGqGvXrqVeO3ToULm6ukoq6sA3ZcoUJScny8nJSSdOnNDBgwcVEhJS6vxPP/1UCxYsUG5urjIzM1VYWFiue0pNTbU4XtwxLzIyslxxAKBGioqyOBwfG6tsX1/zceTkyfbK6Fq33y5t2OC49QEAAAAAAAAAAAAAtYa1XfEkivFwhaysLH3xxRfavHmznnvuORmGoZMnT+rIkSNycnKSYRgymUzKzc01zxk6dKh69Oihr776ShMnTtTgwYM1ffp0i/GvfFDfeOMNeXh4KCUlRc7OzuratWuJuFdLT09XTEyMdu7cqZtvvlnz58/X7t27bXfzAIAbV0SEozMAAAAAAAAAAAAAAKBMTo5OADeO1atX6+6779aRI0eUnp6uw4cP6/HHH9fKlSvVqlUrpaSkSJI++eQT85yDBw/K19dXY8eO1ZQpU7R9+3ZJkpeXl7KyskpdKzMzUz4+PnJ2dta2bdvKLKzLysqSh4eHGjZsqAsXLmj58uWVv2EAwI3P1VW6TodWAAAAAAAAAAAAAABuFHTGg9ny5cs1adKkEmNDhgzRqFGj9Oqrr2rw4MFq3LixIq7oUPTRRx9p5cqVqlOnjtzd3fXPf/5TkjRq1ChNmzZNzz77rNauXXvNWpMmTdLgwYP18ccfKyAgQN26dbtuboGBgYqIiJCfn59uvvlmhYaGXreTHgCgnAxDMpkcnUXpRo6UmjZ1dBYAAAAAAAAAAAAAAJTJZBiG4egkAEfw9/eXJKWmpjo4EwBwMAvFePGxscr29TUfD4qOtmdGRcLDpS+/lNzd7b82AAAAAAAAAAAAAKBWqkxNEdvUAgBQ291odfmurtK4cRTiAQAAAAAAAAAAAACqFbapBQAAfxTk2XrL2hYtpD//WYqIkAYOlD77TNq0ScrOltzcpDp1pEuXpMuXJS+voutGj2ZrWgAAAAAAAAAAAABAtUMxHgAAsJ6LixQYKPXtW1TQt3NnUaHdlYV1krR4sfTEE3+cu/tuiu4AAAAAAAAAAAAAADUKxXgAAMD6jnj5+dKuXUWvq23cKD3/fFHswsJrz8XESCNHSvPmsR0tAAAAAAAAAAAAAKDac3J0AgAAwMFsvTXtlQzj2kK8Ynl50rvvSv36SRcvVl0OAAAAAAAAAAAAAADYAcV4sJrJZFJwcLD59e233zo6JQBAdZSQIE2d6ugsAAAAAAAAAAAAAACoFLaphdWcnZ2VkpJS6vn8/Hy5uPCIAcANrSq74lXEsmXSjBlS06aOzgQAAAAAAAAAAAAAAKvQGQ821bp1az3zzDMKCQnR/PnztX79evXo0UOdO3dW9+7dtWvXLklSenq62rZtq0mTJqlTp07q2bOnTp8+LUm6ePGiJkyYoE6dOikwMFCvv/66JOnw4cO66667FBISopCQECUkJDjsPgEANpaXJy1Z4ugsAAAAAAAAAAAAAACwGm3LYLWCggIFBwdLklxcXLRjxw5JUp06dczvz549q82bN8vJyUm7du3SY489pi1btkgqKsh78MEH9dZbb2nSpElatGiRnnvuOc2cOVMFBQXavXu3nJycdObMGUnSqFGjFBsbK39/fx05ckQRERFKS0uTqYyuTv7+/hbH09LS5OPjo/j4eFt8HABQPcXGWhzO8fEpcRxfynU2Va+exG8yAAAAAAAAAAAAAMCBcnJy5OnpadVcivFgtdK2qR02bJj5/cmTJzV8+HAdPnxYLi4uOnjwoPlc8+bN1bNnT0lSt27d9N1330mSvvrqKy1btkxOTkWNGxs3bqzz588rKSmpROzLly/r9OnTasqWhgBQMxQUODoDAAAAAAAAAAAAAACsRjEebO7KytCJEydq3LhxeuCBB5Sdna2GDRuaz7m5uZnfOzs7Kz8/X5JkGMY1MQsLC1W3bl2LxX9lSU1NtThe3DEvMjKywjEBoMaIirI4HB8bq2xfX/Nx5OTJVZ/L7bdLGzZU/ToAAAAAAAAAAAAAAJTC2q54kuRkwzyAa2RmZqpFixaSpIULF5ZrTr9+/TR//nwVFhZKks6cOaP69evL399fS5cuNV+3a9cu2ycMAHCciAhHZwAAAAAAAAAAAAAAgNUoxkOVmjlzpkaOHKkuXbro0qVL5Zrz3HPPyWQyqVOnTgoKCtL7778vSVq5cqXWrFmjoKAgdezYUW+99VZVpg4AsCdXV2n0aEdnAQAAAAAAAAAAAACA1UyGpT1BgVqgeJva0raxBYBaw2S6ZujqbWoHRUdXbQ7jxknl7KAKAAAAAAAAAAAAAEBVqUxNEZ3xAACAY4WHS/PmOToLAAAAAAAAAAAAAAAqhWI8AABqu6pskuvkVPSyxNW1qCPel19K7u5VlwMAAAAAAAAAAAAAAHbg4ugEAOAa+/ZJ06ZJu3ZJly9LdepIXbtKb7whBQRYnnPqlLR4sZSQIGVnS15eUkSENHq01LSpXdOvUaz5XO35XVRkreJrN26Ufv1VOn9eKiws2qK1bl3J2VnKyZHy8oqKx26+Werfv6hQbd066T//Kbre1VXy9JQKCqTc3KL3bdtKd9zxx7pX5pWRIZ07V7RO/fpS48bSn/8spaQUPeu5uUWxCgqk/Hzbfj43gsLC0s/l5Unvvlv0qihn56LY5Skk9PSUGjWS2rSRbrlF+vpr6bffiuY7OUn16hU9A9nZRb85Tk5Sw4bSn/5U9N2dOVM0fvnyH2vn5f1xbyZT0e9Up05SdHT5n/Urn5MzZ6TMzKJY3t5F+RY/y1LFn/PK/v/HbypuFDyLAAAAAAAAAABb4++e4Qg8d7WGyTCqsh0OcOOqzP7OqCIZGVKvXtL+/aVf4+cnbdkiNWhQdHzxojRlihQXV1QcczVXV2nkyKItMOm8VX7WfK72/C4qspZhXP9aW3Jxkdq3l9LSqn6tKhYfG6tsX1/z8aDoaAdmU82U9ayX9fxeqbiroKWixoo85+X9/4/fVNwoeBYBAAAAAAAAALbG3z3DEXjuqqXK1BRRjIdai2K8G0xGRlF3sczMsq/19i7qbObhId15Z1HleFnCw6X164vm4PouXqz45yrZ77uoSH5/+UtRkVJSknVr1WIU49mApWe9Is9veYWFFXXU++4763KyJjd+U1GVeBYBAAAAAAAAALbG3z3DEXjuqq3K1BQ52ToZALBKr17lK8STiq7r1auoery8BS0JCdLUqVanV6tY87na87uoyFrffUchHhzH0rNekee3vJKSyleIV1pOxfhNxY2CZxEAAAAAAAAAYGv83TMcgeeuVqIYzwHS09PVvn37EmMrVqzQiBEjypzbv39/nT59WpI0d+5cZWVlVXj9ESNGaMWKFdeMx8TEaObMmZKkwsJCjRo1SkOHDlV+fr7GjBmjlJSUCq9VmoiICCVRIINie/Zcf2taS/bvl5YurdicZcuK9mFH6U6eLGqPWxFLl1Z8jrXfhTX5AY505bN+ozy/lv7/syY3flNRFXgWAQAAAAAAAAC2xt89wxF47motivGqmXXr1qlJkyaSrC/GK0thYaFGjx6tnJwcrVq1Si4uLlq8eLGCg4NtvhYgSXrySevmFRRU7Pq8PGnJEuvWqi2WLLG8T/315OdXfI6134U1+QGOdOWzfqM8v5b+/7MmN35TURV4FgEAAAAAAAAAtsbfPcMReO5qLRdHJ4BrxcTE6NChQzp27JgOHz6soUOHatasWZKk1q1bKykpSR988IGOHz+uvn37ytXVVdu3b9eJEyf02GOP6eTJk5Kk119/XeHh4crNzdXYsWOVnJysVq1aycWl9K+9uCNebm6uVq1aJWdnZ0lFnexmzpypsLAwRUREKDQ0VImJiTp9+rRef/11RUdHS5JeffVVLVq0SI0aNVJQUJAKCwu1ePFiZWRk6KGHHtKhQ4fUsWNH5ebmmtdMSkrS1KlTdfnyZfn6+mrJkiXy8fFRTEyM0tLSdOzYMf3666+aMmWK6tWrp0WLFiknJ0effPKJOnbsWObnWbyP89XS0tLk4+Oj+Pj48n0xqDrR0dLAgfZZq149ie+8dM2aSbGx9lnLmu/CnvnVcjk+PiWO4/ncrVf8rN9Iz+/V//9Zmxu/qbA1nkUAAAAAAAAAgK3xd89wBJ67ai0nJ0eenp5WzaUz3g1q7969+uKLL7R7924tW7ZMx44dK3F++vTpatasmb766iulpKSoTp06GjVqlF555RXt2LFD//d//6eRI0fKMAwtWLBABQUF+umnn7R48eLrbg87d+5cnThxQitXrjQX4lmSlZWlrVu36qOPPtJTTz0lSfrhhx+0bNky/fDDD/ruu++0b98+8/UvvviiAgMD9eOPP+q5555TcnKyJOnSpUt64IEH9O6772rPnj2KjIzUlClTzPN++uknrV+/Xjt27FBMTIwyMzOVnJysSZMmac6cOVZ9trgBGYb91qpoN73axp6fjzVr8f2hOip+bm+k5/fqXKzN7Ua6J9QMPIsAAAAAAAAAAFvj757hCDx3tRad8RzAZDKVOT5gwAB5eHhIkvz8/JSenq4WLVqUGvP8+fNKSkrSsGHDzGOXL1/W6dOnlZiYqLFjx8pkMsnX11eRkZGlxgkLC1NycrJ27typ0NDQUq8bMmSIJP3/9u48uuryTBz4k5BAMCEIgiCMZRPZZEdxpLK0QtURAcd1RETtCFOnOlNtdWqHYtWpdo5OtdbT2iqgYl1wgSq/Kta9alEQp2ChikAFUTbZAwRyf38w3nLJQpIbklz4fM7J6X2f7/t93ic336Sc26fvG/37948VK1ZERMRrr70WI0eOjIKCgoiIuOCCC+JPf/pT8tr06dMjIqJv377Rq1eviIhYvHhxtG7dOvr16xcREVdccUXcfvvtyXXOOOOMyMvLi7y8vGjRokWc/X+7p/Xp0ycef/zxcuvb16JFi8qMf7ljXkXvB7XkvPMiNmyonbWGD4944YXaWSsT3XZbxJw5tbNWdX4WtVnfYe6lu++OLccemxx/7eqr67CaDPfls16fnt/9f/+qW5u/qdQ0zyIAAAAAADXNZ8/UBc9dRqvurngRdsarE0cddVRs2K/xaP369dGyZcvkuFGjRsnXDRo0iN27d1eYs6SkJI444ohYsGBB8mvlypXRqlWrKtV20kknxUMPPRSjR49ONtKV5cv6srKyoqSkpMw55TUdVjRn//G+70N2dnZynJ2dfcD3hAzyf82YtWLo0NpbKxMNGVJ7a1XnZ1Gb9UFN+fJZr0/P7/6/f9Wtzd9UappnEQAAAACAmuazZ+qC5+6wpRmvDhQUFESnTp1i1qxZERGxffv2ePTRR6u8Q1uTJk1i8+bNERFRWFgYPXr0iAceeCB5ff78+RERMWTIkHjkkUciImLVqlXx8ssvV5j3tNNOi5///Odx5plnxkcffVTpek499dR49tlnY9u2bVFcXJyyc92QIUOSO+MtWLAg2ejXpUuX+Oyzz2LBggUREfHAAw/Yqe5wdMcd1buvgqOUy5SbG3HFFdVb63BxxRV736eqyMmp+j3V/VlUpz6oS/s+6/Xl+S3r9686tfmbysHgWQQAAAAAoKb57Jm64Lk7bGnGqyMPPfRQ3H333dGnT584+eSTY9SoUXH66adXKce//Mu/xJgxY6JPnz6xa9eumD59ejz99NPRu3fv6N69e9xzzz0RETFx4sTIysqKrl27xuWXXx6DBg06YO4xY8bErbfeGsOHD49PPvmkUvX069cvxo0bF3369ImvfvWr0alTp2jatGlEREyaNCkWLFgQ3bt3j1tvvTUGDBgQEXt3vnvkkUfim9/8ZvTq1SvmzJkTP/3pT6v0PnAI6NUromvXqt3TtWvE5ZdX7Z7LLouo4m6Rh53WrSPGj6/aPZdfXvV7qvuzqE59UJf2fdbry/Nb1u9fdWrzN5WDwbMIAAAAAEBN89kzdcFzd9jKSiQSibougkPH1q1bo6CgIIqLi2P06NExbty4uOCCC+q6rDL16NEjIiIWLVpUx5UQEREbNkR07BixadOB5zZtGrF8eUSjRhFnnBHx6qsHvmfIkIjf/S4iLy/tUg95RUVVf18Tidr7WVSlvlNP3fufr79evbUOYy/dfXdsOfbY5HjUmDF1WE2GKutZr8rzW1lf/WpEVlblnvOKfv+q87vvbyoHg2cRAAAAAICa5rNn6oLnLmOl01NkZzxq1Le//e3o27dv9OzZMzp27BjnnXdeXZdEpmjePOLjjw+8Q17Xrnsb8Y48MqJx44j/9/8irryy/O1dc3P3XvdfWpVXnfe1Nn8WVVnrhRcinn++4rk1KSdn7zNaH44ipe5U9KxX5vndV3b23q+K1pkz58DPeWV+//xNpb7wLAIAAAAAUNN89kxd8NwdluyMx2HLznj12MKFEd/5TsS8eRG7dkU0bBjRv3/EnXdGnHBC2fd8/nnE/fdHvPJKxJYtEU2aRAwduvc8ddu4Vl913tfa/FlUZa0v577wwt7Gz61bI0pK9u4odsQREQ0aRGzbFlFcvLf5qWXLiDPP3Hvv7NkRa9funZ+bG5GfH7FnT8SOHXtfd+wYMWLE39bdt64NG/6242NhYcRRR0V06xbx/vsRf/rT3hx79uz92r27Zt+fNNT7nfEaNNj786jMP2Py8/c2/HboENG5c8Tvfx+xcuXe+7OzIwoK9j4DW7bs/ZuTnR3RrFnEMcfs/dmtW7c3vmvX39YuLt57f8TeZ6hhw4iePSPGjKn8s77vc7J+fcTmzXvjTZvurffLZzmi6s95ur9//qZSX3gWAQAAAACoaT57pi547jJKOj1FmvE4bGnGAyjfSy+9FFu2bEmOR40aVYfVAAAAAAAAAEDtcEwtAAAAAAAAAAAA1CHNeAAAAAAAAAAAAJAmzXgAAAAAAAAAAACQJs14AAAAAAAAAAAAkCbNeAAAAAAAAAAAAJAmzXgAAAAAAAAAAACQJs14AAAAAAAAAAAAkCbNeAAAAAAAAAAAAJAmzXgAAAAAAAAAAACQJs14AAAAAAAAAAAAkCbNeAAAAAAAAAAAAJCmrEQikajrIqAuNGnSJIqLi6NTp051XQpAvbNt27YoKSlJjps0aVKH1QAAAAAAAABA7Vi6dGnk5ubGli1bqnxvzkGoBzJCfn5+bNu2ra7LAKiXPvvss9i5c2dy3L179zqsBgAAAAAAAABqR25ubuTn51frXjvjAQCl9OjRIz744IPk2D8XAAAAAAAAAKBi2XVdAAAAAAAAAAAAAGQ6zXgAAAAAAAAAAACQJs14AAAAAAAAAAAAkCbNeAAAAAAAAAAAAJAmzXgAAAAAAAAAAACQpqxEIpGo6yIAAAAAAAAAAAAgk9kZDwAAAAAAAAAAANKkGQ8AAAAAAAAAAADSpBkPAAAAAAAAAAAA0qQZDwAAAAAAAAAAANKkGQ8AAAAAAAAAAADSpBkPAAAAAAAAAAAA0qQZDwAAAAAAAAAAANKkGQ8AAAAAAAAAAADSpBkPAA4hJSUl8bvf/S7OOeec6N69e7Rq1SqaNWsWRx55ZBx11FHRqlWraNeuXfTo0SOGDRsWEydOjClTpsTmzZurtd6OHTti+vTp0aVLl2jYsGFkZ2dHdnZ2NGzYMLp06RLTp0+v4e8QAAAAAAAAAOqnrEQikajrIgCA6lu2bFl069Ytdu7cWSP52rVrF8uXLy8VX758eXTo0CHt/MuWLYv27dunnQcAAAAAAAAA6hM74wFABuvZs2d07NixxhrxIiJWrVpVY7kAAAAAAAAA4HChGQ8AMtjHH39c4zkLCgpqPOe+duzYcVDzAwAAAAAAAEBd0IwHAKQYPnx4mfFNmzbVSP7Ro0fXSB4AAAAAAAAAqE9y6roAACA92dnZUVJSUmP5jjnmmDLjv/zlL8u9p1+/ftGqVauU2EcffRQffvhhqblLliyJP/7xjzFw4MD0CgUAAAAAAACAeiQrkUgk6roIAKB6Jk+eHGPGjIkzzjgjVq9eXer6N77xjXj++edTYvn5+bFt27Zyc/793/99vPnmmymxtWvXRps2bWL37t2l5o8fPz6mTJlSKp5IJOKrX/1qqVwRexv+Pv3003JrAAAAAAAAAIBM45haAMhgkydPjt69e0eTJk1KXfu7v/u7yM/PLxVv2LBhhTk/+uijUrFHH320zEa8iIgHHnigzHhWVlbcfPPNZV5bvXp1bNy4scI6AAAAAAAAACCTaMYDgENAdnbp/0q/+uqr46mnnqpyrrKa5GbOnFnm3Nzc3MjKyio317Bhw+LYY48t89ott9xS5doAAAAAAAAAoL7SjAcAh4Di4uJSsV/+8pfVytWiRYuU8datW+Pll18uc+4RRxxRYa6srKw4/fTTy7z2yCOPVKs+AAAAAAAAAKiPNOMBwCFg06ZNpWJLly6tVq6TTjopZbxo0aIoKSkpc26jRo0OmG/QoEFlxtetW1f14gAAAAAAAACgnsqp6wIAgPRt37690nO/+OKLCq9fccUVKeM///nP5c7NysqK999/P15//fVYsWJFFBYWRteuXWPQoEHRpk2biIjo3r17mfcWFxfH7t27IyfHP0cAAAAAAAAAyHz+128AOATs2LGjRvKMHDkyRo4cmRJbsmRJufM///zz6NOnT5nXsrOzo3v37nH99deXe//ixYvjhBNOqFatAAAAAAAAAFCfOKYWAA4B5R0jWxU5OTlx5513loqvWbOmWvlKSkpi4cKFcckll5Q756OPPqpWbgAAAAAAAACobzTjAUCG2717d43lGTJkSKn41q1bayR/WT777LODlhsAAAAAAAAAapNmPADIcJ988kmN5fr000/jyiuvTIkdzGa81atXH7TcAAAAAAAAAFCbNOMBQIar7jGy5Zk2bVrKsbc7duwoc15ubm6MGzcunnrqqVi5cmXMmzcvbrrppujSpUul19q4cWO65QIAAAAA2Lm3WAAAGT5JREFUAABAvaAZDwAy3M6dO8u9VlhYWOV8u3btirvvvjs5zsvLK3Nez549Y9q0aTFmzJho27Zt9OvXLyZNmhSLFy+ORCIRAwYMOOBaTZs2rXJ9AAAAAAAAAFAfacYDgAz3ox/9qMx4x44dY8qUKdXKOXPmzOTrgoKCMueUt2Pel955550YNWpUhXNat25d9eIAAAAAAAAAoB7SjAcAGeyJJ56I3//+92Vee+ONN1Ka6qpi0aJFydflNeMVFRUdMM8zzzxT7s56EZrxAAAAAAAAADh0aMYDgAz2rW99q8x4Tk5O/OY3v4kHH3ywWnk3b96cfH300UeXOWfDhg2VyvWd73yn3GudO3euWmEAAAAAAAAAUE9pxgOADLZ+/foy47t3745rr7222nl37dqVfN2lS5cy52zfvr1SuSo6qrZbt25VKwwAAAAAAAAA6inNeACQwRKJxEFfo2vXrmXGi4uLU5r2yvPnP/+5zHh2dnbk5OSkVRsAAAAAAAAA1Bea8QCAUrKz//ZPhB49eqSM9zV79uwD5nr99dfLjBcUFFSvOAAAAAAAAACohzTjAUAGy8/PPyh5GzdunHzdpEmTGDZsWJnz7rzzzgrzJBKJmDlzZpnXRowYUf0CAQAAAAAAAKCe0YwHABnsueeeizlz5kTTpk3LvP7Nb36zVKy8Xe721a1bt5Tx2WefXea8P/zhDxUelfvyyy/HunXryrz205/+9IB1AAAAAAAAAECm0IwHABlsyJAh0aFDh9iyZUupaw0aNIi8vLxS8X13vSvP9ddfnzK+8MILo2HDhqXmlZSUxBVXXFFmjkQiERMnTizzWnZ2drRt2/aAdQAAAAAAAABAptCMBwAZ7NNPP43OnTtHSUlJqWt79uyJe++9t1Q8Jyenwpx5eXkxevTolNhLL70UrVq1KnP+lClT4rnnnisVP//88+PDDz8s855LL720whoAAAAAAAAAINNoxgOADNajR48Kj4ktq0lv06ZNFea86667okGDBimxBx98MD755JNy7znrrLNizJgxsWPHjvjFL34RjRs3jhkzZpQ5NysrK+65554KawAAAAAAAACATFPx1jgAQL22cePGGs03bNiwuPLKK6u1zjPPPFOpI3CnT58eRxxxRHXKAwAAAAAAAIB6y854AEDSsccee1Dz33LLLXHRRRcd1DUAAAAAAAAAoC5oxgMAkrKyssqMd+jQIe28zz//fNx4441p5QEAAAAAAACA+kozHgBksEQiES+88EJMnDgxcnNzD9o611xzTXTs2DGys6v2T4ecnJy48cYbY+vWrTFixIiDVB0AAAAAAAAA1L2sRCKRqOsiAICasX379vjggw9i+fLlsXr16ti6dWuUlJTEkUceGc2aNYvu3btHz549o0GDBmmtM2PGjHj88cdj0aJFsWHDhti1a1dkZ2dHQUFBtGrVKsaOHRvjxo2LwsLCGvrOAAAAAAAAAKB+04wHAAAAAAAAAAAAaXJMLQAAAAAAAAAAAKRJMx4AAAAAAAAAAACkSTMeAAAAAAAAAAAApEkzHgAAAAAAAAAAAKRJMx4AAAAAAAAAAACkSTMeAAAAAAAAAAAApEkzHgAAAAAAAAAAAKRJMx4AAAAAAAAAAACkSTMeAAAAAAAAAAAApEkzHgAAAAAAAAAAAKRJMx4AAAAAAAAAAACkSTMeAAAAAAAAAAAApEkzHgAAAAAAAAAAAKRJMx4AAAAAAAAAAACkSTMeAAAAAEA9N378+MjKykp+LV++vK5LAgAAAGA/OXVdAAAAAABQc3bs2BHz58+PDz/8MNauXRs7duyIxo0bR6tWraJLly7Rp0+fyM3NresyAQAAAOCQoxkPAAAAADJcIpGIWbNmxa9//euYM2dO7Ny5s9y5+fn5cdZZZ8WECRNi2LBhtVglAAAAABzaHFMLAAAAABls/vz5ceKJJ8bo0aPj2WefrbARLyJi27Zt8dhjj8XXvva1GD58eCxdurSWKj287XvE7NChQw/qWsuXL09Zb/z48Qd1PQAAAAD20owHAAAAABnqvvvui5NPPjnmzZtX6lrjxo2jY8eOceKJJ0a7du2iYcOGpea8+OKL0bdv33juuedqo1wAAAAAOKRpxgMAAACADPQ///M/MWHChCguLk7GsrKyYuzYsTFnzpzYsmVLLF26NObOnRvLly+PLVu2xG9/+9sYPXp0Sp4tW7bE6NGj4+mnn67l74CqmDp1aiQSieRX+/bt67okAAAAAPajGQ8AAAAAMsxLL70U1157bUrsK1/5Srzyyivx0EMPxWmnnRYNGjRIud6wYcM466yz4umnn45Zs2ZFixYtktd2794dY8eOjSVLltRK/QAAAABwKNKMBwAAAAAZZMOGDTFu3LhIJBLJWJs2beLll1+OwYMHVyrHyJEj4/nnn4+mTZsmY9u3b4+LL744Zac9AAAAAKDyNOMBAAAAQAa58cYbY9WqVclxgwYNYtasWdGxY8cq5enXr188/PDDKbF58+bFz372sxqpEwAAAAAONzl1XQAAAAAAUDnr16+PadOmpcSuuuqq6N+/f7XynXXWWTF69Oh45plnkrG77rorrrnmmlLH3Na1NWvWxMKFC2Pp0qWxcePG2L17dzRv3jxat24dAwcOjNatW9f4msXFxfHmm2/GwoULY+PGjVFYWBjHHntsDBkyJJo1a1bj6wEAAACQ2eyMBwAAAAAZ4r777ouioqLkODc3N/7zP/8zrZw/+tGPUsZ//etf46mnnipz7vjx4yMrKyv5tXz58kqv88orr6TcO3ny5ArnJxKJeP311+Pb3/52dO3aNVq1ahVf//rX48orr4zvfe978f3vfz8mTpwYo0ePjmOOOSZ69eoV06ZNi927d1e6psmTJ6fU9Morr0RExM6dO+Pmm2+O1q1bx9ChQ+Nf//Vf4wc/+EFcffXVMWbMmGjZsmVceOGFsWLFigrzDx06NJl7X6+++mrKuvt/lfXeVOa9b9++fWRlZUWHDh1S4tOmTatwvalTp0ZExNtvv50SHzFiRKXfy33dfffdKXmuv/76auUBAAAAyDSa8QAAAAAgQzz55JMp45EjR0aLFi3SytmzZ8/o169fSmzGjBlp5awJ3/3ud2Pw4MFxzz33xJIlSw44/09/+lOMHz8+hg0bFmvWrKn2uqtWrYqTTz45Jk2aFBs2bChzzp49e+Kxxx6LAQMGxHvvvVftteqbk08+OXr16pUcv/jii1VquPzSr371q+TrrKys+Od//ueaKA8AAACg3tOMBwAAAAAZYOvWrfH++++nxP7xH/+xRnLvn+cPf/hDjeRNx44dO0rFmjVrFt26dYuBAwdGnz594phjjik154033oivfe1rKTsIVtbGjRvjtNNOiwULFiRjbdu2jQEDBsQJJ5wQjRo1Spm/bt26OPvss2Pz5s1VXqu+mjhxYvJ1IpGI+++/v0r3v/3227Fw4cLkeOjQoXHcccfVWH0AAAAA9VlOXRcAAAAAABzYH//4x1JHsPbv379Gcu+fZ9WqVbFixYpo165djeSvriZNmsS5554b//AP/xCnnHJKmc13q1atiocffjhuu+222LhxY0RELFq0KG644Ya46667qrTetddeGx9//HHk5OTExIkT49/+7d+iU6dOyevbtm2LX/ziF/Ef//EfUVxcHBERK1eujFtvvTVuv/32UvnuuOOO+OKLLyIiYvjw4cl4r1694o477ii3jo4dO1ap7i9Nnz49ioqK4vPPP4+xY8cm4yNGjIjvfve75d7Xo0eP5OuLL744vve978XWrVsjImLKlCkxefLkaNCgQaVq2HdXvIiwKx4AAABwWNGMBwAAAAAZYPHixSnj/Pz8OP7442skd9++fUvFPvjggzptxrvsssviv/7rv6KwsLDCeW3bto3rr78+Lrzwwhg2bFgsW7YsIvY2hf3whz+M5s2bV3rNjz/+OBo1ahRPPfVUnHnmmaWu5+fnx7XXXhutW7dOaXabOnVq3HLLLZGbm5syv7xmyWbNmsVpp51W6boqa9CgQRERpY6WPeaYYyq9XmFhYVx00UXJprpVq1bF7NmzY+TIkQe8d8uWLfHYY48lx0cddVScc845laweAAAAIPM5phYAAAAAMsCGDRtSxq1atYqsrKwayd2yZcvIzk79qHD/9Wpb//79D9iIt6927dql7MpWVFQUjz76aJXXve2228psxNvXxRdfHAMHDkyO16xZE/PmzavyWvXVhAkTUsb773ZXnt/85jexbdu25PiSSy4pdbQvAAAAwKFMMx4AAAAAZID9m+OaNm1aY7mzsrJKNb59ebxqJvn617+ecpTtm2++WaX727ZtG1dddVWl5l5wwQUp4/nz51dprfqsf//+MWDAgOR49uzZsWrVqgPe54haAAAA4HCnGQ8AAAAAMsCWLVtSxvn5+TWaf/98mzZtqtH8taV9+/bJ1++9916V7j3nnHNKHTVbnj59+qSMP/nkkyqtVd/tuzvenj17YsqUKRXOf//99+Pdd99Njk855ZTo3r37QasPAAAAoD7SjAcAAAAAGaBJkyYp432PA60J++fLy8ur0fzpWL58efzkJz+J8847L7p16xYtW7aMRo0aRVZWVqmvt956K3nfunXrqrTOvrvBHcjRRx+dMs7U5sXyXHTRRSm7Jd5///2RSCTKnW9XPAAAAICInLouAAAAAAA4sObNm6eMa7L5K5FIxObNm1Ni+x9bWxdWrFgR11xzTcyaNavCRrDybNy4sUrz92+wq8j+OwkWFRVVaa36Lj8/Py655JL4+c9/HhF7GyLnzJkTI0aMKDW3qKgopk+fnhwXFhbG+eefX2u1AgAAANQXdsYDAAAAgAywfzPemjVrqtWgVpa1a9dGSUlJSqxVq1Y1kru65s6dG717946ZM2dW+/vctWtXleansxtgTf0s6pN9j6qNiPj1r39d5rwZM2akND5efPHFccQRRxzM0gAAAADqJTvjAQAAAEAG6Nq1a8p469at8eGHH8bxxx+fdu733nuvVKx79+5p562u9evXx5lnnllq979evXrFqaeeGscdd1y0adMmGjduHHl5eZGVlZWcc+2118b//u//1nbJh6SePXvGKaecEm+++WZERMycOTPWrl0bLVu2TJnniFoAAACAvTTjAQAAAEAGGDhwYDRo0CD27NmTjL377rs10ow3b968lHFBQUF07Ngx7bzVdeutt8b69euT486dO8fDDz8cJ5100gHvtSNbzZowYUKyGW/Xrl0xbdq0uO6665LXlyxZEq+//npy3L9//+jbt2+t1wkAAABQHzimFgAAAAAyQEFBQfTu3Tsl9uSTT9ZI7v3zDB8+PLKzS390uO8OdFW1ffv2Ss997LHHkq/z8vLid7/7XaUa8SIiNmzYUOXaKN/5558fzZo1S473P6p2/7Fd8QAAAIDDmWY8AAAAAMgQ5557bsr4t7/9baxbty6tnAsXLoz58+enxEaNGlXm3Ly8vJRxUVFRpddZu3Ztpeb99a9/jU8//TQ5Pv300yu9S19RUVEsW7as0jVxYHl5eXHppZcmx0uWLInXXnstIv62U96X8vPz45/+6Z9qvUYAAACA+kIzHgAAAABkiCuvvDIaN26cHBcXF8ctt9ySVs5JkyaljFu0aBHnnXdemXMLCwtTxp9//nml13nnnXcqNW//nF26dKn0Gq+//noUFxdXen5t2ndXwUQicVDX2n9Xw3TXmzBhQsr4V7/6VUREzJw5M6XJ8sILL4wmTZqktRYAAABAJtOMBwAAAAAZ4qijjopx48alxO65556YN29etfI9++yz8fTTT6fErr766jjiiCPKnN+uXbuU8XvvvVepdYqLi0utU579G8d27dpVqfsiIu69995Kz61t+fn5yddVObI33bVqYr2uXbvGkCFDkuMZM2bEF1984YhaAAAAgP1oxgMAAACADHLrrbdGmzZtkuM9e/bEqFGjqnw863vvvRdjx45NiR133HFx3XXXlXtPv379UsaPP/54pdb62c9+lnL0bEVat26dMn7jjTcqdd/s2bNj5syZlZpbF5o3b558vXz58oO6VmFhYTRo0CA5romjeydOnJh8vWPHjrjllltizpw5yVjPnj1j4MCBaa8DAAAAkMk04wEAAABABjnqqKNi6tSpKceerlq1KoYNGxavvfZapXI8++yzMWLEiNi0aVMylpubG1OnTk05Bnd//fv3j6OPPjo5fvvtt+ORRx6pcK3Zs2fH97///UrVFRHxla98Jdq2bZscv/POO/HYY49VeM/cuXNLNRbWNz169Ei+XrduXbzyyisHba3c3Nw4/vjjk+MFCxbE0qVL08p5zjnnRMuWLZPjO++8M2UXQ7viAQAAAGjGAwAAAICMM3z48Lj99ttTYitWrIihQ4fGJZdcEr///e9jz549KdeLi4vjueeei3POOSdGjhwZ69atS17Lzs6OBx98MAYNGlThurm5uTF+/PiU2GWXXRZ33HFHbNu2LSW+dOnSuPrqq+Pss8+OnTt3RqdOnSr9/e1/FO+4cePixz/+cWzevDklvnLlyvjBD34QgwcPji+++CLy8vKiffv2lV6nNo0YMSJlPGbMmLjhhhviiSeeiBdeeCFefPHF5NfHH39co+vt2bMnBg8eHDfddFM8/fTTMWfOnJT1Vq9efcB8DRs2jMsuu6zMa3l5efW+GRIAAACgNmQl9v2/LwIAAAAAGePee++Na665Jnbv3l3qWuPGjaNNmzbRvHnzWLduXXz66aexc+fOUvMKCgpiypQpce6551Zqzc2bN0f37t1j1apVKfFGjRpF586dIy8vLz777LNYuXJl8lrXrl3jxz/+cYwZMyYZ++EPfxiTJ08uc40NGzZE7969U3JEROTk5ESXLl0iPz8/1q5dG8uXL0/Zne2+++6L6dOnx6uvvpqMVfTx5+TJk+Omm25Kjl9++eUYOnRohd//l5YvXx4dOnRIji+99NKYOnVqufM3bNgQXbp0SWmCLE9Z78348eNj2rRpyfGyZcsqbDz8y1/+Er17944dO3YccL0pU6aUarIsy9KlS6Nz586l3tOxY8fGQw89dMD7AQAAAA51dsYDAAAAgAz1rW99K958883o06dPqWtFRUWxdOnSeOedd2LZsmVlNuINHjw45s6dW+lGvIiIwsLCmDVrVrRo0SIlvnPnzli4cGG8++67KU10vXv3jhdffDGOPPLISq/RvHnzmDVrVrRq1Solvnv37li0aFHMnTs3li1blmwKy87OjjvvvLNeH5XavHnzePLJJ1OO+T2Yjj/++HjooYeioKCgxnJ26tQpTjvttFLx+vy+AwAAANQmzXgAAAAAkMFOPPHEmD9/fjz11FNx5plnRsOGDSt13w033BCvvvpqdOvWrcpr9uvXL+bNmxcXXXRRNGjQoMw5Rx55ZEyaNCnefvvtaNu2bZXX6Nu3b8ybNy/Gjh1b7hpZWVkxfPjwePvtt+Pf//3fq7xGbRs8eHAsXrw47rnnnhg5cmR06NAhmjRpEtnZB+dj2nPPPTf+8pe/xG233Rbf+MY34thjj42CgoLIysqqds7LL788ZdylS5cYPHhwuqUCAAAAHBIcUwsAAAAAh5CioqKYN29efPjhh7F27dooKiqKRCIRM2fOjAULFiTnNW3aNF577bXo1atXWutt3LgxXnnllfjkk09i06ZN0bRp0+jRo0cMGjQoGjVqlOZ3s9eGDRvitddeixUrVsSWLVsiPz8/OnToEKecckqt7TTHXpMmTYqbb745Of7v//7vuO666+qwIgAAAID6QzMeAAAAABwG1qxZE6ecckosXbo0GTvmmGPirbfeinbt2tVhZWSKPXv2RPv27ZPHEDds2DBWrlwZLVu2rOPKAAAAAOoHx9QCAAAAwGHg6KOPjueffz5lJ7nVq1fH6aefHuvXr6/DysgUs2fPTjbiRUSMGTNGIx4AAADAPjTjAQAAAMBholOnTvHcc89FQUFBMrZ48eIYOXJkFBUV1WFlZIKf/OQnKeOrrrqqjioBAAAAqJ804wEAAADAYWTAgAExY8aMyM3NTcbeeuutuOCCC2LPnj11WBn12dSpU+ONN95IjgcMGBCnnnpqHVYEAAAAUP9kJRKJRF0XAQAAAADUrtmzZ8fcuXNTYqNGjYq+ffvWUUXUF6tXr45FixZFRMSaNWvixRdfjAcffDClWXP27Nlxxhln1FWJAAAAAPWSZjwAAAAAAJKmTp0al112WbnXzz333HjiiSdqsSIAAACAzOCYWgAAAAAAKmXgwIFx//3313UZAAAAAPVSTl0XAAAAAABA/ZSVlRWFhYVxwgknxIUXXhgTJkyI3Nzcui4LAAAAoF5yTC0AAAAAAAAAAACkyTG1AAAAAAAAAAAAkCbNeAAAAAAAAAAAAJAmzXgAAAAAAAAAAACQJs14AAAAAAAAAAAAkCbNeAAAAAAAAAAAAJAmzXgAAAAAAAAAAACQJs14AAAAAAAAAAAAkCbNeAAAAAAAAAAAAJAmzXgAAAAAAAAAAACQJs14AAAAAAAAAAAAkCbNeAAAAAAAAAAAAJAmzXgAAAAAAAAAAACQJs14AAAAAAAAAAAAkCbNeAAAAAAAAAAAAJCm/w/7asQcG16X6AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = [20, 4] , dpi = 150)\n",
        "plt.scatter (dataset[\"Date\"] , dataset['Price'] , color = \"red\")\n",
        "plt.title (\"The relationship between Date and Price\" , weight = 'bold', fontsize = 25)\n",
        "plt.xticks (range (0 , 1000 , 5) , fontsize=20)\n",
        "plt.yticks (fontsize = 20)\n",
        "plt.xlabel ('Date', fontsize = 20 )\n",
        "plt.ylabel ('Price' , fontsize = 20)\n",
        "plt.grid ()\n",
        "plt.show ()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "2Uy7zFIgg_m9",
        "outputId": "b8cefcce-075f-4710-e1d9-eba3434ceb17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = [25, 10] , dpi = 150)\n",
        "plt.scatter (dataset[\"Quantity\"] , dataset[\"Price\"] , color = \"blue\")\n",
        "plt.title (\"The relationship between Quantity with Price \" , weight = 'bold', fontsize = 30)\n",
        "plt.xticks (range (0 , 10000 , 1000) , fontsize = 20)\n",
        "plt.yticks (range (0 , 10000 , 1000) , fontsize = 20)\n",
        "plt.xlabel ('Quantity', fontsize = 30 )\n",
        "plt.ylabel ('Price' , fontsize = 30)\n",
        "plt.grid ()\n",
        "plt.show ()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "CwkveDgvh-px",
        "outputId": "32b06495-bc14-4bac-f26a-62f205cb950f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3750x1500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lk1tARLflEAp",
        "outputId": "ad4a8883-ab02-4899-ed69-f9dd9c045544"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['BillNo', 'Itemname', 'Quantity', 'Date', 'Price', 'CustomerID',\n",
              "       'Country', 'Description'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = pd.DataFrame(dataset , columns = ['BillNo', 'Itemname', 'Quantity', 'Date', 'Price', 'CustomerID',\n",
        "       'Country', 'Description'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjF82_pir05z",
        "outputId": "0edad1e0-7f94-41cb-af08-a2164878ee2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLQUH7ZBsmze",
        "outputId": "e154b0ae-0c72-4dbf-ee1e-c4bfad45f30c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['BillNo', 'Itemname', 'Quantity', 'Date', 'Price', 'CustomerID',\n",
            "       'Country', 'Description'],\n",
            "      dtype='object')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = dataset['Quantity']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Or7-IAORsoU5",
        "outputId": "6f3d4a2d-923c-4091-ec99-6322cd56143f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist (y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "cEQMX_vqJT5e",
        "outputId": "4605fc53-cda1-4f75-b576-87c76c172c02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([7.80000e+01, 5.21983e+05, 1.00000e+00, 0.00000e+00, 0.00000e+00,\n",
              "        0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 2.00000e+00]),\n",
              " array([-9600. ,  -540.5,  8519. , 17578.5, 26638. , 35697.5, 44757. ,\n",
              "        53816.5, 62876. , 71935.5, 80995. ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Percentage calculation\n",
        "((y == 0).sum ()/len (y)) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MH8LkNFCJhSO",
        "outputId": "c5b960ea-b5ee-48c4-e376-68c1e8b458e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**K-MEANS MACHINE LEARNING ALGORITHM**"
      ],
      "metadata": {
        "id": "WI4OFoEhJq6N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Assuming my_rules contains the association rules data with columns like 'antecedents', 'consequents', etc.\n",
        "# Create a binary target variable based on the 'lift' column (1 for lift > threshold, 0 otherwise)\n",
        "threshold = 1.2\n",
        "my_rules['target'] = my_rules['lift'].apply(lambda x: 1 if x > threshold else 0)\n",
        "\n",
        "# Select features and target variable\n",
        "features = ['antecedent support', 'consequent support', 'support', 'confidence', 'lift', 'leverage', 'conviction', 'zhangs_metric']\n",
        "x = my_rules[features]\n",
        "y = my_rules['target']\n",
        "\n",
        "# Cluster the association rules using KMeans\n",
        "n_clusters = 6\n",
        "kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n",
        "my_rules['Cluster'] = kmeans.fit_predict(x)\n",
        "\n",
        "# Create a 3D scatter plot for the clustered data\n",
        "fig = plt.figure(figsize=(10, 8))\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k']\n",
        "for cluster, color in zip(range(n_clusters), colors):\n",
        "    cluster_data = my_rules[my_rules['Cluster'] == cluster]\n",
        "    ax.scatter(cluster_data[features[0]], cluster_data[features[1]], cluster_data[features[2]], c=color, label=f'Cluster {cluster}')\n",
        "\n",
        "ax.set_xlabel(features[0])\n",
        "ax.set_ylabel(features[1])\n",
        "ax.set_zlabel(features[2])\n",
        "plt.legend()\n",
        "plt.title(\"3D Scatter Plot of Association Rules Clusters\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "bFLjN0nSKzCT",
        "outputId": "2c5332b1-fa26-4238-ad73-3fce11fd0657"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}