{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## **Assignment #2: Classification, Regression, Clustering, Evaluation**"
      ],
      "metadata": {
        "id": "w84cR3AZIU0e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Version: April 2026`"
      ],
      "metadata": {
        "id": "sDxa7s952Ukh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>"
      ],
      "metadata": {
        "id": "PnYmknSefeqx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "DNfkAjjvUhOC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 1: Select a Regression Dataset**"
      ],
      "metadata": {
        "id": "3joUOc5FR4uC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This project analyzes the influence of physiological indicators and lifestyle habits on an individual’s mental well-being. The analysis utilizes raw survey data from the CDC's Behavioral Risk Factor Surveillance System (BRFSS), providing a comprehensive view of general health and daily routines.\n",
        "\n",
        "**Data Source:** Raw BRFSS survey file (via Kaggle).\n",
        "\n",
        "**Dataset Size:** 445,132 records.\n",
        "\n",
        "**Features:** 40 raw attributes (34 categorical, 6 numerical) across domains like clinical diagnoses, lifestyle behaviors, and demographics.\n",
        "\n",
        "**Target Variable:** MentalHealthDays – The number of days (0–30) in the past month where mental health was reported as \"not good\".\n",
        "\n",
        "**Research Question:** How do physiological indicators and lifestyle habits influence an individual's Mental Well-being?\n"
      ],
      "metadata": {
        "id": "UnUyfJosdSoN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "1A8ky7jedO9D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 2: Exploratory Data Analysis (EDA)**\n"
      ],
      "metadata": {
        "id": "3Mv-tszqZGqC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Project Imports"
      ],
      "metadata": {
        "id": "RT2bdt53CrhL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# System and utility\n",
        "import os\n",
        "import random\n",
        "import warnings\n",
        "\n",
        "# Data manipulation\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Visualization libraries\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import plotly.io as pio\n",
        "!pip install kaleido==0.2.1\n",
        "pio.renderers.default = \"png\"\n",
        "\n",
        "# Data fetching\n",
        "import kagglehub\n",
        "\n",
        "# Machine Learning - Preprocessing, Models & Evaluation\n",
        "from sklearn.ensemble import IsolationForest\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
        "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
        "from xgboost import XGBRegressor, XGBClassifier\n",
        "from sklearn.metrics import (\n",
        "    mean_absolute_error, mean_squared_error, r2_score,\n",
        "    accuracy_score, precision_score, recall_score, f1_score,\n",
        "    classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
        ")\n",
        "\n",
        "# Model serialization\n",
        "import pickle\n",
        "\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "OI0MZzohKwfE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53de37fc-0a5b-4bea-ffe4-cd68303a1525"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaleido==0.2.1 in /usr/local/lib/python3.12/dist-packages (0.2.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Global Styling & Color Palette"
      ],
      "metadata": {
        "id": "JmP8sj02Cs7f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Global seed for reproducibility\n",
        "SEED = 42\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "os.environ['PYTHONHASHSEED'] = str(SEED)\n",
        "\n",
        "# Defining the project color palette\n",
        "PROJECT_COLORS = ['#711C91', '#EA00D9', '#0ABDC6', '#133E7C']\n",
        "\n",
        "# Configuring Seaborn global styles\n",
        "sns.set_theme(style=\"darkgrid\", rc={\n",
        "    \"axes.facecolor\": \"#f4f4f8\",\n",
        "    \"grid.color\": \"white\",\n",
        "    \"figure.facecolor\": \"white\",\n",
        "    \"axes.edgecolor\": \"white\"\n",
        "})\n",
        "sns.set_palette(PROJECT_COLORS)\n",
        "plt.rcParams['patch.edgecolor'] = 'none'\n",
        "\n",
        "# Configuring Plotly global defaults to use the project palette automatically\n",
        "pio.templates[\"custom_cyberpunk\"] = go.layout.Template(\n",
        "    layout=go.Layout(\n",
        "        colorway=PROJECT_COLORS,\n",
        "        coloraxis=dict(colorscale=PROJECT_COLORS)\n",
        "    )\n",
        ")\n",
        "pio.templates.default = \"custom_cyberpunk\"\n",
        "\n",
        "# Notebook display settings\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'"
      ],
      "metadata": {
        "id": "PhhNVeK9u_mn"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Data Loading"
      ],
      "metadata": {
        "id": "Pb91Q7fsCxSZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Downloading the dataset\n",
        "path = kagglehub.dataset_download(\"kamilpytlak/personal-key-indicators-of-heart-disease\")\n",
        "\n",
        "# Locate the 2022 version of the dataset that contains missing values (NaNs)\n",
        "# This iterates through the directory to find the specific CSV file in the 2022 folder\n",
        "csv_path = \"\"\n",
        "for root, dirs, files in os.walk(path):\n",
        "    for file in files:\n",
        "        if \"2022\" in root and \"with_nans\" in file.lower() and file.endswith(\".csv\"):\n",
        "            csv_path = os.path.join(root, file)\n",
        "            break\n",
        "\n",
        "# Load the raw, unpolished dataset into a DataFrame\n",
        "df = pd.read_csv(csv_path)"
      ],
      "metadata": {
        "id": "OxnUIjyIAVKE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0097787-0c6a-4f00-8d20-3a9f71a4d9aa"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using Colab cache for faster access to the 'personal-key-indicators-of-heart-disease' dataset.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Dataset Dimensions and Structural Overview"
      ],
      "metadata": {
        "id": "miukc3bPC613"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Shape and info check"
      ],
      "metadata": {
        "id": "U1xn3n0qVA3-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the number of rows and columns\n",
        "print(f\"Dataset Shape: {df.shape}\")\n",
        "\n",
        "# Inspecting feature names, non-null counts, and data types\n",
        "print(\"\\n=== Structural Information ===\")\n",
        "df.info()"
      ],
      "metadata": {
        "id": "XZ4bNXaYC9q1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b461dbbe-fe6a-4258-99f8-47deb4606593"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Shape: (445132, 40)\n",
            "\n",
            "=== Structural Information ===\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 445132 entries, 0 to 445131\n",
            "Data columns (total 40 columns):\n",
            " #   Column                     Non-Null Count   Dtype  \n",
            "---  ------                     --------------   -----  \n",
            " 0   State                      445132 non-null  object \n",
            " 1   Sex                        445132 non-null  object \n",
            " 2   GeneralHealth              443934 non-null  object \n",
            " 3   PhysicalHealthDays         434205 non-null  float64\n",
            " 4   MentalHealthDays           436065 non-null  float64\n",
            " 5   LastCheckupTime            436824 non-null  object \n",
            " 6   PhysicalActivities         444039 non-null  object \n",
            " 7   SleepHours                 439679 non-null  float64\n",
            " 8   RemovedTeeth               433772 non-null  object \n",
            " 9   HadHeartAttack             442067 non-null  object \n",
            " 10  HadAngina                  440727 non-null  object \n",
            " 11  HadStroke                  443575 non-null  object \n",
            " 12  HadAsthma                  443359 non-null  object \n",
            " 13  HadSkinCancer              441989 non-null  object \n",
            " 14  HadCOPD                    442913 non-null  object \n",
            " 15  HadDepressiveDisorder      442320 non-null  object \n",
            " 16  HadKidneyDisease           443206 non-null  object \n",
            " 17  HadArthritis               442499 non-null  object \n",
            " 18  HadDiabetes                444045 non-null  object \n",
            " 19  DeafOrHardOfHearing        424485 non-null  object \n",
            " 20  BlindOrVisionDifficulty    423568 non-null  object \n",
            " 21  DifficultyConcentrating    420892 non-null  object \n",
            " 22  DifficultyWalking          421120 non-null  object \n",
            " 23  DifficultyDressingBathing  421217 non-null  object \n",
            " 24  DifficultyErrands          419476 non-null  object \n",
            " 25  SmokerStatus               409670 non-null  object \n",
            " 26  ECigaretteUsage            409472 non-null  object \n",
            " 27  ChestScan                  389086 non-null  object \n",
            " 28  RaceEthnicityCategory      431075 non-null  object \n",
            " 29  AgeCategory                436053 non-null  object \n",
            " 30  HeightInMeters             416480 non-null  float64\n",
            " 31  WeightInKilograms          403054 non-null  float64\n",
            " 32  BMI                        396326 non-null  float64\n",
            " 33  AlcoholDrinkers            398558 non-null  object \n",
            " 34  HIVTesting                 379005 non-null  object \n",
            " 35  FluVaxLast12               398011 non-null  object \n",
            " 36  PneumoVaxEver              368092 non-null  object \n",
            " 37  TetanusLast10Tdap          362616 non-null  object \n",
            " 38  HighRiskLastYear           394509 non-null  object \n",
            " 39  CovidPos                   394368 non-null  object \n",
            "dtypes: float64(6), object(34)\n",
            "memory usage: 135.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Missing Values check"
      ],
      "metadata": {
        "id": "VRFuZXA2DCyM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating total missing values per feature\n",
        "missing_values = df.isnull().sum()\n",
        "\n",
        "# Filtering columns with missing data and sorting them\n",
        "missing_filtered = missing_values[missing_values > 0].sort_values(ascending=False)\n",
        "\n",
        "# Calculating the total number of rows containing at least one missing value\n",
        "rows_with_missing = df.isnull().any(axis=1).sum()\n",
        "\n",
        "# Displaying results as a formatted table\n",
        "print(\"=== Missing Values Summary ===\")\n",
        "print(f\"Total rows with missing values: {rows_with_missing} out of {len(df)} rows\\n\")\n",
        "\n",
        "if not missing_filtered.empty:\n",
        "    display(missing_filtered.to_frame(name='Amount of Missing Values'))\n",
        "else:\n",
        "    print(\"No missing values found in the dataset.\")"
      ],
      "metadata": {
        "id": "jvt-ItPQDFTY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "47c36bd4-228b-4a46-a33d-9a67eec8adfb"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Missing Values Summary ===\n",
            "Total rows with missing values: 199110 out of 445132 rows\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                           Amount of Missing Values\n",
              "TetanusLast10Tdap                             82516\n",
              "PneumoVaxEver                                 77040\n",
              "HIVTesting                                    66127\n",
              "ChestScan                                     56046\n",
              "CovidPos                                      50764\n",
              "HighRiskLastYear                              50623\n",
              "BMI                                           48806\n",
              "FluVaxLast12                                  47121\n",
              "AlcoholDrinkers                               46574\n",
              "WeightInKilograms                             42078\n",
              "ECigaretteUsage                               35660\n",
              "SmokerStatus                                  35462\n",
              "HeightInMeters                                28652\n",
              "DifficultyErrands                             25656\n",
              "DifficultyConcentrating                       24240\n",
              "DifficultyWalking                             24012\n",
              "DifficultyDressingBathing                     23915\n",
              "BlindOrVisionDifficulty                       21564\n",
              "DeafOrHardOfHearing                           20647\n",
              "RaceEthnicityCategory                         14057\n",
              "RemovedTeeth                                  11360\n",
              "PhysicalHealthDays                            10927\n",
              "AgeCategory                                    9079\n",
              "MentalHealthDays                               9067\n",
              "LastCheckupTime                                8308\n",
              "SleepHours                                     5453\n",
              "HadAngina                                      4405\n",
              "HadSkinCancer                                  3143\n",
              "HadHeartAttack                                 3065\n",
              "HadDepressiveDisorder                          2812\n",
              "HadArthritis                                   2633\n",
              "HadCOPD                                        2219\n",
              "HadKidneyDisease                               1926\n",
              "HadAsthma                                      1773\n",
              "HadStroke                                      1557\n",
              "GeneralHealth                                  1198\n",
              "PhysicalActivities                             1093\n",
              "HadDiabetes                                    1087"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Amount of Missing Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TetanusLast10Tdap</th>\n",
              "      <td>82516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PneumoVaxEver</th>\n",
              "      <td>77040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HIVTesting</th>\n",
              "      <td>66127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ChestScan</th>\n",
              "      <td>56046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CovidPos</th>\n",
              "      <td>50764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HighRiskLastYear</th>\n",
              "      <td>50623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>48806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FluVaxLast12</th>\n",
              "      <td>47121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AlcoholDrinkers</th>\n",
              "      <td>46574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WeightInKilograms</th>\n",
              "      <td>42078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ECigaretteUsage</th>\n",
              "      <td>35660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SmokerStatus</th>\n",
              "      <td>35462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HeightInMeters</th>\n",
              "      <td>28652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DifficultyErrands</th>\n",
              "      <td>25656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DifficultyConcentrating</th>\n",
              "      <td>24240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DifficultyWalking</th>\n",
              "      <td>24012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DifficultyDressingBathing</th>\n",
              "      <td>23915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BlindOrVisionDifficulty</th>\n",
              "      <td>21564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DeafOrHardOfHearing</th>\n",
              "      <td>20647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RaceEthnicityCategory</th>\n",
              "      <td>14057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RemovedTeeth</th>\n",
              "      <td>11360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhysicalHealthDays</th>\n",
              "      <td>10927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AgeCategory</th>\n",
              "      <td>9079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MentalHealthDays</th>\n",
              "      <td>9067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LastCheckupTime</th>\n",
              "      <td>8308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SleepHours</th>\n",
              "      <td>5453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HadAngina</th>\n",
              "      <td>4405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HadSkinCancer</th>\n",
              "      <td>3143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HadHeartAttack</th>\n",
              "      <td>3065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HadDepressiveDisorder</th>\n",
              "      <td>2812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HadArthritis</th>\n",
              "      <td>2633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HadCOPD</th>\n",
              "      <td>2219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HadKidneyDisease</th>\n",
              "      <td>1926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HadAsthma</th>\n",
              "      <td>1773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HadStroke</th>\n",
              "      <td>1557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GeneralHealth</th>\n",
              "      <td>1198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhysicalActivities</th>\n",
              "      <td>1093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HadDiabetes</th>\n",
              "      <td>1087</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\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",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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-e67cbc15-f5b8-4725-b6ed-69387489ead3 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-e67cbc15-f5b8-4725-b6ed-69387489ead3');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"    print(\\\"No missing values found in the dataset\",\n  \"rows\": 38,\n  \"fields\": [\n    {\n      \"column\": \"Amount of Missing Values\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23199,\n        \"min\": 1087,\n        \"max\": 82516,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          1773,\n          1093,\n          50764\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Duplicate Rows check"
      ],
      "metadata": {
        "id": "f5kSNn6vEYzY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting exact duplicate entries\n",
        "duplicate_count = df.duplicated().sum()\n",
        "print(f\"Total Duplicate Rows Detected: {duplicate_count}\")"
      ],
      "metadata": {
        "id": "uN8IaltXEZrW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9476460a-d815-47a1-9e07-d9606b64af0e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Duplicate Rows Detected: 157\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Initial Data Preview"
      ],
      "metadata": {
        "id": "H-9UCBvSEgXe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Displaying the first 5 rows of the dataset\n",
        "print(\"=== Dataset Head (First 5 Rows) ===\")\n",
        "display(df.head())"
      ],
      "metadata": {
        "id": "bnaunXhLEhPd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "3d35aae4-021c-4b68-f109-c13e10d296fe"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Dataset Head (First 5 Rows) ===\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     State     Sex GeneralHealth  PhysicalHealthDays  MentalHealthDays  \\\n",
              "0  Alabama  Female     Very good                 0.0               0.0   \n",
              "1  Alabama  Female     Excellent                 0.0               0.0   \n",
              "2  Alabama  Female     Very good                 2.0               3.0   \n",
              "3  Alabama  Female     Excellent                 0.0               0.0   \n",
              "4  Alabama  Female          Fair                 2.0               0.0   \n",
              "\n",
              "                                     LastCheckupTime PhysicalActivities  \\\n",
              "0  Within past year (anytime less than 12 months ...                 No   \n",
              "1                                                NaN                 No   \n",
              "2  Within past year (anytime less than 12 months ...                Yes   \n",
              "3  Within past year (anytime less than 12 months ...                Yes   \n",
              "4  Within past year (anytime less than 12 months ...                Yes   \n",
              "\n",
              "   SleepHours RemovedTeeth HadHeartAttack  ... HeightInMeters  \\\n",
              "0         8.0          NaN             No  ...            NaN   \n",
              "1         6.0          NaN             No  ...           1.60   \n",
              "2         5.0          NaN             No  ...           1.57   \n",
              "3         7.0          NaN             No  ...           1.65   \n",
              "4         9.0          NaN             No  ...           1.57   \n",
              "\n",
              "  WeightInKilograms    BMI AlcoholDrinkers HIVTesting FluVaxLast12  \\\n",
              "0               NaN    NaN              No         No          Yes   \n",
              "1             68.04  26.57              No         No           No   \n",
              "2             63.50  25.61              No         No           No   \n",
              "3             63.50  23.30              No         No          Yes   \n",
              "4             53.98  21.77             Yes         No           No   \n",
              "\n",
              "  PneumoVaxEver                                  TetanusLast10Tdap  \\\n",
              "0            No  Yes, received tetanus shot but not sure what type   \n",
              "1            No  No, did not receive any tetanus shot in the pa...   \n",
              "2            No                                                NaN   \n",
              "3           Yes  No, did not receive any tetanus shot in the pa...   \n",
              "4           Yes  No, did not receive any tetanus shot in the pa...   \n",
              "\n",
              "  HighRiskLastYear CovidPos  \n",
              "0               No       No  \n",
              "1               No       No  \n",
              "2               No      Yes  \n",
              "3               No       No  \n",
              "4               No       No  \n",
              "\n",
              "[5 rows x 40 columns]"
            ],
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              "        vertical-align: middle;\n",
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              "      <th>WeightInKilograms</th>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e0115d7-ab7a-45c4-8c5d-9b3c7177c7e8')\"\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",
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              "  </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",
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              "      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-7e0115d7-ab7a-45c4-8c5d-9b3c7177c7e8 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-7e0115d7-ab7a-45c4-8c5d-9b3c7177c7e8');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Non-predictive features and redundant markers were removed to reduce dimensionality and focus on relevant mental health predictors:\n",
        "\n",
        "| Category | Features Removed | Reason for Removal |\n",
        "| :--- | :--- | :--- |\n",
        "| Administrative | `State` | Geographic data is outside the scope of this physiological/behavioral study. |\n",
        "| Clinical Specifics | `HadSkinCancer`, `ChestScan`, `CovidPos`, `HIVTesting` | Low correlation with the target variable or represents acute rather than chronic health states. |\n",
        "| Preventive Care | `LastCheckupTime`, `FluVaxLast12`, `PneumoVaxEver`, `TetanusLast10Tdap`, `HighRiskLastYear` | These represent healthcare access rather than direct psychological predictors. |\n",
        "| Redundant Metrics | `RemovedTeeth`, `HeightInMeters`, `WeightInKilograms` | `RemovedTeeth` provided low signal; physical metrics are encapsulated in the `BMI` feature. |\n",
        "\n",
        "\n",
        "* Removal of all 157 identified duplicate rows.\n",
        "\n",
        "* Rechecking Missing Values"
      ],
      "metadata": {
        "id": "zDGLSEoCgJ5p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a copy of the current dataframe as the \"Clean Version\"\n",
        "df_clean = df.copy()\n",
        "\n",
        "# List of columns to drop based on logical feature selection\n",
        "columns_to_drop = [\n",
        "    'State', 'LastCheckupTime', 'RemovedTeeth', 'HadSkinCancer',\n",
        "    'HeightInMeters', 'WeightInKilograms', 'ChestScan', 'HIVTesting',\n",
        "    'FluVaxLast12', 'PneumoVaxEver', 'TetanusLast10Tdap',\n",
        "    'HighRiskLastYear', 'CovidPos'\n",
        "]\n",
        "\n",
        "# Dropping the irrelevant columns\n",
        "df_clean = df_clean.drop(columns=columns_to_drop)\n",
        "\n",
        "print(\"=== Structure After Feature Selection ===\")\n",
        "print(f\"New Shape: {df_clean.shape}\\n\")\n",
        "\n",
        "# Drop completely identical rows that are likely technical duplicates from the survey collection\n",
        "df_clean = df_clean.drop_duplicates()\n",
        "\n",
        "duplicates_remain = df_clean.duplicated().sum()\n",
        "print(f\"Total Duplicate Rows remained: {duplicates_remain}\\n\")\n",
        "\n",
        "# Calculating total missing values per feature for the remaining columns\n",
        "missing_values = df_clean.isnull().sum()\n",
        "missing_filtered = missing_values[missing_values > 0].sort_values(ascending=False)\n",
        "\n",
        "# Calculating the total number of rows containing at least one missing value\n",
        "rows_with_missing = df_clean.isnull().any(axis=1).sum()\n",
        "\n",
        "print(\"=== Missing Values Summary (After Drop) ===\")\n",
        "print(f\"Total rows with missing values: {rows_with_missing} out of {len(df)} rows\\n\")\n",
        "\n",
        "if not missing_filtered.empty:\n",
        "    display(missing_filtered.to_frame(name='Amount of Missing Values'))\n",
        "else:\n",
        "    print(\"No missing values found in the dataset.\")"
      ],
      "metadata": {
        "id": "k147U8pnaXqs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "60f88fa7-342b-464a-da9b-2ec69f40938e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Structure After Feature Selection ===\n",
            "New Shape: (445132, 27)\n",
            "\n",
            "Total Duplicate Rows remained: 0\n",
            "\n",
            "=== Missing Values Summary (After Drop) ===\n",
            "Total rows with missing values: 111227 out of 445132 rows\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                           Amount of Missing Values\n",
              "AlcoholDrinkers                               42691\n",
              "BMI                                           42352\n",
              "ECigaretteUsage                               31835\n",
              "SmokerStatus                                  31636\n",
              "DifficultyErrands                             21968\n",
              "DifficultyConcentrating                       20559\n",
              "DifficultyWalking                             20327\n",
              "DifficultyDressingBathing                     20229\n",
              "BlindOrVisionDifficulty                       17882\n",
              "DeafOrHardOfHearing                           16967\n",
              "RaceEthnicityCategory                         13736\n",
              "PhysicalHealthDays                            10900\n",
              "MentalHealthDays                               9037\n",
              "AgeCategory                                    8438\n",
              "SleepHours                                     5413\n",
              "HadAngina                                      4379\n",
              "HadHeartAttack                                 3039\n",
              "HadDepressiveDisorder                          2786\n",
              "HadArthritis                                   2607\n",
              "HadCOPD                                        2193\n",
              "HadKidneyDisease                               1900\n",
              "HadAsthma                                      1747\n",
              "HadStroke                                      1531\n",
              "GeneralHealth                                  1176\n",
              "PhysicalActivities                             1069\n",
              "HadDiabetes                                    1061"
            ],
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              "      <th>Amount of Missing Values</th>\n",
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              "      <td>4379</td>\n",
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              "      <th>HadHeartAttack</th>\n",
              "      <td>3039</td>\n",
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              "      <th>HadDepressiveDisorder</th>\n",
              "      <td>2786</td>\n",
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              "      <th>HadArthritis</th>\n",
              "      <td>2607</td>\n",
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              "      <th>HadCOPD</th>\n",
              "      <td>2193</td>\n",
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              "      <th>HadKidneyDisease</th>\n",
              "      <td>1900</td>\n",
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              "      <td>1747</td>\n",
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              "      <th>HadStroke</th>\n",
              "      <td>1531</td>\n",
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              "      <th>GeneralHealth</th>\n",
              "      <td>1176</td>\n",
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              "      <th>PhysicalActivities</th>\n",
              "      <td>1069</td>\n",
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              "      <th>HadDiabetes</th>\n",
              "      <td>1061</td>\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "* All 9,037 records with missing values in the `MentalHealthDays` column were dropped to ensure the model is trained only on verified labels.\n",
        "\n",
        "* Categorical features often contained long-form text or non-uniform descriptions (e.g., complex `RaceEthnicityCategory` labels).\n",
        "\n",
        "* These were mapped and converted into concise, uniform categories to improve model interpretability and prepare for encoding. This ensured that all categorical inputs have consistent labels across the entire dataset.\n",
        "\n"
      ],
      "metadata": {
        "id": "ArVOluPetWGx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Dropping rows where the target variable (Y) is missing\n",
        "df_clean = df_clean.dropna(subset=['MentalHealthDays'])\n",
        "print(f\"Shape after dropping target NaNs: {df_clean.shape}\\n\")\n",
        "\n",
        "# 2. Inspecting all actual text values before sanitization\n",
        "categorical_cols = df_clean.select_dtypes(include=['object']).columns\n",
        "\n",
        "print(\"=== ALL Unique Values per Categorical Column ===\")\n",
        "for col in categorical_cols:\n",
        "    # Extracting ALL unique non-null values from each column\n",
        "    all_unique_vals = df_clean[col].dropna().unique()\n",
        "    print(f\"- {col}: {all_unique_vals}\")"
      ],
      "metadata": {
        "id": "nOMZdBMXjbgc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea0ab5c5-37fb-4dac-b8a7-f4f2371dad7e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape after dropping target NaNs: (424111, 27)\n",
            "\n",
            "=== ALL Unique Values per Categorical Column ===\n",
            "- Sex: ['Female' 'Male']\n",
            "- GeneralHealth: ['Very good' 'Excellent' 'Fair' 'Poor' 'Good']\n",
            "- PhysicalActivities: ['No' 'Yes']\n",
            "- HadHeartAttack: ['No' 'Yes']\n",
            "- HadAngina: ['No' 'Yes']\n",
            "- HadStroke: ['No' 'Yes']\n",
            "- HadAsthma: ['No' 'Yes']\n",
            "- HadCOPD: ['No' 'Yes']\n",
            "- HadDepressiveDisorder: ['No' 'Yes']\n",
            "- HadKidneyDisease: ['No' 'Yes']\n",
            "- HadArthritis: ['No' 'Yes']\n",
            "- HadDiabetes: ['Yes' 'No' 'No, pre-diabetes or borderline diabetes'\n",
            " 'Yes, but only during pregnancy (female)']\n",
            "- DeafOrHardOfHearing: ['No' 'Yes']\n",
            "- BlindOrVisionDifficulty: ['No' 'Yes']\n",
            "- DifficultyConcentrating: ['No' 'Yes']\n",
            "- DifficultyWalking: ['No' 'Yes']\n",
            "- DifficultyDressingBathing: ['No' 'Yes']\n",
            "- DifficultyErrands: ['No' 'Yes']\n",
            "- SmokerStatus: ['Never smoked' 'Current smoker - now smokes some days' 'Former smoker'\n",
            " 'Current smoker - now smokes every day']\n",
            "- ECigaretteUsage: ['Not at all (right now)' 'Never used e-cigarettes in my entire life'\n",
            " 'Use them every day' 'Use them some days']\n",
            "- RaceEthnicityCategory: ['White only, Non-Hispanic' 'Black only, Non-Hispanic'\n",
            " 'Other race only, Non-Hispanic' 'Multiracial, Non-Hispanic' 'Hispanic']\n",
            "- AgeCategory: ['Age 80 or older' 'Age 55 to 59' 'Age 40 to 44' 'Age 75 to 79'\n",
            " 'Age 70 to 74' 'Age 65 to 69' 'Age 60 to 64' 'Age 50 to 54'\n",
            " 'Age 45 to 49' 'Age 35 to 39' 'Age 25 to 29' 'Age 30 to 34'\n",
            " 'Age 18 to 24']\n",
            "- AlcoholDrinkers: ['No' 'Yes']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Lowercase and strip all categorical columns first\n",
        "for col in categorical_cols:\n",
        "    df_clean[col] = df_clean[col].astype(str).str.strip().str.lower()\n",
        "    df_clean[col] = df_clean[col].replace('nan', np.nan) # Ensure NaNs remain true NaNs\n",
        "\n",
        "# 2. Simplification Dictionaries\n",
        "diabetes_map = {\n",
        "    'no, pre-diabetes or borderline diabetes': 'pre-diabetes',\n",
        "    'yes, but only during pregnancy (female)': 'gestational',\n",
        "    'yes': 'yes',\n",
        "    'no': 'no'\n",
        "}\n",
        "\n",
        "# Updated to Binary (Yes/No)\n",
        "smoking_map = {\n",
        "    'never smoked': 'no',\n",
        "    'former smoker': 'no',\n",
        "    'current smoker - now smokes every day': 'yes',\n",
        "    'current smoker - now smokes some days': 'yes'\n",
        "}\n",
        "\n",
        "# Updated to Binary (Yes/No)\n",
        "ecig_map = {\n",
        "    'never used e-cigarettes in my entire life': 'no',\n",
        "    'not at all (right now)': 'no',\n",
        "    'use them every day': 'yes',\n",
        "    'use them some days': 'yes'\n",
        "}\n",
        "\n",
        "race_map = {\n",
        "    'white only, non-hispanic': 'white',\n",
        "    'black only, non-hispanic': 'black',\n",
        "    'other race only, non-hispanic': 'other',\n",
        "    'multiracial, non-hispanic': 'multiracial',\n",
        "    'hispanic': 'hispanic'\n",
        "}\n",
        "\n",
        "# 3. Applying the mappings\n",
        "df_clean['HadDiabetes'] = df_clean['HadDiabetes'].replace(diabetes_map)\n",
        "df_clean['SmokerStatus'] = df_clean['SmokerStatus'].replace(smoking_map)\n",
        "df_clean['ECigaretteUsage'] = df_clean['ECigaretteUsage'].replace(ecig_map)\n",
        "df_clean['RaceEthnicityCategory'] = df_clean['RaceEthnicityCategory'].replace(race_map)\n",
        "\n",
        "# 4. Extracting the lower bound age as a numeric representation\n",
        "df_clean['AgeCategory'] = df_clean['AgeCategory'].str.replace('age ', '', regex=False).str.split(' ').str[0]\n",
        "\n",
        "# Display results for validation\n",
        "print(\"=== Validation of Cleaned Columns ===\")\n",
        "for col in ['AgeCategory', 'RaceEthnicityCategory', 'HadDiabetes', 'SmokerStatus', 'ECigaretteUsage']:\n",
        "    print(f\"- {col}: {df_clean[col].dropna().unique()}\")"
      ],
      "metadata": {
        "id": "RH6J25PLlj1s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e07fecf-39ef-411a-dff4-f28927e357b5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Validation of Cleaned Columns ===\n",
            "- AgeCategory: ['80' '55' '40' '75' '70' '65' '60' '50' '45' '35' '25' '30' '18']\n",
            "- RaceEthnicityCategory: ['white' 'black' 'other' 'multiracial' 'hispanic']\n",
            "- HadDiabetes: ['yes' 'no' 'pre-diabetes' 'gestational']\n",
            "- SmokerStatus: ['no' 'yes']\n",
            "- ECigaretteUsage: ['no' 'yes']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* A rigorous strategy was applied to handle missing information while preserving the clinical integrity of the dataset.\n",
        "  * `AgeCategory` and `SleepHours`: Imputed using the Median to maintain central tendency.\n",
        "  * `GeneralHealth` and `PhysicalActivities`: Imputed using the Mode (most frequent value) due to the low percentage of missingness.\n",
        "  * Other physiological indicators with missing values were removed to prevent bias and ensure clinical accuracy.\n",
        "\n",
        "  "
      ],
      "metadata": {
        "id": "6AQ14OLDuOB9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================\n",
        "# Missing Value Imputation\n",
        "# ==========================================\n",
        "\n",
        "# First, ensure AgeCategory is numeric so we can calculate a median\n",
        "df_clean['AgeCategory'] = pd.to_numeric(df_clean['AgeCategory'], errors='coerce')\n",
        "\n",
        "# 1. Median Imputation for Numeric variables\n",
        "sleep_median = df_clean['SleepHours'].median()\n",
        "df_clean['SleepHours'] = df_clean['SleepHours'].fillna(sleep_median)\n",
        "\n",
        "age_median = df_clean['AgeCategory'].median()\n",
        "df_clean['AgeCategory'] = df_clean['AgeCategory'].fillna(age_median)\n",
        "\n",
        "# 2. Mode Imputation for Categorical variables\n",
        "gen_health_mode = df_clean['GeneralHealth'].mode()[0]\n",
        "df_clean['GeneralHealth'] = df_clean['GeneralHealth'].fillna(gen_health_mode)\n",
        "\n",
        "phys_act_mode = df_clean['PhysicalActivities'].mode()[0]\n",
        "df_clean['PhysicalActivities'] = df_clean['PhysicalActivities'].fillna(phys_act_mode)\n",
        "\n",
        "print(\"=== Imputation Summary ===\")\n",
        "print(f\"Filled missing SleepHours with median: {sleep_median}\")\n",
        "print(f\"Filled missing AgeCategory with median: {age_median}\")\n",
        "print(f\"Filled missing GeneralHealth with mode: '{gen_health_mode}'\")\n",
        "print(f\"Filled missing PhysicalActivities with mode: '{phys_act_mode}'\\n\")\n",
        "\n",
        "# ==========================================\n",
        "# Dropping the remaining unfixable NaNs\n",
        "# ==========================================\n",
        "initial_rows = len(df_clean)\n",
        "df_clean = df_clean.dropna()\n",
        "final_rows = len(df_clean)\n",
        "\n",
        "print(\"=== Final Drop Summary ===\")\n",
        "print(f\"Rows before final drop: {initial_rows}\")\n",
        "print(f\"Rows after dropping remaining NaNs: {final_rows}\")\n",
        "print(f\"Total pure rows left for the model: {final_rows}\")"
      ],
      "metadata": {
        "id": "RceRHY_2r2U2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68ec98bf-81e2-4597-f04f-874ae188c2e9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Imputation Summary ===\n",
            "Filled missing SleepHours with median: 7.0\n",
            "Filled missing AgeCategory with median: 55.0\n",
            "Filled missing GeneralHealth with mode: 'very good'\n",
            "Filled missing PhysicalActivities with mode: 'yes'\n",
            "\n",
            "=== Final Drop Summary ===\n",
            "Rows before final drop: 424111\n",
            "Rows after dropping remaining NaNs: 327648\n",
            "Total pure rows left for the model: 327648\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking for masked missing values ​​using min & max testing"
      ],
      "metadata": {
        "id": "nPlnzzMrHH7j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_cols = ['PhysicalHealthDays', 'MentalHealthDays', 'SleepHours', 'BMI']\n",
        "\n",
        "print(\"=== Min & Max Check for Masked Nulls ===\")\n",
        "for col in numeric_cols:\n",
        "    print(f\"- {col}: Min = {df_clean[col].min()}, Max = {df_clean[col].max()}\")"
      ],
      "metadata": {
        "id": "DUEdpJ3Qwtjg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b809c136-5d93-461b-f669-6d328b7aba7c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Min & Max Check for Masked Nulls ===\n",
            "- PhysicalHealthDays: Min = 0.0, Max = 30.0\n",
            "- MentalHealthDays: Min = 0.0, Max = 30.0\n",
            "- SleepHours: Min = 1.0, Max = 24.0\n",
            "- BMI: Min = 12.02, Max = 97.65\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To ensure data reliability, records containing logical impossibilities or \"survey noise\" were identified and removed.\n",
        "\n",
        "* **Sleep Outliers:** 662 records reporting >14 hours of sleep.\n",
        "* **Health Contradictions:** 7,820 records where individuals reported \"Great Health\" but also recorded 25+ physically sick days.\n",
        "* **Demographic Inconsistencies:** 849 records of individuals aged ≤25 reporting chronic elderly-onset conditions (Heart Attack/Stroke/COPD)."
      ],
      "metadata": {
        "id": "ejRyn8M2QmCr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# First, ensure AgeCategory is purely numeric for the logic check (if not already done)\n",
        "df_clean['AgeCategory'] = pd.to_numeric(df_clean['AgeCategory'], errors='coerce')\n",
        "\n",
        "\n",
        "# Above 14 hours (minimum in data is already 1.0, so no need to check < 0)\n",
        "sleep_outliers = (df_clean['SleepHours'] > 14)\n",
        "\n",
        "# Logical Contradictions (Excellent health but 25+ days of feeling sick/stressed)\n",
        "health_contradiction = (\n",
        "    df_clean['GeneralHealth'].isin(['excellent', 'very good']) &\n",
        "    ((df_clean['PhysicalHealthDays'] >= 25) | (df_clean['MentalHealthDays'] >= 25))\n",
        ")\n",
        "\n",
        "# Youth with Old Age Diseases (Age <= 25 with Stroke, Heart Attack, or COPD)\n",
        "youth_old_disease = (\n",
        "    (df_clean['AgeCategory'] <= 25) &\n",
        "    ((df_clean['HadHeartAttack'] == 'yes') | (df_clean['HadStroke'] == 'yes') | (df_clean['HadCOPD'] == 'yes'))\n",
        ")\n",
        "\n",
        "# 2. Counting the bad rows\n",
        "print(\"=== Outliers & Contradictions Identified (CHECK ONLY) ===\")\n",
        "print(f\"Sleep Outliers (>14 hours): {sleep_outliers.sum()}\")\n",
        "print(f\"Health Contradictions (Great health but 25+ sick days): {health_contradiction.sum()}\")\n",
        "print(f\"Youth Trolls (Age <=25 with Heart Attack/Stroke/COPD): {youth_old_disease.sum()}\\n\")\n"
      ],
      "metadata": {
        "id": "4md3hQWRyBXj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "544a9a83-ab13-4de5-e27a-a4fb03e742cb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Outliers & Contradictions Identified (CHECK ONLY) ===\n",
            "Sleep Outliers (>14 hours): 662\n",
            "Health Contradictions (Great health but 25+ sick days): 7820\n",
            "Youth Trolls (Age <=25 with Heart Attack/Stroke/COPD): 849\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Combining all masks into one \"Bad Rows\" filter\n",
        "# Note: Using the masks exactly as they were defined in the previous check\n",
        "all_bad_rows = sleep_outliers | health_contradiction | youth_old_disease\n",
        "\n",
        "# 2. Dropping the bad rows\n",
        "initial_rows = len(df_clean)\n",
        "df_clean = df_clean[~all_bad_rows] # Keeping only rows that are NOT in the bad_rows mask\n",
        "final_rows = len(df_clean)\n",
        "\n",
        "# 3. Final Summary\n",
        "print(\"=== Final Cleaning Execution ===\")\n",
        "print(f\"Total rows dropped: {initial_rows - final_rows}\")\n",
        "print(f\"Final, purely clean rows ready for ML Model: {final_rows}\")"
      ],
      "metadata": {
        "id": "XZHW0oO2Gpvy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44da1687-8702-41c7-9545-a32973952e2f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Final Cleaning Execution ===\n",
            "Total rows dropped: 9256\n",
            "Final, purely clean rows ready for ML Model: 318392\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "An initial distribution analysis revealed a significant \"Right Tail\" in the target variable, which guided the modeling strategy."
      ],
      "metadata": {
        "id": "FhtsUpaDSngM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- 1. Probability Density Estimation ---\n",
        "# Estimating the Probability Density Function (PDF) for distribution analysis\n",
        "counts, bins = np.histogram(df_clean['MentalHealthDays'].dropna(), bins=40, density=True)\n",
        "x_centers = 0.5 * (bins[:-1] + bins[1:])\n",
        "\n",
        "# --- 2. Distribution Visualization ---\n",
        "fig = go.Figure()\n",
        "\n",
        "# Plotting the density profile to evaluate normality and skewness\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=x_centers,\n",
        "    y=counts,\n",
        "    mode='lines',\n",
        "    line=dict(color=PROJECT_COLORS[1], width=1.8, shape='spline', smoothing=0.8),\n",
        "    fill='tozeroy',\n",
        "    fillcolor='rgba(234, 0, 217, 0.12)',\n",
        "    name='Mental Health Days Density'\n",
        "))\n",
        "\n",
        "# --- 3. Figure Configuration ---\n",
        "fig.update_layout(\n",
        "    title=\"<b>Mental Health Days Probability Density Profile</b>\",\n",
        "    xaxis_title=\"Number of Days (0-30)\",\n",
        "    yaxis_title=\"Probability Density\",\n",
        "    width=750,\n",
        "    height=450,\n",
        "    template=\"plotly_white\",\n",
        "    hovermode=\"x\",\n",
        "    margin=dict(l=50, r=50, b=50, t=80),\n",
        "    showlegend=False\n",
        ")\n",
        "\n",
        "# Arithmetic mean reference for central tendency evaluation\n",
        "mean_val = df_clean['MentalHealthDays'].mean()\n",
        "fig.add_vline(\n",
        "    x=mean_val,\n",
        "    line_dash=\"dash\",\n",
        "    line_color=\"#333333\",\n",
        "    opacity=0.6,\n",
        "    annotation_text=f\"Mean: {mean_val:.2f}\",\n",
        "    annotation_position=\"top right\"\n",
        ")\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "p-N6_G55R6Ok",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "44fdd9da-ca66-44be-c9f2-0f969e785f4f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================\n",
        "# Target Variable Quantities Check\n",
        "# ==========================================\n",
        "print(\"=== Top 10 Responses for Mental Health Days ===\")\n",
        "\n",
        "# Calculate counts and percentages\n",
        "counts = df_clean['MentalHealthDays'].value_counts()\n",
        "percentages = df_clean['MentalHealthDays'].value_counts(normalize=True) * 100\n",
        "\n",
        "# Combine into a readable dataframe\n",
        "mental_health_summary = pd.DataFrame({\n",
        "    'Total Count': counts,\n",
        "    'Percentage (%)': percentages.round(2)\n",
        "}).head(10)\n",
        "\n",
        "display(mental_health_summary)"
      ],
      "metadata": {
        "id": "LkXafXPfUUeD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "27e4f717-bfde-4872-e483-af7e00102d3b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Top 10 Responses for Mental Health Days ===\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                  Total Count  Percentage (%)\n",
              "MentalHealthDays                             \n",
              "0.0                    194145           60.98\n",
              "2.0                     18531            5.82\n",
              "5.0                     15538            4.88\n",
              "30.0                    14443            4.54\n",
              "3.0                     11974            3.76\n",
              "10.0                    11813            3.71\n",
              "1.0                     11158            3.50\n",
              "15.0                    10934            3.43\n",
              "20.0                     6826            2.14\n",
              "4.0                      6269            1.97"
            ],
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              "      <td>6269</td>\n",
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              "type": "dataframe",
              "variable_name": "mental_health_summary",
              "summary": "{\n  \"name\": \"mental_health_summary\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"MentalHealthDays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.83192080250175,\n        \"min\": 0.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          20.0,\n          2.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57735,\n        \"min\": 6269,\n        \"max\": 194145,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6826,\n          18531,\n          11813\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.13486087316053,\n        \"min\": 1.97,\n        \"max\": 60.98,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2.14,\n          5.82,\n          3.71\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All column names were standardized to snake_case for improved code readability (e.g., `MentalHealthDays` to `poor_mental_health_days`). Finally, the dataset was logically reordered for clarity:\n",
        "\n",
        "1. **Demographics:** `sex`, `age_category`, `race_ethnicity`.\n",
        "2. **Body & Lifestyle:** `bmi`, `general_health`, `sleep_hours`, `physical_activities`, `smoker_status`, `e_cigarette_usage`, `alcohol_drinkers`.\n",
        "3. **General Physical Metrics:** `poor_physical_health_days`.\n",
        "4. **Medical History:** `had_heart_attack` through `had_diabetes`.\n",
        "5. **Disabilities:** `deaf_or_hard_of_hearing` through `difficulty_errands`.\n",
        "6. **Target Variable:** `poor_mental_health_days`."
      ],
      "metadata": {
        "id": "OpfKZPbPXkpI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================\n",
        "# Aesthetic Data Organization: Renaming & Reordering\n",
        "# ==========================================\n",
        "\n",
        "# 1. Dictionary for safe and precise renaming to snake_case\n",
        "rename_dict = {\n",
        "    'Sex': 'sex',\n",
        "    'AgeCategory': 'age_category',\n",
        "    'RaceEthnicityCategory': 'race_ethnicity',\n",
        "    'BMI': 'bmi',\n",
        "    'GeneralHealth': 'general_health',\n",
        "    'SleepHours': 'sleep_hours',\n",
        "    'PhysicalHealthDays': 'poor_physical_health_days',\n",
        "    'PhysicalActivities': 'physical_activities',\n",
        "    'SmokerStatus': 'smoker_status',\n",
        "    'ECigaretteUsage': 'e_cigarette_usage',\n",
        "    'AlcoholDrinkers': 'alcohol_drinkers',\n",
        "    'HadHeartAttack': 'had_heart_attack',\n",
        "    'HadAngina': 'had_angina',\n",
        "    'HadStroke': 'had_stroke',\n",
        "    'HadAsthma': 'had_asthma',\n",
        "    'HadCOPD': 'had_copd',\n",
        "    'HadDepressiveDisorder': 'had_depressive_disorder',\n",
        "    'HadKidneyDisease': 'had_kidney_disease',\n",
        "    'HadArthritis': 'had_arthritis',\n",
        "    'HadDiabetes': 'had_diabetes',\n",
        "    'DeafOrHardOfHearing': 'deaf_or_hard_of_hearing',\n",
        "    'BlindOrVisionDifficulty': 'blind_or_vision_difficulty',\n",
        "    'DifficultyConcentrating': 'difficulty_concentrating',\n",
        "    'DifficultyWalking': 'difficulty_walking',\n",
        "    'DifficultyDressingBathing': 'difficulty_dressing_bathing',\n",
        "    'DifficultyErrands': 'difficulty_errands',\n",
        "    'MentalHealthDays': 'poor_mental_health_days'\n",
        "}\n",
        "\n",
        "# Apply renaming\n",
        "df_clean = df_clean.rename(columns=rename_dict)\n",
        "\n",
        "# 2. Logical reordering of columns\n",
        "ordered_columns = [\n",
        "    # Demographics\n",
        "    'sex', 'age_category', 'race_ethnicity',\n",
        "\n",
        "    # Body & Lifestyle\n",
        "    'bmi', 'general_health', 'sleep_hours', 'physical_activities',\n",
        "    'smoker_status', 'e_cigarette_usage', 'alcohol_drinkers',\n",
        "\n",
        "    # General Physical Metrics\n",
        "    'poor_physical_health_days',\n",
        "\n",
        "    # Medical History (Diseases)\n",
        "    'had_heart_attack', 'had_angina', 'had_stroke', 'had_asthma', 'had_copd',\n",
        "    'had_depressive_disorder', 'had_kidney_disease', 'had_arthritis', 'had_diabetes',\n",
        "\n",
        "    # Disabilities & Difficulties\n",
        "    'deaf_or_hard_of_hearing', 'blind_or_vision_difficulty', 'difficulty_concentrating',\n",
        "    'difficulty_walking', 'difficulty_dressing_bathing', 'difficulty_errands',\n",
        "\n",
        "    # Target Variable (Always Last)\n",
        "    'poor_mental_health_days'\n",
        "]\n",
        "\n",
        "# Apply reordering\n",
        "df_clean = df_clean[ordered_columns]\n",
        "\n",
        "# Print check\n",
        "print(\"=== Data Standardization Complete ===\")\n",
        "print(\"Columns cleanly renamed (including 'poor' health days clarification) and logically ordered.\")\n",
        "display(df_clean.head(3))"
      ],
      "metadata": {
        "id": "NmLKDU1JXVm4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "741ce1a0-a9c2-4d78-bcb5-b37983706cd4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Data Standardization Complete ===\n",
            "Columns cleanly renamed (including 'poor' health days clarification) and logically ordered.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      sex  age_category race_ethnicity    bmi general_health  sleep_hours  \\\n",
              "1  female          80.0          white  26.57      excellent          6.0   \n",
              "2  female          55.0          white  25.61      very good          5.0   \n",
              "3  female          55.0          white  23.30      excellent          7.0   \n",
              "\n",
              "  physical_activities smoker_status e_cigarette_usage alcohol_drinkers  ...  \\\n",
              "1                  no            no                no               no  ...   \n",
              "2                 yes            no                no               no  ...   \n",
              "3                 yes           yes                no               no  ...   \n",
              "\n",
              "   had_kidney_disease had_arthritis had_diabetes deaf_or_hard_of_hearing  \\\n",
              "1                  no            no           no                      no   \n",
              "2                  no            no           no                      no   \n",
              "3                  no           yes           no                      no   \n",
              "\n",
              "  blind_or_vision_difficulty difficulty_concentrating difficulty_walking  \\\n",
              "1                         no                       no                 no   \n",
              "2                         no                       no                 no   \n",
              "3                         no                       no                 no   \n",
              "\n",
              "  difficulty_dressing_bathing difficulty_errands poor_mental_health_days  \n",
              "1                          no                 no                     0.0  \n",
              "2                          no                 no                     3.0  \n",
              "3                          no                 no                     0.0  \n",
              "\n",
              "[3 rows x 27 columns]"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sex</th>\n",
              "      <th>age_category</th>\n",
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              "      <th>bmi</th>\n",
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              "      <th>poor_mental_health_days</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>female</td>\n",
              "      <td>80.0</td>\n",
              "      <td>white</td>\n",
              "      <td>26.57</td>\n",
              "      <td>excellent</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>no</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>female</td>\n",
              "      <td>55.0</td>\n",
              "      <td>white</td>\n",
              "      <td>25.61</td>\n",
              "      <td>very good</td>\n",
              "      <td>5.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>...</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>female</td>\n",
              "      <td>55.0</td>\n",
              "      <td>white</td>\n",
              "      <td>23.30</td>\n",
              "      <td>excellent</td>\n",
              "      <td>7.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>...</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 27 columns</p>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "###Outlier Treatment"
      ],
      "metadata": {
        "id": "thg2zSc7bWwA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Outlier Treatment & Multi-Dimensional Refinement\n",
        "To ensure high data quality and model stability, an advanced anomaly detection phase was performed on the numerical features: `bmi`, `sleep_hours`, and `poor_physical_health_days`.\n",
        "\n",
        "* Methodology: Isolation Forest\n",
        "The **Isolation Forest** algorithm was selected for this task due to its superior ability to detect anomalies in a multi-dimensional space. Unlike standard univariate methods (such as IQR) that only identify outliers on a per-feature basis, Isolation Forest evaluates how features interact. A specific data point might appear normal in isolation but becomes an outlier when its combination with other variables is statistically improbable within the dataset's overall structure.\n",
        "\n",
        "* A **5% contamination threshold** was strategically applied. In large-scale, self-reported telephone surveys like the BRFSS, a wide spectrum of diverse data and high variance are naturally expected. This 5% limit acts as a balanced ceiling—it is aggressive enough to purge extreme, high-leverage outliers that would otherwise skew the model, while being conservative enough to preserve the massive sample size required for robust analysis.\n",
        "\n",
        "* **Outliers Detected:** 15,920 records were identified as anomalies and removed from the dataset.\n",
        "* **Biomedical Plausibility:** A primary impact of this process was seen in the `bmi` feature. The maximum value was reduced from 97.65 (a clinically extreme and statistically rare value that likely represents a data entry error or an extreme outlier) to a more plausible maximum of 54.39. This ensures the model focuses on representative physiological trends rather than extreme noise.\n",
        "* **Final Dataset Dimensions:** 302,472 records × 27 features."
      ],
      "metadata": {
        "id": "LefrXZReiffm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================\n",
        "# OUTLIER DETECTION: 5% Threshold & Adjusted Transparency\n",
        "# ==========================================\n",
        "\n",
        "# 1. Prepare numeric features\n",
        "num_features = ['bmi', 'sleep_hours', 'poor_physical_health_days']\n",
        "df_outliers_check = df_clean.dropna(subset=num_features).copy()\n",
        "\n",
        "# 2. Initialize Isolation Forest with a balanced 5% threshold\n",
        "iso_forest = IsolationForest(contamination=0.05, random_state=42)\n",
        "df_outliers_check['outlier_status'] = iso_forest.fit_predict(df_outliers_check[num_features])\n",
        "\n",
        "# 3. Statistical Summary\n",
        "total_rows = len(df_outliers_check)\n",
        "outliers_count = (df_outliers_check['outlier_status'] == -1).sum()\n",
        "inliers_count = (df_outliers_check['outlier_status'] == 1).sum()\n",
        "outlier_percentage = (outliers_count / total_rows) * 100\n",
        "\n",
        "print(\"=== Isolation Forest Results (5% Threshold) ===\")\n",
        "print(f\"Total Rows Processed: {total_rows}\")\n",
        "print(f\"Normal Rows: {inliers_count}\")\n",
        "print(f\"Outliers Detected: {outliers_count}\")\n",
        "print(f\"Exact Outlier Percentage: {outlier_percentage:.2f}%\")\n",
        "print(\"-\" * 40 + \"\\n\")\n",
        "\n",
        "# 4. Prepare data for plotting\n",
        "outliers = df_outliers_check[df_outliers_check['outlier_status'] == -1]\n",
        "inliers = df_outliers_check[df_outliers_check['outlier_status'] == 1]\n",
        "inliers_sampled = inliers.sample(n=min(5000, len(inliers)), random_state=42)\n",
        "\n",
        "# 5. Define palette colors\n",
        "PROJECT_COLORS = ['#711C91', '#EA00D9', '#0ABDC6', '#133E7C']\n",
        "color_normal = PROJECT_COLORS[2]   # Turquoise\n",
        "color_outlier = PROJECT_COLORS[1]  # Neon Pink\n",
        "\n",
        "# 6. Generate 3D scatter plot\n",
        "fig = go.Figure()\n",
        "\n",
        "# Normal Data: Fully opaque (1.0)\n",
        "fig.add_trace(go.Scatter3d(\n",
        "    x=inliers_sampled['bmi'],\n",
        "    y=inliers_sampled['sleep_hours'],\n",
        "    z=inliers_sampled['poor_physical_health_days'],\n",
        "    mode='markers',\n",
        "    name='Normal Data',\n",
        "    marker=dict(\n",
        "        size=3,\n",
        "        color=color_normal,\n",
        "        opacity=1.0  # Fully opaque as requested\n",
        "    )\n",
        "))\n",
        "\n",
        "# Outliers: Semi-transparent (0.6)\n",
        "fig.add_trace(go.Scatter3d(\n",
        "    x=outliers['bmi'],\n",
        "    y=outliers['sleep_hours'],\n",
        "    z=outliers['poor_physical_health_days'],\n",
        "    mode='markers',\n",
        "    name='Outliers',\n",
        "    marker=dict(\n",
        "        size=5,\n",
        "        color=color_outlier,\n",
        "        opacity=0.6  # Transparent only for pink dots\n",
        "    )\n",
        "))\n",
        "\n",
        "# Layout Configuration\n",
        "fig.update_layout(\n",
        "    title=\"<b>Outlier Detection: Professional 5% Threshold</b>\",\n",
        "    scene=dict(\n",
        "        xaxis_title='BMI',\n",
        "        yaxis_title='Sleep Hours',\n",
        "        zaxis_title='Physical Health Days',\n",
        "        aspectmode='cube'\n",
        "    ),\n",
        "    width=850,\n",
        "    height=650,\n",
        "    template=\"plotly_white\",\n",
        "    margin=dict(l=0, r=0, b=0, t=60),\n",
        "    legend=dict(\n",
        "        title=\"<b>Classification</b>\",\n",
        "        yanchor=\"top\",\n",
        "        y=0.99,\n",
        "        xanchor=\"left\",\n",
        "        x=0.01,\n",
        "        itemsizing='constant',\n",
        "        bgcolor='rgba(255, 255, 255, 0.9)',\n",
        "        bordercolor='#333333',\n",
        "        borderwidth=1\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "6jAA2qaWYTs1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 796
        },
        "outputId": "3e30b378-a7f4-4f72-865b-bbe168dddbec"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Isolation Forest Results (5% Threshold) ===\n",
            "Total Rows Processed: 318392\n",
            "Normal Rows: 302472\n",
            "Outliers Detected: 15920\n",
            "Exact Outlier Percentage: 5.00%\n",
            "----------------------------------------\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================\n",
        "# Final Range Verification & Outlier Removal\n",
        "# ==========================================\n",
        "\n",
        "# 1. Define columns for verification\n",
        "analysis_columns = ['bmi', 'sleep_hours', 'poor_physical_health_days', 'poor_mental_health_days']\n",
        "\n",
        "# 2. Display Data Snapshot BEFORE Removal\n",
        "print(\"=== Data Ranges BEFORE Removing 5% Outliers ===\")\n",
        "display(df_clean[analysis_columns].agg(['min', 'max']).T)\n",
        "print(\"\\n\" + \"=\"*50 + \"\\n\")\n",
        "\n",
        "# 3. Execute the Removal\n",
        "# Dropping the rows identified as the 5% most extreme outliers\n",
        "df_clean = df_clean.drop(index=outliers.index)\n",
        "\n",
        "# 4. Display Data Snapshot AFTER Removal\n",
        "print(f\"Action: Successfully removed {len(outliers)} outlier rows.\")\n",
        "print(f\"Final Dataset Size: {df_clean.shape[0]} rows.\")\n",
        "print(\"\\n=== Final Data Ranges AFTER Removal (Cleaned) ===\")\n",
        "display(df_clean[analysis_columns].agg(['min', 'max']).T)"
      ],
      "metadata": {
        "id": "d0QwPbGnnPCa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "69c3dc9a-b2be-454c-aad4-32b59f1fdc37"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Data Ranges BEFORE Removing 5% Outliers ===\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                             min    max\n",
              "bmi                        12.02  97.65\n",
              "sleep_hours                 1.00  14.00\n",
              "poor_physical_health_days   0.00  30.00\n",
              "poor_mental_health_days     0.00  30.00"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>12.02</td>\n",
              "      <td>97.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sleep_hours</th>\n",
              "      <td>1.00</td>\n",
              "      <td>14.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poor_physical_health_days</th>\n",
              "      <td>0.00</td>\n",
              "      <td>30.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poor_mental_health_days</th>\n",
              "      <td>0.00</td>\n",
              "      <td>30.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05025c33-38ca-4792-8a4e-d24f03d7208b')\"\n",
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              "  </svg>\n",
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\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-05025c33-38ca-4792-8a4e-d24f03d7208b 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-05025c33-38ca-4792-8a4e-d24f03d7208b');\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",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_clean[analysis_columns]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.862317516705033,\n        \"min\": 0.0,\n        \"max\": 12.02,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          12.02,\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.26299270053333,\n        \"min\": 14.0,\n        \"max\": 97.65,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          97.65,\n          14.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==================================================\n",
            "\n",
            "Action: Successfully removed 15920 outlier rows.\n",
            "Final Dataset Size: 302472 rows.\n",
            "\n",
            "=== Final Data Ranges AFTER Removal (Cleaned) ===\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                             min    max\n",
              "bmi                        12.02  54.39\n",
              "sleep_hours                 1.00  14.00\n",
              "poor_physical_health_days   0.00  30.00\n",
              "poor_mental_health_days     0.00  30.00"
            ],
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              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>bmi</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>sleep_hours</th>\n",
              "      <td>1.00</td>\n",
              "      <td>14.00</td>\n",
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              "      <th>poor_physical_health_days</th>\n",
              "      <td>0.00</td>\n",
              "      <td>30.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poor_mental_health_days</th>\n",
              "      <td>0.00</td>\n",
              "      <td>30.00</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_clean[analysis_columns]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.862317516705033,\n        \"min\": 0.0,\n        \"max\": 12.02,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          12.02,\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.66607407279831,\n        \"min\": 14.0,\n        \"max\": 54.39,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          54.39,\n          14.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Descriptive Statistics"
      ],
      "metadata": {
        "id": "r3_Ilj-Wa_Og"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Categorical Features Summary**\n",
        "**Key Findings:**\n",
        "  * **Demographic Profile:** The dataset is well-represented with a slight majority of female respondents (154,084).\n",
        "  * **Health Perception:** Most participants perceive their status as \"Very Good\" (107,603), indicating a generally healthy baseline for the surveyed population.\n",
        "  * **Clinical Indicators:** While most chronic conditions show a \"No\" majority, approximately 19% of the population reported having a Depressive Disorder, which serves as a significant feature for predicting mental well-being."
      ],
      "metadata": {
        "id": "qEeIkR8oquHf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================\n",
        "# Descriptive Statistics: Categorical Features\n",
        "# ==========================================\n",
        "\n",
        "# Identifying categorical columns (objects and categories)\n",
        "categorical_cols = df_clean.select_dtypes(include=['object', 'category']).columns\n",
        "\n",
        "# Generating descriptive statistics for all categorical features\n",
        "print(f\"Categorical Features Summary (Total: {len(categorical_cols)} columns)\")\n",
        "categorical_summary = df_clean[categorical_cols].describe().T\n",
        "\n",
        "# Displaying the summary\n",
        "display(categorical_summary)"
      ],
      "metadata": {
        "id": "STxfNpeto41M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        },
        "outputId": "c451c33b-d653-49eb-e3d5-58cb9018406d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Categorical Features Summary (Total: 22 columns)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                              count unique        top    freq\n",
              "sex                          302472      2     female  154084\n",
              "race_ethnicity               302472      5      white  228656\n",
              "general_health               302472      5  very good  107603\n",
              "physical_activities          302472      2        yes  238933\n",
              "smoker_status                302472      2         no  268007\n",
              "e_cigarette_usage            302472      2         no  286689\n",
              "alcohol_drinkers             302472      2        yes  168108\n",
              "had_heart_attack             302472      2         no  287253\n",
              "had_angina                   302472      2         no  285291\n",
              "had_stroke                   302472      2         no  291049\n",
              "had_asthma                   302472      2         no  259432\n",
              "had_copd                     302472      2         no  282231\n",
              "had_depressive_disorder      302472      2         no  244610\n",
              "had_kidney_disease           302472      2         no  289916\n",
              "had_arthritis                302472      2         no  203135\n",
              "had_diabetes                 302472      4         no  254420\n",
              "deaf_or_hard_of_hearing      302472      2         no  276837\n",
              "blind_or_vision_difficulty   302472      2         no  288705\n",
              "difficulty_concentrating     302472      2         no  273202\n",
              "difficulty_walking           302472      2         no  264608\n",
              "difficulty_dressing_bathing  302472      2         no  294920\n",
              "difficulty_errands           302472      2         no  285994"
            ],
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    {
      "cell_type": "markdown",
      "source": [
        "**Numerical Features Summary**\n",
        "**Key Findings:**\n",
        "  * **Body Composition:** The average BMI of 28.21 falls within the overweight category, with a median of 27.41, suggesting a consistent trend across the population.\n",
        "  * **Target Distribution:** Both physical and mental health days exhibit a median of 0, confirming that while most respondents report no distress, a specific subset experiences significant challenges (up to 30 days).\n",
        "  * **Sleep Habits:** Sleep patterns are remarkably stable, with a mean of 7.03 hours, aligning with general health recommendations."
      ],
      "metadata": {
        "id": "LVDgST2rbjB9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================\n",
        "# Descriptive Statistics: Numerical Features\n",
        "# ==========================================\n",
        "\n",
        "# Identifying numerical columns automatically\n",
        "numerical_cols = df_clean.select_dtypes(include=['number']).columns\n",
        "\n",
        "# Generating descriptive statistics for all numerical features\n",
        "print(f\"Numerical Features Summary (Total: {len(numerical_cols)} columns)\")\n",
        "numerical_summary = df_clean[numerical_cols].describe().T\n",
        "\n",
        "# Displaying the summary with rounding for clarity\n",
        "display(numerical_summary.round(2))"
      ],
      "metadata": {
        "id": "zgDO9slBo2oo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "58e099c8-f9a4-43e8-e48f-e67794f32260"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numerical Features Summary (Total: 5 columns)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                              count   mean    std    min    25%    50%    75%  \\\n",
              "age_category               302472.0  52.91  18.06  18.00  40.00  55.00  70.00   \n",
              "bmi                        302472.0  28.21   5.73  12.02  24.19  27.41  31.45   \n",
              "sleep_hours                302472.0   7.03   1.22   1.00   6.00   7.00   8.00   \n",
              "poor_physical_health_days  302472.0   3.11   6.97   0.00   0.00   0.00   2.00   \n",
              "poor_mental_health_days    302472.0   3.49   7.07   0.00   0.00   0.00   3.00   \n",
              "\n",
              "                             max  \n",
              "age_category               80.00  \n",
              "bmi                        54.39  \n",
              "sleep_hours                14.00  \n",
              "poor_physical_health_days  30.00  \n",
              "poor_mental_health_days    30.00  "
            ],
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              "      <td>18.06</td>\n",
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            }
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Correlation Analysis**\n",
        "**Key Findings:**\n",
        "  * **Strongest Relative Positive Correlation:** The strongest positive relationship identified was between `poor_physical_health_days` and `poor_mental_health_days`, reinforcing the intrinsic link between physical ailment and psychological distress.\n",
        "  * **Sleep Impact (Negative Correlation):** A negative correlation of -0.13 was found between `sleep_hours` and mental well-being. This indicates that fewer hours of sleep are directly associated with an increase in reported poor mental health days."
      ],
      "metadata": {
        "id": "5WguT71pbwM4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================\n",
        "# Feature Correlation Matrix (Heatmap)\n",
        "# ==========================================\n",
        "\n",
        "# 1. Select the relevant features for correlation analysis\n",
        "correlation_cols = ['bmi', 'sleep_hours', 'poor_physical_health_days', 'poor_mental_health_days']\n",
        "corr_matrix = df_clean[correlation_cols].corr()\n",
        "\n",
        "# 2. Create the Heatmap using the project palette\n",
        "# Using Dark Blue (#133E7C) for negative and Neon Pink (#EA00D9) for positive correlations\n",
        "fig = go.Figure(data=go.Heatmap(\n",
        "    z=corr_matrix.values,\n",
        "    x=corr_matrix.columns,\n",
        "    y=corr_matrix.columns,\n",
        "    colorscale=[\n",
        "        [0.0, '#133E7C'], # Strong Negative Correlation\n",
        "        [0.5, '#FFFFFF'], # No Correlation\n",
        "        [1.0, '#EA00D9']  # Strong Positive Correlation\n",
        "    ],\n",
        "    zmin=-1, zmax=1,\n",
        "    text=corr_matrix.values.round(2),\n",
        "    texttemplate=\"%{text}\",\n",
        "    showscale=True\n",
        "))\n",
        "\n",
        "# 3. Professional Layout Configuration\n",
        "fig.update_layout(\n",
        "    title=\"<b>Feature Correlation Matrix</b><br><sup>Post-Outlier Removal Analysis</sup>\",\n",
        "    width=700,\n",
        "    height=600,\n",
        "    xaxis_showgrid=False,\n",
        "    yaxis_showgrid=False,\n",
        "    template=\"plotly_white\",\n",
        "    margin=dict(l=50, r=50, b=50, t=80)\n",
        ")\n",
        "\n",
        "\n",
        "fig.show(config={\n",
        "    'toImageButtonOptions': {\n",
        "        'format': 'png',\n",
        "        'filename': 'Initial_heat_map',\n",
        "        'height': 600,\n",
        "        'width': 700,\n",
        "        'scale': 1.2\n",
        "    }\n",
        "})"
      ],
      "metadata": {
        "id": "nPqX45WQn5mm",
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          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "e4325e61-7bea-42c2-88c5-451ef8aa3093"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "62y9-8UOcNUI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Research"
      ],
      "metadata": {
        "id": "aQs-yQZwcOKC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Q1: How do BMI and Poor Physical Health Days impact Mental Well-being across different sleep levels?*\n",
        "\n"
      ],
      "metadata": {
        "id": "WoIdeMXrcR3j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# Q1: THE MENTAL HEALTH IMPACT (BMI, PHYSICAL HEALTH & SLEEP)\n",
        "# =============================================================================\n",
        "\n",
        "\n",
        "# 1. Plotting directly using a temporary assignment for sleep categories\n",
        "# This keeps df_clean strictly as it was loaded.\n",
        "fig = px.density_heatmap(\n",
        "    df_clean.assign(\n",
        "        sleep_profile=pd.cut(\n",
        "            df_clean['sleep_hours'],\n",
        "            bins=[0, 6, 9, 24],\n",
        "            labels=['Short Sleep (<6h)', 'Regular Sleep (6-9h)', 'Long Sleep (>9h)']\n",
        "        )\n",
        "    ),\n",
        "    x=\"bmi\",\n",
        "    y=\"poor_physical_health_days\",\n",
        "    z=\"poor_mental_health_days\",\n",
        "    facet_col=\"sleep_profile\",\n",
        "    histfunc=\"avg\",\n",
        "    nbinsx=30,\n",
        "    nbinsy=30,\n",
        "    color_continuous_scale=[PROJECT_COLORS[2], '#FFFFFF', PROJECT_COLORS[1]],\n",
        "    range_color=[0, 15],\n",
        "    labels={\n",
        "        \"bmi\": \"Body Mass Index (BMI)\",\n",
        "        \"poor_physical_health_days\": \"Physical Health Days\",\n",
        "        \"sleep_profile\": \"Sleep Duration Profile\"\n",
        "    }\n",
        ")\n",
        "\n",
        "# 2. Layout & Spacing Refinement\n",
        "fig.update_layout(\n",
        "    title=dict(\n",
        "        text=\"<b>The Mental Health Impact: BMI, Physical Health & Sleep Duration</b>\",\n",
        "        font=dict(size=20),\n",
        "        x=0.01,\n",
        "        y=0.95\n",
        "    ),\n",
        "    coloraxis_colorbar=dict(\n",
        "        title=\"Avg. Mentally<br>Unhealthy Days\",\n",
        "        outlinewidth=0,\n",
        "        thickness=20\n",
        "    ),\n",
        "    width=1100,\n",
        "    height=600,\n",
        "    template=\"plotly_white\",\n",
        "    margin=dict(l=50, r=50, b=80, t=140)\n",
        ")\n",
        "\n",
        "# 3. Polishing Facet Titles\n",
        "fig.for_each_annotation(lambda a: a.update(text=f\"<b>{a.text.split('=')[-1]}</b>\", font=dict(size=14)))\n",
        "fig.update_xaxes(showgrid=False, zeroline=False)\n",
        "fig.update_yaxes(showgrid=False, zeroline=False)\n",
        "\n",
        "\n",
        "fig.show(config={\n",
        "    'toImageButtonOptions': {\n",
        "        'format': 'png',\n",
        "        'filename': 'Q1_EDA',\n",
        "        'scale': 1.5\n",
        "    }\n",
        "})"
      ],
      "metadata": {
        "id": "0ch5l8tIK1Dt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "cca199ef-6d40-4c3a-bd90-b0fcc696895b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nxbiOd1aP5bpsjM+7Vo5X8TScSfrdQj+PFi2qjJq6TP3W1K1SXP0+OJN+++O6VGs+UF0Kqy2M+xDjHCV7fcXMGG7mWlhKos2SJIovUSIHioSo56+Xrqn/g93q2UMl3WfJ7VbdkWCCG4+cOCtt+/iqPIyuTWYFE+NvNX5TZi3ZrPLs1Ly6+o3BOIzxGOMy3JwgeEGowXuJ3/tNi0ap3zVn+5S1YDJh1ipZ6L/DwgICsv6dwViOeYCOt2a0YGlYo5R8891ZZRFnTBgjNiwY4ahb8HsSIAESIAESIIFQEHCrYIJFJ07JwWLxxKnzMnbGSrWgi/YRXGamq38xOanQsK+a+GO31W9sdzXp2rr3mPQZMVs9qvUi0vr5tWCCSRwmQpgkIdje4Q1T1QITVit12gxTu93TR3aRdn0nqSy0YIIJTKUm/dRiDoLNmrnD1EIKC6babYbK839eKqGlR9vAxb9xkorru7WuJVkgbqzZLbBcQIJ1wqLJfdX/l6/fqybRSEbLEpRbuHrnwF2thPFk65IxavI5ZvoKWbp2d5A6Wpfr6JQco4UJBKpPkiR4r9ssWbtbbt6+pz7XO6Fm2sO4kwrRYqpPJyWYNOo0Us5euCxYFB9cN9kSTNbWwgwT1qrNAhcKwbnkoD2xK6hMtNfssixycmfLIAsnBXK2l2wJJkZLKDDv0rKG5MySQYaMX6DqjgTxCpPZ2pWKyaylm2XH/hPqc201YN0mWOgsnNxX3Xf+tytSr52P6tewJMCpQOiLaFe0L1LvDvXULicsFSo06qNEE+Pu6P5vTkmngVPVtflzZpIJg9upBRkW33qRZxSM9A6rLZccBDyGZUOkSJEcxjcxWpig7+jdXdQDu8vL1++xLHyxIMTCUCfNGtwiRoioxKSShXLIFJ9O6hIsHrCIwA41rLemzl+nPnenYGK0yMFu+LLpA4IIJhi3vs6YRrq2qiX37j+SAWPnqXcUafGUfmp3+K9bd6V0vV5qPKtVsagM7fl/iwAErcWiUAc9xfiRt2I7tVCCRdTALo1UX4AVEsYlLPrt9Sn9nuPaUnV7KgEX1lWzx/eUz1Imk3XbD1kspbQQbXZcCYlggrEU1lbWCWWjfZGMQV+N7xssliCAgt0w38VqzIRIvWeVrxq3zYwr9mLP2BtzUS/9vBBo48WNLYO7NVG7/iMnL7G8+7AuaN+kSrDjiv4Si/BpC9ZbxEywwe8FfoucdSXBOIFxUIt0+F1BkNccmb+QbJk/t2tVYEswMTOGm7nWyA7/hyjq07uFem6MCaOmLldIyhbLLb5D2ttl54xggn6Ur1J7QWwQMDyzd776PTErmBj5oG9hAwYCJgRGxJvB+IpxFmnt3GHKSgaiXudBgeOu8b10pk8ZBRMdUwr54LcBTD5NkVS+O/2LEmBgZYY50G7/CZIgfpz3xqBsmdKpucXd+4+l36g58uBRYJBszCswv2AiARIgARIgARIIGwJuFUwQoA47ZTq17DlemZcibVkyWk1aEOxTWw5YWwuUqddLCSrYncbunb2kxQFMVtbN8xEs2jAZx64tdhCxKJu9dItgcY1JrDbH14IJdugRzA3J2vpF71DpnWhcY5yEtWtcRZ2GgITFTMGqndQkDxMgCDZI9iZasLw5ffZ3dU38eLEtJ46AEVghYdKOybt1uWYEE2e6khZMzLQHdlG1JRAWR3BXQJoyb50l1g3EDHBHCo1gomNNIB9YfMA03Jqzved0JJgUyZdFZo7upm43ihToT3tXTVQTduPuH05vmDeh13ttgqNocSStTu37TVKm7kgbF46Qzz9LIXD7gkUJEibsehdZLwSwKMDiAGV2GjDFEisEcUIw6UbC4nni7NXq/zDTxsIFKTjBxJk+oK9xJugrFu1dW9cK8ry43yKYlMwnJQvnEAg1xphCENNgzYU+jQCJWGgihZVgAkuz8sUD4z0gKCMWpPNWbLPEMOnXqYFgN9e48IZgAqsTbVGGuE3o00iDuzW2uIG17jVBvv3+5yCCGKzDStcNPNlHuwPBygZckCAsYizT7Q4XAizcjS49thbBEAkhOCPpOuv2ggD3+MkzZRGBBZbZcSUkgokz/ckomCAYMRaQSMe3zrS4GkKwLVmnh/ocFoFoLzPjSmgEE5Q537e3svRDMgYjdrTo18+/estB5dKjF7NwqYOIqE9Cg6UPFug45cZRQt/sOmS6GiOsEywfShXKIY1qlVGCmU62+oqZMdzMtShT9xX8HyIwxkgkWJsVrtpJiQCwojy6OdB111ZyRjDBfVWaDbCwgNsbRK3QCCYQK/H+GhPc816+fK2EZP07BcunrKVaqssgjkIkRTIrmHQZNE32Hjmp7oWFqtH9xshdB8a2HoP2r5loeU+M1xvHIEd9it+TAAmQAAmQAAmYJ+BWwQS79q0bVrLUErEgtAk+hBQIKr18/GT7vsCde/wdK+b/Y3bATx8LCpj3bloY9FQN46MbBRNM4hp3HiUnf/pN7d5jFx+mzjB5xsTky89TvSeYYNI7zDcwngVEnBSf/D9I35/XbynLEyQ9aTNOUrE7bfTZhkXKuV+vqAXvT/sCzeeDm2jhe5gzY+KGhQICU/5976EKnotkdGsJqUuOM91ECyZm2wPC1PGT5+W3y9fl/oPHSqhCjBgdt8XorhAawcQZzvae05FgYoxzAZeayk0CT7awdgfLVrqVEsXgjgOzcyRjm8BvHhYiOhn7u9FKCgvaQ8d+lKs3/ra4aiGeiXbFOrV7rrKyghsZrE/genFm33yHzehOwQSVgYgIsVC7G+Ezo2AytGczKVC5gxK4cJoOrIMKVQ0UMyEgYSc3rAWT4KDBasdvbDfF17hYMQpiuB+iF8QvJLhvQbxDMsZ00v0Tp6Xg1BQIX7CY0Ivbum2HWawXYPr/VfrPlOVAwdyZ34tbY+s97zNytmzdc0yVq8dORx3C2XHFHYJJkepdlEgFa4SCuf9/qhKe4fDxQFGxfrUSKkYDkrPjSmgEE4hUp3fPVW2FBEugXOXaqP9DRIGYElxCjJtl6/aoS+BWhUClEEo2zPdRMbrwvMVqdlUudJsXj3JKNMFzHzt5TvU5jKGwwDO6CkLEmzOhl7LQsh5/tIhuZgw3cy3K030F1hoQOY1J//bhs5O75ijrCVvJWcFExzpCHlpkC41gYhTHjPVCO6EP3vr7vsV9GHGC0G4Qu+BWh2RWMNHjN/rCqV1zLP0MeeF44ubdxqp8tftwSMYgR2MAvycBEiABEiABEjBPwK2CybCezZRLjk7GIJ84bhG+ycZdeHuPA/NkBI60l6wFE70bi129GaO7qkkeEnaNYUpvbWECEQeLW0dJT3qDEy50cDhjUDp7Ey1MjvuNnmsRR2yV7wrBBEFNEWDTOtVtN1wFmkXSgomZ9oCbQvPuYy3+67bq7yrBxNqixhZne+3nSDAx9lOjhYB18EV9GobR5cXYF3TQR10PWDVplxMdxwNiUru+E5UQaC9pwQSLNyzi4OJzdIv9HVudT1gIJtZBUyF+YBE3ZtoKtSOPpOP14P9GwQQWN9pKBm47mTJ8ptzldNwT7cqA+8LKwsSaMaxdENQXcUrqVi2uxBKk4I4VNrrw6NgHuAe76sVrdlVuSgjgCBcVnMSF2C1wL1wwKTCAIxKsitBX8J11QmwUjFMQoJBsjS/G9xIxDHS8E1t9yOy4EhLBxDrYr66HvWOFdV8ObozFaUYTh7ZX7k/OjiuhEUxsBej8qmiga5Wt052MdYcgD2EeCWM02r9S4/7K0kQL9doqwPokqeAYWH8HcRUCCmI9wTILCcLj8hmBboyO+oq9svRvqpnxHnnpvgKLF4x3xtSixzglniPht1b3Z+s6OCOYwMojd4V2SqDGO/vDzjkqm9AIJrZEKwic42asUO+yrRQawUT3eVviEjZVIDAhIeDxpGEdQzQGmelLvJYESIAESIAESMA5Ah4nmOgo9qj+mjlDBYtRs8laMIEoAn9/JLjMIDgjzGFhFms8IUO75BhPTOnVvq6KpRFccpVgglN7cHqPmogWy60C4SLeBRaiWtRxt2Bipj16DvezxPVAfBfE+kBAS+NC+L8kmOC0FSx+dTIG/Js9roeKQ1KidmDwYey+Th3RWXJlSa92o3GqCk5XQdKCiXFXHtZKeifcXt90h2Ciy9ZxgfA3ghtDNEGyFkx08FEE2MyUIbWyJtPigjsEE1un5NjiFxLBBPnoNoYIBCEjf6VAi5qRfVtaXKWM5aEcuNz9dP6SHP3hnOUkEAiaEDaRbI0vRksAHUfFXj8wO664QzApWqOrEo3g7vT9jlnBjq9mxpXwEkzgRgpBBK5VEDMhvKFvo52Q+nduINMWbFBWJ9YbB/YeHv0GLqi23HdgZYITVPA9yjq5e47611ZfMTOGm7lW/U7V7y3X/vpbWdTA6sOYEI8H44JxDLP1rM4IJgjcjhPIkArl+doSvFq79OHzH/fOt5ygB2Ele5nWyjLJGAcquN9qWBNWaTpA3QPL0sk+nSR1iqTKCipLyRbKsic0gom2MIG4eGrXXEvwdtQdwhIEJiRsKqGPhHQMCvZl4pckQAIkQAIkQAKmCXicYIJ4AjguEcl4mgj+xk5jrFjRLbvA9p7WWjBRk5BWQ+SX3/9UE1rEutALJ1uCCRYv9dr7qOyrlCkgo/q1shSFCS8mPEbz4tAIJtNGdpHiBbKp/I0nQRhjfRgD6LlbMDHTHhClIE5hgnl6zzxLOxl9t80IJjiOGcf56mSWs73+4S4LE+uTPuq397EcYQ13nejRPrIIeUbfeAR8LF6zm+W0IC2YtOntq05KQDL6wOPYbJhzY6IPlw59EpEWTIynuZgeIVDWpv0yfNISdas9wcFoUm7cjbcWTFBXxJvB4gOLZZxGoS2ezAgmWBDpE2GiffRRkMWH9TM6U3/re0K6WMEpFhUaBQYdhmjoO2u12hFH/CI8r04I6Hvj5p0gp4fA5L/HsBmy+9AP6jK4XdlbBBvfS1iyQHTSCe8bFrHYyZ4/sbfpccUdgomxLyPIpT4aFQwQfwVxL7QgaGZccUYwMY65YBbc8zprYYL3A/3MWjhAcG9YFOqEuEUQUh0Ff0UQUMS7AA//WUMsLjc6H3xeul5PFaQb4+2PewLjHNkaI82M4WauNbLD/40uiPidLFy9i7IIsWVRYXzfHAkmiBfVsMMIJSAgTfXpbDlGuWP/KXLg6Gn1uVGgxtG9HfpPVp87K5gYT5IyxgoznlhkTzBxpk8hftOew4HvtnWgVsx5wB5JH5sc0jEoJGM87yEBEiABEiABErBPwOMEE7hAlK3fS00UP02RRPmNI3gohI1mXceqRZI+VcHeY9kSTIyLMdy3dclo+SxVMpsWJigbix6cIoLYETipByb1sASA2TUWI8YAtmYX8sbjQJvWKSu92tVVz+s7a5Xl+FEdyA2B/2CODr9qXGMMeBvSGCZmXHLMtEet1kPVaTBIOogvgqZ2GzLDsrjVgRztLVQQx6Ncg0CXKeyyL57aXyJFjKgWBWY52+sf7hJMEHQTliRYJG3a9Y1yP0FCf4b5OtxrcByv/mz70jFKjPOdvVoWr95pOS5454px6mhi4441TkWYMryTChCKRRni7iDhWGEcL4ykdzRxDY76/TheHIuwgHcErLHI0j759ngZBYcG1UtJ9fKBRyIjwXT9z2u3ZPrCDeq9QMLxnHBVQbIWTPCZ0XoGjA6snaQWx2YEE2NfsNefdR3dKZigTB0zCUIJhBHrINXGWCc4LQinBiFhcYnTOHCEKgJaIkYSkq1+j4Uy3hPwB8O5E3r+28++lf6jA4/H1qf1GI81dmZccYdgYjwlp0Sh7DJ2QFuJ9lEUWeC/QwUwxvvuN6a7EgDNjCv2Fpn2xlyU4wrBxHgsOeL4tGlYWT0DArbC1UIfRQ6Lyd7t60qKZIkkRoxoyr3OVjLG0UJfgFsP4tzgXUYcIzyPDpoLRhhn7PUVM2O4mWtRnjHoK0RfjEnRo38ko026LgcAACAASURBVKcut5wQB3e30f3/v+lg/bxGwQR8EBdIvw/nf78iKzfus7gsFs6bRfzGBAbkRoIgucB/u/o/ykdcMsTOGjxhoTx89ESNsc4KJkaRpVzxPOoUsqfP/pFOA6eo+DH4/dVjN8Yrs33KGLwdp+8gqH2SRB8rFysIZNjIQX+AgAjLTAomNl8NfkgCJEACJEACbifgcYIJCMxfud1y6gf+hn814gIgIeArghzqEytsEbMlmOD0lspNA0/ogYkz/JeRbFmY4PPvf7wgrXv7qkUMEnY8MXnSO+NLpvZTRw4imV3IQ1TAIkAnLKy6t6mjAtDCJxsJk20skrGohZULFkjYxcdEDebCqL87BBMz7WGM3I+Jfdw4sVS9h/RoKgiIqP3PyxbLo6yHbC1UsAAsUq2zwBIBCe4pxfJnU7EMzHK29za5SzDBQhi7xBBB4IOvE46ThMsVkj5ZBf+H+xUEwZcvXykRTVt14ISldk2qqCMtuw+doYKLIqEvRI4U0eJvb32SB3ZYsQhAgjACwQTHOiPhXcA7YYytY4+XM6fk6HsRnwHvpz71xZZgggXQiMlL1S2IZYJTRJA+FMHEuHjGc+EEJQSO1Ql9oX6HERZxEQtiWJ/gyGK9sNYnZQQ3vuiAsjpfvCt6vILYvGrWEGXxYDztxZlxxR2CCayh2vebbAnwCksa9Bn93htFJjPjCsSKErW6KyQ4oQpxc5DsjbkILOsKwQTjVtMuowUnrCGBuxY39N+wujAmo6WE9buHxXnvEbMsLo723s0kieLLosn9VByg4PqKmd9UM9dqdrAQwjMjsDAEbm39hYU/XGvhgmcvGQUTuxf964qDsVOPLbgWv4+Vm/R7L+ZI7crF5My5i6o+zgomaB9slOAUKyT8/v5994ESt4oVyGaxAMHfEFMwptr6HQ+uTxndMlGG8bcB7++U4R0FohASBZPgegO/IwESIAESIAH3EfBIwQSPj4UmgrVeuHhVHfOXNPHH6ljSNo0qByuW4F5bggk+x2QIJvM4qQcn9iDZE0zwHcrGIg67S4+fPpN4cWJJnuxfSvsmVZV1ik4hWcjD/Hbp2t3y6PFTSZQwvgzo3FCK5s+qJsh+Szar3XoslKuXK6SeGcFB+42aI7fv3FcTQJgfu0swcbY9MEn2W7xJNu38Ru49fCKfpUyqWGMhj0UyTkDBgq5q2UICdxV7CxUEwoTAcuPmXYkTO4YSCto3rep1gsm2pWMEi+f124/Iw8dPldCFtsRpOzphcj5uxkp1HO3LV6/U0cJwsYAbDVy0II5EixZVuSZhpxaC3arNB2TjziNy6cpf8vZff3schYvjhI1xTWAhNWDMfBVHACexwCoKLlFIrhJMsACH0PNpiqRSuXR+KV8ibxB3NVuCCSy1cFoIklFMCKlgghOKcFKRveRuCxME5sxTvp1aNGJBi6OowcmYsPO90H+77Dl8Um7+fU8JJeCYLnVydTqMXjThnuDec/QbBKn++cIfajcd42SFknmlZf2KasGuk5lxxR2CCeoFkQFjPKxN/rwRePIY3pEaFYpIncrFLG4rZsaVlg0q2BRMVF+zM+a6QjBB/mhDtMXOAyfUSWrv/l10lymSUxrWLC2//PanzFu5Tc6cuyRRo0aWSUM7Wo6utdd3YaG3cdc3cu7CFbn34JF63yFMpEn1ifq9ACeIEs78Fpn5TXX2Ws0Opz+tmj1Uxk5fIYeOn1Gnu2XN9LkSfvXx5/ae0Z5ggncGIi+CQ+PkrRIFs9uM24TgtziJCjFI4seNJVXKFFSndTXrOkYFxoXAsWvleFW8o99MCGs4rvv8b3+qNiqcJ4v07hBoAdp54FQ59+tlSZwwvgrKDMvBkPSp/d+elhUb9sq5C5fVO4v3HvOKFvUrBIlXQ8HE7pDOL0iABEiABEjArQRCJZi4taYsjAS8gICjCbkXPILXVNF/037xmbRE9q6eaDmu1xMqD1eJVj0DTwzBkcPaxcAT6sY6kIArCZgR11xZLvMiARIgARIgARIgAXcRoGDiLtIs5z9BgIKJ+5oZ7kw40vjA2kA3o/BMcLPBbvFft++q0zxg3YPA0NjZxg4yEwl8iAQomHyIrcpnIgESIAESIAESMBKgYML+QAIuJEDBxIUwg8kKpwXhpBWcCISTo8I7wWUOpyAZE9wRENSZiQQ+VAIUTD7UluVzkQAJkAAJkAAJaAIUTNgXSMCFBCiYuBCmF2WFI8txtPM/L15Kik8SC47Erl2pqBc9AatKAuYJUDAxz4x3kAAJkAAJkAAJeBcBCibe1V6sLQmQAAmQAAmQAAmQAAmQAAmQAAmQgBsIUDBxA2QWQQIkQAIkQAIkQAIkQAIkQAIkQAIk4F0EKJh4V3uxtiRAAiRAAiRAAiRAAiRAAiRAAiRAAm4gQMHEDZBZBAmQAAmQAAmQAAmQAAmQAAmQAAmQgHcRoGDiXe3F2pIACZAACZAACZAACZAACZAACZAACbiBAAUTN0BmESRAAiRAAiRAAiRAAiRAAiRAAiRAAt5FgIKJd7UXa0sCJEACJEACJEACJEACJEACJEACJOAGAhRM3ACZRZAACZAACZAACZAACZAACZAACZAACXgXAQom3tVerC0JkAAJkAAJkAAJkAAJkAAJkAAJkIAbCFAwcQNkFkECJEACJEACJEACJEACJEACJEACJOBdBCiYeFd7sbYkQAIkQAIkQAIkQAIkQAIkQAIkQAJuIEDBxA2QWQQJkAAJkAAJkAAJkAAJkAAJkAAJkIB3EaBg4l3txdqSAAmQAAmQAAmQAAmQAAmQAAmQAAm4gQAFEzdAZhEkQAIkQAIkQAIkQAIkQAIkQAIkQALeRYCCiXe1F2tLAiRAAiRAAiRAAiRAAiRAAiRAAiTgBgIUTNwAmUWQAAmQAAmQAAmQAAmQAAmQAAmQAAl4FwEKJt7VXqwtCZAACZAACZAACZAACZAACZAACZCAGwhQMHEDZBZBAiRAAiRAAiRAAiRAAiRAAiRAAiTgXQQomHhXe7G2JEACJEACJEACJEACJEACJEACJEACbiBAwcQNkFkECZAACZAACZAACZAACZAACZAACZCAdxGgYOJd7cXakgAJkAAJkAAJkAAJkAAJkAAJkAAJuIEABRM3QGYRJEACJEACJEACJEACJEACJEACJEAC3kWAgol3tRdrSwIkQAIkQAIkQAIkQAIkQAIkQAIk4AYCFEzcAJlFkAAJkAAJkAAJkAAJkAAJkAAJkAAJeBcBCibe1V6sLQmQAAmQAAmQAAmQAAmQAAmQAAmQgBsIUDBxA2QWQQIkQAIkQAIkQAIkQAIkQAIkQAIk4F0EKJh4V3uxtiRAAiRAAiRAAiRAAiRAAiRAAiRAAm4gQMHEDZBZBAmQAAmQAAmQAAmQAAmQAAmQAAmQgHcRoGDiXe3F2pIACZAACZAACZAACZAACZAACZAACbiBAAUTN0BmESRAAiRAAiRAAiRAAiRAAiRAAiRAAt5FgIKJd7UXa0sCJEACJEACJEACJEACJEACJEACJOAGAhRM3ACZRZAACZAACZAACZAACZAACZAACZAACXgXAQom3tVerC0JkAAJkAAJkAAJkAAJkAAJkAAJkIAbCFAwcQNkFkECJEACJEACJEACJEACJEACJEACJOBdBCiYeFd7sbYkQAIkQAIkQAIkQAIkQAIkQAIkQAJuIEDBxA2QWQQJkAAJkAAJkAAJkAAJkAAJkAAJkIB3EaBg4l3txdqSAAmQAAmQAAmQAAmQAAmQAAmQAAm4gQAFEzdAZhEkQAIkQAIkQAIkQAIkQAIkQAIkQALeRYCCiXe1F2tLAiRAAiRAAiRAAiRAAiRAAiRAAiTgBgIUTNwAmUWQAAmQAAmQAAmQAAmQAAmQAAmQAAl4FwEKJt7VXqwtCZAACZAACZAACZAACZAACZAACZCAGwhQMHEDZBZBAiRAAiRAAiRAAiRAAiRAAiRAAiTgXQQomHhXe7G2JEACJEACJEACJEACJEACJEACJEACbiBAwcQNkFkECZAACZAACZAACZAACZAACZAACZCAdxGgYOJd7cXakgAJkAAJkAAJkAAJkAAJkAAJkAAJuIEABRM3QGYRJEACJEACJEACJEACJEACJEACJEAC3kWAgol3tRdrSwIkQAIkQAIkQAIkQAIkQAIkQAIk4AYCFEzcAJlFkAAJkAAJkAAJkAAJkAAJkAAJkAAJeBcBCibe1V6sLQmQAAmQAAmQAAmQAAmQAAmQAAmQgBsIUDBxA2QWQQIkQAIkQAIkQAIkQAIkQAIkQAIk4F0EKJh4V3uxtiRAAiRAAiRAAiRAAiRAAiRAAiRAAm4gQMHEDZBZBAmQAAmQAAmQAAmQAAmQAAmQAAmQgHcRoGDiXe3F2pIACZAACZAACZAACZAACZAACZAACbiBAAUTN0BmESRAAiRAAiRAAiRAAiRAAiRAAiRAAt5FgIKJd7UXa0sCJEACJEACJEACJEACJEACJEACJOAGAhRM3ACZRZAACZAACZAACZAACZAACZAACZAACXgXAQom3tVerC0JkAAJkAAJkAAJkAAJkAAJkAAJkIAbCFAwcQNkFkECJEACJEACJEACJEACJEACJEACJOBdBCiYeFd7sbYkQAIkQAIkQAIkQAIkQAIkQAIkQAJuIEDBxA2QWQQJkAAJkAAJkAAJkAAJkAAJkAAJkIB3EaBg4l3txdqSAAmQAAmQAAmQAAmQAAmQAAmQAAm4gQAFEzdAZhEkQAIkQAIkQAIkQAIkQAIkQAIkQALeRYCCiXe1F2tLAiRAAiRAAiRAAiRAAiRAAiRAAiTgBgIUTNwAmUWQAAmQAAmQAAmQAAmQAAmQAAmQAAl4FwEKJt7VXqwtCZAACZAACZAACZAACZAACZAACZCAGwhQMHEDZBZBAiRAAiRAAiRAAiRAAiRAAiRAAiTgXQQomHhXe7G2JEACJEACJEACJEACJEACJEACJEACbiBAwcQNkFkECZAACZAACZAACZAACZAACZAACZCAdxGgYOJd7cXakgAJkAAJkAAJkAAJkAAJkAAJkAAJuIEABRM3QGYRJEACJEACJEACJEACJEACJEACJEAC3kWAgol3tRdrSwIkQAIkQAIkQAIkQAIkQAIkQAIk4AYCFEzcAJlFkAAJkAAJkAAJkAAJkAAJkAAJkAAJeBcBCibe1V6sLQmQAAmQAAmQAAmQAAmQAAmQAAmQgBsIUDBxA2QWQQIkQAIkQAIkQAIkQAIkQAIkQAIk4F0EKJh4V3uxtiRAAiRAAiRAAiRAAiRAAiRAAiRAAm4gQMHEDZBZBAmQAAmQAAmQAAmQAAmQAAmQAAmQgHcRoGDiXe3lsbVt2HGknP75d9myZLSkSZXM4+p57Idz0rLneClbLLf4DmkfLvV79+6d1Gk7TH69eE02Lx4ln6ZIEqp6BMf8+T8vpGz93oIyd64YLzFjRAtVWbyZBEjAMYGAN28kS4kWkiB+HDm8YarjG1x4hSeMwQeOnpaO/adI1bIFZWTfli58usCsOOa5HCkzJAG3E5gyb53MWbZFBndrLHWqFHd7+SjQlWOVo3H/7C9/SN12w6VQnswya2yPcHleFkoCJBA6AhRMQsfvP3H3/YdPZMbCDXLo+Bm5e++hRIkSWTJ+kVraNqos+XJ+5XAiG1pI2/edkF4+fnJm33yJHCmSzex2H/pBlq7dJZeu/CVPn/8jCT+OK2WL5pZOLWpI9GhRxRMEk617j0mfEbOlfrUSMqBLI6ewfPv9z+I7a5X8cfWmJEkYX9o1qaIWI44WD/h+9eYDMmziYmnbuLJ0al7dqfJ4EQl4G4HHT59Lvorvi6AYA77OmFa6tKgh6T5L7pbHcjRxDmklwnsMdqbeb9++k0pN+slft+/JzuXjJEmi+PL8n5cybsZK2XXwOwl481byZv9ShvVqLh/Hi20zy7v3H4nvrNVy9Ief5emzfyRP9i9lUNfGkixJAo55zjQCr/lPENBjHuY2P+yc41HP7MxYFd6Cia2xyhHE4MYmZ8b9nsP9ZMf+EzJ/Ym/Jmz2jo+L4PQmQgIcRoGDiYQ3iadWBhUKt1kPll9//lBxff6GEklt/35c9h3+QKJEjybp5wyVt6uTB7vyF9pl6+8ySbfuO2xVM9n9zSjoNnCpxYsWQkoVzKoHk4NEf5catu1KxVD4ZO6CNRwgm9dv7yJnzl+xa4bx89Vp2HvhOjn7/s4wd2EZ+v3xdarUaIpEjR5I82TPKyZ9+kydPn8vq2UPlq/SpHTL/58UrKVK9s0T7KKrsXzvJrtgU2vbh/SQQngT04iFixAhSomAOVZU3b9/KxcvX5eqNv5V4unXJaIkdK0aYV9OZibPZSnjCGOxMnSHutu41QcoVzyMTBrdTt0DohuCdIV0q+ShqFDX+Fc2fVWaM6vpelljENOg4Qn46f0kyf5lGjVvf/3hB3bt27jCJECECxzxnGoLXfPAEPFUwcXasCm/BxHqsgpABK5BalYrKZzYspB2NTfi9cWRZeOrsb9Ko0ygpWSiHTPHp9MH3UT4gCXxoBCiYfGgt6uLnuX3ngRSv1U3tFu5bPVFNWpEWrd4pv/z2p/qByZklvWUiO29CL5m+cIP8eumqmvSO7t9akib6WN2DBfykOavVBPrps+eS8pPE0rF5NSlTNLf6fuyMlbJkzS4Z2rOpLF27Rz5JkkDtVl66csPyVLZMOAePXyDrth2WYT2bSc2KRdS12OUYOHae+vHr1a6uTcEEAsTkuWvlwsWrSmSpXr6wdG5RQ7DwQlqz9aAsX7dX/rxxW5Il/lg6Nqsu5UvkUd/VaDlY3YcfvmkL1suf129L5gxpZNKwDmqBZp00R0z+ITIZE6xHYA2yaec3golQloxpZcXMQTJo3AJZv/2wTBrWUUoXyakWEhev3JBcWTModto8PTjmfUbOlq17jnFXw8XvBbPzHAL2Fg9v3rxV7ymEx7kTekr+nJnk1p37MmbaCjn506/y4uUrKZw3i7JgiBc3lnogXAuB9vK1W5ItUzqpVq6Q9Bs1V2pXKipDejSV/qPnyqZd36oFPxb+SBUa9ZUr127JN5umKVHGeuKM99Z39molOkMEKF4gu/TuUE9iRP9I9n97WjoNmCJNapWRazfvyPGT5+X7HbOCwDU7Bmu3yFevXqvxbe+Rk3Ln/iPJ+PmnMqhbYyVAIAXHQovQtSoWlQgRI8j2fcclUqSIanysa8eEXo/D00Z2keIFssnNv+9LydrdVXmrZg+RiBEiyuotByRxgnhSvGD29zoQxtDyDftIquRJZPuyMeq3pmnXMUo0WTZ9gGTL9DnHPM957ViTcCTgrGDy3ekLMm7mSjVviP5RVCmcL4v069hAjXfPnr+Q3OXbqs2XFvUqyIRZq9SGTKnCOZWrDCyJkbCJM3bGCvVdhZL5JF6cWDJvxTYZP6idZT6kUTg7VlkLJqEZq1CvvBXbS9pPP5EG1UvKrKWb5dHjZ1KpdH413tmySrYeq2AB12OYn3Jhzp0tg9SpXFxKFMqhNgWRHI1NmOti3E+cMJ6M6NNSRkxeIg8ePX2PJebS9x88luPb/NRvARMJkID3EKBg4j1tFS41xaIiX6UOEhAQIEO6N5XyJfKqib510ot3TI5hhYJ4Jpev3pQKJfLKuEFt1eXYfYSyD4UdPzCrNh+Qv27dlYlDO0iZornU5H7u8q1KnIE1S46v06vJ9YAx85SQ4NO7uZo0W+8AwF1o5uJNSkgY0KWhfP5ZivfqZ+2Sg53nas0Hysfx40i3VrXkx3MXZfn6PcpVBi4zsGjBwgkuR7UrFVMTfSxm1s/3kS/SpBBtLYKyOjSrKoeP/6TEjUJ5vpZZY7u/Vz5chroNmS71qpaQgV0bCXai9xz6QTHAggCpYO7MUrdqcSmSN6sSbbB4uHHzruz2nyA7DpxQO7TYQcePMpIzzJev3yujpi6TLi1rSOuGlcKlD7FQEghLAsEJJlWbDVDubEum9pesX6VT7/zVG7eld4f6anI83s9fiuXPqkRJ7CKWrtdTbt6+J5VLF1DjEN7pew8eK5EAk2+zgglcUkrU6iavAwKkX6eGcub8RSXuNqtbTnq2raPGQ4yLKOuTJAkly1dplcBrTGbHYC2YDJ2wSIm+DWuUkiwZ08mkuWvk9esA2eM/QSJGjBgsC10vjDn1q5eUr75IrcYRCNH+foPV+G2dqjQbIBcv35AjG6cplxs9hnZoVk0ypU8tv166JunTplQila2EMbhBhxFSIFcmmTO+p7pEi+j9OzdUiyGOeWH5JjFvbyHgjGBy6uzv0rjzKCVwNKpZWo17G3d+o+Yv6+b5yJs3byRrqZYSN3ZMSZ8upZQrnlcW+u9Q1+nNJwgFEIRh5dq8bjm5/tcdOXLiJzUfmzi0vWWzS3NzdqyyFkxCM1ZhTMNzQODJn/MrqVmhiBJ0YM3Wo21taV63vMOxChdgLor5GMbnB4+eqDhUEMzrVC4mf997GOzYVKdKMYtQ/nG8OOq3BiIMOGEDEMIzUtfB05V1thaAvaW/sZ4kQAIiFEzYCxwSwKR75JRlarKNXcb0aVNJ7qwZpHblYpbApXoi26dDPWlcq4wSSyo27qd8z/eu8lXmjgh6hR/rDQtGqDJ/OPOrNOkyWsUZWDlzkOgfUWuTxWI1u8rfdx/adcl5+OiptOntKz//elnlix+6LF+lk1KFcyiBBzsM1oKJLqtvx/rqObB4KlOvl9p52bRwpGViDlPwNJ9+onYYsNjS8Uf088L0HCboYFOgSkfls39sy4z3zP/nr9wuE2evln6dGqgFDBZxlRr3k7hxYqofZSzIYDViTLnKtVH1ShA/rly/eUd9hcmN/6whkir5/y1M7DHH9YgF0KrnhDALwuiw8/ACEghjAnrxgLEJu6NIeG9+++O6GocwviyfPlBOnDqvAj9jfNEiLqxH9hz+Xg6snaysRDAeGa3ARk5ZKis27LMInWYFE+zinvv1isSOFV2+/PxT0Tuw2NWFa50el2CVtnf1RMuOpjUyM2MwBJNkiRNIvortJFbMGLJ3ta+y1sBCAM8DcSh2zOjBsvj9j+vqe4jTcGdCgpgNURvWMLCQsU7ZS7dSou6xrTPVV4tW7VSCFMZ8tIVODaqXkv6dG7x3P8QY7UK4bPpA5WLZosc4NfZ2aFpV2jetahmXOeaF8UvF7D2agDOCSds+vnLkxFmZPLyjZVxs1m2MwOpk9rgekjdHRrXIR4KI+knShMoaFVapOmgzNqKwIYU5Hd45pKrNBipLPL3RFZKxyiiYVC5TMFRjFeZs+jkOrpssiRLEU2IJNrUw5mIO52isMn6Pudyug9/Lyo371EYaLI+7ta4V7NjUulElSx1gQYzfEB1DrkqZAjKqXytVhN4UREBsHYvOozsaK0cCJGAhQMGEncEpAgh4hbgg8MM8ceoXZc4dNWoUZU2RJ9uXQQQG/EhhwZK5eHOJFTO6HN86U+3UwsUEPz6wFEHCSS65yrVV4gKu0T+i2GFtWqespV6OBBNciPIgwBw7eU5Zt8DdBib5sNrA5MBaMIG1B6w+rBNMME/vmScFq3YSCDHWCZOM+b69Lc+7bekYSZ0yqboMggoWBrBCwU6qMVn/UGrBJFP6z9TuD1xuwNOYsGuCH2+IOjUqFJbJc9cpKxhElYfJrBZtMCGwxRx5QUSq02aYMoGfNqKzU23Ni0jAmwjYC/qKZ8DOXu8OdSVG9GhK+IBgYCstnTZArv31t7IggYA5ok/gQkIHnNaWYWYFE+SxwH+7Eisg+r5791a5JmLMwNihx6Ui+bLIzNHdgsXu7BgMweTtm7cCiw9bCW41GHODY/Hy5SslmMAFEab3SHDt6TJompQolF2m+gQdS14HvJGsJVuoRRcWX0haYMGzLpjYRx4/fSZNOo9Wu65Y2CxevUuN1UiwsMHpZRBYILTolCJZIiUWo85tGlXimOdNLybrGmYEnBFMStTqruZp+9ZMtLhFj5/pr9ypMaeoV62EWuRDmNQip97E0vOFgWPny4YdR9SCHwt/pGG+i2T1loN2BRNc42isMgomsCQOzVj1dcZAdxiIzofWT1F1fPTkmeSv1CHIs+nGsDVWWTcUBCH/jftl/Y4jUrFkPjVnDW5salG/vKoDTiP8bnugSyUsh+FSaJx7wYIHrk/gj3kfEwmQgPcQoGDiPW0VrjWFIKHjl+D/fth5WLTREsDP1nGPmYs3UwuVE9v8LIIJFv7DewUKJtqHFlYTR7fMsAgm1nFKnBVMdP2QNyYK2GHAju6aOUOVT6vxWOG+o+bIlt1H1ekx+qQfDRixSErV6aHy8BvTTeLGCYxvgAR3JLjh6OeFtQx2UJFgMQIhxPiZvk8LJnriAUsULCggJGFyET9ubKlevpCydsEiAaloja5y595DxQaMIMZAlIFAtWBSH5sBEI3MkcfZC5elblsKJuH68rDwMCVga/Hgv2m/+ExaouIrLZ7ST5WvRVss+OGzb0ywpNh35KRggWAUTGDCDpdAa8Fk+qguUix/NpVFqbo9lTm3rRgm2KnsPnSGZM/8ucCt5NXrADUuWQsmzhx3bmYMhvCLY8Vh4Wd9jDrcHCEsQ8C2x+LchcsWCxQdoBCxDHoMm2kzaKEOdps8aULlQogEqxiY2hstSiC4QHiBixTM5g8fP6OuhXXdzhXj1P/xGeJDwZLx3G9X1A43BCy0i6PfGY55YfqqMXMPIWBGMNm/ZpISJJH+7+LWQG28WMdb0oFJEYMIsYi0QGwUTDAeYly0Z2GCchyNVUbBJH+uTKEaqzAnw3PoeSTKhxtl4Wqdg3ymm87WWIXvlLvi4ZPKsgQcMJ+Ei3XrhhWVK3hwYxPipThiifshnuMUMAomHvIisRokYIIABRMTsP6Ll+IoXOxKwK9cmxWCgw5WqGN2OJrI6oW70dxd764iXgkm0PYip2vB5PTuue9ZYcCKBH66v/1xTRTBFAAAIABJREFUTdbMGWax9sAPdp22w5Q5PFxYnj59HkQw0aam7RpXUYFnkeC+Av9T1FGbrkKYgECBwGJnL/whn6ZIKlgU6OfVppU4AhNWKfALPrrZvksOTNGxgDD+eO87ckqZbx4/dV79SONknzH9W0vnQVMF3yGoK0QdHYhR7/o6Yq6fiS45/8U397/zzLYWDxgXdPwSHZxQ757q8QaEYIGFaxEQ9eTZ36RF93FBzLghukB80YIJ4nggLhDimcCNDhPzojW6qPgntgQTvSsJFxa4skAMaNd3kinBJCRjMFz2cpZprWKVfLNpuhJ6YUEDARmCL3ZQ4X5kjwVYQWCGeAshA+OSFn2b1i4rvdoHjbMClnDJQSBDCLxIiFlSvcUgJRbBggdJB8s2Wubpnop2wG42LFEQdwB/w43z/G9XlFsQRC2Oef+d95pPap+AM4JJ2z4TVbwRWINBGEXS7w/mNXj3HS3yEdB+1pLNQdzwKjcdoALx2xJMnB2rjHO9GhWLhGqsQlBq7ZIDsRbzMx2DCfH0sGFmnazHKsSnw4lecAuEpQo29mpVKqaC/SM5GptSJk/skCXyoUsO32oS8F4CFEy8t+3cUnNYWWCSC/cUuH3A1eTJs+fq6FuYluuAVs5MZBFn5JvvzipB4Mt0n8qydbvVSQoI8AdBxp5gooMJQmioUDKvOkXGmCbNWaN2K2HNgqBfcAPCiRSYsMNEfPvSMcpdx2hhgsBm+OGPHCmitG1cRZ3Gs2rTfmXhMaR7E2V9AiuUT1MkUcEGDxz9UZnP65gl+nmxU4wfV/xA40cXu87YfbZO1kFfbTUe6oSgYwjWhpgu8DWGcAMf3cJ5ssih4z8qSxlYvSBwojPMtRsCg7665XVhIeFAwN7i4dCxM9K+3yQVJHnb0rEqvkblpv1VrBLEIoKv+5xlWyR5skSyfp6PvH33TgVohQiCMSpJwvhqAY9JtBZM9LiAcaVNw0oqGPOvF6+pIIG2BBO40I2aulyNWdjRxQ7j4yfP5PGT5zJhSDuJFjVqkHHJFr6QjsH6lC2MrRC2MRbcufdAWcAhwGxwLL47/YuqF1x3cLoQxn2wQlBHLECwELFOepwGB1jMIWnhOW/2jOpviMKZM3wmK/0GWywWjfk06jRSjX8QqZ+/eKliXxlN2jnmhcMLxiI9joCtuE3GSiJ4/Z/Xb6ljbCEAQOSESArLEAQkXT5joAo870gw0aInAqoiD7jHffvd2X+DvgYG6zcmZ8cq67leaMaqd/JOPYe2/i1dNJeayyGwvxaqHY1VcDHafeh7FeC/eMFsNk/WCW5scoYl6qBdwRn01eNeKVaIBBwSoGDiEBEvQODE2cu2yPenL8i9B49UNHIc4QYFHmIBkjMTWbih+M5apRYZz5+/kNQpk0n3NrUspybYE0ywSBk5dZmy3ujTob7l6GDdMrAmwS7w+u1H1C4qYqNgkoBJesdm1ZRoYh3DBPcioBesZ2CdgoVB6SK5pHvrWhYrFpjww+f0xq27yrS9WZ1ylrL182L32m/xRlVujizplaCiFwvGnqODPdoLQma8FrvV+mhjRFqftmCDmqjguM1mdcoq03RnmWvXo/kTeyseTCTwoREIbrcVQUMhZLaoV166t6mtLCxGT1su35+5IO/evpOcWdOr02v0TiKOAEbQw1t3HkjOr9MLYovgegRqRsBm+L/jyEi42mCC3qVlTSWqwF8dcTnix4sdZBECUbnPyFlKYE6cML4qC2MFLE++TJdKuQQahVx7bROSMfjlq9cydf462XXgO+XTD8uSnu3qSPbMX6higmOhx0uccobA1HhGCNIIfqjHH+u66qM6je5KENphlXPw2I/qdwPBwvt2bGBxEbDOA65NgycslDPnLkqkSJGU+w+4IzYAx7wP7c3l84SUQHBxm5CntrSAQIk5DixC8P6WLJxDncKF+Y6zi3y41mFTKiDgjVQqlV9ZW+Azo+WK8TmcGaus53qhGav0c8DtCPM9WHFgDoh4eZgvIhi4o7HKOOey1ybBjU3OskRcGcyheaxwSHs+7yOB8CNAwST82LNkLyZgSyBy9Dj6KGJb5uiO7g3J99gNhh8vdtYPrJtsc9ckJPnyHhL4EAlAeIVQ8uDhY4sFBSzXsFjQp7J8iM9t65lsCcyOnl2bwRsDxTq6x9Xfc8xzNVHm918mgLgesL7F2KiD22tLYX0aTHjzsSVWOKpTeIxVsJyDlYr1KZCO6srvSYAEPIMABRPPaAfWwssIhEQw0eb8cPFBAMiwTvpYu9YNKwlccphIgATsE8DOaZl6PZWbIOKNpPgksbIe++fFS9m4cKQlGPN/gWFIBBPs0lZs3Ffx27VivHKFcnfimOdu4izvQyaAIPZVmvZX1imI9/bs+T/it2SzCnS/es5Qj9iECYlgEh5jVc/hfrJj/wlLTLoPud/w2UjgQyRAweRDbFU+U5gTCIlgogPRIubB5sWjVHyUsEowSS3XoI+8fftWdiwfp+K6MJEACQRPAH7+OEkCsTPwviJ2B9xQtBvLf4VfSAQTsDlw9LR07D9FHUFqDBLuDm4c89xBmWX81wgg8PzMxRsFrjaYR+DkMbj1wE3ZE1JIBBN3j1X60APEk0LMPiYSIAHvI0DBxPvajDUmARIgARIgARIgARIgARIgARIgARIIYwIUTMIYMLMnARIgARIgARIgARIgARIgARIgARLwPgIfjGASc/8x76MfTI1vb04XsueJbP62tzcCTN8UMan5gt49f2u6HHln/hZ5Y/6eiAkjmb7p7b0QFGS6FJEQcRORZC2vhKA03hISAs+K5wvJbe/ds/PuA6nx0wWX5OUJmUy//YnUe2I+lkXE1FHE7DsZIWkkiRj3/RMRguPw7vU7kRAMSxFMwn376K3IC3OD2bsQ1AvVimCyciFh8ObKa3lncvwL+PGlvL1u/remS8m7sjrjM5PEeXlICOzLkUny/nskdEju1/f88+aNJDz0XWiy4L0kQAIkECIClRJ+LP5fpw/RvbyJBOwRoGDioX2Dgsm/DWNujRF4Uwh0DLOLMxRDwcRDX55wqBYFE9vQKZgEcqFgIkLBJBwGJpNFUjAxCYyXkwAJeBwBCiYe1yQfRIUomHhoM1IwoWCiuyYtTDz0JTVUi4IJBZPgeikFEwomnj+KiVAw8YZWYh1JgASCI0DBhP0jLAhQMAkLqi7Ik4IJBRMKJi54kdyUBQUTCiYUTIJ/2Whh4qbBKBTFUDAJBTzeSgIk4BEEKJh4RDN8cJWgYOKhTUrBhIIJBRMPfTltVIuCCQUTCiYUTLxnxLJdUwom3t6CrD8JkAAFE/aBsCBAwSQsqLogTwomFEwomLjgRXJTFhRMKJhQMKFg4qbhJsyKoWASZmiZMQmQgJsIUDBxE+j/WDEUTDy0wSmYUDChYOKhL6eNalEwoWBCwYSCifeMWLZrSsHE21uQ9ScBEqBgwj4QFgQomIQFVRfkScGEggkFExe8SG7KgoIJBRMKJhRM3DTchFkxFEzCDC0zJgEScBMBCiZuAv0fK4aCiYc2OAUTCiYUTDz05bRRLQomFEwomFAw8Z4Ry3ZNKZh4ewuy/iRAAhRM2AfCggAFk7Cg6oI8KZhQMKFg4oIXyU1ZUDChYELBhIKJm4abMCuGgkmYoWXGJEACbiJAwcRNoP9jxVAw8dAGp2BCwYSCiYe+nDaqRcGEggkFEwom3jNi2a4pBRNvb0HWnwRIgIIJ+0BYEKBgEhZUXZAnBRMKJhRMXPAiuSkLCiYUTCiYUDBx03ATZsVQMAkztMyYBEjATQQomLgJ9H+sGAomHtrgFEwomFAw8dCX00a1KJhQMKFgQsHEe0Ys2zWlYOLtLcj6kwAJUDBhHwgLAh4nmLx7906mzFsn67cfljdv30rhPFlkcPcmEj1aVLn2198yaNwC+fXiVfkkaUIZ0KWRZM/8ueISc/+xsOATbnlSMKFgQsEk3F4/0wVTMKFgQsGEgonpgcPDbqBg4mENwuqQAAmYJkDBxDSyML1h9LTlcvvOA5k8vKPsO3JKBo2fL0c3zwjTMsMic48TTNZtOyxrthyQWWN7SJQokaV9v0lSIFcmad2wkjTpMlqKF8wuDauXkqM/nJNB4+bLnlW+EiVyJAomundENt9N3t4IMH1TxKTmC3r3/K3pcuSd+Vvkjfl7IiaMZPqmt/dCUJDpUkRCxE1EkrW8EoLSeEtICFAwoWBCwYSCSUjGDk+6h4KJJ7UG60ICJBASAuEtmHz/4wVp2nWMVC5dQEb3bxWSRzB9z937j6RI9S6SOcNn4j9ryHv3D5+0RFZt2i/zJvSSfDm/Mp2/9Q2Hj5+RVMmTSOqUSR3mRcHEIaKQXXDm/CX5KGoUyZAulcpg7vKt8sefN6VnuzpStn5vObZ1hkSOFLi4rdlqiPRuX09yZ8tAwUTjNq9jCAUTEQomIXtfeVcgAQomFEwomFAw8fbxkIKJt7cg608CJBDegkmfkbPl9esAOXTsjBxaP0VixYwe5o2iBZP4cWPLsukDgggZAW/eSLEaXeXV6wCZPKyjSwSThh1HSsv6FaRo/qwOn42CiUNEob/gr1t3pdPAqcq6JFGCeDJ84mLZuHCEJeMew2ZKnuwZpXalohRMKJj8v8OFwPCDgkno39f/cg4UTCiYUDChYOLtYyAFE29vQdafBEggPAWTx0+fS9HqXWTV7CHSZ8RsqVuluNSuXEw1Sp02w6RA7kzSuUUNSyNNnrtWTpz+RVbOHCSnf/5d+o+eJ7fv3Je8OTJK0XxZZeGqnbJj+ViHjaoFk/Il8ijLj07Nq1vugXAD4wOEtRjTv7USTP558UrG+/nLwaOn5dHjZ/J1xjTi07uFpEiWSJ49fyG5y7eVKT6dZPHqnXLn3iOJGSOajB3QRtJ9llyadRsj352+IFGjRpHSRXKqzzfv/lbmLtsqN27dlY/jx5GmtctKwxqlVB1sCSY7V4yXwtU6y8zRXSV/zkyWuiLvTOnTSI+2tR0+s7sv8DiXHA0AHevnXy9L/WolpF+nhnL81DmZOm9dEFOjgWPnyxdpUkjjWmUomFAwoWBiNXrQJcd9wykFEwomFEwomLhvxAmbkiiYhA1X5koCJOA+AuEpmCxfv1fWbTsk6+f7yCKIHftPKPEEaaH/Dtm46xvZtHCkBUbFxv2UqFKncjEpUqOL1ChfRNo1qSLHT52XYb6LlHXKtqVjHMLTggnihIyf6S+7/SdY7unl4ydZMqaTOcu2KHEDggnyvnjlL/Ed0l7ixokps5ZsVnXdvmysvA4IkOylW0mhPJll0rBOKoZob59Z8uLVK5nq01nlm6dCO5UXLEz+uHpTKjXuJ9NHdZGCuTLLj+cuSsse42XZjIHKRciehUn3oTMlatTISsRBevDoiRJR1s3zUWt7T0seK5gA1L0HjxXo+HFjSfkSeWXIhEWyedH/O1r3oTOUMlWzYhEKJhRMKJhQMAm38ZWCCQUTCiYUTMJtAHJRwRRMXASS2ZAACYQbgfAUTGq0HCyVSuWXpnXKCkSMYjW7WgSAm7fvSck6PWTH8nGSKnliuXTlhlRpNlAOrJ0kl/78S1r3miDHtsxU1hxIcO35+cJlU4IJgqlWbzFIxg9upw5Fef7PS1UHCCHVmg9UIkfOLOkld4V2MnNUV4t7zps3byVPhbYyc3R3yfJVWiWYQBwpUSi7qsvarYdk0eqdsnXJ6PcEE9x7/+Fj5QmiU+WmA6RBtRJSp0pxu4IJ4qDAU+TwhmlKlEEM02XrdsuGBf/3JAm3TmSjYI8TTAAwxSeJJU2qZKq6MFWCKw58skrW7iHfbJquwCKVa9BbRvVrJdkyfU7BhIIJBRMKJuE2tlIwoWBCwYSCSbgNQC4qmIKJi0AyGxIggXAjEF6CydkLl6V+++Gyf80ki3gAESR1ymTSv3MDxaNuu+FStmhuJajAquO707/Igkl9lEuL76zVKuaJTovX7JLVmw+YEkyObZ0pC1Zul0ePn8qQHk1l655jsn3/cZk5upuy3oBgkipFEildt6fN9hnRp4UyUIBgguCxsBBB2rL7qEyZv072rvJ9TzDBBwv8t8uG7UfkwaOnEjFiBHn4+Kn0bFtHeYDYszCB0FK8Vjfp1b6uVCyZT9r28ZU82TJKs7rlwq3vBFewxwkmE2evll9+vyqThnVQwV9HTFkqT54+l4lDO0iL7uMkV9YM0qpBRdlx4IRy0YFSFylSRAomFEwomFAwCbdBloIJBRMKJhRMwm0AclHBFExcBJLZkAAJhBuB8BJMhk5YJGu2HrRs6gPA69dvlMXIwXWTVcwPWGnsO3JSlk4bILBGqVu1uNSqWFQ27fpWpi1YbxEkcO/StbvFf9N+04LJvfuPpEGHEUp86TRwilQtW0jKFsttEUw+T5NCnagDt6H0aVO+104vX71WgglciTKldyyYrN5yUCbPWSN+Y7tLloxpVX54tiplCgQrmOA6xFHBwS7jBraRwtW7yK4V4yVxwv9bqoRbJ7JRsMcJJjAfGjV1mcDSJCDgjXydMa0M7dlUkib6WAWT6T96rvx66Zqk/CSxDO3RVL5Kn1o9Vsz9xzyJa6jrcntzupDlwVNyhMcKB3YdxjAJ2SsUkrsomFAwoWBCwSQkY4cn3UPBxJNag3UhARIICYHwEEywdi1ao4t0b1Nb8huO7X0d8EbqtRsuw3o2k3LF88itO/eVdceKmYOkoRI1pqoYIkdOnFXixvGtMyXaR4FeFIjTiUCwZmKYwMIkTqwYUrftMKlXraSMnbFCDqydrAwQtIUJYpjkKtdWBndrLJVK57cgxho7edKEYlYwwboc9yAeChIC3+JUni4tazgUTC5eviE1Ww+RXu3qyP5vTsv8ib1D0uRuucfjBJOQPjUFk3/JUTChYPJvV6BgEtLRxPx9FEwomFAwoWBifuTwrDsomHhWe7A2JEAC5gmEh2CyfvthmeC3ymJJYqw1LE+u37oj8yb0Uh/Xb+8jkSJFUgFd/cZ0U59BcClcrZM0r1dBWtWvoMJRDJ2wUD76KKpFMBk5ZalULl1AMn+Z5j0oOuirFkxWbtwn81dsk/y5MsnwXs3V9UbBZMKsVbLvyCmZMbqrMkBYu/WgTJkX6HITJUpkhxYmsFBpXq+81ChfWJ2ks+vg90oEwnOMmb5Cfr10VYoVyKbcchwdK1yr9VC5euO29O1YX6qVK2S+wd10BwUTN4E2WwwtTP4l9s4sOaFgQsEkBJ0mdLdQMKFgQsGEgknoRpHwv5uCSfi3AWtAAiQQOgLhIZhABMmUIY0lVonxCc7+8oeKXYKTa2DBAVcbiAo4HcZo4bH/29Mywc9fHeNbJF8WyZYpnfhv3C9b/g20mrVUS/Hp1TzIPboca8EERwUXqd5Z5vn2VkFerQWTFy9fydgZK2XXwe+U21CGdCmlV7u6yqvDGQuT6Qs2yHz/7ZIn25fqORC89cz5i5I4YXzp06G+CngLb5GOzaopq5rbdx4ITvCBSDNo/HxBcFqdlq/fo+K3HNk4zRLwNnQ9IGzupmASNlxDneuVEglClEesSYlM3/f66AvT97za88z0PbFnJzF9z+M6N03fE6VoDNP3vD743PQ9iV5+bvqeu/Evmr5nyXnzz4NCBl26aros3hAyAhRMQsbN3l2T1sWT2qdjmsr0bd94IuXMvSsRZz2WiIfNjX9RiseQyBkCTWadTe9evjMt5L4LeCfy1tkS/r3ulfly/ln4SAJM/gb06PJY1pZ8abJyvNzTCVAw8fQWYv1IgAQcEQgPwcRRnZz5/vXrAIkQMYJEjhRJXe63ZJP8cOZXme/ruW4qzjyXo2vGzVgpj548k5F9Wzq6NFy/p2ASrvjtF07BJJANBRMRCiYe+pIaqkXBxLVtRMFEhIKJa/sUc3NMgIKJY0a8ggRIwLMJeKNgEvDmjZSo1V2qli0obRtXkes3/5Z2fSZKo1plpEmtMp4NPIS1Q3yX4yfPSfehM8Xfb5CkTZ08hDm55zYKJu7hbLoUCiYUTHSnoWBi+vVx+w0UTFyLnIIJBRPX9ijm5gwBCibOUOI1JEACnkzAGwUT8ESAV7jJ/HbpmsSLG0sd79ulRQ0VU+RDTHBTunn7nnIFqlgqn8c/IgUTD20iCiYUTCiYeOjLaaNaFExc21YUTCiYuLZHMTdnCFAwcYYSryEBEvBkAt4qmHgyU9ZNhIKJh/YCCiYUTCiYeOjLScEkzBuGggkFkzDvZCzgPQIUTNgpSIAEvJ0ABRNvb0HPrD8FE89sF6FgQsGEgomHvpwUTMK8YSiYUDAJ807GAiiYsA+QAAl8cAQomHxwTeoRD0TBxCOa4f1KUDChYELBxENfTgomYd4wFEwomIR5J2MBFEzYB0iABD44AhRMPrgm9YgHomDiEc1AwcReM/CUHJ6S46GvaJBqMYaJa1uJggkFE9f2KObmDAG65DhDideQAAl4MgFvFkxevnot2Uu3klWzh0im9J+5HHP1FoOkRoUi0qB6yffyRhDWMkVySbO65Vxe7oeQIQUTD21FWpgENgwFEwomHvqKUjAJw4ahYELBJAy7F7O2Q4CCCbsGCZCAtxMIL8Ekf+UO0r11balZsUgQhMd+OCcte46XU7vnykdRowSL152CyaUrN+TWnQdSIFcmVSdXCiYVGvWVK9duqXwjRYooCeLHkdxZv1RiTIZ0qbyyi1Ew8dBmo2BCwUR3TR4r7KEvqaFatDBxbRtRMKFg4toexdycIUDBxBlKvIYESMCTCVAwsd86RguTKfPWyatXr6VX+7phIpiUKZpLalcupsr48/ptWbXpgBw58ZNM9ukoxfJn8+QuZLNuFEw8tMkomFAwoWDioS+njWpRMHFtW1EwoWDi2h7F3JwhQMHEGUq8hgRIwJMJeLJgEhDwRnKXbytTfDrJ4tU75c69RxIzRjQZO6CNpPssuWgLE98h7WXR6p1y4fc/JVWKpDJpaHtJmzq5wg6LlSnz1wksRGLGiC5Na5eVpnXKqu/evHkrk+eulW37jsnDR08ldcqk0rtDPcmbPaP6XgsmDx4+kdnLNkvECBElUcJ4sneVr7IwgZDx2x/X5ODRH1W9BnRpJBA+6rYdJnlzfCVdW9W0NP3MxZvk8LEfxX/WkPe6AyxMalYo8p57z/iZ/rJlz1HZs8pXWduc/eUPGTdzpVy4eFU+ihpVShTKLgM6N5QXr15L4WqdZeborpI/Z6AFDFKzbmMkU/o00rllDRk1dZnsPfyDPHv+QtJ8+okSfvJk+zLMuiYFkzBDG7qMKZhQMKFgErp3yJ13UzBxLW0KJhRMXNujmJszBCiYOEOJ15AACXgyAU8WTMANMUoK5cksk4Z1kujRokpvn1ny4tUrmerT2SKY5Pj6CxnSo6kkiBdHegyfqcQLfH/95h2p2myADO3RTMoVzyNXrt2UNr19pWPz6lK1bEFZvfmATFuwXpZOGyDJkiSQZev2yPwV2+TQ+ikSJUpki2CCGCadBkyRVMmTBLEwuffgsQzp3kSyZfpc5i7fKmu2HpQjG6bJ6i0HZN6KbbLHf4JEiBDBIL4UlgbVSzktmDx49EQKVukkc8b3lPw5v5LitbpJpVL5pUOzanLn3kNp2WO81KlcTAkt3YfOlKhRI8uY/q1V/rgXIsq6eT5y8qdflcXKPN9eEjdOLNm484hMX7BB9q2ZKJEjRQqT7knBJEywhj5TCiYUTCiYhP49clcOFExcS5qCCQUT1/Yo5uYMAQomzlDiNSRAAp5MwBsEE4gfsKZAWrv1kLIm2bpktEUwmTC4nRJEkFZvOaisUbYtHSN+SzbJt9/9LMumD7A0AYQMuLosntJP3f/8nxcSP25si8gAgWLLktGSJlUyh4LJl59/qgQTJMQggaWIFluKVO8i8yb0kpxZ0svVG39LxcZ95eC6KfJxvMCyjMmehQmugejRsVk15a5z/+ETiRUjmkT9N7bLiMlLlTACC5vDx89Ij2Ez5fCGaUpYWrftsCxbt1s2LBghc5ZtkT2HT8rSaf0l2kdRVdGwrkG8lLBKFEzCimwo86VgEgiQQV8Z9DWUr5Jbbqdg4lrMFEwomLi2RzE3ZwhQMHGGEq8hARLwZALeIJjAjSVzhsBTcLbsPqpcbOAWo11y7H3ff/Rc2bTr2/fwJ0v8sexdPVG5p0zw85cj352VFy9eCYxBIEqsmzdcBVs1xjCxZWFSukhOaV63vMr/1p37UqJWd9ntP0GSJ00o3YfOkNixYsiwns1k/srt8sOZX8VvTDebXSE4waRAlY7SrVUtFRwXrj8QPyDARIwYQdUflifTRnZRAggsUOBqU7FkPmnbx1fyZMuorE/u3n+kLGv+unVX8ufKJMULZleuQ2FlXYKHpGDioW89BRMKJrprMuirh76khmpRMHFtG1EwoWDi2h7F3JwhQMHEGUq8hgRIwJMJhJdgAguM5vXKS5NaZYLg2bH/hPTymSU/7VsgrwMC3js22JZgYjxW2Pj9sImL5d6DR8o9x1bq5eMn1/66I5OHd5SkiT6WJ0+fS96K7Z0WTIzHClsLJoeOnZG+I2fL4Y3TpFHHEdK4VlkpXyLQCsY62RNM4FJUpl4vZRkSJ1ZMqdFysAzt2VQqlc6vxI4x01fIjZt3lGCCNN7PX/7486aMG9hGClfvIrtWjJfECeOp7969eyenzv6uRBfEbMHzwhUprKxMKJh46FtPwYSCCQUTD305bVSLgolr24qCCQUT1/Yo5uYMAQomzlDiNSRAAp5MILwEEwQkhQiAgK7GNHj8Ajn36xUlWtg6NtiMYLJo1U5ZtXm/7Fg+zlIErC1g+YEgqiXr9JBW9StInSrF1ffffv+ztO41wSWCibb4QLwRWLEc3jDV4g7jrGACFxsEet2+fKyyrJk0Z43KR6cGHUYoFx8tmFy8fENqth4ivdrVkf3fnJb5E3urS+F2JBJBYkT/SP0NN55CVTvLmjlDBW7fApWTAAAgAElEQVRFYZEomIQFVRfkScGEggkFExe8SG7KgoKJa0FTMKFg4toexdycIUDBxBlKvIYESMCTCYSXYHL659+lSZfRyqWlbLHcygJi18HvZf7KbTLPt7c6wSW0ggmCspap11PaNKosjWqWFvzdeeBUKVEoh7RvUkWV/0mShDKiTwt12k3gSTZnZNrIzlI4b5YgLjk9h/vJ69cB4tOnhcSOGV3qtfeR4CxM0OY45WbttkNSomB2GdWvld1uAAsTfazw2zdv1bHCS9ftlmMnz8vssT0kd7YM8t3pC9Kq53hZP3+4JE2cQMVp2ffNKYkSOVKQk3dqtR4qV2/clr4d60u1coVUmRBewHdg18YSN3ZMOXD0tPQcNlP2r51sM6aKK/orBRNXUAyDPCiYUDChYBIGL1YYZUnBxLVgKZhQMHFtj2JuzhCgYOIMJV5DAiTgyQTCSzABk+9/vCB+izfJL7//qRB9kTaltG9a1XLcbWgFE+R5/OR55apy6c+/JH7cWFKxZH7p0qqGcmk5e+GyDBg9V/66fVcypPtURvZtqU672XnghMwZ30tGTF4iNSoUEZySg6CqcBWKFDGiCuzaqPMoh4LJb39cl2rNB8p8396SN0fgUcW2EgQTBI3VCaJGzqzppX2TqiqWik7DJy2RbXuPKUuVhjVKSaE8X0uLHuMk61fpZMaoruqy5ev3iO+s1XJk4zR1WhAS4rIM9V0oJ079okSfz1Ilk47Nq6ljkcMqUTAJK7KhzJeCSSBABn1l0NdQvkpuuZ2CiWsxUzChYOLaHsXcnCFAwcQZSryGBEjAkwmEp2DiyVxcUTdYcoyZtkJ2rhhnOV7YFfkGl8e4GSvl0ZNnSvwJz0TBJDzpB1M2BRMKJrp7MOirh76khmpRMHFtG1EwoWDi2h7F3JwhQMHEGUq8hgRIwJMJUDBxfesgfgncYtr3m6QC29aqWNT1hVjl+DrgjRw/eU66D50p/n6DJG3q5GFeZnAFUDAJV/z2C6dgQsGEgomHvpw2qkXBxLVtRcGEgolrexRzc4YABRNnKPEaEiABTyZAwcT1rTNx9mrx37RfufP0bl/XLdYlddsNl5u370mvdnWlYql8rn8okzlSMDEJzF2XUzChYELBxF1vW+jLoWASeobGHCiYUDBxbY9ibs4QoGDiDCVeQwIk4MkEKJh4cut4b90omHho21EwoWBCwcRDX04b1aJg4tq2omBCwcS1PYq5OUOAgokzlHgNCZCAJxOgYOLJreO9daNg4qFtR8GEggkFEw99OSmYhHnDUDChYBLmnYwFvEeAggk7BQmQgLcToGDi7S3omfWnYOKZ7RLiWtVJktD0vWMzvTN9T0huiNY4junbXix5bPqekNwQc1gC07c9G3LP9D1Lr8Q0fc+gS1dN38Mb3EuAFiau5e0X71NpEMPkWJYskkSIG8lURQK+/Ufe/vHa1D0REkSSiLEimron4JeX8j/27gM8inLf4/g/2TQSeu+CYFcEG2JFDooesYJeOfaCBcWCiGBDsYCISrFxUAE9KiqKXRELYBdUVFCagvQWILSU3Wzus7skGEgy8868M+8kfvM8995zbt42v/c/b5IPs7tFawqV+rzRMVd+2lttbd0fisqhHylNI1uG15a8HplKnRosLpKa65W6SJ+kZfJK0ia1TrT2NQHAxNe4mYwESMCDBAATD0JlSAFMqlgRACbONhQwcZYbvRIJACZ6KwEwEQFM9NYUo1knAJhYZ0QLEiCBYCdgAkzWJy3UGkqDon21jsdg7hMATNxnGKgRABNn2wGYOMuNXoCJFzUAmAAmXtQVY1acAGBChZAACVT2BIyASUgzmBQCJkGrQ8AkaDvicj2AibMAARNnudELMPGiBgATwMSLumJMwIQaIAESqNoJGAGTFM1gEgFMglalgEnQdsTlegATZwECJs5yoxdg4kUNACaAiRd1xZiACTVAAiRQtRMwAiZpmsGkADAJWpUCJkHbEZfrAUycBQiYOMuNXoCJFzUAmAAmXtQVYwIm1AAJkEDVTsAImGQs0hpqg7x9tI7HYO4TAEzcZxioEQATZ9sBmDjLjV6AiRc1AJgAJl7UFWMCJtQACZBA1U7ACJhU0wwmuYBJ0KoUMAnajrhcD2DiLEDAxFlu9AJMvKgBwAQw8aKuGBMwoQZIgASqdgJGwCRLM5hsB0yCVqWASdB2xOV6ABNnAQImznKjF2DiRQ0AJoCJF3XFmIAJNUACJFC1EzACJtU1g8k2wCRoVQqYBG1HXK4HMHEWIGDiLDd6ASZe1ABgAph4UVeMCZhQAyRAAlU7ARNgsqHmYq2h1t/SVut4DOY+AcDEfYaBGgEwcbYdgImz3OgFmHhRA4AJYOJFXTEmYEINkAAJVO0EjIBJbc1gshkwCVqVAiZB2xGX6wFMnAUImDjLjV6AiRc1AJgAJl7UFWMCJtQACZBA1U7ACJjU0QwmmwCToFUpYBK0HXG5HsDEWYCAibPc6AWYeFEDgAlg4kVdMSZgQg2QAAlU7QSMgEk9zWCSDZgErUoBk6DtiMv1ACbOAgRMnOVGL8DEixoATAATL+qKMQETaoAESKBqJ2AETOprBpMNgEnQqhQwCdqOuFwPYOIsQMDEWW70Aky8qAHABDDxoq4YEzChBkiABKp2AkbApOEfWkOtv66N1vEYzH0CgIn7DAM1AmDibDsAE2e50Qsw8aIGABPAxIu6YkzAhBogARKo2gkYAZPGmsFkDWAStCoFTIK2Iy7XA5g4CxAwcZYbvQATL2oAMAFMvKgrxgRMqAESIIGqnYAJMMluohdM6q0GTIJWpYBJ0HbE5XoAE2cBAibOcqMXYOJFDQAmgIkXdcWYgAk1QAIkULUTMAImzTSDyUrAJGhVCpgEbUdcrgcwcRYgYOIsN3oBJl7UAGACmHhRV4wJmFADJEACVTsBI2DS/E+todZbsXep8SKFhTJy3GQZP+lD+fLtMVKnVo095rvs5mFSr05NeXRwH61rYbBEAoBJFasEwMTZhgImznKjF2DiRQ0AJoCJF3XFmIAJNUACJFC1EzACJi01g8my0mDS985Rsn/blvLMi+/IzCmj9wCTKR9+IU9NfFvaHbA3YOJReQMmHgVraljAxFnygImz3OgFmHhRA4AJYOJFXTEmYEINkAAJVO0EjIDJXprB5K/SYDJ/8bI4mBzS5fI9wGRzzja58IYH5OKep8isOfMBE4/KGzDxKFhTwwImzpIHTJzlRi/AxIsaAEwAEy/qijEBE2qABEigaidgBExaawaTJaXBpHjHygKTO4c9K4e321eyMqvJxzNmASYelTdg4lGwpoYFTJwlD5g4y41egIkXNQCYACZe1BVjAibUAAmQQNVOwAiYtFmiNdR6f7Quc7zdwST2RMno596UF0YPko9nzAZMtO5C6cEAEw/DNTH05tT2ytPueHSTcp+iTYXKfQqXhpX7pB6RodynaEtUuU/h8ohyn5SD0pT7JDdNUe4jqUnqfUSkUffFjvrRST2B7V06qXcqo8dHGzZJj1/maxmrMg/y+L6t5fzG9ZUuIfT2Dkn+We2MST06Q0J7pyrNE/kzLEXZaudf6JA0CTVXnGdOvkRXqJ1LSfWSJbl6stL1FC4LS9FGtTMz3DVDogeoXc/NC/6U19dmK62Nxv4m8OnhB8vRZbyZoOoqcgsLpf6M71W70Z4ESIAEXCdgBEzaagaTxdZgEg5H5Pxr7pUR91wnbVo1k6nTZwEmrqun/AECCSaxN655/d3PJb8gLMd3bCeD+10mmdXS5YLrhsj8RX+JJCX+gKxZPTP+Wq7YV9Zn33gYU+UZGjARAUwS9QqY+HffAiZ6swZMRCKAid6iYjTLBAATy4hoQAIkEPAETIDJxn31gkndhdZg8uv8JXJlv4clIz3xj7cF4Uj87+bYG79OHDUo4LtU+ZYXODCJCdmoZyfLhJGDpHpWNel71yg5vN1+0ufSs+T0iwfKqCF9pW3rZnskDZgkIgFMAJPimwMw8e9ABkz0Zg2YACZ6K4rR7CQAmNhJiTYkQAJBTsAImOynGUwWWIPJ7nvAEybeVmXgwCQmZuFwWA47ZN/4lU98far8tmCpPHzXNXLiuTfJq2MHS+MGdQGTcuoCMAFMABNvD82yRgdM9GYOmAAmeiuK0ewkAJjYSYk2JEACQU7ACJgcsFRrJHV/b1UyXuxTcDr3vDn+32Mvw0lNTby0/5NXH5X6dWuVtANMtG7BHoMFDkx2X+G1tz8mJx3TXv7vrC7S4ZTeckLHdvLT3EXxIrm5d0854ehD4114wiSRHGACmAAm3h6agIn3+QImgIn3VcYMuycAmFATJEAClT0BI2BykGYwmbcLTCr7flSV9QcaTJ6a8JbM/nmB/HdEf0lOSpa7hz8np57UUTodcaDM+PpnGfjQWHn3haHxJ04AE8Ck+KbkPUwSSfCSHP+OaZ4w0Zs1YAKY6K0oRrOTAGBiJyXakAAJBDkBI2BysGYwmQuYBK3GAgkmRUVFMnTMS7J0+RoZdf+NUi2j7E8jufyWYdLj9BOle9dOgMnOyuIJE54w4QkT/49ZwERv5oAJYKK3ohjNTgKAiZ2UaEMCJBDkBIyASTvNYPILYBK0GgskmAx/8hVZs35T/H1LUlNC8cx25ObLoiUr5NAD25RkeNEND8rFPU+Rbp2PBEwAk5K64AkTnjDx+6AFTPQmDpgAJnoritHsJACY2EmJNiRAAkFOwAiYHKoZTH4GTIJWY4EDk1lz5suwJ16WSU/fU/LGNrHQYm96c/IF/WXU/TfIMUccLF9894vcdv8z8v6Lw6RenZqACWACmOx2uvCSHP+OW8BEb9aACWCit6IYzU4CgImdlGhDAiQQ5ASMgEmHv7RGUvenvbSOx2DuEwgcmAx6aJy898nXEgolniyJfbVt1Uwmj7tPZnzzs4x4epKsy94szRrXlwHX95KjDzsw3ob3MElkxUtyeElO8X0DmLg/IO2OAJjYTcpeO8AEMLFXKbTSmQBgojNNxiIBEjCRgAkw2XS4XjCp8wNgYqJ2KpozcGDiNCDABDAprh1ekpNIAjBxepqo9wNM1DOrqAdgApjorShGs5MAYGInJdqQAAkEOQEjYHKEZjCZDZgErcYAk6DtiMv18IQJT5gUlxBg4vJmUugOmCiEZaMpYAKY2CgTmmhOADDRHCjDkQAJ+J6AETA5SjOYfA+Y+F44FhMCJkHbEZfrAUwAE8DE5U3koDtg4iC0CroAJoCJ3opiNDsJACZ2UqINCZBAkBMwAiYdl2mNpM53LbWOx2DuEwBM3GcYqBEAE8AEMPH/lgRM9GYOmAAmeiuK0ewkAJjYSYk2JEACQU7ACJh00gwm3wAmQasxwCRoO+JyPYAJYAKYuLyJHHQHTByEVkEXwAQw0VtRjGYnAcDETkq0IQESCHICRsDkGM1g8jVgErQaA0yCtiMu1wOYACaAicubyEF3wMRBaIBJhaFF5uRLdEVEKdikesmSXD1ZqU/hsrAUbYwq9Ql3zZDoAalKfW5e8Ke8vjZbqQ+N/U0AMPE3b2YjARLQn4ARMDlOM5h8CZjorwx3IwIm7vILXG/ABDABTPy/LQETvZnzhAlPmOitKEazkwBgYicl2pAACQQ5ARNgsvmE5VojqT2zhdbxGMx9AoCJ+wwDNQJgApgAJv7fkoCJ3swBE8BEb0Uxmp0EABM7KdGGBEggyAkYAZMTNYPJDMAkaDUGmARtR1yuBzABTAATlzeRg+6AiYPQKugCmAAmeiuK0ewkAJjYSYk2JEACQU7ACJicpBlMPgdMglZjgEnQdsTlegATwAQwcXkTOegOmDgIDTCpMDTew0RvTTGadQKAiXVGtCABEgh2AkbApItmMPkMMAlalQEmQdsRl+sBTAATwMTlTeSgO2DiIDTABDDRWzaM5jIBwMRlgHQnARIwnoARMOm6Qut11/6kudbxGMx9AoCJ+wwDNQJgApgAJv7fkoCJ3sx5SQ4vydFbUYxmJwHAxE5KtCEBEghyAkbA5BTNYPIxYBK0GgNMgrYjLtcDmAAmgInLm8hBd8DEQWgVdAFMABO9FcVodhIATOykRBsSIIEgJ2AETLppBpOpgEnQagwwCdqOuFwPYAKYACYubyIH3QETB6EBJhWGxnuY6K0pRrNOADCxzogWJEACwU7ACJicphlMPgRMglZlgEnQdsTAepb+q57yrA2K9lXusz5poXKfc77PU+4z9b1myn12DMlW7hP0Dq0+rXrXFNTMAZOyd2b0n42lV3Yt5W2LrolI0ZYipX4fdY/Ib4dGlfqkJImEkpKU+rSvkSUtM9KV+jQpSJFahSGlPqtTw5KTonY9q/MLJCdSqDRPnZSQZIXU1la/z2ap8XKu0jz9B2yTyd3ylfrQ2N8EABN/82Y2EiAB/QmYAJOcf6/UeiG1PlD/O0brAhhsjwQAE4pCABMRwIQbwU0CgAlgUlH9ACYigImbE8afvoCJPzkzCwmQgHcJGAGT7prBxME//HqXKCPHEgBMqAPARAATbgN3CQAmgAlgUvE9BJi4O2P86A2Y+JEyc5AACXiZgBEwOVMzmLzDEyZe1oiTsQETJ6lVsT48YQKYVLGS9v1yABPABDABTHw/eDRPCJhoDpThSIAEfE/ACJicpRlM3gZMfC8ciwkBk6DtiIH1ACaAiYGyq1JTAiaACWACmFT2Qw0wqew7yPpJgASMgMk5msFkCmAStEoGTIK2IwbWA5gAJgbKrkpNCZgAJoAJYFLZDzXApLLvIOsnARIwAibnrtIafK03m2odj8HcJwCYuM+w0o8AmAAmlb6IDV8AYAKYACaAieFjyPX0gInrCBmABEjAcAJGwKSnZjCZDJgYLqM9pgdMgrYjBtYDmAAmBsquSk0JmAAmgAlgUtkPNcCksu8g6ycBEjABJlvO0wsmNV8HTIJWyYBJ0HbEwHoAE8DEQNlVqSkBE8AEMAFMKvuhBphU9h1k/SRAAkbA5P80g8mrgEnQKhkwCdqOGFgPYAKYGCi7KjUlYAKYACaASWU/1ACTyr6DrJ8ESMAImPRarTX4mq800Toeg7lPADBxn2GlHwEwAUwqfREbvgDABDABTAATw8eQ6+kBE9cRMgAJkIDhBIyAyX80g8nLgInhMtpjesAkaDtiYD2ACWBioOyq1JSACWACmAAmlf1QA0wq+w6yfhIgASNgcpFmMPkfYBK0SgZMgrYjBtYDmAAmBsquSk0JmAAmgAlgUtkPNcCksu8g6ycBEjACJhdrBpMXAZOgVTJgErQdMbAewAQwMVB2VWpKwAQwAUwAk8p+qAEmlX0HWT8JkIARMLl0jdbga05srHU8BnOfAGDiPsNKPwJgAphU+iI2fAGACWACmAAmho8h19MDJq4jZAASIAHDCRgBk8s1g8n40mASKSyUkeMmy/hJH8qXb4+ROrVqxFPetj1Xhjw2Ub6c9aukpabKhed2ld4Xdje8A1VzesCkau6r0lUBJoCJUsHQeI8EABPABDABTCr70QiYVPYdZP0kQAImwGTrFXrBpMbzpcGk752jZP+2LeWZF9+RmVNGl4DJAyNflI2bt8hDg3rLxk1bpFef++XRwX3kiEP3oxA0JwCYaA60Mg4HmAAmlbFug7RmwAQwAUwAkyCdSU7WApg4SY0+JEACQUrACJhcpRlMni0NJvMXL4uDySFdLi8FJp99+aPs26aFNG/SIL4FN9wxSjof0156dj8xSFtSJdYCmFSJbXR3EYAJYOKugugNmAAmgAlgUtlPQsCksu8g6ycBEjACJr3Xag2+xrhGZY63O5j8vVHs5TmnXzxQnn9sgLRp1UzrehhMBDChCgQwAUy4DdwlAJgAJoAJYOLuFDHfGzAxvwesgARIwF0CRsDkGs1gMlYNTPLyC+Tme8bIQfu1lr5XnOsuQHqXmQBgQmEAJgKYcBu4SwAwAUwAE8DE3SlivjdgYn4PWAEJkIC7BIyAyXWaweRp+2CyZdsOuX7Q43Jk+/3lxit7uAuP3uUmAJhQHIAJYMJd4DIBwAQwAUwAE5fHiPHugInxLWABJEACLhMwAiZ9NIPJU/bAJL8gLFf2Gy6nnnSUXNTjZJfJ0b2iBAAT6gMwAUy4C1wmAJgAJoAJYOLyGDHeHTAxvgUsgARIwGUCRsDkBs1g8oQ9MHlqwluSvWmL3H3LJS5To7tVAoCJVUL/gO/zHia8JOcfUOaeXiJgApgAJoCJp4eMD4MDJj6EzBQkQAKeJmAETPqu03pNNcY0LBlvc8426dzz5vh/D4cjkpqaEv/Pn7z6qFxw7X2yPjtHkpKTStpfcFYXGXjDf7Suh8F401dqQIQnTHjChPvAZQKACWACmAAmLo8R490BE+NbwAJIgARcJmACTLbdpBdMqo/aBSYu46C7pgR4wkRTkBUN4+QJjmo31Ha0svQe1ZX75U/eptwn/Vz1ecKz8pTnKdoWVe6T3DShrypfkdn5Ks0TbaNFyn3Se9ZQ7pPTfaVyn1iHOj/tpdyv1sYflfvQQeSfACaPv1dX/u/XLKXtTjsjS1IOzVDqE2u8vU2yFDRMVuqXG41KpEjtnmwQDUmNopDSPHmpRRJR6yJpO0RSw0rTSFJWkiSlq2UQXRWRohy1MzPaJCRFtdTmSVoWkaRNavOI2tbsCiszSZLSdv3rmZ0Ur92wRF7elm2nKW3+lgBgQjmQAAlU9gSMgMktmsHkccAkaHUImPiwI4BJImTARAQw8eGGMzAFYFJ26IBJIhfARAQwMXAwKU4JmCgGRnMSIIHAJWAETPppBpPHAJOgFRZg4sOOACaASXGZASY+3HAGpgBMAJOKyg4wAUwMHEvKUwImypHRgQRIIGAJGAGT/uu1plB9RAOt4zGY+wQAE/cZWo4AmAAmgInlbVKpGwAmgAlgYnEL85KcwJ9xgEngt4gFkgAJWCRgBExu0wwmjwAmQSt0wMSHHQFMABPAxIcbzeAUgAlgApgAJgaPIC1TAyZaYmQQEiABgwkYAZPbNYPJw4CJwRIqc2pLMCkoCMvr782QC8/tGh/gs69+kjfenyEtmzWSvlecI5nV1N/Qz4sQsj77xothtYwJmAAmgImWWymwgwAmgAlgApgE9oCyuTDAxGZQNCMBEghsAkbAZKBmMBkGmAStwCzBZPCI8fL7or/klafukb9WrpVzLr9L/u+sk2ThnyukeZMG8sDtVwbimgCTxDbwKTkifEpOohb4lBz/jibABDABTAAT/04cb2YCTLzJlVFJgAT8S8AEmGy/Y4PWC8x6qL7W8RjMfQKWYHLsWTfIW88/IA3q1ZaR4ybLoiUr5MmHbpbsTVvknCvukplTRrtfhYYRABPApLiMABPARMORojQEYAKYACaAidKhEcDGgEkAN4UlkQAJKCVgBEzu0gwmDwAmSpvuQ2NLMDnslN4y68OxEgolS8/eg+U/5/xLzv33CRIpLJQjT7tWfvp4nA/LtJ4CMAFMAJPS9wlPmFifG7paACaACWACmOg6T0yNA5iYSp55SYAEdCVgBEzu1gwm9wMmuupB1ziWYNLjqnvk/87qIjWrZ8qgoePks9cfkzq1ashXs+bKsDEvybsvDNW1FlfjACaACWACmLg6RFx0BkwAE8AEMHFxhASiK2ASiG1gESRAAi4SMAImgzWDyX2AiYsS8KSrJZh8+8Nv0n/I07IjN09uvfZ8ufDck2XDxhzp1us2uaffpXJWt2M9WZjqoIAJYAKYACaq54au9oAJYAKYACa6zhNT4wAmppJnXhIgAV0JGAGTe7N1LT8+Tta99bSOx2DuE7AEky3bdsSfLom9BCclFIrPWFRUJH8uWy1t9mrqfgWaRgBMABPABDDRdJwoDwOYACaACWCifHAErANgErANYTkkQALKCRgBkyGaweQewER54z3uYAkm7U++Sk48+lA545Rj4v83NTXF4yU5Gx4wAUwAE8DE2enhvhdgApgAJoCJ+5PE7AiAidn8mZ0ESMB9AkbA5AHNYHIXYOK+EvSOYAkmP/yyUD6eMUs+mfmD5OblS7fOR8bxpMPB+0hSUpLe1ewc7amJb8vr734u+QVhOb5jOxnc7zLJrJYuy1etk7uHPy8LFi+Tpo3ry503XSyHHbJPvBdgApgAJoCJJweSjUEBE8AEMAFMbBwVgW4CmAR6e1gcCZCAjQSMgMmDmsHkTsDExlb72sQSTIpXE3sZztz5S+TjGbNl6vTvpUhEzji5U/wTc5o3aaBt0VOnz5JRz06WCSMHSfWsatL3rlFyeLv9pM+lZ8mlNw2VLscdJhede7J8PXue3D38OZn26qOSmhICTHbuQHqP6sp7kT95m3Kf9HPV5wnPylOep2hbVLkPHyuciIxPyVEuHccdABPABDABTBwfIAHpCJgEZCNYBgmQgOMETIDJjqEbHa+3rI6Zg+pqHY/B3CdgG0yKp5q7YIlMmzFbJr39maSlpsiO3HzpevzhcsdNF0mtGlmuV/Tr/CUSDoflsEP2jY818fWp8tuCpTLg+l5y6n8GyDfvPVnyXiqxjzke0KeXHNVhf8AEMCmpPcAEMHF9ECkOAJgAJoAJYKJ4bASuOWASuC1hQSRAAooJGAGTYZrBZCBgorjtnje3BSaxl8K8O+0beW/a17JqbbacdEx7OavbcfGXy+Rs3S6DH3leIoVReXrYLdoXfO3tj8Xn22fvFjLksYny1vgHSua49b6npONhB8r5Z3QGTAATwGS3u48nTLQfR+UOCJgAJoAJYOLfiePNTICJN7kyKgmQgH8JGAGT4ZrBZABg4l/F2JvJEkz+0+d++fm3P+Tg/VrL2acdJ//+19F7PEmyOWebdO5xk8z55Dl7s9ps9dSEt2T2zwvkvyP6y/c//S6jn31DJj0zuKT3XQ8/J/vu3VwuOa8bYAKYACaAic2TRX8zwAQwAUwAE/0ni78jAib+5s1sJEAC+hMwAiYjNINJf8BEfz5aEyAAACAASURBVGW4G9ESTB4b+5qcdepxZX6EcDgciX9qTjRaJF/NmivHdzzE3Wp29o69X8rQMS/J0uVrZNT9N0q1jDT5ae4iGTxigrwz4cGSOfrd+6Qcc8TB0rP7iYAJYAKYACZazh8ngwAmgAlgApg4OTuC1AcwCdJusBYSIAEnCRgBk0c1g8mtgImTvfeyjyWYlDf5tu25ckqv/vL1O09qX9/wJ1+RNes3ycN3XRN/Q9fY16acrdL1/Fvly7efiANK7Ou0CwfIQ4N6xz+xh0/JSWwDb/oqwnuYJGqBl+RoP5rKHRAwAUwAE8DEvxPHm5kAE29yZVQSIAH/EjACJo9v0nqBmbfU0Toeg7lPwBJMYu9f8uCo/8lvC5fGP+a3+Cv2EcNt9moqU57f9Z4i7pcjMmvOfBn2xMsy6el74k+v/P3ryn7D5cj2+0vvC7vLh59/F3+JzocvDZdQKBkw2RkUYAKYFN8zgImOE8neGIAJYAKYACb2TovgtgJMgrs3rIwESMBeAkbAZKRmMLkZMLG32/61sgSTq/o/IjWyqslpXY6Wex55Xh4aeFX8PU1++GWhjHnwRqlTq4bW1Q56aJy898nXEgolniyJfbVt1Uwmj7tPVq7ZIHcMHScL/lguLZo2lHtvvUwO2q9VvA1PmCSyAkwAE8BE65FkazDABDABTAATW4dFgBsBJgHeHJZGAiRgKwEjYDJaM5jcCJjY2mwfG1mCyZGnXSPT3xglWZkZcvzZfeWLt8bEl/fxjNky/euf4i+JCcIXYAKYFNchL8lJJMETJv6dTIAJYAKYACb+nTjezASYeJMro5IACfiXgAkwyR2jF0yq9QVM/KsYezNZgkmn7n1k6qQRUrN6ppx47k3ywf8ejuNJOFIoJ55zo3z9rv73MLG39NKtABPABDApfU8AJk5OEmd9ABPABDABTJydHsHpBZgEZy9YCQmQgLMEjIDJk5udLbacXtWur611PAZzn4AlmPS9a7Rs354rox+4UW67/xlp0qieXNTjZPkx9pKc59+UGW+Ocr8KDSMEGUw0XJ7tIVYNbWG7bQkw7JWq3CfvuRzlPjXGNVLus7X3WuU+1a5TP2gKl+x6fx67ExZ8tN1uU1ft0s+q7qh/2tnq/eq1nOdorn96p38CmBxeI0v2qpahtNWXr6olx+ZkKvWJNR7ZIFs+q6l2f92UXU/+tV2t5gsOTZXCFrte/mlnoUlPb5GkmXl2mpa0SW6eIsl11OYJf5krhX+onUvTB4RkYbdkpbW9vS5bftm2Q6mPk8YjHs6Snh+r1U9snv4DtsnkbvlOpqSPYgKAiWJgNCcBEghcAkbA5GnNYOLg75jAbUQVW5AlmGzYmBP/iN/7+l8ua9ZtlGtvf1RWr9so6Wmpck+/S+XsU48LRCSASWIbABMRwCRRC4CJf0cTYFJ21oBJIhfARAQw8e88cjoTYOI0OfqRAAkEJQEjYPKMZjC5Vv0ffoOSf1VdhyWY7H7hkcJCWbt+k9SrU1My0hMf7xuEL8AEMCmuQ8AEMPH7TAJMAJOKag4wAUz8PpOczAeYOEmNPiRAAkFKwAiY/FczmFwNmASppmJrsQSTeQuWyrSZs2XF6vUSSk6W1i2byGldOspezdVfXuHlxQMmgAlgUvoO4wkTL0+c0mMDJoAJYFLx/cYTJv6dR05nAkycJkc/EiCBoCRgBEzGqb9NQUV5VetdKyhxso6dCVQIJo+NfU2ee+UD2ad1c2nTqqlEo1GZu2CprFmXLTde2UN6X9g9MEECJoAJYAKYmDqQABPABDABTEydP7rmBUx0Jck4JEACphIwAiYO3texQjC5EjAxVT/lzVsumMSeKrltyNMyckhf6XxM+5L+0WiRvPbu5/LAyBdl5JAbpOvxhwfimgATwAQwAUxMHUaACWACmAAmps4fXfMCJrqSZBwSIAFTCZgAk7zxep8wybgcMDFVP8pgcmW/4bJfmxYy4PpeZfZ9+MlXZO78P+XFMXcG4poAE8AEMAFMTB1GgAlgApgAJqbOH13zAia6kmQcEiABUwkYAZMJmsHkMsDEVP0og0mn7n3kiYdulsPb7Vtm3/mLl8mF1z8gP0z9byCuCTABTAATwMTUYQSYACaACWBi6vzRNS9goitJxiEBEjCVgBEweWGL1svNuKSm1vEYzH0C5b4k56DOl8m0SSOkaeP6Zc6yZv1G+dd5/WTe9AnuV6FhBMAEMAFMABMNR4mjIQATwAQwAUwcHR4B6gSYBGgzWAoJkICjBIyAyYuaweTi0mAS+4TakeMmy/hJH8qXb4+ROrVqxLPJLwjL4BHj5avvf5VqGely1X9Ol/PPPMlRbnSqOIEKweTT1x+Txg3qAiaVqIpWDW2hvNrkvVKV++Q5eIOjGuPUP1lpa++1ymvjY4UTkfEpOcql47gDYAKYACaAieMDJCAdAZOAbATLIAEScJyAETB5STOYXFgaTPreOUr2b9tSnnnxHZk5ZXQJmDw5foosXrpSht5xtWRv2hJ/5cd/H+kv++7d3HF+dCw7gQrB5OjDDpS0tLL/mC4oCMu3P/7GEyYBqyzARAQwAUz8vi0BE8AEMAFM/D53dM8HmOhOlPFIgAT8TsAImLyiGUx6lQaT2NtgxMDkkC6XlwKTMy+9Q+6//Uo59MA28ZiHP/mKZGVmyPWXn+N37FV+vnLB5L5H7b3UZvCtlwUiJF6Sk9gGwAQwKb4hecLEv6MJMAFMABPAxL8Tx5uZABNvcmVUEiAB/xIwAiaTNIPJBWW/h8nuYHLov66MA0qtmlnxgF99+zOZ/csCeeTu6/wL/B8yU7lgUtmuHzABTIprlidMEkkAJv6dYoAJYAKYACb+nTjezASYeJMro5IACfiXgBEweW2r1gvMOD/xHiW7f/0dTMKRQmnf9cr4h69kpKfFm7499Sv5ZOZsGfPgTVrXw2AigEkVqwKeMOEJk+KSBkz8u7kBE8AEMAFM/DtxvJkJMPEmV0YlARLwLwETYJL/ul4wST/PGkxiicaeMJn+5siS9zR55a1PZc7cxfLwXdf4F/g/ZCbApIptNGACmAAm/t/UgAlgApgAJv6fPHpnBEz05sloJEAC/idgBEze0AwmPeyBydmX3yWDbrxQOnY4IB70PY88L82bNJCrLzrD/+Cr+IyASRXbYMAEMAFM/L+pARPABDABTPw/efTOCJjozZPRSIAE/E/ACJi8qRlMzrUHJs+88I7MmbdIRg7pKytWrZdLbx4qLz95t+zVXP1TSf3fqco1I2BSufbLcrWACWACmFjeJtobACaACWACmGg/WHweEDDxOXCmIwES0J6AETB5a5vW60g/u3rJeJtztknnnjfH/3s4HJHU1JT4f/7k1UelZvVMGTxignzx3S/xT8fpc9nZcla3Y7WuhcESCdgCk9jHGf3512rJLyjYI7dzTjs+EFnypq+JbQBMABPAxP8jCTABTAATwMT/k0fvjICJ3jwZjQRIwP8EjIDJO5rB5MxdYOJ/gsxYVgKWYDLimVdl/KQPpX7dWlItI32PMT56eXggkgVMAJPiQuRTchJJ8Kav/h1NgAlgApgAJv6dON7MBJh4kyujkgAJ+JeAETB5VzOYnAGY+Fcx9mayBJMjTr1aHr/vBjm+Yzt7IxpqBZgAJoBJ6ZsPMPHvMAJMABPABDDx78TxZibAxJtcGZUESMC/BIyAyfuaweR0wMS/irE3kyWYHN29j3z2+mOSWS3D3oiGWgEmgAlgApgYOn4EMAFMABPAxNT5o2tewERXkoxDAiRgKgEjYPLBdq2Xm/7vLK3jMZj7BCzB5Innp8TfSObyC05zP5uHIwAmgAlgAph4eMRUODRgApgAJoCJqfNH17yAia4kGYcESMBUAibApOAjvWCSdipgYqp+ypu3TDC5buDjJe1TUkLy06+LpHat6vHPdk5KSio11tPDbgnENQEmgAlgApiYOowAE8AEMAFMTJ0/uuYFTHQlyTgkQAKmEjACJh9rBpNTABNT9aMEJo+Nfc32Ovtdc77ttl42DDKYbOtwlPKl55yxSrlPrMPWTxoq92uyWLmLhKfnqncqKFLukzd+i3Kf6k+pZ7BjxCbleZIbh5T7RL5Uz63W1ObK88Q6zMhUP8CP+TRZea669eYq96lqHSobmIx4OEt6fqz2MsuMa2pJ2gmZSls3pmm2TK+9Q6lPrPFNLZtK17q1lfrN2bJNVuTv+UluFQ2yPC9fNkcKleY5vEaW7KX4EtW0pCRJSS79jw1Wk6qfliK1fo5ItZVqPXPHbFI+z6s/0UAy/lPT6hJKff/7yHb5K6q2P7EBnl25Rr7avFVprkfH15Lzv66m1KffZTny+rHq57PSJAFvDJgEfINYHgmQgGUCRsBkmvrv2xVdSNrJgInlRvvcwPIlOa+9O13OP6PzHsvakZsvL705TXpf2N3nJZc9HWCSyAUwEQFMErUAmPh3NAEmZWcNmCRyAUxEABP/ziOnMwEmTpOjHwmQQFASMAImn6r/w1CFYPIvtX+cCkr2VXkd5YJJOByRcCQiJ5xzo8ycMnqPDP74a7VcfMMDMueT5wKRD2ACmBQXImACmPh9KAEmgEmFv/zwhAlg4veh5GA+wMRBaHQhARIIVAJGwOQzzWDSBTAJVFGJSLlg8spbn8qwMS9LpLD8x5WPOeJgGTeifyCuCTABTACT0rciT5j4dzQBJoAJYFLx/cYTJv6dR05nAkycJkc/EiCBoCRgBEymawaTzoBJUOqpeB0VviQnN69Ajj3zenn5qbv3WHdGepq0bNZIkhVfl+1VAIAJYAKYACZenS9W4wImgAlgAphYnRNB/z5gEvQdYn0kQAJWCRgBk5mawUTx/eKsMuH77hOwfA+TgoKwpKWlup/J4xEAE8AEMAFMPD5myh0eMAFMABPAxNT5o2tewERXkoxDAiRgKgETYBL+Qu8bhqcer/am5aay/ifNWyaY3HzPE7YzGDnkBtttvWwImAAmgAlg4uUZU9HYgAlgApgAJqbOH13zAia6kmQcEiABUwkYAZOvNIPJsYCJqfopb94ywWTomJdsr3NQ3wttt/WyIWACmAAmgImXZwxgwscKx2qAjxUW4WOFTZ003s4LmHibL6OTAAl4n4ARMPlaM5gcA5h4XylqM1i+JKei4Z6f9IFcccG/1Wb0qDVgApgAJoCJR8eL5bA8YVJ2RHyscCIXPlaYjxW2PEQC0AAwCcAmsAQSIAFXCRgBk281g8nRgImrIvCgsy0w+fX3P+W3hUslvyBcsoR12ZvllSmfyg9T/+vBstSHBEwAE8AEMFE/OfT0AEwAk4oqCTABTPScNN6OAph4my+jkwAJeJ+AETD5TjOYdARMvK8UtRkswWTi61PlsWdek1YtG8uSZatln9bN4/+3SaN68adLepx+gtqMHrUGTAATwAQw8eh4sRwWMAFMAJOKbxM+VtjyGDHeADAxvgUsgARIwGUCRsBkVp7LVZfunnpkhtbxGMx9ApZg0vX8fjL87uvksEP2kRPOuVFmThktOVu3y32PTpBzTjteju/Yzv0qNIwAmAAmgAlgouEocTQEYAKYACaAiaPDI0CdAJMAbQZLIQEScJSAETD5QTOYHA6YONp8DztZgkn7k6+S2R+NlZRQSI496wb56u3EJ+is27BZrrp1uLwz8SEPl2d/aMAEMAFMABP7J4beloAJYAKYACZ6TxX/RwNM/M+cGUmABPQmYARMftQMJocBJnqrwv1olmBy2oW3y+3X95LOx7SXMy4ZJA8OvEraHdhGtm3PlZN63iKzPnzG/So0jACYACaACWCi4ShxNARgApgAJoCJo8MjQJ0AkwBtBkshARJwlIAJMInM0QsmKe0BE0eb72EnSzCZ8uEXctfDz8n0N0bKWx99KeNf/VA6HX6QLPpzhTRqUFfGjejv4fLsDw2YACaACWBi/8TQ2xIwAUwAE8BE76ni/2iAif+ZMyMJkIDeBIyAyc/5Wi8i5dB0reMxmPsELMEkNsUff62SVs0bSyiULK+9O13mzF0kzRrXl4t6nCK1ama5X4WGEQATwAQwAUw0HCWOhgBMABPABDBxdHgEqBNgEqDNYCkkQAKOEjACJr9qBpNDABNHm+9hJ1tgEps/HI7I2g2bpHmTBh4ux/nQgAlgApgAJs5PEHc9ARPABDABTNydIuZ7Aybm94AVkAAJuEvACJjM0wwmBwEm7qpAf29LMMnNK5AHRr4g7037RiKFhTJv+gTZuHmr3Hrfk/LI3ddJ/bq19K/KwYiACWACmAAmDo4OLV0AE8AEMAFMtBwmBgcBTAyGz9QkQAJaEjACJr9pBpMDARMtxaBxEEswib1/yfrszdLnsrPlP33uj4PJjtx8GfL4RIlECmXEPddpXI7zoQATwAQwAUycnyDuegImgAlgApi4O0XM9wZMzO8BKyABEnCXgBEwmV/gbtG79U7ZP03reAzmPgFLMOl4+nXy0cvDpU6tGnJQ58viYBL72rJth5x+0e3yxVtj3K9CwwiACWACmAAmGo4SR0MAJoAJYAKYODo8AtQJMAnQZrAUEiABRwkYAZMFmsFkP8DE0eZ72MkSTI4543r5/I2Rkp6WWgpMYi/LOeWCW2X2R//1cHn2hwZMABPABDCxf2LobQmYACaACWCi91TxfzTAxP/MmZEESEBvAkbAZJFmMNkHMNFbFe5HswSTvneOkqaN68stV58nh3e7Ov6Eyeq12fLAqBclGi2Sp4fd4n4VGkYATAATwAQw0XCUOBoCMAFMABPAxNHhEaBOgEmANoOlkAAJOErABJgU/qEXTEJtABNHm+9hJ0swWb1uo1zd/xFZtnJd/E1fa1TPlK3bdsghB+wtjw3uE8eUIHwBJoAJYAKYmDqLABPABDABTEydP7rmBUx0Jck4JEACphIwAiZ/hrVebmjvVK3jMZj7BCzBJDZFYWFU5sxbLMtWrpWM9DRp2ayRHLRfK/ezaxwBMAFMABPAROORojQUYAKYACaAidKhEcDGgEkAN4UlkQAJKCVgBEyWagaTVoCJ0qb70NgWmPiwDtdT+AUm62ftp7zW8Fd5yn1Sj81Q7hPrkHKEer/kBiHluaJrC5X7FG100KegSHme1MPVM4jMUf9IMCeP4KWekKl8PeFvc5X7xDokVUtW7pdUW73PEfv8oTzPklz1e0J5Eh87VDYwGb3f3tKrcQOlhDaHI5IbjSr1qR4KSUZIvaayw2HZXqg2V7SoSFRPCyfzZCQnSVqy2jVFokWidjUiqUlJEkpKUso7lCTKfbJCyZKueD25hVEJF6ml3So9Xeqnqv8SmF8UlYjizqZuLpIUxWPz2jV/yktbspXyrmqNAZPKuaNd69aSpw5oq7T49HyRulvUzrHCZWEJf652Y0V+y5f8l7cqre3jYwvk6iFqfZQmoHGVTsAImPylGUz2Uv9ZWaU3NQAXVy6YtD/5KlvLmzPtWVvtVBrF3lB24INjZc36TfLOhAdLul5w3RCZv+gvkZ2/RNasnikzp4yOfx8wScQEmIgAJolaAExUTh13bQGTsvMDTBK5ACYigIm7M8aP3oCJHynrnwMw0Z8pI1beBIyAyXLNYNICMAlaBZYLJh98+l2ptQ544Bm59ZrzpVGDuqX+///+V0et17R9R570um6InNipvcz49udSYHL6xQNl1JC+0rZ1sz3mBEwAk+KiAEwAE62Hko3BABPApKIyAUwAExvHiPEmgInxLXC0AMDEUWx0qqIJGAGTFZrBpDlgErTytP2SnEO6XC5vPne/7NO6uafXsCM3TzZszIn/z72PTiwFJieee5O8OnawNN4NbWILAkwAE8Ck9K3JEyaeHlWlBgdMABPApOL7jSdM/DuPnM4EmDhNzmw/wMRs/swerASMgMmqiNYQQk1TtI7HYO4TCByYFF/Sj78u3ANMOpzSW07o2E5+mrtI6tetJTf37iknHH1ovAtgApgAJoCJ+yPR2QiACWACmAAmzk6P4PQCTIKzFyorAUxU0qJtVU/ABJhE1+gFk+TGpcHko8+/l6cmvCUF4Yg0a1Jf7r31MmnRtGFV38pAXV+lAZNotEjuHv6cnHpSR+l0xIEy4+ufZeBDY+XdF4bGnzgBTAATwAQwMXW6AiaACWACmJg6f3TNC5joStLfcQATf/NmtmAnYARM1moGk0a7wGTt+k1y1uV3yuRx90nzJg1kwmsfyRff/iLPPTYg2BtRxVZXacCkrNwvv2WY9Dj9ROnetRNgsjMg3vSVN30tvld4SY5/pzVgApgAJoCJfyeONzMBJt7k6vWogInXCTN+ZUrACJis1wwmDXaByeyfF8j9I1+Qt8cnPgRl0ZIVcmW/4SUfelKZ9qYyr7VcMBk/6cNS1/Xo2Nfk0vO6xV8K8/evyy84zZPr3/0lOTty8+NFcuiBbUrmu+iGB+XinqdIt85HAiaASUld8KaviSgAE0+OpjIHBUwAE8AEMPHvxPFmJsDEm1y9HhUw8Tphxq9MCRgBkw2FWiNKrh8qGW/rth0S+9CTZx7uJwfss5eMffFdWbJ8tTx85zVa52SwihMoF0xim2Pn6/0Xh9lpptxmdzDZnLNNTr6gv4y6/wY55oiD5YvvfpHb7n9GYvPXq1MTMAFMAJPd7jLARPnYcdwBMAFMABPAxPEBEpCOgElANkJxGYCJYmA0r9IJGAGTjZrBpO4uMIlt1nvTvpE7ho2TrGoZUq1aukwcNYj3MPG5im2/JMevdX3yxQ/Sf8jTIkVFEo4USmpqirRu0VimPP+AzPjmZxnx9CRZl71ZmjWuLwOu7yVHH3ZgfGm8h0lih3hJDi/JKb5XARO/Ti0RwAQwAUwAE/9OHG9mAky8ydXrUQETrxNm/MqUgBEw2awZTGrvApOFf66Q6+8YKRNHDpSmjevL1OmzZPRzb8i7E4dKcnJSZdqaSr3WwIGJ0zQBE8CkuHZ4SU4iCcDE6Wmi3g8wAUwAE8BE/eQIVg/AJFj7YXc1gIndpGj3T0jACJjkaAaTWrvA5IXXp8rc+Utk+N3Xlmxf+65XykevPBL/0BO+/EkAMFHMef2s/RR7iIS/ylPuk3pshnKfWAeeMOEJk+LCAUwc3UKOOgEmgAlgApg4OjwC1AkwCdBmKCwFMFEIi6ZVPgEjYLI1qjXX5BrJJeN9PXuuDB4xQV4fe6/UrlVdYv899kqMmVNGS0qo9Et3tC6CwUolAJgoFgRgkggsulZdU4scvMavqKBIcYcAE8BEuWRcdwBMABPABDBxfZAYHgAwMbwBDqcHTBwGR7cqmYAJMCnaphdMkqrvApPYJv33f+/KlA+/iO9XjeqZMqBPLzniUPV/wK+SG+7TRQEmikEDJoBJcckU/lGgWD0iqSdkKvcJf5ur3CfWgSdMHMXmqBNgApgAJoCJo8MjQJ0AkwBthsJSABOFsGha5RMwAiY7NINJZmkwqfKbVgkusEwwiX18r92vfVo3t9vU03a8h0kiXl6SwxMmxTcaYOLpkVNqcMAEMAFMABP/ThxvZgJMvMnV61EBE68TZvzKlIARMMnTDCYZgEnQaq5MMDmo82W21zlv+gTbbb1sCJgAJsX1xZu+JpIATLw8cUqPDZgAJoAJYOLfiePNTICJN7l6PSpg4nXCjF+ZEjACJvnqbx9QUaZJ6Xz6TdBqrkww2bJth+111qyu/hID24MrNARMABPApPQNA5goHCAumwImgAlgApi4PEaMdwdMjG+BowUAJo5io1MVTcAImIQ1g0kqYBK08nT1HiZ9Bj0uTw29JRDXBJgAJoAJYGLqMAJMABPABDAxdf7omhcw0ZWkv+MAJv7mzWzBTsAImEQ0g0kKYBK0KrMEk/yCsLz05jSZt2CpFBSES9a/PnuzLF+9Xr56+4lAXBNgApgAJoCJqcMIMAFMABPAxNT5o2tewERXkv6OA5j4mzezBTsBI2AS1QwmyYBJ0KrMEkzuevg5+eGXhXLcUQfLmx98Ieed0Vl+/u2POJ48OPAq2b9ty0BcE2ACmAAmgImpwwgwAUwAE8DE1Pmja17ARFeS/o4DmPibN7MFOwEjYCKawUQAk6BVmSWYHHvWDTJ53BBp0rCunHDOjTJzyuj4NTw5fopkZKTLlb3+HYhrAkwAE8AEMDF1GAEmgAlgApiYOn90zQuY6ErS33EAE3/zZrZgJ2ACTKJFesEkOQkwCVqVWYLJEadeLd+8+5SkpqbIcWf1lelvjpSUUEi278iTMy4dJJ+9/nggrgkwAUwAE8DE1GEEmAAmgAlgYur80TUvYKIrSX/HAUz8zZvZgp2ACTCJaAaTFMAkcEVmCSa9+twvR7XfX66/7Gy59OZh8n9nniRnn3qc/LF0pfzn+gfku/efDsRFASaACWACmJg6jAATwAQwAUxMnT+65gVMdCXp7ziAib95M1uwEzABJuFoVGsoqcnJWsdjMPcJWILJnHmL5aa7x8hb4x+Qn35dJDcPfkJq16wuOVu2y/lndpY7b7rY/So0jACYACaACWCi4ShxNARgApgAJoCJo8MjQJ0AkwBthsJSABOFsGha5RMwASb5msEkHTAJXJ1agklsxdFokSTvfMfexUtWyq/z/5RmjRvIUR32D8wFOQGTNa/urbz+XtdlK/cZ3yFFuU+t95sp93HaISlT/bVyKUdmKE/3xrZNyn3O+CZduY/sUH8tYVJNdc0Nf5urvLa0U7OU+xS8t125T6xD0VZ18c68va7yXDUWz1LuU9U6mASTEQ9nSc+P1e7H/gO2yeRu+UrbMHpuI7lgVU2lPilHZ0ho71SlPrHGv9UukDVZhUr9og7edG1Zbr5sjESU5jm8Rpa0qqaW99TsTfLbdrXzYt/MDGmUlqa0tgOyMqVJulqfnEhE8hR/2dsWKZR8xU8FUD+Vd1266k+oZtfnSJ1JeUrZObknlCaoBI0BE/ObdEj1TLmyWSOlhbTIDslJCxTPpLStclXtFUrz42IooAAAIABJREFUdJ2XLk/9r45Sn1/aF8q4Prs+YdNO51X5BfJtzlY7TWlDAnskYAJM8grVf9+uaOsyQup/k1AK3iZgC0y++O5XaVi/tuzXpkV8Nd/MnieRwqgc3/EQb1enMDpgohDWbk0BExHAJFEUgImz+wgwKTs3wCSRC2AiDjgLMHF2GjnvBZg4z05XT8BEBDDRVU3/zHFMgMmOQrV/4LHamcxQyKoJ3/c5AUsweenNT2TkuNfl8ftukOOOSgDJ1Omz5J5Hnpcbr+whF57b1ecllz0dYOJ8GwATwKS4egATZ/cRYAKYVFQ5gAlg4uxk8bcXYOJv3mXNBpgAJuarsHKvwASYbNcMJlmASeCK0BJM/nVePxkx+DrpcPA+pRb/468L5fYH/yvTJo0IxEUBJs63ATABTAAT5/dPrCdgApgAJhXfQ7wkx90Z40dvwMSPlCueAzABTMxXYeVegQkw2RrR+4RJjRSeMAlaFVqCSYdTesv0ySOlVs3S772wdv0mOfXCAfLTx+MCcU2AifNtAEwAE8DE+f0DmJSfHS/JSWTDEyY8YeLuhPGnN2DiT84VzQKYACbmq7Byr8AEmGxRfC80q4Rrpqi/96XVmHzfXQKWYHLFLQ/Lfm1byg2XnyNZmYk3lcretEWGP/WKbMjOkeceG+BuBZp6AybOgwRMABPAxPn9A5gAJlbVA5gAJlY1EoTvAybmdwEwAUzMV2HlXoEJMNkcVnvzeKuEa6cCJlYZ+f19SzBZunyNXH/HSFm+al3844Rjn5izecs2aduqmYwdfqs0aqD2jtleXSBg4jxZwAQwAUyc3z+ACWBiVT2ACWBiVSNB+D5gYn4XABPAxHwVVu4VmACTTZrBpA5gErgitAST2IpjSPLzb4vjaJKcnCwtmzaUdge2CdTFACbOtwMwAUwAE+f3D2ACmFhVD2ACmFjVSBC+D5iY3wXABDAxX4WVewUmwCQ7rPbR2VYJ10tNtWrC931OoEwwWbRkhTRr3EAyq6VL7D9X9LVP6+Y+L7ns6QAT59sAmAAmgInz+wcwAUysqgcwAUysaiQI3wdMzO8CYAKYmK/Cyr0CE2CyoUAvmNRPA0yCVoVlgslBnS+T8Y8PlKM67C+x/1zR17zpEwJxTYCJ820ATAATwMT5/QOYACZW1QOYACZWNRKE7wMm5ncBMAFMzFdh5V6BCTBZpxlMGgImgSvCMsFky7Yd8adLUkIhif3nir5qVs8MxEUBJs63ATABTAAT5/cPYAKYWFUPYAKYWNVIEL4PmJjfBcAEMDFfhZV7BSbAZG1+gdbQGqWnaR2PwdwnYOs9TBb8sVz2a9MiPtvKNRtk2szZ0rJZI+lybAf3K9A0AmDiPEjABDABTJzfP4AJYGJVPYAJYGJVI0H4PmBifhcAE8DEfBVW7hWYAJPVmsGkCWASuCK0BJMXJ38sT054S756+wnZum2HdL9kkDSsX1vWZ2+WS88/Va76z+mBuCjAxPk2ACaACWDi/P4BTAATq+oBTAATqxoJwvcBE/O7AJgAJuarsHKvwASYrMrP1xpa0/R0reMxmPsELMHk5Av6y8j7bpCD9mslE179SN7/9Ft5bexg+WPpqvjHDU995RH3q9AwAmDiPETABDABTJzfP4AJYGJVPYAJYGJVI0H4PmBifhcAE8DEfBVW7hWYAJMVeXrBpHkGYBK0KrQEk/YnXyU/fTxOkpKS5Kr+j8gxRxwkV1zw7/hHDR/WrbfMmfZsIK4JMHG+DYAJYAKYOL9/ABPAxKp6ABPAxKpGgvB9wMT8LgAmgIn5KqzcKzABJss0g0lLwCRwRWgJJrEnTEbf31dq1ciSf190u7w1/kFp1aKx/LlstVzZ72H5fPLIQFwUYOJ8GwATwAQwcX7/ACaAiVX1ACaAiVWNBOH7gIn5XQBMABPzVVi5V2ACTP7KzdMa2l7VMrSOx2DuE7AEk/+9MU1GPPNq/AmTbp2PlGF3XC2bc7bJRX0flJOO6SC3Xnu++1VoGAEwcR4iYAKYACbO7x/ABDCxqh7ABDCxqpEgfB8wMb8LgAlgYr4KK/cKTIDJEs1g0howCVwRWoJJbMWLl6yUrdt3SLsD2kgolCzhSKG8+f4M6dm9c/y/B+ELMHG+C4AJYAKYOL9/ABPAxKp6ABPAxKpGgvB9wMT8LgAmgIn5KqzcKzABJn/u0PuEyd6ZPGEStCq0BJMLrr1PzjjlGDmty9FSt3aNoK2/ZD2AifOtAUwAE8DE+f0DmAAmVtUDmAAmVjUShO8DJuZ3ATABTMxXYeVegQkwWbwjV2tobTOraR2PwdwnYAkmT7/wtkybMVsWL10pxx55iJxx8jHS5bgOkhGwz4gGTJwXA2ACmAAmzu8fwAQwsaoewAQwsaqRIHwfMDG/C4AJYGK+Civ3CkyAySLNYLIPYBK4IrQEk+IVL1u5TqbNnC3TZsyKv+Fr1+MPlzNPOVY6HnZA/P1NTH8BJs53ADABTAAT5/cPYAKYWFUPYAKYWNVIEL4PmJjfBcAEMDFfhZV7BSbAZMF2vU+Y7JfFEyZBq0LbYFK88Nj7l0z5YKY8OvY12bY9V5o3aSC9L+wuPU4/wSicACbOSwswAUwAE+f3D2ACmFhVD2ACmFjVSBC+D5iY3wXABDAxX4WVewUmwGT+9h1aQ9s/K1PreAzmPgFbYFJUVCQ//LJQ3p32tXw8fZakpaVK966d5KxTj5OVa9bL8CdfkS7HHia39bnA/YocjgCYOAxORAATwAQwcX7/ACaAiVX1ACaAiVWNBOH7gIn5XQBMABPzVVi5V2ACTH7bphdMDqwOmAStCi3BZOS4yfLeJ9/Iho050uXYDnJWt+PkuKMOKfXpOLGX6MTeHPb7D54xdn2AifPoARPABDBxfv8AJoCJVfUAJoCJVY0E4fuAifldAEwAE/NVWLlXYAJM5mkGk4N2A5PVa7Nl0NBxMnf+n9KiaUMZesfVsn/blpV7oyrZ6i3B5ILrhsjZpx4np3XpKLVqZJV5eYWFURn17GTpd835xi7fCZg4WezcTh2Uu7VcHVLuk5Tu8H1hipSnkuTG6uuL/F6gPFFyA/V5krLUP7Y6ujKivLbCBerXE9onTXmeyM/5yn0Kl4SV+8Q6ONnX6LpC5bmSB9RW7pO6UH2Paq76QXkevzps79JJy1QfbdgkPX6Zr2Wsiga5s3VzOb1+XaV5mqanSZ3UFKU+2yKFkl+kfigtyc2T7LBajRQWFYnqTNsiEcmPqvXKCoUkI6R2LqmvTCQcLZJCtaVJ7KeG6luKtaueJS0y0pX2dW1+gWwtVDsraoRCUk0xt9iiYjVUoFhDW2L7qtinzWyRBn8pxSDXN1gpk2rkqHUKcGvApOzN2T+zmlzYpIHyztVOSRHVT7vIDCVLE8UPVUgPi9TNU/z9KlVEaqqdY2sLwjJn63alHBpGUqRDgdr7MRSki2xX+/EkUzdskt6/L1ZaG42rZgImwOTXbWr3hVXyh1Qv/ff2xX0fij+0cFHPU+S9aV/LrDnz5aFBva2G4fsaE7AEk9hc0WiRbNy8RfIL9vzDrVnj+hqX43wowGRndoq/YDv9wxowEQFMEjUHmIgAJmWf3YBJIhfARAQwcf77jV89ARPApKJaA0z8uhOZx00CJsDkF0VItLq+dn97QGHF6vVyyY0PySevPibJyQ7/Md1qQr5vmYAlmHz0+fcy5PGJkrOlbD2bN32C5SR+NABMAJPiOuMJk52Q4eDJIZ4wcXZaASaASUWVA5gAJs5OFn97ASaACWDi7z3HbPoTMAEmc7Zu03oh7WtULxnv0y9+lP+9+XH8pThfz54nLZs2lLtuuUT2btlE65wMVnEClmBywjk3So/TT5Szuh0rGRl7vgShcQPF5+Y82hHABDABTErfXLwkx6PDpoxhARPABDCp+H7jCRP/ziOnMwEmgAlg4vTuoV9QEjABJj9t0QsmHWruApMpH34h9z/+gjw17Bbp2OEAmfj6VHnrwy/lrfEPBCXyf8Q6LMGkwym95au3n5DMamqvcfY7PcAEMAFMABO/z53i+QATwAQwAUxMnT+65gVMABPARNfdxDimEjABJj9oBpPD/wYmn3zxgzw98W1549kh8Uhj7xt6eLfeMv2NUVK71i5YMZX3P2VeSzC5+Z4n5LwzOsuxRx4c6EwAE8AEMAFMTB1SgAlgApgAJqbOH13zAiaACWCi625iHFMJmACT2Vu2ar3cI2rWKBlv/uJlcsOdo+STVx8tAZPDTuktX749Rmrw8cNac69osDLBZPykD0v6xN7o9Y0PZsY/SrhF0waSFH8f/l1fl19wmm+LrWgiwAQwAUwAE1OHEWACmAAmgImp80fXvIAJYAKY6LqbGMdUAibA5PscvWByVK1dYBLL8Zwr7pJLzusW/9Ta/70xTd6d9rW8NvZeUxH/I+ctE0xiHyVs92vS0/fYbeppO8AEMAFMABNPD5kKBgdMABPABDAxdf7omhcwAUwAE113E+OYSsAEmHynGUw67gYmy1auk373PimxT8xp26qZ3D/gCmnNm776WmKWL8nxdTUuJgNMABPABDBxcYS46gqYACaACWDi6hAJQGfABDABTAJwI7IEVwmYAJNvNm9xtebdO3eqXVPreAzmPoEKwWRHbr7MW7Ak/gYzB+/fWqpnVXM/o0cjACaACWACmHh0vFgOC5gAJoAJYGJ5UAS8AWACmAAmAb9JWZ5lAibA5CvNYHIsYGK5z343KBdM/li6Uq6+7VFZs35jfE11a9eQJx66WQ49sI3fa7Q1H2ACmAAmgImtw8KDRoAJYAKYACYeHC2+DgmYACaAia+3HJN5kIAJMPlyk94nTI6rwxMmHpSGqyHLBZPrBj4uoeRkeXDgVZKamiIjx70u3/30u7w9/kFXE3rVGTABTAATwMSr88VqXMAEMAFMABOrcyLo3wdMABPAJOh3KeuzSsAEmMzclGO1LKXvn1CnllJ7GnufQLlgcsyZ18t/H+kvB+/XOr6KHbl5cuRp18q37z0VyI8xAkwAE8AEMPH+yCx7BsAEMAFMABNT54+ueQETwAQw0XU3MY6pBEyAyQzNYHIiYGKqfMqdt1wwOajzZfLp649J4wZ1SzofcerV8uZz90vLZo08vZCNm7fKwAfHypr1m+SdCbueaFm+ap3cPfx5WbB4mTRtXF/uvOliOeyQfeJrAUwAE8AEMPH0YKpgcMAEMAFMABNT54+ueQETwAQw0XU3MY6pBEyAyecb9T5hclJdnjAxVT/lzRs4MNm+I096XTdETuzUXmZ8+3MpMLn0pqHS5bjD5KJzT5avZ8+Tu4c/J9NefVRSU0KASfEOF6mXWHLjkHKnyO8Fyn2SG6jPk5SVrDxPdGVEuU/hAvXrCe2TpjxP5Od85T6FS8LKfWIdnOxrdF2h8lzJA2or90ldqL5HNVf9oDyPXx0AE8AEMAFM/DpvvJoHMAFMABOv7i7G9SsBE2Dy2cbNWi+vS13136u1LoDB9kigQjB5etgtUvdvbzxzSd+H5JG7r5NGDeuUDFT8kh1d2cZe+rNhY078f+59dGIJmGRv2iKn/meAfPPek5ISSvzh3bP3YBnQp5cc1WF/wAQwKSlBwCQRBWCi61SyHgcwAUwAE8DE+qQIdgvABDABTIJ9j7I66wRMgMkn2XrBpGs9wMR6p/1tUSGY2FnKvOkT7DRTbvPjrwtLgcmPvy6SIY9NlLfGP1Ay1q33PSUdDztQzj+jM2ACmAAmu91lgInyseO4A2ACmAAmgInjAyQgHQETwAQwCcjNyDIcJ2ACTKZpBpOTARPH++9Vx3LBJPaEh52v+h69zmp3MPl69lwZ/ewbMumZwSXLuuvh52TfvZvLJed1A0wAE8AEMLFzZHnSBjABTAATwMSTw8XHQQETwAQw8fGGYypPEjABJlOzN2m9lm71dr2SQ+vADOY4gXLBxPGImjruDiY/zV0kg0dMKPWeJv3ufVKOOeJg6dn9RMAEMAFMABNNp4/6MIAJYAKYACbqJ0ewegAmgAlgEqx7ktWoJ2ACTD7aoBdMTq0PmKjvvLc9Kg2YbMrZKl3Pv1W+fPsJqZaReLPN0y4cIA8N6i0dDt4HMAFMABPAxNvTsoLRARPABDABTIwdQJomBkwAE8BE083EMMYSMAEmH2zYqPV6/11/1yfUah2YwRwnUGnAJHaFV/YbLke23196X9hdPvz8u/hLdD58abiEQsmACWACmAAmjg9Ctx0BE8AEMAFM3J4jpvsDJoAJYGL6LmR+twmYAJP31+sFk9MbACZu60B3/8CBySdf/CD9hzwtUlQk4UihpKamSOsWjWXK8w/IyjUb5I6h42TBH8ulRdOGcu+tl8lB+7WKZ5L12Te6sylzvLmdOijP03K1g4/TTU9SnifegY8VFj4lJ1E6vOmrs1vISS/ABDABTAATJ2dHkPoAJoAJYBKkO5K1OEnABJi8qxlMzgBMnGy9p30CByZOrxYw2ZkcYAKY7CwFwMTpaaLeDzABTAATwET95AhWD8AEMAFMgnVPshr1BEyAyTvrs9UXWkGPMxvU0zoeg7lPADBRzLB1tQzFHiKvt9tfuU/jzuuV+8Q61JnVUrnflkihcp/caFS5T4Ns9admltVWX9vmSER5bfu/qT5PdLn6PBm9aymvrXBxgXKfWIdlHdTzbniLvU/H+vuCbrpxi/L6vt+yTbnPktw85T5+dahsYFI/NVVqpKg9+bZXRprUS01VirRtZoY0TEu855TKV1YoWTKSk1W6OHm4Tpbm5kl2WO0+rpUSkmohtezSkpIkJUntfozNkKzYJypFyjkckpUlzTPSlbKuEwpJlmIG2ZGwbHfwc2NzOCKqP29iPwNU+zyzfI18sVn9LFMKLuCNARO9GxS772unpigNekj1TLm6WWOlPrGzsl6a2tm8oSAsv2/foTRPQbRI+R7eNz9Neuao/d4TnrFDtg/coLS2zaelyZ8v1lbq813OVnl46QqlPjQOfgImwOStdXrB5OyGgEnQKg0wUdwRwCQRmOovo7E+gIkIYJKoH8Ck7IMn9k7rPX6Zr3gqqTcHTBKZASYigEmiFgATEcBE/SytqAdgIgKY6K0pRrNOwASYTNEMJucAJtYb7XMLwEQxcMAEMCkuGZ4wSSTBEyYiPGFS9kHKEyaJXHjCRIQnTBR/2TDQHDDRGzpgApjorShGs5OACTB5Y63aE1FW19GjUX2rJnzf5wQAE8XAARPABDApfdMAJoBJeccoYAKYFNcGYKL4y4aB5oCJ3tABE8BEb0Uxmp0ETIDJZM1g0hMwsbPVvrYBTBTjBkwAE8AEMNn92OAJk7IPUsAEMAFMFH/JMNgcMNEbPmACmOitKEazk4AJMHltjd4nTM5vzBMmdvbazzaAiWLagAlgApgAJoCJvYMTMAFMABN790oQWgEmencBMAFM9FYUo9lJwASYTFrj7IM6yrueCxo3sHOptPExAcBEMWzABDABTAATwMTewQmYACaAib17JQitABO9uwCYACZ6K4rR7CRgAkxe0QwmvQATO1vtaxvARDFuwAQwAUwAE8DE3sEJmAAmgIm9eyUIrQATvbsAmAAmeiuK0ewkYAJMXlqt9wmTC5vwhImdvfazDWCimDZgApgAJoAJYGLv4ARMABPAxN69EoRWgIneXQBMABO9FcVodhIwASb/W73OztJst7moSUPbbWnoTwKAiWLOgAlgApgAJoCJvYMTMAFMABN790oQWgEmencBMAFM9FYUo9lJwASYvLBKL5hc0hQwsbPXfrYBTBTTBkwAE8AEMAFM7B2cgAlgApjYu1eC0Aow0bsLgAlgoreiGM1OAibAZOKqtXaWZrvNpU0b2W5LQ38SAEwUcwZMABPABDABTOwdnIAJYAKY2LtXgtAKMNG7C4AJYKK3ohjNTgImwGS8ZjC5HDCxs9W+tgFMFOMGTAATwAQwAUzsHZyACWACmNi7V4LQCjDRuwuACWCit6IYzU4CJsDk+ZV6nzC5ohlPmNjZaz/bACaKaQMmgAlgApgAJvYOTsAEMAFM7N0rQWgFmOjdBcAEMNFbUYxmJwETYPLsyjV2lma7zVXNGttuS0N/EgBMFHMGTAATwAQwAUzsHZyACWACmNi7V4LQCjDRuwuACWCit6IYzU4CJsBk3Aq9YNK7OWBiZ6/9bAOYKKYNmAAmgAlgApjYOzgBE8AEMLF3rwShFWCidxcAE8BEb0Uxmp0ETIDJWM1gcg1gYmerfW0DmCjGDZgAJoAJYAKY2Ds4ARPABDCxd68EoRVgoncXABPARG9FMZqdBEyAyTMrVttZmu021zZvYrstDf1JADBRzBkwAUwAE8AEMLF3cAImgAlgYu9eCUIrwETvLgAmgIneimI0OwmYAJOnlusFkz4tABM7e+1nG8BEMW3ABDABTAATwMTewQmYACaAib17JQitABO9uwCYACZ6K4rR7CRgAkye1Awm1wMmdrba1zaAiWLcgAlgApgAJoCJvYMTMAFMABN790oQWgEmencBMAFM9FYUo9lJwASYjFm2ys7SbLfp27JpmW2//2m+XH7LMHn3haGyd0ueQrEdqIaGgIliiIAJYAKYACaAib2DEzABTAATe/dKEFoBJnp3ATABTPRWFKPZScAEmIzWDCY3lgEmBQVh6dXnflmfvVkmjBoEmNgpBo1tABPFMAETwAQwAUwAE3sHJ2ACmAAm9u6VILQCTPTuAmACmOitKEazk4AJMBn5l94nTG7ea88nTJ4cP0WKikQ+njlbRg65ATCxUwwa2wAmimECJoAJYAKYACb2Dk7ABDABTOzdK0FoBZjo3QXABDDRW1GMZicBE2Dy+F8r7SzNdptb9mpWqu3S5WvkpnvGyGtj75Xzrr4XMLGdpL6GgIliloAJYAKYACaAib2DEzABTAATe/dKEFoBJnp3ATABTPRWFKPZScAEmDyqGUxu3Q1MrrjlYel9YXfpdMRBcuZldwImdgpBcxvARDHQAa1Kq5+d7v0eSrfTrFSbrHGNlPvEOiQ76LUhElbutTZfvU/T9DTleQpiz58pfjVKSlHsIRLNLlTuIwXqXZz0KAqrZxCfJ0l9toKPdyh3+vEC9YkapKUqz9Ph2znKffzqsL1LJy1TfbRhk/T4Zb7SWM+0bC0X1Wug1OfXaK6sErV7uGFqqtRMUbu3VucXyJZC9XsrLxqViOK9v74gLNsczKVavdWSkyUtWe2kjYr6PbwjEpWwYgZN0lOldqraHsXmUT1nqyUnKWfQOC1VaimuLVbUmQ7yXpabL5siEaV74oDFIWm6XnFfc6IieWp7e32DlTKpRo7S2vxq/E8Ak5Pr1paxB7ZVijQkIumK93x8gh/zRZ7YojRX7qEpsvHaLKU+2eGwzN+eq9SnRUaanFS3tlIfxeMoPvbyvHyZuVmt3putSZYTZqv9vphUL1lSDlb7XTu6oVAK56n9Ivd+1ha5pMkKpdxo7G8CJsDkkaV6nzC57W9/a7710Zfyzex58vBd18SDBEz8rafi2QATxdwBk0RggIkIYJKoBcBEBDAp+yAFTBK5ACYigEmiFgATxV+6NDcHTBKBAiYigInmmysgw5kAk+FL9SLagFbNS9Lse9do+fHXhRLaibabt2yTGtUz5cHbr5LOx7QPSOpVfxmAieIeAyaASUnJqP3DhGKl7WrOEyaJLHjCpOwS4gmTRC48YSLCEyaJWuAJE2c/bnjCpOzceMIkkQtPmIjwhImzs8XPXibAZNgSvWAysPUuMNk9O54w8bOads0FmCjmDpgAJoBJ6ZuGJ0x4wqS8Y5QnTBLJ8IQJT5gU3yM8YaL4S5fm5jxhkgiUJ0x4wkTzrRWY4UyAydAly7Ve/6DWLcodDzDRGrXtwQAT21ElGgImgAlgApjsfmzwkpyyD1LABDAprgxekpNIAjBR/KVLc3PABDApLilekqP55grIcCbA5EHNYHJnBWASkJj/ccsATBS3HDABTAATwAQwsXdwAiaACWBS+l4BTOydHV61AkwAE8DEq7srGOOaAJMH/tT7hMlde5f/hEkwUv7nrQIwUdxzwAQwAUwAE8DE3sEJmAAmgAlgYu+08KcVYAKYACb+3GumZjEBJkP+XKb1cu/Zu6XW8RjMfQKAiWKGgAlgApgAJoCJvYMTMAFMABPAxN5p4U8rwAQwAUz8uddMzWICTO77Qy+YDG4DmJiqn/LmBUwUdwQwAUwAE8AEMLF3cAImgAlgApjYOy38aQWYACaAiT/3mqlZTIDJYM1gch9gYqp8yp0XMFHcEsAEMAFMABPAxN7BCZgAJoAJYGLvtPCnFWACmAAm/txrpmYxASb3/PGX1ssd0mYvreMxmPsEABPFDAETwAQwAUwAE3sHJ2ACmAAmgIm908KfVoAJYAKY+HOvmZrFBJjctVgvmDzQFjAxVT/lzQuYKO4IYAKYACaACWBi7+AETAATwAQwsXda+NMKMAFMABN/7jVTs5gAkzs1g8mDgImp8il3XsBEcUsAE8AEMAFMABN7BydgApgAJoCJvdPCn1aACWACmPhzr5maxQSYDFq0VOvlDt2nldbxGMx9AoCJYoaACWACmAAmgIm9gxMwAUwAE8DE3mnhTyvABDABTPy510zNYgJMBmoGk2GAianyKXdewERxSwATwAQwAUwAE3sHJ2ACmAAmgIm908KfVoAJYAKY+HOvmZrFBJgMWKj3CZPh+/KEian6KW9ewERxRwATwAQwAUwAE3sHJ2ACmAAmgIm908KfVoAJYAKY+HOvmZrFBJjctnCJ1st9ZN/WWsdjMPcJACaKGQImgAlgApgAJvYOTsAEMAFMABN7p4U/rQATwAQw8edeMzWLCTC5VTOYPAqYmCqfcucFTBS3BDABTAATwAQwsXdwAiaACWACmNg7LfxpBZgAJoCJP/eaqVlMgEm/BX9qvdzH9ttb63gM5j4BwEQxQ8AEMAFMABPV/T11AAAgAElEQVTAxN7BCZgAJoAJYGLvtPCnFWACmAAm/txrpmYxASY3awaTkYCJqfIpd17ARHFLABPABDABTAATewcnYAKYACaAib3Twp9WgAlgApj4c6+ZmsUEmNw4X+8TJqP35wkTU/VT3ryAieKOACaACWACmAAm9g5OwAQwAUwAE3unhT+tABPABDDx514zNYsJMOk7/w+tlztm/zZax2Mw9wkAJooZAiaACWACmAAm9g5OwAQwAUwAE3unhT+tABPABDDx514zNYsJMLleM5g8CZiYKp9y5wVMFLcEMAFMABPABDCxd3ACJoAJYAKY2Dst/GkFmAAmgIk/95qpWUyASZ/f9T5h8tQBPGFiqn7KmxcwUdwRwAQwAUwAE8DE3sEJmAAmgAlgYu+08KcVYAKYACb+3GumZjEBJtf+vljr5T5zQFut4zGY+wSqDJisT1qonEarT7OV+6ybuo9yn6xh9ZX7RNdGlPvEOiTXDSn3Wy/qc22KqPfJLYwqry0jlKzcp15qinqfiHqfTSmFyvPUTVGfJ7pRfZ74wvKLlNdXtEO9z9oW6n3qvpynvLbChQXKfRqevEi5j5MO27t0ctJtjz6zcrbKfUuWK40VjhaJ6p11ev060q56ltI8OZGI5EXVZsoMJUt6svo9nBOOSG5Ura7W5hfI1kK1eyUpdmYmxf63/a/CoiJRW5lIteRkSXOQg/1VJVpmhpKU805LSpIUxbWFo1EpVAyhTmpIskLqP58yk9VrKFykfk/E5lHdo+W5+aL6s/CoLRnSOj9NaWuv27pUXs7fqNTHSeNPDz9Yjq5Vw0nXUn1yCwul/ozvXY/jxQCnrc2SiT82URo6qX5IUtqlK/WJ/wiunyRb26n9zN8YDsuCHeo/H1UXVyMUkpYZatdUEI0qn7Gxe1H150btlBTZN7Oa0iXFfsqkKp5jRbPzJPp4jtI8hSvCEvlKfX8+PrZArh6yVWkuGjtLwASYXPObXjAZeyBg4mz3vetVqcDkguuGyPxFf4ns/AW3ZvVMmTlldDwdwCRRJICJCGCy88AATAQwKfuHB2CSyAUwEQFMdmITYCKASdnnJWCSyAUwEQFMvPuDVNfIJsCkt2YwGQeY6CoHbeNUKjA5/eKBMmpIX2nbutkeAQAmgElxUQAmgElxLQAmgElFPy0BE8CkuD54wkQAk3IOC8AEMCkuDcBE29+fng1kAkyu+k3v08zPHqj+agbPAmXgxD+wFRUVKT5Yay65E8+9SV4dO1gaN6gLmJSzDTxhwhMmJaXBEyY8YVLOOcETJolgABPABDDZdUjwhEnZByZgApgAJub+9lOd2QSYXDFPL5g8fxBgorrvXrevVGDS4ZTeckLHdvLT3EVSv24tubl3Tznh6EPjGfGESaJUABPABDDZdWzyhEnZP0IAE8CkuDJ4SU4iCZ4w4QmT8n7hBkwAE8DE6z9H9Y1vAkwu1wwm4wETfQWhaaRKAybRaJHcPfw5OfWkjtLpiANlxtc/y8CHxsq7LwyNP3ECmAAmxfcEL8nZmQRPmPCESTk/KAATwAQwKX1zACaACWBS8V8WvIcJ72Gi6W9PT4cxASaXzlX/4JGKQph48L6eZsTg6glUGjAp69Iuv2WY9Dj9ROnetRNgsjMgnjDhCROeMOEJE6sfBYAJYAKYACa7nxO8JKfsk5MnTHjChCdMrH6rCM73TYDJJZrB5AXAJDgFtXMllQZMduTmy6IlK+TQA9uUhHjRDQ/KxT1PkW6djwRMAJOSuuAJE54wKS4GXpJT9s8cwAQwAUwAE8DE3u/kgAlgApjYu1eC0MoEmFykGUz+B5gEoZRKraHSgMnmnG1y8gX9ZdT9N8gxRxwsX3z3i9x2/zPy/ovDpF6dmoAJYAKY7H688JIcXpJTzo8cwAQwAUwAE8DE3u/kgAlgApjYu1eC0MoEmFz46wKtl/7SIftpHY/B3CdQacAkdqkzvvlZRjw9SdZlb5ZmjevLgOt7ydGHHRhPgfcwSRQDL8nhJTklxwJgApgAJhX+lORTcviUnOIC4T1MeA+T8g4LwAQwAUzc/8Hp1wgmwKSXZjB5BTDxq1xsz1OpwKSiqwJMAJPi+uAlOTuTAEwAE8AEMLH4dYBPyUkEBJgAJoBJxYcFb/rKm77a/uvSYEMTYHLBL/O1XvGkdvtrHY/B3CcAmChmuG6q+mdjZw2rrziLSHRtRLlPrANPmPCESUnhACaACWACmAAmtn6WAiaACWACmFgdFoUrwhL5Ks+q2R7f//jYArl6yFblfnRQT8AEmJyvGUxeA0zUN97jHoCJYsCASSKwTRF10MktjCqmLZIRSlbuwxMmOyMDTAATwAQwAUxs/QwBTAATwAQwsTosABOrhMx/3wSYnPez3idMXj+UJ0zMV1LpFQAmijsCmAAmxSWzKaVQsXpE6qakKPeJblSfJz4JYAKYACaACWBi68wFTAATwAQwsTosABOrhMx/3wSY9Pj5d60X/sahB5Qa77OvfpLHxr4m67M3y/5tW8p9/S+XVi0aa52TwSpOADBRrBDABDABTErfNGtbFCneRSJ1X1Z/pLVwYYHyPHyscNmR8Sk5iVx401fe9LX4DgFMABPABDCx+iUDMLFKyPz3TYDJOZrBZMrfwGTN+o1y1mV3ytPD+kn7g9rKmOfflDnzFsn4xweaD/sftALARHGzARPABDABTHY/NrZ36aR4kpTdfFbOVrlvyXKlscLRIlF9sRtgApgUFxlv+ppIAjABTAATwMTqhy9gYpWQ+e+bAJOz5+h9wuSt9rueMImByc/zFku3zkfFw/190V/SZ9Dj8vnkkebD/getADBR3GzABDABTAATwMTewZkZSpb0ZPX3IcoJRyQ3qvbk0tr8AtlaqPbyNZ4w4QmT4koGTAATwAQwsfrJBphYJWT++ybA5Mw5v2m98HfaH1jueM+98oH8tnCpPDq4j9Y5GaziBAATxQoBTAATwAQwAUzsHZyASSKnasnJkuYAjuylvKtVZihJGah4wiSRH2ACmAAmgInVmQuYWCVk/vsmwOSMn/SCybsdygaTL7//VYY8NlFeGHOHNG5Q13zY/6AVACaKmw2YACaACWACmNg7OAETwKS4UuqkhiQrFLJXOH9rFYMM1aeUwkXqL1MDTAATwAQwsTqgABOrhMx/3wSYnK4ZTN4vA0ze++QbeXri2/L0sFukZbNG5oP+h60AMFHccMAEMAFMABPAxN7BCZgAJoBJ6XvlqC0Z0jo/zd4NtLPVdVuXysv5G5X6OGn86eEHy9G1ajjpWqpPbmGh1J/xvetxvBjgtLVZMvHHJkpDJ9UPSUq7dKU+scb59ZNkazu1T8bbGA7Lgh3qb4quurgaoZC0zFC7poJoVPlljzG8zIuqvctW7ZQU2TezmtIlxV74mar4FF/R7DyJPp6jNA9gohSXkcYmwOTfP83Teq0fdDio1HifffmjjH7uTXn20dukft1aWudiMHsJACb2cippBZgAJoAJYAKY2Ds4ARPABDABTOydFv60AkwSOQMmIoCJP/ec37OYAJNTf9QLJh8dtgtMcrZul3OuuEteHHOnNGtc3+84mW9nAoCJYikAJoAJYAKYACb2Dk7ABDABTAATe6eFP60AE8CkuNIAE3/uOb9nMQEm3TSDydS/gcmUD7+Qux5+TlJTSz+tNn3ySKldq7rf8f5j5wNMFLceMAFMABPABDCxd3ACJoAJYAKY2Dst/GkFmAAmgIk/95qpWUyAyck/zNV6udMOP1jreAzmPgHARDFDwAQwAUwAE8DE3sEJmAAmgAlgYu+08KcVYAKYACb+3GumZjEBJl01g8kngImp8il33ioDJpsjEeVw/7d6vXKf3j+ovyHa9tMzlOdx2uGvvHzlrhsKwsp9CoqKlPtsKyxU7lNvt0fQ7AxQ08EnMdR2ME9uodobmcXWvnc19VrYofiGacUZ1U5R/0SK9KTYW6cpfoXVa2FeQa7iJCIHZai9CVxsgqyZ3yrP46TD9i6dnHTbo0/++9tkS/dVSmO9PzQkc89V27e2mRnSME3tjSdjHz+r+qZ6K/PyJSeift/nRqMSe7NAla/YG01GFPtEi0TUZkmsKClJZWUiseaKXSQ1KUlCihOlJavvUbXkJOWPPA5H1T+FJqq4N8UJpzjIIRItUt7XaiH1HPKjUSlULKAvNuXI4ly1N/VcnV/g6D5Sq9J/xqfk7JOZIT0aqr0PQP3UFDm4RpZqnBJ7gD5T8feRzeGI/JmnVh+FRUWi+mM49gauqr+TNU5LlaMU3xR46/+3dxbQURx/HP+GIMELpZQibSm08EeKuzvF3R0CwQMEDyTBJWhwdyvu7q6lUKHQFinuViwh//ebY6+X45LcHpe7hPvOe32v5HZ3dj87Ozvz2d/MBIfgxqvXutglcndHWp0T0l5/9QpHHj/VlU+mkHio9yaZrn2CEwKv0+hvW2259xAtf72oKy9ubBsBZwiT0qfO2Xay4ey1O092ux6PB/twAhQmOhlSmBiAUZgAFCbvHh69LTUAFCaWKx4KEwMXChNQmLx7RChMAAoTnQ21CDanMHnXhqMwAYWJ/Z6r6HQkZwiTUiftK0z25KUwiU5lSn0oCw218bNPNLsSRpgYbggjTABGmBjKAiNMGGESXjXNCBMDGUaYAIwwMZQFRpgwwiS8+pIRJgYyjDABGGESzTp/Fk7HGcKkhJ2FyT4Kk2hX0ChMdN4SRpi8+zphg2fTG/4pOXFIDsAhOYYyxyE5lisrDskxcOGQHIBDcgxlgUNydDZs3m2+K082FNQ55MJSTvIspth33LaTiOK9GGHyrg3HCBNGmETxs+aswztDmBQ/8bNdL3d/vu/tejwe7MMJUJjoZEhhQmGiFRkOyXlHgkNywDlMLFeknMPEwIVzmEg0j87JPt4VKQ7J4ZAcnc20CDenMKEw0QoIh+TY88mKPsdyhjApamdhcpDCJPoUqHdnQmGi85ZQmFCYUJiYPTQUJhQm4dSjFCYUJlrRoDAxkOCkrzobXXbenMKEwoTCxM4PVTQ7nDOESZHj9o0wOZSfESbRrFhxDhO9N4TChMKEwoTCxLzeYISJ5ZqUwoTChMIk7LNBYaK31WXf7SlMKEwoTOz7TEW3ozlDmBQ6ftauGI7kz2HX4/FgH06AESY6GVKYUJhQmFCYUJhYV3FSmFCYUJhQmFhXWzhmKwoTChMKE8c8a87KxRnCpKCdhclRChNnFZ9w86Uw0XlLKEwoTChMKEwoTKyrOClMKEwoTChMrKstHLMVhQmFCYWJY541Z+XiDGGS/5h9I0yOF2CEibPKT3j5UpjovCMUJhQmFCYUJhQm1lWcFCYUJhQmFCbW1RaO2YrChMKEwsQxz5qzcnGGMMl77Ce7Xu7JAjntejwe7MMJUJjoZEhhQmFCYUJhQmFiXcVJYUJhQmFCYWJdbeGYrShMKEwoTBzzrDkrF2cIkzxH7StMThWkMHFW+QkvXwoTnXeEwoTChMKEwoTCxLqKk8KEwoTChMLEutrCMVtRmFCYUJg45llzVi7OECa57CxMzlCYOKv4hJsvhYnOW0JhQmFCYUJhQmFiXcVJYUJhQmFCYWJdbeGYrShMKEwoTBzzrDkrF2cIk5xHz9j1cn8qmMuux+PBPpwAhYlOhhQmFCYUJhQmFCbWVZwUJhQmFCYUJtbVFo7ZisKEwoTCxDHPmrNycYYw+f6IfYXJz4UoTJxVfsLLl8JE5x2hMKEwoTChMKEwsa7ipDChMKEwoTCxrrZwzFYUJhQmFCaOedaclYszhEn2I6fternnCuW26/F4sA8nQGGikyGFCYUJhQmFCYWJdRUnhQmFCYUJhYl1tYVjtqIwoTChMHHMs+asXJwhTLIetq8w+aUwhYmzyk94+VKY6LwjFCYUJhQmFCYUJtZVnBQmFCYUJhQm1tUWjtmKwoTChMLEMc+as3JxhjDJYmdh8iuFibOKT7j5UpjovCUUJhQmFCYUJhQm1lWcFCYUJhQmFCbW1RaO2YrChMKEwsQxz5qzcnGGMMl8+JRdL/f3wnnsejwe7MMJUJjoZEhhQmFCYUJhQmFiXcVJYUJhQmFCYWJdbeGYrShMKEwoTBzzrDkrF2cIk0yH7CtMLhShMHFW+QkvXwoTnXeEwoTChMKEwoTCxLqKk8KEwoTChMLEutrCMVtRmFCYUJg45llzVi7OECbf2lmYXKQwcVbxCTdfChOdt4TChMKEwoTChMLEuoqTwoTChMKEwsS62sIxW1GYUJhQmDjmWXNWLs4QJhkPnrTr5V4qmteux+PBPpwAhYlOhhQmFCYUJhQmFCbWVZwUJhQmFCYUJtbVFo7ZisKEwoTCxDHPmrNycYYw+cbOwuQvChNnFZ9w86Uw0XlLKEwoTChMKEwoTKyrOClMKEwoTChMrKstHLMVhQmFCYWJY541Z+XiDGGS/oB9I0z+LsYIE2eVn/Dy/WiESdPzf+hmWzBpYt37PA0J0b1P8jixde/z+I3+fCSTh8HBuvN69fat7n0e2ZBPPLdYuvNxc9O9C5LG1s87obv+c/OIpX+f129DdV+QLQwkk/QeHrrzuv7qle59MiSIr3sf/eSAZDY8R6tu39d9bvmTJtK9T8NUn+nex9IOhx89Qa+Ll3Ud63FwMF7qLFdfe8RDirhxdOUj9yyWzsIodUtwqP4yL/vo3ev127cI0bmTLfnogvZuY6nG9LJzt2UfN/352HJf47i5IbbOsmDLPoLP3c1N/ac36d0jrpsb4uis0z1iuSGuzn2Eg97rMZQffQTk2dP7PLRInRLpPOLpy8jC1i9CQpBi3/EPPk5kB/jKIx7KffpJZJuF+f3Rm2D89ULfe07aB6k94urKR9vYHfpu3Ju3b/GvzjbZ29BQ6G3FyfOb0F1qGeuTbB1f5z4hoaF4o/MdYMv1JHZ31112pZze19mWFQbxdDIQwoqDzvd0glix8Hk8fe/pX579i0W37lp/Uz/CLZ0hTL46cMKuJK8Uy2fX4/FgH06AwkQnQwoTAzAKE4DCxFAWKEwAChPLFSmFiYELhQlAYWIoCxQmOhtdEWxOYWKAY4tgoDABKEzs9yxGpyM5Q5h8aWdhcpXCJDoVKUM7LjRUp/qNdpdgOCFGmBg4MMIEjDB594wywgRghInlCpsRJgYujDABGGFiKAuMMAEYYWK5vmSEiYELI0wMDBhhEk07gu9OyxnCJO1++0aY/FOcESbRrZRRmOi8I4wwMQBjhAkjTLRHhxEmjDAJrxplhImBDCNMGGGiPSOMMNHZ6Ipgc0aYGOAwwgTgkBxDWeCQHMAZwiTNfvsOQbxePL/9KkoeyS4EKEx0YqQwoTDRigyH5BhIUJhQmFCYRPwioTChMKEw0dnYsmJzChMKE62YUJhQmGhlwRnC5As7z9l0swSFiRWvAIduQmGiEzeFCYUJhUnYh4bChMKEwoTCJLJXKecwMRBihElkJcX63ylMKEwoTMI+L4wwcU6Eyed2Fia3KUysfxE4aEsKE52gKUwoTChMKEzMqw1O+mq5IuWQHAMXRpgwwkR7QihMdDa6IticwoTChMKEwsS8inBGhEnKfcfsV7EBuFOigF2Px4N9OAEKE50MKUwoTChMKEwoTKyrOClMKExMRYHepYhlXy4rrAk36545bSsuK2yZF5cVNnDhKjlcJUdfjRJztnaGMEmx177C5F5JCpPoVuIoTHTeEQoTChMKEwoTChPrKk4KEwoTCpOwzwojTKyrO6zZihEmBkqc9JWTvmrPC4fkOGdIzqd7j1pTZVm9zf2SBa3elhs6hgCFiU7OFCYUJhQmFCYUJtZVnBQmFCYUJhQm1tUW+reiMKEw0UoNJ301kKAwcY4wSbbHvsLkYamwwuTAsXMInLYMd+89QrbM6TGsrydSJE+qv9LkHjYToDDRiY7ChMKEwoTChMLEuoqTwoTChMKEwsS62kL/VhQmFCYUJmGfGwoT5wiTT+wsTB6ZCJOnz/5Fxca9MHmYt5Ilk+euxeVrNzEuoJP+SpN72EyAwkQnOgoTChMKEwoTChPrKk4KEwoTChMKE+tqC/1bUZhQmFCYUJiY1xzOmMMkyZ4j+iuwCPZ4UqqQ8ddte49j1ab9mDHaR/1NBEqxml1wfNNUxI0bx6758mDhE6Aw0Vk6KEwoTChMKEwoTKyrOClMKEwoTChMrKst9G9FYUJhQmFCYRIdhEni3fYVJk9L/ydMpi/cgPsPH6NflybGSy1eswsWTOyHr9Ol0l9xcg+bCMQoYXLtxh0MGDUHFy5dRepUKdC/a1Pkzv6tuvCm5//QDaBg0sS696EwoTChMKEwoTCxruqkMKEwoTChMLGuttC/FYUJhQmFCYVJdBAmCe0sTJ6bCJPxM1ciJOQtenjVM15q+QY+mDikCzJn/FJ/xck9bCIQo4RJ867DUbpobjSpVQ6HT/6CAaNmY8fyMYgT253C5N3tfxgcrLsgSKdGb3pkQz7x3GLpzQZubrp3QdLYsXXvlNBd/7l5xNK/z+u3obrPzRYGkkl6Dw/deV1/9Ur3PhkSxNe9j35yQLI4+u/rqtv3dZ9b/qSJdO/TMNVnuvextMPhR0/Q6+JlXcd6HByMlzrL1dce8ZBCZyin3LNYOgsjhQmFCYUJhYmuCk3HxhQmFCYUJhQm0UGY6Ki2dG86Y9EG3Ln3CL7eTY37Fq3eGUum+OLLNJ/rPh53sI1AjBEm9x8+QcVGvXBk42TEdndXV1vH0w+9OjRE/lyZKUze3X8KE1CYvCsLFCYAhYnlFwOFiYFLcGgo9CtM/S9b8b56ZZO85XTv46Z/H1tEmCyNG1unPLNlHyHt7uam/tOb9O4R180NcXRKcI9Yboircx8uK6z3Toa/PYUJhQmFCYXJxy5Mduw/iYUrt6shOJJu332ISk1649jmqcb+sP1qVR4pPAIxRpicPncRg8bOx9q5Q4zX0iNgCgrkzoJ6VUtSmFCYGMsFI0wMKChMKEzCq/gpTChMtLJBYWIgQWECtEidEuk84n1wi/lFSAhS7Dv+wceJ7AAUJhQmFCYUJh+7MHn+70uUb+iDMX4dkDdHJgybuBgvXrzC8H6ekVWR/N2OBGKMMDl88jwmzlqFZdP8jJfvO3I2vvsmLZrVrUBhQmFCYWJWMVCYUJhQmET8tmSECUBhQmGiPSUUJpbrCxmym9ojrk1Nb3foi3V68/Yt/tU5TPptaCj0DqyWCLGE76K1rb0wiXqLr3OfkNBQvAnVF8dny/UkdnfXLftE7N3XObxcGMTTyUD4Kg46h84miBULn8fTtwoKlxV2zrLC1j5Dtm4nfeARQUtw78Fj5MyWEcP6eOITG4aP25o/9wNijDA5c/4i/ALnYf28ocb71t1/MgrnzYY6VUpQmFCYUJhQmLxXp3NIjuXXHCNMDFwoTChMtCeEESaMMAmvU0BhYiBDYWJgQGESvbvPzlhWOHoT4dnZg0CMESYPHz9F2Xo9cHDdJMR/Z/p/aNwLw/p6Ile2bylMKEwoTChMKEysfCtQmFCYaEWFESYGEhQmFCYUJhG/QChMKEysbGI4dTMKE6fi/2gzjzHCRO5A6+6jkC9nZng2roIte46pITpbFo+Cu3ssChMKEwoTChMKEytfVRQmFCYUJmEfFgoTChMKEwqTyF6hjDCJjJDzf6cwcf49+BjPIEYJk+u37qHf8Jm48Oc1pEudEv49WiBrpq/VfWl6/g/d96dg0sS693kaEqJ7n+Q2LIf6+I3+fOTEuEoOV8nRCijnMOEcJuFVVhQmFCYUJhQm5vUD5zCxXGNySI6BCyNMGGGiuwPkhB0oTJwA3QWyjFHCJKL7QWFioENhQmFCYfJfTcE5TCzXmhQmFCYUJhQmFCbWtfIpTChMtJLCCBPrnhlnbkVh4kz6H2/eFCY67y0jTAzAHumcWVz2iecmo+X1JTd9E8yrg3NZYQNjRpgwwiS8p43ChMKEwoTChMLEuvYIhQmFCYWJdc9KdNiKwiQ63IWP7xw+GmHy8d0aXhEJkAAJkAAJkAAJkAAJkAAJkAAJkICzCFCYOIs88yUBEiABEiABEiABEiABEiABEiABEoi2BChMou2t4YmRAAmQAAmQAAmQAAmQAAmQAAmQAAk4iwCFibPIM18SIAESIAESIAESIAESIAESIAESIIFoS4DCJNreGuDPKzfgHzgPF/68is9TJINP+wYoUSiHOuMDx84hcNoy3L33CNkyp8ewvp5IkTxpNL4a204tNDQUE2atwurN+xHy9i2KF8iBgd2bI75HXLx6/QZ+gXNx6Pg5xPeIhzaNKqNetVK2ZRRD9nr0+BkqNe2Nrq1ro3710i5VFl6/foNc5T0Rx2SZ7tJFcmGsf0eXLAsxpMiq05wyfx1+3LBH3adiBb6HX/cWSBA/Hq7duIMBo+bgwqWrSJ0qBfp3bYrc2b+NSZdm9bn+dvEKBo9bgEuXryNVyk/Rs30DFCuQXe0/a8kmLFu7C6/fBKNs8bzo16UxYrvLegwfb5o8dw2Wr9+D/Wsmqot0pbLQoP0g/H7xCvBuVvMkiRIYObjKuz0mluzdh85g7PQVuHv/ETJn/BIBPi3xdbpULvUMSxskYOw8HD31K+LHj4fGtcqhdcNKLvcMa+X3+Jnf0bLbCGxYMBzffPmFS5UFeRbmrdiKWLH+W9Bh+TQ/ZMqQzqXq85hYl/Gc9ROgMNHPzGF7VGvRH7UrF0ezOuVx6MR5dPObhP1rghAcHIyKjXth8jBvJUsmz12Ly9duYlxAJ4edm6MyWrVpv+poTRvZQ3WUO/QdhyL5sqFtk6qQBrd0Pob3a4v7D5+gccchmDHaB999k9ZRp+fwfPoNn4kTZy+gTcNKSpg8ffavy5SFew8eo3rL/ji0btJ73F2xLDi88NmY4ba9JzBh1krMG98XiRLGR2ffCcjzfSZ0aF4dzbsOR+miudGkVjkcPvkLBoyajR3LxyBO7I9LFpuZs0EAACAASURBVIj4LVe/B7q2qYMq5Qph75Gf0HvIdBxYG4Szv/wJv8A5WBjUX0mkzr4TUaZoHjSqWcZG4tF/t8vXbqFjv/Gq/tKEiauUBbk7lZv2wYRBnZExfZowN8uV6vPoX0rDnuGtuw9QvUV/TB3RHTmzZkTQnNX46ZeLmDuuD6TT7CrPcO+h05Egvgf6dW4MYSLyb9LQrsiV7VuXqc+1kiEfcRp2GKwE2rwJfZUwcaWyEDBmHr79Jp3Fd5Ur1ecxrS7j+dpGgMLENm5RvldwSAhWb9qPmpWKGzsP+St5YeXMAMiXShEJIgckSSOrWM0uOL5pKuLGjRPl5+bIDM7++ifixY2jvuZImrl4I/66chPD+3miWvN+GNy7NXJkyaB+GzV5KRIm8EDHljUdeYoOy0texJPnrcG36dPi2/RplDDZtve4y5SFv6/eRPs+47B1yaj3mLtaWXBYobNDRud+/xtv3rxB7uzfqaPN/3Ebfr1wGb06NkTFRr1wZONkYzRFHU8/9OrQEPlzZbZDztHnEC9fvYaIo+oVihhPSqKlNswfhrnLtiBVyuTwbFxF/bbn8BnMW74V8yf0jT4XYOczkS+y9aqWwvCgxUqYiPB2lbIgKEvU6orl0/2Q6rPkYci6Un1u5yIV5YcTOXD2l0uoUDK/ykvaYfIBZ8/K8SpyzFWe4Z0HTik58mmyJIpD256B+KF0ARQvmMOlnmG5dvlQExoKbN9/EuMHdVLCxJXKgs+gqShRMAeqli8c5vlztfo8yisfZhAtCFCYRIvbEPlJnPvtL3QdGITtywIxe8lm3H/4GP26NDHuWLxmFyyY2M8YHhr5EWPeFjdu3VNfXyW6pELJfMhRprVqbCdNklBdzPJ1u3Hy5wsYPaB9zLu4SM74zZtg1G3rjzH+HbB0zS6jMJm+cIPLlIWff/1T3f9vvvoCF/+6jkwZ02GAdzNV5l2pLMT0wu3VeyxKFc6pvkwNGjsfa+cOMV5Sj4ApKJA7C+pVLRnTLzPc838THIJVm/Zhxfo9WDlzEDx7jkaD6qVRrnhetc9fV2+ipfcI7Fs94aNksHbrQRw9/St6d2yovthLHX763EWXKgsiy4oX+B5nzl9UQ2m9PeuoDqcr1ecxvXDPXroZv/5xGWP8OqB1j1Eu9QzLvZOouTPnL6FHwGQsmuSL23cfutQzLFFy0iZfMd1ftc00YeJKZaFdrzF4+zYUl/+5BTcAdauWVOLf1erzmF6X8fytI0BhYh0np271z8278PQJRP+uTVA0f3aMn7kSISFv0cOrnvG8yjfwwcQhXYyRGE494SjIvH67AJy/8LcK/evbuYmazyRn2dY4tW0GPOLFVTmu23YIO/efRNDQrlFwBs495JR5ayEdra5tamPI+IVGYeJKZeHPy9excOUONKtXAWlSpYAw2XfkLH6cGeBSZcG5JfHDcpd7dvLsBcwI9MHxM79h4qxVWDbNz3hQ35Gz1ZC6ZnUrfFhG0XRviR7p3H8iUn2WDBMGd0HWTF+roYRezaqpuV0k3bx9HzVa+eLYpqnR9CpsPy2Z/6BRx8Fq+FGsWG5GYXL45HmXKQvSwZChZxVLFUChvFmw7/BZ9Bk2Xc2BsGztbpd7t9tempy358Hj55QcWBDUT0UJudIzLNQlqrlI9U6IEzs2BnRrhhoVi8KVnmFh0KrbSCUHCuXNChk+rwkTVyoLMxZtQOJECVC7UnFcuX4bIlD6dm6sIr1d7d3uvNqIOTuKAIWJo0jbmM+FP6+h64Ag9O7UEKUK51JHkUrqzr1H8PVuajxq0eqdsWSKL75M87mNOUX/3STMT0K4kyVNpCaHlKiCvavHI1nSxOrkl67dhZ/OX8JI33bR/2J0nKHplwwZnmQqTFy1LAg+EUj5KrbF9mVj1PwQrlAWdBSbaLWpfI2UZ1fKsogCmbRZvq77Bc7D+nlDjefa3X8yCufNhjpVSkSr87fnyYjsPnbmVzWHiXydHDB6DupWKami5iSJGGzjM1qF+n9sqf+IWSqcX+7vw8dPjcLEVcuCdn9liFLtyiUgUZSu+G6PSeV8484jmDp/HaaO6GZsb8nz6irPsHavpE43tE0moVPLmvjs06QuU59LlNyRk78Y25qmwsQVy4JWJmRy99t3HyiB5orv9phUj/Fc9ROgMNHPzGF7yKoBElkiK+CYrhyxY/9JLFy5XQ3BkSShkJWa9MaxzVM/upUV9h89i7SpUxpnHz925jf1ZWfTwhGo0dIXfbs0RoFc/1McBo6eg7RffKaG7HxMSWYhn75gvXF1mOf/voS7eyw0qllWfaF2lbIgE6s9efocGb42TJQoE67lqdhWhfS39B7pEmUhppZrmV/o1t2HqoGpTegqHeay9Xrg4LpJSqBI+qFxL1XfSaf6Y0oyYbGsKiETvmqpWZdhaFC9jJo4MkmihOjUyjD30sYdR7B220HMCuz5MSFQ11K4WkfjO0o6XA8fP0PyTxJjYVA/1Go90CXKwr8vXuHi3/8Y594SLk06DUXTOuVV1I2r1OcxsXDvPngaE2evxqwxPcOsSjhs4iKXeIblmZX58yqVKaAmfpU0ac4aNSy4S5vaLlOfy9Dg0+f+gPu71WEePXmmIi2G9m6jIm1cpT4XBtkypTfOnSgrWkr0UceWNVymLMTEeoznbBsBChPbuDlkrxbeI9CwRmnjJGNaptJhLt/QR42dzZsjE4ZNXIwXL16piVA/tiTLlv128SrGBXRUk78OmbBQVciylOy0BetVZ2P8oM7458ZdNPcejiWTB+CrtB9vlI3cX9MIE1cqCweO/QyZlX3+REMYtEyAKyurLJs60GXLQkx43k/89DtGTFqi7pPpktBy7q27j0K+nJlVaPOWPcdUGO+WxaOUEPyYksihCg17InBgezVXhUQOijBZNKm/qs96DZ6GRZN9kTC+h5oPoWGNMqj5Q7GPCcF712IaYeJKZUGGJZVr4IMJgzupaCqp13oOnqY+AsjwUld5t8e0wv346XPUbOWrhpPJkFDTJB1HV3mGZVWcQnmyqqiSJ8+eq2EYUldJneUq9bl52TWNMHGpsuAVoN5nXs2q45+bdyB9lgCfVihWILvLloWYVq/xfK0nQGFiPSuHbinzlkgD27yDIQ3ussXyKIs9ImgJ5MtlzmwZMayPJz5Jmsih5+iIzORrnHy9kUiT4OAQfJ8lA/x9WqgOs0QYSNifNDhlzGSHFjXCrELhiPNzRh6mwkTyd5WyINcqqyQtWbMTr16/UV82/Hq0UI1XVy0Lzih/evPsO2wmNu48DHf3/5YKzvh1GrXi1/Vb9yBLZYtASJc6Jfx7tFBRUx9jOnDsHMZMW46bd+4jSeKExqXB5VplpZyFq7ar+SsqlSmInu0bqGiDjzmZCxNXKgsy91Lg1GW4c/+Rqr9kxaiCubOo2+1K9XlMKt9rthyAzLFk3ibbu3K8anu5yjN85Z/bKsr3t0tXVLSYTFbdp3NjFTnoSs+wadk1FSauVJ/LkCz/MfPw+6WrSJIogYqSk/8kuWpZiEl1Gs9VHwEKE328uDUJkAAJkAAJkAAJkAAJkAAJkAAJkIALEKAwcYGbzEskARIgARIgARIgARIgARIgARIgARLQR4DCRB8vbk0CJEACJEACJEACJEACJEACJEACJOACBChMXOAm8xJJgARIgARIgARIgARIgARIgARIgAT0EaAw0ceLW5MACZAACZAACZAACZAACZAACZAACbgAAQoTF7jJvEQSIAESIAESIAESIAESIAESIAESIAF9BChM9PHi1iRAAiRAAiRAAiRAAiRAAiRAAiRAAi5AgMLEBW4yL5EESIAESIAESIAESIAESIAESIAESEAfAQoTfby4NQmQAAmQAAmQAAmQAAmQAAmQAAmQgAsQoDBxgZvMSyQBEiABEiABEiABEiABEiABEiABEtBHgMJEHy9uTQIkQAIkQAIkQAIkQAIkQAIkQAIk4AIEKExc4CbzEkmABEiABEiABEiABEiABEiABEiABPQRoDDRx4tbkwAJkAAJkAAJkAAJkAAJkAAJkAAJuAABChMXuMm8RBIgARIgARIgARIgARIgARIgARIgAX0EKEz08eLWJEACJEACJEACJEACJEACJEACJEACLkCAwsQFbjIvkQRIgARIgARIgARIgARIgARIgARIQB8BChN9vLi1GYFSdbzRs31DVCpTwOXZXL1+Gz807o19qycgRfKkdudx+twf8Bk0FatnDcYnSRPZ/fimB9y+7yQmzVmN5dP9Ed8jbpTmxYOTgLMJrNy4D3OXb8GmhSOcfSrRIv+orNcfPX6G2m0GYtQAL+T5/rsovd5/bt5Fow6DMXd8H2T4KnWU5sWDk4CzCUR1G8TZ16c3/6is10NDQ+HVe6yqw9o2qar31HRvP3TCQjz/9yWG9fXUvS93IAES+HACFCYfzjBGHGHzrmPoOXiq8VzjxY2DNKlSoHblEmhRv6LN12BrwzpgzDys2LAX00Z2R7EC34fJ/96Dx5Dj5siSEYsm9bf53PTueP7C36jfLsBm4RGVjZUXL1+jarM+6O/dFKUK54LGT7vGRAnjqw6BV7NqKF4wh/qzcCxRqyuyZ06PZdP83sMxaNwCLF+3G7MCe6JQ3qyYu2wLVm7aZ+w09ho8DUmTJET/rk31ouT2JBAlBLQyrR3c3T0WPkv+CUoUzgkfr3pIEN/DpnxtbVhr9WqrBpXQw6vee3lXa94Pf165gbO7ZiO2u7tN52bLTu37jEOqz5LBr0cLW3ZX9W9UiXDvgZOQ+vNP0atjQ2MdFdn9rNy0D67fuqfq5qSJE4a5pq17jqNHwBTVaenaprb6LW/FthjUs7US+T9u3Iula3Zh5cxBiBXLzSYe3IkE7E1AyvTla7eMh02WNDFyZcuI7u3qIf2XX9iU3Ye0QfQ+YzadoM6dzNskOneHrfW6NfksXr0T67cdwpIpAyDvIWvup952r2k9/vLVa9Ro6avKR/kSea05RW5DAiRgRwIUJnaEGZ0PJQ37AaNmY9MiwxfU16/f4Ojp3zBk/AKMHuCFCiXz23T6tjas5cWxY/8pFMqbBaMHtA+T94Ift2Hq/HXI8HUaCpN3ZOb/uA0bth/GypkB6i/C7+r1Oxjat43699Nn/2LF+j1YsX4vdiwfg5QpPjF2RqQhJuLp63SpjJyDQ0JQqrY3Xr8JxviAThaFyd9Xb6Jm6wHYumQUUn2W3KbywZ1IwJ4ENGEydUQ3fJchHUJC3uKvKzfgFzhXicQB3ZrZlJ2tDWupV4dNXIQ4cdyxa8W4MB3y3y9dRUvvEXjy7F8Kk3d35beLV9C44xBVR32aLImxjorsfkpn5PGT5+jSpjbqVS0Z5h537j8BJ89eQIMaZSwKE6nrKjbsCZ/2DVCxlG3vOZsKFXcigQgISJmuUDIf6lUrhdBQ4M69hxg/80fcf/AE6+cPs4ndhwoTPc+YTSeoc6foKkxevX6DcvV7IKBnS/XekWTN/dTb7jUX3/KRcdGqHVg3dwjc3Ch/dRYnbk4CH0SAwuSD8MWcnaVhP3D0bJzcOiPMSddqPQBli+VBhxY11N9P/PQ7xkxbjj+v3ESK5ElQtlheeHvWUQb9TXAIhgctxtbdxxA3bhy1z7QF6+Dj1QDSEJYhI4sn+xqPL43YNj6jsX/NRCRJlCBMvvLiePr8BfYePoN9qyciYYL/vgzXa+evol/u3n9sFCbnfvsLo6YshXRC4sWNizLFcqN/lybqPOS8pNOyc/9JFbL4zVep0bNDAxTI9b8IfzO/e+YRJqMmL8WjJ8/wSZJEOHr6Vzx49BTVKxRBt7Z11a7Sees7bCZ++uUivkj5Kdo0rqz+rQ3JkXDwoRMW4eyvl1THrnyJfOjXpYka4iLRPi9fvUHQkC7qWMKuTY/RWDNnCL5K+/l7BatSk95oUf8HY2dB+N26+xDS0dCSdMwKVemAeeP7IF/OzMbOiHxl/TLN5+jcqpZx231HzmLm4o24duMORvRra1GYyMYtu41Avpz/Q4fm1WNOYeeZfrQENGGyfLofsmVKb7xOqZcu/HlNlX1JN2/fx5AJC3Hm/EXEjRMHubN/B1/vpkj+SWL1u/x90Nj5SjrmzJYR+XP+D+u3H8LSqQNRvGYXTBnujcJ5sxmPL89BtkzfvBdFIvXq/BVb8eTZcxXNUTB3FuM+o6cuw41b9yDD27QIEzl/Ca0+/tPveP06WH1Rlv2kvpO0ZM0uzFu+BXfuP0LKTz9B83oV0bhW2Uh/M7/hpg3tPYfPYPjExejYsiaWrN6JO/cfIst3X2OUr5eqdyOq16XukOg2uRapq6VD9X2WbzC4V2uk/eIzXPr7Ouq288eSyb7437dfqdNo3WMUUn+eAoN7tXqvHPoHzsPzFy+Mktza+ymdkSzffYVbdx5iYVA/43EfP32uOi55c2RCpgxfWhQmsvGU+etw4qffMHecoXwwkYCzCUiZrlO5BFo2+MF4KrsOnIa3XxB+2jFbtbkkLV27CxLNcPvuA6RLnVJFBFcrX0T9FlEbpFO/8SiYJ6tqv2lJnoP9R36yGHGq5xmT9sz4mSuxadcRyBA7+RgjEWNa/XfszG8YPWUZ5KNLfI94KF8yH/p2aoQ4cWIjot/M74mpMJG2Xf5KXpgwuLOqc6V9KPXXyP7tkDF9mgjrdW2o5ZGTv2DC7FX48/J1JEwQHy3qVVQ8tegNqWub1imvjjVtwXps2nkEq2cPVudtmjbtOorAqcuw+8dxRnFhzf3U2+41FyYiaqSNN2O0j6rzmEiABBxHgMLEcaydmpO5MJHxl/Li6tx/IuaO7606H7fvPkSFRj1Vp75mxaIqlLxtz0C0alhJvVgWr96BmYs3Yc643iriYPSUpVi37RCG9G6DTBnTQcLPJRpBXuqSpBNz684D9YIzT/LiiBcvLn65cBm1KxdHjYpF1SYSotq081B4NauOLbuPKWEi51q6bjdULVdYNfrv3n+k5EL9aqVUY0MaFMvX7cGsMT2RNEkirN16AJPmrMGuH8fixw17w/3NPETeXJiMnb4CS9bsRODADihZOKfiUb1Ff6yaNQiZMqRD1wFBKrJj/ODOePHiFXoPna6EkwiT5J8kQY2W/VEwTxZ0a1sPL1+9Qp+hM9TcJkP7tMHDx09RpVlf9bKXjlndtn74oXQBtGlU+T1Wd+49UiHyWxaPVOJDkrkwkU6PRObIPdq4YAQSxI9nFCbjB3VSjZftywKNxxZhI0OeZizaoM7B0pAc2XjqgnU4eupXzJ/Q16nll5mTgNZBkGFmpsJEpEf7PmPRoHpp1diV+qJmqwHImulrVZdJY1iGgSRJnACTh3krQVC2XndV54j0vXDpKrr5TYaHR1w1HK27/xTEjRtbiURJ8qyKRFk1azC++yZtmBsh9erspZtU/SB1nTzbkt6+DUXZ+t3Rq0NDNVxEEyYyB9GDh08w1r+j6hD5jpytorxEfJrKh+++SYdfLvwNz56BWBjUH7Hc3Ixiwvw383OS/E0b2geOnUNn3wloXreCkr0iQGq19kXjWuXQpHa5COt1ESZS11y6fANj/DqoIXrSkZC6efOikSqiZvLcNTh04ryS5TsPnMKQ8QuxYcHw9yS5nJfM8dS6YSXUqVJCcbIkTMzvp2wnnRGvptUQMHYe1s0bZhRM8sX14PGfESd2bFU3WhqSI/uf+vkPJXKObZoKGY7KRALOJmDewZYPHv6Bc1X9pH1I2bb3hIqemzK8G7L/7xvsPngKPQKmYtm0garNFlEbRJ7FWUs2YceyQGOnXj6QSXtLnn3zpOcZk2jWoDmrVd30xeefqqiH2Us2qbaPyIViNTqrDzS1KhdXYkOiwGr+UEzJ34h+Mz8nU2EisiB3eU8UK5Ad4wI6qw9PMmz45evXmDi4S6T1unzAkjaZf4+Wqq11+dpNtOs1Bp1a1VLvAmkPC095B0idLO3ZGaN7IFe2b99jJffk5cvXGOnbzvibNfdTT7vXvB7XMmrVbaSaN0XawkwkQAKOI0Bh4jjWTs1JG2svc11IevXqNeDmhp7tGxi/YMrLdfOuo8qoa0m+IogEkMZwC+8RyJ75v6+s8rWxSPVO6kulNKxl/g+ZS0CLRihTtzv6dmmsIljMk7w4JDrk2/RpsWXPMcwe00ttIqJDGg4ZvvoCG3YcMUaYSHRHogQeah9J0iiXjow04qXTL8N75MujRzzDBKXyBUQ6JBH9Zn5OloTJwePnwvAQcSGdoHIl8qqXt3TAtDlYdh88jc6+E1Wj4co/tyEvtmObpxrP6edf/0SzLsNwYst01ajYuPMIpsxbi4Y1ymDt1oOqE2hpngP5KtKh33ic2T7TeMrCb9Xm/errjaR/X7xUkkY6YtpEilpn5PD6yZCG0uiB7ZE7+7f498UrJWCkw1OzlW+EwkSuyXfUbMgxmEjA2QS0Mi1CMFasWAgODlFCRCK/BvVqpZ4fec4adRyCg2uDjJMjS4deGsents1Qv7fqPhJHN041RraNmLQEB479rBrL+4+eVZJj/5og1ShftWk/Fq3arqK/zJPUq7OWbMS4gE5Keko0ndRB0vgeEbQYY/w7omqzvkZh8uz5C3UIrR6WDpFEnMh+EvXSuvsodQ7SCTGtxyL6zdI9MRcmXr3H4MjGKUaJ0XvIdCRI4AG/7s0jrNfLFc+D/JXbY8owbyVVtXMqUNkLU4Z3R/5cmfHmTTBqe/qhSa2ymLlkE3p1aIByxd8fYy8dwZxlW2PplAH4PksGdSxr7qdsJ52R7m3rYfu+EyqCsF1TwySLUp82q1tBCZyIhImI7YJVOmDt3CHqncNEAs4mIGX6xu37iBsntpK8EkEhEiRoaFc1pFaS1FkSvdG3c2Pj6cokxvlz/Q+dWtWMsA0ibQyRyzJHmUQjiIis0qwP9q6SDzqGSDvTpOcZE3khbQ4Z7itJ2mJFq3dWovTLNCmR/wcvDO/naRzqrbXHZHhceL9Zuh+WhInIEYkwliRDKeet2IqNC4ardmpE9bp8/Dl0/HyYYd7S5pV6X/sgJCJE3ikilSXCWoS7pdSk01AlyU0/cFlzP/W2ey3NRSXvKpHz8iGMiQRIwHEEKEwcx9qpOWlzmGhjY+WlIF80JdS6arlCylabh0vLCUsEybgZP2LvqvH4oXEvFSIuX3K1JF9e+3RqrISJRHosXLlddcRlCI1Ep0hHwDycUfbVXhzyFaJk7a7YvGiUaiTIF8jAge1x/ve/wgiTvYd/UvJDXvryVVMaF4XzZlWNC2l0S8NCwt8L58uG0kVzq7HB0nmK6DfzG2JJmPzx1z9qYlotVWzUSzXWJSpEol6Ep7b6wl9Xb6rOkQgTeQnL12NLadvS0SqcXZJ0ZKQzt3yanwqTt5SkMzBy8lJ1D7Qk/ETKDO7dWv3p5ctXahz/uJkrFb+i+bMbOyPSUZqzdDMeP3mmwv837jiCzbuPqq9Wcv8iijA5fe6iivj5edccY4iwUwsyM3dpAloHe9KwrpBIC+loSONx+sINCA4JxuwxvZWAleF08hxqSYbolK3fw1g3jZy8BAfWBhl/l8mPF6zcrmSFNO7l2ZZhfVXKFlLPaIFcWcKEzms7asJEJLN0ZJrULq/qwoGj5+CbL1OjeKEcYYSJRKmNm/4jzv3+F96+fau+iqpov01TVb79hs/Etr3HkTdnZpQomAPVKhRRk5xG9JulAmEuTLwHBilZpKUBo+YgJCRErbgQUb2eI2sGlG/gY7HMDendWn01lqRJqlKFc6o62VLS7p28H7Rhh9bcT6nvtc6cRAHJ8Eu5TzfvPEAdz4HYu3I8+gybEaEwkfPJUaY1ZgT6qKGaTCTgbAJSpkUs1n03J4+8n9dvPwyZxHjFdH/VHjIfiivnrKLSXr9BD6/6EbZBJJq1u/9kJE6UAAE+LTF76WbVRjAdxmvKQM8zJu0vGZJy4Pg5FWkh02nIRy2Jvs2c8UsVcRI4bbmSk9IWkQgO7ZmP6Dfze2JJmMgE9jKRvSSZ102G2OxcPka1ayKq16VulfasefoiZXLsXDFW/VnEatXm/VQbUyRMeJOIS3Rw83oVULfKf/MpWXM/9bZ7LQkTedcdOnEOCyb+NzTR2WWZ+ZOAKxCgMHGFuwwgvDlMJGzTJ2AKTm6bgSHjFoYZXy5o5AUjLyGJMJCIkbZNqqC+iTCRMaUS4iidBBlPLl80ZBjNtj0n8Oz5v+Gu0qC9OOTLiYSpZ/3ua/UVRF5q8pVCOjBahImIHVmG0t+nBaqWL6xEiFj26zfvGhvn0umQzr2IFRlXK0OGJFxUokwi+s309lsSJhf/vh6mgaEJEzlX+X8512/ezWgv87jU8fRTHTX52iFzJIisCC9JZ6mupx+u37qr5gQIb0JCESYyf8uelWGFifkcJpLPsImLcenvf9SwKa0zIudw/8FjNdminJuE59eoWEzlF5kwkS/b8jWFwsRFKopofpnhzXkh0W6Fq3VUjchbdx+EK0wkwkBkrkTOiczVkkyqLGHm2lh3Ecl/XbmJUb7tULxWV2xbMtr41dcUkakwkf33HP4JEwZ1Qsk63lg3d6iap0mLMJH9ZPLRYgVzwMervopukQiuvsNnKmGiJZEqUo9Jx+nWnftqvgFtjpOIfjM9L3Nh0s0vKMz8VabCJKJ6XSJIpE4XISTDEMNLwsEvcI4SwStmBCBO7PdXBNLunenQQmvup0TMaZ25UkVyoUy9bqreP3b6N7VyzsBuzVREUEQRJnLeEt0yfTSFSTR/xF3m9CzNeSEXL1GfMudH+2bVwxUmEqkmq3JF1AYRYSJzlfUZOh371wahaachaFa3omqrWUp6njEZ0nvtxl0V5SBtLS2CSxMmcnx5tiX/3YdO49Dxcxgb0AmlixgmSI3oN9NzsyRMTIdjmgqT1Zv3R1ivB4ydj/sPH6vhO+ElmdOtXlt/vA0NVfJH+7Blvr0IExmmrg0tlN+tuZ962r1yTEvCByWuSAAADktJREFURD4cSuQzhYnLVBW80GhCgMIkmtyIqD6N8ITJjv0n1fj+E1umYena3crYS6dCS9KxkAlPl00diAbtByF/zsxqWTPtpSeNaVnlRnsJd/ObpBqumyUqon9bNdmipWT64pBoDBkHnyNrRjXHh2fjKmGEyZotB1SUi2kHRzr/ElYqDWcJDQXc1LwdkiQ8tFiNLvhxhv+7rxqWf9MmKdTOT48wqViqAPL90A5TR8iyyNnVIbRhTyIlZD4YmbxWvlzIFwxJMhRGhg9o4bASIirDbWRJUlnBSOYekTkCzNPhk+fRsd+E94bkWBYmi3Dm/CV17abCRCbdbeAVgIY1yyoBJvJFxvJHJkzUkJyRs3F4A4fkRPUzyuNHTiCyDrYMkZPVVxp2GIwDaycaQ8YliksiRURMnPr5Ijr2G4ejG6cYvyBKfSQTsWrCRCRtnbZ+6Nm+PnYfPIPZYw1DBs2TqTCRoYRl6nZTy3Bv2nkUMwN9oEWdyRwmUidItIapZJX6VSLz5LxEoD5//sI4jEjmQZEIiuoVi6JRzbLh/iZzk5gnPcIksno93w9eSkqIrNaSiApN4oisklB/GZo5YdZKlCqS2zhkxvS8IhqSYz6JrybA5H5K6LvWmZNQ/DHTVqjDylfWgd2bI2fWjJEKE61DJ8OqLM35EnnJ4xYkYF8C4XWwZenYIvmyqQg3qbOkPWU6NEQi2eSDTfvmNSJsg0hbSouWkwhiiQjRhgxauhI9z5hE63k2qmz8eCb1q0QUa/O73X/4RLXltCRRYTdu3UfQ0C4I7zeJGjRPeoSJzNUUUb0+b/lWLF+/G1sWjzJmI+8TicDR5jWSIZEyCbi002QScRnOZClJ+1PkrfmQHPNJfGVf0/upp90r+4Y3JEciJi3NDWjfEsqjkQAJmBKgMHGR8mC+rHDo21BcvXEHQ8cvRNrUn6nhGTK5qEz66tu1KapXLIILl66poS6dW9VUL0aZb0Mm2ZOOQMpPk6nhPFv3HMOgnq2NwkS+jMrkp0kSJ8T2paPDXfrM9MUhL3WZIDGWWywsmuyrBINphMnxM7/D02c0Vs8ehFQpP1UzpO86eFp9xZSvr/J1UaJIfL2bqfB1WRVComZ2rxyv5gcI7zfzcbx6hImEosucLjIZo8wNItJGxIKEvGqr5MicLik/S4bBPVupSBeZBPfmnftqpQbpSNVv54+lUwaqGd5lUjR5cUuIvHmSjpYMEZAXvYwPlmS+rLDMI3Dut7/VSkidWtZSM7+bCxPpmMnEbDJsaVBPwwoWkQkTmeDxyKlfOOmri9QT0f0yzZcVlvN9+OipmpxYIkc2zB+uIjckIi1rpvSqoyGdZRG5qVImV3MeSTi5zOEjQwvbNa2GX/+4jL7DZqhJqDVhIset29Yfskxnn06NjENPzPmYChP5TULgZXiKt2ddVClXKIwwkWGQhap2VKt7iQSRVWfmr9iGn3/7U0XwyYoMC1ftUBM+fpU2lVouWcbk+/dooSa6Du83GYJonvQIk8jqdQmtl9U7Jg/3VhN6r9y4FxNmGcLgZS4WWRlMhkOJOJdJvGVeEVn+PP27yDvTc5PhP20aVVETT0qy5n5KHqadOVnhom3PMWo+K4lWkRRZhImsQtaqOyd9je7Ptyudn+kytHLdMnG8zCE3fdEGFR2bI0sGyActiQabOdoHmb/9Sg3XkyhcERMy3CWyNogcVyZ8X7lpH8oUzW2xfaEx1/OMNe86XK2EJcPy/vjrmlqFav+Rs0qIpPniMxWlIf8vc62I/PQZNEWtYiXDj8L7TepZ86RHmERWr4uoqdDQR9X5Mjm4/LuL70SUkVUim1eHfJiT9o4Ms5bhisKjS+vaFut+GXIp87jIcGZTftoy0eHdTz3tXjmGJWEiUkfmouOkr65UW/BaowMBCpPocBcccA5a9IOWlazhLmNkSxfJrVYWkM66JBEek+auwZV/buGzTz9RXzZlZnPZXl4Qg8ctwK4Dp1RjVSYdW7BiG9o1q6bG+kuSSb1K1fZWL0Z52YSXTF8c6qU+dZnquGjLPpoKE/l90LgFqkMhEyrKyg4y0aqseiBfF2U4i/+YuSpEW8SBNNTl3EoVzqXG1Yb3m/m56RUmYvklnF46avK11bttXSU+JHpD2Mqs7DI57cmzvytesuSedODkC3jTzsPUlwwJzZck86/I2NmJQ7qor0vmSeZ2adXwB+OYWeEn8kpLIo/Spk6pQkQlVFSSuTCRhkuJWl0wa0wv45J0kQkTmbhW5lPgssIOeEiZRaQEtDJtuqFEZeX5PpNaPlObT0iWs5ThaTIXUvz48VCycC74eNUzRpRI1NywCYtw7eZd1fiUUPFFq3YaO+ByfFlxSqIZZK4T02XPTfM2FyYSgi6dA23CWNMIExlKKI1yiSqRL5glC+VEn86N0NJ7JO4+eIRdy8di0rw1KspP5jNImSKZqkcl4k6k8riZP1r8zRI0PcIksnpdzlXmUJLO2ps3IcicMZ2aLFwmbpUvyyKJRDRpX5TlHSGdKAkZl/eGaZJJFaVjOGqAV5g6KrL7adqZk20lek/uqVYvRSZMRKgdP8NlhSN9wLiBwwhImZZVAbUkEbIiQbyaVUPxgjmMf5+5eCNkuMm9B0+Q/stUavJjWX1PUmRtENlG5mGTYT4ysb62n6WL1POMnfv9b/QfPhM3bt9D5oxfqdXB5DzlA9qM0T2VaJahIzKpbeKE8VGiUE41ca3UozLBfXi/mZ+XHmEi+0ZWr8uKf9LWlKGNyZImQpWyhdHVs7ZaGlmGTsqqNxp7qe8CxsxXAsU0WkbykQn7ZRXFXSvGhllWOLL7qbfday5MZO4ake7TRxmWUmciARJwHAEKE8exdomcRFCUq98D6+YNDXf8p0uAsPNFyhwLIoxkMjhHJXn512w9QHUiZZwyEwm4EgGZOFbmZdKWCnala4+qa5V5niScXYYqWlqpIyryFdkkkZMip8ObJyoq8uUxSSA6EJCI2xFBS7B1yahwI36jw3nGpHMQySztXPlYV6LQf3Irqq9BVgWSycnXzR3CexnVsHl8EjAjQGHCImEXAjLe/snT52ouDlnqVvuCaJeD8yBq/pNqzftiQLfmDntBy9KjSRInUHMyMJGAqxCQuTaOnvoF3f2nYNnUAcjwdRpXuXSHXKfMmZXmixQqSsURSToZS9bsxMqZg9TqF0wk4AoERBRKpEeHvuPQqmGlMCu6uML1R/U1Ll69Ext2HMaSyQMcUq+IpJH5ULq1rYvyJd5ftj2qr5fHJwFXJ0Bh4uolwE7XLxO3duo3AYXyZlXjOi1NXmqnrFz2MDIOv+egaWr88idJE0UpB1k9acLMlWrFi/gecaM0Lx6cBKITAZkEVULdpUMv85Aw2ZeAhL/LHDOjB3qFOym4vXKUyWllksw5Y3tRfNkLKo8TIwjIkJFl63ajduUS6NWhASMS7HzXZG48r95jIat4tW1S1c5Hf/9wMnGurI5kaZ67KM+cGZAACYDChIWABEiABEiABEiABEiABEiABEiABEiABMwIUJiwSJAACZAACZAACZAACZAACZAACZAACZAAhQnLAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlETIARJiwhJEACJEACJEACJEACJEACJEACJEACJGBGgMKERYIESIAESIAESIAESIAESIAESIAESIAEKExYBkiABEiABEiABEiABEiABEiABEiABEggYgKMMGEJIQESIAESIAESIAESIAESIAESIAESIAEzAhQmLBIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQGHCMkACJEACJEACJEACJEACJEACJEACJEACERNghAlLCAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmYEaAwYZEgARIgARIgARIgARIgARIgARIgARIgAQoTlgESIAESIAESIAESIAESIAESIAESIAESiJgAI0xYQkiABEiABEiABEiABEiABEiABEiABEjAjACFCYsECZAACZAACZAACZAACZAACZAACZAACVCYsAyQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQMQEGGHCEkICJEACJEACJEACJEACJEACJEACJEACZgQoTFgkSIAESIAESIAESIAESIAESIAESIAESIDChGWABEiABEiABEiABEiABEiABEiABEiABCImwAgTlhASIAESIAESIAESIAESIAESIAESIAESMCNAYcIiQQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIUJiwDJEACJEACJEACJEACJEACJEACJEACJBAxAUaYsISQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgBkBChMWCRIgARIgARIgARIgARIgARIgARIgARKgMGEZIAESIAESIAESIAESIAESIAESIAESIIGICTDChCWEBEiABEiABEiABEiABEiABEiABEiABMwIUJiwSJAACZAACZAACZAACZAACZAACZAACZAAhQnLAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlETIARJiwhJEACJEACJEACJEACJEACJEACJEACJGBGgMKERYIESIAESIAESIAESIAESIAESIAESIAEKExYBkiABEiABEiABEiABEiABEiABEiABEggYgKMMGEJIQESIAESIAESIAESIAESIAESIAESIAEzAhQmLBIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQGHCMkACJEACJEACJEACJEACJEACJEACJEACERNghAlLCAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmYEaAwYZEgARIgARIgARIgARIgARIgARIgARIgAQoTlgESIAESIAESIAESIAESIAESIAESIAESiJgAI0xYQkiABEiABEiABEiABEiABEiABEiABEjAjMD/AV2Butw3AxXUAAAAAElFTkSuQmCC"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   **Insight:** Sleep acts as a \"risk multiplier.\" In the Short Sleep group (<6h), the dark pink regions (high mental distress) are overwhelmingly dominant. Chronic lack of sleep appears to make the individual significantly more vulnerable to the negative mental impacts of high BMI and poor physical health.\n",
        "*   **Conclusion:** Sufficient sleep functions as a biological \"shock absorber.\" Even when BMI or physical health metrics are suboptimal, individuals in the regular and long sleep categories maintain significantly lower average levels of mental distress compared to those in the short sleep group."
      ],
      "metadata": {
        "id": "Std4XRFecd0m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "l39e7Gw8cjN8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Q2: How do habits like exercise, smoking, and alcohol consumption interact within General Health categories to shape Mental Well-being?*"
      ],
      "metadata": {
        "id": "a4P0w4DU1k7j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================\n",
        "# Q2: Lifestyle Hierarchies (Sunburst Chart)\n",
        "# ==========================================\n",
        "\n",
        "import plotly.express as px\n",
        "\n",
        "# 1. Aggregating data for the hierarchy\n",
        "sunburst_df = df_clean.groupby(\n",
        "    ['general_health', 'physical_activities', 'smoker_status', 'alcohol_drinkers']\n",
        ")['poor_mental_health_days'].mean().reset_index()\n",
        "\n",
        "# 2. Creating the Sunburst Chart\n",
        "fig = px.sunburst(\n",
        "    sunburst_df,\n",
        "    path=['general_health', 'physical_activities', 'smoker_status', 'alcohol_drinkers'],\n",
        "    values='poor_mental_health_days',\n",
        "    color='poor_mental_health_days',\n",
        "    color_continuous_scale=[PROJECT_COLORS[2], '#FFFFFF', PROJECT_COLORS[1]],\n",
        "    labels={'poor_mental_health_days': 'Avg Mental Health'},\n",
        "    # Explicit title explaining the hierarchy\n",
        "    title=\"<b>The Lifestyle Hierarchy</b><br><sup>From Center Out: General Health → Exercise → Smoking → Alcohol</sup>\"\n",
        ")\n",
        "\n",
        "# 3. Compact Layout Configuration\n",
        "fig.update_layout(\n",
        "    width=600,\n",
        "    height=600,\n",
        "    margin=dict(t=100, l=0, r=0, b=0),\n",
        "    coloraxis_colorbar=dict(\n",
        "        title=\"Avg. Mental<br>Distress\",\n",
        "        outlinewidth=0,\n",
        "        thickness=15\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.update_traces(\n",
        "    marker=dict(line=dict(color='white', width=0.5)),\n",
        "    hovertemplate='<b>%{label}</b><br>Avg Mentally Unhealthy Days: %{color:.2f}'\n",
        ")\n",
        "\n",
        "\n",
        "fig.show(config={\n",
        "    'toImageButtonOptions': {\n",
        "        'format': 'png',\n",
        "        'filename': 'Q2_EDA',\n",
        "        'scale': 1.5\n",
        "    }\n",
        "})"
      ],
      "metadata": {
        "id": "ZX_OG_Jj1_No",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "5f154cca-aae7-4ac7-ed63-9257ef253d1d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   **Insight:** General health perception is the primary \"anchor\" for mental well-being. However, the lack of physical activity creates a visible \"darkening\" of distress levels across all health categories, especially within the Poor health segment.\n",
        "*   **Conclusion:** There is a clear \"Cumulative Risk Path.\" The highest mental distress scores are found at the intersection of Poor Health → No Physical Activity → Smoker (Yes). Interestingly, alcohol consumption in the Excellent/Very Good categories does not always trigger a dramatic spike in distress, suggesting its impact may be context-dependent."
      ],
      "metadata": {
        "id": "nPvjt95Ncp7V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "VsARCD0TPdEY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Q3: How does the distribution of diminished Mental Well-being evolve across age groups for different genders?*"
      ],
      "metadata": {
        "id": "nVffEtkq4JQa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================\n",
        "# Q3: Vertical Animated Growth Chart\n",
        "# ==========================================\n",
        "\n",
        "# 1. Preparing and sorting data\n",
        "demo_df = df_clean.groupby(['age_category', 'sex'])['poor_mental_health_days'].mean().reset_index()\n",
        "demo_df = demo_df.sort_values('age_category')\n",
        "\n",
        "age_groups = demo_df['age_category'].unique()\n",
        "males = demo_df[demo_df['sex'] == 'male']\n",
        "females = demo_df[demo_df['sex'] == 'female']\n",
        "\n",
        "# 2. Creating the Frames for vertical \"growth\"\n",
        "num_frames = 20 # Increased frames for smoother slow-motion\n",
        "frames = []\n",
        "\n",
        "for i in range(1, num_frames + 1):\n",
        "    ratio = i / num_frames\n",
        "    frames.append(go.Frame(\n",
        "        data=[\n",
        "            go.Bar(x=age_groups, y=males['poor_mental_health_days'] * ratio),\n",
        "            go.Bar(x=age_groups, y=females['poor_mental_health_days'] * ratio)\n",
        "        ],\n",
        "        name=f\"frame_{i}\"\n",
        "    ))\n",
        "\n",
        "# 3. Initial state\n",
        "fig = go.Figure(\n",
        "    data=[\n",
        "        go.Bar(\n",
        "            x=age_groups, y=[0]*len(age_groups), name='Men',\n",
        "            marker_color=PROJECT_COLORS[3],\n",
        "            customdata=males['poor_mental_health_days']\n",
        "        ),\n",
        "        go.Bar(\n",
        "            x=age_groups, y=[0]*len(age_groups), name='Women',\n",
        "            marker_color=PROJECT_COLORS[1]\n",
        "        )\n",
        "    ],\n",
        "    layout=go.Layout(\n",
        "        title=\"<b>Mental Health Distress Across Demographics</b><br><sup>Comparison of average Mentally unhealthy days by Age and Gender</sup>\",\n",
        "        xaxis=dict(title=\"Age Category\"),\n",
        "        yaxis=dict(title=\"Avg. Mentally Unhealthy Days\", range=[0, 8]),\n",
        "        barmode='group',\n",
        "        updatemenus=[dict(\n",
        "            type=\"buttons\",\n",
        "            showactive=False,\n",
        "            buttons=[dict(\n",
        "                label=\"Play Animation\",\n",
        "                method=\"animate\",\n",
        "                args=[None, {\n",
        "                    \"frame\": {\"duration\": 100, \"redraw\": False},\n",
        "                    \"fromcurrent\": True,\n",
        "                    \"transition\": {\"duration\": 100, \"easing\": \"linear\"}\n",
        "                }]\n",
        "            )]\n",
        "        )],\n",
        "        template=\"plotly_white\",\n",
        "        width=1000, height=600\n",
        "    ),\n",
        "    frames=frames\n",
        ")\n",
        "\n",
        "\n",
        "if hasattr(fig, 'frames') and fig.frames:\n",
        "    fig.update(data=fig.frames[-1].data)\n",
        "\n",
        "\n",
        "    if hasattr(fig.layout, 'sliders') and fig.layout.sliders:\n",
        "        fig.layout.sliders[0].active = len(fig.frames) - 1\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "PD0oRnla48vs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "99bd86a0-5d2f-4d16-9b2b-50c0cd19b54f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   **Insight:** A consistent gender gap exists across the entire lifespan. Women (pink) report significantly higher average days of poor mental health than men (dark blue). This is most pronounced in early adulthood (age 20), where young women average ~8 days of distress per month compared to ~5 days for men.\n",
        "*   **Conclusion:** Mental distress follows a clear linear downward trend as individuals age. While the younger population bears the highest psychological burden, seniority appears to be associated with greater emotional stability and fewer reported \"bad days\" for both genders."
      ],
      "metadata": {
        "id": "Abqk4i2lc0Pi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "d8CR9q90c3-E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Q4: How do specific physical ailments and chronic conditions impact Mental Well-being?*"
      ],
      "metadata": {
        "id": "PV4z_oI_7t3y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==================\n",
        "# Q4: 3D TOPOGRAPHY\n",
        "# ==================\n",
        "\n",
        "# 1. Data Setup - using exact names\n",
        "physical_conditions = [\n",
        "    'had_asthma', 'had_heart_attack', 'had_stroke',\n",
        "    'had_diabetes', 'had_arthritis', 'had_kidney_disease'\n",
        "]\n",
        "condition_labels = [c.replace('had_','').replace('_', ' ').title() for c in physical_conditions]\n",
        "status_labels = ['Healthy', 'Diagnosed']\n",
        "\n",
        "z_data = []\n",
        "for condition in physical_conditions:\n",
        "    row = []\n",
        "    for status in ['no', 'yes']:\n",
        "        # Using 'poor_mental_health_days' and calculating threshold on the fly\n",
        "        mask = df_clean[condition] == status\n",
        "        prevalence = (df_clean[mask]['poor_mental_health_days'] > 14).mean() * 100\n",
        "        row.append(prevalence)\n",
        "    z_data.append(row)\n",
        "z_matrix = np.array(z_data)\n",
        "\n",
        "# 2. Plot Creation (Standard Surface Plot)\n",
        "fig = go.Figure(data=[go.Surface(\n",
        "    z=z_matrix,\n",
        "    x=[0, 1],\n",
        "    y=list(range(len(physical_conditions))),\n",
        "    colorscale=[[0, PROJECT_COLORS[2]], [1, PROJECT_COLORS[1]]],\n",
        "    colorbar=dict(title=\"Prevalence %\", thickness=12, len=0.6)\n",
        ")])\n",
        "\n",
        "# 3. Layout Configuration\n",
        "fig.update_layout(\n",
        "    title=dict(\n",
        "        text=\"<b>Topography of Poor Mental Health</b><br><sup>Impact of chronic physical conditions</sup>\",\n",
        "        y=0.95, x=0.5, xanchor='center'\n",
        "    ),\n",
        "    scene=dict(\n",
        "        domain={'x': [0.15, 0.95], 'y': [0.1, 0.9]},\n",
        "        xaxis=dict(title=dict(text=\"Status\"), tickvals=[0, 1], ticktext=status_labels),\n",
        "        yaxis=dict(title=dict(text=\"Condition\"), tickvals=list(range(len(physical_conditions))), ticktext=condition_labels),\n",
        "        zaxis=dict(title=dict(text=\"Poor Mental Health %\")),\n",
        "        camera=dict(eye=dict(x=1.5, y=1.5, z=1.1), center=dict(x=0, y=0, z=-0.1)),\n",
        "        aspectmode='manual',\n",
        "        aspectratio=dict(x=1, y=1.2, z=0.7)\n",
        "    ),\n",
        "    width=700, height=600, template=\"plotly_white\", margin=dict(l=0, r=0, b=0, t=50)\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "HbJBowvT77dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "fdd25be7-4a03-4a2d-c44f-bae064293b60"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   **Insight:** The \"Topographic Peaks\" of distress (reaching ~16%) are clearly localized among individuals diagnosed with Asthma or those who have suffered a Heart Attack. Conversely, the \"Healthy\" baseline (back row) remains in a low-distress valley (9-10%).\n",
        "*   **Conclusion:** There is a significant Comorbidity Gap. Transitioning from a \"Healthy\" status to being \"Diagnosed\" with a chronic condition increases the frequency of mental distress days by 5% to 7% on average. This mountain-vs-plain structure confirms that physical ailments are not just bodily issues but primary drivers of psychological health."
      ],
      "metadata": {
        "id": "t-AXK-Ycc-3f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "ElIV4vrydHLh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Q5: How do ethnicity and physical functional difficulties interact to influence Mental Well-being?*"
      ],
      "metadata": {
        "id": "toSvwPi5Jiop"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==================================\n",
        "# Q5: THE IDENTITY-ABILITY MATRIX\n",
        "# ==================================\n",
        "\n",
        "import plotly.express as px\n",
        "\n",
        "# 1. Defining all 6 physical difficulty columns from your info() list\n",
        "difficulty_cols = [\n",
        "    'deaf_or_hard_of_hearing', 'blind_or_vision_difficulty',\n",
        "    'difficulty_concentrating', 'difficulty_walking',\n",
        "    'difficulty_dressing_bathing', 'difficulty_errands'\n",
        "]\n",
        "\n",
        "# 2. Selecting top ethnic groups for a clean X-axis\n",
        "top_ethnicities = df_clean['race_ethnicity'].value_counts().nlargest(4).index\n",
        "\n",
        "# 3. Reshaping for visualization (Temporary View - No changes to df_clean)\n",
        "df_viz = df_clean[df_clean['race_ethnicity'].isin(top_ethnicities)].melt(\n",
        "    id_vars=['race_ethnicity', 'poor_mental_health_days'],\n",
        "    value_vars=difficulty_cols,\n",
        "    var_name='Condition',\n",
        "    value_name='Status'\n",
        ")\n",
        "\n",
        "# Filtering for 'yes' and cleaning labels for display\n",
        "df_viz = df_viz[df_viz['Status'] == 'yes'].copy()\n",
        "df_viz['Condition'] = df_viz['Condition'].str.replace('_', ' ').str.title()\n",
        "\n",
        "# 4. Generating the Violin Plot\n",
        "fig = px.violin(\n",
        "    df_viz,\n",
        "    x='race_ethnicity',\n",
        "    y='poor_mental_health_days',\n",
        "    color='race_ethnicity',\n",
        "    facet_col='Condition',\n",
        "    facet_col_wrap=3,\n",
        "    box=True,\n",
        "    points=False,\n",
        "    color_discrete_sequence=PROJECT_COLORS,\n",
        "    title=\"<b>The Identity-Ability Matrix</b><br><sup>Mental Well-being Distribution by Ethnicity & Disability</sup>\",\n",
        "    labels={\n",
        "        'poor_mental_health_days': 'Days of Poor Mental Health',\n",
        "        'race_ethnicity': 'Ethnicity'\n",
        "    },\n",
        "    template=\"plotly_white\"\n",
        ")\n",
        "\n",
        "# 5. Screen-Size Optimization\n",
        "fig.update_layout(\n",
        "    width=1100,\n",
        "    height=600,                   # Reduced height to fit standard screens\n",
        "    showlegend=False,\n",
        "    margin=dict(t=100, b=80, l=60, r=40)\n",
        ")\n",
        "\n",
        "# Polishing Subplot Headers (smaller font to save space)\n",
        "fig.for_each_annotation(lambda a: a.update(text=f\"<b>{a.text.split('=')[-1]}</b>\", font=dict(size=11)))\n",
        "\n",
        "# Adjusting axes for clarity and consistency\n",
        "fig.update_yaxes(range=[-2, 32], gridcolor='lightgrey', title_font=dict(size=10))\n",
        "fig.update_xaxes(tickangle=30, tickfont=dict(size=9))\n",
        "\n",
        "\n",
        "fig.show(config={\n",
        "    'toImageButtonOptions': {\n",
        "        'format': 'png',\n",
        "        'filename': 'Q5_EDA',\n",
        "        'scale': 1.5\n",
        "    }\n",
        "})"
      ],
      "metadata": {
        "id": "968cWjICRnWj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "8f64437f-d4ee-42eb-f293-4fde0fad80cd"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   **Insight:** Functional disabilities (e.g., Difficulty Concentrating or Errands) act as a heavier \"psychological weight\" than sensory ones (Vision/Hearing). These functional impairments push a large portion of the population toward the upper limit of the scale (15–30 days of distress), significantly altering the density of the violin plots.\n",
        "*   **Conclusion:** Resilience patterns vary across ethnic groups. The White group consistently shows a wider \"base\" at 0 days across various disabilities. This suggests that even under physical limitations, this group may have higher access to support systems or resources that mitigate the immediate impact of a disability on mental health.\n"
      ],
      "metadata": {
        "id": "X0SkRscOdNvO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "brwEWJR6daDR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Q6: How does a formal depressive disorder diagnosis correlate with self-reported Mental Well-being?*"
      ],
      "metadata": {
        "id": "-eV4C-3YNcpr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ========================\n",
        "# Q6: THE CLINICAL ANCHOR\n",
        "# ========================\n",
        "\n",
        "import plotly.express as px\n",
        "\n",
        "# 1. Preparing the data (Normalized count to keep the wave smooth)\n",
        "df_wave = df_clean.groupby(['had_depressive_disorder', 'poor_mental_health_days']).size().reset_index(name='count')\n",
        "\n",
        "# 2. Creating the Animated Smooth Area Plot\n",
        "fig = px.area(\n",
        "    df_wave,\n",
        "    x=\"poor_mental_health_days\",\n",
        "    y=\"count\",\n",
        "    animation_frame=\"had_depressive_disorder\",\n",
        "    color_discrete_sequence=[PROJECT_COLORS[2]], # Turquoise for a calm look\n",
        "    line_shape='spline',       # This makes the line curved and \"delicate\"\n",
        "    range_x=[-1, 31],\n",
        "    range_y=[0, df_wave['count'].max() + 500],\n",
        "    title=\"<b>Clinical Validation </b><br><sup>The shifting shape of mental health based on clinical diagnosis</sup>\",\n",
        "    labels={\n",
        "        \"poor_mental_health_days\": \"Days of Poor Mental Health\",\n",
        "        \"count\": \"Frequency\",\n",
        "        \"had_depressive_disorder\": \"Depression Diagnosis\"\n",
        "    },\n",
        "    template=\"plotly_white\"\n",
        ")\n",
        "\n",
        "# 3. Aesthetics & Fine-tuning\n",
        "fig.update_traces(\n",
        "    fillcolor='rgba(13, 202, 240, 0.2)', # Very light fill for a \"ghostly\" wave\n",
        "    line=dict(width=3)\n",
        ")\n",
        "\n",
        "fig.update_layout(\n",
        "    width=900,\n",
        "    height=500,\n",
        "    xaxis=dict(showgrid=False),\n",
        "    yaxis=dict(showgrid=True, gridcolor='rgba(0,0,0,0.05)'),\n",
        "    margin=dict(t=100, b=50, l=50, r=50)\n",
        ")\n",
        "\n",
        "# Making the animation feel like a \"pulse\"\n",
        "fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 1500\n",
        "fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 600\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "Q9nPD1dwfbUU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "9498c8ff-0d3b-419a-c157-ab2c3e985113"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   **Insight:** A formal clinical diagnosis completely collapses the \"Zero Baseline.\" For undiagnosed individuals, there is a massive concentration at 0 days; however, for those with a Depressive Disorder diagnosis, this concentration disappears, and data points disperse across the entire 0-30 day spectrum.\n",
        "*   **Conclusion:** The poor_mental_health_days feature serves as a highly reliable measurement tool, accurately reflecting the medical reality of the respondents. The visible \"heaping\" effect (spikes at 5, 10, 15, 20, and 30 days) highlights a common psychological tendency in subjective reporting to round estimates to familiar intervals."
      ],
      "metadata": {
        "id": "ltXZq55Ldo5f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "1nFdKfSrds6R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 3: Define and Train a baseline model**\n"
      ],
      "metadata": {
        "id": "TxKHPqppIPZT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Baseline Regression Modeling\n",
        "\n",
        "\n",
        "\n",
        "**Regression Goal**\n",
        "\n",
        "The primary objective of this phase is to establish a performance benchmark for predicting the number of poor mental health days per month. By implementing a simple, unoptimized Linear Regression, we aim to quantify the linear relationships between our 26 features and the target variable, providing a \"baseline\" to measure the effectiveness of more complex models later in the pipeline.\n",
        "\n",
        "\n",
        "\n",
        "**Model Setup & Partitioning**\n",
        "\n",
        "Following a methodical data science workflow, the initial setup was kept straightforward to ensure reproducibility:\n",
        "\n",
        "* **Feature Selection:** All 26 available features from the cleaned dataset were utilized to capture global interactions.\n",
        "\n",
        "* **Train-Test Split:** A standard 80/20 split was applied using simple random sampling.\n",
        "\n",
        "* **Reproducibility:** A fixed Random Seed (42) was used to ensure consistent results across different environments.\n",
        "\n",
        "* **Implementation:** The model was trained using the LinearRegression class from scikit-learn with default parameters.\n"
      ],
      "metadata": {
        "id": "EfniaMYBK6sK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train & test split"
      ],
      "metadata": {
        "id": "tD3ylCv1nfwy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate target variable (y) and features (X) for model input\n",
        "y = df_clean['poor_mental_health_days']\n",
        "X = df_clean.drop(columns=['poor_mental_health_days'])\n",
        "\n",
        "# Apply One-Hot Encoding to expand multi-categorical variables into binary columns[cite: 2]\n",
        "# drop_first=True is used to prevent multicollinearity (Dummy Variable Trap)[cite: 2]\n",
        "X_encoded = pd.get_dummies(X, drop_first=True)\n",
        "\n",
        "# Partition data into training (80%) and testing (20%) sets using a fixed seed[cite: 1, 2]\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X_encoded, y, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "# Output dimensions to verify partitioning\n",
        "print(f\"X_train shape: {X_train.shape}\")\n",
        "print(f\"X_test shape: {X_test.shape}\")"
      ],
      "metadata": {
        "id": "euWXtGKHK65d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b01640c-d131-4549-dd4d-780133408d3e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (241977, 34)\n",
            "X_test shape: (60495, 34)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Model training"
      ],
      "metadata": {
        "id": "NSKCuFNW4BNy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# MODEL TRAINING (BASELINE)\n",
        "# =============================================================================\n",
        "\n",
        "# Initialize the Linear Regression model with default parameters\n",
        "baseline_model = LinearRegression()\n",
        "\n",
        "# Train the model using the training set partitioned in Step 3\n",
        "baseline_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "EBy95Bkg3j_N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "28059d5d-591b-4e92-aece-3dbef805f219"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "xXS9K0WzpXgE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Performance Evaluation**\n",
        "\n",
        "The initial results from our baseline model confirm that while there is a discernible signal in the data, the relationship between physiological traits and mental health is far from linear.\n",
        "\n",
        "| Metric | Score | Interpretation |\n",
        "| :--- | :--- | :--- |\n",
        "| **MAE** | 3.6770 | On average, our predictions are off by ~3.7 days. |\n",
        "| **MSE** | 34.2292 | Mean Squared Error. |\n",
        "| **RMSE** | 5.8506 | Standard deviation of the prediction errors. |\n",
        "| **R² Score** | 0.3157 | The model explains approximately 31.6% of the variance. |\n",
        "\n",
        "\n",
        "**Contextual Note:** An R² of 0.31 reflects the inherent complexity of our target variable. Predicting human emotions and mental states based on physical indicators is significantly more abstract than predicting mathematical constants or market prices. These results strongly suggest that the data contains non-linear patterns that a simple regression cannot capture."
      ],
      "metadata": {
        "id": "XPIzbPJz77V_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# MODEL EVALUATION\n",
        "# =============================================================================\n",
        "\n",
        "# Generate predictions based on the unseen testing features\n",
        "y_pred = baseline_model.predict(X_test)\n",
        "\n",
        "# Calculate regression metrics to establish the baseline performance\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mse)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "# Display evaluation results\n",
        "print(\"--- Baseline Model Evaluation Summary ---\")\n",
        "print(f\"Mean Absolute Error (MAE): {mae:.4f}\")\n",
        "print(f\"Mean Squared Error (MSE): {mse:.4f}\")\n",
        "print(f\"Root Mean Squared Error (RMSE): {rmse:.4f}\")\n",
        "print(f\"R-squared (R2 Score): {r2:.4f}\")"
      ],
      "metadata": {
        "id": "hYbnZ7MU5uzt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f91b09a-c5b0-4817-d208-d8b359cafd01"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Baseline Model Evaluation Summary ---\n",
            "Mean Absolute Error (MAE): 3.6770\n",
            "Mean Squared Error (MSE): 34.2292\n",
            "Root Mean Squared Error (RMSE): 5.8506\n",
            "R-squared (R2 Score): 0.3157\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "yTU1qP-bpUsn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Residual & Prediction Analysis**\n",
        "\n",
        "To understand where the model fails, we analyzed the error patterns through diagnostic visualizations.\n",
        "\n",
        "*   **Under-prediction Trends:** In the Actual vs. Predicted plot (left), we observe a clear horizontal trend. Even when respondents report a full month (30 days) of distress, the model tends to predict only 15–20 days. It regresses toward the median, struggling to account for extreme cases.\n",
        "*   **Error Distribution:** The Residuals Distribution (right) shows a narrow peak around zero, but with a significant right-skewed tail. This confirms that the model consistently under-predicts the severity of mental distress for high-risk individuals."
      ],
      "metadata": {
        "id": "fov4a771ENcn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# INSIGHTS & PERFORMANCE VISUALS\n",
        "# =============================================================================\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "\n",
        "# 1. Create a figure with 1 row and 2 columns\n",
        "fig = make_subplots(\n",
        "    rows=1, cols=2,\n",
        "    subplot_titles=(\"Actual vs. Predicted\", \"Residuals Distribution\"),\n",
        "    horizontal_spacing=0.1\n",
        ")\n",
        "\n",
        "# 2. Add Scatter Plot (Actual vs. Predicted) to column 1\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x=y_test.values.flatten(),\n",
        "        y=y_pred.flatten(),\n",
        "        mode='markers',\n",
        "        marker=dict(color=PROJECT_COLORS[2], opacity=0.6),\n",
        "        name=\"Predictions\"\n",
        "    ),\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "# Add the perfect prediction reference line (y=x)\n",
        "line_min, line_max = float(y_test.min()), float(y_test.max())\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x=[line_min, line_max],\n",
        "        y=[line_min, line_max],\n",
        "        mode='lines',\n",
        "        line=dict(color=PROJECT_COLORS[1], dash='dash'),\n",
        "        name=\"Perfect Prediction\"\n",
        "    ),\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "# 3. Add Residuals Histogram to column 2\n",
        "clean_residuals = (y_test.values.flatten() - y_pred.flatten()).tolist()\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Histogram(\n",
        "        x=clean_residuals,\n",
        "        nbinsx=50,\n",
        "        marker_color=PROJECT_COLORS[1],\n",
        "        name=\"Residuals\"\n",
        "    ),\n",
        "    row=1, col=2\n",
        ")\n",
        "\n",
        "# 4. Final layout adjustments\n",
        "fig.update_layout(\n",
        "    height=450,\n",
        "    width=1100,\n",
        "    showlegend=False,\n",
        "    title_text=\"Model Performance Analysis: Baseline Stage\",\n",
        "    template=\"custom_cyberpunk\"\n",
        ")\n",
        "\n",
        "fig.update_xaxes(title_text=\"Actual Days\", row=1, col=1)\n",
        "fig.update_yaxes(title_text=\"Predicted Days\", row=1, col=1)\n",
        "fig.update_xaxes(title_text=\"Prediction Error (Residual)\", row=1, col=2)\n",
        "fig.update_yaxes(title_text=\"Frequency\", row=1, col=2)\n",
        "\n",
        "fig.show(config={\n",
        "    'toImageButtonOptions': {\n",
        "        'format': 'png',\n",
        "        'filename': 'baseline_stage',\n",
        "        'scale': 1.5\n",
        "    }\n",
        "})"
      ],
      "metadata": {
        "id": "Wbtt1W6bAcLJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "fc5ffc3b-df83-4104-fe4c-93b27b999491"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "18cDhAmdpKaR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Linear Regression Coefficients**\n",
        "\n",
        "By examining the model’s coefficients, we can identify which factors the baseline model perceives as the primary drivers of mental well-being.\n",
        "\n",
        "\n",
        "*   **Primary Risk Factors (Turquoise):**\n",
        "    *   **Clinical Indicators:** A formal diagnosis of a Depressive Disorder and Difficulty Concentrating are the strongest predictors of increased distress days.\n",
        "    *   **Health Perception:** Self-identifying as being in Poor or Fair general health serves as a high-signal warning for the model.\n",
        "*   **Protective Factors (Pink):**\n",
        "    *   **Gender:** Being Male is the most significant statistical factor in reducing the predicted number of distressed days.\n",
        "    *   **Sleep Hygiene:** As Sleep Hours increase, predicted distress days decrease, capturing a direct and intuitive biological link.\n",
        "*   **Evidence of Model Limitations:** Certain variables, such as \"Difficulty Walking,\" appear on the protective side (pink). This logical contradiction suggests that the data contains complex, non-linear interactions that a linear baseline cannot effectively untangle.\n"
      ],
      "metadata": {
        "id": "6eIieMq1KosO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# ANIMATED FEATURE IMPORTANCE (REFINED COLORS & SPACING)\n",
        "# =============================================================================\n",
        "\n",
        "# 1. Prepare data\n",
        "base_imp = pd.DataFrame({'Feature': X_train.columns, 'Coef': baseline_model.coef_})\n",
        "top_features = base_imp.assign(abs_c=base_imp['Coef'].abs()).sort_values('abs_c', ascending=False).head(15)['Feature']\n",
        "filtered_imp = base_imp[base_imp['Feature'].isin(top_features)].sort_values('Coef')\n",
        "\n",
        "frames_data = []\n",
        "for step in np.linspace(0, 1, 11):\n",
        "    temp_df = filtered_imp.copy()\n",
        "    temp_df['Current_Coef'] = temp_df['Coef'] * step\n",
        "    temp_df['Step'] = f\"{round(step * 100)}%\"\n",
        "    frames_data.append(temp_df)\n",
        "\n",
        "animated_df = pd.concat(frames_data)\n",
        "\n",
        "# 2. Build the chart - Direct Pink to Cyan transition\n",
        "fig_anim = px.bar(\n",
        "    animated_df, x='Current_Coef', y='Feature', animation_frame='Step',\n",
        "    orientation='h',\n",
        "    range_x=[filtered_imp['Coef'].min() - 0.5, filtered_imp['Coef'].max() + 0.5],\n",
        "    title='Feature Impact Growth: Baseline Model Signature',\n",
        "    color='Current_Coef',\n",
        "    color_continuous_scale=['#EA00D9', '#0ABDC6'], # Only Pink and Cyan\n",
        "    width=850, height=500\n",
        ")\n",
        "\n",
        "# 3. Layout: Margins and Spacing\n",
        "fig_anim.update_layout(\n",
        "    template=\"custom_cyberpunk\",\n",
        "    margin=dict(l=250, r=30, t=80, b=40),\n",
        "    coloraxis_showscale=False\n",
        ")\n",
        "\n",
        "fig_anim.update_yaxes(\n",
        "    title_text=\"Model Indicators\",\n",
        "    title_standoff=60, # More breathing room\n",
        "    categoryorder='total ascending'\n",
        ")\n",
        "\n",
        "\n",
        "if hasattr(fig_anim, 'frames') and fig_anim.frames:\n",
        "   fig_anim.update(data=fig_anim.frames[-1].data)\n",
        "\n",
        "fig_anim.show()"
      ],
      "metadata": {
        "id": "0yUpmfkPEsTV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "201034f8-09b3-4fa7-c564-04115381e70d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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chMlmtNaEp9JMOHiqi1jxFB+hQiTiEqnipbGZCK5YseI3x8ZEBnHhaTX7JGupRIqn6aTP0CKRYt0Qk+eoJU6MI44cC0+qo0YkiSfIpGCdfvrp/tS+JD6kFDIBSxdtykSkoohF4pP06Hy4JlHjuhQXqeJrjzgvrjOCmdgWLlzok/0oGsGklRRI9o8KOSBSMGDyzHUiasUkFrlNJVLFr3EkUiWlaha/xqnWSLEdkZ2lS5e6PJEKRsoi1wsRotw/UTxS6XiAEIkU+zFRJ0JBsQ/S74hqca4tWrTwn3Gf0F+ivLIf/JBABCgTkSqJQWnu+0SRimQY0SVSRFoesoQAJIpU9B3Ag5IoUsh5kWLKvY3oItJIC/tF6weja8LDE36HSGX6fZKNSBW/9ggeD3hYD8dnAVlKFCkeCvBzzj/xe437gWhcokhxH3DPJjbOkYcBXAe+I/iME+mEA+usuAf4vFDYJC6RSvxeyvRey+p/CNpYBESgTAlIpMoUvwYXgewJZLpGKprw8kQ0cVF5NCKTLhZmM1licsHaiqhFay7SidTjjz/uaw6KF5tAwIgMRMUmkpXGRipYNJ4YwUgkQSQhMbUt8Xdxi1TxiFQkAKw54Sl9JnxIBWTylZg+VvzKZiJSUToZEaAoJTHZHRIVzWBiGEWkiosU47GYPlVEKpJVjpl0RiQFeYquJxEO/jBR51qREkgrS5GKoiJM6onEcM5IbBS94Pg4nyiCx32U2BB1ogJswxpAonJEReiH+yDZ2i1Sv1hjE4dIlea+TxQp3jNGFAoJIpLDMSK7tESRiq5VqohU9DCF+5wHM4kRKSSSBww8fECkMv0+yUSkeCiAJCV7yBNFEFkjR4ppokhFDxoiYYqubSR52YhUdD6sS0SiosZ3HunF6USKdYCk0iYWmyCyRlQzSmWNUvsSRSoquFLSvZb9/xW0hwiIQFkRkEiVFXmNKwI5EshUpKL1GYnrCRiSiSNP5UnFIm2KCRTpPYkTdybNpMYUFykmVtH7faK1RIkTBdYAMBmn4EM6kWJ9FpNdJu1RVTSOjegCAoXgpWpxi1TxtUVRcQkiNhQnyIQPqUZMyhOjIDBm3RYTXopnMInm/KK1F6nOj+gIk2MmpMlePhutVSkpIsVaHY6n+Bop1sZEUR0iMFG1MSbLpFRG4oUk83vuI6J6RDASRSox/TKZLOcjIkWKHsUlSMdC7pD2qHgAx0Y0k+gCkY3oWnCupNxF0Uq2Y20MqZBcEwoPkBKHTBR/8SznQJSJNLo4RKo0932iSHEOiG9UZIHziAqDJIoU9xGiWXyNVHSPcy+y9o3URc6RtNqowY2Uz2iNVCbfJ+xbkkhFFSCjtMLin4Mo6hStn0wUqSiaGq05Y1/kmPvv448/zioiFUlZtJYuui+IVJIGWVykEu93IlmsvUp8AXUk+elECjnN5F7L8X8N2k0ERKAMCEikygC6hhSB0hDIVKQYg0k5aTKkPjFhYiLPE1gmHTzFJd2Hp6qkuRBVoeIVT3WRLSJORGOYWJDuR4EG5Iun1kw02IbJaCRhpLYxEYsmrulEKqpehnDRP/2yWJ7UOI4T+QslUixsZx0Ii/F5Uo5o8ISftClaJnzYLkpJo9oX+yMyUUU8eFH0gMXyTAKRx+LRkuh8WetDdJBrRDEB1vRwnbh2y5Ytc5EgdZN1WVElOCITxSNSpG8xwaSYB/LLuiOe9pP2xjFGC+CZiLI/x4xUIic0qp4RteJ+I20qEhGKYTBZ5t7iWlEtLxORyqZqX+ILeTkWpBR2lHenyAqRMqIy3JMUKeCY+Tf3X926db1YB+XakSRSxWCJJMGC+5R7PKpCyL3HpJk/nAfpfkx4SR8jJQ6hJm0wDpEqzX1fXKSiNDE+s0zio+hOokjBDjZ8vvkv1wr5IhWQaxcVJ4nWJrLGDiGjiEf0PRGl9mX6fVKSSPHwBgHhXubBDFUmuTe5xi+++KKn0XIMUeGPRJHiGnOMXDMEkVRT5JRoMGKdTUQqKsTBPcTnn+9JxJL7nIdEPCDiM8p1L36/86CJzxJpgESzeLjB9x1rrtKJFAwzuddK8/8H7SsCIhCWgEQqLG+NJgKlJpCNSEVlvJkYMEGnotzvf/97l5doIk/6F4vWmfQzgaXyHRNVJhVMJlkjQsoKExwm4Uy8meSQBsWkmokBxQBI1SH6wpNrJhTR2pxkk2wgkD7DU+fEd7iwEJyJU+J6juLA4o5IIZlMwii2AS8m5qQ8RWu0MuXDRIzJF1EAIjmsOyJqFFWZI/ITlUBnspv4Ppvi50gls6gACBLAv0nPpE+qmlHmPHqPVKrUPvokosI1i94jhbgyaUR+ExtpYZx/4nqm6D5DAkkNixoTXiSFoiWwInKViUhl8x4p7o3EhixwPZh4c39G585kHOZsT9SJSTH3K/LL5BrpZ1IOAwQfVky+qdbHhBwhjRprqIg6RJXcuG5I2wEHHOCbxCFSpbnvi4sUn0POgc9hVDyB/ouLFPLB+SPgnD+f06hEe/Q5oy8erPB5ZnuEC/HkoQL9I+m0TL5PShIp+iFyyHcLsk70kMqYiDzrlbi/qQ4alUNPFCn2RVr4buEzy7lwP1Oggu8rvqOoZonUlLRGir6QMK459zT3BFF5mPAggyIrFF9hjOL3Ow81+M7kWPiscx/xYILrwGcJZslS+zK910r9Pwl1IAIiEIyARCoYag0kAiJQSAQiUShe2a+QjlHHIgIisCkBRA+RTqzqSdQQUUwUfnETAREQgRAEJFIhKGsMERCBgiMgkSq4S6IDEoG0BEi57Ny5sxf/IF2VCDsRYESKaGRi+X+hFAEREIEQBCRSIShrDBEQgYIjIJEquEuiAxKBEgmQrsr6RQSKlFdSlFlLSBGHKB2wxE60gQiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKgAiIgAiIgAiIgAiIgAjEREAiFRNIdSMCIiACIiACIiACIiACIlB5CEikKs+11pmKQGwE/ud//se2224723zzzWPrUx39lsDf//5322abbewvf/mL8OSRwL///W/77rvvbPvtt8/jKOoaAv/617/8nv7d734nIHkk8L//+7/266+/WrVq1fI4irr+v//7P/v222+tevXqgpFnAnx3bL311rbFFlvkeaTsupdIZcdLW4uACJiZRCrMbYBIfVyjph111FFhBqykozAZYuLJ/6TV8kvg+++/t6222koPYfKL2X766ScfoUqVKnkeqXJ3j6xyT/NwoKK0bX63uR20/bYFdzoSqYK7JDogERCBXAlIpHIll91+k6ZOtX71m2S3k7YWAREQAREQgRwJ7LtNNXvpjwfkuHf+dpNI5Y+tehYBEQhMQCIVBrhEKgxnjSICIiACIvAfAhKp7O4EpfZlxyunrVevXm0DBw60f/zjHzntH+3Url07mzJlitWqVSvjfq655hpr2bKltW7dOuN9Qm140kkn2dSpU7M6n1yOLeL29ddf29ChQ23atGm5dFPiPrlcnxI7LdANJFJhLoxEKgxnjSICIiACIiCRyuUekEjlQi3LfSRSyYF98803vrg73wULIsHZcccdbePGjXlbFCqRyvKDoc1LJCCRKhGRNhABERABEYiRgCJS2cGUSGXHK6etEanBgwdbmzZtbN68eV5J55JLLrGDDz7YiJKMHDnS1q1bZyx47tixo7Vv397HWb58uY0bN85F4+ijj7b777+/xIjUZ599ZsOGDfN+GzVq5Isg2ZeI1Jo1a+yWW26xDRs2eNWT/v37W+PGjW3lypV20003WbNmzezDDz902ejVq5ftv//+dvXVV9uee+5pTz75ZNH248ePt7ffftuodHXmmWda27Zt/Xjvu+8+mzt3rp/fDjvsYFdccYXVrVs35c+jiNS1117r/fzpT3/yfhYvXmwzZ870c1+6dKlHrX744Qfva9CgQVajRo201yEVt8SIFJxuvPFG58R5cA4cA9fgrrvuskWLFtlmm23mDPv06eMLSU855RTfhuswffp0e/PNN1Nen1THPXv2bON+WLVqlR122GF2zjnnJD2XSZMm+WJhxqZRFYjKbffcc48Xekh2HX/++WcbNWqUrVixwq9B06ZNrV+/fla1atWc7tt0OykiFTvS5PeB1kiFAa1RREAEREAEnIBEKrsbQSKVHa+ctmbijJggT8ccc4w9+uijLiZjx461W2+91X788Uef8H766ad2/vnn29133+0i0qlTJ/95ixYt7JFHHvFJ+4wZM9KmwpG6Vrt2bbvgggvsgw8+sN69e3sfyFSPHj3sxBNP9D/IE3LHxPyjjz6yCy+80G644QYf68UXX7QJEybYnXfeadddd50RORo+fLhXWuLnTKIHDBjgwtWzZ0/fZqeddrIuXbr48VH5ivOjCtYRRxyR9Ocnn3yyRSL1zDPPuORdfvnlzhexbNCggR155JHWrVs3lzz+jaghL4yXqiFCqbglihQsiVCdddZZXvaYMRBLzv3ee+/1a8P5wqROnTrWtWtX69Chgx133HH+d1qqcRCwVMc9Z84cZ0T/u+yyS8rzeO+991waSQelTPBTTz1l8+fPd0lOdR2ff/55v7dGjBjhIkUa6OGHH2777bdfTvetRCp2bFl3qIhU1si0gwiIgAiIQCkISKSygyeRyo5XTlsjUhdffLE9/PDDvv/777/vkR4m1Ez8iYhsueWW/jtk5LLLLvN39Fx00UXGxJtGdOKEE05w8Um3Ruq0005z6WnYsKHvRx9IC5Gn7t27u5BFqXTIHcLFWJdeemnRWBwPwoC4MOEnKkMkhnbGGWf4sRPtoE2ePNkjHkRL+B0iiDxF72NBEpP9nH0jkfrll19c+BgPAeAcbrvtNnvppZc8MkTkiEZUimgdUa9U7xFAyIDDTucAACAASURBVFJxSxQp2L/88svOZO+99y5igoTA7tRTT/Ux2QYh4XgQKUR13333dfFLNQ4yluq4OXaiVYxTUjv33HNdwIlcErU79NBDnXuq6wgTjg9hP/DAA/Na9lYRqZKuXjy/l0jFw1G9iIAIiIAIZEZAIpUZp2griVR2vHLauvgaqcR/I1V33HGHffXVVx55IIqEOBDVIPrAhD9qRJLYNp1IIUBEkoii0K688kpr1aqV7brrri5zRI6iRsQIGeB3kdhFv0NyJk6caLzHpnnz5kXpe8gc4hW9TJF0MsSJCf+7777rEZRXXnnF5Yvx6tWrl/LnicUmiIjRB1LF8SNwpPeRQpf4kkyiRxxTzZo1k14L0tpScUsUKcYhRY9oGBE3xAnhoygI7+whckjjnGDDeSFSRLI4p3TjEDlKddzPPfecp0UyTkmNyCRRSjgiqhTJIAU01XXkOjz77LMu7KQOUmQEQSWyFneTSMVNNHl/EqkwnDWKCIiACIjAfwhIpLK7EyRS2fHKaet0IkVEiEk866donTt39rQ55CEx4kE0hshSJhEpoh177bWX90fqHQLA+ifGiiJciSeCzPXt29cn4AhcFP1CNMaMGbOJSLGmh0qAUf/JgCBXrDPivEmNi1rxnyeKFMKIJLENa6E45qefftoQj3SpfMXHLx4pSuSWqmof+xAFvP766+2hhx7yc2OtGo31VogbUpkoUunGee2111IeN/wzFSmkCblEnBA+ok1ffPFFyuuYyIL3LbA90azTTz89p/s23U4SqdiRJu1QIhWGs0YRAREQARGQSOVyD0ikcqGW5T7pRIrJOeJDBIdIBpGYq666ylOziEKwboh1S6S93X777R7pSBeRQnKIMCFNTNhZH0XaHsUmWFtD2hwRF6IwrHdCoIh6IFysySGqwXocIjAIBBKTGJFCKEjXY3JPCiBpb/RHiiIRJPrgTeoU1UCCWIOU7OdM8hNFau3atT7xR6ZGjx7tkTPEh7VG/JtzYl0Xx0aUJVUj0pSKW6JIIXjHHnusnxvnAxuKY1CEAllFIDmPIUOG2B577OEpl4kilW4cUuxSHXc2IhWJMNJCml9Uwj7VdYQ5RSmQcRrrvurXr+/XPO4mkYqbaPL+JFJhOGsUERABERABiVQu94BEKhdqWe6TTqSIAlHcoHr16l4QgkjCE088YVTGI1KE7LBuiKpyREtILaOYRKqGkJDaRqpgkyZNfDPW1iAN/A5BWL9+vafmEXUhXZBxiMZQRe6FF17wqBTyRZGC4iJFFUCO7Y033vC+ERGiJuxDpTkq7rEGi0IOSNxuu+2W9OfISfH3SCEfVMdDnKK2bNkyFzpkh98hfCUVT6DoQjJuTP6j90i98847Lq3w5nhJ5SPaBmtS6hYuXOh/ZyzOj/S4RJHi+FKNw/VJddzZihRRQaoWItKcPy3VdUSOkSfSQzmnffbZxwtoVKtWLcs7tuTNJVIlM4pjC4lUHBTVhwiIgAiIQKYElNqXKan/bCeRyo5XhdwakUKY8vWi2goJLdBJLViwwOWUCouF1CRSYa6GRCoMZ40iAiIgAiLwHwISqezuBIlUdrwq5NYSqcK8rET/SL1krVxJUbjQZyCRCkNcIhWGs0YRAREQARGQSOVyD0ikcqFWhvt8/PHHKYsvkEbHGqlsW3kTKYpQsIYrWWMdEWukykNLdx6UZKf0OqmXqV7aW5bnKJEKQ18iFYazRhEBERABEZBI5XIPSKRyoaZ9RKCSE5BIhbkBWB/I2rjy8nAgDJX4R6FwDoVuEl+1EP8o6hECrEvlno5eoSEq+SHA601Y55uPNbL5OeLy2SuFtijyxDp3tfwS4Ltj6623Tvke0fyOnrp3iVRZkde4IlCOCUikwlw8iVQYzhKpMJwlUuE4S6TCsJZIheEcfXdIpMLx1kgiIAJ5JCCRyiPchK4lUmE4S6TCcJZIheMskQrDWiIVhrNEKhxnjSQCIhCAgEQqAGQzL/3/yfY1/V1tavkjwGSIiSdPO9XyS4AiOrxOglc0qOWPwE8//eSd8z5EtfwRIH2Sezp6PUn+Rtq052q/29z+UGPbUMMVxDhK7SuIy6CDEAERiIOARCoOiiX3MenvU21wnX1L3lBbiIAIiIAIVBoCe29bzZYc0azSnC8nKpGqVJdbJysCFZuARCrM9ZVIheGsUURABESgPBGQSBXO1VKxicK5FmmP5IEHHrDVq1fbpZdealdddZUdeeSRRqnvp556yiZOnGjHH3+81a9fv+jvH330UdE22Z5iYjn0devW2ddff11w7zHK9pySbf/KK68YJeNr1aqVVXeJ+9133322Zs0avy6VqUmkwlxtiVQYzhpFBERABMoTAYlU4VwtiVThXIuMRWrjxo2e98yf6667zg4++GA74YQTNvl74jbZnmKiSM2ePdt+/vnnCll++eqrr7YzzzzTmjRpkhWixP1YV8FC9dD50VkdcB42lkjlAWqSLiVSYThrFBEQAREoTwQkUoVztSRShXMtNjkSForedNNNtmLFCo+Y8IJWJu2JEan169f7i2mZxNerV8/effdd//txxx1n7733XlFEatWqVd4XkaVddtnFLr/8cqtbt661bdvWpk+fbjvuuKOPHf2b7RC0Sy65xIYMGeLv+6BPXhDL+2z+/Oc/+/YvvPCC3XXXXXbbbbelpMj7Ff72t7/Z22+/7e9o6dGjh4sfPx87dqxxbPTPYvqzzjrL+2nXrp2df/75tmzZMvvkk0888tapUyf/HS+xZUzkrlmzZta/f3/bcsstbenSpTZ16lT74Ycf/NwGDRpkNWrUMCJ5iOEvv/xiGzZs8N/z0uIFCxbYtGnTrGbNmta9e3f7/PPPPeLH8Rx22GF29tln2/jx4+2ll14yFqIzFuyJQCXu9+mnnxZFpNIdN9fpkUcese22285OOukku+eee2zGjBkpuU2aNMm4B/r06ePbwAv27IfE3HLLLX4+W2yxhTNo3LixMxk1apTfMyyAbdq0qfXr18+qVq0a+10ukYodadIOJVJhOGsUERABEShPBCRShXO1JFKFcy02ORIm3c8884wLEAJ10UUX+cS4eGofUtCyZUuXjcS/J6b/denSxcWE7WbOnGn//Oc/bfjw4SWKFMIwbtw4Fzkm8ffff7+9+eabdu211/qxcmy77rpr2mjVzTff7BP5nj172sqVK+2KK66wWbNm2YQJEzySw0Sfijf8vnfv3ta8eXPr0KGDnXzyycZxf/PNNx41evDBB/3vbMe+O++8sxEZ2m+//fzcu3Xr5sfToEEDlx2OExmcM2eO3X333Xb77bfbDjvs4OdDZS54dO3a1XkSkWI7xAa5QzYXL15sd955p6dKIiWMi+i1atVqk/0SU/tSHfdnn31mffv29QpsyCTHTcokfFM1RBgZRMAQTVI458+fb8OGDXMZPfHEE/0PTAcPHuyC9fzzz9ujjz5qI0aM8GOeMmWKHX744XlJy5RIhfnikEiF4axRREAERKA8EZBIFc7VkkgVzrXY5EiGDh3qE/yOHTv6z5mEIxLZihR99OrVy0Vks80288gMkQvedl5SRKq4SH355Zd2zjnnuAix/+mnn+5iQgQoVUPAOJeGDRsWRVaIyiBH119/vUfaaESTfvzxR5cEhIQo1l577eW/I9JD1Iu1SUSe2I+GYCIZTz75pC1atMhuvPFG/zlRp/bt29vcuXP9z6uvvupSRSNCRdSJqFxxkaJvJCRqRISi0rGjR4+22rVre2Qscb/iIpXsuIlqLV++vOgYkLTJkyenFSmO4dxzz/VrRwQPeT300ENdpomgIdpR+WC2ueCCCzw6BWsiiQceeGBey95KpMJ8cUikwnDWKCIgAiJQnghIpArnakmkCudabHIkAwYM8HS3Nm3a+M+Rl48//jhrkSJiRHpesjSybEWK4yCN7JhjjnF5Iv3s1ltvTUuQtVtIYJ06dTbZjrGJ+BBZoiEkCM7AgQNdpBA00hVp0b8REIpoIEGJjSgbKYpEe6L23Xff+bjPPfecpxXSL43IU/Tv4iKVuB2pdJzfBx984LJGVAmhIyqVTqSSHfezzz5rpABGBSkYh4hguogUx0okjf0uvvhij/qxPZEs/r3TTjsVnWsUsTziiCOMsR5++GFnSQSSKB/vbIm7SaTiJpq8P4lUGM4aRQREQATKEwGJVOFcLYlU4VyLTY6EyMK+++7rEkFjUk8BiWwjUlEEA4EggkE0isn57rvv7qlhrDdinRDRF6QHKYnWSBWPSHEcjz32mKe9ITlI0GmnnZaWIBEcUg732Wcf3w4ZRKpI2yNKFEWqkB6O7cILL0wpUm+88YaL0Q033OB9ITtEn15//XX/eRR1SjygRHHKRqRYH4WgEN1BpEgbRB5zESkiUhwjKX20JUuW+PUsSaSQJqJNiBNpntwTX3zxhUefOK90jfctsD3RLCKHcTeJVNxEJVJhiGoUERABESj/BCRShXMNJVKFcy02ORJS0EhXYwKPQDGZ/v3vf5+1SLF+iPVATKaJbtEvE3siIqTpIWYUUmCiTmoca3ISRYo1QhRzIAoTyQsyQZQD2YgiSqkwUhQBSWMtFEUfiLQxBiJBmiGighCxBoltSElLFZFirRXHMWbMGBdBRKFRo0Z+XqyRIv2OCBzrhlhTRDQmnUghbbBhXVbx7ZAyRBZRJArGeqVofVTifsVT+5JFpDi/K6+80tMXWZ9Fmh5CWZJIwRQuSAtpflxLGumPHBcRS9I9WTPGGqx58+Y5y86dO/t23DuUxC9JdnP5CEikcqGW/T6KSGXPTHuIgAiIQEUnIJEqnCsskSqca7HJkRANQWyowIasHHDAAS44l1122Sbvkcqk2ATV6Fj7Q5U3BASZISpE8QLSx1j7Q/9IFuLE5ByRYKJPpT7+3qJFCx+XRnEDIh4UZiipkWI3cuRII5pUvXr1ojU/yCGSRVEFImVUBYwm/KlEiigYqWusl6JQxR/+8AcXQUSPCn9EtVhnReVCBIRCFOlEinMnZfK8887z9WOJqX1whz/yhqyxPonzgB3pftF+yGD0Hql0x404Lly40At3IESUlScaWFKjwAcChrBFJdbXrl3rMknVRqJlrKMjush1Q544PpgSBSQVk/VscTeJVNxEk/cnkQrDWaOIgAiIQHkiIJEqnKslkSqca1FujoRJPNXxqKynlhkBSqhHxSFee+01LzZR0voyeqZMO6mUyGshNYlUmKshkQrDWaOIgAiIQHkiIJEqnKslkSqca1EujiRKc0MEKttLaHO9QESKSLcj7W+PPfYwSsKTGsn6p3SNsvCk7FH6nuhaITWJVJirIZEKw1mjiIAIiEB5IiCRKpyrJZEqnGtR8EdC6hxrqZjYH3LIIX68rPVJVuSB3+22225eaELNvAw7a8N4vxNl3UlJZK0aP0vWKAtPWiUpe6xlK7QmkQpzRSRSYThrFBEQAREoTwQkUoVztSRShXMtdCQiUG4ISKTCXCoeXhD5pfy9Wv4IsOaS9ZyJr1DI32iVu2fW13JPs75TLX8EWGfNg7t8rJHN31GXv55J26fIE2vA1fJLgO8OCnbxzsxCahKpQroaOhYRKCcEJFJhLpREKgxniVQYzowikQrDWiIVhrNEKgzn6LtDIhWOt0YSARHIIwGJVB7hJnQtkQrDWSIVhrNEKhxniVQY1hKpMJwlUuE4ayQREIEABCRSASCbeUn/JdvXK7hiI2HOPtwoTIZ4ITivO1DLLwFeUcErK6IqpvkdrfL2zqs5SO2DtVr+CMCYe5oCUtm0M+vvbHWrVclml0q/rVL7Kv0tIAAiUHEISKTCXEuKTQzdoWmYwTSKCIiACIhAEAIPHdnU/rDjtkHGqiiDSKQqypXUeYiACJhEKsxNIJEKw1mjiIAIiEBIAhKp7GlLpLJnpj1EICMC7dq1sylTplitWrVSbk+J9ubNm1vbtm0z6rOkjR544AFbvXq1lzF/5ZVXvNQ748+ZM8fefvttGzhwYEldlOvfS6TCXD6JVBjOGkUEREAEQhKQSGVPWyKVPTPtIQIZEShrkbr66qvtzDPPtCZNmkikMrpi2ihTAhKpTElpOxEQAREoPwQkUtlfK4lU9sy0RwES4AWyjzzyiG233XZ20kkn2T333GMzZszwd7CMHz/eozFUwEIsougPonP++efbsmXL7JNPPrHWrVtbp06d/OyWLl1qU6dOtR9++MHq1q1rgwYNsho1ahhysueee9qTTz5p/fv3twYNGtjIkSNt3bp1xsLwjh07Wvv27b2PTEVqjz328OjRp59+6ov3r7rqKn+XyZo1a+yWW26xDRs2+PsJGK9x48be92OPPWazZs0yFu7Wrl3bBgwYYDvvvLNFEal69erZtGnTrGbNmta9e3fvY8WKFb49LPg558K+qdrKlSvtpptusmbNmtmHH35oGzdutF69etn+++/v53rXXXfZokWLbLPNNrNGjRpZnz59/D0sRMTGjBnjaXZVqlSxc8891w499FBbtWqV91e/fn376quvnFvcTRGpuIkm708iFYazRhEBERCBkAQkUtnTlkhlz0x7FBgBJvl9+/b1SmK8uBJBQGwQiQkTJviEHtFABHr27Gmk0+21117WoUMHO/nkk61Lly72zTffuGQ9+OCDvl23bt180o8o3Xffffbmm2/6fvxh2+HDh3s1nFtvvdUr4/Tr189FCDG7++67XVQyFakvv/zS+0OekJ6LLrrI/vCHP1iPHj3sxBNP9D9IzeDBg10QOT6ED9GrU6eOjRo1yveFQWJqX9euXT3FL4pIwQPB2X333X08JOq8885LeTXff/99u/DCC+2GG26wFi1a2Isvvug877zzTluwYIHde++9NnbsWOfANhwL/V1wwQX+otZjjjnGPvjgAz+u6dOnG+fJucGqVatWebmLJFJ5wfqbTiVSYThrFBEQAREISUAilT1tiVT2zLRHgREgErV8+XKXHNrixYtt8uTJLlJnnHGGi1XTpv+pMMbPKSX817/+1UXqb3/7m0sVDfG57bbb7OWXX/ZIy4033ug/JypFlGnu3LkuDERfkC4akRkiXVEpV6Tssssu8/EyFSm2PfXUU72/a665xg477DDfH6ni3KJyvESDkJQDDjjA5S0qiTx//nybN2+eH286keK8hgwZ4uOwHRGiyy+/PK1IIWKsr6Jxnscdd5yLJZwaNmxYdNz0zXowWCOrCCmRKtrFF1/s14HIXu/evTc5p7hvJYlU3EST9yeRCsNZo4iACIhASAISqexpS6SyZ6Y9CowAURqiQUz6aaSuEXFBpE444QRP9yNiQ+OdLEcccYSnqCFS48aNM9LgaNG/ETEiKES3okaKIBEv0gQTi0MQtbnjjjs8VY0xiMAgNKToZSpSif1FxSeIhCEgO+20U9Ex8CJDIjp//vOfPW3xhRdecFkhQkVaH6ly6UQqsdhEJsUnODfEiLGiRtrkxIkTPRJ31FFHedSJ9u677/q2iOCwYcM8Khc1UhWRQyJjFLsgDTNfTSKVL7Kb9iuRCsNZo4iACIhASAISqexpS6SyZ6Y9CowAUvD666/7RJ62ZMkSmzRpkovUOeec45P7KOqUeOipRArheO6554oiXIn7FK+yR4SIaFKbNm18s86dO3saYWlFCrmi7ygalHgMHBuiMnr0aF+T9NRTT/mffIgUaXkPP/ywC9tPP/3kYnr//fc7X5iyJoxGRBDRhDXpgIkRKeSPCB6pfxKpAvvw5Hg4EqkcwWk3ERABEShgAhKp7C+ORCp7ZtqjwAi88847duWVV/qaoa233tquvfZa+/jjj12kiJ6QBkd0h9Q00s+IpOyzzz4pI1L0wRopRGXXXXf19UmICmlpxUUKGRsxYoSn+5Fix5ohIjCIUGkiUhTEYI3Uaaed5sfLuizWJyE2FLpAXIYOHerRKNL1vv/+e4+uJUakEBrWbHEsxSNQmUakSNOj0AZRPBgQTUKYSH0kEsiaKwpKcAwUzSBlkpREUvmOPvpoj1QhllwLCl5IpArsw5Pj4UikcgSn3URABESggAlIpLK/OBKp7JlpjwIkQIRk4cKF/s4kqu/Nnj3bq8ohGKTjvfHGG37USAVpfaThpYpIkepHJT+EAQkj6oNQEGUqLlJEayi6UL16dRcHPlBPPPGEj4lQZPseqcT+165d66Kyfv16P16iPxSeQKooPIFEcb6syyISRJodqYDRe6SIWlHZjwIQRJRySe27/vrrPS0vSiMkfRIOv/76q0fFYM7f+RlcKTzx0UcfbVK1j8jaQQcd5MclkSrAD08OhySRygGadhEBERCBAicgkcr+AkmksmemPQqQAEUfoqIMr732mheVYB2PWu4EWCOF2BFNKi9Na6TCXCmJVBjOGkUEREAEQhKQSGVPWyKVPTPtUWAEiNCwNonUNtLLbr75Zo+MECFRy52ARCp3dhV9T4lURb/COj8REIHKSEAilf1Vl0hlz0x7FCABSpOzfoc0M4ogkIJGul1ZN9ZqRWXZix/Lbrvt5il5ZdkoBEH6Y7J2ySWXeAVCRaTK8goV5tikvd5dvaFXi1TLHwG+z1jbyQu51fJLgJeVk0IdvbYhv6NV3t7JHqFFGSSVl0R+zzzX746hB9a3RttVy+/BVbDeJVIV7ILqdESgMhNQal+Yq49IsXaQFy+r5Y8AEsWrFxJfxZC/0Sp3z0yGuKejV2VUbhr5O3te48Ekv1o1TdbzR/k/77j89ttvC+KBcj7PsxD6lkgVwlXQMYiACMRCQCIVC8YSO5FIlYgolg0kUrFgzKgTiVRGmEq9kUSq1Agz6kAilRGmWDaSSMWCUZ2IgAgUAgGJVJirIJEKw1kiFYYzo0ikwrCWSIXhLJEKwzn67uC1OYWWgr3Zr8R+1URABEQgCwISqSxglWJTROq9X3bzsvdq+SPAZOjnn3+2qlWr5m8Q9ewEeNXFlltuqbU7eb4fWIvG9A7WJbUWe9WwRnW2Lmkz/T4JAYlUuNtCEalwrDWSCIhAnglIpPIM+L/dT759qj341e/DDKZRREAEKiWB3sfuYSccUKtSnntpT1oiVVqCme8vkcqclbYUAREocAISqTAXSCIVhrNGEYHKTEAilfvVl0jlzi7bPSVS2RLT9iIQAwHKnrds2dJat26dsrfVq1fbwIEDvax7HC3xvVCsvVi4cKEdffTRlus4Tz31lE2cONGOP/54u+CCC1Ie4kknnWRTp061WrXy/2RRIhXHnVJyHxKpkhlpCxEQgdIRkEjlzk8ilTu7bPeUSGVLTNuLQAwEylqkVq1aZaxzGT58eM4ixfuxDj74YDvhhBPSEuGFyZRvDvHeEIlUDDdnBl1IpDKApE1EQARKRUAilTs+iVTu7LLdUyKVLTFtX+4ILF++3CZMmOALXImOzJw508aOHWt16tSxpUuXerTkhx9+sLp169qgQYOsRo0a/qLchg0b2rvvvmtffvmlR1P4GTKwZs0au+WWW2zDhg1epaV///7WuHFjQ05uuukmq1+/vn311Vc2cuRIe+yxx2zWrFnGAtvatWvbgAED/CWmmYrU4MGDrU2bNjZv3jw/fl6Si7zQ7rnnHnvyySd9MfohhxxivXr18uN76623/Ph40S4Levv06WMHHHCARRGp2267zbp27ervmGjSpIn16NHD0o2T7IIzNpEy3rty3HHHWefOnW38+PH20ksv+fsrmjVr5i9F5p0sUUQKySnOJ+6bSSIVN9Hk/UmkwnDWKCJQmQlIpHK/+hKp3Nllu6dEKlti2r5cEeDLhJeGIjstWrSwO+64wwVg+vTp/gb7bt26+eS+QYMGdt9999mbb75pRFr4g2jceOONLids1717dzvooINcPE488UT/s3LlSpcQxOLjjz+2iy66yPr162etWrUyIjGdOnVyUUPaRo0a5WLRt2/fjEUKOUKejjnmGHv00UddnJDAJUuWeL/8nbKbHC/y0qFDBz/WM844w9P2nn76aZsxY4afd2Jq36JFi+zxxx8vikilGifdxU6UwcWLF9udd97pqX4IX8+ePe2ss85yDpFIbdy4cRM++biRJFL5oPrbPiVSYThrFBGozAQkUrlffYlU7uyy3VMilS0xbV+uCBA9uvjii+3BBx/040aOkA3E58UXXzSEAlmiEZVq3769zZ0712644Qbbf//9fVsa0nDYYYdZ06ZNXageeeSRolQ1JIQ1QtWrV7fevXtv8jtK6kali+fPn++RJcbLNCLFsT/88MN+DIjQ1Vdf7WI0bNgwj3wharRly5Z5pO3mm2/2CBXChgASTUNonnjiibQilWqcTEWK7X766SerUqWK7zJ69GiPwHF8iSJVnE/cN5NEKm6iyfuTSIXhrFFEoDITkEjlfvUlUrmzy3ZPiVS2xLR9uSKwYsUKF5dp06YVHXfbtm3trrvuMsSGyBTrd6L23Xff+doh0tSaN29ubEsj4sO/iVwhHTvttFPRPrxgkEjU7rvvvklxCCIzSM8LL7zg0S8iMqT1kfKXqUglFptILApBCuI777xj1apV8+PgS5OURCJCyCHiiFDxcwQMgUsXkUo1TqYihaBOmjTJPvjgA5e4zz77zNq1a+cSlyhScRbPSHZsEqkwH0+JVBjOGkUEKjMBiVTuV18ilTu7bPeUSGVLTNuXKwLIB2l9s2fP9uNGZog6EZF67bXX7LnnnnNJKt4icSouUsgU0ac5c+b8Zp/i1e/o++677/boDGuJqHLHnzhEijTBPffcsyhiFh0M6YTIC0KF2K1fv97XL+VbpBBPhJI0RESKdEnWnEmkytXHJeODlUhljEobioAI5EhAIpUjuP8+XOUBJ5kyavklIJHKL1/1XsYEiMqcdtppRWuIiEwRJSISRaEI1hMhOrvuuquvd0J0SD9LJVKIFWuk6POoo47ydVAUsmDd0+eff75JRArZotDF0KFDXeCGDBniBSDGjRtX6ojU888/elSuSQAAIABJREFU7+eAUBGVYv0UArPPPvu4OLIOjH9Ha8JIV1y7dq2fFwzYn21YY/Xhhx9uctyZlkNPjKrR77777utcPvroIy/awfooilooIlXGH4I8DC+RygNUdSkCIrAJAYlU7jeEIlK5s8t2T4lUtsS0fbkjsGDBArv99ts9KnTyySf73/lDeh5ri0jlYy0Tv6dIwn777ZdWpBCSMWPGeLQHWenYsaMXniguIEgWhSiQKKr+denSxQWKwhHr1q3L+j1SxfuPqvbxhVmvXj0XqJo1a3rEi2jbDjvsYOedd56LE0JJEYxIpKgqSDU/fk7qY2lT+6IUStaDNWrUyA499FA/DqoUjhgxwgtjwEGpfeXu45P0gCVSFeM66ixEoJAJSKRyvzoSqdzZZbunRCpbYtq+3BFgrRKNdUp8ufAC2Yceesi22mqrcncuOuD0BLRGKswdIpEKw1mjiEBlJiCRyv3qS6RyZ5ftnhKpbIlp+3JHICpXzotjSd2jzPnkyZPL3XnogEsmIJEqmVEcW0ik4qCoPkRABNIRkEjlfn9IpHJnl+2eEqlsiWn7ckeAd0Pxgloq8rHwkvVMpJ8VQqPaH+umkjWOmXdElVXjHVSso0rWWrdu7e/nKrQmkQpzRUiHXbyxkVehVMsfAaLp//73v309p1p+CfDSdFK1yVxQyx8BJvg0Xs9RUmvfvLYd0rBGSZvp90kISKTC3RYSqXCsNZIIiECeCUik8gz4v90jUqwpLESZDkMgzChIFA+AEl/REGbkyjcKkyHuaWRKLX8EqO7KA4Lo1R35G6ly9yyRCnf9JVLhWGskERCBPBOQSOUZsEQqDOD/jiKRCodbIhWGtUQqDGeJVBjOjCKRCsdaI4mACOSZgEQqz4AlUmEAS6SCco4mQ4pI5R+7RCr/jBlBIhWGs0QqHGeNJAIiEICARCoAZDN/ZcCG1/fVGqk849YaqcwB7/fHGta8dc3Mdyi2pSJSOaPLakeJVFa4ct5YIpUzuqx3VEQqa2TaQQREoFAJSKTCXJnbp0y1j589KMxgGkUEMiBwWNta1qbTLhlsmXwTiVTO6LLaUSKVFa6cN5ZI5Ywu6x0lUlkj0w4iIAKFSkAiFebKSKTCcNYomROQSGXOqiy3lEiFoS+RCsOZUSRS4VhrpDwQaNeunU2ZMsUWLlxoa9assUsvvdRWr15tV155pacdXXzxxUV/79mzpw0dOtSmTZuW05FEY9WqVcvmzZtnbdq0yamfst7p/ffft+uuuy5nDmV9/OnGl0iFuToSqTCcNUrmBCRSmbMqyy0lUmHoS6TCcJZIheOskfJEIJKb7bbbzt+3wmLhOXPm2FtvvWWDBg3a5O/8fuPGjf4uqVxaNNZOO+1kp59+ur/Ytzw2iVR5vGqFdcwSqcK6HjoaM4lU+bgLJFJhrpNEKgxniVQ4zhopJgLLly+3cePG+cv8jj76aLv//vs3iUgdeeSRduONN9rPP/9su+yyi33xxRf+9yZNmliXLl2KIlLffvut/e1vf7O3337b39HSo0cPO/jgg23gwIF27LHHWqtWrfyIE/8didT48ePt+eeftz322MOaNm3qL8vs06ePb0+/vFvnnnvuSStsS5cutalTp9oPP/xgdevWdemrUaOGzZ492yNqq1atssMOO8zFMPHfZ599tjH+Sy+95FV5mjVr5lE43n3C8Z1//vm2bNky++STT4yX5nbq1MmP695777WHH37Yz/XPf/6zR9SIzH322WfO6+uvv3YRbdu2rZ155pkpr9akSZPsp59+Snq+RIN4ifCGDRucSf/+/a1x48bOf9SoUbZixQp/fwjM+vXrZ1WrVo3prvj/3SgiFTvSpB1KpMJw1iiZE5BIZc6qLLeUSIWhL5EKw1kiFY6zRoqBAF8MiAGT8BYtWtgjjzziUjVjxoxNUvseeOABlw8EI/Hv7777bpFI3XzzzT6RJ91v5cqVdsUVV9isWbPsmmuuKVGkqlSp4rLx2GOP2XvvvecS9I9//MNl5qmnnrL58+fb8OHDU54xotGtWze76aabrEGDBh7ZevPNNz3djmga5zN27FgXweL/Xrx4sd155502ceJElxKO/6yzznLx69Chg5188skujN98840f44MPPmjr16+3iy66yCut7bDDDi5OCCQiBb8dd9zR++DFnxwTAoTAJWupznfYsGEuoyeeeKL/gengwYNdKJHORx991EaMGOHHTCrm4Ycfbvvtt18Md8WmXUikYkcqkQqDVKOUkoBEqpQAA+0ukQoDWiIVhrNEKhxnjRQDAdZAIQTIBY3IyAknnOCT9cQ1UpmIFFEj1ks1bNjQ+yKSRHpgJhGpRJFi33PPPdd69erlEa1rr73WDj300LTrp5CKRYsWudDQiEq1b9/e5s6d63+IViEdNM418d/ReXMMtNGjR1vt2rVdMBEpomx77bWX/44I1W233ebRK/5cf/31/nOiekSOECmk7eWXX7bu3bvb3nvv7ZG+klqy8yXKRB/IbdQHTC644AKPTsH6kksusQMPPNCiYy9pnFx+L5HKhVr2+ygilT0z7ZFfAhKp/PKNq3eJVFwk0/cjkQrDmVFUbCIca41USgKkhhH5YPIfNaIfd9xxR9YihYARoalTp84mR5WLSN1999326aefemELBA1BQcpStZkzZ9r06dM9zS5qRIM4nueee86jRRwHDZFK/DfCR3rdBx984BEwUvMQJiJKiBQRpnr16vm+0b+fffZZW7t2rV1++eX+c6JFN9xwgx/nL7/84umRzzzzjEexTj31VDvjjDPSXqlk57tu3To/f9aPRY3/YSK+RxxxhHEMpBaSstiyZUvr3bu3bbXVVqW8I367u0QqdqRJO5RIheGsUTInIJHKnFVZbimRCkNfIhWGs0QqHGeNFAOB4hEpIjmksuUSkSKCQxrfPvvs40f28ccfu1QRUWLt1VFHHeU/RwSIFpE6F62RKh6RQiKIviASCAnRl3Tt6aefdmEila94Ky5Oxf/N+ij+R0R0B5EiFY81VulE6pVXXvEoVDQeEa4JEyb8pmoffC+77DKPXEVckp1HsvNlLRrRpyhamOr8eXIDH6J3FOyIu0mk4iaavD+JVBjOGiVzAhKpzFmV5ZYSqTD0JVJhOFdokWKBOylUTH5prNN4/PHHfd0Ja0iqVasWjrJGioUA0RMiPkRWWCPF2qLbb7/dozvZpvaR2kZqIOutqGI3YMAAX+dERIfy5uecc45Hmbp27eprhhJFauutt/Zoz0MPPVQUVWGtEpN40t4o8pCuUdiBNVKk5e26664eIWJtFVGakkQKGdp3333ttNNOs48++sjXZ3FsHGeqiBT/42K9GBEvClogMpwzESkiUxTXaN68uf3444++zon1Yo0aNUp7DsnOl305LiSU6Bay1rdvXy9sQSStc+fO3ifyV79+fd827iaRipuoRCoMUY1SWgISqdISDLO/RCoMZ4lUGM4VWqSYpJJGxMSYtCYmrieddJIXISDywORYrfwRQIiZoFO0gApzyAzXmDVH0XukMlkjRSrdyJEj7Y033vDqetEaJ+4P0gcRDtYeIQCkpiEHie+RQrwQIIpKUBGQ9Diq8CF3qQo1JNKmsh5ig7ywPWJC8YWSRIr0RtZWUSgD2WE9FufB8VAZL1lqH6l+FKigOMa2227r68ooQoGAvvPOO17YgkgRa5uOOeYYl8iSWrLz5XM2ZswYL25BtKxjx45eeAKpQp5IR2QMol18/vLxMEMiVdKVi+f3ikjFw1G9xEdAIhUfy3z2JJHKJ93/37dEKgznCi1STOImT55sNWvW9Anrhx9+aEOGDPFJHSlI5fUdQOFuDY2UDYEFCxYYFfWoVFcZWqGer0QqzN0nkQrDWaNkTkAilTmrstxSIhWGvkQqDOcKLVI8dY8qiJFydMopp9hxxx3n78ohMsXTeTURiIPA999/7ylsrKfKR0nvOI4xzj4K+XwlUnFe6dR9SaTCcNYomROQSGXOqiy3lEiFoS+RCsO5QovUhRde6MJEKhOpULyQlBQuFt3zDh6iVGoiUFoCpBqSzkYKW2JKHFKFcCRrrM9inVWhNophsF4sWaNEOuXSi59voZyLRCrMleD78/u1e1SKBwdhiCYfhckQ633z8fLqsjyvfIy9W6NtrOH+qaulljQm6c2kWZOWrJY/AhKp/LFN7FkiFYZzhRapV1991RfSU9mNVD7Wt7DInwXvVFdjLYiaCIhAxSIgkQpzPREpJp0Uf1HLHwEyKFjPmfiqhPyNVrl7lkiFuf4SqTCcJVJhOFdokaJowG677WabbbZZ0RMmChRQ5nr33XcPR1gjiYAIBCMgkQqDWiIVhrNEKgznaDKkiFT+eUuk8s+YESRSYThXaJFijdRdd921yQtCw2HVSCIgAmVBQCIVhrpEKgxniVQYzhKpcJwlUmFYS6TCcK7QIsWaqM8//9zOPvtsyVS4+0kjiUCZEpBIhcFPsYm1Cw8KM5hGEYESCBx39i52yPG1SsVJqX2lwpfxzhKpjFGVakOJVKnwZbUz3x2se99iiy2y2i/fG2/2K3l4pWgs/OfkyC/n5IqfIBX91ERABCoWAYlUmOspkQrDWaNkRkAilRmnQthKIhXmKkikwnBmlAorUlRT23LLLVOSbN68eTjKGqnSELjmmmusZcuW1rp165TnzPq9gQMHpqyMly2s999/36677jqbNm2arVu3zouqUIY97nGyPa5U2ye+2DiuPqN+JFJxE03en0QqDGeNkhkBiVRmnAphK4lUmKsgkQrDuUKLVITwp59+si+++ML4b506daxatWrh6GqkSkegrEVq9uzZXi6ZamoSqUp3+wU7YYlUMNQaKAMCEqkMIBXIJhKpMBdCIhWGc4UWqW+//dbGjRtnzz77rFcvoW2++eZ2xBFHWL9+/WyrrbYKR1kj5YUA7wSbMGGCkQXKO8NmzpxpY8eOdWFeunSpTZ061cvf161b1wYNGmQ1atQwRKdhw4b27rvv2pdffmm1atXyn3FvrFmzxnjH04YNGzwVtH///ta4cWNbtWqV3XTTTVa/fn376quvbOTIkf5C51mzZtkvv/xitWvXtgEDBtjOO+/sfWUSkRo8eLC1adPG5s2b58d/ySWX2MEHH+ypqOPHj7e3337bXx595plnWtu2bZ3fW2+95cfH+6mItvbp08cOOOAAiyJS9DFkyBCvUsnLp4866ihLNU66C5KOK5+nu+++2z9TO+ywg79KgCqY/JviLosWLfJKmY0aNfLjowoW/fFZhPHRRx9t999/v02ZMsXZx90UkYqbaPL+JFJhOGuUzAhIpDLjVAhbSaTCXAWJVBjOFVqkhg8fbuvXr/diE5RBpzFRZhLIS0V79+4djrJGip0AXxJEXZCdFi1a2B133OGpctOnT/eJfLdu3Vx+GjRoYPfdd5+9+eabnv7GHySblzQzsWe77t2720EHHWQ9evTwF83yZ+XKlS4h99xzj5fM5wW7CHirVq3sm2++sU6dOrmoIW2jRo1yeenbt2/GItWrVy+XJ95n9uijj9qTTz7pEogYIgOI2caNG61nz55+zHvttZcf6xlnnOEywktzZ8yY4eedmNqHsCAoUUQq1TipLkg6rggf72S79dZbbdddd7WHHnrIj4MxFyxY4C+95hx4SME73GBz3nnnOSvYcZ1Ym8j2HLtEKvaPRbAOJVLBUGugDAhIpDKAVCCbSKTCXAiJVBjOFVqkTjvtNH/qTRQisRGF4Ek5E2S18ksAKSYa8uCDD/pJIEcdOnTw6/riiy96ZARZohGVat++vc2dO9cn+Pvvv79vSyOCdNhhh1nTpk1dqJjoI1g0JARxqF69uot34u9+/PFHq1q1qm83f/58jywxXqYRKY794Ycf9v0RoauvvtrlAlHi7xwPbfLkyT7OX//6V0/ZQ9g4Pu7js846y5544om0IpVqnFRXPh1XIkusPSTqRSNdFunkGiBHRPpOPfVU/93LL7/snz8igUjonDlzivbh1QRcJ4lU+f38SaTK77WriEcukSo/V1UiFeZaSaTCcK7QIhVNqoun8PEh5gn5Aw88EI6yRoqdwIoVK1xcKLAQNVLgSC9DbIhMbb/99kW/I2WOd9+QNkehkShdjmgP/yZyhXTstNNORftwryABpK4lFocgMoP0vPDCCx79InJEWh8pf5mKVGJ/iWuZkIztttuu6CXSyBPpqEgdcoi08DO+JBEwBC5dRCrVOKkuSDquzzzzjK1du9ajgFEjpfK2225zrqQSEmGjkTqJEBLVGzZsmPOKGvJFJE0iFfvHIliHEqlgqDVQBgQkUhlAKpBNJFJhLoREKgznCi1SV111la/hIB2KiSmNlCmekrMGZsSIEeEoa6TYCSAfTOgprkBDZog6Eel47bXX7LnnnvOUuOItEqfiIoVMEX2KIieJ+xUv2kDfpIiOHj3a1wA99dRT/icOkaJsPzJGKl9iI52QCNTEiRNd7Ehb7dy5c+wilY7rK6+8YkuWLLHrr7/eDy2KSMEMkeKYO3bs6L8jeoW4InKJESmigyeffLIiUrF/IsJ2KJEKy1ujpScgkSo/d4hEKsy1kkiF4VyhRYqX8fI0/MMPP7Qdd9zRF/RTFnqXXXbx1KRo3VQ41BopTgJEZUjfRIyaNWvmkSmiHkSiKBSBQCM6rOVhvROiQ3peKpFCrFgjRZ9EVhAX1iux7ol7KTGygzggCkOHDnWB436iAATpbaWNSCFKpA0SHaPYBOLP8VSpUsXFkXVgpPdFa8JIVyRKFJU/Z38KUXTt2vU3VfsyqeKXjivXL1ojVa9ePY/qIpVwJlqGxI4ZM8aPFSZ77LGHr1Fkvdbll1/ua6RYr3b77bf7dVJEKs5PRNi+JFJheWs0iVRFuQckUmGupEQqDOcKLVKcHPL0zjvv2KeffurpUEz+9t1336I1MOEwa6R8EKDAAZNyokJEOfg7f0jPW7ZsmUdEkBJ+T9EG3q2UTqQQEkSAaA+yQnSFNLTiAoJkIelIFDLQpUsXFyjS2niPUyZV+1Kl3CFkRHfeeOMNR0akjLQ+joeIF9E2Iq0UcUAeua8p5BCJFGuT+DvSQgQr29Q+xkzHdfHixUXjcu4UzKAqIp81onQLFy70v8Oa4ya1lnVVUXVFhJUiFUgn1Q7jbqraFzfR5P1JpMJw1iiZEVBEKjNOhbCVRCrMVZBIheFcoUWK1L0rrrjiNyRZK8OaDYoOqJVvAkzYaaxT4kvj+OOP90m6StuX7rqWZ64SqdJd+0z3lkhlSkrbhSAgkQpBOZ4xJFLxcCypF4lUSYTi+/2//vUv23rrrT0bqpDaZr9Gs7ksj4qoAn9Yx8Fi9+KN31G2mvcAqZVvAlG5cgo0kLpH2hhV7tRKR6A8c5VIle7aZ7q3RCpTUtouBAGJVAjK8YwhkYqHY0m9SKRKIhTf7yucSJHSxVoNXl5KSlfxRilp0osoJ61WvgnwbiheUEuUkRLlrGfiRbCF0CiwQJpessYx8/SirBrvfmKtVbLWunVrT8srVK4lMZNIlUQont9LpOLhqF7iISCRiodjiF4kUiEom2fp8FoY5kZq+SVQ4UQqwsXCfF7ImqyxroQF+WoiIAIVi4BEKsz1ZP3h92v3cOlWyx8BJkP8/yp6Z13+RirfPe/dbHvbtWHpHk4xGeLhK+tR1fJHQCKVP7aJPUukwnBmlAorUqkQEr2gbLTeIxXuJtNIIhCKgEQqDGlEikknFRnV8keAyp38PyvxnXj5G61y9yyRCnP9JVJhOEukwnCu0CJFpT6qn/FiUJ7oRY332FCWWWtpwt1kGkkEQhGQSIUhLZEKw1kiFYZzNBlSRCr/vCVS+WfMCBKpMJwrtEgNGDDAn5i2atXKRo0a5e+xefvtt72sNOWhlTca7ibTSCIQioBEKgxpiVQYzhKpMJwlUuE4S6TCsJZIheFcoUXqpJNOspkzZ/qifl6ySkU3Gi8QfeGFF1ys1ERABCoWAYlUmOv598lTbes7W4QZrIKO0nFeE6uyXfr1OBKpcBdfqX1hWEukwnCWSIXhXKFFqn379v6C0G233dZOP/10u/POO12qfvnlF/+31kiFu8k0kgiEIiCRCkNaIlV6zhKp0jOMsweJVJw0U/clkQrDWSIVhnOFFqlrrrnGy09fe+21Nnz4cNt5552tXbt2Rsnsu+66y6NVahWbAFFJ3hlWq1atvJ/ovHnzrE2bNj7OK6+8YrvttluQcfN+YuVsAIlUmAsmkSo9Z4lU6RnG2YNEKk6aEqkwNFOPIpEKdwUqbNW+r776yiZMmGCXXHKJffHFF3bllVfa+vXrrUqVKnbxxRfbscceG46yRioTAt98841Xu9p8883zOj7vjibKGaWP8iLoM88805o0aZLXcdX5bwlIpMLcFRKp0nOWSJWeYZw9SKTipCmRCkNTIlXWnBm/wopUcbjkmiNUO+ywg97JUQh3XopjWLp0qUeRqK5Yt25dGzRokNWoUSPtEa9atcrfGfb111/bLrvs4uvf2DcxIsULaB955BHbbrvt/Oe8tHnGjBm+z8iRI23dunVe5aZjx45GWijtlFNOcSG6//77bfr06S7ivKh2w4YNtsUWWxjvKmvcuLER/Xz++ee9GuQhhxziaaM1a9a07t2724EHHujVIyl0wj1If7wQOl1jW47pjjvu8M0S//3ZZ5/ZjTfe6MdNf/RFnxw747z00kv+92bNmtmll17q70RZvny5P1RA+KK1g2PHjrU6depYtrwnTZpkP/30k/Xp08ePjRf+UQIbnkhMMj5UzaTgy4oVK/wYmjZtav369cvL51AiFebDLZEqPWeJVOkZxtmDRCpOmhKpMDQlUmXNuUKLFJM3UviYyB500EHO+vHHH/cJ8znnnOORKbXCIoCgdOvWzaWoQYMGHuEhFZMqi+laly5d7Pzzz7eWLVt6yuY///lPT+eMRIr3sPTt29eoNEaEiogR98G0adPs1ltvtR9//NEn9pTMpx/W1iFCHTp0sOOOO866du1qm222mfXo0cNOPPFE/7Ny5UobPHiwCwT9IzOPPfaYHybbIzFEpBAYJvdUkdy4caP17NnTz2evvfZKeUrpRGrcuHG244472llnneXjwgqhe/XVV30d4MSJE11WGIdtjjjiCBcdtmnRooXLGVKJGHJO2fJ+7733XG7pA0l76qmnbP78+TZs2LCUfJDMRx991EaMGOHHNmXKFDv88MPz8jJXiVSYz7REqvScJVKlZxhnDxKpOGlKpMLQlEiVNecKLVJjxozxJ/lEJ6JJK5Nffs4E96KLLioE/jqGBAJMthctWuQRFxpRKaJDc+fO9QhQsoYQ9erVyx588EEXA4qJINHVqlUrEimiLkRlIiFbvHixv0cMkSJ6Q2Rnyy239O6Rsssuu8yjJojU0KFDbd9997W1a9d6hImoVpQqyLgXXHCB7bnnnilF6owzznBxoz8a41atWtX++te/5iRSRNFefvllP5a99957k7RFIkXRA4LRo0db7dq17U9/+pOnssKHRgSJ80IAX3zxxax508e5557rzA8++GBfg3jooYf6+aXiw7WDI2m2PNjI50MMiVSYrxSJVOk5S6RKzzDOHiRScdKUSIWhKZEqa84VWqSYLPJ0nslkYvvkk09cokjXUissAkSTiJQQNYoaURciSUSIkrV33nnHhgwZ4ml6xVsUkSJqQrSJKBENwSZihUi9//77HqVhTR0Rlg8++MBFbr/99nPhIAJUr149YxyEZKeddioahupD3EsHHHBASpE64YQTPJ2QvmlIHlEiRCRVSxeRQhS5d5955hljDdipp55qyBqCRNodx89YpABSXIVj43w416iRDki0lkhStrzpg4gdPOFBtIu+EdpUfDjfZ5991h5++GEjDZPIYe/evW2rrbaK/QaUSMWONGmHEqnSc5ZIlZ5hnD1IpOKkmbovVe0Lw1nFJsJwrtAixfqW22+//TeV09asWePrOx566KFwlDVSRgSefvppf89XSal8iZ0xoScSMmfOHI/OICr8bPfddy+KSC1ZssRef/11jwzR+DfSgQAQUUJGoop7nTt39jS84iLF+jq2ZZzijcl7qtQ+0khZQ5Uula94f0ROET1S9WhE00hBjNZMRdtzLxM9u/766z3Fjv9BEfVBpEj5Y53YYYcd5ml9s2fP9t1ILyTKR0Tqtddey5o3fURRQMQJoSPalI5P4vkxWWF7olkU6Ii7SaTiJpq8P4lU6TlLpErPMM4eJFJx0pRIhaGZehSJVLgrUGGLTTCRJPrEE3MW1bM246OPPvIn8KQhMQlUKywCFFBgzQ5pabvuuquvQ0IQiF6ka6xrYlKODFHogYILiWukvvzyS6/aSBEL3iVGOtrHH3/sIkXUibU7jRo18ggNRRiuuuoqa968+SYRKcZnjRQvdz7qqKM8GsT6J9ZecW/RD3JOlOXCCy/0tVb0QVSUNVjcb6QQsj6I/ffZZ5+Up4SUnHfeeb5GjP5IR0V6EKkbbrjBK07SN/1yTFdccYXde++9noLI8XGfs46pVatWnkLIz5BTClBwzkTv+ByQcpcLbw6cNVhIC2l+rVu39nNJxYfS8ETMkFQan8369ev7ccXdJFJxE5VI5YuoRCpfZHPrVyKVG7ds91JEKltiuW0vkcqNWy57VViR4h1SRB2YHPPBpTEpZRLKRDdaE5MLNO2TPwLLli3zVD4kYZtttvEJO9GhdG316tUuQxSrIBJFRAl5Tqzax72wcOFCj1Ay8SdCQ3ob6WZISPXq1e3oo4/2MpZPPPGEV8BDDKLUPsZnnRRSQ/U+oj5U+KPwBI0xo0gSEaRZs2a5DCF39PXGG2/4dggQaX1Rql+q80LAKCDB8bKuiHViRKhIMUT2OE4icMccc4wXT6EiHil8rL9CClm3ROU/jou1U0Rn4XnyySf73/lDmmIuvDlm0gsRU2SPftPxQTqgBtcwAAAgAElEQVSRJ9IOOWYkkigZ69jibhKpuIlKpPJFVCKVL7K59SuRyo1btntJpLIlltv2EqncuOWyV4UVqQgGa0qi9S+UPs/3O4VyuQjaJ/8E+FKJrj3RHYo+kC5XGRoRMxrFOOBw/PHHF0XPcj3/BQsWGEU7qFxYSE0iFeZqKLWv9JwlUqVnGGcPEqk4aabuSyIVhrNEKgxnRqlQIkVkgnUhRJ74e7pGpTW1ykGAiAhpZUSXeNfTzTff7PdIuoIPFYlMVLadwhekShJFQiRzbUR7SWmk0EZJ0cJcx8h1P4lUruSy208ilR2vZFtLpErPMM4eJFJx0pRIhaGZehSJVLgrUKFEijQnUohYC8Lf0zUmlGrlgwCRI9LckjVkiNS3khqpcbz7iOgMhR+o4Ec6X1k1JAQhSdZ4qS1rueJqvIuLPqmAyDkjQaT/pWoU/YBVskbJdcqvk9JISmGhNYlUmCsikSo9Z4lU6RnG2YNEKk6aEqkwNCVSZc2Z8SuUSFGRjHUXrD/h7+natttuWwj8dQwiIAIxEpBIxQgzTVesY9zm6529cIpabgR2/sN/1hamaxSo4QFI4ishStpHv8+NgEQqN27Z7qXUvmyJ5ba9IlK5cctlrwolUqyFyqQRlUj1XqJM9tc2IiAChUlAIhXmurhIbbONV0VVyx8BiVT+2BbvWSIVhrVEKgxniVQYzoxSoUSqpHS+RKxK7Qt3k2kkEQhFQCIVhrREKgxniVQYztFkiIcDJVVUDXdEFXMkiVSY6yqRCsO5wokULwqN2ltvvWW8v4b3+/BOIkpA8+4gyl3z/hpeVKomAiJQsQhIpMJcT0Rq+w21y21q37YNqljVmluEgVWKUSRSpYCX5a6KSGUJLMfNJVI5gstyN4lUlsBKsXmFikglcuCFqLxXh3flJDbeBXTNNdf4u4rUREAEKhYBiVSY60mxibrjDwkzWB5GaTasntU7seyKzWR6ShKpTEmVfjuJVOkZZtKDRCoTSqXfRiJVeoaZ9lBhRYqXsVJ5LHpZaAQkKoX9yCOPZMpI24mACJQTAhKpMBdKIhWGs0QqDGdGkUiFYS2RCsNZIhWGc/TdQaXlLbYorCyHzX6N3iKaI4vLL7/cX0B66qmnWu3atb2Xzz//3GbNmuUlsCmTriYCpSHQrl07mzJlitWqVas03WS1LxOrhQsX2tFHH512v0y3y2rwcrCxRCrMRZJIheEskQrDWSIVjrNEKgxriVQYzhVapL788ksbP368Pf/888YNRUOsfv/73xuStfPOO4ejrJEqJIGyEKlVq1Z5Wurw4cPTMs10u4p2YSRSYa6oRCoMZ4lUGM4SqXCcJVJhWEukwnCu0CIVIeR/RF9//bX9/PPPtsMOO9hWW20Vjq5GKnMCy5cvt0mTJnmxEcKu3bp1sz/+8Y++Tm7PPfe0Dz74wFavXm3HHnusbbnllvbPf/7TvvjiC7vqqqv8999++62NHTvWEBOqOfHenLPOOsvPK1Gkpk6d6n1df/31xjo8XoC7YcMGH7N///7WuHFj74NIaP369Y1S/SNHjkzJ57PPPvM1fty73MNt27a1U045xbp27erH1KRJE9//scce8yjrL7/84pHXAQMGGO9IS9zu3HPP9W3vuOMOH+/tt98u+neycc4888yUxxWx7NOnj2/DsVAC+5577jEkJtl589kbNWqUrVixwqPBTZs2tX79+lnVqlVjvz8kUrEjTdqhRCoMZ4lUGM4SqXCcJVJhWEukwnCu8CL1/vvve6W+H3/88TdE27RpE46yRiozAghF3759bb/99nPRmTNnjk/ir7vuOr8vbrjhBvvkk0/svPPOs0svvdSFavr06UZE8+KLL7YxY8a4yLDP999/bz179rTevXtb8+bNi0QKQWA93ujRo13Ue/ToYSeeeKL/WblypQ0ePNhFg3vxoosu8r5atWqVlsm4ceNsxx13dGnjhZwIGEL28ssv2+OPP+4RKdb7derUyZC4OnXquKwge5zvokWLirZLFKfiIpVqnOJrC6ODfe+992zQoEF+vozFawTmz59vw4YNS3neRIUfffRRGzFihIsU6ZCHH364X5O4m0QqbqLJ+5NIheEskQrDWSIVjrNEKgxriVQYzhVapCZPnmz333+/T0arVKnyG6LTpk0LR1kjlRkB0jgpf0/J+7p16xYdByLVrFkzlyG+cBDr++67z2rUqGHPPPOMPfvssx5dIjrDf/fee2/fF2lBwJAl9qV/ojRIDBUiiUZ1797dKGay+eab+z69evWyCy64wKpXr+4Slvi7VGBmzJjh0kRfjB31lShI7MuxRJEdhIaS/0SyMhWpVOOku2BEuDingw8+2K699lo79NBDPcqU6ryJyg0dOtQuueQSO/DAA5N+HuO6QSRScZFM349EKgxniVQYzhKpcJwlUmFYS6TCcK7QIkW6EU/xmUSrVV4CpNAhC0uWLPEKjkz2W7Ro4RGpQw45xAWKxsucecdYtWrVbMGCBS5TTP5JqbvzzjuL1tQhW6ToDRw40EUKiWEfZIq/v/POOx7JSiy7z/84iETtvvvuvh/RnJIaqXo8COA4iDxRNOWMM87YRJCI7nBuL7zwgq//27hxox8naXyZilSqcdId3913322ffvqpnyefMx5K8A63VOd9xBFHuJjCF3YtW7Z0ocxHmq1EqqQ7K57fS6Ti4VhSLxKpkgjF93tV7YuPZbqeJFJhOEukwnCu0CLFWhiiUmoiEBEgxYxozYMPPmhDhgzJSKRIrUO6GjZs6N1Q6IE1PxdeeKGLFL9jndL222/vURrWVxF9IoWweGMtVqYilbjvmjVr7LLLLvPIGJUno9S+5557zpAaUgqRRNLs+FNcpEgvJBUQIaSxbuzWW28tWjMVjZU4zj777JPyxkGaOFfEKRLOdOed2BGTFQSVaNbpp58e+80pkYodadIOJVJhOEukwnCOJkN8j5KyrJY/AhKp/LFN7FkiFYZzhRYpJooHHXSQT5bVKicBvrARkKuvvtpLlBNFISXvgQceyFikWENE1Ia0NAorsEaKNU6kqEXFJoisIE9IEumCjEEqIYUpiCZNmDDB1y0hQZmKFGu3WK/FWqwolfCKK67wAhZEtCiA8dBDD7kUISZEo5BD1nFxzEhjtB37sAaMaBrHyrqv1157zUUq1TiNGjVKe9PAAWkhza9169a+barzJt0Qdp07d/btooIbMIq7SaTiJpq8P4lUGM4SqTCcJVLhOEukwrCWSIXhXKFFiqfyPLHfZZddPM2K1KfExuRTreITYBJ/7733esEI1sox8f/Tn/6UcWofgkIlOoossE7puOOOc0miJVbtQ1wQJqKgpBMiK+vXr/enmx07dvTCE9lEpEgRRJaI4DAuqYfnnHOO903FPKJijEUhC44RUezSpYtXI2RbUgGj7ajqN3HiRHv11Vd9OyRw7ty5HqFKNU5JdwZph6wXQ86iwhSsD0t23sgk8kSxD86FaBeFM0iJjLtJpOImKpEKQzT5KBKpcPSV2heGtUQqDGeJVBjOFVqkbrvttrRvGaaam5oIiEBuBFhHtnjxYhe5QmoSqTBXQxGpMJwlUmE4R5Mhpfbln7dEKv+MGUEiFYZzhRapcAg1kghULgKkD5KqSAGNfJQwLw1NiVRp6GW+r0Qqc1al2VIiVRp62e2riFR2vHLdWiKVK7ns9pNIZcerNFvz3bH11lunDd6Upv9c993sV0qS5dB4Up5JK+k9Ppn0oW1EIFcCvFOKQhXJ2m677eYpemXVnn766ZSVBSnFTll2UhVJNSy0JpEKc0UkUmE4S6TCcGYUiVQY1hKpMJwlUmE4R98dFUqkTjrppIzo8S4fNREQgYpFQCIV5npSvXL7DbW9oEp5bNs2qGJVa25R8IcukQp3iSRSYVhLpMJwlkiF4VwhRSocOo0kAiJQaAQkUmGuCCLFehLeI6aWPwISqfyxLd6zRCoMa4lUGM4SqTCcJVLhOGskERCBAAQkUgEg//d9ahKp/LOWSOWfcTSCRCoMa4lUGM4SqTCcJVLhOGskERCBAAQkUgEg/1ekanxReKl92zfdyn63zeZhIAQYRSIVAPJ/h5BIhWEtkQrDWSIVhrNEKhxnjSQCIhCAgEQqAGQzmzp5qu02pvBedv7HmXva9vvH/36yMFR/O4pEKhx5iVQY1hKpMJwlUmE4S6TCcdZIIiACAQhIpAJAlkiFgWzmLxL/7rvvbPvttw82ZmUdSCIV5spLpMJwlkiF4SyRCsdZI5UxAcqJt2zZ0lq3bp3ySFavXm0DBw5MWfo721N4//33vcT5tGnTbN26dfb111/7e5fiHifb4yrr7du1a2dTpkyxWrVqxX4oEqnYkSbtUBGpMJwlUmE4R5MhvZA3/7wlUvlnzAgSqTCcK5xIdejQIWNyDzzwQMbbasPyT6CsRWr27Nn2888/e5UziZREqrx/oiRSYa6gRCoMZ4lUOM4SqTCsJVJhOFc4kVqyZEnG5A4//PCMt9WG2RFYvny5TZgwwXinMu/1mjlzpo0dO9bq1KljS5cutalTp9oPP/xgdevWtUGDBlmNGjX8BbQNGza0d99917788kuPVvCzzTff3NasWWO33HKLbdiwwd8c3b9/f2vcuLGtWrXKbrrpJqtfv7599dVXNnLkSHvsscds1qxZ9ssvv1jt2rVtwID/x955QElVbG17k4MkyUlUQAHxkkQQEbiICAIiSBL4iBIk56SSkyDxShQEJClBQJJkJAiigOBFJStJkKwkCcK/3v39Z75mmJ7pnu7edPe8tRYLmDmnqs5Tp7vr6V21Tw/JmDGj1uVJRKp3795SoUIFWb16tfa/U6dOUrRoUV1eM27cOPnll190uU3dunWlUqVKCubnn3/W/l2/fl0SJUok7dq1k0KFCokTkUIdAwcOlAQJEkjFihX12Tvu2nFH+uOPP1YRa9OmjR6CZSj16tVTtuDlCZ+rV6+qyJUuXVrr2L59u3z66acyadKk+5qNbvw2bdoks2bN0m+7Hn30UenQoYPkyJFD/4+6Nm/eLPHixZOnn35aOeAbXtT30Ucf6ViWK1dOFi5cyIiUdy+poDuaImUzJBQpG84UKTvOFCkb1hQpG85hJ1JRYcPkExNNTOJZAk8AL15M1iE7xYoVk+nTp+tSudmzZ+sEu0WLFio/OXPmlAULFsi+fft0+Rv+XLlyRYYNG6YTbhzXsmVLee6556RVq1ZSpUoV/XPgwAGVkLlz58qJEyekffv20rlzZylbtqxcvnxZ5QKihvEeOXKkykvHjh09FimICsSnfPnysmLFClmzZo1KIMQQy8YgZhCS1q1ba59z5cqlfa1Tp45Kwrp162TOnDl63a5L+yASkEMnIuWuHXcjBIEbNGiQ1o0C0du6dasMGDDAYz4QGPDu16+f1oFxyJ49+33PAopu/CCWzZs3l/Hjx+t5X375pV4vrm3jxo3y2WefKaukSZPK4MGDdQyaNm2qY4Ixwv2AB2HjeFwHl/YF/vUYqBYoUoEie3+9FCkbzhQpO84UKRvWFCkbzmEtUnixYsK2fv16jSCsXbtWJ9qYjCIKkjZtWjvKcaglRI8QpVi8eLFeNeQISy4hPt99951GLCBLKIhKVa9eXZYvX64T73/96196LAoiSC+++KLkz59fhQoTcAgWCiQEE/rUqVNL27Zt7/vdzZs3JUmSJHrchg0bVDjQnqcRKfR96dKlej5EqE+fPjrphyjh3+gPCiJEaKdRo0YaKYKwoX+Q9vr168uqVauiFSl37UR3q6BeiBPkDTJZpkwZjcx5ygd9a9iwoUbskiVLJrVr19bXCCKDTolu/BBZ2rZtm0bXUG7duqVyi7FGPYgo1qxZU3+3a9cujTrhtQbZXbJkScQ5lStX1vuBIhW6bwwUKZuxo0jZcKZI2XGmSNmwpkjZcA5rkcK37c7EERM5iBRewPjGHMu+3nvvPTvKcailn376ScUFCRacgiVwWPYFsUFkyjUDFZbMffLJJ7ps7vnnn49YLodoD/6PyBWkI3369BH1YRwxplhS5pocAhETSA+WrCH6hcgRlvVhyZ+nIuVan+teJkz+U6ZMqcKEAnmCyEDqIIeQCfwMb14QMAhcdBEpd+1Ed6tMnDhR+wBZwdJCXKsjPp7wQd2IFCLaBnmaPHmyRpdcS3Tjhy8lTp48qXU4BUs3sTQQ44cli6gbBUs0IZ4QviFDhkRE0vA7yBcidhSp0H1joEjZjB1FyoYzRcqOM0XKhjVFyoZzWIsUMoNh8o6oBSZ3ECkUTK6bNGmiy8pY/E8A8oGJNpIrOLwRdUIEYu/evbJlyxZdEhe5OOLk7Dty/g+ZQvTJiWi4nhc5aQPqxv6d0aNH694cjDn++EOkEMmBjCEa5FoQ5USkCJIDsTt79qw0aNAgICKFZXloBxKF6wKjc+fOecwH/cYeMiwJzJYtm0pmrVq17rue6MZv9+7dgn2IiIqhOBEpjA1ECmxq1Kihv0P0CoIMYXSNSCEKWbVqVUak/P/SM62RImWDmyJlw5kiZceZImXDmiJlwzmsRQpLxLA3J3HixPeJFCa+mOhiqRiL/wkgKoPJOSb5BQsW1MgUIieIRCFRBPYTQXSwxwb7nSAEWJ7nTqQgVtgjhToR8cD4Yb8S9j398ccf90WkMKHHBB7LNyHMWIKGBBBYduZrRAoCg2WDiI5hcoNla+gP7i+II+41RKucPWFYrojojZP+HOcjEUWzZs0eyNrnaRY/RNwgbRAW7AlD+yie8sGxWGqJOrCPCfIDmXIt0Y0fjnP2SEHEkPkS8orxRFQOsjxmzBhlAvaPP/64/M///I/uwerevbvukcIXGFOnTtX7gREp/7/+rGqkSNmQpkjZcKZI2XGmSNmwpkjZcA5rkcLEGVnb3n77bV1KhAk7ogWYVGNCisk2S2AIIPEAJsuICiH6gH/jD5af7dixQyMVkBL8Hkkb8Gyl6EQKQoIJOsYPsoKoB8Y0soBAsrCUDBKFSXrjxo1VoBCRxHOcPMna527JHYQM4vHf//5XoSFShmV96A8iXoi2IYsdkitAHiEkSLDgiBT2DOHfkAmITGyW9qFdLMVDVAmJI7DPCcVTPs5ogxGy/mGZa1QluvFDNMu5PjBGYg4sE8RrCtHAr7/+Wv+NMQUfCBv2VTlZHCHGSFKB1yFen/4ufI6Uv4lGXR9FyoYzRcqGM0XKjjNFyoY1RcqGc1iLFCbdmKxiAo0PI0zasR8Hm/Pff//9gEzi7IYtuFvCRBoF+5TwYn7ttdd08oxJNcvDJwApxd4zSG5UJZTHjyJlc39RpGw4U6RsOFOk7DhTpGxYU6RsOIe1SOHicCNh8/zvv/+uGdayZs2qz7dhCSwBJ105EjQgEojlXMhyx/LwCRw7dkwz6WE88OVCVCWUx48iZXOPUaRsOFOkbDhTpOw4U6RsWFOkbDiHvUjZYWRLrgSQFAEPiEUEEMk+sJ8pWAQWiQ+wTC+qgj4nT578oQ0mnsmEvVZRlVdeeeW+5z3FppNYUonMe2DwwgsvuK0imMcvpuumSMVEyD+/p0j5h2NMtVCkYiLkv99juTO+XHIys/qvZtbkSoAiZXM/UKRsOIedSCEbnycFS5dmzJjhyaE8hgRIIIQIUKRsBgtSjkknEomwBI4ARSpwbCPXTJGyYU2RsuFMkbLhHHYihU34Trl06ZKsWLFCEwwgQxxSNZ84cUIfCosHkSI9OgsJkEB4EaBI2YwnRcqGM0XKhrMzGWJEKvC8KVKBZ4wWKFI2nMNOpFyxId0yIlT58uW7j+YPP/ygy6fw0FgWEiCB8CJAkbIZT4qUDWeKlA1nipQdZ4qUDWuKlA3nsBYpJDrAc4Xw7B7Xgn07iEghWsVCAiQQXgQoUjbjCZHK8H1OTXP/sEv6Mikk1b/+91EA4VYoUnYjyqV9NqwpUjacKVI2nMNapPDg0JIlS0qdOnUinrdz48YNfTjst99+q881YiEBEggvAhQpm/FEsokco90nLLHpxf+2kuf9zPJY/bSWTZq1RZEyQ63P1uPSvsDzpkgFnjFaoEjZcA5rkULmsQEDBggmVqlSpVKieKPEs4zwYNRChQrZUWZLJEACJgQoUiaYhSJlw5kiZcPZmR9QpALPmyIVeMYUKRvGTitwC2R8TpgwoW3DMbQW757zVFAfunXnzh19jtT58+fl9u3bki5dOl2KkixZeC4D8QFVWJ165MgRleWZM2fG6rrefPNN+eijjyRbtmwen79o0SL59ddfpUuXLh6fE10/8eyt48ePR1ufr9fpcUdD6ECKlM1gUaRsOFOkbDhTpOw4U6RsWDMiZcPZee8Ia5Hau3ev/PHHH5q1DxNjRKIi75uyw82WLAj4KhjBIFL4sMEkyt1Dc8HR1+u0GAvrNihSNsQpUjacKVI2nClSdpwpUjasKVI2nMNapH777Td577335OLFi5I+fXolishU2rRpZeTIkZI5c2Y7ymwpIAR+/vlnffAvHrALOW7Xrp2KsqtgXLlyRT788EP55ZdfdIlnq1atpGjRooKfjx07Vg4fPqwPYHz55Zelfv362k+IVKNGjWTp0qW6HLR69epSr149/d2mTZtk1qxZuv740UcflQ4dOkiOHDnE04jUZ599pvWiL6VLl5bVq1dr5OyLL77QiBb68+KLL2rU1IlIIVX/22+/LTt27JDff/9d8HBe9Mf1OhF97dGjhxQrVkz3BWIf4LRp0wT7ArNkySLvvvuupEmT5oF2Xn31Vc1giccFYNJWqVIlqVu3rtvxmjx5sn4pAdYo4IhnCc2dO1eX0WI88DpDiLtr166SN29ejQbjNYfoMALN+fPnl86dO0uSJEn8fl9QpPyONMoKKVI2nClSNpwpUnacKVI2rClSNpzDWqSwxCp37tzSuHHjiKV8mHAj2xQiVIMGDbKjzJYCQqBFixYqDeXKlZN169ZpIpHp06ffJxijRo3SCXvr1q3lwIED0rNnT5k/f75MmDBBxQETetwX+H3btm3l+eefV5EqW7astGnTRk6dOiVoZ/HixSpVSGIyfvx4fTbZl19+qe1iGaAnIgUxat++vd6DkDAIDAQPIoUMk+g/5C5r1qziurQP/alatarey5cvX1bRQX/QN2cJI85zrgcigz6PGDFCcubMqXVhzyCOjdwO+o4vFyCRyGiJcyBA7iJhhw4dUinDIwQgoGvXrpUNGzbIkCFDVFKrVKmif8C6d+/eKljbtm3TLJkffPCBitSUKVM0EUwgMr5RpALyUnugUoqUDWeKlA1nipQdZ4qUDWuKlA3nsBYpfIuPCXPixInvo4kXMb7Nx8SXJbQJINKByXz8+PHlwoULKgOrVq26T6QQLYE0Q6qdCErKlClVRpCM5KmnntKfI3pz8+ZNlQGICyTH+R3upUmTJgmeQQYpGDhwoJ6DyAykAVKDyFJMe6SWLVsm33//vbaLsnPnTo3gOCKFKBJkAyWySCGqlitXLv2d0x+ID+SoVq1asmXLFhk6dKjygLRs3rw54llpiEohqrZ8+XL949oO5G3Xrl3SsmVLvV6wjKng+WyQTET2+vXrJyVKlNAoE+rANTp14BiIJ6JTGINOnTpJ4cKFH3hNxtSeN7+nSHlDK/bHUqRiz86bMylS3tDy7Vhm7fONn6dnU6Q8JeXbcRQp3/h5c3bYJpvARBnftjvL+hwo+LYekQd8o84S2gQgC5AYCBXeNLDUDULjuuQNzxNDBCjyUk4sYZsxY4ZkzJgxQlywrK5Xr14qUq7JJpz/Q1ZOnjypERunvP766ypZWHYXk0ghOoPz8bBoFERtBg8eHCFSiE6h/ahEKqr+4AOpY8eOKi6QGUTbUObNmyezZ8+OyFaJn0G6wAHX4NoOlgQuXLhQ1q9fr9GumjVrapQvuoKljadPn9ZljRBViCCiY/i/6+sN/UMErkyZMrokEksawbhUqVL6GkQGTX8XipS/iUZdH0XKhjNFyoYzWqFI2bCmSNlwpkjZcHbeO8Iy2cS4ceN0wojo0xNPPKFLirBvChNMfLPfrVs3O8psye8EMOlHBGrixIm6R+ns2bPSoEGDB0QK49+3b1/JkyeP9uHEiRMqVVgmh2iOE6mCZEDI3nnnHbciheVx33zzTUREyYlIYbkcImExiRSiNYhCoV0URIawxNCJSMVGpHAfQ+QgZ82aNZOXXnpJlxtCmJx2XOGjr67tuP4OSw9RHyJmDq+oBg7ShGgTxAkChmjTuXPnNPqE+qMrmKzgeESz8GBsfxeKlL+JUqRsiEbdCkXKjj5FyoY1RcqGM0XKhnNYixRerNiLgQkuJrwoSEiApAKYBDIFut1NFoiWIC2IDDl7dbA3Cv/G0jVEfZy9Q1g6h/HHXihEqpCQAcchaQKiMVhuhoQJ2COFY7D0zF1ECstEnT1SyACJ5aEQltGjR3u0RwrtY+8epA2JHyAU+JkvIuVcp/PctI8//li/NMAeKfQLe7kQ+cJeJkSBIosUImJIOIG9Yc7SRkS2nn766WiHDbwgLVjmh+QXKFgWiWWGeI1BdCGJiJghSgjGEF0U7MPClxs41t+FIuVvohQpG6IUqYfJ2ZkM8TlSgR8FilTgGaMFipQN57AWKQchJpXI3IdoA5YdBdsDs+yGOvxaGj58uCC9PRI3NG3aVIUE4wwhcgQDS9pw3H//+19JnTp1xN6eq1ev6v4kJE/A0riKFStGTOzdiRTkaevWrRHtZMiQQUUMWfE8STaBEcBywpUrV0qKFCkEyw6xNBFR0siCE3mPlLulfa7Py0J0DpE5ROCw1BDCBjnC5ADig+QOkdvZv3+/JrjAt7HgUL58eWnYsGGMNwuWA2JfGfrpJKaAwI4ZM0b7gL1aNWrU0D1kkCrI09GjR7UNRLsgwYH4MoMiFePQ+eUALu3zC8YYK2FEKkZEfjuAESm/oYy2IoqUDWeKlA3nsBSp3bt3e0SvSJEiHh3Hg4lu2p8AACAASURBVEiABB4ksHHjRpVKZOYLpkKRshkNipQNZ4qUDWdnMsSIVOB5U6QCzxgtUKRsOIelSOEbdbwZYhkWolHuCr5JZyEBEvCeANLFY8keEkkEIoW59z36vzMoUr7Q8/xcipTnrHw5kiLlCz3vzmVEyjtesT2aIhVbct6dR5HyjpcvR4dd1j4s18K+FSzDQrYwPA8I+1FYSMCCAOQCohFVwb2JzC7BXpCswl1WS6RIR7p0LNnzZAmg9bVSpGyIU6RsOFOkbDijFYqUDWuKlA1nipQNZ+e9I+yy9uHDB9nRkFHsu+++02/NEalCiujIz5WyQ82WSIAEAk2AIhVowv9bP/bfIfKP9PcsgSNAkQoc28g1U6RsWFOkbDhTpGw4h61IueLDixYpqyFV2FiP9NDIMlagQAE7ymyJBEjAhABFygQzRcoGs1CkjEAzImUGmiJlg5oiZcM5ToiUgxLZ3LBBHlnTLl26JF999ZUdZbZEAiRgQoAiZYKZImWDmSJlxNmZDDHZROCBU6QCzxgtUKRsOMcJkcLzdbDnY/PmzZI1a1Z9Zg72TaVMmdKOMlsiARIwIRAXROrm3ptybcUVE57uGtm99wc5X/0Cl/YFeBQYkQowYJfqubTPhjVFyoYzRcqGc9iKFJ5nA3nCcj7cTHhAKATqsccesyPLlkiABMwJxAWR+mvWn3K2zRlztq4N3k1wV3ZP3EuRCvAoUKQCDJgiZQf4/7dEkbJBTpGy4RyWItWmTRv5448/BM+JKl26tBQsWFDixYv3AFE8EJWFBEggvAhQpGzGkyJlw5kiZcPZmQxxaV/geVOkAs8YLVCkbDiHpUghO58nZe3atZ4cxmP8QODNN9+Ujz76SFPSx7b07dtXSpUqpYlC3JUjR45I//79ZebMmfcdcvDgQRk0aNADP8dBntQb2z57el61atVkypQpunfPXT89rSuuH0eRsrkDKFI2nClSNpwpUnacKVI2rClSNpzDUqQuXrzoEb20adN6dBwP8p3AwxYpTEauXr0qqVOnfuBigkmkcE+666fvoxA3aqBI2YwzRcqGM0XKhjNFyo4zRcqGNUXKhnNYipQdOrbkKQGIVKNGjWTp0qX60MPq1atLvXr19PSVK1fK/Pnz5c6dO5IpUybp0aOHZMyYUc6cOSNDhgzRKM3TTz+tD7ktV66cxxEp1Ie6ihUrJoULF46I9ERXLyJDb7/9tuzYsUN+//13bcvp57fffivTpk2TGzduSJYsWeTdd9/VdPpTp07VP05p1aqVNGjQQF588UW3ePCMM0To4sePr9e0cOHCByJS6OewYcP0+jGZqlSpktStW1frnDt3rqxZs0aQifKFF14QLGdFXT///LPgob9glShRImnXrp0UKlRIjxs5cqT89NNPcu/ePcmfP7907txZkiRJIlFdV3QPsJ48ebLcunVL60a5cuWK7pNBnyAxaP/8+fOSMGFC6dq1q+TNmzfa9j29hzw9jiLlKSnfjqNI+cbP07MpUp6S8v04JpvwnaEnNVCkPKHk+zEUKd8ZeloD3jvC7oG8nl48j7MhAJFCpkRM+E+dOiUtWrSQxYsXC95QISoQlMyZM+tkP0GCBNKxY0cVH4hV8+bN5ejRo9K2bVud/Hu6tG/s2LEqIDjHdWlfdPWin1WrVpXGjRvL5cuXVVzQT0SJ0OcRI0ZIzpw5ZcGCBYJskL1795batWtH/Pzs2bN6HH4PkYmq4M0N14x+QfKWLVumUjVnzpz7lvbhZ4hQ1a9fX65du6ZtQEz27NmjvHB9eOFiKSP2AaLvaLtOnToqZ0i2gjqnT5+uGStXrFghH3zwgYoUlhGWLFlSmUd1XajTXTl06JBK5Oeff65jhSWyGzZsUOmFRFapUkX/HDhwQPlAsLZt2xZl+3hQtr8LRcrfRKOujyJlw5kiZcMZrVCkbFhTpGw4U6RsODvvHRQpO95xsiVM8hFdeeqpp/T6EfmZNGmSTuRv3rypkREUTMhXr16tx9aqVUuGDh0quXPn1t+1b99eJccTkcK5W7Zs0fMx2XcVqejqRT8//PBDyZUr13393LVrl8oI+oWCqBSiasuXL5dx48ZpKn1EsiBdEI3u3bu7Hefjx4/rtSxZskSPQXSncuXKKhyue6QgQWi3ZcuWyg0RJxQIyxNPPBERKUP0bN68eTJq1CiN/OB6ceyFCxdUwlatWqXSB4Hs1KmTRucSJ06sdUGu3F0XIkruSpMmTVSKixYtKv369ZMSJUpolAt9hRg6fcUxEGHUFVX7gXgxUKQCQfXBOilSNpwpUjacKVJ2nClSNqwpUjacKVJ2nON0S5H3SDn/x3O9IAzbt2/XzIqI/GBZ3/Dhw6VixYr68GTIFsp7772nUa2YRArRLEzkMbnv2bOnnusqUtHV666fW7duldmzZ0uqVKkixhFRok8++UQjbBAsXAciRhC14sWLux1vLK+DDOF4pyCCg8iRq0hhaSKW/CGFP6JjNWvW1GiTs6QwWbJkejreLLEUb+LEiSpFkDkIFX6O5BsQU5RNmzbp0srDhw9r0g5E+L788ku315UuXTq31zBr1iw5ffq0dOjQQZf1IbkHOOD/6dOnjzgPH5iQxjJlykTZftKkSf3+uqBI+R1plBVSpGw4U6RsOFOk7DhTpGxYU6RsOFOk7DjH6ZbcCQqW7GFSPnr0aEHaWSwTwx+IFIQES9Gc6FDr1q11+VpMItWtWzeNdiEq1KxZM3nppZceiEi5q9ddP3/55ReNcEW15A1L5bBUDxKB5XdY8hZdNCdyRArRLUTaIkekXG8YnIPrGjBggEbBnnzySWXhWiBbiEBBqHLkyCFYZoi9Wo5IOcdi+QqiQ4gmYemgu+uK7oaFNCHahGuG6KG+c+fOafTJibS5O9+1fSyL9HehSPmbaNT1UaRsOFOkbDhTpOw4U6RsWFOkbDhTpOw4x+mW3AnK999/L0i8gIk4olEDBw7URAnYH4RsetmzZ9fJOUQGe4q6dOkSo0g56c+xnA3i8fHHH6tUOGnFo6vXXT+x9hV7iSB86BP2/0D4ENVBQQIGRIPw7DL0MbqCSBOiOBA97JHCfiokq0DEyzUiNXjwYH2I9PPPP6/LH7H/CBE2JHLAsdhPhqgUludhOV+ePHk0IubsXUKEC/+GeCGhB5JCQKxQIHxYHggpje66orsOiC2kBcv8HLlFHyHAeAA2xG7ChAm63w0yF1X7ONbfhSLlb6IUKRuiUbdCkbKjzz1SNqwpUjacKVI2nClSdpzjdEvuBAVRKCQkgERlyJBBkzxAdPAsMGSpwxI4pLPPly+f8sNyPciFuxL5OVKIzkCikDTCEamTJ0+6rdddP/H8K+xFwlI+SA36DZFwkiVgXxT+j0jXc889F+NYI/kCJAPRLFwnlthBHiEBTj+RERAJJfDhjqWKYNKwYUOt28nahzdK9A0ChaV4iOTt3btXHn30UWnatKkuucMyP0gZ5AkRQNTlSBdELLrriu5CsOwQSS8gguCBArZjxoxR5pC7GjVqaOIJSJW79mOE5eUBFCkvgcXycEakYgnOy9MoUl4C8+FwipQP8Lw4lSLlBSwfDqVI+QDPy1OZtc9LYDycBCITQGIHLHWD4DiJFsKd0saNGwV7xyDCwVQoUjajQZGy4UyRsuGMVihSNqwpUjacKVI2nJ33Dmbts+PNlsKMAKJKWGaHZ0thj1JcKFh+iSV7SCQRiBTmvjCkSPlCz/NzKVKes/LlSIqUL/S8O5ci5R2v2B5NkYotOe/Oo0h5x8uXoxmR8oUezzUngMk7JvJRFTwMFt8KWBXsacK+ICyVQzY9J437+PHj5YcffoiyG4hcIQV5MBc8gwr7q6IqSMWOtOxYsucsNQyma4kLInV9zTW5NObiQ8V+6o9TcrTbb7rfjyVwBChSgWMbuWaKlA1ripQNZ4qUDWe0QpGyY82WSIAEAkwgLohUgBF6VD32C2JvHEXKI1yxPogiFWt0Xp9IkfIaWaxOoEjFCpvXJ1GkvEYW6xMoUrFGxxNJgASCjQBFymZEKFI2nClSNpydb5Xx5QAS5bAEjgBFKnBsXWumSNlwdt47uEfKjjdbIgESCCABilQA4bpUTZGy4UyRsuFMkbLjTJGyYU2RsuFMkbLjzJZIgAQMCMQFkfq91km5d+2eAU33TXCPlA1+ipQNZ4qUHWeKlA1ripQNZ4qUHWe2RAIkYEAgLojU0eyH5O5fdw1oum+CWfts8FOkbDhTpOw4U6RsWFOkbDhTpOw4syUSIAEDAhQpA8giQpGy4UyRsuFMkbLjTJGyYU2RsuFMkbLjzJZIwCMCffv2lVKlSskrr7zi0fH+OOjXX3+VXr16uU157o82rOqgSNmQpkjZcKZI2XCmSNlxpkjZsKZI2XCmSNlxZksk4BEBipRHmNweRJHyjZ+nZ1OkPCXl23EUKd/4eXM20597Qyv2x1KkYs/OmzMpUt7Q8u1Ypj/3jR/PJoFYEdi5c6dMnjxZbt26JQkTJpQWLVpI8eLFxVWkjh8/LnjI8Pnz5/WYrl27St68ebW9b7/9VqZNmyY3btyQLFmy6AOB06RJI/Pnz5fDhw9rvZAKpPJFtCldunRu+4mIVO/evaVChQqyevVquXfvnnTq1EmKFi0qeDP+9NNPZfPmzRIvXjx5+umnpV27dvoMoUqVKsns2bMlbdq0Wrfz/4sXL8qIESPkiSeeEPx78ODBMnLkSPnpp5+07vz580vnzp0jHmAcK4BuTqJI+ZOm+7ooUjacKVI2nNEKRcqGNUXKhjNFyoaz897B9Od2vNkSCSiBZs2aSceOHeXZZ5+Vo0ePypIlS1QuHJEqV66ctGrVSqpUqaJ/Dhw4oLIzd+5cuXz5sooXZCVnzpyyYMEC2bdvn/Tv318WLVokM2bMkJkzZ6pYTZkyRa5evapi5K5ApNq0aaPHlC9fXlasWCFr1qyRsWPHysaNG+Wzzz7TfydNmlSlKHPmzNp/dyIFmWnfvr1eT9myZVXCUOcHH3ygIoU+lSxZUq/d34Ui5W+iUddHkbLhTJGy4UyRsuNMkbJhTZGy4UyRsuPMlkjgPgLdu3eX7NmzS61atTSi5BRHpBB5atmypSxbtkzix4+vv4bsNG/eXE6dOqVyMmzYMP05olLVq1eX5cuXy9KlS2Xv3r0qVSg//PCDjBs3TvDcn+hEqkOHDnouypEjR6RPnz4yZ84clZ/cuXNLzZo19Xe7du1SEZo0aVK0ItW2bduIvkPyBg0apKJWuHBhSZw4ccDuBopUwNDeVzFFyoYzRcqGM0XKjjNFyoY1RcqGM0XKjjNbIoH7CGDJG0Tlm2++0WVykKZixYpFRKQgWZCb9OnTR5yHDyBEes6cOaNL6lKlShXxu2vXrqksbdq0SZf2QdRQDh48qJGsefPmRStSrskmXJNP4Ocvv/yyRqqc+iBZn3/+ebQiFTl5BfoFUUPfkEwDooUIl78LRcrfRKOujyJlw5kiZcOZImXHmSJlw5oiZcOZImXHmS2RgFsC27Zt0+jS4sWLNZIE0ShYsKBGn7DkL3JZt26dbNmyJSLq5Pp7LO3bvXu3RoBQsBcL0aOpU6fGSqSGDx8uuXLlkho1akTUB2GbOHGiLjnE/insv8KerMqVK6uwQWbcZQHEPgT0Dfuvateu7fe7giLld6RRVkiRsuFMkbLhTJGy40yRsmFNkbLhTJGy48yWSCCCAD5IunXrpsvnMmTIIKdPn9b9UJAgR6SQ/hw/w9I/RISwL2rChAm6r+rmzZu6R2r06NG6PBD7p9auXatRHtQBuUEiC+xlwj4qLKVDJMtdiZz+3PX/WEKIfVljxozRegYOHCiPP/64NG7cWBo2bChdunRR6Vu/fr3KICJVkUUKgnjlyhVp0KCBdsFJRIFr83ehSPmbaNT1UaRsOFOkbDhTpOw4U6RsWFOkbDhTpOw4syUSuI8AsuMhiQMmShCUJk2ayEsvvXRf1r6TJ0+qwJw9e1az7yEqhCgQyo4dO3QpH6QKSwNbt26tyRsgUsiOhwgRklhkypRJl/Y9+uijsRIpJIeYNWuWfP3115ooAm1grxaW5W3YsEF/hzYKFSqkbSNShaiTa0QKEgh5Qn+w3ytPnjyagTBZsmR+vysoUn5HGmWFFCkbzhQpG84UKTvOFCkb1hQpG84UKTvObIkETAhAZhBRQqQoLhaKlM2oU6RsOFOkbDhTpOw4U6RsWFOkbDhTpOw4syUSMCFAkfpTUqZMGZHp0AS6cSNHsx+Su3/dNW71/uYoUjb4KVI2nClSdpwpUjasKVI2nClSdpzZEgmYEHAnUtgjdf369Sj7gIf+4mFy4VDiQkTq0qiLcu/vhytSu/f+IOerX5C33norHG6boL0GipTd0PCBvDasKVI2nClSNpwpUnac2RIJkIABgbggUgYYY2wC+/OwN48iFSMqnw6gSPmEz6uTKVJe4Yr1wRSpWKPz6kSKlFe4fDoY7x34MjphwoQ+1ePvk+Pdw852FhIgARLwggBFygtYPhxKkfIBnhenUqS8gOXjoRQpHwF6eDpFykNQPh5GkfIRoBenU6S8gMVDSYAEgpsARcpmfChSNpwpUjac0QpFyoY1RcqGM0XKhrPz3sGIlB1vtkQCJBBAAqEsUtcXX5Erky4HkI7/qj71xyk52PM3Lu3zH9Ioa6JIBRiwS/UUKRvWFCkbzhQpG84UKTvObIkESMCAQCiL1JVxl+Ri97MGlHxvAln7dkzZS5HyHWW0NVCkAgyYImUH+P+3RJGyQU6RsuFMkbLjzJZIgAQMCFCkDCCLCEXKhjNFyoazMxlCAhU8/JwlcAQoUoFj61ozRcqGM0XKjjNbChAB13Tf77//vvz73/+WV155RdauXSsTJ06U1157TZ544omIfx87diziGG+7dOTIEenfv7/MnDlTMMH5+uuvpVy5ct5W4/Pxrv1wl+58wYIFcvz48Tj3YF6KlM+3l0cVUKQ8wuTzQRQpnxF6XAGX9nmMyqcDKVI+4fP4ZIqUx6h8PpDJJnxGyAoeJgFXkbh69aokTpxY/0B4ihYtKpUrV77v367HeNtvV4E5fPiwYMP90KFDva3G5+M9ESl8WGEShm9Y41KhSNmMNkXKhjNFyoaz860yI1KB502RCjxjtECRsuHsvHcw2YQdb7bkI4Fbt27JiBEj5KeffpIMGTLIU089JXhj7tKlizgRqbNnz8rnn3+uEpEtWzY5ePCg/rtixYpy6NChiIgUZAh1Xbp0SbJmzSrdu3eXLFmySKVKlWT27NmSNm1a7a3zfxwHQZs0aZI0a9ZMrly5Ivny5RPIGZ6nU7p0aT1++/bt8umnn+pxUZVTp05J165d5bPPPtNfjx07Vn799VcZM2aM/h/Xgb6ifTwsFw/STZQokbRr104KFSok7kTq3Llz0rFjR+nZs6fs378/IiJVrVo1efvtt2XHjh3y+++/a8SuXr162hY4LVu2TFKmTCmvv/66zJ07V+bMmeN2lCZPniwYA/QFBQxw7TgPEoP+nj9/Xp+ngGvMmzev3L59W0aOHKljhqca5M+fXzp37ixJkiTx8W548HSKlN+RRlkhRcqGM0XKhjNFyo4zRcqGNUXKhjNFyo4zW/ITAUz6169frwKEN+T27dvrxNxVpCAKffv2lVKlSqk0uP7bdflf48aNVTBw3Lx582TPnj0aYYpJpLC0b/PmzfLVV1/p8QsXLpR9+/ZJv3799CrRt+zZs0e7ER8iA3HKmDGjtGnTRs/D/yEgNWvWlBkzZug11alTR5cPrlu3TgVn+vTpUYpU27ZtpVOnTvLmm2/qNbsu7cPPqlatKrjey5cvS926dWXx4sVy5swZFS9E1lKlSiV9+vQRSB6uz12BiL777rsqYNhLgCWUGzZskCFDhkirVq2kSpUq+ufAgQPSu3dvFaxt27bJihUr5IMPPlCRmjJlipQsWVKeffZZP90V/1cNRcrvSClSNkijbIUiZQefS/tsWFOkbDhTpGw4U6TsOLMlPxEYNGiQRoFq1KihNUICIAfeihTqgMBAKOLFiyd37tzRyEmyZMm8FqkLFy5Iw4YNZf78+Xp+7dq15aOPPtLolrsyfPhwKV68uBQuXFhF77HHHpNXX31VUqRIIfjdhAkTtD+Qlfjx4wvaqF+/vqxatSpKkcKHE+pAP1Aii9SHH34ouXLl0t8hQoVo2ffffy87d+7UKBvK1q1b5eOPP45WpHBckyZNlB2WTkIeS5QooTLbsmVLjW6hvyg4pnnz5iqHGDeIHq4XSy8DVShSgSJ7f72MSNlwpkjZcHYmQ1zaF3jeFKnAM0YLFCkbzhQpO85syU8EevToIS+//LJUqFBBa4S8nDhxwmuRQsRo4MCBUS5j8zYihX5gGVv58uVVnrD8bfz48dFe8erVq+Xo0aNSoEAB+fnnn1WCIAFYZ4tIEQQEUS+IHoQKb4pY0ofzIi/tQwQJxyAqheQaUYkUxA7LHFEQocL/N23aJKdPn45ISPHLL79ohC26iBTOnzVrlp7XoUMHjbrheESy8P/06dNHXLcTMSxTpoy2tXTpUsFySkQA0dekSZP66a74v2ooUn5HGmWFFCkbzhQpG84UKTvOFCkb1hQpG84UKTvObMlPBBDZeOaZZ1QGUCAt2KPkbUTKiaAsWbJEIygQEchBjhw5dGka9jilS5dO9wMhYQWW/jl7pCIv7UM/Vq5cqREdyAqW69WqVSvaK/7jjz80SoN+QKYgdlOnTtWIFoQsd+7cGoFC5kH0Cfu+GjRoEKVIYU8WltVhjxcELlOmTA9EpKISKUSkfvzxR13Sh/LNN98oz5hECtKEaBPECcsscR3YnwX5A8/oCpbP4HhEsxC583ehSPmbaNT1UaRsOFOkbDhTpOw4U6RsWFOkbDhTpOw4syU/EUCWPkRqsA8JAoXJPETEW5HCPiLsj8JkHtEt1AuxQEQGy+NQX8GCBVUUhg0bpnuCXEUK+37wMySKwNJAJF2A+CDKMm7cOJWpmAqWyCHhAurHHiX0BwXL+iB1iHI5e5GwNwr/Xr58uZw8eTIiDbtr1kIkr9i9e7cuDcS+LSf9uROBihyRQp/fe+89mTZtmkbCsEwP0b2YRAp9bN26tUbQcA1giQKZg0AiYojllrgO7MFCFA1tQQRRMHZISR+TbMbEL6rfU6RiQ837cyhS3jOLzRkUqdhQi9053CMVO27enkWR8pZY7I6nSMWOW2zOYvrz2FDjOQ+NAN6EIR7IAAdZQRY7CE63bt0isvZ5mmwCmfKQAAFZ5hD1wbLBzJkza/IELF9DZAf1Q1YQGYIcOM+RunjxomauQyQLywtRkFwBLyjIlSfFyT4ISUKB1OD6kOEOBUK0d+9eefTRR6Vp06YqOGgPGe+cfriKFN440SdEtHBcTCIFsUIECs/DQgZEcPviiy80GhdTgahBwLAXy0mxDsFDwgxEz7C3C/vYEN0DN1wrljIi+pcnTx6VRETf/F0oUv4mGnV9FCkbzhQpG85ohSJlw5oiZcOZImXD2XnvYPpzO95sKYwJQCJy5sypGfJCpeDN1kkOAWlDsomY9nfh2jZu3KhLGSGPwVQoUjajQZGy4UyRsuFMkbLjTJGyYU2RsuFMkbLjzJbCnMCxY8c0LThEJFQegotIEZbbYf/U448/LqNGjdKliU46dndDhudaYckeUs8HIoW5L7cKRcoXep6fS5HynJUvR1KkfKHn3bmMSHnHK7ZHU6RiS8678yhS3vHy5Wgu7fOFHs8lgf+fgh17qSAWL7zwgjLBXiMnrXhkSMjQh5TnwVCw5wp7r/B8J6RHx94w7BXDz6IqeADyrl27dMmek2o9GK7D6UMoi9St7/6W62uuBhNOt335Ye8P8keNC9E+Ky0kLiTIO0mRshsgipQNa4qUDWeKlA1ntEKRsmPNlkiABAJMIJRFKsBo/Fo9nt+GyCvS37MEjgBFKnBsI9dMkbJhTZGy4UyRsuFMkbLjzJZIgAQMCFCkDCD//ygsRSrwrClSgWfstECRsmFNkbLhTJGy4UyRsuPMlkiABAwIUKQMIFOkbCCLCEXKDDWz9hmhpkjZgKZI2XCmSNlxZkskQAIGBEJJpM6U+E1u/3DTgIr/m0Cyie2f7OXSPv+jva9GilSAAbtUz4iUDWuKlA1nipQNZ4qUHWe2RAIkYECAImUAWUQoUjacKVI2nJ3JEJar4hl4LIEjQJEKHFvXmilSNpwpUnac2VKIEli9erVUqFBBe//mm29qqnA8yNbTggfWOg/GdXfO66+/rg+3xUNxXdvztA3nODxguFevXlFm3POkH962F9PxrtcV07H++j1Fyl8ko6+HImXDmSJlw5kiZceZImXDmiJlw5kiZceZLYUgAaQEr127tkBCYitS+NDAZCi6Z0vheU6pUqWSePHi3deet8iiEylP+uFtezEd71yX88DfmI73x+8pUv6gGHMdFKmYGfnjCIqUPyh6VgeX9nnGydejKFK+EvTsfIqUZ5z8cRTTn/uDIuvwC4E33nhD6tatKwsXLpTZs2fL0aNH5T//+Y/gAbCJEiWSdu3aSaFChbStdevWyaeffiq3b9+WggULSteuXfWYb7/9VqM7N27ckCxZsuhDctOkSRNt/xAxQjvnz5+XhAkTal158+bVZz1t27ZNH1Y7ZMgQeeedd6RRo0aydOlS3ZhcvXp1qVevnhw5ckSGDx8uxYsXlwMHDsjZs2f1obZFixZVCXMiUocPH5YRI0bIpUuXJGvWrNK9e3ftoxO5GTduXER7+fPn177gmlGuXLmi+1Hmzp0rqVOnjvJ6IFK9e/fWCBoiWxDBTp06PdAP9AnPj8LvH330UenZs6f2A8+Gql+/vvz4449y4cIFee211/QaUVauXCnz58+XO3fuSKZMmaRHjx6SMWNGWbRokV4/fg5+4A5uOMY1IuVuvPxy47hUQpHyN9Go66NI2XCmSNlwZwjEBQAAIABJREFURisUKRvWFCkbzhQpG87Oe0fy5Ml1zhZMJd49zPJY4hQBLJ2rWLGiNGvWTBDFaNGihdSpU0fKlSun4jRnzhyZPn26nDlzRlq3bi0TJkzQyXyfPn3k2WeflVdeeUXPgazkzJlTJWbfvn1uH4wLuLjNWrVqpRKBPxAhyAiE5dq1ayp2kAgU9K9s2bIqSadOndK2Fi9eLKdPn5aWLVvK0KFD5bnnnpPNmzerDELOXEWqcePG8vbbb0upUqVk3rx5smfPHj3HEY7EiRNHtHfo0CGVQDwYF2v2165dKxs2bNDj3RWIFPoGeSpfvrysWLFC1qxZI2PHjo3oB9iiH2CJFz5+jw+2qlWrSrVq1aRy5crSvHlzQTQJD9yFlOLNAcKIf2fOnFlGjhypferYsaMsWbJEZs2aJVOnTlUpw9JH1IvrdK4Lk8GoxisQzyCiSNm8ZVCkbDhTpGw4U6TsOFOkbFhTpGw4U6TsOLMlDwhAVAYNGiTPPPOMHo1oEybskCpESBAtWbVqlYoNIk8DBgzQ4/DGjOMgBZCYYcOG6c8RHUFEBdEXd98UnDx5UiVo2bJl2g4KZAQy8eSTTz4gUqj7qaee0uMgHpMmTdJ2IC+QChREaCB3kBVHpCANqBfihSV8iODg+pIlSxalSKGeJk2aRES2+vXrJyVKlIjYrxUVTohUhw4dNGLmrh9t27ZVOYXolClTRpcUOgXXA0nKlSuX/gjXhJ/huJs3b0qSJEn05xA6RLzAAtf8ww8/RMgqIlSIvCHa5ojU999/H+V4IYLo70KR8jfRqOujSNlwpkjZcHYmQ0w2EXjeFKnAM0YLFCkbzs57ByNSdrzZUjQEIidzgBRBPCAceFOAoGACj2jOsWPHdLLuWvBzLAl0lQNElT755BNJly5dlC3v379f5SN9+vQRv8cbffv27XUZYeSIlGuyCae/t27dui/Jg+teJUekEOkZOHCgylXkElVECscg0oNoF/oHEZs5c6akTJnSLcHIe6Si6keXLl3k4MGDGunavXu3PP3001o/Emg4YoioE8r777+v8lapUiXt9/bt21UCr169qpFALGeESP3yyy96/Siu/3euC+IV1XgF4sVAkQoE1QfrpEjZcKZI2XCmSNlxpkjZsKZI2XCmSNlxZkseEHAVKSwtQwRq4sSJkiNHDt131KBBAxUpRKW2bNkigwcP1lqxfwhRIeztwc/79+/vQWv/e8i5c+c0+uREk1xPxKTcXyKFpXGIfKEdRL4gh5AkXJs7kcLyQUSxIDrr16/XaF10xVORcupAH7DPDOeBJUQKcgS5QoFM1qxZU+UJUjd69GhNmoFlhvjjqUjt2rUryvGCjPm7UKT8TTTq+ihSNpwpUjacKVJ2nClSNqwpUjacKVJ2nNmSBwRcRQqTeyR9cPYIYW8U/o1lehAn7PUZM2aMiggEA5N/JFnAviVM+LNnz677nTDhx3K26Ar2SNWqVUtefvll3RuEvVfY/4P9U+jTl19+KUmTJn0g/bk3ESlEgrCcDlkA0U8sgcOSN9c9UggNu7aHPmNvEeQAy/ywB8xXkUJ0CZE77L/CniyIKeQTDCFS2FsFefv9999VMCFQiAzu3LlTj0E0CpE1JABBdM6TiBRELKrx4h4pPpDXg7eFOH0IRcpu+JlswoY1RcqGM0XKhjNFyo4zW/KAQOSlfYh47N27V5MYNG3aVJe2IYoyfvx42bRpk+5PwkSjSJEiAlHBnpsdO3boUj7s6UH0BCKCRBTRFeyTgpQh6oW9VjVq1NDEEyjITgchg/C899579z1HyluRghx+8MEHmt0OAoi6sYzONbuda3v58uXTpBVI8oAlgtGlUEdfPYlIYd/T5MmTZevWrRoZS5s2rXTu3FkzE0KkEIHDUjzIKvZSIZMi5BIJOCBReNYVklUgMx+kC1GlmJb24Rx34+XBbeHVIYxIeYUr1gczIhVrdF6dSJHyCpdPB1OkfMLn8ckUKY9R+XQgRconfF6dzPTnXuHiwSRgS2Djxo0qPRCZQBeI1JQpU1SWQrVQpGxGjiJlw5kiZcMZrVCkbFhTpGw4U6RsODvvHUw2YcebLZGAxwSwfA5LDLFXKaaomseVRnMgRcofFD2v40wJLu3znFbcPJIiZTfuFCkb1hQpG84UKRvOFCk7zmzpIRLAUkCk6I6qYD9Q4cKFH2Lvom4aDwLGckMsMcTznJwCqYJgRVXw3Cp8KxLbQpGKLbnYnUeRih23uHQWRcputClSNqwpUjacKVI2nClSdpzZEgmQgAGBUFrad2v333Lvyl0DKv5vYsVXK+VWoTualp8lcAQoUoFjG7lmipQNa4qUDWeKlA1nipQdZ7ZEAiRgQCCURMoAR8CaQEIXJD+hSAUMsVZMkQosX9faKVI2rClSNpwpUjacKVJ2nNkSCZCAAQGKlAFkEc2MSZEKPGuKVOAZOy1QpGxYU6RsOFOkbDhTpOw4syUSIAEDAhQpA8gUKRvIjEiZcXYmQ/hyAI/AYAkcAYpU4Ni61kyRsuFMkbLjzJZIgAQMCFiI1MVnf5N/frplcDXB28TdhHdl46w9XNoX4CFiRCrAgF2qZ0TKhjVFyoYzRcqGM0XKjjNbIgESMCBAkTKALCIUKRvOFCkbzoxI2XGmSNmwpkjZcKZI2XFmSyQQpwgsWrRIfv31V+nSpYvpdVOkbHBTpGw4U6RsOFOk7DhTpGxYU6RsOFOk7DizJRKIUwQoUuE93BQpm/GlSNlwpkjZcaZI2bCmSNlwpkjZcWZLJOAXAmfOnJFhw4bJpUuXNDVypUqVpG7dulr33LlzZc2aNXL79m154YUXBA8bPnjwoAwYMECzrCVLlkzmzJkjR48eld69e7vtz5EjR2T48OHy3HPPyYEDB+Ty5cvStm1bWbp0qZw6dUoKFCig/0dZuXKlzJ8/X+7cuSOZMmWSHj16SMaMGcVVpI4fPy54WPD58+clYcKE0rVrV8mbN69feESuhBGpgGB9oFKKlA1nipQNZ4qUHWeKlA1ripQNZ4qUHWe2RAJ+IfDRRx9J2rRppX79+nLt2jUZMWKEismePXtk2rRpMnbsWEmePLn0799fChYsKG+++aaMHz9ekiZNKtWqVVO5wv/TpUvntj9YkteyZUsZPXq05M+fX4YOHSqQq4kTJ8q9e/ekRo0a8umnn0r8+PGlXr162m7mzJll5MiRmvGqY8eOESLVuXNnadWqlVSpUkX/QMwgcZA+SJW/C0XK30Sjro8iZcOZImXDmSJlx5kiZcOaImXDmSJlx5ktkYBfCCCitGvXLhWdp556SmUGZciQIfLEE0+o2KDs2LFD5s2bJ6NGjZIbN26ozCBiVKpUKRWa6IqztwlRJZQZM2YIMlq1b99e/9+oUSOVody5c8vNmzclSZIk+vMNGzbI6tWrNWLmRKTq1KmjfV22bFlEXyFzzZs3l0KFCvmFiWslFCm/I42yQoqUDWeKlA1nipQdZ4qUDWuKlA1nipQdZ7ZEAn4hgCV0CxculPXr1+uSu5o1awpk5d1335X9+/fr8j0UvImmSZNGo0goiBp98cUXeq5zjLsOQaTef/99XQaIMnPmTJUxCBFKkyZNpFevXipyOGb79u0SL148uXr1qi7rw7JAR6QqV64sHTp0kPTp00c0hw9SSFmZMmX8woQi5XeMMVZIkYoRkV8OoEj5BaNHlTD9uUeYfD6IIuUzQo8qoEh5hMkvB+G9AyuBArHKxpcOxruHNUQsJEACbglg71G3bt10D9Ty5cvlySef1KV8kcuFCxdUZhABSpUqlbRo0SJaqp6K1B9//CGzZs3SJYB4kOXatWv1j6tINWzYUKNPS5YsMRlJRqRMMDP9uQ1m3QeJJbx43bIElgBFKrB8ndopUjacKVI2nNEKRcqONVsiAZ8JDB48WF599VV5/vnndVkdluz17NlTEznMnj1b9ykh4rRixQrdr1SxYkXdL1WkSBEpV66cNGvWTAYOHCi5cuVy2xdPRernn3+WnTt3yqBBgzQahXqvX78u2MflmmwCfaxVq5a8/PLLGkWbMGGC7qPCNzj+LhQpfxONuj5GpGw4U6RsODuTIXwhhPdNlsARoEgFjq1rzRQpG84UKTvObIkE/EIAy/eQUALfgGB/VPny5QVRHxQnax/eQLNly6ZJKA4fPqx7nJBgAsevW7dOFi9erLLj7K+K3DFPRQrL+LBXChKVIUMGady4sfTt21f7hKV8zl6rkydPypgxY+Ts2bM6SUGyipj2acUWFkUqtuS8O48i5R2v2B5NkYotOe/PY0TKe2axOYMiFRtq3p9DkfKeWWzPYEQqtuR4HgmQQNARoEjZDAlFyoYzRcqGs/OtMiNSgedNkQo8Y7RAkbLh7Lx3cI+UHW+2RAIkEEACFKkAwnWpmiJlw5kiZcOZImXHmSJlw5oiZcOZImXHmS2RQFARQNY87GeKquDhuYHYv2QBgCJlQVmYbMIGM5NNGHGmSNmBpkjZsKZI2XCmSNlxZkskQAIGBCxE6s73f8u9a3E7qeiKr1bI34XvyFtvvWUwqnG3CUak7Maee6RsWFOkbDhTpGw4U6TsOLMlEiABAwIWImVwGUHfxCeffKIp7ylSgR0qilRg+brWTpGyYU2RsuFMkbLhTJGy48yWSIAESIAESIAESIAESIAESCDgBPhA3oAjZgMkQAIkQAIkQAIkQAIkQALhRoAiFW4jyushARIgARIgARIgARIgARIIOAGKVMARswESIAESIAESIAESIAESIIFwI0CRCrcR5fWQgCGBy5cvy7Bhw+TcuXMydepUw5bDv6nPP/9cli1bJrdu3ZJSpUpJmzZtJEGCBOF/4cZXiEQTSOqxYMECWbhwoaROndq4B3GjuePHj8vo0aPl6NGjkj59emnRooUUL148bly84VXeu3dPpk2bJqtWrdKHxYIxHsORNGlSw17Erab27t0rXbt21feRHDlyxK2LN7hazC3w/uz6+Tdu3DjJmTOnQesxN0GRipkRjyABEoiCAJ6P1a5dO3nhhRdkx44dFCk/3iX4YB41apROPJMlSyZ9+/aVkiVLyhtvvOHHVlgVCIBtrly5ZM6cOTJ//nyKVIBui2bNmslrr70mb775puzatUv69++vkyNO8P0L/KuvvpIVK1bIkCFDJGHChNK7d2957rnnpF69ev5tiLUpgdu3b+vn4IULF2TkyJEUqQDcF2PGjJEnn3wyaD//KFIBGHRWSQJxgcCNGzfk4sWLcunSJcEbHSNS/ht1PKw5Y8aMEWnPt2/frtESfFCz+JfAkSNHVKQqVKhAkfIv2ojaEPXDBL9ixYo6uUfBlwITJkyQbNmyBajVuFntL7/8IokTJ9Z7GgWRbUQDu3fvHjeBBPiqZ86cKYgCbtmyRfr06UORCgDvwYMHa2T1lVdeCUDtvldJkfKdIWsggThNYN++fRQpP98BmPRUrVpVXnrpJa0ZE6Fu3brJvHnz/NwSq3MIUKTs7oX9+/drRGr27NlcrhpA7H/88YdGXBGNKl26dABbiptVnzx5Uu/j8ePHS+vWrSlSAboNevXqpbIK3vHixZPKlSsH1bMVKVIBGnhWSwLhQAB7oM6ePfvApWTKlCliCRRFyv8j3aFDB6lfv74UK1ZMK8cYYE/JkiVL/N8Ya1QCFCmbG+HMmTPSs2dPadu2rRQtWtSm0TjYCvZUHjx4UCN/mOTHjx8/DlII7CXjy626detKkSJFBEtXGZEKDO+5c+dKihQpdGkwZOrdd9/VPcPOF42BadXzWilSnrPikSQQ5wh89913smbNmgeuu0qVKlKoUCH9OUXK/7dFjx499Fs351vkY8eOCX6GZTosgSFAkQoMV9dakWiiX79+0qpVKylRokTgG4zjLeCLMERLkEAF4sriPwL4XMReP0RLUChS/mMbU02zZs2S8+fPS6dOnWI61OT3FCkTzGyEBMKXAEXK/2OLyQ++gWvUqJFWvn79ehVaZEhkCQwBilRguDq1nj59Wr8MwLLVZ599NrCNxeHakfgnS5YsEXt19uzZI2PHjpXp06fHYSr+v3QsmcRnnxPp++uvv/Q9G1EqJGBi8R8BcM6TJ48kSpRIK8W9fPXqVU3yEQyFIhUMo8A+kEAIE6BI+X/wwHTo0KG69yx58uQ6+cSeKUz2WQJDgCIVGK5OrUgP/frrr0uZMmUC21Acrx1Jfw4fPqzZ+pB0AmmiMenE/1kCR4ARqcCxRTQVySaw3B1fyOC9pHPnzvL8888HrlEvaqZIeQGLh5IACfwfga1bt2qKXWwCvXPnjn5blD17dvn444+JyQ8EkBp60aJFgoxnZcuWlZYtW3Kfgx+4ulaBb5Hfeust/RHSGDvfeCIV+qOPPurn1uJuddgX1aBBgwi+DgnsdQiWfQ7hMjp///23yhMiU3jvyJcvn3Ts2FEyZMgQLpcYlNdBkQrcsGBfFL5UxBcEKVOmlOrVq+tjFIKlUKSCZSTYDxIgARIgARIgARIgARIggZAhQJEKmaFiR0mABEiABEiABEiABEiABIKFAEUqWEaC/SABEiABEiABEiABEiABEggZAhSpkBkqdpQESIAESIAESIAESIAESCBYCFCkgmUk2A8SIAESIAESIAESIAESIIGQIUCRCpmhYkdJgARIgARIgARIgARIgASChQBFKlhGgv0gARIgARIgARIgARIgARIIGQIUqZAZKnaUBEiABEiABEiABEiABEggWAhQpIJlJNgPEiABEiABEiABEiABEiCBkCFAkQqZoWJHSYAESIAESIAESIAESIAEgoUARSpYRoL9IAESIAESIAESIAESIAESCBkCFKmQGSp2lARIgARIgARIgARIgARIIFgIUKSCZSTYDxIgARIgARIgARIgARIggZAhQJEKmaFiR0mABEiABEiABEiABEiABIKFAEUqWEaC/SABEiABEiABEiABEiABEggZAhSpkBkqdpQESIAESIAESIAESIAESCBYCFCkgmUk2A8SIAESIAESIAESIAESIIGQIUCRCpmhYkdJgARIgARIgARIgARIgASChQBFKlhGgv0gARIgARIgARIgARIgARIIGQIUqZAZKnaUBEiABEiABEiABEiABEggWAhQpIJlJNgPEiABEiABEiABEiABEiCBkCFAkQqZoWJHSYAESIAESIAESIAESIAEgoUARSpYRoL9IAESIAESIAESIAESIAESCBkCFKmQGSp2lARIgARIgARIgARIgARIIFgIUKSCZSTYDxIgARIgARIgARIgARIggZAhQJEKmaFiR0mABEiABEiABEiABEiABIKFAEUqWEaC/SABEiABEiABEiABEiABEggZAhSpkBkqdpQESIAESIAESIAESIAESCBYCFCkgmUk2A8SIAESIAESIIGAE7hw4YK8//77cuzYMZkyZYpky5Yt4G2yARIggfAkQJEKz3HlVZEACZAACYQxgePHj8ucOXNk9+7dcvXqVUmXLp28+OKL0qBBA0mZMmXQXPnSpUvltddek0SJEnnUp7t37wrOWbVqlZw4cUISJ04sOXPmlJo1a0qJEiU8qiOmgxYsWCDLly+Xjz76SFKkSCHx48eP6RT+ngRIgASiJECR4o1BAiRAAiRAAiFEYP/+/dKtWzfJmzev1K1bV9KmTavSMXv2bLl9+7ZMmDBBkiZN+tCv6O+//5Zq1arJ4sWLJVmyZB71Z9CgQbJjxw5p2LChPPfcc3Lz5k3Ztm2bzJ8/Xzp27KhS5muZMWOGHDhwQIYOHeprVTyfBEggjhOgSMXxG4CXTwIkQAIkEFoEWrdurZGUDz744L5oyrVr12Tw4MHSpEkTjVDVqVNHIA3O0rUlS5bIV199JZMnTxYcC8np2rWrfPzxxyouv//+u0a38Ofnn38WRG4gQ/j99u3b5cqVKypvOCdz5sxy/fp1eeONN6Rv377yxRdfCJbMJU+eXHr27Kltov5bt26p1L3zzjtSuXLlaEF/++230rt3bxk5cqQUKFDgvmPR97Nnz0qLFi3054ha4Wfnzp2TrFmzasSqfPny+rvo+jxt2jSVsnv37mm0a+LEiZI9e/bQugHYWxIggaAhQJEKmqFgR0iABEiABEggegKQifr168uwYcOkSJEibg++ePFitCIFwYHYIOrTpk0byZAhg0a0Vq9erfX/+9//ljRp0siYMWN0LxH2FGHJIJYTbty4UQXtzp07Wsfzzz8vffr0UWFClAdRpH79+smRI0dUoCA9nkSkRowYISdPntQ2oyubN2+WUaNGCaJXELtvvvlGBXLcuHHy9NNPR9tnLOObPn26HDx4kBEpvthIgAR8JkCR8hkhKyABEiABEiABGwJ79+7ViNC8efN0SZ+74qlI9ejRQ1555RWtZurUqQJJmTlzpv4fywQRcYKwONKGPUzOz/Lly6ciBWkqWbKknrNy5UpZuHChIPLjrUh16dJFHnvsMV3CF13p1auXHofInFPat28vhQoV0j1i0fW5YMGCFCmbW5WtkECcIECRihPDzIskARIgARIIBwI//vijQDjmzp2rUSRfRWrs2LHyzDPPRIjU4cOHdckgypkzZ1RMoiqQubJly6pIIRKUJ08ePWzdunUqKohceStSqDNLlix6fdGVxo0bS61ate5bKohoFsQPyxqj63OFChUoUuHwQuA1kECQEKBIBclAsBskQAIkQAIkEBOB8+fPa4KJgQMHygsvvPDA4VhulzBhQvE0IjV+/HhdDoeCiNRvv/2mESiUS5cuSe3atXVPFTLnRS7O8kDXOnwRqdGjR8uhQ4c0WUZU5Z9//pEECRKIO5HCvi9EpqLrM+rl0r6Y7jL+ngRIwFMCFClPSfE4EiABEiABEggCApAFSAWiSZAmp0Ak2rVrJ2+//bZgCVv16tU1UcSTTz6ph0CIkC4df0clQZFFCudUrVpV5cRZ/oefIVKFZBP+FqmdO3cKlu25LhV0rm3ZsmWaEh3Rr/fee0+TWWBvl1PQRySoaNasWbR9pkgFwQ3MLpBAGBGgSIXRYPJSSIAESIAEwp8Alt917txZ9wk1atRIMmbMqOnPZ82apQkfsMwNGelq1Kihy9yQPQ9JKrB0DkkfvBEpiBjSjw8YMECz42EPFPY/Yekeng2FpX3uIlLoU9OmTfX3OXLk8CglO5JVYJ8WEl7guVFYrrd161bNCgjJKl26tP4f14gliLlz59bjkXxj0qRJKo3R9fmRRx5hRCr8XyK8QhIwI0CRMkPNhkiABEiABEjAPwTwQF4khdizZ4+mIcd+KWTaw7I/5xlSEAxIBYQHERxk6MODaD/55BOPI1LIwAdB2bRpk2bpy5Url6YgR6KJmCJSSEwB+cG+LgjfW2+9FePF4xykNUf0CRn8cC1YelivXr37UqJ//vnnmsodyw8hlIhEFS5cWOuPrs+MSMU4BDyABEjACwIUKS9g8VASIAESIAESIAESIAESIAESAAGKFO8DEiABEiABEiABEiABEiABEvCSAEXKS2A8nARIgARIgARIwDsCixYting+VVRnFi9eXJcBspAACZBAKBGgSIXSaLGvJEACJEACJEACJEACJEACQUGAIhUUw8BOkAAJkAAJkAAJkAAJkAAJhBIBilQojRb7SgIkQAIkQAIkQAIkQAIkEBQEKFJBMQzsBAmQAAmQAAmQAAmQAAmQQCgRoEiF0mixryRAAiRAAiRAAiRAAiRAAkFBgCIVFMPATpAACZAACZAACZAACZAACYQSAYpUKI0W+0oCJEACJEACJEACJEACJBAUBChSQTEM7AQJkAAJkAAJkAAJkAAJkEAoEaBIhdJosa8kQAIkQAIkQAIkQAIkQAJBQYAiFRTDwE6QAAmQAAmQAAmQAAmQAAmEEgGKVCiNFvtKAiRAAiRAAiRAAiRAAiQQFAQoUkExDOwECZAACZAACZAACZAACZBAKBGgSIXSaLGvJEAC0RL47bffZPLkyXL48GG5e/euZMmSRZo2bSpFihTR81avXi0VKlQIGMXPP/9cli1bJrdu3ZJSpUpJmzZtJEGCBDJx4kS5cuWKxI8fX7p27RrR/meffSaJEiWSmjVrBqxPrJgESIAESIAESCAwBChSgeHKWkmABB4CgcaNG8ubb74pVapUkXjx4snmzZtlxIgRMnfuXEmRIoXUrl1bFixYEJCe7d27V0aNGiWjR4+WZMmSSd++faVkyZIqcVOmTJEBAwZoXypXriz58uWTs2fP6s/Gjh2rssVCAiRAAiRAAiQQWgQoUqE1XuwtCZCAGwJ37tyR1157TebNmydp06aNOOrEiROSLVs26d+/v2zbtk0ef/xxGTJkiPz999/yn//8R86fPy8JEybUSFHevHnlwIEDKjwFCxYURLiuXr2qkaV//etf0bJHXRkzZpS33npLj9u+fbssXLhQatSoIT/88IPWMXv2bMmUKZOUL19eJapatWpSoEABjikJkAAJkAAJkEAIEqBIheCgscskQAJRE3j//ffl4sWLKi+FChWSdOnSRRz4559/St26dWXlypVy7949adWqlUau8Afy1Lt3b41cHTt2TN555x0ZPHiwFCtWTL777juZMGGCzJgxQwVr69atDzQOiRo3bpxUrVpVXnrpJf398ePHpVu3btK5c2fZsWOHtG/fXuvIkSOHpE6dWtasWSNFixaVr7/+Wn/WsmVLDisJkAAJkAAJkEAIEaBIhdBgsavhT2DjjoNBcZFliz+t/Qi2/sQEB3uTVqxYIVu2bJH9+/fLY489JvXr15fSpUuLq0idPHlSxQX7mbBvCQURo+bNm0vKlCmlS5cusmTJEv35P//8IxUrVtQlgWnSpHHbhQ4dOmhbkC8ULN1r0aKFfPLJJxp9GjNmjPTq1UsFbvjw4frvgQMHqqRheV/ZsmWlcOHCMV0if08CJEACJEACJBAkBChSQTIQ7AYJOOLiSMzDIgJ5chW0VcuWAAAZD0lEQVSpYOqPN0xu3ryp0SMIDMQla9asEREpSBbEJ3369BFVYqkfokbZs2eXPn36yJw5cyJ+9/rrr2vCCPzOXenRo4fuf4K0oSCyhZ8hAcWXX34p33zzTUTSCywlxJI+JJvAXioI3fXr16VOnTreXCKPJQESIAESIAESeIgEKFIPET6bJoHIBFwl5mHScfoRbP2JjgkiQEeOHJESJUrcdxgiPy+++KIKjrO079y5cxp9cqJOriegjo4dO8rSpUs1YQWiXBAk7HdC0gh3S/sgQ0ho0ahRI61u/fr1unxv2LBhEdWjXSc6dejQIY1yYUkhzoXI1apV62EOO9smARIgARIgARLwggBFygtYPJQEAk0g2MQl2PoTHX8klWjbtq0mjUC2PEjQjz/+qEkmPvzwQ8mcObNm9EN0KGnSpLrEDuLy8ssvy+XLl3WJHQTq9OnT0rp1a3n33XelTJkysnbtWo0qYYledGXfvn0ydOhQjYAlT55cunfvrnumXNOtDxo0SH+GaBTSoffs2VPGjx+vf7Bfqnjx4oG+xVg/CZAACZAACZCAnwhQpPwEktWQgD8IBJu4BFt/YmK8e/dumTlzpiZ6gEjhOVKIQkGsULDUDoklIDzYCwXpQSQL6ceRoAKJJxCRQtQIUSxk3kM92DP17LPPxtS8RpgWLVqk+6qw5wn7sJw9WMjct2rVKt0b5RQsH9yzZ4/2BYkynGNjbIgHkAAJkAAJkAAJPHQCFKmHPgTsAAn8H4FgE5dg64/FvQKRQhQLQsZCAiRAAiRAAiRAAu4IUKR4b5BAEBEINnEJtv5YDBVFyoIy2yABEiABEiCB0CdAkQr9MeQVhBGBYBOXYOuPxVBTpCwosw0SIAESIAESCH0CFKnQH0NeQRgRCDZxCbb+hNFQ81JIgARIgARIgARCnABFKsQHkN0PLwLBJi7B1p/wGm1eDQmQAAmQAAmQQCgToEiF8uix72FHINjEJdj6E3YDzgsiARIgARIgARIIWQIUqZAdOnY8HAkEm7gEW3/Cccx5TSRAAiRAAiRAAqFJgCIVmuPGXocpAW/FZf78+frsIzwA1p/F6Ye3/UEfbt++Lffu3YuyO3gmU6JEibzuamz64drIP3fvytVrN+Xq9Zv645SPJJEUyZPwuU1ejwRPIAESIAESIAEScAhQpHgvkEAQEfBWGCpVqiRJkyaVmjVrSrVq1fwmVL6IVK1ateTy5ctRUs2YMaPgIbTeFm+5OPWfvXBFTp65JH9duykpkieW5MmS6K+uXb8p127ckpQpkkqOzGkkQ9qU3naJx5MACZAACZAACcRxAhSpOH4D8PKDi4C3wgCRQgRIoywpUwokBkKVLFkyny4sHETqp0OnBZGox7Olk6RJoo6C/X3ztvx26oIkTBBf8ufO4hMznkwCJEACJEACJBC3CFCk4tZ482qDnIAvIuVcGoSqdu3a8sYbb8RaqEJZpO7cuStbdx+Wpx7PKOk9jDSdu3hFDv92Vl4qmlulioUESIAESIAESIAEYiJAkYqJEH9PAoYE/CFSTndTpUqlESr8SZAggVdXEcoihb4XK/CkJEzonRDdun1Hdv90XMo8/5RXrHgwCZAACZAACZBA3CRAkYqb486rDlIC/hSpIkWKSKNGjeSZZ57x+mpDVaSwnC9NqmQeR6Iig0Fk6q8rN+QZLvPz+p7hCSRAAiRAAiQQ1whQpOLaiPN6g5qAP0SqcOHCKlD58+eP9bWGokghscSZ839JnpyZY33dOHH/0TOSNUMqJqDwiSJPJgESIAESIIHwJ0CRCv8x5hWGEAFfRKpQoUIqUM8++6zPVxyKIrXrp+OS+/GMbhNLeArlxt+35eiJc1Lkmcc8PYXHkQAJkAAJkAAJxEECFKk4OOi85OAlEBuRwtK9hg0bSoECBfx2Yb6IVK9eveTPP/+Msi/p0qWTgQMHet3PmLggO9+WnUekROGcXtcd1Qnbdh/xSz2shARIgARIgARIwHcCZYs/7XslAaiBIhUAqKySBGJLICZhiFzvvn37/BKBilyvLyIV22uP7ryYuPx55YYcPHZWCuTJ7pfmf9x/Up58LL2kfCSpX+pjJSRAAiRAAiRAArEjgC83KVKxY8ezSCBOEYhJGKxghJpInfrjsvx59W/JlSODXxAdOX5OHkmWWDJnSO2X+lgJCZAACZAACZBA7AhQpGLHjWeRQJwjQJGKeshj4kKRinMvFV4wCZAACZBAHCIQrDLFpX1x6CbkpQY/gZiEweoKQi0i9dfVG3Lg17NSIC+X9lndI2yHBEiABEiABCwIBKtE4dopUhZ3ANsgAQ8JUKRiF5G6e/eubN55WEoUzuUh6egPY7IJv2BkJSRAAiRAAiTgFwLcI+UXjKyEBMKbAEUqdiKFs3b/fEL3SCVNksinm+Tvm7cFe6SY/twnjDyZBEiABEiABMKeACNSYT/EvMBQIkCRir1Inbt4RX4/95fk9fGBvL8cOS3ZM6WR9I+mCKVbh30lARIgARIgARIwJkCRMgbO5kggOgIUqdiLFM786fBpSZ0ymWRImzJWN9rZC1fkyrW/5ZlcmWN1Pk8iARIgARIgARKIOwQoUnFnrHmlIUCAIuWbSOHsTd8dkueezSGJEiX0asRv3boju386JmWKBedD/7y6GB5MAiRAAiRAAiQQcAIUqYAjZgMk4DkBipTvIvXPP3dly67DkvvxjB5HphCJOnrinLz0XC5JED++5wPGI0mABEiABEiABOIsAYpUnB16XngwEggGkXLtQ7D1x5sx+/nwabn9z115PGs6SZY06gQUSCzx68nzkjhRQi7n8wYujyUBEiABEiABEmD6c94DJBBMBCAuKA8rzWfk9oOtP96OFRJQnDhzWfCcqUeSJZFHkifRKq5dvynXbtyU1CmSyWNZHmViCW/B8ngSIAESIAESIAGKFO8BEghGAo7AWPfNncAFW3+85XL37j25ev1vuXLtpp6a8pEkkiJ5UokfP563VfF4EiABEiABEiABElACXNrHG4EESIAESIAESIAESIAESIAEvCRAkfISGA8nARIgARIgARIgARIgARIgAYoU7wESIAESIAESIAESIAESIAES8JIARcpLYDycBEiABEiABEiABEiABEiABChSvAdIgATiFIGiRYvKzp07Q/qaeQ3BMXzhMA4gGQ7XwWsIjtcE7yeOg78JBPtrmyLl7xFnfSRAAkFNINjflD2Bx2vwhFLgjwmHceDEN/D3iact8H7ylFTgjwuHsQiHawiF9yeKVOBfj2yBBEggiAiEw4cLryE4bqhwGIdQmKh4MtrhMBbhcA28nzy5W22O4f1kw5kiZcOZrZAACQQJgXD4cOE1BMfNFA7jwIlvcNxL4TIO4XId4fDaDodrCIX7iSIVPO+h7AkJkIABgXD4cOE1GNwoHjQRDuMQChMVD4aC+7w8gWR0TDi8LngNRjeLB80E+1hQpDwYRB5CAiQQPgSC/U3ZE9K8Bk8oBf6YcBgHilTg7xNPW+D95CmpwB8XDmMRDtcQCu9PFKnAvx7ZAgmQQBARCIcPF15DcNxQ4TAOoTBR8WS0w2EswuEaeD95crfaHMP7yYYzRcqGM1shARIgARIgARIgARIgARIIIwIUqTAaTF4KCZAACZAACZAACZAACZCADQGKlA1ntkICJEACJEACJEACJEACJBBGBChSYTSYvBQSIAESIAESIAESIAESIAEbAhQpG85shQRIgARIgARIgARIgARIIIwIUKTCaDB5KSRAAiRAAiRAAiRAAiRAAjYEKFI2nNkKCZAACZAACZAACZAACZBAGBGgSIXRYPJSSIAERE6fPi0jR46UI0eOSKZMmaRt27by7LPPyo0bN2T48OGSIEECKVCggFStWjUC17vvviuNGjWSPHnyPFSEGzZskNGjR0u3bt2kdOnSEX35/vvv5eOPP5YLFy5oH/H7tGnTyq+//iqffPKJHlevXj155pln9N/Xrl2TLl26yKhRoyR58uRm14R2//Of/wj6mzhxYnnjjTekbt262n4ojcvGjRvl008/lcuXL0vu3LmlU6dOki1bNrl165aOz86dOyVp0qRSp04dqVKlil7fxIkT5cqVKxI/fnzp2rVrBPPPPvtMEiVKJDVr1jQbB9eG/vrrL2ncuLE0adJEXn/9df3V559/LsuWLdPrKVWqlLRp00ZfF7j/tmzZIv/884/eP6lTp9bjf/rpJ5k3b54MGDDA9BqmTp0qCxYs0L45Zdy4cZIzZ06391OwvSbQ7z179sjYsWPl/Pnz+t7Tq1cvSZEiRcjcT6tXr9b+u5bbt2/LF198IalSpQqZ+wmvT7x+Dx8+rPdUpUqVpFatWnpZofIee+/ePZk5c6asWrVK/v77b33/efvtt0PiPRbvp8OGDZNz584JXttOcffZECrjQpEy/VhgYyRAAoEmgAngiy++KNWrV5ddu3apVM2ePVs/eG7evCk1atSQd955RzAhS5gwoWzdulU/RDFZfphl4cKF8t///ldlqXbt2hEiBTlp2LChDBw4UCUKH6InTpyQPn36yODBg3Uyj8kMJghDhw7VSxg/frzky5dPXn75ZdNL+uijj1Q+unfvrn+3a9dO3n//fZ08hsq4nDx5Utq3b6/3zeOPP64f+JByTADA/rfffpMePXrIpUuXpGPHjsoc99GUKVNUNEaMGCGVK1dW/mfPntWfYRLqKgOWg4IvD3788Ue9TyBSe/fuVcHG/ZIsWTLp27evlCxZUqW3WbNmKuzr16/X+/Ctt96Su3fv6nVi8p8lSxbLrsuYMWPkySef1L5FLu7uJ4xTML0mMHlv2rSpvg5wT+A+yZEjh45FKN5PGIfdu3cLviD48MMPQ+p+wpc8KHh9Y1zwOYD7GvdYqLzH4kueuXPnKntIFb60Qd/LlCkT1O+x169f18+DF154QXbs2HGfSLl7LePzOhTGhSJl+rHAxkiABAJJAJN3vPEuXrw4YuLaqlUr/cDctm2b/Otf/5KXXnpJIzr4AwGBQGHyhX8/zILJOr5pxyQd3zI6EalNmzapBDqSBLHCt6hffvmlfjDhAzVlypQaUUMUBfVMnjxZo2/WBYxxDZkzZ9amIXv44ITYhsq4nDlzRkX1+eef12v45ZdfZNCgQTJnzhz95hcTF0yIUSZNmqQRP0StfvjhB43sQNoRCS1fvrxKVLVq1VQkH0aBNGGy/sQTT+gfTN4xmcyYMaNKEsr27dsFEo++4rUAkYLQIwqBa12yZIlOOhs0aGB+CfiioHjx4vLKK6/c13Z0r3NEBoPpNQGO+EIHUe/IJdTuJ/Qf0Uq8pzoCEkr3E+5nSDmisCj9+/eXIkWK6Ht/qLzH4osafMHjRNKWLl2q7z0dOnQI6vdYrAi5ePGifgGFL0iciFR0r2X8LhTGhSJl/tHABkmABAJFYN++fTpRxGTQKZgEFy5cWE6dOqURHXxzhwkjviGGjGDSjyjEsWPH5N///rdOgB9mQTTHVaQwgccHCibpTsGHqBOBwmQTIoVJ2YwZM3TyC6nC0i18C4h/P/XUU+aXBOHDN/EQOkzEQ3FccA0TJkzQyA2WiFasWFGXmoE3ChhDOsqVK6ffsuKbbowBIg5YFrdmzRopWrSofP311/qzli1bmo0Dll7hnnHuc0ekcH9hWSu+UEA5fvy4fqkACcfxWCqKiRn6jOWAEAD8HBEISCM4WH3pgMk6vnXH6zNevHga6YMARvc6X7lypUZqg+U1gfvnzp07eg2///67SjV4PvLIIyF1Pzk3Lu5pRPDfe+89/VEo3U/4kgP3O/r8559/aqQV9wqWs4bKe2xkkcJyXCy7xZdqofAei9euq0hF91rGmITCuFCkzD7W2BAJkECgCeCb3+nTp+uyPafggwdRknTp0smBAwf0Wzt8e9e7d29dvoVv2tetW6cCgkkijrfcVxSZSWSRmjZtmn4L3Lx584hD0ed+/frJihUr9Bt7TMrwb3y7ij0i2J+EZVj4/wcffKDXaVmwJAPf9j799NO6PycUxwUyDmnC/jpEayBTr732mixfvlySJEmiONeuXatLQyFQOAYTBEz+8Y09BBL/xpJMTKaxvK9s2bIq9RZl1qxZOoGHDGHJpSNSuPfr168vxYoV025g+WGLFi008oTILaI5+JYbrxdMcvDtPSZquC+xVwrROoypRcESJuwlAneIiCN1GAt3r3McF0yvCdwH4AauadKk0chy+vTpVapD6X5yxhtf2OC+RhQWJZTuJ+wp6ty5s36phn9jCTWuJ5TeYyFO+HIN7+lYLoz3Hey9w2dXKHz2RRap6D4bIFGh8NlHkbL4NGAbJEACJgQwYUGkxnUjKyayzz33nFSoUEH3DuFDFBv/EY2CVGH5E5IBYAkWolf4Xd68eU36G1UjkUUKk0nsV8E3jk5BHzExR8IDfAuJDxtMmCGB+IAdMmSIThCw9v9//ud/dLmZVbl69apKasGCBSMm3KE6LphsIeoEYcJySUx8IRVOEgYIB64NE0vcT998843KKwr2TSH6gEgO9iE5EULs3wl0gUxAZHG/Q6pdRQpLRxHZcZaOIhKLnyEBBaJruFcyZMigy+mwRBb1QLwwecO3+VjOiPvrYRTIISaNr776qtvXOSQqmF4TGAMkIIFco4Ax+oe9UohwhsL95Iz1wYMHdRm0k+AGPw+l+wnv74899ph+eYalZhBzLPXDct5QeY/FnkUs14VQ4X0IX4ggQggxD4XPvsgiFd1nA0QqFMaFIvUwPg3YJgmQQEAIYLkGJn3Y8wHJQMHSNshJ/vz5I9pEFAGZ17C0AxMZfMON5U74oMW3lIikPKwSWaTQ10WLFmmCABRMJBERwMTdNYEBJmzI2oeoByYI+GDFWnrIIj54LQqywKH/WD6JZB9OCaVxwR4zZLpzIkeQVAgURKNnz57SunVrKVSokF4axgRLQ5Ex0SnISOVEpw4dOqRRLYglRApi5uxtCOR44P6H+EDmUDBpxL2Cexx9QJQHrwsUJJbAci1MkJ2CyRqWv0IQcX2uIoVIHV4nFgWTLizHxRcdKPjGHaKOe9qT13kwvCbw2kWWOLwuUCBS6BeEFJHAULifnLHG8k9Em9Fvp+BaQuV+wpdl6C8ycKLgSw4st4R8h8p7bOTXHT6/IIL4TPDkNfGwP/sii1R0nw3YTxUK40KRsvg0YBskQAJmBDBhQTQEabexzwOTL0wA8K0wCiYCToIJ7KP49ttvdekZ9i3gDyaUmBg8rBJZpLDPCd+gOtnvMBHAZNiZmKGfR48e1YiJMxnGvyGOiIhgLwMiEhYFEQN8+GGpW+QSKuOCb3cR1YMkZc2aVSUD0YP58+drtix8g4oIE1L2ItsUogvOxAzXDMmAsIA99oZBvjBm+IP9Upi0WRfXiBQmMlhehmWIWMLq7HFBxNYpiLRhguMkmMDrAucg+9/PP/9830Q6kNeC5UrghQkieGP5LZZmIRFITPdTsLwmsMEe2RARLcY+OSy1RbIPyAhkN5TuJ3whgL11rvdKKN1PWIaIpaqI6GMPIb5wKlGihEYGQ+U9Fgl98KUMxgKvUbwmnIyQMb0mguGzL7JI4f3DXb/xORcK40KRCuSnAOsmARIwJ4Bv57AfAZEFTIQRdXKNMCEZACYyeIYICr59x/I/fNONKIRrdMGy81j6g2VW2NcC6cMfLJtBdAeih302kBREnfDB47rhH8kCsPQPEzUU7HvB3oz/194d3DYMA1EUbMRVuWxVFTwBBHyKb5toMbrlRHGWkvlDmm41pUloE/iJq7bainFCa212Ulx9e1JdCk2t+HXYRN816/4LRk2+2j5zXde9itnKyOfhJB3S0ClTreScq8lyvyNUaG/C82kzUZPa+AxS/d0qWf/pbXy0gtnq5bmvJmdNMAtaZyWo1du2/LXC1djr+Zm42qLYfbSik1+rnO/3+27623j6L89E99pBJAXuJrJt/eydVIh92njqO3RtGT4nWp4x8JTx1NbU6tD7sav3Ys924/wp79g+H/onT4Gq72q2dfv8Pty3Z+IvP/taCWtLcIfH1IfMX6/XfTDUb/f9hLoIUhOfBtogQIAAAQIECBAgQGCVgCC1qpw6Q4AAAQIECBAgQIDAhIAgNaGsDQIECBAgQIAAAQIEVgkIUqvKqTMECBAgQIAAAQIECEwICFITytogQIAAAQIECBAgQGCVgCC1qpw6Q4AAAQIECBAgQIDAhIAgNaGsDQIECBAgQIAAAQIEVgkIUqvKqTMECBAgQIAAAQIECEwICFITytogQIAAAQIECBAgQGCVgCC1qpw6Q4AAAQIECBAgQIDAhIAgNaGsDQIECBAgQIAAAQIEVgkIUqvKqTMECBAgQIAAAQIECEwICFITytogQIAAAQIECBAgQGCVgCC1qpw6Q4AAAQIECBAgQIDAhIAgNaGsDQIECBAgQIAAAQIEVgkIUqvKqTMECBAgQIAAAQIECEwICFITytogQIAAAQIECBAgQGCVgCC1qpw6Q4AAAQIECBAgQIDAhIAgNaGsDQIECBAgQIAAAQIEVgkIUqvKqTMECBAgQIAAAQIECEwI/ADvkmuGoMkaggAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "K20H7qOApBg_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 4: Feature Engineering**\n"
      ],
      "metadata": {
        "id": "wnzIOsrRVc_i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Log Transformation & Encoding & Scaling**\n",
        "\n",
        "*   **Target Log Transformation:** To handle the significant right-skewed distribution of `poor_mental_health_days` (0-30 days), we applied a log transformation. This stabilizes the variance and allows models to better differentiate between varying levels of distress.\n",
        "*   **Encoding & Scaling:** All 22 categorical features were transformed. Binary features were mapped to 0/1, while multi-class variables (like `general_health`) were expanded via One-Hot Encoding, resulting in 34 initial features. All numerical data were standard-scaled to prevent unit-bias.\n",
        "\n"
      ],
      "metadata": {
        "id": "XGkTq09o_3z6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# ENCODING, LOG TRANSFORMATION & SCALING\n",
        "# =============================================================================\n",
        "\n",
        "# 1. Target Transformation: Log(x+1) to handle the right skewness\n",
        "# This helps the model deal with the high volume of '0' days vs '30' days\n",
        "df_engineering = df_clean.copy()\n",
        "df_engineering['log_mental_health_days'] = np.log1p(df_engineering['poor_mental_health_days'])\n",
        "\n",
        "# 2. Dummy Encoding (Categorical to Numerical)\n",
        "# We use drop_first=True to avoid the Dummy Variable Trap\n",
        "categorical_cols = df_engineering.select_dtypes(include=['object', 'category']).columns\n",
        "df_encoded = pd.get_dummies(df_engineering, columns=categorical_cols, drop_first=True)\n",
        "\n",
        "# 3. Scaling the Features\n",
        "# We separate the target variables (original and log) and scale the rest\n",
        "features_to_scale = df_encoded.drop(['poor_mental_health_days', 'log_mental_health_days'], axis=1)\n",
        "scaler = StandardScaler()\n",
        "X_scaled_array = scaler.fit_transform(features_to_scale)\n",
        "\n",
        "# Convert back to DataFrame for easy manipulation in the next steps\n",
        "X_scaled = pd.DataFrame(X_scaled_array, columns=features_to_scale.columns)\n",
        "y_log = df_encoded['log_mental_health_days']"
      ],
      "metadata": {
        "id": "uQHNkN1nVdTm"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# SANITY CHECK (Visualizing the Change)\n",
        "# =============================================================================\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Plot 1: Original Distribution\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.histplot(df_clean['poor_mental_health_days'], kde=True, color='#EA00D9') # Pink\n",
        "plt.title('Before: Original Target Distribution', color='white')\n",
        "\n",
        "# Plot 2: Log-Transformed Distribution\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.histplot(y_log, kde=True, color='#0ABDC6') # Cyan\n",
        "plt.title('After: Log(x+1) Transformation', color='white')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig('before_vs_after_log.png',\n",
        "            dpi=300,\n",
        "            bbox_inches='tight')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(f\"\\n--- Data Preparation Complete ---\")\n",
        "print(f\"Total features after encoding: {X_scaled.shape[1]}\")\n",
        "print(f\"Scaling check (Mean should be ~0): {X_scaled.mean().mean():.4f}\")\n",
        "print(f\"Scaling check (Std should be ~1): {X_scaled.std().mean():.4f}\")\n",
        "print(\"\\nFirst 5 rows of scaled features:\")\n",
        "display(X_scaled.head())"
      ],
      "metadata": {
        "id": "fQuFR8878-uM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 882
        },
        "outputId": "736b579b-9c6d-4d6a-ad82-899023cabbf9"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 1182,
              "height": 481
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Data Preparation Complete ---\n",
            "Total features after encoding: 34\n",
            "Scaling check (Mean should be ~0): 0.0000\n",
            "Scaling check (Std should be ~1): 1.0000\n",
            "\n",
            "First 5 rows of scaled features:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   age_category       bmi  sleep_hours  poor_physical_health_days  sex_male  \\\n",
              "0      1.499846 -0.286107    -0.846715                  -0.445594 -0.981343   \n",
              "1      0.115899 -0.453756    -1.666745                  -0.158815 -0.981343   \n",
              "2      0.115899 -0.857163    -0.026685                  -0.445594 -0.981343   \n",
              "3     -0.714469 -1.124354     1.613374                  -0.158815 -0.981343   \n",
              "4      1.499846 -0.371678    -0.026685                  -0.302205  1.019012   \n",
              "\n",
              "   race_ethnicity_hispanic  race_ethnicity_multiracial  race_ethnicity_other  \\\n",
              "0                -0.322523                   -0.149967             -0.233683   \n",
              "1                -0.322523                   -0.149967             -0.233683   \n",
              "2                -0.322523                   -0.149967             -0.233683   \n",
              "3                -0.322523                   -0.149967             -0.233683   \n",
              "4                -0.322523                   -0.149967             -0.233683   \n",
              "\n",
              "   race_ethnicity_white  general_health_fair  ...  had_arthritis_yes  \\\n",
              "0              0.568177            -0.362118  ...          -0.699299   \n",
              "1              0.568177            -0.362118  ...          -0.699299   \n",
              "2              0.568177            -0.362118  ...           1.430003   \n",
              "3              0.568177             2.761528  ...          -0.699299   \n",
              "4              0.568177            -0.362118  ...          -0.699299   \n",
              "\n",
              "   had_diabetes_no  had_diabetes_pre-diabetes  had_diabetes_yes  \\\n",
              "0         0.434590                  -0.149621         -0.384251   \n",
              "1         0.434590                  -0.149621         -0.384251   \n",
              "2         0.434590                  -0.149621         -0.384251   \n",
              "3         0.434590                  -0.149621         -0.384251   \n",
              "4        -2.301017                  -0.149621          2.602467   \n",
              "\n",
              "   deaf_or_hard_of_hearing_yes  blind_or_vision_difficulty_yes  \\\n",
              "0                    -0.304302                        -0.21837   \n",
              "1                    -0.304302                        -0.21837   \n",
              "2                    -0.304302                        -0.21837   \n",
              "3                    -0.304302                        -0.21837   \n",
              "4                    -0.304302                        -0.21837   \n",
              "\n",
              "   difficulty_concentrating_yes  difficulty_walking_yes  \\\n",
              "0                     -0.327318               -0.378279   \n",
              "1                     -0.327318               -0.378279   \n",
              "2                     -0.327318               -0.378279   \n",
              "3                     -0.327318               -0.378279   \n",
              "4                     -0.327318               -0.378279   \n",
              "\n",
              "   difficulty_dressing_bathing_yes  difficulty_errands_yes  \n",
              "0                        -0.160022               -0.240035  \n",
              "1                        -0.160022               -0.240035  \n",
              "2                        -0.160022               -0.240035  \n",
              "3                        -0.160022               -0.240035  \n",
              "4                        -0.160022               -0.240035  \n",
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              "      <th>had_diabetes_yes</th>\n",
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              "      <td>-0.846715</td>\n",
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              "      <td>-0.233683</td>\n",
              "      <td>0.568177</td>\n",
              "      <td>-0.362118</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.699299</td>\n",
              "      <td>0.434590</td>\n",
              "      <td>-0.149621</td>\n",
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              "      <td>-0.304302</td>\n",
              "      <td>-0.21837</td>\n",
              "      <td>-0.327318</td>\n",
              "      <td>-0.378279</td>\n",
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              "      <td>0.115899</td>\n",
              "      <td>-0.453756</td>\n",
              "      <td>-1.666745</td>\n",
              "      <td>-0.158815</td>\n",
              "      <td>-0.981343</td>\n",
              "      <td>-0.322523</td>\n",
              "      <td>-0.149967</td>\n",
              "      <td>-0.233683</td>\n",
              "      <td>0.568177</td>\n",
              "      <td>-0.362118</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.699299</td>\n",
              "      <td>0.434590</td>\n",
              "      <td>-0.149621</td>\n",
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              "      <td>-0.304302</td>\n",
              "      <td>-0.21837</td>\n",
              "      <td>-0.327318</td>\n",
              "      <td>-0.378279</td>\n",
              "      <td>-0.160022</td>\n",
              "      <td>-0.240035</td>\n",
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              "      <td>0.115899</td>\n",
              "      <td>-0.857163</td>\n",
              "      <td>-0.026685</td>\n",
              "      <td>-0.445594</td>\n",
              "      <td>-0.981343</td>\n",
              "      <td>-0.322523</td>\n",
              "      <td>-0.149967</td>\n",
              "      <td>-0.233683</td>\n",
              "      <td>0.568177</td>\n",
              "      <td>-0.362118</td>\n",
              "      <td>...</td>\n",
              "      <td>1.430003</td>\n",
              "      <td>0.434590</td>\n",
              "      <td>-0.149621</td>\n",
              "      <td>-0.384251</td>\n",
              "      <td>-0.304302</td>\n",
              "      <td>-0.21837</td>\n",
              "      <td>-0.327318</td>\n",
              "      <td>-0.378279</td>\n",
              "      <td>-0.160022</td>\n",
              "      <td>-0.240035</td>\n",
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              "      <td>-0.158815</td>\n",
              "      <td>-0.981343</td>\n",
              "      <td>-0.322523</td>\n",
              "      <td>-0.149967</td>\n",
              "      <td>-0.233683</td>\n",
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              "      <td>2.761528</td>\n",
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              "      <td>-0.302205</td>\n",
              "      <td>1.019012</td>\n",
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              "      <td>-0.240035</td>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "**Polynomial & Interaction**\n",
        "\n",
        " Using the top coefficients from our baseline, we engineered interaction terms between the most impactful variables: *Difficulty Concentrating, Depression Diagnosis, Sleep Duration, and Poor General Health*. Each combination was added as a new feature, bringing the dataset to 40 columns."
      ],
      "metadata": {
        "id": "6C1vvGQXG4GH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# ADDING INTERACTION FEATURES TO DATASET\n",
        "# =============================================================================\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "# 1. Selection of top-tier features for interactions\n",
        "impact_features = [\n",
        "    'had_depressive_disorder_yes',\n",
        "    'difficulty_concentrating_yes',\n",
        "    'general_health_poor',\n",
        "    'sleep_hours'\n",
        "]\n",
        "\n",
        "# 2. Creating the interaction terms\n",
        "interaction_tool = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)\n",
        "interaction_array = interaction_tool.fit_transform(X_scaled[impact_features])\n",
        "interaction_names = interaction_tool.get_feature_names_out(impact_features)\n",
        "\n",
        "# 3. Converting to DataFrame and removing original columns to avoid duplicates\n",
        "df_interactions = pd.DataFrame(\n",
        "    interaction_array,\n",
        "    columns=interaction_names,\n",
        "    index=X_scaled.index\n",
        ").drop(columns=impact_features)\n",
        "\n",
        "# 4. Merging the new interaction features into our main scaled dataset\n",
        "X_scaled = pd.concat([X_scaled, df_interactions], axis=1)\n",
        "\n",
        "print(\"--- Interaction features successfully added! ---\")\n",
        "print(f\"New dataset shape: {X_scaled.shape}\")\n",
        "display(X_scaled.head())"
      ],
      "metadata": {
        "id": "2ZVUTVmyVddO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "outputId": "17673ed1-73a0-4938-fe11-352a7e28bdff"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Interaction features successfully added! ---\n",
            "New dataset shape: (302472, 40)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   age_category       bmi  sleep_hours  poor_physical_health_days  sex_male  \\\n",
              "0      1.499846 -0.286107    -0.846715                  -0.445594 -0.981343   \n",
              "1      0.115899 -0.453756    -1.666745                  -0.158815 -0.981343   \n",
              "2      0.115899 -0.857163    -0.026685                  -0.445594 -0.981343   \n",
              "3     -0.714469 -1.124354     1.613374                  -0.158815 -0.981343   \n",
              "4      1.499846 -0.371678    -0.026685                  -0.302205  1.019012   \n",
              "\n",
              "   race_ethnicity_hispanic  race_ethnicity_multiracial  race_ethnicity_other  \\\n",
              "0                -0.322523                   -0.149967             -0.233683   \n",
              "1                -0.322523                   -0.149967             -0.233683   \n",
              "2                -0.322523                   -0.149967             -0.233683   \n",
              "3                -0.322523                   -0.149967             -0.233683   \n",
              "4                -0.322523                   -0.149967             -0.233683   \n",
              "\n",
              "   race_ethnicity_white  general_health_fair  ...  \\\n",
              "0              0.568177            -0.362118  ...   \n",
              "1              0.568177            -0.362118  ...   \n",
              "2              0.568177            -0.362118  ...   \n",
              "3              0.568177             2.761528  ...   \n",
              "4              0.568177            -0.362118  ...   \n",
              "\n",
              "   difficulty_concentrating_yes  difficulty_walking_yes  \\\n",
              "0                     -0.327318               -0.378279   \n",
              "1                     -0.327318               -0.378279   \n",
              "2                     -0.327318               -0.378279   \n",
              "3                     -0.327318               -0.378279   \n",
              "4                     -0.327318               -0.378279   \n",
              "\n",
              "   difficulty_dressing_bathing_yes  difficulty_errands_yes  \\\n",
              "0                        -0.160022               -0.240035   \n",
              "1                        -0.160022               -0.240035   \n",
              "2                        -0.160022               -0.240035   \n",
              "3                        -0.160022               -0.240035   \n",
              "4                        -0.160022               -0.240035   \n",
              "\n",
              "   had_depressive_disorder_yes difficulty_concentrating_yes  \\\n",
              "0                                           0.159195          \n",
              "1                                           0.159195          \n",
              "2                                           0.159195          \n",
              "3                                           0.159195          \n",
              "4                                           0.159195          \n",
              "\n",
              "   had_depressive_disorder_yes general_health_poor  \\\n",
              "0                                         0.079462   \n",
              "1                                         0.079462   \n",
              "2                                         0.079462   \n",
              "3                                         0.079462   \n",
              "4                                        -2.976872   \n",
              "\n",
              "   had_depressive_disorder_yes sleep_hours  \\\n",
              "0                                 0.411810   \n",
              "1                                 0.810641   \n",
              "2                                 0.012979   \n",
              "3                                -0.784684   \n",
              "4                                 0.012979   \n",
              "\n",
              "   difficulty_concentrating_yes general_health_poor  \\\n",
              "0                                          0.053477   \n",
              "1                                          0.053477   \n",
              "2                                          0.053477   \n",
              "3                                          0.053477   \n",
              "4                                         -2.003410   \n",
              "\n",
              "   difficulty_concentrating_yes sleep_hours  general_health_poor sleep_hours  \n",
              "0                                  0.277145                         0.138336  \n",
              "1                                  0.545555                         0.272313  \n",
              "2                                  0.008735                         0.004360  \n",
              "3                                 -0.528086                        -0.263593  \n",
              "4                                  0.008735                        -0.163332  \n",
              "\n",
              "[5 rows x 40 columns]"
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              "      <th></th>\n",
              "      <th>age_category</th>\n",
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              "      <th>had_depressive_disorder_yes difficulty_concentrating_yes</th>\n",
              "      <th>had_depressive_disorder_yes general_health_poor</th>\n",
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              "      <td>0.159195</td>\n",
              "      <td>0.079462</td>\n",
              "      <td>0.411810</td>\n",
              "      <td>0.053477</td>\n",
              "      <td>0.277145</td>\n",
              "      <td>0.138336</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.115899</td>\n",
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              "      <td>-0.233683</td>\n",
              "      <td>0.568177</td>\n",
              "      <td>-0.362118</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.327318</td>\n",
              "      <td>-0.378279</td>\n",
              "      <td>-0.160022</td>\n",
              "      <td>-0.240035</td>\n",
              "      <td>0.159195</td>\n",
              "      <td>0.079462</td>\n",
              "      <td>0.810641</td>\n",
              "      <td>0.053477</td>\n",
              "      <td>0.545555</td>\n",
              "      <td>0.272313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.115899</td>\n",
              "      <td>-0.857163</td>\n",
              "      <td>-0.026685</td>\n",
              "      <td>-0.445594</td>\n",
              "      <td>-0.981343</td>\n",
              "      <td>-0.322523</td>\n",
              "      <td>-0.149967</td>\n",
              "      <td>-0.233683</td>\n",
              "      <td>0.568177</td>\n",
              "      <td>-0.362118</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.327318</td>\n",
              "      <td>-0.378279</td>\n",
              "      <td>-0.160022</td>\n",
              "      <td>-0.240035</td>\n",
              "      <td>0.159195</td>\n",
              "      <td>0.079462</td>\n",
              "      <td>0.012979</td>\n",
              "      <td>0.053477</td>\n",
              "      <td>0.008735</td>\n",
              "      <td>0.004360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.714469</td>\n",
              "      <td>-1.124354</td>\n",
              "      <td>1.613374</td>\n",
              "      <td>-0.158815</td>\n",
              "      <td>-0.981343</td>\n",
              "      <td>-0.322523</td>\n",
              "      <td>-0.149967</td>\n",
              "      <td>-0.233683</td>\n",
              "      <td>0.568177</td>\n",
              "      <td>2.761528</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.327318</td>\n",
              "      <td>-0.378279</td>\n",
              "      <td>-0.160022</td>\n",
              "      <td>-0.240035</td>\n",
              "      <td>0.159195</td>\n",
              "      <td>0.079462</td>\n",
              "      <td>-0.784684</td>\n",
              "      <td>0.053477</td>\n",
              "      <td>-0.528086</td>\n",
              "      <td>-0.263593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.499846</td>\n",
              "      <td>-0.371678</td>\n",
              "      <td>-0.026685</td>\n",
              "      <td>-0.302205</td>\n",
              "      <td>1.019012</td>\n",
              "      <td>-0.322523</td>\n",
              "      <td>-0.149967</td>\n",
              "      <td>-0.233683</td>\n",
              "      <td>0.568177</td>\n",
              "      <td>-0.362118</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.327318</td>\n",
              "      <td>-0.378279</td>\n",
              "      <td>-0.160022</td>\n",
              "      <td>-0.240035</td>\n",
              "      <td>0.159195</td>\n",
              "      <td>-2.976872</td>\n",
              "      <td>0.012979</td>\n",
              "      <td>-2.003410</td>\n",
              "      <td>0.008735</td>\n",
              "      <td>-0.163332</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 40 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-b87882a5-2f10-4f56-bfaf-432cdab25b1d')\"\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-b87882a5-2f10-4f56-bfaf-432cdab25b1d 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-b87882a5-2f10-4f56-bfaf-432cdab25b1d');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Applying Clustering (Unsupervised Learning):\n"
      ],
      "metadata": {
        "id": "Vq9Nh-D1Vdh6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The Search for Optimal Groups**\n",
        "\n",
        "  We first utilized the Elbow Method (integrated with K-Means training) to observe the inertia trend across different 'k' values. While the curve showed a general improvement, it lacked a definitive \"break\" point. To resolve the ambiguity between 3 or 4 groups and to benchmark our approach, we employed the Silhouette Score to compare K-Means (k=3, k=4) against DBSCAN."
      ],
      "metadata": {
        "id": "LZ-2flklYFnv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import plotly.express as px\n",
        "\n",
        "# =============================================================================\n",
        "# SELF-CONTAINED ELBOW METHOD\n",
        "# =============================================================================\n",
        "\n",
        "# 1. Calculate WCSS for k values from 1 to 10\n",
        "# This measures the \"tightness\" of clusters as described in Lecture 05\n",
        "wcss = []\n",
        "k_range = range(1, 11)\n",
        "\n",
        "for k in k_range:\n",
        "    # Initialize KMeans and fit to your scaled data\n",
        "    kmeans_temp = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
        "    kmeans_temp.fit(X_scaled)\n",
        "    wcss.append(kmeans_temp.inertia_) # inertia_ is the WCSS value\n",
        "\n",
        "# 2. Create visualization\n",
        "fig = px.line(\n",
        "    x=list(k_range),\n",
        "    y=wcss,\n",
        "    title='Elbow Method: Optimal k Search',\n",
        "    labels={'x': 'Number of Clusters (k)', 'y': 'WCSS (Inertia)'},\n",
        "    markers=True,\n",
        "    template='plotly_white',\n",
        "    width=500,  # Compact width\n",
        "    height=500  # Compact height\n",
        ")\n",
        "\n",
        "# 3. Apply your custom color #EA00D9\n",
        "fig.update_traces(\n",
        "    line=dict(color='#EA00D9', width=3),\n",
        "    marker=dict(size=8, color='#EA00D9', symbol='circle')\n",
        ")\n",
        "\n",
        "# 4. Refine layout to match professional standards\n",
        "fig.update_layout(\n",
        "    title_font=dict(size=16, family='Arial', color='#2c3e50'),\n",
        "    margin=dict(l=50, r=50, t=60, b=50),\n",
        "    xaxis=dict(tickmode='linear', tick0=1, dtick=1, gridcolor='#f0f0f0'),\n",
        "    yaxis=dict(gridcolor='#f0f0f0'),\n",
        "    hovermode='x unified'\n",
        ")\n",
        "\n",
        "\n",
        "fig.show(config={\n",
        "    'toImageButtonOptions': {\n",
        "        'format': 'png',\n",
        "        'filename': 'elbow_plot',\n",
        "        'scale': 1.5\n",
        "    }\n",
        "})"
      ],
      "metadata": {
        "id": "935GjHVFVaVz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "f59cbbc7-a8b6-49be-a288-36bc14f9cb26"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.cluster import KMeans, DBSCAN\n",
        "from sklearn.metrics import silhouette_score\n",
        "import pandas as pd\n",
        "\n",
        "# =============================================================================\n",
        "# THE FINAL NO-STRESS TOURNAMENT\n",
        "# =============================================================================\n",
        "\n",
        "# 1. Quick Sample for the Score (10k rows is statistically robust)\n",
        "sample_indices = np.random.choice(X_scaled.index, 10000, replace=False)\n",
        "X_sample = X_scaled.loc[sample_indices]\n",
        "\n",
        "tournament_results = []\n",
        "\n",
        "for k in [3, 4]:\n",
        "    km = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
        "    # Fitting on the FULL data (K-Means can handle it), scoring on sample\n",
        "    full_labels = km.fit_predict(X_scaled)\n",
        "    sample_labels = full_labels[X_scaled.index.isin(sample_indices)]\n",
        "\n",
        "    score = silhouette_score(X_sample, sample_labels)\n",
        "    tournament_results.append({'Algorithm': f'K-Means (k={k})', 'Score': round(score, 4)})\n",
        "\n",
        "# Running DBSCAN on sample\n",
        "db = DBSCAN(eps=0.5, min_samples=5)\n",
        "db_labels = db.fit_predict(X_sample)\n",
        "\n",
        "if len(set(db_labels)) > 1:\n",
        "    db_score = silhouette_score(X_sample, db_labels)\n",
        "    tournament_results.append({'Algorithm': 'DBSCAN', 'Score': round(db_score, 4)})\n",
        "\n",
        "# 2. Displaying the Winner\n",
        "df_tourney = pd.DataFrame(tournament_results).sort_values(by='Score', ascending=False)\n",
        "print(\"\\n--- Final Results: Choose the highest score ---\")\n",
        "display(df_tourney)"
      ],
      "metadata": {
        "id": "bM-KXPKvQ_K4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "outputId": "89fe3aed-6d90-4c4e-8368-f286556adb1f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Final Results: Choose the highest score ---\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       Algorithm   Score\n",
              "0  K-Means (k=3) -0.0011\n",
              "1  K-Means (k=4) -0.0053\n",
              "2         DBSCAN -0.3257"
            ],
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              "      <td>-0.0053</td>\n",
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              "      <td>-0.3257</td>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-9f3475d2-33ec-48fa-8238-a7bf6235eac9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-9f3475d2-33ec-48fa-8238-a7bf6235eac9');\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",
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              "\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_tourney')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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              "        document.querySelector('#id_16c02b9c-1ff1-4973-b10a-e53ffbd01233 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_tourney');\n",
              "      }\n",
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              "  </div>\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_tourney",
              "summary": "{\n  \"name\": \"df_tourney\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Algorithm\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"K-Means (k=3)\",\n          \"K-Means (k=4)\",\n          \"DBSCAN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1862073038309722,\n        \"min\": -0.3257,\n        \"max\": -0.0011,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.0011,\n          -0.0053,\n          -0.3257\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Reconciling Low Scores with Visual Evidence**\n",
        "\n",
        "  Although the absolute Silhouette scores are low (near zero), this is expected in high-dimensional, large-scale social surveys where boundaries between human behaviors are naturally fluid. We chose to move forward with K-Means (k=3) as it yielded the highest relative score. To validate this, we projected the 40-dimensional space into 2D using PCA (Principal Component Analysis).\n"
      ],
      "metadata": {
        "id": "Zl-z3jSfY7a_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "import plotly.express as px\n",
        "\n",
        "# =============================================================================\n",
        "# PCA VISUALIZATION (THE REALITY CHECK)\n",
        "# =============================================================================\n",
        "\n",
        "# 1. Running the winning K-Means on the full data to get final labels\n",
        "final_km = KMeans(n_clusters=3, random_state=42, n_init=10)\n",
        "final_labels = final_km.fit_predict(X_scaled)\n",
        "\n",
        "# 2. Reducing dimensions to 2D for visualization\n",
        "pca = PCA(n_components=2)\n",
        "components = pca.fit_transform(X_scaled)\n",
        "\n",
        "# 3. Creating a DataFrame for plotting (sampling 5,000 for smooth viewing)\n",
        "# Visualizing 300k points in Plotly will freeze your browser\n",
        "df_pca = pd.DataFrame(data=components, columns=['PC1', 'PC2'])\n",
        "df_pca['Cluster'] = final_labels.astype(str)\n",
        "\n",
        "df_pca_sample = df_pca.sample(5000, random_state=42)\n",
        "\n",
        "# 4. The Final Visualization\n",
        "fig = px.scatter(\n",
        "    df_pca_sample, x='PC1', y='PC2', color='Cluster',\n",
        "    title='Cluster Visualization (PCA 2D Projection)',\n",
        "    color_discrete_sequence=['#EA00D9', '#0ABDC6', '#711DB0'], # Your style\n",
        "    template='plotly_white',\n",
        "    width=700, height=500\n",
        ")\n",
        "\n",
        "fig.update_layout(title_x=0.5, legend_title_text='Group ID')\n",
        "\n",
        "fig.show(config={\n",
        "    'toImageButtonOptions': {\n",
        "        'format': 'png',\n",
        "        'filename': 'PCA',\n",
        "        'scale': 1.5\n",
        "    }\n",
        "})"
      ],
      "metadata": {
        "id": "o7BAbiUHXgaP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "ef3cd6a2-ee24-4173-987b-28a011519032"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PCA Analysis:**\n",
        "\n",
        "The visualization reveals a surprisingly clear structural distinction.\n",
        "* The low Silhouette score likely stems from the partial overlap between Cluster 0 and Cluster 1.\n",
        "    \n",
        "* However, Cluster 2 emerges as a completely isolated \"island\" of outliers, representing the most extreme health cases.\n",
        "\n",
        "* The visual clarity of these three groupings provided high confidence that the model successfully captured distinct risk profiles despite the statistical noise."
      ],
      "metadata": {
        "id": "r7AbU7vU66P9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "uvrnLC6CLAKt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cluster Profiling:**\n",
        "\n",
        "The \"Human Story\" Behind the Clusters\n",
        "To understand who these clusters represent, we performed an anomalous feature detection for each group, measuring how much each feature deviates from the Global Average in terms of Standard Deviations (SD)."
      ],
      "metadata": {
        "id": "1L4WHghbK3uC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# ISOLATED CLUSTER PROFILING (EXTREME DEVIATIONS TEST)\n",
        "# =============================================================================\n",
        "\n",
        "# 1. Create a temporary DataFrame just for the analysis (no permanent changes yet)\n",
        "analysis_df = X_scaled.copy()\n",
        "analysis_df['Cluster_ID'] = final_labels\n",
        "\n",
        "# 2. Calculate the mean values for each cluster\n",
        "cluster_means = analysis_df.groupby('Cluster_ID').mean()\n",
        "\n",
        "print(\"=== CLUSTER PROFILES (k=3) BASED ON EXTREME DEVIATIONS ===\\n\")\n",
        "\n",
        "# 3. Find the extreme values for each cluster\n",
        "for cluster_id in cluster_means.index:\n",
        "    print(f\"--- Cluster {cluster_id} ---\")\n",
        "\n",
        "    # Sort features from highest to lowest mean\n",
        "    sorted_features = cluster_means.loc[cluster_id].sort_values(ascending=False)\n",
        "\n",
        "    # Print the top 3 highest (positive extremes)\n",
        "    print(\"Top 3 ABOVE Global Average (Positive Extremes):\")\n",
        "    for feat, val in sorted_features.head(3).items():\n",
        "        print(f\"  + {feat}: {val:.2f} standard deviations\")\n",
        "\n",
        "    # Print the top 3 lowest (negative extremes)\n",
        "    print(\"Top 3 BELOW Global Average (Negative Extremes):\")\n",
        "    for feat, val in sorted_features.tail(3).sort_values().items():\n",
        "        print(f\"  - {feat}: {val:.2f} standard deviations\")\n",
        "\n",
        "    print(\"-\" * 50 + \"\\n\")"
      ],
      "metadata": {
        "id": "iKrq3HwN0Qp0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a809974-4702-4039-c255-2fc6a55b5725"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== CLUSTER PROFILES (k=3) BASED ON EXTREME DEVIATIONS ===\n",
            "\n",
            "--- Cluster 0 ---\n",
            "Top 3 ABOVE Global Average (Positive Extremes):\n",
            "  + had_depressive_disorder_yes difficulty_concentrating_yes: 6.28 standard deviations\n",
            "  + difficulty_concentrating_yes: 3.06 standard deviations\n",
            "  + had_depressive_disorder_yes: 2.06 standard deviations\n",
            "Top 3 BELOW Global Average (Negative Extremes):\n",
            "  - difficulty_concentrating_yes sleep_hours: -0.81 standard deviations\n",
            "  - had_depressive_disorder_yes sleep_hours: -0.55 standard deviations\n",
            "  - difficulty_concentrating_yes general_health_poor: -0.50 standard deviations\n",
            "--------------------------------------------------\n",
            "\n",
            "--- Cluster 1 ---\n",
            "Top 3 ABOVE Global Average (Positive Extremes):\n",
            "  + had_depressive_disorder_yes general_health_poor: 0.04 standard deviations\n",
            "  + general_health_very good: 0.03 standard deviations\n",
            "  + age_category: 0.02 standard deviations\n",
            "Top 3 BELOW Global Average (Negative Extremes):\n",
            "  - difficulty_concentrating_yes: -0.19 standard deviations\n",
            "  - had_depressive_disorder_yes: -0.12 standard deviations\n",
            "  - difficulty_errands_yes: -0.07 standard deviations\n",
            "--------------------------------------------------\n",
            "\n",
            "--- Cluster 2 ---\n",
            "Top 3 ABOVE Global Average (Positive Extremes):\n",
            "  + difficulty_concentrating_yes general_health_poor: 18.70 standard deviations\n",
            "  + had_depressive_disorder_yes general_health_poor: 7.31 standard deviations\n",
            "  + general_health_poor: 6.12 standard deviations\n",
            "Top 3 BELOW Global Average (Negative Extremes):\n",
            "  - general_health_poor sleep_hours: -2.21 standard deviations\n",
            "  - difficulty_concentrating_yes sleep_hours: -1.10 standard deviations\n",
            "  - physical_activities_yes: -0.87 standard deviations\n",
            "--------------------------------------------------\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# INTEGRATED FEATURE ENGINEERING: CLUSTERS, DISTANCES, AND INTERACTIONS\n",
        "# =============================================================================\n",
        "\n",
        "\n",
        "# 1. Create a fresh copy for regression to keep X_scaled pristine\n",
        "X_ready_for_regression = X_scaled.copy()\n",
        "\n",
        "# 2. Add 'Distance_to_Centroid' using the already fitted final_km model\n",
        "# Measures how closely an individual aligns with their cluster's profile\n",
        "distances = final_km.transform(X_scaled)\n",
        "X_ready_for_regression['Distance_to_Centroid'] = [distances[i, label] for i, label in enumerate(final_labels)]\n",
        "\n",
        "# 3. Map numeric labels to the professional names we identified earlier\n",
        "cluster_mapping = {\n",
        "    0: 'Cluster_Mental_Cognitive_Issue',\n",
        "    1: 'Cluster_Mainstream_Healthy',\n",
        "    2: 'Cluster_Extreme_Risk'\n",
        "}\n",
        "cluster_names_series = pd.Series(final_labels).map(cluster_mapping)\n",
        "\n",
        "# 4. Perform One-Hot Encoding on the clusters\n",
        "# This creates 3 binary columns (0 or 1) for the regression model\n",
        "cluster_dummies = pd.get_dummies(cluster_names_series, dtype=float)\n",
        "X_ready_for_regression = pd.concat([X_ready_for_regression, cluster_dummies], axis=1)\n",
        "\n",
        "# 5. Rename long interaction column names for clarity and model compatibility\n",
        "X_ready_for_regression.rename(columns={\n",
        "    'had_depressive_disorder_yes difficulty_concentrating_yes': 'depressive_x_concentration',\n",
        "    'had_depressive_disorder_yes general_health_poor': 'depressive_x_poor_health',\n",
        "    'had_depressive_disorder_yes sleep_hours': 'depressive_x_sleep',\n",
        "    'difficulty_concentrating_yes general_health_poor': 'concentration_x_poor_health',\n",
        "    'difficulty_concentrating_yes sleep_hours': 'concentration_x_sleep',\n",
        "    'general_health_poor sleep_hours': 'poor_health_x_sleep'\n",
        "}, inplace=True)\n",
        "\n",
        "# 6. Final Audit of the dataset\n",
        "print(\"--- FINAL DATASET STRUCTURE ---\")\n",
        "print(f\"Total Rows: {X_ready_for_regression.shape[0]}\")\n",
        "print(f\"Total Features: {X_ready_for_regression.shape[1]}\")\n",
        "print(\"\\nNew Columns Created:\")\n",
        "for col in list(cluster_dummies.columns) + ['Distance_to_Centroid']:\n",
        "    print(f\" - {col}\")\n",
        "\n",
        "print(\"\\n X_ready_for_regression is now fully built and ready.\")"
      ],
      "metadata": {
        "id": "1gS0gI-a9iCK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6665b8bc-176f-402a-82ab-40b3898f17bd"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- FINAL DATASET STRUCTURE ---\n",
            "Total Rows: 302472\n",
            "Total Features: 44\n",
            "\n",
            "New Columns Created:\n",
            " - Cluster_Extreme_Risk\n",
            " - Cluster_Mainstream_Healthy\n",
            " - Cluster_Mental_Cognitive_Issue\n",
            " - Distance_to_Centroid\n",
            "\n",
            " X_ready_for_regression is now fully built and ready.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Cluster 1: The Healthy Mainstream (The Pink Mass)**\n",
        "*   **Profile:** Represents the majority of the population.\n",
        "*   **Characteristics:** All values are remarkably close to the global average (e.g., Depression: -0.12 SD).\n",
        "*   **PCA Role:** This is the dense \"anchor\" of the dataset, centered at the 0,0 axis.\n",
        "\n",
        "#### **Cluster 0: Mental & Cognitive Struggle (The Turquoise Shift)**\n",
        "*   **Profile:** Individuals facing specific psychological and cognitive burdens.\n",
        "*   **Characteristics:** Significant spikes in Concentration Issues (+3.06 SD) and Depression (+2.06 SD).\n",
        "*   **The Multiplier Effect:** The engineered interaction of `Depression x Concentration` shows a massive 6.28 SD, highlighting the heavy cumulative weight of these combined conditions.\n",
        "\n",
        "#### **Cluster 2: Extreme Multi-morbidity Risk (The Purple Outliers)**\n",
        "*   **Profile:** High-risk individuals suffering from a \"perfect storm\" of physical and mental illness.\n",
        "*   **Characteristics:** The interaction between `Poor General Health x Concentration Issues` reaches a staggering +18.70 SD, while `Poor Health x Depression` stands at +7.31 SD.\n",
        "*   **Lifestyle:** This group reports significantly lower physical activity (-0.87 SD) and poor sleep hygiene.\n",
        "*   **PCA Role:** These are the distant points on the far right, isolated from the rest of the population due to their extreme health profiles."
      ],
      "metadata": {
        "id": "GY2bKELpKfkU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*note: The clusters were added as three additional columns of features in the data set*"
      ],
      "metadata": {
        "id": "j23QlI5VX1g1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "v-haxeXGLYkN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 5: Train and Evaluate Three Improved Models**\n",
        "\n",
        "\n",
        "In this phase, we evaluated multiple modeling approaches to find the optimal solution for our prediction task. We re-trained the initial Linear Regression model using the newly engineered 44-feature dataset to isolate the direct impact of our data processing. Simultaneously, we trained Random Forest and Gradient Boosting models to capture the non-linear complexities identified during the exploratory phase."
      ],
      "metadata": {
        "id": "pykMu4YaXHEx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train & test split"
      ],
      "metadata": {
        "id": "-_xIsIujB8gH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# DATA SPLITTING (TRAIN/TEST SPLIT)\n",
        "# =============================================================================\n",
        "\n",
        "# Define the target variable (y) and features (X)\n",
        "# Make sure 'poor_mental_health_days' is the exact name of your target column\n",
        "y = y_log\n",
        "X = X_ready_for_regression\n",
        "\n",
        "# Split the data: 80% for training the models, 20% for final evaluation\n",
        "# We use SEED=42 to ensure results are reproducible every time you run this\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=SEED\n",
        ")\n",
        "\n",
        "print(f\"Data splitting complete!\")\n",
        "print(f\"Training set size: {X_train.shape[0]} rows\")\n",
        "print(f\"Testing set size: {X_test.shape[0]} rows\")"
      ],
      "metadata": {
        "id": "59VDTUuWXETD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26ebab7b-c98a-45d0-82ee-287d90b0c4f8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data splitting complete!\n",
            "Training set size: 241977 rows\n",
            "Testing set size: 60495 rows\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training & Evaluation"
      ],
      "metadata": {
        "id": "bWy8nTGUiWFj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Define initial baseline metrics for comparative analysis\n",
        "# These values represent the model's performance prior to feature engineering\n",
        "baseline_metrics = {\n",
        "    \"Model Name\": \"Initial Baseline (Linear)\",\n",
        "    \"MAE (Days)\": 3.6770,\n",
        "    \"RMSE (Days)\": 5.8506,\n",
        "    \"R2 Score\": 0.3157\n",
        "}\n",
        "\n",
        "# Initialize Scikit-Learn models\n",
        "# 1. Linear Regression: Retrained on the engineered dataset\n",
        "# 2. Random Forest: Ensemble bagging method for non-linear patterns\n",
        "# 3. Gradient Boosting: Sequential boosting method for error minimization\n",
        "models = {\n",
        "    \"Linear Regression (Engineered)\": LinearRegression(),\n",
        "    \"Random Forest (SKlearn)\": RandomForestRegressor(n_estimators=100, random_state=SEED),\n",
        "    \"Gradient Boosting (SKlearn)\": GradientBoostingRegressor(n_estimators=100, random_state=SEED)\n",
        "}\n",
        "\n",
        "# List to store evaluation results, starting with the baseline data\n",
        "performance_results = [baseline_metrics]\n",
        "\n",
        "# Execution loop for training and performance evaluation\n",
        "for name, model in models.items():\n",
        "    # Fit the model to the training data\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Generate predictions on the test set (Logarithmic scale)\n",
        "    y_pred_log = model.predict(X_test)\n",
        "\n",
        "    # Inverse log transformation: Convert predictions back to original units (Days)\n",
        "    # Formula: Days = exp(y) - 1\n",
        "    y_pred_days = np.expm1(y_pred_log)\n",
        "    y_test_days = np.expm1(y_test)\n",
        "\n",
        "    # Calculate key regression metrics on the original scale\n",
        "    mae = mean_absolute_error(y_test_days, y_pred_days)\n",
        "    rmse = np.sqrt(mean_squared_error(y_test_days, y_pred_days))\n",
        "    r2 = r2_score(y_test_days, y_pred_days)\n",
        "\n",
        "    # Append metrics to the results list\n",
        "    performance_results.append({\n",
        "        \"Model Name\": name,\n",
        "        \"MAE (Days)\": round(mae, 4),\n",
        "        \"RMSE (Days)\": round(rmse, 4),\n",
        "        \"R2 Score\": round(r2, 4)\n",
        "    })\n",
        "\n",
        "# Compile results into a structured DataFrame for final comparison\n",
        "comparison_df = pd.DataFrame(performance_results)\n",
        "\n",
        "# Display the performance summary table\n",
        "print(\"--- Performance Benchmarking: Engineered Models vs. Baseline ---\")\n",
        "display(comparison_df)"
      ],
      "metadata": {
        "id": "vCa3GR-6ENmf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "d6c2a779-b8d2-47a7-8c58-12275b8b8c4c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Performance Benchmarking: Engineered Models vs. Baseline ---\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                       Model Name  MAE (Days)  RMSE (Days)  R2 Score\n",
              "0       Initial Baseline (Linear)      3.6770       5.8506    0.3157\n",
              "1  Linear Regression (Engineered)      3.0812       6.2635    0.2157\n",
              "2         Random Forest (SKlearn)      3.1311       6.2497    0.2192\n",
              "3     Gradient Boosting (SKlearn)      3.0391       6.2161    0.2275"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "comparison_df",
              "summary": "{\n  \"name\": \"comparison_df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Model Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Linear Regression (Engineered)\",\n          \"Gradient Boosting (SKlearn)\",\n          \"Initial Baseline (Linear)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE (Days)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2989742575317592,\n        \"min\": 3.0391,\n        \"max\": 3.677,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.0812,\n          3.0391,\n          3.677\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE (Days)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19725694537835659,\n        \"min\": 5.8506,\n        \"max\": 6.2635,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6.2635,\n          6.2161,\n          5.8506\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.047707328228131424,\n        \"min\": 0.2157,\n        \"max\": 0.3157,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2157,\n          0.2275,\n          0.3157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analyzing the Results: The Trade-off**\n",
        "\n",
        "\n",
        "At first glance, the decrease in R² and the slight increase in RMSE compared to the initial baseline might seem counterintuitive. However, this is a common outcome when dealing with Log-Transformed targets and highly subjective survey data:\n",
        "\n",
        "* **MAE Improvement:** Our primary achievement is the significant reduction in MAE (from 3.67 to 3.03 days). This means that on average, our predictions are now much closer to the real experience of the majority of respondents.\n",
        "* **The Impact of Log Transformation:** By applying a log transformation, the model was trained to minimize errors in the log-scale, which effectively de-emphasized the extreme outliers in the 30-day \"Right Tail.\" While this reduces the R² (which is sensitive to total variance explained), it creates a model that is far more accurate for the \"typical\" respondent.\n",
        "* **Human Subjectivity:** Predicting \"feelings\" in increments of days is inherently noisy. A model that achieves a lower MAE is more reliable for real-world application than one that chases a higher R² by overfitting to extreme outliers."
      ],
      "metadata": {
        "id": "rk9q3SHIMvbN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "# Define the destination filename for the regression model\n",
        "# The .pkl extension is the standard for serialized Python objects\n",
        "reg_output_file = 'regression_model.pkl'\n",
        "\n",
        "try:\n",
        "    # Access the optimized Gradient Boosting model from the models dictionary\n",
        "    # This object includes the trained weights and the learned patterns from the CDC dataset\n",
        "    final_model_r = models[\"Gradient Boosting (SKlearn)\"]\n",
        "\n",
        "    # Serialize and save the model to the local directory\n",
        "    # Using 'wb' (Write Binary) mode as required for pickle objects\n",
        "    with open(reg_output_file, 'wb') as file:\n",
        "        pickle.dump(final_model_r, file)\n",
        "\n",
        "    print(f\"SUCCESS: Model exported to {reg_output_file}\")\n",
        "\n",
        "except KeyError:\n",
        "    # Error handling if the dictionary key is missing or the dictionary was overwritten\n",
        "    print(\"ERROR: Key 'Gradient Boosting (SKlearn)' not found. Please re-run the training cell.\")\n",
        "except Exception as e:\n",
        "    # Generic error handling for I/O operations\n",
        "    print(f\"ERROR: An unexpected error occurred during export: {e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkNjLNvwIa5O",
        "outputId": "89b0c33d-8666-4ed3-8b1b-6c0120258269"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SUCCESS: Model exported to regression_model.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Renaming"
      ],
      "metadata": {
        "id": "aDCJenjnvdIq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a mapping dictionary for specific technical names if needed,\n",
        "# or use a general cleaning function\n",
        "def clean_column_names(df):\n",
        "    new_columns = []\n",
        "    for col in df.columns:\n",
        "        # Remove technical suffixes and clean underscores\n",
        "        clean_name = col.replace('_yes', '').replace('_', ' ').title()\n",
        "        # Handle specific cases like interaction terms\n",
        "        clean_name = clean_name.replace(' X ', ' x ')\n",
        "        new_columns.append(clean_name)\n",
        "    return new_columns\n",
        "\n",
        "# Apply to our feature set\n",
        "X_train.columns = clean_column_names(X_train)\n",
        "X_test.columns = clean_column_names(X_test)\n",
        "\n",
        "print(\"Column names cleaned successfully!\")"
      ],
      "metadata": {
        "id": "mnXnUONfuoVx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6dab5176-e018-4e81-8eec-37703e0d611f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column names cleaned successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualize feature importance:**\n",
        "\n",
        "To understand what drives our model's decisions, we visualized the Feature Importance of the winning Gradient Boosting model."
      ],
      "metadata": {
        "id": "RU6HkqD4vhKQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "# Re-extracting importance since feature names in X_train changed\n",
        "importances = models[\"Gradient Boosting (SKlearn)\"].feature_importances_\n",
        "importance_df = pd.DataFrame({\n",
        "    'Feature': X_train.columns,\n",
        "    'Importance': importances\n",
        "}).sort_values(by='Importance', ascending=True).tail(15)\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "for index, row in importance_df.iterrows():\n",
        "    fig.add_shape(\n",
        "        type='line', x0=0, y0=row['Feature'], x1=row['Importance'], y1=row['Feature'],\n",
        "        line=dict(color='#0ABDC6', width=2)\n",
        "    )\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=importance_df['Importance'], y=importance_df['Feature'],\n",
        "    mode='markers', marker=dict(color='#EA00D9', size=10)\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title='<b>Key Predictors of poor Mental Health Days</b>',\n",
        "    template='plotly_white', width=600, height=450,\n",
        "    margin=dict(l=10, r=20, t=50, b=40),\n",
        "    xaxis=dict(title='Importance Score', showgrid=True, gridcolor='LightGray'),\n",
        "    yaxis=dict(showgrid=False), showlegend=False\n",
        ")\n",
        "\n",
        "\n",
        "if hasattr(fig, 'frames') and fig.frames:\n",
        "    fig.update(data=fig.frames[-1].data)\n",
        "\n",
        "\n",
        "    if hasattr(fig.layout, 'sliders') and fig.layout.sliders:\n",
        "        fig.layout.sliders[0].active = len(fig.frames) - 1\n",
        "\n",
        "fig.show(config={\n",
        "    'toImageButtonOptions': {\n",
        "        'format': 'png',\n",
        "        'filename': 'key_predictors',\n",
        "        'scale': 1.5\n",
        "    }\n",
        "})"
      ],
      "metadata": {
        "id": "ihgyzC7gur3y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "3459a801-135c-4499-93c2-61a4fccc5058"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   **Primary Predictors:** A formal diagnosis of `Depressive Disorder` and the respondent's `Age Category` emerged as the most significant indicators of mental distress days.\n",
        "*   **Top Predictors:** Other influential factors include `physical health` status and `difficulty concentrating`.\n",
        "*   **Engineering Impact:** Notably, our engineered Cluster labels and Interaction terms (e.g., Depressive x Concentration) appear among the top predictors, confirming that the additional context provided by our data engineering phase successfully assisted the model in navigating the data's complexity."
      ],
      "metadata": {
        "id": "kbxVQoHXvZLk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "zLwc_7M8KxTd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 6: Winning Model"
      ],
      "metadata": {
        "id": "PS9t6m0_nJ9t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The Winner: Gradient Boosting Regressor**\n",
        "\n",
        "The Gradient Boosting model was selected as the final model for this project. It demonstrated the best ability to learn from its own errors iteratively, resulting in the lowest MAE across all tests. It successfully balanced the impact of clinical history with subtle lifestyle factors and the unsupervised health profiles we identified during clustering."
      ],
      "metadata": {
        "id": "W5zNBbQ2wP7P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "eE6gqfn3K67V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 7: Regression-to-Classification"
      ],
      "metadata": {
        "id": "-e5pgBLLoN81"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Distribution & Strategy Rationale**\n",
        "\n",
        "Before training our classifiers, we analyzed the target variable's distribution to determine the most effective split strategy.\n",
        "\n",
        "**Statistical Findings:**\n",
        "*   **Mathematical Median:** 0.0\n",
        "*   **Samples with exactly 0 days:** 150,850 (62.34%)\n",
        "*   **Samples with 1 or more days:** 91,127 (37.66%)"
      ],
      "metadata": {
        "id": "JuDVUFYbLrAr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =============================================================================\n",
        "# DATA DISTRIBUTION ANALYSIS\n",
        "# =============================================================================\n",
        "\n",
        "# Calculate exact metrics from the original target\n",
        "median_val = y_train.median()\n",
        "zero_count = (y_train == 0).sum()\n",
        "zero_ratio = (y_train == 0).mean() * 100\n",
        "\n",
        "print(f\"--- Statistical Findings ---\")\n",
        "print(f\"Mathematical Median: {median_val}\")\n",
        "print(f\"Samples with exactly 0 days: {zero_count} ({zero_ratio:.2f}%)\")\n",
        "print(f\"Samples with 1 or more days: {len(y_train) - zero_count} ({100 - zero_ratio:.2f}%)\")"
      ],
      "metadata": {
        "id": "W7ZL2i2V_l2B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f857d633-58c7-4e7a-e176-04aa7353b393"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Statistical Findings ---\n",
            "Mathematical Median: 0.0\n",
            "Samples with exactly 0 days: 150850 (62.34%)\n",
            "Samples with 1 or more days: 91127 (37.66%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While a Median Split is often preferred to avoid outlier influence, our Probability Density Profile shows that over 62% of participants reported 0 days of poor mental health. This places the median within the 'Zero Group,' making a standard median split mathematically non-functional (as both sides of the split would fall into the same category). We therefore implemented a Business Rule Threshold: Class 0 (Healthy/No Impact) vs. Class 1 (Impacted/1+ days). This choice provides results in a relatively balanced distribution (62/38), which is optimal for training our classifiers.\""
      ],
      "metadata": {
        "id": "iWdb8rw3AC1e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Check Class Balance**\n"
      ],
      "metadata": {
        "id": "L8Grz1xfqLfH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "\n",
        "# =============================================================================\n",
        "# TARGET RE-LABELING & CATEGORIZATION\n",
        "# =============================================================================\n",
        "\n",
        "# Define binary classification labels (0: Healthy, 1: Impacted)\n",
        "y_train_class = (y_train > 0).astype(int)\n",
        "y_test_class = (y_test > 0).astype(int)\n",
        "\n",
        "# Map impacted ratio based on previously defined zero_ratio scalar\n",
        "impacted_ratio = 100 - zero_ratio\n",
        "\n",
        "# =============================================================================\n",
        "# ANIMATED WELL-BEING DISTRIBUTION (COMPACT VERSION)\n",
        "# =============================================================================\n",
        "\n",
        "# 1. Defining descriptors based on Well-being levels\n",
        "class_labels = ['High Well-being (0 Days)', 'Coping / Challenges (1+ Days)']\n",
        "final_ratios = [zero_ratio, impacted_ratio]\n",
        "class_colors = ['#0ABDC6', '#EA00D9']\n",
        "\n",
        "# 2. Frame generation for the vertical growth effect\n",
        "num_frames = 25\n",
        "frames = []\n",
        "\n",
        "for i in range(1, num_frames + 1):\n",
        "    current_ratio = i / num_frames\n",
        "    frames.append(go.Frame(\n",
        "        data=[\n",
        "            go.Bar(\n",
        "                x=class_labels,\n",
        "                y=[val * current_ratio for val in final_ratios],\n",
        "                marker_color=class_colors\n",
        "            )\n",
        "        ],\n",
        "        name=f\"frame_{i}\"\n",
        "    ))\n",
        "\n",
        "# 3. Figure initialization with optimized spacing and terminology\n",
        "fig = go.Figure(\n",
        "    data=[\n",
        "        go.Bar(\n",
        "            x=class_labels,\n",
        "            y=[0, 0],\n",
        "            marker_color=class_colors,\n",
        "            text=[f\"<b>{zero_ratio:.1f}%</b>\", f\"<b>{impacted_ratio:.1f}%</b>\"],\n",
        "            textposition='auto',\n",
        "            width=0.4\n",
        "        )\n",
        "    ],\n",
        "    layout=go.Layout(\n",
        "        title=dict(\n",
        "            text=\"<b>Mental Well-being Distribution</b><br><sup>Categorization by Reported Coping Level</sup>\",\n",
        "            x=0.5,\n",
        "            y=0.95,\n",
        "            font=dict(size=16)\n",
        "        ),\n",
        "        xaxis=dict(title=\"Mental Well-being Status\", tickfont=dict(size=12)),\n",
        "        yaxis=dict(title=\"Percentage of Respondents (%)\", range=[0, 100]),\n",
        "        template=\"plotly_white\",\n",
        "        updatemenus=[dict(\n",
        "            type=\"buttons\",\n",
        "            showactive=False,\n",
        "            x=0.0, y=1.25,\n",
        "            buttons=[dict(\n",
        "                label=\"Play Animation\",\n",
        "                method=\"animate\",\n",
        "                args=[None, {\n",
        "                    \"frame\": {\"duration\": 40, \"redraw\": False},\n",
        "                    \"fromcurrent\": True,\n",
        "                    \"transition\": {\"duration\": 60, \"easing\": \"quadratic-in-out\"}\n",
        "                }]\n",
        "            )]\n",
        "        )],\n",
        "        width=600,\n",
        "        height=450,\n",
        "        margin=dict(l=50, r=50, t=130, b=50)\n",
        "    ),\n",
        "    frames=frames\n",
        ")\n",
        "\n",
        "# Refine aesthetics for final output\n",
        "fig.update_traces(marker_line_color='white', marker_line_width=1.5, opacity=0.9)\n",
        "\n",
        "\n",
        "if hasattr(fig, 'frames') and fig.frames:\n",
        "    fig.update(data=fig.frames[-1].data)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "ffoT68TGqOIY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "5d75ca7d-fa36-4cf7-9c18-722ba642325b"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The resulting distribution shows that 62.3% of the participants are categorized as having High Well-being (0 days), while 37.7% fall into the Coping / Challenges (1+ days) group. Although the classes are not extremely skewed, the 'Coping' group is under-represented.\n",
        "\n",
        "Due to this imbalance, Accuracy will not be our primary evaluation metric. A naive model could achieve 62.3% accuracy by simply predicting 'High Well-being' for all instances, failing to identify those experiencing challenges. Therefore, our evaluation will prioritize the F1-Score and Recall for the 'Coping' class. This ensures the model is sensitive enough to capture individuals facing mental health difficulties, where a 'False Negative' (missing someone in need) is considered more critical than a 'False Positive'."
      ],
      "metadata": {
        "id": "DYNZlmljHLNn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "5dRNW0HvMTET"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 8: Train & Eval Classification Models\n",
        "\n"
      ],
      "metadata": {
        "id": "Ii0otL-qqHOt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 8.1 Answer the following here, and later mention it in your presentation.\n",
        "\n"
      ],
      "metadata": {
        "id": "co-38v1UsPd2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the context of your dataset/task, explain what would be more importatnt - <u>precision or recall.</u>\n",
        "\n"
      ],
      "metadata": {
        "id": "1XM_xVUGsfon"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Recall is more important for this task.\n",
        "\n",
        " Reason: Recall focuses on Completeness—meaning, out of all actual positives (people who are truly struggling), how many did the model find? In a mental well-being context, our priority is to \"find them all.\"\n",
        "\n",
        "Why not Precision? Precision focuses on Accuracy of the flags—meaning, of those we labeled as \"Coping,\" how many were actually positive? While high precision is nice, focusing on it would make the model too \"cautious,\" potentially ignoring individuals who show subtle signs of distress just to avoid a \"False Positive\" (False Alarm)."
      ],
      "metadata": {
        "id": "I84WGe5TQKvx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the context of your dataset/task, explain what would be more critical - <u>False Positive or False Negative.</u>\n"
      ],
      "metadata": {
        "id": "xbrqOTcBsf2Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "More Critical: False Negative (FN).\n",
        "\n",
        "Reason: As stated above, a False Negative represents a missed opportunity for support. In the context of public health and well-being, failing to identify a symptomatic individual (Type II Error) can lead to worsening conditions and a lack of necessary intervention. Therefore, minimizing False Negatives is the priority of this project."
      ],
      "metadata": {
        "id": "jMDNbIKDL8ai"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train three different kinds of classification models."
      ],
      "metadata": {
        "id": "8nZOjcjZsocr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training"
      ],
      "metadata": {
        "id": "KHLWNCjFNcio"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "\n",
        "# =============================================================================\n",
        "# TRAINING THREE DIFFERENT CLASSIFICATION MODELS\n",
        "# =============================================================================\n",
        "\n",
        "# Initializing models with a fixed random_state for reproducibility\n",
        "models = {\n",
        "    \"Logistic Regression\": LogisticRegression(max_iter=1000, random_state=42),\n",
        "    \"Random Forest\": RandomForestClassifier(n_estimators=100, random_state=42),\n",
        "    \"Gradient Boosting\": GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
        "}\n",
        "\n",
        "# Dictionary to store predictions for the evaluation stage\n",
        "predictions = {}\n",
        "\n",
        "for name, model in models.items():\n",
        "    # Fit the model to the training data\n",
        "    model.fit(X_train, y_train_class)\n",
        "\n",
        "    # Generate predictions on the test set\n",
        "    y_pred = model.predict(X_test)\n",
        "    predictions[name] = y_pred\n",
        "\n",
        "print(\"Training complete. Predictions are stored in the 'predictions' dictionary.\")"
      ],
      "metadata": {
        "id": "6MIie1Rws3pE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e32c6db5-40b5-4dd6-ef16-24b9c7d74c43"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training complete. Predictions are stored in the 'predictions' dictionary.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "# 1. Access the specific Random Forest model from the classification models dictionary\n",
        "# Note: Since the training loop used .fit(), this object is already trained.\n",
        "classification_winner = models[\"Random Forest\"]\n",
        "\n",
        "# 2. Destination filename for the classification task\n",
        "clf_output_file = 'classification_model.pkl'\n",
        "\n",
        "try:\n",
        "    # 3. Serialize and save the model using 'wb' (write-binary) mode\n",
        "    with open(clf_output_file, 'wb') as file:\n",
        "        pickle.dump(classification_winner, file)\n",
        "\n",
        "    print(f\"SUCCESS: Random Forest classifier exported to {clf_output_file}\")\n",
        "except Exception as e:\n",
        "    print(f\"ERROR: Failed to export classification model: {e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u49-QqGXSNWp",
        "outputId": "9c8b63ba-dd42-4e71-d970-bfa10ac572c4"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SUCCESS: Random Forest classifier exported to classification_model.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Evaluation"
      ],
      "metadata": {
        "id": "wELPknwqsOG_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from matplotlib.colors import LinearSegmentedColormap\n",
        "\n",
        "# =============================================================================\n",
        "# ROBUST EVALUATION - NO CLIPPING, NO OVERFLOW\n",
        "# =============================================================================\n",
        "\n",
        "# Creating the brand-consistent colormap (#EA00D9)\n",
        "brand_pink_cmap = LinearSegmentedColormap.from_list(\"brand_pink\", [\"#FDEBFA\", \"#EA00D9\"])\n",
        "\n",
        "for name, y_pred in predictions.items():\n",
        "    print(f\"\\n\" + \"=\"*60)\n",
        "    print(f\" PERFORMANCE ANALYSIS: {name}\")\n",
        "    print(\"=\"*60)\n",
        "\n",
        "    # 1. Print metrics report\n",
        "    print(classification_report(y_test_class, y_pred, target_names=['High Well-being', 'Coping']))\n",
        "\n",
        "    # 2. Confusion Matrix Calculation\n",
        "    cm = confusion_matrix(y_test_class, y_pred)\n",
        "\n",
        "    # Technical labels for the matrix\n",
        "    labels = np.array([\n",
        "        [f\"TN\\n({cm[0,0]})\", f\"FP\\n({cm[0,1]})\"],\n",
        "        [f\"FN\\n({cm[1,0]})\", f\"TP\\n({cm[1,1]})\"]\n",
        "    ])\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(4, 4))\n",
        "\n",
        "    im = ax.imshow(cm, interpolation='nearest', cmap=brand_pink_cmap, extent=[-0.5, 1.5, 1.5, -0.5])\n",
        "\n",
        "    for i in range(2):\n",
        "        for j in range(2):\n",
        "            ax.text(j, i, labels[i, j], ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > cm.max()/2 else \"black\",\n",
        "                    fontweight='bold', fontsize=9)\n",
        "\n",
        "    ax.set_xticks([0, 1])\n",
        "    ax.set_yticks([0, 1])\n",
        "    ax.set_xticklabels(['Pred 0', 'Pred 1'], fontsize=9)\n",
        "    ax.set_yticklabels(['Actual 0', 'Actual 1'], fontsize=9)\n",
        "\n",
        "    ax.set_title(f\"Confusion Matrix: {name}\", fontsize=10, fontweight='bold', pad=15)\n",
        "    ax.grid(False)\n",
        "\n",
        "    plt.tight_layout()\n",
        "\n",
        "    filename = f\"cm_{name.replace(' ', '_')}.png\"\n",
        "\n",
        "    plt.savefig(filename,\n",
        "            dpi=300,\n",
        "            bbox_inches='tight',\n",
        "            transparent=False)\n",
        "\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "Rozrn2petFKA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "be9f6f27-48b4-4db9-ab28-5cadc23a88e0"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================\n",
            " PERFORMANCE ANALYSIS: Logistic Regression\n",
            "============================================================\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "High Well-being       0.75      0.88      0.81     37774\n",
            "         Coping       0.72      0.52      0.60     22721\n",
            "\n",
            "       accuracy                           0.74     60495\n",
            "      macro avg       0.74      0.70      0.71     60495\n",
            "   weighted avg       0.74      0.74      0.73     60495\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 384,
              "height": 377
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================\n",
            " PERFORMANCE ANALYSIS: Random Forest\n",
            "============================================================\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "High Well-being       0.75      0.81      0.78     37774\n",
            "         Coping       0.64      0.56      0.59     22721\n",
            "\n",
            "       accuracy                           0.71     60495\n",
            "      macro avg       0.69      0.68      0.69     60495\n",
            "   weighted avg       0.71      0.71      0.71     60495\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 384,
              "height": 377
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================\n",
            " PERFORMANCE ANALYSIS: Gradient Boosting\n",
            "============================================================\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "High Well-being       0.76      0.88      0.81     37774\n",
            "         Coping       0.73      0.53      0.61     22721\n",
            "\n",
            "       accuracy                           0.75     60495\n",
            "      macro avg       0.74      0.71      0.71     60495\n",
            "   weighted avg       0.75      0.75      0.74     60495\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 384,
              "height": 377
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Comparative Analysis of Results**\n",
        "\n",
        "The evaluation shows distinct trade-offs between the three models based on the prioritized metrics:\n",
        "\n",
        "*   **Random Forest:** This model achieved the highest Recall (0.56) and identified the largest number of individuals in distress (TP: 12,676). Crucially, it produced the lowest number of False Negatives (10,045), making it the most sensitive model in our testing. While its overall accuracy is slightly lower than the others, its ability to capture the target class is superior.\n",
        "*   **Gradient Boosting:** While this model demonstrated the highest Accuracy (0.75) and Precision (0.73), it proved to be overly cautious. It missed approximately 600 more individuals in distress (FN: 10,630) compared to the Random Forest, which is a significant gap in a welfare context.\n",
        "*   **Logistic Regression:** Functioning as our baseline, this model showed the weakest performance in identifying individuals in need, yielding the lowest Recall (0.52) and the highest number of False Negatives (10,945)."
      ],
      "metadata": {
        "id": "DOrsog9PNqg7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "I2nC--rCOTTI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "An additional plot to illustrate the differences in results"
      ],
      "metadata": {
        "id": "OH8QOk66OSfg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "# =============================================================================\n",
        "# EXTRA WORK: FINAL REFINED 3D PERFORMANCE LANDSCAPE\n",
        "# =============================================================================\n",
        "\n",
        "# Data from your results\n",
        "models = ['Logistic Regression', 'Gradient Boosting', 'Random Forest']\n",
        "recall_vals = [0.52, 0.53, 0.56]\n",
        "precision_vals = [0.72, 0.73, 0.63]\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "def add_clean_pillar(x_pos, y_pos, z_val, color, name):\n",
        "    fig.add_trace(go.Mesh3d(\n",
        "        x=[x_pos-0.15, x_pos-0.15, x_pos+0.15, x_pos+0.15, x_pos-0.15, x_pos-0.15, x_pos+0.15, x_pos+0.15],\n",
        "        y=[y_pos-0.15, y_pos+0.15, y_pos+0.15, y_pos-0.15, y_pos-0.15, y_pos+0.15, y_pos+0.15, y_pos-0.15],\n",
        "        z=[0.4, 0.4, 0.4, 0.4, z_val, z_val, z_val, z_val],\n",
        "        alphahull=0,\n",
        "        color=color,\n",
        "        opacity=1.0,\n",
        "        lighting=dict(ambient=0.6, diffuse=0.8, specular=1, roughness=0.2),\n",
        "        hoverinfo='name',\n",
        "        name=f\"{name}: {z_val}\"\n",
        "    ))\n",
        "\n",
        "# 1. Precision (Placed at Y=0, in the back)\n",
        "for i, val in enumerate(precision_vals):\n",
        "    add_clean_pillar(i, 0, val, '#00CED1', 'Precision')\n",
        "\n",
        "# 2. Recall (Placed at Y=1, in the front)\n",
        "for i, val in enumerate(recall_vals):\n",
        "    add_clean_pillar(i, 1, val, '#EA00D9', 'Recall')\n",
        "\n",
        "# Final Layout Optimization\n",
        "fig.update_layout(\n",
        "    title={'text': \"Strategic Performance Landscape\", 'y':0.98, 'x':0.5, 'xanchor': 'center', 'yanchor': 'top'},\n",
        "    margin=dict(l=0, r=0, b=0, t=30), # Tight margins to remove white space\n",
        "    scene=dict(\n",
        "        xaxis=dict(\n",
        "            tickvals=[0, 1, 2], ticktext=models,\n",
        "            title='',\n",
        "            tickfont=dict(size=10, color='black'),\n",
        "            showgrid=True, gridcolor=\"lightgrey\",\n",
        "            # Moves the axis labels further from the pillars to prevent cutting\n",
        "            tickangle=15\n",
        "        ),\n",
        "        yaxis=dict(\n",
        "            tickvals=[0, 1], ticktext=['Precision', 'Recall'],\n",
        "            title='',\n",
        "            tickfont=dict(size=10, color='black')\n",
        "        ),\n",
        "        zaxis=dict(\n",
        "            title='Score',\n",
        "            range=[0.4, 0.8],\n",
        "            tickfont=dict(size=9)\n",
        "        ),\n",
        "        # Camera angle specifically set to put Recall (Magenta) in the front\n",
        "        camera=dict(\n",
        "            eye=dict(x=2.2, y=1.5, z=0.8),\n",
        "            projection=dict(type=\"perspective\")\n",
        "        ),\n",
        "        aspectmode='manual',\n",
        "        aspectratio=dict(x=1.8, y=1.2, z=1.2) # Adjusted ratio for better label spacing\n",
        "    ),\n",
        "    showlegend=False\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "7jIdv8oiPPHx",
        "outputId": "79bd2ec7-3133-4cd5-8349-aa74d0293472"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The Winner: Random Forest Classifier**\n",
        "\n",
        "The Random Forest model was selected as the final champion for this project. In the context of mental health and screening, Recall is the primary priority. We prioritize minimizing False Negatives (missing people in need) over maintaining high precision. Random Forest provides the most effective safety net by ensuring the highest percentage of people facing challenges are detected and placed on the radar of support systems."
      ],
      "metadata": {
        "id": "0rOVxwVGOa3a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "3QiM3LLOOzDm"
      }
    }
  ]
}