ML_classifications_models.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": [
        " # **ML Classification Models**"
      ],
      "metadata": {
        "id": "ME7p3VRECPqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Introduction:**\n",
        "This report aims to provide a detailed understanding of the processes and methodologies involved in implementing **ML Classification Models**. The study is conducted on a tabular dataset, which comprises a mix of <b>categorical</b> and <b>numerical</b> features.\n",
        "\n",
        "The dataset is subjected to rigorous <b>preprocessing</b> techniques, which includes *data cleaning, handling* of *missing values, outliers*, and other data quality issues, and encoding of categorical variables. The dataset is thoroughly examined to gain insights.\n",
        "\n",
        "The main section of this report includes the implementation of two most basic classification models, namely **Linear Classifier** and **Logistic Regression Classifier**. Both are implemeted using <i>skLearn</i> Library as well as *manually*.\n",
        "\n",
        "The dataset is split into Training and Testing sets. Both models are first trained on Training set and then tested over unseen Testing set and are evaluated using appropriate performance metrics such as **accuracy**, **precision**, **recall**, and **F1-score**. The performance of the two models is then compared to understand their effectiveness in the given classification task.\n",
        "\n"
      ],
      "metadata": {
        "id": "6gNVveTfQyEh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Dataset Selection:**\n",
        "\n",
        "###***Source of Dataset:***\n",
        "\n",
        "The dataset  titled  Bank Marketing Dataset\n",
        " is taken from the https://archive.ics.uci.edu/dataset/222/bank+marketing\n",
        "\n",
        "The dataset is related with **direct marketing compaigns of a portuguese banking institutuion**.They used phone calls to try and get people forsubscribing a term deposit.Sometime ,they had to  call the same person multiple times to see if they were interested or not .\n",
        "\n",
        "####**Goal of the marketing compaign:** The goal was to find out if the person eventually said **\"yes**\" for subsribing a **term deposit** , or if they said **\"No\"** and didn't subscribe  \n",
        " for **term deposit**.\n",
        "\n",
        "\n",
        "##  ***About Data***:\n",
        "\n",
        "**Data Charateristics**: Multivirate\n",
        "\n",
        "**Associated Task**: Classification\n",
        "\n",
        "**Feature type**: Categorical,Integer\n",
        "\n",
        "**Instances** :45211\n",
        "\n",
        "**Features**:21\n",
        "\n",
        "## ***Features_Name***:\n",
        "Here we outlines the features in our analysis, each of which serves as a potential risk factor. These variables can be categorized into three main groups:\n",
        "\n",
        "<b>\n",
        "1.age:\n",
        "\n",
        "2.job:     type of job (categorical:  'admin.', 'entrepreneur' ,\n",
        "'housemaid' ,'management', 'retired' , 'self-employed' ,'student','technician','unemployed', 'unknown')\n",
        "\n",
        "\n",
        "3.marital:  marital status(categorical: 'divorced',  'married',  'single'  ,'unknown';  note: 'divorced' means divorced or widowed)\n",
        "\n",
        "\n",
        "4.education: (categorical:'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
        "\n",
        "\n",
        "5.default:        has credit in default?\n",
        "\n",
        "6.balance:      average yearly balance\n",
        "\n",
        "7.housing:      has housing loan?\n",
        "\n",
        "8.loan:        has personal loan?\n",
        "\n",
        "9..contact:      contact communication type (categorical: 'cellular','telephone')\n",
        "\n",
        "10.day_of_week:    last contact day of the week\n",
        "\n",
        "11.month:      last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
        "\n",
        "12.duration:\t   last contact duration, in seconds (numeric).\n",
        "\n",
        "13.campaign:      number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
        "\n",
        "14.pdays:       number of days that passed by after the client was last contacted from a previous campaign (numeric; -1 means client was not previously contacted)\n",
        "\n",
        "15.previous:      number of contacts performed before this campaign and for this client\n",
        "\n",
        "16.poutcome:        outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
        "xvii.emp.var.rate:           employement variation rate\n",
        "\n",
        "17.cons.price.idx:            consumer price index\n",
        "\n",
        "18.cons.conf.idx:             consumer confidence index\n",
        "\n",
        "19.euribor3m:                  euribor 3 month rate\n",
        "\n",
        "20.nr.employed:               number of employees\n",
        "\n",
        "\n",
        "21.y:      has the client subscribed a term deposit?<b>\n",
        "\n",
        "##*Target variable*\n",
        "**term deposit(y)**: The target varaible is the term deposit which will contain value either Yes or No (\"Yes\" for subscribe and \"No\" for not subscribe)\n",
        "\n",
        "\n",
        "Further details about the the dataset are at\n",
        "[dataset documentation](https://archive.ics.uci.edu/dataset/222/bank+marketing)"
      ],
      "metadata": {
        "id": "zt1Y_wFYefV1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Preprocessing:**\n",
        "\n",
        "**Data Preprocessing** is a fundamental step in data analysis.It is the process of **preparing** and **cleaning** data for analysis. The **goal** of data preprocessing is to improve **data quality**, **remove inconsistencies**, and prepare the data for effective use.\n",
        "\n",
        "Common data preprocessing task include:"
      ],
      "metadata": {
        "id": "1eRu7ZvsQyKp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data Cleaning:**\n",
        "\n",
        "It involves identifying and correcting errors and inconsistencies in the data, such as **missing values**, **outliers**, and **duplicates.**"
      ],
      "metadata": {
        "id": "gOaF-3VBbgag"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Missing Values:**\n",
        "\n",
        "For checking whether the dataset contain missing values or not **isnull().sum()** function is used which summarize the count of missing value per column. It returns a **Series** where each element represents the count of missing values in the corresponding column of the original DataFrame."
      ],
      "metadata": {
        "id": "MBKVFBjxbnfw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('bank.csv')\n",
        "# Check for missing values\n",
        "\n",
        "missing_values = df.isnull().sum()\n",
        "print(missing_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjB_NJD8btaL",
        "outputId": "6c59a821-303e-4bbe-ed6a-1468971cacd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age               0\n",
            "duration          0\n",
            "marital           0\n",
            "education         0\n",
            "default           0\n",
            "housing           0\n",
            "loan              0\n",
            "contact           0\n",
            "month             0\n",
            "day_of_week       0\n",
            "job               0\n",
            "campaign          0\n",
            "pdays             0\n",
            "previous          0\n",
            "poutcome          0\n",
            "emp.var.rate      0\n",
            "cons.price.idx    0\n",
            "cons.conf.idx     0\n",
            "euribor3m         0\n",
            "nr.employed       0\n",
            "y                 0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Dropping Duplicates:**\n",
        "\n",
        "Duplicate rows are rows that have **same values** in all columns. For dropping duplicates **drop_duplicates()** function is used which remove duplicate rows from DataFrame.\n",
        "\n",
        "**inplace=True** modifies the DataFrame in place, means it updates DataFrame itself and doesn't return a new DataFrame."
      ],
      "metadata": {
        "id": "9USdVjZGb_km"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle duplicates by dropping duplicate rows\n",
        "df.drop_duplicates(inplace=True)\n",
        "\n",
        "# Reset the index after dropping duplicates\n",
        "df.reset_index(drop=True, inplace=True)\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZneWpEtcE6j",
        "outputId": "53bb42b8-24cd-4a5e-b3b3-af2901c2d652"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41176, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Encoding Categorical Data:**\n",
        "\n",
        "Categorical data is data that can be divided into categories, such as \"male\", \"female\". This can be done using techniques such as **one-hot encoding.**\n",
        "\n",
        "In **one-hot encoding** each category within a categorical feature is transformed into a binary column. For each unique category, a new binary column is created, where **\"1\"** indicates the presence of the category, and a **\"0\"** indicates its absence."
      ],
      "metadata": {
        "id": "b2fao4YacMjt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify which columns should be one-hot encoded\n",
        "categorical_columns = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'poutcome']\n",
        "df=pd.get_dummies(df,columns=categorical_columns)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "sFW5-skrcRRh",
        "outputId": "a3bd8e85-fd10-44c4-aa3a-0538d7924191"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  duration  campaign  pdays  previous  emp.var.rate  cons.price.idx  \\\n",
              "0   56       261         1    999         0           1.1          93.994   \n",
              "1   57       149         1    999         0           1.1          93.994   \n",
              "2   37       226         1    999         0           1.1          93.994   \n",
              "3   40       151         1    999         0           1.1          93.994   \n",
              "4   56       307         1    999         0           1.1          93.994   \n",
              "\n",
              "   cons.conf.idx  euribor3m  nr.employed  ... month_oct  month_sep  \\\n",
              "0          -36.4      4.857       5191.0  ...         0          0   \n",
              "1          -36.4      4.857       5191.0  ...         0          0   \n",
              "2          -36.4      4.857       5191.0  ...         0          0   \n",
              "3          -36.4      4.857       5191.0  ...         0          0   \n",
              "4          -36.4      4.857       5191.0  ...         0          0   \n",
              "\n",
              "   day_of_week_fri  day_of_week_mon  day_of_week_thu  day_of_week_tue  \\\n",
              "0                0                1                0                0   \n",
              "1                0                1                0                0   \n",
              "2                0                1                0                0   \n",
              "3                0                1                0                0   \n",
              "4                0                1                0                0   \n",
              "\n",
              "   day_of_week_wed  poutcome_failure  poutcome_nonexistent  poutcome_success  \n",
              "0                0                 0                     1                 0  \n",
              "1                0                 0                     1                 0  \n",
              "2                0                 0                     1                 0  \n",
              "3                0                 0                     1                 0  \n",
              "4                0                 0                     1                 0  \n",
              "\n",
              "[5 rows x 64 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d1617158-5a43-4aa4-b112-eea6ae74a549\" 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>age</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>...</th>\n",
              "      <th>month_oct</th>\n",
              "      <th>month_sep</th>\n",
              "      <th>day_of_week_fri</th>\n",
              "      <th>day_of_week_mon</th>\n",
              "      <th>day_of_week_thu</th>\n",
              "      <th>day_of_week_tue</th>\n",
              "      <th>day_of_week_wed</th>\n",
              "      <th>poutcome_failure</th>\n",
              "      <th>poutcome_nonexistent</th>\n",
              "      <th>poutcome_success</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>226</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56</td>\n",
              "      <td>307</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 64 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-d1617158-5a43-4aa4-b112-eea6ae74a549')\"\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-d1617158-5a43-4aa4-b112-eea6ae74a549 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-d1617158-5a43-4aa4-b112-eea6ae74a549');\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-eff1735a-97c3-4c90-b8d6-6ab293e34ac5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eff1735a-97c3-4c90-b8d6-6ab293e34ac5')\"\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-eff1735a-97c3-4c90-b8d6-6ab293e34ac5 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": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Exploratory Data Analysis (EDA):**\n",
        "\n",
        "\n",
        "**EDA** is a process of investigating and analyzing data sets to discover **underlying patterns**, **correlations**, and **anomalies.** It is performed using a variety of data visualization techniques, such as histograms, scatter plots, heatmaps, and box plots which can be used to identify patterns and trends in the data, as well as to identify outliers."
      ],
      "metadata": {
        "id": "NwNEKWHBcWX8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Insights:**\n",
        "\n",
        " * **Basic statistics of columns:**"
      ],
      "metadata": {
        "id": "kM8QwURScegB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "oXhlvq4McnVv",
        "outputId": "aeb7793b-08c1-44d9-ecf7-15be11d4f6a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age      duration      campaign         pdays      previous  \\\n",
              "count  41176.00000  41176.000000  41176.000000  41176.000000  41176.000000   \n",
              "mean      40.02380    258.315815      2.567879    962.464810      0.173013   \n",
              "std       10.42068    259.305321      2.770318    186.937102      0.494964   \n",
              "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
              "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
              "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
              "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
              "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
              "\n",
              "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m  \\\n",
              "count  41176.000000    41176.000000   41176.000000  41176.000000   \n",
              "mean       0.081922       93.575720     -40.502863      3.621293   \n",
              "std        1.570883        0.578839       4.627860      1.734437   \n",
              "min       -3.400000       92.201000     -50.800000      0.634000   \n",
              "25%       -1.800000       93.075000     -42.700000      1.344000   \n",
              "50%        1.100000       93.749000     -41.800000      4.857000   \n",
              "75%        1.400000       93.994000     -36.400000      4.961000   \n",
              "max        1.400000       94.767000     -26.900000      5.045000   \n",
              "\n",
              "        nr.employed  ...     month_oct     month_sep  day_of_week_fri  \\\n",
              "count  41176.000000  ...  41176.000000  41176.000000     41176.000000   \n",
              "mean    5167.034870  ...      0.017413      0.013843         0.190062   \n",
              "std       72.251364  ...      0.130806      0.116841         0.392355   \n",
              "min     4963.600000  ...      0.000000      0.000000         0.000000   \n",
              "25%     5099.100000  ...      0.000000      0.000000         0.000000   \n",
              "50%     5191.000000  ...      0.000000      0.000000         0.000000   \n",
              "75%     5228.100000  ...      0.000000      0.000000         0.000000   \n",
              "max     5228.100000  ...      1.000000      1.000000         1.000000   \n",
              "\n",
              "       day_of_week_mon  day_of_week_thu  day_of_week_tue  day_of_week_wed  \\\n",
              "count     41176.000000     41176.000000     41176.000000     41176.000000   \n",
              "mean          0.206722         0.209297         0.196377         0.197542   \n",
              "std           0.404960         0.406812         0.397261         0.398150   \n",
              "min           0.000000         0.000000         0.000000         0.000000   \n",
              "25%           0.000000         0.000000         0.000000         0.000000   \n",
              "50%           0.000000         0.000000         0.000000         0.000000   \n",
              "75%           0.000000         0.000000         0.000000         0.000000   \n",
              "max           1.000000         1.000000         1.000000         1.000000   \n",
              "\n",
              "       poutcome_failure  poutcome_nonexistent  poutcome_success  \n",
              "count      41176.000000          41176.000000      41176.000000  \n",
              "mean           0.103264              0.863391          0.033345  \n",
              "std            0.304307              0.343438          0.179537  \n",
              "min            0.000000              0.000000          0.000000  \n",
              "25%            0.000000              1.000000          0.000000  \n",
              "50%            0.000000              1.000000          0.000000  \n",
              "75%            0.000000              1.000000          0.000000  \n",
              "max            1.000000              1.000000          1.000000  \n",
              "\n",
              "[8 rows x 63 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0ef62297-48df-4f34-88ce-ea5388520203\" 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>age</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>...</th>\n",
              "      <th>month_oct</th>\n",
              "      <th>month_sep</th>\n",
              "      <th>day_of_week_fri</th>\n",
              "      <th>day_of_week_mon</th>\n",
              "      <th>day_of_week_thu</th>\n",
              "      <th>day_of_week_tue</th>\n",
              "      <th>day_of_week_wed</th>\n",
              "      <th>poutcome_failure</th>\n",
              "      <th>poutcome_nonexistent</th>\n",
              "      <th>poutcome_success</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>41176.00000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "      <td>41176.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.02380</td>\n",
              "      <td>258.315815</td>\n",
              "      <td>2.567879</td>\n",
              "      <td>962.464810</td>\n",
              "      <td>0.173013</td>\n",
              "      <td>0.081922</td>\n",
              "      <td>93.575720</td>\n",
              "      <td>-40.502863</td>\n",
              "      <td>3.621293</td>\n",
              "      <td>5167.034870</td>\n",
              "      <td>...</td>\n",
              "      <td>0.017413</td>\n",
              "      <td>0.013843</td>\n",
              "      <td>0.190062</td>\n",
              "      <td>0.206722</td>\n",
              "      <td>0.209297</td>\n",
              "      <td>0.196377</td>\n",
              "      <td>0.197542</td>\n",
              "      <td>0.103264</td>\n",
              "      <td>0.863391</td>\n",
              "      <td>0.033345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.42068</td>\n",
              "      <td>259.305321</td>\n",
              "      <td>2.770318</td>\n",
              "      <td>186.937102</td>\n",
              "      <td>0.494964</td>\n",
              "      <td>1.570883</td>\n",
              "      <td>0.578839</td>\n",
              "      <td>4.627860</td>\n",
              "      <td>1.734437</td>\n",
              "      <td>72.251364</td>\n",
              "      <td>...</td>\n",
              "      <td>0.130806</td>\n",
              "      <td>0.116841</td>\n",
              "      <td>0.392355</td>\n",
              "      <td>0.404960</td>\n",
              "      <td>0.406812</td>\n",
              "      <td>0.397261</td>\n",
              "      <td>0.398150</td>\n",
              "      <td>0.304307</td>\n",
              "      <td>0.343438</td>\n",
              "      <td>0.179537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.400000</td>\n",
              "      <td>92.201000</td>\n",
              "      <td>-50.800000</td>\n",
              "      <td>0.634000</td>\n",
              "      <td>4963.600000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>32.00000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.800000</td>\n",
              "      <td>93.075000</td>\n",
              "      <td>-42.700000</td>\n",
              "      <td>1.344000</td>\n",
              "      <td>5099.100000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.00000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>93.749000</td>\n",
              "      <td>-41.800000</td>\n",
              "      <td>4.857000</td>\n",
              "      <td>5191.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>47.00000</td>\n",
              "      <td>319.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>93.994000</td>\n",
              "      <td>-36.400000</td>\n",
              "      <td>4.961000</td>\n",
              "      <td>5228.100000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>98.00000</td>\n",
              "      <td>4918.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>94.767000</td>\n",
              "      <td>-26.900000</td>\n",
              "      <td>5.045000</td>\n",
              "      <td>5228.100000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 63 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-0ef62297-48df-4f34-88ce-ea5388520203')\"\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-0ef62297-48df-4f34-88ce-ea5388520203 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-0ef62297-48df-4f34-88ce-ea5388520203');\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-8501b716-7248-43e0-8c64-9bd624c38c76\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8501b716-7248-43e0-8c64-9bd624c38c76')\"\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-8501b716-7248-43e0-8c64-9bd624c38c76 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": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Data types of columns:**"
      ],
      "metadata": {
        "id": "pc1ov_IPcrVw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eZWm_ik5cvDY",
        "outputId": "952b4a69-74ae-4912-c4e9-376a569c8529"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                     int64\n",
              "duration                int64\n",
              "campaign                int64\n",
              "pdays                   int64\n",
              "previous                int64\n",
              "                        ...  \n",
              "day_of_week_tue         uint8\n",
              "day_of_week_wed         uint8\n",
              "poutcome_failure        uint8\n",
              "poutcome_nonexistent    uint8\n",
              "poutcome_success        uint8\n",
              "Length: 64, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***uint8*** ( \"*unsigned 8-bit integer*\") is used to represent integers within the range of 0 to 255. It's a memory-efficient data type, uses only 8 bits to store each value. This data type is often used when working with data that doesn't require negative values, such as categorical data."
      ],
      "metadata": {
        "id": "S1Dk1MldczVz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Insights From Boxplot Of Outliers:**"
      ],
      "metadata": {
        "id": "yeysobYTdF2R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# List of features for which you want to create box plots\n",
        "features = ['age', 'duration', 'campaign', 'pdays', 'previous']\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Set the x-axis limits to 1000 or 2000\n",
        "\n",
        "# plt.xlim((0, 2000))  # Set the x-axis limits to 2000\n",
        "\n",
        "for i, feature in enumerate(features):\n",
        "    plt.subplot(2, 3, i+1)\n",
        "    ax = sns.boxplot(x=df[feature], color=\"white\", width=0.5, linewidth=1.5, whis=3, boxprops={'facecolor': 'pink'})  # Customize boxplot colors, outlines, and limit the range of outliers\n",
        "    plt.title(f'Boxplot of {feature}')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "ssGTpyR1dKgu",
        "outputId": "4b02bea7-9fbf-4808-9167-53519ee3ea25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 5 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Insights From Heatmap:**"
      ],
      "metadata": {
        "id": "5hC_onRtc3V0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "#Compute the correlation matrix for the specified features\n",
        "features = ['age','duration', 'campaign', 'pdays', 'previous','emp.var.rate','cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'y']\n",
        "correlation_matrix = df[features].corr()\n",
        "\n",
        "# Create a heatmap\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
        "plt.title(\"Correlation Heatmap\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        },
        "id": "5YGLgN4lc6WI",
        "outputId": "47f42381-ce86-4d7c-af77-20ff1e8e481a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-42-4bb690184c31>:5: 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",
            "  correlation_matrix = df[features].corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing The necessary Libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix"
      ],
      "metadata": {
        "id": "tdASRS3r8fM-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loadingt the CSV file into a DataFrame\n",
        "csv_file_path = 'bank.csv'\n",
        "df = pd.read_csv(csv_file_path)"
      ],
      "metadata": {
        "id": "lHaJEKrv8bKW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('bank.csv')\n",
        "print(\"Total Rows and Columns: \",df.shape)\n",
        "# Check for missing values\n",
        "#The isnull().sum() method, on the other hand, is typically used to summarize the count of missing\n",
        "#values per column in a DataFrame. When you call df.isnull().sum(), it returns a Series where\n",
        "#each element represents the count of missing values in the corresponding column of the original DataFrame\n",
        "\n",
        "missing_values = df.isnull().sum()\n",
        "print(missing_values)\n",
        "\n",
        "#df.head()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wq6UIV178MZZ",
        "outputId": "f6f5a541-d0c6-4147-85cf-bd1d47ef8735"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Rows and Columns:  (41188, 21)\n",
            "age               0\n",
            "duration          0\n",
            "marital           0\n",
            "education         0\n",
            "default           0\n",
            "housing           0\n",
            "loan              0\n",
            "contact           0\n",
            "month             0\n",
            "day_of_week       0\n",
            "job               0\n",
            "campaign          0\n",
            "pdays             0\n",
            "previous          0\n",
            "poutcome          0\n",
            "emp.var.rate      0\n",
            "cons.price.idx    0\n",
            "cons.conf.idx     0\n",
            "euribor3m         0\n",
            "nr.employed       0\n",
            "y                 0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle duplicates by dropping duplicate rows\n",
        "df.drop_duplicates(inplace=True)\n",
        "\n",
        "# Reset the index after dropping duplicates\n",
        "df.reset_index(drop=True, inplace=True)\n",
        "df.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uv5FyQN38SWU",
        "outputId": "219e6872-dbdc-45dd-831d-0b472de6b276"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41176, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify which columns should be one-hot encoded\n",
        "categorical_columns = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'poutcome']\n",
        "df=pd.get_dummies(df,columns=categorical_columns)\n",
        "# Use get_dummies to one-hot encode the categorical columns\n",
        "#encoded_df = pd.get_dummies(df, columns=categorical_columns)\n",
        "df"
      ],
      "metadata": {
        "id": "Rd-2uixL8PfJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "14d216bf-5baa-43d6-ae03-701afabfa594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  duration  campaign  pdays  previous  emp.var.rate  cons.price.idx  \\\n",
              "0       56       261         1    999         0           1.1          93.994   \n",
              "1       57       149         1    999         0           1.1          93.994   \n",
              "2       37       226         1    999         0           1.1          93.994   \n",
              "3       40       151         1    999         0           1.1          93.994   \n",
              "4       56       307         1    999         0           1.1          93.994   \n",
              "...    ...       ...       ...    ...       ...           ...             ...   \n",
              "41171   73       334         1    999         0          -1.1          94.767   \n",
              "41172   46       383         1    999         0          -1.1          94.767   \n",
              "41173   56       189         2    999         0          -1.1          94.767   \n",
              "41174   44       442         1    999         0          -1.1          94.767   \n",
              "41175   74       239         3    999         1          -1.1          94.767   \n",
              "\n",
              "       cons.conf.idx  euribor3m  nr.employed  ... month_oct  month_sep  \\\n",
              "0              -36.4      4.857       5191.0  ...         0          0   \n",
              "1              -36.4      4.857       5191.0  ...         0          0   \n",
              "2              -36.4      4.857       5191.0  ...         0          0   \n",
              "3              -36.4      4.857       5191.0  ...         0          0   \n",
              "4              -36.4      4.857       5191.0  ...         0          0   \n",
              "...              ...        ...          ...  ...       ...        ...   \n",
              "41171          -50.8      1.028       4963.6  ...         0          0   \n",
              "41172          -50.8      1.028       4963.6  ...         0          0   \n",
              "41173          -50.8      1.028       4963.6  ...         0          0   \n",
              "41174          -50.8      1.028       4963.6  ...         0          0   \n",
              "41175          -50.8      1.028       4963.6  ...         0          0   \n",
              "\n",
              "       day_of_week_fri  day_of_week_mon  day_of_week_thu  day_of_week_tue  \\\n",
              "0                    0                1                0                0   \n",
              "1                    0                1                0                0   \n",
              "2                    0                1                0                0   \n",
              "3                    0                1                0                0   \n",
              "4                    0                1                0                0   \n",
              "...                ...              ...              ...              ...   \n",
              "41171                1                0                0                0   \n",
              "41172                1                0                0                0   \n",
              "41173                1                0                0                0   \n",
              "41174                1                0                0                0   \n",
              "41175                1                0                0                0   \n",
              "\n",
              "       day_of_week_wed  poutcome_failure  poutcome_nonexistent  \\\n",
              "0                    0                 0                     1   \n",
              "1                    0                 0                     1   \n",
              "2                    0                 0                     1   \n",
              "3                    0                 0                     1   \n",
              "4                    0                 0                     1   \n",
              "...                ...               ...                   ...   \n",
              "41171                0                 0                     1   \n",
              "41172                0                 0                     1   \n",
              "41173                0                 0                     1   \n",
              "41174                0                 0                     1   \n",
              "41175                0                 1                     0   \n",
              "\n",
              "       poutcome_success  \n",
              "0                     0  \n",
              "1                     0  \n",
              "2                     0  \n",
              "3                     0  \n",
              "4                     0  \n",
              "...                 ...  \n",
              "41171                 0  \n",
              "41172                 0  \n",
              "41173                 0  \n",
              "41174                 0  \n",
              "41175                 0  \n",
              "\n",
              "[41176 rows x 64 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09b8d5da-23b9-4046-8a2f-759e59876aa3\" 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>age</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>...</th>\n",
              "      <th>month_oct</th>\n",
              "      <th>month_sep</th>\n",
              "      <th>day_of_week_fri</th>\n",
              "      <th>day_of_week_mon</th>\n",
              "      <th>day_of_week_thu</th>\n",
              "      <th>day_of_week_tue</th>\n",
              "      <th>day_of_week_wed</th>\n",
              "      <th>poutcome_failure</th>\n",
              "      <th>poutcome_nonexistent</th>\n",
              "      <th>poutcome_success</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>226</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56</td>\n",
              "      <td>307</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41171</th>\n",
              "      <td>73</td>\n",
              "      <td>334</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>94.767</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>1.028</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41172</th>\n",
              "      <td>46</td>\n",
              "      <td>383</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>94.767</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>1.028</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41173</th>\n",
              "      <td>56</td>\n",
              "      <td>189</td>\n",
              "      <td>2</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>94.767</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>1.028</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41174</th>\n",
              "      <td>44</td>\n",
              "      <td>442</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>94.767</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>1.028</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41175</th>\n",
              "      <td>74</td>\n",
              "      <td>239</td>\n",
              "      <td>3</td>\n",
              "      <td>999</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>94.767</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>1.028</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41176 rows × 64 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-09b8d5da-23b9-4046-8a2f-759e59876aa3')\"\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-09b8d5da-23b9-4046-8a2f-759e59876aa3 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-09b8d5da-23b9-4046-8a2f-759e59876aa3');\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-9ad9321c-f8ea-44f6-b0cf-ccbe57b4e482\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9ad9321c-f8ea-44f6-b0cf-ccbe57b4e482')\"\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-9ad9321c-f8ea-44f6-b0cf-ccbe57b4e482 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": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Exploratory Data Analysis (EDA):**\n",
        "<h4>Conduct exploratory data analysis to gain insights into the dataset.\n",
        "• Generate descriptive statistics, visualizations, and correlations to understand the\n",
        "relationships between features</h4>"
      ],
      "metadata": {
        "id": "Wze2IFNpXrBc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classification:\n",
        "\n",
        "Classification in machine learning is the process of teaching a computer algorithm to categorize or label data points into distinct groups or classes based on their features.\n",
        "\n",
        "\n",
        "![](https://miro.medium.com/v2/resize:fit:1400/1*CS-OYdiRLCBMBiOpEURy0g.png)\n",
        "\n",
        " The process typically involves two main steps: ***training*** and ***prediction***.\n",
        "\n",
        "###***Training phase:***\n",
        "  The algorithm is provided with a labeled dataset, containing input features and their corresponding correct labels. The algorithm learns to recognize patterns in the data and determine the relationships between the features and the classes(extract insights from the data).\n",
        "\n",
        "  An optimization process  is used to adjust its internal parameters (like weights in a neural network or coefficients in a logistic regression model) in order to minimize a specified loss function(error minimization).\n",
        "  \n",
        "  The loss function measures the difference between the predicted labels and the actual labels in the training data. The algorithm iteratively refines its internal parameters until it achieves the best possible classification performance on the training set.\n",
        "\n",
        "\n",
        "###***Prediction Phase:***\n",
        "Once the training phase is complete, the prediction phase begins. The trained model is presented with new, unseen data points that it has not encountered before.\n",
        "\n",
        " The model applies the learned patterns and relationships to these new data points and predicts the corresponding classes. This prediction process is based on the features of the input data and the model's internal parameters, which were optimized during training.\n",
        "\n",
        "\n",
        "![](https://www.nltk.org/images/supervised-classification.png)"
      ],
      "metadata": {
        "id": "geQq1fz54M9o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Classification Model Building:**\n",
        "\n",
        "\n",
        "\n",
        "1.   Linear Classifier\n",
        "2.   Logistic Regression\n",
        "\n"
      ],
      "metadata": {
        "id": "m5Dnkql5X0ss"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Spliting the Data (For Linear Classifier)**"
      ],
      "metadata": {
        "id": "p4JOFlePYQW5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# making the copy of dataframe\n",
        "df2 = df.copy(deep=True)\n",
        "# Spliting the data into features (X) and target variable (y)\n",
        "X = df2.drop('y', axis=1)\n",
        "y = df2['y']\n",
        "\n",
        "\n",
        "# Spliting the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "#print(f\"Training Set Size: {len(X_train)}\")\n",
        "#print(f\"Testing Set Size: {len(X_test)}\")"
      ],
      "metadata": {
        "id": "GxfjH9dN7z30"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1. Linear Classifier**\n",
        "\n",
        "Linear Classifier is a type of supervised learning task . It uses a linear decision boundary (a hyperplane) to separate different classes in the feature space.\n",
        "\n",
        "\n",
        "## ***GOAL OF LINEAR CLASSIFICATION***:\n",
        "Categorize data points into one of several predefined classes or categories\n",
        "\n",
        "\n",
        "## ***Linear Function***\n",
        "The linear function for different type of varaibles is of the form\n",
        "\n",
        "f(x)=w1​x1​+w2​x2​+…+wn​xn​+b\n",
        "\n",
        "where w1,w2,....,wn are the weights and x1,x2,....,xn are the inputs and b is the bias term.\n",
        "\n",
        "\n",
        "##*How the classifier works*\n",
        "\n",
        "A linear calssifier classify data points into different categories on the basis of feature.Decision is made by drawing linear boundaries in feature space,Which results in seperating different classes\n",
        "\n",
        "The classifier works by assigning weights to each feature that are than combined linearly with a bias term.\n",
        "\n",
        "This determines the decision boundary, which can be visualized as a line (in two dimensions) or a hyperplane (in higher dimensions).\n",
        "\n",
        "**The goal of training a linear classifier is to find the optimal set of weights and bias that best separates the classes in the training data.**\n"
      ],
      "metadata": {
        "id": "JTv0F4l5YhEy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####     ***i. Using skLearn***"
      ],
      "metadata": {
        "id": "DzkD4IRMYhP7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "linear_svc = LinearSVC(max_iter=5000)\n",
        "\n",
        "# Trainining the model on the training data\n",
        "linear_svc.fit(X_train, y_train)\n",
        "\n",
        "# Making predictions on the test set\n",
        "class_predictions = linear_svc.predict(X_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZFvNOICGHke",
        "outputId": "675fecfd-51de-41d6-ac00-660a1fb8b4ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "## **Warning:**  Liblinear failed to converge, increase the number of iterations\n",
        "\n",
        "This warning indicates that means that the Liblinear solver was not able to find a solution to the SVM problem within the specified number of iterations.\n",
        "This may be encountered because:\n",
        "\n",
        "* The training data is not normalized.\n",
        "*There are strong correlations between the features."
      ],
      "metadata": {
        "id": "oCrjgjGHIMMo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Confusion Matrix, Accuracy , Precision , Recall , F1-score**"
      ],
      "metadata": {
        "id": "9tuVY2OKNYMK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Confusion Matrix\n",
        "conf2_matrix = confusion_matrix(y_test,  class_predictions)\n",
        "print('Confusion Matrix:')\n",
        "print(conf2_matrix)\n",
        "\n",
        "true_negative, false_positive, false_negative, true_positive = conf2_matrix.ravel()\n",
        "\n",
        "# Accuracy\n",
        "accuracy = (true_positive + true_negative) / np.sum(conf2_matrix)\n",
        "print(f'Accuracy: {accuracy*100:.2f}%')\n",
        "\n",
        "# Precision\n",
        "precision = true_positive / (true_positive + false_positive)\n",
        "print(f'Precision: {precision:.2f}')\n",
        "\n",
        "# Recall\n",
        "recall = true_positive / (true_positive + false_negative)\n",
        "print(f'Recall: {recall:.2f}')\n",
        "\n",
        "# F1-Score\n",
        "f1 = 2 * (precision * recall) / (precision + recall)\n",
        "print(f'F1-Score: {f1:.2f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kh6U_9thNYlY",
        "outputId": "fdb72956-c007-42aa-e98f-2b33341b9bb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[7115  150]\n",
            " [ 669  302]]\n",
            "Accuracy: 90.06%\n",
            "Precision: 0.67\n",
            "Recall: 0.31\n",
            "F1-Score: 0.42\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ***ii. Manually***"
      ],
      "metadata": {
        "id": "_mbTsf7aaDHF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Convert categorical target variable to numerical values\n",
        "y = y.map({'no': 0, 'yes': 1})\n",
        "# Add bias term to X\n",
        "X_b = np.c_[np.ones((len(X), 1)), X]\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_b, y, test_size=0.2, random_state=42)\n",
        "# Make sure that y_train and y_test are numpy arrays\n",
        "y_train = y_train.values.reshape(-1, 1)\n",
        "y_test = y_test.values.reshape(-1, 1)\n",
        "\n",
        "# Initialize parameters for linear classifier\n",
        "theta_init = np.random.randn(X_b.shape[1], 1)\n",
        "\n",
        "# Set hyperparameters\n",
        "learning_rate = 0.01\n",
        "num_iterations = 1000\n",
        "\n",
        "# Linear hypothesis function\n",
        "def linear_hypothesis(X, theta):\n",
        "    return np.dot(X, theta)\n",
        "\n",
        "# Mean Squared Error cost function\n",
        "def compute_cost(X, y, theta):\n",
        "    m = len(y)\n",
        "    predictions = linear_hypothesis(X, theta)\n",
        "    cost = (1/(2*m)) * np.sum((predictions - y)**2)\n",
        "    return cost\n",
        "\n",
        "# Gradient descent for linear classifier\n",
        "def gradient_descent(X, y, theta, learning_rate, num_iterations):\n",
        "    m = len(y)\n",
        "    costs = []\n",
        "\n",
        "    for _ in range(num_iterations):\n",
        "        predictions = linear_hypothesis(X, theta)\n",
        "        errors = predictions - y\n",
        "        gradient = np.dot(X.T, errors) / m\n",
        "        theta -= learning_rate * gradient\n",
        "        cost = compute_cost(X, y, theta)\n",
        "        costs.append(cost)\n",
        "\n",
        "    return theta, costs\n",
        "\n",
        "# Train the linear classifier\n",
        "theta_trained, _ = gradient_descent(X_train, y_train, theta_init, learning_rate, num_iterations)\n",
        "\n",
        "# Make predictions on the test set\n",
        "predictions = linear_hypothesis(X_test, theta_trained)\n",
        "\n",
        "# Convert predictions to classes based on a threshold\n",
        "threshold = 0.5\n",
        "class_predictions = (predictions >= threshold).astype(int)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GmWwVsQbXJ-X",
        "outputId": "0a5c5cf7-0d30-41cf-ec3d-2ef6555ace71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce\n",
            "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
            "<ipython-input-62-0a147a79a6bf>:27: RuntimeWarning: overflow encountered in square\n",
            "  cost = (1/(2*m)) * np.sum((predictions - y)**2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Warning:** overflow encountered in square\n",
        "\n",
        "This warning suggests that there is an overflow issue in the computation of the cost function. The values involved in the square operation is too large.\n",
        "\n",
        "This warning can be addressed by scaling the features, and by handling the outliers properly"
      ],
      "metadata": {
        "id": "e7tQfKOkqH7g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Confusion Matrix, Accuracy , Precision , Recall , F1-score**"
      ],
      "metadata": {
        "id": "l3qpNAl5NQDy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Confusion Matrix\n",
        "conf2_matrix = confusion_matrix(y_test, class_predictions)\n",
        "print('Confusion Matrix:')\n",
        "print(conf2_matrix)\n",
        "\n",
        "true_negative, false_positive, false_negative, true_positive = conf2_matrix.ravel()\n",
        "\n",
        "# Accuracy\n",
        "accuracy = (true_positive + true_negative) / np.sum(conf2_matrix)\n",
        "print(f'Accuracy: {accuracy*100:.2f}%')\n",
        "\n",
        "# Precision\n",
        "precision = true_positive / (true_positive + false_positive)\n",
        "print(f'Precision: {precision:.2f}')\n",
        "\n",
        "# Recall\n",
        "recall = true_positive / (true_positive + false_negative)\n",
        "print(f'Recall: {recall:.2f}')\n",
        "\n",
        "# F1-Score\n",
        "f1 = 2 * (precision * recall) / (precision + recall)\n",
        "print(f'F1-Score: {f1:.2f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWRb_XMBNQVv",
        "outputId": "adceb4f3-b6ac-4309-aecc-780c3ba80664"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[7265    0]\n",
            " [ 971    0]]\n",
            "Accuracy: 88.21%\n",
            "Precision: nan\n",
            "Recall: 0.00\n",
            "F1-Score: nan\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-52-17d7c0ff78ea>:13: RuntimeWarning: invalid value encountered in long_scalars\n",
            "  precision = true_positive / (true_positive + false_positive)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Decision Boundary For Linear Classifier**"
      ],
      "metadata": {
        "id": "ahC3pe0TfsD7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Extract the \"age\" and \"duration\" columns\n",
        "X = df[['age', 'duration']]\n",
        "y = df['y']\n",
        "\n",
        "\n",
        "# Standardize the features for SVM\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Convert categorical target values to numerical values\n",
        "y_numerical = pd.factorize(y)[0]\n",
        "\n",
        "# Fit a LinearSVM model with feature names\n",
        "svm_model = SVC(kernel='linear')\n",
        "svm_model.fit(X_scaled, y_numerical)\n",
        "\n",
        "# Plot the decision boundary with different colors for each class\n",
        "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=y_numerical, edgecolors='k', cmap=plt.cm.coolwarm)\n",
        "ax = plt.gca()\n",
        "xlim = ax.get_xlim()\n",
        "ylim = ax.get_ylim()\n",
        "\n",
        "# Create a meshgrid to plot the decision boundary\n",
        "xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 100), np.linspace(ylim[0], ylim[1], 100))\n",
        "Z = svm_model.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "# Plot decision boundary with different colors\n",
        "plt.contourf(xx, yy, Z, alpha=0.7, cmap=plt.cm.coolwarm)\n",
        "\n",
        "plt.title('Linear SVM Decision Boundary')\n",
        "plt.xlabel('Standardized Age')\n",
        "plt.ylabel('Standardized Duration')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "pJqzxUfsfsSO",
        "outputId": "d8fc21d0-93cc-43e0-f79b-d27fe34223a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Spliting the Data (For Logistic Regression Classifier)**"
      ],
      "metadata": {
        "id": "iPKxOtcxL_5H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# making the copy of dataframe\n",
        "df3 = df.copy(deep=True)\n",
        "# Spliting the data into features (X) and target variable (y)\n",
        "X = df3.drop('y', axis=1)\n",
        "y = df3['y']\n",
        "\n",
        "# Spliting the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "#print(f\"Training Set Size: {len(X_train)}\")\n",
        "#print(f\"Testing Set Size: {len(X_test)}\")"
      ],
      "metadata": {
        "id": "fYeDxKrbMsLg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2. Logistic Regressison Classifier**\n",
        "\n",
        "Logistic regression is a regression algorithm used for classification. It models the probability that a given data point belongs to a particular class.\n",
        "\n",
        "####***Usage:***\n",
        " Logistic regression is widely used  statistical technique  for bianry classification  due to its simplicity, interpretability, and effectiveness in a wide range of applications.\n",
        "\n",
        "## ***GOAL OF LINEAR CLASSIFICATION***:\n",
        "The goal of it is to model probability that given points belongs to particular class which is done by estimating realtionship between dependent and independent variable.\n",
        "\n",
        "\n",
        "## ***Logistic Function***\n",
        "Logistic regression uses the logistic function (also known as the sigmoid function) to map the output of a linear combination of features to a probability value between 0 and 1.\n",
        "\n",
        "Y = 1 / 1+e^wx+b\n",
        "\n",
        "where w are the weights associated with the features.\n",
        "wx represents the dot product of the feature values\n",
        "x and the corresponding weights,b is bias term and e is base of natural algorithm.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "##*How the  regression classifier works*\n",
        "\n",
        "Logistic regression is a statistical model used for binary classification tasks. It works by modeling the probability that a given data point belongs to a particular class.\n",
        "\n",
        " The model combines input features with associated weights and a bias term, and passes this linear combination through a logistic function (also known as the sigmoid function), which maps the output to a probability value between 0 and 1.\n",
        "\n",
        "This probability is then thresholded to make a binary decision.The model adjusts its weights and bias using optimization techniques like gradient descent to minimize the difference between predicted probabilities and actual labels durng training set.\n",
        "\n",
        " This iterative process updates the model's parameters, allowing it to make accurate predictions on new, unseen data.\n",
        "\n"
      ],
      "metadata": {
        "id": "bXtsi_m5aN7Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####     ***i. Using skLearn***"
      ],
      "metadata": {
        "id": "zT-K7syBacc_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Logistic regression model\n",
        "model = LogisticRegression(max_iter=1000)\n",
        "\n",
        "# Training the model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Predictions on Testing set\n",
        "y_pred = model.predict(X_test)\n",
        "\n"
      ],
      "metadata": {
        "id": "DAmM4vHU8xtT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Confusion Matrix, Accuracy , Precision , Recall , F1-score**"
      ],
      "metadata": {
        "id": "fdjT0MVJNNxU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the confusion matrix\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print('Confusion Matrix:')\n",
        "print(conf_matrix)\n",
        "\n",
        "\n",
        "true_negative, false_positive, false_negative, true_positive = conf_matrix.ravel()\n",
        "\n",
        "\n",
        "# Accuracy\n",
        "accuracy = (true_positive + true_negative) / np.sum(conf_matrix)\n",
        "print(f'Accuracy: {accuracy*100:.2f}%')\n",
        "\n",
        "\n",
        "# Precision\n",
        "precision = true_positive / (true_positive + false_positive)\n",
        "print(f'Precision: {precision:.2f}')\n",
        "\n",
        "# Recall\n",
        "recall = true_positive / (true_positive + false_negative)\n",
        "print(f'Recall: {recall:.2f}')\n",
        "\n",
        "# F1-Score\n",
        "f1 = 2 * (precision * recall) / (precision + recall)\n",
        "print(f'F1-Score: {f1:.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CFHw4QP_MFb",
        "outputId": "2b5d2690-0279-4247-e2a6-4f2c90aaf58b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[7066  199]\n",
            " [ 578  393]]\n",
            "Accuracy: 90.57%\n",
            "Precision: 0.66\n",
            "Recall: 0.40\n",
            "F1-Score: 0.50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ***ii. Manually***:"
      ],
      "metadata": {
        "id": "hz3Imhvoagx0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert categorical target variable to numerical values\n",
        "y = y.map({'no': 0, 'yes': 1})"
      ],
      "metadata": {
        "id": "sjWh0GW-82Y_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Add bias term to X\n",
        "X_b = np.c_[np.ones((len(X), 1)), X]\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_b, y, test_size=0.2, random_state=42)\n",
        "#print(f\"Training Set Size: {len(X_train)}\")\n",
        "#print(f\"Testing Set Size: {len(X_test)}\")\n",
        "\n",
        "# Make sure that y_train and y_test are numpy arrays\n",
        "y_train = y_train.values.reshape(-1, 1)\n",
        "y_test = y_test.values.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "_9H_RurZ83MR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Sigmoid function\n",
        "def sigmoid(z):\n",
        "    return 1 / (1 + np.exp(-z))\n",
        "\n",
        "# Cost function (log-likelihood)\n",
        "def compute_cost(X, y, theta):\n",
        "    m = len(y)\n",
        "    h = sigmoid(np.dot(X, theta))\n",
        "    cost = (-1/m) * np.sum(y * np.log(h + 1e-15) + (1-y) * np.log(1-h + 1e-15))\n",
        "    return cost\n",
        "\n",
        "# Gradient descent\n",
        "def gradient_descent(X, y, theta, learning_rate, num_iterations):\n",
        "    m = len(y)\n",
        "    costs = []\n",
        "\n",
        "    for _ in range(num_iterations):\n",
        "        h = sigmoid(np.dot(X, theta))\n",
        "\n",
        "        gradient = np.dot(X.T, (h - y)) / m\n",
        "        theta -= learning_rate * gradient\n",
        "        cost = compute_cost(X, y, theta)\n",
        "        costs.append(cost)\n",
        "\n",
        "    return theta, costs\n",
        "\n",
        "# Predict function\n",
        "def predict(X, theta, threshold=0.5):\n",
        "    probabilities = sigmoid(np.dot(X, theta))\n",
        "    predictions = (probabilities >= threshold).astype(int)\n",
        "    return predictions\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Initialize parameters\n",
        "theta_init = np.random.randn(X_b.shape[1], 1)\n",
        "\n",
        "# Set hyperparameters\n",
        "learning_rate = 0.01\n",
        "num_iterations = 1000\n",
        "\n",
        "# Train the model\n",
        "\n",
        "theta_trained, _ = gradient_descent(X_train, y_train, theta_init, learning_rate, num_iterations)\n",
        "\n",
        "# Make predictions on the test set\n",
        "predictions2 = predict(X_test, theta_trained)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdSb8E-b-RlL",
        "outputId": "9888875f-3e33-4c4c-f336-e086a230f136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-59-fe869f2b5b9b>:3: RuntimeWarning: overflow encountered in exp\n",
            "  return 1 / (1 + np.exp(-z))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Warning:** overflow encountered in exp\n",
        "\n",
        "This warning indicates that the exponential function (np.exp) encountered an extremely large value, resulting in a potential overflow. This often happens when the input to the exponential function is very negative."
      ],
      "metadata": {
        "id": "sM9MtjM1odLY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Confusion Matrix, Accuracy , Precision , Recall , F1-score**\n"
      ],
      "metadata": {
        "id": "8W5ZDOCfM2ce"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Confusion Matrix\n",
        "conf2_matrix = confusion_matrix(y_test, predictions2)\n",
        "print('Confusion Matrix:')\n",
        "print(conf2_matrix)\n",
        "\n",
        "true_negative, false_positive, false_negative, true_positive = conf2_matrix.ravel()\n",
        "\n",
        "# Accuracy\n",
        "accuracy = (true_positive + true_negative) / np.sum(conf2_matrix)\n",
        "print(f'Accuracy: {accuracy*100:.2f}%')\n",
        "\n",
        "# Precision\n",
        "precision = true_positive / (true_positive + false_positive)\n",
        "print(f'Precision: {precision:.2f}')\n",
        "\n",
        "# Recall\n",
        "recall = true_positive / (true_positive + false_negative)\n",
        "print(f'Recall: {recall:.2f}')\n",
        "\n",
        "# F1-Score\n",
        "f1 = 2 * (precision * recall) / (precision + recall)\n",
        "print(f'F1-Score: {f1:.2f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a07o887iFfzr",
        "outputId": "f7954e81-5c09-468c-88db-766b44a61232"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[7265    0]\n",
            " [ 971    0]]\n",
            "Accuracy: 88.21%\n",
            "Precision: nan\n",
            "Recall: 0.00\n",
            "F1-Score: nan\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-60-25f53f36d38b>:13: RuntimeWarning: invalid value encountered in long_scalars\n",
            "  precision = true_positive / (true_positive + false_positive)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Decision Boundary For Logistic Regression Classifier**"
      ],
      "metadata": {
        "id": "NExsATJtfbQ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Extract the \"age\" and \"duration\" columns\n",
        "X = df[['age', 'duration']]\n",
        "y = df['y']\n",
        "\n",
        "# Convert categorical target values to numerical values\n",
        "y_numerical = pd.factorize(y)[0]\n",
        "\n",
        "# Fit a logistic regression model with feature names\n",
        "model = LogisticRegression()\n",
        "model.fit(X, y_numerical)\n",
        "\n",
        "# Plot the decision boundary with different colors for each class\n",
        "plt.scatter(X['age'], X['duration'], c=y_numerical, edgecolors='k',cmap=plt.cm.coolwarm)\n",
        "ax = plt.gca()\n",
        "xlim = ax.get_xlim()\n",
        "ylim = ax.get_ylim()\n",
        "\n",
        "# Create a meshgrid to plot the decision boundary\n",
        "xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 100), np.linspace(ylim[0], ylim[1], 100))\n",
        "Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "# Plot decision boundary with different colors\n",
        "plt.contourf(xx, yy, Z, alpha=0.5, cmap=plt.cm.coolwarm)\n",
        "\n",
        "plt.title('Logistic Regression Decision Boundary')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Duration')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "OuOtWTlDfbhz",
        "outputId": "55424ee6-8dc8-4daf-81ca-75f0200b405c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkYAAAHHCAYAAABa2ZeMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3RU19Xw4d+dPqPRqBcECERH9I7ozcgYg3vFxjWJHWwHnDiJvzdv4sRJnNeJ4ziJSxI74AIuuAM2xTQbECBA9F4lIdRRl6ae7w9JA0ISRkIwAvazFmsxc8/cu+/VSLPn3HP20ZRSCiGEEEIIgS7QAQghhBBCtBaSGAkhhBBC1JDESAghhBCihiRGQgghhBA1JDESQgghhKghiZEQQgghRA1JjIQQQgghakhiJIQQQghRQxIjIYQQQogakhiJa964ceMYN25ci+2vY8eOPPjggy22PwGapvHcc88FOozLbs2aNWiaxpo1a5r0OnkPNt+1+l4TZ0hiJFqNefPmoWkaW7ZsCXQo32vDhg0899xzFBUVXdLjdOzYEU3T/P+CgoIYOnQo77zzziU9rqj23HPP1bn+NpuN+Ph4pk2bxty5c3E6nYEOsVWp/R0++190dDTjx4/n66+/DnR4QlwQQ6ADECLQli9f3uTXbNiwgd/+9rc8+OCDhIaG1tl24MABdLqW+87Rv39/fvrTnwJw6tQp3nzzTR544AGcTic/+MEPWuw4rVllZSUGQ+D+XL3++uvY7XacTicnT55k2bJlPPzww/ztb39j8eLFtG/f/pIcd8yYMVRWVmIymZr0upZ+DzbV7373OxISElBKkZOTw7x587jhhhtYtGgRN954Y8DiEuJCSGIkrnlN/dD5PmazuUX317ZtW+677z7/4wcffJBOnTrx8ssvX/bEqLy8nKCgoMt6TACLxXLZj3m222+/ncjISP/jX//618yfP5+ZM2dyxx13sHHjxktyXJ1O16xzb+n3YFNNmTKFwYMH+x8/8sgjxMTE8P77719TiVGgfl/ExZFbaeKKk5aWxpQpU3A4HNjtdiZOnNjgB9POnTsZO3YsVquVdu3a8fvf/565c+eiaRrHjx/3t2tojNE//vEPevXqhc1mIywsjMGDB7NgwQKg+vbKM888A0BCQoL/lkHtPhsa31FUVMScOXPo2LEjZrOZdu3aMXPmTPLz85t8/lFRUfTo0YMjR47Ued7n8/G3v/2NXr16YbFYiImJ4Uc/+hGnT5+u1+65554jLi4Om83G+PHj2bt3b724a2+LrF27lh//+MdER0fTrl07//avv/6a0aNHExQURHBwMFOnTmXPnj11jpWdnc1DDz1Eu3btMJvNtGnThptuuqnO9d+yZQvJyclERkZitVpJSEjg4YcfrrOfhsZ9XMj7oPYc1q9fz9NPP01UVBRBQUHccsst5OXlXeglb9CMGTN49NFH2bRpEytWrKizbdOmTVx//fWEhIRgs9kYO3Ys69evr7ePkydP8sgjjxAXF4fZbCYhIYHHH38cl8sFNDzG6NChQ9x2223ExsZisVho164dd999N8XFxf42Db0Hjx49yh133EF4eDg2m43hw4ezZMmSOm1qj/fRRx/xhz/8gXbt2mGxWJg4cSKHDx9u9rUKDQ3FarXW6/UrLy/npz/9Ke3bt8dsNtO9e3f+8pe/oJTytzl+/DiapjFv3rx6+z33fVF76/Pw4cP+3tyQkBAeeughKioq6rzW6XQyZ84coqKiCA4OZvr06WRmZtY7xokTJ/jxj39M9+7dsVqtREREcMcdd9R5D0Pjvy+rV69G0zQ+++yzevtesGABmqaRkpJyAVdRXC7SYySuKHv27GH06NE4HA5+/vOfYzQa+de//sW4ceNYu3Ytw4YNA6o/cMaPH4+maTz77LMEBQXx5ptvXtA36f/85z889dRT3H777fzkJz+hqqqKnTt3smnTJu69915uvfVWDh48yPvvv8/LL7/s70mIiopqcH9lZWWMHj2affv28fDDDzNw4EDy8/P58ssvyczMrNMTcSE8Hg+ZmZmEhYXVef5HP/oR8+bN46GHHuKpp57i2LFj/POf/yQtLY3169djNBoBePbZZ3nxxReZNm0aycnJ7Nixg+TkZKqqqho83o9//GOioqL49a9/TXl5OQDvvvsuDzzwAMnJyfzf//0fFRUVvP7664waNYq0tDQ6duwIwG233caePXt48skn6dixI7m5uaxYsYL09HT/48mTJxMVFcUvf/lLQkNDOX78OJ9++ul5r8GFvg9qPfnkk4SFhfGb3/yG48eP87e//Y0nnniCDz/8sEnX/lz3338///73v1m+fDnXXXcdAKtWrWLKlCkMGjSI3/zmN+h0OubOncuECRP47rvvGDp0KABZWVkMHTqUoqIifvjDH9KjRw9OnjzJxx9/TEVFRYM9mS6Xi+TkZJxOJ08++SSxsbGcPHmSxYsXU1RUREhISINx5uTkMGLECCoqKnjqqaeIiIjg7bffZvr06Xz88cfccsstddr/6U9/QqfT8bOf/Yzi4mJefPFFZsyYwaZNmy7ouhQXF5Ofn49SitzcXP7xj39QVlZWp+dTKcX06dNZvXo1jzzyCP3792fZsmU888wznDx5kpdffvmCjtWQO++8k4SEBF544QW2bdvGm2++SXR0NP/3f//nb/Poo4/y3nvvce+99zJixAhWrVrF1KlT6+0rNTWVDRs2cPfdd9OuXTuOHz/O66+/zrhx49i7dy82m61O+3N/X8aNG0f79u2ZP39+ves8f/58OnfuTFJSUrPPVVwCSohWYu7cuQpQqampjba5+eablclkUkeOHPE/l5WVpYKDg9WYMWP8zz355JNK0zSVlpbmf66goECFh4crQB07dsz//NixY9XYsWP9j2+66SbVq1ev88b65z//ud5+anXo0EE98MAD/se//vWvFaA+/fTTem19Pt95j9OhQwc1efJklZeXp/Ly8tSuXbvU/fffrwA1a9Ysf7vvvvtOAWr+/Pl1Xr906dI6z2dnZyuDwaBuvvnmOu2ee+45BdSJu/bnMWrUKOXxePzPl5aWqtDQUPWDH/ygzj6ys7NVSEiI//nTp08rQP35z39u9Pw+++yz7/2ZK6UUoH7zm9/4H1/o+6D2HCZNmlTnWs+ZM0fp9XpVVFR03uP+5je/UYDKy8trcHvtOd5yyy1KqeqfZ9euXVVycnKd41VUVKiEhAR13XXX+Z+bOXOm0ul0DZ577WtXr16tALV69WqllFJpaWkKUAsXLjxv3Oe+B2fPnq0A9d133/mfKy0tVQkJCapjx47K6/XWOV7Pnj2V0+n0t33llVcUoHbt2nXe49Ze73P/mc1mNW/evDptP//8cwWo3//+93Wev/3225Wmaerw4cNKKaWOHTumADV37tx6xzv3fVH783r44YfrtLvllltURESE//H27dsVoH784x/XaXfvvffW22dFRUW946akpChAvfPOO/XO/dzfF6WUevbZZ5XZbK7zfsvNzVUGg6HOsUTrILfSxBXD6/WyfPlybr75Zjp16uR/vk2bNtx7772sW7eOkpISAJYuXUpSUhL9+/f3twsPD2fGjBnfe5zQ0FAyMzNJTU1tkbg/+eQT+vXrV+/bIlTfCvg+y5cvJyoqiqioKPr06cO7777LQw89xJ///Gd/m4ULFxISEsJ1111Hfn6+/9+gQYOw2+2sXr0agJUrV+LxePjxj39c5xhPPvlko8f/wQ9+gF6v9z9esWIFRUVF3HPPPXWOpdfrGTZsmP9YVqsVk8nEmjVr6t3Oq1U7cH3x4sW43e7vvRbQtPdBrR/+8Id1rvXo0aPxer2cOHHigo7ZGLvdDkBpaSkA27dv59ChQ9x7770UFBT4r015eTkTJ07k22+/xefz4fP5+Pzzz5k2bVqdsTi1Gntf1PYILVu2rN6tofP56quvGDp0KKNGjaoT+w9/+EOOHz/O3r1767R/6KGH6vRYjR49Gqi+HXchXn31VVasWMGKFSt47733GD9+PI8++midnsCvvvoKvV7PU089Vee1P/3pT1FKXdQstscee6zO49GjR1NQUOB/X3z11VcA9Y49e/bsevuyWq3+/7vdbgoKCujSpQuhoaFs27atXvtzf18AZs6cidPp5OOPP/Y/9+GHH+LxeOr0oonWQRIjccXIy8ujoqKC7t2719vWs2dPfD4fGRkZQPW4gC5dutRr19Bz5/rFL36B3W5n6NChdO3alVmzZjU4PuRCHTlyhN69ezf79cOGDWPFihUsXbqUv/zlL4SGhnL69Ok6H1yHDh2iuLiY6OhofxJV+6+srIzc3FwAfyJw7nUIDw+vd2uuVkJCQp3Hhw4dAmDChAn1jrV8+XL/scxmM//3f//H119/TUxMDGPGjOHFF18kOzvbv6+xY8dy22238dvf/pbIyEhuuumm750G35T3Qa34+Pg6j2vPtbGE7UKVlZUBEBwcDJy5Ng888EC9a/Pmm2/idDopLi4mLy+PkpKSJr8vEhISePrpp3nzzTeJjIwkOTmZV199tc74ooacOHGi0etVu/1sF3u9hg4dyqRJk5g0aRIzZsxgyZIlJCYm8sQTT/jHT504cYK4uDj/tfu+mJri++I/ceIEOp2Ozp0712nX0DWqrKzk17/+tX8cVGRkJFFRURQVFTV43c/9fQHo0aMHQ4YMYf78+f7n5s+fz/Dhwy/ob5K4vGSMkRDn6NmzJwcOHGDx4sUsXbqUTz75hNdee41f//rX/Pa3v73s8URGRjJp0iQAkpOT6dGjBzfeeCOvvPIKTz/9NFA9oDo6OrrOH96zNTb+6UKc/Y259lhQPc4oNja2XvuzB9jOnj2badOm8fnnn7Ns2TL+93//lxdeeIFVq1YxYMAANE3j448/ZuPGjSxatMg/Df6ll15i48aN/h6Zi3XuN/ha6qxBvs2xe/du4EyiWXtt/vznP9fprTyb3W6nsLCw2cd86aWXePDBB/niiy9Yvnw5Tz31FC+88AIbN26sMzj+YrT09dLpdIwfP55XXnmFQ4cO0atXrwt+bWO9Z16vt9HXtGT8Tz75JHPnzmX27NkkJSUREhKCpmncfffd/p/32c79fak1c+ZMfvKTn5CZmYnT6WTjxo3885//bHI84tKTxEhcMaKiorDZbBw4cKDetv3796PT6fz1ZDp06NDgLJoLnVkTFBTEXXfdxV133YXL5eLWW2/lD3/4A88++ywWi+WCboHV6ty5s/8DtCVMnTqVsWPH8sc//pEf/ehHBAUF0blzZ7755htGjhzZ6B9mqL4uUH0dzv5mW1BQcMG9AbXfsqOjo/0J2/e1/+lPf8pPf/pTDh06RP/+/XnppZd47733/G2GDx/O8OHD+cMf/sCCBQuYMWMGH3zwAY8++mi9/TXlfXCpvfvuu0B1wgpnro3D4TjvtYmKisLhcDT7fdGnTx/69OnDr371KzZs2MDIkSN54403+P3vf99g+w4dOjR6vWq3X2oejwc408vWoUMHvvnmG0pLS+v0Gp0bU21vz7nFVC+mR6lDhw74fD6OHDlSp5eooWv08ccf88ADD/DSSy/5n6uqqmpycde7776bp59+mvfff5/KykqMRiN33XVXs89BXDpyK01cMfR6PZMnT+aLL76oM1U2JyeHBQsWMGrUKBwOB1D9QZWSksL27dv97QoLCxvtUTlbQUFBnccmk4nExESUUv5xMLW1SS7kj+Ntt93Gjh07Gpyu29xv4L/4xS8oKCjgP//5D1A9C8fr9fL888/Xa+vxePxxTpw4EYPBwOuvv16nTVO+uSYnJ+NwOPjjH//Y4Lig2mnwFRUV9Wa6de7cmeDgYP+tstOnT9e7BrU9LY3dTmvK++BSWrBgAW+++SZJSUlMnDgRgEGDBtG5c2f+8pe/+BOAs9VeG51Ox80338yiRYsarPTe2PuipKTEn2DU6tOnDzqd7ry3H2+44QY2b95cZ1p4eXk5//73v+nYsSOJiYnff8IXwe12s3z5ckwmk/9W2Q033IDX66333nv55ZfRNI0pU6YA1UlmZGQk3377bZ12r732WrPjqd333//+9zrP/+1vf6vXVq/X1/t5/OMf/zhvj1VDIiMjmTJlCu+99x7z58/n+uuvb/KMVHF5SI+RaHX++9//snTp0nrP/+QnP+H3v/89K1asYNSoUfz4xz/GYDDwr3/9C6fTyYsvvuhv+/Of/5z33nuP6667jieffNI/XT8+Pp7CwsLz9vhMnjyZ2NhYRo4cSUxMDPv27eOf//wnU6dO9X+zHTRoEAD/8z//w913343RaGTatGkNFnN75pln+Pjjj7njjjt4+OGHGTRoEIWFhXz55Ze88cYb9OvXr8nXaMqUKfTu3Zu//vWvzJo1i7Fjx/KjH/2IF154ge3btzN58mSMRiOHDh1i4cKFvPLKK9x+++3ExMTwk5/8hJdeeonp06dz/fXXs2PHDr7++msiIyMvqCfM4XDw+uuvc//99zNw4EDuvvtuoqKiSE9PZ8mSJYwcOZJ//vOfHDx4kIkTJ3LnnXeSmJiIwWDgs88+Iycnh7vvvhuAt99+m9dee41bbrmFzp07U1payn/+8x8cDgc33HBDozFc6PugpXz88cfY7XZcLpe/8vX69evp168fCxcu9LfT6XS8+eabTJkyhV69evHQQw/Rtm1bTp48yerVq3E4HCxatAiAP/7xjyxfvpyxY8fywx/+kJ49e3Lq1CkWLlzIunXr6lVUh+pSAE888QR33HEH3bp1w+Px8O6776LX67ntttsajf+Xv/wl77//PlOmTOGpp54iPDyct99+m2PHjvHJJ5+0eJXsr7/+2t/zk5uby4IFCzh06BC//OUv/UnrtGnTGD9+PP/zP//D8ePH6devH8uXL+eLL75g9uzZdcb/PProo/zpT3/i0UcfZfDgwXz77bccPHiw2fH179+fe+65h9dee43i4mJGjBjBypUrG+xRvvHGG3n33XcJCQkhMTGRlJQUvvnmGyIiIpp83JkzZ3L77bcDNPglRrQSAZsPJ8Q5GpvqW/svIyNDKaXUtm3bVHJysrLb7cpms6nx48erDRs21NtfWlqaGj16tDKbzapdu3bqhRdeUH//+98VoLKzs/3tzp2u/69//UuNGTNGRUREKLPZrDp37qyeeeYZVVxcXGf/zz//vGrbtq3S6XR1pu6fO1VaqepSAU888YRq27atMplMql27duqBBx5Q+fn5570mHTp0UFOnTm1w27x58+pNY/73v/+tBg0apKxWqwoODlZ9+vRRP//5z1VWVpa/jcfjUf/7v/+rYmNjldVqVRMmTFD79u1TERER6rHHHqv382hsKv3q1atVcnKyCgkJURaLRXXu3Fk9+OCDasuWLUoppfLz89WsWbNUjx49VFBQkAoJCVHDhg1TH330kX8f27ZtU/fcc4+Kj49XZrNZRUdHqxtvvNG/j1qcM4W69rXf9z5o7BzOnQbfmNrp37X/LBaLateunbrxxhvVf//7X1VVVdXg69LS0tStt97qfw916NBB3XnnnWrlypV12p04cULNnDlTRUVFKbPZrDp16qRmzZrlnyp/bpxHjx5VDz/8sOrcubOyWCwqPDxcjR8/Xn3zzTd19tvQe/DIkSPq9ttvV6GhocpisaihQ4eqxYsXN3hdzi0HcL4p82dr6HfYYrGo/v37q9dff71eeYrS0lI1Z84cFRcXp4xGo+ratav685//XK9dRUWFeuSRR1RISIgKDg5Wd955p8rNzW10uv655RVq4zq7vEZlZaV66qmnVEREhAoKClLTpk1TGRkZ9fZ5+vRp9dBDD6nIyEhlt9tVcnKy2r9/f71rfCHlRpxOpwoLC1MhISGqsrLyvNdSBI6m1EWOPhTiCjJ79mz+9a9/UVZW1ugAzWtRUVERYWFh/P73v+d//ud/Ah2OEFclj8dDXFwc06ZN46233gp0OKIRMsZIXLUqKyvrPC4oKODdd99l1KhR13RSdO51gTNjK85dGkUI0XI+//xz8vLymDlzZqBDEechPUbiqtW/f3/GjRtHz549ycnJ4a233iIrK4uVK1cyZsyYQIcXMPPmzfOvdm6321m3bh3vv/8+kydPZtmyZYEOT4irzqZNm9i5cyfPP/88kZGRDRaGFK2HDL4WV60bbriBjz/+mH//+99omsbAgQN56623rumkCKBv374YDAZefPFFSkpK/AOyG5vqLYS4OK+//jrvvfce/fv3b3AxXNG6SI+REEIIIUQNGWMkhBBCCFFDEiMhhBBCiBoyxugC+Hw+srKyCA4ObtJSEEIIIYQIHKUUpaWlxMXFXXAhU0mMLkBWVtZlW3tJCCGEEC0rIyPjghdZlsToAtQuA7Hlry9iP88CnUIIIYRoPcoqKxn89M/rLFT8fSQxugC1t8/sVivBkhgJIYQQV5SmDIORwddCCCGEEDUkMRJCCCGEqCGJkRBCCCFEDUmMhBBCCCFqBDQxeu6559A0rc6/Hj16+LdXVVUxa9YsIiIisNvt3HbbbeTk5NTZR3p6OlOnTsVmsxEdHc0zzzyDx+Op02bNmjUMHDgQs9lMly5dZK0aIYQQQjQo4D1GvXr14tSpU/5/69at82+bM2cOixYtYuHChaxdu5asrCxuvfVW/3av18vUqVNxuVxs2LCBt99+m3nz5vHrX//a3+bYsWNMnTqV8ePHs337dmbPns2jjz4qq4gLIYQQop6AT9c3GAzExsbWe764uJi33nqLBQsWMGHCBADmzp1Lz5492bhxI8OHD2f58uXs3buXb775hpiYGPr378/zzz/PL37xC5577jlMJhNvvPEGCQkJvPTSSwD07NmTdevW8fLLL5OcnHxZz1UIIYQQrVvAe4wOHTpEXFwcnTp1YsaMGaSnpwOwdetW3G43kyZN8rft0aMH8fHxpKSkAJCSkkKfPn2IiYnxt0lOTqakpIQ9e/b425y9j9o2tftoiNPppKSkpM4/IYQQQlz9ApoYDRs2jHnz5rF06VJef/11jh07xujRoyktLSU7OxuTyURoaGid18TExJCdnQ1AdnZ2naSodnvttvO1KSkpobKyssG4XnjhBUJCQvz/ZDkQIYQQ4toQ0FtpU6ZM8f+/b9++DBs2jA4dOvDRRx9hDWCF6WeffZann37a/7ikpESSoytQZkYmGzdu5Njx46CgY8cODBs2jPgO8YEOTQghRCsV8FtpZwsNDaVbt24cPnyY2NhYXC4XRUVFddrk5OT4xyTFxsbWm6VW+/j72jgcjkaTL7PZjMPhqPNPXFm2pKby1n/f4tCRTAyWzhitXTh8NIu58+ay8Ty3UYUQQlzbWlViVFZWxpEjR2jTpg2DBg3CaDSycuVK//YDBw6Qnp5OUlISAElJSezatYvc3Fx/mxUrVuBwOEhMTPS3OXsftW1q9yGuPjnZOXz11dcEh/YkJv5mQiMGEhIxgJj4m3CE9WbZ8uVknTwZ6DCFEEK0QgFNjH72s5+xdu1ajh8/zoYNG7jlllvQ6/Xcc889hISE8Mgjj/D000+zevVqtm7dykMPPURSUhLDhw8HYPLkySQmJnL//fezY8cOli1bxq9+9StmzZqF2WwG4LHHHuPo0aP8/Oc/Z//+/bz22mt89NFHzJkzJ5CnLi6h1NTNGIw2QiOHoHFm4UANjZDIQZhMwWzenBrACIUQQrRWAR1jlJmZyT333ENBQQFRUVGMGjWKjRs3EhUVBcDLL7+MTqfjtttuw+l0kpyczGuvveZ/vV6vZ/HixTz++OMkJSURFBTEAw88wO9+9zt/m4SEBJYsWcKcOXN45ZVXaNeuHW+++aZM1b+KpadnYra1Q9Pq5/0aGmZbe9LTMwIQmRBCiNYuoInRBx98cN7tFouFV199lVdffbXRNh06dOCrr746737GjRtHWlpas2IUVx5Np0Mpb6PblfKi07equ8hCCCFaCfl0EFedrl06UVWegc/nrrdNKQ9V5el06dIpAJEJIYRo7SQxEledwUOGoGk+CnO+w+dz+Z/3+dwUZH+HUm6GDB0awAiFEEK0VgFfEkSIlhYaGspdd97Bhx8t5NSxhZhtcYCGszIL8HHH7bcRERER6DCFEEK0QpIYiatSl65deeqpJ9m2dSvHjh1H4aNjh+EMGjQIR4jUpRJCCNEwSYzEVSs4OJix48YxdlygIxFCCHGlkDFGQgghhBA1JDESQgghhKghiZEQQgghRA1JjIQQQgghakhiJIQQQghRQxIjIYQQQogakhgJIYQQQtSQxEgIIYQQooYkRkIIIYQQNSQxEkIIIYSoIYmREEIIIUQNSYyEEEIIIWpIYiSEEEIIUUMSIyGEEEKIGpIYCSGEEELUkMRICCGEEKKGJEZCCCGEEDUkMRJCCCGEqCGJkRBCCCFEDUmMhBBCCCFqSGIkhBBCCFFDEiMhhBBCiBqSGAkhhBBC1JDESAghhBCihiRGQgghhBA1JDESQgghhKghiZEQQgghRA1JjIQQQgghakhiJIQQQghRQxIjIYQQQogakhgJIYQQQtSQxEgIIYQQooYkRkIIIYQQNSQxEkIIIYSoIYmREEIIIUQNSYyEEEIIIWpIYiSEEEIIUUMSIyGEEEKIGoZAByCEuLZlnTxJamoqOaeyMZpM9OjZk/4D+mO1WgMdmhDiGiSJkRAiYFavWsW3331HsM5AjM+AEx8rT2ayft067n9gJjExMYEOUQhxjZFbaUKIgNi7Zw/ffvcd/TUbN6oQhuqCGK0LZjqhmJ1uFrw3H6/HG+gwhRDXGEmMhBABsWH9BtpoJhI1K5p25nmrpmOECqKkrJR9+/YGLkAhxDVJEiMhxGXndrk5eSqLDpga3O7Q9ITrTBw7duwyRyaEuNZJYiSEuOwUCgDtPG00QCl1WeIRQohakhgJIS47k8lETFQ0Gbgb3F6mfBT6XLRvH3+ZIxNCXOskMRJCBMSw4cPJVE6OKmed591KsYlyrBYrvfv0DlB0QohrlUzXF0IExIABA8jMzGRj2jYOay5ifXqcKNI1Nxj0zLjnboxGY6DDFEJcYyQxEkIEhgbTpk2je/fubElNJT0nG6PByODEQQwZMoSQ0JBARyiEuAZJYiSECBwNunXvRrfu3QIdiRBCADLGSAghhBDCTxIjIYQQQogakhgJIYQQQtRoNYnRn/70JzRNY/bs2f7nqqqqmDVrFhEREdjtdm677TZycnLqvC49PZ2pU6dis9mIjo7mmWeewePx1GmzZs0aBg4ciNlspkuXLsybN+8ynJEQQgghrjStIjFKTU3lX//6F3379q3z/Jw5c1i0aBELFy5k7dq1ZGVlceutt/q3e71epk6disvlYsOGDbz99tvMmzePX//61/42x44dY+rUqYwfP57t27cze/ZsHn30UZYtW3bZzk8IIYQQV4aAJ0ZlZWXMmDGD//znP4SFhfmfLy4u5q233uKvf/0rEyZMYNCgQcydO5cNGzawceNGAJYvX87evXt577336N+/P1OmTOH555/n1VdfxeVyAfDGG2+QkJDASy+9RM+ePXniiSe4/fbbefnllwNyvkIIIYRovQKeGM2aNYupU6cyadKkOs9v3boVt9td5/kePXoQHx9PSkoKACkpKfTp04eYmBh/m+TkZEpKStizZ4+/zbn7Tk5O9u9DCCGEEKJWQOsYffDBB2zbto3U1NR627KzszGZTISGhtZ5PiYmhuzsbH+bs5Oi2u21287XpqSkhMrKSqxWa71jO51OnM4zyxSUlJQ0/eSEEEIIccUJWI9RRkYGP/nJT5g/fz4WiyVQYTTohRdeICQkxP+vffv2gQ5JCCGEEJdBwBKjrVu3kpuby8CBAzEYDBgMBtauXcvf//53DAYDMTExuFwuioqK6rwuJyeH2NhYAGJjY+vNUqt9/H1tHA5Hg71FAM8++yzFxcX+fxkZGS1xykIIIYRo5QKWGE2cOJFdu3axfft2/7/BgwczY8YM//+NRiMrV670v+bAgQOkp6eTlJQEQFJSErt27SI3N9ffZsWKFTgcDhITE/1tzt5HbZvafTTEbDbjcDjq/BNCCCHE1S9gY4yCg4Pp3bt3neeCgoKIiIjwP//II4/w9NNPEx4ejsPh4MknnyQpKYnhw4cDMHnyZBITE7n//vt58cUXyc7O5le/+hWzZs3CbDYD8Nhjj/HPf/6Tn//85zz88MOsWrWKjz76iCVLllzeExZCCCFEq9eqF5F9+eWX0el03HbbbTidTpKTk3nttdf82/V6PYsXL+bxxx8nKSmJoKAgHnjgAX73u9/52yQkJLBkyRLmzJnDK6+8Qrt27XjzzTdJTk4OxCkJIYQQohXTlFIq0EG0diUlJYSEhLD/9X8Q3Mi4JCGEEEK0LqWVlfR4/EmKi4sveFhMwOsYCSGEEEK0FpIYCSGEEELUkMRICCGEEKJGqx58La5cTqeTvXv2cPp0EVarlV69euEIkbIHQgghWjdJjESLS9u2ja+XLsPj8WA02vB4qljxzQqGDB5M8vXXo9NJR6UQQojWSRIj0aL27d3Ll4sWYQ/pSlR4fwyGIHw+N2XFB0jdshWdTk/y9VIqQQghROskX91Fy1GwatUarEHtCYsegcEQBIBOZ8QR1htHeH82p6ZSXlYe4ECFEEKIhkliJFpMbl4u+QV52EO7o6HV2x4c0h2lFPv37w9AdEIIIcT3k8RItBhnlRMAg8He4Had3oxeZ8TprLqcYQkhhBAXTBIj0WLCwsLQNA1nZU6D212u03i8TsLDwy9zZEIIIcSFkcRItBh7sJ2uXbpSVrQHr7dur5BSPkoKthFks9O1W7cARSiEEEKcnyRGokUlX5+MweAlN3MxpUV7cFblUl56hLyTX1NVfpLp06eh1+sDHaYQQgjRIJmuL1pUeHg4jz76CCu/Wcm+fVvxKR8AHeI7MH78DXTo2DGwAQohhBDnIYmRaHFhYWHcfsftVFZWUlpSitVqJdgR3CL7zs7OJnVzKiczM9Hr9XTt1pVBgwa32P6bTEEDE/CEEEJcoSQxEpeM1WrFarW22P42bdzI0mXLsOkMxPkMeFBsyFlHSspG7r33nsvWG5WXl8eG9RvYu3cvbrebiPBwBg8ZzODBQ9Ab5DahEEJcyWSMkbginDh+nKXLltETKzepEIbqghihs3MzoYR74P0F71NZUXnJ4zh+7Dj/+fe/ObRrN93deoZoNoJOl7F82XLee/cdPG7PJY9BCCHEpSOJkbgipGxIIUxnZIBmQzvr1pVR0xhBEB6Pm7S0tEsag8fjYeHCj4jw6rhROeijs9FFszBSszNRc5CZkcm6dd9d0hiEEEJcWpIYiSvCsePHiVfGBsfzWDQdMcrIsWPHLmkM+/bupaKykiHY0Gt1A4nSDHRSJrakbsHn9V3SOIQQQlw6khiJK4NS5x3jXL1NXdIQTp06RYjeSLDW8DiitpqJ8soKSkpLLmkcQgghLh1JjMQVIT4+ngzN02Du41KKHM1DfHyHSxqDXq/HrVSj+Ze7ZoNBL3MahBDiSiWJkbgiDE8aToHPxR4q6yQmXqXYSDmaXsfAgQMvaQxdu3ajwufhFO4Gtx/FSWx0DHZ7w2vFCSGEaP0kMRJXhM5dujB+3Dh2qAqWaCVs91WwxVfOF1ox2ToPd9xxJ0H2oEsaQ3x8PO3i2rJJq6BAnZl95lWKnb4KspSLUaNHSV0jIYS4gkmfv7hsfF4fBw4c4ODBA3g8HmJjY+k/YABBQReW0IwZO5aOHRPYvHkTJzOqCzz269aPoUOHEh5xGRam1eCue+5m/rvvsSw3h3DNiNWnka/z4lI+Jk6YQK/evS99HEIIIS4ZTSl1aUesXgVKSkoICQlh/+v/ILgFCxZeS4qKinjv3fkUFOZjtkSg05lwVuWhaXDT9On06dsn0CFeMJ/Px8EDB9m7dy8ul5PIyEgGDhpEePhlSM6EEEJcsNLKSno8/iTFxcU4HI4Leo30GIlLzuf18d578yktcxEbPw2TOQIAr9dJUf5mPvv8M0JDQ2gfHx/gSC+MTqejR88e9OjZI9ChCCGEaGEyxkhccgcOHKCgIJ/w2LH+pAhArzcTHjMSkzmM9es3BDBCIYQQopokRuKSO3jwAGZLRJ2kqJaGDmtwZw4dOoTc1RVCCBFokhiJS87j8aDpTI1u1+lM+JQPn08qRgshhAgsGWMkLrmYmFj27juAz+tEpzfX215VcZKIiEj0elmZvjUoLysnLW0b6ekZaJpGp04J9OvfH4vFEujQhBDikpMeI3HJDRgwAA1FUX4qirq9QpUVmVSWpTNs6JAARSfOdujgQf72yiusXvMtmacqyThZzrLlK3jlb38nIz0j0OEJIcQlJz1G4pILsgcxffp0Pv/ic9yuQmzBndHpzFRVZFJRlk7XLl0YNGhwoMO85hXkF/DhRwsxW+MIix6JvqZ3z+upoCDnW+bPX8CTTz5xyQtpCiFEIEmPkbgs+vbry4MPPEiH9pEU52+lIGcdFlMZ1ycnc9ddd6HTy1sx0DZv3oROZyIidow/KQLQG2xExI7D7fGQlrYtgBEKIcSlJz1G4rKJ7xBPfId4lE/h8/nQG67+MUVVVVVs27qNHTt3UlFeQUhoCIMGDqRv376t7vwPHDyMJagjmlb/z4Jeb8ES1I4DBw4xavToAEQnhBCXhyRG4rLTdBp6XetKCi6FkuIS5s6bR0lxCVZ7BwzmWE4XFbJo0SLStm/nvvtmYDI1PlvvcvN6vehMjf9J0DQjHq/rMkYkhBCXn9y/EOIS+fiTTyivcBPb8WYiYscQEjGAyLiJRLefQlZWNiuWrwh0iHW0jWuDsyITRf16Ukr5cFaepH27uABEJoQQl4/0GAlxCWRnZ5ORkU5km/EYDMF1tpkt0dhDe7F9xw4mTprYaqbBDxs2lAMH3+V03iZ8XheuylzQNMzWWMCHx13B4MEye1AIcXWTHiMhLoHMjAw0TYc1qH2D2232Dng8bnKysy9zZI1L6NSJTgkJlBXtx1WZiy24I9ag9lSVZ1JecoR+ffsSHRMd6DCFEOKSkh4jccnk5+VTVHQaq9VKXNu2aJoW6JAuG03TqL4j1fAyJ7W3q1rTNcnMyOTosWM4wnoTEjkIjerYQiMHU5i7nl27djNm7FjCw8MDHKkQQlw6khiJFncqK4slX33NyZOZ/udCQ8KYNGkCvXr3DmBkl0/HjgkofFSUHScouHO97RUlRzGZzLRp03rG7GzatAmTOaROUgSgaTrCo5M4VXGSLalbmJw8OYBRCiHEpSWJkWhR2adOMXfu2+gMwUS2GY/JEonHXUZZ0R4+/uQTXC43AwYOCHSYl1xEZARdOnfh+IktGE2hdRbQrSzPoKxoL0lJwzCajAGMslppaSmVlZUcP5GOxdahTlJUS9MMmG1tOX4iPQARCiHE5SOJkWhRy5YvR2ewE9VuCrqaejgGQxBmazRazjqWLV9O7z69MRoDnxBcarfcegvvvPMu2emLsNri0BvteFyFVFXm061rN8ZPmBDQ+E4cP8Hq1as5kX6i5hkNozEde0g3DMbg+i9QCl0ruvUnhBCXgiRGosUUFxVz/PhxImJH+5OiWhoaIeH9yTp+hAP799O7T58ARXn52Gw2fvDoo+zZs4cdO3ZQXl5CWGwEAwdOpkvXLgEdX3To4EE++OBDjJYIImJHYzAG46rKo+T0HnIylhDd/gaMRoe/vc/npqoik079hgUsZiGEuBwkMRItpqSkBKDObaOzGYzB6PVmiotLLmdYAaU36Onbry99+/UNdCh+Pq+PL75YhNnWlsg249G06smpZks0tuDO5KQvpigvlai4idXtfW4Kc75Dw8egwYMCGboQQlxykhiJFmMLsgHgdhVjNIXW2+71VODzuggKkkVIA+ngwYOUV5QR22GiPymqpddbcIT3oTA3hfzstWjoqKrIQMPHnXfeSUhISICiFkKIy0MSI9FiIiIiiGvTlsKivViD2tf70C0p2oPeYKBHzx4BilAAFBYWoNebMZnCGtxutsYAYDGWYjabGdB3KIMHDyYk9OpOiirKKzh69Agej4fY2Fhi27QJdEhCiACQxEi0qOuum8g7775H/qlVOCL6YzJH4PWUUXp6L6VF+5g4ofVUer5Wmc0WfD43Pq8Tnd5cb7vHUw7AXXfdQXT01V/Q0evxsmzZMrZt24bX5/U/37ZNHDfdcjNRUVEBjE4IcblJ5WvRojomJHDP3Xdj1JWSk76YjEPvkHXsE1wVR7lu0nWMGjUq0CFe87r36I6maZSVHKi3TaEoK9pPZEQU0VFXf1KEgs8++5RtW7fQR5m5TQvjbi2CsVow5Tl5zPvvXIqLigMdpRDiMpIeI9HiunbryuzZP+Ho0aM1la9tdO3atVXU7BFgt9sZOmQwmzanAjrsId3R6Yx4POWUFG6nsjyDG6fcQQPljK46WVkn2bN3LyM0Ox21M71nbTERqYx85Spmw4b1TLnhhgBGKYS4nCQxEpeEptPo3KV+xWfROlw3eTJer48tW7dQUrgdg8GK212OQW9g2o3TSOyVGOgQL4udO3di1xnooOrfUjRrGp18JrZv38GUKTdcE4miEEISIyGuSTqdjhum3sDIUSPZu2cvlZUVhIaG0at3L8zm+knC1aqsrBy70tFYSSmHpsflrsTj8WAwyp9LIa4F8psuWi2f10dGRjpVVU4iIiKIjIoMdEhXnZCQEJJGJAU6jIBxOII5qvnwKdA1kBydVl6sZgsGg/ypFOJaIb/t4rJzu9x4vB4sFkvD1Z8VbNmyhbVr1lBWUe5/Or5de264cSoxMTGXMdrWwefzkZubi8/rIzIyEpPZFOiQrgr9+vcnZeNGjmhVdKXubMkK5eOYzsWggcPkNpoQ1xBJjMRlc+TwEdat+47jJ6rX5goJDmbwkKEkJSWhN+j97davX883K7+hE2ZGaSHY0JGLhz1Z2cz971weffTRa6b3SCnFxpSNbEhJoaysFACj0cSA/v2YOHGSJEgXKSYmhkEDB7Fl2zZKlZfOmgUTGlm42a1VYQkKYsSIEYEOUwhxGUliJC6LbVu3smjxYqJ0JoZpdoxonCp1s2b1Ko4ePcKMGfehN+ipqKhg9erV9MTKAJ3N//p4TLRRRpZ5Sli1ciV33n1XAM/m8vn6q6/YsmUrQY4uRLcbhU6np7Isg63bdpB5MosHH3zgmliQ91KaOnUqDkcwKSkp7HcWAdVr+3Xt3IWpN95IkF0qtQtxLZHESFxypSWlLFnyFV2xMEQF+W9LxGsmEpSJVSdOkJq6meFJSezetRuUj0TNWm8/Rk2jmzKz7eABKisqsdrqt7maZJ3MInXLFsKihxMccqZauMkcidXenlMZX7F1y1aGJw0PYJRXPk2nMWbsWEaMGElGRjoej4fo6JirvtK3EKJhUuBRXHJpaWnogf6ard5YjWjNSDwmUjenAlBaWoJNZ8DcyDShMAz4lKKsvOwSRx1427Ztw2gKxh7Srd42kzkSm70DW7duC0BkVyeD0UBCp0507dZNkiIhrmGSGIlLLjc3lwgMGBtJdtpgpLDoNF6Pl6AgO5U+L26lGmxbghcNjSDb1X97o/B0IUZTOFojv6YmSxRFRacvc1RCCHF1k8RIXHJGoxGn1nCiA1CFD52mQ6fX0at3L3zAAarqtfMqxQHNSZfOnbEF2erv6Cpjs1rxesob3e5xl2GxXN23E4UQ4nILaGL0+uuv07dvXxwOBw6Hg6SkJL7++mv/9qqqKmbNmkVERAR2u53bbruNnJycOvtIT09n6tSp2Gw2oqOjeeaZZ/B4PHXarFmzhoEDB2I2m+nSpQvz5s27HKcnavTs2YPTPjf5ylNvm0/BUc1Nj5r1u4KDgxkxYgQ7VQVpvgrKlQ+lIEe5WUUZ5Zpi/IQJATiLy6937z44q/JxVuXU2+b1VlFZdpS+fXsHIDIhhLh6BTQxateuHX/605/YunUrW7ZsYcKECdx0003s2bMHgDlz5rBo0SIWLlzI2rVrycrK4tZbb/W/3uv1MnXqVFwuFxs2bODtt99m3rx5/PrXv/a3OXbsGFOnTmX8+PFs376d2bNn8+ijj7Js2bLLfr7Xqq5duxETFc16rZxc5YaazqNK5WMDZZQqLyNHjvS3nzhxIhPGj+eI0csX6jTvqwJWqhJUWDD3z5xJm7g2ATqTy6tb927ExbWl4NRqykuPopQXhaKqMpv8rBUYjTqGDZOB10II0ZI0pRoZzBEg4eHh/PnPf+b2228nKiqKBQsWcPvttwOwf/9+evbsSUpKCsOHD+frr7/mxhtvJCsry1/074033uAXv/gFeXl5mEwmfvGLX7BkyRJ2797tP8bdd99NUVERS5cuvaCYSkpKCAkJYf/r/yDYKrcumqO0tJT35y/gVE42Dp0RExqFPjcGo4FbbrmVHj171HuNy+XiyOHDOJ1OwsMjiI+Pv+YK7VVWVvLpJ59y+MhhdDoDmqbH63USERHJHXfcfk0WuxRCiAtVWllJj8efpLi4GIfDcUGvaTXT9b1eLwsXLqS8vJykpCS2bt2K2+1m0qRJ/jY9evQgPj7enxilpKTQp0+fOh8OycnJPP744+zZs4cBAwaQkpJSZx+1bWbPnt1oLE6nE6fT6X9cUlLScid6jQoODuYHP/ohR48c5eDBA3g8HobGxtK3bz/MlobX5jKZTPRMvDYWM22M1Wplxn0zyM3N5cjhI/h8XuLi2pKQkHDNJYlCCHE5BDwx2rVrF0lJSVRVVWG32/nss89ITExk+/btmEwmQkND67SPiYkhOzsbgOzs7HrfmGsff1+bkpISKisrsTbQA/TCCy/w29/+tqVOUdTQNI3OXTrTuUvnQIcScPn5+aRuTuXEsWOgaSR0SmDwkCFEREQ02D46Opro6OjLHKUQQlx7Ap4Yde/ene3bt1NcXMzHH3/MAw88wNq1awMa07PPPsvTTz/tf1xSUkL79u0DGJG4muzcsZMvvvgcs6ajra+6avX2gi2kpqZy6623ktirV4AjFEKIa1fAEyOTyUSXLl0AGDRoEKmpqbzyyivcdddduFwuioqK6vQa5eTkEBsbC0BsbCybN2+us7/aWWtntzl3JltOTg4Oh6PB3iIAs9mM2dzw7R0hLkZubi5ffPE5HZWJodjR1Ux/GKQUmyjn008/JSY2ttGeIyGEEJdWq6tj5PP5cDqdDBo0CKPRyMqVK/3bDhw4QHp6OklJSQAkJSWxa9cucnNz/W1WrFiBw+EgsWZsSlJSUp191Lap3YcQl1Pq5s1YND1DNTu6s8YI6TWNYQRhRGNLamrgArxSKPyzG4UQoiUFtMfo2WefZcqUKcTHx1NaWsqCBQtYs2YNy5YtIyQkhEceeYSnn36a8PBwHA4HTz75JElJSQwfXj1FefLkySQmJnL//ffz4osvkp2dza9+9StmzZrl7/F57LHH+Oc//8nPf/5zHn74YVatWsVHH33EkiVLAnnq4hp17OhR2vsM/p6is+k1jbY+A8eOHrv8gV0JFOzevZuNmzaRlXUSTdPRKSGBESOSSOjUKdDRCSGuEgFNjHJzc5k5cyanTp0iJCSEvn37smzZMq677joAXn75ZXQ6HbfddhtOp5Pk5GRee+01/+v1ej2LFy/m8ccfJykpiaCgIB544AF+97vf+dskJCSwZMkS5syZwyuvvEK7du148803SU5OvuznK8T39XJoaNC6Kmi0Dgq++moJqVu2YA2KIzRqGEp5ycg6xjvvvsuU66cwdNjQQEcphLgKtLo6Rq2R1DESLeXLL77k4M5dTFchnLt0nE/BF1oRfQYNYsoNUwITYCt1YP8BPvjwA8JjRmB3nFlUV6Eoyt9C2em9/HjWj4mMjAxglEKI1qY5dYxa3RgjIa5mQ4cNpcLnZYsqr9Mx5FOQqspxKh9DhgwJXICt1ObNm7FYo+skRVDdwxYaMRC9wcLWLVsCFJ0Q4moS8FlpQlxLYmNjmTbtRhYtWkyW5qGdz4ACMnUeqvBy0003ExklvR7nyjp1CnNQ/eroAJqmx2Rtw8msU5c5KiHE1UgSIyEuswEDBxIX15bNmzdx4thx0CCxUyeGDB0qRRwbodfrUT53o9uVz43BIH/OhBAXr9l/SYqKiti8eTO5ubn4fL4622bOnHnRgQlxNYuJjWHa9OmBDqNRSikqyivQNA2bzRbw5Ud6dO/Gzl0HCIkYgKbp62zzeCqoqjhJ926TAxSdEOJq0qzEaNGiRcyYMYOysjIcDgfaWaNINU2TxEiIK5TyKTZv3symjRs5XVwEQHRkFCNGjqBfv/4BS5CGDRvO9u07KMheS1j0CPR6CwAeTykF2d9itVrp179fYIITQlxVmpUY/fSnP+Xhhx/mj3/8Y/W3SSHEFU8pxWeffcru3XvooJnorQWjgOMFJXz+xRfk5OQyOTkwvTJR0VHceeedLPz4Y04dW4jJGgN4cVbkYrMFcd9992GxWAISmxDi6tKsxOjkyZM89dRTkhQJcRXZv28/u3bvZpQWTLxm8j8fj4kDGEjZmEJiYiLt2rcLSHzdundjzpzZbE/bTmZmBjqdjk6dhtKnTx+MJmNAYhJCXH2alRglJyezZcsWOkm1WSGuGlu2pBKtMxGPqd62blg4qHOxZUtqwBIjAJvNxoiRIwJ2fCHE1a9ZidHUqVN55pln2Lt3b/W3NWPdb2vTW/GgUiFEw/Jy8+jo0zdY3UzTIManJzcnt/5GIYS4ijQrMfrBD34AUGfpjVqapuH1ei8uKiGawO12s2vnLnZs3055WRmO0BAGDBhIr1690OmbVsNUKYXyqSa/7mpgMhqpxNPo9irNh8lcvzdJCCGuJs1KjM6dni9EoFRUVPDOvLfJzcslTjMRpfQUFZfz6fHjbNu6lXtnzLig8Scnjp9gw/r1HD5yBJ/yER0ZxdBhwxg4cCCaLsBz1S+Tnr16sXlDCv2VwnTOeiXlykeWcpOc2CtA0QkhxOVx7X0tFleVLz7/nJKCAqZooYzVghmoszFBC2aS5uBkRgbLli373n1sT0vj7bffJu/ocQZgZZhmx1xQwpIlS/j444Uo37WxnOCQoUPQmQysoYxidabXt1B5WKOVYbfb6ddPpsQLIa5uzU6M1q5dy7Rp0+jSpQtdunRh+vTpfPfddy0ZmxDnVVhYyMFDh+inrISeU/QvWjOSiIUdO7ZTWVnZ6D5KSkpYtGgxnTFzvXLQXbPQWTMzWrMzSrOzb98+tm/ffonPpHVwOBzMmDGDUgMsUUUs8hXxhe80S1UxHrOB+2fOxGwxBzpMIYS4pJqVGL333ntMmjQJm83GU089xVNPPYXVamXixIksWLCgpWMUrVhGegYLP1rI//3pRf70p/9j/nvzOXL4SMsepJEOm/QT6QANzqIC6IAJj9dL1smsRnedtm0bemCAZqu32n17zUScZiZ18+bmRH3F8fl8fPvtt7jdbtpgJBQ94RiIxUh5ZSXr169v9GchhBBXi2aNMfrDH/7Aiy++yJw5c/zPPfXUU/z1r3/l+eef5957722xAEXrtXXLFhYvWYLJHIrV3h1N05Fx8gSH57/HuLHjGDtubLP3XVBQQMqGFPbs3o3T5SI8LIxBgwczZMgQDMbat231p/TFjAA6dSqbKGXA2Mg4ojgMbMnNqT7UVT7UaM/uPRw6fJjxmoM2Wt1xWceUk5Qd2+nXry8dExICFKEQQlx6zeoxOnr0KNOmTav3/PTp0zl27NhFByVav7y8PJYs+Yrg0J7ExN9ESHh/HGF9iW5/IyERA1mzdg0njh9v1r4z0jP497/+zb7tO+ji0jFYsxFcVM4336zgnXfexu2qXkw0Pr4DAOm4GtzPCVwY9Hri2sY1eiyDwYD7PAmPC4VBp7/qkyKArVu30EYz1UuKABIwE6ozsnXr1gBEJoQQl0+zEqP27duzcuXKes9/8803tG/f/qKDEq3fltQt6A0WQiMHo52VNWhoOML7YLaEsWlT029B+bw+Fn70EaEeuFE56Kuz0VWzMEKzcx0Osk9msWbNGgDCI8Lp1qUr27VKilTdEhF5ysNequjXrx9Wq7XR43Xv3o085aJE1S8xoRQc19x07969yedxJSrMLyBS6RveqEGkT09BfsHlDUoIIS6zZq+V9tRTT7F9+3ZGjKiuQrt+/XrmzZvHK6+80qIBitYpI/MkZlvbeiudQ3VyZLa1JyPzeJP3e+DAfkrLyxithWI4Z9BPhGagi8/Mtm1bGT9hPAaDgZtuvpl33n6br/PyiMOIQ+k5rXnJVi46tI8nOfn68x6vZ2IiYatW811pOSNVkH8Qt1MptlJOqfKQNCKpyedxJTJbLFRUlDW6vULzYZHB10KIq1yzEqPHH3+c2NhYXnrpJT766CMAevbsyYcffshNN93UogGK1kmv06Ea6GWppXwedLqmd0ieOnUKu6564G9D2mpG9jlLKCoqIjIyEluQjUd+8Ci7du5ke9p2csrKCAkN4daBg0jslYhe30gPSA2DwcB999/Pe+++y1fFRURoJowK8vGATsetN99GXNu2TT6PK1HvPn1Y/+239FM+LFrdn12J8nJKubmxT98ARSeEEJdHsxIjgFtuuYVbbrmlJWMRV5CuXTvz7bfr8fmc6HR1exGU8lJVfpz+/RKbvF+9Xo8HH0pRb5YYgLtmwLVBf+atazQaGThoEAMHDWry8aD6ltysJ59g/759HDx4CK/XQ+82cQwYMABb0LWzUPLgIYPZkprKqqoyhigbURhQQDZuUrUKwkPD6NOnT6DDFEKIS6rZiZG4tg0cOIj161MoOLWW8Ngx6PUWAHw+F6dzN+DzOhkydGiT99u1a1fWrF3LSc1Fuwam4R/FRWR4BKGhoRd7CnXo9Xp69e5Nr969W3S/V5KgoCAeePBBPvzgA1YUFmDVGVAoqnxe4mLacNfdd8sq9kKIq94FJ0bh4eEcPHiQyMhIwsLC0Br6Ol+jsLCwRYITrZc92M69997Ngvc/4NSxhZhtcaBpuCpOgaa4/fbbiIqKavJ+4+La0jE+ntTMk5iVjiit+i3qU4p9VJGhnNw06voLmiV2uvA0qampHD96FAV0TOjI4CFDiIiIaHJc14rIqEh+/MQsjh09RkZGOpqmkZCQQPv28dfEzDwhhLjgxOjll18mODjY///zJUbi2tChY0d+8pOn2J6WxtGjR/EpRYf4EQwcOIhgR3DzdqrBHXfexfz33mNF9inCNSNWn0aBzkuVz8vYMWPo37//9+5m3969fPLJpxiAtj4DGrA9fwubN6dyyy0301tuCTVK0zQ6de5Ep86dAh2KEEJcdppSSmrZfo+SkhJCQkLY//o/CD7P1G/RcpRPcejQQXbv3oPT6SQiIpyBgwYRGRn5va8tLCzktVdfo50yMJwg9DVJvE8pNqtyjmtuHnvsMaKim96jJS4Pj8fD3j172b17F1WVlYSHRzBw0MDq2lXynUwIcYFKKyvp8fiTFBcX43A4Lug1zRpjpNfrOXXqFNHR0XWeLygoIDo6Gq+38dlK4iqkoLS0FKUUwcHBLbIavabT6Na9O92aUUModXMqRqiTFAHoNI2h2MnWitm4MYXRo8dgsVqwWCwXHa9oOWVlZbz79jvk5ucRo5kIUhrHTuWyY9dOBvQfwLTp06THWghxyTQrMWqsk8npdGIyNbxulbgKKdi6dSsbUlIoLKwu/OdwhDBs6FCGJw1v1nT9s50+fZp9e/fhdFYRERFBz8REjMbvH/x77OhR2voM6BtI0CrxYfAp0tK2sy0tDU3T6NqlC+PGjadNXJuLircpKsorSEtLI/3ECdCgY8cE+vfvj9UmPZIfL1xIWeFppmghhGkG0KqLbR7VnGzenkZkZCQjRo4IdJhCiKtUkxKjv//970D1GIQ333wTu93u3+b1evn222/p0aNHy0YoWicFX3/9FZtTUwkK7khkXD80NCrL0vlm5UpOnjzJ7Xfc3qxv9l6Pl8WLF7Njxw4MmoZJ01Hh8/D1V18zbfp0Ent9TxkApepU465VprwsVyVoQD/NSjgGSvFy6Mhx5h79L/fdfx/xHTo0Od6mOnL4CB999CFej5cYVf3Bv/LwYdauWcPd99xDx4SOlzyG1upUVhYn0tMZowVXJ0U1NA06YyZfediYktIiibcQQjSkSYnRyy+/DFT3GL3xxht1iueZTCY6duzIG2+80bIRilYpPT2dzamphEUPJzjkTDJsDWqPxd6OvftWs2/vvu9PYhqweNEidu3axSDNRifMGNAo07zscFfy8ccfc7/1PhI6NT4wOL5jR3YXpuFT4ENRiAeAPaoSAxrJmgNzTQHDWIx0UmbWqDLeX/A+1iA7Pq+XuLg2DB06tMWTlNOnT/PhBx8Q7dMxnFDMNb1aVcpHiqec9xcsYNYTT+AIubB74VebI0eOYtL0tG2gVANAR83EkfIS8vPz693KF0KIltCkxKh2gdjx48fz6aefEhYWdkmCEq3f1q1bMZlDsIfUHwNkC+qAxRZL6pYtTU6MCgsL2b5zB0O0ILpqZ8b+2DU9I5SdCq2UNavXnDcxGjJ0CFu2bGEpRZTj8xeF1IAYjOjP6U3Saxr9lJXlrmI0YzRGUwhHj51k3/63GT1qFBMmTmzSOZxP6uZUdD7FSOx1ljyxaDpGKTufe4vYunUL4ydMaLFjXkmU8qHTQGtkSkjtz87n813GqIQQ15Jm9UWvXr1akqJrXG5ePiZLTIO3rADMlhjy8vKbvN+9e/Zi1PR0ov6aXJoG3TCRnplBaUlpo/uIjIgkOjqKErx0x8INWihTtVB6YyUfD2tVKb5zPngjMaBDw2KLIzRyMNHx0wmNHMx369ZxYP/+Jp9HYw4fPEh7Zay3DhyAUdNop4wcOnCwxY53pWnXrh1VPi+5uBvcnqFcWMxmIiO+f3aiEEI0R7MrX2dmZvLll1+Snp6Oy+Wqs+2vf/3rRQcmWjez2UhxWWWj273eSszmpg/EdzqrsOh06FXDCZetZg01p9NJMA3XStq/fx85ubmM1xy00c4M1u6j2YhRRr5RJaTjpONZyZcThUKh6ap/JTQ0HGG9qarIIGXjJrq30Ng5r9eL8TzzzY1olF3Dszo7JiQQFRHJ1tPFjFf6Omu25So3hzQnwwYNx2CUov1CiEujWX9dVq5cyfTp0+nUqRP79++nd+/eHD9+HKUUAwcObOkYRSvUK7EX6UuX4vGUYjDUTVC83ioqy44zcMTwJu83PDyCMq+HCs2HTavfoZmHG4Nef94Cktu2biNKZ6IN9WewRWtGYpSRI8pJR+1MYnSYKkCHNah9nfbWoA5kpG8BRYvUz2nbrh0nSg/Qv4G14JSCkzoPXdu1q/N8ZkYGGzZs4MjhI3h9PuLi2jBs2DB69ep91dX00TSNO+68k7fnzWNRVTEdfCaCNB35eMlSTjp06MC4ceMDHaYQ4irWrFtpzz77LD/72c/YtWsXFouFTz75hIyMDMaOHcsdd9zR0jGKVqhfv344gh3kZ32DsyoXVTOOx+UsID/rG4xGPYOHDG7yfnv16oXRaGQHFZxbFaJC+Tiguejduzdmc/1bbbWKi4oI9zX+1o7AQDnVvTJepTioqtipKrGH9vCv+XZGI6vZNtOQoUMo8bnZQyWcfX4KdqoKyn0ehg4d4n96x/bt/Pe/czl18Ag9vUb6Kwvuk7l8/MknLF68uO4+rhJR0VE89vjjJI0aSUGIhYMmH8SGc+ON07jv/vult0gIcUk16y/Mvn37eP/996t3YDBQWVmJ3W7nd7/7HTfddBOPP/54iwYpWh+T2cTMB+7n/QUfkJPxFSZTcPVaac4SHI4Q7rl7pn8Jmabu98Ybp/LZZ59ToZXSVZmxoSMXNwc1F6YgGxMmnH8wtC0oiLLTjY9BKsFLBYqvtFLKlQe38hEU3InQyLqJnEJRWXacjh1artpy+/h4xo8bz+o1qzmp8xCvqlewT9fcFCo31026jjZxcQAUFxXz5ZeL6IyZISrIn591x8JRnGzctpVOnRJI7NWrZYJrRezBdsZPmHDNDkIXQgROsxKjoKAg/7iiNm3acOTIEXrV/HHOz2/6gFtxZQoPD+fHsx7n6JEjHD16DKUU8fHxdOve7aJqzPTp2xer1cqa1WtYdyoLAL1OT+9evZg4adL3rsPWt19fFmdkUISXUE1fZ1up8pKFm+49uuNwhGA2m9i0aTNebwXK50bTV/dEKXwUF+ygqjKPESMmN/tcGjJm7Bji4uLYtHEju/0FHjtyY1JSndl2W7duxYDGQM1Wr9Oqk2bmGC42b9p8VSZGl5qzyklpWSlWi5Uge1CgwxFCtCLNSoyGDx/OunXr6NmzJzfccAM//elP2bVrF59++inDhzd9XIm4cmmaRucuXejcpUuL7rdL16506dqV4qJinE4njhDHBS/d0advXzambGT16SIG+CxE1hQKLFQe0nRVhIWGcfPNt1RXadegS5euzJ+/gKzjC7EExaPTGXFWnsTtKmPSxIktfm7V59eFLl3Pv9+skyeJVnoMjSyx0hYju2oSR3FhiouKWbVqFXv27MHrq76dmtAxgfHjx9E+Pj6gsQkhWodmJUZ//etfKSsrA+C3v/0tZWVlfPjhh3Tt2lVmpIkWFRIa0uTXGI1GZj7wAG+/PY+NBQX+qfk6wOEIpVv37rz26qsUl5Zgs1jp268fDzwwk0OHDnLgwCE8nnK6JnRhyJChl3WZkHPpDXo857mF50ah1+kbbyDqKDpdxJtvvoXLrQgOH4DJEonXXUZW9n7mzXube+65my5duwY6TCFEgDU5MfJ6vWRmZtK3b1+g+raaVLsWrYqC1atXUVBQQAfNTIeaKsrpuDheVMSmjRtJwER3LYjSKh/bU1PZuWMHDz78EGPGjg1w8Gd07dqVrw4dphwfQefM0FMKjmtuunZr+iK716ply5bh8kB0+xvR62vWpLOCLbgT+adW8fkXX/L0nDno9LLUiBDXsib/BdDr9UyePJnTp09finiEuGhHjx5lW1oawzQ7IzQ7bTUTbTUTSTWPfSjaaSa6aBYG6GxMVSGYXR4+/fiTVjXLq2/ffgTZbHynlVGuzlR6divFRsooVx6SkpICGOGl5fV6OXb0GPv27iX7VPZF/WzKyso4cPAA9tDeZ5KiGpqmIyRyEOXlZRw8eO0W1xRCVGvWrbTevXtz9OhREhISWjoeIS7ali2phOmq10A7VwfNzH5VxWHlpK1W3ZNk1jT6Kyurc3PIzMykXft29V4XCCazifvuv5/33n2XLyuKiMaIQUGu5sGnadxy863+GWxXm61btrB69VrKK8r8z8XGtmHajVOJa9u2yfsrOl2EUgqLNabB7SZTGAaDmYKCgmbHLIS4OjQrMfr973/Pz372M55//nkGDRpEUFDdWR0Ox7W5AKZoHfLz8oj26RvtD43BSAZ1q7XH1qyhdupU1kUlRgUFBWSfykav19OxY0cs1gsbMN6YmNgYnvzJU+zauYvDhw/j83oZ2bYtAwcNbFY5hCvBxpQUli1fTpCjC7HxPTEY7Dircjl9eidz573NIw8/RGybpo39MtVUYfd4KjCa6i9n5PM58Xrd562PJYS4NjQrMbrhhhsAmD59OtpZ84iVUmiahvcaXtJABJ7JZKZSK2t0eyU+DOcUJvKi8AF6ffOKB1bXHPqCozULLQMY9QYGDR7E0GHD2L9vPxUVFYSEhNC7d+8mJUwmk4lBgwcxaPCgZsV2JamqqmLlqtUEh/YkLGqY/3lrUHvMtjbkZizhm29Wct/99zVpv9FR0URERFJWvB+LLa7eGn9lxQfRgB49W2bpFyHElatZnwKrV69u6TjEFcDtdnPwwEHKykoJDnbQrVu3VlmFuFfvXqzM/oYKVX9ZkSrlIwMXvbS640yO4QSgc5fOTT5eeXk5c//7X7zlFYzQ7MRhwoPiiLeKTZs2sWnTZjSdHoPRittVzrLly7lu0iSGDhva/JO8Su3dsxev14sjrE+9bTrNgD00kSNH11NaWtq0HjMNxo8bx8effMzp3BRCwvujN9jw+dyUlxyiuCCNIYMHYbfbW/BshBBXomZ9qo1tRTN3xOWRujmVVStXUuVyYtB0eJQPq9nCdZOvY0ArWR+vqrKKyspKEhN7kbIhhTWVZQxTNiIwgFZdx2ijKkMHdKpdQFZBFi62U0nvXr0ICWl6eYCNKRupLCvnBhz+RMyEhleBQiM0ciD2kB7odEa83kpKCnfw9dKvsVjM9O3XrwWvwJWvtLQUg8GC3mBrcLvJHA5AWWlZk28l9urdi6qqSpYuW055yWGMJjteTyU+r4eBAweSnHx9g6/Lzc0lLzcPk8lIx44JGE311+ATQlw9mpUYffvtt+fdPmbMmGYFI1qnbVu38tXXX9EFC4laGHZNRyle9jgr+XLRIvR6fUA/4LOzs1mzajUHDx9CKYVep6Nzp87k5uayrKQYu656PftS5cZsMuFyeVmulRLq01GmUxT73HRKSODG6dOadfztaWkkKCO2s6p9O5ViP1WEhPet0/uh11sJjRqG11PJ6tVr6dO3b53b0dc6u92Ox1OF11tZb/YYgNtVBNDsatWDBg+mV+/e7N61m6Ki01itVnr16k1oWGi9tnl5eXz55SIyMzP8z5lNZkaMSGL0mDHycxPiKtWsxGjcuHH1njv7j4SMMbp6eL1eVq1cRSfMDNWd+TAK1vQMx45XwcpvVtKnT1+0Rio0X0qZGRm888472Hwag7Hh0PQUKg8Hjx7DZzJw49SpFBYWohR07NiBrl27kZOTTVpaGkVFRUTZgujbt2/1DMvmhK+grKKcEK3uB3UWLnwo7KH1x6xoaNhDe5KbuZRTWaeIa3t1zixrjsReiSxdupTS03vqr12nPJQV7SWhY8JFTfCwWCzfu8Dx6cLT/Pe/c/EpC5FtxmOxtcHrraKs+ABr1qyloqKS66c03MMkhLiyNSsxOreGkdvtJi0tjf/93//lD3/4Q4sEJlqHY0ePUV5ZQU8ttP5GDXpgYVlZMenpJ+jQsePlDU7BF59/QahXx3jsGGqS8xiMdFYWVrhK2bN7NzMffLDOy2LbtGFKE2c1NUoDuy2I4gpvncSqXPnQ0DXY6wFgMFQnUlVVlS0Tx2Xi8/lwuVyYTKaLWg+vMVarlbFjx7By1Sp8PjfBYYkYDEE4q/IoKdiO113MxIm3tPhxz/Xtt2vxenXEtL8eXc36eTqdibDIIej1VjZv3sywYcMIC68/w00IcWVrVmLU0DiM6667DpPJxNNPP83WrVsvOjDROlRUlAMQ3Mjc92D0Ne0qmn8QBVlZJ8nLy8NoNNG5c2fMlu+fNp2efoL8wgImag5/UlTLpGn0UmY2nDhBYUEh4RHhzY/ve/QfMIDNG1LoedZgbyc+FD6cznzM5sh6r6mqzAbA5XJfsrhaUklxCevWfceO7TtwedwYDQb69u3LqNGjCQ0NbdFjjRo1GoPByNpvv+XU8QP+5yMjoph21/20bdf0OkZN4fF42L17D/awvv6k6Gz20B6Unt7Fzp07GNtA77kQ4srWolOKYmJiOHDgwPc3FFeM2g+9ArxENfB2KcQDQEhIaLP2n52dzeeff0FOTrb/OYPByPBhw5gwYcJ5b8/l51cX44um4cGwMTXPFxQUfG9iVFBQwOZNmzi4/wBer5c2beMYOnQYnTt3/t5bbMOThrNr506+KS+lr7IQhwkjGho6ivO3ERU3EU07s6aZ1+ukpHAXoGG5gAQw0AoLC5n71n/xVlXRzWciVLNQ7PWyb/tO9u3dx0MPP0xkVP3kr9m06ms6ePBgjh49SlVVFeHhYbRr1755tzubqKqqCo/X02C9I6ieHWc0BVNaWnrpgxFCXHbNSox27txZ57FSilOnTvGnP/2J/v37t0RcopVoHx9PeGgou4srGKeCObtjxqdgN1VER0YR14wKzAX5Bcyb+zbogohqOwmLNQ6vt5Ky4gOsX7+eqqpKpt54Y6OvN9cU7avCh7WBHq1KfHXaNebwoUN8+OGHGJVGvM+AEY2sI8d579AhkoYPZ/Lk5PN+IAcFBfHQww+z6Msv2XDsaJ1tzopscjK+wh7aA6PJgauqgNLTe/F6nWhARGQLJhSXyOIvv0RX5WKycmA56/ZZN2VhpauULz7/nEd+8GiLH9dgNNCte7cW3+/3sVgs6PUG3K7TWIPa19vuUx7crlLs9quzwKYQ17pmJUb9+/dH0zSUqrt40fDhw/nvf//bIoGJ1kHTNG6YeiMLFixgJaX0VGZC0FOEl71UcVrzcf/UG5r1TX7t2rX4MBDTNhmdrjp5MRiCCI0YiF5vYcvWzSQljajT26N8isOHD3Py5EmU8mHQ6znoraKfVn9690FVRXCQvbqnoREVFRUs/GghsT4DIwlCX9ND1VfBQa2KlI0bad++PT0TE897LiGhIdw38/6zKl/rWL7iG8orFDqdkcKc9QBo6LAEtQVnAV06d2711asL8gs4duIEIzU7lnNqQpk1jT7KwndZJ8nJziEmtuHlNq40BoOB3r16sXffQeyO7vVup5UVH8DrddJPSi0IcVVqVmJ07KzqvgA6nY6oqCgslotb/kBcvKKiIlI3p7J/717cHg8xMTEMGTqEbt26N/s2ROcunbnvvvtYsXw5a8+65dUuri0zJ08mvkN8k/fpcXvYu3cvweED/EnR2ewh3Sgp3MHOnTsYN348AKeyTrHwo484XVyEVWfAqxQe5WUvlZiURlcsGDQNl1LsU5UcxckNYyaed7X07Wnb8Xq9DCMY/dndYRp0w0IGbjZt3PS9iVGtiIgIIiIigOqlcd5++128yoMjvC96vQWPu4zKsqPYrGamTJnShCsWGLm5uQDE0nCvW5ua25W5uVdPYgQwevRodu/ZQ07m14REDvD3ZpYXH6Tk9G4Se/aUgddCXKWanBj5fD5WrlzJp59+yvHjx9E0jYSEBG6//Xbuv/9+qe0RQOkn0pk/fz6ax0sHZcSERvbxDN4/eoSBAwYwbdr0ZidHCZ0S+OFjPyIvL6+6uJ4jmMiLuA1UVVWF1+fFaGq4oKKmGdAbgigrq17ao6ioiHfeeZsgt49kLYQIDCggExcplLNdVbBbq8KmGShXHpQOJoydwJAhQ84bR0ZGOtEYMDfyvm2PkW2ZmdUru5/dRFXPznS5XISEhjT4pSCubVse/cEjfPftd+zduwevz4vJZGbQwH6MGjUae/BFVFlWcPz4MbZu3UpBXj5mq4VevXrTt19fTKbz3zpsCmNNZXMXPszo62131Sx5bzBcXUUPq9el82DzlpGfdabSvwEdQeg5fvw4Ho8Hg6H1VX6/ZBScPHmSkpISgoKCaN++fUBKdAhxqTXpt1opxfTp0/nqq6/o168fffr0QSnFvn37ePDBB/n000/5/PPPL1Go4nzcLjcffvABYR4YQwjG2ltCwFHNyca0NNq1a3fRVaqjoqKIioq66HgtVguapsPlLGx4HIfPjdtVgs9XPU5o08aN4PYwXoVgqkliNA3aayZsSscyVUyPXr0ICgoiJMRBnz59L6gIoKbpakYiNcwH9ZL9/fv2s2bNWnJyq3vP9PrqWy+TJk2ql+xERUVx6223ctNNN+F0ObFYLBc9zV0pxeJFi9iWlkaozkikT0+ldpqvTqSzYf16Zj74QIvNFOvQoSNmk4nDLicDGrhdeVhVYTQY6NSpU4scr1VQ1e+3DpqZEZqdIuWlGA8GNGIwUomPRZVF7N2zl779+gY62svi2NGjfPX1UvLz8/zPhYSEMvm6SST26hXAyIRoeU1KjObNm8e3337LypUrGV9ze6PWqlWruPnmm3nnnXeYOXNmiwYpvt/u3buorKpkkhaG8ZwP8k6amcyaW0KtZfkOj8eDUj7Ki/ZjD+mOXl+3x6WseB9Keajtptm9axcdfSZMDXxDjdAMhGEgNzeXzp07VScyF/hFtlOnThzYv58K6q+rhoITmouEhI7+/W3buo1FixdhDWpLZNwE9Horzsps9uzdy/ETJ5g0cSJFRUXo9Xq6du1KaGgou3btYsfOnVSUVxAaGsLAgQPp3r17s79tb960ibS0NIZrdjopM7XjzkvxsqqklP/8+99ogMvtISoyksFDhtCvf79mJWRGk5GkpCTWrv2WIKWjC2Z0moZPwVGq2EsVSUNHXFB5hZZSWVFJdnY2Op1GXFzbFl+io6KigtPFRfTWqsd/hWp6Qs/qLQtGT5hmIiMj45pIjI4dPcZ7783HZI0hut1kjKZwPO4SSk/vZuHHH3Obz0fvPvXXthPiStWkxOj999/n//2//1cvKQKYMGECv/zlL5k/f74kRgGQkZFBuM6EvZF6Q/EY2ZCX6y/OF2geT/U0f83rJDdjCY6IAWeqCxcdqF4FXdOj11d/IFU5nQRp9W9XKQVpqoIiPOhzc6kqKKTM52XFihWMGzeeUaNGnTdJ6tu3L6tXrWK9s5wxKghzTXLkU7BTVVCg3NyQNKI6hsoqvl66FHtIN8Kik/wrtJstUdiCE8g+sYhPPv0Es06PVymWr1iO2WjC6XZVF3TU9BQWlXHo8CG6dOnC3Xfdjd5Q//bU+SifImVDCgmY6aTVTUYq8FGlfBgrnXTCjEUzkZNTyKJFX3LgwH7uvPOu8463asyYsWMpLy8ndcsWduuqCEZPmeaj0udhQP8BTJg4ocn7PJvT6WTXzl3k5uZgMBjp0aMH8fHx9X5uVVVVLFu6jN27duHxVVfXN5tMDBkyhPHjJzTr3BpSm7B6UY228aIuSYHLVkfB0qXLMFmjiYq7Dq3m90Ovt2BqMx6y17J06XISE3u12PUXItCalBjt3LmTF198sdHtU6ZM4e9///tFByWaTtO08/wZx3+7SLschWAuQJAtCLstiIgKN1VuJznZZ9bfM6GnF1b2qkpiYqoH9IaHhZFXUEr3c/azS1VwgEr6aTa6YsGoNJwo9vkqWblqZaPLP1RVVVFWVobVauXeGTOY/957fO4qJq5mun62zkuF8pA8eTKdOlffJtq1ayder5eQiAH1rqPBYCc4rCdlBTu5SYWgA47hItVdPUbK5HESjI4ivHiBI4ePsHLlN0xOTm7SdTtddJri0hIGanWXxPAoxTpVRhQGxmjB/oKXPYAsTHx78BAbN25kxMgRTToe1M5MnMrQocPYsWMHpaUl2O12+vbrR3R0dJP3d7b9+/bx2Wef43G7CdUZceIjZWMK8e3ac9c9d2OzVd++c7vdvDPvbQpy8+iNmXaaCS+KEy4XG9ZvoLCgkDvuvLNF6hxZLVbaxMRyIreQjtTvCStUHkqU++q6fdiI7OxscvNyiGo7yZ8U1dLQcIT3I/vEFxw5cpiu3S5/aQUhLoUmpfiFhYX+D6qGxMTE1Fsu5HxeeOEFhgwZQnBwMNHR0dx88831CkRWVVUxa9YsIiIisNvt3HbbbeTk5NRpk56eztSpU7HZbERHR/PMM8/4eyRqrVmzhoEDB2I2m+nSpQvz5s274DivBAkJCRT6XBSrhtepO4GLtnFxrWZlcE2nMXjIYE5qHvppVqZpoYzRgpmgObhJC6FU82E0mujTt7qLftDgIWQqF/nqzM/VXbNQayJWEjWr/xaiWdPor7ORgJlv167F5z0ziuh04Wk++fgT/vznv/Dqq6/yl7/8hVWrVnHrbbcxfsIEtPYxOGPD6TWwP48/9jjDk5L8ry0sLMRkDml0mQ+zJRovPqpQ6DSNPNxoaIzSgrlJC2GiLphbtBAGakEoFJtTt+ByuZp03WpLZJz7+Z+OCxc+hmpB9aqAx2kmOmomUjdvrldioykioyKZOGkiN99yC5Ouu86fFPl8PnJzcsnOzsbtPlPJWylVPci+kbUTMzMyWLhwIbEejelaKNcTzE0qhPGag9ysLN6fv4DabD9t2zayc3LoocykKzeLVRFLVTGFeOmJhb3793HsnBpSzaZB0ogRnFQu9qpKzr5kZcpLilZBRFg43a6BRKC2iKXR1HCBVKMpFNAoKZFil+Lq0aQeI6/Xe95ZGHq9vl5Ccj5r165l1qxZDBkyBI/Hw//7f/+PyZMns3fvXoKCqgfOzpkzhyVLlrBw4UJCQkJ44oknuPXWW1m/fr0/pqlTpxIbG8uGDRs4deoUM2fOxGg08sc//hGoLi8wdepUHnvsMebPn8/KlSt59NFHadOmDclN/MbeWvXsmUhI8DeklJczVtmx1ny7Uwr2Uskp5eKOEU3vLbiURo4cxdGjR1mZeZJ2ykAwejwotmmVlODljlvuwGyu/sY+cNBA9uzZzerMk3T1VfcYnFQuPCi6aGZOKTfHlZMqfNjQ0Umz0E2zcKy8mMzMTOI7xFNQUMBbb/0Xj0fDYmuPpulRykdmViEffPgh9824l5GjRjYar8ViweupRClvnUrWtaoqsgBYpUrQKSjBSx+sxGtnbl3qNI0eWChRXo54qziZmUlCE3oeQkND0Ws6MpSLWO1MklugPISgx95AXABtMXG0pJiKigr/79bFUkqxMSWFlA0plJZX94xZTGb69uuLwWBge9p2Kqoq0et09OzRk1GjR9eZ0v/dd98RohkYqexnCodq1SUARqggVmWd5NixYyR0SmDb1q0EoWMnFVhtcYTbO6CUj9Mlh8l2FmDV9KRtS2vStTyfPn36kJeXx3frvuOwzkW0T0+Vpjil3Djswdw7Y8Y1MSPLXjOBweMqwmCoP/je4yoGFHb7RcywFKKVafKstAcffND/YXUup9PZpIMvXbq0zuN58+YRHR3N1q1bGTNmDMXFxbz11lssWLCACROqxzHMnTuXnj17snHjRoYPH87y5cvZu3cv33zzDTExMfTv35/nn3+eX/ziFzz33HOYTCbeeOMNEhISeOmllwDo2bMn69at4+WXX75qEiO9Qc+9M2bw7jvv8EVFEW0xYVKQo/NS5vMwZvSYVjd7xGA0cNNNN/PhRx9yIjcPqO49MRpMjBw2gh49z6xMbzAYuO/++/lmxTds27aNvd4zi69uUuVk4yYEPQ705OLhiCqmfU3tndr35ddffY3L5UP53HjcJ7BoelzKiw/QG4L47PMvmP2Tn5z5wFNw9OgRtmzZQn5uHjq9Do+nivLSo9gdXeucS1HBNkpO7yYYPW0x4cZHJT72UUW0MhKj1e2p66yZOayqyM/Pb9KHeWlpKV7l4whVtFVG4mqSLh3grYm5odtJteNldu7YgcVipUuXLgQ7LqK4pILFixaTlraNTlgYqjnQo3HC5WRL6hZ0QGfMRGnBlCsvR/Yf4K0DB7h3xgw6JnTE6/Fy6PBhBmGjoUoJsRhx6Izs27ePhE4JFJ4uwo2X8JiRda69PbQHxflbKTm9m7z8/Oafz7k0mDBxAj179mTLli3k5eZgMZmYmphInz59MX1PNfXLobCgkG3btpGfn4/JZCIxMZFu3btd9NinI4cPszFlI+np6QCYjCaK8rcSEx+LdtZNBoWi5PQurFYbXbp0uahjCtGaNCkxeuCBB763zcUMvC4uLgYgPLy623br1q243W4mTZrkb1M7MDMlJYXhw4eTkpJCnz596tziS05O5vHHH2fPnj0MGDCAlJSUOvuobTN79uwG43A6nXWSvJKSkmaf0+UUHRPNrCeeYPv2NPbt20eFy02X2FiGDBlCXNumL9lxqZWVlvH22+9QWeUhLHpo9eBrTxXlxQf4bt06TCYTo0aP9rdPP3GCHTt24vX5MFujUD43LmcROXgZRhCdNQto1b1kJ3CyUVX3YkRERFBcVMyRo0cA6IqF3poVq6bDjeIgVez0lFFSAkePHqVzl84opVj05SLStqcRpjP6eww0NE7nbkQpL0GOLug0A6XF+ykp3El/zUYiVn9iMlApvlOlfKdKmU6Yv8wAgKmmUUMLMp9PbY9sBAbWqFJilJFoDBTjpRQv+XiIbODX+qiqXoJkxTcrUUqhaRq9e/UiOjqaPbt3U1lRQWh4GIMGDaJXr97fO5A2IyOdbWnbGKoF0eWsQfFHVBVG4DotBMdZvVfdlIW1qoxPP/mE2XPm1MxKVP7B7vVoYEGrc6vRbImql5BqaIREDKCs+BBOV9O+mF2INnFtmDZ9Wovv92KtX7eOlStXoTeYMZoj8fkK2LV7F9HRMdx//33N7sH57ttvWbV6NRE6E72UEQWk46ZQFZB94gvCY0ZiMkfgdhdTWrib8tKjTJ82vcmTCIRozZqUGM2dO/dSxYHP52P27NmMHDmS3r17A9UD/0wmU72aLDExMWRnZ/vbnDvuqfbx97UpKSmhsrISq7XumJEXXniB3/72ty12bpeTxWpheFJSnbExrdW3366lotJFdPsbq2dtAUYjWKyx6I12Vq9eQ7/+/QkODiYvN48PPvgQo6UNbdoO97d3OfMpOLWW3e5KOmJGj4amQUfMFCkvBzQnwcHBZGSkowHtMDFEC/InL0ZNoxdWvEqxh+pp4J27dCZ1cyrbt9efEt8XL8tUCadzN1KcvwW93ozHXU4kBhK1uu8jo6YxAjufq9Mcw0l3ziQQWTXjj+LaNm2l+NCQUMwmE1EuHd00C4eVkyM4MaBhRccGVcpYHITUJCU+BXtUBdm4cYT3JSRiAMrnprzkMLv3bIXdu4nHRAQ6Csqz+TT9M7anbeeee+/FYGz8z8O2bdtw6Ix0VmfOya0UJ3CRqFnrJEUAek1jgLLxdXkRBw8epHuP7gQH2ckpd9NBq9/74lKKQjz0ramZpdCwBLVrMBZN02MNaoteX3+cS35ePseOHUMpRfv27WkT1+b7L3Irt2f3Hr5ZubL65xneF02r/jk5q/IozF7DB+9/yKOPPtLkgeiZGRmsWr2avpqN3upMgp+orOykgt2uYnIyvvK3D7LZuWn6TfQf0L+FzkyI1qHVlG2dNWsWu3fvZt26dYEOhWeffZann37a/7ikpIT27Rtfb0s0ndfjZfuOndgcPf1JztkcYX0oL9rPju07GDV6FCkpG9B0FiLbjPV/EACYzJFEtp3IqeOfcwIXnc6aRZSgmdmrKsnMzKC0tAwFdK/pVTpXN83CblXJ6dOF/rEzHbT6U+KDNT3jCWaZKqZf3z44HMF89913dGhg9hKARdMRrYzkKHf1sakewLtHVdKjR/cmf7M3GA0MGDCArZtTmYSZibozs9NKlJdlqpglqohoTFgU5ODBiQ9HeF9CI6prWGk6E8GhiegNNvJPraG7ZiGy5prm4GbtiROsXr2K6yZPbjSOwoJCInw6zu7wKcOLB+VfJuRcYZoeq2YgNzeHHj17MHjIEL5bu5bOykzEWT9TFGynAqVp/g9ds8mE8rkb3C+Az+ci+KzimhXlFXz66accOXoETdOhoeFTXtq1bc9tt9/aYgUwLzsF361bhzWonf/nWctsiSI0egQnT64gPT29yUv1bN68GYfOSK+zkiIANOiDjeOam9iEDvTu3YegoCASOiX4y2kIcTVpFYUnnnjiCRYvXszq1atp1+7Mt8LY2FhcLhdFRUV12ufk5BAbG+tvc+4stdrH39fG4XDU6y0CMJvNOByOOv9Ey6qsrMTtdmG2NLysiE5nwmgO8f/s9+0/gDW4U52kqJbRGIrFGkOmqjvDy1Dz193r9fo/NG2NvOUtmg4dEBoaRmlpKaeLi+jQyPpgERiw64xYrRbGjRv/vcvgKKoHYh9XTrb6yvmaEqwhDm6YOvW8r2vMuPHjiYyOZgWlbPKVc0w52aMqWauVofQ6Ro0cRVjXjug7xuHWadhDutf7EAWw2jtgNARxXJ25BRWjGemuzNW3sV2NJyIWq4Vyre4MN33N9XY1Ujiiem07n3/5kKQRScTGtWElpWzxlXNSuTiqnKyglMOqiqlTb/APFO/RozsVZUdrin7W5fGUU1WRRY/u1cUcvB4v77zzLifSs4iIHUO7zjNo22UGkXETyM0rZt7ct6msrKy3nytBWVkZOTnZBDk6N7jdYovDaAzi0KGDTd73yYxM4nz6Bsd8aRrE+QwUFxXRr38/unTtIkmRuGoFNDFSSvHEE0/w2WefsWrVKhISEupsHzRoEEajkZUrV/qfO3DgAOnp6STV3CpKSkpi165d/sUuAVasWIHD4SCxZuHPpKSkOvuobZN0BdxuulqZzWZ0mg6Pu+Fpvkp58bjLsNmqE1ePx4Ne33h1ZU1nphwfx5STHOVGKTiJC52mERMb61/LLI+GZ00WKg8+QKfTOG9BKICawtqqZqBzh/gOnNDcDb6uUvnIxUO5DjaoMjIsGsNGjODRH/6g2eNAzGYzDz78EKPHjiXPbiRFlbFHc5KQ2JNHf/ADJk6ayN333MO9M2bg83kxWxtewkVDQ290UHXOoijtNRNOl4vc3JwGXwfQu3cfcpWLQuVBKSitKRPhQM9hVdXga07gwq18dK9JYIxGIzNnPsCIUaM4adWxVpWyUZVhax/LfTNm1KnSPnz4cJTPRUH2t3jPGnjvdpdQcGoVNquNvjWr3e/Zs4ec3Gwi4yYRFNwJTdOjocMWFE9k28mUlpWxbeu2C7jSrU9t6QNN13CvnIaGTm/E42m4RML56A2GRn47qnlQ6PWt5iaDEJdMQN/ls2bNYsGCBXzxxRcEBwf7xwSFhIRgtVoJCQnhkUce4emnnyY8PByHw8GTTz5JUlISw4cPB2Dy5MkkJiZy//338+KLL5Kdnc2vfvUrZs2a5Z8999hjj/HPf/6Tn//85zz88MOsWrWKjz76iCVLlgTs3K91RpOR7t27c+TYAUyWKMpLDuNxFqHpDFiDq6diezxV9OlTveRCdFQ0p0tOERxaf2ZdWfFBqsozqcRHSs2AazN6fPgwW8zs2rmTgoJCNGC3qqqesXfW12Kfgh2qEh0aBoOBYEcwoY4Q0kur256rUHkoVW46duwAwPCkJBYcX8BurZJeyur/xu1WihTKMZtM/GTObAx6Q3W5ixaY5W0ymRgzdgxjxozB4/Wg1+vr9VwZ9AZs1iBcVQUEBdefNaSUF4/zNEENLA4LsG7dOux2OwkJCfTo0bPOgOzExMTqAcD5+RgUVNYkVwY0SvCy3VdBr5raUkpVL/a7lQoSe/QkIjLCvx+jycj4CeMZO24sFeUVGAwGLNb6Fc4joyK5+647+eijj8k69hF6vQ1QeDwVBAXZuf+++/zJ786dO7Ha2mAyR9Tbj8Fgx2rvwI6dO89bmqG1Cg4OxmYLoqo8E6ut/pgrt7sEZ1URcc0YS9W1W1e2btrMQKXq1cJyK0Wm5mHYNVC7SYiAJkavv/46AOPGjavz/Ny5c3nwwQcBePnll9HpdNx22204nU6Sk5N57bXX/G31ej2LFy/m8ccfJykpiaCgIB544AF+97vf+dskJCSwZMkS5syZwyuvvEK7du148803r5qp+lcUVV0o0ePxMHLkCA4cnEdOxhIs6InFQCWKnIqT6NDo3q07UdHVvR29eyeyfMUKKsrTsQWdGTtRVnKIwtwN2IITcIT3wWgMweUsoLhgB+6Kk7irqlizchWq+tBUaBpfqxJ6YiYcA2V42a+cnMaLQmE0mtA0jQEDB7BmzRriMNLhrHFGVao6+Qqy2ujatfpDomu3rkwYP55Vq1dzTOeijc+AG0Wm5kZnMHDvvff4P7RbnEbjtcU0GDhwACkbNxMclojBUHd6flnxATy+KjppoXWeP65caMCxE3lALlu2biXEEcq9M+7B7XLXVL4Opm27tuTk5RKHmQTNjB7IVC4OUsU+KjmEkzAMVGg+ynweunTqzE233NxgqDqdrt4CvOcKCrJjMZsoLXdh8lSnYh7AbrPVSabKKyrQGxsvRWAwBlNe3nhvWGum0+sYMngw361bjy04AbPlzKQSn/JQlLcJq9VGz5re8qYYPGQIqamprPeWMVzZMdckR07lYwPloNcxeHD9KvJCXG0CmhhdSBVei8XCq6++yquvvtpomw4dOvDVV181uh2qk6+0tLQmxyi+n1KKoqIilFKEhoY2Wkdlx/btrPtuHfmFBUB1j4bP56VvzTT32vJBJcrLGkopyM9H+RSaTqO4uAQdGvlZq7HZO2C1x+PzuSjKSyUouBPhsaPPWrssmqi2E8nLXIG3Mofxyo4C1lKOISgOhWJrWQaq5t6XxRqL3RxKefFBunSt7lkpLS1Dh8Z6VcZ+VUU0RqrwkU510oDLicvl8i+eOnrMGDp16kxq6mayTp7EoDcwokd3Bg0afHH1gi5S0ogkdu/ZQ17m19hD+2AJaovyuSgrPkRZ8QE6YvbPYIPqxOYAlQSFdCc8uvpWs8t1moKsVfz7jX/hVXVvuw3Wguh21nT9GM1IW2VilSqhc7eumM0WLBYLvXv3rh4/2MzesuLiYubNm4vdrZiihRCmGUBVDxZPyctn3n/nMuuJJzAYDYSFhnK6KBeFanAJHHdVAWFX6uBrYNSoURw/cZz09GXY7PGYrW3weiqoKDuC8jmZce895y3E25iwsDDuuutuPvrwQz73FhHrM6CAHM2NwWDknnvuwREi4y3F1U9uGItmU0qRunkzKRtSKCqprkFltwUxdNhQRo4cVefWy7drv2X1mtW0w8SAmiHQe71VRGGk9znT3B2anhHKzvLCAg4fPkzXbl3ZuXMHPhRGUxguZwEVZcfPtA/vW+8DUEOHI7wPuSdPsZzqOlR6NFxlJwiNHERo1DB8Pic6nQlnZS6nczfQv38/goOD8bg97Nyxg55YiNAMHFZOMnFhQKO3ZqUtJpZ6i9m1ayeDhwzxH7Ntu7a0bXdLC1/li2Oz2Xjk4Yf5+uuv2b8/ldN5mwCwWm0E2WycqKjApRR2pSNf81Ko3Fht7QiLGubfh9dTjsddRgwGeml2QjGQokopxktX6veExWpG2mKmtKSUu354d4ucx9o1a1FuDxO0s+pBaRCDkQk4WFxSxPbtaQweMoSBAweyb/98KsvSsdk71NmPsyqXyvJMJk24sUXiCgSD0cD9993Pli1bSN2yhcLcjRiNJnol9mTEiBH+Xtbm6NylMz+ZPZu0tG2cOH6iutBlxwQGDBiA1dbwUjhCXG0kMRLNo2DJ4sVs27aNjpqZ/poDHZBR4WLN6jWcPHmSu+66G02ncbrwNKvXrCYWI3l4yeTM7LEBWv1lBgAiMeDQGWsWp+xKldOJwWAjNv5G0HQ1tXiOcDpvEwZTw0USq9dxAntIN4JCulFRepzS03soyt9KSeEuzJZwPO5S3O5yEnv2ZOKkSRw5fJji4hJcHjfRmrX6Q76BOjsOnZGCgoKLvowXKy8vj8KCAsxmC/Hx8Q0WZrQH27njzjsoKysjLzcXg8FAXNu2+Hw+du/axc6dOykur6CsuBiLsR2RbSf6E02Foih3EzEYGa8FnzV+CqIxNjiDCSAKPftb8Prs3bOHBMx1xobVcmh6YpSRzZtTGTxkCJ27dKZnjx7sP7AWZ2hPgoI7gaajsuwEZUV7aNe+PX379mux2FpaZUUlpaWlWG1WgoMb7m3UG/QMGz6MYcOHNVrtvLlsQTZGjhrFyFGjWm6nQlxBJDESzXLixHG2btvGMM1O57PG4MRqRuKUkbUHD7J37x569e5NWloaejSycVdPHQ/pCkqRk7HEP8W7Hq26h8fn89U81GFzdPWvUabpTBiM1WNS3K4iTKawertwuaoXNDYYHZjNkZjNkVhtceSeXI5ep+jaORqbrSO9e/fiwIEDvPK3V3C5zyRt21UFYwn2rztXy6egCm+DS+MopaiqrEKv11/SZSNysnNYsmQxGZmZ/ufstiDGjhvL4MFDGvygtNvtdWbC6fV6Bgwc6J/99dJf/oreHFGn981VlYfbXUovzVEnCTKhUXHObLazVShfo0sHNYfb7camNd5jYUNHds2Cp5qmcfvtd7BmzWo2p26h9PQeoHqpmYED+nPddde1ykrNhQWFfPPNN+w/cABVc8uyY4eOTJgwnvbx56lJdPUv2SbEZSWJkWiWrVu2EqIz0lnV//Brq5mI0UxsSU2lV+/eZGVl4UURFjXEP6tMoTAagsj0uGjfQI9MqfJyWrlo2/bMzBudvm47iy0Onc5MScEOItqMrfOBrpSPksKdgIb+rMUvLbY4zJZodJRyy623gILPP/uMXbt20QMLnbRQjGhk4WanqmCFKiGZEP9AVKhebqTK562z9pzX62XTxo1s3rSZ4tLqW3fx7dszatQoujYyk8fpdJJ9KhtNg9jYNhecSOXl5TF37lxsHh+jtWCiMFCBj4MVVSz56iucTlezZlzFxERz8tQpYMCZ8/JUABB2zp+KeM1EiiqjSHkJPafKtUspjmtuhvQZ1OQYzidHuetVF4fqsgk5uNHOStB1eh0TJk5k9JgxZJ/KRilFTEyMf0xYa5Ofn89bb/0Xr89AaOQQTJYIPO5STuXsY97b7zDj3nvo1Lnh2kVCiJYliZG4YNnZ2ezcsYOysjKOHTtGrE/faCWsaJ+e4/nVt1LKysrQ6czYQ84sCquhERTak+P5W4lXrjq3qzxKkUoFNquVXjXJh8FooKosE8fZ0/VrkpWKsuOoLB+O8N4YjCG4XQUUF+zCWVld20p3Ts0XszUaj7P6Az8r6yQ7du0kSbOTcNYHa2fMxGJkiSpil6pgsBaETymO42JLzbTz6OhoAHxeHx++/wFHjh6hozLRRwvGg+JoZg4L3n+fKddfz9BhZ8bsuN1uVn7zDdu2bcftqe6hMhnNDBo0gAkTJ37vwNlVK1di9niZpBwYa66BBR3DNTtmn441q1czYOAAbLaGb1M2ZsiQwRz58EPKS48QFFz9IazTVyciJXjrrMEWj4m96FmjShiGndia22qFykOqVoneaGTo0KFNOv756A0GTnncnFJu2pyzIO9BqqjAR3gDxVqNRiPt41t/1fqlXy/Fp0zEtLsBXU29LrMlGps9gfxTK/n8iy+ZM3v2mQWOhRCXjCRG4nv5vD6+/PILduzciU1nIBgdlT43Fed5+1Tgw2yu/qCyWC2YzOH+22C1gkMTcVXmsrY8nRhlpI1mpEopTuhceHU6Ztx1l3+9Lq/Hg8t1ioqyY9js1YVAqypO4fM5CYkYSHnxwTrrOBlNoYSE96W4cAced1md43rcpThqZoqlpaURhJ6ODSzpEaTp6KTMHKaKU5oXp/LhUl56JSZy0803+9tt27aVI0eOME4LJvasJKyTMrONcpYuW0a37t0JDQ3F5/WxYP4C0jMyCQ7the3/s/ef0XHlZ3Yv/PufULkKhQyCYCbBnJrsZucc1JoZtTySxhrNSBOuLV+Hub73y3yylz/ed9l+vTz2ute+156kNDOKncVudk7sZs4ASZAEiBwrh1MnPO+Hc1AACIBS61XolrDX4lpcFU6dUKj/Ps+zn72T630bgcINPjp2gomJSb7yB19ZdrKvUq5w+coVDhKrk6L52KGiXJEqFy9c4M6PSUy2bt3Gvr37OHv2PSrFm0QTvp+UrnQuSoWHSNbbNrpSPEKKw5LjTckT0XR0FCVxSCcb+NqXv/xznWBS+OaRb0uBTRKmS4XwEG6IxU1qpNAJh371ifc/C7LZLNeuX6O5/f46KZqFUhqp5v2M33yRa9f62LxlyzJbWcEKVvDzwgoxWsFPxGuvvcb5c+c5FASqKuXfpZ+kREFckrcQHks8biqb+/bsBqC1pZXh4Z5F49NKaTR3PoyMvMlkaYisqQiZJrt3HODQ3Ydoamqqv1Y3DDRSTI2+QzR+g2hiDdXSCACppt2kmnZhVSbw3Cq6EScUacFzKuRmzuK6c07MjlOgUhrkgXufAGBiYpI0S8cgAKSVjidgGzohM8zd+/fz4IMPLdCoHD92nC4VouOWSgYK9hDjOjanTp7k0cce4+LFC/QP9NPW9RSR6JwJX6i5kXC0nWvXj3C59zLbd2xfcn+KpaJvi7BENApAWCliyiCfzy94XETov3GDsbExdN1gS/cWGhtv0WUp+Nwzn6OrazUffniMqbF3/HOQbmQ4m+FdiuyUCGkMcrhcokIVj4cefBACPVhXVxdbtmxZsrJRqVQ4c/oMPT291GybVR3tHDx4kNVdPzlINxIO0+E45HC5hsXVwF1bR7GeMFW8j10h+6QgM+Nr4ULRtiWfD4db0DSDmZmZX+ZurWAFv7FYIUYruC2q1aqvFSKyQGS9UYW5LFXekDx3k6A9CA6dxuG4KhMKRzgQmMHt2bOHEydPUCkOEEusX7B9cW1sa4o7Dhzgt357+eywrd3d9F7pp6ntEIXsFWbG368/59pFDDNJJNqx4D21WhYAqzLG9Pi7aFqIammQhlQD+/f7OppypUwVd9nJnry4KMCquVQti3fefZdjx47z9X/2dZ9YCExOT3GQpRdlQylaPZ2JiUkATp46TTTWuYAUzSIaW00k2sapU6eWJUbxWBwVuEu3iMEoNQp4mChWE0IBFXGJx+dE1qMjI3z/e99nJpvBRMNFOHz4MDt37OB3nvkcoXmVFqUUBw4e5MCBg1StKkopwuEwly5e5JXDr3C4mKu/NhlP8LtPfpbde/Ysua/zMT4+zje+8U2qlSqReBeanuRSzzXOnD3Dgw88wCOPPnrb969a3cnVK1cRFPGGLYQirYBQLQ7TXxoA4NH16267jVl4rsfVq1fJZjNEo1G6t279xRlw/hSIBLon1ylhmourbK5b8aNdwr+6fVzBCn6TsEKMVnBb3Lh+A9t12KQWjg0bSvEoKd6SPK9LnqjS0ZSi5Dk0NTTytS9/uT4BtWbNGrq3dNN37T1cp0w8tRmlmVjlUXLTJzEMuPe+exd99tTkFJcuXcKyqqTTaRy7hFWZoL3rKZRmIOIycuP75DMXaWq7e8F7reoU0yN+Pl6sMg2VaTI46Erjqae+WBfhRiNRpphikBprbon/qIjHNSxiqU00tz+A59Uo5q6QnTrF//h//wd//ud/DgpM3cBylzcrtTRoNH3imM3mMMPLV0jMcDOZ7NSyz8fiMTZv2sSF6zc4L2XKeBgoXAQNRRM6nhJ27doF+JNOf/PXf0PCEZ5QDbRi4CD0Y3Hq0iXK5RJf/dofLSaFigVkYcfOnWzbvp3+GzfI5wskkwk2bNi4pD2A4zicOnmKEydPkslkCIfD2LaN0hJ0rP8tjEAMLwiFzAXeefdd2tra2blrcdzLLKanZ/AQWlc/TjQ2d/4SqW4K2UtkJo8xObn8eZtFb08vL770EqVSEU0z8DwHwzB54P77ePDBh34lE14dq1bRmG6imO0lHO1Y5MlVyPai6zrdW1fiOFawgl8GVojRCm4Lx/ET1kNLrBhxpfE4KX4gGTZs66a5uZk1a9ayefPmha0UBV/60pd46aWXOHvuBJnJ4yilEPFobW3jC1/4owVtHcd2eO7ZZ7lw6SJhTSeiNApBHlil2E+ldJNwtB3xnICs9KKUItm4C8OIY1UnmBw8TBqN+1S63uoriseHlHju2edYvbqLZCpJa2sLY0NDfCBFdhNlE2FMFMPYnJYyokdIN/vTVZoWItW4C6UZZCY+5Pz5c+zes4dt27dx42IPOySCdktPLiMOU1Ljke2+8Dwei5EtLB2c6x97gcbU7Y30duzcSd+1PtoxeVAlaVIGlnhcweKClFm1qrMer/H666+hOS6PqXRdk2Sg2EyECBrv9Pdz8+YAa9f95GqLpmk/cTLKrtl881vfYmhoiGhiLcnGdVRKo9RqQ6xa93SdFIEvwE817saqjPLB0aO3JUYzMxkisdULSNEsEult5DMX6bt2bcHjU5NTHD9+jGt91xDxaGxq4vp1vw3bse4xQqFGXKdMIXuJN996CxHhoVviiX4ZUErxyCMP88Mf/RBt4kNSTXsxjFj9u12YOcf9999HdAlx+QpWsIKfP1aI0Qpui/Z2vz01ir2oojL7OMBjjz1OU3PToudnYZgGz3z+GR599FH6+vpwHIeOjg7WrFmz6C79+eeeo7enh0MqwQYJoaEoK4+zXpl+cdmzeyeWVUXTdHI5ndGRUcrZyxSyvWhKxxMHHcUjKrVgzD6hNB6UBM/ZWU6cOMEjjz7Cvn37OHX6NB2YnJMKZyjXX28aSVq7nlgw7g+QSG0mO3mSox9+yO49e7j33vu4dPES71HioMSIKc1vseFwVJVoaWxm2za/NbZ3724Ov/Iqtp3DNBcaU9ZqGSqlYfY+stiVeWx0jL6+PjzPpaenl0Zl8jCpeoxKWGnsJooOnB0ZJZ/Lk0qluNx7mR0qsqRQu4sQcTQ++OCDn4oY/TR48803GBkepa3rM4QjvmbGtrKEwk11w81bEU1sYGTkfWq12oK23nyICJFY+5LPKTQi0Q6q5YH6Y5cuXuQHP/ghYaWxxjNQQF+2n1C0jeZVD6OCcUrdiJFuOQhK8d5773PXoUO/EgKye89uLKvKK68eoZS/imnGcZwKIh533303jzxy+1bjClawgp8fVojRCm6LtvY2ujpXc2ZkjDYMwvPMDqvicU4qbFi37rakaD6SqST779i/7PPTU9Ocv3hhkXFkTGncLQlKqsDI8DB79+2jVqvR03OJgyrOesIMUaOKRx9CM8YCUjSLkFKs9Ux6e3p45NFHWLNmLdu3buPKlStslwhRNPI4XMGisf2eJTUfShnoRpTp6WleO3KElpYWfvcLX+DZZ3/Ec3YGUzQEsPFoa27lD/7wD+stp127dnPktdeZGDxMY9shovG1gFAuDpCZPIZpmnWLAoByucwPvvc9rvf3E1K+HaYlLveoBEtNbm8hwgVV5eLFCxy8805c8UioZTwVFCREZ2Jyctnr8XFg2zYnT50m3rCtToqAIJFu+R6VCvbv29/6NuFwiG3btrN7927M0EIxu+tUlt2G45TqAvpMJsMPf/hD1mJytyTQNN8X67JUaWzcWSdF85FM76CQuUhvT0/d8PKXjYN33snu3Xu4ePEC2WyOWCzGzp07f6VZeytYwW8iVojRCn4iEskkIwzzkuTYLH7oaEZcrlHFRkgkf35j2T09lwgpnfVLVKeUgi0S5v3pKd586208z0OA9YQxlWJDMHJ/XawlW3+zCKFh23awUfjCF7/Iq6++wqmTp3A8d/bTsKpTRGKdi97vulVcu4iLx/mPjlNwHQxdx/O8us7HVsK0eNRqNeygHQlw5cplHMemCZgafWtB9EYTJjOuTW9vLxs2biAUCvHtb36LzMQED6gkXYTI4/ASORIs7dxsKkVU6ZTLZVSw9WlxFng0zcITIYODUbV4+aWXSKcb2bN3D4lEAhGhVCqhlCIei/9U2puZ6RlqNYvG9oW+QeFoK6V8H45dwFgi9b5c6Ecpnamshrhlrva9yNvvvMPXvvZVmpubAdA0RSnfR0PzPjRt4bHYtSxWZYxU8D08eeIEhijuIl4nj1ZAz5YiugC6HkXXTcrl8pLP/7IQjoS548DP1xhzBStYwcfDCjFawW1RLBa5cuUKO4liIVymii1CCJ+I6Ch6ei5RqTz9c2lBWFaNsKahy9IrcSy422/t+i2syjiZiaOLYkUaMRjFRoTFY/gCY5pDe8dcxIJu6Dz92c/y8MOP0N9/A8uq8fzzz1HMXiKR2ryglSYI+ZmzCMIuouyRGEOqxrtugXWEOaji9TyvHC7vFUt86xvf5F/+2b/CMAxOnzpNpwrxsEqSFZcJbBR+7piB4lXJ8dzzzyPioZSGiMe9KlF3B4+KjgbM4NC6xJ9vVTxKnkM4HCGXy6FpGtc8i26JkLrFVqGXKjUEr1Lh2qlz5MXh9ddfY8uWbibGx8nksgC0tbRyz733sG/f/tsSpFnvJRF3weOx5EayU6eYHn+f1s5H0bQ50lvK91EpDdLYdjfJwADUtvNMj77Bt771bf7sX/0rNF3DNE0sy2Ji6AhN7fcQCjcjiD9xOPYeoBFPxAHov9FPpxgY80pqs9+bWnVqyZaebedxHIt0evFztyKTyfDh0Q+5cP48Vq1Gc1MjBw7eyR0H7viZUu1XsIIVfLKw8le8gttioH8ATzy6VZSwUhyQOC6CjkIpKInHJbfC4M2bdG/d+rN9iMDg4CCTk5NUKhWKrk1JeURRjONgIyTRaVQ649hoSscw4hD4vgxRY+28CtMWFaFfclymyrZb0t+vUiXj2TTZDn/7N39DPB5n9+49dHd3E41F2b5jB57n8dJLL+G6NcZvvkiyeTeRaAeuU6GY7aVcuglAPKja9ItFEp2NKsxHUiQjDhqKTkzuIMqb+RyXLl5iz9495PN5OkUH5XskpYNtFMTlsORx9DCp9HZC4SZsO0cx08Mxp0wSnWZlEFKKNRKmVyqsJ7ywXShwQfx20+tvvM7rb7we8BidVyXPDiJ0EqKGxzWxuIGFQmcLIfYTw8LjNclz5cpl1hFml0oiwMB0nueef56JiQmefOqpZS9jS0sLqWQDpXzfAjsCTRm0dj7CxNARRm58j1hyI5oeploapmZNE09tIdEw990xzRRNHQ8yNvA8ly/3sn3HDsTz0BAca4axmy9gGDFEPFy3ioaGiaAHLTmlFLfOCMaURoeEmJk5TzSxboEbuiDkp88QiUTZunUbt8Pw0DDf/OY30ByPDZ5JTEWYnC7wyuHDXLp4kT/46h9imuZtt7GCFazgk40VYrSC22I2zHJWlaGUP9U0i9kaxGzY68fF6Mgoz/7oR0xMzelcFPCOFCgjWMxVH9JiUsIlmtqEppmEQo1Eou2crkzTjEE8WBhblcEmCXNKSgxLjfUqjAJuUmNE/AiOiRsDNIjGqBIuXrrE2q41fOUP/oBwJEyhUMBxHUIoHLdCduIYEiy1RnAmwiiGsNAEhrFpxuANydOAzlrC2Ag3sOgTiwZlcOXK5XqbKl9YPFZ+XMp4ZoyONb+FrvtkLkoXiVQ3k0OHedfK0iEGHpBAYwThFcmykxhts1lpUmWQGkl09qoYYRTjYnMZC1czOOtV6+JyQ4/R2HSISnGAUsU3DpzAN1C8XyVZOy+iZS0hLmNw9MMP2bFjB11rlo7YUJri3nvv4fArhwmFW0ikt87T82jomkE8EUbTprBrNjWrQCK9jcbWQ4tG1EOhJkLhNB8dO0YulyccDtNkC1kcKoDmVBDABZIoNKWRSvltsg0bN/DR6BiOCMY84niHivGqnWf85oukmvcQirTi2kUK2UtUSkP8o89/vu60vhQ8z+N73/0uKQcelhRmUJHqxhfavzk0xDtvv81jjz++7DZWsIIVfPKxQoxWQLlUplqtkkgmCIVCjI+Nc/z4cYZu3sQTnxBcE4ttarHB3CA1FIrO1T/ZvfhWTE5O8jd/8zckHeFRlaIdEwuPNyRPBodEQzeN6e0YeoxqdZzc1GmcWo7G1Mb6Npo6HmBy8Me86GRZLyFSSmdGHG5SQwEZA8YdPxIkGU+gSjVaMZgRlwIOmvhtrNHhYZ5/7jm+9I9/b64lhE/83DotAi34Xw1hGJth8fVDE9jsVzG2E623m/ZLjPekwJjYWJZPyPbt38dLIy+TZS58tSguY9Robj5UJ0Wz0DSThpYDTAy/yqARQzciWNWpwI9SOCbFefsG7Rg8phrq+9CuTNZLmFe8PNGGbuKpjSilY4YaQSmKmYuEAvJyVaq0Yi4gRbPoJsIVrcaJEyeWJUYAhw4dYmZmhmPHP6KYu4gZasFzS1Qrk3R0rOKrf/iHxOIxalaN//P/838SjrQuIkWCkJs+jW3luDmQZeTmILZ4FIED+K3KqaAq16FMlMAbkucz+/YCcODAQY5+cJQPpcTdEq+ToxQ6qzEYsPNMj71b/7zm5hY+91u/x7btS5tqzuLqlSvkCnmeVg2LpvxalcFmL8zJEyd5+OFHFjijr2AFK/h0YYUY/Qbj5sAAb735JjcG/DFnUzdoX9XB8NAwUU1ntWfgImRRnKbEjGdTQKgoISYanehcUTW2bdtav1v/OHjn7bcJux6PMheGikAOj1TjbtItcyLUWHwtkegqxm4+TyFzsd6qMYwEbWt/m9EbP2RQaoj4epLdxLhOlXRbG3/0x3+Mbdv8xV/8BQaKXBCIqvyPo4CLJ8Kl3h7+3//+/wCgazqa5/HbKo1CUcLFRHFFqlymynaibFH+FNv3ZYZGdLbfkvxuKMXdJPiRZJCAYO7Zu5fjx47zxvQ0eyXKWkLM4AAQvsW5exbhmH+syabdJBq6cZ0yMxNHsUpDPKxSaMCw1LhClftVapEOKKV0NkuYy7kr2JVxlGYQSaxBN+LYTpH1yr92eVw2LZEZB36lsN3TmRifuP1FVfD0Z59m3769nDx5ipmZGaLRFnbtepitW7fVp/NCoRDNTS1USoP1wNpZ5KZOUshcYCdRtqoIYaWRxeWMlDlBiR0SZa+K4QE3sDinKjQ2pDn8yqv84Ic/IplIsnPXTi5cuMizkiXtaYAirzwsPH7nt3+bDRs3kMtmiUSjdLR3/FTi8uHhEWJKp3GZOJYuZdJr5clkMrS0tvzkDa5gBSv4RGKFGP2G4nLvZb773X+gEYN7VIIYGgOuRd/QENuIsF/izE557xaPlyVHPzbReBdmOE2pmuFceQhTN3n8iSc+9ufbtk1PTw97JEIVj3NelQwuVTxAkWxcbPanaSbJxp1kJj7Cdav16kq5OIAnNg+qFCl0Qig0pUiIxgcjw+QLeYqFIlbNwkRRQ5hQgmmmcJwSrmcTRsNDMMdnMFGEECoI17HYQBgXqOJxmSp7VYyNhBnGxhYPF2HtElNfABGl0S5mXQRumiZf++M/4oXnnufYlSt8xFzAredWwYgv2oYXjKkrzf9z1Y0YLaseZuzGDxhwLe7WEgxIjfQyFgUAbcqgRyqsqpVxEYaDqlMzJm1BnIuBorJInTOHqvIIhX+6oNZVnZ38dufiib46FNx99128/PKPKSf661ExrluhkLnEbhVj1zyimVY6D5HkiOS4RIWLgZZKU74wO5srEEtuIJbaSLWW4dz5ixiGTs22mQiIJwJdXWvY0r2FZDK5OCvuJ8B1XRzxJyGXOs1OcO50faVatIIVfJqxQox+A+E4Ds8/9xydhHiARP1Hvk+qpNDZr+ILfvhPShlPM+lY8xSh0JxfUc2aYnL4CG+++RZf+MLvLvwQgeHh4Xoe1br16xcsGFbVwvU8ZnA5TRZNCxGOrqJWnURT2qKW0ixC4UZAqJSHMc0EpVwfxfxVkui8IXk8/AV+nYTYHLT+pqemsW2/lWUjpJr2kmrchaaZiLiU8tfITHyIAJtVhA5lIgInpMRpKXOG8oJ21ZjYnKMSULjZcfvlIYoFgtxYLMbjTzxBJBql/8YNECFfKlHMXaapbXE0SjF3GaX0Ba7PSulEGzYzOHORnUGeWwVv6Um84DkF3KnimEpRFY+3pEAJFwfBQNFFiKtU2Sex+mTdLEriMSI2T+1Y3p364+LAgYMMDNzkwsW3iMY7CUc7qZZHUHh0s/j6KwXbiPKeFHjqyadIJBKcPHmSoZFJmjvuoVoexapMoBtRdCOB65ZparubWHIjSinKxZuMjZ/gb/76b/n61/9pPRbmJ2FiYoLJiUmmp6epIYxQY/USdhLXxUKDRf5LK1jBCj5dWCFGv4G43NtLuVrhUZVesIiO47CF8ILHSuIxjEVjy70LSBFAKNxCqmkvly6e4Kknn6zHUAz0D/DSiy8yOT0nMk7E4jzy6CN1j5ZINIKuaQx4FqnG3TQ070Upg0L2ItmpU3iuhaYvXrhsKwvATF0joqHh64A6A7WMCwxTYyjQ/4RCIWq1GqART24g3TxnMKmUTqKhG8+z/JFycehQJhU8hqkRQ2OnitKG/9jVQOAcja+hqf1+ND3ExM2X6Ley/gTcLaSkLB6T4nBgw4b6Y+fPnefZZ58lpBSrPf9PsIBHMXcF3YiRTO8MSJtDMXeV3Mw5NKWj9IULrqaFsfF4QbL1x4aWyHwTgatisYpQvWUZURr3k+B5yXJMiqwnTBYHB+FNKXA3cRoCDdSMOLwvJQSp+wrVt+0JfX1XuXbtGp4ndHWtZsfOnT/V2LrSFL/7hd+lu7ubY8ePMzFxHvE8TLRFxGwWiUAPtX79OkLhMD/44Q8IRVqYGn0Lw0xihhooFwbwvCrtaz67wGgyntxIKNLC2MCznD59irvvuee2+zc5OckLzz3P4PBQ/TEN+EhKPIRGc9BS8wQuU+EmPvmulCv1nMAVrGAFnz6sEKPfQExNTRPTDFK3mAQKLCo3TGEjQCy5dGRELLGezOQxhoaG2LZ9G0ODQ3zrm9+kSXQeVSmaMSji0Vuu8MKLL+I4LncdugvDMNA0AyPcREPLHfXKSyyxgezkSQrZSzTMIzAAnjgUMhcBxTbCGCguUCGERhWPUWyiKMp4KBQ6gqHp6LpOX99VwCORXtpSIJHqJjt1kkIwBXde/CrRZ1QD0aCn2IBOByYnpcSV0jAgft5Xyz4mh1/jnJTZTax+CmsivC9FdEPn7NnzfPjRcWKxKMNDQ2wg5BsQBpNNB0R4RXLkps9QmLmAaSZxnCKu50exDEqNanm0XjWyquPkp88wv1algKNSxCPOGsJoyhd2n5YyORwOqoURJAml0ywG/dTol5rflgo1kXMqvORmSWIiCEVxMM0kBsK5c+fYtNnXBGVmMnzn299mamaalGaiozh+4jivvvIq//jL/5g1a9fyk6CUYvee3ezesxuAV195lQ8/PEpJvPqU4XxMz+qxIhGGh3zCYlsZmjseJJbcgEIxPvRjkNQCUjQL00wRTazlo4+O35YYZWYy/PVf/RXhmssDKkkHJlU8eqTKNaq8IjmaxSCGxhQOFTxWYfr+WbetH65gBSv4pGOFGH2KUSgUOHvmLJnMDJFIlF27drLqdrqOAOFwiJp4fgtlHhFqw+CmWOyeN1k1C1nmt352EZjdzJFXX6VBNB4lUQ9UbUTnHpXA8BSvv/46+/bvw7ZtbKdGc8uWBVNJuhEj2bSL3MxZPK9GIr0d3YhhVcbJT53CsQsohF6q9fdU8dijYnQTIRS0iS5KhctUUZ7LX/7VX9Zfqy3TolN6CIVGEh1HhAFq7FTROimaeyHsIsZVsZgYOoyuR9D0CCogaTfEYpWEcBCGqOEhiAMzOTDMNkbH+gmjuOuWSA9DKT5DAz+SDA0C6VqFCAbrVZwkOt+RaVzb1yPVrCkmhl4lFJDKcLQd1ymTz1yglO3lfSkSokRYNAq4GCjuV0lalxANa0AykWTdunVcuXKFcLSDdMsBysUBatVJFNASW0U03kVm4kOGhke4eOEikUiEF194Aa9Y5inVQHPwU1JQLseqZb71rW/zv/7z//Vj63hM0wAU56XM3SQWfA9rIvSI5ceiVC0cxwEUqaY9xJNzk4pOrUA00bX8Z4TS5DNDyz4P8M47b6PVHB6XVL16ZaJzl4qT8DTOUCaMwkFYQ4j1KsxR8a/PrVN2K1jBCj5dWCFGn1Ic/eAor732mj96HU7jOmU+OPoBW7u38oUvfuG2JnPbtm/n1VePcF1ZC7QcW1WE1yTPWSmzJ6h8tGKiUJSLN+rOxPNRLtxA13TWrFlLJpPh5tAg96vkopR5gB0qSp+dobenl/YOPxB0VlA8Hw3N+9E0k9zUaQrZnvrjIXQEYSsR9qgYVXxReDeRBULdMBolPDSgmyjrVAgLX1NjlUcxGxZP0NWqEwgeLcqgiuAEeWtLIawUcdFwa3naqDBEjWhAPq6JxTQOenC8zRi8KXniqc3EEuuxSoOsJbRkzpmuFGskxAwud2pzIuxC4CRdq05T0vrITp7EMBO0dj2FFpAdw0jQ1Ho3uh4hP32GTcF1HcQigU5nUHXKiIOuFKsJEUIxhYNRKjHWcwXPsylmL6HpYRqa9i4gG+Bnlc3kpvn+D75ff+x3VJrkPEftpNJ5UBK84OY49tFHPPWZzyx5DusQuHy5l7feeotMJoPnCSiN62JhiX+t40FV5oJUKSufpOu6HpB1IZ7asnCT4lKrziz7kbXqDHIb3y3Hcbhw/gI7JbxkS69bRbgoFWJo7FVxJrA5LiXKeGhKW8k2W8EKPuVYIUafQpw/d45Xj7xKMr0dM5TGdSt+zILS6Os7yXPPPscXv/TFZd+fTqfZt28vp8+eQxP8aA+lfHdlDC5SYQCbLs/EQ9BQ5KZOEgo3E4601rdTrYxTyJxj9+7dxOIxpqenAb/ltBTiSsMQRaGQZ+tW3/yvUhwkFl/YplMoYsn1ZKdOoqMIo1HFpYbLXmLs1HwSNCIuLsLWW/yVhqkxRI2HVJLV8zx5uiTE2Mw5IvE1GPNiPjzPJjN5AhONNsygeQhFXGAxwXRFqOKxgTAHtTjf9WbYrCKE0DBQGIHuyUDRjEEjBuVCMHklt58MV6hFrZheqaKAYv4KxfwV/xy37K+TovlIprdTmD5PVGlsUxEaRecDKfIDyWDjYepRPM/mrJQJB/u5UyJ4wEZMinhcmj6NbkRJpLrr23WcApXSENsIs1vFeFPyGKgFpGgWplKs90wuXbx0e2Ik8J3vfJu+vj7A9xmq+jU2kukdTJaGGLbz9ZdHY52ElU5IL9DS0sLY2BjAAhdrAF2PULOmqJSHiMYWVo5q1jSV0uBtqzrVahXHm/OZuhVGMPHYFxh4AjRhEFIam3dsJxJZuiq5ghWs4NOBFWL0aYPA22+/SyjcQjl/DderEUEPmja+F87FSxd5dOZRmpqWT7x/6qnP0N8/wLFshlOUiYhGKdDXbN60mVgsytDNQTRNY9+GnQwNDTE++BLR2CqMUAN2LUu1PMbaNWt5+rNPA3PTODncJclRWTxshGrVIhwJ09TUyMzMNeLJjQvCWkUcMuNHAYWLohxoeTYQqpMi8NPrAaK3pKVfE4sWjAWkCOCAivOqk2ds4DmS6W2EIi3Ydp5SpgfXKeEFholmsGxekSobiSyq7lzHqpMnxBd+58ThJcpoWphwvAvxbCZKw1ykSgpFrjzCxNBhPISb1LhD4oumxzzxKzyrAvF0STx6pMLVoG34W5/9LJqm8cKLL2KGFuqFZqFpYXQjQtXx/NgRdDQUWrSNjrZDhEKNiHiUi/3MjL2HoDhLFV2FcMS3M2jGIDd1pl6JqVUnmRl71/eHUjFMpdBELTrvc9cPKvghtP/5//ufiEQj7Nq9hx07dtDb20tPby92zQaE8Ylx1hDigIoTUxoicFjyZHNXMG5pezpOGbuW5ZGnn0ZpilWrfH+nanmIWGJO3B5NrsWZucjUyFukmnYFU2ka5eIA+elzKKURiy4/kRaJRDA0nZy4LGVb6opQxKMDk9UqhCUeA5qNFg7z6GOPLbvdFfgol8vYNZtEIrFihLmCTyRWiNGnDFPTU0zP+NNe6wizV6VJKB1bhD6qnKmModC43HuZe+5dXlz648OHyeXyvsBZPDxxaNAj2LUcfdf62LB+PUpTKE0jFDL5whe/wNjoKKfPnKFQmKGtKcn+/V9gdVcXH7z/ATeuX6dSqaCAHqnQtUS76JL4zxcKfhVgzZoupmdmmBg+QjS+hkh8Fa5TpZS7iutWUAjxhm4MM0lu6iRNt1RIkgH5mrolULWIS8cSlZ640niKFG97ebIz55CAcK0lRLdKckTyDFFDw8/ayuPytuTZR4xGZdTP8VkpE5qtOSiIicYANZLpHaRbDqCCSoPjlJkeeZ0pawblObRUZhjDpoJwUkocYI4cicBxKWIh9GMx6Nm4eP40GjoiLul0Gk3zt12zZghH2hcdo+tWcZwKsaC12CNVdCNG6+rH6xUmpTRsawZBaGjeTzK9FU0L4zgFclNnmC5cA9dhbOAHgMKuFYhh8JhK1qfaUuiMUFtkD+CJH+cyQo02MWktOpSKed5843XeePNNFIpIvAtNb6BaGq5fx9mQV6Vgs4Q4LiWaHYutKkkcnSlsLtbyoBts2rQZgJbWFtra2pmZPEko0o7rlHDdCuFIG3kuYugR8jMXyE2f8beNhm4mcGyLLVu6OXvmDNFolI2bNi2YojMMg127dnHl/AU2S2RRO+1qQIzHsBkTG4Wie/MWnnrqM6Qb04uuyQp8XOvr45133uHm4CAAkVCY/Xfs58GHHlqpsq3gE4UVYvQpg2P7aeytmNyr5jyITKX8OArgjJQpFovLbiObyXLu7FnSbYcW6YbymYsAjPXfpBEdDzg1c5zjx47zpd/7Pb72ta/VX3ut7xr/9//1f4Hr0SkmcWAGP/n9dcmxmxgts1NpUuE6vnC2Wq0innD1ylUMFC5+VaJSGkQpPdAd+Q0lM9xIsmEbpWwvWcde0IdqwySJzlkp8whJ9OBkhNAosrSGJKY0kqKTw+XzqhET5QvQxRcid2BySCX4oWToIsQoNX4sOQxRuEGVaA0hhqjV20gGGmaogXTrnQtaNIYRo7nzEUZu/ACBgHT5+32VKsNSY42EEPxolQoeLRhkjQjJxu1oepRofA2lgu+z1Nzcws2bvkt5ceYS8eSmBUn1/vU7j0JYRxgRPx8u0bBjQdvNdasUsj00NO+joWnPvP1N0tRxP55boVIeo3vzWmzb4crVAk+rJGGlURKPKh6rlUmfVLlEhZ3MVfHOSZkxajyiUqxSPjn1RBgTB9dM0bb6CfSgjSkIhcxFLk2doBGddYRxRDhLmfWEuVfNia/T6KwhzKuS58iRV/nHX/4yIkK1UsZ1Sozd+F5QM/WhAMcpopThu6QrRa0yiWPnUcCZs2c4c/YMANFwhIcfeZi77jpU/7w1a9dw9txZjpBjD7H6VFqfWPRQIRqJ8Ed//MfUrBqNjY11q4oVLI2zZ87w3HPP06r8360wGuM1m5MfHef6tev8yZ/+6U/tK7WCFfyisUKMPm0IksO3qsiSRn6biXCWMpVKZdlN9Pb2oDSdeGrzgserlTGyU8dJolPEZSQgF5ooYqLxve9+l//tX/9rkqkk+Xyef/iHv6fN1biXuUDNU57iMlXKQebZLMJohFDYCO3t7ZTKJUqVMpoyaF/7W4RCc9NLgjAz/j7l/DVq1SlogFjDFvqnz7JTXBIBIVEKDhHnDclzWHJsJ0oKnSiKQWrkxSV1i06kKB6Dgd/MSSkRRmM9YQSpm0MqoAWDm0GD0jQb0Iwomng41gzD4o+MrycM4keKpFKbltStGEaCcLSNWGWKvSrGUSnRjMEBFeOKVBnG91rqJES3itAnVfJKJ5meM1KMJzeRmfiQmzdv1kX1nlNiYvBlUs37gqm0EoVsD6X8NdLoZPFDcF0Ew1zopl0p3kTEW5BoPwuFItG4g0p5hEOHDgFw5eoVBqkx4NUYD/YXII7GWSkzITYbVRglil4qbCFSJ0Xgk7MqLqtWPVQnRbOflWrchVUeoac8yToVZpAaNsIeFVskxgorxVYJc+rKFcqlMpOTE+QLBcAntDtUlAZ0srhclArj2Ig4VCvjC2w42zDZraK0YFLEpdeq8uPDh3Fdl3vu9Q0233v3PdLo6CjelUJ9H0wUGwhzo1qlWCiwafPCv6EVLEalUuGlF19iI2HumlclXaVMNkiYV6emeffdd34mB/0VrOAXgRVi9CmDeP6Pe3wZfYepFKZot80us6wauh5aJN4tZC6ioVHRTRpb7iYWX+s7Qxeuk58+jXgeJ0+e4OFHHuHUyZPgej4pCn7pHPEjNLYTZZ+KMYVDEZcwGu2YjFLjbSmwenVXENSqiDd0LyBF4C+Y6ZYDlPPXcWx/UUo2bKOc7+NVu8BeonRh4uCbUgqKAsKHMlcl0/CDRQ8Sr7sUj0iND/BDV8MBSZvB5qpU662xa1hcCwS1ALHkBmrlMaxKrt4Gsms5dLuEOW+5VcsIdQE0pRNHp1OFWCW+MHwfMQ5pC6sMIjCEgxldKBhWgWWA67o0N/u6MR0hUSsxNfpW/XURdOJo5HB5PSClCo1qeQKlhShle7Gr0wgeSml4nrWkw7hh+PuVyWTZvWc3yUSCE8UiDejcqxKkMcjh0CM+AZ7AZlTmCNNa5VerRqhRxGNQLEKhBsxQesnzE01uYKY8ghNodyJoJJbwMAKfsHoi5HI5stlcvcr3kErVF9yO4Pv2puSZwCbZvJ9ofBWZ8Q9JWDkeUal6mzcVjOAbHrz15lvcceAAIkIml+VulWCjCjMgFhPiYKLYGuTjjYvN+x988CsnRrVajXwuTygUIrXEtOUnAefOnsN1Xfaq5KKbuQals8kzOXXyFI8++lg9S28FK/hVYoUYfcrQ0NCApvwx66YlLl9RXCy8+gK6FFpamrHtMradxTTT9cet8ggCtK75LKY59yObatyFGU4zOfwavb29PPzII1zru8ZqMeuVIvBF1zXET2dX/iLWMm8fOyWEjuL73/9ecPMuRIKA1Fuh61GMUAOe5y+4mh6mretppkbe4CNrio+C12lKJ5Hehl0r4NbG+Sf/9J/w3HPPMTIygsLXu+gBgZlthR1QcbYEomqfjNT4ICBVptLQ8b2QdC2EVRhgAyHaVYIKQl9plCoeDh7vSIE2ZWCiKBcHSKYXp7O7bhWrPE5LkKW2RUXolxwXqbBL5vlFid+GKuPQnl7Y3iyXbgLw4osvzvlGoSji0ICOg08EBam3EGPJjURiHdSqM5Rylynlr9CMyWplUhPFDRzG+5+jefVjC6JGAKyqHxRbLBZRKAzdn6x7XKXq7Uq/tRXidclTwuNp1cAVqXKeCpPYvCdFKrhoSsfDg1qe7PQpGpr3L6qszRK/WcJqIdgiixLsgfrxRaNRJiYm8IBdKrZowVUKdhJlTGzARWkhLGuKu1RySauE7SrKZSdLb08Pq1b5gwC1wHRz1lQSfEH+ViKYKCYnJhdv6KeE67j09PTQ398PCGvXrmPHzh0/lWM4QKlU4o3XX+f8+fPYjr9/q1d18tDDD7Glu/snvPuXi6mpKdKaSWSZm7l2ZdJrFSiXyystyRV8IrBCjD5liMVjbN26jd4rV1knIcLz7qxF4CwVIuEw27YvXKRdx6W3t4fx8Ql0XSccjpCdPEkivZ1KsR/PrSEixJLrF5CiWURiqwmFmigWS8Fnecv8zPmwRRjAoiQeIaVYS7gusG12FO2YnMXBdcpLvl/w8NwKnucyMfQKRqiBRLobPZREd6s0th5EKZ1wtA0Ehq7/A4auUSmXA+M/fF8ZIImG4Le8NhFZMN6vlK8Z2k+ME1KiQfzXVhGUZ/O4Si4QfXcT4V0pMILNMDVGxNd8eZUxirnLC9pTIi6ZiQ9QePXU+lZlsIcY56TMIDXW42uM+sUii0syvYNweC6Z3XEKZCePY6KxR0XRBI5Twg28llw9TDjahuWUqVUn0Gcn+QrXKReuM0uZDqkEm2aDbhXslRjvSJHxkbfo3PhFNM1/znMtCjPnAUUsFmNsbJRMLsvDKlnfz0pQ1ekixB4V43XJk8cjFVDQs1IhEuuko+UOQuFmPxg220N+5jyIR7rl4IJrXS70E0ZnAhsRQRCuUmXHPO2Sfz7hCharOztJN6axLH9ar3EZe4jG4OfNc2t+QC8scnufRVRphJRW1+Yp4CxlUug8rJK0Y1JF6roqAZLLxJb8JIyPjfPt73yHQiFPONIEKE6eOsWrR47wld//fTpX396ktVwq81f/8y8p5/NslzDtKu7H1YxN8Z2/+zue+dwz7Nu/72fat18EQqGQb8Nwmxw/WMmYW8EnByvE6FOIJ558gr8cGOAVq8A2CdOCQQmPK1hMiM3v/tbvLjB4vH7tOj/8wQ8oVcokNJOaeNTERVlDVEqDGGay3j4xw0s7FSsUZrgRQ/leRWvWruXM+ASeSN3MMR0k25+VMpM4uAhxNKoinKHMKkxchH0qRpMyGPccZrKXiTdsQd1CsyrFQVzXb3HplXGqlXGKuV4AGlvvrqexizhMDL8KgOM6/M3f/m2wv7AKk3tVkpBSTInDq5KbIwe3YANhTlJiKqgOKGAz4UWTcJqCA8QYDvLJZHa2TZnMTBwlO3Wy/loRD8TlQZVYQGB3qSgGcFrKnA30ShKc40K2F9cpE4o0YddylAv9GCJ8VqWIK508Loif5B6JrcaxZigX+1FomGYDtp0jGu8ikd6KrkeZGn6dVtdedNy6UtxNnGclQ2byOMn0dqzqJMWZC+CUAeHoBx/UK1TT4vA+JWw8dGXiioNBmd2BkWRRXLpUCBFFKNxMS+ej1KxpSoVraFqIVNMelNLIT58j2bgTXfdJTzF/NfAV0ng70PJoyuSs+ORjS+BmnheXc1SYFJs/fPRRAOJxXzuVx62ToPnIB/YTuhFBN/zPywZVNvAn6LTgYmfFwRKXc2fP0dvTW69ePa7mWsVxFHtVjIhonJQSTU0fz9Ub/FH1b3zzm7hehFXrPl9vL9p2jsz4+3zzW9/iX/yLf04yubxJ5FtvvUU5n+dJSdb1dgBrJcRHlHj55ZfYtn3bJ2bSa8eOHXxw9AOG1NI5ftdUjU3rNxIOr4ivV/DJwAox+hSisbGR/+Wf/hNeO3KEU729eEFeR2fHKr7y6CNs3jLnBDw2Osrffec7tInOIypNKpg0u6ksjkkJLdRE+9rfQSnFaP+PsK3Mkp8pCDVrhtXr/Pypg3feybFjxzgu5bqgUleKNjEZosbmwI06pjQcEfqwOC0lomh1srFLRXi9lmF69B3SrXdiGHEEj1LhOpmx9wEIoYiiU8NB4ROI3MxZXKeEiEMpfw3PswlH20g17sIINZCdPIFd8h2487iMi113jzZZ+i7f9y5SxFKbMMwGctMn6wJiEbARDEBTioTSSYhvRriWENexkEBjY5gJovE1eJ5NqXAdcd0lPzEjLhE0Pq8aUQosEd6VPBM4VIs3qRRvBsfrEUPDCIijIx6g0DUTuzzKRkK0qgRlPK7aBRwUifR2orFORDwct8xatXR7Iqo0WsRgMt9HKd+HQrEakzXEOEoRfSqLGVDW81RINHTT2rQbw0jiOCXyM+c5HZDVk6rMWVUFEaKJLsZuvoBdy9Y/S9PCJBt3IAhTI28SjnVQLQ1Rs2ZINHTT2HoI17VQSkfTTWbG3udc4RrnqRBSGlVxiUWifOl3vsjGTX5W25133sV7777HJalwH8mFYm3x7SE0/IqR79BucsGrkBWXG1iU8TBRdIifcaYAczqHHphwdqvIku28zYQ5R/m2PmHL4fSpU1SrFqvW/3adHAKYZgPNqx5jbOD7nDzh6/iWgmM7nD1zhm4vREK7pfqlYC9RbjhZzp87z5133fmx9+8XgdWrV7Npw0Y+6h9AxK/QKuX7mp2mTEYcfufBB3/Vu7mCFdSxQow+pWhsbORLv/d7lEtlcrkckUiExiXuYN95513iaDw4L7tMU/5ElYHindoMNWuCcKSdeMNmctNnSNk5THOhgWC1PIxdy3Dnnb6ZY3NzM5/73DM899xzjCuHtZ5POUax6STEXSpeX6gMpdhGBBfhvJSpikdEabQpk/tIcLR4k5HiAGaoAc+t4rlVDBT3qqQvnFZ+a+6CVOihgrgWhexllAIRIRpfTUvno/Wqk2cX6AzEt5P4olm/IuPvX3KJdsoYNh5ComELmhYmN32SiricF5s+/PaRAlZLiO0qio2fNXc9mHADaGw7RLJhroWZbrmDqdG3eac0xDOkiSoNW4QeqXADiyYMfiQZED+nbhYJFE0Y5HGZwaOIx2HJoqNj4QGC8hyeVKkF7sxbifC2FJkafYfIxi8FR6wWjLHfCgG6CPkkFo2I0njf83VZD6sUAjwrWbTkWhrb7qnrgwwjTlPb3Yi4lPPXOHj3IcrlMmfOniU/c55QuJm2ricJRdr8ibnMJXLTp1HKwKpOUqtOBdUoBWigVN2N3HHKOHaWUDhCIhGnZtVoTad58MEH2TxP7JxIJli7fh39/f0oKbKD+VNpZYaokYgnsMp9iAht7a2Mj47SS5WNhGlWBgVx6aEStMxSxJQfSDwgtWUd3HWlSIgeDBB8PFy61Esk3rWAFNW3q4eJxNdxqad3WWJULBapOTZtavH7ASJKI6WZzMxMf+x9+4VBwZd+7/f4/ve+x3vXrxHVDMJKI+fZmKbJF575IuvWLx1SvYIV/CqwQow+5YjFY8TisSWfcx2Xy5d72Udsyeyy1YSIBXEV4Ug7iYatlPJ9TAwepqFlP9FgKq1cuE5u+gydq1YvmMLZu28vbW1tvPPO29y40Y/nebhOENGxRJlkCxHO42trtgQtmLUqRElcTlMmEuugWp7AdavcpeILnKtNpdhPjIw4wRh2rR5sm2rag+fWKOYu49pFXKfCGDYmiodUks6gfP+65DkvFToxF7QgaiKcljIaCjPUhKb59aNTlIOojDBtyqSCR59UeV1yeEAkvoZwuJnczFki0fYFpAhAKYOm9vsYuf5dnpcsSdEo4NVF4C7ClkB71E8t0ECFOTTPv2dMbN6RAmUEM5xGPAdVy7CV8KLICl0pDhLjRS9LuTjgO4pH27lRmfbP9y3XpCAuUzgcIkFTYGB5ziszQA2Fr8mq4FHFpb1x55J2BKnGnZTyV1m/fgONTY2cOXsOw0zR1vUkKqgMamYDTW33oGkm+cxFYol1NK96CKs8SmbyOMXcZSrFAaLxLjzPolIaQimF57loRhO63sTE5BTf/s632btnL8888wwqUFBvWL+e/v5+BrDonzdNOLun99xzD/fe54/gP/fsc8yMjfMEybqNww0sPIEHVJIoGllx/XYhiixLO187IpSURzL58afAaraNri/fJtP1CHZteVIz224qL+PT5QlUcT9xbalwJMwffPUPGR0Zpaenh1qtRltbK7t27SYUDv3kDaxgBb9ErBCjX2PYto0nQmyZ0WelIIZGxfOrHpoWoq3rM0yNvs3M+AfAB8HrdDQ9wuTUJOVyua7tqJQrvPXmm1zpuxosmv6CH1tGlh1SCl0UNZkXGCY+KTCMBOnWQ0wM/hgTxRoW/7CPYtc1QE0YOAh5XKaGjuCKja/S0ZBAW/KUaliQ5XU/CV6VHC9Ljo0yVzHow8JD8BCq5SGi8TUIgoviSZWicZ7OaAsR3pMCw9SolgaR0ggA0cTSd7y6HiEc66BWHiGB7k8z4bAjsDSYPQ+7JMZpKXGZKt1E64LiDmWyLxCGe9Xp+lme7xM0HymlExMDu+a3RJONO5msvM45KbOLWH0iqyIe70kRBZyjxDWvSg4XB2ETYa5hBdU2H6ax9GJumH6brlwuoWn+3qWadtZJ0XwkG3eSz1zE83y36Eisk/Y1TzPa/yM8t0oyVsIMGeT1FMVyjfY1T2AGVg6CUC5c59y592htbeG+++/Hrtl88MEHbCZMFY+RIKhFAasxMdF4/733OHToELZtc+HCeXYRXuBtNSY2zRiMYXMpmLAj2MYVqbKZCOFbbiquUKUmHrt3717ynNwOHe1tXOkbrLuuz4cgWJVR1q9tW/b90ViUjes3cHVgiA0SXiRmHsCi6rns2Llz6Q38irGqcxWrOpeeRF3BCj4pWDGN+DVGOBImFokyOc9jZj5sETLiYMybQtP1KJFoO0rpNHXcT8uqR+jc8CU61n4Ox3E5dfIU4FejvvmNb9B/7TodmCSUQTTg2ZMs/Xn+3bhQm62YiHBKSmRwcJ0iQ33fxqpO+LqWW37wc+LyjhRox+QZ1chntAZ+W0vzlGpACwTMITTSyg9yBT8zbX4XKaI0nlJpYmj0UeWoFLlEBSvQmmiAbWWxqlO+xoTIAlIEgfhaxRF8AvgFlfbnvgIN01KQQE/0oJYkgU4Cjb23GBgqBftVnAgaveLrYKriL9IbApJ4kBhP4V+r2jLtsVk91KyvUjS+hobmO7hAhWclw1GvyNtegWclQxGXe0iwnggpdLarKM+oRrYGbZoQqj6obllTS36eVfUfv3LlChcvXgJY1q9I16NoehiRufF3TQuRSG9DgD/5kz/mqSefJJvL0th6D0ozyWcvkp0+RSnfRzS+lnjDFj788Bie63HjxnWsWo2b1BjFYT1h9qs46wgzjM0QNcrVCpcvX2ZoaBDHdevVw/r5wp+KOi4l2jB5TKV4WqXZRhQLj1clx4DUsETIictJr8QZ8ScpJycnljzO2+HOOw9Ss3IUsz2Lnivlr2JVp7nzzoNLvHMODz70EBkc3qdIMfjeeSJcF4vjlNmxbTttbcuTqxWsYAW3x0rF6NcYSikOHDzAh+9/wGaJ0DC/9SJwXip4yAIHbLuWoZC9RCjcTCK50LwuElvNpZ4eHnjwAS5dusjo+BgKxZRmEEmuB7eKKg5wSaqsYQkrASmj4QelljyXcWxqCOsJ049FONJCrTJJGXeRa3WvVIigeEAlFrQFB6SGi3CnirOJMJpSOAiXg0yzMHNRKeC7J3eJL5j+vEqjoSgGhogC2HYBrRZD8Ks1SyEexIrY+JWvDjGZyl8nuUS7yXGKWJUJ0sE9yBQOXYH49FYUcNHx2zs3grbQKjHZpYIIjeA9SqBPqnQF+qv58J2j3Xoob602g1XxU+ijKHJBNa0ZgykcpnE4oC10xh4IMu3aMBmXGqDIT58lEu1YYGQp4pGbPgtoXOrprfsR1awMoXmWA7NwnTKea+E6VXIzZzHDjURjXUEgrh86e/36DXQ9RKU0yNTIG2goQmgUcMhOHCPRuJ18rsDU1BSW5UfM6Cg+oxrmTCEV7JIoRyQPCN/7/vfq+1DDg3naoTgaZTz2qhg75+l2GlWMdjF4Rwq8P8/5OhxMpt1QNteuXae7e2u9rffTYO26ddxz9z0c/fAoVnmUaHI9oKgU+ykXb3LwwIF6FtxyWLd+HV/60pd47tnneKGWJT5v0nTnjh088/nP/9T7s4IVrGAxVojRrznuu+9+Lvde5sj0DN1eiFUqhBVoZUaw0bQQpXwfhpnAqkxQyl9DKR3HKfkOyfOKikrTKAYRDCdOnPSnuJLraWq/H6V0KuUhysUBangclhxbiQZZaS6XpUoGB8GP3bAQNhJms4owgk2/WNSCYFOFH9fxEMk6CRoKdEnzSZElvt/NLhVjyzxvIkMpdhIN0umrbCW6oAI1iYPHnM1AAp07iPOuFOb5/8wuoosh4j+XDv581hJipDZDbuqkb2AYkAfXrTA18hbgj9cf80rU8LCWqPZkxeU1yRFC404VpzEQX/dKhdclB/hkxm9WKUawOSNldhL1J6cERrD5KGiPTQweRmkGnldDoXGPSrDhlpH9y1LlpJTYIpE6CS2KyyWpsAqTsFKE0UCEWnWa8cEfk2raiRluxLayFDIXqVWnAc8XvyuDyeEjFDIXiSU3LHBW9zybiaHDgCB2nur0OXK4GHqMcKwDUIyMDDM1NYnnuZRyV9mvYmwmjKEUJfE4K2X6Z84F18A/hwLsU7FFTtlRNBJoQZPUhwZcF4u2eYS3hkcIfzjgVnSqEJslwjWq3KMSmGi0BdYYPV6VU6dOcvzEcRob0hw4eJC7Dt21wCZjOTz55JN0dHTwwdGjjI+9C0BraxuPP/I59u/fv6Q+71Zs276dTZs3c+niJaampgiFQmzfsZ2WlsWEdBZ2zWZmZgbd0Glubkb9jD5MK1jBrztWiNGvOcKRMH/yp3/Km2++wZnTZ7jg+BlqGn7shl3Lkp85h4iLrsdINe7EjLQwNfI6Ti1X13i4bpVKYRBdh//6F/+FXD6P0gwa2++rEwGl/Mm0NDpRNM5IqU4t2jFpx2QMmxwugksZj4j4bS2FRiTShlWdxPMsRrF5SXJsJkwMvzoTumXxG8WvFm1eQo8EsEWF6ZMqk9i0B2qZUbGZxCZ8y8TRakJo+IGwBhoWLlfFYh3hRQvVMDUshL1BJaoX/5zmMxco5/sIx1cjru2LiAGFUERxMxTBrtncwCLuaQvywE5IiRgah1SCQakxSZUIirtUnNNSJoNLAzplXECIJTfSU7jBFbFoEn+xLuEQibRhV6cJ47HWU1wDNhNaRIrA10tdoMxHUqKbCNPBBJ4baLfOeWWfQKJhRlsBYWr07XnfrTbCsVXY1gzR2GqqlXFAcOyCL+Bv3ks40obtFJkeeR1xKhxSCdYTQleKjDicdMtMBkT0e9//fn3b+4mRROOYlKiJRwKd7SpCWTymlUtzSzNnzpwB/Km6+RCBt6RADpf9Ks76IPDlhBS5jkWD6GwNSPZU4IGkL0MSmpXBFfHbsiaKKXR6qPrxIBIhpjQmcxXefOMNent6+OoffY1Q6CeIiRXs2buHPXv3YFUtBCESXnpg4XYwTZO9+/b+xNfVrBpvvPEGp0+fpmb7esLGhjT33X8fBw4c/Nifu4IV/LpjhRj9BiASjfD0Zz/L448/QTaX5fSpUxw/cYZk4y4Uiqb2+0FcUDoKVY+EmDX3c5wK4wPPIri0uibpbJUsQjS5dkFVwDD8FtQEDhsI85RqCNyBqDs9N2KwSYUJoRgRmzP4eg0DRXN5ggoe0/h3/CEUZ6Rcv+MfFItt8ypDTvBMeBmp3OzjGRwMUdyUGpepomsRkt6tmqDgaCMt6JFmyN9gwqtyQkrsIUYoqMqMYvOhFGnCoEXzyVYOl2YMDqk4fa7FdH4QHT/odxUhfixZwKNWy6IpHTPUzAVrmpBobFMR8uIygU0nJq9IjjAaDeiM49IrVTowcRBGqQX0DZLpraRb7qCUu0rRLqBpJm3J9YSjHYzc+D4djs0dKs5VsWheQggNvl4qLQbj2EyKja4MYg07cJ0ypeINLgSEz0BhVcaIJzfSsPpJUHowrXiDUv4qTe33BuQ4EC5rJiIukyOvL/i8+0igUHwkJRwR0ugcUn4IcBSNh1SK3sCS4SY1psUhGQjWp7G4IlVWE8IVj0K+gOf5n+ciC/yphqgxgc1jKkX7vOrQfSTRpchpKXORCk3iZ75F0WYdNhehFNg0ZGLtiFtl0JqiBYNHVAojIFObVJhucXh9dJS33nyLJ596csnzvRR+0Ynytm3zjb/9WybGxtgqYTpVAzbCjXyFF196iWw2y2OPP/4L3YcVrODThhVi9BsEM2TS2trKpk2bOfrhh9Sqk4QjbT51mbd4+uaCGqX8dUpco5S9jCEuT6iGujnjkGcvajQZZpJIpA29Os0QNfrFIjKvnbGJMHepRF1fs16F2Shh3pQ8u4iyXfMrMBlxeFsKzAQuxTE0pnCYxOF9r8h9QfjqbCtrHJsOFrcwxgIR+KlALKtrIaKJTZTyfWy6xfTwZiBnbu64F9NMk269i7GB57lay3BNLNKBoWMpGOW+H//9s6G+61WYtDI4uAQJaRMTK9CxzIjDFSuLpgxOSZkpz65HIoxiBzluvlZKBPqx+Ej8GJa3Aq2LQmFVxkk17qGhef+Cz3KcAq5TYhqNd6SAhu/qzBIVIxHqmiMAVxxK+T48z6IDk7XKjyu5IRZTOFTLNykVrsNsQ0/ppJr3YVsZxvp/gDePbHquRTK9A00PUykOolkznKdCXnwSGUZxmSoXpUIXIYapBaJ7hQr2y0SjMG//ougMB75RllVl+/ZtHD9+jBtBcPEsrotFK8YCUhScOO5WCcqexxi2b/uAT36GqdXDhmdhi3BVqr7QPrEGu5bDsqa4UyXqpGgWTcpgixfm9KlTPProoxjmJ+On9fix44yNjfIEKZq0uX1ahUkanffef589e/bS2tb6K9zLZSAwPjFOtVKhIZ0mnU7/qvdoBb8h+GT89a7gFwuBgYF+jh8/wdjYOIZpEo3GyE5+REvnk+j63KJpVScoZHtQeohS7ioAnjjsU3GfFIlfF+jC4HLxJp44C6pGDa0HmRg8TBM6bZho+FNq00Fb49aORYcyWSshrs1b3BqVwb0keE3y7FcxVqkQrgiXqHJeyhQ9l20qgi2Chh+t8TipBS7FVfE4L2U0pdPc6UdI2FaW/MwZkhi0YTAlDuFg8uqkVIjGVtdDdaulIexahnhqE7qRoOoUUcqkwYiRnznPK5LnbokTVVrdT2k5KHwd02oVYnWgW3lF8lQQxkIJQFC1DN1L5LhtIEwZj3NSZj1hOpXJWSmTn/F1PHYth1PLozSTSKyLyeE3AY1iYAwJ0IuF6Sl2aXN+V54Ir0ueKh4hNGLoFHDwPIs1hHhAmxvP30KED6XIddcKKJGvAxNxyU+fIYzO+oCYXkURS6zH83xfqdlpPQOFATytGuqTfrYIZ6XMFfwcMwdBFwIqJCRSm2lu3IFhJLCqE+SnTqOsaUBIJlN0rFpFLBrjXKVMWTzfNRzBxqMJfUFczSyy4tbPy+xlU8D7UuQAfttNxw9pPibFwFATZiaO1l/bLxZ7WRxc26lMLtXyZLNZWlqX1/r8MnHyxAnWEloUbQOwjSiXtRqnTp3iqc889SvYu+Vxufcyr7/2GpPTc9OQG9av56nPfIb29vZf4Z6t4DcBK8To1x0Cr7xymA8/+ohwuJFQtINq2cKyJhCpMjbwQ2LJjehmglp1gkrhJigVhG7OeRO1YnLKK3GNGjYeJhoiwszYezR33F/3rQmFW4jEu8iUbi5IJW/H9NtRS2CVCjEgxQULWRsmCXRGxGaV8jUpu4mSEYcRHN6XYv39BVxelixbiNCAzow4XJ31JhKPyeEjwSt9l6Mi8EKQdUZwlLqRoKXjgfpj+ZnzhKMdNLXdj1Udx6nlUJpRn6KaGn0zyPbSUMCAWAtIzSyq4jGBzT41N/kVVRq7iHBMSqDpwV7BpuD9FfGo4BFGI640NhPmLGXaMFinwrRh8qKXY+zGDwMNkO9uPXu1Eg3bSKa3oulRquUR8tNnOGcXcD3Yq2LYCG9Knhkc7lYJ1hNGU77R5SWpcIkK18ViY1BlGqVGPxZR/NZfGoMcLpelQhmPg0RZF4TQKg+u5vtoXfMZmtoO4ThlclOnqZRuct8tnlCmUhwkzrQ4ZHC4KOW6T1WycSeNLXORFtHYasJr2pm4+SK1Ws7300rE+cIXv8g3vvlNLlODeTXMCTxeljxPzSPMWXE5IjliaNyrEjRjUMSjRyqMB8L1Eyh0fDsEXy9nsE/F6MCkGgwtXKBCTcR3d58HO/hb0Y2lHbN/6RDIZLNsVksbwGoKmjyNmZmZX/KO3R4Xzp/nBz/8IZ0qxCMqRQKNaRwu3Rzmr//yr/iT/+VPV8jRCn6hWCFGnxI4jsPY6BgiHm1t7T+1NuHcuXN8+NFHNLYeIpHeVh8nd90qU8Ov4nkFxBmiVLJoaGjArYWxbT+oVOaZ3b0iOUSZxNM7SIXS1GoZStnLlIsDVK+PEEuuBzQqxX5ct8o+YpTxyONSxK3feS8FK9BxLBh1V2AIi961SYUZkhqJVDe2XcCqjBIORq794FHfOE8QGjCozSNnBCQijsYOFaUJgwIuPVJlyilhVSeIxdfiuhWs6gQNTXsZH/gRNTtfJx2a0kmkt6NpITzPX4xVUGG44JX90frgMBwRjkoRHcXGWwTis60/z7XrlYcqHm96ZUbnxYy0Soi9s4QreGEJDxePVkz2qRjNysASjyvBoq2Uqovm48mNRGKdjA08x0W3Qo9U63NaB1ScZgwuSBlLhITyiU9RXC5KhQ34BoIfSJEEOk+qhjq5XYXJJsIckRzHKLFOfJH6XhVjSgpM3HyZaHId4Wgbjp0nib601kn5LdZj4jBIre7PZFen8TwLTZs7b5oySDbuYnr8Pf7n//zLee0qIRJbRapxF2bw3czPnCdfGeddKfCo8r2fTgcC9ydVQ50sJdFZhckHUmQAizgaOuDge0/ND5GNorFbxQiLxgkpsW3eNB/AdWq0NrfQmF4czfMrgYJwOETJ8pYWWAuUNaH5ExI2C/7v3Msvv8x6FeZe5hzgk+islhBH3AKvvvIKX/3a1361O7qCX2usEKNPODzP49133uHDj45RrQZiWMNk3969PPHEEz/RTv/o0Q+JxrtIphfGVeh6hKZVDzHa/yOe/szTdG/t5qMPP+T9995HR7FTRVhDCBdfY9JLhUisnYaWAygUcSCZ3sH4wIu4XoVq7hoALi6rMDlLBaU0TDOFbRfwxGFanEWLo4ivCbnV26coHjncutngLGZFtsX81eARhaWH6FjzGQwjjuf5FgS1WobJwVdA4B6VwEM4JiUSaNyjkgyKxRg2YTTuUDEuSIXx8aNEN3TVDQgLM+dpQmefStEWVAyuSpULmYsoZdKGSbeKMCE217A4H1Ra1kgYG2EgGMx/UCUXVcuqAQFo7rgXEY+JocO8LUV0M0lT052Ewk3YtRz5zEVet/w7+nQwSXdMiqTQeUSl6jYEYeUv2roozmZ7STbuwjDi9WudSG8nN32a3US4iU0Wh0lxOEkJXQthGHFsO88ZqbCJMAVqvCsFXBEshDtVbNExmEqxjxhvSYEB/Ak+QykeJ8k7UmCs4Ifhgm8WuRxmr+nTKo2BYpAaH1UmmBp5k9aupxYQZjPskw4zuhaldIq5K0Riq2ld/VjdWiJqxIhEVzE5fISxyjgZzwGlGMX2x+5vrVwq2E3Ut4xASAQVvK3LhMhuCkJk+8Vij4rV27yDYvH5+5/+RE157dq1m0unz7BDZNGxTGCT8Wye3rXrl7tTAp54S2bNXbl8mUq1ym6VXnQeTaXYLmGO3rhBLpejoWEuz1E8YWBggEIhTyKRYN369T9Tlt0KVgArxOiTDYFnf/QsFy5eJNGwlYa2Tb5fUPEmp8+cY3RsjD/+oz9aVuhZq9UYGx+lqf1+KqUhirlenOoMSjOIJNaRSG8jFG7i+ReehxeAoNby+C0tj/0qRrMYvFcaZKjvWyC+8DYS76xXMUQP+74oTolRbFKNu0g17UbTwniew+j1v+ddKfAAyTo5ssTjpJTJ4/r5YAFcEY5LkRCKdbcIYofFRgM6MLDwmMalofVgPfRW1/1th8MtpJr3kp06SZsY5JSfUJZA57Bk0TQTM9SIYxfodXOsIoTjVqiWh4nEOlFoxFE8qpJ1oW0kqBiYonFKSnQSY60KsVaF2CRhXpU8RTyuUEHwq107iC5pFHlVqhh6lFC4hWp5GFBooQba1nwWLZh2C4WbiSXWMTl8BLsyQRM6FfHI4nK3SixyB4fZPLoq5cINUo1zC14o3AgILZhkcMmhGMSmqf0+4smNKKXjuRb5zHn6MhcAyOHUKzhLiduBug3ChDisC1pvulK0iMGk5tG56fcpZC8xM3m8Hh58K4bFrseloHxfKAN4qzKGVRknEu2ov9a2/KiTVNNuSvk+wKOhee8Cvy0ApTRSzXuoDr3Cj8nVBUVNy/zkJZWOKYoOTO5QMX4oGVK3CZGNicYVqsyIy4xyqXouDz/08E81Pv/LxD333sP58+d4yylyQKI0KQNP/Mm9E6pM16rVC4J5f5EYGx3jg/ffp6e3B8d1aW5s5OCdd3LnnXfV24/ZbJawpi8Z9Ay+lxdAPpevE6Penl5eOXyYbD5Xf10qkeTJp55k5y+b9K3g1wIrxOgTjP7+fs5fOE9zxwOEIi1USyP4bYNOIrFORoZ+zJkzpzl4551LbyBYDMr5PqqVMRox6cTAcmv0Zy5SzPbUE5sE0BC6CC2KwQB/sUqiU0IjkuyiVp2hXBxAKYO2rqcIB4vXyPXvEo62kW45iOMUqVlZPM/CFQdB4xXJ0SA6oUA3IMGY/CWp+DoO8biGhYXHA/NICRBoh6p0E+EOLc5HXpFpXGLL5JTFkuvITp1ggFp9QRyiRrrlDhLpHWjKQMSjXLzB2Nj7AGQmT2CYCQSPbSq+aPoIYHNQMZgNgwVfMN4tEXqp1OeoFNBDhYRobCSCpnzBca9UuIFFY/PdKKXhuhYgNDTvrZOiWSil09ByB+ODLzOBU28tLrdwmEoRQQs0YnOwazkI2ojtmNykRmPrnSRSW+qv0fSwf93sApXiIIV5jcwKHqElPrMSnANj3u29P01nE4r5Eazx1CZyU6c4LmXuYyGhGxWbm1iLIlJWESKOQaXYXydGnjjkMxcgmMpzav5CaIabljwXoeDxaHwtoUgzuenTFAM/qFtRFQ8HoYLfklRA5jYhskU8QtEoqbVr2dDcxB13HKCpeen9+EVAPOH8+fOcPHGCqalJQmaIHbt2ctddh2hIz1VSmpqa+OpXv8b3v/c9DudzRJWBi1ATl03rN/KFL33xYzl3/6zou3qVv//7fyCOxk4JE1Ea49kyR149wpXLl/nKH/4hhmEQi8WoeR5VtTSJnp1SjMb8SvLl3st897vfpVOZHFINpNHJ4XKxWOX7P/gBIrBr9wo5WsHHwwox+gTj1KlThEINlPM3mB57Fy0gMS5CONxMJLqKEydPLUuMQuEQqVQD+fwYh1SCTfPMCvdJjLckzww2u4mRVDpHpbjk9AoAClrEwA430NLxEIJQKd5kevRtCpkeatUpXKeM61YIx1YzMXwkqITMvl3jADF0pRgS31N6jYqxjhDvS5ERbIbF9v1wtDDiVjgmJTaLQ0zpTIjNABYKxeZAczO7bIvIkhEbs+7Ini+YAjTiqU2kGvfM7ZfSiCU2UIpdo1oexbFzOLa/4C5HPoygYnBrXlmHMumRCnfjj3Nfkyqj2ByTEmcpExf/R9sDGpr3kWjYCjBXIYosnW8Vivij1FekSjj4Dszg0LrEn29VPMrikjbnKnCeV6OQ7QGEa1jkcIJzsWXR+wES6W2UiwNz5wg/guTALWJj8CtfCurCcxFfy1PAob1xR3B8YZpWPcDQyFu8KBk2ESGMYlT8PLNOQnRgMiAWOop2TEyliIqiZJfwPBurOkFu+gx2LYdCmBl/v74PTi2HUjqlfB+uW0E3YsRTm/FcX6sVT21C6b6rVY9U6VwikuWK+ERyAt8AVILHthBeEG0DcJkqDsLD997Dffffv+Q5/EXCcz2+993v0nvlMqtUiC0YVKo1Tn54jFMnT/HVr32VztVzlG5112r+t3/9r+nru8rIyAi6brCle8vHEjA7tsPZs2c4eeo0+XyeeCzOvn17uOOOAz9R71ir1fjB93/AKjG4fx4x3kSYzdi8eXOQD97/gAcfepBt27bz8ksvc9mt+mR5HkTgMhar2jtoaW5BRHj1lcN0KpMHSdavaRMGD5DgPYEjr77Cjp07VtpqK/hYWCFGn2DMZDK4ThVqRe5RCdYFDr6j1Dhp5SirPBnnNn/wAkqE1YTYdIuPTUgp7iXB85IlqjTWqhBnRaMg7rIaiTwemu4vgApFLLEOu3kvuenTVMsjzL4xO/EhRihJc/v9hKItOLUihcxF3q+Mci8JDmkLPYQ2EWYiCLoVcVCen1ZfLg5wEQtPPEwjQSK1lWKmlzckDzIX2VEu3iCR6l60v+XAc8dBAv8bj0TDwtcJwszE+1TLo6QadxJv2AwoxvqfJYOzZAupJkIZj9gtC+bs/nQqk0hwTs95ZS5QCZy1FRYelhEj1bS3rp3RArsE1yliGAsXA9etMj3yJuD7HM1W93oCcfQC3Y/ABakAinC0DRGXanmE7NQpXMf3crqE77SslLGoOjULPbjGycZdGGaC/Mx5LjslIqKxlQiGUjhBHEtPYAJ5Tsro4uuDqng0NO8nHJlbeKOxNYSibZQqU5wXCw8XDUUjOjU8fixzbRATxWYJM4ONVxpk6Nq3Ad+VXEfYoWKsIYSDcETyTI2+hWMX0PUIhpmiUhwiP3Mew0wCKnDr9knsJDbvSYHdxEgrnap49EqVS1SCq6FAKcKmiVWzeEUK7CFCByYVhD6pcpUqum5w112Hljx/v2gcPfoBV65c4SGVZLUKWs0K9orwllPk7//u7/nf/4//A02fH+ej2NLdzZbuxX8nPwlW1eIb3/wmoyMjRBNrMMObKFXzvPbaG5w4cYo//pM/IplMLvv+C+cvYNVqHFDpRe3fNmWywQtx4vhxHnjgASLRCPfdfx9vvf024sE2FSGiNHLico4K42LzlcceAwVDN4eYyWZ5QjUsvjFSsIMIh4s5+m/cYOOmTR/7uFfwm4sVYvQJhngermfxmGqgZV4lp5MQaQxekCyatvxESa1WI1fIs/MWM8NZJJROkxhMis1GFWaDCnNJKuyWxYv+pDhMY9OSmvuBETzE8zU/XiBY1tBAM2nrerq+wJpmmkh8NdNjb3O8MEhXEAkxi6K4KOAOYnj4mpOJ4gC6ESca76Kx7W4UiuzUSTyxqaAIRVvRPAesGbKTJzDDTYTnBZdWyyPkZ84DvjB2Fpq+kCDWqlOU8tdoar9vQVspltxAb2GAjUQI3/Kr2xOE726YP2km0CcWLRgLWgA7VZTLUmUMmzZMXPxg2fz0WZRmYFXGg1aaRiFzidCq1jphEvGYGj6CWBnuV0m6At/rASyOSpHDkmMXUdowKActoJv4oa9jA8/X92HWfXy+ubOITc2aWjLstVoeRaGRatyJrkdJpLYyMfRjzlYnuEiVODolcXEQovG1VEo3mcLBRagGNgPFzCUQj3C0HccpUsj04NRytHQ+QiTeBUBm4kOyucsk0LlfJVmFiYVwLSAqAPcEFYYZcemlwpOqYYGAv0MMRu0ijW13k0htQQWu3MXcZTKTxwBIouEBJYT9KkaPVHlZshiicPG9sBLoFHHRNBOlmVi1EqAo4fDBPGsIDYWmNH7/y/8YM3QLsRQYHh5ieHgETdPYtGkTjU0/nwm1bDbLtb4+HMfh6AdH2UB4jhQFMJXiTonxcilLb28vO3bu+Ll89uHDhxkfn6R97W8t+L7Ydp6p4Vd49kfP8tWvfXXZ94+NjZLWTeKy9E3camXSVypQKpdIJBI89NDDKKV477336HEqGGjY4hGPxvjSb3+RzVt8TVSx6F+XpVqjMDesUCgUl3x+BStYDivE6BMNoQVjASmaRUxprJcQo7d7u5rdyu0+YQ5b8AMzX5M8dxDz4xcQBqhxSsqEI61E42uC9/ntjHL+OpuIsE75FZFBsbjiWcyMvUtL52P1xHWFoqF5P6OFfoaxWRuIqm0R+oLprW4VRSnYLlGOSYlrTgkCFVS5eIN85gLRxBqa2u6tky6rOsHE0KuM33yRcLQDM9RAzZqhVp1EodhOlA0qjIXHG1KgWh7BbEjVj7mU78MwEsRTC+8oG5r3M14a5hUvx26itGNSCabSrmOxgTCRQPBbE+GclBnH5iG18M5ZV4pWMRhVHqPhNHYtA16N/MwZFIp2DAQYx6Nc7EebCJNq2oNhxCkVrmFZ0zx5CzFeR5gEOkckx4fzFm09oEACNLbdi6Y0HKdEbvoMIETQfY0ZwggOmckTtK1+vO5BBeA4peA8r0XXfR2HUoq2rs8wfON7oHQqRhRTj9LSvA8jlPIF+eATJjy2E6HoefTPnCfH2fr1j8S7iMS78JwKnjhYlUlMNJ5QqXq7ysRPr4+JxnEpkVAarcqkR3J0EVpAimwRJnBJNe0m2bCt/rhSOsn0DhynRCFziblvoP+eZ1QjI9R8nZBAHzUymqKl/QGi8bUopWHXsmQmPqJaGVvwV2KaBvfedx+bbhEsT09N84Pvf5/R8TH0wLXcQ9i+bRvPPPP5nzn6o1ar8cLzz3Px4qXA0sJvpa9RS1do0konpUyGhgZ/LsSoXCpz/vx5kk37F5Fo00yRaj7A9RvvMDU5tayppa4b2CLLxq7MtqSNYHACBQ8+9BB3HTrE5d7LVCplGhsb2bxlC7o+R4KSSf+GL4tD2xKV3WygR7pdNes3HRMTE5w/d45SqUQqlWLv3n0/NzL/acYKMfoEQ9e0ZXUu4E/SDC/K/JpDKBSio62dGxMzS4aI5sUlg8P2QB8SVorHaOBtydfjJCT4Fwo307r68TrRsSqjlPLXuFclWD9v263KoFNCvFEeply8QTw5RzhMswFNmeQDN+QJbE5JebHHkYK9RLkusyaTvihaN2K0dDxUD60FP8i0fe1vMzbwLFZlnFplgtmMt4dI0KnN3lXrdEmI0emzRONdGIb/o+o6Zcxw46KpJsNM0rb2s0wNv8FRe67N4wfNKm5gMSE2MdGYwcVDuFPFF93Fgy9QjibW0tLxEHYty9jA83Sgc69K1AlBVTyOSJ5i7gql3BV0LYzjVf1MtiWIcXMg9r482xpDI5LcQDS5nqmR13GdIsnmOxi7+QIg7CLKLhWrtzJuSo33K+OMDjxPMr0dw0xSq05SyPaiaSbp1oW6tWKuF3EtXDx0p0IVD6s8TCKwgajiEQvMLvuweFo1cIAYVfwcs7NS4mppkOG+7+AFbVMF7CK6SMMDsIkIZylzTio8pkyKuKy95dyOYeMu0R6dRaKhm0LmIpVwA5oeRcrD9FBlFSG6gm2NYTMlNm2rniQS66y/1wylaVn9GGP9PyLlWNyjEr7Bo2Px5ltvoet6XV9ULBT5m7/+a/RqjUdUig5MPCX0U+P05at85zvf5o//+E8+vshZ4B/+/u8Z7B/goIqxgTA1hGclw7J/9QKekp+bpmZ0dBTXc4kllxlwSKxjGhgcHFyWGHV3d/PhRx8yrubCnOfv73VqrOnqIhJdWP2ORCK3nfJb3dVFc2Mjl7IlWsVc2E4TuEiVhmSSDRs2/DSH+hsFz/V48cUXOH3mDJFgCjCPy9vvvMO999zDE088+YmynfhlY4UYfYKRbmzk5ujEsnda07g/MT8omUpxdWKcHqmwjeicmaB4vC9FYmj16g1ArH5/vdBc0bELuG6tbrhXzF6hAYP1SyTbdyiTdgmRy15eQIxc168UnMPmovg58aGgwgELDzOsNFJiUCj0M1YewXXKNDTtWUCKZhEKpQlHO6hVxoihKCM0YcwjRT4OqBhH3AJjA8+TaOgmFGnBdSp47iy5uMWnx2wgEl+Nk83zkEpi4FccjlJEC85VEh0XnxjcauII1F2dWxLr/fOWv4oBiybuIkrjMzTwI5mhnRDNojGATmKZgFyAuPJds7s2/QEoVd9/3Yj5mWeujW1N04LJnlumvtaqEBoJ3rHzZCY/AvyaihFK07b6CfR5WqfZtlQ3EXaoKDGlURWPS1KlN5gSM1HoKAwUJVxekTy7VIQWTCbFpi8wrQxFWoinu1FKZ2rkDRpuE3CbEoNS8C0Mo1GUhUaFtUBcP6t7W7SNoM3seS5Kc9C1CJpncURyrJYQLcrgulgYZpJwbNUS+2CQSG8lO3WaFDoNSqcdvyX0xhtvsGfvXpLJJB999CG1SpXfJlVvo+ooNhEmIRqvDw5y9epVurd+PH3PjRs3uH7jBg+rJJ0BkTNE0YBOv1isWYKET+JQ9Bw2btz4sT5rOahZOw5Z2qB19nG11PRDgA0bNtDZsYqjE5PcL/E60XfqldYav/8ziNiVUjz1mc/wd3/397ytCuyQCI2BK/slKgxJjS899blfytTdpw1Hjhzh7Jmz3KXibBR/YtYV4Yqq8sHRo8Rice67/75f9W7+yrBCjD7BuOOOOzh/4QIDyjfPm48ZcRiWGp85cGDZ94snDN68STMGp6VMHxYd4hsVzoZx7iNWX1BrIhyWLKXAG3l+xUi8GqP9PySe2owZTlMrj9KJsexdRRs603Z+wWOZyWOooJ7jBtt3EFKBh8383y8RqOAS8TyaPGEQUPryZpa67kdlPKaleNHL0rLEVzumNJ4iydtegZnMxWBPfPVNpXhz0di/65Qp5a8iCJ2YzPYy9nsxPqJEGY8yHlE0qni8LQXuJE5S6SC+WPqolAiFGonG1yAIVnGQdYSWtAEwlWKNhJnEZi8xJrGZwkHE/+hycF1iaCgFU+JgmIl6Fc8/by6ea/vHVBpGgA0qtOR16lIhmsQgg8MzqpFLUuGaU0LNE2WLeOSnT7OBMAe1uam0iPKNMR1PuEYVO9zCjB7CqYyBeBRu0eaAIpneRrr1kN/uEw9NGWTFWVQJAj/LLY9DBA1X/JZyPxa7JUo0OF4noNTV8jCx+OKKxuxUpNIMdCPmT04ipNEp4DIhfhCyYcQXkeJZ6EYcD+G0lDDRWEOIHSrCFSy+/73v09zczKVLl+gSg8gSVZp2ZdKkTM6fP/exidH58+do0Ew6Zd75UX7G2UdSpFeqbCVSv9kpiMuHqkRbcysbfk7EaPXq1RiGSTl/fVFgMUC5cAOFYv369ctvRMHvf+UrfPtb3+LViXEalUlEFNO42Mrj8UcfJ5fL88LzL2AYOt3dW9m4aeNtydYstnR38+Uvf5lXDv+Y17LZ+uPpVAO/99QzbN/x89FZ/TqhUq5w4sRxdqlofcIX/Lb/dqKUxOOD99/n0N2HMIzfTIrwm3nUnxKsX7+BXTt3cfTiRabFYb0Ko+NP/vRisapzFfv3L/6xmoVlWVRrFgdVkhgaV6XqL7T4YR+N6JyizGWpEhOd6WBMOYHOHSpGJyG8usaohI3g5q9TRVAIpdt8fUp4qMDY0HUrTI+9R7U8TAsGm1SEUDCufZ0qeVw6MHnTy2OgWKNCTIhNDd91uRQIiqvFIVLpnYs+yxMHqzzK+mB/IkGI6lKIKI2YaGTwWL3xy0yPv49bGmJm9B2clv3EU5vRNJNKaYjc1Al0z12wZGbE5QwVdHw9jIaiGpzPcWxekCwR0XAR7KAKFQklGb7xXTy3ilI6ORQ1kSWz40wUZTxenpfl9qEUmcEjhx0cn06XmNzEIp3es+D95WI/IjYda38HTY8xcuMfggbX0tDxOVNMaWwjwnUvz9TIG6RbD+I6ZWpWBsetsk01LPn+bSpCn1RpaN5DNL4Gz7WYmfiAavEmGwhRxWMKB1tpNDTvrxMQpTRiqU1czfWxVSKL2ml9+C7UNVz+QWbq+/qa5DlInA7MoCmjyE+eIhzpWBCG7LpV8tOnAUVD025iifVMDL2CqoxjBX5FOr742q1OB47pi3Uq1fIYCo0hatginKdMBA0Hj5HRKSamKtRshxt4NEmV7iXy8hKeolQqLXsNlkO5XCbhKW4tGm4iTB6XU1LiHGUM8auuFh7pZAO//5Wv/FSk4qdBOBLmwB13cPzESUKRVqKBcB4ILBROsm3bNtKN6dtuJ5FM8E//2de5euUqPT2XqNVstrS20tCQ4vArr+I4DuFIE55X49jx43S0r+Irf/D7P5U+qHtrN1u6tzA0OEg+XyCZTLBm7dqf2zn4dcO1a9dwXJfNS8grADarCFeqWYYGB1n/G9qG/JUSo3feeYf/8B/+AydPnmR0dJQf/ehHfP7zn68/LyL8u3/37/gf/+N/kM1mue+++/hv/+2/sWXL3PTQzMwMf/Znf8YLL7yApml84Qtf4C/+4i9IJOYmsc6dO8e//Jf/kuPHj9Pa2sqf/dmf8ed//ue/zEP92aDgH/3uP6K5uZljH31ErxUY2ukGe/bu48knn1zW9RrADJnomk5RfH1Gy+x0mvjZZ1U87lMJpsR3OM6i8IDHVKo+lTab85VC51XJkUbjUS1Fn1Q5LiXy4i7IiwK/TTdADdexGOr7Fp44KKCbCAdVvF69WKNCrJcQr0ueSRxWBbEbH0ixLjSdk70KtcoouekzNDTvm/eokJ08juvV6Eejz7PQ8FtbGXEWmVUWxGWIGroRx7ZzhKKt5Eo3WUuYwakTZKdO1F/bgomFIo1fcRGBdySPILiAhRBBUcb1J/OCPa7WSZm/YFXLYyQbujHMFDVriqn8NV6VPI+RrJtExgMt2RA24fgaUk27UcpgfPBlbohFNL6GltRmlKZTKQ5yLXcVlE40vrZ+HirFm2QmPvQNDcPNvr8TcFOsRXYN4IfVTuEQRpEXlygadxHjg8oYYzdfXHD248to3eLBqu0GhpKaHqa540FGb/wA5bo8rKV4zstAuHFB7hn47tUThQFe9QrsIUonJhYefWLRQ4UwGhsIYQVVnk5M3qTAm5JHR9Uz38QuMD7wLPH0VsxQGtvyc/yUVwOE/NRJ8lOnqNl59hBlh4oxGoivRYTTUiafOU+6+Y4F+1ezpikXrrOVEHdocTyBVyVPRgktHQ8RTaxFofBci9zMGU5ke4iiLWhxiUBG89j8M+SnNTSkGdQ8v2J4yxo/e+xhVN1KYBQbEan7d/288PgTjzM9PU3ftdcIR1swQ004dp5qeYzOztV87nOf+6m2o2kaW7dtZes2379rZHiEv/zLvyQSX0Nr1yEMI+ZXVSvjTI+/y7e+9W3+2T/7+k+ll1JKsWbt2v+/jvM3Bbbt32CFl2nTh4Mf6NnX/SbiV0qMSqUSe/fu5U//9E/53d/93UXP//t//+/5L//lv/C3f/u3bNiwgX/7b/8tTz31FJcuXSISBB/+wR/8AaOjoxw5cgTbtvmTP/kTvv71r/Od73wHgHw+z5NPPsnjjz/Of//v/53z58/zp3/6p6TTab7+9a//Uo/3Z4GmaTz8yMPc/8D9jI2O4Xkebe1t9eNfDq7j4rgOO7Zv51pPL90SmWvfKLifJK9Kjg+kSKcKEQ+qHBsILxrVB2hRBi1B2wV8J+xTlHld8hwiwaog8WoiMDScFUBLfYxfLXI3Bt/HZJ2EmcbhAc2/O/zIK3KNID8Nf3nWUeRwyM6coVoeId6wGfEcCtkrOHYWgIrSMIw4llMEURyWPIckxvrgLn6IGieC9o7jlBgffHn2dDBKDT2oBPiPKSp4lIP4jdn3l/EwUDwQjM8r5RPBM1LmOhappj00NO3F82xK+T5yU6cJR1rrGXPQTSK9g/GbL/CCZOvtoHigVyrjQmkY28rgOGX8yIs7aGiaqwxFY13EkuuZGHqVkf4foutRRGw8zyYSW01zxwP+MSiFZiQYdYpcu4UcuSL1ibYqwouSna3loGkmyabdGGYC1y6QnTrFFDadLG55TQXfB8+16o8ppRNLbWQ404MdkDPXmQv4nYVhJGhd+zRTQ6/xvlOYe39wvW2EXqooNASLS/XrowU2mb5xqAl0uB4D0+dw8TDQaEFnHA8TRZddQwHDaJynQgRtroWgwBM4M3MO25oh3tCNpoWoloYoZHtJi+brs4A8LjPYNLc/sKDtqulh0q134dRyXChPsoa51uUNLAqewx13LF/ZXQ7779jPsePH6FNVtjD3995HlctUOajibJnXSiuKy1ulEn/3ne/wz//lv/i5VUwMw+ArX/kKV69e5dSpU+RyeRItCfbu/QLbd2xfMCn2cfD+++9jhFI0dzy0YHo1Eu2gqeNhxgdfou/qVbq3bv25HMcKfLS1+UayY9isWmKabyyoTLe0tv5S9+uThF8pMXr66ad5+umnl3xORPjP//k/82/+zb/hmWeeAeAb3/gG7e3tPPvss3z5y1+mp6eHw4cPc/z4cQ4ePAjAf/2v/5XPfvaz/Mf/+B/p7Ozk29/+NrVajb/6q78iFAqxc+dOzpw5w3/6T//pU0GMZmEYBl1run7i624ODPDeu+/Rd+0agpCMJyjj8SYF9kuMFgwchJEgyby9vR1lmEyXy0im6utjlkEy0GYADGPj4AduviV5QvW2klfPmIoEmowbWDRjYAbGgJM4eAhpDOJKY5Uy6RcLLwgsvR6knA9So+H/x95/B1mWXeed6G8fc71Ln1lVWb7LV1dVV1d7b+BBADQiSEmkniTyPYU4TwpNhBQKRSiCDOkp9MfIxWhGlEYjDikDASABor33pqrLe+8yq9Ln9ebY9f7Y5970DYKE6SZ6RXR0983j3V77W9/6PkxSGEzg4SHEUPitKWZbk/OOTFHo3U8mvxXDsAlDh0rxDJXZk3xInUOiyxh6SNXnsUMl6caiSsB5aTKFr1GZ3n1zpbSZ48SCOZ2UUdGNxfeqzAJUIKEM7iVDVUKqjXFUz12YpkkuEkicHnuz4/klCJWZ4yABw8Tpi8w0JvC4HvG+tE1IExAMM7HA86yzz+QQyfQamvVbGEGTAJ2AWHYWlIHvV5HQp3foUSZHnuOg1LgsLVarGK6EXItKVaBd49vIlYuQTA1RmTnRSWoVBgelzpexNTIVIWUxFKdECyMuJkArQ2sSfScqg+HXadVvLSjFgE6O2iO7iU6CQzR/KJ4c0py00ANl4bQmAUUmv4VUZi0iAfXKFRq169zC5W5SpJXJjPicpEF3dG2vopO2AiZ5bA5JnW6sjsr7DpXEFsXh+i2a9dHOMzWAxSPzSPKjuJjKJhUR6RecL4p0fivTjduM4xETxTVxuIjD3j17WLNmeMk6PywGBwc5cPfdHD58mJIEbFRxbBQnpckwsSVlu4wyuU9SvDwzzdUrV5ZICvxFQhmKLVu3/Mg8qZVCRDh/4QK57n0LOHLtiCf6iCe6OXbsOIlEknwhv8A09i9DSCjMFmeRUOjq7vpzJ5g/aqxevZrB/gFOTM/SK9YCc+GWhJxWLTat30hX189v2/4nlmN07do1xsfHeeqppzq/5fN57r33Xj744AO++c1v8sEHH1AoFDpJEcBTTz2FYRgcPHiQb3zjG3zwwQc88sgjxGJzA9nnP/95/uW//JcUi8W/VDf/1MlTfO9736PLsNivUtp2oe5xA6FkCi8HZWLKxBcNxG+O1GAvX72CiBa6mxF/eUK1aHQgBA6FdW7iMIDNkyrHND4TET+pD4sBbN6XGiV8Dhhp3DCkSMCpsMEFWp0BWQGrJEYBEwM9uGjbDz0wPqlyur036pg4I01OR8J/c6HIde9ekDwYRpxCz10EfoNG5Sq70S7pV8QhRPicync+BgVM1hDjbaky1rjFzOgMQkgs0Uuhdz/FiQ95QyrsIMkkHjGURgQWH4WCO4jzfmtS21JEGkDJzDosO0e9coVEcpBm/SaN2nXWEWMcj2vRoJ3CIB/5PBkSkMGgAiSSA8sOHACJ1BBOfZRfMbpxRbhMixPlCzSr1whCvV1DmcTTwzj1W8ziU4ySnXaSaKBYhU1GmZyVFoYRp1m/Ta57N6nMOgShUblKtXiGP5Fip4Sj0HwoP8IGLXuhiGizNoJppSj07ac4dRjxG8yOv03XwANRCcrA8yqUJg/hexUMNCFfoS0dSvg4zTEGiZFGMYmvS5epVXT130NbXiGZHqZaOkdx6iAHqaOk87gyEymX36t0b9+IuDq5QfGOVEmJSRzFOhWLut+EL6p8R1F7m0osGDQCEQzTXrYzEsC0dKLyuuimg5hl8+C9D/LEE0/8uVufv/ilL5HPF/jg/fe51JyTjVhOfgOgF4uMYXH16rUfa2L0444wDAnDYIng6vxQKs75C+c5f+E8ABs3bOTzX/h8B/H4tIaIcPDDg3z4wQeUq/pZSSdTHLjnAA89/PBPPkFS8PVvfIM/+C//hef9CpslRg6TovhcMVzMRIIvf/UrP9lj+ITHJzYxGh8fB1ji5zMwMND52/j4+JKXxLIsuru7FyyzWMeivc3x8fFlEyPHcXCcudJApaIfXu/yKbz4yp1RP8sIg4DUxCh/84G9ZDE73+HWRIlLVyd5I6yyd89e+vv7sCybeDzGD37wDFmMaKZtcEFajOIuy825gUuVAAvt0wWRl5jSH+PFXWBZMZiIBtH1Ks4NqXKaJltJsknFiaG4jcdpaXRUoZWCUqiTr3tUhoF5rvSmUtypUhTDgNu49K7+HPXyRRq162TmifvNj0xhG/XKZZ2AYXOYOg+ozILBDnRScycpbkmJocCjC5PR+jiz9RHseDezTpF3pNo555UqFG3BxzbSAjrZs2P5jqlrvXSBWISGbYyEMQW4IQ5XcShg8rTKYSuDt8MqU5GVx3KhbT70wcSUYgdJTBRHwjp3kyKrLKbF40L9FioiiLfZJwpNsn9S5WgR4kYJThg6DAx/kfg837ZY735iyT6mb7/OFhKsV3HKBJyTJjUCLDNJPDn3ntbKF3CdaXpXPUEqvZZ69RrJmkMshPGxN7GMOIYRw/WrHWL4MHFWKY0yXZYWAuwjxSaVwEOIR00HHzbGqBbPkOvaPe8+b6VSPI3lN1hFjCk8aoTsUSl2qmRnuQ0qzjXRquEBigwGVQLeizhteUy6lIWI5llMir9AlyqvTLyghueVse2l6EWzrm1xMhFDruF7HDl8mG3btrF6zXJ2tD88lFI8+NCD3Hf/fYzdHqNcLvPdP/7uyiIOCgylVmyv/6SEaZp0dXXjNMYWKM63IwxdnNYU6exGcj134ramGb19mv/7P/8X/ubf+n99epMjgWefeYZjx3Sn536VQwGjTZd33n6bW7du8c1f+7WfuLfbwOAAv/Xbv80777zD6VOn8MOAmGWzZ+8+Hnr4YXK53A/fyF/i+MQmRj/L+Bf/4l/wu7/7u0t+T3z9V0lklhppfhKiUqkQLxbpnedznpy+RuXwcXYPFLj6/imuXb3K1772NYIw4F//b/+KATF5hGynTb4fm1ekzKtSYSdJ1hAjAK6Lw/nIS2ofae4wErwTVpnB/xiNJb+jwdPWm7lbpbljHvy/iThD2DwnJRoRZ6TZ5oUsg8oAbFJxbomLhB6+pzkyppVcdlnTnOOGtLlRy7XxA3QpE1MUg8pmq0pwp2iz0COOLgX9kuriujgcpUFjGcsUgHHxMJXdQYtA26Z4TpFEWg+MbkvLQT6ssgvKcYPKZlBs3pMaU/isIsY6FWO0NYXrzBCL9yzYV5vDJCwk2rbRtyM0ENHcpY3EuBwJGz6ishwSzeFaR4zXpEqFdiKnSKZWL0iK2pFMD2PHuim7Vc03w4rWr1AMHCrF0xjKolG7jtOcIFvY3lFJN80kNQK+oXKUCRgNXYLQp4LNbTyeULkFSfBWErwvVY7T4JjoxNBCsZ4464gxWjxLtrBzHi/FIJEcJFkd4X4jw8GwhuCxgyQIka66aDK3inNRWsRQ3B959s2IzxtSoUyAKyExZbBRElymxSaJd5oLhokRw6Q0+RG9qx5fgBw16jepRhY0DWWC6EQ0cDz+63/9I/7e3//7P5QX+HFhmiZrhtewevVq8i/nuFl1OtpG86MkAZXQY3j4k09EvufA3bz8yqukm1tIJAc7vwtCafooSEi+dz+Wlca2CyTTa5kcfZ6XXnzpYy1IPslx48Z1jh47pk2956F+A0rLMrxx+TJnTp9m9513fsxWfjzR3dPN177+Nb7y1a/gOA6JeGKBv97Pc3xiE6PBQf2iTExMMDQ0J742MTHB3r17O8tMTk4uWM/3fWZnZzvrDw4OMjExsWCZ9v+3l1kc//gf/2P+wT/4B53/r1QqDA8PczLcSzr88WXSIoLjasf4WDz+I6HtoYSMjY1x+/Ztms0WCou49LFpnt7Rmi2bWPWLd8Kf/CGff2Anp8YncFyHK5evUG82eGyRqaOlFE+R53WpcEIaHGduUIqhBfzaKtebVII3pcII7pLS0pT4jOGxmhgfhjVu4ZLAYBNLB4aUMtgkCS7SpBqhRRZqRVQmFl2l6bE3Or85zXESyaUCfU5TG6bkMGhEM+g6IZllOqyaEhJEyAQASg/QN8VlGo/XpUYzSt6OSZ0H5rl5A5Ql4BIOqfy2BRYbjcpVfL9GOheVNSSgF2tZcb51xDlHi8uiB71h4uRoMX3rNboHHyKeGkKh8NwSs5MfEobePGxQl4rekyoZTO5QCRIoxsXjEg5WhNB9W4qdXPYUTZKp1fR37cS0M4zf+FNiyeXVixWKeLKXojtXzrGUYg8pXpcK1emjtDWv8j13keveje9VI2FQXT69hMM2ldDcHoHvyizriS9IikAjePtJc1Nc+rEYJk6DgKu4WvsqCHVHYWwO7Q38OjbgiFCM5B+u43BeWp2kOIvJVhIMYXMFhxnRHXk9yuIAad6TGq9LNUqA9HV9ScpslQQDyqaFkAJKjVtaJLSwDdNK0arfola5jB3L0dV/L/HkIIi2eClNHsRzXI4dPcb9D9y/7LX9UUIZinvuvZfXXn2VNeJ2FLxBT0AOqQb5TI6tnwLC8oED93Dx4iVu3HiFVHYjifRqwtClVr6E25qiq/8+LGtuImoYMbKFXVy99i6VcoVc/tOHahw5fIS8YbNJlpYQh5TNEDGOHD7yU0mM2mGaJqlU6ocv+HMUn9jEaMOGDQwODvLaa691EqFKpcLBgwf5O3/n7wBw//33UyqVOHLkCPsjocPXX3+dMAy59957O8v8k3/yT/A8D9vWH+BXXnmFrVu3rsgvisfjxONLH9wr11skU3/xUpqIMDIywq1bo7iuJtwmE0nWDA8ztGqIlcTm2hEEASdPnqRSqWDH8phWAdeZxQxahCqkDeFcutpiaM9GnvjF36Dn5gnWvfEWauw68dlbPL5pmLXLKDUDfFUKXMbBRqspu4TEohl721A1J5CmQIWA0rzhWYAWAQ+RAQxMM86qoEkGg64VeBF7JUcvLkXgDvQsPk58gRlrO5ri8TBZ4qnVBEETzyliWldJ59RCocMwoCajhBvX0C4gPUwXMUxyUTfZ4u0+QoptJBYY3D4uWW6ztG11GoPuSJiyjhY0vI8M8VQK07qKiBB4NZz0JGxcjWGcxTASjIpP/zIJIgAK+sTqdIUYCp4gy9tBjclbL2OaKZRh4XsVTDOFYdgMB4IvQojuMltDjAfVXNK2TsXZLD6vSAXTSpPp2k4YONQrlwn8JunC1gVWGJ5bWe7IOn8LFiFUfdEn5F6VpgeLZ6WEYcaYGn2RVnNuQmKiOCp16hKwWSVQooneq9TSrhjQhPZusZjEZxIt9zCI3SnjMq9U5LklWs1xJoA/llkMwEO4Ig6ribFDZVAoRsXliNTJYtIi5CXRSV6X2JFjHZQJqEkTNyKzC3CGJqel2b5FDGJjeU1uTR3sMK4MZdG/5gsdDz+USTq7CdPKMDn6AqdOn/oLJUYiwuzMLEEQsP/u/dwaHeXt8+foVzEGxKIpITeVhxmz+Y1f++anYuZvWiZ/9a/+VT744AMOffQR02OXAN3V2Lf6KZKppc0msYRO3Mvl0qcyMZqZnqY3NJZoU7WjD4urMzM/3YP6LJbEzzQxqtVqXL58ufP/165d4/jx43R3d7N27Vr+/t//+/yzf/bPuOOOOzrt+qtWrepoHW3fvp0vfOEL/NZv/Rb/4T/8BzzP43d+53f45je/yapV+mP/67/+6/zu7/4uf+tv/S3+0T/6R5w+fZp/+2//Lf/6X//rH/l4e5IuqaTzwxf8mAgl5Dvf/g4XL1xgAzHWEHFMcPlIHO4+cIAvfemLH5scvfjSS3z00WH6Vn+eWCIFCK16nalbr9Jt5BYYKo4Br7OBofXrufNpk0I+gzOQod6dpIvEsuJ/VQJ6xMGO5REE362wQcWXIC0ZQq6KQxB1iyl02aIHUGYSxEMkQET7ZXWrxLLn5eHRKx7r0W37VQIcDIZUfMHxNQlBWnTPOwLoAxSmqYglejDMBEHQxG1OYctGECGe7MMwEzRrIwC0lEU/FjEMfIQZfELx2KbsJWaUgocj8xIjpYjFewgDh5ZXpYUmOA/E83hOGWQudVB0MUgPNUKUYZO5PcOv9ezhvffPrXhvG1EpsR0pZbBXkryORyI1iGElse0czfotWvURbgPfFqezxj6VYhyP0dDFR8grk43E2UScq6FLtrAdhUG+ew/T428xM/Y28Q2/osURRWhWr+P37sOyFgrruc4MTnN8AULVPl7QROwcJmlMypOHyGKwX2XoxaZOwAVpMYLLFRwuSGtuu4sSrXkXXnfIYfCYyjKNzzmZU4gyzaTWvGmMMTPxLgYGO4jTpSzOSpMZfO5SabbNK92uVTFWS4z3pIod66Jn8CF8r0a1dBanOcEdxNkacZJaaAmGWXz2kGJA2QQIo+JxniZbSPBXVDc+8CdSIp3fPJcUzYtEcoBYvJtqpbrkb3+mEDh69CjvvfsOs5Gyc9yOsWv3LjZt3MT1a9eYFj25ymbzPP300wwOLUVPP6lhWiYPPfwQDz70IK1mi9OnT/PCCy8sKR23w/f0dUwmP50IRzyZoKlWeOaBugTE459MusbPU/xME6PDhw/z+OOPd/6/Xb76zd/8Tf7gD/6Af/gP/yH1ep3f/u3fplQq8dBDD/Hiiy8uqNX/t//23/id3/kdnnzyyY7A47/7d/+u8/d8Ps/LL7/M3/27f5f9+/fT29vLP/2n//Rn1qp/9sxZzp8/zyMqu4DYuZoYfVgc/ugjdu3axboVxMpcT8PymfwOYok5nYlEejWmYfNuWOUhI0s/FqBwD73F9w/WeeC3/zaJHb/CFvMWicEtdA1tpYHZIQwv2AcBvQCqTSYOaAKJeY+LoF2te9Ck1TazSx0JhQAA5uhJREFUKUCoEOAhmFEJzkUIEOrL7E9/Inz6o+33EGIToFBMoVvJ2whAi5DuzjpzkcHEQzpcElD0RKW/OgF2rAtQpFzNF1IophEM5kQChzHIYOIv2naIT68yseNdICG+XycMWlh2NuIw6f15ziwiPimMjv9bizAyV1WIEceOF8jdOsqDIiQnajSulxbsqybaqmWfmvsw+iIcp4ECPLeM4TepFc8hCAbCWhL0KYsz0kSA96TGND7Z6FrfFJeTNNhMgiB0CQMH00yilEl3//3cvvZd6tXLWlFcGSAhkyMvUeg7EHGEQhq1GxSnPgIM4ouu/kVpYaG4KFpbJ4jMZD+nch2SexqD/qhN/joOj5DVx0qVK+KweZ4WTzumogR5DTG6lEUXFmuJ86KU8BAmbvwAAYKwhYnBF1SOQsT5uSIt0hhsXQaZW6diXBCLCkIs3kMs3kMyM8zEzee54sxySfTEx47EJeuElAnYHiVMfcomKQbHpM4WEpH9i2iZhBXCsnNYVnnFv39cvP7667zz7jusU3H2qBw2iluey4mjxwiBLcQZVFph/HKtwR//8R8Dws5dSyUePsmhlCKZSrJr9y5eevllqqVzSwQ3BaFaPEtfXz+9vcuXfD/psWvXbp69fp0Kywvj3lQeD+zevcLan8VPK36midFjjz32sSqtSil+7/d+j9/7vd9bcZnu7u6OmONKceedd/LOO+/8uY/zxxlHDh9mwIixehly8WYV56LhcOTw4RUTo9mZWVzXoSuzUBfFdWYIQo1svBZWyGEQw2A2khC89vv/B2cf+jJ33rlJl3lC3UWUwyQRddGECHXCDpfGjgb4tgihCfMSIP17D9YSFCGMUJhklGyAdFr90xgkIxzIRagREABdWFFZKqBBSBcWjSixgDnkue0VFo/8yRqEkRaQPv62x5tC2z3M4GOYCURCJBwmjsKJyiRtL7gwOtc8ZgehkuhaNAixF6k2+16VoO0pJtHSMtg5h/lRJ9D2KEaMWLybjUNd7H3Co3b4BC6Cf00PmBN4HIwEF2+Iw0iEAmj0LGQTcXynFt0jgzI+n1OFTjJwTppUCHFRC2QOXBGOS4OLaJRmduIDDDNGMj1MMj1MLNGH25oGIJ7op9UcIww9psfeQAsr6mTTMGKAdBA1V4Tz0uQCLdqCjFr1W7hnUZs7AAp2keSKtLiFSx8WWUxm8TkkNfaR7likzIjPe1JFAbvmdZXFIy+nw1KHcA65fYB05zoAFAlYhb0iT20Im6I3h+BUi2dwnWmSqTWkC3dgGHFa9VEul84TFy0hca9kOtu7gzinaPCilCmInoA4zSmyhaX7EkKc1hSbtm1Y+scfEtNT07zz7jtLuut6lcWwxHhFKiSU0SlHbpA4H6gaP/jBD7jjji3E/gwdtBIKo6Mj1OsNCoU8g4NDHcn5Wr2GQpFOz6nV/6QjmUgSj8WozJ4EIFvYjmkm8bwS5eljtJpjrB7a9DN1fq9Vaxw6dIjTJ0/SbLXo7u7irv13s3fvXkzr41vtd+/ezfvvvceb5QoHJMVg9MWYxucj1SAWT3D3gbs/dht/3pBQqNfrGIahOUU/w2v4SY9PLMfoL2tMT0+zXsxlH0qFoi80mZ6aWnH9dhtnGC7ENsozJwBt5+EijOISAGuIsYE4b0uV4+8+x/F30U7soge5cTwSKJIYlCM94TwmTxo5wigdqUYcFQfBQttDtBOabSrBPpVi8Ql9JDUmxOMrhuZxTYvHq1JZgvYoYANx7o06hE5Igxvi8AtGm/+lu3suSosT0uBzRp4uLKrANWnxodT5ZaOLGiHXxYkQGpMBLI5InQq6y0gBjoRkMNirUkzgUSckHnGFTkkTH2E1MWyluCWutqLoO0C2a86fLQxdpm+/gdMYwzDj0Ue7jBK4R6XYuEh0L0T4XlhEpYboX/M5Hnk8TWL9N1i7dg+pP/lD6gM5Riam8UmwnxxBhLQlogTOR0hjsJ44RqTSfJ4WPZgLSMsHJEWDkPXEloh0PiXdbMTR/mBWApGAILiIoW6wcU0aw0ySyk7ipzdTr2hemcxLhsLQ66BrChhHq1J3keRhNKl5nYrRlJDLOGwm3jF6nR954FFJdxLt+fPiSXTC66ORtv1kOtudH7skJIGDZWWi82iygyQBQokAH+FuEmQxyS9DcAfYIGkKhOQKk4SBywuXjpLr3k2hZ86QOZEcJJXdwMTN56LkWToTAFMp0qJb/TMYWMB47cay3YP18mUCv97hPP4ocfToEZKGxXZZinx1K4u1EuOKOOyIkialYK+kuOmVOHXqFPvvXtlgGuD0qdO89uqrlCpzaNZAXz8bNm7kwsWLFIsaYe3p7uX+++/TPM6f8GB648YNGs0GG4hzc/YU1dlTGMokEJ84JuuIcfPGDVqt1l+oy+/PG1OTU/w/f/AHeC2HdWKTVibTE0Wee+45Tp86yV/9a3/9h9o0/cZv/ib/81vf4o3xMRKGnow1Qp+eQje/8c1fXWBn9eOIMAj54IP3OXjoI6qRbtLgwBAPPvgAu3Z/upDFn1Z8lhj9lCMei9FsrKxN00SWJX63o7evl2wuR6NymURqjkvgOyVyGPREg+XQIkRqI3FmxCffdzcSetTLF5nwW+xXaRpR6askAXEUD6sMF6TFTER6lUiRep9KcYdKYKKF/drJSgzFhqjIklIGChUZbbYLZcK7UqULk/tUhhIBAUIeM9IyarJGYqxWMbowOUtICZ8CFg7ajPWytBhWMbrmPbIDSiM2r4cVZglIoMhichuXc+iBVreC63Umlc+hsMZRafBFI09sXlmvhRaQnMDr8Fu6Bu4nk1/Y3TMz9jZua5reVY+TzAwDWqW6PHWEg5XLJDEYmjcgGyh6lcVslCj8j//4fzByzxf5la9vZvgX/wY9N08wVC0ToBOCrDJJLjiusONjllUmAYIV6UzZKBxCnaCKxst6VyAz50QjV4YZAxTKsAj8FkiAFcthWikk9PHcTUjoY6A6RxFGySlofyUTrZWTQNEQjappXSTYA+TU8iXaEMEQnzQGaWXiElKNugEXRwJjSakBtFFqWoIFv7XLtYPR9V4dba9HLUUzBcEUn2GliCcHaJ04ysObhrmm9izZVyzeQzq3iXrlyoKzCUSjiWlM9qoU4+IxSYOJkRfJde8imRlGQp965Qq18kXu2ncXq1f/cNX6xTE7O0t3aGAYy2cjfcrimjgLJDNSyiBn2ExPrzy5Ajh54gTf+/73GSbGPSrfQe9OTxU5OPUh8eQQvUOPA0Kjdp1nn3uW8fExvvzlr/xEk6Pbt29hK4P7ybAfYQQXl5CMSrIamwohN4IS01NTrBn+0dXE/yIhInzn298m5nh8nhyJeVpDU/i8MTLKG2+8ztOf+9zHbieXz/Fbv/3b3Lx5k2vXdKPG8PAwGzdt+rEb34ZhyLe+9S0uX7lCOruJ3lV3I6FPuXqVP/6TP2Z2doZHHn30x7rPvwzxWWL0U44du3bx4XvvsSdCK+ZHjYAxXL74MfwAQxk88MADvPTii8QSvWQKWwEDZVjzFIyWRrvEk4m8oHLddzJ9+3VO1G/zdaOAjcEVcejF4nkpY6AYUjaBCOP4mGgIvz3QWCh2qARj4nJKmpyMFKlN0STXqgTko4Tkpjg0EZ4ysmQwyc977LrF4gYOH0oNJe1yHXwQ1oihFY9Bf4sTEuKoueuWwiSHQZGAe1WaDSqOQnFOmpyQBk8YuQWE8X5snjByPBOWuCgtdqkUIcJVcTgrTfqxeNLIUxSfF2UpJ8R1ZmjVR+kZeozkPK8s00zSPfggvlfmdKu0KCkV6tJOSABl8eHBFxhSv8CqPRvZNtSPDGkoPYFBdpmEokFIjZDeqHdqBs1lai7QoRZMYHaZV7q9vkInESGas4VS2uX0zxgGuvTaLlf6CNUIV2wncw1CTHRpdPHTWCekHp3HXNKlzwfD1npUIvhelQwGqWWuRTFChdrH0STEQ5P7E1Hptx5dFzM63vaz76NLkx7aasWO5RlctY5d8l3u8KZ49eZS0nI8NUitcomRyOajD4tLOHgIHgHfkyIAq1etolAocOHiacozxwBIpzI89eSTPPDgA3+uZCKRSDBpyIpaYQ3R3aLz/yaiu0LnK/0vjsAPeOnFl9hAnPtVprP+EDYD2LwiFWqh1/GDS2XWU0te5PCR99mxYycbNv7oZcE/a5imqfWfEGJKLZAfAc25ay/3047r164zNTPNUyq3pGO2T1lsCeMcOXKExx5/vNMBvWIoWLtuLWvX/dn0pjzPY+TmCL7v0z/QT6FQ+DOtd+L4CS5fvkzv6icXdPmlsxspzx7njTffZPv2HfT1//z6oi0XnyVGP+U4cOAAhw9/xJtOjQOk6MYChEl8DtEgn8uz54doWNx77z3Mzs7y0aGD1EpnsOK9hEGLIgFNwgWIQztu4WHbGQxDv7BKmXQPPMDtq9/h9bBCQelB9xYe61WMAyqjhzalUYt3wypvhVW+YhSixEQ4Ig0m8RlWMdZHCkAjuHoWCzyEhvgn8OjBWtLVJgjvU6NKSD8Wg/OUj0sEdGFyr0qTwmASn4vS4pWwzFNGngQGTjTg71bJBSWsUXFZrWLL6hWlMBlWMc5IkxFxqUcDazcmfVi8IBX8qBW8VjxLOre5I+TXrN7AMBOkMst9zBTp/Damm+/QUmEHMRkRlzIBqjbCyKU/hAjdePfD57n/UIaxex6lLgHXcNis9FpF0YO3BRQiZOicNFlNjC5lclG0LpBl50ikVmFYCdzmJK3Gbbap5IKUpCYB19EJ74CyO12BHiHXo3KhQkhh0EKT5JURI5O/Q3OogDBwaVSuIKGzBNvpwmK1smmPrnUCrolDBoMBZZOMymPT4jGNTx/2Et2iSfGYxMOy86AUvlsGhAFsuqNk3CVkQjzKBKQwFtzvG+J0PPwM6KBb7Xb7eMShc6LkUIBUdiNWDL72yC7WfyNJ4dp5nuIkExWTUyUtcOl5Fe1rB7wbKZ9bME8Ok46Vyfj4OPv27eOrX/0FpqenMAyTgYEBmq0mb77xBseOn6DRaJDNZtl/1z4OHLiHeGJlZBhg585dnDh5kknlLemW9ES4grNEcmMEl1YYsH379hW3e+nSRRqtJjtVYUnCZSjYSYK3nWk8t4QdKwCQzt9BvXyOw4c/+okmRps2byZ48UVuKpcNy8iJXBWHTCrNwMDyGnQ/yRgdHSVumPTL8knPsIpx1i0zMz3D4NCP5/gkFN5++20+/OADWq7+rioUmzdt4stf/coP9Y/76KPDJNNrlpU+yHXtpl6+wJEjh/nCCp6lP6/xWWL0U45sNstv/MZv8q3/8T94qVImY1qEQCPw6e/r45u/9msfO9sD/WJ86YtfZO+ePRw9dpTZmVnqjSyTE00+Cms8ZGQXtLnfFpeb4pAv3EnnSyghldnTgJ6F16JkIIXBfZHuSzsSGDxkZPnTsMhVcdiukp1EZbGa9WpirMbmXanxvtRQUkPQCFNdAtLzyiMXpMWIuDykMgxHGkd+hOCswuZBY+44BomxUcV5KSxzUhrcozKMiUcIc07pUehEZ2UdlzYSUYoG0wwGFSXMihs51QcQBHhuhelbr5DvvZtYopcwcDTys4J3WVuBW/uH6VLjMZoowyKd24wVy+G2pmhUr3MLn++FRVYdfJ6mBEwT0FJxLouDYdjYsXwkjuiwXsW5Lg6uitOtMlyWEs1YnsG1XwFVASqEocvY1dcoi8mD8+7fYangSshuI8/8UTAGDBLwXFiiB5PPGQUCNKH6pDTJ996FHdPriATMjL1NPyY7VZKCsjgS1hnD4+tG14JnJQP0iss7Uu14n7UBj50qyYBKsliywRGH01JjAAsPbTljozgTcZpiKFpoSYg1KsYZcVhjdBHH4LK0+EjqbFZxdqhkh/92VppciRL0dPQ2NAgJ5+3XMBNUZh5lz95NfH1zjP44JA4doSdxQRv5+g2MoU2klW5kCBHKEnRKfxYKIzJGDhGYHaV5zqE/rbsKndItJsfHWRuErN+7E2VYSOgRtGYZ/+BVBgYHl0U+fM/DDwJWGQZ/87F78FyXTNRwoKLnuyYBv8hwp6Qq6DLjeglYp6BQuo1XnViybYD+eoW/9sDejgbV4uhFGBYfO17GMOe8Cf3BbRjKw7t4fNn1fhyRT6TZtmUrRy5dJiVGp4lABC7T4gotnnrg6Z+JTpNhKEKRlQC8znPx47TzeP755zhy5ChbSbBJFYihGMPj9LXr/Jf//H/zt3/7tz6WkzQ9PU26a2mZGPTkOJYYYHLy48uuP4/xWWL0M4jBwUH+v3/v73Hx4kVGRkZQSrFh40Y2btjwI9WYV61a1dFr+t73v8/U5DS3xOPZsMRGFSeOwW1xuY1HMjNMtjA3iyxOHaJeOs8eleIOFcfG4LvhLOujctTiSKC7X8bw2B51F2Ux2LyMaOOwitMrLWbx2aq0HcSIuDwrJZ4gR5+yAeGStFinYp2kCHTZzUPYa6SWHEcGk60qwRlpso8UXjTMxRYtl1UGU+KhHUWXnsukaPaTjYEXoU5E9OLArwPtAV1wGxNM3HwWpUydMKFVlk1rqdZIq6G9sl4JywQInlJYVob+tV+aZxOynVzXbiZHXiAQl1GZU/K5LA75vrvJFrR6tkhIo3KZ65MfAFpd+yJNKhLQ3bVzQYJmGDG6Bh9m5PZbPC9lNqs4cbTy9f5lyPGgy0xdmHPaQCjuUAmuiEN5+uiCZZMYPKKy2NE+W4SsUvay5dshFWM7Sc7ike+7m1bjFn7tFjuWSYpAJ6gW8LiRQ6Eo4vNOWMWCDletgMWdKoVSGjWoR3pPp6TBRhXngJobHDKY3KM0yXtEhfjxXo2KxbtwWlN4rVkMM4lScO7Iu5w+9Ar89v+H3es/z+rCAI2xEVrRRGGNsilEn0kXYVZamGjhzHSUYAswhceE+NRMg4ceehiAY8ePU9/UJJXbjBhzKEMQOFQrl1E9XezYsaPze61W5fLly5TLcyKbqZ27UYbBSL1O2/TXRzodlRU0IuYj+Ogkv05Ia9NG1qxZnoPjT01y7ew5BlRiybsDutxYFpdMYcsCe5tG7QaJWEDXXXctWWe5EAnxPA/TNDHNP9tQM/NH/w9f37eFUymD546fo8uwyYSKohFSC33u3n/3skKZvudz8eIFqtUqmUyGLVu2Ysd+SDnrR4xNmzbx6muvMaqWqv0DXBOXbDpDb9+PR0pgcmKSw0eOcGDR5HMjcQbF5oVahQ/ef/9jOU22bRP6i4235yIMW8RiH486/TzGZ4nRzygMw2Dbtm1s27a8AeqPGrVaFZGQ+1SacTzOSUsTdVFYdoaeocc7A2ng16mVLrBXpTr6LKCHThNdDrksDtN4KBRD2GxUcV3SkBAUVCRYUJpZHIPKZlZ89igtxFZXAa+EZV6XCgPYeCLUCNmy6BEcF29em//S6Fc2p6TJ9yJri/Y68wnPm0jwFlVuiMu6RYnbqLia06IMkt27CCs3CLwySMhapTv4bKW4LR7nZY7FYyEEQIiiNPURPUOPMF++1nNL1ErnSaG5TB7CLfEo9N+7YHABsONdZHvupDJ1hN0qQVF8RpRPJr+VXNccv0wpg3R+iyZ3Tx9jAp+JyKDWsjOEoUujeg3fraAMm1R2PfnefZSnj3Y8xoCOc3yFkKL4mCgGlIWNgYXqqJn7CK+HFTyE/SrNsIohCNfF5bTo9nSEjpzDoNgrcmdcEUQJbmsaZcTwCLkkLbaphdeiRchlaXX4YaDLcw8aGV4OK5gIBWUyIz4vS5lVURkjFiV9LYStarnuJMU2leRGWCbXs6fTqBCGHrevfpswbJFMr8Fzyyg/5Lu//39SufcrfO7rdxFfuxULzfm7gtAV8ZS0dIJuGphBsVSfOKCBcNpbBSJMFUys/gLBMg7yQe9WRr0q+GswTRPXdRmfGccsHCBVMLDRchNVQt3E0KvfTYUu3QkaCTMAN/o9hdbu8lXASCpDXbqX7BdAejbgbljFpTAygl4URQJ8w6Q2r8NOJMTN9GB3FbggHz+QhmFIuVymVqsShvp+JZNJ8vn8xzaWAPR/4zfIjJzgTkOxdt063rt6i0ajwfauLvbtu4uhVUt5YMeOHuXll1+h5bQwDZsg9IjHEjz55OMcuOeej93fjxKDQ0OsX7uWw6O3SYuhrW3QaNZVHK7S4ukHnv6xIUbHjx8naVhsWqYrMaUMNoYxjh09xtNPf27F93Dnzu0cO3GWXPeeDo2iHZ5bpNUYZ8eOv7hVzV+2+Cwx+ksSIkIWgw0qwQYSnRfltri85VUpTx8h37sPpSwa1RsYsATt6VUWV8XhtDSx2uRrhJPS4Iw08NEf5++EMwiQEGPFF7IZ8TkmxCOmFEqIEgtAIK0MKqI4Kg0E2KIStCJyrkeoO5iW2Xh7Jr+BOFllciXqjOtRVqfLrG2s+r7UmMBjbcQyuYnTKa8gWltIQq3vs0elOm3P+lrYrFExXg7LWHaWRHYdhmETBi7V4hmc1hSGEUc3tis8twyENBGuozWIUIpkes5uY36k0sOUpw7TG5UKRsQjk9+y7LLp/BbK08cW/FYrnadVu4lIQFpZunst4sMoYI2KkUAxJh7npcVNcTsq1QCWaLf5aXz2opPXq+JQIuALRr6DkgDsUEm6sXhDKgxgsUUluBEhkS3CJR1oZ6XJRVqYoohXb3T2e0waVCVgi5EgjsGYeJySBka0j/nRg00Ok35lcUBlCFVUmpQG6ShxnprngbZctE2Mw2BO88gw7E6nWe+qJ2jWRpi+/RoPG1kyH73Pe/NKvYJwWRwSKIZVnKvSooWwQyVY7sGvSsANHDZtSmNaJhcvjJHr7gcFYdAiDH0Mw8Yw40hoUy2OsWt3Lz3dPRw/fg6nUmMjsc5zHyJciAxv16l4B50ThImIs7WeOJlFx3yeFsPr1rNu3crt7Ldup7l86RJdWPQqiziKJqFGvQhIpNcQi1YP/Cat+k0MFXDgwMaPJRZ7nsfxYydxWk26RCuhewizSutx7dy5i56e5VWtAfKFPQyv28BuQA6f4Cu7NmJv2bvi8idPnOQHzzxDOreZoaE7se0cvl+lMnua5194AcMw2H/3Ul0g3/ep1+vE4/Efqe3/l3/lr/BHf/iHvDg1ST8x0qKYMUIqocdd+/Zx3/1Lk4yx22OcOHGcWq1GJpNl7969fyYOUqVSIS8GKzQl0qVMzjk1PN9b8Z7ce999HD9xkpmx1yn0349t5xAEpzlJafJdurt62LFzx7Lr/jzHZ4nRX5LIZrOUl+G+rFIx7iLF0eIZauWLJFJDuK0ZbHQpaX7kovb5DSrG3W3yNVrh+pWoSyuL2VGUvo1HjWAJuuNEmkIB8LpUQOY6mr5gZElHy4dKOCHaQf2kNBe0bV8XhwFlMyIuHkIWkzXYnI08q27hYoqiF4sRXJ4LS9yhEuQwOSx1LGC7SnJZHK6gB8YEip0q2SEvV2aOI6FPDLXAOqId3VisU3FueFXc1gxh0MJzinrW7tUp4JBSBpPi0e4K26NSrFYxroqj1ajDALXMDDKMkJ8PqBNGnTbGMsgCoO06FkWjeo31Ks4eI0sKkxDhGg6Hpc4qFeMhpZWYGwS8IGUU8IDKsErZuGgfsfa13BglyNekxRoVW5AUtWNQ2fSIhaUUW1WS9SrOM2GJd8IqDxvZTnLUTlS3qQS7VBI7EokcweVDqXMFh8vzxBkLmDxs5Egtk9xo1ESHESFARXzGxEMQ0soA0V1ti8nJALPz0LX5YZjxTlm0Wb9JTln0ReurQ3NCsAoIpMn70uBXjW5Gwyqz+Ow0lkdiWnicDiv0pTzyhQKnD75M92CD8tQRwmCunGHaGXI9e5kdf5e9WwZQTZf3Xv4OD6gM5rzJylVpcVLqfNUoYM+7PgoYQjgZlplRBo+pOc+wS1FH5iP3/C90f4x9Ue+mXrzidV5/7TWarTmLlmwmgx+GNBsNYok8iOA6FfKFAr/+679Ofy4EVt7uc288z8kjR3iaHLl5x9yF8J7U+P7xN/kH/+v/im3N3a9yucyRo0eZGB/HtEye/savwLqvsWF4D+H3/nBFTpOIkJ25xV9/+N6IJF6P/gE2rsd3u5DyGO6FYx2KQhAElEslavU6Emr0O5lIki8UfiiaBZqb9zefvI9Go0G9XiMMQmzbJpPJEE8k8C+dWHB8MzPT1Gt1tlgKqxDHx6F26kPGr2Xo7un5WOrEw8PdNAo76F6h3zhDyEYlcO3MMm6OOnLA//KVx5mamiIMrmAYtmZAhj72pm309/cjV0+vuD6ASqSx1t7xQ6/NX6b4LDH6SxKrhlZx6uRJyuJzC49RcQmiMsQdKsGAWEyEPq3GOBDiIFRkoSz9TXFJYXDvIvL1cdEfG0HzD9qzQIAXwzKPqix9SisAF/E5GGrWjhXrwlAGYejie1UenJcUgR7s9qkUk+JRsRL0DdxPqzFGtXiGQ1JHovb9GIpmxKsI0eTpNSqGj3AzSsAChFPzjD43qDi7IsXgalRKykTK1g4hV8RlYN0vMH7jWXpleXQKtKnjdRycxlhn23EUjxm5jqZSqIRzEWk5FqEZG1WcM9KkXr22LBJUr1wGZeJaKY3F+zVajdukc0s/QK367ei/FKadJvRq9GJxn0p37pOBYlOU3B2SOhUVkMPkfKRM/bmokw/ABu5UKTIYHJQ65yKEsEbIqmW4E+3oikpaKK1n9JiR5Y2wwvfDIkPKxhDd1bgae4GtiUKxVsXxEA5JnXuUfgo+kDp5ZS5bNm0SMovPukXHs4EE13H5Tljs/HYibPCkkVtwD0OEU2j+RCyxEKFo1m6ChMxOvIfTnKB7JdgTzXFqq6OvU3Fui9fR2Foct8RFAZlMho0bNoCC4vi7KLQVSRaTkgSMejWK4+9iWRbDa9YweusWAD2YTOJpDpUEVNH3cDnZAiIU6YQ0NBdO6W7O07S4a/9ddHctn7zNj7vvvpu9+/Zy5cpVGvU6hUKB9evXE0rI2bPnuHDhAkrBtq3b2L59+w8tEbmey4njx9kq8SUaVAaKvSrFs60SZ8+e63TeHj16lGeffRZLKfrExFPw7/7577H/ka/yxFP38eQ3foPECo9kq9XCmhgnFu/pdFDOj1jo4zrTSP8AyWSSIAiYHhsnCANSZkojxhIQBg1KoU9/fz/JZHKZPS2NJLAy7qVjtljEr1Tow1iAqrYIqRDSyufp+pi2+x7HxR2/DZhLpF30t9inJ5sl0f3x9zoB5EWo1xu4rqOR7ESSZCKxIuLfDuvKCSqHT8DNSz9XydFnidGnOESEGzdvUpydJZlMYpoWL/ka2VmtYsSjUsp1icicyiSW6OkgHy9T4YuS63SKNQmXEGSrBIxFJYs9KsUWlcBCKx9fkCanpMmrUiEpmu9Qn9cSHfeq9EYlky7MBTPIuVBsUHGO+HUSqVUk0quplc6DBNylUmyK9lcl4HjYYBSXe1S6wynar9K8E1aZ6KRqOozO1tWS/RpRAWzs2p8AMIliBq8jjjk/2ufzhJFjXDzOSJMHVHaB0KSBYqdKMYPPeWmyIZIKGFYxbk0dwrIzEcdFX5l65TK1ojaStawUYegR+FCZPkYitRrTmjPIDEOH8vSRjuZQGLid0uNy/K71Ks4xaTAiDjtVkmvisEnFlxVc3BANrGelhYUudZYJlizXjrIECxSte7H5kirwAykxZad111VrekmX4PxjOyJ1nOg5OywNborLBtwFHLEQ4XCoycYbFpV77ejhslKDKGUSNG4zLT5/GhaJofChcy51QvoH7mf+13967A2N+ikTzykS+A1mxMdT4RIEFWAclyRa7LIXCxP4KKzzuJFbMI+fweeStBBgYGCAdEb3wsWBp4z8XPKnoIjPa6FWgY/FYtqeAZ3QTohPFs1f8UWYJeDVsMKjRnaBICm02wXgf8osIpq3eOCee/nc00+veA8Xh2VabFi/nlarRTKVJAxD3nr7bQ4fOtRBkq5evsLU9DQPP/wQprGyflClXMHzPAaM5ZOLLCYZ02JqchKA69ev88yzz7CZOHskRaD0e+sYwjvvvoBCYX3+wRVRlUajztT0ALFEP2rZTlHBaU7QSy/pdIbp6WkajV7seE9HgqO9nOeWYNZjzZrhH4vIYhiGjI6OkBK1YDLYjhoBzRlhzfAwxgpdrgBTzSka9foyVkohYiiG4quwpv8cw/g8cO3jYnjdBjYM7yH43h8iF4//3KBHnyVGn9K4fv06P3jmGYqzs53fFNrO43Ej1xkIRQnnpcVxadDVfx+ZvH6oXWeG6Vuv8XxQ4T5JdXgK7VfURyjjcyv0UMBWlVjAA7FR7FKpiDzrdAi5oJOi+YKL71OlIfMbpRdGWzRSEHynhEjAfpVmy7wBNovJQ0aGV8MKZ6TZGUgtFA8YGb4fFklkhskUtlOePsZIa5r9aikSJAgj4qLMOL1Dj+F7FarFs7ziVnhCZReUZDxCrkiLLkz6sbmNRtTaStqLYwNx3qVGEyGJ4l6V5i2pMTX6Mna8gBXL4zen8fw6GQyahk3fms9TLZ6h7MxiBg4TN75POr8NO96N75Wpl84TBq2OEKOEmr+0nOVG+1rGosQ1QH9E8yu85grtD9fC72jzjIpLRfnkFq0zhccUPveToYUWFjRQmEqBgGmnUcoCpol/zLGZEWl6h0oxjM1VXN6UKmuIMYSt0Txc6lGJdrH33C3xMJRF36onUIaN55aYvPksrdCnG4u8MilKwDj6uS1PHyed3wQYVItn8JxZct13ku3ejWHY+F6NsWvf5aQ0l3TvTeFxI+oa/FY4957N4PNMWGSTSpDC6CzXjlWrV3Hs6DFCEfapzBJErAuLXSrJMb/B9evXWbd+HYlEnImWw3aVoEFIXUKSymAXMS5Ik0NS75RH23Ez4su1JwQx2yKfy6FWIKVMT08zNjaGaZps2LCBcqXCW2+9yYULF5BQMC2LdDpFtVxhi0qw1sghwM2Wy9tvvcXExDh/5Vf+yoqJQ7sLrBU1aCyOEMGVEDuSI3nvvffoUhZxMXhOSrSiZ7wXi80qxuG3f8C6PptdKwje1idu89/+03+ib83TJFKrl/zdaU4wOfICv/k3/gaJgQH+87/938h07SXXvdSk1XNLjF//Pr/0y7/Mrp07l/x9cVSqFc6dPae90rq62L59O7ZtMzY2xujoKBOTExw5fISvGYVly8Q1Ap4JS/zar/0aW7Yszy0EGEjFeendNzl65AhBMPcNXTU0xNd/8RdJBg4EK5c2/6Jx+noK1m9gQ0SKrx09hX/z0k9sfz+J8Fs/+vX5LDH6FMbI6Cj/9b/+V+x4H/3DXyCe7KdSPEt56jAPzuN7gB78tqsk43gUS+c7iVEs3kPPqseYvPk871LrfF1vioODFo8L5qki37ECCnCHSnBJHLoxyWJwA487VJwNKs64aD8yA8UM/rJEXYBRPGLxLpQyKc+cwGSO9zI/FLqd/AOpLRCyjKP1Tqa9Or5XJZXbSKk1xTFpcJea3/avDVUbhPT03x+hOEOkc5uYGn2Fd1vTfI08ptLHeySs4yI8GpWFQog+ccsPDG0Eod3JZmPwBFl+QBHDqZBxdUK00cgRivBaWGVm7E0CX3eQ3UOSidDnavEUflTeS6OoIp1risCrUmFSvEj2YGHUItPaHCYmOoEt48MyYnmCUCZgrYpxt0ozIdrI9tWwwh6VYq2KEaIFFNvWLx/K3LPSi0VP9AkJOuU+GBeXnmWSxxI+btRpBdBQgmUXyBa2Mlm6wKhbQimTZGYtXalBihMfMCEeg1ESPIvPBRxS+a2d0kl5+iiWhDwReei1b88svu6wc4vMjr+nf1Ymyex68r1z7eaWnaFr4D4uTnzIrPhsUnFiGNyOhEpjKHwzSbZnN6HfojJ7grtVWh+LNDst8ntUihnxGMXj4IcHOXvuLKBJ8MvFsIpxTBpcu36d1WtW4zoOWQzOSYs8Jt3KoiYBp2mSwWBEXGpqjs93WGpMqUAnpWacMPRoOS4vv/wyo7dGse0YruvQ09PL5s2befPNN7l+7Vpn/4ZpggimnSHfewDbzuG2ZqiWzmIpgy0q0dlXn7LpF4t3z53n3//7f4/nOqTTae7cu499+/YSj+lnK5/LMzQ4yOWJGRwRrkiLKgE2BmtVjDQGroRs37aNUEIuX7lMRgzO02SjijNEDA/hCi2OSoOUMrl46dKKidHQqiH6+vupzJ4knhxciAJJSGX2BIWuLtatXcvExARBEJBIDRH4DeqVK/heBcOIkcpuIJboxYqlPtanEjQS9OJLL3H4o48wgJhh0AwCnnvuObKZDDOzswsE5RejfO1oyyT4vr/s39thmiZf+uKXeOzRx7hy9Sq+7zM4MMDQ0NLuvJ9E9CYdTl9PMVLYw7Ztm9gIWKbCsj49qYPdWFmuYKX49Jzdz0GEEnLxwkWOHz9GtVwhk8uyZ+8+tm3duqC+/9qrr2LFCvSufrrzMXCbk+RXLFfBOmKMOzNI6HUGlXiiD9vOssp32KaSvBSWKSNU8Mh230kys1b7PRXPLqumDXNWELMEzEZlmKIE/ECKNOYVtxRwaBnxyevicFtcursOAFpKIB61kS8XbcKtNy8xuiQtJvEInFmKE1rzxzBsLoYtbkUt+wo9wNcISefuIJVdP3dsyqLQt5+Jm8/zXWaxRKtTm8AjKtspsXVjcYEWFZYiKgCjuCRQeCJUVUAWgzE8mghPqGxH8XkWn3ciJWW7fhtLBA+4gcuDKsNdpPCUFhN8VSrco9Id/hBKI1MXpMV6FV8A00uU+NkRpwcUGQwui76/i5PSa+LQQlhDjDgGa1WcHixekDKHpM4hWYy1C71YdCnNlZnCZzrCmvarFA7CMWlwPlJknt8tFiAclQZag1oLIo6LR1dhK5nCdjKF7Sz0vhAqs6c47jXYRMgUHjfFJZbo6SQ2gV+jWbvJAZVeUNps36vdKsnRoEG+dz8iPpWZE0u87wAy+W0YZoKZ228yHRG2k9GEoiQ+t8ImpcmD+n6h2KTibCbBPSqz4JhvYzIiHi+99FJn23PJ9MJoz/0TiQTnz18gFKgR8oDKRMa5ChRM4/FWWI3enzp9yuIGLlXRnWOFvruxYwUk9KiWzlOZPsLZM2cpGBZJUVxWF3j3nXcwzDg9Q4+QSA8TBg4TN5/FimfpX/P5COmDRHoN6fwdTN58jiNBg0fnIVTDKk6XNKnPzLJJJajWirz80ksc+egjfvNv/I2OyOCDDz/Md7/zHSYirtk6FadJyLVIn2zdunX09/cThDqpaxDyhJHrkN8B1hPjKA0uSAtnHjF8cSgUX/rSl/ijP/ojpkZfJFPYiR3L43lVaqUzuK0pfukbv45SqiOaWytfoF65jCmQUxYNQqrFMyTTqwl854eK6+qk6BB7SLFZxbHFYEZ5vO5WaM2WeFhlWa1sivi8JBVuixu9iwvjtmi688DAwMfurx2pVIrdH2MV9ZOM3qQDjsP5sQz1dV9nQ3htRd7XJzHqtT9DzXBRfJYYfULC8zz++3//71y/fj0S0VNMTUxy8eIl1g4P81f/2l8jFotRrpS5ceMG3YMPEwYt6uXL+F4FpzmxDCYwF8vrDSkMM4bpu53OB0HoH/48sYS2RSAr1IpnmRCPVcvMfieiF/xhlSGnTEbE5Yw0MVE8pXL0KYs6IUfCOrfw+NOwyIb2rFxcpvBJ5zaRzm0GtHp0g5AGwbIQ9JT4zPfmuiQtDkudTSrOdpUki/ZOOy1NRtE8k/MRKTsECv33LhC6bEcs0QfKICZa+8lFuFulF5xzLLqKh8I6jy3imEyIxxVxUMALkd5PItrOEFan/NYk5M2wQhqDJ42MTrAUnIk682LU2amSpDC5TD2yv1h4Z+9UKSbE48WwzBaVoB+bBiEXpcksAQZwROrERUXiiVp08k6VWtKVZgIHpcaHUuuQxu9SKQ5KnS0qQS8WR6SOj/CYkZsrNUaD9hthBR9NUu5CP7e+MnlRKmwmRi/6/l/GpRb1HYbRNQStPj3/eZz/36aZoOzVOCJ1TXpPraJ39ROdgdxpaq7K2mWeyxDp8MPK00c6W6+XLxJP9nW20Y5UZj1Fw2anWNooWeANasyqgHRuM4nUKmrlS6jGeOddakV2MskokW+XhL+o8oQIL0mF6+Isi7ZeFwelYO/ePRw+fKRTrl6su9WLzb7ofsyYIdUYNJoBdryL3lVPdLg1yrBo1W8SUwYPqYy+T0qXxE+rBueCFmBgGDZua4owaFHofXzJdTCtFNmePdyeeJ+GWvgO9iubcfG4M9ImqxDwRrHE97/3Pf7aX//rAExMTGAoxWNkF1i/7FJJ3pAqxdlZwjDENEwsw2CNWAuSova9361SXJbWD0VU1q9bx6/88i/z7HPPMTP+VgTVKFLpFL/4jW+wedMmALq6u8jmclTLl9imEuw0ksSijsmbuBys30YQTNPkyJEjrFmzZknSUqlWOPzRR9yJ1n8LEBxCbkf8xqfmdVd2RyjbSWnQr+wFk5ImIadUiw3r13+sdMEnLWyn3kGPPk3RrFd++EKL4rPE6BMSzz3/HDdu3ADATPRhJbrxnSJeY5yR0VGeeeYZfumXfol6XZdd3NYUs+PvogwDO9aFSEiJYNn2eYCbuLpcNV+B12/gOLMUIk8zUYpkes1cUgTEEj3EYgVOuFX6lL2A8+ESckqa9GGxJvqg71Tak+uVsEKFgD5s0pg8YuT4MKxxDYdL0kKhOsrBYRjgNKfw/TpK6T2clCb3qTTzB8smIRekyVoVw8boaCxtVPFo9q6jC4uHVIa3qDIuHo+rLKZSvBxWiMWX7+AIAwckRM9PNdJ1cZHw4AQeJtrw9U/DIt2YmErRkpAZAhSwhQSrjRi+aGuTEdwIXdLbuBwJbz5m5BZ0muyMSn4npdHRzmkhrFUxFie1CQyeNvK8E1Y5I01OM9eN149Fn7IZFTeyJtFWHGO4vD+vDGahB+JZ8WkScodKMIvPSWl0kJ5+rMiTTtirUkta4nux2aNS2jNPPPYYKUwRgtDDTg1ysTXD+bAFkZM9rWmQgJYyuKECEEWrcZtUdgO+W8Z1iyhlkUgOIBLgOrN0Y7JKxbgkDkHQXESaXT4E4X2pMSpuJ9lQaP7Uuep1pvw6fWs+v2BbbmuaMPToUQkSGJyiwSwB+d67cBpjVKY+QgA/Eqq8Li7TUZOzgWKDioHojsWc0t2PXWJyXOoUlDlv8NfCn2ekyabNm0kmksRiMQRNTgedcDUjHlcak3UqzkGps3HzJh577HH+4+//PpnC9gWEY6c5hdOc4tFFPDkL3Q1WImBm9gSp7DrtRacM4sm593zB8xWJYVYkIDXvGtUJFrz/OUz2SJIPrl5lenqarq4ujnz0EZuJL/HDi2FwQKV5qVrmlVdeoaenBz8MlyzXDhtFr7J+qBlrqVTiueefp9XyyBZ2Yse78N0SjeolXnjxRYaGhjrJhxKJOibnuGS6YzLGdXEYw+PFF1/sbDufy/HNX/s1Bge15tC5c+c7JPx3pdpRrTfQ927xRO4eI8NrYZnnwhKbVJw8FiV8rimXWCrFV3/hFz723D6J0UaPPk3huY0fvtCi+Cwx+gREo9Hg5ImTgEGmcAdOcwK3chXTSpIpbKdeucTpM2f43Oc/RzaCrGul82S7d3UUTbVX1rf5IKzxmJGd12GjRerml6tAK9kWJw9iCmww5map85MiHYquoYeZGnmBF8ISW1WCPCZFAi5KCx/hASO3YI1ebFYpmyvizJWAgD1GimuhQx8WdxlpLkmLkoQ0a9dp1a7PHRu6zNMgZAuJjlbQOWnhoMXtjlKnJJqzslgcUB+1YjtJxiL17m4sUsqkVjqvB+l5EYYukyMvAHqQ0/5XOtF8M6xwwNAEWl+07UI6sl2YwMcUbSqqgH0qxdb2sSitIdVGgkT0B/WytFgb2bUsjh0qwZi4TON3bB90e/xSa5NE1LlUEp8vGAVOh9rQdxIfEa3hFEfxrlRxEZ5QeSoqWKB8baH4gZRYo2Kd496mdMcUaFTuWqRZs24Frsw6FeeINLSa+DxOlNMYxzDjJNKrCbw6TgdpEVZv+ibKsJkceYF6+TJeawbXmSM3G4aFaedAQhooLkoLG0XdKVItniEbqYPr+6i4Ie4CVGZcvCUefABdShsVv9qcZGb8HUwzgWHGSWbWUpo6REKZDCmt83JRHMxYhtLUYbqUzSAmjoRcAw5LHUOZnSQzRLiK1lbqjyQhAJ5QOV6UMq+GFXoiYvis+JQIGBgY4Fe/+asA9Pfrd64pIaeocmsekbtX2dxJAgWsW7sOIyI+L9ZmatVHiEfHvzQUG4kz5hQJfO3dh4SR999SNCuINJeseSTrKgG3xOMulVqw7LCK8YHAufPnGRwcpNFsskYt/B60oxuLJIoPP/xw7rgX9JLOD6GlIJn6+Pb5Z555BseB/rW/QOg3CfwGycxaMoUdTN16kW9/+9vs3LmTer1OpVpln8qy+F06LNr3b5tKsFlp8dFxcTlRqfF//af/xP/77/wd+np7aTWbmEp1EN99KkUKgw+ktqzIaBaTzxl53girXMAhlBaJeJx9++7hgQceIJvNLlnns/hkxGeJ0ScgLl66hIhgWDFq5YukMmtJZTfgOSVq5QtakM73OX/uPHcfuBvDNIknV1PonVN0NYwYvas/x/ToS3w/LLJOxSPDQZ9SxJto1kYREcLAoV6+qLWFVKZDELRQBH5tyfHF4j30r/0KkyMvcDSYy74zGHzOyC/7UejG4goL+QG65ABj+DwXalmBdmt/X+T+7iAdPsKkeEzgMf/bqYAm2metzdPIrsB/ykWDl4ugUGwixqnqNUwrRa57N4aZIAw9Jm48A16Ve1WadSqOCUwqn6NhnQl8nglLHQ8q0MjVPpViU+QxV8TneNjguDToVRY982bs21WS89LiorS4iJ5dLudPJQgfSo1JfFIYdCuTsgRUCXk5LPOEkV9QumtFvA1Bf4ATyiAU4VGV5Zw0NUk62t9labEtEr+cry9zPUo+189PHrDYphKcliavyBwEvZL1S/vKl9GWLxWE/SqFieJc0KRVv42JbrvfTILnpUy9coVMYSuOM4tCsJ0S+1WGoej+X5EW56NEqT1wth3lSlOHqVeuksquRyRAKZPj0qBbzRHBr+BQiOQSFkcfNkPYjFevk1UWTQmozMyJ8n2bkpZFQMCtcJ/KdCQDfCVcD2cRoBeDLSpJSplMicc5HBxCZiKcTqGwlWJIbC7jMIPPrAQaw1OwdevWjoHs4OAAhqF4V2oYVpqurruIJXrxvQqV4hneaGml+XKlwrnz50EpnOYkidScqrqIRnNWuk9tpEcibhLKoFa+SK77ziXLVmZOYADvhTUU2nuujE8araw/F8KlSDn+9dde6/x6Xpr0KqtTWpxbWgiBbSrBXpXi9bDClejZXNw9OoVPOfTZvn1lVebZ2VmuXr1KtmsXM6Mv47rlufO1M4QSMjlZpjQ93fk9vqijrozPZXGWdMEOqzj9yua5UJcKf+u3fot8oYAXhvRj8aiR65zfGWlqMdFlLn0KA8Mw2L5jG1//2tcxLXPFe/RZfHLis8ToExBT7U4IEQbX/UKk4qoj7+5hYlTDuwcPHuTCxQuEQUAqu3HJduLJfgbWfY2x69/jugpQho1hZjGDFoYRYKpZihM3oqX1y3kBB4ksOmKiNXZyPXuX+Hu5zuwCawXQL/1KdgzViNsyIg4xDPojrpEPpPNbSaRXMXP7DQSWzOzvVEneDKsU8VmLLjPYKKbxqRBwr8poOQEJuECLIsES8i1AMUoIT0iDM9LstJnXimeplc5hW2k8v45IyGMqu0BLpx+bJ4wcz4YlujDpUlaHrHzXIlPHLiweNbK8FJY5LU0enTdrN9Dq3AHCw0aWt0Nd3luMAp2UJjfE5T6VYX27fKZ02eVdqfJmWOFxI4eBRkTaXmgB8JZUqEhAC8FHeNLI40QeWyLCy1LhlbDMXpWexzFqcToqS/YsunZrVIxT0iTfexeGEaM4+SEj4rBlGWTuZoRuzCf99yqbbqwFaGE74qJo1m+Rym2E0MNGLRCfjAP7VJoUBkelQQ6DLxoFpkSX+abx8ZxZqo4WeGwPti+HZQaUTQGTcfEi3tHyA1CPsiiKz5dVnlDpRPwjqYMySOW3gDKpF8+wRsUW6Chdi3SK1qv4AnHNHmWxjjgvSpkWISUJ6FIWJ6TBZRxiyX48p6gbH8wYtl3g7bffJplKcd+995JMJEmnMzQd6F/7FYwIwY0leklm1jN961VazTGOHDoUySMItdI5MvnNmJZGjux4D7PiR6T/pe/kGB6GMilNH9blytQQ5eljGEaMdH4zSlmEocvM2Ju06rdJYbBO6Xfvhri0EPaopOZpERDD4GhY56pyQSAW8bac5gS3I/7bF438goRnTDwchOHo+d5npHklLPOOVLlbpaNGAk3K/1A1GBocZPPmTcveQ4CJSA+pWjxDv7LYqbIUlEVZAs76zUgWIsGdpChJwIuUGZeFemXXxCGOWtYMO47BHSrBmdu38QMfyzQRYJ+RXpD0bVRxjkqDWbRkxPy4KS4l8fjqvrs+VZ1cP+/x2Z36BERbbTXfu3dBUgRgxfIUeu9idvw9/NkSldkKCiiOv4NhWHr2t2D5LMqwCEIPQh/8JoODg5SKRWrV6rwlBWXEmA4jroSeyGKIMDXyAoX++0ikhhARqsXTlKePkkB1lIA9hEl8ZmSpMGKNgBFxCYF3I+QiJQap6DNp2ZnIZ0wTZ4cXfZRsDO41MjwXlhhUsQ6i4SO8HJa5jsOjKscoDhcFTocNHjKyC2ZiIcJpaWIAXZgopahIgI9wIHJer/sek6IIWb4EEYuIz1fE4XGV50IkhLiclICBlhL4SOq4KlzQpluPvN8OS50EBpO4C8qMfoSAbVMJNqg4LiEtAuIYrFYx9pLiqDT4TjiLgU6GujDpwWLKNJhN9hN4VZRb5iB1QtHXNY5BTQUUxGIST3fCzUPf7lBx7pqnUN0OP1ooluglFu+hWjzLSU9zzPKYuAg2ihoBx6VBBoMnjTy3xGVSqtQk6Bhszg+XEBchTqitUtByD8tJONwRoVaNSO5hQNncQ5qXpEKAIEoTZxEhVAa5rl2UW5MU/QaBH1ANgxVVfeuEnVJmWzHcj7rmTCtN221v7SLV7duiSbZ7FomggkZDt6oEJ6RBWXzSyuCCOKBMvOZk57JL4OIFkyhl8fZbb3Pg7rupVqtUq1W6Bx/qJEXtUMog37ef1o1neEBlKIjFM5QQCRm/8QzZ7t0kkoMow0Qpg8NhnUeM7IKBexqvg7BmapoqXBJff0cmP6QyfQTTSuN5FURCtqjEApmLvSrFEalzQhqcknmOe0phx/L0Dj2OFTm0h4Gjt1m9xomwwT5DP19FfA5JnR4seqNhpxuLh1WWd6XKD6RE3rDxCGlIwOqhVXz1F36Bjw59RLPZpFAosGPHjgVdY5apz7JfWTyusggaWexVFo+R5W3RRtJ3qlQHmj4flWWvRNprPkIBawli1Y48OhmqVqvMzM4SN4wlk7CNKsE1cXg9rLBDJVmjYtqiRxwu4LB9x3Y2bNyw7PY/i09mfJYYfQKirXybzKxb9u/69/fYgfaoaqmQg1Jj7Pbr9Aw9Rhg6mFYaO97N9K1XkNAjjiJumFRCn4nxcXKY3KuynXLFZWlxOmxixfJ0DzyIMiymbj7HHdiMeQ2mRl/GMCxEQkRC7IgMnEaRVyYl8WmgNXV6xKIeDSYZFJWIiHwfafLKwiXkKk5HBK/dJQQwvIIFRQ6TPCbTeKyP+u0sFFui5MNRIa1on6N4vCVVtpMkF/E4zkiTWXwenYcEBUo4JDUOS50vGnlyWLxFJUoWlv8wZqJEoJ1RJDCWlAjmltWDrYt0zmoSjzIBFtCQkFLU3n5I6p1W3hI+HkI/9gJSJ8CQstmOTqDWqRjdWDrpEHhNKuR7dJed5xQZv/GnGPFuPnBmOYxBDIO6RMalKklv4LNBxTpt9b3Yyw4I18RBoZgafRkAO17AQ/FiWO6UPtv/toGnlB4UC8pECVyQVgcVmB9tZWjNDdJqT4UVEEcDrVg+jc+LYYk0BpMqwLAzdPffRyK9CpGQZu0GxcmDNBu3yHXtIgwcKsUzTEaI4+JBrEHATXHYuYgrszFSDNcq420z4oXhozvQluuWBLQtjkCJgKY4+gwlYL2Ks0MltCUIAaekyS1xaTZ9RkZGOl55K5GhY/EelNKl3L5IUFNCjzwm5akjlOc9mxN4PBuW2KziJDGYwOdGpMf0eZXvqNyXlM87oVaUWh+aeF6TGVG0MBZpf+ky6l0qzU1xyWCyWyU5LHVqEtK76kkse44rY5hxugcfxmmOc95vciN0CREchDwGDxsLOT5Dyma9xLiiPMqhB0qxZniYbDbLf/yPv48SiBsmzdDnhRde4Atf/CL79u4FwLJtBNhInENS50ZkD2SguW8bVJz3pMak+BSi8/YQjkgDCyhEZOgKwYqm1W0leEMZmIaBL0KgZME3wELxhJHjvbDGCWlwIkJz4/EYDx54kMcef+yz8tmnLD5LjD4BkWy7O6+gDt02vGy/WgkMHlJZ/jQsMn379c5yCjraO6uUZhy8p6pMisdTRq6DYiTQqtVJDA65ZQzDwo53Y5hxGoHPl1SOSeUzLVqU8UKUgjyhcnNdJArGxOUtqTJNxJ0AWspARH8EP6JOEI0uQ9iswebWIuuOAOG2uFymRVl0+W2NinGHShBGXI35kY94Q615Okb7VYpL4vB6J8nRH8dHF5XHTBT3qgzjUuSitLhbZSIeltf5MIbRmbSFHGfQM+tXIwuHOiHOPMRhfsygpQQSURvwqLgcirR7cpg8aeQJ0FYqJ6QZedrN2Te+LzViKO5SKQrKoioBF6TFW2ikLx4RrkfF5aLoEk1bsNOOd5HKbqRRu67JyUqBhOQMC6cxhtecYt88f7dJPI5InZQyOl1Mgm7jvywOdryLXPceIKRWuoAiJB4lpgUsygRclCYB4CghCaQ7yawedO80UhSwcAi5GJXuADL5LRiGZlqtZEESIlQIsNFcsVviEmAyNPzFjmWKUiap7EYsO8vEzeeYGXuLdspmAm+EFfardJSk6VKONhheWjqxo4Q31bOHXPcexq9/lxu+w7p5IhjdymImajhYTmerHr2/52h1ENh22a399nZh8fC8jslischA1PXke3UseylxOQiaiITElEEchY2iT1k8qnK4hNTQE5csBlfE4ZDUOa0cwjAkZtvYnuKrqhDJgOgoYPGIkeX5sExeWaxXcZ6VImuWSWhBJ6rDKsa4eDTQk5JYcmBBUtSOavFURPK2kWQfgVcHt4yhjAVvjSC8IzVu4ZJMryOZXkMYukxMXmJ0ZIS1KsY9Ko0tBnUVcNpr8oM//VMS8Tjbt2+n2dTP01HR9jE7VbLzbF6SFrfQE7EGAQPYpFA00M0aO1USC0VZfJ6XMpeltaRU7EQdiAr4N//m33R+v46zpExsY2AqRXehiy9/9auYhsHQqiFi9qdI8Oez6MRnidEnIIpFzZeoV64sS4ZsVK4CC4XiTLTH2BVxeEhlqUddYu2BRkWD/C1x2aGSyyqwblBxjkuTSvEsmcIWRClGxKWkAvqx6Vc2F6PB7E6VWtJaW4p0abowWK90qnATjylCylHyYCh9rEXxcaKkow+LnSrJW1LlhDRoIHRhsiZCM85Lk4vSwkXYt6hFfCbiDbW1kixgWny+pPLMqgBHQo5KnT4sCsrilDSYiFCafiw2qzjDKsZEmywp4CAclTo1QsaiRCUXHc/1iODsEGKiO9BOL2MfoaUEdLniGSkTIHgSahSgNUtJfJ4LS6SVwUbibCTGVVx6MGkRUkdIRHyb9r3qVzbrVZzXwwoz+FyQFhdkjtCuW7bnXuF8310066NUi6dBKQxlEoY+MWXwuKEp6tfFwQT2kuJVKrwWVihgklUm01Hrfjw1SP+az3fOr1Y6T0aZPK3mJAZWA5tVnNfCCofCOp8zNGp0t5HmtbDCOB63wjI2qlOaA1CGTa18EcPQ/JVL0mIzCaYjZM2MEuMJ8XTHIQn2qDQ/oAS5TQt85NoRS/RFHBeT/jVfYPTiHxBGR/++1DCiJCVATwocZEE5DXSpx0eIJbTjea5nL7fG3+MizYiUq0ul56XF1WUGUYme24W/wTYSLEYj53dMPvfssxiGgWlZ1EpnSaQGlyxfK57DQLFa2TQRPIQNUcIWw6B73nlsUnFO0uSu++7jySef5P/3z/85O1ViQVLUeV4i5fJr4rBexSPa+EpdYnQmBm3hT6M1Ra18cYFJcr1yhfL0MXI9e8h1746eT+1ZNn37dd4NazwZda1dEodb4tK7+kmS6eHONrJd25kdf4/RylVW4wKKtDK4hzQtJbzx+uts276NdDqNQkskPGXkl302PQIS6MYELWaqJSY610BZdInJkUgRf5NKEEfb1pyQRqRDZrNBxXHQ8iBHouS6LaXhEXJGmoyKy9cf+7I2EP4xxuzsLAcPHeLiuXP4vs/A0BAHDhxgy9YtnyFRP6H4LDH6GcXly5c5ePAgIzdv4gc6manMHCcW717AG2o1blOZPgqAt2gb2q8KBpUN0cv7TljloNT4uurCR/tlLVTDnqfUKx6BArdymUblcmeJV8IKu1WSIexOIrJ+0Qy7iM9xabBDJdmjkp1tbiHJBZoclQaS6CKZHibwqjSq1/RLLCFZDIZUDFOggXCvSke8Hb2NfSoVCQfqmV47RsTheITA1ETzA3y0RpMnVXaQJBNxoJoIPwhLWq8lswZQzNRHORe2GMLq2HY4CDEUl8ShC5P9KoWNtoRoCyACfEkVOEOTM1HS1iDgDhIklcFEJCXQHlLswlbiRpxYopfy1CFEfAaxO2W+96h1CM93GWlmQ58jNNitUksSWBPFHiPVSWCSGFrlG5gZe4tq6TyJ1CCBX6dRvQ7RVRwUi0CESSAm+oM+xWKxPH29y4ZBBUEwQMIo+dDPieeWcJqT3K0yGKiOjEIyQhDuVCnelmqHeNqHzaNK8zv0MyudPSmgEIaUp491rr+L8AMpEqIHuACt3g364zSCS1W0rk82alEX8fHcMgoDO5YHZWDZOXyv2nnCLeCrRhc1go4Iaa+y6MbimbDERWlxX6R9FUalRctKkow8t9K5zbitWY6UznFBWgwpm2aECB0Vrek+3+D4nbBKOWLf2IkeJPTx3TIvUeUJySyxb2kjn7FQsMOQKiHN2gizE++T79mLaaUJA4da6TyV2ZPsjCY39Wjis5IyPCgsgSAIUIYiCDXStFLEI8T0LanQQrgpLnct4zEYRH9bq2LcrzJUCDgnLa5NvA8QJUdCtXiKZGaYfM++BccUTw7SPfAgk7ff4DotBlSMs7RIZtYtSIp0GBT67qFevcYHbfV10d+xDSrOielppqamSSaTCLBbpZYguDGMzrM5hwqzBOmpEVAkYACLy+Jwbt7EoydCOzPK7KhXbyDGc1LmfalxXJmkIlpBgPDU00+z586lE9u/SFy9do1v/ff/jhkKa8UmhmL82k2+deUK++/ez5e//OXPkqOfQHyWGP0M4s033+Stt96i27DZKhZTIowR0I3B1K1XicW7sSOBR6c1Q7eymEV/xObHmHgU5iEGBop9RornwjKj4jKsYlgRWuOiCb56Vq6h9Gl84qkhurp2YFkZnNYklZmT+EGTYzQ7nU962wvjsrRIYnDnvKSoHVtVgqu4OFaKfM9eAPK9dzE18iKeX9OdVmGFAM0x2rjoYxXH4D4jwwthmYu02ESc6+JwTBoMYXPASHd4HkV83g2rjOF1ZPZB2ysk02voHnoEw9Bwdhh6FMff5VZNd+Z9Pyx2zG/vUHHunlfy2ECc9bidAV4phStCGoNdKsVpafAG1U7JZI2KsQabD6ROprAdy84yNfoShlfjS0Z+zshVaS+x9nYDoXMM/SuI3fVF6w6rGLtUCpeQw1LnhriEfp16+RLKiJEp7MBtTuI0x5kw0ERe36cG+AQ8qDKsUjHK4vOyVFCRU7qEc+VNw0zQqFzFtvNku3fhRd1fNUK+HxbxEeJoRe8jUu9oSFXmEa5LBIRotWMtEKmFQLepBFPid5KidvRGulZdUffeDXE4HA2ITULSEY7hNCcpTX1EvXyRMNT32rKSpAs7cVtTxBK9+hyUxRq0GnUhQg7nx1oV45K0SIkWCb2FS01B7+DjHX4RKLr678EwbCqzJxkVr4OmDKsYRyMuSZt7B5BIr6Z74IGIwB0ZNd9+g9e8ChnRQpltJfNUm49m2HjKQIIWBlAvX6ZevhRJSTgd0632M5AUPQzewmXVMvy8Cj41QirVSvtxY2yRzlM7dAOFRyweJ7dhA+MXLuCKlo24V2U6PBof/VuAsDviH+WxuE9lUMCNqcOkcxsJ/CaeUyLfc9eSfQEkM8Mow+aDsA7R/e3OrFl2WcOME0/2092c4SGVZUZ8TqPJ3wCtVot6TTd2rCQSOahsEJ1ctxPJxe36V8XBRvFIpMU2IR4BQl6Z5LE4LnUui9PhXVnK4Asqz/fCIrF8lt7BQbb39nHgwIEfuy6R4zh8+1vfojc0eIhMR1dqN3BFtTh0+AjDa4bZs+fTpUS9OATh5s0Rxm7fxjRNNm/eTFdX18/0mD5LjH7Kce36dd566y3uVCl2kgClS17fkVnSotipMlx1azTcKgUUG1WWEXFooCJkKNqOtJjC5yEWCr3lsIihmMBjLXHWE+M8LURgtbLZTIImIWekRSq3kZ7Bh2knA3a8QKtxm2b1BqadQUkIEuIHLS6Lw/Z55YNZ8Ts8pqWhWI3NheacIaNppSn038vUrVfx0UiRsLydA+jELYuxgMyYRPFwVBJyCVGoqFU+x3NhifUqxgbinJEmUwb0DD26QOnbMGy6hx6mdXWMeOCxScW5IDot2btIZRtgtYqxSmwmI6yubTOxStmsVwVK6C63DCYJDM5GpRTDTOC5RVqNMR5QmSXu9oMqxjaSnJUmKQz9wRMiJGbp7L6dOLX/HcPgfpVhWko0A5eugft0iap0HteZpnvwYVLZDShl4HsVilMf4dRGSCmdLIxG5yOhT6awnWzXDiw7g9uaoTxzjFb9FuXZ45RnjnWO4YQ0uEPF2RHZlTQJOSdNTkbnPCoudQJGxe145p2RJjEUHoKF7gjqVxb3kyGO4qho/OOxeZowJoqNKoGN1vXZRZLdKsW5sMmJ+ihu/RZbVZw1RpJA4HrgcHX6MABd/ffrbdgplKfJzyPiai0sNAKQRHvH+cCZeYrh6fz2BbpARH9pJ2AOIWH0DD6osuxVuvPSQ0sfOFaC3lULLTZMM4ESbUvSqyyyEdn3mDQwActKM7jhl1EKWo1xZm69BuKTxSQe+PgYlAgwlMk7UuNBSdOI0sor4jBMfME3wUc4FGq+zYULF/j3//v/DgpuiceouAsMbbWvXh0fWD88zK/+6q/yz//ZPyMfwIi4jEmR1SrW4cqFwAMqs0QKYLtKcjUsMTvxQUd00jBWUqs2MA0bI/R1MwJCGLgrLAsSOCSiBHdA2fSR41WpMItPoZDHdd3OvVmO89doc74iFKjNM5vfRdsgJI/ZSZxWL/oedWPh0cKLkGXQXpAWilKxRKlY4qJxkWKpyFNPPkU+n19yHM1Wk7Nnz1Gr1chmMuzYsYNEYmmiujhOnDyJ67ncq7qWIISbVIJR5XHwww8/1YnR5OQkf/zd7zI5NYWp5jie27dv52tf+1rHoPinHZ8lRj/lOHTwIAXD1klR9LArBaYobuJhRYqqGUxqBJyTJtdxsYFnwxKgSwW6Hh5fImKny0tCu6k2oQxE2oRsvew1cQCJ4O65F65RvU6zqtGUmN9kDRYBcBPVcVdvQ9FmhBqsFB6yxL4hkV6NUiYiIQ0lHaK0G4kVViKOybCK0YeFoMuFBUym8NlAnIvS4pK0qEXn14vFdpWkHwsfYVDFOEyTVHb9gqSoHUpZpLIbqJUvcUFaeGhi+EqliVUqFiFRQlP04HhGmuxTaQrzXp9W5FUGIKFLq34bBcuKDIJOCM9Ik6YKMUN9Fy5Kk/vV0lnnxYgAmp83KKmIY3YmbDIz9nbn956hx0hl1xMGLYLQx7TS9A49zviNP+VDt8aAsjrIWqZrB11993TWjSV66Vv1JBOjL+K2pnT5RhTHabBOxbh7nu1KEoO7ItmDq+IwgcuEaBuH7dicj0qL7X98dAfa4+Qwog9gTULujMQgF8caFSMpCkfph8TU8j08YWQX+LUNYNOLxSGpdywyYsl+RtzLTERE4QImBlr4UaF5RvtVmh5lU4sI7uOlcyjDptA7h3a4rRlqZW0D0W6L8NDij2lMtkUThdO0yOXuWOI7Njv2Dnbg8JRRWGDTU1I+r4YVDDuD25oEhGb1OkiwsMEB7Uf2mlRwEN5mofjqG1JhtdgMKJsmIVfF6byTEoTMzGiRTAN4V6qsJsZqbDx0K3kxSmBHR0f4N//qXyEIs4Q8rrLcxmNKfLxIe+xplaN3GWQmFyUkjcoVfUuU1qmKp5a6v3tOEd9v0ItFNyYX0UKz2a7tLMakXWcG1ymyat4zZ6DYphK8JzUuXLhIKpUiHotx2XO4a9G1rxDwimjBxzwmJtro+nykPN+mF+hSom4eWW6SVyXEYK50OSIO70mNbiy2qgRpZTAlPhfPnOP/unqNv/W3/zaFQkHfA4T333ufN998gyAISBgmrTDgxRde4PEnnuD+++9fsr/5MTIyQq+yVzTwXiM2h8bHI42lT99QXi6X+YM/+C8knIDHVY5BZeGjzb6Pnb/At+r/g9/4zd9EqeW/zT/J+PRdzU953BodZa1YOhuKYjpq196mElwVh6viYKEHEws6H+a+SE12JGp778ZkMcpxNSIAi8BtXC5Ki7UqtsAMtUaAZSaWdJRUZk8BuhS2b17L7n4lvCfVTot5t7LwEGbExVFLZ2sBwg1c4pmFTuYimtxpxbuxUkMYpbOcjVSaBf0BcxAuSItuTGqEEYcpxf8MZ7iNR1kC1qkYe4jhI1zH4R2pkp9nxeAgxD7GU2txwubOKbMsCTciXbeQyJBVIx8NtL9YCs0xOi3NzqB0++p39H5W3Orc30ToyA5cF5cEujwVx8CLurnaM97FJr5tNeNfMbo5InVuGAplWEyOPN8xWDUMm3hqkMCrUiXEmpfLuo0xAr+xkNCsDNLZjbjNSUbQbfsCc1Yni2KbSnBFHNKY9CkbEz1DH1Yx9qgUWUw8Qi6LwwlpcIwG+9EJVQgdv7yl10f7hLWJ29fEYTX2Er820K32Z2hFBrEDhGFAEF2f+WXMGgEfhjWK+PQr7eGXi2w03pcaI7MnSaRWYVkpGrXrVKaPovn5bXEC/U6OiLvQ7FV06Wd+eG6JVnOcB1RmiXdhAYvdKsnR5kTHikahu6oWl4VymOwnxXvUOiTg96VGKtJNuhZd1zYZ2ERxXjSP7iGV5TtSRJRJQrR20ShuJ8k2UYhpQ8tltWNwQ6ABHJQ6+1WavUaKogS8JOUVJ0GlKLlqd8IelwYXS+dIZdd3SpugeWHFyQ+JK6NDvr4YOnhumdnx9yj03dO5hq4zw+ztN8gqawHKBVoSAoHnn3tOXzcFF0Qn6ndEnC9HQl6RMnF0G327E7MoPq9JhRfDEpsig+QQ/f5dF2eRqjcdNHCdimNE/LePpM5q7AW6ab3KZgNxXm5WeOXVV/iVX/4VAA4ePMSrr77KNpVgu8qREIOmCjkXNHn55ZexbJsDd9/NSqGU+pip51yy/rNIHH4c8cEHHxA6Ho+TI67a7gsaDUuJwZs3bnD16lU2bVpZ5PMnFZ8lRj/lMAyDYBEJtk3q3Kl02eCWuDSjAfm4NBjA5kEj0/E/u1vpLqqPpEEqMtlsC4q1eUFjeJ0Oq9WLuAgxFEHoEIZuh38D4LslssqM/JAW6nQ8oDL8iRQ7M8m2tepbYZWHjEyH8+MQ8qHUcIHuRS72zdpNkJCewYew410IQrF4hg0qxl6V7rS5j4nH+6LtCDzRcEoqKis8orIL4O6NxDlBk7PSpEesqNMspFm9QVffvfM4I+0QmtUbGBLyy0YPF6XJEWlQwl+AAIEunV0RhxD4Xqi5Ngp9LNPic3OeZUYXJoutCgVdYlqrlsLBN8XtpLXtD5yBRocuSosUBs2ofGNG278SaeP0oAeMUXGJo7DQJHylDKZvvUqvstmnMiTQ3TWXaiPYKJ5SefJRl9CE+LzvVpi+9QoD675Ke8beqF6lNHUQC0W3sqhFz2ZmhQQmHd33KgGB6H/3YfGgynQGDhujU4Y9IQ22qwRJdOv5ND5rWXp9vCgR7cOiSkCNcIkQaDsUij5Mblaucat2Ewn1+/WYkV2gOZTB5FEjxw9CLdewLxK3VCjuVCluisvU6IvzNqwwDZtM1y4SqSGqxbO0qtf4SOqY6C4xhcKMUJJs187Oqm5URl4JMVwT8ZR2qyQmiuPSWLGsvEbFtJ6PUh1xxG0qwRaVXJKwtsVCA8BSWoIgmduM07hN06tioJ/LEgHJzDCBVyMWlGkSMKxiXJYWDiFvSxVT5lo1zkiTQbVY90qLqCZRDEXXYrdKMSVVJkeeJ5ndQCI5iO/XqZcvIH4r6o6ck1QYUjFuV67SqF4jnuwnDBxcp0hamTymsiwmgZci6ZKnVY6UMrguDidpclwanKZFFpNKRIb+vJFfkJR2KYsvk+cZKXEpsugBiMfjHHIbtETYFFkpTYjPManTJGRz9HyOiKsNlY051fN2JDDYKnGOnztHo9EgFovx9ptvslnFO88ZzCGtHsJbb7zBXfv2daxhFsfGjRs5dfIkVWM5NXPhpnJZN7wW0/jhxsqfxDh18iQbxO4kRfNjSNkUlM3JU6c+S4x+HmL9xo1cOH6CPSrVeenbiEs5cqNvz0ZPR90vD8xLikBDyvtVmtvi8bZUyYpu+W4rEoPwVVXAR3hOyouUg2CtinMsbFCZORF1HU1o6EJC1i1DpgY9uK2O2qh/0dAO9TN4vBFW+dOwRL+yUCgmI3XgbPdurNgcIuU6MxQnPyCeGsKOa2Kd72hi+b3zBlGFYpWKcT8Z3hZNqv5IanjottnFHABQ7FJJLR4YHXYcRTVoUZz+KCoVdfAZStNH8P06CvhBWKTdLfW21HhYZTqzy1ZEcK4TYsVyFPoOMDvxAaHf4BEjSwGTWQJ8EbJKi/59EFa5icu2aOZ5TRyOSJ2CshZ0Bo6JyxlpIkrx2rzkyorKkxkMdCOwQTXilEwTUDRCDGVx3q9hR4jSndHAmMTA9xtsVHHumWdXMYS2tXglrHANh71YEHE2HibNK06RZv0WyfQwnjPLzPg7rCfGASONhWISj9ekwrT4SxAr0FIJAHeRZsCweSYyGl6uLLFZxTkpDUbEZatKslHFuSQOmyMft/lxVpr4COcixEyh9WhWirqEOo0MNadnFfayQow2inVRUjl/wMpGPJMF0gLKon/tVzraQqaVwkbz2t6RKmkxSGIQEurEo3ZjTqQ1msUHLC8c2E6GT8kcz+mqOOxVK3tpzYrPu5Gm1eLr1Q4LRQqjg/AME+NG9aq2I5m3XwW06rcQCfEMi1Ysi+eWEZkras2/2jP4vBEpO3cpS3elhU1u4/FA1LHY3v8TKss7UmW8epVG5QoKhYXw9PwmBBTrVZwx8fiSynETl1JzFgWMorsqM4uQ3RDhnDTpw+qU9XaoFD3YvC4V7MxaXMNGNcYZ9FtLkDoAWxl0i248aV+HMAxZtXo1J2/f5njY0KVekc634TWp0CU6QY+hyGJSjIQzvYhjuEHF6VM2YdigVC5Tq9VotlpsNZZyjgC2qARXG2Wu37jBpo1L7Z0Adu7cwWuvvML7zTqPkOmU1CRKSCfF45sPPLDsun/WmJmZ4eTJk9QbdXLZHHfuuZNCvvAX2uafJQSh0WqSZanyvg5FWqDZWDzd/OnEZ4nRTzkSiQQthA+kpsXLojmcAs6ETdaqgOs4NEWLqOUiyHtxqOjDci4iuFoYlAkw0G39swSsiVrir4oTCdrp7SQxWI3NaOksoEhl1mHaGWrF0x977G3NkHb0YPOwyvC6VJmKlGPMWB7fLVOdPUWzdpN4sh/frejkS+m23Ub1OmHo0GqMc6daOvsCWKVsEqKoK8VILIXjOCs4h2u+02DEswA9C21Ki1rxHE5tlGRuA6BoVq/huRUs9Ed/ndLdboadwZGAF/0ymUjor0IASpFID2vJhMlDEHqkmLME6MHq5FzFyNk+RKM+IXTQnufCEquxySqTGfF127xSZAvbtRkq0Khco1Y6RyqSG6gRRkiQAjNO39DDETlY4TlFZiffRzWn6cbilrg00AnBnkWqxaBLN5tUnKviLPh7LzZ5ZdGoXu9YfyQxuFelOwOdF233jDQZUPaCZ1Hbrmj5BFspytFsvq2uXMGnKiGxCOmoR9s6IdpwN4eJjfY526YSrFIxHAm5LK0OQdxEz8brEQ9tudbsIvqaahQlwYthmcTHtqgbkQLXXDiEBAjrVYxVxPiQOunC1igpCgkChzDUWly7VZJdKsnNiHw9KBaXcJm+/QbJ7HpSmbUEvk54rq/QEXZdHAzgfpXBQnETl/PSoknI/fMmCqCRCq0ybpBAQdRiPrjMuXmE1Ak7pOyCmNyISvPbVJLVKoYnwjVaXItK8kMbfhnTTCChx9StV3GaE/RiLfBKm4kUot+cZyujgAOR8fL8MIG6BNjI/5+9/4qyLDvvO8HfPu56Fz4iIyO9q8rK8gblUL5gSBAgaKQZEXRSt3pEqVt8Enue9KS1RrO0OANxLU5LZFMUQRIkAMIVyqAcyldWZlZ6nxkZ3l7vzj1uz8M+90TciEgAaqKKlFjfU+aNe48/e3/7+/6GL2t9HA/NmHOoFvyidPBQyes0Du/KJndpSW4lgQ8IGlyTHXwpOSCUBEdRepyVbSr4TGDxcqjCPixM9og4/Zi0Ao++0cdYnvkBca/X17F7bV4NalTw2SksBjFpEnDNtZmbneWue+5m27ZxPNcll8/zwxdfpFYq0x9i1FypUcfnDVlnTjroegzDSOI4VU4FrchXLxaLsRr6X95MJb3bQrZte8u/A5iGya/+o3/En/yf/yffCcqMoaor86F33R133sGBAwdu+vsfF0EQ8IPnf8DxY8eJaTopoVOXHq+//joPPfQQTzz5xE0T9J9FCAT5bJZircPeLf4ukZSFZKyQ/8iO4cfFJ4nRxxzXrl0nlhhmxl5hTlYYQwFAJbAYtr+GMRgTFhXps4jLy0GVx9cpV3ej+9jWUc7aB0WCCSxeklXelw1OSKH0bvA4IVscJoEjJLqEeeFhGGmGtn8mohd7TpXp5jy3RQX0tXAJmJNOjwP1lOzwrmyoVXhYq5pxanhADo2U26bt3iANxIXFlHSorWM6AVv6ZKlzE8SEhqOZKuUSa7TorcKWSpH6Q5oIqfSNRjAxvQ7LpXNIYBidAJN5XDJo3C6SVPBYQsPzGmgQgboFChPlOSWQPrtch/mQhcaG69PE59WgRgqNT2lZhkLQ+pTscFy20JAs4rIsPbqU78GxJ3v0qmLxIeLJEVbnX0NDta6aYSttePxprFh/9F0zVmBw2zMsTH6D1/01/7tcyI7bKoYxuYRNBxlOriqhsKVPp3ZNgWeFoCA1HLH2nSA82xIePwyqHBIJckKnJhUxoCvy+Z5sRndnWnY4LhusdmsOEixUwh5HY4+IRSJ6XYzKOdnuqZ6AUjTfHeJGStLlZakEKe/RUgyF4Pw56XJUNqL7qaGUoeelixRbA2o3ylyAsjERwF0ihQSCQGLF+qmunqBZvYQfGigL4F3Z4BmR4+5uxUlAKtB5nybtxpQCUodxUjbJCX0dNkoyK13Oyzb7RDxqs45hMYbJ27LBBG6EranicTwULH0qrD68LetcljZ7RAwdQTtMouNoXAyT8ttRuLELtBHAU1puzR4lBK3nQ0mO6spx+kYeomOv0mkvsV/EcIALoXL0sNApoHNVdrhfpLDQ0IF3ZJ1J2WFcWNFz5yP5UDaph0mwQDAkTK7IDh/IBjdCRmA3suhU8PhhUMPSNHwp8UOJghkhuRFUo+9aKMzNHC5jQtmBXJBtLsg2Q5jUPSUBYMb6WLSLbLT5OC3b1Al4Zv21QDHrXgtqnDh2nCeffJJEXFVhx8e38fLLL3Pm9Gk8b61+No9H38gjpDK7QGjKhqZ0iuvl8+RyWfr6CrTCSseq9HqYg93oVlp/Ei39m9/6Jn6gRFdXAhdkgNAKaG6FUydPc88997JtbCOb8ifHy6+8wonjx7lbJBmVJi5gSsE0Hd566y0SySQP/gRw+N827rz7Ht547TUOhknz+rgqO7Skx5133PmRHsPN4pPE6GOOVqtFLLmX/tFHaVQvs9JewnXq4Ck11ce0DAPrWDelsIT9gWzyUA9jSTItOyTQGBUmFhp5dF6RXe1riR9WouIILkmbyyFtvxv9w5+KkiKATOEWlhvTnKLNkXVGmT6S9wM18XUTo7r0eVc2GA8F37qVhLuF0re5Ijs8IBI9btM52pyWLXIDdyE0i9rKByzibsJAgWpl1aSPYWSIJYZxOxWuY3M4xGR0z01DUAsrBgBNw1AWBMASLpYUEc5jNsQIwBr1PSYFvqvaWbtCfyUTwZx0uSDbeOG27tLSjIQWKEU8+teBgC9KpUHzuMiyLF1Oo8xrx4TFw6TVKhvlK2YjsBIDm8x/QdH8hWYSBC61boKmGar6sAFeo2kmqew+6qFastNepRO4N2XXdM+3e5+WwzaoZqbJ5w+FdP1VqpWLvChrPC0UPicf+n/dIhKsoLBf3Qyoq2B+RraJ5w9gGClqpVNcCOxNA4uLmgSfFVl8obAw+0SceanMbW8TCUaFxUzgcI42d4lkj7p0nzB5hhw/lFVeCWpYQiMAPBmgaQZBoAQ/LQSaEDSlEiC8ZQMGZ1J2KOJxd5g42CHA/Zxsc2sIeu+206orx5B+i+3SJCni+Cidmwo+3w7KKvEWgrpUFRwLwWESjAiVzE/KDpexeSWo0YdOThiUpEcVn22Y3LHBr20irAC/LxssE6MufeZxSaHxsLb27h8RSV6SFb4bVAiQUcsrgaCNZGSddpMTVsE2esaBepfPyzbN+jU03aTVmCKG4LLsYBhJEhm1lp+pT+J5yl/srGyTEToxlBL4eWmraoZQzM6FULF8BFOZU6MwUoZUk11C6HhhZVEXAlsG+MDOXTvp7+/H9wNOfvghheFPkcruo9NeJPA7VFaO4XhNdgqLe8MqG4AjAt4JGizgEgsNeNO5AyxWLnKONreF11ixJ20OiMSma2EiuEdL8WJQ5aWXXuIXvvALACQTSb7w81/gmWeeoVyu0Go2+bM/+zPyQ/eTyq7hXjQ9Rn7wXlynRhC2xsfHtzE4MMDZUpUhek1qfSTnhM3w4BCjo1vV/VScOXOGSrlCYfhTpHO9lSHfa7Fw42/43ve+yz//n//5TbexVbTtNh8cfZ9d0mIGh+O0ond6VJhsx+LtN9/kvvvu/UjZbvffdx/nzpzhlWKJgzLGNmFGLNerssPd99zN6OhmduPHEZ8kRh9z5PM5ytVVdOOOSB12Ze4VfK/JYZFYS4rC6MPgSOhu3RJ+WJqVnJHtaPJcCa0cnDBZAAWKjaPArW0kGmogHMXkhGxS03TiGyi1scQI+cF7OL9yjBuyw/YQ1D0lHZyw0lCRPlLAu7KODjywLikCtUK8W6SixGJ9MrdPxDgjW1RDJW+Aa/jsErGeBEqGq06ExtD2z6JpMZqVi3SAF4MKAYpGq66PHgHVpZFkbNcvsTL7Aqn2Kg+INFekHeGetocebMeCZpRIda/hXSLZA2TNC4NtwuTFoGvRKRkVJlmp817Q5DEtE7VkbsgOY5g8T4229DH0BIH0OBOo9lMajQaqNYrQQhPV3ui0l1iefRErViDbdwQz1ofn1KiVz7A69woDY49tMhnWDdUWG9z2DJ32MsszP2BOupuYPBJlGjwqTEwEEsm7somZGGJw/OmIZp5IT5DO72d56vt8GLR4SGTIoDMiTCZlh6e0LFKoJKurL/PDoIpAUBi8FyF0WvXreJ0yOXQOiySDQrXQLkuba7LDc9RoBmpitITGXmJsx+K67HCLSHBKeGhys0Jx9558ijRvygZW/iC6HieeHEM3kixM/jXXZSei0Gt6nFN+i0XphHY1gmnZYS6crI+H7TzVulHEh73EuCZtfCQJBB2vSQGdGzjRxGEhogRkHrenrfS0luvB/hSEQZ/UeVc2KeFTDhOCbZg8so7VtD6GhUlF2sxIxR4zEXQIKIbveBBiWswQR3QwlKqwkVyWNu1Q4PG7QTnShOniDgOUpYeGauVoqBbnnHRpVC9D4NFBWc0Uhu6lizbKDdzF4o1v47l1YuH2ytJnCkeNK8QpSQ8byU4RYwcWr8s6PvAjWUNIoipREO+nP38QTY/TaS3QqFxASJ8bkze4MXkjvJiq5V5afBO7MU0g/RCnBHeT6pHWsNB4SEvzN0EZ12titxZwnQqaHuOs32ZFeOyQFp1QdmDbTdrxfaH+29SNqU1/i8fijI6M8O677yI0vScpWgtBOn+Q1bmX+X//v/49hq4zNDrCZLnES7LOQRkLzbd9Lgqbhib58hNPcPToBwS+z/4DB+jv6+vZ4jvvvIOmx0ll923am24kSecOsLR0jkAGaD+mdbwxrl29huf53MAnj86DIk1W6JSlx0Vp08DHb8PM9Ay7fsb2JusjFovxG7/5m7z40oucPXOGU76qsqWSSZ588Cke+lvip/428Uli9DHH3Xfdxfe+/z0cexkrrty0A1+1EDb26ruxQ1gck02OBk3yoaFmLcQRlKTH57Q8vpQ8J9UK8hEtE1U0HAJOyhbXZIcxLIaFiSZvDmjLFA4jhEF5+T2uherJVmo3RuDQql3jR3JN7XlHqKy9MQQK4HpV2pSEh4UgjY4VTqb7iHEwBEyfpc3LQZU9Is4wJjYB16RNCR8r1ocQBkGg6jxpNKoEjGNyq0jgoeQJ2kiyaDRCNlIQaqVk0VW7Y8Mh9gsjKmVX8KLV78bIo8w1J2WHvwgUMHQQgwo+3wsqPY29aeFixgcZGXoAM9anmHHNGVaX3kbzlU/bE1qW52QNz21s2JOkvHIUK9bP0PbPRHIChpkhnhplZe41ystHlXXCugHQbi+ghVT7WGKQeHKUd1uLPEAqMgNt4fOhbFHGZ0xqzOKwjEtL+gwP3rtJe0c30qT7jjCzcpQ3ZA1DCIYwWMHle0El8j7rVu30MNGyWwtYsT7cMCl6IjTMrRNgIejH4BodbCOJqSlmnETjglMhJQQNGUQkAQNxU12pZEjXTmX39LQX48ltzLeWOEgCW/pI3ycTJg/vhyrLWTQK6JRDLF7XXT2JRhWP76xr5UlUWtBG8qBIMyzUs3k5sLkWtph0I4luJHE7ZaT0mZcO2Q0Vqh0ixmnZphPKO3RbdjfDb7TDFtQvaKrF0pYBz8sKb8g6XTBwEB7bI+u0ydS+LN6TDaakwxgmaaGzKF0uyw6L0g0JGipy6BwUcSXhYGUZ3fmLzFz+E3QjQWFwLSkCZVXku3U+JdLsCA15EcpS6C1Z5zoKhCyBDqoy220aF6UX3ddkdg/9Iw/TfSHjyTGS2T0sTX8fpMdnRI7r2FzGZWnqOxhSsl/ESIkYRTympcOrssaTIttDRjHRGBcW026dldkXEZrgwIGD7JiY4Ny5sxydnYu+60i5pY5GEFbYW+0W1ycnGRwY2KRkbXc6CKFveme60X1vRVu1XaevXicgwBjo592VlSiJ3jmxA7PV4q/+8i+jMeSHL71E30A/X/nKV8hmFNi/1W5hxvKRPtfGMGMFkBK7bZNMbvYQvFl0HCcayx7TslE1qyAMJkSMl4MqFXwcd6MJ1c8+EokEX/yFL/LMM8+wsryCruuMjo7elKn3ccUnidHHHEduP8LJkyeZm/sh6fytJDM7fvKPwijhUZU+A8LgPhFnTjpUw7XYEi5NAp7Wsj1tHguNe0WKsvS4QDt0mdYoBy52c27Llo7rVEAI+saeJJYYBEBKn3ZjBhncXKm2G3V8ZqWLC7wYYgT6hckAOi6S89iclzY6yhF8m7CitgMoKf/d0uB6p8Ts1T+LbBEaBDwk0j30930ixgnZ4pK00XU1SQg9Tt29OaixIddMKQIUiHor5hDh367TiQDby+H1zqDRL9TvJmUHocUY3Pb0mqik0Eikd6DpcZZnnkfBlgV7sDjVmMJz65GOlNsp49pFBrY9uUljCTRy/bezNP197NZ8dL86rQXsxgyG1XVjF/SPPU5x/jXeai1gSaHYeZFAHcyvs00RmtGjM7M+4qlRKiuwHEujCYMpeyWalMeEGTmYT4V2Cg4S32vhhBWRXSLGe7LBrOxVv9E0A9+tMyBMYgiW8JAyoCHWGInDmCzgUmcrijKR71mjchGhmSSSY8RT25ColtYxGrioab1ryNs1RzURBCgrGAfJM1qOs0GLM7RpyoC7RJLdYSt1MaRrNwnoFwZxNAUCFwFC6PSPfppESnnwBYFDrXiKD8vnSKL1PJ/XZAcb1S7q6lHN4XJZtjcZ0doETEsn0qcCyfsoK457RIpdIoYOLEqXD2WL92SDZ0UukkwQCO4QKaakw2hYHUVIJqXDe7LBQFh9dlHg627CaHUlO4RGIr2zJ/mWgUujcoF9xKjj892gQouAOIr8cUvYSs32345upGjXp7BbKhFJhPiipgyUVpu9gu+1e3SzTCtPOneAeuU8FoJtWFyRHbLoPLnOGBbgkPB4Oajyo6BOSmjh86Im827V2jAMTMsin89z991388ADD+C4Dh9+eJIXn3+e63QY3aJ1PyMdfMC3O/zXP/1ThBDsP3CAWw4d4ujRoxSLq/i+T+B7uJ1yxKyN7l1zltX5VxGoBZwP1KRS+F5dXWFoaJB2q006nWZhcZFOp8NOYbEjPMNZHK6uFvmD//gH/Ot//a+Jx+Mkk0kaK1XWUuHecJ0KILh06RJWLMae3bt/KkVt31PN4tu05KZxz0Bwq0jwlmygaT99FepvG8lEkh07fvq58KOOTxKjjzkM3eCf/JN/wg9ffpmTH56kVjwZ/W1adrYU0psKy+qf0XIRyyFA8o5sRCvGORwyaJHOyfoQKJuFY7KJLyQJNIQQlJfeZXD7sxEdGSSt+hSNyiVAUiufRq8liCfHSKQnSKTGadUnIVwNzkoHT8ie1X0dn5dkDd9MUCjchhUfwHMb1CvnKLZX0M002cJhhNApLb3NfpFkn4hzu0jSCSsQq9LlDRoYZoZ0/iC6kaSy/D4DvreFJpDgdpHkuuyghYlGMruH+fa7VIWyV+gCfHPotAiYxQkbkjIEXPtsBFR3o4GPQIkousA1afOhbNEgoCG72xGk8vu3VNqOJYYxrDwyxCrtETEuyw4rsy9SGHqAeGpbVEFaXwFZH1Zcfd5uzoDQaDemaVYvkxY6zXC7AJpmkem7jU5rEQcZUba7DDmhGcSTozidCr7XIAjcLe0bfE9N4H0jD2PF+lmZfRHZWuQxkaWMaun0Y7BfxPlRiKkIfJsgBCifl20MYGcoOFiSHmV8UhI+reWjhCdAcibUoBIIJaQZWmicDJo9InoAc9LhVGjj0WkvI6VPo3weTY8R+B2EZjBlJBCeRxD4nJdt7hDJ8PlU29FQhshXwiSla/PwqQ0J94gweVJk+UFQ4YJsc69IU8FjUbr0jzzaY3yqaRb5wXvwnApnW0tMYAGCq9LmA9lkd5hAZEItrjOB0s5ykdwaymMUcTka4vgmwol7SXosSHeTdteosOgTBs8FFS5Jm7s26OSkUewpFUohfQGHeelGshrjWKH3WHuDOOUGtp69QhB4zBHQkQE7RYwCiq5/PWTWgapuprJ7AYndmuNOkVyTbRAK6P+626A4/ypDE59n/bsWS45QL59DCqhINWnfHYLgL8k2bQKyIbEgAFbwkFIZQU+iBC67uLBRX6PS6vDeu+/y4YkT/NIv/RKLS0ssLMwjUfphBZRvX1dPaUl6fEALIQSakWJo/Bns5jxXrnzI5YsXkSicZoAEISivfMDgukWM61RYnX+VUXTu13IREL2Oz9tBnYr0aS4X2S4slpurdKTHvSLF3nVV6hEUG/JHTp3nX3ieL33xSzz00MN88xvfoFm7Hl7btbBbCzRKZwHJd7/7XQBMw+C+Bx7giSce/7GtNd1Qc8RWcwWgDI+lYq79Q41PEqO/g7Asi89/7nM8+eQTLC0tMzMzwysvv8xp2WJAGD0VnxIep2SLUbGmy+IQ8IFsYhNEYOgOXQ2jrSsf3QkgCPtgUkoM32Zh8m9IpLahmxmc9hJOpxRiYiRWYx6EoFi9jGmm0QzVlhrZ8SUWb/wNHvC+bPCpdTomx2WLwEgyMvHz0YBrxQdIZiZYnX+dTnuZVG5fNIkm0Wjhc012lMGthFnhYSVHeyoopcU3Gdui3QVrdP2uM1squ4dG6RwvulU0xLoWjYquRcVfBsoyoUbAonQZ2YDNcVA2C92pwkTR+AUKADsRXtXvykoPiH1jGGaawGlSFh4JNJ4UGV51a6zMvdyjgN5qTJHZIIoJ4DkKvN2oXKJRuYQQgmFp0I/OBW2N49OsXaO0+CZ96OwVceJoXJJtlvBI5w+SH7gboZn4XpP569+gWbu65f4a1UsYZgYr1ofvNbBbC0wIi5elMv41dAsvsNEkjAuTaemw3leM8PpOSod4WFGSQF84aXdDQ3C7SLCCx6p0eTtosBDiVuZw+WFQY39ouzAnHS7IDlZigL7hhzCtPCDp2CsUF95AA0Z2fimindcrF7iwegILbRMA20BEk+5JWiTQthRitNDYLeJckm3uFSmuSBuhGaqqsikEqdx+VptzNIXCYJ2WSlfq/nW2FnkMHtbS/Cioc0a2uSptBELpZaHe3oOaOt4bdMiibYmLiaGxK2zzrk+MJJIOcpN22Q5iTOFQlz7ZsBV0i0hwUdpE1kQIWvVJ8iFeTG1QTY4+ks+sS2q7v385qNJBIqXiLzZKZ5gQVoT16kYBg/tJ8iN7lU57hVhiKPqb76nW/guyElX7JqXNjVBOoFuVFKiW9APrBGWj5AOfHFoIoBeU8Hm7U+drX/sapqYhw6qzFR/klL3CBWwG0GkgqUkPy8ojnQqmlccws1gJFxm4DAmTu0WSHCoROy1bXGjNszj1XTL5g+hmmurqh1hS8rCW6cFbZtB5VMvw3aCCAO4UKd6VdVy0UD6lN8aExZA0uHj+AnzxS+zetUsRcJbexnebpHL70HSLZvUKleX3yaJzh0gyErZ5r/od3nn7LdrtNj//cz+3afvdSCTUvWkSbKn11AyT6p+m+vQ/anySGP0dRjwWZ8fEBKOjI7z26qv4QcBLQY0RYZJHp4rPglTCbPPS5VVq6FLR+kGZOnbVmj0kZXzsEBi7MRZwSKFhItClGgSFlNwq4iy1lnBYZBCNjIiHg6USlixgUBEeb3tN6m4D0DCtHAgdK1ZgplNkUVaYwCSQaj+Fvns2WSSAFoE4280ZEqkJNM3kctBmSXroKJ+tIh6elAwM3d/TVtKErvABN4kOEqGpayGEDppOgBIVnAhxEdPS4ZLc3GITwFuywV0k2RG2K5bx+DBo4iLJhxrV9TCB2y/iLOJE5rYCqJfPkUzv3HTeUvo49jKLuLwQthWz6KFwZI5UdjdIaNavUVl+H6QkU7ilZxu18lmEEByRcQwhlOo2Dqu4GLFhmrWrgKC89DY7RYwH1mlDfSCbmLE+CkP3050AdSNFKreXysoxNM0kmdmNEBpB4Cj9qfoN+oYfAgSOXYTw2qXzh8j23YZuJPH9NvXSWabL5wDw/U5UfXKQTAiLI1tYgsRordHcw6u3C4sVXFZQCZaPpC+cYt5dx4LTNIvBbU+r+20vIxAKl7XtGRZufIt2Y5p0TlXusn1H8L0256uXOEB83YQlI12gk1I5CiqUxdYLigw6HqqNWpU+mm7eFPOh6WoicaRkBcWAPLRFBVggOCQSLEhXPbfhCfrAp0Ummqw6BKHI4c2PTaXta9XO6ZAoITYUQLv/VNpo4fEiGBIGxfD/hpmOLDr6Rh5ECANNV8d/OLyX6yOOxh0ixZuyjtAMPLeB6zbYtYXfHyjGkyUFdnN2LTGSAY3KRTQEt4gEU7JDBZ9JHI6IBPtEHAuNo7LBtHR4VMv2VKgz6DymZfl2UKZCEL1jQ6F211XZ4fMyi4fk+1RxOkX6Rh7B7ZQouzU0zaI/NU61eAqEwG7NM3Plv6AJk6SE7cLk3aBBJfRxHBcmB4lz0alSXn4/urYHRJyt9OaS6IxiRoKSNekzJMyb3tNhYbLqtvnB8z+guFpESCigUSp+2GPqnEDjaW0Na5VE54hIEpeC48eP8+CnPkV//9YV6L1792BZJpfd3mpjeEO4JG2ymQzbt2+GWXwUEciAK1eusjA/j67r7Nu3j5GRm7P1Po74JDH6exCmaZJIJnHcGOn8fiq1axS9FpqRoZDZQ7N2FWGXMCSRx5SJoBCu/CQSM5R9PiFbfGqDaOKSdLkhu4rMAi/8jWvEOee1lfw6OivSYw4Hw8qBU4sYNnkMPi3SfE9WQAgC3wYkZqxAfvA+KisfcL1TRooAJD2rwZ7ztPII3cJz6oi0hpUcYbExw34R54hQqsInaXJFD5OvdRFP72CyPslh1Qjs+Vsdn2XpktQsOu1F6uULuJ0yj4hMD0OrX5iMYPK6rLMNkxFhcjWsVJkI3pfNyO7BDZlJAQozsyAdZsIW3BVp04fBA6GNyTIel50aS9PfZXjiCz3JUb18jsB3yPbfoVqRjRvUSmfJ9h0hN3An3QEy23+EysoxKitH0Y0kycxOPLdOvXxWtc3QOI8NUlXZdmIxiQPtJUrtJQi3dKemmGo2AY4MaBOQz+1j40BcGLofGbiUFt+isvIBupHCc6pI6ZMbuItUbh0TRghSmd1hcqVC1xPkB+9FSo9G9TKNyiV0Q6kfDWDw4E+wBFkvfNedUFKYmEKd37Ts0C9MfoE8rpC8Khvo2V1KV6h2NbL90PQ46fxBYslR2o0p0rn90XbT+QM0KhdYxmU0fA4uSJtaCL7eL+JclTZl/E2aN90oh+B8HSVu6ntt3LCysDHs5hwgeEGuae9kbqIrlQ1B5IHQ0YwEuA2kEMzhMBiaGifRQmd7SUl4zEmXAEkfBtuFRRmPRFglDsKE7ygtNM3AkL3nMh0a6G5UzK7JAN9rUln5AMPK4TpV2vXrLDRniKd34IXaQGM3YXNtC9suMvCi6tJWCQKohFCDqHXsuQ0qq8dwO2UsNA6JOE3pUcHnNpHg1nVyBkvSZedNyB5xNMaxqODxoJahGmpsXQ2rvW/KBgkU5s5BUFp8k3h6O/HEMJ7boLT0NlL6aFqMdP4gmm5RWT6Kgc4x2WKbMNlNPDK77jL71jeazJucM4AlRDQ+Gwhaoc3RVtFSNX2uHD9JM1ANwnaYOAvNQAidwFeLM3OLZ2uPiHNWdDh1+jRPPP741sdjWjzyyKO88soraCgdp1hYuT8v20xJh1944rP/TUy3/6sxNzfHX//VX1Gt1UjoOr6UvPrqq+zetYsv//IvkUz89KDyn2V8khj9PYiFhUWajQaD2x4gnhrfpFlhWDlWZl/kQOgI/r2gQgd4LqiiC4ErAwQCKQRTskNZuuwRcWJoLOCErQ41KUyHPmwg8Hz1+XIIspNSh8DHc6ocZPMKKIagI33mrv0loFo3zdp1QJJIbcPz2rj2Kr7b2HLiCHwbGbhoIUhaem0GMLh7nTebEbZkpAx6VuaZwq0s1a/zVqgY3q2KVfB4K1DSAa2uUCHKW0wlRTIaWOIIRoXFgDSYw2UuTBFB0CIggUZeKJXbpgyUnxRa5D8Ha2y8B9ZN/F3bjRfdCstzL1EYvI8gcGnWrtCuTwECITSs+AD1ynkMM9OTFHW3nB+4m2b9OsWF1ykuCgU6D69BmyAEmSps12Q4ua8fnLMoZe1zsk1xnYyetgWLRgiD/tHHcJ3v4nZKyFB80rByZPtu6zkupCS9oYrVjXT+lhCT5uKHLJaf1hKkG11j01ZmHOnbLLYWsIQy560Kn1EsPClxm3MEfodM4VaS6R1I6dOsXadWOoVupHrNcFHJG8AkHSrSZyZUcO6qcN8pUsSk4BRtroQaN+ujgc81aTOKpYQ2hcksHpWVowyM9QLl3U6ZRuU8O4TJMCa10Mm9gr+lhlA5ZEUms3voG35QGacuvsWVTpmrsoMOEfLtB7JCXSrAs4HgIjYxqZL3APierNIhwJUB8eQodmuB3LpWzZTsRCrXWnhbJJK3ZYM6PsKt027ORAmLDgwH0KpPIWRAB3qsUtZHt2UnhIZuptE0pcQ+vIXZbwlF6ac+Sae9hO81FYA+s5N2/QbfDSp0wid6o7aZD5sEbteHJQS6VE/diDCZEBavBjWKeNTjBZqagdNeQkofw8zgtFewm3MIoSMDn1hyhMFtT6nEI3CoLB+ltoU34y0iwVtBnXlcHhJp8kLnraDBvHQ5fBO22/w6CY240JiWzpbkgg4BN0Jtus/KLD6S50WVhhD0Dz9MIr2DIHCYv/Z1cjdR1NYRpIVOo7GR+dobpqnuz0XZ4aK0sYROR/qRIa1hfPSpQalU4k//9E/JesrXrk8aSCQzwuHY1DR//mdf47f+6W9/LAnaxvgkMfp7EMWiKmZbIQNsY3SZYa9GcvwK0CgRJDK7SSeG8L0GjcplpN/BQuNk6LOWRmMiVJ2Oo8QOV6XLqgjQ9TiD257C8xoEfgfDzOB7LYoLP8JYh32pSI8XZZUAlbgoDJKPJ1WCNbT9s8QSQ9itJVbmXlCig6ltbFwWqQlUtT869godezX0q1r73jZhcSZo027cIJlZ8xAyYwX6Rx9nfv5Vvi3LDGBE7cPua9OlkBM2J87LFpOyE2kVpdHYL+Lk0ChpBvH0BAKNQHq06zdoE2CvY6whBJ6RpK//DuLJEeqVi9RLZ7e03ciis18kuGAXI8d0w8rSN/wgzfoNHFshoBx7lUR6YtO1UfvTSKYn6FSvsgsTX0iWpEsTRc3uVj3uEZJTssUFaXNEJNgvEhwPWWBvyDpDwuBB0sQQvE6DdnO6twIURuDbuE4ZgCGpsSw9PKdKo3KJdP4gQIgdAcNIb/o9gGGqUvweESOFxmnZjixBNoaJFq7a1ybZGdlhRjogdAZGHwE0PLfO6twPEW6d12WdpNSU2YrbZHjic1jxgRCjJigM3YcVH6C0+OamZNxuLwAqkZzDYUCYPEqak7KFheC4bOKEz8YJ2aKGzx4Rx0IwH/rZ+cAMDl8P8WiggK+LU98hnTuAbiTptJdoVq+QRXBPqAwtUfpfZ2WLh0UviDxAck4qRepm9Sqt2jWsxHBEA+9i4EA9JQ6ST4tMZNRaw+eDoMEKnnqGzTQJzaQvNUGtdBpQquK1sJJaWud6Vpc+aeCobDCLS37wXtL5A0oWw7eplc5QL59jDhdtnUbTddnhzi0S7Emp7kM8OaYSRWFwJbDZFkqDdMORAW/JusLUSYkfVqI0PU67fiPEUlm4KF2wl2WNT4tMpBieRWdeOhwRmysIJekxGVbWuq20EWGyU8RYlR75wXtDg1qb0tI7yswaAUIgpVoc9Y88vEa3DwHq28Vmb0YNwb1amu8EZWwCssQ4IpK8Ketck/YG/S31njrIKFFsS4Ulez2ocZ9IMywUsqyIxwdBbzKzikdd+gyOPRWxUTXNQgiNMt6WwrheiJnKZrOb/ub7PpVKhSAIeO3110nl9pMfuItWYwrfa5MyUiTSOygtvcXLr7zCrbfeGiVKH0W8+9676J7PY6y1BAWCCREjjsYr8/NcvXKV/fv3/4Qt/ezjk8To70FMT08BqrRsxTaD8roruXT+IFZ8gGrxJIFvM7z9c0ozJ4xM4TCrsy/R6pT5ZfpArJW1m7JKNWTV1PFBQjw1xsrMD/DXgWZNK0siPcGV5hy3hm2r16SSU7xLJNkb9tI9lKDcKakSqcFtT+J7NaWr0ZynuPgWuf47FfA46NCoXKJa/BBNt5RuSXd/G1aBBQxGhcXS0rtqNbmOEt1pLyKR7BKxkFEm2EGMs7JNoMdI5Q9gWjmcTolq5RKnpM1YSFEWKLn7ExEuyMRzm/huHd9rYZgZPLdBbvBeEILK8lE0Pc7QxM9F2BEpAzLCuKn/0TAG51FsrlhiKKTjCxq1qwRBNxnoDsZbhwx8UmgRDscXkleDGidki8+H2ARFy04yL12KeJgIJogxicOe0ES2m3iNSYO5xgyt+g2SmZ1r+5EBpeV3QSql5MdFlm/JCjJeoLz8Hq3GFMnMDjxHPXsde7mHidWNTugiv0vEyKJzRrZZlS59W0yiDXzaSEqhdMQCLkvSJRYfDNlPLpoWwzAz9I8+xuKUYtu0CEAI4slxOu1ligtv4Lmhmni8n0z+VgwzQxCs+WMFfofa6ocMCJOnxdok8YasUQtbIchw26gK46x0uCrXrD+2C4uU1LgoOhSGHqRVn8RuLYAE321SWTmmNiIEOanxtJbpGeDvFknekg1el3X2iTgxlH3HJWmziocQOvmBO9U51m/g2KvR29CtBErg0fVq+Kgk4dNalu8HFdrNGYKQzdisXY1U36dxWVM9EhhWAd8p8VLIIhRAMrOLTOHWaLuaHic/eC++1yRozHAodEasSFX9yqKzW8TCJE9Zm5wM36f5yW+oo5USw0jxqldjGxZDGLRCjJmPJJneTTp/AF1PYLfnqRZPYQqNx8kqjSpQuKWgxptBnS9oBUwEcQRLYQVvffJRwuMlWUO3MuQLhzFjfbhOhVLpLMuOSpI8t0YsMYSmx+kf/TTzk99AIEjnD9CsXUMIo4c8IaWPRG6ZeIDC9/RhRAnnuDDZS4yjssm0dNguLHwkk7iUpYumWSxKl53E8JGMYVHH51VZIy7V+9wmIB0C6ufC6t40HUwzHS4yVQihk8zu4UrtOvuIb/INvCxtXBlw+5Ej0Wee5/GjN97g2LFj2O129HnfyGHVit7QocgUbmV55nlmZ2fZvn3zO/+zirOnz7BbWphbVISGMCloJmfPnf0kMfqHGgvzCyAE9fJ5+kce2fT3evk8XUPTWLyf8vJ7ZAq39iRFENKGh+5nafo5loTLGKqVdFK2WMUjhcagMJASqkLSrF5hn4hxQMuTRGNVepx22xTdBlIG1ISPKyUdAg6JeE+rwUCBJW0CLnvNaBIDNei2QmdtzUio1X1YebDiQ6Rz+xCaRXH+FRakw/iGAeghkeJ12WB17hUMI4lupHA6JaT0OSwSSJT/myYlC8JDs3IMb/9shO1JZnaTyR9iafo58D22h22FcSzGUP5uQgY47SU0oWPFB3HsVUBSWTkaHUcmf0uUFKnra2CHesJb226sMV+6EgieW8dpLxNLKDBhPDVGq3Y9ZP70vn4ycGk3bjCx7rXUERwRSV6VNZbx1nluCXaIGOdlCwQRbub2dW1JgAdI810qFBdep1EdI5EaJwgcmrUr0SQaE5oCkgvVSssUbqFeuUB56b1wVxrV4kniydGeY5bSp1r8EF1oDIacqnFhcUHa7BCxnkFbhoweITSWhMYiDqaVpy9/CN9r0rFXEOsqDGasDyM0JM6gUZcS32tQWfkAKzFI3MqqyoPfobT4BmasgNupYDdncTplGpUL+H4bWwrOh35hV6RNk4ADIh7ZfwQoC4LjsskuYoyHVYtBDJJCZ5IOUtoks7to1K6gGwkGtz1NeeUDOq2F7oWghk+TgPy6c94uYuyQDlM4LMp1iw+UerZv5cgUDqtnrXCYWukU1dVeL8E8+iY1fFDv3x4R45xs02dXaRNQDY2PARKp7ehGErs1j+fU8JwSu7DYJiw8JNdkh5X6JFZ8oCc5AqV8vVy/waCmGLLfl6qqeFQ2OYtNAY0qkob01HsgNLJ9hxGaSbs+hWOvKG8v32HBrSGEju/LEFd319p+rCyJ1ARLU9/hXNDmXtLR9XlAS/PdoMIN2WGfiFPEx7ByHHWqzOCyHeWV9iFtjFiB4e2fjeQyFAt2JyszL9LprOI6NdqNaax4vyIeZPfQrF0j23c7nttUBtfRgxpAqMd1s/Zh929rd1pwr0iRQOOsbEf3Op4cZbBwmGb9OrP1SU7IJi0haUuXUUwq+KH/o4zudUl60XvjINGNNBury9m+Iyw3pnkpqHFbaEGjhHE7XA7JJeVKhUKhgO/7fO1rX2N6eoZkbj+D/RO0G9M0KhcwrK1B8l19tdZH6GwvkdhOhyQ3Z/MmJdjtm+vRfZTxSWL0dxjlSpm52Tnq9TpIqRIJzQyZPyl8r0UtBN8CrM79MPptYgthRlCDgq5blAKPMVQL7YK0e3RFTtGkKm0OiTh3rGMlDAuTJzB4SdaoEDAvHeZCO42tlKFB4UYuSZts3xES6XGKC28hpc/A+FM0yhfw3BoCHbs1R7bvNnIDd0e/TRcOc714ih3CXTfhq2QgFo5JWa9Dzve5IX3GMVULQjOIJUfx3RZOp8jg4GYWnG6kyPbfwfzSOzSFH4ng7Qz9qJQmSxxHSqbtVQSSdN9hsn1H8NwmS1PfifSDomue3kGteJppnE0q5RKp6NwQAcd9r8nqwutKLVdT+8/kDtGoXKK48AZ9Iw+jheJ6QTjBE/gIYXBcNiMRvaHQr2yN0aKiWw8ABUDPom9aQVpC4wvkeUXWqLQX6LTm1S/XrdKm8dF1A8+TUL+OFetjePvngQDPbbAw+S3cTpml6efChFwlIfXKedxOGcIkOovBHSLJD2WVF4Iqh0ScwVDg75Jss4xH/+ijPS3SIHBZvPE34UXsVSXutja69i9up4zQLZz2CmYsjwx8PLeOZsTVcQArcy/TVV5PyBgXsEPtI3W1hjC4a13yqCHYK+IU8bgmHa6FFaOY0NgrY6r1occRQkfTLByvhaZbDI0/o+66lMxe+a8IzeAVWecW4oyHyceFoM1UqC92QCRCTS2Py9LGQeJ2Sj1A7mzfEZrVq/S5bR7VMrwR1G+qAA6qciGBx7UMEviurOIYSYa2PxtOqOrJbFavUFp6hz5hRouEXSLGh7LFxZUPSKQnoskQiLBarpTYBNSRZPuPYMWHqBVPsei3FZur7zCB71Befo9Udi+GmSFbOEyjeony0rv0jz1GMr2TWuk0teIpMn2HN52DbiRJ5Q9yvXiau8UaAD6JTh6d87LNCi4tApKpcbT0DpYqF1kMmuHZQT67Z5OGmBAG2YG7WJl9kXrpNHX1IYn0DkwzGyU/idQ4zeplGtXL2M0Z2s1ZkIrhei5oswsLfUNFo4TyuzvC+raeIIuOBPrHHie5rl1eK5/BC8eHbho0jcPtIhna1SiBydNh262LPUqjMdMpIqXXsyAxzAxD2z/H8uwLvOutsTZjCI6IBNeFy6VLl9i9axcnPvyQG1NTDI0/Gy3OQNKoXMCxV7Him+EbTkdBO/L5/Ka//axCIOjLF1ittNjc5CckHASMb7BJ+bjik8To7yCazSbf/e53uXzlco+e2kER50r1Eo3KRXTNwg+cdcOiKl93o7z8PgNjT2wCnEoZIAMfLby1F2SbEUwOioRaVUrlpSRgSyqxjuAgcd6jwSm5Vna9mWt793MpPXQjjefWSWZ3szL9HEHgERM6tvQRmkG2//ae32b7DuO0Fni1vcSEsBjBxEZyDYcGHiDIYfCASHNDdpjFJZXdQ37oATTNpFG9jLP0DvHU1u7SidQ2yiiadRf3UsHDRVV3ukrEeWHgyIBW9Qr5gbuj6+w5NUiubduK9ZNIjXO0NQeSUAZAlcFPyVYEeF6ZewUIsFvzaHoMTY+jhQaXQtOJJ0ZoN6aZv/b1EDsQ0G7OIaRSaL6Ag2mm8bwWp4M2u8PJrJf5Iplap7EECqC9VTXLEhr7iIfU/X40TVV3Ou1Fktk9yMAhCFzMxBAy8KisfICUklR2l2odAYPbnqJePktp8a1ou7HkKIPbnmZl9kXOyjZ5DAaEwdMix+uyxnG5Zk7ZbTD5XpsgcNA0k057Se3LU8/Z/PWvo+sm8cwukukJ3E6JQyLOIZHgW0EZhIZlFSgMPxgmnxLHXqG4+BaBcNBkwM9reSw0FqTD2yhwbzKzCyEErfoUy77NVdnpEde7LNtclx1iiWFSuf1ouoXdnON89TISSTqtSvmp7B7s5hyV1eNk+47QbkwR+C4gSedvwfPqnKpPcjJYk3HoD61gugnOCCa7RYwfBlVcJJ5bX4eNEiTSEzQrlzDR6BMG18IW1FZMr+XQYBYEC9JRNi+jj6xLitQ2U7n92K0FLjZm2Ee3FSa4TSTVu1a9HD73KjrtRQDS6JyWLUAS+C6r868g0NDNDJ5bp7jwRtSGaVavRNWgdO4Ardo1GpWLJNM7cTolDCsTLQI2hhUfIEDpLykmqOQD2aSMj4GyFZFIGqE0RFoYTIgEMsQjVVY+QNOtTZ5iXXZsbuBuUtk9tBvTVIsnaQezGGaKWuk0mmai6QnKS+9gWnnyA/eg6XHs1hyt+iTfp8ZnZRYrTI6qeLwd1Mmi9+hLSSSXUAsjtV91v9xOmU5rMfzOWjwqsj0YrL0izqAweD6o0gwXAnkMZKBwX11fzWh/MsD3HbJo3C5SGChhRh3BrKjheWosOnbsGMn09nVJEcSTI+hmimrxJIPbnmS9qraUHvXSaUZGRxke3uzp+LOMu++5h1defpkDeD1emQCXpE1b+tx19903+fVHG58kRh9zOI7Dn/yX/0Kl0qBv6EESmR0Evsfi5F/TQfIlkWcGh7YMCEhwljZC6OQG7iSZ3YMQOu3GFJXVEyzPvsDwxM/1DDjtxhSB9CkInaJ0KeNzp4jxpqwzK9fsPGKITdWFbuRCKvGDIs2sdJgOWwEbgYhAVDaWkhD3oSpfu0SM27Q0KXTepMZKvH9T60gIg4Hxpyktvs10fZIpHITQSKQnGMjuZXXuFexwkJCAbqbJFA5TWX4/wqQABL7T0/LqRldEsjuplPF4WdbBStPXdxtWbADPrVEvn6PTXga/Q7s5SyxcRTUq50nl9vYcd//op1ma/j7vOFWOS0EcjRo+OnBIxLkgbXyvEXpO3YfQYpQW36BVv47dWiDwVcUrnhrHbs4q3JQMIAR9Z/uOkOm7FU2LKTuG6mWur3yAYI0yLUPwbiWyQFXRCZMlB8kMLh6QQ2MPSulZCA3DyhB4bTrtRXQjpYC/8QHF1LFX8dw6upGkunqc6uqxaNuGmVaTYAic1YwU6dwBtfpGrXjncXGlJIeODaRy+5TdQ/kcnVAxvbLyAZWVD1TFKgTvd3E6QeAQBA5u6Rz10lk0FIj+TIhj0TSTgW1PrVPrFljxIQbHnmLhxreQCJLoNPF5RzaJpyfoH30kun/5wfuprBzlg8pF+oRBHwYtfI7LFunCIQqD90XXM5HaTjK7m+WZ52nWrtKoXg6rfpJW7Tqt2nWEZqKHz12tfIa+4YcoDN6H0ynRql2nWbvK7ZHy9lpYaNwqkrwrG2Fite6ZDVSitYyrmHhIzss1l/hurOIyLZ0QBaQsgQwjuWUFACCZ3c1qfZKWCKLqqYFgEJ1SWG0DBcivFU8B4AhJPaysNCoXep9N6dGoXApxVuB5vcDhRHqCyuoJ7OYsrl3E9ztI6W9hebMmYNrVXjohm9yQHe4RKXaHdh/z0uFHss4tIsHtoVo4qNbxB7LJtcV3iMWHlNRIGN1Wse8rG5J0/iBSBlRXjuI5VVrFk7hhiz+R3kEiPU67MYuUHqZVoG/4YUpLb/E3ssxouGgrSg+BknpwkMQQVPE4FSi4gmHlQzZkiLVceisCnHejD70nKepGDoNxYTEnHRakEwqKmtSKp3A7FVK5fei6Rbs5R71yAU23aHhttoWgfIAWPuXA5VOhDlCpWCJduGPDnjQKQw+wOv8qyzMvkuk7rLCZdolG5SyeW+Fzn/3KVo/RzzTuvfdezp87x6uLi+yTMbYJE1cqNfMp6fDQQw8xNLj18/xRxyeJ0cccJz78kGKxyPDEF6KVoiZM0AwmA+U91dV5eTXUQxnY9gTx5BoAL5XdRyw+zMLUt2lULpLtO4KS4l+gvPQuIHhd1qPvn8Mm0GMU+h4gnhqjUb1CvXTmpmKQlRAcbEjlwi2A07LFkDB6wNIOAWdCdo1hphCaqmmMCJMH1gGAY2gEbpOtbDeEMDBjBVqNKcb3/t/CgTOkjFo5hKNEFhECw8ywOPUdND1GIjWO73ewvRaN6uXwGvRGo3oZS2gMhBPjCdlCmBkFqA4nVzNWIJHezsrcq9itOZq16yRS27HiAzh2kZXZl8gN3E0sMYTv2zQql3CdGtuxyArl/baPODuFFYlHCqEjZYDdWqDdmI3aViopMknl9pFK78JuztI/+hiaFmNp5nuksnt7MBhCM8kUblVA4tIZTgQtLCGYxaMhvSi5cpF0QrbLu6gqTSK1Dc1IMNOcZ9JTgNvBsWfC+38ptNTwGNr+uXW6U5Jm9SqlpbcByA/dH1rHvMfK7Et4boM+YVJAo+K2KbYWMMw0AviCKJAQQtkryAYeAdnkKIFvoxlJfBS+qjD0KezWQkSbriy/j++1SOcPEk+NEfgKqN9pL0X4nwQaCEEyu3tLCxPDyhJLjuKEK/PL0kZqesg0WhvihNAoDN1HuzHDeb/NwyLDVdlBaDq5/rs2PZux+BDJzG7ajSkKQ/fjezb18lmkDCgMPUAquxshDKUDtHqc0uKbDG5TDCLXqULtqmqDbhHDof6Ppq39vdNeVmKdMuCVECSN0Dkr25Tw2Y2FiWAelyuyg26msNdZwnQrQf8tYcuAwG8rV/pOkUb5HCJcUHRkQAaNZSFIZff0PpvCUM9m0KFWPINh9OJVZKDEBlbmXo4+a9au9ehMAWGCdUH9W6iq51XZ4fbQKqiOT4eAq9hk0HqSou453yNSzOBSr14Kk1sV9YrCZsow+bRb81RWjjIuLG4XSbLonKPFaWnjdIq0G1PEEsNoeoxm7QqB38GM9+N2KswRyodIJftxRdpcljYGSjYhhsAAPKfC7LU/BymRgQtCYMX6yPbfgWnlWZ19kYJ3c3PWPDqzEI3fsdgwqdw+aqUzrIbXUmgGqexeTKtAefldpUaPaj+dkC1Mw+S2I7eFv49FyuLrI5HazuC2p1mdf43VuVeiz8e3b+fZZ36B8fGtoRp+4GPbNjEr9rem9JumyVd+/dd59bXX+PDEcc47qnJcyOf5/MPPcPfdd/2ELXx08Uli9DHHyQ9PkkhN9FCL7fZSJFh3hQ6XA6Ur4eBjxgo9SVE3DCtLMrOTWvGk0gRxG7hOlQFhUkYSy+4hnduvJjShMTLxcxHzIls4TKN8jvOyvUn51FvXYnqDtVVgg4Dngyp7RZxc6D92VdqRq7aUPiJc4e9HCUl2YzsW19w6dmuBeLK37aUEAi9hxgqUl4+iaQaJ9A6s+CBB0GEel2+EVOlOa4F0/hD5wXvWrEKW3lYAYCNJKrMbhKY8tCqqJbkvXHE2Qupyf//tW0yuGvmBu1icmo1WmfnBe1meeR7fXlX0+3WrvhFMHtLWdIxA2Uso3ykLw8wSBE7obaYS33ThEIaZxekUaVav0K5PAmqyttvzSitoAzukG+n8QWql00zpAk3omIkRhvKHaNaugRDR9RFh8ji47el1mJGAevk8lZVj+L4aeDynBkgKQ/dvEOMUpHL7cDqrNKqXMa28GtyBwG3wuMj02KYsC5fX3RppNBJh8jcsTO4lxWuyQXHhjXWbVqwyw8yQSBvIwKdZvYzvtxma+FyPT1wys4vy8vs0Kxf5rMiRFDp/IUuRLtFW0W0pz0mH6zgk0ju39K4DjWR2F/Pl81yVNtOygxkf2DLhAgWgbdWukcruxXdb1IonKQx/qmeC140U/SOPhIKFxxlJbUMPMW9tgi1ZjO2wEtrV9LJbC6zM/VDhdPqOhB6DYTWztciybjAfVmV03SKdO4xmJKgsH0XKgEFMLnp1nE5xS8+9dv0GSaGTXLewKeMpZpVdVK70ITarjzgnaPGObCgAsoR07uCW1yedO0iteBrdXF/RCmjWr5FITVAYuo/iwpt02kuUl98jCBzSuX1omkXHXqG6cjwSkTwTqPYZKHzNC7JGuWt6DJFA7cbQEIxjMhu2fQPfpl4+T6NyUf02TD5rqycYECYPr9Mgq0sfoSmD55GdX4zGZSk9qqsnqZfPAopx6sqAUri/p0WGEj4uknTIUnudOjXpI/21yrymxxkcfzaq6htWnoq3Duy9ISr4aJrGv/xX/4qvfvWrOJ0Sg5mdpLJ78JxatKAQmkl5+T10ocRQO0iuC4eGCPjlL/8KMUs9f7cevpUTJ06R7b99UytT01VV+qmnn2ZsbIxsNkv/TTA9jUaDN998k5MnT+E4HTRN49Att/Doo4/+rao6lmXxmWef5cknnqBUKqHrOv39/R+pTMBPE58kRh9z1BsNjHiv3Hngt9WKOL2TwtADtBvT+H4Lp3h6E/NsfZhWHhvJQGsZE40dIsMIJt8SFUwzq/SPhCCVP9BDR9X0GLmBu7i0cgwXqfyouqy0UM/FjA+QSG3DsYvYzVkGMIgLwZnQRkEHJkSMhvRZxaPTXsbpqAk6uaEKNSJMBjApzb9OfuRBkqkJEFpovvgavtvEd1v4bhMpferl85ixAoHXJp4aR4TGqYaVpTC01u4AKAw9EAKX36KycgzDyuI5FQJf2UtckR1uhBonwE1bDWasD4SG0NUEGUsMUxh+IGJmGVJpJElU2+KStNkjYphoXJU2J2SLdOEW5UcWJm2+12Jl/hV8t6mMczWTFHvWGHOh6GOtdAZgE16sG7qhEoLswF3RhOy5dZq1a2hajMLQfbQa07TrN+gfebQHSAsamcJh7PYS9fJZUtnd6toIjUR6x5b7S2X30KhcQkqfIFCD/G0iuclLbgiT20WSD2WLdiiQWcHjDZoYVpZs/x3EEkOhifB5KitHqVfO44fyEwiNdG7fFhO5IDdwJ83qZSZxuJUEQqoV/1aVQaTCc0kkb4RaOT8OtAzK++uD0F0eeyVUI7+VjROv73VxdoJW44a6h9k9W2xRkMkforjwI2av/gUybEFdCasfG6ML1K8WTyHEGezWPIaZY2Ti81FCZ1o5EqltymPQXmF015cBiW6kEEJXK32hqkTbQi/F0uJbDI0/29NabtVv0Kxd4xaRoEu1X8bjnaBBCo2nRQ5PSOIIfAnPhQr36y14us/gxtj4uQxcyitH8Zw6/SMPoxspZGh/E08OqxbtynFEuIAxzAy5/juprh5nIWz/CpRFTywxzEDhVgwzzcrsC11i65YRIHE7ZRaufR0/sBGSUG8twDDTeG6Djr3KPaJ3QWMjkYFH//inexarQhjkB++mYy/jtFf4tJZFR1DB482gznuyyWe1HAKBS8CLsk5LM+kbfIBEagIpfVr161SLJ1mdf42h8acBjWRuL8XmLEu4m9ppNTxmpYNpxcjlchiGQafToV4+R7bv9p42oetUaNauIGXA+zTRNI0DBw/y0EMPsW1sbfH5wAP3c+rkSVbnf0h+8IHwXZO0m3NUlt9hcHCIB+6/H13fWoIEoFar8Z//6I9otR1SmQNk4/14bp3LVy5x+dJ/5itf+bWbVph+2jBN8yPHNP23xCeJ0cccmUyaelP19KX0cJ0qjl0KPbJuRdNjkRhfrXgGt1NiqxYUKGBfGp1H1um0FHFxgoBMLEfgu8jAw4oNbD6OwmGEZjC5/D7X1+m/AFiJITynFmENEEowblyaPC6yJNBoE3BGtlgRShPJdSqRUvXL1HmCTASoEwg+LdK8HtQpzr9OUWhqYAyrZKrVJNGNuMIiBK5qRaBW0roeUwledu+m6yCEzsDYEyxNP4frlDCMNLH4IEHg0apeQqIMdIMQJO25jQ2Jgwrfa4EMCPwO7cY0nlOlVj4HCIxYnmR6gkb1CtJroRkJPvRanJQtdJQYnxnLUxi8t+f4dCPJwOjjLEx+k2b9elQRMswM2f7bqSy/H7rCqwGy017sYWx1ww5bRJoeU5WoxjTV1eNAQP/oY8STI9RKZ9HNNFZ8870GSGV2UVx4Qwl5WlkE4qaeX932k6ZbBGFisHMLfJn6PMYJ2WJZuuwQMU7JNsJIMTTx+WiFqhspYokhyvp7NKpXKAw/iKZZFBde7wGFrg9Ni2HF+ql3VEvJBDqtRdqN6VAgcy1q5XMEnsJujez4BZamn6PdmN7E5lEhadVvEE+OKXFTt0G9fD7EUwU9iZeUfsQIBULV9tgW2yQ8T5UApTK7CAKHVn2S87KNhWCfiGMgcAi4IG2uh+w3KX1ViZSSXP+RLapcGrn+O1mc+g5upxyde7s5Q7s5gwCui47CJMoA16kwf/2vSWZ2RnR95XcnOC/bzOHhIWlKD00YSOmziEMBgynpckK28EP6vbKlabI69zJ2a3HLhLALLG5Wr2A357Fbc8jAozD8IFZ8KLxuPoHfCqUJBK36DaT0Ma1c6GmnWj2tLjxZCBKp7QyMPU73fYqndzBVu87tbLZucVF2KAC7A4nEooIf+ZNpejzSuEpvwDgJVPV96/dGjTlOezkCTucxeFDL8FJQZS5UtL4qOzTwGdn+8z3JS6ZwGDNWYGX2h7Sbcwq3lpqgkRjiDXuF22Sih5V2KsSTDvSrYxkYGGBubp7q6ochxmiNGFALW7oA//v/83/H0I0tqyyFfIFf+7Vf4+t/9VcsTX0P00ohpY/n2mwbH+dXf/VXfmxSBPD8Cy/Qtn2Gt/98zwI7nTvAyvwP+da3/oZ/+S9/5++8yvOzjE8So4857rzzTl544QVYfIt2c7qn7LpRV8IwU7idcvhS9WbkrlOh3bjB7ayt2FwCjoWYn8B31CArNDy3tuWxpLP7qax+CP5aYiQ0C8deJZ0/QDJc+TRr12nVrzMnAmblum0JDcNI0z/6aFSJcTtliktv8WqnzOdENmojBBIaoZeaphnK5T1MjJLpHeQH7wlfuoB2Y4bi4ltIKRkce4J4ahszl/9LD8V8Y+hGHM816B99lMDvsHTjb8gInU+JdJigSb4jq9TL54gnR9iYYNUr5wGBa6+yOv8qINDNFHqIm0rnD1IvXwChMbLziwR+h3r5PL7v4DUmt0za1D1MK1Xw5nxPqyyR2kYFlfSkMrto129QXf2QeHJbj/SAKukfByEozr/Ws+38wD3huSjwq74FAD26VeGEIJHEEkNI6WO3FoknRzd9t9WYRmg6ppWPEqOtfMTU5+FxAnYo8VDou2cLBpIg23eERuUyAkikt4MQ6yoyG0MSeK1ogNqGyRQOq/OvkczuiixBWrVrtJtzQKh9ZGaw4oPYzdnQDHVN0RgCyivH8N0GA6OfBlTrsTB0P0JoVIunSOcOoOkx1RZbeR/Pa6hEUugYVhbfbeC59S2T6057SRElBu+mVb9Bqz5JOn+Ik5ULnKGtgOHSRwo1adbLZ8n23YYQOiuzizdNas1YAYRGs3aVIHCwGzO0GlPRdT8q13BGup7EjPXRas5E2mG6mWZk4gu0GkpEUgidwfQ4hpljYfIbvCebPZSpwW1PRc+FaRWIJUeplU4p3Nq6Z0xKj8rqcYRmREl7Ir1DgdPrN7Di/Vix/nAcK7I49R1AkMzsQjcS2K0FiguvR8ngsyLHxdCrS+GZ1p65TP4Qi9WrHKXJvSIVESpcAt6RTQIEEhnp+OTRORAaYhtWNnqnNoqPxoR200QX1jBgwbrFaT8GOXTmURps18LWrbHB3xEgntyGGeujWb2qBFKFprA9Cz/iw+Zsj92QFesj6JSwYup6CE1V9zKFW2k1pmhF7XedRGYXnfYivtfANLZuA3dj27Zt/G//6//K5cuXmZubQ9N19u7dy/j4tk0M1o1Rb9S5dOki+YH7e5IidXwmuf67WZ55nj/64z8mZlmMjo5y9913UygUfux2/77HJ4nRxxy3HznCK6+8Qqt+nUzhFpLpHXTay1RWPsCxV3vwRMnsHqqrH1Kcf5Vs/x2kQlZaqzFFdfUEEiWHfxUlXHcNB1dooCnAnRAaycwuGtVLpPOHNuEomvXrSL+3WiQDh8HxZ3smzHhqXK34l9/DsHIYZga3U8L32gyOP9MzSZixAkPbnmF+8q85GjQ5oiVpSJ/3ZYtANxgYfphEepwg8Ji7+ueYsT76QysIFarF0z8iWJ1/lWbtKvHUNtVOq98gW9ishRIEHdrNeUxLVc4atSv4focntLwC7gIguJMEbzdnKS6+GapyZ/D9No3yBeqls5EprqaZtJuz1Mvn0DQTt1Nn/trXAVXt0bRY2MJ6AJDMXJnckm3TDcVm6u0DdBlznluLqhSe11BWE4VbsGJ9eE6Vevl8qHyufNPUBJ3D91pUS6cJpEssMaw0ffwarlPdZMALqp2imyl0PY6eiGNaecrL7zG0/TM92B3HXg1bbnvRNCuarGeks6WWVXelPiCMSEXaukn7VzdSaEYcz2uqwT21nUb1Mun8wU3VK7s5h+s12R5WQ7u+YGkEfn2K1dp1ADLCUB5+yKgll+27Fbs5Q6txA/v6AsnMDkCj3biB77UpDD2wqaWaKdxKvXyO+clvhhN5Rd03KaNnLpneSVk7SmX1OAOjj7Ke5ux7ql2oAOIWhqko86nsbjWx1a/j+zZZI0kyszu8zspqRYbXTVUzt7By8JogA9qNadVSFl3xA5XPPECKGj7nsRkYe2JdgiWZufJfyeQPoemWasNuAD/HkiPI1hKf1jIclS3seP+mZLkw9ADLMz8In81be59Nr8nQtmeIJdcqf6nMboqLP2Jp6nsIzVCVYSEwrTyD256OkpRc/5206tcpLryBhqBP6HihhtBGe5fuODG58CYz0mFcKIHHWVwCNKzkKLK9xKdIkhAaphS8HC7ilqefi7azUXy0H4PJThnfa26a+AHajRnSwsDakEAYdO2HlHp6OnbzRMCKFSKYASjMkwglM1K5fQih4To1Oq15zFiB1VX13fn5eQwrS37wXvKDd+N2Kqr9aGXRtBj18lkqK8dwPfcnJkeapnHw4EEOHtwaK3azWF0tIgO5CRvajVhiCITG0nIN0yowNf0B77z7Dp//3Oe5+++Iav+ziE8So485Ll68iOs4SnArHIDazXkQgmrxFLHESDTJpnL7qBVPE5c+9dUPqa6eiLZjxvpJp8ZZqF9j2m2gayaJ7D7y2T0szTwfVQ+yfUdYmp5meeYFCoP3EEuOKOXj6hUqqydAaJixPvL9d7Iy/zLx5LYtqwjp/AFqpTN4bj3EnrjEU9u2XDlreoxkZjcLtSsshN5FAIOjj0fb7lYilCfX5kpQIr1dCfc5FbVNIxFN2kqpVw1UUvqUFt8B6ZMJzU/t+iTbhbkuKVIxIZQk/9H6JK3a9WiVi5TEkmMhDkBt14oPkExPsBjal/SPPU556V18v72hRSOw4oO0GtOk84c2nUfg23RaS5s0nBrVKwrHYa9QL51RbSu/oxKelWObtoPQSKTGEZqB3Zwj8B10I6m8seRaC6K09JaafNZVbLrVC6EZ1MrnkH4H160DAQuT3yKV3YNhpunYK8pHSqrKIaj2UCwxwun2EoPCIL9uyKjhc1K2SKFhE0SYFM+tbVn9CHybwO9EiVi27zaWZp6nuPAa+cH7wmcpoN2YpbT4FgPCDL2kYAEPRFhxkZBBJ0BSD+nToDBIoPBhibRS+NV0i3ZzFikDAq9N/+inSWZ2bTo23UgiNDOsZhohLqWOYeVJhN8Xmkm27wjV1eMsuQ3S+UMYZopOe4l6+Xy4glZ6Mz1aMWNPbmjReVRLp9T51K6o6VVo1Mtnw/djQzWzfJ61NAg0KUmgharJYAhBSfroRnLrqtOPbXEo77rjQZOakKS38Gs0rRzDE59nZe6VnmdTaAbDE5/fhBGLp7aRSO+iWb1Mrv9OmvVJXHtVCZpuEGJVrD+VxPoSNCGQgafYjBsqoMnMbmTgU1p6mznDQtMTpFJjpLJ7WZr+HkkpqQllFtzV+IolhknnDyBlgK4nKC2+wYtBjUMo3SAtfGZLS+8yMPZ4zwKn3VRWOndtYMLZBJTwqEioUsMP8U1bh8TplPC7CyGnSrV0mnb9BkDUqjVjSp/Lc+tIR/0t8ANMq7to0TY7HYTvUaPe+MgqNF2zWd+3MdictHfHz0zhMOncflVFXPmA7z/3ffoHBti5Y2sc49/3+CQx+pjj+PETJFLboqQIVJUGKXFCBlSm7zCWVcB1KuhmGjtMDvox0ICVsHee7b89dGlfK/NWiycAqVoVauuYVh6ns8ry7ItE+jFCkEiN027MYBjJyI8nlrgZAE4QS47gtJcZ3fVllqa/d1OwMChQpqbHGRx7kuLim4CM2j6wxhS5GagTBLoej8rsXcBwZeUYzdo1EukJZODRrF8n8NqYsT5S4QQmA/emgpS7RJxy4HMZNfAG0kNoOoPbnmLjhGTG+khmdtGqX0cGHrqZxrVXaVQu9dgoZPIHKS68oaof61bkUvqUlt5BIiPxOcWYuxgNiHZrUWGIpB/R70GlikF4HazEIINjT6zzbPOprHygWDdCY3TXL7I6/xqeW8PtVJif/AapzC40PUGnNU+nvaw8pDolasWTavCXPtn+O0D66hqGJsL5gXvptBepl8+q0n2oWyQQvBBUGcciL3SqqAlI02N4UvLDoNtiFdRKZxUrbEMVqB7SsruebVZ8kIGxxyku/IiFyW9iWFkC3yHwbdSELbhChxSa4ipJn1T+EFasQMdeRQiN/sQwzdp17NYsnlOnvHI0FNVbCFti9R4NGXkTBK/n1pGBiy+DEHCungXPqbAw+ddYcWVC6tirCM3A6RQpLb4ZnrK6nqn0xLqJXyM/cDfFhTdYmn2BbN9tmGYWp1OiVjqN69SIJ0dpN2aUKKcwlO7NwhshtVtVBevl89RDYUMdgR7ilAwjAUEHAp+3ZMhW22QdIoglhmjXp8jkb9l0zoFv02kvYiEoCIM2XshY3ByGmVXVUt2if+RRHLtIceF1As+GDfaOvteiVb9Gtw3UqF7BsLKbqkDdSGZ20qpP8hq1sOooqVcubBI1VPiwSRCCwe2fC8cfSXn5fQLfQaJxRraIo3GLSGAiONFeom/k4WgBN7T98yzP/IBj/vr2ocBuzbNw41uqUqrHsJtz2M05+jHYv65S2hWflIDUEzjpcahdo9WYIrtFtdZuzkVJ08yVP43G3mRmN636dfpHHyWR2h6Oc5KlqW+zb99aMuHYq6Eg6maMn91SC2rD/Oim8bHRUdKZDM3qpQ0MVhXN6hUQa04MQhgUhh7AsVd49513PkmMPomfLkrlMmZ8b89n3QEjP3A3rcYUxfnXo79Z8QGS/XdSK35IPZZVYE2nitspsjL3Q/IDd2HFB/C9Zs8gunDj22iaGQEbNT2hJhzZpQnHiSXHcDpl1hICEblebxW+20A30/h+G8PKhQq5WwPDO60FTCuPFR9ABi6Gle35nq4nVPWjtbClOanvt3Gd6trgKH2yhcO0mzO4nXJUSerK9w9seyLavmEVWHTnbnpsS7hoehzDTBH4Haz4wE2ByLHEEK3atXASVNuqrHygaMf5g+h6HE1PohlKPbdZvUwiPRH6kV2LKmNLM8+FLchyOPGryBRuwXNrtOtT9A1/CgVOvY7nNvHcGnZrgYHRx3pWz0LoFIbux27N43ttpSUkVJVjcNtT1CsXadenCKSHaeXpH30MITRW519ldOcv0m5MU145qsD+mkm6cIuq5BhJNM3CjBVoN6YjK5MgcJCBy04sVkMjYlCVwZGdX0ZoOq5TwXNqFBd+hOuUw2fzTqz4YM8En+0/0nMu8eQ2dD2JF9SiSVkA94gkczgcW4efWc9KTK1LQOOpbcxf+zoSH7s5h24kyQ3eQ710lnhyG4Wh+wl8h6WZ71MvnyWZ2bEBVyJV5U0ICCtSCs8mQAh0Ix1ioSS6kVRtat2K2o2eU6HVmFIyDI1pEuntBIGnqm9CI/A7PVoxCA3TytE3/Cl0I4Xn1liY/Bbp/EFa9cmwumciAxehGaTzB2lULmKlt2NaOWIyCAHMoJupKJltVC7i2Cs9bcJM/hCr869RL58jU7gleoal9MIFC/RhcJ9Ic1m2Od6cwXOqm/AyrlPGbs6SH7ofw8ygGwkMK8fq/KvkB+8hmd2LJnTazRnKKx8ocHVI+hCaTuD/GOPkcEzqLviAkPghyOQPoulxPLdGdfUkdmseIXQa1cuApFm7hu82uFeketTMQeGPTsgWnfZylBh5XhN/3fsHYMX76Rt+iHrlHPXKhZAUItGNBEWvzZuywRgmnRCu0CJAaBbx1Db6hh9SjMjAY3n2BXL9d5JMr2Eza6VTCrCvWcQSg+tsUy7TakyqDkGYFFVWT+B0qtx335fXXRuf6uoJCkP3s34s67SXaNWvq4WL79/02v5tQ9M0HnnkEZ7/wQ/QzQzZwq3q2ZQBrfo1KqsnSGX3bVgkq8Tv6rUPuZmv5N/3+CQx+pgjkYhjd+nKYQjdAiFoNZSnje828L0WmpHEMDNK38TKMrzj5+m0l1ieeQEAx15RtO9umX3d5C6ljx8ORmr7kB+6j3hyVInoVS9TWX4fzUhE2CPDTNOsXSPXf+emkrfbKYdmi0LhbUJdn0bl4qYWUqt+g057mf7RxwCVhDn2ajjQh6taoZHK7adZvUQ6u3dDmViGiroiAiybsQJOp8jIjl/Adco49qoSxKteQQiDpanvk87tw7BySCQ16XGNTo8TN6AsBPDB9/GbqvXSVezeKony3QYIjf6RR/B9m+rqcWWwWzoTDd4go9aTYxcjryEQGFYez6kox3LfDoXv1toi9ZCqD7Ay/xpD489ExqIrcy8TT47epDKnRPeqxZPMXP5TdQxCR2gx8gP3kB+4p+fb1eJJVXFbPaH864SO2ylRLZ5cM0MVGslQMwXAdBsYCHQpqaK8uRJoCAI0BFW/Q7s5Qyq7R7VTwspMfvA+6uVzLE3/YO1chQChh5NlAGghwPnoJnKABIaEyV7iuCLARfJdWQtbYFvo2GgW8fQ47fo0MlBVj2prEYRGPDmqVrVITKugPN9mnifXdwQrPojn1qmXz9NuTKlth60UTTMJAg8IcJ3yuqqTID/0AG6nRLN6JUr6cwN343ttGuVzOHZZ6T/JgNFdv4RhpnGdCoHXRjdT+J7N6vzLzF//a6zEkKoYo3BO+cF7aDdm8b2GEjJNT6BpMRrVywpjFB6FbqZB+vhuk+LC6+GhCUqLbzE4/mz0zMRTE8RT46rCWL1MMr0jZMxdJwg8BDoxoRibcTR0BEuzL1AYvD9kwEmVSC+/r9r9qydo1a7huQ2V4AtBefl99fd1zyZIAt+mvPweBB6+2wi9uTa3+rp6XEKzVGIakjJqxVMqsdBUm7nbEpQyCBeAimr/qEizbYN3Yfc5AjV2KbHVaRrVSxF4OT9wZ/j8L2LG8vQNP0zfsGoPzV39c/KD9xIEDivli8w5FfW79ARDhVspr7xPt6abzOyiUblELD5AeekdykvvRO9TIrWddmOKvsF7Q4KGGrerxQ8B1T5FaHSa0zidGs888wwTXUf78NVRCW+RdMhKazfnQiFQ9bVYbPO5/yzj3nvvodls8uYbb9CsnMeM5XCcGoHXIZnZFSZtvSGEgQyCmw2rf+/jk8ToY47bbruNN954E7//rmjwEtDTSsv23RYZdZYW38KxV0ikJ1Sv2lOGmGasj+GJz9JpL+O5NQWUTQyzMvOCMtbUTMX8chsgJcMTn48AoaBaZqaZpVr8EC08jlz/XRQXf8Ty7Iv0DT8YDmISuzlHceltEBq5viOqLeOUqRVPU15+H7u1QDK7G4FGq3GDVu06yczOEPQKmb7DlBbeoFo6FXoyiXB/d2A3Z1maeY507iDx5KgCQ1cu4dirGFY2tK5Ioptp2vUbEV3btAqAoFm9wtD4M0rNu3IxolSbsQJHO2UWcNmB8jSbDqXmN4bnVLcUnwwCJ1yZQnHhRwDhuVdABmhhq8/3msjAoTB0P4nMTmV2KnSseD9C6Mxf/2t8r43QdCRKEbjb0jRjBUDidiq4nSJL088xsuMLIdYh+LGgbjWJaxSG76VZvY5jL1MvnyPXf0fP13yvpdSFZUCrdjX6fHn2BcxYX9hqSIcYrvO0GzMAHCKGgeB6iGa5gA1Ci6omSCgtvknHXiGd24fr1ABB4HcY2/WL2K3F6Nns2EUa5fMU51+lrMdUm8up3QT/IngjqPFZkccUGiZsIZG4+TfqXMMKU1j9UT5acRAirN4J3E6J1fUMv2hBIUOzWEOdXygj0a1KdisJnlOlb/hB+oYfpDvyB36HxenvYsb7Gdz2FK3qNSqrx6IFhmnlIawMG2aWwW3PsjT9PaVhFrZ9PbeGYWaiVuPa/VPga91MYxgpvE4Z320oE9nUKIWhBygvH8VuzuC7NRYmv0EiPRHR9d1OBSs+gGYk1IQqNJIZlfw2KheYgui9MIwkvtdWyda68+8uhHQzgUQ9+4Hf6WGzrV135Txvei306jWyEspCo7j4BkPjz/QY3NbL51X7WGjEwqTJbi0BSgYEKSOiQndfuYE7yfYdIfBt5q9/nRoBmyVw186pXj5LvXwWTY+p6+c1yfYdRjdSJDO7lNTAOoam8k+L4dhF8oP3huKWazO8lB5upxKNF5n8IZrVK/hei4GxJ1QFTKhjL68cBSFo1q7hdso4nTKd1jy6mcF360h3Bk3T2L9/B/fddx/b1+kBpVNpGo0GGgLHXqVkr/Rc427EYzdno/4sQiB4/LHHuPPOOzh18hSVapVyqcT0zAyFoQe2HKPs5gwjo6P/3VL4/0ElRn/wB3/Av//3/57FxUVuv/12vvrVr3Lffff95B/+DOOee+7m2LFjrM6/SG7gfuLJUZWACEE6tx/HLvYM2la8HzM+oHRLGtPR55nCLQhhhC/nGCBZnn0JP+jQP/qYcncWMHPlz0jnDvQkReu3USufiXrgycxOOu0lGtWLLE1/H81IgPRD6r/ByI41G5MEE6Ry+1m88R3azdm1YxM6CF3hV8KBJJXZRXX1Q+qls7idEqnsmh6H5zZB+tQra23A7u8Cv9NzLYRmKbp2ZqdqV4Ulcbu1SGHofgpD9yFlN5mQrMz+kNnWPDM467aqqk/Zvtux4v2q/bP4Bqvzr1EYeoBkRmFjOu0VyisKuxBLjtE/+jCaUDIDqt35GoaZVcrFof5RIr0DXU+gJ9dwU636DXyvFbJPjLA9No8VJiTd6+l2yhQX3wzbUK+QyR9ASkmnNX9TjEG7foNYYoh07iCd9gqOvUyteBLfa4arS4UxqpZOEQQOQosxPPF5Ar/N8uwLxJNjDIw9GbURY4kRktk9LE19D196nAipxEIYIEMft1BrKwgc6uXz1IonaVYv0QxVhkGBhZPpHdGzqQxH3yTTdxvJ9HZaocaQnt1HrXwWTTPJD9xFPLktqmbWy2f5jixzB0lS6KSkoFm/HiZ9vYNtl8KuDIbvw+2UqZVOYzfnsBLDDG//DISTS3HxLTy3qs7Bd6JJRmgW/SMPhdg8JT5aXn6fTmsBoRmM7/2/IwPVfmpULuD7bTIh+NpuLVErncT3lEhpl8Go7v+1LVWj283pELz882iayeLUd0IpibFN51cP9bTyrg1ehwIaHhY3cBgsHA7bRGHyJgMMKZGNGVzAkBIXcDol0vEDZEYOhR58VyI8mx62xjy3jue1SIZMOqe9TBCEchFSKjak30a6PkJoaEacwFNtddVWNGg1pum0FgiAT4mUEgUVUJYeP3TrzF//JvHUeEjXn4uU5gU6dii70D0mP/TtU4sPGxl00PR4JEyq6XHiqXHONGYYEAaD6zBWRVxOhs9vV+7AtYsEgUPf8Np71x1/iws/on/00Qj8nszsjBiT6vqu3ZN66RwycNGNJE6niNNeYU2w9tUerSChWeQG7sJuztFuzqAbSfqGH6RZu0JfPsH/8v/45zdtNQ0ND9FoNJCohYGS51TCm53wqoHE872fyEr7WUQ+l+fTn/40AI1mg9///f8P5ZX36Bt+pAeK0AwlNJ595ksf+TF9VPEPJjH6+te/zu/+7u/yh3/4h9x///38/u//Ps8++yyXLl1iaGgzqOyjilQyxW/8xm/wV1//K5ZnX0I3LGQo8NasXWVo/FmEZuF7LXQjgZS+ap3J3qWZGcv3/L/TXqLTWmBg25MRZket6IKb6qMIzVTA7HAlohINGe0r8J0Ik9Q/8ugm8KSuJygM3U9x4UcMbv8MpplldeFHuPYqS9PfJ5XdG1ob1JGh/5rdXMAOW1gIEfqKiQ3nJ0nnD1EYuj8CxRYX30LTEyRS22hULkSaHpoep7p6HMPKKNZWmBQ1a9ew26pFlMruQzfT1MpnMM0swxOfizAmhplhdOcvsjj1HUqLb1JeeifERDjoZhqh6WqFt47Snkxvp6zHcOzldRdTsDzzPLmBu0M5Apdm9YpqYSHI9t+BYaSYv/5XCDQGtz3Vg7UxYwUGtz3F/OQ3FGA6ZFiBoLz0Ln0j6wcftdJ27FUGxp5Q9z9sh6Vy+7Gbs2H7SEU8NY6mWbidCroew3MqIGWo0t2LrdL1BNm+I5RX3md015cRQlNg7tz+Hq8sTbPI9d+hqmqVC/SPPo6mm6zOv44QGkszz5HM7CaWGKJZvar8yPpuQ2hmhIEpL7+LQDA88fno+mp6jPzgPRhmivLy+7y/DmOEU6eycpz84F10qyxSepQW3wJUJUHTLGKJYQa3PcXK3A+xW4sEgRtJDwxv/wzz1/96XRVCPXcDY4/1VAwVtfxJFm78TaTILjSTgbEnKC29TbN2NWIWAcSSowhhqGqhZqnrKgSVleNKCygiNUjazVlqpTOqotOcAwRCmNjNeVbnXyc3cCemlV+HGzwPKKFSC8GUdHCRFIYeiKochpnGcxIMjD1BvXyOdn2SQPqYZoZcbj9Op0ijejHyJdP0GAjlsNXF2hlGGsdrI9AxrULENnOdMq36VFgZ9cOkUgmxpnL7I2wcQDp/iGbtKqXFt+isKycVhMFhGeMU7RAnJCJsUTK7h/6Rh5GBh+/bLNz4FpkwIVHEAJtYYggr1k955SiLN/4GM5ZHSlW9E8DLQY1BDArCoCI9llFsxS4eRoGedZABldUP8L2W8j/0mkgpCYIOK7MvhQBzVS1CwNL0c2T7blvz8atephVKRShfSvWOGlaWwGuzY8cOJiYm0DSNeCLBD196CbsxTTp/K2Ysj9sp06icw3PKfP7nvvJj8TeFQh9CU+1hYWawpa/OQ7fAbQACTQND//in8XQqzS9+6Ut845vfZKmzSiKtZCrs1gx2c5E77ryDI6Ff23+P8Q8mMfoP/+E/8M/+2T/jN3/zNwH4wz/8Q5577jn++I//mH/zb/7Nx3os/X19/PP/5X9memqamZkZhBCMjY2ph2z6OWLJUaywXWWHVP4uEFQN5BLXLvXQZJVOTXqTECRwU4FHZBBhBRanvhOpTSvsz2WQa6C+eGprHYvuZNJpLWLm0jjtJQpDD+D7LRrVKzQqFxGaQTKzi2RmFyuzL6kyv5khlhgmmR6ntPQezdoVBsaeDMUVJdl+RW/ugiYzhUOUFt8mUzjI6K5fVBUQNCSwOPVdVudewbByyiW6UwqB4il8t0UsOaySkMAj23dkk6CbpscYHH+axRvfwYoPEk+NYcX6iafGqKwco145j5RyXVlYQ9MUXbhv5CGWZ36AFR9EINbwHqBwVNk9NGvXaVavqETCV335jVRkUJTxRHoCuznL2K5fQmgm7eYMxYU3sCcXSWX2IDSDdmMqqvJVu155oSJ33/CnkFLi2CvIwMOM5dCNNM3qZUpL7xAEHVynitCtm9rNxJIjIRuthd1aACnJbCFFAIqR1yifx7FXyA/eQ6ZwkFrpLMnMzjBBuwwIBXBfr6MlA5q1a2QKt27pgZbK7adS/BDpOxhWAd9rIAM3ZMtdJ9EVeKxPIqXPwNjjG3RoBNm+27Gb8zQql8iGUg6aHieZ3U2rPkk8NU67fgPDzGwpUSGEQTp3QGmGSRF1/QpDn6LdmCGeGiOV2YNhZTDMLO3GFKvzr4Xts0BVQ4TG8szzWPEBhZOzV8N7J0J80I+6O0PoMdrNaYV3Cplu6ysVC7gIZV1GKrcvlLpQYcb6aFQuoukWfSMPw8jDrG//VMKqj26oakYXHwQCw0hjJMdw7FUAmrUrtJszYUtPKiNkGSClxIz3qyTbqSADd5PaO0Aqu5dm9Qrn7SI7iOGExrCnZZuJCaXc3XEchoYGmZ2do1qdplo8SSI1HpIAUlRWj9M3/Cn6Rx5FCE1VKEtnQAYkkgnarQoA/f0DZLMZJicnWRUBRRxkiM3RQvkCp6OcBeLJETrtFQLPDpXj154VoSlxWLs1h5Q+seSIEnRFqmu38kH4/MTW2oV6HN1I4Dp1PKfK9u3b+fXf+PWeZGd0dJSXX36Z2ZnXo8+2T0zw9FNfYPv2zaST9XH48GGOHztGMrsHuzm7lswHDrHEML5b5dZb9/+dtatuueUW/llfH++99x6XLl3C931GR0e57zNf5tbDt/53Cbruxj+IxMhxHI4fP87v/d7vRZ9pmsZTTz3Fu+++u+n7nU6HTmdN+LBWu0li8bcIgWDHjh3sWEdn/Nf/+n/jnXfe5fixY9jNEqZlomkaZnwI08rTaS+qVZLboFY+o9o+XTp7WNrtGaTCakCjeolM/tAmu4FmfTLSC4klhkll9yqzQrfRY4UAqpq0hg1Yi64xqSodq9V9LDmCaeXJ9d+pVtpCj45L6BZWfDCaqICwqnA5SvR0Pblpskxl99BuzLA6/yrJ9M5wYnRpVq/iu3XMWEEBrwOXWHwQGeuL2nueW0eE7DArvtlgE5TCL0JD02M9mjOGlY2S0e45eG4dz60TT45FoONUZjfp/MEQGF5ChMBfTY/je601QLYQWyZF3VDnrUXfSaZ3IodVVUSpcxMej0am7zaa1cuRWaYyLhUIITbJLmihLIIQBpquGE9B0EHTNgM3uzgdTbMiE9mb+7ipz7ueatm+23HsEq3adazEEMnsbmWx4jXogq4BAqnsahRWbHMIoaskt72C51Z6sCxB4EU+UciAkR2/EGK1eqNbVQ02sJBUchCEgGvF7LoZQrSbbMmgA1qICRQauplSrezUGrqle880zVSV0MCLJjPHXlXPQFih0s00+cF7CLy2apUYCaqrH+JFlSy1KBGa4KGHHuaRRx5mZmYW3/N48803mZufJFO4Jbp+qcwuKisfUF75YJ34ZKgO7VRoVC4wODhIPBHHMAweuP8BxsfHOXb8OKdOnabVnCOfy3PX7Q/x/tGjeK5No3pJbUMGWJbFbUeOcO7cOZxOBSkl8eT4FhYmKhLpCSrtJb5BCS/EKN173308++yzaNpaldK2bV559VVOnTwZWRDl8wWSqVHm596mVjyBbqRUIiZ9nnjySR55+GH8QJlWd7e1vLKitlGvk06n2blzJy+99BKl4jRWPIcQqlWXSCb5ws9/iQsXL7CyvEIikcA0TS5dvkzHXg5bxXHs1hyGmYisgrptysDvcPi22xgYGOD06VM4jstg/xCPPfYY+/bt23QddkxM8Nu/9VuUSiUajQbpTJq+ws39L3t+u2OCXbt2MT09Q37gXqxQYyrwO9SKJxB4PPTQQz/Vtj6qGB0Z4Utf/OLf6TF8FPEPIjFaXV3F9/1NJnXDw8NcvHhx0/f/3b/7d/zbf/tvP67Di8LQDR595BEefeSR6LMrV6/y9a9/Hc8pEU9NIIROuzGF59RZmvkBuf47sBJDCM2IeuhdPIqmGRhmFs9rsDz3EvmBe4glhlSbp3aVyopaNeX6b+9hlnVCgK5upBS1VQY0Kpd7WindaFQvgxAkkmt2AZ5TC9tuoqc64/ttpO9usq7o4gy0EJTr++2w/bF+0NUYGH2M4uKbCuAdttJAEE9tw24t4DoVVeb3OghNj2jOhpkN1afXAK4bIwK4bkgA3E4pbPl1WzcBlZWjgFCA83C1FoQJhGkVNk32ge+E2KGXkTLAbs0CayD0tZDYzdkeLRRF+72KZsQZ3fllNM2gtPwezRCHk8ruIZYYplY8hduphi3YzUmM3ZxHaCa6HieRmqDM+zQql3sS1O4xNCoXVfUtlg9XzufotBe3NJ3ttJRTeFejSilaKz0mx14JJxZJ4LVp1acicUVNmOqZdcrAzk3bldLHc6pYiSGGt38OkMxPfksBVqXb03qVcmu6stupAJuT4U57EdPKkc4fpLJybDNjcsN3NyazQdDBc6okN1wPJTApomcG4OChQ3zhCz/P8WPHKZZKjAwPMzo6yp997WsU519DMxRyxPdaGKbJV77yFfwgYGlpiXwux6FDh6LJf89u5aO3fft2/r9f/SpL098nnTtILDmC77XR9Bjt+g0WnZqSkjASdFqLSvk+neQ3f+s3ScR7FxwbxxuAxx9/nAsXLjA1rZh6O3fu5ODBA+iazs99/vMA/MVf/AXTs/Utr7u6Ri6GafLpT3+aWCzGwYMHSac3L6zi8Tif/9znePqppyiVyxiGQX9fH0IIlpaWOHP2LO12i3z+dm6//QjZjBIZ1LVewO/Q4CBPP/10z2f79u3lypWrXLt2jSAIGB9/lFtvvQXTMHsUoAMZ8Nprr/P+e+/3sETHt2/n53/u16jVaiwtL2MaBvv37yefzwPw6Ucfven5b4y+vj76buJcf7MQCH71V3+Vb3/721y8+DaabqBpBp5rk83m+PI/+icMDmwNk/gk/nbxDyIx+m+N3/u93+N3f/d3o//XarWfWPb8qGLf3r38zr/4F3xw7BhXr6oXfN+RW9ixYwevvfZaL7sG1VpZK28Lsn23UVp6G89tsDzzfLjyUROJpsWQUotMa0ExmLrmqQoXpAM+tdJpNCNBOgQRS+nRqFykUT4faiINAwIz3k+9fI5EepyNitb18jmE0HpMQBUQ9FKEy+gbfpDVuVdoVC5umrQlEtepEEuMMDD6GDLwmL/xTTQtxtjuX6HdmAp1fVIk0hOszr8WtvHUhCI0g1rp7JYA11r5LAjRo1DtuTWFjxE6rfqkYndVL4d6O0qErluVatauhO7svefsOVWlLZMYAiGIJ0exm3M0q1d6tHhA0XI9t046tR3HXsVza9RCwDrA3LU/p7uCV/tRSdP66l5l5YMNFivgdIo0q5eVjk3IKkvnDlAtnlAJZG4/QigmVq10inZjhv7RRwFl5il0i2rxJLHkWE+yKgOXSlGpp3tuU2G/QvAtQhBLjDI0/gxSSuYn/4ri4lsEQYdUdm9EHGhULoWWFb3JcrN2RYH+EVRWjql2oRtOxOvxaEJQK53uMRztPi210pmojduNdnOGTmuR/tFHSWZ2E/iOcnwvnQ4Zk2vhdIo0a9fQtFgPFqtWPIOUfkS/BlWVUVggyZe//GU0XWfnzh0kEypJffjhh3u2/Xv/5t9w/MQJLlw4DxIOHDjAPffegxbuZ++ezYat3UgkEvyrf/Uv+d73vs+lSxcj0kJffz933H4fly9fZnZWUcYN0+Tee+7i8ccfJx7/6dhLuq5z+PBhDh/ebL/TjX379nH5yg+29o2TAe3GNQ6Gbu8/TViWxcgWC9e/jeO6JjQO7N/Pgf37f+L3nnziCR5++CEmr0/ieh7DQ0MR9nRoaIi9e/f+2G18VBGLxfjVX/1VVotFLl+6hOd5jIyMsHff3uhZ+SR+9iGk3IDq/R8wHMchmUzyjW98gy+uK/v9+q//OpVKhe985zs/9ve1Wo1cLscf/dUkyeTmasPfZSwvL1Mul4nH4zz//PMsLS0RS46Rzu9H12K0m3PUK+dCqm0aw8wgpY9jlyD03cn2HQmtDZapl88TBB10PU06vw8p/ajEDcpx3TAzuE4tarNk++8g138EUKrH1dXjxFPj5Afuwoz19YhPJtIT9I9+GiF0HHuV8sr7OO1V0vlD5AfuDMGX30YpM98eiSg69iqV1eN0WovEk2OkIvn54wR+m0Rmp5I5sHI4dpFa6RR2c175ro2qlV1x8U3a9Rsk0hPk+u/EjCnsSq18jkb5AoaZYWDsCTTdpN2YpVo8qVy5pSDyOusqh28Rqexe8kP3RRU7z6myMv8qntugMHgvZixPxy5SWz2BlD6J9PZo0m7Wrkcsod5QgAklBKfMcEGEoN2tX10zViCdP4CuK6PORvWyUokeeTQUH1TAcKUVFVLUjXhU3QGF8UlmdoIMqJXO0mkvoZspsoVblZSEU6FePofnKNHPwGshCRRo2O8QeG2y/beTTO/A8xrUSmdx2kvRNRRCizy0DDNDbuAuEslt+H6HZvWyElyENXxd1M5UYPJEejtIJYonA4dEeoJs35EI4FotnsZuzmDGB8j134EQGq36DZq1K+G92kcsMYjr1KmH+4qntik2n6a0YhqVC0gCUtl9ZPIH8X2bRuViaDUSI9d/B7qRpNNeplG9hJQ+n//c57jnnl4NqY8y/MCn1WxhWmYPbdvu2LiOSzKV3FRd+VlEx+nw1a/+RzzPom/08Sg5CoIOleX3aTVu8E9/+7cZG9sam/hJfBIfV7RadX77V3ZRrVbJZjfbmmwV/yASI4D777+f++67j69+9asABEHAxMQEv/M7v/MTwdd/nxOj9eH5Hn/+53/O5OSNdatqQSweI5NOs1os0p1odV2xt3w/2MR4i6IL6lu3rZtNxl1TRJUsiUjHYy2RWPttpKi8ji79Y2Odjcl6TZWeY9q4nY3f6R7P+r9tcWw3OQDWMEabhFt6PhNCj/zoFI33x5zf+r/9pOvw3/LdLX93k+v0M9pfz/3f6vs/dhsbr+tPuB9bHdvN7v/NzmPjdflpz/nHnNtnP/vZj13+4+8ylpaX+bP/+mc0mnViiWGEMHDsJQSSL37pSxy+9dafvJFP4pP4iOP/SmL0D6aV9ru/+7v8+q//Ovfccw/33Xcfv//7v0+z2YxYav8jhKEbfOXXvkK9Xuf0mTM4nQ4TExPs3rMbgcD1XGrVGoZpkM1mEQjsjs03v/lNatUau3bv5tlnn0EgKJfLnD17Ft0wuOOO20kmkqysrPAnf/In2LZNIpngt37ztygUCnz/uec4deokINl34CC/8su/guu5/Of//J8plUrE4yl+8zd+k/7+fn7w/A84duwYQeCTy+X4F//iX2AYBv/xD/4j5VIZTdP44he/yOHDh5m8cYOvfe3PCPyAbC7H7/zO71CtVvnLr/8lqyurIASHD9/KZz/zWQD+6I//iHqtTl9/H//0t/8phmHwN9/+NmdOn0YiOXTLLfzKL/8yS0tL/Kf/9J+UlL6AX/6lX+KWW27h5KlT/OC55wiCgL379vIrv/Ir+L7PH/7hH1IqldB1jV/9lV8N2whX+O53voPruoyMjPCP//E/xnEcvv5XX2dpcRGhCe6//34ef/xxzp49yw+ef57A94nFYvziL/4i+/bv48MPT/LKKy8jEDz9zDPcfuQIbbvN//H/+z+o1+vEE3H+p3/2P5HN/v/bu/egqMu2D+Df5bCIclKMk7KIQiAqCGK4mo81MqkxnqIkBhsMmVGDVzCnJEmpMR6dcWpGx0k7oFaaij5K5iElUAojDsZBogEURtFEKlwBDyDs9f7xrPu26vs8UOb2W7+fmZ2R330vXN+5cfbid3RCQ0MDjh0/hu7uboSFjcNErRYqlQo1P/2EmpoaDBw4EE9NmQKogB+rf0Refh66Orvg4+ODZ6Y9A2dnZ+z4bAcuNF2AtZUNno2KwtiQEHR2dmLr1q241nYNvr6+mDdvHlRQobyiAvl5ebCyssLMWbPgN2IEum534dChw/j550vw8PDAzJkzoVarkZeXj9LSEogI/P2DMHfuXFy71obs7D3QXdVBrVZj+owZCAoaCb1ej6Ki79He3o6xY8fC0+Pf5yYVl5Sgorwc/fv3x6xZszDAYQB+/LEGhYWFuHH9Bjw83fGPf0yBRuONX1p+wanvTsHGxhZPP/0UHAY4oLOzE/v+tQ+//vor3B5zQ3R0NNRqNfR6PX4oL0f37dsIGxcGta0aPT092H9gPy5dvARHR0fMi5kHRwdHXG5uxnfffYfbXV3w8/NDWFgYrly5guO5x3H5cjNsbWwwcdIkjAsLQ09PD6qqqtDW1gaNRgP/x/0fuUMb7m5uWLr0f1BdXY36s/Xo6enBEK/JCAsLu+/5RERK8cjsMQKATZs2GW/wOHbsWGzcuBEREffezvxuStljRERERP+He4z+i+TkZCQnJ5u7DCIiIvqberT2/RIRERH9B2yMiIiIiAzYGBEREREZsDEiIiIiMmBjRERERGTAxoiIiIjIgI0RERERkQEbIyIiIiIDNkZEREREBo/Una//qDtPTbl5o93MlRAREVFv3fnc7svTzx6pZ6X9URcvXoS3t7e5yyAiIqI/oKmpCUOHDu3VXDZGvaDX6/Hzzz/D0dERKpXK3OXco62tDd7e3mhqaur1Q/KUwpKzAcynZJacDWA+JbPkbEDf8okI2tvb4eXlBSur3p09xENpvWBlZdXrTtOcnJycLPI/AWDZ2QDmUzJLzgYwn5JZcjag9/mcnZ379H158jURERGRARsjIiIiIgM2RhbAzs4OGRkZsLOzM3cpD5wlZwOYT8ksORvAfEpmydmAvz4fT74mIiIiMuAeIyIiIiIDNkZEREREBmyMiIiIiAzYGBEREREZsDFSiLVr12L8+PFwdHSEm5sb5syZg9raWpM5t27dQlJSElxdXeHg4IDo6GhcuXLFTBX3zebNmxEcHGy8YZdWq8XRo0eN40rOdrd169ZBpVIhNTXVuE3J+d566y2oVCqTV2BgoHFcydnuuHTpEubPnw9XV1fY29tjzJgxKCsrM46LCFavXg1PT0/Y29sjMjIS9fX1Zqy4d4YNG3bP2qlUKiQlJQFQ/tr19PRg1apV8PX1hb29PUaMGIE1a9aYPDdLqWsHAO3t7UhNTYWPjw/s7e0xceJElJaWGseVlO2bb77BzJkz4eXlBZVKhZycHJPx3mRpbW1FXFwcnJyc4OLigoULF6Kjo6PvxQgpwrRp02Tbtm1SXV0tFRUV8uyzz4pGo5GOjg7jnMWLF4u3t7fk5eVJWVmZTJgwQSZOnGjGqnvv4MGDcvjwYamrq5Pa2lpZuXKl2NraSnV1tYgoO9vvlZSUyLBhwyQ4OFhSUlKM25WcLyMjQ0aNGiWXL182vn755RfjuJKziYi0traKj4+PLFiwQIqLi6WhoUGOHTsmZ8+eNc5Zt26dODs7S05OjlRWVsqsWbPE19dXbt68acbK/7uWlhaTdcvNzRUAcuLECRFR/tplZmaKq6urHDp0SBobG2Xv3r3i4OAgGzZsMM5R6tqJiMybN0+CgoKkoKBA6uvrJSMjQ5ycnOTixYsioqxsR44ckfT0dNm/f78AkAMHDpiM9ybL9OnTJSQkRL7//nv59ttvxc/PT2JjY/tcCxsjhWppaREAUlBQICIiOp1ObG1tZe/evcY5P/30kwCQoqIic5X5pwwcOFA+/vhji8nW3t4u/v7+kpubK1OmTDE2RkrPl5GRISEhIfcdU3o2EZEVK1bIk08++f+O6/V68fDwkPXr1xu36XQ6sbOzk127dj2MEh+YlJQUGTFihOj1eotYu6ioKElISDDZ9txzz0lcXJyIKHvtbty4IdbW1nLo0CGT7WFhYZKenq7obHc3Rr3JUlNTIwCktLTUOOfo0aOiUqnk0qVLffr5PJSmUNeuXQMADBo0CABw+vRp3L59G5GRkcY5gYGB0Gg0KCoqMkuNf1RPTw92796N69evQ6vVWky2pKQkREVFmeQALGPt6uvr4eXlheHDhyMuLg4XLlwAYBnZDh48iPDwcLzwwgtwc3NDaGgoPvroI+N4Y2MjmpubTTI6OzsjIiJCMRkBoKurCzt27EBCQgJUKpVFrN3EiRORl5eHuro6AEBlZSUKCwsxY8YMAMpeu+7ubvT09KBfv34m2+3t7VFYWKjobHfrTZaioiK4uLggPDzcOCcyMhJWVlYoLi7u08/jQ2QVSK/XIzU1FZMmTcLo0aMBAM3NzVCr1XBxcTGZ6+7ujubmZjNU2XdnzpyBVqvFrVu34ODggAMHDiAoKAgVFRWKz7Z792788MMPJsf/71D62kVERGD79u0ICAjA5cuX8fbbb2Py5Mmorq5WfDYAaGhowObNm/Hqq69i5cqVKC0txdKlS6FWqxEfH2/M4e7ubvI+JWUEgJycHOh0OixYsACA8n8vASAtLQ1tbW0IDAyEtbU1enp6kJmZibi4OABQ9No5OjpCq9VizZo1GDlyJNzd3bFr1y4UFRXBz89P0dnu1psszc3NcHNzMxm3sbHBoEGD+pyXjZECJSUlobq6GoWFheYu5YEKCAhARUUFrl27hn379iE+Ph4FBQXmLutPa2pqQkpKCnJzc+/5684S3PnrGwCCg4MREREBHx8fZGdnw97e3oyVPRh6vR7h4eH45z//CQAIDQ1FdXU1tmzZgvj4eDNX9+BkZWVhxowZ8PLyMncpD0x2djZ27tyJzz//HKNGjUJFRQVSU1Ph5eVlEWv32WefISEhAUOGDIG1tTXCwsIQGxuL06dPm7s0ReOhNIVJTk7GoUOHcOLECQwdOtS43cPDA11dXdDpdCbzr1y5Ag8Pj4dc5R+jVqvh5+eHcePGYe3atQgJCcGGDRsUn+306dNoaWlBWFgYbGxsYGNjg4KCAmzcuBE2NjZwd3dXdL67ubi44PHHH8fZs2cVv3YA4OnpiaCgIJNtI0eONB4uvJPj7qu1lJTx/Pnz+Prrr5GYmGjcZglr99prryEtLQ0vvvgixowZg5deegnLli3D2rVrASh/7UaMGIGCggJ0dHSgqakJJSUluH37NoYPH674bL/XmyweHh5oaWkxGe/u7kZra2uf87IxUggRQXJyMg4cOID8/Hz4+vqajI8bNw62trbIy8szbqutrcWFCxeg1WofdrkPhF6vR2dnp+KzTZ06FWfOnEFFRYXxFR4ejri4OOO/lZzvbh0dHTh37hw8PT0Vv3YAMGnSpHtujVFXVwcfHx8AgK+vLzw8PEwytrW1obi4WDEZt23bBjc3N0RFRRm3WcLa3bhxA1ZWph9z1tbW0Ov1ACxj7QBgwIAB8PT0xNWrV3Hs2DHMnj3bYrIBvVsnrVYLnU5nsrcsPz8fer0eERERffuBf+rUcXpolixZIs7OznLy5EmTy2tv3LhhnLN48WLRaDSSn58vZWVlotVqRavVmrHq3ktLS5OCggJpbGyUqqoqSUtLE5VKJcePHxcRZWe7n99flSai7HzLly+XkydPSmNjo5w6dUoiIyNl8ODB0tLSIiLKziby71ss2NjYSGZmptTX18vOnTulf//+smPHDuOcdevWiYuLi3zxxRdSVVUls2fP/tteFn23np4e0Wg0smLFinvGlL528fHxMmTIEOPl+vv375fBgwfL66+/bpyj5LX76quv5OjRo9LQ0CDHjx+XkJAQiYiIkK6uLhFRVrb29nYpLy+X8vJyASDvvfeelJeXy/nz50Wkd1mmT58uoaGhUlxcLIWFheLv78/L9S0ZgPu+tm3bZpxz8+ZNeeWVV2TgwIHSv39/mTt3rly+fNl8RfdBQkKC+Pj4iFqtlscee0ymTp1qbIpElJ3tfu5ujJScLyYmRjw9PUWtVsuQIUMkJibG5B4/Ss52x5dffimjR48WOzs7CQwMlA8//NBkXK/Xy6pVq8Td3V3s7Oxk6tSpUltba6Zq++bYsWMC4L71Kn3t2traJCUlRTQajfTr10+GDx8u6enp0tnZaZyj5LXbs2ePDB8+XNRqtXh4eEhSUpLodDrjuJKynThx4r6fcfHx8SLSuyy//fabxMbGioODgzg5OcnLL78s7e3tfa5FJfK7W4ASERERPcJ4jhERERGRARsjIiIiIgM2RkREREQGbIyIiIiIDNgYERERERmwMSIiIiIyYGNEREREZMDGiIiIiMiAjRERWayioiJYW1ubPAOMiOg/4Z2vichiJSYmwsHBAVlZWaitrYWXl5e5SyKivznuMSIii9TR0YE9e/ZgyZIliIqKwvbt203GDx48CH9/f/Tr1w9PP/00PvnkE6hUKuh0OuOcwsJCTJ48Gfb29vD29sbSpUtx/fr1hxuEiB4qNkZEZJGys7MRGBiIgIAAzJ8/H1u3bsWdHeSNjY14/vnnMWfOHFRWVmLRokVIT083ef+5c+cwffp0REdHo6qqCnv27EFhYSGSk5PNEYeIHhIeSiMiizRp0iTMmzcPKSkp6O7uhqenJ/bu3YunnnoKaWlpOHz4MM6cOWOc/+abbyIzMxNXr16Fi4sLEhMTYW1tjQ8++MA4p7CwEFOmTMH169fRr18/c8Qior8Y9xgRkcWpra1FSUkJYmNjAQA2NjaIiYlBVlaWcXz8+PEm73niiSdMvq6srMT27dvh4OBgfE2bNg16vR6NjY0PJwgRPXQ25i6AiOhBy8rKQnd3t8nJ1iICOzs7bNq0qVffo6OjA4sWLcLSpUvvGdNoNA+sViL6e2FjREQWpbu7G59++ineffddPPPMMyZjc+bMwa5duxAQEIAjR46YjJWWlpp8HRYWhpqaGvj5+f3lNRPR3wfPMSIii5KTk4OYmBi0tLTA2dnZZGzFihXIz89HdnY2AgICsGzZMixcuBAVFRVYvnw5Ll68CJ1OB2dnZ1RVVWHChAlISEhAYmIiBgwYgJqaGuTm5vZ6rxMRKQ/PMSIii5KVlYXIyMh7miIAiI6ORllZGdrb27Fv3z7s378fwcHB2Lx5s/GqNDs7OwBAcHAwCgoKUFdXh8mTJyM0NBSrV6/mvZCILBz3GBERAcjMzMSWLVvQ1NRk7lKIyIx4jhERPZLef/99jB8/Hq6urjh16hTWr1/PexQRERsjIno01dfX45133kFrays0Gg2WL1+ON954w9xlEZGZ8VAaERERkQFPviYiIiIyYGNEREREZMDGiIiIiMiAjRERERGRARsjIiIiIgM2RkREREQGbIyIiIiIDNgYERERERmwMSIiIiIy+F/BaroNsebKugAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conclusion and Recommendation:\n",
        "\n",
        "\n",
        "###***Comparison on the basis of SK learn outputs:***\n",
        "\n",
        " The  linear and Logistic classifier have perfomed equally well on this dataset.They have  achieved identical perfomance across all metrices .\n",
        "\n",
        "Both models have an accuracy of 90.57% which indicates that around 90.57%  of the prediction were made by model were correct.Precision is 0.66%  which indicates that out  of all the deposit terms that are  predicted susbscribed (\"yes\") about 0.66% of them were true.\n",
        "\n",
        "0.40% for recall  which indicates that  out of all term deposit that are subscribe about 0.40%  were correctly predicted.\n",
        "\n",
        "The confusion matrices for both models show the same counts of true positives, true negatives, false positives, and false negatives.\n",
        "\n",
        "THe F1-score of  0.50% indicates that both models strikes between\n",
        "correctly identifying truly positive and minimizing false positive and false negative.\n",
        "\n",
        "\n",
        "###***Comparison on the basis of Manaual outputs:***\n",
        "\n",
        "## linear classifier\n",
        "88.21%  high accuracy for this classifier , but model is not  capturing positive cases.There maybe a imbalance significant class that lead to it.\n",
        "Precision is undefined because there were no positive predictions.\n",
        "model failed to capture any of the actual positive cases in recall case.\n",
        "FF1-Score is undefined because there were no positive predictions.\n",
        "\n",
        "##logistic regression calssifier:\n",
        " 75.91% of the predictions made by the logistic regression model were correct.\n",
        " out of all deposit terms that are predicted subscribe  by the model, only 30% were actually correct. This suggests a relatively high number of false positives.Model a captures 75% of all deposit terms that are subscribe . This\n",
        "The F1-Score, which balances precision and recall, is 0.42 which suggests a trade-off between precision and recall .\n",
        "\n",
        "The linear classifier has high accuracy due to a large number of true negatives, but it completely fails in capturing positive cases on other hand The logistic regression model outperforms the linear classifier in terms of capturing positive cases (higher recall), but it has a relatively low precision\n",
        "\n",
        "##Recommendation:\n",
        "Feature Engineering to potentially enhance perfomance\n",
        "\n",
        "Regularization\n",
        "\n"
      ],
      "metadata": {
        "id": "7YzhMJF2I_xg"
      }
    }
  ]
}