random_forest_gridsearch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('Preprocessing_pipeline/final_data_sets/train_set_smote.csv')\n",
    "df2 = pd.read_csv('Preprocessing_pipeline/final_data_sets/test_set.csv')\n",
    "\n",
    "df = pd.concat([df1, df2], ignore_index=True)\n",
    "df.drop(['Sex', 'Q8'], axis=1, inplace=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the data set:  (771, 22)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of the data set: \", df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. One-hot encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the variables for one-hot encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 771 entries, 0 to 770\n",
      "Data columns (total 22 columns):\n",
      " #   Column               Non-Null Count  Dtype\n",
      "---  ------               --------------  -----\n",
      " 0   Age                  771 non-null    int64\n",
      " 1   Relationship Status  771 non-null    int64\n",
      " 2   Occupation           771 non-null    int64\n",
      " 3   Social Media User?   771 non-null    int64\n",
      " 4   Time Spent           771 non-null    int64\n",
      " 5   Q1                   771 non-null    int64\n",
      " 6   Q2                   771 non-null    int64\n",
      " 7   Q3                   771 non-null    int64\n",
      " 8   Q4                   771 non-null    int64\n",
      " 9   Q5                   771 non-null    int64\n",
      " 10  Q6                   771 non-null    int64\n",
      " 11  Q7                   771 non-null    int64\n",
      " 12  Facebook             771 non-null    int64\n",
      " 13  Instagram            771 non-null    int64\n",
      " 14  YouTube              771 non-null    int64\n",
      " 15  Snapchat             771 non-null    int64\n",
      " 16  Twitter              771 non-null    int64\n",
      " 17  Discord              771 non-null    int64\n",
      " 18  Reddit               771 non-null    int64\n",
      " 19  Pinterest            771 non-null    int64\n",
      " 20  TikTok               771 non-null    int64\n",
      " 21  Difficulty_level     771 non-null    int64\n",
      "dtypes: int64(22)\n",
      "memory usage: 132.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Relationship Status', 'Occupation', 'Time Spent', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7']\n"
     ]
    }
   ],
   "source": [
    "# Initialize the list of categorical variables\n",
    "cat_variables = []\n",
    "\n",
    "# Loop through the dataframe columns (ignoring the first one)\n",
    "for col in df.columns[1:]:\n",
    "    # Check if the column is of object type (categorical) and has more than 2 unique values\n",
    "    if col == 'Difficulty_level':\n",
    "        continue\n",
    "    elif df[col].nunique() > 2:\n",
    "        cat_variables.append(col)\n",
    "\n",
    "print(cat_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply pandas pd.get_dummies function to one-hot encode\n",
    "\n",
    "- data: data frame(data)\n",
    "- prefix: A list of prefixes(cat_variables)\n",
    "- columns: A list of columns that will be one hot encoded(cat_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shape of the dataset now\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns after one-hot encoding before encode 'platforms used?':  61\n"
     ]
    }
   ],
   "source": [
    "# This will replace the columns with the one-hot encoded ones and keep the columns outside 'columns' argument as it is.\n",
    "features = pd.get_dummies(data = df, prefix=cat_variables, columns=cat_variables)\n",
    "\n",
    "features.head()\n",
    "\n",
    "# How many coluns now\n",
    "print(\"Number of columns after one-hot encoding before encode 'platforms used?': \", len(features.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Age  Social Media User?  Facebook  Instagram  YouTube  Snapchat  Twitter  Discord  Reddit  Pinterest  TikTok  Difficulty_level  Relationship Status_0  Relationship Status_1  Relationship Status_2  Relationship Status_3  Occupation_0  Occupation_1  Occupation_2  Occupation_3  Time Spent_0  Time Spent_1  Time Spent_2  Time Spent_3  Time Spent_4  Time Spent_5   Q1_1   Q1_2   Q1_3   Q1_4   Q1_5   Q2_1   Q2_2   Q2_3   Q2_4   Q2_5   Q3_1   Q3_2   Q3_3   Q3_4   Q3_5   Q4_1   Q4_2   Q4_3   Q4_4   Q4_5   Q5_1   Q5_2   Q5_3   Q5_4   Q5_5   Q6_1   Q6_2   Q6_3   Q6_4   Q6_5   Q7_1   Q7_2   Q7_3   Q7_4   Q7_5\n",
       "0   24                   1         1          1        1         0        1        0       0          0       0                 0                  False                   True                  False                  False         False         False          True         False         False         False          True         False         False         False  False  False   True  False  False  False  False   True  False  False  False   True  False  False  False  False   True  False  False  False  False  False  False   True  False  False  False   True  False  False  False   True  False  False  False\n",
       "1   46                   1         1          0        1         0        0        0       0          0       0                 0                  False                  False                   True                  False         False         False          True         False          True         False         False         False         False         False  False   True  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False\n",
       "2   30                   1         1          1        0         0        0        0       0          0       0                 1                  False                  False                   True                  False         False         False          True         False         False         False         False         False          True         False  False   True  False  False  False  False  False  False   True  False  False  False  False   True  False  False   True  False  False  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False\n",
       "3   56                   1         0          0        1         0        0        0       0          0       0                 0                  False                  False                   True                  False         False         False         False          True         False          True         False         False         False         False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False  False  False   True  False  False  False   True  False  False  False   True  False  False  False  False\n",
       "4   19                   1         1          1        1         0        0        0       0          0       1                 1                  False                   True                  False                  False         False          True         False         False         False         False         False         False          True         False  False  False   True  False  False  False  False   True  False  False  False  False  False   True  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False  False   True  False"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Every feature True or False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v6/jvlsn67j71g102qybf_hsvxr0000gn/T/ipykernel_32571/3132694753.py:10: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  features[feature] = features[feature].replace({1: True, 0: False})\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Social Media User?</th>\n",
       "      <th>Facebook</th>\n",
       "      <th>Instagram</th>\n",
       "      <th>YouTube</th>\n",
       "      <th>Snapchat</th>\n",
       "      <th>Twitter</th>\n",
       "      <th>Discord</th>\n",
       "      <th>Reddit</th>\n",
       "      <th>Pinterest</th>\n",
       "      <th>TikTok</th>\n",
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       "      <th>Relationship Status_0</th>\n",
       "      <th>Relationship Status_1</th>\n",
       "      <th>Relationship Status_2</th>\n",
       "      <th>Relationship Status_3</th>\n",
       "      <th>Occupation_0</th>\n",
       "      <th>Occupation_1</th>\n",
       "      <th>Occupation_2</th>\n",
       "      <th>Occupation_3</th>\n",
       "      <th>Time Spent_0</th>\n",
       "      <th>Time Spent_1</th>\n",
       "      <th>Time Spent_2</th>\n",
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       "      <th>Time Spent_4</th>\n",
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       "      <th>Q1_1</th>\n",
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       "      <th>Q1_3</th>\n",
       "      <th>Q1_4</th>\n",
       "      <th>Q1_5</th>\n",
       "      <th>Q2_1</th>\n",
       "      <th>Q2_2</th>\n",
       "      <th>Q2_3</th>\n",
       "      <th>Q2_4</th>\n",
       "      <th>Q2_5</th>\n",
       "      <th>Q3_1</th>\n",
       "      <th>Q3_2</th>\n",
       "      <th>Q3_3</th>\n",
       "      <th>Q3_4</th>\n",
       "      <th>Q3_5</th>\n",
       "      <th>Q4_1</th>\n",
       "      <th>Q4_2</th>\n",
       "      <th>Q4_3</th>\n",
       "      <th>Q4_4</th>\n",
       "      <th>Q4_5</th>\n",
       "      <th>Q5_1</th>\n",
       "      <th>Q5_2</th>\n",
       "      <th>Q5_3</th>\n",
       "      <th>Q5_4</th>\n",
       "      <th>Q5_5</th>\n",
       "      <th>Q6_1</th>\n",
       "      <th>Q6_2</th>\n",
       "      <th>Q6_3</th>\n",
       "      <th>Q6_4</th>\n",
       "      <th>Q6_5</th>\n",
       "      <th>Q7_1</th>\n",
       "      <th>Q7_2</th>\n",
       "      <th>Q7_3</th>\n",
       "      <th>Q7_4</th>\n",
       "      <th>Q7_5</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>24</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Social Media User?  Facebook  Instagram  YouTube  Snapchat  Twitter  Discord  Reddit  Pinterest  TikTok  Difficulty_level  Relationship Status_0  Relationship Status_1  Relationship Status_2  Relationship Status_3  Occupation_0  Occupation_1  Occupation_2  Occupation_3  Time Spent_0  Time Spent_1  Time Spent_2  Time Spent_3  Time Spent_4  Time Spent_5   Q1_1   Q1_2   Q1_3   Q1_4   Q1_5   Q2_1   Q2_2   Q2_3   Q2_4   Q2_5   Q3_1   Q3_2   Q3_3   Q3_4   Q3_5   Q4_1   Q4_2   Q4_3   Q4_4   Q4_5   Q5_1   Q5_2   Q5_3   Q5_4   Q5_5   Q6_1   Q6_2   Q6_3   Q6_4   Q6_5   Q7_1   Q7_2   Q7_3   Q7_4   Q7_5\n",
       "0   24                True      True       True     True     False     True    False   False      False   False                 0                  False                   True                  False                  False         False         False          True         False         False         False          True         False         False         False  False  False   True  False  False  False  False   True  False  False  False   True  False  False  False  False   True  False  False  False  False  False  False   True  False  False  False   True  False  False  False   True  False  False  False\n",
       "1   46                True      True      False     True     False    False    False   False      False   False                 0                  False                  False                   True                  False         False         False          True         False          True         False         False         False         False         False  False   True  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False\n",
       "2   30                True      True       True    False     False    False    False   False      False   False                 1                  False                  False                   True                  False         False         False          True         False         False         False         False         False          True         False  False   True  False  False  False  False  False  False   True  False  False  False  False   True  False  False   True  False  False  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False\n",
       "3   56                True     False      False     True     False    False    False   False      False   False                 0                  False                  False                   True                  False         False         False         False          True         False          True         False         False         False         False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False  False  False   True  False  False  False   True  False  False  False   True  False  False  False  False\n",
       "4   19                True      True       True     True     False    False    False   False      False    True                 1                  False                   True                  False                  False         False          True         False         False         False         False         False         False          True         False  False  False   True  False  False  False  False   True  False  False  False  False  False   True  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False  False   True  False"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of social media features to be converted\n",
    "binary_features = [\n",
    "    'Social Media User?', 'Facebook', 'Instagram', 'YouTube',\n",
    "    'Snapchat', 'Twitter', 'Discord', 'Reddit', 'Pinterest', 'TikTok'\n",
    "]\n",
    "\n",
    "# Convert 'Yes' to True and 'No' to False only if the column exists in the dataframe\n",
    "for feature in binary_features:\n",
    "    if feature in features.columns:\n",
    "        features[feature] = features[feature].replace({1: True, 0: False})\n",
    "\n",
    "# Display the first few rows of the dataframe\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing target from the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(771, 60)\n"
     ]
    }
   ],
   "source": [
    "features.drop('Difficulty_level', axis=1, inplace=True)\n",
    "print(features.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table to clarify every feature is a binary variable other than \"Age\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature Index</th>\n",
       "      <th>Feature</th>\n",
       "      <th>Categories</th>\n",
       "      <th>Number of Categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Age</td>\n",
       "      <td>24, 46, 30, 56, 19, 21, 47, 23, 20, 25, 26, 65, 52, 35, 22, 44, 17, 60, 34, 48, 27, 36, 29, 32, 18, 42, 31, 50, 37, 69, 28, 14, 16, 43, 38, 40, 91, 51, 49, 15, 55, 13, 33, 53, 54, 59, 58, 74, 90, 83, 45</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Social Media User?</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>YouTube</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Snapchat</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Twitter</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Discord</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Reddit</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Pinterest</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>TikTok</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Relationship Status_0</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Relationship Status_1</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Relationship Status_2</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Relationship Status_3</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Occupation_0</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Occupation_1</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Occupation_2</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Occupation_3</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Time Spent_0</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Time Spent_1</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Time Spent_2</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Time Spent_3</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>Time Spent_4</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Time Spent_5</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Q1_1</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Q1_2</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>Q1_3</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>Q1_4</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>Q1_5</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>Q2_1</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>Q2_2</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>Q2_3</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>Q2_4</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>Q2_5</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>Q3_1</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>Q3_2</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>Q3_3</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>Q3_4</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>Q3_5</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>Q4_1</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>Q4_2</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>Q4_3</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>Q4_4</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>Q4_5</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>Q5_1</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>Q5_2</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>Q5_3</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>Q5_4</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>Q5_5</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>Q6_1</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>Q6_2</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>Q6_3</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>Q6_4</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>Q6_5</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>Q7_1</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>56</td>\n",
       "      <td>Q7_2</td>\n",
       "      <td>True, False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57</td>\n",
       "      <td>Q7_3</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>Q7_4</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>Q7_5</td>\n",
       "      <td>False, True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature Index                Feature                                                                                                                                                                                                  Categories  Number of Categories\n",
       "0               0                    Age  24, 46, 30, 56, 19, 21, 47, 23, 20, 25, 26, 65, 52, 35, 22, 44, 17, 60, 34, 48, 27, 36, 29, 32, 18, 42, 31, 50, 37, 69, 28, 14, 16, 43, 38, 40, 91, 51, 49, 15, 55, 13, 33, 53, 54, 59, 58, 74, 90, 83, 45                    51\n",
       "1               1     Social Media User?                                                                                                                                                                                                 True, False                     2\n",
       "2               2               Facebook                                                                                                                                                                                                 True, False                     2\n",
       "3               3              Instagram                                                                                                                                                                                                 True, False                     2\n",
       "4               4                YouTube                                                                                                                                                                                                 True, False                     2\n",
       "5               5               Snapchat                                                                                                                                                                                                 False, True                     2\n",
       "6               6                Twitter                                                                                                                                                                                                 True, False                     2\n",
       "7               7                Discord                                                                                                                                                                                                 False, True                     2\n",
       "8               8                 Reddit                                                                                                                                                                                                 False, True                     2\n",
       "9               9              Pinterest                                                                                                                                                                                                 False, True                     2\n",
       "10             10                 TikTok                                                                                                                                                                                                 False, True                     2\n",
       "11             11  Relationship Status_0                                                                                                                                                                                                 False, True                     2\n",
       "12             12  Relationship Status_1                                                                                                                                                                                                 True, False                     2\n",
       "13             13  Relationship Status_2                                                                                                                                                                                                 False, True                     2\n",
       "14             14  Relationship Status_3                                                                                                                                                                                                 False, True                     2\n",
       "15             15           Occupation_0                                                                                                                                                                                                 False, True                     2\n",
       "16             16           Occupation_1                                                                                                                                                                                                 False, True                     2\n",
       "17             17           Occupation_2                                                                                                                                                                                                 True, False                     2\n",
       "18             18           Occupation_3                                                                                                                                                                                                 False, True                     2\n",
       "19             19           Time Spent_0                                                                                                                                                                                                 False, True                     2\n",
       "20             20           Time Spent_1                                                                                                                                                                                                 False, True                     2\n",
       "21             21           Time Spent_2                                                                                                                                                                                                 True, False                     2\n",
       "22             22           Time Spent_3                                                                                                                                                                                                 False, True                     2\n",
       "23             23           Time Spent_4                                                                                                                                                                                                 False, True                     2\n",
       "24             24           Time Spent_5                                                                                                                                                                                                 False, True                     2\n",
       "25             25                   Q1_1                                                                                                                                                                                                 False, True                     2\n",
       "26             26                   Q1_2                                                                                                                                                                                                 False, True                     2\n",
       "27             27                   Q1_3                                                                                                                                                                                                 True, False                     2\n",
       "28             28                   Q1_4                                                                                                                                                                                                 False, True                     2\n",
       "29             29                   Q1_5                                                                                                                                                                                                 False, True                     2\n",
       "30             30                   Q2_1                                                                                                                                                                                                 False, True                     2\n",
       "31             31                   Q2_2                                                                                                                                                                                                 False, True                     2\n",
       "32             32                   Q2_3                                                                                                                                                                                                 True, False                     2\n",
       "33             33                   Q2_4                                                                                                                                                                                                 False, True                     2\n",
       "34             34                   Q2_5                                                                                                                                                                                                 False, True                     2\n",
       "35             35                   Q3_1                                                                                                                                                                                                 False, True                     2\n",
       "36             36                   Q3_2                                                                                                                                                                                                 True, False                     2\n",
       "37             37                   Q3_3                                                                                                                                                                                                 False, True                     2\n",
       "38             38                   Q3_4                                                                                                                                                                                                 False, True                     2\n",
       "39             39                   Q3_5                                                                                                                                                                                                 False, True                     2\n",
       "40             40                   Q4_1                                                                                                                                                                                                 False, True                     2\n",
       "41             41                   Q4_2                                                                                                                                                                                                 True, False                     2\n",
       "42             42                   Q4_3                                                                                                                                                                                                 False, True                     2\n",
       "43             43                   Q4_4                                                                                                                                                                                                 False, True                     2\n",
       "44             44                   Q4_5                                                                                                                                                                                                 False, True                     2\n",
       "45             45                   Q5_1                                                                                                                                                                                                 False, True                     2\n",
       "46             46                   Q5_2                                                                                                                                                                                                 False, True                     2\n",
       "47             47                   Q5_3                                                                                                                                                                                                 False, True                     2\n",
       "48             48                   Q5_4                                                                                                                                                                                                 True, False                     2\n",
       "49             49                   Q5_5                                                                                                                                                                                                 False, True                     2\n",
       "50             50                   Q6_1                                                                                                                                                                                                 False, True                     2\n",
       "51             51                   Q6_2                                                                                                                                                                                                 False, True                     2\n",
       "52             52                   Q6_3                                                                                                                                                                                                 True, False                     2\n",
       "53             53                   Q6_4                                                                                                                                                                                                 False, True                     2\n",
       "54             54                   Q6_5                                                                                                                                                                                                 False, True                     2\n",
       "55             55                   Q7_1                                                                                                                                                                                                 False, True                     2\n",
       "56             56                   Q7_2                                                                                                                                                                                                 True, False                     2\n",
       "57             57                   Q7_3                                                                                                                                                                                                 False, True                     2\n",
       "58             58                   Q7_4                                                                                                                                                                                                 False, True                     2\n",
       "59             59                   Q7_5                                                                                                                                                                                                 False, True                     2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize lists to hold the table data\n",
    "feature_index_new = []\n",
    "feature_name_new = []\n",
    "categories_new = []\n",
    "num_categories_new = []\n",
    "\n",
    "# Iterate over each column to extract the required information\n",
    "for idx, col in enumerate(features.columns):\n",
    "    \n",
    "    unique_categories = features[col].unique()\n",
    "    feature_index_new.append(idx)\n",
    "    feature_name_new.append(col)\n",
    "    # if col == 'Platforms Used':\n",
    "    #     categories_new.append(\", \".join(map(str, social_media_platforms)))\n",
    "    # else:\n",
    "    categories_new.append(\", \".join(map(str, unique_categories)))\n",
    "    # if col == 'Platforms Used':\n",
    "    #     num_categories_new.append(len(social_media_platforms))\n",
    "    #else:\n",
    "    num_categories_new.append(len(unique_categories))\n",
    "\n",
    "# Create the resulting DataFrame\n",
    "table_for_num_of_categories_new = pd.DataFrame({\n",
    "    'Feature Index': feature_index_new,\n",
    "    'Feature': feature_name_new,\n",
    "    'Categories': categories_new,\n",
    "    'Number of Categories': num_categories_new\n",
    "})\n",
    "\n",
    "# Display the DataFrame using the display function\n",
    "from IPython.display import display\n",
    "\n",
    "# Set the maximum width of the display to make it horizontally scrollable\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "# Set the maximum column width to display all data in each cell\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "\n",
    "# Display the DataFrame\n",
    "display(table_for_num_of_categories_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of features after one-hot encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns after one-hot encoded:  60\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Social Media User?</th>\n",
       "      <th>Facebook</th>\n",
       "      <th>Instagram</th>\n",
       "      <th>YouTube</th>\n",
       "      <th>Snapchat</th>\n",
       "      <th>Twitter</th>\n",
       "      <th>Discord</th>\n",
       "      <th>Reddit</th>\n",
       "      <th>Pinterest</th>\n",
       "      <th>TikTok</th>\n",
       "      <th>Relationship Status_0</th>\n",
       "      <th>Relationship Status_1</th>\n",
       "      <th>Relationship Status_2</th>\n",
       "      <th>Relationship Status_3</th>\n",
       "      <th>Occupation_0</th>\n",
       "      <th>Occupation_1</th>\n",
       "      <th>Occupation_2</th>\n",
       "      <th>Occupation_3</th>\n",
       "      <th>Time Spent_0</th>\n",
       "      <th>Time Spent_1</th>\n",
       "      <th>Time Spent_2</th>\n",
       "      <th>Time Spent_3</th>\n",
       "      <th>Time Spent_4</th>\n",
       "      <th>Time Spent_5</th>\n",
       "      <th>Q1_1</th>\n",
       "      <th>Q1_2</th>\n",
       "      <th>Q1_3</th>\n",
       "      <th>Q1_4</th>\n",
       "      <th>Q1_5</th>\n",
       "      <th>Q2_1</th>\n",
       "      <th>Q2_2</th>\n",
       "      <th>Q2_3</th>\n",
       "      <th>Q2_4</th>\n",
       "      <th>Q2_5</th>\n",
       "      <th>Q3_1</th>\n",
       "      <th>Q3_2</th>\n",
       "      <th>Q3_3</th>\n",
       "      <th>Q3_4</th>\n",
       "      <th>Q3_5</th>\n",
       "      <th>Q4_1</th>\n",
       "      <th>Q4_2</th>\n",
       "      <th>Q4_3</th>\n",
       "      <th>Q4_4</th>\n",
       "      <th>Q4_5</th>\n",
       "      <th>Q5_1</th>\n",
       "      <th>Q5_2</th>\n",
       "      <th>Q5_3</th>\n",
       "      <th>Q5_4</th>\n",
       "      <th>Q5_5</th>\n",
       "      <th>Q6_1</th>\n",
       "      <th>Q6_2</th>\n",
       "      <th>Q6_3</th>\n",
       "      <th>Q6_4</th>\n",
       "      <th>Q6_5</th>\n",
       "      <th>Q7_1</th>\n",
       "      <th>Q7_2</th>\n",
       "      <th>Q7_3</th>\n",
       "      <th>Q7_4</th>\n",
       "      <th>Q7_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Social Media User?  Facebook  Instagram  YouTube  Snapchat  Twitter  Discord  Reddit  Pinterest  TikTok  Relationship Status_0  Relationship Status_1  Relationship Status_2  Relationship Status_3  Occupation_0  Occupation_1  Occupation_2  Occupation_3  Time Spent_0  Time Spent_1  Time Spent_2  Time Spent_3  Time Spent_4  Time Spent_5   Q1_1   Q1_2   Q1_3   Q1_4   Q1_5   Q2_1   Q2_2   Q2_3   Q2_4   Q2_5   Q3_1   Q3_2   Q3_3   Q3_4   Q3_5   Q4_1   Q4_2   Q4_3   Q4_4   Q4_5   Q5_1   Q5_2   Q5_3   Q5_4   Q5_5   Q6_1   Q6_2   Q6_3   Q6_4   Q6_5   Q7_1   Q7_2   Q7_3   Q7_4   Q7_5\n",
       "0   24                True      True       True     True     False     True    False   False      False   False                  False                   True                  False                  False         False         False          True         False         False         False          True         False         False         False  False  False   True  False  False  False  False   True  False  False  False   True  False  False  False  False   True  False  False  False  False  False  False   True  False  False  False   True  False  False  False   True  False  False  False\n",
       "1   46                True      True      False     True     False    False    False   False      False   False                  False                  False                   True                  False         False         False          True         False          True         False         False         False         False         False  False   True  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False\n",
       "2   30                True      True       True    False     False    False    False   False      False   False                  False                  False                   True                  False         False         False          True         False         False         False         False         False          True         False  False   True  False  False  False  False  False  False   True  False  False  False  False   True  False  False   True  False  False  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False\n",
       "3   56                True     False      False     True     False    False    False   False      False   False                  False                  False                   True                  False         False         False         False          True         False          True         False         False         False         False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False  False  False   True  False  False  False   True  False  False  False   True  False  False  False  False\n",
       "4   19                True      True       True     True     False    False    False   False      False    True                  False                   True                  False                  False         False          True         False         False         False         False         False         False          True         False  False  False   True  False  False  False  False   True  False  False  False  False  False   True  False  False  False   True  False  False  False  False   True  False  False  False  False   True  False  False  False  False  False   True  False"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numer_of_features_after_one_hot_encoded = len(features.columns)\n",
    "\n",
    "print(\"Number of columns after one-hot encoded: \", numer_of_features_after_one_hot_encoded)\n",
    "\n",
    "# Feature\n",
    "features.head() \n",
    "\n",
    "# For check the data frame still preserved\n",
    "#data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Making train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE = 55\n",
    "\n",
    "X_train = features.iloc[0: df1.shape[0]]\n",
    "X_test = features.iloc[df1.shape[0]: df.shape[0]].reset_index(drop=True)\n",
    "y_train = df['Difficulty_level'].iloc[0: df1.shape[0]]\n",
    "y_test = df['Difficulty_level'].iloc[df1.shape[0]: df.shape[0]].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train samples: 675\n",
      "validation samples: 96\n"
     ]
    }
   ],
   "source": [
    "# printing number of samples in each set\n",
    "print(f'train samples: {len(X_train)}')\n",
    "print(f'validation samples: {len(X_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Building the Models\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for plot graphs\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_graphs(accuracy_list_train, accuracy_list_test, hyperparameter_list, title, xlabel, ylabel):\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    plt.plot(hyperparameter_list, accuracy_list_train, marker='o', label='On training set')\n",
    "    plt.plot(hyperparameter_list, accuracy_list_test, marker='o', label='On test set')\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xticks(ticks=hyperparameter_list)\n",
    "\n",
    "    # Find the maximum accuracy and corresponding min_samples_split value for both curves\n",
    "    max_accuracy_train = max(accuracy_list_train)\n",
    "    max_accuracy_train_index = accuracy_list_train.index(max_accuracy_train)\n",
    "    max_accuracy_train_value = hyperparameter_list[max_accuracy_train_index]\n",
    "\n",
    "    max_accuracy_test = max(accuracy_list_test)\n",
    "    max_accuracy_test_index = accuracy_list_test.index(max_accuracy_test)\n",
    "    max_accuracy_test_value = hyperparameter_list[max_accuracy_test_index]\n",
    "\n",
    "    # Highlight and annotate the highest points on the training set curve\n",
    "    plt.scatter(max_accuracy_train_value, max_accuracy_train, color='blue')\n",
    "    plt.annotate(f'{max_accuracy_train_value}, {max_accuracy_train:.2f}', \n",
    "                xy=(max_accuracy_train_value, max_accuracy_train), \n",
    "                xytext=(max_accuracy_train_value, max_accuracy_train + 0.03),\n",
    "                ha='center', color='blue',\n",
    "                arrowprops=dict(facecolor='blue', shrink=0.05))\n",
    "\n",
    "    # Highlight and annotate the highest points on the test set curve\n",
    "    plt.scatter(max_accuracy_test_value, max_accuracy_test, color='orange', zorder=5)\n",
    "    plt.annotate(f'{max_accuracy_test_value}, {max_accuracy_test:.2f}', \n",
    "                xy=(max_accuracy_test_value, max_accuracy_test), \n",
    "                xytext=(max_accuracy_test_value, max_accuracy_test + 0.03),\n",
    "                ha='center', color='orange',\n",
    "                arrowprops=dict(facecolor='orange', shrink=0.05))\n",
    "\n",
    "    # Rotate x-tick labels for better readability\n",
    "    plt.xticks(rotation=45)\n",
    "\n",
    "    # Show the legend and plot\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    return max_accuracy_test_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters of random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of hyperparameters in RandomForestClassifier:  19\n",
      "bootstrap\n",
      "ccp_alpha\n",
      "class_weight\n",
      "criterion\n",
      "max_depth\n",
      "max_features\n",
      "max_leaf_nodes\n",
      "max_samples\n",
      "min_impurity_decrease\n",
      "min_samples_leaf\n",
      "min_samples_split\n",
      "min_weight_fraction_leaf\n",
      "monotonic_cst\n",
      "n_estimators\n",
      "n_jobs\n",
      "oob_score\n",
      "random_state\n",
      "verbose\n",
      "warm_start\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "print(\"Number of hyperparameters in RandomForestClassifier: \", len(RandomForestClassifier().get_params()))\n",
    "for item in (RandomForestClassifier().get_params()):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyper parameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. bootstrap: If True, bootstrap samples are used when building trees. If False, the entire dataset is used to build each tree.\n",
    "\n",
    "Reasonable values: True (default) or False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. ccp_alpha: Complexity parameter used for Minimal Cost-Complexity Pruning. This parameter controls the trade-off between the complexity of the tree and its ability to generalize to unseen data.\n",
    "\n",
    "Reasonable values: 0.0 to 0.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best ccp_alpha value:  0.1\n"
     ]
    }
   ],
   "source": [
    "ccp_alpha_list = [0.10, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.00]\n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for ccp_alpha in ccp_alpha_list:\n",
    "    model = RandomForestClassifier(ccp_alpha = ccp_alpha,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train,) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "ccp_alpha = plot_graphs(accuracy_list_train, accuracy_list_test, ccp_alpha_list[:], 'Train & Test metrics', 'ccp_alpha', 'accuracy', )\n",
    "print(\"Best ccp_alpha value: \", ccp_alpha)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. class_weight: Weights associated with classes. It can be used to handle class imbalance.\n",
    "\n",
    "Reasonable values: None (default), balanced, or a dictionary specifying weights for each class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. criterion: The function to measure the quality of a split. Supported criteria are \"gini\" for the Gini impurity and \"entropy\" for the information gain.\n",
    "\n",
    "Reasonable values: gini (default) or entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. max_depth: The maximum depth of the tree. If None, nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.\n",
    "\n",
    "Reasonable values: None (default) or an integer between 5 and 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best max_depth value:  15\n"
     ]
    }
   ],
   "source": [
    "max_depth_list = [i for i in range(2, 101)] \n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for max_depth in max_depth_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(max_depth = max_depth,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "max_depth = plot_graphs(accuracy_list_train, accuracy_list_test, max_depth_list, 'Train & Test metrics', 'max_depth', 'accuracy', )\n",
    "print(\"Best max_depth value: \", max_depth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. max_features: The number of features to consider when looking for the best split.\n",
    "\n",
    "Reasonable values: auto (default), sqrt, log2, or an integer between 1 and 65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best max_features value:  32\n"
     ]
    }
   ],
   "source": [
    "max_features_list = [i for i in range(1, 65)] \n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for max_features in max_features_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(max_features = max_features,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "max_features = plot_graphs(accuracy_list_train, accuracy_list_test, max_features_list, 'Train & Test metrics', 'max_features', 'accuracy', )\n",
    "print(\"Best max_features value: \", max_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. max_leaf_nodes: Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity.\n",
    "\n",
    "Reasonable values: None (default) or an integer between 10 and 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best max_leaf_nodes value:  23\n"
     ]
    }
   ],
   "source": [
    "max_leaf_nodes_list = [i for i in range(10, 101)] \n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for max_leaf_nodes in max_leaf_nodes_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(max_leaf_nodes = max_leaf_nodes,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "max_leaf_nodes = plot_graphs(accuracy_list_train, accuracy_list_test, max_leaf_nodes_list, 'Train & Test metrics', 'max_leaf_nodes', 'accuracy', )\n",
    "print(\"Best max_leaf_nodes value: \", max_leaf_nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. max_samples: The number of samples to draw from X to train each base estimator. If None, then draw X.shape[0] samples.\n",
    "\n",
    "Reasonable values: None (default) or a float between 0.1 and 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best max_samples value:  1.0\n"
     ]
    }
   ],
   "source": [
    "max_samples_list = [i for i in np.arange(0.1, 1.1, 0.1)] \n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for max_samples in max_samples_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(max_samples = max_samples,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "max_samples = plot_graphs(accuracy_list_train, accuracy_list_test, max_samples_list, 'Train & Test metrics', 'max_samples', 'accuracy', )\n",
    "print(\"Best max_samples value: \", max_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. min_impurity_decrease: A node will be split if this split induces a decrease of the impurity greater than or equal to this value.\n",
    "\n",
    "Reasonable values: 0.0 (default) to 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best min_impurity_decrease value:  0.1\n"
     ]
    }
   ],
   "source": [
    "min_impurity_decrease_list = [i for i in np.arange(0.00, 0.11, 0.01)] \n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for min_impurity_decrease in min_impurity_decrease_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(min_impurity_decrease = min_impurity_decrease,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "min_impurity_decrease = plot_graphs(accuracy_list_train, accuracy_list_test, min_impurity_decrease_list, 'Train & Test metrics', 'min_impurity_decrease', 'accuracy', )\n",
    "print(\"Best min_impurity_decrease value: \", min_impurity_decrease)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. min_samples_leaf: The minimum number of samples required to be at a leaf node.\n",
    "\n",
    "Reasonable values: 1 (default) to 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best min_samples_leaf value:  1\n"
     ]
    }
   ],
   "source": [
    "min_samples_leaf_list = [i for i in range(1, 21)] \n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for min_samples_leaf in min_samples_leaf_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(min_samples_leaf = min_samples_leaf,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "min_samples_leaf = plot_graphs(accuracy_list_train, accuracy_list_test, min_samples_leaf_list, 'Train & Test metrics', 'min_samples_leaf', 'accuracy', )\n",
    "print(\"Best min_samples_leaf value: \", min_samples_leaf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 11. min_samples_split: The minimum number of samples required to split an internal node.\n",
    "\n",
    "Reasonable values: 2 (default) to 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rb88NAGicTEZdDBXAbwzD0LJlyypUQ5akfv366ZRTTglAVAAAAACAukIi38Bs3rxZJ598sttz55xznv73v5V1GQ4AAAAAoI5R7K6BKSkp+eunryQdLvO6TsXFJR6vAwAAAAA0DqyRb7AsksruZctvJQAAAAA0BYzIAwAAAADQgJDIAwAAAADQgJDIAwAAAADQgLCw2g273a7du3erZcuWMplMgQ7HxcGDBz2es9lsbrelAwAAAADUb4Zh6MCBA+rYsaOCgryPuZPIu7F7927FxMQEOowqW7dunaxWa6DDAAAAAABU086dO3XCCSd4bUMi70bLli0lOT7AiIiIAEfjKiMjQwkJCW7P9evXT199tbyOIwIAAAAA1FRhYaFiYmJK81FvSOTdcE6nj4iIqHeJfHh4uMdzZrO53sULAAAAAPCdL8u7KXYHAAAAAEADQiIPAAAAAEADQiIPAAAAAEADwhp5AAAAACjDbrerpKQk0GGgkQkODpbZbPZLXyTyAAAAAPCXkpISZWVlyW63BzoUNEKtWrVSVFSUTwXtvCGRr8dycnL0/fffuxz7/fffPbb/8898LV261OVYWFiYhgwZoqAgVlEAAAAA3hiGoezsbJnNZsXExPBvaPiNYRg6fPiw9uzZI0mKjo6uUX8k8vXY1KlT9cYbb7g5EySpbbljHbR162+6+OKLK7T+9ttvPe49DwAAAMDh2LFjOnz4sDp27KjmzZsHOhw0MmFhYZKkPXv2qEOHDjWaZs9XTPXYdddd99dPr0nKKfPKl9S9XOvZ5drkyGQarC5dTtKAAQPqJmAAAACgAbPZbJKkkJCQAEeCxsr5BdHRo0dr1A+JfD12/vnn66yzBslsni+pg6TIv15WN61NZc5HSvpDhrFSDz883W8FFQAAAICmoKbrlwFP/PXfFol8PWYymfTIIzNls/0g6dMqXjtLXbqcpFGjRtVOcAAAAACAgCCRr+eOj8rPlGT4eNVaGcZSRuMBAAAA1DuxsbGaO3euz+1Xrlwpk8mk/fv311pMDQ2JfD1XnVF5RuMBAACAwLHZDaVl5unj9buUlpknm93XAbnq27lzp2644QZ17NhRISEh6ty5s+68807l5eXVuO/BgwfrrrvuqnmQf/n+++918803+9z+rLPOUnZ2tqxWd0uM6w9/f07eULW+ATj//POVkDBI6ekzZbONkGM9vCfO0fhFjMYDAAAAdSxlQ7ZmLc1QdkFR6bFoq0UzkuOV1LNmW455sm3bNiUkJOikk07Su+++q7i4OP3666+677779Nlnn+m7775TmzZtauXeToZhyGazqVmzylPM9u3bV6nvkJAQRUVFVTe0RokR+QbAZDLp0Ud9G5VnNB4AAAAIjJQN2Zq4aJ1LEi9JOQVFmrhonVI2ZNfKfSdNmqSQkBB98cUXOu+883TiiSfqwgsv1IoVK7Rr1y49+OCDpW1jY2P1+OOP64YbblDLli114okn6uWXX/bY97hx4/T111/r+eefl8lkkslk0vbt20unu3/22Wfq37+/QkNDtWrVKmVmZmrkyJGKjIxUeHi4zjjjDK1YscKlz/JT600mk/75z3/q0ksvVfPmzdW9e3d98sknpefLT61/44031KpVK33++efq0aOHwsPDlZSUpOzs45/vsWPH9Le//U2tWrVS27ZtNWXKFF1//fW65JJLPD7r77//ruTkZLVu3VotWrTQqaeeqk8/PZ5/bdiwQRdeeKHCw8MVGRmp6667Tvv27fP6OdUWEvkGwre18o7R+FmzHmI0HgAAAKghwzB0uOSYT68DRUc145Nf3f5L3Xls5icZOlB01Kf+DMO36fj5+fn6/PPPddttt5XuU+4UFRWla6+9VosXL3bp75lnntHpp5+uH3/8UbfddpsmTpyo3377zW3/zz//vBISEjRhwgRlZ2crOztbMTExpefvv/9+PfHEE9q4caNOO+00HTx4UCNGjFBqaqp+/PFHJSUlKTk5WTt27PD6HLNmzdJVV12ln3/+WSNGjNC1116r/Px8j+0PHz6sp59+Wm+99Za++eYb7dixQ/fee2/p+Tlz5ujtt9/W66+/rtWrV6uwsFAfffSR1xgmTZqk4uJiffPNN/rll180Z84chYeHS5L279+vIUOGqG/fvvrhhx+UkpKi3NxcXXXVVT59Tv7G1PoGwrlW/oILLpBjVP4iN21mKS6O0XgAAADAH44ctSl++ud+6cuQlFNYpF4zv/CpfcbDiWoeUnm6tmXLFhmGoR49erg936NHD/3555/au3evOnToIEkaMWKEbrvtNknSlClT9Nxzz+mrr77SySefXOF6q9WqkJAQNW/e3O309ocffljDhg0rfd+mTRv17t279P0jjzyiDz/8UJ988oluv/12j88xbtw4jR49WpL0+OOP64UXXlB6erqSkpLctj969KgWLlyorl27SpJuv/12Pfzww6XnX3zxRU2dOlWXXnqpJGnevHkuo+vu7NixQ5dffrl69eolSerSpUvpuXnz5qlv3756/PHHS4+99tpriomJ0ebNm3XSSSd5/Zz8jRH5BsS5Vt79qPzx0Xhf1qUAAAAAaDx8HcGXpNNOO630Z5PJpKioKO3Zs6da9z399NNd3h88eFD33nuvevTooVatWik8PFwbN26sdES+bEwtWrRQRESE15iaN29emsRLUnR0dGn7goIC5ebmasCAAaXnzWaz+vfv7zWGv/3tb3r00Ud19tlna8aMGfr5559Lz/3000/66quvFB4eXvo65ZRTJEmZmZle+60NZHwNiHOtvLtReUbjAQAAAP8KCzYr4+FEn9qmZ+Vr3OvfV9rujfFnaEBc5YXnwoJ9WyrbrVs3mUwmbdy4sXT0uayNGzeqdevWLgXmgoODXdqYTCbZ7Xaf7ldeixYtXN7fe++9Wr58uZ5++ml169ZNYWFhuuKKK1RSUuK1n6rG5K59Vb7McOemm25SYmKili1bpi+++EKzZ8/WM888ozvuuEMHDx5UcnKy5syZU+G66OjaKWLoDSPyDYz7UXlG4wEAAAB/M5lMah7SzKfXoO7tFW21eNxfyiRH9fpB3dv71J/J5G2nquPatm2rYcOGaf78+Tpy5IjLuZycHL399tu6+uqrfe7PnZCQENlsNp/arl69WuPGjdOll16qXr16KSoqqlaLvrljtVoVGRmp778//sWKzWbTunXrKr02JiZGt956q5YsWaJ77rlHr7zyiiSpX79++vXXXxUbG6tu3bq5vJxfZlTlc6opEvkGxl0FeyrVAwAAAIFlDjJpRnK8pIqbRTvfz0iOlzmo+gm1J/PmzVNxcbESExP1zTffaOfOnUpJSdGwYcPUqVMnPfbYYzXqPzY2VmvWrNH27du1b98+ryPl3bt315IlS7R+/Xr99NNPuuaaa6o92l8Td9xxh2bPnq2PP/5Yv/32m+688079+eefXr/QuOuuu/T5558rKytL69at01dffVVae2DSpEnKz8/X6NGj9f333yszM1Off/65xo8fX5q8V+VzqikS+Qbo/PPP15lnDlJQ0ExJP8gwluqhh6ZVGI232Q2lZebp4/W7lJaZJ5u9ZlNNAAAAAHiW1DNaC8b0U5TV4nI8ymrRgjH9am0f+e7du+uHH35Qly5ddNVVV6lr1666+eabdf755ystLa3Ge8jfe++9MpvNio+PV/v27b2ud3/22WfVunVrnXXWWUpOTlZiYqL69etXo/tXx5QpUzR69GiNHTtWCQkJCg8PV2JioiwWi8drbDabJk2apB49eigpKUknnXSS5s+fL0nq2LGjVq9eLZvNpuHDh6tXr16666671KpVKwUFOdLqqnxONWUyarqQoAa++eYbPfXUU1q7dq2ys7P14Ycfet3XT3LsITh58mT9+uuviomJ0bRp0zRu3DiXNi+99JKeeuop5eTkqHfv3nrxxRddCh1UprCwUFarVQUFBYqIiKjGk9Wumf/M1oJlb2vPR/dJilUz6zFFX/uqbujbSzNvcvzhkLIhW7OWZrjsYRlttWhGcnyt/QECAAAANGRFRUXKyspSXFyc14SvMja7ofSsfO05UKQOLS0aENemVkbi4Tu73a4ePXroqquu0iOPPBKwOLz9N1aVPDSgI/KHDh1S79699dJLL/nUPisrSxdddJHOP/98rV+/XnfddZduuukmff758S0hFi9erMmTJ2vGjBlat26devfurcTExGpXYaxvZv4zW69vWafQ7qcotFNPSdtlHXS1TOFH9fqWdZr5z2ylbMjWxEXrXJJ4ScopKNLEReuUsiE7MMEDAAAATYA5yKSErm01sk8nJXRtSxIfAL///rteeeUVbd68Wb/88osmTpyorKwsXXPNNYEOzS8COiJflslkqnREfsqUKVq2bJk2bNhQemzUqFHav3+/UlJSJEkDBw7UGWecoXnz5klyfPMSExOjO+64Q/fff79PsdTXEfmSo4a6T/5Sal4kk0kqyd2mAz99rjZDb5YpyCzDkHQ4VNHRJuUUFrntwyTH1J5VU4bwBwoAAABQhr9G5BF4O3fu1KhRo7RhwwYZhqGePXvqiSee0LnnnhvQuPw1It+gSpynpaVp6NChLscSExN11113SZJKSkq0du1aTZ06tfR8UFCQhg4dqrS0NI/9FhcXq7i4uPR9YWGhfwP3k1c/yZepxfEEPSSyi9oOn1j63mSS1KJYOV7CNyRlFxQpPStfCV3b1l6wAAAAABAgMTExWr16daDDqDUNqthdTk6OIiMjXY5FRkaqsLBQR44c0b59+2Sz2dy2ycnJ8djv7NmzZbVaS18xMTG1En9Nbc91P8peHXsO+K8vAAAAAEDdaVCJfG2ZOnWqCgoKSl87d+4MdEhuxUb6b3pPh5aOvqhsDwAAAAANS4OaWh8VFaXc3FyXY7m5uYqIiFBYWJjMZrPMZrPbNlFRUR77DQ0NVWhoaK3E7E83XtxGT6y0lK6RL6/sGvncwiJ5SsnNJinYbKKyPQAAAAA0QA1qRD4hIUGpqakux5YvX66EhARJUkhIiPr37+/Sxm63KzU1tbRNQxYSbNL43vGS/kray3C+H9/7VM282NHGUyk7myFd9Y803UplewAAAABocAKayB88eFDr16/X+vXrJTm2l1u/fr127NghyTHlfezYsaXtb731Vm3btk1///vftWnTJs2fP1/vv/++7r777tI2kydP1iuvvKI333xTGzdu1MSJE3Xo0CGNHz++Tp+ttsy8KVrju/eTDpebZn/YovHd+2nmTdFK6hmtBWP6Kcrq2ibaatGzV/XW/50WLU8z6J2HZy3NYJo9AAAAANRDAZ1a/8MPP+j8888vfT958mRJ0vXXX6833nhD2dnZpUm9JMXFxWnZsmW6++679fzzz+uEE07QP//5TyUmJpa2ufrqq7V3715Nnz5dOTk56tOnj1JSUioUwGvIZt4UrQeORunVT/K1PbdIsZEW3XhxG4UEHx+DT+oZrWHxUUrPyteeA0Xq0NKiAXFtZA4yKdpq0X9/9jziTmV7AAAAAKi/6s0+8vVJfd1H3l8+Xr9Ld763vtJ2z4/qo5F9OtV+QAAAAEA9wD7yqG3+2ke+Qa2Rh384K9b7qx0AAACAMuw2Ket/0i8fOH6122r9ljt37tQNN9ygjh07KiQkRJ07d9add96pvLy8Gvc9ePBg3XXXXTUPsoxx48bpkksu8Wuf5W3fvl0mk6l0KXdj0qCq1sM/BsS1UbTVopwC95XtTZKirI6p+AAAAACqIOMTKWWKVLj7+LGIjlLSHCn+4lq55bZt25SQkKCTTjpJ7777ruLi4vTrr7/qvvvu02effabvvvtObdrwb/vGhBH5JsgcZNKMZM+V7Q1JM5LjZQ7yVPceAAAAQAUZn0jvj3VN4iWpMNtxPOOTWrntpEmTFBISoi+++ELnnXeeTjzxRF144YVasWKFdu3apQcffLC0bWxsrB5//HHdcMMNatmypU488US9/PLLHvseN26cvv76az3//PMymUwymUzavn27JGnDhg268MILFR4ersjISF133XXat29f6bUffPCBevXqpbCwMLVt21ZDhw7VoUOHNHPmTL355pv6+OOPS/tcuXKl2/t76sPpn//8p3r06CGLxaJTTjlF8+fPLz0XFxcnSerbt69MJpMGDx5cjU+3fiKRb6I8VbZ3Kj5mr+OIAAAAgHrGMKSSQ769igqlz/4uuZ3z+texlCmOdr7052Mps/z8fH3++ee67bbbFBYW5nIuKipK1157rRYvXqyypdGeeeYZnX766frxxx912223aeLEifrtt9/c9v/8888rISFBEyZMUHZ2trKzsxUTE6P9+/dryJAh6tu3r3744QelpKQoNzdXV111lSQpOztbo0eP1g033KCNGzdq5cqVuuyyy2QYhu69915dddVVSkpKKu3zrLPOqnBvb31I0ttvv63p06frscce08aNG/X444/roYce0ptvvilJSk9PlyStWLFC2dnZWrJkiU+faUPA1PomzF1l+69+26OXv9mmv3/ws2LbtlDvmFaBDhMAAAAIjKOHpcc7+qkzwzFS/0SMb80f2C2FtKi02ZYtW2QYhnr06OH2fI8ePfTnn39q79696tChgyRpxIgRuu222yRJU6ZM0XPPPaevvvpKJ598coXrrVarQkJC1Lx5c0VFRZUenzdvnvr27avHH3+89Nhrr72mmJgYbd68WQcPHtSxY8d02WWXqXPnzpKkXr16lbYNCwtTcXGxS5/lZWdne+1jxowZeuaZZ3TZZZdJcozAZ2Rk6B//+Ieuv/56tW/fXpLUtm1br/dpiEjkmzhzkMlli7kBcW2UueegUjft0c1v/aAPbztbv+cdrrCFHQAAAID6oyqbkZ122mmlP5tMJkVFRWnPnj1Vut9PP/2kr776SuHh4RXOZWZmavjw4brgggvUq1cvJSYmavjw4briiivUunVrn+/Ru3dvj30cOnRImZmZuvHGGzVhwoTSa44dOyar1VqlZ2mISOThwhxk0txRfXTZ/G+1Zc9BnffUVzpqO/6HQrTVohnJ8UrqGR3AKAEAAIA6ENzcMTLui9+/ld6+ovJ2134gda44jdztvX3QrVs3mUwmbdy4UZdeemmF8xs3blTr1q1LR6clKTg42KWNyWSS3V61pbUHDx5UcnKy5syZU+FcdHS0zGazli9frm+//VZffPGFXnzxRT344INas2ZN6dr1ynjro3lzx+fzyiuvaODAgRWua+xYI48KWlqCNTbBMXWlbBIvSTkFRZq4aJ1SNmQHIjQAAACg7phMjuntvry6DnFUp3dbTlqO4xGdHO186c/k2yzYtm3batiwYZo/f76OHDnici4nJ0dvv/22rr76apl87M+dkJAQ2WyuW+j169dPv/76q2JjY9WtWzeXV4sWjiUBJpNJZ599tmbNmqUff/xRISEh+vDDDz326Y6nPiIjI9WxY0dt27atwv2dXxSEhIRIkk/3aWhI5FGBzW5o/spMt+ecaf2spRmy2X2fvgMAAAA0akFmxxZzkiom83+9T3rC0c7P5s2bp+LiYiUmJuqbb77Rzp07lZKSomHDhqlTp0567LHHatR/bGys1qxZo+3bt2vfvn2y2+2aNGmS8vPzNXr0aH3//ffKzMzU559/rvHjx8tms2nNmjV6/PHH9cMPP2jHjh1asmSJ9u7dW7qWPzY2Vj///LN+++037du3T0ePHq1w38r6mDVrlmbPnq0XXnhBmzdv1i+//KLXX39dzz77rCSpQ4cOCgsLKy3EV1BQUKPPoT4hkUcF6Vn5yi4o8njekJRdUKT0rPy6CwoAAACo7+Ivlq76lxRRbhlqREfH8VraR7579+764Ycf1KVLF1111VXq2rWrbr75Zp1//vlKS0ur8R7y9957r8xms+Lj49W+fXvt2LFDHTt21OrVq2Wz2TR8+HD16tVLd911l1q1aqWgoCBFRETom2++0YgRI3TSSSdp2rRpeuaZZ3ThhRdKkiZMmKCTTz5Zp59+utq3b6/Vq1dXuG9lfdx000365z//qddff129evXSeeedpzfeeKN0RL5Zs2Z64YUX9I9//EMdO3bUyJEja/Q51CcmoypVEZqIwsJCWa1WFRQUKCIiItDh1LmP1+/Sne+tr7Td86P6aGSfTrUfEAAAAFAHioqKlJWVpbi4OFks7rdp9ond5lgzfzBXCo90rImvhZF4NDze/hurSh5KsTtU0KGlb39oOdvZ7IbLFnZUtgcAAECTFmSW4gYFOgo0YiTyqGBAXBtFWy3KKSiSt+ka/1m7U3sOFOmJzza5TMWnsj0AAAAA1B7WyKMCc5BJM5LjJXks0yFJ+mCdYwp++fX0VLYHAAAAgNpDIg+3knpGa8GYfoqyuk6zj7JatHBMP71940B5mj1PZXsAAAAAqD1MrYdHST2jNSw+yu3697TMPHnL0ctWtk/o2rbOYgYAAABqinrgqC3++m+LRB5emYNMbhPxPQc8b09XnXYAAABAoJnNjsryJSUlCgsLC3A0aIwOHz4sSQoODq5RPyTyqJaqVrYHAAAA6rtmzZqpefPm2rt3r4KDgxUUxEpk+IdhGDp8+LD27NmjVq1alX5pVF0k8qiWyirbm+RYTz8grk1dhwYAAABUi8lkUnR0tLKysvT7778HOhw0Qq1atVJUVFSN+yGRR7U4K9tPXLROJqlCMm9ImpEcz37yAAAAaFBCQkLUvXt3lZSUBDoUNDLBwcE1Hol3IpFHtTkr289amlFhC7oQc5BOjooIUGQAAABA9QUFBcliYYko6i+TQUnGCgoLC2W1WlVQUKCICJLRytjsRmll+/bhoXohdYu+y8pXvxNb6d+3nsWoPAAAAABUoip5KCPyqLHyle07t2uhpOe+0bod+/WPbzJ12+BuAYwOAAAAABoXyjDC7zq1CtOMi0+VJD23fLMydhcGOCIAAAAAaDxI5FErLu/XScPiI3XUZujuxT/qm8179PH6XUrLzJPNzmoOAAAAAKgu1si7wRp5/9h3sFiDn1qpg8XHXI5HWy2akRyvpJ7RAYoMAAAAAOqXquShjMij1vywPb9CEi9JOQVFmrhonVI2ZAcgKgAAAABo2EjkUStsdkOzlma4PeecAjJraQbT7AEAAACgikjkUSvSs/Ir7C1fliEpu6BI6Vn5dRcUAAAAADQCJPKoFXsOeE7iq9MOAAAAAOBAIo9a0aGlxa/tAAAAAAAOJPKoFQPi2ijaapHJS5toq0UD4trUWUwAAAAA0BiQyKNWmINMmpEcL0kek/kZyfEyB3lL9QEAAAAA5ZHIo9Yk9YzWgjH9FGWtOH3eHCT17GQNQFQAAAAA0LA1C3QAaNySekZrWHyU0rPytedAkTq0tOjFLzfr28x8vZi6VXOuOC3QIQIAAABAg0Iij1pnDjIpoWvb0vchzU7Rtwu+1Qfr/tDEwV0V265FAKMDAAAAgIaFqfWoc/07t9b5J7eXzW7o+dQtgQ4HAAAAABoUEnkExORhJ0uSPlq/S1tyDwQ4GgAAAABoOEjkERC9TrAq6dQoGYb03IrNgQ4HAAAAABoMEnkEzN3DTpLJJH36S45+3V0Q6HAAAAAAoEEgkUfAnBzVUsmndZQkPfvFb0rLzNPH63cpLTNPNrsR4OgAAAAAoH6iaj0C6q6h3bX0p91K3bRXqZv2lh6Ptlo0IzleST2jAxgdAAAAANQ/jMgjoDbnHpC7sfecgiJNXLROKRuy6zwmAAAAAKjPSOQRMDa7oVlLM9yecyb3s5ZmMM0eAAAAAMogkUfApGflK7ugyON5Q1J2QZHSs/LrLigAAAAAqOdI5BEwew54TuKr0w4AAAAAmgISeQRMh5YWv7YDAAAAgKaARB4BMyCujaKtFpm8tIm2WjQgrk2dxQQAAAAA9R2JPALGHGTSjOR4SfKYzLcPD9Uxu73uggIAAACAeo5EHgGV1DNaC8b0U5TVdfp8mxYhahZk0s+7CnTzv9aq6KhNNruhtMw8fbx+l9Iy86hmDwAAAKBJMhmGQTZUTmFhoaxWqwoKChQRERHocJoEm91Qela+9hwoUoeWjun0323L041vfq+io3adEhWuPw8fVW5hcek10VaLZiTHK6lndAAjBwAAAICaq0oeSiLvBol8/bFmW57Gvpau4mMVp9c7p+MvGNOPZB4AAABAg1aVPJSp9ajXTo9to/DQZm7POb+BmrU0g2n2AAAAAJoMEnnUa+lZ+co7VOLxvCEpu6BI6Vn5dRcUAAAAAAQQiTzqtT0HivzaDgAAAAAaOhJ51GsdWloqb1SFdgAAAADQ0JHIo14bENdG0VaLx33mTXJUrx8Q16YuwwIAAACAgCGRR71mDjJpRnK8JHlM5mckx8sc5OksAAAAADQuJPKo95J6RmvBmH6KsrpOn28RYmbrOQAAAABNjvt9vYB6JqlntIbFRyk9K19fbcrVy//LkiU4SBf0iAx0aAAAAABQpwI+Iv/SSy8pNjZWFotFAwcOVHp6use2R48e1cMPP6yuXbvKYrGod+/eSklJcWkzc+ZMmUwml9cpp5xS24+BOmAOMimha1vdl3SK2oWHKu/QUX39295AhwUAAAAAdSqgifzixYs1efJkzZgxQ+vWrVPv3r2VmJioPXv2uG0/bdo0/eMf/9CLL76ojIwM3Xrrrbr00kv1448/urQ79dRTlZ2dXfpatWpVXTwO6kiwOUiX9u0oSfpg7R8BjgYAAAAA6lZAE/lnn31WEyZM0Pjx4xUfH6+FCxeqefPmeu2119y2f+utt/TAAw9oxIgR6tKliyZOnKgRI0bomWeecWnXrFkzRUVFlb7atWtXF4+DOnR5/xMkSambcvXnoZIARwMAAAAAdSdgiXxJSYnWrl2roUOHHg8mKEhDhw5VWlqa22uKi4tlsbgWPAsLC6sw4r5lyxZ17NhRXbp00bXXXqsdO3Z4jaW4uFiFhYUuL9Rvp0RFqGenCB21Gfrkp92BDgcAAAAA6kzAEvl9+/bJZrMpMtK1WFlkZKRycnLcXpOYmKhnn31WW7Zskd1u1/Lly7VkyRJlZ2eXthk4cKDeeOMNpaSkaMGCBcrKytKgQYN04MABj7HMnj1bVqu19BUTE+Ofh0StuqKfY1Se6fUAAAAAmpKAF7uriueff17du3fXKaecopCQEN1+++0aP368goKOP8aFF16oK6+8UqeddpoSExP16aefav/+/Xr//fc99jt16lQVFBSUvnbu3FkXj4MaurhPJwWbTfplV4E25TCLAgAAAEDTELBEvl27djKbzcrNzXU5npubq6ioKLfXtG/fXh999JEOHTqk33//XZs2bVJ4eLi6dOni8T6tWrXSSSedpK1bt3psExoaqoiICJcX6r82LUI05JQOkqT/MCoPAAAAoIkIWCIfEhKi/v37KzU1tfSY3W5XamqqEhISvF5rsVjUqVMnHTt2TP/5z380cuRIj20PHjyozMxMRUdH+y121B9X9Hcsg/jwx906ZrMHOBoAAAAAqH0BnVo/efJkvfLKK3rzzTe1ceNGTZw4UYcOHdL48eMlSWPHjtXUqVNL269Zs0ZLlizRtm3b9L///U9JSUmy2+36+9//Xtrm3nvv1ddff63t27fr22+/1aWXXiqz2azRo0fX+fOh9g0+ub3atgjRvoPF+mYLe8oDAAAAaPyaBfLmV199tfbu3avp06crJydHffr0UUpKSmkBvB07drisfy8qKtK0adO0bds2hYeHa8SIEXrrrbfUqlWr0jZ//PGHRo8erby8PLVv317nnHOOvvvuO7Vv376uHw91INgcpEv6dtKrq7L0wdo/NOSUyMovAgAAAIAGzGQYhhHoIOqbwsJCWa1WFRQUsF6+AcjYXagRL/xPIeYgrXngArVuERLokAAAAACgSqqShzaoqvWAO/EdIxQfHaESm11Lf2ZPeQAAAACNG4k8GoUr+jv2lH9z9XZ9vH6X0jLzZLMz2QQAAABA4xPQNfKAv7S0OP5Tztx3SHe+t16SFG21aEZyvJJ6smMBAAAAgMaDEXk0eCkbsvX3D36ucDynoEgTF61Tyobs0mM2u6G0zDxG7QEAAAA0WIzIo0Gz2Q3NWpohd+m4IckkadbSDA2Lj9LyjBzNWpqh7IKi0jaM2gMAAABoaBiRR4OWnpXvkpiXZ0jKLijSi6lbNHHRugpt3Y3aAwAAAEB9RiKPBm3PAc9JfFlzU7d4HLWXHKP2TLMHAAAA0BCQyKNB69DSUuM+nKP26Vn5NQ8IAAAAAGoZiTwatAFxbRRttcjk4bxJUquwYJ/68nV0HwAAAAACiUQeDZo5yKQZyfGSVCGZd74ff3asT335Y3QfAAAAAGobiTwavKSe0Vowpp+irK6JeJTVogVj+un2Id0rHbWPtlo0IK5NrccKAAAAADXF9nNoFJJ6RmtYfJTSs/K150CROrR0JObmIEf6PiM5XhMXrZNJclv0bkZyfGlbAAAAAKjPTIZhUKq7nMLCQlmtVhUUFCgiIiLQ4cBPUjZkV9hHPsgkPT+qj5J7dwpgZAAAAACauqrkoYzIo8koO2qfU3BED/83Q38ePip2nQMAAADQkLBGHk2KOcikhK5tdWm/E3T9WbGSpLe/2xHYoAAAAACgCkjk0WSNHnCizEEmpW/P16acwkCHAwAAAAA+IZFHkxUZYdHw+EhJjMoDAAAAaDhI5NGkXXdmZ0nSknV/6GDxsQBHAwAAAACVI5FHk5bQta26tG+hQyU2ffTjrkCHAwAAAACVIpFHk2YymXTtQMeo/KLvfhe7MQIAAACo70jk0eRd0e8EWYKDtCnngNb+/megwwEAAAAAr0jk0eRZmwfr4t4dJTlG5QEAAACgPiORBySN+avo3ae/5CjvYHGAowEAAAAAz0jkAUmnndBKvU+wqsRm1/s//BHocAAAAADAIxJ54C/X/jUq//aa7Vq9dZ8+Xr9LaZl5stkpgAcAAACg/jAZlOmuoLCwUFarVQUFBYqIiAh0OKgjR0ps6v/och0usbkcj7ZaNCM5Xkk9owMUGQAAAIDGrip5KCPywF++3rynQhIvSTkFRZq4aJ1SNmQHICoAAAAAcEUiD0iy2Q3NWprh9pxzysqspRlMswcAAAAQcCTygKT0rHxlFxR5PG9Iyi4oUnpWft0FBQAAAABukMgDkvYc8JzEV6cdAAAAANSWZoEOAKgPOrS0+NzOZjeUnpWvPQeK1KGlRQPi2sgcZKrlCAEAAADAgUQekDQgro2irRblFBTJ0yr4ELNJW/ce1OT317tMw6eqPQAAAIC6xNR6QJI5yKQZyfGSJE9j6yU2Qw99tKHCWnqq2gMAAACoSyTywF+SekZrwZh+irK6TrOPtlo06+J4BZvdp/hUtQcAAABQl5haD5SR1DNaw+KjKqyBT8/K11Gb5yS9bFX7hK5t6y5gAAAAAE0OiTxQjjnIVCEZp6o9AAAAgPqCqfWAD6pS1R4AAAAAahOJPOADZ1V7b5vMRVsd0/ABAAAAoDaRyAM+8KWq/fT/i2c/eQAAAAC1jkQe8JGnqvZO+YdL6jgiAAAAAE2RyTAM9ssqp7CwUFarVQUFBYqIiAh0OKhnbHbDpar9L7v26/FPNyks2KzP7hyk2HYtAh0iAAAAgAamKnkoI/JAFTmr2o/s00kJXdvqpnO6aGBcGx05atO9//6JveQBAAAA1CoSeaCGgoJMevrK3goPbaYffv9Tr/xvW6BDAgAAANCIkcgDfhDTprmm/5+jGN6zX2zWr7sLlJaZp4/X71JaZh6j9AAAAAD8plmgAwAaiytPP0FfZORoxcY9GjlvtY6VSd6jrRbNSI5XUs/oAEYIAAAAoDFgRB7wE5PJpGHxkZLkksRLUk5BkSYuWqeUDdmBCA0AAABAI0IiD/iJzW5o7ootbs850/pZSzOYZg8AAACgRkjkAT9Jz8pXdkGRx/OGpOyCIqVn5ctmN1hDDwAAAKBaWCMP+MmeA56T+LKWZ+Ro8vvrXZJ+1tADAAAA8BUj8oCfdGhp8anda6u3Vxi5Zw09AAAAAF+RyAN+MiCujaKtFpmqcS1r6AEAAAD4ikQe8BNzkEkzkh17yZdP5n1J7suuoQcAAAAAT0jkAT9K6hmtBWP6KcrqOs0+ymrRjWfH+tSHr2vtAQAAADRNFLsD/CypZ7SGxUcpPStfew4UqUNLiwbEtVF6Vr5eXb290ut9XWsPAAAAoGkikQdqgTnIpISubV2OOdfQ5xQUydMq+BahZp0R27r2AwQAAADQYDG1Hqgj3tbQOx0qtunRZRtlGIZPe82zHz0AAADQ9JgMw+Bf/uUUFhbKarWqoKBAERERgQ4HjUzKhmzNWppRYR/580/uoHe/3yHDkM7p1k5b9xxUTqHnveY99cN+9AAAAEDDU5U8lETeDRJ51Dab3aiwht4cZNK/f9ip+z742e01zlH8BWP6SZImLlpXYYp+2TYk8wAAAEDDQSJfQyTyCBSb3VD/R5Zr/5Gjbs+bJEVGhEoyuYzWl28TZbVo1ZQhMgdVZ1d7AAAAAHWtKnlowNfIv/TSS4qNjZXFYtHAgQOVnp7use3Ro0f18MMPq2vXrrJYLOrdu7dSUlJq1CdQn6Rn5XtM4iXHXvM5hcUek3hnG/ajBwAAABqvgCbyixcv1uTJkzVjxgytW7dOvXv3VmJiovbs2eO2/bRp0/SPf/xDL774ojIyMnTrrbfq0ksv1Y8//ljtPoH6xJ97yLMfPQAAANA4BXRq/cCBA3XGGWdo3rx5kiS73a6YmBjdcccduv/++yu079ixox588EFNmjSp9Njll1+usLAwLVq0qFp9usPUegRKWmaeRr/ynV/6enfCmRW2wAMAAABQPzWIqfUlJSVau3athg4dejyYoCANHTpUaWlpbq8pLi6WxWJxORYWFqZVq1ZVu0+gPnHuNe9pZbtJUlREqKIivLeJtjoK6AEAAABofAKWyO/bt082m02RkZEuxyMjI5WTk+P2msTERD377LPasmWL7Ha7li9friVLlig7O7vafUqOLwgKCwtdXkAgeNtr3vl+5sWnaubF3vejn5EcT6E7AAAAoJEKeLG7qnj++efVvXt3nXLKKQoJCdHtt9+u8ePHKyioZo8xe/ZsWa3W0ldMTIyfIgaqLqlntBaM6acoq+vskyirpXRbOU9tJGn82bFsPQcAAAA0Ys0CdeN27drJbDYrNzfX5Xhubq6ioqLcXtO+fXt99NFHKioqUl5enjp27Kj7779fXbp0qXafkjR16lRNnjy59H1hYSHJPAIqqWe0hsVHud1r3lOb1Vv26f21f2jZL9m6Z/jJahEasP+9AQAAANSigI3Ih4SEqH///kpNTS09ZrfblZqaqoSEBK/XWiwWderUSceOHdN//vMfjRw5skZ9hoaGKiIiwuUFBJo5yKSErm01sk8nJXRt63aqfNk2D1/SUzFtwpRbWKyXvtoagIgBAAAA1IWATq2fPHmyXnnlFb355pvauHGjJk6cqEOHDmn8+PGSpLFjx2rq1Kml7desWaMlS5Zo27Zt+t///qekpCTZ7Xb9/e9/97lPoLGyBJv10EWOtfP//F+Wtu87FOCIAAAAANSGgM69vfrqq7V3715Nnz5dOTk56tOnj1JSUkqL1e3YscNl/XtRUZGmTZumbdu2KTw8XCNGjNBbb72lVq1a+dwn0JgNi4/UuSe11zeb9+qR/2bo1XFnBDokAAAAAH4W0H3k6yv2kUdDtnXPQSXN/UbH7IZeH3eGzj+lQ6BDAgAAAFCJquShVMMCGpluHcJ1wzlxevmbbZq19FeZg6Q/Dx91WzBPkmx2w2tRPQAAAAD1CyPybjAij4buQNFRnf3ElyosOuZyPNpq0Yzk+NLt6VI2ZGvW0gxlFxR5bAMAAACg9lUlD21Q+8gD8M3qrfsqJPGSlFNQpImL1illQ7ZSNmRr4qJ1Lkl8+TYAAAAA6h+m1gONjM1uaNbSDLfnDEkmSTM/+VWSSe6m4zjbzFqaoWHxUUyzBwAAAOoZRuSBRiY9K7/CKHtZhqScwmLlFHpvk11QpPSsfP8HCAAAAKBGSOSBRmbPAc8JeiD7AgAAAOAfTK0HGpkOLS1+74vK9gAAAED9QSIPNDID4too2mpRTkGR2zXwJkmREaGSTMotdN9GkoJMkslEZXsAAACgvmH7OTfYfg4NnbMivSSXRN05hr5gTD9JcttG5dp7+jLA2Q/JPAAAAFBzbD8HNHFJPaO1YEw/RVldp9lHWS2lybenNtFWi567uo8u7dPRY4LvPD5raYZsdr4LBAAAAOoSU+uBRiqpZ7SGxUd5XdvurU1UhEUfrt/tsf+yle0TuratgycCAAAAIJHIA42aOchUaZLtqY2vFeupbA8AAADULabWA3DL1+r3/qySDwAAAKByJPIA3HJWv/e2yVy01TEVHwAAAEDdIZEH4JY5yKQZyfGS5DGZHx4fyX7yAAAAQB0jkQfgkafK9s1DzJKkf333u95L3yGb3VBaZp4+Xr9LaZl5VLIHAAAAahH7yLvBPvKAK5vdcKlsf3rn1npkWYb+lfa7JCnC0kyFRcdK20dbLZqRHM8e8wAAAICPqpKHksi7QSIPVM4wDN305g9K3bSnwjnnZHvnnvW+KP9lQfmt8vylru4DAAAAVEVV8lC2nwNQLXZD+jW70O05Q45kftbSDA2Lj6o0UU7ZkK1ZSzOUXXB8K7vaGNWvq/sAAAAAtYk18gCqJT0rXzkFnveQNyRlFxQpPSvfaz8pG7I1cdE6l+RaknIKijRx0TqlbMj2R7h1dh8AAACgtpHIA6iWPQc8J/G+trPZDc1amiF363ucx2Ytzahx8by6ug8AAABQF0jkAVRLh5aWyhtJah8eKkluK9unZ+VXGCEvy9dR/crU1X0AAACAusAaeQDVMiCujaKtFuUUFLkd6Xaav3Krdv55WHNXbKmwNn1YfKRP9/J19L+m19f0PgAAAEBdYEQeQLWYg0yakRwv6XiVeifn+2CzSau25mnKf36pMCKeXVBUun1dZXwd/a/p9TW9DwAAAFAXSOQBVFtSz2gtGNNPUVbXBDjKatHCMf303zvOUbMabu3WoWWoBsS1qVEfse2aVxpHZETN7wMAAADUBabWA6iRpJ7RGhYf5XZv9rTMPB3zsYCcSXI7RT/YbFLRUZtahPr2x1X5feJPbNNc1726ptI4WjcPkWE4N84DAAAA6i8SeQA1Zg4yKaFr2wrHfV1zfsPZsfpsQ47L9PsOLUN15KhNu/YX6e7F67VwTH8FVWM/erPJJJthqFOrMN16XlfNX7nV5Xz78FAVFB3VppwDenTZRs28+FSfYgYAAAAChUQeQK3xdc35sPgoPXhRfIVR/Z/+2K9R//hOX2Tk6rkVm3XX0JPcjvxLx/eJLz/ubjMcR24b3FXXntlZ1ww8sUIfyzNydeuitXrj2+06Jaqlrjw9xuN9AAAAgEAzGYbBxsnlFBYWymq1qqCgQBEREYEOB2iwbHZD58z50mNle5Mc6+lXTRniMVH+z9o/dM+/f5IktQoL1v4jR0vPRVstmpEcr2HxUTpnzpdet5iLruQ+L6Ru0bPLN8scJLUKC1HeoZIK90nqGV35QwMAAADVUJU8lGJ3AGqNL5XtZyTHex3tvrz/CaXb1JVN4iUpp6BIExet07wvt3hN4qXK94m/Y0g39evcSja7XJL4svdJ2ZDt9R4AAABAXSCRB1CrvFW2XzCmX6Wj3Da7oV/+KHB7zvjrNXfFFp9i8bZm325Iu/884vE+kjRraYZsPhbvAwAAAGoLa+QB1Dpvle0rk56Vr5xC76PtvqbW3tbsO+5T7PUezlF9d4X9AAAAgLpCIg+gTniqbF8ZXyvfW8OCVXjkqNe1+N72iff1Pr62AwAAAGoLU+sB1Gu+Vr6/4ew4SdVfi+/rfXxtBwAAANQWEnkA9dqAuDaKtloqJOhOJjmqyt8+pFuN1uJXdh/9dR9vo/oAAABAXWBqPYB6zVn5fuKidTLJdT18+dH2mqzF93Yfp7uGdmc/eQAAAAQcI/IA6r2qVL53rsUf2aeTErq2rVLi7ek+zj4Wf79TxcdsNXgSAAAAoOZMhmGwl1I5hYWFslqtKigoUERERKDDAfAXm92o1mh7Te/TvmWoLpu/WoVFx3R5vxP09JWnyWRiZB4AAAD+U5U8lETeDRJ5AOX9b8teXf9auuyGNO2iHhp/dpzXLxXq6ksHAAAANA4k8jVEIg/AnddWZenh/2bIJKlV82D9efho6bloq0UzkuOV1DNaKRuyNWtphrILityeBwAAAMojka8hEnkA7hiGoTH/XKPVmXkVzjnH2m8+N04vf5NVoVie87wvFfQBAADQ9FQlD6XYHQD4yG5ImXsPuT1n/PV6+X8Vk3jneUmatTRDNjvfnwIAAKD6SOQBwEfpWfnKKSzy2sbbHCdDUnZBkdKz8v0bGAAAAJoUEnkA8NGeA96T+LruBwAAAE1Ts0AHAAANRYeWlsob+dRPqCQq2wMAAKB6SOQBwEcD4too2mpRTkGR23XwkhRkckyv97YK/tVVWdr15xE9s3wzle0BAABQZUytBwAfmYNMmpEcL+l4FXon01+vCYPiPJ6XpGZBJq3YuEf3fvCzSxIvSTkFRZq4aJ1SNmT7O3QAAAA0IiTyAFAFST2jtWBMP0VZXafZR1ktWjCmn6aOiPd4fuGYfvrPxLM8Tp+nsj0AAAB8wdR6AKiipJ7RGhYf5bK+/YzY1qUJurvzzvXvaZl5XpP0spXtE7q2raMnAgAAQENCIg8A1WAOMlVItI0ye8+5Oy/5XrGeyvYAAADwhKn1AOAnJlPlFed9rXzvrwr5AAAAaHxI5AGgDjkr33tK+U1yVK8fENemLsMCAABAA0IiDwB1yFvle8mxRn5Gcjz7yQMAAMAjEnkAqGOeKt9LUu8TrOwjDwAAAK8odgcAAVC+sv3RY3b9/T8/66c/CvTN5r0696T2gQ4RAAAA9RQj8gAQIM7K9iP7dNIVp8do3FlxkqSZS39VyTF7gKMDAABAfUUiDwD1xF3DuqtdeIi27T2kN7/dHuhwAAAAUE+RyANAPRFhCdbfE0+RJD2fukV7Cv2zl7zNbigtM08fr9+ltMw82exG5RcBAACg3mKNPADUI1f0P0Fvp+/QTzv3a07Kb3rmqt416i9lQ7ZmLc1QdsHxLwWirRbNSI6nqB4AAEADxYg8ANQjQUEmzbr4VEnSf9b9oTe/3e51JN3baHvKhmxNXLTOJYmXpJyCIk1ctE4pG7Jr92EAAABQKwKeyL/00kuKjY2VxWLRwIEDlZ6e7rX93LlzdfLJJyssLEwxMTG6++67VVR0/B+pM2fOlMlkcnmdcsoptf0YAOA3fWJaKaFLW0nSjE9+1Z3vrdfoV77TOXO+dEm+UzZk65w5X2r0K99VaGOzG5q1NEPuJtE7j81amsE0ewAAgAYooFPrFy9erMmTJ2vhwoUaOHCg5s6dq8TERP3222/q0KFDhfbvvPOO7r//fr322ms666yztHnzZo0bN04mk0nPPvtsabtTTz1VK1asKH3frBkrCAA0HCkbspW2La/CcedI+oIx/SRJExetq5Co5xQU6dZF63T+Se0rjMSXZUjKLihSela+Erq29UvcNrtRup1eh5YWDYhrI3OQyS99AwAA4LiAZrjPPvusJkyYoPHjx0uSFi5cqGXLlum1117T/fffX6H9t99+q7PPPlvXXHONJCk2NlajR4/WmjVrXNo1a9ZMUVFRtf8AAOBnzpF0d5xJ+/3/+VmSyeto+1eb9/p0vz0H/FNQj7X4AAAAdSdgU+tLSkq0du1aDR069HgwQUEaOnSo0tLS3F5z1llnae3ataXT77dt26ZPP/1UI0aMcGm3ZcsWdezYUV26dNG1116rHTt2eI2luLhYhYWFLi8ACIT0rHyvI+mStP/IMe0/ctQv9+vQ0lLjPliLDwAAULcClsjv27dPNptNkZGRLscjIyOVk5Pj9pprrrlGDz/8sM455xwFBwera9euGjx4sB544IHSNgMHDtQbb7yhlJQULViwQFlZWRo0aJAOHDjgMZbZs2fLarWWvmJiYvzzkABQRf4aIZekVmHB8jaxPdrqmP5eE6zFBwAAqHsBL3ZXFStXrtTjjz+u+fPna926dVqyZImWLVumRx55pLTNhRdeqCuvvFKnnXaaEhMT9emnn2r//v16//33PfY7depUFRQUlL527txZF48DABX4Y4TcafzZcZLkMZm/rF8nn9ewe6qOX9kMgrJr8QEAAOAfAVsj365dO5nNZuXm5rocz83N9bi+/aGHHtJ1112nm266SZLUq1cvHTp0SDfffLMefPBBBQVV/F6iVatWOumkk7R161aPsYSGhio0NLQGTwMA/jEgro2irRblFBS5HeU2SYqMCJVkUm6h5zZRVotuH9JNJ0eFV1i73jzErMMlNr357e+6tG8ndevQ0mtMnta/P3RRvNZkVSzK544/ZxoAAAA0dQEbkQ8JCVH//v2Vmppaesxutys1NVUJCQlurzl8+HCFZN1sNkuSDMP9tM2DBw8qMzNT0dEUWwJQ/5mDTJqRHC+p4ki68/3Mi0/VzIu9t5mRHC9zkElJPaO1asoQvTvhTD0/qo/enXCm1k4bpgFxbXSw+JhufPMH5R0srvJe9NkFRbrtnXV6M+13n57LnzMNAAAAmrqAVq2fPHmyrr/+ep1++ukaMGCA5s6dq0OHDpVWsR87dqw6deqk2bNnS5KSk5P17LPPqm/fvho4cKC2bt2qhx56SMnJyaUJ/b333qvk5GR17txZu3fv1owZM2Q2mzV69OiAPScAVEVSz2gtGNOvwih4VLkq8L60kRxfDpTfYm7Btf008qXV+j3vsBJmf6kSm730nLPa/LD4KI/r38sKD22mQ8XHvM4OqOlafAAAABwX0ET+6quv1t69ezV9+nTl5OSoT58+SklJKS2At2PHDpcR+GnTpslkMmnatGnatWuX2rdvr+TkZD322GOlbf744w+NHj1aeXl5at++vc455xx99913at++fZ0/HwBUV1LPaA2Lj/K6L7svbTxpGx6qcWfF6tFlG12SeOl4tfk7LuhWaQV9SZowqIvmrtgsk+Q2mXfODgAAAIB/mAxPc9KbsMLCQlmtVhUUFCgiIiLQ4QCA39nshs6Z86VPiXplnh/VR6HNgirMDpCkc09qp3/dMLDG9wAAAGjsqpKHBnREHgAQGL7sV++rDi0tSuja1mV2wL4DxXpk2Uat2rJPv+4u0KkdrX65FwAAABrY9nMAAP/wtYq8t73oTXLdi965Fn9kn066cVAXXXRatOyGNOuTDI8FSQEAAFB1JPIA0AT5WkXe01705avju/PgiB6yBAcpfXu+PvlpdzUjrR02u+GxUj8AAEB9x9R6AGiCfNmv3tte9O6q45fXsVWYJg3upmeWb9bjn27U0B6RahEa+L92UjZkV3ieaB+eBwAAoL6g2J0bFLsD0BQ494iXXKvNO8fXF4zpV5rY2uxGtarjFx21adhzX2tn/hHdel4XnXdShyr3UV51Y5GOP3P5v/jcPXNtxgEAAFBeVfJQEnk3SOQBNBV1MTr9xa85uvmttRWOV+c+NYm3skr9zlkIq6YMqTQhZ1QfAAD4G4l8DZHIA2hKantk+bNfsjXx7XUVjld1FLymo+lpmXka/cp3ld7n3QlnKqFr21qLAwAAwJ2q5KEUuwOAJq5stfmErm39msTb7IYe/m+G23PORHjW0oxKi83Z7IZmLc1wu57f1358rdTvrZ0/4gAAAKgpEnkAQK2pbL96Q1J2QZHSs/JrvR9fK/V7a+ev5wEAAKiJwJcPBgA0WlUdBfc0zd8fo+lx7VqoWZBJx7yMlkdYmun0zq1rNQ4AAICaIpEHANQaX0fBDcPwWEBu9IAT9cWvOTW6X25hka7953dek3hJKiw6pkvmr9beA8Xac6DYJY77Ek/Wd5l5NYoDAADAHyh25wbF7gDAP5yV4j3tV+9P7cJDtOaBoZLkMpp+QuswXffqGm3PO6yOVotuPa+rFnydWeELg/NObq8Pfvij0mTfm6pUvgcAACirKnkoI/IAgFpjDjJpRnK8Ji5aJ5Mq7ldvSDo1uqV+zT7gtZ/mIWZNvfAUTf/4V6lcP077Dx/V/f/5Wf/buk85ZZJ0s0myGVJMmzC9c9OZimnTXNee2bnC1HlJWpGRq30HSzzG0SzIpOvOPFFvfPu7xzhmJMeTxAMAgFpFsTsAQK1K6hmtBWP6KcrqOt08ymrRwjH9NO3/Tq20j8MlNnXr0NJtP5ERoTrtBKuO2Q39e+0fLkm85EjiJWnieV0V06a5JPeV+tOz8r0m8ZJ0zG5o+Knun0eSzj2pPVvPAQCAWseIPACg1iX1jNaw+Ci3BeQ+Xr/Lpz72HCjSyD6d3PZjGIb6PbJchUXHPF7/4pdbdfUZJ3ocLa9KIbvycew9UKxHl23U/7bsVcbuQsV3ZFkWAACoPSTyAIA64RwFL6+q28K56yctM99rEi8d3xbOXQz+iOPHHfu17JdszfzkVy2+5UyZTEyvBwAAtYOp9QCAgBoQ10bRVos8pb0mOYrROdexu+OPbeFqGscDF/WQJThI6dvztfTnbJ/iAQAAqA4SeQBAQDkL4kmqkEQ731dWQK6qo+m1EUenVmGaNLibJOnxZRt1qNj7DAEAAIDqIpEHAASct4J4C8b0q7SAnD9G9f0Rx4RzuyimTZhyCos0f+VWr20BAACqi33k3WAfeQAIDJvdcFsQzxcpG7I1cdE6SRW3uZPkUyLujzi++DVHN7+1ViHmIH125yDtOVBcrX6qoibxAgCA+qEqeSiJvBsk8gDQMKVsyNaspRnKLrMFXbTVohnJ8XW2LZxhGBr7Wrr+t2WfQpsFqfiYvVZjqQ/PDAAAao5EvoZI5AGg4aoPo9Ovr87SrKUZFY5XZ3aAN85ZCOX/Ii9/n/rwmQAAAO+qkoey/RwAoFHxtM1dXbHZDb38zTa35ww5kuxZSzM0LD6qRsm0zW5o1tKMCkl8+fvY7dIjyxixBwCgMaHYHQAAfpSele+SNJdn6Pie9r6w2Q2lZebp4/W7lJaZJ5vdqNJ9bntnXYV2OQVFmrhonVI2sE0eAAANESPyAAD4kT/2tHfytv5938GSasfoz5kBAACg7pHIAwDgR1XZ097b2nVP69+zC4p066J1qmnuXXZmQELXtqyjBwCgASGRBwDAj5x72ucUFLldvy5JzUPMyi0s0jlzvnQ72j4sPsrj+ncnuyEFm006aqtZzdo9B4qofA8AQAPDGnkAAPzIHGTSjOR4Scerx5d3uMSmuxav97h2fd6XW7yuf3e6/fxuMrm5T1XG0VM35mriItbRAwDQkJDIAwDgZ0k9o7VgTD9FWV2n2UdbLbp9SFeP0+KNv17zvtrq031i27Vwe58oq0Xzr+mraKul0qT+k5+yPVa+lxzr6J0F9gAAQP3A1HoAAGpBUs9oDYuPqrDuPD0rX/O+zPR6ra/T5Tu0tCiha1u39zEHmRQUZNLERetkklySdWdyf3psa32//U+P/ZdfRw8AAOoHEnkAAGqJuz3tfa1qHx5q1qFim9vRcpMco+4D4tp4vI90fGZA+fXvUX+tfy8+ZveayJeNmWJ4AADUHyTyAADUIV+r2k8Y1FVzV2z2OJo+Iznep0Ta08wAc5BJaZl5PsWydc8Bj4X5KIYHAEDdq9Ya+a+++srfcQAA0CQ4q9p7SsFNcq6l7+Zx/fuCMf2qlEA7R+xH9umkhK5tS78AqCwWpxe/zPSpGJ7NbigtM08fr9+ltMw81tYDAFBLTIZhVPlv2dDQUJ1wwgkaP368rr/+esXExNRGbAFTWFgoq9WqgoICRUREBDocAEAj49wjXnI/2l42Ua/tKe3eYjEkmUySp38pOKf4r5oyRMszctjCDgCAGqhKHlqtEfldu3bp9ttv1wcffKAuXbooMTFR77//vkpKSqoVMAAATYmnqvbuRts9jabXRSx3D+3uMYmXjhfDe+ijX3zewo5RewAAaq5aI/JlrVu3Tq+//rreffddSdI111yjG2+8Ub179/ZLgIHAiDwAoC7UpwJy7mL578+7ded762vUL6P2AAD4pip5aI0TeUnavXu3Xn75ZT3xxBNq1qyZioqKlJCQoIULF+rUU0+tafd1jkQeAAApLTNPo1/5zi993XF+V837KrNCFX53ywkAAGiKan1qvSQdPXpUH3zwgUaMGKHOnTvr888/17x585Sbm6utW7eqc+fOuvLKK6vbPQAACDBfCvO1Cgv2qa8X3STx0vF1+bOWZjDNHgAAH1Urkb/jjjsUHR2tW265RSeddJJ+/PFHpaWl6aabblKLFi0UGxurp59+Wps2bfJ3vAAAoI6Yg0yakRwvSRWSeef78WfH1vg+zrX26Vn5PrVnnT0AoKmr1j7yGRkZevHFF3XZZZcpNDTUbZt27dqxTR0AAA2csxhe+bXtUX+tbR8WH6X3vt+pnIIityPuJknWsGDtP3K00nvtOVBUaZuUDdmsswcANHl+WSPf2LBGHgAAV94K81W2nd5dQ7vruRVbKr3HuxPO1IC4NpXex5d19vWpkCAAAL6oSh5arRH52bNnKzIyUjfccIPL8ddee0179+7VlClTqtMtAACop5zb4LlT01F7STKbpB+252vy++vdjrYPi4/SrKUZHtfZm+RYZz8sPorq+ACARq9aI/KxsbF65513dNZZZ7kcX7NmjUaNGqWsrCy/BRgIjMgDAFB11Rm1r4xzDP2OId30wpdbK21/99CTNHfFZqrjAwAanFqvWp+Tk6Po6Ip/CbZv317Z2dnV6RIAADRwzlH7kX06KaFrW5ep7M5R+yirxeWaaKtFT19xmizB7v9JYvz18iWJl6QXv9xCdXwAQKNXran1MTExWr16teLi4lyOr169Wh07dvRLYAAAoHFJ6hmtYfFRFUbt07PyVXTU7pd7HPOSpJetju9pmQAAAA1BtRL5CRMm6K677tLRo0c1ZMgQSVJqaqr+/ve/65577vFrgAAAoPFwt9bel2r1kmPP+oIjRz1Wxw8PNetAsa3Sfny9HwAA9VW1Evn77rtPeXl5uu2221RSUiJJslgsmjJliqZOnerXAAEAQOPWoaWl8kaSxp8dp7krNssk99XxbxrUxafq+L7ezx+ong8AqA3VSuRNJpPmzJmjhx56SBs3blRYWJi6d+/ucU95AAAATwbEtVG01eJ1L/ooq0W3D+mmk6PCa7SnfZTVkUzXBfa8BwDUFvaRd4Oq9QAA1K3K9qL3dY/4yqrjz7+mn0acVvtJdFX2vAcAQKqDfeQl6YcfftD777+vHTt2lE6vd1qyZEl1uwUAAE1QZXvRl016q7OnvdOWPQf9H3w5Nrvh8573TLMHAFRHtRL59957T2PHjlViYqK++OILDR8+XJs3b1Zubq4uvfRSf8cIAACaAE9V7aua7Lrr5/e8Q7p/yS96bsVmnRQZrgt71d5oeHpWvtsvEZyong8AqKlqJfKPP/64nnvuOU2aNEktW7bU888/r7i4ON1yyy1u95cHAADwhbfR9pr0k9C1rX7LPaDXV2/X5Pd/UqfWYTpUbKuVInS+VsWnej4AoLqqlchnZmbqoosukiSFhITo0KFDMplMuvvuuzVkyBDNmjXLr0ECAADU1IMjemjrnoP635Z9uuSl1Sq75bw/i9D5WhW/LqvnAwAal6DqXNS6dWsdOHBAktSpUydt2LBBkrR//34dPnzYf9EBAAD4STNzkC7p00mSXJJ4ScopKNLEReuUsiG7xvc5vXNrhQWbPZ43yfHFQV1VzwcAND7VSuTPPfdcLV++XJJ05ZVX6s4779SECRM0evRoXXDBBX4NEAAAwB9sdkNPf/Gb23POvH7W0gzZymf5VbzHlCU/68hRm8c2hqQZyfEUugMAVFu1ptbPmzdPRUWOdV0PPviggoOD9e233+ryyy/XtGnT/BogAACAP/i7CF35bfD6nthK933ws5b+tFvmIJPGnRWrT3/JrnDPZkEmdevQsqaP4zUWf675BwDUP1VO5I8dO6b//ve/SkxMlCQFBQXp/vvv93tgAAAA/uTPInQpG7IrbHEX2ixIxcfsCjab9OLofkrqGaUHRvQok2CH6qWvtmrV1jzd8/56/WfiWWpmrtbkyEpj8eeafwBA/VPlRL5Zs2a69dZbtXHjxtqIBwAAoFb4WlyubYsQSZ5HuVM2ZGvionUV9okvPmaXJE0Y1EVJPaMkVayeH9uuhRKf+0Y//VGg+Ssz9bcLulcaj7fRdk+xONf8LxjTrzSZr2zUnlF9AGg4qjW1fsCAAVq/fr06d+7s73gAAABqxYC4Noq2WpRTUFQh8S3ryc9/U9a+w5q/cmuFUe6HLuqhR5Zt9Hr9hz/u0j3DT3abBEdbw/TwyJ66a/F6vZC6Reef3EG9TrB67MvbaPuw+CjNWprhNhZDjqJ6s5ZmaFh8lJZn5HgdtWdUHwAaFpNhGFWu6PL+++9r6tSpuvvuu9W/f3+1aNHC5fxpp53mtwADobCwUFarVQUFBYqIiAh0OAAAwE+cI9iSXBJg01/vw4KDdOSo3e21pnLXePPuhDM9rrM3DEOT3lmnT3/JUfcO4fpo0tn6+Y8Cn0f+nV8PjDojRu9+v7PSWO66oLueT93isZ+bz43Ty99keTxfdlQfAFB7qpKHViuRDwqquJ7LZDLJMAyZTCbZbJ4rtZb30ksv6amnnlJOTo569+6tF198UQMGDPDYfu7cuVqwYIF27Nihdu3a6YorrtDs2bNlsViq3Wd5JPIAADRe3kaf4ztadcEzK3XUVv3K9ZL0/Kg+GvnXVnfu5B8q0fDnvtG+g8VqEWLWoZLj/3YqO/LvrTifryr7AiLIVHE7vrLXRlktWjVlCNPsAaCWVSUPrdbU+qysrGoFVt7ixYs1efJkLVy4UAMHDtTcuXOVmJio3377TR06dKjQ/p133tH999+v1157TWeddZY2b96scePGyWQy6dlnn61WnwAAoGlJ6hmtYfFRbteDp2Xm1TiJlypfj9+mRYiu7H+CFnyd6ZLES4717be982ONY3Cq7Gm87bZX00r+rLMHgNpRrRF5fxk4cKDOOOMMzZs3T5Jkt9sVExOjO+64w20l/Ntvv10bN25Uampq6bF77rlHa9as0apVq6rVpzuMyAMA0DR9vH6X7nxvfbWv93UE22Y3dM6cL2s84t4qLFgFR466TdZNkqxhwdp/5GiN7iFVPsNAono+ANRUrY/I/+tf//J6fuzYsZX2UVJSorVr12rq1Kmlx4KCgjR06FClpaW5veass87SokWLlJ6ergEDBmjbtm369NNPdd1111W7T0kqLi5WcXFx6fvCwsJK4wcAAI2Pr5XtpYpT1p1p+4zk+EpHoSvb095X48+O09wVmz3GMv7sWD23YkuN71PZ51KV6vkAgJqrViJ/5513urw/evSoDh8+rJCQEDVv3tynRH7fvn2y2WyKjIx0OR4ZGalNmza5veaaa67Rvn37dM4558gwDB07dky33nqrHnjggWr3KUmzZ8/WrFmzKo0ZAAA0bpVVtneOuD90UbweWeY6+hxVhdFnX/e098QZx+1DuunkqPAKI+FRZSrbv/f9Tq+V+oNMkmF4noJvDQvWgLg2HmOx2Q2fq+czzR4A/KNaifyff/5Z4diWLVs0ceJE3XfffTUOypOVK1fq8ccf1/z58zVw4EBt3bpVd955px555BE99NBD1e536tSpmjx5cun7wsJCxcTE+CNkAADQgJiDTJqRHK+Ji9Z5HXFP6hmtxJ7u19n7wp8j/97W/DvbeXueCYMcVes9FcUrOHJU732/Q9cO7Ox2DXxlswvKr7NnHT0A1Fy1Enl3unfvrieeeEJjxozxOvrt1K5dO5nNZuXm5rocz83NVVRUlNtrHnroIV133XW66aabJEm9evXSoUOHdPPNN+vBBx+sVp+SFBoaqtDQ0EpjBgAAjV9Sz2gtGNPP4yi3c8TdHGTyqQCcO/4e+fcWiy/P0/fE1m7Xt58S3VJfbdqrBz/coJ927tf/tuyr0GZET8//xiprz4Ei1tEDgJ/4LZGXpGbNmmn37t0+tQ0JCVH//v2VmpqqSy65RJKjMF1qaqpuv/12t9ccPny4wtZ3ZrNZkmNP1ur0CQAAUF5lo9w1VVcj/74+j6fzQSbpiZRN+sfX2/T+D39U6De7oEivrt7uUwz/+DpTGdkHKhx3t46eUXsA8K5aifwnn3zi8t4wDGVnZ2vevHk6++yzfe5n8uTJuv7663X66adrwIABmjt3rg4dOqTx48dLchTN69Spk2bPni1JSk5O1rPPPqu+ffuWTq1/6KGHlJycXJrQV9YnAACAL2oy4u6Luhj5L6uyfjydv2/4yXr7u991sNjm5irfuUvipYrr6Jdn5DBqDwCVqFYi7xztdjKZTGrfvr2GDBmiZ555xud+rr76au3du1fTp09XTk6O+vTpo5SUlNJidTt27HAZgZ82bZpMJpOmTZumXbt2qX379kpOTtZjjz3mc58AAAD1RW2P/PvD99v/9DmJ9zS74OozYvTe9zs9XudcRz/vy62au2JzpdXvGbEH0NQFdB/5+op95AEAABw+Xr9Ld763vtJ2N5wdq8825LgdSS8+ZvepD0uzIBUds7s9561uACP2ABqDWt9HHgAAAE2DrxX2h8VH6cGL4t2OlKdl5vnUh6ckXjo+an/bO+sqnGO/egBNTVDlTSq6/PLLNWfOnArHn3zySV155ZU1DgoAAAD1g7PCvqeJ6yY5RsSdSXtC17Ya2aeTErq2LZ3uXlkfkhQWXK1/lko6Pp1/1tIM2eyOdza7obTMPH28fpfSMvNKj5flSxsAqI+qNSL/zTffaObMmRWOX3jhhVVaIw8AAID6zdcK+97WqPvSx63nddVzK7ZUO86y+9UXHCmptGAeW+EBaMiq9dXnwYMHFRISUuF4cHCwCgsLaxwUAAAA6g9nhf0oq+s0+yirxefp7JX1cfuQ7pWO2vti4deZunXROpcEXTo+/T5lQ7ZSNmRrYiVt/IVRfwC1oVrF7gYMGKD/+7//0/Tp012Oz5w5U0uXLtXatWv9FmAgUOwOAACgIn9Ui/fWhzPBliqO2vsr/Q0PMUsmkw4WH3N73llUb9WUITWuhM+oP4CqqEoeWq1EfunSpbrssst0zTXXaMiQIZKk1NRUvfvuu/r3v/9dYXu6hoZEHgAAIDA8Jb8PXdRDjyzbqJyCIo9JvSU4SEVHPRfMq4p3J5yphK5tq32980uJ8rE6vxqgMB+A8mq9an1ycrI++ugjPf744/rggw8UFham0047TStWrNB5551XraABAACApJ7RGhYf5XbUPijI5HWd/bUDTtSrq7f7JY49B4oqb+SBzW5o1tIMt184GHLEO2tphobFR9V41B9A01Tt7ecuuugiXXTRRf6MBQAAACitfl+ec519+RH7qL+mq1vDQvyWyHdoaan2UoL0rPwK6+/LKluYryaj/gCarmol8t9//73sdrsGDhzocnzNmjUym806/fTT/RIcAAAAUJa3EXub3VC01eJx+r1JUmREqCSTcgs9T9GXpI9+/EN3v79eOdVY3+7raH5NRv0BNG3Vqlo/adIk7dy5s8LxXbt2adKkSTUOCgAAAPDE0371zm3uJFWofu98P/PiUzXzYvdtylr8wx8uSbzke1X7Di0tXs9XtR0AlFetRD4jI0P9+vWrcLxv377KyMiocVAAAABAdfiyVZ6nNtFWi14a3VctLe4nrTpH8GctzfC6jdyAuDZq06LiVs1lNQsyqXuH8MofCADcqNbU+tDQUOXm5qpLly4ux7Ozs9WsWbWX3QMAAAA15m36fWVt0rPydaDI/dZ0km/r23fkH1bRUZvXGI/ZDY15dY0W3TRQrZuH1HhbPwBNS7Wy7uHDh2vq1Kn6+OOPZbVaJUn79+/XAw88oGHDhvk1QAAAAKCqPBXMq6xNTde3FxYd1YR//aDDJTbFtWuuIyU25RQWl56Ptlp087ldtGBlpjblHND/vbBKdsPQngOubdhrHoA31Urkn376aZ177rnq3Lmz+vbtK0lav369IiMj9dZbb/k1QAAAAKCuVHV9e9nK9u3CQ/XKN5nauuegoiIsWnxzgtqGh7odbR98cgddOn+1cgorfiHgXIvPXvMAPKlWIt+pUyf9/PPPevvtt/XTTz8pLCxM48eP1+jRoxUcHOzvGAEAAIA6MSCujdfK907//Xm39h4o0uzPNlXYaq5ZkEmvjD1dHSIcyb67mQEntmmuYLP7clXsNQ+gMtVe0N6iRQudc845OvHEE1VSUiJJ+uyzzyRJF198sX+iAwAAAOqQs/L9xEXrZJJckvmy799es0Nvr9nhto9jdkO79h9WrxOsHu+TnpWvvWWm05dXfi1+dfe09zdf4qgvsQKNWbUS+W3btunSSy/VL7/8IpPJJMMwZDId/5/TZvNe3AMAAACor5xV7WctzXAZbY/6a+26pZlZN7z5vTwVrvdlNL0qa/FTNmRXiCUQ6+h9iaO+xAo0dibDMLzNGnIrOTlZZrNZ//znPxUXF6c1a9YoPz9f99xzj55++mkNGjSoNmKtM4WFhbJarSooKFBERESgwwEAAEAAeBpZTsvM0+hXvqv0+ncnnOmx4J6vfSTEtdV3WXkVpvk7vx6oq3X0KRuyNXHROq9xSKq0Dck84FlV8tBqjcinpaXpyy+/VLt27RQUFCSz2axzzjlHs2fP1t/+9jf9+OOP1QocAAAAqC88Vb6vaWV7yfe1+GlZeW6P1+U6epvd0KylGW7jdMYx85NfJZm8tmHNP+A/7itsVMJms6lly5aSpHbt2mn37t2SpM6dO+u3337zX3QAAABAPVPVyvbuONfiS8dHrJ1Mf71G9Izy2n/ZdfS1KT0rv0JBv/Jx5BQWu63AX7ZNVWK12Q2lZebp4/W7lJaZJ5undQw1VFf3AfytWiPyPXv21E8//aS4uDgNHDhQTz75pEJCQvTyyy+rS5cu/o4RAAAAqDcqG003ybGefkBcG6/9VLYWv/iYXZ9uyKk0Hl9nCFSXP/v3pa+6WmfPen40ZNVK5KdNm6ZDhw5Jkh5++GH93//9nwYNGqS2bdtq8eLFfg0QAAAAqE8qq2wvSTOS432aQp7UM1rD4qM8rsX3ha8zBKrLn/1X1pentfg5BUWauGid39bZ19V9gNpSrUQ+MTGx9Odu3bpp06ZNys/PV+vWrV2q1wMAAACNUWWj6VVJAj2txffXyL9TdbaOi+8Yofe/d7/NXtk4IiNCJZmUW+h5zX90JbH6sha/7Dr7yp7H0/mq3geoj6q9j3x5bdr49gcIAAAA0Bh4G033B28j//rr/YMX9fDpftXdOs6Z+Drv72kGwsyLT5Ukj7FKjmTfZjc8xuvLWnznOvuCIyVen8fb81rDQny+j6ddB4BAq9b2c40d288BAACgvnCXlDqT5XFnxZYm0d6ur+7WcU73Dj9J3TqEV+vLgDbNg1VYdEzH7IaGnNJBL47uq5//KKjw5cfH63fpzvfWV/p5DD6pvVZu3lvhuPN5bj43Ti9/k+XxWeKjWyoj+0Cl93l+VB+N7NOp2iP/QFVVJQ8lkXeDRB4AAAD1Sflkcf/hEk18e50kafZlvTR6wIkerztnzpdeR6BbhARJMulQic3teecU/lVThkhSlafnD4hro28z92nCv35Q0VG7QpoFqeSYvbR9tNWie4adpNWZ+/Thj7ur8Km452lGQFUtGNOvdJp9dUb+WWOPqiKRryESeQAAANR3877coqe/2KxmQSa9dcMAyWRyWzBv9Cvf+eV+7044s0ZTzZ9fsUXPrdhcoxgszYJUVOZLgOoKD22mg8XHvLYxB0k2N7eqbOS/7EwHknlURVXyUL+tkQcAAABQdyad302bcg7ovz9n65pX16js8Fy01aL7Ek9W6sZcv92vJtvQ2eyG3qukaF6zIJPGnx2rf/4vS5L7tfjXDjxRr67eXu04nK46/QS9/lc/5e9jSIptG6bteUfcXuts72n6PgXzUBeCAh0AAAAAgKozmUwaFh8pSSo/xza7oEiT3/9Jy36pfB96X9VkG7rKCtlJ+msNfaQWjOmnKKvrvaKsFi0Y009D46OqHUNZw+KjPN5n4Zh+mn3ZaZX24W1ac9mCeUBtYEQeAAAAaIBsdkNPfLbJaxuzSQq3BKvwyFGPW9hVtnVcVbe5c8fX0fw9B4o0sk8nj7sB2OyG1y35JCnI5Phio7JnMQeZPN7n4/W7qvuoFZ4HqA2MyAMAAAANkC+j3DZDuuHsOEnHp6c7ld06bubF8V7bzEiOr9EUcV9H853tzEEmJXRtq5F9Oimha9vSezu35PMUq0nShEHen7fss3i6T01mH5TVqnmwJMeXLmmZefp4/S6lZebJZqdMGWqGEXkAAACgAfJ1tDe2XXMtGNOvQnX1qHLV1X1pU10D4tp4HUmvyqh/Us/oSmPte2LrGj1LZfFK3kf+nZ74dJO27T2kl7/ZRmV7+BVV692gaj0AAADqO18r0jurzfuy33lt7onu3M9ecl/IrqpV3mt7f/fK4nVWrXd33pD3yvhUtoc7bD9XQyTyAAAAqO+ce8RXNsq9asqQelM5vaHtu15ZvN7O9z6hlc57aqVK3O1hp/r5+4PAIpGvIRJ5AAAANAT+HuWuC7U56l8bqjvyX9UZEzWNAw0f+8gDAAAATYAv68XrG2eBuYaisng9na9Kpf7KNLSZDKh9JPIAAABAA5bUM9rjNmoInKpW6vfEOeui/DTqnIIiTVy0rl7OukDtI5EHAAAAGriGNsrdFPhS+b59y1CvlfptdkOzlma4vd6QYwnFrKUZGhYfxRc3TQyJPAAAAAD4mXPP+4mL1pVWsi+v+KhNWfsOKq5duNsZFelZ+S7T6cszJGUXFCk9K9/nnQkqU1dr8f2xi0JTrhtAIg8AAAAAtcBTDYPIiFA1CzJp1/4iXfLSt7IEB2nfwZLS887170VH3Ve8L2/PgSK/rKOvq7X4vtynJjsGNIWlBlStd4Oq9QAAAAD8xd3IceGRo7r4pVXamX+kQnvnCH6bFsHKP3S00v4HdW+rVVvyKoz6l9+9wNsItqe1+P7eAcGX+0jy2ubmc+P08jdZtR5rXWP7uRoikQcAAABQm2x2Q2c9karcwmKv7TxNy/eVc7/6hy6K1yPL3I9gD4uP0jlzvvQ4jd9fe97b7Eal94mMCJVkUk6h5yUFQSbJ7uFD8VesgVCVPDSojmICAAAAAPwlPSu/0iRekm4d3FUmHR9tdnIeGxYf6fV65zr6295ZVyGBdla+n/flFp/X4kuOhDwtM08fr9+ltMw82Txl1eX4suY/p7DYaxIveU7i3cXaWLFGHgAAAADqmK/7zJ8S1dLtOvuov0bTi4/ZtTwjt1oxOPPhF7/c6lP7mq7F9/WZ/aEu7xUIJPIAAAAAUMeqss98Qte2GhYf5XZ9e1pmXo1jOebjiPqabXl6N31ntfe0b908uAZRVo2vn29DRSIPAAAAAHWssn3mnWu9nfvMm4NMSujatsr9+Co8tJkOFR/z2sc76TvdHi+/p72kCl867Mg/rDkpm7zGUHaNfG6h5+cJMkmG4bl2QGREaOnn1liRyAMAAABAHfO2z7xzPfyM5PhKC7ZV1o+vyf2EQV00d8Vmj330iGqpjTkHPF7vXJs+78uteu/7HS5T761hwTpy1KaSY3Y1DzHrcInN4zPPvPhUSfL6uUwY5Kha7+n5TDIp72CxOkQ03lF5it0BAAAAQAA495mPsromnFFWS5W2UPPWz/xr+iraaqlQLM/JJMca99uHdPPYx8Ix/XTr4K4+xfLcis0VCtoVHDmqkmN2de8Qri/vGayFlTxzZZ/L1BHxbs+3bxmqVs2DlVNYpKv+kabd+49UuzBffcf2c26w/RwAAACAuuJtf3d/9OPcu11yP8Jd9ksDT32kZeZp9Cvf1eg5o8tsC+fLM1fWxt353fuPaPQr3+mPP4+obXiIgkwm7T1Q7BKDL4X5AoF95GuIRB4AAABAY1KTavPS8T3ga7oW/90JZ7pd6+9Pu/cf0ch5q7T3YEmFc+6+vKgvqpKHskYeAAAAABq5pJ7RHivf+8Jfa/HrYlu4yAiLTCb3z1W+MF91Zj7UB6yRBwAAAIAmwFn5fmSfTkro2rbKSay3tet3D+3uUx91sS2c48uKYo/nnYX50rPyaz2W2sKIPAAAAADAJ55G9iXpve93+rydXm3yddS/LmYH1BYSeQAAAACAzzztae+P7fT8wddR/7qYHVBbmFoPAAAAAKgxf22nV1MD4tr4tOVeXcwOqC2MyAMAAAAA/KKmRfX8obLCfFLdzQ6oLWw/5wbbzwEAAABAw1bTLffqGtvPAQAAAACatPowO6C2kMgDAAAAABolT4X5GjqK3QEAAAAA0IDUi0T+pZdeUmxsrCwWiwYOHKj09HSPbQcPHiyTyVThddFFF5W2GTduXIXzSUlJdfEoAAAAAADUqoBPrV+8eLEmT56shQsXauDAgZo7d64SExP122+/qUOHDhXaL1myRCUlJaXv8/Ly1Lt3b1155ZUu7ZKSkvT666+Xvg8NDa29hwAAAAAAoI4EfET+2Wef1YQJEzR+/HjFx8dr4cKFat68uV577TW37du0aaOoqKjS1/Lly9W8efMKiXxoaKhLu9atW9fF4wAAAAAAUKsCmsiXlJRo7dq1Gjp0aOmxoKAgDR06VGlpaT718eqrr2rUqFFq0aKFy/GVK1eqQ4cOOvnkkzVx4kTl5eV57KO4uFiFhYUuLwAAAAAA6qOAJvL79u2TzWZTZGSky/HIyEjl5ORUen16ero2bNigm266yeV4UlKS/vWvfyk1NVVz5szR119/rQsvvFA2m81tP7Nnz5bVai19xcTEVP+hAAAAAACoRQFfI18Tr776qnr16qUBAwa4HB81alTpz7169dJpp52mrl27auXKlbrgggsq9DN16lRNnjy59H1hYSHJPAAAAACgXgroiHy7du1kNpuVm5vrcjw3N1dRUVFerz106JDee+893XjjjZXep0uXLmrXrp22bt3q9nxoaKgiIiJcXgAAAAAA1EcBTeRDQkLUv39/paamlh6z2+1KTU1VQkKC12v//e9/q7i4WGPGjKn0Pn/88Yfy8vIUHR1d45gBAAAAAAikgFetnzx5sl555RW9+eab2rhxoyZOnKhDhw5p/PjxkqSxY8dq6tSpFa579dVXdckll6ht27Yuxw8ePKj77rtP3333nbZv367U1FSNHDlS3bp1U2JiYp08EwAAAAAAtSXga+Svvvpq7d27V9OnT1dOTo769OmjlJSU0gJ4O3bsUFCQ6/cNv/32m1atWqUvvviiQn9ms1k///yz3nzzTe3fv18dO3bU8OHD9cgjj7CXPAAAAACgwTMZhmEEOoj6prCwUFarVQUFBayXBwAAAADUuqrkoQGfWg8AAAAAAHxHIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg8AAAAAQANCIg/UV3u+kVYmSx92lN4xSTs/qtjmSK6UNs7RZnFz6askqXBL1e91aIe08iJHH//pIP14n2Q/Vvl1u5ZJnw+UFodJ/24tfXOJ6/mcVOmLs6T3W0pLoqQfp/jWLwAAAACPSOSB+urYIal1b+n0l9yfNwxH4nxwm3Tux9KFP0otOktfDnVc6yu7zZHE20uk4d9KCW9K296Qfp7u/bod/5HSrpO6jJcu/EkavlrqfM3x83/+JK0cIUUnOWI7e7G06xNp/f2+xwYAAACggmaBDgCABx0vdLw8ObBFyvtOGrFBanWq49gZCxwj39vflbrd5Nt9cr6QCjOkISuksEipdR/ptEek9VOkXjMlc0jFa+zHpLV3Sn2fkrreePy4Nf74z78vllqdJvX66wuBlt2kPk9Kq6+Ses2Qglv6Fh8AAAAAF4zIAw2Vvdjxq9ly/JgpSDKHSntX+d7PvjTJ2suRxDtFJ0pHC6WCX91fk79OOrJLUpD0WV9pSbT01YXS/g2u8ZWNTZKahUm2Iil/re/xAQAAAHBBIg80VBGnSM1PlH6aKpX8KdlKpIw50uE/pCPZvvdzJEeyRLoec74vynF/zcFtjl9/mSmdOk0a/F8ppLWUOlgqzneci06U9n3rmB1gt0mHd0m/PPzXPasQHwAAAAAXJPJAQxUULJ27RCrcLH3QRnq/uZT7lRR9oWNkvlbZHb/0fFA68XKpTX/pzNclmaQd/3acix4u9XlK+v5WaXGotPQkqeMIx7lajw8AAABovFgjDzRkbfpLI9ZLJQWOYnWW9o4q8m1O972PsCgpL931WFGu41dLlIdroh2/RpRZE28OlcK7SId3HD/WY7J0yt2OEfiQ1tKh7Y4ZBOFdfI8PAAAAgAuGxYDGIMTqSOILt0j5P0gnjPT92nYJUsEvUtGe48dylkvBEa7F68pq018KCpUO/Hb8mP2oI1Fv0dm1rckkNe/oWB//+7tS8xipdT/f4wMAAADgghF5oL46elA6uPX4+0NZ0p/rpZA2UosTHcd2/FsKbe94v/8XRyX5Ey5xTGv3VdRwx8j6t9dJfZ90rJn/aZrUfZJjlF2S9qVL342VhqRKzTs5kvzut0o/z3Ak5i06SxufcrQ98crjfWc8JXVMkhQk7VwiZTwhnf2+FGSuwQcDAAAANG0k8kB9lf+DlHr+8ffrJjt+jbteSnjD8fORbMfxolzJEi3FjZV6PuTaT9o4x0j50JXu7xNkdhSrS58ofZEgNWvhuMdpDx9vYzssFf7mGHV36vuUZGrm2Ev+2BGp3UBpyJeOKfRO2Z9Jvz7mqGDfqrdjv3tvW+oBAAAAqJTJMAwj0EHUN4WFhbJarSooKFBERESgwwFqZsV5UofzpdNmBjoSAAAAAB5UJQ9lRB5ozEoKpAOZ0nnLAh0JAAAAAD8hkQcasxCrdOkfgY4CAAAAgB9RtR4AAAAAgAaERB4AAAAAgAaERB4AAAAAgAaENfJAE3XkyBF9//33Kr9xhdls1oABAxQSEhKgyAAAAAB4QyIPNFFz587VAw884Pbcyy+/rAkTJtRxRAAAAAB8QSIPNFGHDx9WdJtm+ubBYy7He00N0uHDhwMUFQAAAIDKkMgDTViw2aRuUa7HgqicAQAAANRr/JMdAAAAAIAGhEQeAAAAAIAGhEQeAAAAAIAGhEQeAAAAAIAGhEQeAAAAAIAGhEQeAAAAAIAGhEQeAAAAAIAGhEQeAAAAAIAGhEQeAAAAAIAGhEQeAAAAAIAGpF4k8i+99JJiY2NlsVg0cOBApaene2w7ePBgmUymCq+LLrqotI1hGJo+fbqio6MVFhamoUOHasuWLXXxKAAAAAAA1KqAJ/KLFy/W5MmTNWPGDK1bt069e/dWYmKi9uzZ47b9kiVLlJ2dXfrasGGDzGazrrzyytI2Tz75pF544QUtXLhQa9asUYsWLZSYmKiioqK6eiwAAAAAAGpFs0AH8Oyzz2rChAkaP368JGnhwoVatmyZXnvtNd1///0V2rdp08bl/XvvvafmzZuXJvKGYWju3LmaNm2aRo4cKUn617/+pcjISH300UcaNWpULT8RUP/88ccfFb4cy87O9th+586dWrduncuxjh07KioqqlbiAwAAAOC7gCbyJSUlWrt2raZOnVp6LCgoSEOHDlVaWppPfbz66qsaNWqUWrRoIUnKyspSTk6Ohg4dWtrGarVq4MCBSktLc5vIFxcXq7i4uPR9YWFhdR8JqJdOPvkkHT58pMLxU2PMFY61tEjPPPOMnnnmGZfjJ8acoN937Ky1GAEAAAD4JqCJ/L59+2Sz2RQZGelyPDIyUps2bar0+vT0dG3YsEGvvvpq6bGcnJzSPsr36TxX3uzZszVr1qyqhg80GCMvHqlPl76vlPvsCinzf31MW1uFtj8+alf2/uPvC49IiXNMGjnyklqPEwAAAEDlAr5GviZeffVV9erVSwMGDKhRP1OnTlVBQUHpa+dORh3RuDw0fboKDxv6aYfUL+74q31ExbbRrV3brPpNMpmbaYqbpS4AAAAA6l5AE/l27drJbDYrNzfX5Xhubm6la3EPHTqk9957TzfeeKPLced1VekzNDRUERERLi+gMenRo4dGXX21HvukmUqO+X5dwWHp2RSzbp5wizp16lR7AQIAAADwWUAT+ZCQEPXv31+pqamlx+x2u1JTU5WQkOD12n//+98qLi7WmDFjXI7HxcUpKirKpc/CwkKtWbOm0j6Bxuyh6dP1R55Nr3/t+zUvfi4dPhrEaDwAAABQjwR8av3kyZP1yiuv6M0339TGjRs1ceJEHTp0qLSK/dixY12K4Tm9+uqruuSSS9S2bVuX4yaTSXfddZceffRRffLJJ/rll180duxYdezYUZdcckldPBJQL1V1VJ7ReAAAAKB+Cvj2c1dffbX27t2r6dOnKycnR3369FFKSkppsbodO3YoKMj1+4bffvtNq1at0hdffOG2z7///e86dOiQbr75Zu3fv1/nnHOOUlJSZLFYav15gPrsoenTderixXr9a+mWC7y3ZTQeAAAAqJ9MhmEYgQ6iviksLJTValVBQQHr5dHoXDN6tFat+EBbnznmUsG+rILDUtzdZo0ZN1EvvPhi3QYIAAAANEFVyUMDPrUeQN3yZa08o/EAAABA/UUiDzQxla2VZ208AAAAUL+RyANNkLdReUbjAQAAgPqNRB5ogjyNyjMaDwAAANR/JPJAE+VuVJ7ReAAAAKD+I5EHmqjyo/KMxgMAAAANQ8D3kQcQOA9Nn65e77+nlJ8km106Pc7QlPsmBzosONlt0u/fSgdzpfBIqfNZUpA50FEBAAAgwEjkgSasx5//Uu58k9q2NCRJl55hl74+Sep6r3TW7ABH18RlfCKlTJEKdx8/FtFRSpojxV8cuLgAAAAQcEytB5qqb6dKWU+oTbjhejzomJT1hOM8AiPjE+n9sa5JvCQVZjuOZ3wSmLgAAABQL5DIA03RsRIp82lJkslU7pzzfebTjnaoW3abYyRehpuTfx1Lud/RDgAAAE0SiTzQFG1YKJmPHU/ayzPJcX7DwrqMCpJjTXz5kXgXhlS4y9EOAAAATRKJPNAUHdjm33bwn4O5/m0HAACARodEHmiKWnbxbzv4T3ikf9sBAACg0SGRB5qinrdKtmbul2FLjuO2Zo52qFudz3JUp/e27iGik6MdAAAAmiQSeaApahYidbjW8XP5ZN75vuu9jnaoW0FmxxZz3iQ9wX7yAAAATRiJPNAU2e3S9iwpO0yyuf4xYBwzSYdPlhIeD1BwUPzF0lVvSqZyybqllXTVv9hHHgAAoIkjkQeaovWLpOz10tG20sjfpfjnpZg7pdiHZPq9tbQrW/rts0BH2bS17SYZNikoWOp6gePYqZeQxAMAAEDNAh0AgDp2ZL+0Ypbj58H3S9YTpD5/O37+sFla9az0+VSp6xAp2BKQMJu8zZ87fu0yWOpzjZSZKuX8EtCQAAAAUD8wIg80NV/PkQ7vk9qdLA24ueL5QfdILaOlP7dLafPqPDz8Zctyx68nJUrRvR0/52yQbEcDFxMAAADqBRJ5oCnZs0la8w/Hzxc+IZmDK7YJDZeGPeL4+X/PSAW76i4+OBz5U9q5xvFz9+FS6zgpNEKyFUv7Ngc2NgAAAAQciTzQVBiG9NnfHeuuT/k/x7R5T3pdIcWcKR09LC2fXncxwmFrquP3qf0pUuvOUlCQFHWa41z2T4GNDQAAAAHHGnk0HHab9Pu30sFcKTzSsY82W3B5V/Yzy8+Usr6WzKHS8Ee9X2cySSOelP5xnrThA6n/OMfxmnz2lf3+Nbbf35o8z5YvHL92H378WHRv6fdVjkS+zzX+jxcAAAANBok8GoaMT6SUKVLh7uPHIjo69tumird77j4zybHmuk1c5ddH93Yk8Gtfl966VLKXWZtd1c++st+/xvb7W5Pnsdtc18c7OdfJMyIPAADQ5DG1HvVfxifS+2MrJqSF2Y7jGZ8EJq76zNNnJkkbl/r+mZ1whuNXe7kCa1X57Cv7/fviocb1+1vT/153rZWO5EuhVilm4PHj/8/eeYdFdT19fJYqXREpIoKKCqhYsYC9YS+xR0VN7CbGXmKL0RijKSYxiYmJxliivqaZX2LvNVbsvfcuCChl9/v+cbiXXbYCy7KL83keH657z86Ze3buuXdOmZEd+ZNEKpV5dWYYhmEYhmFsCnbkGetGpRQzmwQdJzM/2zhJlGMEBtssE1PaTKUk2qFvCb6JbW/090NmZPxC8vuaw16ltHOhTTWDEfqUJ3JwIUpPFtskGIZhGIZhmNcWduQZ6+bGft2zyjIgSrwjyjECc7WZOeQYlUFEMDS7bGO/rzna7FKmI18+VvNzO3si/yrimJfXMwzDMAzDvNawI89YN0kPzFvudcBcbWYOOeb6XWzl981rmyXeJbp/iogUROVbaJ+Xl9fH50Y7hmEYhmEYppDAjjxj3bj7mbfc64C52swccsz1u9jK75vXNpOi1QfWJHLz0T7PAe8YhmEYhmEYYkeesXaCo0W0b1LoKaAg8gwU5RiBudrMHHKMyiAihZ3h87b0+8rXqw8jbSZFq1dPO6eOuiMPAzEQGIZhGIZhmEINO/KMdWNnL1J26STT+Ws117bzjZsbjTbL7iDnoM3MIUeWocvpVIh/9d7RU0cmsR/bzu9rZ09Uf6yBAtDfZhmpRFd2iOMKehz5EmFE9k5ErxKInt/Is7oMwzAMwzCMbcKOPGP9RHQgajhe+3PPkkTdf7HNPOP5TUQH0Tau3pqf57TNJDmeAZqfewSYLieiA1GZxtqfS7q0nKW7DsmxT000TVdr4WqmM27vrOOkHZFPBd3fu7FPRKR39yPyr6q7jIMTkW+EOObl9QzDMAzDMK8tDgWtAMOYhKOL5v/rjhAOoK3M1BYEER3EzO36d4Tz13qeWNKd0zaL6EAU1lZEWl/3FlHyQzFLbupgAED0+KI4bj6TyKuUcFbVdVGvI+mBOH/3ONGWaUTbZorzRbxypndBcHkb0fn/ESnsiQbtEPngpes5+B3RhX+INkwgivuLSJFtBcLFzP3x5VsQ2RkYYw2oKoLd3TtBFNEx3y6FYRiGYRiGsV7YkWdsA2n20c6BSJUhHHt24o0jpUIrVYuoTIPcy7GzF9+v3IXov++IrmwlqtzJtO8+OE304i6RoytRnaFEjkUM1yERVIfo+HIxCLBrHlHsR7nX3xIo00WOeCKiOkOI/CtpnvcqRXR5K9G1XcLZD2+veV5f2rnscMA7hmEYhmGY1x5eWs/YBpLTElJf/E24XXC62BIJt8RfryDzyJP2bl/aYnqwtYuZDmqZRvqdeF04OBG1+lgc/7eI6NEF079bEBz6QQw6uPoQNZqofd67DFH0u+J40/tE6S+zzj2+TPT0KpGdI1G5Jobr4YB3DMMwDMMwrz3syDPWz8vnRM+uieOKbcVfduRNQ2onr1LmkRccQ+ToRpR03/QZYSmlmr4AboYIbU5UsY1YhbFxkvU6rkkPiXbOFcfNZxC5FNVdrsEYIo+SRM9vEu3/OutzaTY+OJrI2cNwXX6VxNL95EdEL+7nWXWGYRiGYRjG9mBHnrF+7p8Sf4uWJvKvIo6lmWbGMOZ25B2cs2aMJQfdEClPiW4fFsf6UqoZI/YjEan9ynaiC//mTkZ+s22mCMoXUI2oWh/95ZzcRGwHIqI9nxM9z7RjadVCBSPL6onEtpISFcUxL69nGIZhGIZ5LWFHnrF+JGcloGqWQ5p4l0ilKjidbAHA/I48UZZDLjmfhri8lQgqIr/KudfBu2zWkvQNk0RAuVPriK7tIVIpcy5PpRTfzYsMdTm75hEdXyE+az3PcKA6IhFnoHQ0UcZLos1TRTte3yvOlWtuWt0FsU/elHYzV9taA4XpWhiGYRiGKXRwsDvG+lF35D0CiBR2RKp0ET3dw79gdbNmUp4KZ5GIyDPQfHIlR/7OUaLkx0RuPvrLSs5+bmfjJeqPITq8hCjhJtGKN7I+9ywp8tSbGkH/7HqijROzggDmRoY+OY4uIkK9MRQKotafEH3fkOjsn+KfxIpOpukSUJXoxK+Wc+RNaTdzta01UJiuhWEYhmGYQgnPyDPWj+zIVyOydxB7jIl4n7wxpO0H7n5iSby58Awg8o8kIoigd/pQZogZeSLTlowb4sp2olfPtD9PvEe0Nk44XsY4u16UVXfOcirDkJz0V6bLeXadiHTs9zdVF0vOyJvSbuZqW2ugMF0LwzAMwzCFFnbkGesmLTkrB7nkvEhLtHmfvGHyY1m9hOSYXzKwvP72YaJXz4lcihGVisp9XSqlmB3VSaYzvHGS4aXPsgxdwfJMlGEuOea4HilWROJtsSoivzB6vSD6awTRX+8YKEOmta01YC47YRiGYRiGyWfYkWesm/uniQhE7v5E7r7iM9mR5xl5g+SnIy/lOr+8XeRP14Xk5Ic2Fznic8uN/dqzoxqAKPGOKJefMqxJF2cPouKh4jg/Z+WN6koiyF9qgoECJratNWAuO2EYhmEYhsln2JFnrBv1/fES7MibhrlzyKsTWIPItbhw4G79p7vMxcyo9uXzuKzelH3nxsqZQ4a16WKJ5fWm6mppWfmFuX4bhmEYhmGYfIYdeca6YUc+9+TnjLydPVFoC3GsK3r981tED8+IwIShzfJWl7tf3suZQ4a16WIJR95UXS0tK78w12/DMAzDMAyTz7Ajz1g3Oh35zBlm3iNvmPx05ImIymc68rryyUuflapN5Oqdt3qCo0XEcFLoKaAQUfmDo02QoQ8TZJhLjjmuh8gyjrwpunqUNM/1WANGf1+ynWthGIZhGKZQw448Y72kvyJ6dE4c84x8zslvRz60GZHCnujReaJnNzTPSY685OznBTt7kfaLiHQ7iyBqNdfwPnw7e6Lo9wzXY0yGli7ZUZgmx+D1mCiDKDNzABE9u0b08rnhsrnFlOtt/Yl5rscasLMXqQ4N0XK2bVwLwzAMwzCFGnbkGevl4VkiVQaRi7emMyodpzwhSkspGN2snYy0rH28+bFHnkhEow+qI47VZ+XTXxJd3SWO85p2TiKiA1H3X0TqOy0URN5lDX8fyAq+51Ak29ftibovMz0/eFg7ImdP7c89SwodTZGj73pyIsPVm6hoaXF8/5Tx8rklogPRG4u1P1fX1RzXYy1c2SH+2mdL2ajIfFwm3rGsPgzDMAzDMDpwKGgFGEYv6svqFWozfUW8iJw8iNJeiJdqn/IFo5818+IuEUE4ra7F86+eCi2Jbu4X++RrDxKfXd9LlPFSLEH2q2y+uiI6EIW1FRHDkx6IfcqHFhOd+0ukBOv3t6adqHNhg8hFb+9ENGSP+P7zG0R/vycGi0qEma7HveMiUrujO1GPFUQvnwhdgqNzNlOr63pyKiOgKtHzm+JeKdPA9O/llGIh4m+RYkRtP9Wtq3Q9a/sRnf+bqHIXMQBgS7PXl7cSXfiHyM6BaPBOMVgo/TbPrhGtf5do5ydEVboTefA+eYZhGIZhCg525BnrRdf+eCLhrHmVEsvuE26xI68L9WX1+pxbc1A+lmjrB0TX94jVEU6uWcHvyrcwf9129poOa7FgMdN+fQ/R2T+JKnXW/k76K6JNk8VxvXeISlQQ/6gB0enfhIN/cRNRiYqm6SBF4w9tIv7lhezXk1MCqhKd+zt/98kTEd2LF3+DooiqdNVfTrqe838TZaTalhOfkUa0YZI4rj2EyC9C83xwDNGRpUR3jxFtm0nU6VvL68gwDMMwDJMJL61nrBd9jjwR75M3Rn7vj5fwDRdL9zNeEV3brbmEPa9p50yhaGmi+qPF8aapurdaHFhI9Ow6kUcAUYOxmuckHXUF7NOHJa/PGAHVxN98d+Qz5Uv78g1RvJz4++RK/umTHxz6gejJJSK3EkSNJ2qft7MjajNfHMevJLp9xLL6MQzDMAzDqMGOPGOdKNOJHpwRx+zI5xw5h3w+O/IKBVH5luL40iaiRxfEUm97Z6KyjfK3bomY94i8ShMl3ibat0DzXMIdoj2fieMWs4ic3TXPV8jU/eYBolcJxut68YDo7nFxLF13QSI51o8vEqUl5189hgbVsuOd6cg/vUqkUuWfTuYk6SHRrsyAfc2mi+07uihVi6hab3H873jbuT6GYRiGYQodvLTeVlEp87a31pK65EbXxxeJlKkiqFixMtrnc+vIW1O7GSMvukrt4pnPjjyRCGh35CexxPtVovgsOIbIyS3/6yYicnQhip1NtDaOaO8CosgeRIl3RbsdXUaUnkIUVFf3knDvskTFy4uZ2CvbdS/NV+fyFvE3oJp17JH28CNy8yNKfkC072uikBjz23RGKtFDHdkj9FG0NJGdo7h/E29nBeQzFVPs3hx9jnqZE7+KuAclqxNV62NYv2YziM6uF0vs41eI/ikvupqrTRiGYczVV3CfwzA2ATvytsjZ9UQbJwpnRcKzpEgBZeno0MZ0ya2u6kt57XQsHMlNLnlrajdj5FVXSy2tJ8py3pMfEZ1eJ47vHhXXYKl2De9AVKahWN7/XbRY6q9OWFv9+/UrxBIduCT2vhtz5KX9/+aKxp9Xzq4nSn0ujnd9TLSLzG/TD88RqdKJihQ1zSm3syfyLiMG455cyZkjb4rdm6PP0VWGiCi8ve7+Rh0PP7H0fvNUESwRarPyOdXVXG3CMAxjrr6C+xyGsRl4ab2tcXa9mHnM/gKaeE98fna99eiyeVrudTW2lDenM/LW1G7GMIeulnLkz64n+n2Q9uevEi3brgoFUYXW4ji7E09EtGW6fl2kJfKXtxheKp2RlpWazBr2x0t2kpGq+bm5bVpf9ghDFA8Vf59cNr0eU+zeHH2OPhlERNtmmdZunoHiL7LZS050NaUeW+q3GIYpOMzVV3CfwzA2BTvytoRKKUZJCTpOZn62cZIoV+C6QAQZy62uJjvyd4zvU7WmdjOGOXQF1Bz5fMohT2Rd7apSEh342nAZfbqUrifSGSY/Eqnl9HHzgEh56FZCLMEuSCzZ9jnZHy/hXVb8fXrVtPKmXM+GiXnvcwzKyMRYu6mURJun6Dlpqq4m1mMt9xfDMNaLufoK7nMYxuZgR96WuLFf9yySDERe9Rv7rUAX0p6t0jypX1eViujeSXGsz3nwLElECrEPN+VxHnW1YLsZwxy6vnpOlJYkjr0CzamdJtbUrnnRxcGJqFxmGjkptZwupMj2oS2ML7/ObyzZ9rlx5HM6I2/K9by4m/c+x6gME9rNLLqaqR5r6bcYhik4zNVXcJ/DMDYHO/K2RNID85bLC+aqQ5ecp1eI0pOJHFz054i3dxTpxIiM75O3pnYzlw6Gykmz8a4+IhBcfmFN7ZpXXaQ971JqOV3I++OtIFq9pdpemaGWPaKa6d/LaQo6a7j31DGkjzl1NUc91tZ2DMNYFnP1FdznMIzNwY68LeFuYpRsU8vlBXPVoUuOHOiuiuEoqabuk7emdjOXDobKWWp/vDW1a151CW0h/t49LlLMZefpVRHZ3s6BqFzT3OloTizV9k8uEWW8JHJyz1oubwpSCrpn10UqSWNYw72njiF9zKmrOeqxtrZjGMaymKuv4D6HYWwOduRtieDorCXlOlGIIEzB0VagCxEp7AycN6DrvXjx19hSXlMdeWtqN2PIuurDBF0t5chbU7vmVRcPv6wZZynFnDrSkvvS9fTnGLcklmp7Y9kj9OERQOToSgQl0fObxsubcj0eJfPe5xiVYUK7mUXXnNSjDyvqtxiGKTjM1VcER4u+K69yGIaxGOzI2xJ29iL9BxFpvyBm/r/VXMvk+tTQJTsK8a/eO5q6aQD9upq6J9dUR96ormS5djOGnT1R0+l6Tpqoq7TVID8D3RFZsT3mUhdpef1FHcvrpSX35a1gWT2R5do+N/vjiYTTL83gm7JP3pR7tPUnRq7ZUJ9jqgwy3m6mtL3Besj0euqO0HPSyvothmEKDjt70RfoxcD7VnY5IfX1nOQ+h2GsEXbkbY2IDkTdfyHyDND83L2E+NySOT4jOhB1+lb7c8+SQpeWs3TrSkRk56Q78jeQA0c+B7nkIzoQdV9GWi/Vkq7WlBtVun47B83PTdXVkjnk9dljQbRrXnWRUspd2SFSzUmkJhFd3yuOrSV/PJH+6y3iZb62z60jT5TzffIRHYi6/qT9ufrvZ+w31tfn5ESGKe1migx9ZYiIGo4zXg9AdGGDOHYokntdGYYp/GR/X1DHyYOoTEPjMhLvEp3/RxwXKap5jvschrFKDNz5luGbb76h+fPn0/3796lq1ar09ddfU+3atfWWf/78OU2ZMoV+//13evr0KQUHB9OCBQuoTZs2RET0wQcf0MyZMzW+U7FiRTp//ny+XodFiehAFNZWRA7932ixj7XZBwXTwUrLudz9iGLniL/B0Vkjtuq6Jj0gcvcl2j6H6NYBoi3TiLr9rCnv+Q2iVwlE9k5EJcIM1y1FZDc1l3xANdJIq6KwIxpxmMjZzbTvW4KH54kO/SCOe60msnMkWtmVSJVO1PdPohIVjMuwpCNPpOM3zmYDliQvupSsLlLLJT8SqebKNhKfX9tFpEwjKhpM5GNC+1sS9es9soTozO9EpWqbpy8wJXuEIaR98k9NdOTVv+PoRtT+SyIPf+3fz9hvbIoNmMNmc1PP2b+Jzv1JdOZPooYTRMYEfZz9k+jGXuHEDz8oZPw1nMi+CNHIE0QOjqbryjBM4SX9FdHGyeI4ZhRRaHPR37gUJ/p3HNHTy0S75hG1mmNYztYPRKDhUrWJBmwg+qISUdJ9sbqo9iCeiWcYK6RAHfk1a9bQmDFjaNGiRVSnTh1asGABxcbG0oULF8jX11erfFpaGrVo0YJ8fX1p3bp1FBgYSDdu3KCiRYtqlKtUqRJt3bpV/r+DQ4GPV5gfO3uiMg1Eh/3kEtH9UwWjhzRjV7ouUZWuustIukq0KUr0QyOiM38Q1Xpb85wkzzfC8EsukelL67PL9q8iRp5TnhA9vkAUWMO07+c3ANGGCWJfcVg7ovKZAdgCqhLdOUL04FQOHfl8XlqvTvbfuCDJrS52diLo3YlVItWc5MjL0epjiRQG9mcXFNL1unoLR/76HqL0l3nPWPDsGlHaC+FI5mYAI6cp6Iiy7tFStYgiu+kvZ+w3NsUGzGGzOa2nfAuim/tEn33oB6Lod3R/Jy2FaNNUcVx/NJF3GaKipYn+GSuCDz6/rj+jB8MwrxcHFopJEI8AoobjiZzds861/oRoZReiQ98T1Ygj8tUzQXLzINHJNUSkEN+xdxB9TNJ98WxhJ55hrJICXVr/+eef06BBg2jAgAEUERFBixYtIldXV1qyZInO8kuWLKGnT5/Sn3/+STExMRQSEkKNGjWiqlU1Z4scHBzI399f/ufj42OJyykYpJky6QXY0uRm6W1AJFHNAeJ44ySR4io38iRHNfmRcFxM1rVawbebLs7/T8z+2jsTtZyd9XlOdFWmE724J44tNSNfmJBSy0nOO0B0KTP4nbXsj9eHbwSRZynh6F3bk3d5UtBJv8ripS6nyEvrr+agzjws5bcFingRNZshjnfO1Z0hgYho3wKixNtEXqWJYt4Tn9nZi0FIIuvqtxiGKTgS7hDt+Uwct5il6cQTEZVvTlSxDZEqg2jjRPFMy45KKSYRiIhq9M2a3MjJ9kWGYQqEAnPk09LS6OjRo9S8efMsZezsqHnz5nTgwAGd31m/fj3Vq1ePRowYQX5+flS5cmWaM2cOKZVKjXKXLl2ikiVLUtmyZal3795086bhqMmpqamUmJio8c9mkF54758US2EtTW5fvJtOFXuwHpwmOro0d/JcionI2ERiht0Y6rKtzZFPf0m06X1xHDNSzMBJ5ETXF/eIoBJbE9xKmF/Pwk65pmKv4ZNLIuXcg9NEL+4SObgYCAJkJSgUWQMRUnC+vJBXp1qakU+4JZZ+WqJOW6Bab7GNI+0F0bYPtc8/u060d4E4jp2tubJC7gvi81lJhmFsgi3TidJTiIIMrIqM/Ui8E1zdmbUHXp3jy0Xf6+ylGWw3p6seGYaxOAXmyD9+/JiUSiX5+Wnmo/Tz86P79+/r/M7Vq1dp3bp1pFQq6d9//6Vp06bRZ599RrNnZ81e1qlTh37++WfauHEjfffdd3Tt2jVq0KABvXjxQq8uH3/8MXl5ecn/goIsuCQ5r/hUEEtf05KE42FJXiVmLZv1z+GLt6u3cOaJiLbPJkp5KkaK78aLz0x5kVco1B40RkaMAbW0dtWsz5Hf/7VI0+UZKJbSqhMQKf7eO6F7NF0d6YHrGZizdGGMoIiXSDFHJGbipZn5so3yvlTdEkgB+y5uNm4rxsirU+1aXLwYEsQyfWMoM8TACVFWKsDCiJ0dUet54jh+BdHto5rnN08lUqaK4FTh2WIdqPcFDMO83tzYT3R6HREpiNrM07/1y7ssUfS74njT+5oDqy+fZQ0oNpksAidLsCPPMFaPTW0eV6lU5OvrSz/88APZ29tTzZo16c6dOzR//nyaMUMsV2zdurVcPjIykurUqUPBwcG0du1aevvtt3XKnTx5Mo0ZM0b+f2Jiou048/YOYunrnSPCUfUJtVzd0ku3Z6Bm528qNQcQHf1ZyNn2IVHZxkQpj4nIznigOwmvUkSPL4rlZYZ4cV8swVfYEflVInIrnnkNZ8RydHsLB45SKbMCYJGCaLe0NO5DIqdswfd8I8Qs8ctnYsCiaGn9ci0d6K4wUr6l2Gd+YrUIvEgkYlHYAmUaioG9hJtEj84T+YbnTk5OskfoQ6EQy+vvHhMDfsZ0eXyRKOMVkZN7Vuq6wkpQbaKqvYhO/CqCUTWfSZT8UKyoOfc3kSIzxV32F3P1AUggZzEb1PscfcH9jJUxhwxz1WOua2ZsF3PZoznqMQc5uZ4X94h2ZAavq9nfeD9dfwxR/K9iL/3+L4lKR2cG4PxTxAsqEUYUNVDzO7Ijb+T9imGYAqPAHHkfHx+yt7enBw809wg+ePCA/P39dX4nICCAHB0dyd4+q2MLDw+n+/fvU1paGjk5aQdHK1q0KFWoUIEuX9YfcMnZ2ZmcnZ1zeSVWgBQM7d4J/Uur8oO8vujbO4igKj+3Fcvr5SX2KqJvosSLrLHo26aOGEu6+lQkcnIlKlZGzBamJhA9ukDkXzl315Abzq4Xe9WybwfwqUhUuYt2eQdn4QTdPyWuw6Ajb6Ec8oUZ+8x+5O6xrM92zxcvVtaeesfJlSikAdHlzNUEuXXkE26JgSM7x9zLIFJz5E2IXC8Ho4x8PVaTNP9ABPy8e4zol/aa58o1JfKL0P5OiXDxm7xKEC/kxUJMq0tXn+NZUrOPNVbGHDLMVY+5rpmxXcxlj+aoxxzk9npIQVQqyrh8Z3eRnvO3t4l2fEwaGXyIiCI6ak9oyHvkeUaeYayVAntbcnJyopo1a9K2bdvkz1QqFW3bto3q1aun8zsxMTF0+fJlUqntBb948SIFBATodOKJiJKSkujKlSsUEKAjl29hoaCWieclNZVEylPdnyfeI1obJx5chjA1GEv2QQeFomCWqZ5dL65L157+xxfEbJwuTP2NeUY+b5xdLwIwZifpoWn2aA1Iue4vbc69DDl7RJgYSMotOYlcf98M/YktceuQWIGgi8tbdduag1OWg29qv6Wvz1HvY42V2Twt7zLMVY+5rpmxXcxha6bYgKXsKC/XQyD6a4RpusiOuo5tV7vmacuQUvymJmStTmMYxqoo0GmPMWPG0OLFi2nZsmV07tw5GjZsGCUnJ9OAASKieVxcHE2ePFkuP2zYMHr69Cm99957dPHiRfrnn39ozpw5NGLECLnMuHHjaNeuXXT9+nXav38/de7cmezt7alXr14Wvz6LoR7wLq/7YnNCXmfkVUoxuqyTzOvYOEmU00dOZ+TVdbX0AIh8vfp+I4X+65X2DLMjn38Y/H1MtEdrQIquf/OgmFXPDeYKOifnkjchfsfrEOhOwmDfl4neviAH/ZYpNr1hopEyEOmt8iTDTPWYcv8VlvuY0Y1ZbJqM24Cl7CjP10Om6aJS6h6kNiTDyU0EFSbi5fUMY6UUqCPfo0cP+vTTT2n69OlUrVo1io+Pp40bN8oB8G7evEn37t2TywcFBdGmTZvo8OHDFBkZSSNHjqT33nuPJk3K6pxu375NvXr1oooVK1L37t2pePHidPDgQSpRohBH8PbNXG4p7aG2BOkvxR5coty/eN/YbyTaPIgS74hy+jDVkdc122dpRz4v1yvretJwHezI5x5z2KM1UCxY7HeEkujK9tzJUE/VmBeKZ+51NzYjr1KZZ4WPrWCWvsCEfsuUel7cNZ71A4Yyopgiw0z1mHL/FZb7mNGNWWzaBBuwlB1Z+/VwwDuGsWoKPNjdO++8Q++8847Oczt37tT6rF69enTw4EG98lavXm0u1WwHeQ/1SeN7qM3Fg7PCUXArQeSRy20LSXpyKOeknPpDRl/wp+QnWQMcUh5mIrWVDKfEKHR+B0HKy/X6VRKB+pLui8B9HrrjSGQ58rxHPseYwx6thfItxUDbxc264y4Yw9wz8kkPiFJfEDl76C737JpIx+ZQRGTiKOzkxdakwZW78cYD3tmCreYUY9dUmO5jRhtz/m6GZFnKjqz9eryCxDsS55JnGKvkNYgo9Jpg6dllKZWbf2TOIier4+5nvIyxcp6Ze7gyXurfb38/s028yxEV8cz6vHioyEOfnmxaMK68kpfrdXLLcnD0zcq/SiBKTRTH0t42xnTMYY/WgrRP/vKWnC/9fHFfvMxJGR7ygktRIlcfcWzoHpP6E79KIghmYScvtuZXSUS1T3ksIlebox5bwtg1Fab7mNHGnL+bIVmWsiNrvx6ekWcYq4Yd+cKCxR15M8zYBUeLqKykbyBAIRz14Gj9Mhycsx48+kaM9elqZ581Q2+Jdsvr9Rr7jaUHrYu3dgo7xjjmsEdrIaiOyMqQ8oTozjHj5dWRMzxUMI8dSQHvnhpy5F+j/fFEebM1RxeiEhXFsbGtNqbU41HSSBkSgzp5kmGmeky5/+Rr1luJ7dzHjDZmsWkTbMBSdmRKPWa9nhzKYEeeYawaduQLC6YGQzMX5njxtsvMlUxE2g+XzP+3mmt8ybuxB40hXWXnON6IsmZA43qzY8L1GtOV98fnDXPZozVg70gU2lQcX9qUs++a26kunrm8/omBgHevmyOfV1szdeDWlD6n9SdGdFEQ1XvHwHlTZOS1nkxMuf8MXnMO5DDWSZ5tmogIxm3Azp6oyVQDipggwxTs7Imi39NzMgf3lynXkxsZ7MgzjFXDjnxhQd5D/UAsjc1PMtKIHp4Vx3l98Y7oQNT9FyLPbPvsPUuKz03J02oWR95CAyDS9Tp7an5uyvUaC3jHOeTzjjns0Voon7m8/qK1OPJ6At4Br58jT5Q3W8tJvxXRgajJFO3P1esxpkvLWcZ1NeV6cluPg0vO7r+IDkTueuKIRL9jW/cxo01EB6KO32h/boqtERH5RhCFtzdez+1D4q+do/Y5J3eiMg1yprcugKxUodlTfeb0/jJGbmRwLnmGsWpeg82IrwlOrkQ+FYkenRMvd/qCoZmDR+eJlGli6W6xkLzLi+hAFNZWREtNeiCWygdHmz7SbSiX/KuErNRXhhx5KXVfbvf754SIDkSn/o/o3Hqiyl2JavY37XqlbQAJN0U8AFdvzfM8I28e8mqP1kJocyJSCNtOvKf7hVYX5naq5RR0epbWJ9wSGTfsHMQL9utEbm0tpwOQ6cnib5lGRDXidNdjTBdTdDVHGfXzN/YR7fxYOFIVW5t2rUQiHkPSfRFLoOcqorQkostbiU78SnTmT6LG74tnJmO72GW+vhYLIWo6zTRbg4rozxFiIuL8/ww783fjiY4uE8d9/yQiCBmuxYk2TCJ6fJ5ox8dEbebl7ToubiS6so3I3oloyF5RR17uL2PkVIb0PpF4xzJBgRmGyRHsyBcmAiKzHHkp2FV+IL/o5yHQXXbs7HM/um1oRv7+qcwypbUdXyKRpsveSTj8z2+YZ2DCFKR0eDX6mn7dRbyIvMuKgYl7J4jKNdE8z468+ciLPVoL7iWIAmsQ3Tkqgt7ViDP+HX0ZHvKCtEde34y81J/4hmvPSL0O5MbWpN8m8TZR8mMiNx/D5S9mzvhV70tUpWvudTFFV3OUkc4HRxMd+kHEerj1H1FIfcNyJS5tEX+Do4kqthLHFdsQXdsj7Hv/V0SNjeTUZqwbactQpTdyZtOPLxLtnk+06X0x2Onoov0dQORuJxBV6UZUJpvdtfmE6JeORId/JKrZL/dBQTNSiTZOFsf1RhCVqCD+GcIcz6acyHD3E4Mmqgyx2pMD6TKMVcFL6wsTllombm3LYA058uqDDrqwd8x6CFtqef3LZ0TProtjfz166cPQb8yOPJOdnC6vlzM8lBUDR+bAOzOX/MtnujNLWFt/Ygs4e2QNkBjrt57fInp4Rmy9Cm2W/7qZEzv7zJUllLMtIpKTpz6g7eRKFDtbHO/9guj5TfPoyFgeZYZYYUGU80mL+qNFULfnN4n2f627zKl1RLcOiqw2zWdqny/bmCi8g0jBu2GicPxzw4FvROpNjwCiBuNyJyO/sbPPCsbHy+sZxupgR74wYXFHvlr+1mMqJjnyBpwEyZm2lCMvrRIoqmeVgCFMcuR5jzyTSYWW4u/VnWL2xxj54VQ7uWalidSVgs7a+hNbwdT+Xtp/W6p2zvsba6B8pg1L12GM1CSi63szv5vNyYvoRBRcnyjjFdFmQ4HMGKvm9iGxis6lGFGpqJx918lNxGIgItrzuRjoUic1iWjLNHHcYKz+GeiWs4kcihBd30N09q+c6UBElHiXaPen4rj5TCJn95zLsBSGti8yDFOgsCNfmJD3UN8SS2TzA5UyyxG1lhk06SGTdF/bWTHFMbGl1H36dFVmiBcDIp6RZ7LwryqWRqYliT2Rxsiv2XFpVl7XPnmekc8dOXXky7fIX33yi9BmYq/7o/NEz24YL39tl4jhUiyEyKe85jmFQkQAV9gJ5+va7nxRmclnpNUZoc1zt2e70htEwTFEGS+znHaJPZ8Rvbgn7EfOoqCDYsFEMaPE8eapRGkpOdNhywwRu6JUbaLI7jn7rqXhyPUMY7XwHvnChPoe6vsniMo1Nf4dlTJngVMeXxIPP0e3rGjUBY1rcTEynvFKOLPeZcTnacliPxyREUe+mvh7N960gHc5bbPs5MVx8c/8ztMrRK8SiYpkRr9Pui+W+dk5Cp0YhojIzk44cMdXEB1ZKvYa67JZyaav7xP/96tsXj2Kh4qZq+z75F/cF/eRwi73+0xfV0xx5NNfEV3dJY7zM25KfuJSjCioDtHN/WJQovYgw+UlJ698rO6+3L8yUa23iQ4vJvp3gki5lfI4f4NamvLMMFYmr88dc8oxly65RR6cyqVNSwM63zckOvMHUY3+Qv/7J0X8BCKi2I+JHIsYlhPzHlH8SjF5svcLojINTfv9Up4QnVpLRAoRLM8SQXbzQm4deUvZiTnuHWu5Rwv63mJsDnbkCxsBVdWCoRlx5M+uJ9o4MWsml0jshWr1if5UJtJLo38V6+lcFArxoHlyWTxoJEf+wRkRpdbdz3AUf78IMeOT8liMxEv7wXSRmzbLTl6WErsVFysQEm6JlREhMeJz6QHrWVI4bwwj4ZK5nPrcX+IfkabN6rLpv94hajPffGm65BR02WbkpXvBp4JY8sqYjrQl6Nk1opfPiVyKape5vlcMvHoGmn9wxpJUaGmaIw9kBbqTtpXoosn7RCdWieCwyztmfZ7TvtwUTHlmGCtjjueOqbpYQkZeeH5LRJ3Pa8wH/ypEtd4SAetWdhHB3CTsnYlU6cZlOLmKJfb/149o9zzxT8LY70ckskiUrJ77a7AUuXHkLWUn5rh3rOUeLeh7i7FJ+I2/sGHqcsuz64nWxmk/WBLvic/Prtf9PWtdBqvrQWOqro4uInq9+nd0kds2Uyc1SaxqMEUvfeja08/74xldnF2vO6CTZLObp+m26aQHptu0KUgp6LLPyFtrf2ILuHqLbBxEWdudsiMFfSvfwvpn/Qwhzbxe2214CfP9U0Qv7oogZcEGItxf3ytWbGUnJ325KZjyzDBWRt89mlNdzfH8MoeMvGLOmA+BNcVfdSeeiEiZSrS2n2nXo+++Mvb7EYltIJZos7yS01zylrITc9w71nKPWsO9xdgk7MgXNkxx5FVKMepHuiKtZn62cZIolx1rffHW6cjHi7+m6Gqs3fLSZuo8OC3KewQQufsa18tUXaUgNLw/npEwarMgOrDQwHkyzaZNQYqw/vSqZoRna+1PbAUpG4eufgvQXGZuy/iGC2ci45XYoqEPaeCiTCP9y6Ll+0IXZrR7U+6//40W//L7HjXH88tcz8C8IjnyhlZcmIJKSbR9luEyJrWJvjSGxn4/E+uwBuT3KxOC3VnKTvL8fDPx/rPEPWot9xZjk7AjX9iQ91BfFVFddXFjv+7RYRkQJd7RDo6lUmXlP7e2F29dUVVz4iQYc+Rz22bZMYfjotOR59RzTDaM2iyJrSf6T5pm06ZQLEQshU1LErP9EuzI5w1pe46ufuvxRaLnN8Qy4bKNLKqW2VEosoL1GUpDd9EEJ89cfbkxTLn/Uh6Lf4Ywxz1qjmu2VLsZIv1lVsyH8nl05C3SJmS5PjY/kbKOvHpOlPrCcFlrur8Mtj2Zdv9Z4h61hnuLsVnYkS9sSHuoifQvt1R/kTZE9nLPrxOlJooXwxIVc61ivpB9Rj4jlejhOXFsDkc+t22WHXM68o8vZC0zlR15PalymNcPU23WEnIcnES6RaKsffLJT7IG3qSMG0zOMNRvSQ5vSP3CEX9AWlVwabPuvN3JT4huH84sa8DJM1dfnt/fN2dd5rhmS7WbIcwZ88GSbWIuXQqKIp4imDIRUcIdw2UL4/1lDqz93mJsFnbkCyPyy91J3edNjWqevZz0suhXicjeMXe65RfZHfmHZ8W+N5dipu0b969MRAox6pn0SPt8btssO+Zw5D38idx8xSjwgzPiM94jz2THXNkLzCUn+z75+5n3gnfZrJdEJmfIg3oXtfd8y0uQbXxZvUSZhiI7ScKtrEFada5sIyIIB8/QyiRz9eX5/X1z1mWOa7ZUuxlC3ipihpgPlmwTc+lSkJi6T74w3l/mwNrvLcZmYUe+MGJsdjk4msjVx4AAhRjxDo7W/Nial8GqP2QATV1NeeA7e2Tt472vo92Cow1Hs9fXZuqkv8rZKgG9VSnUfuN48Zf3yDPZkW3WgP0r7AycN8Gmc4K8Tz5zRv6elW7TsSU8/Ijc/YkIRPdPZ33+KoHo5gFxnNclyNaCkytRSANxfEnH8nrZyTNyvUbvCzPZvSn1eJS0zD2a22d+dhkeAXmTkRcAteCNZhicMocdWFsfm5+Yuk/e4veXAYy1vdH7z0L3qCnXYit2wlgcduQLI6YEvDO21LLVXO30ctbsyEudYHoy0ctnudPVULvZ2RPVHWH4+7raTJ2HZ0Sud9fiWXvOcou6rq8Ss+Ih5FUuU3iwsxdpa4hI+yVDIf7Ve8fAeTJu0zkhewo6a+5PbAld/daV7WJFkk+FrHSchQFpdYG0F15CmUF0eatmGX0YvC8yMYfda9STncx6W3+Sh3uUiAim65qbZ746dvYG7tV86C+y8+gC0fOb5ov5YLR/JNPaxJr62PzE1BR0Bu2eKEc2m+t6TGx7o/efKWXMcI/a2Ys0hoZo+ZFt2AljcdiRL4zo2kOtzvEVIgiSg2vmbE42KnXWzlmZfZbb2nB0yZpxSLhtfkceILq4URw7ZIuG7OhC1P0X43k+1Wcg87osUF3XxMw9a0W8xF42hpGI6CBs0zPbTJpnSfF5y1mGz5szdy078vmDrn5LcnQLy2y8hHQ9t/4TA7YStw+LQFwuxYhKRRmXo+++cHQ1r91HdCCqEaf9ufr9ldt7VMKUFGzHl+f8mZ+dh+ezBktci2fTobj5+4vsSLPx5oz5YKztTbkea+pj85Oc5JKP6EDUZr7uc97liMLamUeniA5EvhHan+ek7U2xgTzdowoR7NUYz29mFs/ulmW+K/L+eEYPDgWtAJMPePiLvTRJD8Qe6iC1F5uXz4m2zRTHzaYR1RkiImEmPRD7LHd9QnThX9GpSMGpiISzmPKEyM5Bd8dpDXiVEtFFn13LWmYqRXU2BUOO/Nk/RdojhyJEww+Kh9n1fUS7PiaycySq2Ma4fHM6LpKMh+eyHCPeH8/oIqIDUVjbrPvc3U8s0ZNG942dNxfSHvmnV0U/JC2x92dHPk9k77dUKqLLW8RxYXPkiwUTlQgjenSe6PI2oipdxeeSkxfa3HS7Vbf7K9uJ9n5O5OhmPidD4sU98bdGP7HPX9f9lZt79PRvREeXEm2YSDR4F5G9nte5l8+Itn0ojptPJ6o92LRnvjqASI+lyiCq2Jaox3IhY+fHRDf2EVXplv8OqZyRwMwxH8zR/1lLH5uf5DSXvFvmxErxUKLGk0VcpT+GiX7/5Gqiam/mXadXicKGiYg6/yDaMzdtb44yus4f/ono7B/iHh3wr/4JnMR7RLsyBz46LBT3oSTj8UWif8YQ7Zwj+js3Q1tkmNcRduQLKwFVRbCje/GajvzOucIh96lIVHuQ6ITKZO47BEQndH0P0eapYoRRQnpJLBGuPz9vQeNVSlzvle1EylQiJw+iYjlYVirlZH52Xbz8uBQT/09LIdo0VRzXHy2WqnqXEZ34oe+JXj4lun3I+P4lczryRUsTFSkqZqGkWRLeH8/oQ/0+z815c1C0tBj0UqZmrW7xChKZNpjcI/Unj86JOBwPzhAlPxL9X+l6BatbflC+pXDkL23OcuTlFQg5dPIkuw+qQ3ToB6KUR+IZEljDPLqmpRBd2y2O6wwRgWKN6WLqeb9KRGf+IHpwWjj0tQfp/p70zC8RRhQ10PRnvjrn/yG6ulMsa4/9KEtGyhPhyF/eQkRz9eueV14+z9+YD+bo/6yhj81PcpJLnijrfSekftZ9+uw60ZbpRFtmiAGzvK4gvLpTDC55lyWq2kN/OVPa3hxlsp/3LiOedTf3i4E3qR2ys3WG2BZaKoqoai8iO7VZ+eBooqM/i9TP22cRtf/SsI7MawcvrS+s6JpdfnhOvKwQEbWeqx15XqEQ+4EUdkRn/8rK16oux5qXwUojxhc2iL8BkZodojFcihEVDRbH6qn79i0gSrxN5FWaKOa9rM/t7MUMEJHh3MZERMr0rAjz5mhD9YB30vWyI89YM3b2Wfu1T/8u/lpzf2IreJUicvEWL7QPz2bNTpdrItL+FTakGdlLW4hUSqLnt0T8EYUdUWiz3Ml0cCIq2zhT7maDRXPE9T1EGa+IPEuZfyWbqzdR08wB5u2ziVKeapd5cJbo0GJx3CqHz3yJ9JdEmyaL4+h3NWMulGsiVuk9uZy1Miw/uLJdxJcpbDEfbAnp/SLxrrjvjKHrnbHOMLEyK/kh0W49S+9zgjmDH+YHXqWIGowVx5unaWcWISK6+R/RyTVElHkvZn9ntbMnaj1PHB9dRnQ3Pj81ZmwQduQLK9kdeUAs74FSjISWa6r7e36VxKg9kSivzNCUY80v3tKDRtpLlBtds7fbs+tEexeI49jZYj+8OtLsgLGXv0cXxEyks1fOVgmYomvSffGXHXnG2pEi11/ZJv5ac39iK2hksTiRNahYWNLOZSeojuhHXz4lunM0q+8tFWXafnF9yIH0jAzK5gT5t2iZ97gouqg5QKTbe/VcOPPqSMvh5Wd+E90y9D3zJfYvFMvuPUoSNRijea6IV9aqD3MOgGRHkl3YtorYEu7+RAp7IlU6UdJDw2X1xVRycBIDSkREB78jenwp9/qoVGIwj0jcX9ZK9LtigujFXaI9n2ueUymJNkwQx9X7EAXW1C0juJ7YvkIQ5YF8VZmxLdiRL6yo76HOSCU69zfRtV1ZS+MM0XiymOF5dI7oyE/iM1ty5CXM4chvnioc8DINicJ17AEMbSZmMx6eFTND+pDbL9J8L3TZr4/3yDPWjndZ8VeV6SxYc39iS0jtKG2nIiIKbVFg6uQr9o5EoZkD0Rc3mc/Jk75/95hxR8UUADXd8mlQxd5BzOIRieX16ivJzq0Xy/pz+8wnEvuh93wmjlvO0h1kLj8GQNTRcNgK6eCULWDvkJUdyNg+eSmmksKeyDfbdpIKLcX9oEon2jgp907p/RNi0sbRjSg4JncyLIFjkaz7b//XRE+vZZ07vkL0186eRM1mGJbT4kNxrbf+Izr1f/mmLmN7sCNfWPEKEnuoVeliFPCfzOU9MSONR9DMvmTv2IqsgD2+4fmlcd7xyJaH069yzmVIwfFuHCDaOlMMgJCdSD+iywF39SYqVVsc68ptLJEfAyHZA/mlPDFtyRvDFBTZl8Xm5h5ltJG32fwr/hYPLdxBkSTH+OQaEfSOKGubU27x8FcbENmSN1lEYhA94ZYIkFqmYd7l6SOkvog6DxXRvxOE8x6/iuifceJ8zHs5f+af+4fo1DoRnCzjpZh1r9xF93el3+LGPqLUJLNckoxKSXRkiQhi6+ia9axlCgZT98lL7zu+emIqtcoMEnx5q3g/PbWO6NqenL2/SHExyjUhcnA2/XsFQVg7sXVHmUq0aYq41qPLxEQREVHjSUTuJQzL8CxJ1DDzPX7TVDFwlpt2Ywod7MgXVs79Lfa2ERHtmiv2JCnsRJA7U6jZX+wJT00kWq+WP/3bOkRn15td3Txzdj3R2r6an63slnNdpQGLxNsiijERkZOL2AOoD2lZV/bcxupIDzb/yJzpY4gHZ0gjb+mGCUQLKlvn78MwZ9cT7Zij+dlPzdlezUHyY83/P7lcuPsCqMTfhFtisJqIaHWvvF+v5JQaGpQ1FTldWgMiJ9e8yzNEi1lEdk4iqNay9kR/Dst85tuLfeWmoP7MX/Mm0W9vE13PDNRXsa3+lWQ+5cXSYWWaCD5mLs6uFzb8b6bzkp5C9HX1wmvTtoCpKeiMTVwUL5e1umL7h8LWlrXLWZ8l74+34mX1EgpFZi56O6IL/4hr/XukuNfsHLQnofRRdwSRmy9R8gOiVd1z125MoYMd+cLI2fVEa+PE6J86UBH9Psi0G/78P0QJN7U/T7wnZFtTpyFdr7RXXOJFDnU9u55o/bvan6elGJYjvfxd2501eKKOSpm15NFcM/Jn1xP9Xz8iyrYszRp/H4aR7tGUJ5qfs73mnbPrs/ZZqlNY2/bseqK/Rmh/bo7rlZyLKztEgNK8kF/p0nRx9ziRKk37cyjz/swnItoyTb8MhUItAKGZltdL/UXiXc3PC6tN2wrmcuTPrhf2lh1Tf9+kR0R3joljW3DkiTLT5Km0P1dlEK0bYJpNX9osBuiyw/fFaw078oUNlVIEuMnu4KmzcZLhpTiyDF3ANBmWwuD15kDXvMjxq0TkGSiWIF7bo33+yRWRWsTBRcxe5BVzXTPDWAK21/zjdWvb/L7ekjWIXH3ETJmU7iw3vHwm9rIS5b+jYfB5nUmenvkmyJBXMmzJeyCu182mbQlzOPLm+H0vbxFl/SOJPAOMKG0F5Ps9yvfF6ww78oWNG/u1R7E1gAhEcmN//sqwFObSNS9yFArD0evlZfVVRCqRvGJLvw/DsL3mH69b2+b39drZEZXPDBKYl+Btl7eJ2fASYUTFgnMvxxSs4ZkfUl/sYX9xT+S7zguvm03bElJAXUN75JMeZm5RVOiOgWKO39fWMnNYwz3KFFrYkS9sSKnX8lLOHDIshbl0zasc9aWF2WckpCjS5lpWb0u/D8OwveYfr1vbWuJ6JUc+L+nULJkuzRqe+Y5FiMo0EseGYsXkpY7clmPMhykz8vcyB3J8yhM5u2ufz+vvq0wnurJdHFtr/vjsWMM9yhRa2JEvbLj75b2cOWRYCnPpmlc5ZRqKND/Pb4qc8eqYO2K9Lf0+DMP2mn+8bm1riest10wEiXt8UTNVlKmolCIaN5FlHHlreeZLQV/zmk/+dbNpW0Jy5F8+JUpL1l3G2MRFXn/fmwfF1hfX4kSBNUyTVdBYyz3KFErYkS9sBEdn5vrUl6tcIfZzB0fnrwxLYS5d8yrHyY2oTANxrB7wB8gaoTaXI29Lvw/DsL3mH69b21riel2KEpWuK45zk4buzjER1NHZK0tOfmItz3xp0OL2YaLkJ/rLGSM4WsQp0Eshs2lbooiXyHlORJRwR3cZYxMXRm2NDP++0vtVaAvzbFW0BNZyjzKFEnbkCxt29plpLoi0b/jM/7eaa7gDNIcMS2EuXc0hR3qRUV9a+Ow6UWoCkb2T2C9pDmzp92EYttf843VrW0tdrxzzJBf75KXvlGtCZO+YNz1MwVqe+V6lMvdEI2tFQm4AiBxd9JwshDZtaxjLJW/MkTdoa5k0nab/95WzQdhItHoiC9yjRETg++I1hR35wkhEB6Luv2hH8/QsKT6P6GAZGZbCXLrmVY708nfzANHL5+JYeqj5RhA5OJmmhyV0ZRhLwvaaf7xubWuJ65Vinlzbo38JsT4KIhCXtTzz8zIAInF0qXASHV2JPPxzrwuTPxjaJ//yGdHzG+LYP1K/DH22Zucg/krL87Pz7DrR4wti60u5ZjnRuuDJz3tUwsMGIvgzZkcB5DVXSOEjMTGRvLy8KCEhgTw9PQtandyjUooIlkkPxL6Z4Oicj9aZQ4alMJeueZGzMErsrey6lKjyG0RbZxLt/ZyoRj+iDl/lXJf81JVhLA3ba/7xurVtfl4vQLQgUuRV77WaqGJr076XeI/o8zAiUhCNu0TkXsI8+phKQT/zbx4kWhIrlmCPv0pk75CzulOeEn1VnejVc6I2nxLVeuv1smlb4H+jiY4sIWo4gajpFM1zV3cR/dKBqFgI0XsnjMvKbmsZr4hWdhWO+tC9RH4RmuX/+4Fow3ii4BiiAf+a7ZIsSn7co/GriE6sIipZnWjgdpF9g7FpcuKH5rCXZWwKO/usfdsFKcNSmEvXvMgp31I48pc2C0fe3IHusmNLvw/DsL3mH69b2+bn9SoUYunu4R/FDLupjrwU6C2whuWdeKKCf+aXiiJyKSZmZm8fyvl+3e2zhRPvV5mo5oDXz6ZtAc9A8VfXjHxO33d0/b5h7YjO/0/kTI9bL+5FCWmlhyWCSOYX+XGP+lQgOvc30d3jRPEriWr0zZt8xqbgYRuGMSdyGrotYtRUfrBVKzCVGIZhmBwipba6tFk7pag+5LRzNpIWy9zY2ROFNhfHF3O4vP7+KbGsnoio9Sc5n81nLIOhXPLmmLiI/UhkALq2m+jc+qzP01LEVhci28kfbyk8/IgaTxTH22YSvUooWH0Yi8KOPMOYk9L1RFTXlMdE5/8RfxX22kvEGIZhGOulTAMiBxeixDtED84YL5+RSnRlhzi2pUBc5kZ9AMRUAKINE4mgIqrUmSikfv7oxuQdQ3vkzeHIFwshinlPHG+aSpT+Uhxf202kTCXyKm2+wMGFidpDiIqXJ0p+RLRrXkFrw1gQHvJkGHNi7yiiFZ/9i2jLDPGZV5CIWs8wDMPYBo4uRGUaiuW8h74nKtNI955Wab/qle1E6clEbr5E/vm0lcoWCG1GRAqih2eJDn4vBrF17QNW3+f76ALRjX1i4KTFrAJRmzERyZFPvEOkUmXtx059QfTksjjOq/3XHy32fSfcJNq7QAzsHPxWnCvfXHO5PSNwcBJR61d2IfpvEVG13iINpr69+Kbs1TdHGUvV8xrDjjzDmBs3X/H32VXx9/l1ogWVReoQjrbLMAxjG0hR04/9Iv4RiSjTUl9+dr3Yy5t4N+s7aUlij+/r2tdf3ysGtJVpRBsniM/U24xId7sREYW1ISoaZFl9mZwh5TJXponZXw8/8fn900QEsYc+r/EhnFyJWs4iWjeAaNdcol1q587+RVS2yet7fxmifHOiim2ILvxL9ENjsYJBwli/Zco9mtMylqrnNYej1uug0EStZyzP2fVEa+OIKPttlTmCzKlzGIZhrB9jfXn0u0T7v9ZxPrPM69jXm/L8I9JTJrPc69hutsZn4UQv7ooI6aVqis8OLhLOVoXWRG+uznsdZ//KtJPs8LuUQQ4tJvp3nI4TxvotU+7RHJSxVD2F1A5y4oeyI68DduSZXKFSipn37LMMMgoxijjqFC8JYhiGsVaM9uVEpLATe7p1n3z9+npTnn8eAeL9m5+Rts2PLURWgm7LiCp1Ep/9MUykQGs0iajJ5LzJ53ep3GGOfsuUe9RoGUvVU3jtICd+KAe7YxhzcWO/4Y6NIPaV3dhvMZUYhmGYHGK0LycDL6lEr2Vfb8rz78VdfkYWBtT3yUvcPyn+miPVLr9L5Q5z9Fum3KNGy1iqHrYDInbkGcZ8JD0wbzmGYRjG8pirj36d+npzXuvr1G62SPbI9emviB6eE8fmcOT5XSp3vK7t8bpedybsyDOMuXD3M285hmEYxvKYq49+nfp6c17r69Rutkj2XPIPzxBBSeTqkxkML4/wu1TueF3b43W97kzYkWcYcxEcnRXRVScKEdE1ONqSWjEMwzA5wWhfTmIPKPf1WZjy/PMoyc/IwkD2GXn1/PHmSA3H71K5wxz9lin3qNEylqqH7YCIHXmGMR929iIdBhFpdzyZ/281t9AF5WAYhilUGO3LFUT13jFwnl6/vt6U51/rT/gZWRgw5MibA36Xyh3m6LdMuUeNlrFEPZmwHbAjzzBmJaKDSIfhGaD5uWfJQpsmg2EYptBhrC9vOYv7+uyY8vzjZ6TtIznyyY+I0l+a35EnYjvJLebot8xxH+d3PURELWezHRCnn9MJp59j8oxKKSJpJj0Q+3eCo1/7UUOGYRibw1hfzn29Nqa0Cbeb7QIQzQkkSk8mGv4f0fcNiJRpRCPjibzLmLcutpPcYY5+yxxl8qOeo0uJru8lCo4h6v+PebZzWBmcRz6PsCPPMAzDMAzDMDpYWJvo8QWilh8RbZ5C5OxFNOlGoXSqGCvj+S2ihVFEGS+Jui4lqvxGQWtkdjiPPMMwDMMwDMMw5kdaXn/+H/E3IJKdeMYyFA0iqj9aHG+eRpSWXLD6FDDsyDMMwzAMwzAMYxqSI3/roPhrzv3xDGOMmJFEXqWJEm8T7V1Q0NoUKOzIMwzDMAzDMAxjGlIueajE34BqBaYK8xri6EIU+5E43vcl0bPrBapOQcKOPMMwDMMwDMMwpuFZUvP/fpULRg/m9SW8PVGZRkTKVKKN7xNd20N0ap34q1Jql1cpjZexQTjYnQ442B3DMAzDMAzDZOPseqL/jSZKeZz1mWdJkfOb04ExluThOaJvo4lIpfl5dns8u55o40SixLv6y1gRHOyOYRiGYRiGYRjzcXY90do4TSeeiCjxnvj87PqC0Yt5PXl8ibSceCJNe5RsVt2Jz17GhmFHnmEYhmEYhmEY/aiUYlaTdC3kzfxs46RCs2SZsXJke9RFpj1umCj+FWKbZUeeYRiGYRiGYRj93NivPaupAYgS74hyDJPfmGKPL+6Kf4bK2LjNsiPPMAzDMAzDMIx+kh6YtxzD5AVz2pkN2yw78gzDMAzDMAzD6Mfdz7zlGCYvmNPObNhmC9yR/+abbygkJISKFClCderUoUOHDhks//z5cxoxYgQFBASQs7MzVahQgf799988yWQYhmEYhmEYRg/B0Zlp5xR6CiiIPANFOYbJb0yxR4+Shd5mC9SRX7NmDY0ZM4ZmzJhBx44do6pVq1JsbCw9fPhQZ/m0tDRq0aIFXb9+ndatW0cXLlygxYsXU2BgYK5lMgzDMAzDMAxjADt7ka6LiLQdo8z/t5oryjFMfmOKPbb+pNDbbIHmka9Tpw5FRUXRwoULiYhIpVJRUFAQvfvuuzRp0iSt8osWLaL58+fT+fPnydHR0SwydcF55BmGYRiGYRgmGzpzcgcKh8gKc3IzhRxT7NHGbDYnfmiBOfJpaWnk6upK69ato06dOsmf9+vXj54/f05//fWX1nfatGlD3t7e5OrqSn/99ReVKFGC3nzzTZo4cSLZ29vnSiYRUWpqKqWmpsr/T0xMpKCgIHbkGYZhGIZhGEYdlVJE+k56IPYXB0fb9KwmY+OYYo82ZLM5ceQdLKSTFo8fPyalUkl+fpoBBvz8/Oj8+fM6v3P16lXavn079e7dm/7991+6fPkyDR8+nNLT02nGjBm5kklE9PHHH9PMmTPzflEMwzAMwzAMU5ixsycq06CgtWAYgSn2WEhttsCD3eUElUpFvr6+9MMPP1DNmjWpR48eNGXKFFq0aFGe5E6ePJkSEhLkf7du3TKTxgzDMAzDMAzDMAxjXgpsRt7Hx4fs7e3pwQPN3H0PHjwgf39/nd8JCAggR0dHsrfPWgoRHh5O9+/fp7S0tFzJJCJydnYmZ2fnPFwNwzAMwzAMwzAMw1iGApuRd3Jyopo1a9K2bdvkz1QqFW3bto3q1aun8zsxMTF0+fJlUqlU8mcXL16kgIAAcnJyypVMhmEYhmEYhmEYhrElCnRp/ZgxY2jx4sW0bNkyOnfuHA0bNoySk5NpwIABREQUFxdHkydPlssPGzaMnj59Su+99x5dvHiR/vnnH5ozZw6NGDHCZJkMwzAMwzAMwzAMY8sU2NJ6IqIePXrQo0ePaPr06XT//n2qVq0abdy4UQ5Wd/PmTbKzyxprCAoKok2bNtHo0aMpMjKSAgMD6b333qOJEyeaLJNhGIZhGIZhGIZhbJkCzSNvrXAeeYZhGIZhGIZhGMaS5MQPtamo9QzDMAzDMAzDMAzzusOOPMMwDMMwDMMwDMPYEOzIMwzDMAzDMAzDMIwNwY48wzAMwzAMwzAMw9gQ7MgzDMMwDMMwDMMwjA3BjjzDMAzDMAzDMAzD2BDsyDMMwzAMwzAMwzCMDcGOPMMwDMMwDMMwDMPYEOzIMwzDMAzDMAzDMIwNwY48wzAMwzAMwzAMw9gQ7MgzDMMwDMMwDMMwjA3BjjzDMAzDMAzDMAzD2BDsyDMMwzAMwzAMwzCMDcGOPMMwDMMwDMMwDMPYEA4FrYA1AoCIiBITEwtYE4ZhGIZhGIZhGOZ1QPI/JX/UEOzI6+DFixdERBQUFFTAmjAMwzAMwzAMwzCvEy9evCAvLy+DZRQwxd1/zVCpVHT37l3y8PAghUJR0OroJTExkYKCgujWrVvk6emZb2WsRUZhq8eWdLVUPbakq6XqsSVdC1s9tqSrpeqxJV0tVY8t6WqpemxJV0vVY0u6WqoeW9LVUvXYkq6FsR5rAAC9ePGCSpYsSXZ2hnfB84y8Duzs7KhUqVIFrYbJeHp6GjVIc5SxFhmFrR5b0tVS9diSrpaqx5Z0LWz12JKulqrHlnS1VD22pKul6rElXS1Vjy3paql6bElXS9VjS7oWxnoKGmMz8RIc7I5hGIZhGIZhGIZhbAh25BmGYRiGYRiGYRjGhmBH3oZxdnamGTNmkLOzc76WsRYZha0eW9LVUvXYkq6WqseWdC1s9diSrpaqx5Z0tVQ9tqSrpeqxJV0tVY8t6WqpemxJV0vVY0u6FsZ6bA0OdscwDMMwDMMwDMMwNgTPyDMMwzAMwzAMwzCMDcGOPMMwDMMwDMMwDMPYEOzIMwzDMAzDMAzDMIwNwY48wzAMwzAMwzAMw9gQ7MgzDFNoMSWWp7EyHA+UsSR5tUe2V8acWMLeVCpVnvVgGCLzPPONYYq9mqMea4Lb1XphR96GyYsx37t3j86ePWuwjFKpNFhPSkoKpaWlGZRx+/ZtOn78eO6UzESlUpl8g7+uJCcnm1zWHJ1gQb+8ZWRkGPze8+fPiYhIoVDoLfPo0SMCYLDMjRs3aNOmTURk+kPGEPwAEqSmplq0PnO1uzEbyK29Ehm3WbbXgsPS9mpOcvsbStesUCh0ynj06JF8Xh/379+nx48fG6zn2rVr9OOPP5JSqdRZj2THhuphtLFVm80veyUyj80as1ci67RZbtfCCzvyNkZycjK9ePGCEhMT9Rrz06dP6fz583Tp0iWdjvadO3eoSpUqNHXqVDpy5IhOGfHx8dSpUydKSUnRWc/p06epe/fudPDgQb0PjDNnzlB0dDStWLGCiHS/WN6+fZvWrl1Lv//+O506dUrr/NmzZ6l///7UvHlzGjx4MK1evVpnXYbIj5dRAPJAhz6ePn0qd3D6uHz5Mh0+fNjg+T/++MPggMmFCxdo6NChdPv2bZ3nU1JS6NmzZ/Tq1SsiMr0TVG+3e/fu0aFDh2jTpk2kVCp1ypDaQ58D8eTJEzp//jwdPHiQiIjs7Oy0yt65c4e2bNlCy5Yto4yMDJ0PjwsXLtDUqVPp8uXLOuuJj4+n9u3b08mTJ/Ve2+nTp6lBgwb03Xff6dX39OnTFBoaSuPHj5f1Vefq1au0YMECGjt2LO3du5devnypJePu3bt0+PBh+t///pejF6vs15xTG86Nzb948YJSUlIMlrl58yadP39e7/mrV6/S7t27Dcq4cOECTZkyhdLT0/WWSUtLy9HgFJH57ZXIuM2aw16JjNusrdmrvs9y8n1TMGazxuyVyLjN5pe9Emle961bt2jz5s20YsUKevbsmc4+X+rDDfHgwQM6fPgw/f3330Sk/QJ+8+ZNWrduHX3++ed6nxkXLlyggQMH0o4dO3TKiI+Pp+joaNq7d69ePU6dOkX16tWjX375hZKSknSWOXnyJEVERNDMmTN11nPp0iWaMWMG9e/fn5YvX05PnjzRknHr1i3aunUrLVmyhB48eGC0D5PgPjZvfSyRcZs1h70SGbdZY/ZKZB6bNWavRMZt1hR7NUdf8Lq1K1He+gKbHrQGYzOcOXMGLVu2RPXq1VGyZEmsWLECAKBSqeQyp06dQvXq1VGlShU4Oztj1qxZyMjI0JCzY8cOODg4oGnTpoiLi8PRo0flcyqVCvHx8XBxccHEiRM1vifVc/r0aRQtWhRDhgzBzZs3deoaHx8PV1dXlClTBv7+/njw4IFWmZMnTyI4OBi1atWCn58f2rdvj8uXL8vnz507h2LFiuHtt9/GZ599htjYWISGhuKdd97RWeeFCxcwYcIE9O/fHwsWLMDFixe1dH/w4AGePXum8/sSV69exeeff44xY8Zg9erVOut577330LZtW8ycOROPHz/WKnPlyhWULVsW06ZNw507d3TWc/z4cXh6euKHH37Qef7EiRMoUaIEBg0apFeG9FspFAosXbpU6/zp06fRrl07hIeHo1OnTvjf//6nVeb8+fOYNGkS+vTpg/nz5+P48ePyOZVKhRMnTiA4OBgVKlSAl5cXwsLCsGrVKjx58kQud+rUKTRu3Fi2B6VSqVHHyZMnUbt2bVSsWBG+vr6IjY3VqEMqU6FCBdSoUQNubm6oUaMG0tLSNMqlpKQgKioKCoUCQ4cO1bA/yXYdHR0xfvx4reuU6pHsasyYMbhx44bOdj1+/Djc3NzQtm1blCtXDr/88ovW9fj6+qJ169YoW7YsQkJCcOLECY0yJ06cQEhICKKiohAQEICQkBB8//33ePTokVzm0qVL+PjjjzFp0iSsWrUKL1680NBX3bbU73N1bty4gSVLluCzzz7Dli1btM5funQJ06ZNQ58+fbB48WKdMi5cuIDIyEgsWbIEycnJOsscO3YMJUqUwP/93//pPH/ixAkEBATg7bff1nm/S2WKFCkChUKh0xYB4OzZs+jZsyeioqIwaNAgrXa9dOkS5s6di/Hjx2Pp0qV4+PChfM5c9goYt1lz2CsAozZrS/YKwKjNGrNXqZ682qwxe5Wu2ZDNmsNepesxZrP+/v6oUqUKPD09Ubp0acyePRu3bt2Sy50+fRp169bFzp079V7PyZMnERkZiYiICLi7u6NWrVpISUmR6zl58iRCQkIQHR0Nb29vlClTBvfu3dOQkZaWhi5dukChUKBfv37Yt2+fhq7x8fFwdnbGhAkTtOqXfu8LFy6gePHiGDt2rIb9qCO9I/Tp0wfly5fHRx99pHUtxYsXR48ePVCnTh1UrlwZ69ev1ygj/X6NGjVCYGAgypYti4kTJ2q0myX6WKkeQzZbmPpYqR5DNmsOe5XKGLJZY/YKwCw2a8xeJV0N2awp9mqOviC/21V6XlpLu5rattl59eqVfK22DDvyNsKZM2dQvHhxjB49GitXrsSYMWPg6Oio4XRJZcaNG4czZ87g008/hUKh0HK2nzx5gg4dOuD7779HjRo10Lt3b5w+fRpA1gth9pfK1NRUAEBSUhJatmyJYcOGyefOnTuH48ePyy+ZknP5/vvv49GjR6hUqRJmz54NlUol39zXr19HYGAgJk2ahKSkJPz777/w9/fHf//9B0DcYL1798bIkSPlel6+fInq1atDoVCgV69eWu3j5eWFVq1aoUuXLvDy8kLz5s01HqhnzpyBk5MTunbtioSEBJ3tfPLkSZQqVQrNmjVDdHQ07OzsMG/ePI3zvr6+6Nq1K4YMGQInJyd88MEHWnIWLVoEhUKB6tWr46OPPtLoIFUqFY4fPw5XV1eMGTNGpx43btxA6dKldb7cSxw/fhwuLi6YMGECxo0bhwYNGmjUc+bMGRQrVgwjRozAokWLEBMTgzfffFNDhjQo061bNwwdOhRBQUGoUaMGvvvuOwDAw4cPERYWhvfffx9XrlzBnTt30KNHD4SHh2PGjBl4+PAhrl27htDQUCgUCpQvX17uOKXO/vz58/Dx8cGkSZNw4MABbNq0CWXLlsXkyZNlPc6dOwcfHx9MnToVN27cwNWrV+Hj46PzReT999/HgAED4OLigl69euHatWvytbi4uGD69OlyOz958gRXr16Vv6tUKjF48GAMGDBA/v/u3buxZMkSXLhwAc+ePZMfLNOmTUNaWhrq1q2Lvn37yjLu3r2L8PBwfPDBB/IgWUREBL799lu5zK1btxAaGoqZM2fi7t27UKlUeOONN1CkSBGMGjUKd+7ckdu+UaNGaNiwIRwcHNClSxds3LhR/v3s7e0xYsQIWW72F01pMCwmJgYRERFwdHTEsmXL5PPSw61t27bo2LEj7O3tsWjRIq02nTJlChQKBQICArB8+XK8fPlS47zUL4wePVrru4AY/PL398f48eP1vgxL9jpixAj07NkTvXv3RnJyskb506dPw9vbGwMGDMDs2bNRsmRJjT7g1KlT8Pb2Rps2bdC1a1c4OzujcePG+PPPPwGYx14B4zZrDnuVrteQzdqSvUrXY8hmjdkrYB6bjY+PN2ivgHGblZ5jebFX6ZoN2ezTp09Ro0YNTJgwAQ8ePIBSqcTYsWNRp04dxMXF4fr167h+/ToqVqwIJycnBAYGYs+ePVr6Xrx4EX5+fnj//fdx9uxZXLhwAWFhYejTpw8AYdO+vr6YNm0anj59CpVKhZIlS2oN+gDArFmz0LZtW4SFhaFdu3bYvXs3ANEnFSlSRH7mqVQq3L59G/Hx8RrfHzdunPyMViqV+OuvvzB//nxs27YNd+/elX+fKVOmAAB69uyJFi1aID09HYC4j6tVq4apU6fKMhs3bozZs2fL/7937x4qV66MDz74AImJiQCA9957D3Z2dujcuTMuX75skT4WMM1mC0sfC4h3SEM2u3fv3jzbK2C6zeqzV8A8NrtlyxaD9goYt1lT7NUcfcHr1q6mtm12Tp8+jc6dO6N58+aIjY3Frl27ZD/H1mBH3gZ48uQJWrZsqfWC0LhxY7z77rsAhLE3bNgQ7733nnxepVKhVatW2L9/P44fP46bN28iIyMDDx8+RIUKFXD79m38/vvv8qhsrVq1UKRIEXnmKSMjA6NGjZJv5C+++AInTpxA/fr1cezYMWRkZCA2NhZRUVHw8PBA3bp1MWPGDDg7O+P9998HIG7crl27IioqSkOv77//Ho0bN9Z4wLRp0wbff/89li1bhu3bt6NZs2ZyJyE99CZMmIAuXbqgRo0amD9/PgAxyNCnTx8MGjRIlnXp0iX06NEDdevWxZdffon79+8jOjoaTZs2hY+PD7p166blzF+/fh2hoaGYMGGC/FL/008/wc/PDxcvXsTVq1cREhKi4YB+8MEHGD58uNaIXnx8PPr16yc/KGfNmiWvBLh48SKcnZ3lzistLQ3r16/HDz/8gL/++gtJSUn4+++/0aZNG/n8lClT0KlTJwwcOBDLli3DkSNH4OnpKevy66+/wsvLC3v37gUgBlw6deqkYQ9//fUXOnfujAcPHuDFixd48eIFYmNjNQYLbt++jeLFi8PPzw8fffQRzpw5g5CQEBw5ckTj+iZOnIgqVargo48+wsSJE9G5c2ds27YNDRs2RHBwsOwcJSQkoHv37hg+fLj8XaVSiXfffRcdOnQAADx//hxt2rTBqFGjNOqIjY3F4sWL8fnnn+Ps2bNISkoCIDrob775BmfOnIGzszPi4uJw8+ZN+Pj4oFKlSvL3BwwYgJo1ayIgIAANGzbE8ePHkZ6ejvr168svYo0aNULNmjXh5eWFcuXKoWfPnlAoFPJvAwD/93//B2dnZ+zYsQMAsHfvXlSuXFljxUePHj0wbtw49OnTB0uWLMHy5ctRp04dPHr0SH44HD58GD4+PqhevTref/99tGnTRuMF8ujRo6hVqxaaN2+OH3/8EbVr10atWrXg7u4u3+dA1ovm1atXERwcjIkTJ+LVq1d49OgRZs6cierVq+PevXu4ePEiSpcujcmTJ8v2PGjQII0HosSWLVswZcoUTJw4EU5OTvj55581VjC4urrK93R6ejp27tyJP/74Q36w//LLL3jjjTcACHudO3cu3nrrLUydOhXbt2/H0aNH4eHhIctYsGABPD09cenSJfmaEhMT0bx5cw17XLx4MeLi4vDixQs8f/4c0dHRmDx5soZuDg4OqFGjBn7++ec826tSqURiYqJBm3327Fme7TU5ORkffPABQkJCUL16dVlGdps9cuSITdjr77//jjt37hi0WWP2qlKpcOnSpTzbrCn2asxmFy1aBA8PD0yZMgUqlSpX9gpAttlJkybJZbLb7I0bNxAcHIytW7dqXN/XX3+NevXqYciQIZg5cyY6duyIkydPomvXrvDx8dF4gU9OTka/fv0wZMgQjRV4n332GWJiYvDixQv07dsX7777LjIyMuT7p0OHDvj4448xceJEbN26VZ55/fzzz/Hhhx/i2rVrqFixIjp37oz//vsPgYGBCAgIkOX37NkTVapUgZubGyIiIvDbb78hOTkZrVq1wueffw4AiImJQXR0NEqVKoXKlSujfv36Wja7Z88eKBQK/PbbbwCE81yxYkWN+3jAgAHo168f2rVrhxkzZuDXX39FtWrVcPXqVfnF/9q1ayhbtiyioqLw5ptvIjY2Nl/72JzYrC30sc+ePTPaxwIwaLN16tRBdHQ0WrdunWt7BWCSzW7ZsgWPHj3Saa9nz57FqFGjUL16dQQFBeXaZitUqAAiwtChQ/Xaqyk2+9ZbbyE8PFyvvcbFxcmryXLbF7yO7WpKXxAXF6exIu/ixYvw9PTE4MGDMX78eHTt2hUKhQIzZszQu+rNmmFH3ga4f/8+ateuLXfo0sNiwIAB6N27NwCxnHHOnDkaL2sffvghFAoFqlWrhlKlSiE2NlaW0bt3b3lU+p9//oGPj4+8PLRWrVr4888/0apVKzRr1gxjx47FiBEjUKZMGbz55psoXrw4Nm/ejNGjRyM2NhYnTpzAhg0bMH78eBQvXhxdunTR0PP8+fPw8vLSmAFatGgRypYti2PHjgEAZs+eDYVCgebNm6NWrVooUaIEKlSogL59+8o35u3btxEcHIwlS5agT58+aNKkiSyvRYsWGDx4MICsh/CNGzfQv39/NGjQADNmzMCbb76Jw4cP47///oO3t7eGM69UKjF37ly0atUKz58/l+VKM/RnzpzB/PnzMWzYMI0BgIEDB6JevXqIiorC0KFD5eU+8fHxKF++PFQqFWbOnImgoCAsWLAAHTt2RK1atVC8eHF5+VybNm0QGRmJkJAQ2NnZybPjdevWBQA0b94cjRs3xnvvvYcWLVqgSpUqcHR01Bq5b9asGZo2bYr09HQolUo0aNAAM2fOlM+PGzcOISEhCAwMRNOmTTF27FhERUVh1apVACAv9+vWrZu8ImHhwoUoVaqUbDfS8iwAGDlyJMqUKYNPPvkEa9asgUqlwvXr19GgQQPZOXrx4gXeeustre0Da9asQZUqVZCamoq0tDQsWrQI+/fvl8/PmjULDg4OaNKkCcLDw+Hn54c1a9YAAP7991+8/fbbAIBDhw7B2dkZISEhcHNzQ61atTBjxgxERUWhVatW+OGHH/DHH3+gXr16CA4OxqVLl/DGG29gwYIFmDZtGlq2bInLly8jPT0dCxYsQM2aNdG+fXvZjqRR5piYGLm9//77b/j6+mLDhg149eoV5s2bB0dHR7z77rto3bo1oqKi0KRJE5QoUULjmnfs2IH27dsjLi4OAQEBqFmzJmbMmCHbHyBemho1aoTIyEi0aNEC+/btw5o1a+Di4qLxopmamopp06ahY8eOGr/J5s2b4e/vj5s3b2Ls2LEYOHCgxsxPnz590Lp1a7Rq1QpTpkyRR8+3bNmCiIgIAMCQIUPg4uKCP//8EwMHDkRkZCRKlCiBgwcPAgDat2+PqlWrws/PD46OjhgxYgSGDBmC1q1bAxADjNHR0ejVqxcqVqyIOnXqwMnJSWP1SVpaGmrVqoW4uDj5fn3x4gWioqI0VtG88847qFy5MsqWLYt27dohKCgIO3bsgEqlQnJyMjIyMtCwYUNUq1YNzZo1w9q1a43a69y5c/XaKyAGnwYMGGDQZr/++muj9vrrr78atNfAwED06dMH9evXN2izLVu2xJdffompU6fqtNd27doZtNe//vorX+21cePGiI2NxZw5c9CtWzedNpuWloapU6fqtdc7d+4gLS0tTzZbpEgR/N///R9CQkLg7u5u0F4fPHiA0aNH67TZ0NBQ2NnZoVmzZkbttXbt2nrttUuXLpg3bx5q1Khh0Ga3bNmC8PBweTZMfTZq/vz5CA8Px8yZM/Hbb7/Jv3GXLl00XuCVSiWGDBkiD25LbN26FaVKlcKzZ8+wYsUKecUbIN4PpBnqOnXqoGzZspg7dy6USiWOHj0qD7SeOnUKERERKF26NJydnVG3bl3ExcWhZs2aaNu2LdauXYv4+Hh07twZwcHB2LJlCwYNGoSRI0dizpw5aNmyJW7fvg2lUonff/8dsbGxiImJkX/jjIwMpKWloXPnzujSpQsSExPx33//oVy5cvjmm2/w6NEjzJkzB46Ojpg+fTr69++PFi1aoGbNmihevLjGsu9Dhw6hQYMGGD9+PIKDg1GpUiXMmDEDKpVKy2YjIyPRvHlzvX1seno6pk2bhvbt2+u02bt378ozpm+99ZZRm9XXxw4aNAiRkZHw8fExaLODBg0y2Mc6OztrvBPos9latWoZ7GNLlSpl0F5PnTqFW7duGbTZ0qVLy1szlUplruw1LS0Nq1evNslmDx8+rNNePTw8MH78eHkbaW5ttnLlyujfvz9evnyp014BGLXZyMhIODk54dy5c3rt9f/+7/8QFhYmD9zq6gs++OADrFu3TmdfIK3gUl9Fqqtdf/31Vxw4cEBvu0rPSUPtOm7cODRp0sRou7799tt627VSpUpG2/XgwYMoU6aM0b7A29vbYF8gvROoVCr5WarOV199heLFi2PixIm4f/8+bAl25G0EdQddmv2dOnWqxhLKxMREeSbg119/hUKhwJo1a/DkyRPs2rULUVFR8gx3XFycPEvw9ttvo1ixYoiIiEDPnj3Rpk0buLi4oEWLFhr7x1auXAkvLy80atQI77zzDtq1a6exrPLWrVvo06cPhg4dKo/0qVQqPH/+HJ06dUL37t3lz69evYro6GiEhobK+3D+/PNPqFQqPHjwACNHjkT16tVhZ2eHhg0bom/fvnBzc8PAgQMBiE7Fw8MDZ86cQWpqKgYMGICuXbvi1atXGg/tK1euoF69eujYsaM8QwUABw4ckJ15yXHftWuXVlwApVKJkJAQ7NixA7du3dLo/GbNmgV7e3tMmTIFX331FaKiotC0aVN5eXvLli3lZbTz5s2Dm5sbvLy8sGTJEgwePBh169ZFUFAQ2rRpg3PnziElJQVHjhxBYGAgmjdvjqZNm+LHH39EixYtcPv2bQBihmfmzJmoVq0azpw5AwDyyOvixYtRoUIFHD58GAkJCWjZsiU6d+6Mb775BpMnT4aLiwuWLl2KDRs24IMPPkCVKlXg7e2t8TC9desWIiIisGzZMkRGRmLgwIHyi76EtK8IAGrVqoWePXvK/1epVLhy5Yo803nz5k1cv34dr169wrFjx+RZSskpUl+RIQ0k7N69G+XKlcP69evlz9q3b49atWoBALZt24aKFSvKL1etWrWCnZ0dWrdujSFDhsDPzw9t27bV6owjIiLQr18/DB06FNWqVUPv3r3x/fffa5QZN24cwsPDtVZYTJ8+HcWKFZPvhyZNmiAgIADNmjWDs7MzNmzYIJedO3cuAgMDUaJECfTt2xeXL1/G3r174erqirlz5wIAypcvjzJlysij0RkZGfJD+8yZM/D395cHFABxP2d/0VyzZo3WfrLnz58jKCgIJ0+exKVLlzT20M2ePRt2dnYYMWIEZs6cCR8fH3Tq1Anp6elISEhAw4YN5fYeM2YM7O3tUbRoUSxfvhyxsbFo2bIlwsLC0KpVKxw7dgw3btzAP//8AycnJ7Rt2xadOnXC6tWr0bx5c7nt7927h379+qFOnTpy26lUKmRkZOD9999HpUqV5IfvvXv3UKFCBfTr1w/r16/H9OnT4erqiq+++gqrVq1Cx44dYWdnp3HNN27cQLVq1TB//nx4e3tj+vTpqFWrlpa93r17F6dOnZLtVbI7dXsNDAzEtm3bAIh9fceOHdNYlrpo0SKUL19eo72Tk5M17PXy5cs4c+YMOnTooNdemzRpAoVCgdatW+PevXsYPXq0hs3evXtXvrcrVaqEChUqaNnr3bt3cfr0adlesy8JHD16NDw9PeU2b9y4sZa9SvUYstc5c+Zo2Ku0pUrdXqXBzgEDBmjsW5RsVpoRXbt2rcZySHV7lbaIXbx4Ua/NzpgxQ8NmExMTNWx21KhRsLe3h7u7O+rWrYvY2Fgte12/fj2cnJwwefJkLF26FG+88YaGzWZkZODevXto164dmjdvjsePH2vZq9S/3717FxUqVJCvW91eV6xYgT59+sir3KRBHclma9asieXLl8s226FDB1SvXl1+FqWnpyM5ORmvXr1C69atNexZOt+1a1cUL15cttnExERs2bJFXioLABs2bEDZsmU1Zudu376N48ePIzw8HH///TcSEhKQmpqKoUOHomLFikhKSkJ8fDxCQ0ORkJCA5ORktGnTBo6OjmjcuDHGjRuHiIgItGzZUo7dkpycjNTUVDRo0ACxsbH4+OOPUbVqVXTp0kXu85KTk5GUlIQvv/wSISEhWrFlpBnk8+fPAwD69euH0NBQNGvWDK6urvLzIDU1FStXrkTJkiVRunRptGrVCtu2bcOmTZvg5uaGUaNG4fDhw6hbty5KliypZbO3bt3C+vXrERAQoLePvXXrFuLj4/H333/r7WM3b96My5cv486dOzptVnrW+Pj4oHPnznj69KlWH2tnZwcPDw+sXbsWsbGxWjZ78OBBfPnll3Ifm91eAciDLvXq1dPbx8bHx+Po0aN4/vy5TpudOXMm5syZgy5dusDOzg4ff/yxhr3Wrl0bv/76q2yvANCuXTstm5XIbrOSvfr4+MirBnXZ644dOxAaGqplr0qlEidOnJBtVqpr6NChqFChAg4cOCDbKyDeFyR7PXz4MBYsWKBlsxKSzX700UdaNivx5ZdfIjg4WMNmv/32W3h5ecn2qlQqERcXp2WzEj///DPs7e0RHR2tYa/Tpk2DUqmUZ9y7deuGKlWqaLSrUqlEenq63r6gS5cuKF68OPbu3YuUlBSkp6djy5Yt8iz0jh07UK5cOY3Bpuzt+tdff8nPEakv2LNnj0a7tmvXDg4ODnK7fv/996hQoYJGuyqVSnkiKTY2Fl988QUqVaokt6tSqZTfIaW+QP1d7dtvv4WnpyfOnj0ryxs6dChCQkI02lWqR+oLQkND0bx5c422lWxValuJsWPHyo68ut0uWrQIbm5u8tZSXbFzrBF25G0MdcOaMmWKRgCmOXPm4LPPPkN6ejquX7+uEcQOANq2bSvP3vz888+YMWMGhg0bhoCAAFy9ehW///47ypUrh759+2L8+PHyC4K6sxUaGio71QqFQivgxNixY9GwYUOtPVy//fYbFAqF3IkDYtnamjVrMGPGDHTt2lWj/Ny5c1G1alXs2bMHffr0wcCBA/HNN9/I53///XeEh4fLnd3OnTthb2+PL7/8Ui4jdUo7d+6EnZ2dHAdAasODBw+iWLFi8sx8Wloavv32W2zevFnWPy0tDWXKlMHmzZtluVu3bsX58+cxatQobNiwQX7onD17FgqFQnbqGjduLI+sDhgwAJ6envD398e8efOwb98+9O3bF23btsX58+c1Hlzr168HEcHX1xcRERFo3rw5gCyH/ebNm3BxcZFn0iWkFwzpxfngwYNo1aoV3nzzTVSsWBE//fSTLOP+/fsoXbo0OnToAIVCIS/Rc3NzQ79+/ZCQkCDPbm3fvh2+vr7yPqekpCQ8ffoUCQkJGDNmjPwyJLWr9CIYHR2N4OBgXL16FSNGjEDNmjXl7QXLly9HWFiY/IAYM2YM2rVrh4yMDNy+fRtXrlzRqGf+/PmoU6cO0tLScPfuXbRr1w5JSUno06cPSpYsiZ9//hnu7u7o0KEDpk2bJs9eJSUl4dmzZ0hISECXLl3QtWtXJCcno2rVqlAoFHj//feRlJSExMREJCQkYPPmzahataqspyTj2rVrqFChAiZNmiTbxt69e/Hnn3+iZs2auHHjhqzr/v37ERoaim+++QZBQUHw9fWFt7c3hg0bhnPnzuHChQuoX7++vJxLWkb26NEjnDhxAhcuXMCqVatQrFgxeZlXRkYGVq9ejSJFiqB37964dOkSnj59iuXLl+PkyZOyTjdv3oS/v7/G0rSDBw/i2rVr6N27NzZs2IAnT57g3LlzWLt2LYhIngWKioqSAzn17dsXrq6ucHR0xOrVq3H8+HHExMSgRYsWuHbtmizj4sWLWLhwIYoWLQpnZ2dUr15dXv755MkTnD17Fhs3bgQRYdOmTXKbAiLwpLSk9cKFCwDE0rry5cujc+fO8Pf3x/fff49z587h/PnzuHbtGhwcHODg4IApU6bgyy+/hIeHB5ydndG5c2eMHDkS0dHR2L9/P3x8fGR7lbaLdO7cGb169ZLtVf2e27t3LxwcHODi4oL169djxIgRqFWrlvzidvv2bXh4eMDDwwOHDh3SsNkbN27gypUrGvWMHDlSttc7d+7I/W737t1hZ2eHmjVrwsXFBR06dMDt27cxb948/Pbbb7h16xaKFy+OTp064b///kOXLl3QqVMnREZGyvaqXs/ChQu17PX27dvw9vaGh4cH+vbtK7f3nj178Ndff6FmzZo4deqULOPHH3+U7bVUqVKyvfbp00e+z+rXr49OnTpp2KtSqcShQ4fQtm1bLFmyRMNepfadO3cu7OzsMGzYMHnWZfny5XJwraSkJPj5+aF+/fryIJ9kj+o2e/z4cbRr1w47d+6EQqHQstnjx48jKCgIbm5ucHZ2xieffIJ69erJ9gpAlvH555/L8QyKFCki26x0Pjk5GefPn4dCocCmTZs07NXV1RUVKlSQdd26dauGva5cuVKWc+bMGbi6uqJVq1ZwcnKSbdbd3R2lS5fGrl278PHHHyM6OhrXr19HmTJl0KJFC6SmpuLUqVNo27Ytdu3ahXnz5qFBgwYay2ABERjN398fXl5e2LhxI4YMGYKwsDB5oOHUqVOyMysNIo0bNw6NGzfGvXv3cOfOHZw6dQpt2rTBrl278MMPP6Bq1apITEzEq1ev0LlzZxw5cgSlSpVCiRIl8PPPP6NKlSpo3LgxZsyYIb9Mq8sYPny4vJKhQYMGUCgUiIuLw7Fjx+TrOXjwICIiIuQVMCqVSr7e8PBwdO3aVSOw7p49exAWFoZNmzbJMo4cOYLy5ctj5cqViIyMhL+/PwICAvDWW28hKCgIo0ePRteuXdG4cWOtJfvS+VWrVqFo0aJafayzszPc3d0xduxYZGRkYNmyZRp9bFJSEvz9/eHn5ydvr5ECgUk2u2jRIgQFBWHMmDE4ePCg3M+q97HSBIa9vT1Wr16NPXv2aPSxp0+flmUsXLgQXl5eWn3sqVOnEBQUhP79+0OhUODff//V6mNdXV3h5eUlz9Zv2LBBw2Y/+eQTBAUFYdSoUTr7WC8vL7z55ptYs2aNPLOclJSEhw8fyjZ75coVrFmzBr/99hvi4+OxYMECNGjQQMNJunr1KurUqQMPDw8sXrxYy14BMdjn5+eHdevWIT4+XrbX5ORkJCQk4PDhw1izZg3WrFmDkydPYunSpahatSoePXqEzp074+XLl+jevTu8vb0xevRohIaGonHjxvjvv/+wbNky2WZv3bqFVatW4bfffkOvXr20bLZLly5YuXIlfvvtN5w8eRLHjh2TbVZqWymuSExMjMa7tmSz5cqVw8KFC/H777/j2LFjOHv2LEqXLo3Q0FDZXt9++2307dsXTZo0QUhICFq2bImHDx+iatWqaNKkCVJTU3HmzBm5TN26dVGxYkX52ZWeno4zZ86gd+/eKFGiBJydnTFt2jS5bSXnWno/atKkCQYOHIh27drJ7frkyRPs2LEDffv2RePGjTFo0CAMHToUkZGRSExMRLt27fDy5Ut07twZrq6uiIiIQLFixVC5cmX8999/+OOPP/Dbb79BqVTKujRp0gSVKlVCZGQkAOF7KBQKdOjQQT4/aNAgfPzxx3K7SpMnZ86cgY+PD3x9fdG/f3/8888/AMS7zfbt2xEWFoYtW7bIbfLGG28gICAAK1asQExMDHx8fBAQEKAxKde1a1d5VRwA+d1Bah/1gfCZM2fC3d1dbyBva4QdeRtE6kimTJkiL7WaNm0aFAqFVqAJQLxwvXz5Ej169JBHl3ft2gWFQgF/f3+NvSd//PEHrl69Ko/Sq9f5+PFj1KtXDytXrsTu3buhUCjQrl072UEGxPLVgQMHas1opqamomXLlujdu7fGMjVAzCS3bdtWo77Ro0ejY8eOSEpK0hoUuHDhAho1aoR69eppLHP/9NNPYWdnh8WLF+PChQv49NNPcffuXRw9ehTh4eEaQaYkOSNHjkTRokXRvXt3DBgwAA4ODnJgjDNnzmDOnDkICQmRXxwnT54MhUKBO3fuIDk5GRcuXMD8+fNx+/ZtnDx5EjVq1JA79YkTJ2L58uXo06cPPD09cfDgQcyZMweurq747LPPcPXqVWzYsAGnT5/Gp59+Kncq69atQ1hYGFavXg0HBwf4+vpi9erV8vWkpqaiadOmGqshzp49i08//RRz5sxBuXLl5N80KSkJGRkZqFevHr744gtZRlpaGlq0aIGvv/4aP//8M6KiolC/fn2UL19ezooQFxeH6tWrIyUlBb/++it8fHzQsmVLtGjRQi4TExODnj17Ii0tDSqVSiOzgp+fH8LDw6FQKODq6io7QGfOnEGNGjXg7OyMkiVLon379nBxcdFYqpw9Q0OjRo3w1ltvIT09HRkZGahVqxacnJzg4OCAEiVKYMWKFdixYwf8/Pxw584d+QGoLqN27dryvsUDBw6gcuXKCAwMRI0aNVC1alWULFkSbdq0QaNGjeQXdUlGtWrV4OrqitDQUA37lV6K1Otp06YNatWqhadPn+LFixf477//sG7dOjmbhKOjI0JDQ7FgwQK8++67cHZ2xsKFC+XzTk5O6Nu3L8LCwjSivB4/fhwhISHyy1/dunVhb2+v4ahUqVIFDg4OcHR0xKxZszBx4kQoFAo8fPgQT58+1chq4eTkBD8/P3lG4c0338S///6L3r17w9HREWFhYbCzswMRYc2aNTh//jzWrVuHY8eOacho06YNqlSpgoULF8LBwQHVqlXDv//+q5E9IygoSL6HAPHSfOrUKfj6+soDBtKy7SdPniAhIQGVKlVC+fLl5XqmTZuGqKgo9OzZE2XLlkW9evXke7Zp06aoUqWK/OKwevVqFCtWDJ06dcKWLVvkMmXLlkXLli2Rnp4uz1oBWZk8ihYtCiJCkSJFZHtVKpXYsWMH7O3t4erqiri4ODl4nbRCJyMjQ5bRtGlThIaGokOHDrK91q9fHyVKlECxYsVgb2+Ppk2bIjY2Ft7e3vJ9n5KSoiEjLi4OTZs2xfTp03Hw4EGEh4cjKCgIn3zyiVwmIiICNWrUkGf5lEoltm3bBgcHBwQEBKBEiRIag6d///03wsPD5XqaNGmCiIgIRERE4OnTp0hKSsL+/fvl2Ulpb3CrVq3w1Vdf4Z133oGzszPWr1+vkd3k33//RXh4OB49eqQR0VgarJBmOkeOHAl7e3vcuHEDaWlp2LZtGxQKhbzSavLkySAieZXGkydPZDkTJkzAnj17EBkZiYsXL0KlUuHNN9/E119/DQcHB7i7u+PGjRsYOXIkFAoFPvvsM6xbtw6pqakaui5cuBCVK1fGy5cvsWjRIjg4OKBChQooUqSIHH358ePHiI6Olm0gPT0d8fHxcHBwQGBgIG7cuCFf56NHj5CQkIBatWph6dKlcHFxwfjx45GQkID69etj0aJFmDVrFsqWLYvIyEgUKVJEzvgyZcoU1KtXT+6TSpYsiVq1asHT0xMDBw7EzZs38fbbb6NNmzby81GpVMpB3AYNGiS/JLu7u+Pw4cMAhINXtGhRdOrUCX5+fkhPT8f7778PFxcX+T6Uyki6DB8+HN26dcPLly+hUqkQFRUFIoKLiwv+/vtvAGL2NyoqCjdv3sSrV6+0Mtj07t0b7777LlQqFe7du4e6devCxcUFrq6u6N+/P27evImJEyeiVq1a8iC8pMfgwYMxbtw4VK5cWV6BJtUZGhoKT09PuZ5JkyahSpUqsp2cO3cO69evlzPl+Pn5ITY2FrNnz5b72K+++kojk86qVatkm5U4cuQInJ2doVAo5BWADg4Och+bnp4u22ypUqXg7+8v25uky+7duzXqWb9+PSIjI3H9+nW5j+3VqxcUCgWCgoLg6uqq1cceOXJEQ8ZHH32k1cf+73//k8v4+vqiVq1a8u8v9UmSzTo7O6NEiRJyX6PexxYpUkSu5+rVq6hXr55GHztixAg5u5CbmxuKFSsmD7weOHAAfn5+cHZ2RuXKleXsQ926ddOwWSlgYM2aNeHs7Awigqurq4a+J0+ehL+/PxwdHeHn54fy5cvD2dlZtlddWY569+6Nbt26ISUlBU2bNoWLiwvs7Ozk7U0NGzZE5cqVZacsNTVVS05gYCB69+4t26w0yB8cHAxfX1+0b98egwcP1rBZSUZgYCAcHBzQokUL+TkKiNVyjo6OiIyMhJ+fH9q1a4chQ4bINnv+/Hls3LhRzsr06aefwtfXF8WKFcN7772HvXv3olKlSqhevTq8vLzQv39/fPbZZwgMDISrq6u8ykQ9s9O8efPg5+cHIoKjo6PctufOnYOHhwdcXV0xb948lC1bFgqFQp4805UdysvLC6GhoUhOTkbnzp3h6OgIhUKBjh074rPPPkO9evXg7Owsb2fVJScgIAAeHh6YOHEi7t69iwYNGoCIEBMTg1mzZiE2NhbFihWDr6+vRl9QrFgx1KxZE/7+/mjUqBHKlCkjx3q6evUqypQpAy8vL7meMmXKwMnJSY5pc//+fXk2X+ovO3bsKK8GU6lUSE1NRcOGDVG3bl15sF5arXDv3j0EBQXh999/h63AjrwNIr1AzJgxA4MHD8b8+fPh7OysNQOvzrRp01C6dGl5iX5aWhp++ukneWZEXxRUdaZPn47y5cvj+vXrAMRggOQgSSOLXl5eOHXqlM7vf/zxx/D09NRKdyNFnJ83bx5++eUXTJgwAUWLFsXJkye1ZPzvf/+THwRvvfWWxkM4OTkZM2fOlF8+FAoFRo0ahZEjRyI0NFRj/8ylS5fg7e0NhUKBvn37gojg7e0tt6H6+aJFi2Lr1q348MMP4ebmJu/Zunjxolxm8uTJGDVqFOrUqSPXs2TJEhARFAqFXObRo0f45JNP5N8hu4xHjx5h3LhxiI2NRUJCgrxFQupIhw4dipEjR6JkyZLyw0ld1wEDBsDf319evaBUKpGUlISqVavKbTJ27FiMHTtWXokBiNmd7FkRpH2iL1++RFJSEhYuXAg7Ozu5o5WClUh7/bNnVpCihnp6kBJYwAAAJZFJREFUespLhaUyHTp0QGhoKGrXrg0i0lhdoC5n2bJlqFOnDohI7lhPnDgBFxcXlCpVCrNnz9bI4CB1xuoyfvnlF1mGFHlXip5qb28PDw8PlC5dGmXLlgURySkHJRmjRo3CypUr8fbbb4OINIIYHTx4EHZ2dggMDESnTp1QuXJlDRnqcsaNG4dDhw6hSZMmICLs378fd+7cQdeuXUFEaN68OXbu3ImPP/4YRITw8HA8ffpUQ8aYMWMwb948EBGISI6SLp0fPnw4SpQogffff19rAOX06dMaWS0kPaQ+YP78+bK99unTR858QURy8B1pJlc9MwYRoXXr1njy5Ak++eQTKBQKODs7o2fPnti1axeaN28OIpL1UNe3a9eu8ku+QqGQ+5a9e/fCzs4OLVq0kJd/SwOPN27cwLNnz/D8+XONDBw+Pj4oV64czpw5A5VKhT///BMRERHyDEjNmjVhZ2eHtm3baq3OefLkCdq1a4datWrB3t5eY4BSqVTiyZMnqF27tvzCa2dnh7Vr1+qU0aZNG9jb26N9+/Zy206ePBkdOnTAtm3b0KFDB3z33XeoUaMGevbsKfdzUj0dOnTAt99+C39/fxQpUkRe4XPixAk0btwYJUuWhKenJyIjI2Fvb482bdpo6dqhQwd89NFHICLUrl1bPv/s2TOEh4ejTp06qFChAmJiYmBvb4/WrVvLukoRxcePH4/ExERMmjQJvr6+uHjxIh4/fowRI0bIzsHw4cPx8uVLTJo0CVWrVpWXSJ44cUKWkZGRgVWrVsl9qTTIKEXbd3Nzw9WrV/Hhhx/C3d1dI82Rui4AMGnSJDRq1Eh+hkyYMAFEBDc3Nw2nYNSoUfJeVHVdABF8sEuXLnj27BlUKhXGjx8PIkKlSpWwe/du3L9/H1OnTkVwcLDs+EgyunXrBg8PD/zxxx8AsrYZJSQkoFSpUnB2dsb48eORlpaG6dOno1SpUvLA8KNHj9C0aVONjC/du3dHp06dcPXqVahUKhw6dEjeglW7dm107NgRrq6uWLNmjTxznJSUhBYtWmDYsGHIyMjA4MGD4eXlhXXr1uHGjRsamWX27t2LqlWrol+/fnBwcJD7C/UyKSkpmDp1KooWLYp169bh2rVrSEpKQuXKlREUFCT/XmfOnMHx48dlJ05dxsuXLzFlyhR4e3vjjz/+kB3fBw8ewNvbG15eXvD390eLFi1QtGhRrF69WkNXaYvRs2fPQEQYPny4fL0vXryAq6srihYtirfeegt9+vSRZVy5ckW2EylTzvnz51G8eHG4uLjIQWq7dOki91OnT59GREQEGjZsiMjISHn5sSRj0qRJWLRokTygd/jwYfn9SIoK7+7ujiNHjsDX1xdOTk7477//ZMc5e8aehg0bomHDhnj69KncxxIRBgwYIJeJiYnRyByUXUa9evXwxhtv4O7du5g3b548mNu3b1+cOXMGJUqUQNGiRXH79m2NlGtSpgw3NzcEBQXhww8/REZGBpRKpby6pU2bNnj06BHCwsLQtGlTlCpVCjdu3MDz589x8uRJjexCrVq1gouLC/bt2yfHGPHz80Px4sVRrlw5VKhQAc7OznBxcZH7E/UMRQkJCWjVqhUUCoXGdhOpTJ8+fVC5cmV06tQJRCSvaMye5eiPP/6Au7s7vLy85HeLTz/9FM7Ozujfvz+SkpLwv//9D/7+/hpB9tTlPH78GD169NDoy69fv46SJUsiODgY5cqVQ7FixeDk5ARPT095+4+6jNu3b4OI4O7uLr8XSueLFSuGihUrokWLFihSpIiGDPWsTI8ePcLEiRPh7e0tT3z06tULR44cQfHixeHp6Sn3BW5ubggLC4NCoUD37t1lGUqlEhkZGXjrrbdgb28vy5Dq6dKlC6pWrYrRo0fD0dERFStW1JIBiMHkSZMmyTL69OmD77//HqVLl0aPHj0AZE0MVqtWDQqFAm+++abG9Uh9QYkSJRARESG/Y3fv3h1ly5ZF2bJl4e/vj6ZNm+rUdeTIkXj27BkUCgWmT58uZ6rq27cvkpOT4e3trdEXeHt7y9ejHplf6nMHDRqEIkWKyLGRpHfvbdu2oXbt2mjWrJlGILynT58iLCxMHry0BdiRt2GkAHFeXl4aLzHqrF27FiNGjEDx4sXlwHISpu7/+PXXXzF48GAUK1ZMS8b58+cxdepUNG/eHMOGDdPpxEsPlqdPn6JmzZpaM+MAsH37dpQrVw7ly5dH48aNdebiffLkCZo1a4YyZcrIjsr48eM1HHRp36SLiwu8vLzklzz1KKBJSUl466230L9/f3z55ZfyCKZ6xHfp/DfffAMikkeKpXZWLzN16lQQEZycnDT2yh0/fhxVq1ZFhw4d8M0338j6SoMPSUlJGDBggFyPQqFA3bp14e7uLr/cSw/PEiVKwMPDA0SEYsWKydsesuuqUChQqVIllCtXTp4lT0pKkp02X19fEBE8PT1lGdmzIpw7d07ebyqlq5PKDB06FBMmTMDAgQPxzjvvICoqCu+++66WDKVSia+//hpEJO+hf/z4sVxmzZo1UCgU8qCAtPdbvczWrVvRpUsXlCpVCrVq1dKo5+2338bdu3fltpYyOEgrRyQZmzdvRvv27eHv7y/LyF7PDz/8gOnTp2Pu3LmoXbu23utJTExEYGCgHJfiwYMHaNmypRx4sV69eujWrZvcJlIZKZvE8ePHMXToUAQEBKBevXo4ePAgdu3aherVq6NJkyZwcnJCmTJl5Fn7pUuX4vjx44iPj5dlZGRk4O2334a7uzvq16+P/fv3Y/v27ahTpw7ee+89vHr1CpUqVUKzZs2gUCiwZMkSLRmXL1/G1KlT4e7ujpiYGOzbtw8nTpzAt99+i1KlSsm/ubSUt1WrVti3bx+2bdsm1wNAluPg4IAVK1YgPj4ex44dQ+XKleHm5iavyChdurS85D27LoDYT1m/fn3ExsZi37592Lp1K6Kjo9G8eXMoFArUrFkTDRs2hLOzM5YuXYqjR4/i1q1bcgaOkJAQDBs2DC4uLqhcuTLefvttNGrUCN27d8ezZ88wfPhweVbjyy+/lLN0REdHo0uXLsjIyMDdu3fh6+sLOzs7fPrppxplOnfujIcPHyIgIEDuTyIiIrRkrF27Fu7u7ggICJBlDBw4EC1atEB0dDRu3rypN2NIdHQ03njjDTx8+FCOkVG0aFFUqlQJAwcORHR0NPr27YuMjAzMnz8fxYsXx+TJk7Fw4UKdMipUqIDz58/LqyUGDRqEOnXqoFevXjh58iTq1auHIkWKoF27dhptop655OjRoxg8eDDc3Nzk1Ftff/01fvvtN3h6ekKhUMj26uLigvr16yMsLEyOvyBt+0pNTUWlSpVgb2+PsmXL4osvvsCePXtkx65y5cpo1qwZ7OzsZBlffPEFdu/eDX9/f8TGxuLKlSt4//334ejoiIYNG6JixYqYNWsWPD09ERwcjOPHjyMjIwMjR47UyLKya9cuDRlTpkyBs7MzGjRogPDwcHz44YdyZP7AwED4+fnB29sbLi4uCAkJ0dIDEDE5pJU3YWFh+Oyzz3Ds2DF4enqCiFCrVi00bNgQbm5uqF+/PipWrIjPP/8cJ0+elDO+nDt3DmXKlIGdnR1cXV1Rp04d/Pjjj3j16hXq16+P8ePHY8KECShXrhyqVKkiZ4VRL3PkyBH8+OOPICJERETIZb777ju5ng0bNoCIYG9vDzc3Ny0ZX3/9Ndq0aYMiRYqgUqVK8PDwQJ06dfDTTz+hfv362L59u87sNOoyvvzySzRt2hTOzs4aMhYtWiSXWbhwIT777DNUrlwZkZGROnV9+fIlYmNjUapUKVlXSUZ0dDRatmyJNm3aIDAwUKNN1DPlSHEjXFxcEB4eDkAMwkgp1KQ+1svLCw4ODvL7jJQP+/3334dSqcRbb70FBwcHVKlSRX7GSHImTJgg26xCoZAD2Ennpajxly9fRnh4OOzs7OTn+Y8//gh7e3t5mW9aWppGVp/sMi5duoTw8HDY29vLjv6xY8fg6Ogo9zVhYWFwdXXVyNiifj2Szfr4+CAqKgoqlUquR7qGWrVqwcfHB46OjhrveFJ2oQsXLmDChAnw9PREgwYN8P333+Pnn3/GmDFj5OxDX331FSZNmoTy5ctj5syZcvYhSUZ6ejqWLFkChUKBmJgYjQxFUplNmzZBoVCgePHiGmXU6/n333/Rtm1buLi4YMqUKfj555+xb98+fP/994iJidHKhLRo0SKtev7++280a9YMgYGBqF+/vlzPqFGj5Ho2bNiAL774ApGRkZg9e7aWDGm1wdy5c9GwYUNZxnvvvYfGjRsjKSkJPXr0QNu2bVGqVCl88MEHsgxABCcePnw4xo8fj6CgIBw7dgwTJkzAG2+8gZo1a+Kbb75Bs2bN0Lp1a0yaNAlTpkzB+fPnNTI3hYaG4oMPPoBKpcIPP/wgb2WRzs+bNw/NmjVDXFwcFAqFPFmlS8bGjRvRqlUrBAcHo1+/fnjjjTdQr149TJ48GQ0bNtTKIDV+/HiNDFLNmjVD79690bx5c5QsWVLjeqpWrYqwsDB88MEH2LRpE+bPn4+lS5di8ODBWjKkfe2zZs3CuXPnZF0jIyPxww8/oHHjxqhcuTLatm2Lfv364ezZszqzWZ05cwZvvfUWFAqF/Jz28vJC06ZN5SCNf//9tzxAv2nTJmzfvh1Tp06VJwxsBXbkbZjDhw9DoVDII5K6OH36NLp3766x1CSnnDhxQmMWSxdS4AlDSE6lPp48eYL79+/L+z2zk5KSgi+//FJOgSI5g+rOfEpKCr755ht8/fXX2LVrl7wUXt2BlsqsXr0ahw4dQqlSpTTKSOdXrVqFJ0+ewMXFBUSE/v37a8lYuHAhOnfujMDAQJ31fPHFF/Ls7OrVq3XWs3r1aly7dg1Vq1YFEaFfv35a9SxevBgXL16UZzx1yVCpVHKbDB48WEvGzJkzMXv2bAwcOFBDhnpWhMTERCxYsACNGjVCx44d5awI9+7d08qcoFQq5cwJujIrrF+/Hl26dJFlqJe5fPky6tevj5MnT2pkX5DK7Nq1C1euXMH06dNx/vx5g/UA0Cvj0qVLmDBhAs6dO6dVJioqSq8cffXExcXpvB6pnaXAi/qySaxbtw6jR4+GQqFAZGQkAgMDUb58efz77784f/481qxZg27dumlkm2jSpAkGDx6MixcvYsOGDShXrhyGDBkilwkICJD3iz58+FCerckuY8iQIdi6dSt69OiBChUqYNiwYVAoFKhatSpCQkLQqlUr9OvXT2/mi5IlS6J8+fL49ddfcfnyZXTs2BFFixaVZQQFBaFRo0YYN24c9u/fjwMHDmDXrl0YP3683usBxHaeUaNGaV3PqlWrsGHDBgwfPhwtW7bUmYEjJSUFlStXRoUKFXD8+HE5A4e7uzt+/PFH+aWud+/e8v5RqYyHhweWLl0ql2nUqBF++uknrTJLliwBINLzVKpUCSdPntQp48qVK6hSpYocEfqff/5B8eLF4ebmhl9++UVDl+wZQ7LX061bN5w7d07jehYvXmyyDOn8q1evNGSop7zq2bOnPOugL3NJtWrVEB0djbFjx2L48OEoV64c3nzzTbRv3x6VKlXCuHHjULVqVcTExMjZTYKCghAcHIzIyEj8+eefqFWrFlxcXNC7d285+0mvXr0QGxuLyMhIeRVInTp1NDKkSGWklGWurq6oXbu2rEuZMmVQpkwZVKpUSW+WFUlGpUqVULt2bbi4uGjIkHStWbMmFi9ejLp166JmzZoYMmSIlgypTapXry63yYgRIxASEiKXCQ0NRevWrVGxYkWNMuoZX37//XfUqVMHnp6eWLNmjZzxxd/fH9999x1KlCihNyuMv78/Fi9eLJfp3bs3GjZsqFGmRIkS8PT0xObNmzF8+HA5Q4ouGX///TcaNmyIBg0ayDLGjRsHHx8fWYYxPdavX4+YmBjUr19fq4yx61HXVTp/9OhRgzJatmxpMFPOrl27sGPHDnh5eWHhwoU4dOgQpk2bBkAMrq9Zswaff/45PDw85Ew66mX++ecflCtXDv/3f/+nkW3n0KFDmDp1Km7evAkigrOzM/766y+5jLqM06dPo0ePHnIGhYULF8oy1KO5A5pZfdRlnDp1Ch07dkRgYCDc3d3lVXZSmRs3bmDfvn3YtWsXdu/eraWrJEepVOKPP/7A5s2bddbz+++/yw6lepsAIvhXSEiIHPR2xIgRGtmFPDw84OPjozP7UFRUFHx9fREXFydnKDp69CjGjh2rt8yuXbsQGxuLd955R6OMh4cHSpQogWPHjiElJQWtW7fWynLUv39/vZmQstezb98+fPTRR/JzR1c9plyP+jPSVBklSpTAokWL0KBBA/Tt2xfbtm3DzZs3NbIy9e7dG9HR0XKZ7JmbfvrpJ/Ts2RNeXl7y+aNHj+LAgQMaMqR7W8rPfuLECb0yEhMT8dlnn2H37t2yjDfffBONGzfWq0f2enr16oUPP/wQ58+f16rH09PTJBlSmfT0dK0yMTExBsuoZ7NKTU1FixYt5Fg5QFZa6qioKDlw7NmzZ9GrVy85U1alSpUMrm62RtiRt3EMOcYS2fer54bsUZELiuzXKznH48aNkx3X58+fa4ymqZeR9sMkJibKS9OfPn2qVSYpKQnp6el49OgRNm7ciPnz52vJSEhIwIMHD3Dr1i3cunVLQ4Y0sJCYmCgvX9eli7SH/eHDh7h27RoWLlyoVc/z5881VjHokqFUKuVlhrra5NmzZxrbELLLuHjxIjIyMvDgwQOkpKTg6dOnWlkRLl68KMckkGb71ctIjlliYqLezAoXL16UMytIAza6yki/s7R0VVc9L168MFiPtG9YGkHWVUZKcWLoeozVI+3vMpRNQmq37Nkkdu7cierVq8uj0PqyTUyePBmAGDz47rvvtMrUqFFD3mPet29fnTLef/99pKWlYc+ePfj66681ymzfvh21a9eW69GnS40aNTBz5kw5nVj2a6lZs6ZGkBlj16OvTdSvx1gGjq5du8orINQzcLz11lvyHva4uDhZL0NldGXyeOutt3Dw4EHExcVh7NixOs9L2wb69u2rV4a09NJQPYcOHUJcXJy8V1uXHsau57///jNYh7R03VDmktatW+vMXLJixQp4eXlh2bJliIuL05vdxNPTE82aNYOLiwsaNWqkMdi8cuVKFC1aFL/88gvi4uLg6OiI6OhoLRlFixbFypUrERcXJ8+iq5dZvny5Rj269Mguo379+lrX4+7uLu+v1SdjxYoVeq93xYoVGtdjKONLw4YN8c4776B58+Ya22+kjC9DhgxBjx49MGLECLRr104jG0b2Mvoyx/Tu3RuhoaEYNmwY2rVrJy+nV5cxePBgdO/e3agMSQ9d2WmMydB1PYbqadu2rV4Z3bt3N9gmQ4cOlfvf7JlypKjfkuOsL5OOxIMHD3D37l2tMkqlUi63YMECnD59WmcZQDwL9u7di1OnTsnnU1NTtbYw6tJFkpGamort27fjxIkTGteiXsaU69HXJlJWIUPnpexCZcqUQbt27aBQaGYX6tevHzw9PVGmTBm88cYbUCgU+OOPPzSyD9WpUwdRUVEGMxSpl+ncubPBegzJqF27tsn16Cqj63pyWo8pbSKtCjCWlWnlypUGy0gxAQzJWL58Oezs7GQHODcyVq1aZbCMu7s7VqxYkWddTalHuh5DcqSJS0NpqWNiYuTBfUCsRL1z547Ge7KtwI48Y5OoP6ykF/3x48fjzp07GD16NN544w0kJCTIDzxdZTp16oQXL14YlNOpUyfZqTR03pguiYmJBsuo51fO7fV07txZ1kXfeWMy1PXQlxVBfVBHV5n58+fLo666zn/66acag0v5WY8xGeaoxxQZpmaTMHbeUBkpGvvVq1cNnjdFhillzKGrKXL0nW/bti1UKpXBDBxDhgzBq1evjJZ5+fKl3jJly5bFkCFDsHjxYr0yBg8ebFCGueoxdj1ly5bF0KFDjcowpquxzCXjxo3DnTt3MHnyZL1lhg4davC81O9MnDjRaBlDcoYNG2ZSPfktw5Q26dOnj9GML9I++byUqVatWp5lVK9e3SK6mqseUzLl5OS8oTLqGS/ysx5zytBVxlibSc68oexCYWFhWL58ucHsQ+fOnTOaoejs2bMWqcdYGXPUY4qMly9f4tChQzqzMv31119yViZjZbZv327w/LNnz/IswxQ9zKGruep58uQJ0tLSjKal7t69u/xdU+KEWSvsyDM2i/qNuXr1ajmIh4ODgxxUxFAZaemTvjL29vY4fvx4nmTkRJe8yjCma070AAxnRTBWxhwyCmM96ujKJpGT89nLZM/RnRsZuS1TkPUYy8BhrjK2VI85ZBjLXALAaBlD55cvX25UxooVK/JcjzlkmHq9ppQxJeOLOcps377dKmRYsh51DGXKMeW8rjK6Xvbzo578kJHXeoxlFzJ23hQZha0eU2Tosikp7Z60ks9YGWuRUdD1SLEUpDK60lJLg3DZ01LbMuzIMzaN+vKwpk2bwtvbWyvavTnK2FI9eZVhSlYEY2XMIaMw1pOd7NkkcnreUjKsuR5TMnCYo4wt1WMuXbOTPXNJbsqYQ4al6skvXU3J+GKOMtYiw5L1qKMvU46p581Vxlpk5KUeY9mFTMk+ZI4ytlRPTjIyASKt3fDhw+Hp6alz8N+UMtYiw9L1vPnmm3B2dsaWLVs0zqmnpVZHX1pqW4QdecbmycjIkAOI6Yp2b64ytlSPOWSYkhXBWBlzyCiM9RjKJmHKeUvJsJV6TMnAYY4ytlSPuXQFDGcuMbWMOWRYqh5L6GpKxhdzlLEWGZasRxqU0pcpx9h5c5WxFhnmqsdYdiFTsg+Zo4wt1WOKDEDEA/r999/Rs2fPXJexFhmWruebb76Bk5MTFIqsNM4SUlpqhUKBqVOn4tixY3jy5AkmTZqklZbaVmFHnrF5MjIy8OOPP8pLx/OrjC3VYw4ZpmRFMFbGHDIKYz3GskmYkm3CEjJsrR4mfzAlc4mxMuaQYal6LKUrYFrGF3OUsRYZlqxHpTKcKcfYeXOVsRYZ5qjHWHYhY+fNVcaW6jFFBiCcUmO/n7Ey1iLDUvUkJSWhX79+6NOnj0aaZ3UHXalUYtmyZfD390dgYCDCwsJQsmRJm4tOrw8FABDD2DgASKFQ5HsZW6rHHDKSk5PJzc3NoAxjZcwhozDWk56eTo6Ojrk+bykZtlYPkz+kpaWRk5NTnsqYQ4al6rGUrgzDMEzuePnyJS1dupSKFy9OPXr0oLVr11LPnj1p3LhxNH78eCpRooRc9vr163Tz5k1KSUmhKlWqUGBgYAFqbj7YkWcYhmEYhmEYhmFsiuwTJ2vWrKFevXrR2LFjaeLEieTj40MZGRl09+5dKl26dAFqmj84FLQCDMMwDMMwDMMwDJMTJCdeqVSSnZ0d9ejRgwDQm2++SQqFgkaNGkWffvop3bhxg3755RdydXU1ulrVluAZeYZhGIZhGIZhGMZmgYj9RnZ2drRmzRrq27cvlS1blq5cuUKHDx+matWqFbSKZocdeYZhGIZhGIZhGMamkdxahUJBzZo1o/j4eNq5cydVqVKlgDXLH3hpPcMwDMMwDMMwDGPTKBQKUiqVNH78eNqxYwfFx8cXWieeiMiuoBVgGIZhGIZhGIZhGHNQqVIlOnbsGEVGRha0KvkKL61nGIZhGIZhGIZhCgWmpGAuDPCMPMMwDMMwDMMwDFMoeB2ceCJ25BmGYRiGYRiGYRjGpmBHnmEYhmEYhmEYhmFsCHbkGYZhGIZhGIZhGMaGYEeeYRiGYRiGYRiGYWwIduQZhmEYhmEYhmEYxoZgR55hGIZhrISdO3eSQqGg58+fF7Qq+coHH3xA1apVK2g1TEKhUNCff/5JRETXr18nhUJB8fHxBaoTwzAMw7AjzzAMwzBWQnR0NN27d4+8vLwKWhVGB0FBQXTv3j2qXLkyEb0+Ay8MwzCM9eFQ0AowDMMwDCNwcnIif3//glaD0YO9vT3/PgzDMIxVwDPyDMMwDJNPNG7cmN59910aNWoUFStWjPz8/Gjx4sWUnJxMAwYMIA8PDwoNDaUNGzYQkfYM788//0xFixalTZs2UXh4OLm7u1OrVq3o3r17JtW/c+dOql27Nrm5uVHRokUpJiaGbty4QUREV65coY4dO5Kfnx+5u7tTVFQUbd26VeP7ISEhNHv2bIqLiyN3d3cKDg6m9evX06NHj6hjx47k7u5OkZGRdOTIEfk7ks5//vknlS9fnooUKUKxsbF069Ytg7r++OOPFB4eTkWKFKGwsDD69ttv5XNpaWn0zjvvUEBAABUpUoSCg4Pp448/Nnr9AOiDDz6g0qVLk7OzM5UsWZJGjhypcX2zZs2iXr16kZubGwUGBtI333yjV5760vrr169TkyZNiIioWLFipFAoqH///kZ1YhiGYRhzwI48wzAMw+Qjy5YtIx8fHzp06BC9++67NGzYMOrWrRtFR0fTsWPHqGXLltS3b19KSUnR+f2UlBT69NNPafny5bR79266efMmjRs3zmi9GRkZ1KlTJ2rUqBGdPHmSDhw4QIMHDyaFQkFERElJSdSmTRvatm0bHT9+nFq1akXt27enmzdvasj54osvKCYmho4fP05t27alvn37UlxcHPXp04eOHTtG5cqVo7i4OAKgofNHH31Ev/zyC+3bt4+eP39OPXv21KvrypUrafr06fTRRx/RuXPnaM6cOTRt2jRatmwZERF99dVXtH79elq7di1duHCBVq5cSSEhIUbb4LfffqMvvviCvv/+e7p06RL9+eefVKVKFY0y8+fPp6pVq9Lx48dp0qRJ9N5779GWLVuMyg4KCqLffvuNiIguXLhA9+7doy+//NLo9xiGYRjGLIBhGIZhmHyhUaNGqF+/vvz/jIwMuLm5oW/fvvJn9+7dAxHhwIED2LFjB4gIz549AwAsXboURITLly/L5b/55hv4+fkZrfvJkycgIuzcudNkfStVqoSvv/5a/n9wcDD69Omjpeu0adPkzw4cOAAiwr179zR0PnjwoFzm3LlzICL8999/AIAZM2agatWq8vly5cph1apVGrrMmjUL9erVAwC8++67aNq0KVQqlcnXAgCfffYZKlSogLS0NJ3ng4OD0apVK43PevTogdatW8v/JyL88ccfAIBr166BiHD8+HEA0Pq9GIZhGMZS8Iw8wzAMw+QjkZGR8rG9vT0VL15cY1bYz8+PiIgePnyo8/uurq5Urlw5+f8BAQF6y6rj7e1N/fv3p9jYWGrfvj19+eWXGkvyk5KSaNy4cRQeHk5FixYld3d3OnfunNaMvLr+kq7G9HdwcKCoqCj5/2FhYVS0aFE6d+6clp7Jycl05coVevvtt8nd3V3+N3v2bLpy5QoREfXv35/i4+OpYsWKNHLkSNq8ebPR6yci6tatG718+ZLKli1LgwYNoj/++IMyMjI0ytSrV0/r/7r0ZBiGYRhrgh15hmEYhslHHB0dNf6vUCg0PpOWuqtUKpO/D7Vl7IZYunQpHThwgKKjo2nNmjVUoUIFOnjwIBERjRs3jv744w+aM2cO7dmzh+Lj46lKlSqUlpamt35J15zob4ykpCQiIlq8eDHFx8fL/06fPi3rWqNGDbp27RrNmjWLXr58Sd27d6euXbsalR0UFEQXLlygb7/9llxcXGj48OHUsGFDSk9Pz5WuDMMwDGMtsCPPMAzDMIWY6tWr0+TJk2n//v1UuXJlWrVqFRER7du3j/r370+dO3emKlWqkL+/P13///buHyS1MA7j+KNkcMTKaLB0sKCCiAKjf+TQaARG0BA0NEUNWRxqqKVBaHJwcMwghxpEaunPUkMQ0eLWkhTl2lJCU5DcO1253Vs3773dGye+HzjD4f0dzu918vE9vieff5d7Pj09PdsAL5fLqVAoqK2t7adaj8cjr9er6+trNTc3PzuamppKddXV1RofH1cymVQ6ndb29rbu7u7e7MUwDIXDYSUSCR0fH+vs7Ezn5+el8W8/Fnx//lKfL6msrJQkFYvFsuoBAHgvvH4OAIBP6ObmRmtraxoZGZHX61Uul9Pl5aUmJyclSS0tLdrZ2VE4HJbNZtPKysofr6r/yOFwaG5uTolEQhUVFYpEIurv71dvb++L9dFoVPPz86qpqdHQ0JAeHx+VzWZ1f3+vhYUFxeNxNTQ0KBAIyG63K5PJqL6+Xm63+5d9pFIpFYtF9fX1yel0anNzU4ZhyO/3l2pOT08Vi8U0Ojqqw8NDZTIZ7e/vlzVPv98vm82mvb09DQ8PyzAMuVyusj8nAAD+FCvyAAB8Qk6nUxcXFxobG1Nra6ump6c1OzurmZkZSVI8Hldtba0GBgYUDocVCoXU1dX1bvdeWlrSxMSEgsGgXC6X0un0q/VTU1NaX1/XxsaGOjo6NDg4qFQqVVqRr6qqUiwWU3d3t3p6epTP53VwcCC7/ddfY9xut5LJpILBoDo7O3V0dKTd3V3V1dWVahYXF5XNZhUIBLS6uqp4PK5QKFTWPH0+n6LRqJaXl+XxeBSJRMq6DgCAv2X7Uu4f7QAAAN6QSqVkmqYKhcJHt/KmxsZGmaYp0zQ/uhUAAH4LK/IAAAAAAFgIQR4AAIv6/nVtPx4nJycf3d4/t7W19er829vbP7o9AAD+GR6tBwDAoq6url4d8/l8MgzjP3bz/z08POj29vbFMYfD8WxTOwAAPhOCPAAAAAAAFsKj9QAAAAAAWAhBHgAAAAAACyHIAwAAAABgIQR5AAAAAAAshCAPAAAAAICFEOQBAAAAALAQgjwAAAAAABZCkAcAAAAAwEK+As+aN/0MmLezAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best min_samples_split value:  19\n"
     ]
    }
   ],
   "source": [
    "min_samples_split_list = [i for i in range(2, 101)]\n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for min_samples_split in min_samples_split_list:\n",
    "    model = RandomForestClassifier(min_samples_split = min_samples_split,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train,) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "min_samples_split = plot_graphs(accuracy_list_train, accuracy_list_test, min_samples_split_list[:], 'Train & Test metrics', 'min_samples_split', 'accuracy', )\n",
    "print(\"Best min_samples_split value: \", min_samples_split)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. min_weight_fraction_leaf: The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.\n",
    "\n",
    "Reasonable values: 0.0 (default) to 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best min_weight_fraction_leaf value:  0.0\n"
     ]
    }
   ],
   "source": [
    "min_weight_fraction_leaf_list = [i for i in np.arange(0.00, 0.11, 0.01)]\n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for min_weight_fraction_leaf in min_weight_fraction_leaf_list:\n",
    "    model = RandomForestClassifier(min_weight_fraction_leaf = min_weight_fraction_leaf,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train,) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "min_weight_fraction_leaf = plot_graphs(accuracy_list_train, accuracy_list_test, min_weight_fraction_leaf_list[:], 'Train & Test metrics', 'min_weight_fraction_leaf', 'accuracy', )\n",
    "print(\"Best min_weight_fraction_leaf value: \", min_weight_fraction_leaf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13. monotonic_cst: Monotonic constraint to ensure certain features always increase or decrease the output.\n",
    "\n",
    "Reasonable values: Generally None for most cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14. n_estimators: The number of trees in the forest.\n",
    "\n",
    "Reasonable values: 10 to 100 (default is 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best n_estimators value:  93\n"
     ]
    }
   ],
   "source": [
    "n_estimators_list = [i for i in range(2, 101)]\n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for n_estimators in n_estimators_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(n_estimators = n_estimators,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "n_estimators = plot_graphs(accuracy_list_train, accuracy_list_test, n_estimators_list, 'Train & Test metrics', 'n_estimators', 'accuracy', )\n",
    "print(\"Best n_estimators value: \", n_estimators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15. n_jobs: The number of jobs to run in parallel. -1 means using all processors.\n",
    "\n",
    "Reasonable values: 1 (default), or -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16. oob_score: Whether to use out-of-bag samples to estimate the generalization accuracy.\n",
    "\n",
    "Reasonable values: False (default) or True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17. random_state: Controls the randomness of the estimator. Pass an integer for reproducible results across multiple function calls.\n",
    "\n",
    "Reasonable values: None (default) or an integer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 18. verbose: Controls the verbosity of the tree building process.\n",
    "\n",
    "Reasonable values: 0 (default) to 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 100\n",
      "building tree 2 of 100\n",
      "building tree 3 of 100\n",
      "building tree 4 of 100\n",
      "building tree 5 of 100\n",
      "building tree 6 of 100\n",
      "building tree 7 of 100\n",
      "building tree 8 of 100\n",
      "building tree 9 of 100\n",
      "building tree 10 of 100\n",
      "building tree 11 of 100\n",
      "building tree 12 of 100\n",
      "building tree 13 of 100\n",
      "building tree 14 of 100\n",
      "building tree 15 of 100\n",
      "building tree 16 of 100\n",
      "building tree 17 of 100\n",
      "building tree 18 of 100\n",
      "building tree 19 of 100\n",
      "building tree 20 of 100\n",
      "building tree 21 of 100\n",
      "building tree 22 of 100\n",
      "building tree 23 of 100\n",
      "building tree 24 of 100\n",
      "building tree 25 of 100\n",
      "building tree 26 of 100\n",
      "building tree 27 of 100\n",
      "building tree 28 of 100\n",
      "building tree 29 of 100\n",
      "building tree 30 of 100\n",
      "building tree 31 of 100\n",
      "building tree 32 of 100\n",
      "building tree 33 of 100\n",
      "building tree 34 of 100\n",
      "building tree 35 of 100\n",
      "building tree 36 of 100\n",
      "building tree 37 of 100\n",
      "building tree 38 of 100\n",
      "building tree 39 of 100\n",
      "building tree 40 of 100\n",
      "building tree 41 of 100\n",
      "building tree 42 of 100\n",
      "building tree 43 of 100\n",
      "building tree 44 of 100\n",
      "building tree 45 of 100\n",
      "building tree 46 of 100\n",
      "building tree 47 of 100\n",
      "building tree 48 of 100\n",
      "building tree 49 of 100\n",
      "building tree 50 of 100\n",
      "building tree 51 of 100\n",
      "building tree 52 of 100\n",
      "building tree 53 of 100\n",
      "building tree 54 of 100\n",
      "building tree 55 of 100\n",
      "building tree 56 of 100\n",
      "building tree 57 of 100\n",
      "building tree 58 of 100\n",
      "building tree 59 of 100\n",
      "building tree 60 of 100\n",
      "building tree 61 of 100\n",
      "building tree 62 of 100\n",
      "building tree 63 of 100\n",
      "building tree 64 of 100\n",
      "building tree 65 of 100\n",
      "building tree 66 of 100\n",
      "building tree 67 of 100\n",
      "building tree 68 of 100\n",
      "building tree 69 of 100\n",
      "building tree 70 of 100\n",
      "building tree 71 of 100\n",
      "building tree 72 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 73 of 100\n",
      "building tree 74 of 100\n",
      "building tree 75 of 100\n",
      "building tree 76 of 100\n",
      "building tree 77 of 100\n",
      "building tree 78 of 100\n",
      "building tree 79 of 100\n",
      "building tree 80 of 100\n",
      "building tree 81 of 100\n",
      "building tree 82 of 100\n",
      "building tree 83 of 100\n",
      "building tree 84 of 100\n",
      "building tree 85 of 100\n",
      "building tree 86 of 100\n",
      "building tree 87 of 100\n",
      "building tree 88 of 100\n",
      "building tree 89 of 100\n",
      "building tree 90 of 100\n",
      "building tree 91 of 100\n",
      "building tree 92 of 100\n",
      "building tree 93 of 100\n",
      "building tree 94 of 100\n",
      "building tree 95 of 100\n",
      "building tree 96 of 100\n",
      "building tree 97 of 100\n",
      "building tree 98 of 100\n",
      "building tree 99 of 100\n",
      "building tree 100 of 100\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best verbose value:  0\n"
     ]
    }
   ],
   "source": [
    "verbose_list = [i for i in range(0, 3)]\n",
    "accuracy_list_train = []\n",
    "accuracy_list_test = []\n",
    "for verbose in verbose_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(verbose = verbose,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_test.append(accuracy_test)\n",
    "\n",
    "verbose = plot_graphs(accuracy_list_train, accuracy_list_test, verbose_list, 'Train & Test metrics', 'verbose', 'accuracy', )\n",
    "print(\"Best verbose value: \", verbose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 19. warm_start: When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble.\n",
    "\n",
    "Reasonable values: False (default) or True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Limiting Tree Depth\n",
    "   - Hyperparameter: max_depth\n",
    "   - Effect: Restricts the maximum depth of each decision tree in the forest.\n",
    "   - Benefit: Reduces the complexity of individual trees, preventing them from overfitting the training data.\n",
    "  \n",
    "2. Minimum Samples for Splitting and Leaf Nodes\n",
    "   - Hyperparameters: min_samples_split and min_samples_leaf\n",
    "   - Effect: Controls the minimum number of samples required to split a node and to be at a leaf node.\n",
    "   - Benefit: Ensures that nodes are not split too finely, which helps in reducing overfitting.\n",
    "  \n",
    "3. Maximum Number of Features\n",
    "   - Hyperparameter: max_features\n",
    "   - Effect: Limits the number of features considered for splitting at each node.\n",
    "   - Benefit: Reduces the variance of the model by introducing randomness and reducing the correlation between individual trees.\n",
    "\n",
    "4. Pruning\n",
    "   - Hyperparameter: ccp_alpha\n",
    "   - Effect: Applies cost-complexity pruning to remove nodes that provide little predictive power.\n",
    "   - Benefit: Simplifies trees by removing branches that have little importance, thus reducing overfitting.\n",
    "  \n",
    "5. Using Bootstrapping\n",
    "   - Hyperparameter: bootstrap\n",
    "   - Effect: Uses bootstrapped samples (random sampling with replacement) to train each tree.\n",
    "   - Benefit: Adds diversity to the individual trees, which reduces the variance of the model.\n",
    "\n",
    "6. Controlling Tree Count\n",
    "   - Hyperparameter: n_estimators\n",
    "   - Effect: Sets the number of trees in the forest.\n",
    "   - Benefit: Increasing the number of trees generally improves performance, but it also increases computation time. Finding the right balance is key.\n",
    "\n",
    "7. Out-of-Bag (OOB) Error Estimation\n",
    "   - Hyperparameter: oob_score\n",
    "   - Effect: Uses out-of-bag samples to estimate the generalization error.\n",
    "   - Benefit: Provides a way to validate the model without requiring a separate validation set, helping in assessing and preventing overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try all the combinations using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# # Define the parameter grid\n",
    "# param_grid = {\n",
    "#     'bootstrap': [True, False],\n",
    "#     'ccp_alpha': [i for i in np.arange(0.000, 0.030, 0.005)],\n",
    "#     'lass_weight': ['None', 'balanced', 'balanced_subsample'],\n",
    "#     'criterion': ['gini', 'entropy'],\n",
    "#     'max_depth': [i for i in range(5, 15)],\n",
    "#     'max_features': [i for i in range(8, 15)],\n",
    "#     'max_leaf_nodes': [i for i in range(27, 35)],\n",
    "#     'max_samples': [i for i in np.arange(0.10, 1.10, 0.01)],\n",
    "#     'min_impurity_decrease': [i for i in np.arange(0.000, 0.020, 0.005)],\n",
    "#     'min_samples_leaf': [i for i in range(3, 18)],\n",
    "#     'min_samples_split': [i for i in range(2, 17)],\n",
    "#     'min_weight_fraction_leaf': [i for i in np.arange(0.000, 0.030, 0.005)],\n",
    "#     'monotonic_cst': ['None'],\n",
    "#     'n_estimators': [i for i in range(17, 27)],\n",
    "#     'n_jobs': [1, -1],\n",
    "#     'oob_score': [False, True],\n",
    "#     'random_state': [RANDOM_STATE],\n",
    "#     'verbose': [0],\n",
    "#     'warm_start': [False, True],\n",
    "# }\n",
    "\n",
    "# # Initialize the RandomForestClassifier\n",
    "# model = RandomForestClassifier(random_state=RANDOM_STATE)\n",
    "\n",
    "# # Initialize GridSearchCV with 5-fold cross-validation\n",
    "# grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "# # Fit the model to the training data\n",
    "# grid_search.fit(X_train, y_train)\n",
    "\n",
    "# # Get the best hyperparameters and the best score\n",
    "# best_params = grid_search.best_params_\n",
    "# best_accuracy = grid_search.best_score_\n",
    "\n",
    "# print(f\"Best accuracy: {best_accuracy}\")\n",
    "# print(f\"Best parameters: {best_params}\")\n",
    "\n",
    "# bootstrap = best_params['bootstrap']\n",
    "# ccp_alpha = best_params['ccp_alpha']\n",
    "# class_weight = best_params['class_weight']\n",
    "# criterion = best_params['criterion']\n",
    "# max_depth = best_params['max_depth']\n",
    "# max_features = best_params['max_features']\n",
    "# max_leaf_nodes = best_params['max_leaf_nodes']\n",
    "# max_samples = best_params['max_samples']\n",
    "# min_impurity_decrease = best_params['min_impurity_decrease']\n",
    "# min_samples_leaf = best_params['min_samples_leaf']\n",
    "# min_samples_split = best_params['min_samples_split']\n",
    "# min_weight_fraction_leaf = best_params['min_weight_fraction_leaf']\n",
    "# monotonic_cst = best_params['monotonic_cst']\n",
    "# n_estimators = best_params['n_estimators']\n",
    "# n_jobs = best_params['n_jobs']\n",
    "# oob_score = best_params['oob_score']\n",
    "# random_state = best_params['random_state']\n",
    "# verbose = best_params['verbose']\n",
    "# warm_start = best_params['warm_start']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimal hyperparameter values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: \n",
      "ccp_alpha:  0.1\n",
      "max_depth:  15\n",
      "max_features:  32\n",
      "max_leaf_nodes:  23\n",
      "max_samples:  1.0\n",
      "min_impurity_decrease:  0.1\n",
      "min_samples_leaf:  1\n",
      "min_samples_split:  19\n",
      "min_weight_fraction_leaf:  0.0\n",
      "n_estimators:  93\n",
      "verbose:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameters: \")\n",
    "print(\"ccp_alpha: \", ccp_alpha)\n",
    "print(\"max_depth: \", max_depth)\n",
    "print(\"max_features: \", max_features)\n",
    "print(\"max_leaf_nodes: \", max_leaf_nodes)\n",
    "print(\"max_samples: \", max_samples)\n",
    "print(\"min_impurity_decrease: \", min_impurity_decrease)\n",
    "print(\"min_samples_leaf: \", min_samples_leaf)\n",
    "print(\"min_samples_split: \", min_samples_split)\n",
    "print(\"min_weight_fraction_leaf: \", min_weight_fraction_leaf)\n",
    "print(\"n_estimators: \", n_estimators)\n",
    "print(\"verbose: \", verbose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid secrching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid search 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# # Define the parameter grid\n",
    "# param_grid = {\n",
    "#     'bootstrap': True, \n",
    "#     'ccp_alpha': ccp_alpha,\n",
    "#     'class_weight': 'balanced',\n",
    "#     'criterion': 'entropy',\n",
    "#     'max_depth': max_depth,\n",
    "#     'max_features': max_features,\n",
    "#     'max_leaf_nodes': max_leaf_nodes,\n",
    "#     'max_samples': max_samples,\n",
    "#     'min_impurity_decrease': min_impurity_decrease,\n",
    "#     'min_samples_leaf': min_samples_leaf,\n",
    "#     'min_samples_split': min_samples_split,\n",
    "#     'min_weight_fraction_leaf': min_weight_fraction_leaf,\n",
    "#     'monotonic_cst': None,\n",
    "#     'n_estimators': n_estimators,\n",
    "#     'n_jobs': 1,\n",
    "#     'oob_score': True,\n",
    "#     'random_state': RANDOM_STATE,\n",
    "#     'verbose': verbose,\n",
    "#     'warm_start': True\n",
    "# }\n",
    "\n",
    "# # Initialize the RandomForestClassifier\n",
    "# model = RandomForestClassifier(random_state=RANDOM_STATE)\n",
    "\n",
    "# # Initialize GridSearchCV with 5-fold cross-validation\n",
    "# grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "# # Fit the model to the training data\n",
    "# grid_search.fit(X_train, y_train)\n",
    "\n",
    "# # Get the best hyperparameters and the best score\n",
    "# best_params = grid_search.best_params_\n",
    "# best_accuracy = grid_search.best_score_\n",
    "\n",
    "# print(f\"Best accuracy: {best_accuracy}\")\n",
    "# print(f\"Best parameters: {best_params}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The parameter dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'bootstrap': True, \n",
    "    'ccp_alpha': ccp_alpha,\n",
    "    'class_weight': None,\n",
    "    'criterion': 'entropy',\n",
    "    'max_depth': max_depth,\n",
    "    'max_features': max_features,\n",
    "    'max_leaf_nodes': max_leaf_nodes,\n",
    "    'max_samples': max_samples,\n",
    "    'min_impurity_decrease': min_impurity_decrease,\n",
    "    'min_samples_leaf': min_samples_leaf,\n",
    "    'min_samples_split': min_samples_split,\n",
    "    'min_weight_fraction_leaf': min_weight_fraction_leaf,\n",
    "    'monotonic_cst': None,\n",
    "    'n_estimators': n_estimators,\n",
    "    'n_jobs': 1,\n",
    "    'oob_score': True,\n",
    "    'random_state': RANDOM_STATE,\n",
    "    'verbose': verbose,\n",
    "    'warm_start': True\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use values for \"max_depth\", \"min_samples_split\", \"n_estimators\" which gave the best results for the test set\n",
    "\n",
    "- max_depth = 10\n",
    "- min_samples_split = 7\n",
    "- n_estimators = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_model = RandomForestClassifier(**params).fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy score: 0.6059\n",
      "Testing Accuracy score: 0.6667\n"
     ]
    }
   ],
   "source": [
    "training_accuracy_score_random_forest = accuracy_score(random_forest_model.predict(X_train),y_train)\n",
    "testing_accuracy_score_random_forest = accuracy_score(random_forest_model.predict(X_test),y_test)\n",
    "print(f\"Training Accuracy score: {training_accuracy_score_random_forest:.4f}\")\n",
    "print(f\"Testing Accuracy score: {testing_accuracy_score_random_forest:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit Learn random forest implementation gives the best accuracy for now. These models should imrove more. For now I'm gonna compare Scikit Learn Random Forest implementation performances with other models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Get the performance Metrics for the Scikit Learn Random Forest Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.78      0.67        18\n",
      "           1       0.85      0.67      0.75        66\n",
      "           2       0.30      0.50      0.38        12\n",
      "\n",
      "    accuracy                           0.67        96\n",
      "   macro avg       0.58      0.65      0.60        96\n",
      "weighted avg       0.73      0.67      0.68        96\n",
      "\n",
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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Lm6u1nvnDVzo9mbpu3bq69dZb9cILL5gpV25urm6++Wb9+eefGjVqlOrWravAwEDt27dPCQkJBd7Lovq8XKrzHb8oPntF5dyf0Zn376677jrvHAzpdGIinW46tm/frmXLlmn58uV67733NGPGDD355JMaM2ZM8RYO4LJEgwAUk1tvvVUvv/yyNm7cqLi4uItuW61aNeXl5emXX34xv2GUpPT0dB05csS8IlFRKF++/HmvCnPuN5qS5OPjo3bt2qldu3Z6/vnnNX78eD3++ONau3at+Qfkua9DkrZv315g3U8//aSKFSsqMDDwn7+I8+jbt69ee+01+fj4qE+fPhfcbvHixWrTpo3mzJnjNH7kyBGnb5ML26wVRlZWlvr166f69evrxhtv1MSJE3XbbbeZV0pyVbVq1bR161bl5eU5pQg//fSTub4odenSRa1atdL48eP14IMPKjAwUNu2bdPPP/+s+fPnm6fVSKevfHWpqlWrptWrV+vYsWNOKcK5nydPfs4Kq1q1alq1apWOHj3qlCIU9mdUqVIlBQcHKzc397y/a+cKDAxU79691bt3b2VnZ6tHjx565plnlJSUJH9//yL9PAO4/HGKEVBMHn30UQUGBuq+++5Tenp6gfU7d+7UCy+8IOn0KTKSNHXqVKdtnn/+eUmn/0ArKjVr1lRGRoa2bt1qju3fv7/AlZL+/PPPAs89c8Owcy+9ekZ0dLSuuuoqzZ8/36kJ+e677/TZZ5+Zr7M4tGnTRuPGjdO0adMUFRV1we18fX0LfEO8aNEi7du3z2nszB+YRXHn2VGjRmn37t2aP3++nn/+eVWvXt288dil6Ny5s9LS0vTOO++YY6dOndJLL72koKAg8zSfojRq1CgdPnxYr7zyiqSz375b30vDMMzP9KXo3LmzTp06pZkzZ5pjubm5eumll5y28+TnrLA6d+6s3NxcTZs2zWl8ypQpstls6tSp00Wf7+vrq549e+q99947bxJ55j4n0ukraln5+fmpfv36MgxDOTk5kor28wzg8keCABSTmjVrauHCheblFa13Uv7888/Ny1JKUpMmTRQfH6+XX35ZR44cUatWrfTll19q/vz56t69u9q0aVNkdfXp00ejRo3SbbfdpsGDB+v48eOaOXOmrrzySqdJumPHjtX69evVpUsXVatWTQcOHNCMGTNUuXJl3XTTTRfc/6RJk9SpUyfFxcWpf//+5uUnQ0NDL3rqzz/l4+Oj//73v3+73a233qqxY8eqX79+uvHGG7Vt2zYtWLDAPHf8jJo1ayosLEyzZs1ScHCwAgMD1axZM5fP51+zZo1mzJihp556yrzs6ty5c9W6dWs98cQTmjhxokv7k6QHHnhAs2fPVkJCglJTU1W9enUtXrxY//vf/zR16tRCT453RadOndSwYUM9//zzSkxMVN26dVWzZk2NGDFC+/btU0hIiN57771/NA+ma9euat68uR577DH99ttvql+/vt5///3zzg3x1OessLp27ao2bdro8ccf12+//aYmTZros88+0wcffKAhQ4aoZs2af7uPCRMmaO3atWrWrJnuv/9+1a9fX3/++ae+/vprrVq1ymziO3TooKioKDVv3lyRkZH68ccfNW3aNHXp0sX8LDRt2lSS9Pjjj6tPnz4qW7asunbt6vGkBYCX8szFk4DS4+effzbuv/9+o3r16oafn58RHBxsNG/e3HjppZeMkydPmtvl5OQYY8aMMWJjY42yZcsaVapUMZKSkpy2MYzzX/bSMApeXvNClzk1DMP47LPPjIYNGxp+fn5GnTp1jDfffLPAZU5Xr15tdOvWzYiJiTH8/PyMmJgY48477zR+/vnnAsc491Kgq1atMpo3b24EBAQYISEhRteuXY0ffvjBaZszxzv3Mqpz584t1OUYrZc5vZALXeZ0+PDhRnR0tBEQEGA0b97c2Lhx43kvT/rBBx8Y9evXN8qUKeP0Olu1anXBy0pa95OZmWlUq1bNuOaaa4ycnByn7YYOHWr4+PgYGzduvOhruNDPOz093ejXr59RsWJFw8/Pz2jUqFGBn8PFPgOuHs8wDGPevHlO78MPP/xgtG/f3ggKCjIqVqxo3H///ca3335b4DNxoZ/VuZ85wzCMw4cPG3fffbcREhJihIaGGnfffbfxzTffFPnn7EI1Xexna3Wx9+mMo0ePGkOHDjViYmKMsmXLGrVr1zYmTZpk5OXlOW2n81xC9oz09HQjMTHRqFKlilG2bFkjKirKaNeunfHyyy+b28yePdto2bKlUaFCBcNutxs1a9Y0Ro4caWRkZDjta9y4ccYVV1xh+Pj4cMlTABdlMwwPzMYCAAAA4JWYgwAAAADARIMAAAAAwESDAAAAAMBEgwAAAADARIMAAAAAwESDAAAAAMBEgwAAAADAdFneSfmT7w94ugSgRIqLreDpEoASKcDP19MlACWOvxf/FRpw9UC3HevEN9PcdqzCIkEAAAAAYPLi3g0AAADwAFvp/g69dL96AAAAAE5IEAAAAAArm83TFXgUCQIAAAAAEwkCAAAAYMUcBAAAAAA4jQQBAAAAsGIOAgAAAACcRoIAAAAAWDEHAQAAAABOI0EAAAAArJiDAAAAAACnkSAAAAAAVsxBAAAAAIDTaBAAAAAAmDjFCAAAALBikjIAAAAAnEaCAAAAAFgxSRkAAAAATiNBAAAAAKyYgwAAAAAAp5EgAAAAAFbMQQAAAACA00gQAAAAACvmIAAAAADAaSQIAAAAgBVzEAAAAADgNBIEAAAAwIoEAQAAAABOI0EAAAAArHy4ihEAAAAASCJBAAAAAJwxBwEAAAAATqNBAAAAAGDiFCMAAADAysYkZQAAAACQRIIAAAAAOGOSMgAAAACcRoIAAAAAWDEHAQAAAABOI0EAAAAArJiDAAAAAACnkSAAAAAAVsxBAAAAAIDTaBAAAAAAK5uP+5ZLNGHCBNlsNg0ZMsQcO3nypBITE1WhQgUFBQWpZ8+eSk9Pd3nfNAgAAABACbJ582bNnj1bjRs3dhofOnSoPvroIy1atEgpKSn6448/1KNHD5f3T4MAAAAAWNls7ltcdOzYMf373//WK6+8ovLly5vjGRkZmjNnjp5//nm1bdtWTZs21dy5c/X5559r06ZNLh2DBgEAAADwEIfDoczMTKfF4XBccPvExER16dJF7du3dxpPTU1VTk6O03jdunVVtWpVbdy40aWaaBAAAAAAKzfOQUhOTlZoaKjTkpycfN6y3n77bX399dfnXZ+WliY/Pz+FhYU5jUdGRiotLc2ll89lTgEAAAAPSUpK0rBhw5zG7HZ7ge327NmjRx55RCtXrpS/v3+x1kSDAAAAAFi58T4Idrv9vA3BuVJTU3XgwAFdc8015lhubq7Wr1+vadOmacWKFcrOztaRI0ecUoT09HRFRUW5VBMNAgAAAODl2rVrp23btjmN9evXT3Xr1tWoUaNUpUoVlS1bVqtXr1bPnj0lSdu3b9fu3bsVFxfn0rFoEAAAAACrf3B/guISHByshg0bOo0FBgaqQoUK5nj//v01bNgwhYeHKyQkRIMGDVJcXJxuuOEGl45FgwAAAABcBqZMmSIfHx/17NlTDodDHTt21IwZM1zej80wDKMY6vOoT74/4OkSgBIpLraCp0sASqQAP19PlwCUOP5e/DV1wK3T3HasE8sGuu1YheXFPxoAAADAA7zwFCN3Kt2vHgAAAIATEgQAAADAyo2XOfVGJAgAAAAATCQIAAAAgBVzEAAAAADgNBIEAAAAwIo5CAAAAABwGgkCAAAAYMUcBAAAAAA4jQQBAAAAsGIOAgAAAACcRoIAAAAAWNhIEAAAAADgNBIEAAAAwIIEAQAAAADykSAAAAAAVqU7QCBBAAAAAHAWDQIAAAAAE6cYAQAAABZMUgYAAACAfCQIAAAAgAUJAgAAAADkI0EAAAAALEgQAAAAACAfCQIAAABgQYIAAAAAAPloEFDkdn6/Ra+MH6Wn+nfX0B4ttO2L9Rfc9t1ZkzW0RwulfPSuGysESob33n1b/76ju9redJ3a3nSd7rvnTn2+4cK/TwBOS/1qswY9PEDtW9+kJg3qaM3qVZ4uCSWNzY2LF6JBQJHLdpzUFdVrqef9wy663dZN6/X7z98rNLyimyoDSpaIyEglDhqqeQsWad6CRWp6fTM9OnSgft35i6dLA7zaiRPHVadOHSX99ylPlwKUSMxBQJGrd80NqnfNDRfd5sjhg3r/1al68Mnn9Mozj7qpMqBkadGqjdPjhwYO0ZJFb+u7rVtVo2ZtD1UFeL+bWrTSTS1aeboMlGClfQ4CDQLcLi8vTwteeFptut+p6Kqxni4HKBFyc3O1ZuUKnThxQo0aN/F0OQCAy5hHG4RDhw7ptdde08aNG5WWliZJioqK0o033qiEhARVqlTJk+WhmKxZskA+vr5q2aWXp0sBvN6OX37W/fF3Kjs7WwEB5fTscy8qtmYtT5cFAJe10p4geGwOwubNm3XllVfqxRdfVGhoqFq2bKmWLVsqNDRUL774ourWrauvvvrqb/fjcDiUmZnptORkO9zwCnAp9uzcrvUfL1bfQf8p9b98QGFUq15dr7/9vua8/rZ63N5bY5/8j3bt3OHpsgAAlzGPJQiDBg3S7bffrlmzZhX4Q9EwDA0YMECDBg3Sxo0bL7qf5ORkjRkzxmms70Mj9O/EkUVeM/65X3/4Vscy/tLYB86mB3l5ufpg/nSlLFukJ2cv8mB1gPcpW9ZPVapWkyTVrd9AP3z/nd556w099t8xf/NMAMClKu1fYnqsQfj22281b9688/4AbDabhg4dqquvvvpv95OUlKRhw5yvlrN2Z0aR1YmidW3rjrqy8bVOY7PHDVfTVh3VrG1nD1UFlByGYSg7O8fTZQAALmMeaxCioqL05Zdfqm7duudd/+WXXyoyMvJv92O322W3253GyvqdLJIacWkcJ47rUNo+8/HhA/u1b9cvKhcUovKVIhUYHOq0vY9vGYWEhSviiqruLhXwajNefF5xzVsqMjpax7Oy9Nmny/T1V19q6oxXPF0a4NWOZ2Vp9+7d5uN9e/fqpx9/VGhoqKJjYjxYGUoKEgQPGTFihB544AGlpqaqXbt2ZjOQnp6u1atX65VXXtHkyZM9VR7+gT07t2v6k4PNxx/MnSZJuq7NLeo76HFPlQWUOH/9+afGPPGYDh86qKCgYNWsfaWmznhFzW640dOlAV7t+++/03397jEfT56YLEn6V7fbNG78BE+VBZQYNsMwDE8d/J133tGUKVOUmpqq3NxcSZKvr6+aNm2qYcOG6Y477rik/X7y/YGiLBMoNeJiK3i6BKBECvDz9XQJQInj78UX268Q/5bbjnV4/p1uO1ZhefRH07t3b/Xu3Vs5OTk6dOiQJKlixYoqW7asJ8sCAAAASi2v6N3Kli2r6OhoT5cBAAAAlHpe0SAAAAAA3qK0T1L22I3SAAAAAHgfEgQAAADAggQBAAAAAPKRIAAAAAAWJAgAAAAAkI8EAQAAALAq3QECCQIAAACAs0gQAAAAAAvmIAAAAABAPhIEAAAAwIIEAQAAAADy0SAAAAAAFjabzW2LK2bOnKnGjRsrJCREISEhiouL06effmqub926dYH9DxgwwOXXzylGAAAAQAlQuXJlTZgwQbVr15ZhGJo/f766deumb775Rg0aNJAk3X///Ro7dqz5nHLlyrl8HBoEAAAAwMJb5yB07drV6fEzzzyjmTNnatOmTWaDUK5cOUVFRf2j43CKEQAAAOAhDodDmZmZTovD4fjb5+Xm5urtt99WVlaW4uLizPEFCxaoYsWKatiwoZKSknT8+HGXa6JBAAAAAKxs7luSk5MVGhrqtCQnJ1+wtG3btikoKEh2u10DBgzQkiVLVL9+fUlS37599eabb2rt2rVKSkrSG2+8obvuusv1l28YhuHys7zcJ98f8HQJQIkUF1vB0yUAJVKAn6+nSwBKHH8vPtE9ZsD7bjvWrhe6FEgM7Ha77Hb7ebfPzs7W7t27lZGRocWLF+vVV19VSkqK2SRYrVmzRu3atdOOHTtUs2bNQtfkxT8aAAAA4PJ2sWbgfPz8/FSrVi1JUtOmTbV582a98MILmj17doFtmzVrJkk0CAAAAMA/4a2TlM8nLy/vgnMWtmzZIkmKjo52aZ80CAAAAEAJkJSUpE6dOqlq1ao6evSoFi5cqHXr1mnFihXauXOnFi5cqM6dO6tChQraunWrhg4dqpYtW6px48YuHYcGAQAAALDw1gThwIEDuueee7R//36FhoaqcePGWrFihW6++Wbt2bNHq1at0tSpU5WVlaUqVaqoZ8+e+u9//+vycWgQAAAAgBJgzpw5F1xXpUoVpaSkFMlxaBAAAAAAC29NENyF+yAAAAAAMJEgAAAAAFalO0AgQQAAAABwFgkCAAAAYMEcBAAAAADIR4IAAAAAWJAgAAAAAEA+EgQAAADAggQBAAAAAPKRIAAAAAAWJAgAAAAAkI8EAQAAALAq3QECCQIAAACAs0gQAAAAAAvmIAAAAABAPhoEAAAAACZOMQIAAAAsOMUIAAAAAPKRIAAAAAAWpTxAIEEAAAAAcBYJAgAAAGDBHAQAAAAAyEeCAAAAAFiU8gCBBAEAAADAWSQIAAAAgAVzEAAAAAAgHwkCAAAAYFHKAwQSBAAAAABnkSAAAAAAFj4+pTtCIEEAAAAAYCJBAAAAACyYgwAAAAAA+UgQAAAAAAvugwAAAAAA+WgQAAAAAJg4xQgAAACwKOVnGJEgAAAAADiLBAEAAACwYJIyAAAAAOQjQQAAAAAsSBAAAAAAIB8JAgAAAGBRygMEEgQAAAAAZ5EgAAAAABbMQQAAAACAfCQIAAAAgEUpDxBIEAAAAACcRYIAAAAAWDAHAQAAAADykSAAAAAAFqU8QCBBAAAAAEqCmTNnqnHjxgoJCVFISIji4uL06aefmutPnjypxMREVahQQUFBQerZs6fS09NdPg4NAgAAAGBhs9nctriicuXKmjBhglJTU/XVV1+pbdu26tatm77//ntJ0tChQ/XRRx9p0aJFSklJ0R9//KEePXq4/voNwzBcfpaX++T7A54uASiR4mIreLoEoEQK8PP1dAlAiePvxSe6X/fMOrcda/Pjrf/R88PDwzVp0iT16tVLlSpV0sKFC9WrVy9J0k8//aR69epp48aNuuGGGwq9Ty/+0QAAAADu5845CA6HQw6Hw2nMbrfLbrdf9Hm5ublatGiRsrKyFBcXp9TUVOXk5Kh9+/bmNnXr1lXVqlVdbhA4xQgAAADwkOTkZIWGhjotycnJF9x+27ZtCgoKkt1u14ABA7RkyRLVr19faWlp8vPzU1hYmNP2kZGRSktLc6kmEgQAAADAQ5KSkjRs2DCnsYulB3Xq1NGWLVuUkZGhxYsXKz4+XikpKUVaEw0CAAAAYOHOG6UV5nQiKz8/P9WqVUuS1LRpU23evFkvvPCCevfurezsbB05csQpRUhPT1dUVJRLNXGKEQAAAFBC5eXlyeFwqGnTpipbtqxWr15trtu+fbt2796tuLg4l/Z5WSYIVUPLeboEoESKaf6Ip0sASqSP3xrj6RKAEqdtXe+9cp633igtKSlJnTp1UtWqVXX06FEtXLhQ69at04oVKxQaGqr+/ftr2LBhCg8PV0hIiAYNGqS4uDiXJihLl2mDAAAAAFxuDhw4oHvuuUf79+9XaGioGjdurBUrVujmm2+WJE2ZMkU+Pj7q2bOnHA6HOnbsqBkzZrh8HBoEAAAAwMKdcxBcMWfOnIuu9/f31/Tp0zV9+vR/dBzmIAAAAAAwkSAAAAAAFl4aILgNCQIAAAAAEwkCAAAAYOGtcxDchQQBAAAAgIkEAQAAALAo5QECCQIAAACAs0gQAAAAAAvmIAAAAABAPhIEAAAAwIIEAQAAAADykSAAAAAAFqU8QCBBAAAAAHAWDQIAAAAAE6cYAQAAABZMUgYAAACAfCQIAAAAgEUpDxBIEAAAAACcRYIAAAAAWDAHAQAAAADykSAAAAAAFqU8QCBBAAAAAHAWCQIAAABg4VPKIwQSBAAAAAAmEgQAAADAopQHCCQIAAAAAM4iQQAAAAAsuA8CAAAAAOQjQQAAAAAsfEp3gECCAAAAAOAsEgQAAADAgjkIAAAAAJCPBAEAAACwKOUBAgkCAAAAgLNoEAAAAACYOMUIAAAAsLCpdJ9jRIIAAAAAwESCAAAAAFhwozQAAAAAyEeCAAAAAFhwozQAAAAAyEeCAAAAAFiU8gCBBAEAAADAWSQIAAAAgIVPKY8QSBAAAAAAmEgQAAAAAItSHiCQIAAAAAA4iwQBAAAAsOA+CAAAAACQjwQBAAAAsCjlAQIJAgAAAICzSBAAAAAAC+6DAAAAAMDrJScn67rrrlNwcLAiIiLUvXt3bd++3Wmb1q1by2azOS0DBgxw6Tg0CAAAAEAJkJKSosTERG3atEkrV65UTk6OOnTooKysLKft7r//fu3fv99cJk6c6NJxOMUIAAAAsPDWE4yWL1/u9HjevHmKiIhQamqqWrZsaY6XK1dOUVFRl3wcEgQAAADAQxwOhzIzM50Wh8NRqOdmZGRIksLDw53GFyxYoIoVK6phw4ZKSkrS8ePHXaqJBgEAAACwOPcc/uJckpOTFRoa6rQkJyf/bY15eXkaMmSImjdvroYNG5rjffv21Ztvvqm1a9cqKSlJb7zxhu666y6XXj+nGAEAAAAekpSUpGHDhjmN2e32v31eYmKivvvuO23YsMFp/IEHHjD/vVGjRoqOjla7du20c+dO1axZs1A10SAAAAAAFj5unIRgt9sL1RBYDRw4UMuWLdP69etVuXLli27brFkzSdKOHTtoEAAAAIDLiWEYGjRokJYsWaJ169YpNjb2b5+zZcsWSVJ0dHShj0ODAAAAAFjYvPRGaYmJiVq4cKE++OADBQcHKy0tTZIUGhqqgIAA7dy5UwsXLlTnzp1VoUIFbd26VUOHDlXLli3VuHHjQh+HBgEAAAAoAWbOnCnp9M3QrObOnauEhAT5+flp1apVmjp1qrKyslSlShX17NlT//3vf106Dg0CAAAAYOGlAYIMw7jo+ipVqiglJeUfH4fLnAIAAAAwkSAAAAAAFt46B8FdSBAAAAAAmEgQAAAAAAt33gfBG5EgAAAAADCRIAAAAAAWpX0OQqEahA8//LDQO/zXv/51ycUAAAAA8KxCNQjdu3cv1M5sNptyc3P/ST0AAACAR5Xu/KCQDUJeXl5x1wEAAADACzAHAQAAALDwYQ6C67KyspSSkqLdu3crOzvbad3gwYOLpDAAAAAA7udyg/DNN9+oc+fOOn78uLKyshQeHq5Dhw6pXLlyioiIoEEAAAAASjCX74MwdOhQde3aVX/99ZcCAgK0adMm/f7772ratKkmT55cHDUCAAAAbmOzuW/xRi43CFu2bNHw4cPl4+MjX19fORwOValSRRMnTtR//vOf4qgRAAAAgJu43CCULVtWPj6nnxYREaHdu3dLkkJDQ7Vnz56irQ4AAABwM5vN5rbFG7k8B+Hqq6/W5s2bVbt2bbVq1UpPPvmkDh06pDfeeEMNGzYsjhoBAAAAuInLCcL48eMVHR0tSXrmmWdUvnx5PfTQQzp48KBefvnlIi8QAAAAcKfSPgfB5QTh2muvNf89IiJCy5cvL9KCAAAAAHgON0oDAAAALLhRmotiY2MvOqHi119//UcF4fJ04niW3po7U19sWKvMI38ptlYd3Zs4QrXqNvB0aYBXGtHvZo0b3E3TFqzVyMnvFVi/dNpD6ti8ge4Y+rI+WrfVAxUC3uOX77/RyiULtXvHdmX8dUgPJiXrqhtameuXvfWqvvq/Vfrr0AH5limrqjXrqNtdDyq2Dv8PAs7H5QZhyJAhTo9zcnL0zTffaPny5Ro5cmRR1YXLzIznxmn3rp0anDRO4RUqaf2qTzTm0Yc0dc5iVagU4enyAK/StH5V9e/ZXFt/3nve9YP+3UaG4eaiAC/mOHlSV1SvpRvb3arZE5IKrI+IqareDwxXxagY5WQ7tPqDd/Ti6CEaO+tdBYeW90DF8HalPEBwvUF45JFHzjs+ffp0ffXVV/+4IFx+HI6T2rR+jR4b95waNL5GktQ7/kF9tXG9Vny0WH3vfdjDFQLeIzDAT3PHJ+jhcW/psftuKbC+8ZVX6JG726r5vyfqt1XJHqgQ8D4Nm8apYdO4C66/vlUHp8e9+g/W56s+0r7fdqpuk2sv8Cyg9HL5KkYX0qlTJ733XsEYHMjLzVVeXq7K+tmdxv3sdv303RbPFAV4qalJvbX8/77T2i+2F1gX4F9W85ITNGTCu0o/fNQD1QEl36mcHG1Y8YECAoNUObaWp8uBl+I+CEVk8eLFCg8PL6rdSZL27Nmjp556Sq+99toFt3E4HHI4HE5j2Y4c+dntF3gG3C2gXKDq1G+sxW++qspVYxVaPlwb1qzQzz9sU1RMFU+XB3iN2zs21VV1q+imuyaed/3E4T216dtdWrZum5srA0q+bZv/pzmTn1S246RCylfQ4DFTFRQS5umyAK90STdKs3Y7hmEoLS1NBw8e1IwZM4q0uD///FPz58+/aIOQnJysMWPGOI09NDRJDw/7T5HWgn9mcNJYTZ80Vvf3vkU+Pr6qUbuubmrTUTt/+dHTpQFeoXJkmCaN7KlbH5omR/apAuu7tGqk1tdfqRv6TPBAdUDJd2Wja/SfqfN1LPOI/vfZh3p14hN6dNIrCgkr2i83cXkoslNsSiiXG4Ru3bo5NQg+Pj6qVKmSWrdurbp167q0rw8//PCi6wtzRaSkpCQNGzbMaWzHwRyX6kDxi4qponFTXtHJEyd04vgxla9QSc+Ne0yR0Vd4ujTAK1xdr6oiK4Ro48JR5liZMr666ZqaGtC7pV5ZvEE1KldU2vpJTs97a/J9+t83O9Xx/hfcXTJQotj9AxQRXVkR0ZVVo05DPTngDn2+aplu6XWPp0sDvI7LDcLo0aOL7ODdu3eXzWaTcZHLcfzduVl2u132c04n8ss8ViT1oej5BwTIPyBAx45masvmjbr7gfNPegdKm7VfblfTXs84jb085i5t35Wu5+at1OEjx/Tq4g1O61MXP65Hn3tPH6d8585SgcuCYeTpVE62p8uAl/LWuQHu4nKD4Ovrq/379ysiwvnSlIcPH1ZERIRyc3MLva/o6GjNmDFD3bp1O+/6LVu2qGnTpq6WCC/0zebPJUOKqVJNafv26PWXX9AVVaur7S1dPV0a4BWOHXfoh537ncayTmTrz4wsc/x8E5P37P9Lv/9x2C01At7q5InjOrj/7GWBD6fv155ff1ZgcIgCg0P16aL5anz9TQotX0HHMjOU8sl7OnL4kK5p3taDVQPey+UG4ULf9jscDvn5+bm0r6ZNmyo1NfWCDcLfpQsoOY5nHdOCV6fp8KEDCgoO0Q0t2qnvvQ+rTJmyni4NAFDC7d7xk6b8d6D5ePFrL0qSbmjbWX0fGqn0vb/r5TWfKCszQ4HBoapWu66GJ89QTNUanioZXs6ndAcIhW8QXnzx9C+bzWbTq6++qqCgIHNdbm6u1q9f7/IchJEjRyorK+uC62vVqqW1a9e6tE94p+atO6h56w5/vyEA09/NKwi4euBF1wOlxZWNrtHMDz6/4PoHk7hnCOCKQjcIU6ZMkXQ6QZg1a5Z8fX3NdX5+fqpevbpmzZrl0sFbtGhx0fWBgYFq1arVRbcBAAAAUHQK3SDs2rVLktSmTRu9//77Kl+eW5MDAADg8sMpRi7ilB8AAADg8uXyfSB69uypZ599tsD4xIkTdfvttxdJUQAAAICn2Gw2ty3eyOUGYf369ercuXOB8U6dOmn9+vVFUhQAAAAAz3D5FKNjx46d93KmZcuWVWZmZpEUBQAAAHhKaZ+D4HKC0KhRI73zzjsFxt9++23Vr1+/SIoCAAAA4BkuJwhPPPGEevTooZ07d6pt29N3IFy9erUWLlyoxYsXF3mBAAAAgDt56dQAt3G5QejatauWLl2q8ePHa/HixQoICFCTJk20Zs0ahYeHF0eNAAAAANzE5QZBkrp06aIuXbpIkjIzM/XWW29pxIgRSk1NVW5ubpEWCAAAALiTTymPEFyeg3DG+vXrFR8fr5iYGD333HNq27atNm3aVJS1AQAAAHAzlxKEtLQ0zZs3T3PmzFFmZqbuuOMOORwOLV26lAnKAAAAuCxc8jfol4lCv/6uXbuqTp062rp1q6ZOnao//vhDL730UnHWBgAAAMDNCp0gfPrppxo8eLAeeugh1a5duzhrAgAAADymlE9BKHyCsGHDBh09elRNmzZVs2bNNG3aNB06dKg4awMAAADgZoVuEG644Qa98sor2r9/vx588EG9/fbbiomJUV5enlauXKmjR48WZ50AAACAW/jYbG5bvJHLczACAwN17733asOGDdq2bZuGDx+uCRMmKCIiQv/617+Ko0YAAAAAbvKPJmnXqVNHEydO1N69e/XWW28VVU0AAACAx9hs7lu8UZFcxcnX11fdu3fXhx9+WBS7AwAAAOAhl3QnZQAAAOBy5eOl3+y7S2m/DwQAAAAACxoEAAAAACYaBAAAAMDCWy9zmpycrOuuu07BwcGKiIhQ9+7dtX37dqdtTp48qcTERFWoUEFBQUHq2bOn0tPTXXv9Lm0NAAAAwCNSUlKUmJioTZs2aeXKlcrJyVGHDh2UlZVlbjN06FB99NFHWrRokVJSUvTHH3+oR48eLh2HScoAAACAhbdefnT58uVOj+fNm6eIiAilpqaqZcuWysjI0Jw5c7Rw4UK1bdtWkjR37lzVq1dPmzZt0g033FCo45AgAAAAAB7icDiUmZnptDgcjkI9NyMjQ5IUHh4uSUpNTVVOTo7at29vblO3bl1VrVpVGzduLHRNNAgAAACAhY/NfUtycrJCQ0OdluTk5L+tMS8vT0OGDFHz5s3VsGFDSVJaWpr8/PwUFhbmtG1kZKTS0tIK/fo5xQgAAADwkKSkJA0bNsxpzG63/+3zEhMT9d1332nDhg1FXhMNAgAAAGBhk/smIdjt9kI1BFYDBw7UsmXLtH79elWuXNkcj4qKUnZ2to4cOeKUIqSnpysqKqrQ++cUIwAAAKAEMAxDAwcO1JIlS7RmzRrFxsY6rW/atKnKli2r1atXm2Pbt2/X7t27FRcXV+jjkCAAAAAAFj5eehWjxMRELVy4UB988IGCg4PNeQWhoaEKCAhQaGio+vfvr2HDhik8PFwhISEaNGiQ4uLiCn0FI4kGAQAAACgRZs6cKUlq3bq10/jcuXOVkJAgSZoyZYp8fHzUs2dPORwOdezYUTNmzHDpODQIAAAAgIW3JgiGYfztNv7+/po+fbqmT59+ycdhDgIAAAAAEwkCAAAAYGHz1lspuwkJAgAAAAATCQIAAABg4a1zENyFBAEAAACAiQQBAAAAsCjlUxBIEAAAAACcRYMAAAAAwMQpRgAAAICFTyk/x4gEAQAAAICJBAEAAACw4DKnAAAAAJCPBAEAAACwKOVTEEgQAAAAAJxFggAAAABY+Kh0RwgkCAAAAABMJAgAAACABXMQAAAAACAfCQIAAABgwX0QAAAAACAfCQIAAABg4VPKJyGQIAAAAAAwkSAAAAAAFqU8QCBBAAAAAHAWCQIAAABgwRwEAAAAAMhHggAAAABYlPIAgQQBAAAAwFk0CAAAAABMnGIEAAAAWJT2b9BL++sHAAAAYEGCAAAAAFjYSvksZRIEAAAAACYSBAAAAMCidOcHJAgAAAAALEgQAAAAAAsf5iAAAAAAwGkkCAAAAIBF6c4PSBAAAAAAWJAgAAAAABalfAoCCQIAAACAs0gQAAAAAAvupAwAAAAA+UgQAAAAAIvS/g16aX/9AAAAACxIEAAAAAAL5iAAAAAAQD4aBAAAAAAmTjECAAAALEr3CUYkCAAAAAAsSBAAAAAAi9I+SfmybBBqRQV5ugSgRNq59nlPlwCUSDm5hqdLAFAKrF+/XpMmTVJqaqr279+vJUuWqHv37ub6hIQEzZ8/3+k5HTt21PLly106zmXZIAAAAACXylvPwc/KylKTJk107733qkePHufd5pZbbtHcuXPNx3a73eXj0CAAAAAAJUCnTp3UqVOni25jt9sVFRX1j45DgwAAAABYuHMOgsPhkMPhcBqz2+2X9M2/JK1bt04REREqX7682rZtq6effloVKlRwaR/emqAAAAAAl73k5GSFhoY6LcnJyZe0r1tuuUWvv/66Vq9erWeffVYpKSnq1KmTcnNzXdqPzTCMy25m1clTnq4AKJn+PJbt6RKAEolJyoDrqlW4tG/I3WHp1jS3HatTnfKXlCDYbLYCk5TP9euvv6pmzZpatWqV2rVrV+iaSBAAAAAAD7Hb7QoJCXFaLvX0onPVqFFDFStW1I4dO1x6HnMQAAAAAIvL5TYIe/fu1eHDhxUdHe3S82gQAAAAgBLg2LFjTmnArl27tGXLFoWHhys8PFxjxoxRz549FRUVpZ07d+rRRx9VrVq11LFjR5eOQ4MAAAAAWPjIOyOEr776Sm3atDEfDxs2TJIUHx+vmTNnauvWrZo/f76OHDmimJgYdejQQePGjXP5lCUmKQMwMUkZuDRMUgZc582TlD/alu62Y3VtFOm2YxUWCQIAAABgcbnMQbhUXMUIAAAAgIkEAQAAALCweekcBHchQQAAAABgIkEAAAAALJiDAAAAAAD5aBAAAAAAmDjFCAAAALDw1huluQsJAgAAAAATCQIAAABgwSRlAAAAAMhHggAAAABYkCAAAAAAQD4SBAAAAMDCxlWMAAAAAOA0EgQAAADAwqd0BwgkCAAAAADOIkEAAAAALJiDAAAAAAD5SBAAAAAAC+6DAAAAAAD5SBAAAAAAC+YgAAAAAEA+EgQAAADAgvsgAAAAAEA+GgQAAAAAJk4xAgAAACyYpAwAAAAA+UgQAAAAAAtulAYAAAAA+UgQAAAAAItSHiCQIAAAAAA4iwQBAAAAsPAp5ZMQSBAAAAAAmEgQAAAAAIvSnR+QIAAAAACwIEEAAAAArEp5hECCAAAAAMBEggAAAABY2Ep5hECCAAAAAMBEggAAAABYlPLbIJAgAAAAADiLBAEAAACwKOUBAgkCAAAAgLNIEAAAAACrUh4hkCAAAAAAMNEgAAAAADBxihEAAABgwY3SAAAAACAfCQIAAABgwY3SAAAAACAfCQIAAABgUcoDBBIEAAAAoCRYv369unbtqpiYGNlsNi1dutRpvWEYevLJJxUdHa2AgAC1b99ev/zyi8vHoUEAAAAArGxuXFyQlZWlJk2aaPr06eddP3HiRL344ouaNWuWvvjiCwUGBqpjx446efKkS8exGYZhuFaa9zt5ytMVACXTn8eyPV0CUCLl5F52/ysFil21CnZPl3BBX/+e6bZjXVMt5JKeZ7PZtGTJEnXv3l3S6fQgJiZGw4cP14gRIyRJGRkZioyM1Lx589SnT59C75sEAQAAALCwufEfh8OhzMxMp8XhcLhc865du5SWlqb27dubY6GhoWrWrJk2btzo0r5oEAAAAAAPSU5OVmhoqNOSnJzs8n7S0tIkSZGRkU7jkZGR5rrC4ipGAAAAgIU774OQlJSkYcOGOY3Z7Z49/YoGAQAAAPAQu91eJA1BVFSUJCk9PV3R0dHmeHp6uq666iqX9sUpRgAAAICFl17E6KJiY2MVFRWl1atXm2OZmZn64osvFBcX59K+SBAAAACAEuDYsWPasWOH+XjXrl3asmWLwsPDVbVqVQ0ZMkRPP/20ateurdjYWD3xxBOKiYkxr3RUWDQIAAAAgJWX3kr5q6++Ups2bczHZ+YuxMfHa968eXr00UeVlZWlBx54QEeOHNFNN92k5cuXy9/f36XjcB8EACbugwBcGu6DALjOm++D8O2eo247VpMqwW47VmGRIAAAAAAWNm+NENyEScoAAAAATDQIAAAAAEycYgQAAABYuPNGad6IBAEAAACAiQQBAAAAsCjlAQIJAgAAAICzSBAAAAAAq1IeIZAgAAAAADCRIAAAAAAWpf1GaTQIcIvUrzZr3mtz9OMP3+ngwYOa8uJ0tW3X3tNlAV5t3iszNP/VmU5jVapV1+vvfuShioCS49DBdL06fao2b9ogx8mTiqlcRSMeH6cr6zXwdGmA16NBgFucOHFcderUUfcePTXskYGeLgcoMarXqKXnpr1iPvb19fVgNUDJcDQzU0MfjFeTa67TM8/PUGhYee3bs1tBwSGeLg0lRGm/DwINAtziphatdFOLVp4uAyhxfH19FV6hoqfLAEqUd998TZUiIzXiv+PMseiYyh6sCChZaBAAwIvt27Nbvbq0lZ+fn+o3aqL7Hx6iyKhoT5cFeLWNG9apabMbNe7x4dr6zVeqWClSXXvcoc7denm6NJQQpTxAoEEAAG9Vr0EjjXpynKpUra7Dhw/p9Vdn6pEH4/XawiUqFxjo6fIAr7X/j71atuRd9exzt+685z5t//F7zZjyrMqULasOnbt5ujzA63m8QThx4oRSU1MVHh6u+vXrO607efKk3n33Xd1zzz0XfL7D4ZDD4XAaM3ztstvtxVIvALhLsxtbmP9es3Yd1W/QSH26ddTa1SvU5V89PFgZ4N2MvDxdWbeB7h3wiCSpVp16+u3XHfp4ySIaBBROKY8QPHofhJ9//ln16tVTy5Yt1ahRI7Vq1Ur79+8312dkZKhfv34X3UdycrJCQ0OdlknPJhd36QDgdkHBIapctZr+2LPb06UAXi28QiVVja3hNFa1eqwOpKd5qCKgZPFogzBq1Cg1bNhQBw4c0Pbt2xUcHKzmzZtr9+7C/88vKSlJGRkZTsvIUUnFWDUAeMaJ48f1x749Cq9YydOlAF6tQeOrtHf3b05je/f8zvwdFJrNjf94I4+eYvT5559r1apVqlixoipWrKiPPvpIDz/8sFq0aKG1a9cqsBDn2NrtBU8nOnmquCrGpTqeleXU+O3bu1c//fijQkNDFR0T48HKAO8184XJimvRSlFRMTp06KDmvTJdPj6+atehk6dLA7xaj953a8iD9+it+a+oZbuO2v7DNn3ywWINGfWUp0sDSgSbYRiGpw4eEhKiL774QvXq1XMaHzhwoD744AMtXLhQrVu3Vm5urkv7pUHwPpu//EL39Ss4l+Rf3W7TuPETPFARzufPY9meLgEWYx8fqa1bUpWZcUShYeXVqMk16v/QYF1RuYqnS8M5cnI99r9SXMCm/6XotZkvaN/e3YqKvkI9+9zNVYy8TLUK3jtfdHvacbcdq05UObcdq7A82iBcf/31GjRokO6+++4C6wYOHKgFCxYoMzOTBgFwExoE4NLQIACuo0E4zRsbBI/OQbjtttv01ltvnXfdtGnTdOedd8qD/QsAAABQ6ng0QSguJAjApSFBAC4NCQLgOm9OEH52Y4JwJQkCAAAAAG/m8RulAQAAAF7FO68+6jYkCAAAAABMJAgAAACAhbfewMxdSBAAAAAAmEgQAAAAAAtb6Q4QSBAAAAAAnEWCAAAAAFiU8gCBBAEAAADAWSQIAAAAgFUpjxBIEAAAAACYSBAAAAAAC+6DAAAAAAD5SBAAAAAAC+6DAAAAAAD5SBAAAAAAi1IeIJAgAAAAADiLBAEAAACwKuURAgkCAAAAABMNAgAAAAATpxgBAAAAFtwoDQAAAADykSAAAAAAFtwoDQAAAADykSAAAAAAFqU8QCBBAAAAAHAWCQIAAABgwRwEAAAAAMhHggAAAAA4Kd0RAgkCAAAAUAKMHj1aNpvNaalbt26RH4cEAQAAALDw5jkIDRo00KpVq8zHZcoU/Z/zNAgAAABACVGmTBlFRUUV6zE4xQgAAACwsLlxcTgcyszMdFocDscFa/vll18UExOjGjVq6N///rd2795d1C+fBgEAAADwlOTkZIWGhjotycnJ5922WbNmmjdvnpYvX66ZM2dq165datGihY4ePVqkNdkMwzCKdI9e4OQpT1cAlEx/Hsv2dAlAiZSTe9n9rxQodtUq2D1dwgXtz3Df/w/D/Y0CiYHdbpfd/vfvz5EjR1StWjU9//zz6t+/f5HVxBwEAAAAwEMK2wycT1hYmK688krt2LGjSGviFCMAAADAwubGf/6JY8eOaefOnYqOji6iV34aDQIAAABQAowYMUIpKSn67bff9Pnnn+u2226Tr6+v7rzzziI9DqcYAQAAACXA3r17deedd+rw4cOqVKmSbrrpJm3atEmVKlUq0uMwSRmAiUnKwKVhkjLgOm+epJyWmeO2Y0WFlHXbsQqLU4wAAAAAmDjFCAAAALD4Z1OHSz4SBAAAAAAmEgQAAADAwlbKIwQSBAAAAAAmEgQAAADA4p/ewKykI0EAAAAAYCJBAAAAAKxKd4BAggAAAADgLBIEAAAAwKKUBwgkCAAAAADOIkEAAAAALLgPAgAAAADkI0EAAAAALLgPAgAAAADkI0EAAAAALJiDAAAAAAD5aBAAAAAAmGgQAAAAAJhoEAAAAACYmKQMAAAAWDBJGQAAAADykSAAAAAAFtwoDQAAAADykSAAAAAAFsxBAAAAAIB8JAgAAACARSkPEEgQAAAAAJxFggAAAABYlfIIgQQBAAAAgIkEAQAAALDgPggAAAAAkI8EAQAAALDgPggAAAAAkI8EAQAAALAo5QECCQIAAACAs0gQAAAAAKtSHiGQIAAAAAAw0SAAAAAAMHGKEQAAAGDBjdIAAAAAIB8JAgAAAGDBjdIAAAAAIJ/NMAzD00Wg9HA4HEpOTlZSUpLsdrunywFKBH5vgEvD7w5waWgQ4FaZmZkKDQ1VRkaGQkJCPF0OUCLwewNcGn53gEvDKUYAAAAATDQIAAAAAEw0CAAAAABMNAhwK7vdrqeeeorJYoAL+L0BLg2/O8ClYZIyAAAAABMJAgAAAAATDQIAAAAAEw0CAAAAABMNAgAAAAATDQLcZvr06apevbr8/f3VrFkzffnll54uCfBq69evV9euXRUTEyObzaalS5d6uiSgREhOTtZ1112n4OBgRUREqHv37tq+fbunywJKDBoEuMU777yjYcOG6amnntLXX3+tJk2aqGPHjjpw4ICnSwO8VlZWlpo0aaLp06d7uhSgRElJSVFiYqI2bdqklStXKicnRx06dFBWVpanSwNKBC5zCrdo1qyZrrvuOk2bNk2SlJeXpypVqmjQoEF67LHHPFwd4P1sNpuWLFmi7t27e7oUoMQ5ePCgIiIilJKSopYtW3q6HMDrkSCg2GVnZys1NVXt27c3x3x8fNS+fXtt3LjRg5UBAEqDjIwMSVJ4eLiHKwFKBhoEFLtDhw4pNzdXkZGRTuORkZFKS0vzUFUAgNIgLy9PQ4YMUfPmzdWwYUNPlwOUCGU8XQAAAEBxSUxM1HfffacNGzZ4uhSgxKBBQLGrWLGifH19lZ6e7jSenp6uqKgoD1UFALjcDRw4UMuWLdP69etVuXJlT5cDlBicYoRi5+fnp6ZNm2r16tXmWF5enlavXq24uDgPVgYAuBwZhqGBAwdqyZIlWrNmjWJjYz1dElCikCDALYYNG6b4+Hhde+21uv766zV16lRlZWWpX79+ni4N8FrHjh3Tjh07zMe7du3Sli1bFB4erqpVq3qwMsC7JSYmauHChfrggw8UHBxszncLDQ1VQECAh6sDvB+XOYXbTJs2TZMmTVJaWpquuuoqvfjii2rWrJmnywK81rp169SmTZsC4/Hx8Zo3b577CwJKCJvNdt7xuXPnKiEhwb3FACUQDQIAAAAAE3MQAAAAAJhoEAAAAACYaBAAAAAAmGgQAAAAAJhoEAAAAACYaBAAAAAAmGgQAAAAAJhoEAAAAACYaBAAwMskJCSoe/fu5uPWrVtryJAhbq9j3bp1stlsOnLkiNuPDQDwHBoEACikhIQE2Ww22Ww2+fn5qVatWho7dqxOnTpVrMd9//33NW7cuEJtyx/1AIB/qoynCwCAkuSWW27R3Llz5XA49MknnygxMVFly5ZVUlKS03bZ2dny8/MrkmOGh4cXyX4AACgMEgQAcIHdbldUVJSqVaumhx56SO3bt9eHH35onhb0zDPPKCYmRnXq1JEk7dmzR3fccYfCwsIUHh6ubt266bfffjP3l5ubq2HDhiksLEwVKlTQo48+KsMwnI557ilGDodDo0aNUpUqVWS321WrVi3NmTNHv/32m9q0aSNJKl++vGw2mxISEiRJeXl5Sk5OVmxsrAICAtSkSRMtXrzY6TiffPKJrrzySgUEBKhNmzZOdQIASg8aBAD4BwICApSdnS1JWr16tbZv366VK1dq2bJlysnJUceOHRUcHKz/+7//0//+9z8FBQXplltuMZ/z3HPPad68eXrttde0YcMG/fnnn1qyZMlFj3nPPfforbfe0osvvqgff/xRs2fPVlBQkKpUqaL33ntPkrR9+3bt379fL7zwgiQpOTlZr7/+umbNmqXvv/9eQ4cO1V133aWUlBRJpxuZHj16qGvXrtqyZYvuu+8+PfbYY8X1tgEAvBinGAHAJTAMQ6tXr9aKFSs0aNAgHTx4UIGBgXr11VfNU4vefPNN5eXl6dVXX5XNZpMkzZ07V2FhYVq3bp06dOigqVOnKikpST169JAkzZo1SytWrLjgcX/++We9++67Wrlypdq3by9JqlGjhrn+zOlIERERCgsLk3Q6cRg/frxWrVqluLg48zkbNmzQ7Nmz1apVK82cOVM1a9bUc889J0mqU6eOtm3bpmeffbYI3zUAQElAgwAALli2bJmCgoKUk5OjvLw89e3bV6NHj1ZiYqIaNWrkNO/g22+/1Y4dOxQcHOy0j5MnT2rnzp3KyMjQ/v371axZM3NdmTJldO211xY4zeiMLVu2yNfXV61atSp0zTt27NDx48d18803O41nZ2fr6quvliT9+OOPTnVIMpsJAEDpQoMAAC5o06aNZs6cKT8/P8XExKhMmbP/GQ0MDHTa9tixY2ratKkWLFhQYD+VKlW6pOMHBAS4/Jxjx45Jkj7++GNdccUVTuvsdvsl1QEAuHzRIACACwIDA1WrVq1CbXvNNdfonXfeUUREhEJCQs67TXR0tL744gu1bNlSknTq1CmlpqbqmmuuOe/2jRo1Ul5enlJSUsxTjKzOJBi5ubnmWP369WW327V79+4LJg/16tXThx9+6DS2adOmv3+RAIDLDpOUAaCY/Pvf/1bFihXVrVs3/d///Z927dqldevWafDgwdq7d68k6ZFHHtGECRO0dOlS/fTTT3r44Ycveg+D6tWrKz4+Xvfee6+WLl1q7vPdd9+VJFWrVk02m03Lli3TwYMHdezYMQUHB2vEiBEaOnSo5s+fr507d+rrr7/WSy+9pPnz50uSBgwYoF9++UUjR47U9u3btXDhQs2bN6+43yIAgBeiQQCAYlKuXDmtX79eVatWVY8ePVSvXj31799fJ0+eNBOF4cOH6+6771Z8fLzi4uIUHBys22677aL7nTlzpnr16qWHH35YdevW1f3336+srCxJ0hVXXKExY8boscceU2RkpAYOHChJGjdunJ544gklJyerXr16uuWWW/Txxx8rNjZWklS1alW99957Wrp0qZo0aaJZs2Zp/PjxxfjuAAC8lc240Ew4AAAAAKUOCQIAAAAAEw0CAAAAABMNAgAAAAATDQIAAAAAEw0CAAAAABMNAgAAAAATDQIAAAAAEw0CAAAAABMNAgAAAAATDQIAAAAAEw0CAAAAANP/Awx3JCZXApIsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "#X_train, X_test, y_train, y_test = train_test_split(features, data['Difficulty_level'], train_size=0.8, random_state=RANDOM_STATE)  # 80% training and 20% testing\n",
    "\n",
    "y_pred = random_forest_model.predict(X_test)\n",
    "\n",
    "\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report)\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(type(cm))\n",
    "\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=random_forest_model.classes_, yticklabels=random_forest_model.classes_)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix for Random Forest')\n",
    "plt.savefig('confusion_matrix.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macro-average precision: 0.5765\n",
      "Macro-average recall: 0.6481\n",
      "Macro-average F1-score: 0.5958\n",
      "\n",
      "Weighted-average precision: 0.7286\n",
      "Weighted-average recall: 0.6667\n",
      "Weighted-average F1-score: 0.6846\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "# Example: Compute macro-average precision, recall, and F1-score\n",
    "macro_precision = precision_score(y_test, y_pred, average='macro')\n",
    "macro_recall = recall_score(y_test, y_pred, average='macro')\n",
    "macro_f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "# Example: Compute weighted-average precision, recall, and F1-score\n",
    "weighted_precision = precision_score(y_test, y_pred, average='weighted')\n",
    "weighted_recall = recall_score(y_test, y_pred, average='weighted')\n",
    "weighted_f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "\n",
    "print(f'Macro-average precision: {macro_precision:.4f}')\n",
    "print(f'Macro-average recall: {macro_recall:.4f}')\n",
    "print(f'Macro-average F1-score: {macro_f1:.4f}')\n",
    "print()\n",
    "print(f'Weighted-average precision: {weighted_precision:.4f}')\n",
    "print(f'Weighted-average recall: {weighted_recall:.4f}')\n",
    "print(f'Weighted-average F1-score: {weighted_f1:.4f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Flow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Up the Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import mlflow\n",
    "\n",
    "# # Set the experiment name\n",
    "# experiment_name = \"My ML Experiment\"\n",
    "# mlflow.set_experiment(experiment_name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running and Logging an Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import mlflow.sklearn\n",
    "\n",
    "# def train_and_log_model(params, run_name, model_name):\n",
    "#     with mlflow.start_run(run_name=run_name):\n",
    "        \n",
    "#         metrics = {\n",
    "#             \"Training accuracy\": training_accuracy_score_random_forest,\n",
    "#             \"Testing accuracy\": testing_accuracy_score_random_forest,\n",
    "#             \"Macro-average precision\": macro_precision,\n",
    "#             \"Macro-average recall\": macro_recall,\n",
    "#             \"Macro-average F1-score\": macro_f1,\n",
    "#             \"Weighted-average precision\": weighted_precision,\n",
    "#             \"Weighted-average recall\": weighted_recall,\n",
    "#             \"Weighted-average F1-score\": weighted_f1\n",
    "#         }\n",
    "        \n",
    "#         # Save the classification report to a file and log it as an artifact\n",
    "#         report_file = \"classification_report.txt\"\n",
    "#         with open(report_file, \"w\") as f:\n",
    "#             f.write(report)\n",
    "            \n",
    "#         # Confusion matrix\n",
    "#         cm_file = \"confusion_matrix.png\"\n",
    "        \n",
    "#         # Log parameters, metrics, artifacts and model\n",
    "#         mlflow.log_params(params)\n",
    "#         mlflow.log_metrics(metrics)\n",
    "#         mlflow.log_artifact(report_file, artifact_path=\"reports\")\n",
    "#         mlflow.log_artifact(cm_file, artifact_path=\"reports\")\n",
    "#         mlflow.sklearn.log_model(random_forest_model, \"model\")\n",
    "        \n",
    "#         # Register the model in MLflow\n",
    "#         mlflow.sklearn.log_model(random_forest_model, model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_and_log_model(params, 'Smote_boarderline_2_without_Sex_Q8', 'RF_Smote_boarderline_2_without_Sex_Q8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "################################################################################################################"
   ]
  }
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