PREDICTING HEART DISEASE MORTALITY RATE.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "collapsed_sections": [
        "lA_Hmh-Zr7qp",
        "G4kt50GaDMxI",
        "9QTY8ex9C8V2",
        "EyyNCSgSr5Dx",
        "m54N0wz18g-C",
        "dGv9YpWChT3W",
        "mzge81co0aBJ"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# AIN427 TERM PROJECT - PREDICTING HEART DISEASE MORTALITY RATE\n",
        "# B2210765024 - İLBEY GÜLMEZ\n"
      ],
      "metadata": {
        "id": "lA_Hmh-Zr7qp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reading the Data"
      ],
      "metadata": {
        "id": "G4kt50GaDMxI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vkTQENkPAVVK"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTB-XjVBA6E0",
        "outputId": "d079716d-9a37-4235-a91a-cd387119c61e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir('drive/MyDrive/Colab Notebooks')"
      ],
      "metadata": {
        "id": "XvREPOP-2r3_"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('data.csv')"
      ],
      "metadata": {
        "id": "VAaboGj0A_RF"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Exploration"
      ],
      "metadata": {
        "id": "9QTY8ex9C8V2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "FurBmderDb-N"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "32ZPv1bUJQtP",
        "outputId": "e437b0a7-2ad7-4189-d3b2-afee33d87f1f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year LocationAbbr    LocationDesc GeographicLevel DataSource  \\\n",
              "0  2014           AK  Aleutians East          County       NVSS   \n",
              "1  2014           AK  Aleutians West          County       NVSS   \n",
              "2  2014           AK       Anchorage          County       NVSS   \n",
              "3  2014           AK          Bethel          County       NVSS   \n",
              "4  2014           AK     Bristol Bay          County       NVSS   \n",
              "\n",
              "                     Class                    Topic  Data_Value  \\\n",
              "0  Cardiovascular Diseases  Heart Disease Mortality       105.3   \n",
              "1  Cardiovascular Diseases  Heart Disease Mortality       211.9   \n",
              "2  Cardiovascular Diseases  Heart Disease Mortality       257.9   \n",
              "3  Cardiovascular Diseases  Heart Disease Mortality       351.6   \n",
              "4  Cardiovascular Diseases  Heart Disease Mortality         NaN   \n",
              "\n",
              "          Data_Value_Unit                                    Data_Value_Type  \\\n",
              "0  per 100,000 population  Age-adjusted, Spatially Smoothed, 3-year Avera...   \n",
              "1  per 100,000 population  Age-adjusted, Spatially Smoothed, 3-year Avera...   \n",
              "2  per 100,000 population  Age-adjusted, Spatially Smoothed, 3-year Avera...   \n",
              "3  per 100,000 population  Age-adjusted, Spatially Smoothed, 3-year Avera...   \n",
              "4  per 100,000 population  Age-adjusted, Spatially Smoothed, 3-year Avera...   \n",
              "\n",
              "  Data_Value_Footnote_Symbol Data_Value_Footnote StratificationCategory1  \\\n",
              "0                        NaN                 NaN                  Gender   \n",
              "1                        NaN                 NaN                  Gender   \n",
              "2                        NaN                 NaN                  Gender   \n",
              "3                        NaN                 NaN                  Gender   \n",
              "4                          ~   Insufficient Data                  Gender   \n",
              "\n",
              "  Stratification1 StratificationCategory2 Stratification2 TopicID  LocationID  \\\n",
              "0         Overall          Race/Ethnicity         Overall      T2        2013   \n",
              "1         Overall          Race/Ethnicity         Overall      T2        2016   \n",
              "2         Overall          Race/Ethnicity         Overall      T2        2020   \n",
              "3         Overall          Race/Ethnicity         Overall      T2        2050   \n",
              "4         Overall          Race/Ethnicity         Overall      T2        2060   \n",
              "\n",
              "                 Location 1  \n",
              "0  (55.440626, -161.962562)  \n",
              "1  (52.995403, -170.251538)  \n",
              "2  (61.159049, -149.103905)  \n",
              "3  (60.924483, -159.749655)  \n",
              "4  (58.754192, -156.694709)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4e700d1-a9f1-4910-9806-bf7dc227f92b\" 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>Year</th>\n",
              "      <th>LocationAbbr</th>\n",
              "      <th>LocationDesc</th>\n",
              "      <th>GeographicLevel</th>\n",
              "      <th>DataSource</th>\n",
              "      <th>Class</th>\n",
              "      <th>Topic</th>\n",
              "      <th>Data_Value</th>\n",
              "      <th>Data_Value_Unit</th>\n",
              "      <th>Data_Value_Type</th>\n",
              "      <th>Data_Value_Footnote_Symbol</th>\n",
              "      <th>Data_Value_Footnote</th>\n",
              "      <th>StratificationCategory1</th>\n",
              "      <th>Stratification1</th>\n",
              "      <th>StratificationCategory2</th>\n",
              "      <th>Stratification2</th>\n",
              "      <th>TopicID</th>\n",
              "      <th>LocationID</th>\n",
              "      <th>Location 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014</td>\n",
              "      <td>AK</td>\n",
              "      <td>Aleutians East</td>\n",
              "      <td>County</td>\n",
              "      <td>NVSS</td>\n",
              "      <td>Cardiovascular Diseases</td>\n",
              "      <td>Heart Disease Mortality</td>\n",
              "      <td>105.3</td>\n",
              "      <td>per 100,000 population</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gender</td>\n",
              "      <td>Overall</td>\n",
              "      <td>Race/Ethnicity</td>\n",
              "      <td>Overall</td>\n",
              "      <td>T2</td>\n",
              "      <td>2013</td>\n",
              "      <td>(55.440626, -161.962562)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014</td>\n",
              "      <td>AK</td>\n",
              "      <td>Aleutians West</td>\n",
              "      <td>County</td>\n",
              "      <td>NVSS</td>\n",
              "      <td>Cardiovascular Diseases</td>\n",
              "      <td>Heart Disease Mortality</td>\n",
              "      <td>211.9</td>\n",
              "      <td>per 100,000 population</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gender</td>\n",
              "      <td>Overall</td>\n",
              "      <td>Race/Ethnicity</td>\n",
              "      <td>Overall</td>\n",
              "      <td>T2</td>\n",
              "      <td>2016</td>\n",
              "      <td>(52.995403, -170.251538)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014</td>\n",
              "      <td>AK</td>\n",
              "      <td>Anchorage</td>\n",
              "      <td>County</td>\n",
              "      <td>NVSS</td>\n",
              "      <td>Cardiovascular Diseases</td>\n",
              "      <td>Heart Disease Mortality</td>\n",
              "      <td>257.9</td>\n",
              "      <td>per 100,000 population</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gender</td>\n",
              "      <td>Overall</td>\n",
              "      <td>Race/Ethnicity</td>\n",
              "      <td>Overall</td>\n",
              "      <td>T2</td>\n",
              "      <td>2020</td>\n",
              "      <td>(61.159049, -149.103905)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014</td>\n",
              "      <td>AK</td>\n",
              "      <td>Bethel</td>\n",
              "      <td>County</td>\n",
              "      <td>NVSS</td>\n",
              "      <td>Cardiovascular Diseases</td>\n",
              "      <td>Heart Disease Mortality</td>\n",
              "      <td>351.6</td>\n",
              "      <td>per 100,000 population</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gender</td>\n",
              "      <td>Overall</td>\n",
              "      <td>Race/Ethnicity</td>\n",
              "      <td>Overall</td>\n",
              "      <td>T2</td>\n",
              "      <td>2050</td>\n",
              "      <td>(60.924483, -159.749655)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014</td>\n",
              "      <td>AK</td>\n",
              "      <td>Bristol Bay</td>\n",
              "      <td>County</td>\n",
              "      <td>NVSS</td>\n",
              "      <td>Cardiovascular Diseases</td>\n",
              "      <td>Heart Disease Mortality</td>\n",
              "      <td>NaN</td>\n",
              "      <td>per 100,000 population</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>~</td>\n",
              "      <td>Insufficient Data</td>\n",
              "      <td>Gender</td>\n",
              "      <td>Overall</td>\n",
              "      <td>Race/Ethnicity</td>\n",
              "      <td>Overall</td>\n",
              "      <td>T2</td>\n",
              "      <td>2060</td>\n",
              "      <td>(58.754192, -156.694709)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4e700d1-a9f1-4910-9806-bf7dc227f92b')\"\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-c4e700d1-a9f1-4910-9806-bf7dc227f92b 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-c4e700d1-a9f1-4910-9806-bf7dc227f92b');\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-86176050-f809-4fdb-bdaf-e81997d1fab3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-86176050-f809-4fdb-bdaf-e81997d1fab3')\"\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-86176050-f809-4fdb-bdaf-e81997d1fab3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_yiTJlEyEl37",
        "outputId": "f529d942-855b-4c96-9566-64a9fae79ed1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(59076, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9W4YId_cofy",
        "outputId": "061693cb-f5d1-47a0-cf9b-dd796e77c0d2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Year', 'LocationAbbr', 'LocationDesc', 'GeographicLevel', 'DataSource',\n",
              "       'Class', 'Topic', 'Data_Value', 'Data_Value_Unit', 'Data_Value_Type',\n",
              "       'Data_Value_Footnote_Symbol', 'Data_Value_Footnote',\n",
              "       'StratificationCategory1', 'Stratification1', 'StratificationCategory2',\n",
              "       'Stratification2', 'TopicID', 'LocationID', 'Location 1'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the histogram below, it can be seen that data contains a lot of NaN values and they should be handled. Also it can be seen that data is very noisy."
      ],
      "metadata": {
        "id": "8vtqsetPEU25"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_data = data['Data_Value'].fillna(-200) #NaN values are marked as -200\n",
        "sns.histplot(plot_data, bins=30, kde=False, color='turquoise', edgecolor='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "EmT-rZ0NDBJI",
        "outputId": "f3318ab7-5267-4611-9171-c953c144271b"
      },
      "execution_count": 9,
      "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": [
        "## Data Preprocessing"
      ],
      "metadata": {
        "id": "EyyNCSgSr5Dx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for column in data:   # If there is 1 type of column in the data, drop the column\n",
        "  if len(data[column].unique()) == 1:\n",
        "    print(f\"In {column} column all cells contain {data[column][0]}\")\n",
        "    data = data.drop(column, axis = 1)"
      ],
      "metadata": {
        "id": "mWk2tHfbBAIR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a49ce6c-d581-40c2-88db-d38855b7b867"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In Year column all cells contain 2014\n",
            "In DataSource column all cells contain NVSS\n",
            "In Class column all cells contain Cardiovascular Diseases\n",
            "In Topic column all cells contain Heart Disease Mortality\n",
            "In Data_Value_Unit column all cells contain per 100,000 population\n",
            "In StratificationCategory1 column all cells contain Gender\n",
            "In StratificationCategory2 column all cells contain Race/Ethnicity\n",
            "In TopicID column all cells contain T2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In LocationDesc, there are 2015 unique values. Too much dimension in values will result in low accuracy, so it should be reduced. LocationAbbr columns are abbreviations of the exact locations, it also generalizes them too. For example, Aleutians East and Aleutians West are both abbreviated as AK. Thus, LocationDesc will be dropped and LocationAbbr will be used in the model."
      ],
      "metadata": {
        "id": "xyzV-jT0J9H-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"LocationDesc : {len(data['LocationDesc'].unique())}\")\n",
        "print(f\"LocationAbbr : {len(data['LocationAbbr'].unique())}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AlOcdqxeIemD",
        "outputId": "48edb8af-df0e-4d9e-bcb2-8dd6b98ccc35"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LocationDesc : 2015\n",
            "LocationAbbr : 57\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop('LocationDesc', axis = 1)"
      ],
      "metadata": {
        "id": "02nzlgB3Lvr6"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "'Data_Value_Footnote' and 'Data_Value_Footnote_Symbol' are indicators of values being NaN or not. it can already be seen in 'Data_Value' column, so they can be dropped too."
      ],
      "metadata": {
        "id": "1AxQ7il1MqG7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"'Data_Value_Footnote' values : {data['Data_Value_Footnote'].unique()}\")\n",
        "print(f\"'Data_Value_Footnote_Symbol' values : {data['Data_Value_Footnote_Symbol'].unique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfeg3-nZL36s",
        "outputId": "0780e1bc-2c1d-462b-e8cc-5b17af98dc5a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'Data_Value_Footnote' values : [nan 'Insufficient Data']\n",
            "'Data_Value_Footnote_Symbol' values : [nan '~']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop(['Data_Value_Footnote_Symbol', 'Data_Value_Footnote'], axis = 1)"
      ],
      "metadata": {
        "id": "go-I00VRNU4L"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "LocationID won't be necessary for the prediction process. So it can be dropped."
      ],
      "metadata": {
        "id": "32XxzDVIT8oC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop('LocationID', axis = 1)"
      ],
      "metadata": {
        "id": "M2GIcvwaUBOW"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "5rwT6-YFBCYo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "de1804c7-dcb6-48d7-cd4d-09436b41ecf9"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      LocationAbbr GeographicLevel  Data_Value  \\\n",
              "0               AK          County       105.3   \n",
              "1               AK          County       211.9   \n",
              "2               AK          County       257.9   \n",
              "3               AK          County       351.6   \n",
              "4               AK          County         NaN   \n",
              "...            ...             ...         ...   \n",
              "59071           TX          County         NaN   \n",
              "59072           TX          County         NaN   \n",
              "59073           IA          County         NaN   \n",
              "59074           WV          County         NaN   \n",
              "59075           KS          County         NaN   \n",
              "\n",
              "                                         Data_Value_Type Stratification1  \\\n",
              "0      Age-adjusted, Spatially Smoothed, 3-year Avera...         Overall   \n",
              "1      Age-adjusted, Spatially Smoothed, 3-year Avera...         Overall   \n",
              "2      Age-adjusted, Spatially Smoothed, 3-year Avera...         Overall   \n",
              "3      Age-adjusted, Spatially Smoothed, 3-year Avera...         Overall   \n",
              "4      Age-adjusted, Spatially Smoothed, 3-year Avera...         Overall   \n",
              "...                                                  ...             ...   \n",
              "59071  Age-adjusted, Spatially Smoothed, 3-year Avera...            Male   \n",
              "59072  Age-adjusted, Spatially Smoothed, 3-year Avera...            Male   \n",
              "59073  Age-adjusted, Spatially Smoothed, 3-year Avera...          Female   \n",
              "59074  Age-adjusted, Spatially Smoothed, 3-year Avera...          Female   \n",
              "59075  Age-adjusted, Spatially Smoothed, 3-year Avera...          Female   \n",
              "\n",
              "                  Stratification2                Location 1  \n",
              "0                         Overall  (55.440626, -161.962562)  \n",
              "1                         Overall  (52.995403, -170.251538)  \n",
              "2                         Overall  (61.159049, -149.103905)  \n",
              "3                         Overall  (60.924483, -159.749655)  \n",
              "4                         Overall  (58.754192, -156.694709)  \n",
              "...                           ...                       ...  \n",
              "59071  Asian and Pacific Islander  (33.613697, -101.820878)  \n",
              "59072                       Black  (32.306972, -100.919542)  \n",
              "59073  Asian and Pacific Islander    (43.375754, -93.73907)  \n",
              "59074  Asian and Pacific Islander   (38.995666, -80.505332)  \n",
              "59075  Asian and Pacific Islander    (39.82459, -97.646162)  \n",
              "\n",
              "[59076 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b4f204ad-d869-42cf-be1a-c64e16804d14\" 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>LocationAbbr</th>\n",
              "      <th>GeographicLevel</th>\n",
              "      <th>Data_Value</th>\n",
              "      <th>Data_Value_Type</th>\n",
              "      <th>Stratification1</th>\n",
              "      <th>Stratification2</th>\n",
              "      <th>Location 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AK</td>\n",
              "      <td>County</td>\n",
              "      <td>105.3</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>Overall</td>\n",
              "      <td>Overall</td>\n",
              "      <td>(55.440626, -161.962562)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AK</td>\n",
              "      <td>County</td>\n",
              "      <td>211.9</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>Overall</td>\n",
              "      <td>Overall</td>\n",
              "      <td>(52.995403, -170.251538)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AK</td>\n",
              "      <td>County</td>\n",
              "      <td>257.9</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>Overall</td>\n",
              "      <td>Overall</td>\n",
              "      <td>(61.159049, -149.103905)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AK</td>\n",
              "      <td>County</td>\n",
              "      <td>351.6</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>Overall</td>\n",
              "      <td>Overall</td>\n",
              "      <td>(60.924483, -159.749655)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AK</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>Overall</td>\n",
              "      <td>Overall</td>\n",
              "      <td>(58.754192, -156.694709)</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59071</th>\n",
              "      <td>TX</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Asian and Pacific Islander</td>\n",
              "      <td>(33.613697, -101.820878)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59072</th>\n",
              "      <td>TX</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Black</td>\n",
              "      <td>(32.306972, -100.919542)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59073</th>\n",
              "      <td>IA</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>Female</td>\n",
              "      <td>Asian and Pacific Islander</td>\n",
              "      <td>(43.375754, -93.73907)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59074</th>\n",
              "      <td>WV</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>Female</td>\n",
              "      <td>Asian and Pacific Islander</td>\n",
              "      <td>(38.995666, -80.505332)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59075</th>\n",
              "      <td>KS</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Age-adjusted, Spatially Smoothed, 3-year Avera...</td>\n",
              "      <td>Female</td>\n",
              "      <td>Asian and Pacific Islander</td>\n",
              "      <td>(39.82459, -97.646162)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59076 rows × 7 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-b4f204ad-d869-42cf-be1a-c64e16804d14')\"\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-b4f204ad-d869-42cf-be1a-c64e16804d14 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-b4f204ad-d869-42cf-be1a-c64e16804d14');\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-d6246c4e-e00a-44f1-8c95-799a77982e9d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d6246c4e-e00a-44f1-8c95-799a77982e9d')\"\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-d6246c4e-e00a-44f1-8c95-799a77982e9d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5d5ab256-63a2-4f36-aefb-84ebbe6b593d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5d5ab256-63a2-4f36-aefb-84ebbe6b593d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Rename LocationAbbr as location\n",
        "\n",
        "- Rename GeographicLevel as geographic_level\n",
        "\n",
        "- Rename Data_Value as mortality\n",
        "\n",
        "- Rename Data_Value_Type as mortality_type\n",
        "\n",
        "- Rename Stratification1 as gender\n",
        "\n",
        "- Rename Stratification2 as race\n",
        "\n",
        "- Rename Location 1 as coordinates"
      ],
      "metadata": {
        "id": "4MDtUr6jB064"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gHjhkbDCdSUZ",
        "outputId": "cb154279-30c5-4feb-8ee3-adb711f8ccc1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['LocationAbbr', 'GeographicLevel', 'Data_Value', 'Data_Value_Type',\n",
              "       'Stratification1', 'Stratification2', 'Location 1'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.rename(columns = {'LocationAbbr' : 'location' , 'GeographicLevel' : 'geographic_level' ,'Data_Value' : 'mortality','Data_Value_Type' : 'mortality_type', 'Stratification1' : 'gender', 'Stratification2' : 'race', 'Location 1' : 'coordinates'})"
      ],
      "metadata": {
        "id": "pOSVhKTKB1pL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the outputs below, it can be concluded that:"
      ],
      "metadata": {
        "id": "VW83R7N3BLOX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 - location should be LabelEncoded"
      ],
      "metadata": {
        "id": "1MirW7px12TW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "GQvw1GZQs-6F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "location_encoded = le.fit_transform(data['location'])\n",
        "location_encoded = pd.DataFrame(location_encoded, columns = ['location'])\n",
        "\n",
        "data = data.drop('location', axis = 1)\n",
        "\n",
        "data.insert(0, 'location' ,location_encoded)"
      ],
      "metadata": {
        "id": "M2b_UuKIsfNS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2 - In geographic level, there is 58050 rows which is 'County' in 59000 rows. The ones that is not 'County' could be dropped since their contribution to mddel would be insignificant. After that, geographic_level column will also be dropped since it has 1 unique value. This way, not only data is simplified, also dimensions are reduced."
      ],
      "metadata": {
        "id": "89zDYdwu14lg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data[data['geographic_level'] == 'County']\n",
        "data = data.drop('geographic_level', axis = 1)"
      ],
      "metadata": {
        "id": "D6qXLXB01dWE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "3 - mortality should be cleaned and its null values handled. This will be handled after the others."
      ],
      "metadata": {
        "id": "14Ug2J9k4mvp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4 - mortality_type should be handled in a different way or One-Hot Encoded"
      ],
      "metadata": {
        "id": "9ByAhf_S4pxC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Mortality Type column has only : {data['mortality_type'].unique()}\") # It has 1 unique value so column will be dropped.\n",
        "data = data.drop('mortality_type', axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qocjp5gW4ovx",
        "outputId": "95ac9086-490c-4bf1-d9a5-83ae111edf67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mortality Type column has only : ['Age-adjusted, Spatially Smoothed, 3-year Average Rate']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5 - gender should be One-Hot Encoded"
      ],
      "metadata": {
        "id": "JMdSLaDe4sgw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gender_encoded = pd.get_dummies(data['gender'])\n",
        "gender_encoded = gender_encoded.rename(columns = {'Overall' : 'Overall_Gender'})\n",
        "\n",
        "data = data.drop('gender', axis = 1)\n",
        "data = pd.concat([data, gender_encoded], axis = 1)"
      ],
      "metadata": {
        "id": "xkEKrcEm4trc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "6 - race should be One-Hot Encoded"
      ],
      "metadata": {
        "id": "kjwAGltA4xRk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "race_encoded = pd.get_dummies(data['race'])\n",
        "race_encoded = race_encoded.rename(columns = {'Overall' : 'Overall_Race'})\n",
        "\n",
        "data = data.drop('race', axis = 1)\n",
        "data = pd.concat([data, race_encoded], axis = 1)"
      ],
      "metadata": {
        "id": "ghOgwzTO4x1q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "7 - coordinates should be replaced with their euclidian values"
      ],
      "metadata": {
        "id": "kr1C0yAn4zq3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def euclidean_distance(coord_str):\n",
        "    coordinates = np.array([float(x) for x in coord_str[1:-1].split(\",\")])\n",
        "    return \"{:.2f}\".format(np.linalg.norm(coordinates))"
      ],
      "metadata": {
        "id": "zeo41qKnA5je"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['coordinates'] = data['coordinates'].apply(euclidean_distance)"
      ],
      "metadata": {
        "id": "e1yDicuE4z_9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "id": "3Frc9mCzF2Gr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3651840-12ef-42c4-b015-bdb4c38ccc65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "location                                  0\n",
              "mortality                             26745\n",
              "coordinates                               0\n",
              "Female                                    0\n",
              "Male                                      0\n",
              "Overall_Gender                            0\n",
              "American Indian and Alaskan Native        0\n",
              "Asian and Pacific Islander                0\n",
              "Black                                     0\n",
              "Hispanic                                  0\n",
              "Overall_Race                              0\n",
              "White                                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is a awful lot NaN values in mortality column. Multiple solutions will be tried on this.\n",
        "\n",
        "All solutions will be checked and the method with the best performance will be used."
      ],
      "metadata": {
        "id": "noeJ85A5-_UO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 - Multiple Imputation (MICE Imputation)"
      ],
      "metadata": {
        "id": "TYZaWenhZTRN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer"
      ],
      "metadata": {
        "id": "Mz6U7Aptae1K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mice_imputer = IterativeImputer(random_state=100, max_iter=30)\n",
        "mice_imputer.fit(data) #takes a while"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "BJnGur54ZiFT",
        "outputId": "d9f7c385-073c-494a-ba11-18f3b7fc724f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IterativeImputer(max_iter=30, random_state=100)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>IterativeImputer(max_iter=30, random_state=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">IterativeImputer</label><div class=\"sk-toggleable__content\"><pre>IterativeImputer(max_iter=30, random_state=100)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_mice = mice_imputer.transform(data)\n",
        "data_mice = pd.DataFrame(data_mice, columns = data.columns)"
      ],
      "metadata": {
        "id": "_HP8To0cbip6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2 - Regression Imputation"
      ],
      "metadata": {
        "id": "EaJBXw31ZWw-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import KNNImputer"
      ],
      "metadata": {
        "id": "YH6CBWtwpQ1o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_imputer = KNNImputer()\n",
        "knn_imputer.fit(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "9hFDpXuzZiZd",
        "outputId": "1e7b9ef0-04fd-4efa-98c2-5a968f65531c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNNImputer()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNNImputer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNNImputer</label><div class=\"sk-toggleable__content\"><pre>KNNImputer()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_knn = knn_imputer.transform(data) #takes a good while\n",
        "data_knn = pd.DataFrame(data_knn, columns = data.columns)"
      ],
      "metadata": {
        "id": "kogK_aRTpjj-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "3 - Mean Imputation"
      ],
      "metadata": {
        "id": "70woINlcZXJl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer"
      ],
      "metadata": {
        "id": "wnR_pxunqYCe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_imputer = SimpleImputer(strategy='mean')\n",
        "mean_imputer.fit(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "gx1uWFqpZi2U",
        "outputId": "50fb096c-ad80-4b0c-906d-db195c05d1fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SimpleImputer()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_mean = mean_imputer.transform(data)\n",
        "data_mean = pd.DataFrame(data_mean, columns = data.columns)"
      ],
      "metadata": {
        "id": "_cw6lwDzqiCF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "4 - Simply dropping them"
      ],
      "metadata": {
        "id": "7Dl5N-zjZXU6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_drop = data.dropna()"
      ],
      "metadata": {
        "id": "3aFZhldCZjKp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that the all null values are handled, its time to handle outliers."
      ],
      "metadata": {
        "id": "surDGYEzrRaH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "With LocalOutlierFactor, outliers will be labeled as -1, and by returning their index, outliers are assigned to NaN. Then they're filled again with the same method."
      ],
      "metadata": {
        "id": "9lFgmOG74fvT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import LocalOutlierFactor"
      ],
      "metadata": {
        "id": "ALQ4d3CM3hht"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lof = LocalOutlierFactor(novelty=False)"
      ],
      "metadata": {
        "id": "lD9CLRsS1pgv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def handle_outliers(data_cleaned, model):\n",
        "\n",
        "  outlier_labels = lof.fit_predict(np.array(data_cleaned['mortality']).reshape(-1, 1))\n",
        "  outlier_indices = np.where(outlier_labels == -1)[0]\n",
        "  data_cleaned.loc[outlier_indices, 'mortality'] = np.nan\n",
        "\n",
        "  #Now to fill in NaN values again\n",
        "\n",
        "  model.fit(data_cleaned)\n",
        "  data_return = model.transform(data_cleaned)\n",
        "  data_return = pd.DataFrame(data_return, columns = data_cleaned.columns)\n",
        "\n",
        "  return data_return"
      ],
      "metadata": {
        "id": "N1hNXIk45khK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_mice = handle_outliers(data_mice, IterativeImputer(random_state=100, max_iter=5))"
      ],
      "metadata": {
        "id": "AjTnLj4Y5bwM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_knn = handle_outliers(data_knn, KNNImputer())"
      ],
      "metadata": {
        "id": "yuBQQB495KBO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_mean = handle_outliers(data_mean, SimpleImputer(strategy='mean'))"
      ],
      "metadata": {
        "id": "EM4Qyhtu7ty6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data won't be scaled since it contains coordinates."
      ],
      "metadata": {
        "id": "4H6wMWt42KBy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Selection"
      ],
      "metadata": {
        "id": "m54N0wz18g-C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4 Regression models will be tried and evaluated for this case. One with the best performance will be used."
      ],
      "metadata": {
        "id": "41ly4HNFz2q-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "metadata": {
        "id": "rZsaM5gM4FiR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_mse(y_true, y_pred):\n",
        "    return mean_squared_error(y_true, y_pred)\n",
        "\n",
        "def calculate_mape(y_true, y_pred, epsilon=1e-10):\n",
        "    absolute_percentage_error = np.abs((y_true - y_pred) / (y_true + epsilon))\n",
        "    return np.mean(absolute_percentage_error) * 100"
      ],
      "metadata": {
        "id": "_uFh0Coj6d3E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fit_evaluate(model_call, data):\n",
        "  X = data.drop('mortality', axis = 1)\n",
        "  y = data['mortality']\n",
        "  X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size = 0.33, random_state = 123)\n",
        "  model = model_call.fit(X_train, y_train)\n",
        "  predicted = model.predict(X_test)\n",
        "  train_r2 = model.score(X_train, y_train)\n",
        "  test_r2 = model.score(X_test, y_test)\n",
        "  mse = calculate_mse(y_test, predicted) # Wellness of scaling\n",
        "  mape = calculate_mape(y_test, predicted) # How well the impact of outliers are decreased\n",
        "\n",
        "  scores = [train_r2, test_r2, mse, mape]\n",
        "  print(f\"R2 score for train set {model} is : {train_r2:.2f}\")\n",
        "  print(f\"R2 score for test set {model} is : {test_r2:.2f}\")\n",
        "  print(f\"MSE error for {model} is : {mse:.2f}\")\n",
        "  print(f\"MAPE error for {model} is : {mape:.2f}\")\n",
        "  return scores"
      ],
      "metadata": {
        "id": "PGjQCSCd4uG0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Performance for Linear Regression"
      ],
      "metadata": {
        "id": "fNXfsm1fykcA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scores_lr_mice = fit_evaluate(LinearRegression(), data_mice)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W9mO2w0Z3ur2",
        "outputId": "128de74c-9d08-4b6a-b2bf-4cd9c6dd95e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set LinearRegression() is : 0.74\n",
            "R2 score for test set LinearRegression() is : 0.73\n",
            "MSE error for LinearRegression() is : 5149.57\n",
            "MAPE error for LinearRegression() is : 13.37\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_lr_knn = fit_evaluate(LinearRegression(), data_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JMnpg3VSx18g",
        "outputId": "45ec4614-1fb7-489c-fb75-c40d801a3306"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set LinearRegression() is : 0.43\n",
            "R2 score for test set LinearRegression() is : 0.42\n",
            "MSE error for LinearRegression() is : 11243.09\n",
            "MAPE error for LinearRegression() is : 28.89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_lr_mean = fit_evaluate(LinearRegression(), data_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phBOMII5x7zT",
        "outputId": "3af5d317-04e0-4367-eea6-d810ceeb6bc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set LinearRegression() is : 0.21\n",
            "R2 score for test set LinearRegression() is : 0.21\n",
            "MSE error for LinearRegression() is : 8001.74\n",
            "MAPE error for LinearRegression() is : 23.59\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_lr_drop = fit_evaluate(LinearRegression(), data_drop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHmjIFydx_Aw",
        "outputId": "2ef4f491-822e-4b2e-b5ee-3baba910fe64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set LinearRegression() is : 0.49\n",
            "R2 score for test set LinearRegression() is : 0.49\n",
            "MSE error for LinearRegression() is : 9828.89\n",
            "MAPE error for LinearRegression() is : 24.68\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_lr = [scores_lr_mice, scores_lr_knn, scores_lr_mean, scores_lr_drop]"
      ],
      "metadata": {
        "id": "pcGsxpeLNIJg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Performance for RandomForest"
      ],
      "metadata": {
        "id": "BExolyMRy9rV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "metadata": {
        "id": "E7u27Bjly9gd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores_rf_mice = fit_evaluate(RandomForestRegressor(max_depth=None, random_state=123), data_mice)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gq8JvF2AzCCb",
        "outputId": "81383cf3-d026-4242-a3e4-b3a14d3de1ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set RandomForestRegressor(random_state=123) is : 0.97\n",
            "R2 score for test set RandomForestRegressor(random_state=123) is : 0.81\n",
            "MSE error for RandomForestRegressor(random_state=123) is : 3685.31\n",
            "MAPE error for RandomForestRegressor(random_state=123) is : 11.44\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_rf_knn = fit_evaluate(RandomForestRegressor(max_depth=None, random_state=123), data_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCrfsEcLzqYT",
        "outputId": "785405bd-b2c4-432f-ea0f-1dd0c876f6b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set RandomForestRegressor(random_state=123) is : 0.97\n",
            "R2 score for test set RandomForestRegressor(random_state=123) is : 0.84\n",
            "MSE error for RandomForestRegressor(random_state=123) is : 3080.61\n",
            "MAPE error for RandomForestRegressor(random_state=123) is : 10.94\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_rf_mean = fit_evaluate(RandomForestRegressor(max_depth=None, random_state=123), data_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qiUNbFbHzsjp",
        "outputId": "7a66df3c-58b8-49fb-b8cc-15fdedcc4ea5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set RandomForestRegressor(random_state=123) is : 0.92\n",
            "R2 score for test set RandomForestRegressor(random_state=123) is : 0.51\n",
            "MSE error for RandomForestRegressor(random_state=123) is : 4972.40\n",
            "MAPE error for RandomForestRegressor(random_state=123) is : 14.66\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_rf_drop = fit_evaluate(RandomForestRegressor(max_depth=None, random_state=123), data_drop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1hrsx2r7zsy7",
        "outputId": "00244b29-7039-4561-a166-d0c2a02e641e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set RandomForestRegressor(random_state=123) is : 0.95\n",
            "R2 score for test set RandomForestRegressor(random_state=123) is : 0.70\n",
            "MSE error for RandomForestRegressor(random_state=123) is : 5714.83\n",
            "MAPE error for RandomForestRegressor(random_state=123) is : 16.15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_rf = [scores_rf_mice, scores_rf_knn, scores_rf_mean, scores_rf_drop]"
      ],
      "metadata": {
        "id": "jDz9CLSENQDD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Evaluation"
      ],
      "metadata": {
        "id": "dGv9YpWChT3W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Performance for SVR"
      ],
      "metadata": {
        "id": "R3iknZGW7Ex-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "PGhAzstI7KC8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores_svr_mice = fit_evaluate(make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2)), data_mice) #takes a good while"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQZxCuXE7FDA",
        "outputId": "690e622e-b683-4439-fab9-16900761623c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 0.74\n",
            "R2 score for test set Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 0.74\n",
            "MSE error for Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 4995.23\n",
            "MAPE error for Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 13.36\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_svr_knn = fit_evaluate(make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2)), data_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3daNXnvL7FVe",
        "outputId": "57a51ce7-de6a-4892-98d2-bb6451274cdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 0.48\n",
            "R2 score for test set Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 0.48\n",
            "MSE error for Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 10117.84\n",
            "MAPE error for Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 24.43\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_svr_mean = fit_evaluate(make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2)), data_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "99xGS92f7Fkw",
        "outputId": "6095a801-c82a-48fe-c9fc-f193fbf86368"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 0.22\n",
            "R2 score for test set Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 0.23\n",
            "MSE error for Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 7824.04\n",
            "MAPE error for Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 19.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_svr_drop = fit_evaluate(make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2)), data_drop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Lsr8NrC7Fuh",
        "outputId": "31be1ae3-9747-48c8-e4ad-f87c0aa0988b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 0.51\n",
            "R2 score for test set Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 0.52\n",
            "MSE error for Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 9200.50\n",
            "MAPE error for Pipeline(steps=[('standardscaler', StandardScaler()),\n",
            "                ('svr', SVR(epsilon=0.2))]) is : 21.76\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_svr = [scores_svr_mice, scores_svr_knn, scores_svr_mean, scores_svr_drop]"
      ],
      "metadata": {
        "id": "zNjKSF1vNVyK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Performance for XGBoost (Gradient Boosting Regression)"
      ],
      "metadata": {
        "id": "e9lmNYqR0I5H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor"
      ],
      "metadata": {
        "id": "_NSoQ47M8tFd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_xg_mice = fit_evaluate(GradientBoostingRegressor(random_state=123), data_mice)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-Wum9Cd0OIz",
        "outputId": "1c492c4c-1382-4aa6-e029-a48f6b7bb8a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set GradientBoostingRegressor(random_state=123) is : 0.79\n",
            "R2 score for test set GradientBoostingRegressor(random_state=123) is : 0.79\n",
            "MSE error for GradientBoostingRegressor(random_state=123) is : 4041.30\n",
            "MAPE error for GradientBoostingRegressor(random_state=123) is : 13.69\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_xg_knn = fit_evaluate(GradientBoostingRegressor(random_state=123), data_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSpnA0Rw0Ol-",
        "outputId": "2c2e160d-e908-40a0-cd5b-7e0e6c680611"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set GradientBoostingRegressor(random_state=123) is : 0.63\n",
            "R2 score for test set GradientBoostingRegressor(random_state=123) is : 0.62\n",
            "MSE error for GradientBoostingRegressor(random_state=123) is : 7353.01\n",
            "MAPE error for GradientBoostingRegressor(random_state=123) is : 22.51\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_xg_mean = fit_evaluate(GradientBoostingRegressor(random_state=123), data_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EG71iER0Oy1",
        "outputId": "6371777e-0da2-47f9-ca90-044253d038e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set GradientBoostingRegressor(random_state=123) is : 0.43\n",
            "R2 score for test set GradientBoostingRegressor(random_state=123) is : 0.44\n",
            "MSE error for GradientBoostingRegressor(random_state=123) is : 5730.56\n",
            "MAPE error for GradientBoostingRegressor(random_state=123) is : 19.30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_xg_drop = fit_evaluate(GradientBoostingRegressor(random_state=123), data_drop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VEtO0esu0PBh",
        "outputId": "a6a224bd-0372-472b-bf98-11618ef2c26d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set GradientBoostingRegressor(random_state=123) is : 0.65\n",
            "R2 score for test set GradientBoostingRegressor(random_state=123) is : 0.66\n",
            "MSE error for GradientBoostingRegressor(random_state=123) is : 6616.78\n",
            "MAPE error for GradientBoostingRegressor(random_state=123) is : 19.19\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_xg = [score_xg_mice, score_xg_knn, score_xg_mean, score_xg_drop]"
      ],
      "metadata": {
        "id": "wqVNl3VGNdI6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Performance for Neural Network"
      ],
      "metadata": {
        "id": "YD6SVYkJ0PW9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPRegressor"
      ],
      "metadata": {
        "id": "KOUeSAF99PLj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_mlp_mice = fit_evaluate(MLPRegressor(random_state=123, max_iter=250), data_mice)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QCi4_HU0Yh1",
        "outputId": "599be219-649d-4354-ab25-236b5e60f3bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set MLPRegressor(max_iter=250, random_state=123) is : 0.74\n",
            "R2 score for test set MLPRegressor(max_iter=250, random_state=123) is : 0.73\n",
            "MSE error for MLPRegressor(max_iter=250, random_state=123) is : 5102.77\n",
            "MAPE error for MLPRegressor(max_iter=250, random_state=123) is : 14.14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_mlp_knn = fit_evaluate(MLPRegressor(random_state=123, max_iter=250), data_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdUeSnmy0ZHB",
        "outputId": "1358ee15-4ba5-41ec-fff2-dc7f68f396ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set MLPRegressor(max_iter=250, random_state=123) is : 0.50\n",
            "R2 score for test set MLPRegressor(max_iter=250, random_state=123) is : 0.50\n",
            "MSE error for MLPRegressor(max_iter=250, random_state=123) is : 9830.87\n",
            "MAPE error for MLPRegressor(max_iter=250, random_state=123) is : 25.80\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (250) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_mlp_mean = fit_evaluate(MLPRegressor(random_state=123, max_iter=250), data_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eYE6v5l0ZPc",
        "outputId": "d20a4e54-584a-4d3b-9ca5-6c646b9282ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (250) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set MLPRegressor(max_iter=250, random_state=123) is : 0.31\n",
            "R2 score for test set MLPRegressor(max_iter=250, random_state=123) is : 0.31\n",
            "MSE error for MLPRegressor(max_iter=250, random_state=123) is : 7051.10\n",
            "MAPE error for MLPRegressor(max_iter=250, random_state=123) is : 21.30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_mlp_drop = fit_evaluate(MLPRegressor(random_state=123, max_iter=250), data_drop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jzxjksmj0ZjM",
        "outputId": "5c04ac96-46f2-41f5-a3f2-9006b1a847b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (250) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score for train set MLPRegressor(max_iter=250, random_state=123) is : 0.53\n",
            "R2 score for test set MLPRegressor(max_iter=250, random_state=123) is : 0.54\n",
            "MSE error for MLPRegressor(max_iter=250, random_state=123) is : 8887.33\n",
            "MAPE error for MLPRegressor(max_iter=250, random_state=123) is : 24.08\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_mlp = [score_mlp_mice, score_mlp_knn, score_mlp_mean, score_mlp_drop]"
      ],
      "metadata": {
        "id": "Aipizgl_NjOu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing Imputers' Performances:"
      ],
      "metadata": {
        "id": "d8Bh1iuCKJrJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_metrics(scores):\n",
        "  metrics = []\n",
        "  for i in range(4):\n",
        "    arr = [score[i] for score in scores]\n",
        "    metrics.append(arr)\n",
        "  return metrics\n",
        "\n",
        "def barplot_imp(model_title, scores):\n",
        "    imputers = ['MICE', 'kNN', 'Mean', 'Dropping']\n",
        "    metrics = generate_metrics(scores) # 0 train_r2, 1 test_r2, 2 mse, 3 maPE\n",
        "    # Create a 2x2 subplot\n",
        "    fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
        "\n",
        "    # Plot train R2 scores\n",
        "    sns.barplot(x=imputers, y=metrics[0], ax=axes[0, 0])\n",
        "    axes[0, 0].set_title(f'Train R2 Score of {model_title}')\n",
        "    axes[0, 0].set_xlabel('Imputers')\n",
        "    axes[0, 0].set_ylabel('Train R2 Score')\n",
        "\n",
        "    # Plot test R2 scores\n",
        "    sns.barplot(x=imputers, y=metrics[1], ax=axes[0, 1])\n",
        "    axes[0, 1].set_title(f'Test R2 Score of {model_title}')\n",
        "    axes[0, 1].set_xlabel('Imputers')\n",
        "    axes[0, 1].set_ylabel('Test R2 Score')\n",
        "\n",
        "    # Plot MSE scores\n",
        "    sns.barplot(x=imputers, y=metrics[2], ax=axes[1, 0])\n",
        "    axes[1, 0].set_title(f'MSE Error for {model_title}')\n",
        "    axes[1, 0].set_xlabel('Imputers')\n",
        "    axes[1, 0].set_ylabel('MSE')\n",
        "\n",
        "    # Plot MAPE scores\n",
        "    sns.barplot(x=imputers, y=metrics[3], ax=axes[1, 1])\n",
        "    axes[1, 1].set_title(f'MAPE Error for {model_title}')\n",
        "    axes[1, 1].set_xlabel('Imputers')\n",
        "    axes[1, 1].set_ylabel('MAPE')\n",
        "\n",
        "    # Adjust layout\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "raxPOwPFK_s6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Linear Regression:"
      ],
      "metadata": {
        "id": "MSgHQij4MfF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "barplot_imp(\"Linear Regression\", scores_lr)"
      ],
      "metadata": {
        "id": "EqQ7SWPQIAZu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9dae32b8-9b1c-4233-eb8e-1e5b6069a0ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Random Forest:"
      ],
      "metadata": {
        "id": "cPniZ-qrMiCt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "barplot_imp(\"Random Forest\", scores_rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2jvBJqfqMm7C",
        "outputId": "b94be12a-f595-48e9-8c62-e7efa04b2634"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAPeCAYAAADd/6nHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADDxElEQVR4nOzdeVgV5f//8dcBWUQEFzYlErdccsEwDTWXQjFN0zTXAtHIUtQkNW3BLcPSlBYTs9Qy/WiafdoU84PSYpal2WJquS8JihuKCgrz+8Mf59sRUFzOgPB8XNdcl3PPPTPvORwOb9/nnnsshmEYAgAAAAAAAEzkUNQBAAAAAAAAoPShKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAAAAAAAAwHUUpAAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmoygFFLEBAwYoMDCwqMPANbp48aLGjBmjgIAAOTg4qFu3bkUd0jWzWCyaMGFCUYcBAABuceRFAK4XRSmgABaLpVBLcnJyUYdqIzk52SY+R0dH+fj4qGfPntq2bVue/itWrFDv3r1Vo0YNubm5qU6dOnrmmWd08uTJQp0vJydHH3zwgZo3b65KlSqpfPnyuuOOOxQeHq4ffvjhJl9d8TFv3jxNmzZNPXv21Pvvv6+RI0cW2Ldt27Y2P5OyZcuqUaNGio+PV05OjolRF2+Xv07/XrZv317U4eXx559/asKECdq7d29RhwIARcrMnOns2bOaMGFCoY9FXmQO8qKbj7wIpUWZog4AKK4WLlxos/7BBx9ozZo1edrr1at3Q+eZO3euXf4ADx8+XHfffbcuXLig3377TQkJCUpOTtYff/whPz8/a78nnnhCVatW1aOPPqrbb79dv//+u9566y2tXLlSmzdvVtmyZa96nlmzZumhhx5S//79VaZMGe3YsUOrVq1SjRo1dM8999z0aysO1q5dK39/f82cObNQ/W+77TbFxcVJktLS0rR48WKNHDlSR48e1ZQpU+wZ6i3l36/Tv1WtWrUIormyP//8UxMnTlTbtm0Z7QigVDMrZ5IuFaUmTpwo6dJ/2guLvMi+yIvsg7wIpYIBoFCGDh1qFOZXJiMjw4RoCrZu3TpDkrFs2TKb9tmzZxuSjFdeeSVP/8u9//77hiRj7ty5VzxXSkqKYbFYjKioqDzbcnJyjNTU1Gu/gOt04cIFIzMz07TztWvXzrjzzjsL1bdNmzZ5+p47d86oVq2aUb58eePixYv2CPGqJBnjx48vknPnJ7/X6WbJyckxzp49e1OPuWzZMkNSvr9DAFCaFTZnuh5Hjx69pr9f5EXmIC+6+ciLUFpw+x5wA9q2basGDRpo06ZNat26tdzc3PTcc89Jkj799FN17txZVatWlYuLi2rWrKnJkycrOzvb5hiXzym1d+9eWSwWTZ8+Xe+8845q1qwpFxcX3X333frpp5+uO9Z7771XkrRr164813C57t27S1K+w9r/bc+ePTIMQy1btsyzzWKxyMfHx6bt5MmTGjlypAIDA+Xi4qLbbrtN4eHhSktLs/Y5cuSIBg0aJF9fX7m6uqpx48Z6//33bY7z79coPj7e+hr9+eefkqTt27erZ8+eqlSpklxdXdW0aVN99tlnV7yWXBkZGXrmmWcUEBAgFxcX1alTR9OnT5dhGDbnXrdunbZu3XrdtyS4urrq7rvv1unTp3XkyBFr+2+//aYBAwaoRo0acnV1lZ+fnwYOHKhjx47Z7D9hwgRZLBbt3LlTAwYMUIUKFeTp6anIyEidPXvWpm9mZqZGjhwpb29vlS9fXl27dtXBgwfzjeuXX37RAw88IA8PD7m7u+v+++/Pc7vBggULZLFY9N1332n48OHy9vZWhQoVNHjwYGVlZenkyZMKDw9XxYoVVbFiRY0ZM8b6+t2oixcvavLkydafeWBgoJ577jllZmba9AsMDNSDDz6o1atXq2nTpipbtqzmzJkj6dL78Omnn7b+jGvVqqVXXnklz4jFJUuWKDg4WOXLl5eHh4caNmyo119/3foaPPLII5Kkdu3aFdvbeQGguMjJyVF8fLzuvPNOubq6ytfXV4MHD9aJEyds+v38888KCwuTl5eXypYtq+rVq2vgwIGSLv0N9vb2liRNnDjR+tl7PfMAkReRF5EXkReheOD2PeAGHTt2TA888ID69OmjRx99VL6+vpIufTi7u7srJiZG7u7uWrt2rWJjY5Wenq5p06Zd9biLFy/W6dOnNXjwYFksFr366qt6+OGHtXv3bjk5OV1znLn3d1esWPGqfVNSUiRJXl5eV+xXrVo1SdKyZcv0yCOPyM3NrcC+Z86c0b333qtt27Zp4MCBuuuuu5SWlqbPPvtMBw8elJeXl86dO6e2bdtq586dio6OVvXq1bVs2TINGDBAJ0+e1IgRI2yOOX/+fJ0/f15PPPGEXFxcVKlSJW3dulUtW7aUv7+/xo4dq3Llyumjjz5St27d9PHHH1sTy/wYhqGuXbtq3bp1GjRokIKCgrR69WqNHj1ahw4d0syZM+Xt7a2FCxdqypQpOnPmjHVI9fXckpCbyFWoUMHatmbNGu3evVuRkZHy8/PT1q1b9c4772jr1q364YcfZLFYbI7Rq1cvVa9eXXFxcdq8ebPeffdd+fj46JVXXrH2efzxx/Xhhx+qX79+atGihdauXavOnTvniWfr1q2699575eHhoTFjxsjJyUlz5sxR27Zt9fXXX6t58+Y2/YcNGyY/Pz9NnDhRP/zwg9555x1VqFBB33//vW6//Xa9/PLLWrlypaZNm6YGDRooPDz8qq9Jdna2TTIuXUpU3d3drdfy/vvvq2fPnnrmmWf0448/Ki4uTtu2bdMnn3xis9+OHTvUt29fDR48WFFRUapTp47Onj2rNm3a6NChQxo8eLBuv/12ff/99xo3bpwOHz6s+Ph468+hb9++uv/++62v5bZt27R+/XqNGDFCrVu31vDhw/XGG2/oueees/78b8atKQBQEg0ePFgLFixQZGSkhg8frj179uitt97SL7/8ovXr18vJyUlHjhxRhw4d5O3trbFjx6pChQrau3evVqxYIUny9vbW7Nmz9dRTT6l79+56+OGHJUmNGjW65njIi8iLyIvIi1BMFN0gLeDWkt9Q9DZt2hiSjISEhDz98xsSO3jwYMPNzc04f/68tS0iIsKoVq2adX3Pnj2GJKNy5crG8ePHre2ffvqpIcn4/PPPrxhn7jD1efPmGUePHjX++ecfIzEx0ahVq5ZhsViMjRs3XvVaBw0aZDg6Ohp//fXXVfuGh4cbkoyKFSsa3bt3N6ZPn25s27YtT7/Y2FhDkrFixYo823JycgzDMIz4+HhDkvHhhx9at2VlZRkhISGGu7u7kZ6ebhjG/71GHh4expEjR2yOdf/99xsNGza0eY1zcnKMFi1aGLVr177itfz3v/81JBkvvfSSTXvPnj0Ni8Vi7Ny509p2LUOq27RpY9StW9c4evSocfToUWP79u3G6NGjDUlG586dbfrm9775z3/+Y0gyvvnmG2vb+PHjDUnGwIEDbfp2797dqFy5snV9y5YthiRjyJAhNv369euXZ5h6t27dDGdnZ2PXrl3Wtn/++ccoX7680bp1a2vb/PnzDUlGWFiY9WdnGIYREhJiWCwW48knn7S2Xbx40bjtttuMNm3aXOVV+r/fp8uXiIgIm2t5/PHHbfYbNWqUIclYu3atta1atWqGJCMxMdGm7+TJk41y5crleW+PHTvWcHR0NPbv328YhmGMGDHC8PDwuOItBAxTB4D8XZ4zffvtt4YkY9GiRTb9EhMTbdo/+eQTQ5Lx008/FXjs6719j7yIvMgwyIvIi1AccfsecINcXFwUGRmZp/3fE2GePn1aaWlpuvfee3X27NlCPTGjd+/eNt/e5Q4z3717d6HiGjhwoLy9vVW1alV17NhRp06d0sKFC3X33Xdfcb/Fixfrvffe0zPPPKPatWtf9Tzz58/XW2+9perVq+uTTz7RqFGjVK9ePd1///06dOiQtd/HH3+sxo0b5/uNXO63XCtXrpSfn5/69u1r3ebk5KThw4frzJkz+vrrr23269Gjh3UYvyQdP35ca9euVa9evayveVpamo4dO6awsDD9/fffNjFdbuXKlXJ0dNTw4cNt2p955hkZhqFVq1Zd9fUoyPbt2+Xt7S1vb2/VrVtX06ZNU9euXbVgwQKbfv9+35w/f15paWnWSVE3b96c57hPPvmkzfq9996rY8eOKT093XpNkvJc09NPP22znp2dra+++krdunVTjRo1rO1VqlRRv3799N1331mPmWvQoEE231A2b95chmFo0KBB1jZHR0c1bdq00O/bwMBArVmzxmYZM2aMzbXExMTY7PPMM89Ikr788kub9urVqyssLMymbdmyZbr33ntVsWJF6/sjLS1NoaGhys7O1jfffCNJqlChgjIyMrRmzZpCxQ0AKNiyZcvk6emp9u3b23z2BgcHy93dXevWrZMk6wiZL774QhcuXLipMZAXkRflXpNEXpSLvAjFAbfvATfI399fzs7Oedq3bt2qF154QWvXrs3zR+vUqVNXPe7tt99us55boLp87oWCxMbG6t5779WZM2f0ySefaMmSJXJwuHId+ttvv9WgQYMUFhZW6CefODg4aOjQoRo6dKiOHTum9evXKyEhQatWrVKfPn307bffSro0Z0OPHj2ueKx9+/apdu3aeeLMHfq7b98+m/bq1avbrO/cuVOGYejFF1/Uiy++mO85jhw5In9//wLPX7VqVZUvX75Q578WgYGB1ict7tq1S1OmTNHRo0fl6upq0+/48eOaOHGilixZYjOngpT/++ZK7xMPDw/t27dPDg4Oqlmzpk2/OnXq2KwfPXpUZ8+ezdMuXbr+nJwcHThwQHfeeWeB5/b09JQkBQQE5Gkv7Pu2XLlyCg0NzXdb7rXUqlXLpt3Pz08VKlS46vtDkv7++2/99ttvNkn7v+W+5kOGDNFHH32kBx54QP7+/urQoYN69eqljh07Fuo6AAD/5++//9apU6fyzKmUK/ezt02bNurRo4cmTpyomTNnqm3bturWrZv69esnFxeXG4qBvIi8iLyIvAjFE0Up4Abl92jgkydPqk2bNvLw8NCkSZNUs2ZNubq6avPmzXr22WfzTByYH0dHx3zbjUJOjNiwYUPrH7Fu3brp7NmzioqKUqtWrfL8cZSkX3/9VV27dlWDBg20fPlylSlz7R8PlStXVteuXdW1a1fr/fb79u2zzrFws13+2ue+rqNGjcrzTVCuy/9wm+XypKJly5a666679Nxzz+mNN96wtvfq1Uvff/+9Ro8eraCgILm7uysnJ0cdO3bM931zo++TG1HQufNrv5nxXD5/REHy+93MyclR+/btrd8yXu6OO+6QJPn4+GjLli1avXq1Vq1apVWrVmn+/PkKDw/PM8EsAODKcnJy5OPjo0WLFuW7Pfc/xBaLRcuXL9cPP/ygzz//XKtXr9bAgQP12muv6YcffrDOo3M9yIvIi+yNvAi4PhSlADtITk7WsWPHtGLFCrVu3dravmfPniKLaerUqfrkk080ZcoUJSQk2GzbtWuXOnbsKB8fH61cufKGkr5cTZs21ddff63Dhw+rWrVqqlmzpv74448r7lOtWjX99ttvysnJsflWMPd2x6slcbnDq52cnAr8Vulq5//f//6n06dP23wrWNjzX4tGjRrp0Ucf1Zw5czRq1CjdfvvtOnHihJKSkjRx4kTFxsZa+/7999/XfZ5q1apZv4X897d9O3bssOnn7e0tNze3PO3Spet3cHDIN2k3U+61/P333zYTZ6ampurkyZOF+vnUrFlTZ86cKdT7w9nZWV26dFGXLl2Uk5OjIUOGaM6cOXrxxRdVq1atQieBAFDa1axZU//73//UsmXLfP9jfLl77rlH99xzj6ZMmaLFixerf//+WrJkiR5//PGb9tlLXnR15EXkRf9GXgR7YU4pwA5yvxH597cgWVlZevvtt4sqJNWsWVM9evTQggULrE+RkS49UaZDhw5ycHDQ6tWrCxy+m5+UlBTr44b/LSsrS0lJSTZDinv06KFff/01z5NApP97nTp16qSUlBQtXbrUuu3ixYt688035e7urjZt2lwxHh8fH7Vt21Zz5szR4cOH82w/evToFffv1KmTsrOz9dZbb9m0z5w5UxaLRQ888MAV979WY8aM0YULFzRjxgxJ+b9vJFmffHI9cmP+97eO+R3T0dFRHTp00Keffmp9IpF0KbFZvHixWrVqJQ8Pj+uO42bo1KmTpLyx575++T0553K9evXShg0btHr16jzbTp48qYsXL0pSnkdNOzg4WJ/ulPuY5XLlyln3AwAUrFevXsrOztbkyZPzbLt48aL1c/TEiRN5/gYGBQVJ+r/P3twn2t3oZy95EXnRlY5JXkReBPMwUgqwgxYtWqhixYqKiIjQ8OHDZbFYtHDhQlOGDl/J6NGj9dFHHyk+Pl5Tp06VJHXs2FG7d+/WmDFj9N133+m7776z9vf19VX79u0LPN7BgwfVrFkz3Xfffbr//vvl5+enI0eO6D//+Y9+/fVXPf3009bHJ48ePVrLly/XI488ooEDByo4OFjHjx/XZ599poSEBDVu3FhPPPGE5syZowEDBmjTpk0KDAzU8uXLtX79esXHx+eZ0yA/s2bNUqtWrdSwYUNFRUWpRo0aSk1N1YYNG3Tw4EH9+uuvBe7bpUsXtWvXTs8//7z27t2rxo0b66uvvtKnn36qp59+Os/8Azeqfv366tSpk9599129+OKLqly5slq3bq1XX31VFy5ckL+/v7766qsbGmEXFBSkvn376u2339apU6fUokULJSUlaefOnXn6vvTSS1qzZo1atWqlIUOGqEyZMpozZ44yMzP16quv3sil3hSNGzdWRESE3nnnHestshs3btT777+vbt26qV27dlc9xujRo/XZZ5/pwQcf1IABAxQcHKyMjAz9/vvvWr58ufbu3SsvLy89/vjjOn78uO677z7ddttt2rdvn958800FBQVZv40MCgqSo6OjXnnlFZ06dUouLi667777CpwzBQBKqzZt2mjw4MGKi4vTli1b1KFDBzk5Oenvv//WsmXL9Prrr6tnz556//339fbbb6t79+6qWbOmTp8+rblz58rDw8P6H/CyZcuqfv36Wrp0qe644w5VqlRJDRo0UIMGDa45LvIi8iLyIvIiFANmP+4PuFVd/nhjw7jy42/Xr19v3HPPPUbZsmWNqlWrGmPGjDFWr16d51GpERERRrVq1azruY/1nTZtWp5jqhCPQM599PGyZcvy3d62bVvDw8PDOHnypPWYBS1Xe1xtenq68frrrxthYWHGbbfdZjg5ORnly5c3QkJCjLlz59o8FtcwDOPYsWNGdHS04e/vbzg7Oxu33XabERERYaSlpVn7pKamGpGRkYaXl5fh7OxsNGzY0Jg/f77Nca70GhmGYezatcsIDw83/Pz8DCcnJ8Pf39948MEHjeXLl1/xegzDME6fPm2MHDnSqFq1quHk5GTUrl3bmDZtWp5rudZHHxfUNzk52ebnevDgQaN79+5GhQoVDE9PT+ORRx4x/vnnnzw/+9xHHx89etTmeLmPJd6zZ4+17dy5c8bw4cONypUrG+XKlTO6dOliHDhwIN/30+bNm42wsDDD3d3dcHNzM9q1a2d8//33+Z7j8kd2FxRTRESEUa5cuRt6nXJduHDBmDhxolG9enXDycnJCAgIMMaNG2fzqGvDuPTo48sfK53r9OnTxrhx44xatWoZzs7OhpeXl9GiRQtj+vTpRlZWlmEYhrF8+XKjQ4cOho+Pj+Hs7GzcfvvtxuDBg43Dhw/bHGvu3LlGjRo1DEdHRx6DDAD/X345k2EYxjvvvGMEBwcbZcuWNcqXL280bNjQGDNmjPHPP/8YhnHpb1Dfvn2N22+/3XBxcTF8fHyMBx980Pj5559tjvP9998bwcHBhrOz81VzI/Ii8iLyIvIiFG8WwyjioRsAAAAAAAAodZhTCgAAAAAAAKajKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAAAAAAAAwXZmiDuBWkJOTo3/++Ufly5eXxWIp6nAAAEAxYBiGTp8+rapVq8rBofR+z0eeBAAALlfYPImiVCH8888/CggIKOowAABAMXTgwAHddtttRR1GkSFPAgAABblankRRqhDKly8v6dKL6eHhUcTRAACA4iA9PV0BAQHWPKG0Ik8CAACXK2yeRFGqEHKHont4eJBsAQAAG6X9ljXyJAAAUJCr5UmldwIEAAAAAAAAFBmKUgAAAAAAADDdLVeU+uabb9SlSxdVrVpVFotF//3vf6+6T3Jysu666y65uLioVq1aWrBggd3jBAAAAAAAQMFuuaJURkaGGjdurFmzZhWq/549e9S5c2e1a9dOW7Zs0dNPP63HH39cq1evtnOkAAAAAAAAKMgtN9H5Aw88oAceeKDQ/RMSElS9enW99tprkqR69erpu+++08yZMxUWFmavMAEAAAAAAHAFt9xIqWu1YcMGhYaG2rSFhYVpw4YNRRQRAAAAAAAAbrmRUtcqJSVFvr6+Nm2+vr5KT0/XuXPnVLZs2Tz7ZGZmKjMz07qenp5u9zgBAAAAAABKkxI/Uup6xMXFydPT07oEBAQUdUgAAAAAAAAlSokvSvn5+Sk1NdWmLTU1VR4eHvmOkpKkcePG6dSpU9blwIEDZoQKAAAAAABQapT42/dCQkK0cuVKm7Y1a9YoJCSkwH1cXFzk4uJi79AAAAAAAABKrVtupNSZM2e0ZcsWbdmyRZK0Z88ebdmyRfv375d0aZRTeHi4tf+TTz6p3bt3a8yYMdq+fbvefvttffTRRxo5cmRRhA8AAAAAAADdgkWpn3/+WU2aNFGTJk0kSTExMWrSpIliY2MlSYcPH7YWqCSpevXq+vLLL7VmzRo1btxYr732mt59912FhYUVSfwAAAAAAACQLIZhGEUdRHGXnp4uT09PnTp1Sh4eHkUdDgAAKAbIDy7hdQAAAJcrbH5wy42UAgAAAAAAwK2PohQAAAAAAABMR1EKAAAAAAAApqMoBQAAAAAAANOVKeoAgJJu/6SGRR0CruD22N+LOgQAAEot8qTijTwJgL0xUgoAAAAAAACmoygFAAAAAAAA03H7nh0Fj/6gqEPAFWyaFl7UIQAAAAAAUGoxUgoAAAAAAACmoygFAAAAAAAA01GUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgOopSAAAAAAAAMF2Zog4AAIDS4uvWbYo6BFxBm2++LuoQAAAAShVGSgEAAAAAAMB0FKUAAAAAAABgOopSAAAAAAAAMB1FKQAAAAAAAJiOohQAAEAJMmvWLAUGBsrV1VXNmzfXxo0br9g/Pj5ederUUdmyZRUQEKCRI0fq/PnzJkULAABKM56+BwAAUEIsXbpUMTExSkhIUPPmzRUfH6+wsDDt2LFDPj4+efovXrxYY8eO1bx589SiRQv99ddfGjBggCwWi2bMmGHXWINHf2DX4+PGbJoWXtQhAABKAUZKAQAAlBAzZsxQVFSUIiMjVb9+fSUkJMjNzU3z5s3Lt//333+vli1bql+/fgoMDFSHDh3Ut2/fq46uAgAAuBkoSgEAAJQAWVlZ2rRpk0JDQ61tDg4OCg0N1YYNG/Ldp0WLFtq0aZO1CLV7926tXLlSnTp1KvA8mZmZSk9Pt1kAAACuB7fvAQAAlABpaWnKzs6Wr6+vTbuvr6+2b9+e7z79+vVTWlqaWrVqJcMwdPHiRT355JN67rnnCjxPXFycJk6ceFNjBwAApRMjpQAAAEqp5ORkvfzyy3r77be1efNmrVixQl9++aUmT55c4D7jxo3TqVOnrMuBAwdMjBgAAJQkjJQCAAAoAby8vOTo6KjU1FSb9tTUVPn5+eW7z4svvqjHHntMjz/+uCSpYcOGysjI0BNPPKHnn39eDg55v790cXGRi4vLzb8AAABQ6jBSCgAAoARwdnZWcHCwkpKSrG05OTlKSkpSSEhIvvucPXs2T+HJ0dFRkmQYhv2CBQAAECOlAMAULd9sWdQh4ArWD1tf1CEAN0VMTIwiIiLUtGlTNWvWTPHx8crIyFBkZKQkKTw8XP7+/oqLi5MkdenSRTNmzFCTJk3UvHlz7dy5Uy+++KK6dOliLU4BAADYC0UpAACAEqJ37946evSoYmNjlZKSoqCgICUmJlonP9+/f7/NyKgXXnhBFotFL7zwgg4dOiRvb2916dJFU6ZMKapLAAAApQhFKQAAgBIkOjpa0dHR+W5LTk62WS9TpozGjx+v8ePHmxAZAACALeaUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgOopSAAAAAAAAMB1FKQAAAAAAAJiOohQAAAAAAABMR1EKAAAAAAAApqMoBQAAAAAAANNRlAIAAAAAAIDpKEoBAAAAAADAdBSlAAAAAAAAYDqKUgAAAAAAADAdRSkAAAAAAACYjqIUAAAAAAAATEdRCgAAAAAAAKajKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAAAAAAAAwHUUpAAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmoygFAAAAAAAA01GUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgOopSAAAAAAAAMB1FKQAAAAAAAJiOohQAAAAAAABMR1EKAAAAAAAApqMoBQAAAAAAANNRlAIAAAAAAIDpKEoBAAAAAADAdBSlAAAAAAAAYDqKUgAAAAAAADAdRSkAAIASZtasWQoMDJSrq6uaN2+ujRs3Fti3bdu2slgseZbOnTubGDEAACiNKEoBAACUIEuXLlVMTIzGjx+vzZs3q3HjxgoLC9ORI0fy7b9ixQodPnzYuvzxxx9ydHTUI488YnLkAACgtClT1AEAAADg5pkxY4aioqIUGRkpSUpISNCXX36pefPmaezYsXn6V6pUyWZ9yZIlcnNzoygFADDV163bFHUIuII233xtl+MyUgoAAKCEyMrK0qZNmxQaGmptc3BwUGhoqDZs2FCoY7z33nvq06ePypUrl+/2zMxMpaen2ywAAADX45YtSl3LXAmSFB8frzp16qhs2bIKCAjQyJEjdf78eZOiBQAAsL+0tDRlZ2fL19fXpt3X11cpKSlX3X/jxo36448/9PjjjxfYJy4uTp6entYlICDghuMGAACl0y1ZlLrWuRIWL16ssWPHavz48dq2bZvee+89LV26VM8995zJkQMAABRf7733nho2bKhmzZoV2GfcuHE6deqUdTlw4ICJEQIAgJLklixK/XuuhPr16yshIUFubm6aN29evv2///57tWzZUv369VNgYKA6dOigvn37XnV0FQAAwK3Ey8tLjo6OSk1NtWlPTU2Vn5/fFffNyMjQkiVLNGjQoCv2c3FxkYeHh80CAABwPW65otT1zJXQokULbdq0yVqE2r17t1auXKlOnTrl25+5EgAAwK3I2dlZwcHBSkpKsrbl5OQoKSlJISEhV9x32bJlyszM1KOPPmrvMAEAACTdgk/fu9JcCdu3b893n379+iktLU2tWrWSYRi6ePGinnzyyQJv34uLi9PEiRNveuwAAAD2FhMTo4iICDVt2lTNmjVTfHy8MjIyrE/jCw8Pl7+/v+Li4mz2e++999StWzdVrly5KMIGAACl0C03Uup6JCcn6+WXX9bbb7+tzZs3a8WKFfryyy81efLkfPszVwIAALhV9e7dW9OnT1dsbKyCgoK0ZcsWJSYmWr/Q279/vw4fPmyzz44dO/Tdd99d9dY9AACAm+mWGyl1PXMlvPjii3rsscesT5Jp2LChMjIy9MQTT+j555+Xg4Ntbc7FxUUuLi72uQAAAAA7i46OVnR0dL7bkpOT87TVqVNHhmHYOSoAAABbt9xIqeuZK+Hs2bN5Ck+Ojo6SRAIGAAAAAABQBG65kVLStc+V0KVLF82YMUNNmjRR8+bNtXPnTr344ovq0qWLtTgFAAAAAAAA89ySRanevXvr6NGjio2NVUpKioKCgvLMlfDvkVEvvPCCLBaLXnjhBR06dEje3t7q0qWLpkyZUlSXAAAAAAAAUKrdkkUp6drmSihTpozGjx+v8ePHmxAZAAAAAAAAruaWm1MKAAAAAAAAtz6KUgAAAAAAADAdRSkAAAAAAACYjqIUAAAAAAAATEdRCgAAAAAAAKajKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAAAAAAAAwHUUpAAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmoygFAAAAAAAA01GUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgOopSAAAAAAAAMB1FKQAAAAAAAJiOohQAAAAAAABMR1EKAAAAAAAApqMoBQAAAAAAANNRlAIAAAAAAIDpKEoBAAAAAADAdBSlAAAAAAAAYDqKUgAAAAAAADAdRSkAAAAAAACYjqIUAAAAAAAATEdRCgAAAAAAAKajKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAlCCzZs1SYGCgXF1d1bx5c23cuPGK/U+ePKmhQ4eqSpUqcnFx0R133KGVK1eaFC0AACjNyhR1AAAAALg5li5dqpiYGCUkJKh58+aKj49XWFiYduzYIR8fnzz9s7Ky1L59e/n4+Gj58uXy9/fXvn37VKFCBfODBwAApQ5FKQAAgBJixowZioqKUmRkpCQpISFBX375pebNm6exY8fm6T9v3jwdP35c33//vZycnCRJgYGBZoYMAABKMW7fAwAAKAGysrK0adMmhYaGWtscHBwUGhqqDRs25LvPZ599ppCQEA0dOlS+vr5q0KCBXn75ZWVnZ5sVNgAAKMUYKQUAAFACpKWlKTs7W76+vjbtvr6+2r59e7777N69W2vXrlX//v21cuVK7dy5U0OGDNGFCxc0fvz4fPfJzMxUZmamdT09Pf3mXQQAAChVGCkFAABQSuXk5MjHx0fvvPOOgoOD1bt3bz3//PNKSEgocJ+4uDh5enpal4CAABMjBgAAJQlFKQAAgBLAy8tLjo6OSk1NtWlPTU2Vn59fvvtUqVJFd9xxhxwdHa1t9erVU0pKirKysvLdZ9y4cTp16pR1OXDgwM27CAAAUKpQlAIAACgBnJ2dFRwcrKSkJGtbTk6OkpKSFBISku8+LVu21M6dO5WTk2Nt++uvv1SlShU5Ozvnu4+Li4s8PDxsFgAAgOtBUQoAAKCEiImJ0dy5c/X+++9r27Zteuqpp5SRkWF9Gl94eLjGjRtn7f/UU0/p+PHjGjFihP766y99+eWXevnllzV06NCiugQAAFCKMNE5AABACdG7d28dPXpUsbGxSklJUVBQkBITE62Tn+/fv18ODv/3nWRAQIBWr16tkSNHqlGjRvL399eIESP07LPPFtUlAACAUoSiFAAAQAkSHR2t6OjofLclJyfnaQsJCdEPP/xg56gAAADy4vY9AAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmoygFAAAAAAAA01GUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgOopSAAAAAAAAMB1FKQAAAAAAAJiOohQAAAAAAABMR1EKAAAAAAAApqMoBQAAAAAAANNRlAIAAAAAAIDpKEoBAAAAAADAdBSlAAAAAAAAYDqKUgAAAAAAADAdRSkAAAAAAACYjqIUAAAAAAAATEdRCgAAAAAAAKYzrSj17bff6tFHH1VISIgOHTokSVq4cKG+++47s0IAAAAAAABAMWFKUerjjz9WWFiYypYtq19++UWZmZmSpFOnTunll182IwQAAAAAAAAUI6YUpV566SUlJCRo7ty5cnJysra3bNlSmzdvNiMEAAAAAAAAFCOmFKV27Nih1q1b52n39PTUyZMnzQgBAACg2Dp58qTeffddjRs3TsePH5ckbd682TrlAQAAQElUxoyT+Pn5aefOnQoMDLRp/+6771SjRg0zQgAAACiWfvvtN4WGhsrT01N79+5VVFSUKlWqpBUrVmj//v364IMPijpEAAAAuzBlpFRUVJRGjBihH3/8URaLRf/8848WLVqkUaNG6amnnjIjBAAAgGIpJiZGAwYM0N9//y1XV1dre6dOnfTNN98UYWQAAAD2ZcpIqbFjxyonJ0f333+/zp49q9atW8vFxUWjRo3SsGHDzAgBAACgWPrpp580Z86cPO3+/v5KSUkpgogAAADMYfeiVHZ2ttavX6+hQ4dq9OjR2rlzp86cOaP69evL3d3d3qcHAAAo1lxcXJSenp6n/a+//pK3t3cRRAQAAGAOu9++5+joqA4dOujEiRNydnZW/fr11axZsxsuSM2aNUuBgYFydXVV8+bNtXHjxiv2P3nypIYOHaoqVarIxcVFd9xxh1auXHlDMQAAANyorl27atKkSbpw4YIkyWKxaP/+/Xr22WfVo0ePIo4OAADAfkyZU6pBgwbavXv3TTve0qVLFRMTo/Hjx2vz5s1q3LixwsLCdOTIkXz7Z2VlqX379tq7d6+WL1+uHTt2aO7cufL3979pMQEAAFyP1157TWfOnJGPj4/OnTunNm3aqFatWipfvrymTJlS1OEBAADYjSlzSr300ksaNWqUJk+erODgYJUrV85mu4eHxzUdb8aMGYqKilJkZKQkKSEhQV9++aXmzZunsWPH5uk/b948HT9+XN9//72cnJwkKc+TAAEAAIqCp6en1qxZo/Xr1+vXX3/VmTNndNdddyk0NLSoQwMAALArU4pSnTp1knRpeLrFYrG2G4Yhi8Wi7OzsQh8rKytLmzZt0rhx46xtDg4OCg0N1YYNG/Ld57PPPlNISIiGDh2qTz/9VN7e3urXr5+effZZOTo6XudVAQAA3JgLFy6obNmy2rJli1q2bKmWLVsWdUgAAACmMaUotW7dupt2rLS0NGVnZ8vX19em3dfXV9u3b893n927d2vt2rXq37+/Vq5cqZ07d2rIkCG6cOGCxo8fn6d/ZmamMjMzrev5TT4KAABwo5ycnHT77bdf0xd0AAAAJYUpRak2bdqYcZoC5eTkyMfHR++8844cHR0VHBysQ4cOadq0afkWpeLi4jRx4sQiiBQAAJQ2zz//vJ577jktXLhQlSpVKupwAAAATGNKUUq69PS79957T9u2bZMk3XnnnRo4cKA8PT2v6TheXl5ydHRUamqqTXtqaqr8/Pzy3adKlSpycnKyuVWvXr16SklJUVZWlpydnW36jxs3TjExMdb19PR0BQQEXFOcAAAAhfHWW29p586dqlq1qqpVq5Zn7s3NmzcXUWQAAAD2ZUpR6ueff1ZYWJjKli2rZs2aSbo0WfmUKVP01Vdf6a677ir0sZydnRUcHKykpCR169ZN0qWRUElJSYqOjs53n5YtW2rx4sXKycmRg8OlBw7+9ddfqlKlSp6ClCS5uLjIxcXlGq8SAADg2uXmMwAAAKWNKUWpkSNHqmvXrpo7d67KlLl0yosXL+rxxx/X008/rW+++eaajhcTE6OIiAg1bdpUzZo1U3x8vDIyMqxP4wsPD5e/v7/i4uIkSU899ZTeeustjRgxQsOGDdPff/+tl19+WcOHD7+5FwoAAHCN8ptKAABKk5Zv8pCH4mz9sPVFHQJKMAczTvLzzz/r2WeftRakJKlMmTIaM2aMfv7552s+Xu/evTV9+nTFxsYqKChIW7ZsUWJionXy8/379+vw4cPW/gEBAVq9erV++uknNWrUSMOHD9eIESM0duzYG784AACAm2DTpk368MMP9eGHH+qXX365oWPNmjVLgYGBcnV1VfPmzbVx48YC+y5YsEAWi8VmcXV1vaHzAwAAFIYpI6U8PDy0f/9+1a1b16b9wIEDKl++/HUdMzo6usDb9ZKTk/O0hYSE6IcffriucwEAANjLkSNH1KdPHyUnJ6tChQqSLs3F2a5dOy1ZskTe3t7XdLylS5cqJiZGCQkJat68ueLj4xUWFqYdO3bIx8cn3308PDy0Y8cO67rFYrnu6wEAACgsU0ZK9e7dW4MGDdLSpUt14MABHThwQEuWLNHjjz+uvn37mhECAABAsTRs2DCdPn1aW7du1fHjx3X8+HH98ccfSk9Pv66pBmbMmKGoqChFRkaqfv36SkhIkJubm+bNm1fgPhaLRX5+ftYld/Q5AACAPZkyUmr69OmyWCwKDw/XxYsXJUlOTk566qmnNHXqVDNCAAAAKJYSExP1v//9T/Xq1bO21a9fX7NmzVKHDh2u6VhZWVnatGmTxo0bZ21zcHBQaGioNmzYUOB+Z86cUbVq1ZSTk6O77rpLL7/8su688858+2ZmZiozM9O6np6efk0xAgAA5DJlpJSzs7Nef/11nThxQlu2bNGWLVt0/PhxzZw5k6fcAQCAUi0nJ0dOTk552p2cnJSTk3NNx0pLS1N2dnaekU6+vr5KSUnJd586depo3rx5+vTTT/Xhhx8qJydHLVq00MGDB/PtHxcXJ09PT+sSEBBwTTECAADkMqUoderUKR0/flxubm5q2LChGjZsKDc3Nx0/fpxv1wAAQKl23333acSIEfrnn3+sbYcOHdLIkSN1//332/38ISEhCg8PV1BQkNq0aaMVK1bI29tbc+bMybf/uHHjdOrUKety4MABu8cIAABKJlOKUn369NGSJUvytH/00Ufq06ePGSEAAAAUS2+99ZbS09MVGBiomjVrqmbNmqpevbrS09P15ptvXtOxvLy85OjoqNTUVJv21NRU+fn5FeoYTk5OatKkiXbu3JnvdhcXF3l4eNgsAAAA18OUotSPP/6odu3a5Wlv27atfvzxRzNCAAAAKJYCAgK0efNmffnll3r66af19NNPa+XKldq8ebNuu+22azqWs7OzgoODlZSUZG3LyclRUlKSQkJCCnWM7Oxs/f7776pSpco1nRsAAOBamTLReWZmpnWC83+7cOGCzp07Z0YIAAAAxZbFYlH79u3Vvn37Gz5WTEyMIiIi1LRpUzVr1kzx8fHKyMhQZGSkJCk8PFz+/v6Ki4uTJE2aNEn33HOPatWqpZMnT2ratGnat2+fHn/88RuOBQAA4EpMGSnVrFkzvfPOO3naExISFBwcbEYIAAAAxdLw4cP1xhtv5Gl/66239PTTT1/z8Xr37q3p06crNjZWQUFB2rJlixITE62Tn+/fv1+HDx+29j9x4oSioqJUr149derUSenp6fr+++9Vv379674mAACAwjBlpNRLL72k0NBQ/frrr9YJO5OSkvTTTz/pq6++MiMEAACAYunjjz/WZ599lqe9RYsWmjp1quLj46/5mNHR0YqOjs53W3Jyss36zJkzNXPmzGs+BwAAwI0yZaRUy5YttWHDBgUEBOijjz7S559/rlq1aum3337Tvffea0YIAAAAxdKxY8fk6emZp93Dw0NpaWlFEBEAAIA5TBkpJUlBQUFatGiRWacDAAC4JdSqVUuJiYl5RjatWrVKNWrUKKKoAAAA7M+uRamLFy8qOztbLi4u1rbU1FQlJCQoIyNDXbt2VatWrewZAgAAQLEWExOj6OhoHT16VPfdd5+kS9McvPbaa9d16x4AAMCtwq5FqaioKDk7O2vOnDmSpNOnT+vuu+/W+fPnVaVKFc2cOVOffvqpOnXqZM8wAAAAiq2BAwcqMzNTU6ZM0eTJkyVJgYGBmj17tsLDw4s4OgAAAPux65xS69evV48ePazrH3zwgbKzs/X333/r119/VUxMjKZNm2bPEAAAAIq9p556SgcPHlRqaqrS09O1e/duClIAAKDEs2tR6tChQ6pdu7Z1PSkpST169LBO5hkREaGtW7faMwQAAIBbhre3tzZt2qRVq1bpxIkTRR0OAACAXdm1KOXq6qpz585Z13/44Qc1b97cZvuZM2fsGQIAAECx9Morr+jFF1+0rhuGoY4dO6pdu3bq3Lmz6tWrx5d3AACgRLNrUSooKEgLFy6UJH377bdKTU21TuApSbt27VLVqlXtGQIAAECxtHTpUjVo0MC6vnz5cn3zzTf69ttvlZaWpqZNm2rixIlFGCEAAIB92XWi89jYWD3wwAP66KOPdPjwYQ0YMEBVqlSxbv/kk0/UsmVLe4YAAABQLO3Zs0eNGjWyrq9cuVI9e/a05kYvvPCCHnnkkaIKDwAAwO7sWpRq06aNNm3apK+++kp+fn55EqugoCA1a9bMniEAAAAUSxcvXpSLi4t1fcOGDXr66aet61WrVlVaWloRRAYAAGAOuxalJKlevXqqV69evtueeOIJe58eAACgWKpZs6a++eYb1ahRQ/v379dff/2l1q1bW7cfPHhQlStXLsIIAQAA7MvuRSkAAADkNXToUEVHR+vbb7/VDz/8oJCQENWvX9+6fe3atWrSpEkRRggAAGBfFKUAAACKQFRUlBwdHfX555+rdevWGj9+vM32f/75RwMHDiyi6AAAAOyPohQAAEARGThwYIGFp7ffftvkaAAAAMzlUNQBAAAAAAAAoPShKAUAAAAAAADT2b0o9fbbbys0NFS9evVSUlKSzba0tDTVqFHD3iEAAAAAAACgmLFrUeqNN97Q6NGjVbduXbm4uKhTp06Ki4uzbs/Ozta+ffvsGQIAAAAAAACKIbtOdD5nzhzNnTtX/fr1kyQ99dRT6tatm86dO6dJkybZ89QAAAAAAAAoxuw6UmrPnj1q0aKFdb1FixZau3at3nnnHY0bN86epwYAACj2fv31V7300kt6++23lZaWZrMtPT29wCfzAQAAlAR2LUp5eXnpwIEDNm0NGjTQ2rVrNX/+fI0ZM8aepwcAACi2vvrqKzVr1kxLlizRK6+8orp162rdunXW7efOndP7779fhBECAADYl12LUq1atdKKFSvytNevX19JSUlatWqVPU8PAABQbE2YMEGjRo3SH3/8ob1792rMmDHq2rWrEhMTizo0AAAAU9h1TqmxY8dq06ZN+W678847tXbtWn388cf2DAEAAKBY2rp1qxYuXChJslgsGjNmjG677Tb17NlTS5Ys0d13313EEQIAANiXXYtSjRo1UqNGjQrc3qBBAzVo0MCeIQAAABRLLi4uOnnypE1bv3795ODgoN69e+u1114rmsAAAABMYtfb965mxYoVVyxaAQAAlFRBQUE2c0jl6tOnj959910NHz68CKICAAAwj92LUnPmzFHPnj3Vr18//fjjj5KktWvXqkmTJnrsscfUsmVLe4cAAABQ7Dz11FM6dOhQvtv69u2rBQsWqHXr1iZHBQAAYB67FqWmTp2qYcOGae/evfrss89033336eWXX1b//v3Vu3dvHTx4ULNnz7ZnCAAAAMVS9+7dNXPmzAK39+vXL9+RVAAAACWFXYtS8+fP19y5c/Xzzz9r1apVOnfunL7//nvt3LlTY8eOVcWKFe15egAAgFvW4cOHFR0dXdRhAAAA2I1dJzrfv3+/7rvvPknSvffeKycnJ02cOFHlypWz52kBAABuCVu3btW6devk7OysXr16qUKFCkpLS9OUKVOUkJCgGjVqFHWIAAAAdmPXkVKZmZlydXW1rjs7O6tSpUr2PCUAAMAt4bPPPlOTJk00fPhwPfnkk2ratKnWrVunevXqadu2bfrkk0+0devWog4TAADAbuw6UkqSXnzxRbm5uUmSsrKy9NJLL8nT09Omz4wZM+wdBgAAQLHy0ksvaejQoZo8ebLeffddxcTEaPjw4Vq5cqXuvvvuog4PAADA7uxalGrdurV27NhhXW/RooV2795t08disdgzBAAAgGJpx44dWrx4sdzd3TVs2DCNGjVKM2fOpCAFAABKDbsWpZKTk+15eAAAgFvW6dOn5eHhIUlydHRU2bJlmUMKAACUKna/fQ8AAAD5W716tXVag5ycHCUlJemPP/6w6dO1a9eiCA0AAMDuKEoBAAAUkYiICJv1wYMH26xbLBZlZ2ebGRIAAIBp7Pr0PQAAAOQvJyfnqsv1FqRmzZqlwMBAubq6qnnz5tq4cWOh9luyZIksFou6det2XecFAAC4FhSlAAAASpClS5cqJiZG48eP1+bNm9W4cWOFhYXpyJEjV9xv7969GjVqlO69916TIgUAAKUdRSkAAIASZMaMGYqKilJkZKTq16+vhIQEubm5ad68eQXuk52drf79+2vixIlMtg4AAExj2pxSJ0+e1MaNG3XkyBHl5OTYbAsPDzcrDAAAgBIrKytLmzZt0rhx46xtDg4OCg0N1YYNGwrcb9KkSfLx8dGgQYP07bffmhEqAACAOUWpzz//XP3799eZM2fk4eEhi8Vi3WaxWChKAQAA3ARpaWnKzs6Wr6+vTbuvr6+2b9+e7z7fffed3nvvPW3ZsqVQ58jMzFRmZqZ1PT09/brjBQAApZspt+8988wzGjhwoM6cOaOTJ0/qxIkT1uX48eNmhAAAAIDLnD59Wo899pjmzp0rLy+vQu0TFxcnT09P6xIQEGDnKAEAQEllSlHq0KFDGj58uNzc3Mw4HQAAwC2jRo0aOnbsWJ72kydPXvP8Tl5eXnJ0dFRqaqpNe2pqqvz8/PL037Vrl/bu3asuXbqoTJkyKlOmjD744AN99tlnKlOmjHbt2pVnn3HjxunUqVPW5cCBA9cUIwAAQC5TilJhYWH6+eefzTgVAADALWXv3r3Kzs7O056ZmalDhw5d07GcnZ0VHByspKQka1tOTo6SkpIUEhKSp3/dunX1+++/a8uWLdala9euateunbZs2ZLvKCgXFxd5eHjYLAAAANfDlDmlOnfurNGjR+vPP/9Uw4YN5eTkZLO9a9euZoQBAABQbHz22WfWf69evVqenp7W9ezsbCUlJSkwMPCajxsTE6OIiAg1bdpUzZo1U3x8vDIyMhQZGSnp0gNm/P39FRcXJ1dXVzVo0MBm/woVKkhSnnYAAICbzZSiVFRUlKRLT3a5nMViyffbQQAAgJKsW7duki7lQhERETbbnJycFBgYqNdee+2aj9u7d28dPXpUsbGxSklJUVBQkBITE62Tn+/fv18ODqYMlgcAALgiU4pSOTk5ZpwGAADglpGbH1WvXl0//fRToScaL4zo6GhFR0fnuy05OfmK+y5YsOCmxQEAAHAlphSlAAAAkL89e/bkaTt58qT1NjoAAICSym5FqTfeeENPPPGEXF1d9cYbb1yx7/Dhw+0VBgAAQLH2yiuvKDAwUL1795YkPfLII/r4449VpUoVrVy5Uo0bNy7iCAEAAOzDbkWpmTNnqn///nJ1ddXMmTML7GexWChKAQCAUishIUGLFi2SJK1Zs0b/+9//lJiYqI8++kijR4/WV199VcQRAgAA2IfdilL/Hoqe37B0AAAASCkpKQoICJAkffHFF+rVq5c6dOigwMBANW/evIijAwAAsB8evQIAAFCEKlasqAMHDkiSEhMTFRoaKkkyDIMnFAMAgBLNtInODx48qM8++0z79+9XVlaWzbYZM2aYFQYAAECx8vDDD6tfv36qXbu2jh07pgceeECS9Msvv6hWrVpFHB0AAID9mFKUSkpKUteuXVWjRg1t375dDRo00N69e2UYhu666y4zQgAAACiWZs6cqcDAQB04cECvvvqq3N3dJUmHDx/WkCFDijg6AAAA+zGlKDVu3DiNGjVKEydOVPny5fXxxx/Lx8dH/fv3V8eOHc0IAQAAoFhycnLSqFGj8rSPHDmyCKIBAAAwjylzSm3btk3h4eGSpDJlyujcuXNyd3fXpEmT9Morr5gRAgAAQLG1cOFCtWrVSlWrVtW+ffskSfHx8fr000+LODIAAAD7MaUoVa5cOes8UlWqVNGuXbus29LS0swIAQAAoFiaPXu2YmJi9MADD+jkyZPWyc0rVKig+Pj4og0OAADAjkwpSt1zzz367rvvJEmdOnXSM888oylTpmjgwIG65557zAgBAACgWHrzzTc1d+5cPf/883J0dLS2N23aVL///nsRRgYAAGBfpswpNWPGDJ05c0aSNHHiRJ05c0ZLly5V7dq1efIeAAAo1fbs2aMmTZrkaXdxcVFGRkYRRAQAAGAOuxelsrOzdfDgQTVq1EjSpVv5EhIS7H1aAACAW0L16tW1ZcsWVatWzaY9MTFR9erVK6KoAAAA7M/ut+85OjqqQ4cOOnHihL1PBQAAcMuYNGmSzp49q5iYGA0dOlRLly6VYRjauHGjpkyZonHjxmnMmDFFHSYAAIDdmHL7XoMGDbR7925Vr17djNMBAAAUexMnTtSTTz6pxx9/XGXLltULL7ygs2fPql+/fqpatapef/119enTp6jDBAAAsBtTJjp/6aWXNGrUKH3xxRc6fPiw0tPTbZbrMWvWLAUGBsrV1VXNmzfXxo0bC7XfkiVLZLFY1K1bt+s6LwAAwM1gGIb13/3799fff/+tM2fOKCUlRQcPHtSgQYOKMDoAAAD7s2tRatKkScrIyFCnTp3066+/qmvXrrrttttUsWJFVaxYURUqVFDFihWv+bhLly5VTEyMxo8fr82bN6tx48YKCwvTkSNHrrjf3r17NWrUKN17773Xe0kAAAA3jcVisVl3c3OTj49PEUUDAABgLrvevpc7LH3dunU39bgzZsxQVFSUIiMjJUkJCQn68ssvNW/ePI0dOzbffbKzs9W/f39NnDhR3377rU6ePHlTYwIAALhWd9xxR57C1OWOHz9uUjQAAADmsmtRKndYeps2bW7aMbOysrRp0yaNGzfO2ubg4KDQ0FBt2LChwP0mTZokHx8fDRo0SN9+++0Vz5GZmanMzEzr+vXeYggAAHAlEydOlKenZ1GHAQAAUCTsPtH51b79u1ZpaWnKzs6Wr6+vTbuvr6+2b9+e7z7fffed3nvvPW3ZsqVQ54iLi9PEiRNvNFQAAIAr6tOnD7frAQCAUsvuRamiHpZ++vRpPfbYY5o7d668vLwKtc+4ceMUExNjXU9PT1dAQIC9QgQAAKXQzf7iDgAA4FZj96LUzR6W7uXlJUdHR6Wmptq0p6amys/PL0//Xbt2ae/everSpYu1LScnR5JUpkwZ7dixQzVr1rTZx8XFRS4uLjctZgAAgMv9++l7AAAApZHdi1I3e1i6s7OzgoODlZSUpG7dukm6VGRKSkpSdHR0nv5169bV77//btP2wgsv6PTp03r99dcZAQUAAIpE7pdkAAAApZVdi1L2GpYeExOjiIgINW3aVM2aNVN8fLwyMjKsT+MLDw+Xv7+/4uLi5OrqqgYNGtjsX6FCBUnK0w4AAAAAAABzmPL0vZutd+/eOnr0qGJjY5WSkqKgoCAlJiZaJz/fv3+/HBwc7HJuAAAAAAAA3Di7FqXsOSw9Ojo639v1JCk5OfmK+y5YsODmBwQAAAAAAIBCYzgRAAAAAAAATEdRCgAAAAAAAKajKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAAAAAAAAwHUUpAAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmoygFAABQwsyaNUuBgYFydXVV8+bNtXHjxgL7rlixQk2bNlWFChVUrlw5BQUFaeHChSZGCwAASiuKUgAAACXI0qVLFRMTo/Hjx2vz5s1q3LixwsLCdOTIkXz7V6pUSc8//7w2bNig3377TZGRkYqMjNTq1atNjhwAAJQ2FKUAAABKkBkzZigqKkqRkZGqX7++EhIS5Obmpnnz5uXbv23bturevbvq1aunmjVrasSIEWrUqJG+++47kyMHAAClDUUpAACAEiIrK0ubNm1SaGiotc3BwUGhoaHasGHDVfc3DENJSUnasWOHWrdunW+fzMxMpaen2ywAAADXg6IUAABACZGWlqbs7Gz5+vratPv6+iolJaXA/U6dOiV3d3c5Ozurc+fOevPNN9W+fft8+8bFxcnT09O6BAQE3NRrAAAApQdFKQAAgFKufPny2rJli3766SdNmTJFMTExSk5OzrfvuHHjdOrUKety4MABc4MFAAAlRpmiDgAAAAA3h5eXlxwdHZWammrTnpqaKj8/vwL3c3BwUK1atSRJQUFB2rZtm+Li4tS2bds8fV1cXOTi4nJT4wYAAKUTI6UAAABKCGdnZwUHByspKcnalpOTo6SkJIWEhBT6ODk5OcrMzLRHiAAAAFaMlAIAAChBYmJiFBERoaZNm6pZs2aKj49XRkaGIiMjJUnh4eHy9/dXXFycpEtzRDVt2lQ1a9ZUZmamVq5cqYULF2r27NlFeRkAAKAUoCgFAABQgvTu3VtHjx5VbGysUlJSFBQUpMTEROvk5/v375eDw/8Nls/IyNCQIUN08OBBlS1bVnXr1tWHH36o3r17F9UlAACAUoKiFAAAQAkTHR2t6OjofLddPoH5Sy+9pJdeesmEqAAAAGwxpxQAAAAAAABMR1EKAAAAAAAApqMoBQAAAAAAANNRlAIAAAAAAIDpKEoBAAAAAADAdBSlAAAAAAAAYDqKUgAAAAAAADAdRSkAAAAAAACYjqIUAAAAAAAATEdRCgAAAAAAAKajKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAAAAAAAAwHUUpAAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmoygFAAAAAAAA01GUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgOopSAAAAAAAAMB1FKQAAAAAAAJiOohQAAAAAAABMR1EKAAAAAAAApqMoBQAAAAAAANNRlAIAAAAAAIDpKEoBAAAAAADAdBSlAAAAAAAAYDqKUgAAAAAAADAdRSkAAAAAAACYjqIUAAAAAAAATEdRCgAAAAAAAKajKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAlDCzZs1SYGCgXF1d1bx5c23cuLHAvnPnztW9996rihUrqmLFigoNDb1ifwAAgJuFohQAAEAJsnTpUsXExGj8+PHavHmzGjdurLCwMB05ciTf/snJyerbt6/WrVunDRs2KCAgQB06dNChQ4dMjhwAAJQ2FKUAAABKkBkzZigqKkqRkZGqX7++EhIS5Obmpnnz5uXbf9GiRRoyZIiCgoJUt25dvfvuu8rJyVFSUpLJkQMAgNKGohQAAEAJkZWVpU2bNik0NNTa5uDgoNDQUG3YsKFQxzh79qwuXLigSpUq5bs9MzNT6enpNgsAAMD1oCgFAABQQqSlpSk7O1u+vr427b6+vkpJSSnUMZ599llVrVrVprD1b3FxcfL09LQuAQEBNxw3AAAonShKAQAAQJI0depULVmyRJ988olcXV3z7TNu3DidOnXKuhw4cMDkKAEAQElRpqgDAAAAwM3h5eUlR0dHpaam2rSnpqbKz8/vivtOnz5dU6dO1f/+9z81atSowH4uLi5ycXG5KfECAIDSjZFSAAAAJYSzs7OCg4NtJinPnbQ8JCSkwP1effVVTZ48WYmJiWratKkZoQIAADBSCgAAoCSJiYlRRESEmjZtqmbNmik+Pl4ZGRmKjIyUJIWHh8vf319xcXGSpFdeeUWxsbFavHixAgMDrXNPubu7y93dvciuAwAAlHwUpQAAAEqQ3r176+jRo4qNjVVKSoqCgoKUmJhonfx8//79cnD4v8Hys2fPVlZWlnr27GlznPHjx2vChAlmhg4AAEoZilIAAAAlTHR0tKKjo/PdlpycbLO+d+9e+wcEAACQD+aUAgAAAAAAgOlu2aLUrFmzFBgYKFdXVzVv3lwbN24ssO/cuXN17733qmLFiqpYsaJCQ0Ov2B8AAAAAAAD2dUsWpZYuXaqYmBiNHz9emzdvVuPGjRUWFqYjR47k2z85OVl9+/bVunXrtGHDBgUEBKhDhw46dOiQyZEDAAAAAABAukWLUjNmzFBUVJQiIyNVv359JSQkyM3NTfPmzcu3/6JFizRkyBAFBQWpbt26evfdd62PRwYAAAAAAID5brmiVFZWljZt2qTQ0FBrm4ODg0JDQ7Vhw4ZCHePs2bO6cOGCKlWqZK8wAQAAAAAAcAW33NP30tLSlJ2dbX2scS5fX19t3769UMd49tlnVbVqVZvC1r9lZmYqMzPTup6enn79AQMAAAAAACCPW26k1I2aOnWqlixZok8++USurq759omLi5Onp6d1CQgIMDlKAAAAAACAku2WK0p5eXnJ0dFRqampNu2pqany8/O74r7Tp0/X1KlT9dVXX6lRo0YF9hs3bpxOnTplXQ4cOHBTYgcAAAAAAMAlt1xRytnZWcHBwTaTlOdOWh4SElLgfq+++qomT56sxMRENW3a9IrncHFxkYeHh80CAAAAAACAm+eWm1NKkmJiYhQREaGmTZuqWbNmio+PV0ZGhiIjIyVJ4eHh8vf3V1xcnCTplVdeUWxsrBYvXqzAwEClpKRIktzd3eXu7l5k1wEAAAAAAFBa3ZJFqd69e+vo0aOKjY1VSkqKgoKClJiYaJ38fP/+/XJw+L9BYLNnz1ZWVpZ69uxpc5zx48drwoQJZoYOAAAAAAAA3aJFKUmKjo5WdHR0vtuSk5Nt1vfu3Wv/gAAAAAAAAFBot9ycUgAAAAAAALj1UZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAAAAAAAAwHUUpAAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmoygFAAAAAAAA01GUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgOopSAAAAAAAAMB1FKQAAAAAAAJiOohQAAAAAAABMR1EKAAAAAAAApqMoBQAAAAAAANNRlAIAAAAAAIDpKEoBAAAAAADAdBSlAAAAAAAAYDqKUgAAAAAAADAdRSkAAAAAAACYjqIUAAAAAAAATEdRCgAAoASZNWuWAgMD5erqqubNm2vjxo0F9t26dat69OihwMBAWSwWxcfHmxcoAAAo9ShKAQAAlBBLly5VTEyMxo8fr82bN6tx48YKCwvTkSNH8u1/9uxZ1ahRQ1OnTpWfn5/J0QIAgNKOohQAAEAJMWPGDEVFRSkyMlL169dXQkKC3NzcNG/evHz733333Zo2bZr69OkjFxcXk6MFAAClHUUpAACAEiArK0ubNm1SaGiotc3BwUGhoaHasGFDEUYGAACQvzJFHQAAAABuXFpamrKzs+Xr62vT7uvrq+3bt9+082RmZiozM9O6np6eftOODQAAShdGSgEAAKDQ4uLi5OnpaV0CAgKKOiQAAHCLoigFAABQAnh5ecnR0VGpqak27ampqTd1EvNx48bp1KlT1uXAgQM37dgAAKB0oSgFAABQAjg7Oys4OFhJSUnWtpycHCUlJSkkJOSmncfFxUUeHh42CwAAwPVgTikAAIASIiYmRhEREWratKmaNWum+Ph4ZWRkKDIyUpIUHh4uf39/xcXFSbo0Ofqff/5p/fehQ4e0ZcsWubu7q1atWkV2HQAAoHSgKAUAAFBC9O7dW0ePHlVsbKxSUlIUFBSkxMRE6+Tn+/fvl4PD/w2U/+eff9SkSRPr+vTp0zV9+nS1adNGycnJZocPAABKGYpSAAAAJUh0dLSio6Pz3XZ5oSkwMFCGYZgQFQAAQF7MKQUAAAAAAADTUZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAAAAAAAAwHUUpAAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmoygFAAAAAAAA01GUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgOopSAAAAAAAAMB1FKQAAAAAAAJiOohQAAAAAAABMR1EKAAAAAAAApqMoBQAAAAAAANNRlAIAAAAAAIDpKEoBAAAAAADAdBSlAAAAAAAAYDqKUgAAAAAAADAdRSkAAAAAAACYjqIUAAAAAAAATEdRCgAAAAAAAKajKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAAAAAAAAwHUUpAAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmoygFAAAAAAAA01GUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgulu2KDVr1iwFBgbK1dVVzZs318aNG6/Yf9myZapbt65cXV3VsGFDrVy50qRIAQAAzEWeBAAAbgW3ZFFq6dKliomJ0fjx47V582Y1btxYYWFhOnLkSL79v//+e/Xt21eDBg3SL7/8om7duqlbt276448/TI4cAADAvsiTAADAreKWLErNmDFDUVFRioyMVP369ZWQkCA3NzfNmzcv3/6vv/66OnbsqNGjR6tevXqaPHmy7rrrLr311lsmRw4AAGBf5EkAAOBWUaaoA7hWWVlZ2rRpk8aNG2dtc3BwUGhoqDZs2JDvPhs2bFBMTIxNW1hYmP773//m2z8zM1OZmZnW9VOnTkmS0tPTrynW7Mxz19Qf5rrWn+f1On0+25Tz4PqY9T64eO6iKefB9THrfZBxkfdBcXat74Pc/oZh2COc60KehJuFPAkSeRIuIU+CZL886ZYrSqWlpSk7O1u+vr427b6+vtq+fXu++6SkpOTbPyUlJd/+cXFxmjhxYp72gICA64waxZHnm08WdQgoDuI8izoCFAOez/I+gCTP63sfnD59Wp7Xue/NRp6Em4U8CZLIkyCJPAn/n53ypFuuKGWGcePG2XxjmJOTo+PHj6ty5cqyWCxFGFnRSU9PV0BAgA4cOCAPD4+iDgdFhPcBJN4HuIT3waVv/k6fPq2qVasWdSimIk/Ki98HSLwPcAnvA0i8D6TC50m3XFHKy8tLjo6OSk1NtWlPTU2Vn59fvvv4+fldU38XFxe5uLjYtFWoUOH6gy5BPDw8Su0vFf4P7wNIvA9wSWl/HxSXEVK5yJOKVmn/fcAlvA8g8T7AJaX9fVCYPOmWm+jc2dlZwcHBSkpKsrbl5OQoKSlJISEh+e4TEhJi01+S1qxZU2B/AACAWxF5EgAAuJXcciOlJCkmJkYRERFq2rSpmjVrpvj4eGVkZCgyMlKSFB4eLn9/f8XFxUmSRowYoTZt2ui1115T586dtWTJEv3888965513ivIyAAAAbjryJAAAcKu4JYtSvXv31tGjRxUbG6uUlBQFBQUpMTHROknn/v375eDwf4PAWrRoocWLF+uFF17Qc889p9q1a+u///2vGjRoUFSXcMtxcXHR+PHj8wzXR+nC+wAS7wNcwvug+CJPMh+/D5B4H+AS3geQeB9cC4tRnJ5jDAAAAAAAgFLhlptTCgAAAAAAALc+ilIAAAAAAAAwHUUpAAAAAAAAmI6iFFCKtW3bVk8//XRRhwGghEtOTpbFYtHJkyeLOhQAKDTyJABmKO15EkWpUmTAgAGyWCx68skn82wbOnSoLBaLBgwYYO3brVs3mz4pKSkaNmyYatSoIRcXFwUEBKhLly5KSkqy9gkMDJTFYsmzTJ061Z6XBjuYMGFCvu+XLVu2yGKxaO/evZKkvXv3ymKxyMfHR6dPn7bpGxQUpAkTJpgUMQrrWj4LUPzl/jwtFoucnJzk6+ur9u3ba968ecrJySnq8CRderrb4cOH5enpWdShAAUiT8K1IE8quciTShbypOKPolQpExAQoCVLlujcuXPWtvPnz2vx4sW6/fbbC9xv7969Cg4O1tq1azVt2jT9/vvvSkxMVLt27TR06FCbvpMmTdLhw4dtlmHDhtntmmA/rq6ueu+99/T3339fte/p06c1ffp0E6LCzXC9nwUonjp27KjDhw9r7969WrVqldq1a6cRI0bowQcf1MWLF/Pd58KFC6bF5+zsLD8/P1ksFtPOCVwP8iRcC/Kkkos8qWQhTyreKEqVMnfddZcCAgK0YsUKa9uKFSt0++23q0mTJgXuN2TIEFksFm3cuFE9evTQHXfcoTvvvFMxMTH64YcfbPqWL19efn5+Nku5cuXsdk24eb788kt5enpq0aJFkqQ6deqoXbt2ev7556+677BhwzRjxgwdOXLE3mHiJijsZ0FOTo7i4uJUvXp1lS1bVo0bN9by5cut27OzszVo0CDr9jp16uj111+3OVfuiILp06erSpUqqly5soYOHWrqH/uSzsXFRX5+fvL399ddd92l5557Tp9++qlWrVqlBQsWSJIsFotmz56trl27qly5cpoyZYokafbs2apZs6acnZ1Vp04dLVy40ObYufs98MADKlu2rGrUqGHzHsgdBbBkyRK1aNFCrq6uatCggb7++mtrn8uHpS9YsEAVKlTQ6tWrVa9ePbm7u1sTxlwXL17U8OHDVaFCBVWuXFnPPvusIiIi8oxOAW4m8iRcCXlS6UGeVLKQJxVvFKVKoYEDB2r+/PnW9Xnz5ikyMrLA/sePH1diYqKGDh2ab9JUoUIFe4QJky1evFh9+/bVokWL1L9/f2v71KlT9fHHH+vnn3++4v59+/ZVrVq1NGnSJHuHipukMJ8FcXFx+uCDD5SQkKCtW7dq5MiRevTRR61/SHNycnTbbbdp2bJl+vPPPxUbG6vnnntOH330kc1x1q1bp127dmndunV6//33tWDBAmsSAPu477771LhxY5uEesKECerevbt+//13DRw4UJ988olGjBihZ555Rn/88YcGDx6syMhIrVu3zuZYL774onr06KFff/1V/fv3V58+fbRt2zabPqNHj9YzzzyjX375RSEhIerSpYuOHTtWYHxnz57V9OnTtXDhQn3zzTfav3+/Ro0aZd3+yiuvaNGiRZo/f77Wr1+v9PR0/fe//705Lw5wBeRJyA95UulDnlSykScVIwZKjYiICOOhhx4yjhw5Yri4uBh79+419u7da7i6uhpHjx41HnroISMiIsKmr2EYxo8//mhIMlasWHHVc1SrVs1wdnY2ypUrZ7N88803drwyXK82bdoYI0aMMN566y3D09PTSE5Otm4bP3680bhxY8MwDKNPnz7GfffdZxiGYfzyyy+GJGPPnj2GYRjGnj17DEnGL7/8YiQmJhpOTk7Gzp07DcMwjMaNGxvjx48385JQCIX9LDh//rzh5uZmfP/99zb7Dxo0yOjbt2+Bxx86dKjRo0cPm/NVq1bNuHjxorXtkUceMXr37n3zL64U+vfn9eV69+5t1KtXzzAMw5BkPP300zbbW7RoYURFRdm0PfLII0anTp2s65KMJ5980qZP8+bNjaeeesowjP/7DJg6dap1+4ULF4zbbrvNeOWVVwzDMIx169YZkowTJ04YhmEY8+fPNyRZPysMwzBmzZpl+Pr6Wtd9fX2NadOmWdcvXrxo3H777QVeK3CjyJNwOfKk0ok8qWQhTyr+yphfBkNR8/b2VufOnbVgwQIZhqHOnTvLy8urwP6GYVzT8UePHp1n8j9/f//rCRUmWL58uY4cOaL169fr7rvvzrfPSy+9pHr16umrr76Sj49PgccKCwtTq1at9OKLL2rx4sX2Chk3ydU+C3bu3KmzZ8+qffv2NvtlZWXZDF2fNWuW5s2bp/379+vcuXPKyspSUFCQzT533nmnHB0dretVqlTR77//bp8Lg5VhGDbzEzRt2tRm+7Zt2/TEE0/YtLVs2TLPrQUhISF51rds2VJgnzJlyqhp06Z5viX8Nzc3N9WsWdO6XqVKFettLadOnVJqaqqaNWtm3e7o6Kjg4OBiMykpSi7yJPwbeVLpRZ5U8pEnFQ8UpUqpgQMHKjo6WtKlD8orqV27tiwWi7Zv316oY3t5ealWrVo3HCPM0aRJE23evFnz5s1T06ZN851gr2bNmoqKitLYsWP13nvvXfF4U6dOVUhIiEaPHm2vkHETXemz4MyZM5IuzaFx+X+YXFxcJElLlizRqFGj9NprrykkJETly5fXtGnT9OOPP9r0d3Jyslm3WCy35B/NW822bdtUvXp163pxmrcmv/fEtf7nHrAX8iTkIk8q3ciTSjbypOKBOaVKqY4dOyorK0sXLlxQWFjYFftWqlRJYWFhmjVrljIyMvJsz52QDbemmjVrat26dfr000+v+PSf2NhY/fXXX1qyZMkVj9esWTM9/PDDGjt27M0OFXZwpc+C+vXry8XFRfv371etWrVsloCAAEnS+vXr1aJFCw0ZMkRNmjRRrVq1tGvXrqK4FFxm7dq1+v3339WjR48C+9SrV0/r16+3aVu/fr3q169v03b5RM0//PCD6tWrV2CfixcvatOmTXn6FJanp6d8fX31008/Wduys7O1efPm6zoecK3Ik5CLPKl0I08quciTig9GSpVSjo6O1uGC/x4qWpBZs2apZcuWatasmSZNmqRGjRrp4sWLWrNmjWbPnm0z9PD06dNKSUmx2d/NzU0eHh439yJw09xxxx1at26d2rZtqzJlyig+Pj5PH19fX8XExGjatGlXPd6UKVN05513qkwZPmKKuyt9FpQvX16jRo3SyJEjlZOTo1atWunUqVNav369PDw8FBERodq1a+uDDz7Q6tWrVb16dS1cuFA//fSTzbdOsL/MzEylpKQoOztbqampSkxMVFxcnB588EGFh4cXuN/o0aPVq1cvNWnSRKGhofr888+1YsUK/e9//7Ppt2zZMjVt2lStWrXSokWLtHHjxjyjAWbNmqXatWurXr16mjlzpk6cOKGBAwde9zUNGzZMcXFxqlWrlurWras333xTJ06cKLWPS4a5yJPwb+RJpRd5UslAnlS88UlYil1L8lOjRg1t3rxZU6ZM0TPPPKPDhw/L29tbwcHBmj17tk3f2NhYxcbG2rQNHjxYCQkJNyVu2EedOnW0du1atW3bVo6OjipfvnyePqNGjdLs2bN1/vz5Kx7rjjvu0MCBA/XOO+/YK1zcRFf6LJg8ebK8vb0VFxen3bt3q0KFCtZH6UqXfrd/+eUX9e7dWxaLRX379tWQIUO0atUqs8KHpMTERFWpUkVlypRRxYoV1bhxY73xxhuKiIiQg0PBg6K7deum119/XdOnT9eIESNUvXp1zZ8/X23btrXpN3HiRC1ZskRDhgxRlSpV9J///CfPt4RTp07V1KlTtWXLFtWqVUufffbZFefhuZpnn31WKSkpCg8Pl6Ojo5544gmFhYUVqkAA3AzkSfg38qTSizzp1keeVLxZjJJ6YyIAALhhFotFn3zyibp165bv9r1796p69er65Zdf8kzcejPl5OSoXr166tWrlyZPnmy38wAAABQWedKNY6QUAAAodvbt26evvvpKbdq0UWZmpt566y3t2bNH/fr1K+rQAAAAilRJypOY6BwAABQ7Dg4OWrBgge6++261bNlSv//+u/73v/9d96SgAAAAJUVJypO4fQ8AAAAAAACmY6QUAAAAAAAATEdRCgAAAAAAAKajKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAbmkDBgxQt27dijqMYhMHAABAruKSnxSXOAAUPxSlAKAYycrKKuoQAAAAiiXyJKDkoSgFoMRo27athg0bpqeffloVK1aUr6+v5s6dq4yMDEVGRqp8+fKqVauWVq1aZd0nOTlZFotFX375pRo1aiRXV1fdc889+uOPP6x9JkyYoKCgIJtzxcfHKzAw0Lr9/fff16effiqLxSKLxaLk5GRJ0oEDB9SrVy9VqFBBlSpV0kMPPaS9e/daj5P7zeGUKVNUtWpV1alTR5L09ttvq3bt2nJ1dZWvr6969uxpl9cMAACUDuRJAIojilIASpT3339fXl5e2rhxo4YNG6annnpKjzzyiFq0aKHNmzerQ4cOeuyxx3T27Fmb/UaPHq3XXntNP/30k7y9vdWlSxdduHChUOccNWqUevXqpY4dO+rw4cM6fPiwWrRooQsXLigsLEzly5fXt99+q/Xr18vd3V0dO3a0+aYvKSlJO3bs0Jo1a/TFF1/o559/1vDhwzVp0iTt2LFDiYmJat269U19nQAAQOlDngSguKEoBaBEady4sV544QXVrl1b48aNk6urq7y8vBQVFaXatWsrNjZWx44d02+//Waz3/jx49W+fXs1bNhQ77//vlJTU/XJJ58U6pzu7u4qW7asXFxc5OfnJz8/Pzk7O2vp0qXKycnRu+++q4YNG6pevXqaP3++9u/fb/2GUJLKlSund999V3feeafuvPNO7d+/X+XKldODDz6oatWqqUmTJho+fPjNfJkAAEApRJ4EoLgpU9QBAMDN1KhRI+u/HR0dVblyZTVs2NDa5uvrK0k6cuSIzX4hISHWf1eqVEl16tTRtm3bbiiWX3/9VTt37lT58uVt2s+fP69du3ZZ1xs2bChnZ2frevv27VWtWjXVqFFDHTt2VMeOHdW9e3e5ubndUDwAAKB0I08CUNxQlAJQojg5OdmsWywWmzaLxSJJysnJKfQxHRwcZBiGTVthhqyfOXNGwcHBWrRoUZ5t3t7e1n+XK1fOZlv58uW1efNmJScn66uvvlJsbKwmTJign376SRUqVCh03AAAAP9GngSguOH2PQCQ9MMPP1j/feLECf3111+qV6+epEuJUUpKik3CtWXLFpv9nZ2dlZ2dbdN211136e+//5aPj49q1apls3h6el4xnjJlyig0NFSvvvqqfvvtN+3du1dr1669wasEAAC4duRJAOyFohQASJo0aZKSkpL0xx9/aMCAAfLy8lK3bt0kXXpazdGjR/Xqq69q165dmjVrls2TaSQpMDBQv/32m3bs2KG0tDRduHBB/fv3l5eXlx566CF9++232rNnj5KTkzV8+HAdPHiwwFi++OILvfHGG9qyZYv27dunDz74QDk5OdYnzgAAAJiJPAmAvVCUAgBJU6dO1YgRIxQcHKyUlBR9/vnn1vkL6tWrp7fffluzZs1S48aNtXHjRo0aNcpm/6ioKNWpU0dNmzaVt7e31q9fLzc3N33zzTe6/fbb9fDDD6tevXoaNGiQzp8/Lw8PjwJjqVChglasWKH77rtP9erVU0JCgv7zn//ozjvvtOtrAAAAkB/yJAD2YjEuvwEYAEqR5ORktWvXTidOnGAeAgAAgH8hTwJgb4yUAgAAAAAAgOkoSgEAAAAAAMB03L4HAAAAAAAA0zFSCgAAAAAAAKajKAUAAAAAAADTUZQCAAAAAACA6ShKAQAAAAAAwHQUpQAAAAAAAGA6ilIAAAAAAAAwHUUpAAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmoygFAAAAAAAA01GUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgOopSAG5Jqamp6tmzpypXriyLxaL4+PiiDsl0ycnJslgsSk5OLupQAABAMUfuRO4EFEcUpQA7WrBggSwWiywWi7777rs82w3DUEBAgCwWix588EGbbWfOnNH48ePVoEEDlStXTpUrV1ZQUJBGjBihf/75x9pvwoQJ1nPkt6SkpFwxxsDAwAL37dix4815Iexg5MiRWr16tcaNG6eFCxfaPdbLXxsPDw+1adNGX375pV3Pe6sp6L3k5+dX1KHla+XKlZowYUJRhwEAJdqN5EO5Tp48KVdXV1ksFm3bti3fPgMGDMjzt7px48Z67bXXlJmZae1H7kTuVJyQO6G0K1PUAQClgaurqxYvXqxWrVrZtH/99dc6ePCgXFxcbNovXLig1q1ba/v27YqIiNCwYcN05swZbd26VYsXL1b37t1VtWpVm31mz54td3f3POeuUKHCVeMLCgrSM888k6f98nMUJ2vXrtVDDz2kUaNGmXbO9u3bKzw8XIZhaN++fZo9e7a6dOmiVatWKSwszLQ4irvc1+nfypYtW0TRXNnKlSs1a9YskisAMMG15kP/tmzZMut/1BctWqSXXnop334uLi569913JV0qZH388ccaNWqUfvrpJy1ZssSmL7mT/ZE7FQ65E0ozilKACTp16qRly5bpjTfeUJky//drt3jxYgUHBystLc2m/3//+1/98ssvWrRokfr162ez7fz588rKyspzjp49e8rLy+u64vP399ejjz56zftlZGSoXLlyedpzcnKUlZUlV1fX64rnSsfOdeTIkUIljYV1/vx5OTs7y8Gh4AGkd9xxh83r1KNHD9WvX1+vv/46idW/XP463SwXL15UTk6OnJ2db/qxAQD2d6350L99+OGH6tSpk6pVq6bFixcXWJQqU6aMzd+gIUOGqHnz5lq6dKlmzJhhUzQid7ox5E43D7kTSjNu3wNM0LdvXx07dkxr1qyxtmVlZWn58uV5ik6StGvXLklSy5Yt82xzdXWVh4eH/YItwIABA+Tu7q5du3apU6dOKl++vPr37y/p0rDj6OhoLVq0SHfeeadcXFyUmJgoSfrll1/0wAMPyMPDQ+7u7rr//vv1ww8/2Bw7d1j/119/rSFDhsjHx0e33XZbvnHk9jUMQ7NmzbIOcc61e/duPfLII6pUqZLc3Nx0zz335BkmnjufwJIlS/TCCy/I399fbm5uSk9Pv6bXpF69evLy8rL+vHJ9+umn6ty5s6pWrSoXFxfVrFlTkydPVnZ2tk2/tm3bqkGDBvrzzz/Vrl07ubm5yd/fX6+++mqecx08eFDdunVTuXLl5OPjo5EjR9rcivBvy5YtU3BwsMqWLSsvLy89+uijOnTokE2f3J/n/v379eCDD8rd3V3+/v6aNWuWJOn333/Xfffdp3Llyln/A3CzHDlyRIMGDZKvr69cXV3VuHFjvf/++zZ99u7dK4vFounTpys+Pl41a9aUi4uL/vzzT0nS9u3b1bNnT1WqVEmurq5q2rSpPvvsM5tjXLhwQRMnTlTt2rXl6uqqypUrq1WrVtbfwwEDBliv99/D5QEA9nGt+VCu/fv369tvv1WfPn3Up08f7dmzR99//32hzung4KC2bdtKuvS3xUzkTnmRO10fcieUZIyUAkwQGBiokJAQ/ec//9EDDzwgSVq1apVOnTqlPn366I033rDpX61aNUnSBx98oBdeeKFQH/bHjx/P01amTJlCfSN24cKFfL+dLFeunM3Q4YsXLyosLEytWrXS9OnT5ebmZt22du1affTRR4qOjpaXl5cCAwO1detW3XvvvfLw8NCYMWPk5OSkOXPmqG3btvr666/VvHlzm/MNGTJE3t7eio2NVUZGRr6xtm7dWgsXLtRjjz2WZ6hzamqqWrRoobNnz2r48OGqXLmy3n//fXXt2lXLly9X9+7dbY41efJkOTs7a9SoUcrMzLzmb5FOnTqlEydOqGbNmjbtCxYskLu7u2JiYuTu7q61a9cqNjZW6enpmjZtmk3fEydOqGPHjnr44YfVq1cvLV++XM8++6waNmxofa+cO3dO999/v/bv36/hw4eratWqWrhwodauXZsnpgULFigyMlJ333234uLilJqaqtdff13r16/XL7/8YvN+yM7O1gMPPKDWrVvr1Vdf1aJFixQdHa1y5crp+eefV//+/fXwww8rISFB4eHhCgkJUfXq1a/6upw/fz7P+6l8+fJycXHRuXPn1LZtW+3cuVPR0dGqXr26li1bpgEDBujkyZMaMWKEzX7z58/X+fPn9cQTT8jFxUWVKlXS1q1b1bJlS/n7+2vs2LEqV66cPvroI3Xr1k0ff/yx9ec8YcIExcXF6fHHH1ezZs2Unp6un3/+WZs3b1b79u01ePBg/fPPP1qzZo0WLlx41esCANyYa82Hcv3nP/9RuXLl9OCDD6ps2bKqWbOmFi1apBYtWhTqvLkFkMqVK9u0kzuRO+Wem9yJ3AlFyABgN/PnzzckGT/99JPx1ltvGeXLlzfOnj1rGIZhPPLII0a7du0MwzCMatWqGZ07d7bud/bsWaNOnTqGJKNatWrGgAEDjPfee89ITU3Nc47x48cbkvJd6tSpc9UYq1WrVuD+cXFx1n4RERGGJGPs2LF5jiHJcHBwMLZu3WrT3q1bN8PZ2dnYtWuXte2ff/4xypcvb7Ru3TrP69SqVSvj4sWLV40595xDhw61aXv66acNSca3335rbTt9+rRRvXp1IzAw0MjOzjYMwzDWrVtnSDJq1Khh/XkU5nyDBg0yjh49ahw5csT4+eefjY4dOxqSjGnTptn0ze+YgwcPNtzc3Izz589b29q0aWNIMj744ANrW2ZmpuHn52f06NHD2hYfH29IMj766CNrW0ZGhlGrVi1DkrFu3TrDMAwjKyvL8PHxMRo0aGCcO3fO2veLL74wJBmxsbHWttyf58svv2xtO3HihFG2bFnDYrEYS5YssbZv377dkGSMHz++UK9Tfsv8+fNtruXDDz+07pOVlWWEhIQY7u7uRnp6umEYhrFnzx5DkuHh4WEcOXLE5hz333+/0bBhQ5vXMicnx2jRooVRu3Zta1vjxo1tfq/yM3ToUIM/hQBgX9ebD+Vq2LCh0b9/f+v6c889Z3h5eRkXLlyw6RcREWGUK1fOOHr0qHH06FFj586dxssvv2xYLBajUaNG1n7kTv+H3IncidwJRY3b9wCT9OrVS+fOndMXX3yh06dP64svvihwqHrZsmX1448/avTo0ZIufYMzaNAgValSRcOGDct36PHHH3+sNWvW2Czz588vVGzNmzfPs++aNWvUt2/fPH2feuqpfI/Rpk0b1a9f37qenZ2tr776St26dVONGjWs7VWqVFG/fv303Xff5RnyHRUVJUdHx0LFnJ+VK1eqWbNmNhOouru764knntDevXutw5dzRUREXNMkku+99568vb3l4+Ojpk2bKikpSWPGjFFMTIxNv38f8/Tp00pLS9O9996rs2fPavv27TZ93d3dbeYQcHZ2VrNmzbR7926b66pSpYp69uxpbXNzc9MTTzxhc6yff/5ZR44c0ZAhQ2zmpOjcubPq1q2b79NuHn/8ceu/K1SooDp16qhcuXLq1auXtb1OnTqqUKGCTUxX8tBDD+V5L+XOG7Fy5Ur5+fnZvLecnJw0fPhwnTlzRl9//bXNsXr06CFvb2/r+vHjx7V27Vr16tXL+tqmpaXp2LFjCgsL099//20dbl+hQgVt3bpVf//9d6HiBgDY37XkQ5L022+/6ffff7f5u9G3b1+lpaVp9erVefpnZGTI29tb3t7eqlWrlp577jmFhITok08+ydOX3IncidyJ3AlFj9v3AJN4e3srNDRUixcv1tmzZ5WdnW3zh/Jynp6eevXVV/Xqq69q3759SkpK0vTp0/XWW2/J09MzzwSfrVu3vu7JOr28vBQaGnrVfmXKlClwvoLLhyYfPXpUZ8+eVZ06dfL0rVevnnJycnTgwAHdeeedBR7jWu3bty/PsPbc8+Vub9CgwXWf76GHHlJ0dLSysrL0008/6eWXX9bZs2fzTPC5detWvfDCC1q7dm2e5PHUqVM267fddlue2zMrVqyo3377zea6atWqlaff5a/tvn378m2XpLp16+Z5DLerq6tN0iJdet/lF5Onp6dOnDiR57j5ue222wp8P+3bt0+1a9fO85r9+2f0b5f/jHbu3CnDMPTiiy/qxRdfzPccR44ckb+/vyZNmqSHHnpId9xxhxo0aKCOHTvqscceU6NGjQp1HQCAm+9a86EPP/xQ5cqVU40aNbRz505Jl/5+BQYGatGiRercubNNf1dXV33++eeSLj2Jr3r16gXmLuRO5E7kTpeQO6EoUZQCTNSvXz9FRUUpJSVFDzzwQKGfgFKtWjUNHDhQ3bt3V40aNa74KGR7cnFxKfAJKzfjsbVmP/r2Ws/374ShU6dO8vLyUnR0tNq1a6eHH35Y0qXHT7dp00YeHh6aNGmSatasKVdXV23evFnPPvuscnJybI5Z0LebhmFcxxVdm4LOXZQxXe7yn1Hu6zdq1KgCn9pTq1YtSZf+s7Fr1y59+umn+uqrr/Tuu+9q5syZSkhIsPmWEwBgrsLmQ4Zh6D//+Y8yMjJsRhTlOnLkiM6cOSN3d3drm6OjY6GKRWYhdyJ3Mhu5E241FKUAE3Xv3l2DBw/WDz/8oKVLl17z/hUrVlTNmjX1xx9/2CG6m8vb21tubm7asWNHnm3bt2+Xg4ODAgICbuo5q1WrVuD5crffTIMHD9bMmTP1wgsvqHv37rJYLEpOTtaxY8e0YsUKtW7d2tp3z549132eatWq6Y8//pBhGDbfwl1+rbnXt2PHDt13330223bs2HHTr/96VKtWTb/99ptycnJskvTC/oxyb2dwcnIq1H86KlWqpMjISEVGRurMmTNq3bq1JkyYYE2seGIMAJivsPnQ119/rYMHD2rSpEnWUSG5Tpw4oSeeeEL//e9/bW7lupWRO5E75YfcCSUdc0oBJnJ3d9fs2bM1YcIEdenSpcB+v/76a75PdNm3b5/+/PPPfIcYFzeOjo7q0KGDPv30U5tHMKempmrx4sVq1aqVPDw8buo5O3XqpI0bN2rDhg3WtoyMDL3zzjsKDAzM91vWG1GmTBk988wz2rZtmz799FNJ//dN2b+/GcvKytLbb7993efp1KmT/vnnHy1fvtzadvbsWb3zzjs2/Zo2bSofHx8lJCTYzDu2atUqbdu2Lc8tDkWhU6dOSklJsflPyMWLF/Xmm2/K3d1dbdq0ueL+Pj4+atu2rebMmaPDhw/n2X706FHrv48dO2azzd3dXbVq1bJ5bcqVKyfp0re0AABzFDYfyr11b/To0erZs6fNEhUVpdq1a2vRokUmRm5f5E7kTvkhd0JJx0gpwGQRERFX7bNmzRqNHz9eXbt21T333CN3d3ft3r1b8+bNU2ZmpiZMmJBnn+XLl9sMX8/Vvn17+fr6XvF8hw4d0ocffpin3d3dXd26dbtqvAV56aWXtGbNGrVq1UpDhgxRmTJlNGfOHGVmZurVV1+97uMWZOzYsdbHTA8fPlyVKlXS+++/rz179ujjjz8ucPj8jRgwYIBiY2P1yiuvqFu3bmrRooUqVqyoiIgIDR8+XBaLRQsXLryh4dtRUVF66623FB4erk2bNqlKlSpauHChzWOlpUvfgL3yyiuKjIxUmzZt1LdvX+tjjQMDAzVy5Mgbvdwb9sQTT2jOnDkaMGCANm3apMDAQC1fvlzr169XfHy8ypcvf9VjzJo1S61atVLDhg0VFRWlGjVqKDU1VRs2bNDBgwf166+/SpLq16+vtm3bKjg4WJUqVdLPP/+s5cuXKzo62nqs4OBgSdLw4cMVFhYmR0dH9enTxz4XDwCwulo+lJmZqY8//ljt27e3mYD637p27arXX39dR44ckY+PzzXHQO5E7kTuRO6EYqBInvkHlBL/fgTylVz+COTdu3cbsbGxxj333GP4+PgYZcqUMby9vY3OnTsba9eutdn3So811r8eeXulcxe0b7Vq1az9ch+znB/l84jhXJs3bzbCwsIMd3d3w83NzWjXrp3x/fff2/Qp7OtUmHPu2rXL6Nmzp1GhQgXD1dXVaNasmfHFF1/Y9Ml9rPGyZctu+HyGYRgTJkywea3Xr19v3HPPPUbZsmWNqlWrGmPGjDFWr16d5+fRpk0b484778xzvIiICJvX3jAMY9++fUbXrl0NNzc3w8vLyxgxYoSRmJiY78946dKlRpMmTQwXFxejUqVKRv/+/Y2DBw/mOUd+P8+CYiroMd2Xu9LrlCs1NdWIjIw0vLy8DGdnZ6Nhw4bWxx7nyn2s8eWPjM61a9cuIzw83PDz8zOcnJwMf39/48EHHzSWL19u7fPSSy8ZzZo1MypUqGCULVvWqFu3rjFlyhQjKyvL2ufixYvGsGHDDG/v/9fevcdFWef//3+OIIMHDoFyWsEoTcEUT6VkuVgkGrqadtA1JbXMRE0pM7Yi0wrTzEORdFCxvprZplaap1QkzSNKkRlpYVAJ7G7KqCUemN8f/rg+Th5Sg2uIedxvt+t287rer+ua1+WO9N4n77mmod1isfAVxwBQBa5kPvTBBx/YJdnnzJlzwfrMzEy7JPvMmTPtdvvF5ypnY+7kiLkTc6cKzJ3gDBa73QlPXwMAAAAAAIBL45lSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTOT2U+umnn3TffffJ399fderUUcuWLbVz505j3G63KyUlRcHBwapTp45iY2O1b98+h2v88ssvGjBggLy9veXr66uhQ4fq6NGjDjVffvmlbrnlFnl6eio0NLRKvr0CAAAAAAAAl8apodShQ4fUqVMn1a5dWytXrtTXX3+tadOm6aqrrjJqpkyZolmzZik9PV3btm1TvXr1FBcXp+PHjxs1AwYM0J49e7R27VotX75cWVlZGjZsmDFus9nUtWtXNW7cWNnZ2Zo6daomTJigN954w9T7BQAAAAAAwBlO/fa9J554Qps3b9Znn3123nG73a6QkBA9+uijeuyxxyRJpaWlCgwMVEZGhvr166e9e/cqMjJSO3bsUPv27SVJq1at0h133KEff/xRISEhmj17tp588kkVFRXJw8PDeO1ly5bpm2++MedmAQAAAAAAYHDqSqmPPvpI7du31913362AgAC1adNGb775pjGen5+voqIixcbGGsd8fHzUoUMHbdmyRZK0ZcsW+fr6GoGUJMXGxqpWrVratm2bUdO5c2cjkJKkuLg45eXl6dChQ1V9mwAAAAAAAPgdd2e++Pfff6/Zs2crKSlJ//rXv7Rjxw6NHj1aHh4eSkhIUFFRkSQpMDDQ4bzAwEBjrKioSAEBAQ7j7u7u8vPzc6gJDw8/5xoVY2d/XFCSysrKVFZWZuyXl5frl19+kb+/vywWSyXcOQAA+Kuz2+06cuSIQkJCVKuW0x/T6TTl5eX6+eef5eXlxTwJAABIuvR5klNDqfLycrVv314vvPCCJKlNmzb66quvlJ6eroSEBKf1lZqaqmeffdZprw8AAP46CgsL1ahRI2e34TQ///yzQkNDnd0GAACohv5onuTUUCo4OFiRkZEOxyIiIvTBBx9IkoKCgiRJxcXFCg4ONmqKi4vVunVro6akpMThGqdOndIvv/xinB8UFKTi4mKHmor9ipqzJScnKykpydgvLS1VWFiYCgsL5e3tfSW3CgAAahibzabQ0FB5eXk5uxWnqrh/5kkAAKDCpc6TnBpKderUSXl5eQ7Hvv32WzVu3FiSFB4erqCgIK1bt84IoWw2m7Zt26aHH35YkhQdHa3Dhw8rOztb7dq1kyStX79e5eXl6tChg1Hz5JNP6uTJk6pdu7Ykae3atWrWrNk5H92TJKvVKqvVes5xb29vJlsAAMCBq39kreL+mScBAIDf+6N5klMfgDB27Fht3bpVL7zwgvbv36+FCxfqjTfeUGJioqQzzY8ZM0bPPfecPvroI+Xm5mrQoEEKCQlR7969JZ1ZWdWtWzc9+OCD2r59uzZv3qyRI0eqX79+CgkJkST985//lIeHh4YOHao9e/bovffe08yZMx1WQwEAAAAAAMA8Tl0pdcMNN2jp0qVKTk7WxIkTFR4erhkzZmjAgAFGzeOPP65jx45p2LBhOnz4sG6++WatWrVKnp6eRs2CBQs0cuRI3XbbbapVq5b69u2rWbNmGeM+Pj5as2aNEhMT1a5dOzVo0EApKSkaNmyYqfcLAAAAAACAMyx2u93u7CaqO5vNJh8fH5WWlrIsHQAASGJ+UIG/BwAA8HuXOj9w3e8vBgAAAAAAgNMQSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANO5O7sBAABcxcbOf3d2C7iIv2dtdHYLlS4rK0tTp05Vdna2Dh48qKVLl6p3794ONXv37tX48eO1ceNGnTp1SpGRkfrggw8UFhbmnKYBAC6JeVL1VlXzJFZKAQAA1FDHjh1TVFSU0tLSzjv+3Xff6eabb1bz5s2VmZmpL7/8Uk8//bQ8PT1N7hQAALgiVkoBAADUUN27d1f37t0vOP7kk0/qjjvu0JQpU4xj1157rRmtAQAAsFIKAADAFZWXl2vFihW67rrrFBcXp4CAAHXo0EHLli276HllZWWy2WwOGwAAwJUglAIAAHBBJSUlOnr0qCZPnqxu3bppzZo1uvPOO9WnTx9t3Hjh50akpqbKx8fH2EJDQ03sGgAA1CSEUgAAAC6ovLxcktSrVy+NHTtWrVu31hNPPKEePXooPT39guclJyertLTU2AoLC81qGQAA1DA8UwoAAMAFNWjQQO7u7oqMjHQ4HhERoU2bNl3wPKvVKqvVWtXtAQAAF8BKKQAAABfk4eGhG264QXl5eQ7Hv/32WzVu3NhJXQEAAFfCSikAAIAa6ujRo9q/f7+xn5+fr5ycHPn5+SksLEzjxo3Tvffeq86dO6tLly5atWqVPv74Y2VmZjqvaQAA4DIIpQAAAGqonTt3qkuXLsZ+UlKSJCkhIUEZGRm68847lZ6ertTUVI0ePVrNmjXTBx98oJtvvtlZLQMAABdCKAUAAFBDxcTEyG63X7RmyJAhGjJkiEkdAQAA/B+eKQUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdO7ObgAAAAAA4Lo6vdLJ2S3gIjaP2uzsFlCDsVIKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6p4ZSEyZMkMVicdiaN29ujB8/flyJiYny9/dX/fr11bdvXxUXFztco6CgQPHx8apbt64CAgI0btw4nTp1yqEmMzNTbdu2ldVqVZMmTZSRkWHG7QEAAAAAAOACnL5SqkWLFjp48KCxbdq0yRgbO3asPv74Y73//vvauHGjfv75Z/Xp08cYP336tOLj43XixAl9/vnnmj9/vjIyMpSSkmLU5OfnKz4+Xl26dFFOTo7GjBmjBx54QKtXrzb1PgEAAAAAAPB/3J3egLu7goKCzjleWlqqOXPmaOHChbr11lslSfPmzVNERIS2bt2qjh07as2aNfr666/16aefKjAwUK1bt9akSZM0fvx4TZgwQR4eHkpPT1d4eLimTZsmSYqIiNCmTZs0ffp0xcXFmXqvAAAAAAAAOMPpK6X27dunkJAQXXPNNRowYIAKCgokSdnZ2Tp58qRiY2ON2ubNmyssLExbtmyRJG3ZskUtW7ZUYGCgURMXFyebzaY9e/YYNWdfo6Km4hoAAAA1WVZWlnr27KmQkBBZLBYtW7bsgrXDhw+XxWLRjBkzTOsPAAC4LqeGUh06dFBGRoZWrVql2bNnKz8/X7fccouOHDmioqIieXh4yNfX1+GcwMBAFRUVSZKKioocAqmK8Yqxi9XYbDb99ttv5+2rrKxMNpvNYQMAAPgrOnbsmKKiopSWlnbRuqVLl2rr1q0KCQkxqTMAAODqnPrxve7duxt/btWqlTp06KDGjRtr8eLFqlOnjtP6Sk1N1bPPPuu01wcAAKgs3bt3d5hznc9PP/2kUaNGafXq1YqPjzepMwAA4Oqc/kyps/n6+uq6667T/v37dfvtt+vEiRM6fPiww2qp4uJi4xlUQUFB2r59u8M1Kr6d7+ya339jX3Fxsby9vS8YfCUnJyspKcnYt9lsCg0N/dP3B8B1dXqlk7NbwEVsHrXZ2S0ATlNeXq6BAwdq3LhxatGihbPbAQAALsTpz5Q629GjR/Xdd98pODhY7dq1U+3atbVu3TpjPC8vTwUFBYqOjpYkRUdHKzc3VyUlJUbN2rVr5e3trcjISKPm7GtU1FRc43ysVqu8vb0dNgAAgJroxRdflLu7u0aPHn1J9TzmAAAAVBanhlKPPfaYNm7cqAMHDujzzz/XnXfeKTc3N/Xv318+Pj4aOnSokpKStGHDBmVnZ2vw4MGKjo5Wx44dJUldu3ZVZGSkBg4cqC+++EKrV6/WU089pcTERFmtVklnHtj5/fff6/HHH9c333yj1157TYsXL9bYsWOdeesAAABOl52drZkzZyojI0MWi+WSzklNTZWPj4+xsZocAABcKaeGUj/++KP69++vZs2a6Z577pG/v7+2bt2qhg0bSpKmT5+uHj16qG/fvurcubOCgoK0ZMkS43w3NzctX75cbm5uio6O1n333adBgwZp4sSJRk14eLhWrFihtWvXKioqStOmTdNbb72luLg40+8XAACgOvnss89UUlKisLAwubu7y93dXT/88IMeffRRXX311ec9Jzk5WaWlpcZWWFhobtMAAKDGcOozpRYtWnTRcU9PT6WlpV3022IaN26sTz755KLXiYmJ0e7du6+oRwAAgJpq4MCBio2NdTgWFxengQMHavDgwec9x2q1GivSAQAA/oxq9aBzAAAAVK6jR49q//79xn5+fr5ycnLk5+ensLAw+fv7O9TXrl1bQUFBatasmdmtAgAAF0MoBQAAUIPt3LlTXbp0MfYrvmE4ISFBGRkZTuoKAACAUAoAAKBGi4mJkd1uv+T6AwcOVF0zAAAAZ3Hqg84BAAAAAADgmgilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAgBosKytLPXv2VEhIiCwWi5YtW2aMnTx5UuPHj1fLli1Vr149hYSEaNCgQfr555+d1zAAAHAZhFIAAAA12LFjxxQVFaW0tLRzxn799Vft2rVLTz/9tHbt2qUlS5YoLy9P//jHP5zQKQAAcDXuzm4AAAAAVad79+7q3r37ecd8fHy0du1ah2OvvvqqbrzxRhUUFCgsLMyMFgEAgIsilAIAAIChtLRUFotFvr6+5x0vKytTWVmZsW+z2UzqDAAA1DR8fA8AAACSpOPHj2v8+PHq37+/vL29z1uTmpoqHx8fYwsNDTW5SwAAUFMQSgEAAEAnT57UPffcI7vdrtmzZ1+wLjk5WaWlpcZWWFhoYpcAAKAm4eN7AAAALq4ikPrhhx+0fv36C66SkiSr1Sqr1WpidwAAoKYilAIAAHBhFYHUvn37tGHDBvn7+zu7JQAA4CIIpQAAAGqwo0ePav/+/cZ+fn6+cnJy5Ofnp+DgYN11113atWuXli9frtOnT6uoqEiS5OfnJw8PD2e1DQAAXAChFAAAQA22c+dOdenSxdhPSkqSJCUkJGjChAn66KOPJEmtW7d2OG/Dhg2KiYkxq00AAOCCCKUAAABqsJiYGNnt9guOX2wMAACgKvHtewAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMV21CqcmTJ8tisWjMmDHGsePHjysxMVH+/v6qX7+++vbtq+LiYofzCgoKFB8fr7p16yogIEDjxo3TqVOnHGoyMzPVtm1bWa1WNWnSRBkZGSbcEQAAAAAAAC7E3dkNSNKOHTv0+uuvq1WrVg7Hx44dqxUrVuj999+Xj4+PRo4cqT59+mjz5s2SpNOnTys+Pl5BQUH6/PPPdfDgQQ0aNEi1a9fWCy+8IEnKz89XfHy8hg8frgULFmjdunV64IEHFBwcrLi4ONPvFQAAAFK7cW87uwVcRPbUQc5uAQDgApy+Uuro0aMaMGCA3nzzTV111VXG8dLSUs2ZM0cvv/yybr31VrVr107z5s3T559/rq1bt0qS1qxZo6+//lr/7//9P7Vu3Vrdu3fXpEmTlJaWphMnTkiS0tPTFR4ermnTpikiIkIjR47UXXfdpenTpzvlfgEAAAAAAFANQqnExETFx8crNjbW4Xh2drZOnjzpcLx58+YKCwvTli1bJElbtmxRy5YtFRgYaNTExcXJZrNpz549Rs3vrx0XF2dc43zKyspks9kcNgAAAAAAAFQep358b9GiRdq1a5d27NhxzlhRUZE8PDzk6+vrcDwwMFBFRUVGzdmBVMV4xdjFamw2m3777TfVqVPnnNdOTU3Vs88+e8X3BQAAAAAAgItz2kqpwsJCPfLII1qwYIE8PT2d1cZ5JScnq7S01NgKCwud3RIAAAAAAECN4rRQKjs7WyUlJWrbtq3c3d3l7u6ujRs3atasWXJ3d1dgYKBOnDihw4cPO5xXXFysoKAgSVJQUNA538ZXsf9HNd7e3uddJSVJVqtV3t7eDhsAAAAAAAAqj9NCqdtuu025ubnKyckxtvbt22vAgAHGn2vXrq1169YZ5+Tl5amgoEDR0dGSpOjoaOXm5qqkpMSoWbt2rby9vRUZGWnUnH2NipqKawAAAAAAAMB8TnumlJeXl66//nqHY/Xq1ZO/v79xfOjQoUpKSpKfn5+8vb01atQoRUdHq2PHjpKkrl27KjIyUgMHDtSUKVNUVFSkp556SomJibJarZKk4cOH69VXX9Xjjz+uIUOGaP369Vq8eLFWrFhh7g0DAAAAAADA4NQHnf+R6dOnq1atWurbt6/KysoUFxen1157zRh3c3PT8uXL9fDDDys6Olr16tVTQkKCJk6caNSEh4drxYoVGjt2rGbOnKlGjRrprbfeUlxcnDNuCQAAAAAAAKpmoVRmZqbDvqenp9LS0pSWlnbBcxo3bqxPPvnkoteNiYnR7t27K6NFAAAAAAAAVAKnPVMKAAAAAAAArotQCgAAAAAAAKarVh/fAwAAAOA6Cia2dHYLuIiwlFxntwCghmOlFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMJ27sxuoydqNe9vZLeAisqcOcnYLAAAAAAC4LFZKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAA1GBZWVnq2bOnQkJCZLFYtGzZModxu92ulJQUBQcHq06dOoqNjdW+ffuc0ywAAHAphFIAAAA12LFjxxQVFaW0tLTzjk+ZMkWzZs1Senq6tm3bpnr16ikuLk7Hjx83uVMAAOBq3J3dAAAAAKpO9+7d1b179/OO2e12zZgxQ0899ZR69eolSXr77bcVGBioZcuWqV+/fma2CgAAXAwrpQAAAFxUfn6+ioqKFBsbaxzz8fFRhw4dtGXLFid2BgAAXAErpQAAAFxUUVGRJCkwMNDheGBgoDH2e2VlZSorKzP2bTZb1TUIAABqNFZKAQAA4JKlpqbKx8fH2EJDQ53dEgAA+IsilAIAAHBRQUFBkqTi4mKH48XFxcbY7yUnJ6u0tNTYCgsLq7xPAABQMxFKAQAAuKjw8HAFBQVp3bp1xjGbzaZt27YpOjr6vOdYrVZ5e3s7bAAAAFeCZ0oBAADUYEePHtX+/fuN/fz8fOXk5MjPz09hYWEaM2aMnnvuOTVt2lTh4eF6+umnFRISot69ezuvaQAA4BIIpQAAAGqwnTt3qkuXLsZ+UlKSJCkhIUEZGRl6/PHHdezYMQ0bNkyHDx/WzTffrFWrVsnT09NZLQMAABdBKAUAAFCDxcTEyG63X3DcYrFo4sSJmjhxooldAQAA8EwpAAAAAAAAOAGhFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAABUIyNGjNDRo0eN/XfffVfHjh0z9g8fPqw77rjDGa0BAABUKkIpAACAauT111/Xr7/+auw/9NBDKi4uNvbLysq0evVqZ7QGAABQqQilAAAAqhG73X7RfQAAgJqCUAoAAAAAAACmI5QCAAAAAACA6dwvp3jKlCkaNWqU6tSpI0navHmz2rdvL6vVKkk6cuSIxo8fr9dee63yOwUAAHARKSkpqlu3riTpxIkTev755+Xj4yNJDs+bAgAA+Cu7rJVSycnJOnLkiLHfvXt3/fTTT8b+r7/+qtdff73yugMAAHAxnTt3Vl5ennbv3q3du3frpptu0vfff2/s5+XlqXPnzs5uEwAA4E+7rJVSPHgTAACgamVmZjq7BQAAAFNcVigFAACAqmez2bRt2zadOHFCN954oxo2bOjslgAAACodoRQAAEA1kpOTozvuuENFRUWSJC8vLy1evFhxcXFO7gwAAKByXXYo9dZbb6l+/fqSpFOnTikjI0MNGjSQJIfnTQE4o2BiS2e3gIsIS8l1dgsA4GD8+PEKDw/XBx98IE9PT02aNEkjR47Uvn37nN0aAABApbqsB52HhYXpzTff1PTp0zV9+nQFBQXpnXfeMfbfeusthYWFXfL1Zs+erVatWsnb21ve3t6Kjo7WypUrjfHjx48rMTFR/v7+ql+/vvr27avi4mKHaxQUFCg+Pl5169ZVQECAxo0bp1OnTjnUZGZmqm3btrJarWrSpIkyMjIu57YBAABMk52drVdeeUXR0dFq06aN5s6dq++++042m83ZrQEAAFSqy1opdeDAgUp98UaNGmny5Mlq2rSp7Ha75s+fr169emn37t1q0aKFxo4dqxUrVuj999+Xj4+PRo4cqT59+mjz5s2SpNOnTys+Pl5BQUH6/PPPdfDgQQ0aNEi1a9fWCy+8IEnKz89XfHy8hg8frgULFmjdunV64IEHFBwczDJ4AABQ7fzyyy9q1KiRse/r66t69erpf//7n7y9vZ3YGQAAQOVy6jOlevbs6bD//PPPa/bs2dq6dasaNWqkOXPmaOHChbr11lslSfPmzVNERIS2bt2qjh07as2aNfr666/16aefKjAwUK1bt9akSZM0fvx4TZgwQR4eHkpPT1d4eLimTZsmSYqIiNCmTZs0ffp0QikAAFAtff3118YzpaQz33i8d+9eh0cltGrVyhmtAQAAVJrL+vjeli1btHz5codjb7/9tsLDwxUQEKBhw4aprKzsiho5ffq0Fi1apGPHjik6OlrZ2dk6efKkYmNjjZrmzZsrLCxMW7ZsMfpp2bKlAgMDjZq4uDjZbDbt2bPHqDn7GhU1FdcAAACobm677Ta1bt3a2H799Vf16NFDbdq0UevWrdWmTRtntwgAAPCnXdZKqYkTJyomJkY9evSQJOXm5mro0KG6//77FRERoalTpyokJEQTJky45Gvm5uYqOjpax48fV/369bV06VJFRkYqJydHHh4e8vX1dagPDAw0fnNYVFTkEEhVjFeMXazGZrPpt99+U506dc7pqayszCFc4xkOAADALPn5+c5uAQAAwBSXFUrl5ORo0qRJxv6iRYvUoUMHvfnmm5Kk0NBQPfPMM5cVSjVr1kw5OTkqLS3Vv//9byUkJGjjxo2X01alS01N1bPPPuvUHgAAgGtq3LjxH9Z89dVXJnQCAABQtS7r43uHDh1yWHW0ceNGde/e3di/4YYbVFhYeFkNeHh4qEmTJmrXrp1SU1MVFRWlmTNnKigoSCdOnNDhw4cd6ouLixUUFCRJCgoKOufb+Cr2/6jG29v7vKukJCk5OVmlpaXGdrn3BAAAUNmOHDmiN954QzfeeKOioqKc3Q4AAMCfdlmhVGBgoLGk/MSJE9q1a5c6duxojB85ckS1a9f+Uw2Vl5errKxM7dq1U+3atbVu3TpjLC8vTwUFBYqOjpYkRUdHKzc3VyUlJUbN2rVr5e3trcjISKPm7GtU1FRc43ysVqu8vb0dNgAAAGfIyspSQkKCgoOD9dJLL+nWW2/V1q1bnd0WAADAn3ZZH9+744479MQTT+jFF1/UsmXLVLduXd1yyy3G+Jdffqlrr732kq+XnJys7t27KywsTEeOHNHChQuVmZmp1atXy8fHR0OHDlVSUpL8/Pzk7e2tUaNGKTo62gjCunbtqsjISA0cOFBTpkxRUVGRnnrqKSUmJspqtUqShg8frldffVWPP/64hgwZovXr12vx4sVasWLF5dw6AACAaYqKipSRkaE5c+bIZrPpnnvuUVlZmZYtW2b84g0AAOCv7rJCqUmTJqlPnz76+9//rvr16ysjI0MeHh7G+Ny5c9W1a9dLvl5JSYkGDRqkgwcPysfHR61atdLq1at1++23S5KmT5+uWrVqqW/fviorK1NcXJxee+0143w3NzctX75cDz/8sKKjo1WvXj0lJCRo4sSJRk14eLhWrFihsWPHaubMmWrUqJHeeustxcXFXc6tAwAAmKJnz57KyspSfHy8ZsyYoW7dusnNzU3p6enObg0AAKBSXVYo1aBBA2VlZam0tFT169eXm5ubw/j7778vLy+vS77enDlzLjru6emptLQ0paWlXbCmcePG+uSTTy56nZiYGO3evfuS+wIAAHCWlStXavTo0Xr44YfVtGlTZ7cDAABQZS4rlBoyZMgl1c2dO/eKmgEAAHB1mzZt0pw5c9SuXTtFRERo4MCB6tevn7PbAgAAqHSX9aDzjIwMbdiwQYcPH9ahQ4cuuAEAAODKdOzYUW+++aYOHjyohx56SIsWLVJISIjKy8u1du1aHTlypFJf7/Tp03r66acVHh6uOnXq6Nprr9WkSZNkt9sr9XUAAAB+77JWSj388MN69913lZ+fr8GDB+u+++6Tn59fVfUGAADgsurVq6chQ4ZoyJAhysvL05w5czR58mQ98cQTuv322/XRRx9Vyuu8+OKLmj17tubPn68WLVpo586dGjx4sHx8fDR69OhKeQ0AAIDzuayVUmlpaTp48KAef/xxffzxxwoNDdU999yj1atX89s0AACAKtKsWTNNmTJFP/74oxYtWiSLxVJp1/7888/Vq1cvxcfH6+qrr9Zdd92lrl27avv27ZX2GgAAAOdzWSulJMlqtap///7q37+/fvjhB2VkZGjEiBE6deqU9uzZo/r161dFnwAAAC7hUp7h6e/vX2mvd9NNN+mNN97Qt99+q+uuu05ffPGFNm3apJdffvm89WVlZSorKzP2bTZbpfUCAABcy2WHUmerVauWLBaL7Ha7Tp8+XVk9AQAAuKyMjAw1btxYbdq0ueBK9MpcKfXEE0/IZrOpefPmcnNz0+nTp/X8889rwIAB561PTU3Vs88+W2mvDwAAXNdlh1JlZWVasmSJ5s6dq02bNqlHjx569dVX1a1bN9WqdVmfBgQAAMDvmP0Mz8WLF2vBggVauHChWrRooZycHI0ZM0YhISFKSEg4pz45OVlJSUnGvs1mU2hoaJX1BwAAaq7LSpFGjBih4OBgTZ48WT169FBhYaHef/993XHHHQRSAAAAlcDsZ3iOGzdOTzzxhPr166eWLVtq4MCBGjt2rFJTU89bb7Va5e3t7bABAABcictaKZWenq6wsDBdc8012rhxozZu3HjeuiVLllRKcwAAAK7IzGd4/vrrr+f8ctHNzU3l5eWV9hoAAADnc1mh1KBBgyr1GQYAAAC4uKp+hmfPnj31/PPPKywsTC1atNDu3bv18ssvX9ID1wEAAP6MywqlMjIyqqgNAAAAVDDzGZ6vvPKKnn76aY0YMUIlJSUKCQnRQw89pJSUlEp9HQAAgN/7U9++BwAAgMo1YsQILVq0SKGhoRoyZIjeffddNWjQoMpez8vLSzNmzNCMGTOq7DUAAADOh1AKAACgGuEZngAAwFUQSgEAAFQjPMMTAAC4CkIpAACAaoRneAIAAFdRuU/KBAAAAAAAAC4BoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAADg4n766Sfdd9998vf3V506ddSyZUvt3LnT2W0BAIAazt3ZDQAAAMB5Dh06pE6dOqlLly5auXKlGjZsqH379umqq65ydmsAAKCGI5QCAABwYS+++KJCQ0M1b94841h4eLgTOwIAAK6Cj+8BAAC4sI8++kjt27fX3XffrYCAALVp00Zvvvmms9sCAAAugFAKAADAhX3//feaPXu2mjZtqtWrV+vhhx/W6NGjNX/+/PPWl5WVyWazOWwAAABXgo/vAQAAuLDy8nK1b99eL7zwgiSpTZs2+uqrr5Senq6EhIRz6lNTU/Xss8+a3SYAAKiBWCkFAADgwoKDgxUZGelwLCIiQgUFBeetT05OVmlpqbEVFhaa0SYAAKiBWCkFAADgwjp16qS8vDyHY99++60aN2583nqr1Sqr1WpGawAAoIZjpRQAAIALGzt2rLZu3aoXXnhB+/fv18KFC/XGG28oMTHR2a0BAIAazqmhVGpqqm644QZ5eXkpICBAvXv3Puc3dcePH1diYqL8/f1Vv3599e3bV8XFxQ41BQUFio+PV926dRUQEKBx48bp1KlTDjWZmZlq27atrFarmjRpooyMjKq+PQAAgGrvhhtu0NKlS/Xuu+/q+uuv16RJkzRjxgwNGDDA2a0BAIAazqmh1MaNG5WYmKitW7dq7dq1OnnypLp27apjx44ZNWPHjtXHH3+s999/Xxs3btTPP/+sPn36GOOnT59WfHy8Tpw4oc8//1zz589XRkaGUlJSjJr8/HzFx8erS5cuysnJ0ZgxY/TAAw9o9erVpt4vAABAddSjRw/l5ubq+PHj2rt3rx588EFntwQAAFyAU58ptWrVKof9jIwMBQQEKDs7W507d1ZpaanmzJmjhQsX6tZbb5UkzZs3TxEREdq6das6duyoNWvW6Ouvv9ann36qwMBAtW7dWpMmTdL48eM1YcIEeXh4KD09XeHh4Zo2bZqkMw/v3LRpk6ZPn664uDjT7xsAAAAAAMDVVatnSpWWlkqS/Pz8JEnZ2dk6efKkYmNjjZrmzZsrLCxMW7ZskSRt2bJFLVu2VGBgoFETFxcnm82mPXv2GDVnX6OipuIav1dWViabzeawAQAAAAAAoPJUm1CqvLxcY8aMUadOnXT99ddLkoqKiuTh4SFfX1+H2sDAQBUVFRk1ZwdSFeMVYxersdls+u23387pJTU1VT4+PsYWGhpaKfcIAAAAAACAM6pNKJWYmKivvvpKixYtcnYrSk5OVmlpqbEVFhY6uyUAAAAAAIAaxanPlKowcuRILV++XFlZWWrUqJFxPCgoSCdOnNDhw4cdVksVFxcrKCjIqNm+fbvD9Sq+ne/smt9/Y19xcbG8vb1Vp06dc/qxWq2yWq2Vcm8AAAAAAAA4l1NXStntdo0cOVJLly7V+vXrFR4e7jDerl071a5dW+vWrTOO5eXlqaCgQNHR0ZKk6Oho5ebmqqSkxKhZu3atvL29FRkZadScfY2KmoprAAAAAAAAwFxOXSmVmJiohQsX6sMPP5SXl5fxDCgfHx/VqVNHPj4+Gjp0qJKSkuTn5ydvb2+NGjVK0dHR6tixoySpa9euioyM1MCBAzVlyhQVFRXpqaeeUmJiorHaafjw4Xr11Vf1+OOPa8iQIVq/fr0WL16sFStWOO3eAQAAAAAAXJlTV0rNnj1bpaWliomJUXBwsLG99957Rs306dPVo0cP9e3bV507d1ZQUJCWLFlijLu5uWn58uVyc3NTdHS07rvvPg0aNEgTJ040asLDw7VixQqtXbtWUVFRmjZtmt566y3FxcWZer8AAAAAAAA4w6krpex2+x/WeHp6Ki0tTWlpaResady4sT755JOLXicmJka7d+++7B4BAAAAAABQ+arNt+8BAAAAAADAdRBKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAwDB58mRZLBaNGTPG2a0AAIAajlAKAAAAkqQdO3bo9ddfV6tWrZzdCgAAcAGEUgAAANDRo0c1YMAAvfnmm7rqqquc3Q4AAHABhFIAAABQYmKi4uPjFRsb6+xWAACAi3B3dgMAAABwrkWLFmnXrl3asWPHH9aWlZWprKzM2LfZbFXZGgAAqMFYKQUAAODCCgsL9cgjj2jBggXy9PT8w/rU1FT5+PgYW2hoqAldAgCAmohQCgAAwIVlZ2erpKREbdu2lbu7u9zd3bVx40bNmjVL7u7uOn36tEN9cnKySktLja2wsNBJnQMAgL86Pr4HAADgwm677Tbl5uY6HBs8eLCaN2+u8ePHy83NzWHMarXKarWa2SIAAKihCKUAAABcmJeXl66//nqHY/Xq1ZO/v/85xwEAACoTH98DAAAAAACA6VgpBQAAAAeZmZnObgEAALgAVkoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMJ1TQ6msrCz17NlTISEhslgsWrZsmcO43W5XSkqKgoODVadOHcXGxmrfvn0ONb/88osGDBggb29v+fr6aujQoTp69KhDzZdffqlbbrlFnp6eCg0N1ZQpU6r61gAAAAAAAHARTg2ljh07pqioKKWlpZ13fMqUKZo1a5bS09O1bds21atXT3FxcTp+/LhRM2DAAO3Zs0dr167V8uXLlZWVpWHDhhnjNptNXbt2VePGjZWdna2pU6dqwoQJeuONN6r8/gAAAAAAAHB+7s588e7du6t79+7nHbPb7ZoxY4aeeuop9erVS5L09ttvKzAwUMuWLVO/fv20d+9erVq1Sjt27FD79u0lSa+88oruuOMOvfTSSwoJCdGCBQt04sQJzZ07Vx4eHmrRooVycnL08ssvO4RXAAAAAAAAME+1faZUfn6+ioqKFBsbaxzz8fFRhw4dtGXLFknSli1b5OvrawRSkhQbG6tatWpp27ZtRk3nzp3l4eFh1MTFxSkvL0+HDh0672uXlZXJZrM5bAAAAAAAAKg81TaUKioqkiQFBgY6HA8MDDTGioqKFBAQ4DDu7u4uPz8/h5rzXePs1/i91NRU+fj4GFtoaOifvyEAAAAAAAAYqm0o5UzJyckqLS01tsLCQme3BAAAAAAAUKNU21AqKChIklRcXOxwvLi42BgLCgpSSUmJw/ipU6f0yy+/ONSc7xpnv8bvWa1WeXt7O2wAAAAAAACoPNU2lAoPD1dQUJDWrVtnHLPZbNq2bZuio6MlSdHR0Tp8+LCys7ONmvXr16u8vFwdOnQwarKysnTy5EmjZu3atWrWrJmuuuoqk+4GAAAAAAAAZ3NqKHX06FHl5OQoJydH0pmHm+fk5KigoEAWi0VjxozRc889p48++ki5ubkaNGiQQkJC1Lt3b0lSRESEunXrpgcffFDbt2/X5s2bNXLkSPXr108hISGSpH/+85/y8PDQ0KFDtWfPHr333nuaOXOmkpKSnHTXAAAAAAAAcHfmi+/cuVNdunQx9iuCooSEBGVkZOjxxx/XsWPHNGzYMB0+fFg333yzVq1aJU9PT+OcBQsWaOTIkbrttttUq1Yt9e3bV7NmzTLGfXx8tGbNGiUmJqpdu3Zq0KCBUlJSNGzYMPNuFAAAAAAAAA6cGkrFxMTIbrdfcNxisWjixImaOHHiBWv8/Py0cOHCi75Oq1at9Nlnn11xnwAAAAAAAKhc1faZUgAAAAAAAKi5CKUAAAAAAABgOkIpAAAAAAAAmI5QCgAAAAAAAKYjlAIAAAAAAIDpCKUAAAAAAABgOkIpAAAAAAAAmI5QCgAAAAAAAKYjlAIAAAAAAIDpCKUAAABcWGpqqm644QZ5eXkpICBAvXv3Vl5enrPbAgAALoBQCgAAwIVt3LhRiYmJ2rp1q9auXauTJ0+qa9euOnbsmLNbAwAANZy7sxsAAACA86xatcphPyMjQwEBAcrOzlbnzp2d1BUAAHAFhFIAAAAwlJaWSpL8/PzOO15WVqaysjJj32azmdIXAACoefj4HgAAACRJ5eXlGjNmjDp16qTrr7/+vDWpqany8fExttDQUJO7BAAANQWhFAAAACRJiYmJ+uqrr7Ro0aIL1iQnJ6u0tNTYCgsLTewQAADUJHx8DwAAABo5cqSWL1+urKwsNWrU6IJ1VqtVVqvVxM4AAEBNRSgFAADgwux2u0aNGqWlS5cqMzNT4eHhzm4JAAC4CEIpAAAAF5aYmKiFCxfqww8/lJeXl4qKiiRJPj4+qlOnjpO7AwAANRnPlAIAAHBhs2fPVmlpqWJiYhQcHGxs7733nrNbAwAANRwrpQAAAFyY3W53dgsAAMBFsVIKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApnOpUCotLU1XX321PD091aFDB23fvt3ZLQEAAFQLzJMAAIDZXCaUeu+995SUlKRnnnlGu3btUlRUlOLi4lRSUuLs1gAAAJyKeRIAAHAGlwmlXn75ZT344IMaPHiwIiMjlZ6errp162ru3LnObg0AAMCpmCcBAABncIlQ6sSJE8rOzlZsbKxxrFatWoqNjdWWLVuc2BkAAIBzMU8CAADO4u7sBszw3//+V6dPn1ZgYKDD8cDAQH3zzTfn1JeVlamsrMzYLy0tlSTZbLbLet3TZb9dQbcwy+X+73mljhw/bcrr4MqY9T449dspU14HV8as98GxU7wPqrPLfR9U1Nvt9qpoxzTMk3A+zJMgMU/CGcyTIFXdPMklQqnLlZqaqmefffac46GhoU7oBlXF55Xhzm4B1UGqj7M7QDXgM573AST5XNn74MiRI/K5wnP/ipgnuQbmSZDEPAmSmCfh/1dF8ySXCKUaNGggNzc3FRcXOxwvLi5WUFDQOfXJyclKSkoy9svLy/XLL7/I399fFoulyvutjmw2m0JDQ1VYWChvb29ntwMn4X0AifcBzuB9cOY3f0eOHFFISIizW/lTmCf9efx7gMT7AGfwPoDE+0C69HmSS4RSHh4eateundatW6fevXtLOjOBWrdunUaOHHlOvdVqldVqdTjm6+trQqfVn7e3t8v+o8L/4X0AifcBznD190FNWCHFPKnyuPq/B5zB+wAS7wOc4ervg0uZJ7lEKCVJSUlJSkhIUPv27XXjjTdqxowZOnbsmAYPHuzs1gAAAJyKeRIAAHAGlwml7r33Xv3nP/9RSkqKioqK1Lp1a61ateqch3oCAAC4GuZJAADAGVwmlJKkkSNHnncZOv6Y1WrVM888c85yfbgW3geQeB/gDN4HNQ/zpCvHvwdIvA9wBu8DSLwPLofF/lf/HmMAAAAAAAD85dRydgMAAAAAAABwPYRSAAAAAAAAMB2hFODCYmJiNGbMGGe3AaCGy8zMlMVi0eHDh53dCgBcMuZJAMzg6vMkQikXcv/998tisWj48OHnjCUmJspisej+++83anv37u1QU1RUpFGjRumaa66R1WpVaGioevbsqXXr1hk1V199tSwWyznb5MmTq/LWUAUmTJhw3vdLTk6OLBaLDhw4IEk6cOCALBaLAgICdOTIEYfa1q1ba8KECSZ1jEt1OT8LUP1V/O9psVhUu3ZtBQYG6vbbb9fcuXNVXl7u7PYkSTfddJMOHjwoHx8fZ7cCXBDzJFwO5kk1F/OkmoV5UvVHKOViQkNDtWjRIv3222/GsePHj2vhwoUKCwu74HkHDhxQu3bttH79ek2dOlW5ublatWqVunTposTERIfaiRMn6uDBgw7bqFGjquyeUHU8PT01Z84c7du37w9rjxw5opdeesmErlAZrvRnAaqnbt266eDBgzpw4IBWrlypLl266JFHHlGPHj106tSp855z8uRJ0/rz8PBQUFCQLBaLaa8JXAnmSbgczJNqLuZJNQvzpOqNUMrFtG3bVqGhoVqyZIlxbMmSJQoLC1ObNm0ueN6IESNksVi0fft29e3bV9ddd51atGihpKQkbd261aHWy8tLQUFBDlu9evWq7J5QeVasWCEfHx8tWLBAktSsWTN16dJFTz755B+eO2rUKL388ssqKSmp6jZRCS71Z0F5eblSU1MVHh6uOnXqKCoqSv/+97+N8dOnT2vo0KHGeLNmzTRz5kyH16pYUfDSSy8pODhY/v7+SkxMNPU/9jWd1WpVUFCQ/va3v6lt27b617/+pQ8//FArV65URkaGJMlisWj27Nn6xz/+oXr16un555+XJM2ePVvXXnutPDw81KxZM73zzjsO1644r3v37qpTp46uueYah/dAxSqARYsW6aabbpKnp6euv/56bdy40aj5/bL0jIwM+fr6avXq1YqIiFD9+vWNCWOFU6dOafTo0fL19ZW/v7/Gjx+vhISEc1anAJWJeRIuhnmS62CeVLMwT6reCKVc0JAhQzRv3jxjf+7cuRo8ePAF63/55RetWrVKiYmJ5500+fr6VkWbMNnChQvVv39/LViwQAMGDDCOT548WR988IF27tx50fP79++vJk2aaOLEiVXdKirJpfwsSE1N1dtvv6309HTt2bNHY8eO1X333Wf8h7S8vFyNGjXS+++/r6+//lopKSn617/+pcWLFztcZ8OGDfruu++0YcMGzZ8/XxkZGcYkAFXj1ltvVVRUlMOEesKECbrzzjuVm5urIUOGaOnSpXrkkUf06KOP6quvvtJDDz2kwYMHa8OGDQ7Xevrpp9W3b1998cUXGjBggPr166e9e/c61IwbN06PPvqodu/erejoaPXs2VP/+9//Ltjfr7/+qpdeeknvvPOOsrKyVFBQoMcee8wYf/HFF7VgwQLNmzdPmzdvls1m07JlyyrnLwe4COZJOB/mSa6HeVLNxjypGrHDZSQkJNh79eplLykpsVutVvuBAwfsBw4csHt6etr/85//2Hv16mVPSEhwqLXb7fZt27bZJdmXLFnyh6/RuHFju4eHh71evXoOW1ZWVhXeGa7U3//+d/sjjzxif/XVV+0+Pj72zMxMY+yZZ56xR0VF2e12u71fv372W2+91W632+27d++2S7Ln5+fb7Xa7PT8/3y7Jvnv3bvuqVavstWvXtu/fv99ut9vtUVFR9meeecbMW8IluNSfBcePH7fXrVvX/vnnnzucP3ToUHv//v0veP3ExER73759HV6vcePG9lOnThnH7r77bvu9995b+Tfngs7+ef179957rz0iIsJut9vtkuxjxoxxGL/pppvsDz74oMOxu+++237HHXcY+5Lsw4cPd6jp0KGD/eGHH7bb7f/3M2Dy5MnG+MmTJ+2NGjWyv/jii3a73W7fsGGDXZL90KFDdrvdbp83b55dkvGzwm6329PS0uyBgYHGfmBgoH3q1KnG/qlTp+xhYWEXvFfgz2KehN9jnuSamCfVLMyTqj9382MwOFvDhg0VHx+vjIwM2e12xcfHq0GDBhest9vtl3X9cePGnfPwv7/97W9X0ipM8O9//1slJSXavHmzbrjhhvPWPPfcc4qIiNCaNWsUEBBwwWvFxcXp5ptv1tNPP62FCxdWVcuoJH/0s2D//v369ddfdfvttzucd+LECYel62lpaZo7d64KCgr022+/6cSJE2rdurXDOS1atJCbm5uxHxwcrNzc3Kq5MRjsdrvD8wnat2/vML53714NGzbM4VinTp3O+WhBdHT0Ofs5OTkXrHF3d1f79u3P+S3h2erWratrr73W2A8ODjY+1lJaWqri4mLdeOONxribm5vatWtXbR5KipqLeRLOxjzJdTFPqvmYJ1UPhFIuasiQIRo5cqSkMz8oL6Zp06ayWCz65ptvLunaDRo0UJMmTf50jzBHmzZttGvXLs2dO1ft27c/7wP2rr32Wj344IN64oknNGfOnIteb/LkyYqOjta4ceOqqmVUoov9LDh69KikM8/Q+P3/YbJarZKkRYsW6bHHHtO0adMUHR0tLy8vTZ06Vdu2bXOor127tsO+xWL5S/5H869m7969Cg8PN/ar03NrzveeuNz/cw9UFeZJqMA8ybUxT6rZmCdVDzxTykV169ZNJ06c0MmTJxUXF3fRWj8/P8XFxSktLU3Hjh07Z7zigWz4a7r22mu1YcMGffjhhxf99p+UlBR9++23WrRo0UWvd+ONN6pPnz564oknKrtVVIGL/SyIjIyU1WpVQUGBmjRp4rCFhoZKkjZv3qybbrpJI0aMUJs2bdSkSRN99913zrgV/M769euVm5urvn37XrAmIiJCmzdvdji2efNmRUZGOhz7/YOat27dqoiIiAvWnDp1StnZ2efUXCofHx8FBgZqx44dxrHTp09r165dV3Q94HIxT0IF5kmujXlSzcU8qfpgpZSLcnNzM5YLnr1U9ELS0tLUqVMn3XjjjZo4caJatWqlU6dOae3atZo9e7bD0sMjR46oqKjI4fy6devK29u7cm8Clea6667Thg0bFBMTI3d3d82YMeOcmsDAQCUlJWnq1Kl/eL3nn39eLVq0kLs7P2Kqu4v9LPDy8tJjjz2msWPHqry8XDfffLNKS0u1efNmeXt7KyEhQU2bNtXbb7+t1atXKzw8XO+884527Njh8FsnVL2ysjIVFRXp9OnTKi4u1qpVq5SamqoePXpo0KBBFzxv3Lhxuueee9SmTRvFxsbq448/1pIlS/Tpp5861L3//vtq3769br75Zi1YsEDbt28/ZzVAWlqamjZtqoiICE2fPl2HDh3SkCFDrvieRo0apdTUVDVp0kTNmzfXK6+8okOHDrns1yXDXMyTcDbmSa6LeVLNwDypeuMnoQu7nMnPNddco127dun555/Xo48+qoMHD6phw4Zq166dZs+e7VCbkpKilJQUh2MPPfSQ0tPTK6VvVI1mzZpp/fr1iomJkZubm7y8vM6peeyxxzR79mwdP378ote67rrrNGTIEL3xxhtV1S4q0cV+FkyaNEkNGzZUamqqvv/+e/n6+hpfpSud+be9e/du3XvvvbJYLOrfv79GjBihlStXmtU+JK1atUrBwcFyd3fXVVddpaioKM2aNUsJCQmqVevCi6J79+6tmTNn6qWXXtIjjzyi8PBwzZs3TzExMQ51zz77rBYtWqQRI0YoODhY77777jm/JZw8ebImT56snJwcNWnSRB999NFFn8PzR8aPH6+ioiINGjRIbm5uGjZsmOLi4i4pIAAqA/MknI15kutinvTXxzyperPYa+oHEwEAwJ9msVi0dOlS9e7d+7zjBw4cUHh4uHbv3n3Og1srU3l5uSIiInTPPfdo0qRJVfY6AAAAl4p50p/HSikAAFDt/PDDD1qzZo3+/ve/q6ysTK+++qry8/P1z3/+09mtAQAAOFVNmifxoHMAAFDt1KpVSxkZGbrhhhvUqVMn5ebm6tNPP73ih4ICAADUFDVpnsTH9wAAAAAAAGA6VkoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFIC/tPvvv1+9e/d2dhvVpg8AAIAK1WV+Ul36AFD9EEoBQDVy4sQJZ7cAAABQLTFPAmoeQikANUZMTIxGjRqlMWPG6KqrrlJgYKDefPNNHTt2TIMHD5aXl5eaNGmilStXGudkZmbKYrFoxYoVatWqlTw9PdWxY0d99dVXRs2ECRPUunVrh9eaMWOGrr76amN8/vz5+vDDD2WxWGSxWJSZmSlJKiws1D333CNfX1/5+fmpV69eOnDggHGdit8cPv/88woJCVGzZs0kSa+99pqaNm0qT09PBQYG6q677qqSvzMAAOAamCcBqI4IpQDUKPPnz1eDBg20fft2jRo1Sg8//LDuvvtu3XTTTdq1a5e6du2qgQMH6tdff3U4b9y4cZo2bZp27Nihhg0bqmfPnjp58uQlveZjjz2me+65R926ddPBgwd18OBB3XTTTTp58qTi4uLk5eWlzz77TJs3b1b9+vXVrVs3h9/0rVu3Tnl5eVq7dq2WL1+unTt3avTo0Zo4caLy8vK0atUqde7cuVL/ngAAgOthngSguiGUAlCjREVF6amnnlLTpk2VnJwsT09PNWjQQA8++KCaNm2qlJQU/e9//9OXX37pcN4zzzyj22+/XS1bttT8+fNVXFyspUuXXtJr1q9fX3Xq1JHValVQUJCCgoLk4eGh9957T+Xl5XrrrbfUsmVLRUREaN68eSooKDB+QyhJ9erV01tvvaUWLVqoRYsWKigoUL169dSjRw81btxYbdq00ejRoyvzrwkAALgg5kkAqht3ZzcAAJWpVatWxp/d3Nzk7++vli1bGscCAwMlSSUlJQ7nRUdHG3/28/NTs2bNtHfv3j/VyxdffKH9+/fLy8vL4fjx48f13XffGfstW7aUh4eHsX/77bercePGuuaaa9StWzd169ZNd955p+rWrfun+gEAAK6NeRKA6oZQCkCNUrt2bYd9i8XicMxisUiSysvLL/matWrVkt1udzh2KUvWjx49qnbt2mnBggXnjDVs2ND4c7169RzGvLy8tGvXLmVmZmrNmjVKSUnRhAkTtGPHDvn6+l5y3wAAAGdjngSguuHjewAgaevWrcafDx06pG+//VYRERGSzkyMioqKHCZcOTk5Dud7eHjo9OnTDsfatm2rffv2KSAgQE2aNHHYfHx8LtqPu7u7YmNjNWXKFH355Zc6cOCA1q9f/yfvEgAA4PIxTwJQVQilAEDSxIkTtW7dOn311Ve6//771aBBA/Xu3VvSmW+r+c9//qMpU6bou+++U1pamsM300jS1VdfrS+//FJ5eXn673//q5MnT2rAgAFq0KCBevXqpc8++0z5+fnKzMzU6NGj9eOPP16wl+XLl2vWrFnKycnRDz/8oLffflvl5eXGN84AAACYiXkSgKpCKAUAkiZPnqxHHnlE7dq1U1FRkT7++GPj+QURERF67bXXlJaWpqioKG3fvl2PPfaYw/kPPvigmjVrpvbt26thw4bavHmz6tatq6ysLIWFhalPnz6KiIjQ0KFDdfz4cXl7e1+wF19fXy1ZskS33nqrIiIilJ6ernfffVctWrSo0r8DAACA82GeBKCqWOy//wAwALiQzMxMdenSRYcOHeI5BAAAAGdhngSgqrFSCgAAAAAAAKYjlAIAAAAAAIDp+PgeAAAAAAAATMdKKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJju/wO3VaykLt9CNwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For XGBoost:"
      ],
      "metadata": {
        "id": "nRYe6xucMnY8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "barplot_imp(\"Gradient Boost\", scores_xg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "acdi_Rc0Mp6L",
        "outputId": "d6b171c5-0f93-49a5-ca32-618e32efc7fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For SVR:"
      ],
      "metadata": {
        "id": "BS8_Mv89MpGT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "barplot_imp(\"SVR\", scores_svr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KhCkBYbwMo-A",
        "outputId": "45e9625c-c0c0-491b-c887-d992499102d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For MLP:"
      ],
      "metadata": {
        "id": "Rfpfzu58Ms_z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "barplot_imp(\"Neural Network\", scores_mlp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tF8-nYCjMsjl",
        "outputId": "4ac7f08b-0b5b-40fc-b2a0-169f89be7764"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be concluded from above, MICE outperforms other imputer techniques in 3 of 4 models. In RandomForest, kNN outperforms all. Also, simple imputing or dropping NaN values on this dataset resulted on inconsistent low performance. So when number of NaN values are high, it is wiser to multiple impute or regression impute."
      ],
      "metadata": {
        "id": "djAm2AJ3R0iB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing Models' Performances"
      ],
      "metadata": {
        "id": "Jkj66RhuKMuS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scores = [scores_lr[0], scores_rf[1], scores_xg[0], scores_svr[0], scores_mlp[0]]\n",
        "metrics = generate_metrics(scores)\n",
        "models = ['LR', 'RF','XG', 'SVR', 'MLP']"
      ],
      "metadata": {
        "id": "OMmZ7RjXTb4_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a 2x2 subplot\n",
        "fig2, axes2 = plt.subplots(2, 2, figsize=(12, 10))\n",
        "\n",
        "# Plot train R2 scores\n",
        "sns.barplot(x=models, y=metrics[0], ax=axes2[0, 0])\n",
        "axes2[0, 0].set_title(f'Train R2 Scores')\n",
        "axes2[0, 0].set_xlabel('Models')\n",
        "axes2[0, 0].set_ylabel('Train R2 Score')\n",
        "\n",
        "# Plot test R2 scores\n",
        "sns.barplot(x=models, y=metrics[1], ax=axes2[0, 1])\n",
        "axes2[0, 1].set_title(f'Test R2 Scores')\n",
        "axes2[0, 1].set_xlabel('Models')\n",
        "axes2[0, 1].set_ylabel('Test R2 Score')\n",
        "\n",
        "# Plot MSE scores\n",
        "sns.barplot(x=models, y=metrics[2], ax=axes2[1, 0])\n",
        "axes2[1, 0].set_title(f'MSE')\n",
        "axes2[1, 0].set_xlabel('Models')\n",
        "axes2[1, 0].set_ylabel('MSE')\n",
        "\n",
        "# Plot MAPE scores\n",
        "sns.barplot(x=models, y=metrics[3], ax=axes2[1, 1])\n",
        "axes2[1, 1].set_title(f'MAPE')\n",
        "axes2[1, 1].set_xlabel('Models')\n",
        "axes2[1, 1].set_ylabel('MAPE')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gKCg1Ii_WZn5",
        "outputId": "59f62b46-6abf-4d0a-b215-2801e9afb9e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the graphs above, it can be concluded that model that performs best on this dataset is Random Forest. It is predictable because data is unscaled. After that, XGBoost had a relatively good performance despite its high MAPE values. That means Gradient Boost Regression is affected by outliers. Even though it fits well to the data."
      ],
      "metadata": {
        "id": "yguNuIMzbqvy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Report"
      ],
      "metadata": {
        "id": "mzge81co0aBJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Introduction:\n",
        "\n",
        "The goal of this project was to predict heart disease mortality rates based on various factors, including race, gender, and geographical coordinates. The dataset initially contained 59076 rows and 19 columns, with over 25000 samples having NaN values.\n",
        "\n",
        "The project followed a structured approach, involving data preprocessing, imputation, outlier handling, and model selection."
      ],
      "metadata": {
        "id": "_VjSQHZ8f2f0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Data Preprocessing:\n",
        "\n",
        "#### 2.1 Column Selection:\n",
        "Unnecessary columns were identified and dropped, resulting in a dataset with 7 columns: 'LocationAbbr', 'GeographicLevel', 'Data_Value', 'Data_Value_Type', 'Stratification1', 'Stratification2', and 'Location 1'.\n",
        "\n",
        "#### 2.2 Missing Values:\n",
        "Different imputation techniques, including MICE, kNN, mean imputation or simply dropping missing values were applied. The chosen method depended on the model's performance.\n",
        "\n",
        "#### 2.3 Coordinate Transformation:\n",
        "Coordinate values were transformed into their Euclidean distance to convert them into numerical features.\n",
        "\n",
        "#### 2.4 Outlier Handling:\n",
        "Outliers were addressed using the Local Outlier Factor (LOF) algorithm from scikit-learn.\n",
        "\n",
        "#### 2.5 Model-Dependent Preprocessing:\n",
        "Data was labeled and one-hot encoded as needed for different regression models."
      ],
      "metadata": {
        "id": "4UPKFrWcf-c8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Model Selection:\n",
        "\n",
        "Five regression models were considered for predicting heart disease mortality rates: Linear Regression, Random Forest, Support Vector Regression (SVR), XGBoost, and Neural Network. The dataset was split into training and testing sets for model evaluation."
      ],
      "metadata": {
        "id": "X36dCQBmgDrS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Imputation Technique Comparison:\n",
        "\n",
        "A systematic comparison of different imputation techniques was conducted for each model. MICE imputation performed well across multiple metrics, while kNN imputation showed superiority with the Random Forest model."
      ],
      "metadata": {
        "id": "rhvzyoSngHSB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Model Evaluation and Comparison:\n",
        "\n",
        "A function was defined to graphically compare the performances of different models using the best-imputed data. RandomForest emerged as the top-performing model, main reason can be RandomForest's compatibility with unscaled data. XGBoost performed well but showed sensitivity to outliers, indicated by a high MAPE error."
      ],
      "metadata": {
        "id": "KYqfdeGGgJcF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. Conclusion:\n",
        "\n",
        "In conclusion, the RandomForest model, trained on MICE-imputed data, proved to be the most effective in predicting heart disease mortality rates. The decision not to scale data, considering the inclusion of coordinates, was validated by RandomForest's strong performance.\n",
        "\n",
        "The project highlights the importance of thorough preprocessing, imputation, and careful consideration of model characteristics in predictive modeling tasks."
      ],
      "metadata": {
        "id": "ixJkaCrSgMbH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7. Future Work:\n",
        "\n",
        "Future work could set a decision boundary and convert this problem to a Yes/No binary classification problem.\n"
      ],
      "metadata": {
        "id": "qHi9yQSqgMxQ"
      }
    }
  ]
}