AIN2002_Stroke_Predictions.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='grey'><b>Stroke Predictions</b></font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='silver'><i>Data Cleaning</i></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing numpy and pandas libraries \n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Concatenating the data sets</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the datasets\n",
    "healthcareDatasetStrokeData = pd.read_csv('datasets/healthcare-dataset-stroke-data.csv')\n",
    "train_table = pd.read_csv('datasets/healthcare-dataset-stroke-data.csv')\n",
    "\n",
    "# Concatenating the datasets\n",
    "dataframe = pd.concat([healthcareDatasetStrokeData, train_table], axis=0, ignore_index=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Dataframe's First Five Rows</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0   9046    Male  67.0             0              1          Yes   \n",
       "1  51676  Female  61.0             0              0          Yes   \n",
       "2  31112    Male  80.0             0              1          Yes   \n",
       "3  60182  Female  49.0             0              0          Yes   \n",
       "4   1665  Female  79.0             1              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2        Private          Rural             105.92  32.5     never smoked   \n",
       "3        Private          Urban             171.23  34.4           smokes   \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# head() method returns top 5 rows of the dataframe\n",
    "dataframe.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Dataframe's Size</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10220, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape() method returns the number of rows and columns of the dataframe\n",
    "dataframe.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>The dataframe has 20414 rows and 12 columns.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Categorcial Columns' Unique Values</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Male' 'Female' 'Other']\n",
      "['Yes' 'No']\n",
      "['Private' 'Self-employed' 'Govt_job' 'children' 'Never_worked']\n",
      "['Urban' 'Rural']\n",
      "['formerly smoked' 'never smoked' 'smokes' 'Unknown']\n"
     ]
    }
   ],
   "source": [
    "# unique() method returns the unique values of the column\n",
    "\n",
    "# Gender column\n",
    "print(dataframe['gender'].unique())\n",
    "# Martial Status column\n",
    "print(dataframe['ever_married'].unique())\n",
    "# Work Type column\n",
    "print(dataframe['work_type'].unique())\n",
    "# Residence Type column\n",
    "print(dataframe['Residence_type'].unique())\n",
    "# Smoking Status column\n",
    "print(dataframe['smoking_status'].unique())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Statistical Info</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>10220.0</td>\n",
       "      <td>36517.829354</td>\n",
       "      <td>21160.686189</td>\n",
       "      <td>67.00</td>\n",
       "      <td>17740.00</td>\n",
       "      <td>36932.000</td>\n",
       "      <td>54695.00</td>\n",
       "      <td>72940.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>10220.0</td>\n",
       "      <td>43.226614</td>\n",
       "      <td>22.611540</td>\n",
       "      <td>0.08</td>\n",
       "      <td>25.00</td>\n",
       "      <td>45.000</td>\n",
       "      <td>61.00</td>\n",
       "      <td>82.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>10220.0</td>\n",
       "      <td>0.097456</td>\n",
       "      <td>0.296592</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_disease</th>\n",
       "      <td>10220.0</td>\n",
       "      <td>0.054012</td>\n",
       "      <td>0.226052</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <td>10220.0</td>\n",
       "      <td>106.147677</td>\n",
       "      <td>45.281344</td>\n",
       "      <td>55.12</td>\n",
       "      <td>77.24</td>\n",
       "      <td>91.885</td>\n",
       "      <td>114.09</td>\n",
       "      <td>271.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>9818.0</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>7.853667</td>\n",
       "      <td>10.30</td>\n",
       "      <td>23.50</td>\n",
       "      <td>28.100</td>\n",
       "      <td>33.10</td>\n",
       "      <td>97.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>10220.0</td>\n",
       "      <td>0.048728</td>\n",
       "      <td>0.215309</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean           std    min       25%  \\\n",
       "id                 10220.0  36517.829354  21160.686189  67.00  17740.00   \n",
       "age                10220.0     43.226614     22.611540   0.08     25.00   \n",
       "hypertension       10220.0      0.097456      0.296592   0.00      0.00   \n",
       "heart_disease      10220.0      0.054012      0.226052   0.00      0.00   \n",
       "avg_glucose_level  10220.0    106.147677     45.281344  55.12     77.24   \n",
       "bmi                 9818.0     28.893237      7.853667  10.30     23.50   \n",
       "stroke             10220.0      0.048728      0.215309   0.00      0.00   \n",
       "\n",
       "                         50%       75%       max  \n",
       "id                 36932.000  54695.00  72940.00  \n",
       "age                   45.000     61.00     82.00  \n",
       "hypertension           0.000      0.00      1.00  \n",
       "heart_disease          0.000      0.00      1.00  \n",
       "avg_glucose_level     91.885    114.09    271.74  \n",
       "bmi                   28.100     33.10     97.60  \n",
       "stroke                 0.000      0.00      1.00  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe() method returns the statistical summary of the dataframe\n",
    "dataframe.describe().transpose()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Missing Values</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0\n",
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "ever_married           0\n",
       "work_type              0\n",
       "Residence_type         0\n",
       "avg_glucose_level      0\n",
       "bmi                  402\n",
       "smoking_status         0\n",
       "stroke                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isnull() method returns the number of missing values in each column\n",
    "dataframe.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>The 'bmi' (body-mass index) column is the only column with missing entries (201 entry).</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>To fill the missing values, The approch that is going to be adapted is filtering the data by age ranges and filling each missing value with the average BMI in that range in order to achieve more accuracy.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/1669062343.py:9: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_data = dataframe.groupby('age_range')['bmi'].mean()\n"
     ]
    }
   ],
   "source": [
    "# Defining the age ranges and labels\n",
    "age_ranges = [(0, 10), (10, 20), (20, 30), (30, 40), (40, 50), (50, 60), (60, 70), (70, 80), (80, 90)]\n",
    "age_labels = ['0-10', '10-20', '20-30', '30-40', '40-50', '50-60', '60-70', '70-80', '80-90']\n",
    "\n",
    "# Creating a new column 'age_range' and assigning the age range of each person\n",
    "dataframe['age_range'] = pd.cut(dataframe['age'], bins=[r[0] for r in age_ranges] + [age_ranges[-1][1]], labels=age_labels)\n",
    "\n",
    "# Calculating the mean of bmi for each age range\n",
    "grouped_data = dataframe.groupby('age_range')['bmi'].mean()\n",
    "\n",
    "# Filling the missing values of bmi with the mean of bmi of the corresponding age range\n",
    "for age_range, avg_bmi in zip(grouped_data.index, grouped_data.values):\n",
    "    dataframe.loc[(dataframe['age_range'] == age_range) & (dataframe['bmi'].isna()), 'bmi'] = round(avg_bmi, 2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>Checking for null values</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "gender               0\n",
       "age                  0\n",
       "hypertension         0\n",
       "heart_disease        0\n",
       "ever_married         0\n",
       "work_type            0\n",
       "Residence_type       0\n",
       "avg_glucose_level    0\n",
       "bmi                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "age_range            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='grey'>The process was successful</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Duplicated Rows</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5110"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duplicated() method returns the number of duplicate rows\n",
    "dataframe.duplicated().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>The dataframe has no duplicate rows.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='silver'><i>Data Visualization</i></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib and seaborn libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Categorical Data</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='lightgreen'>Since the dataset is large, the graphs are only going to be giving a general overview. So the method groupby() is going to be used to get the exact values of individuals with or without a stroke.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>Hypertension Plot</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the style of the plot\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# plotting the graph\n",
    "hypertension_graph = sns.countplot(x=\"hypertension\", hue=\"stroke\", data=dataframe, palette=\"Set2\")\n",
    "\n",
    "# set the plot title and labels\n",
    "hypertension_graph.set_title('Number of people who had a stroke by age range')\n",
    "hypertension_graph.set_xlabel('hypertension ')\n",
    "hypertension_graph.set_ylabel('Number of people')\n",
    "\n",
    "# save the plot\n",
    "hypertension_graph.figure.savefig('hypertension_graph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hypertension  stroke\n",
       "0             0         8858\n",
       "              1          366\n",
       "1             0          864\n",
       "              1          132\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of people with or without hypertension and stroke \n",
    "dataframe['stroke'].groupby(dataframe['hypertension']).value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>Accroding to the dataset, individuals <b>without</b> hypertension have a <b>higher</b> risk of having a stroke.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>Heart Disease Plot</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph\n",
    "heart_disease_graph = sns.countplot(x=\"heart_disease\", hue=\"stroke\", data=dataframe, palette=\"Set2\")\n",
    "\n",
    "# set the plot title and labels\n",
    "heart_disease_graph.set_title('Number of people who had a stroke by age range')\n",
    "heart_disease_graph.set_xlabel('Heart disease ')\n",
    "heart_disease_graph.set_ylabel('Number of people')\n",
    "\n",
    "# save the plot\n",
    "heart_disease_graph.figure.savefig('heart_disease_graph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "heart_disease  stroke\n",
       "0              0         9264\n",
       "               1          404\n",
       "1              0          458\n",
       "               1           94\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of people with or without heart disease and stroke\n",
    "dataframe['stroke'].groupby(dataframe['heart_disease']).value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>Accroding to the dataset, individuals <b>without</b> a heart disease have a <b>higher</b> risk of having a stroke.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>Age Range Plot</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph\n",
    "age_range_graph = sns.countplot(x=\"age_range\", hue=\"stroke\", data=dataframe, palette=\"Set2\")\n",
    "\n",
    "# set the plot title and labels\n",
    "age_range_graph.set_title('Number of people who had a stroke by age range')\n",
    "age_range_graph.set_xlabel('Age range')\n",
    "age_range_graph.set_ylabel('Number of people')\n",
    "\n",
    "# save the plot\n",
    "age_range_graph.figure.savefig('age_range_graph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/1094516152.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  dataframe['stroke'].groupby(dataframe['age_range']).value_counts()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age_range  stroke\n",
       "0-10       0         1012\n",
       "           1            2\n",
       "10-20      0         1034\n",
       "           1            2\n",
       "20-30      0         1090\n",
       "           1            0\n",
       "30-40      0         1336\n",
       "           1           12\n",
       "40-50      0         1448\n",
       "           1           30\n",
       "50-60      0         1548\n",
       "           1           98\n",
       "60-70      0         1090\n",
       "           1           98\n",
       "70-80      0          978\n",
       "           1          210\n",
       "80-90      0          186\n",
       "           1           46\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of people who had a stroke by age range\n",
    "dataframe['stroke'].groupby(dataframe['age_range']).value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>Accroding to the dataset, individuals whose ages are between <b>70 and 80</b> years old have the <b>highest</b> risk of having a stroke.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>Gender Plot</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jJ/35y5+jwMBABAYGljiefL1YabVS1WA4IrWpX78+AgICMG3aNGzatKnUPvKFl3JlLbCsiMePH0MQBIVX0w8fPhR/+darVw/NmzdHUFBQqfvL11MoQx6iHj58qHCVA3gWsqSvtJUl/4UqJ18DYmJiAkNDQwDAzp07S/3D/8Ybb4hre5SpKSMjo9Tn6vk/MHL16tXDW2+9hXHjxpXYpqVV9q+dbt26oaCgAOfPn0dcXBwWLlyIdu3awcDAAOfOncOpU6dKXEkoT/369fHw4cMS7Q8ePACAl1qzIQ8hAQEB6NWrlxg4hw4dKvaRj//w4UO88cYbYvvzP7/nxcfHY+HChfDx8cGECRNgbm4OAPjss8/Kvdri7u4Od3d35OTk4MyZM9i1axdWrlwJBwcH2NvbK/XY5Fdby3runn/ewsLCYGBggHfeeQchISHw8/MDAKXOxReRXu2V1iM/9+rWrYvevXvjhx9+QNu2bZGVlYV33nmnzPHkwTotLQ0WFhZiu3QNVd26dSGTyTB27NhSQ518vZsyGjRogPT0dBQVFUFTU1Nsl/785c/RggUL4OzsXGIM+e8Pqj6cViO1evvtt9G/f39s2bJF4V0gwLOrQSkpKQptf/zxR6UdOz8/X1zwCzxbTHry5Em4uLgAAJydnXHv3j2YmJjA1tZW/BcbG4vIyEiFX3Tlsbe3h46ODg4dOqTQHh8fj7t37ypMCyrr559/Vvj+yJEjaNy4MZo2bQonJycAQHp6ukLtGRkZWLduHTIyMlSqqaCgQFxgCgC5ubniwujSODs7IzExEW3bthWP3b59e+zYsQNHjx4t8zGZmZnBxsYGn3/+OR48eABnZ2doamrCyckJX331FW7cuKEwpaYMJycnnDhxQuFKWFFREb777jvY2tpCR0dHpfGkzp8/j9atW2Po0KFiMLp//z6uX78uXrlydXUFgBKLhk+cOPHCsS9cuIDi4mLMnDlTDEZFRUXitGNp73YCnr2Da+jQoRAEAXp6evD09BRv+Ch/l9nzVzFK06JFC5iZmZU4P27duoWLFy+WOGdNTU3RunVrjBs3Dnv27MGFCxcAQKlz8UWk5x0AfPfdd9DT01MIeUOHDsX169exbds2uLq6vjBwderUCZqamvjpp58U2qXfGxgYwMbGBjdu3FCouU2bNggLC1NpGrxz584oLCzEsWPHxLb8/HyFqbKWLVvCxMQEt2/fVjieubk51q5dW+IqHVU9XjkitVu2bBnOnDlT4hWqp6cnIiIisHnzZjg4OODkyZPlvn1cFdra2liyZAnmzJkDAwMDbNmyBbm5uZg6dSoAYMiQIYiOjsa4cePwwQcfwMLCAr///ju2bt0KHx+fEtNML2JkZIRJkyYhLCwM2tra6N69O27fvo3169ejdevWGDJkiMr179ixA7q6unBwcMBPP/2EEydOYO3atQCerUkYOHAgli1bhjt37qB9+/ZISkpCSEgI3nzzTTRv3hyampoq1bRkyRLMmjULJiYmiIqKQnZ2NqZMmVJqbVOnTsV7772HyZMnY8SIEahTpw5iYmJw7Ngx8Z1GZfHw8MDGjRvRokUL8VW+i4sLVq9ejYYNG5Y5zVmW6dOn49SpUxg9ejQmTZoEHR0dREdH49atW4iMjFRprOfZ2dkhPDwcW7ZsgYODA5KTkxEREYH8/HzxVgPNmjXD8OHDERISgsLCQrRt2xbffPMN/v7773LHBoCPP/4Y3t7eyMrKQnR0tPhOuezs7FKnkzt37ozt27dj0aJFGDhwIAoKChAZGQkjIyMxqBkaGuLChQuIjY2FjY1NqcfX0NDAnDlzsHjxYsyePRvvvPMO0tPTERYWhvr165d6VRAApk2bhu+++w5+fn746quvlDoXX+Snn35Co0aNxNsLxMTE4MMPP1R47J06dULLli1x9uzZMq/0yjVp0gTe3t4IDg5GQUEBrK2tcfToUTGsyoPjnDlzMGnSJMydOxcDBw5EUVERtm3bhkuXLpV53pemc+fO6NKlC/z8/JCWlobGjRtj165dePTokXj1S1NTE7Nnz4a/vz80NTXh6emJrKwshIeH4/79+yqf8/TyGI5I7YyMjBAQEIDp06crtE+ePBmPHj3Ctm3bUFBQAA8PD6xYsUKlX0wvUr9+fcyfPx9BQUF48OAB7O3tER0djZYtWwJ4ttZkz549WLt2LdasWYPHjx+jcePGmDt3LsaPH6/y8WbMmAFTU1NER0dj//79MDIyQu/evTFr1iyVLtPLLVmyBF999RUiIiLQsmVLhIaGKizyXbVqFSIiIrBv3z6kpKTAxMQEffv2xaxZs8SrXqrUFBAQgJUrV+LRo0fo2LEjPv/8czRr1qzU2qytrbFnzx6EhIRgwYIFEAQBlpaW2LhxY5m3OpCThyPp9IL8ap6Hh0e5i4qf16ZNG+zduxfBwcFYsmQJZDIZ7OzsxMXOL2Py5MlIT0/Hrl27sHHjRlhYWGDQoEGQyWSIiIhAZmYm6tevj48++kh8njMzM+Hu7i7eeqIsLi4u8Pf3x/bt23HkyBGYmprCxcUFYWFhmDZtGs6fP6+wqFeua9euCAoKwrZt28RF2J06dcKuXbvEqbKRI0fir7/+gq+vL1atWlVi/ZrckCFDULduXURERGDatGkwMDCAu7s75syZU+aaKV1dXfj7+2Py5MnYvHkzZs6cqdS5WJZFixbhyJEj2LFjB8zMzLB48eJS1xR5eHjgwYMH6NGjxwvHA569INPX18e2bdvw5MkTdO7cGVOmTMHGjRvFNWZdunRBVFQUwsLCMHPmTGhra6Ndu3bYvn27wps9lBEWFoagoCCEhoYiLy8Pffv2xbvvvovjx4+LfYYNG4a6desiMjISMTEx0NfXR8eOHREUFFShaXd6OTJB4KcPEr1K4uLiMHr0aOzatUsMDVVpw4YNCAsLK/dKB5G6CIKAAQMGwMXFpcT9ip6XkZGBU6dOwd3dXWHd1KeffoqDBw9W++cDUs3EK0dERPRKevLkCXbs2IHLly/j5s2bL7w7vZyenh5WrFiBtm3bYsyYMdDX18cff/yB3bt344MPPqiGqulVwHBERESvJF1dXezbtw/FxcVYsWIFmjZtWu4+derUwY4dO7Bu3TosWrQIOTk5aNq0KRYtWoSRI0dWQ9X0KuC0GhEREZEE38pPREREJMFwRERERCTBcEREREQkwQXZFXDhwgUIgqDSTQCJiIhIvQoKCiCTyRQ+LLg0DEcVIAgCuI6diIjo1aLs326GowqQXzGytbVVcyVERESkrMuXLyvVj2uOiIiIiCQYjoiIiIgkGI6IiIiIJBiOiIiIiCS4IJuIiKiWKioqQkFBgbrLqBba2trQ1NSslLEYjoiIiGoZQRCQkpKCjIwMdZdSrYyMjGBubg6ZTPZS4zAcERER1TLyYNSwYUPo6+u/dFio6QRBQHZ2NlJTUwEAFhYWLzUewxEREVEtUlRUJAYjExMTdZdTbfT09AAAqampaNiw4UtNsXFBNhERUS0iX2Okr6+v5kqqn/wxv+w6K4YjIiKiWqi2T6WVprIeM8MRERERkQTDEREREZXqdf2QdYYjIiIiKiExMREjRoyolLFu374NKysrHDx4sFLGq2oMR0RERFTCDz/8gAsXLqi7DLVgOCIiIiKSYDgiIlGxUKzuEmoEPg/0urhy5QrGjBmDTp06oUOHDhg7diwuXbqEDRs2ICwsDABgZWWFDRs2iF+HhYXB29sbnTp1Qnh4OADg5s2bmDlzJtzc3ODg4IBRo0bh/PnzZR5XEAQsWrQItra2OHXqlNh+7NgxDBkyBLa2tnBzc8Py5cuRnZ1dhc9A6XgTSCISacg0sOfSr7j/JFPdpahNI4P6GGnvru4yiKrckydPMHHiRLi4uCA0NBQFBQXYtGkTJkyYgG+++QYpKSn48ssvERMTA3Nzc3G/TZs24cMPP4SVlRXMzc2RmJiId999F82aNYOfnx+0tbWxa9cujBkzBtu2bYOzs3OJYy9fvhyHDx/Gxo0b0bVrVwDAoUOHMG/ePAwYMACzZs3CnTt3EBISgsTERGzfvr1ab03AcERECu4/ycSdrEfqLoOIqlhiYiIePXqEUaNGoVOnTgCAli1bYt++fdDQ0BADkYODg8J+dnZ2mDRpkvj9rFmzxEBUr149AICHhwf69++PNWvWYP/+/Qr7r127FjExMdiwYQO6desG4NmVpKCgILi7uyMoKEjs27x5c4wdOxa//PILPDw8KvspKBOn1YiIiF5Dbdq0gbGxMaZMmYKPPvoIP//8M8zMzLBgwYIXfjaZpaWlwvdnz56Fp6enGIwAQEtLC/369cPly5fx9OlTsX3Pnj3YsmUL+vbtC09PT7H9xo0bSElJgZeXFwoLC8V/Tk5OMDAwwOnTpyvxkZeP4YiIiOg1VLduXezZswfdunXD999/jylTpqBz587w9/dHXl5emfuZmpoqfJ+ZmVmiTd5PEAQ8efJEbLt27Rrc3d1x+PBhXLlyRWzPyMgAAAQGBqJdu3YK/548eSJ+oGx14bQaERHRa6ply5ZYs2YNioqK8Oeff+Kbb77B559/jjfffFPpMerXr4+HDx+WaH/w4AEAoEGDBmK4+fDDDzFmzBj0798ffn5+2L9/P7S0tGBoaAgAWLBgQalrlOrXr1+Rh1dhvHJERET0Gjpy5AhcXV3x4MEDaGpqokOHDggICIChoSFSUlKgoaFcRHBycsKJEyfw+PFjsa2oqAjfffcdbG1toaOjI7abmpqiTp068Pf3R0JCArZt2wbgWUgzMTHB7du3YWtrK/4zNzfH2rVrkZCQULkPvhy8ckRERPQa6tixI4qLizFt2jRMmjQJdevWxQ8//IDHjx+jZ8+e+OeffwAAhw8fhr29PZo0aVLqONOnT8epU6cwevRoTJo0CTo6OoiOjsatW7cQGRlZ6j7u7u7o06cPwsLC0LNnTzRv3hyzZ8+Gv78/NDU14enpiaysLISHh+P+/fto165dlT0PpeGVIyIiotdQw4YNERkZiXr16mHp0qWYPHkyrly5gg0bNsDV1RU9e/aEra0tFi1ahKioqDLHadOmDfbu3QtTU1MsWbIE8+fPhyAI2LVrF956660y91uyZAm0tbWxbNkyCIKAYcOGYe3atfjjjz/wwQcfICAgAG+++SZ2795dZjCrKjLhdf1UuZdw+fJlAICtra2aKyGqfMGnD7/Wb+VvbGiMOW791V0GUYXl5uYiKSkJLVq0gK6urrrLqVblPXZl/37zyhERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxEREdFrolgofq2OW1H84FkiIqLXhIZMA3su/Yr7TzKr7ZiNDOpjpL17hfYtLi5GWFgY9u/fj6ysLHTq1AkfffQRmjVrVslVKmI4IiIieo3cf5L5ynx+Ynh4OPbt24dVq1ahUaNGWLNmDXx9fXH48GHo6OhU2XE5rUZEREQ1Tn5+PrZt24YZM2agW7dusLa2RkhICO7fv4+jR49W6bEZjoiIiKjGuXbtGp4+fQpXV1exzdDQEDY2Njh37lyVHpvhiIiIiGqclJQUAICFhYVCe8OGDXHv3r0qPTbDEREREdU4OTk5AFBibVGdOnWQl5dXpcdmOCIiIqIaR1dXF8CztUdSeXl50NPTq9JjMxwRERFRjSOfTktNTVVoT01Nhbm5eZUem+GIiIiIahxra2sYGBggLi5ObMvKykJCQgIcHR2r9Ni8zxEREdFrpJFB/VfieDo6OvDx8UFQUBCMjY3RuHFjrFmzBubm5ujRo0clV6mI4YiIiOg1USwUV/hu1S97XA2Z6pNVM2fORGFhIfz8/JCbmwsnJydERUVV6Q0gAYYjIiKi10ZFAoo6j6upqYn58+dj/vz5lVzRi3HNEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZGE2sPRnTt3YGVlVeLf/v37AQBXr16Fj48PHBwc4OHhgaioKIX9i4uLERoaCnd3d9jb22P8+PFITk5W6FPeGERERERyar8J5N9//406derg2LFjkMlkYnu9evWQnp6OcePG4e2330ZgYCAuXryIwMBAGBkZwdvbGwAQHh6Offv2YdWqVWjUqBHWrFkDX19fHD58GDo6OkqNQURERCSn9nB0/fp1tGjRAg0bNiyxbefOndDR0UFAQAC0tLTQqlUrJCcnY+vWrfD29kZ+fj62bduG+fPno1u3bgCAkJAQuLu74+jRo+jXrx+++OKLF45BRET0uhCKiyHTqP5Jo8o4bnh4OGJjY7F79+5Kqqpsag9Hf//9N1q3bl3qtvj4eDg5OUFL639lurq6IiIiAmlpabhz5w6ePn0KV1dXcbuhoSFsbGxw7tw59OvXr9wxTExMqu7BERER1SAyDQ1kHvscRemp1XZMzQYNUf/tES81xo4dOxAaGgonJ6dKqurF1B6Orl+/DjMzM7z//vu4efMmmjVrhqlTp8Ld3R0pKSmwtLRU6C+/wnT37l2kpKQAACwsLEr0uXfvHgCUO0ZFw5EgCMjOzq7QvkQ1kUwmg56enrrLqDFycnIgCIK6yyBSWV5eHoqLi1FUVISioiKFbZqamihKT0XhwzvVXtfztSjj/v37WLZsGc6fP48WLVpAEIQXjlNUVITi4mLk5OSguLi4xHZBEBSW8JRFreEoPz8fN2/ehJ6eHhYsWAB9fX18++238PX1xfbt25Gbm1vik3fr1KkD4NkPPycnBwBK7ZOZmQkA5Y5RUQUFBbh69WqF9yeqafT09GBjY6PuMmqMpKQk8XcM0atGS0urxN84DQ0Ntb4Ays/PLzWwvMjFixdhYGCAmJgYbN26FXfv3kVubm6Z/fPy8lBYWIgbN26U2ef5TFAatYYjHR0dnDt3DlpaWmKx7du3x7///ouoqCjo6uoiPz9fYR/5D1tfXx+6uroAnj3h8q/lfeQnQHljVJS2tnaZ04FEryJlXk29TuSvUoleNXl5ebh79y7q1Kmj8LdR3ZQJJc/r1asXevXqBeDZVS8NDY1yH5OWlhaaNm0qXgiRSkxMVOq4ap9WKy2gWFpa4rfffoO5uTlSUxXnReXfN2rUCIWFhWJb06ZNFfpYW1sDQLljVJRMJnupcEVENRunGOlVpaGhAQ0NDWhqakJTU1Pd5YhethaZTAaZTPbCceQBSk9Pr9QQpeyLQLXe5+jatWvo0KED4uPjFdr/+usvtG7dGk5OTjh//rzC/GJsbCxatGgBExMTWFtbw8DAAHFxceL2rKwsJCQkwNHREQDKHYOIiIhISq3hyNLSEm3atEFgYCDi4+Px77//YtWqVbh48SI++OADeHt748mTJ1i6dCkSExNx8OBB7Ny5E5MnTwbw7BKdj48PgoKCcPz4cVy7dg2zZ8+Gubk5evToAQDljkFEREQkpdZpNQ0NDWzevBlBQUGYNWsWsrKyYGNjg+3bt8PKygoAEBkZiRUrVmDw4MEwMzPDggULMHjwYHGMmTNnorCwEH5+fsjNzYWTkxOioqLEuU0TE5NyxyAiIiKSU/uaI2NjY6xcubLM7XZ2doiJiSlzu6amJubPn4/58+dXeAwiIiIiObWHIyIiIqo+mg1KfiJFbTpeZWA4IiIiek0IxcUvfbfqih73ZT8+ZPXq1ZVUTfnUuiCbiIiIqo86PldNncetqFerWiIiIqIqxnBEREREJMFwRERERCTBcEREREQkwXBERERUC72OH5xcWY+Z4YiIiKgW0dbWBgBkZ2eruZLqJ3/M8uegonifIyIiolpEU1MTRkZGSE1NBQDo6+sr/Wn0rypBEJCdnY3U1FQYGRlBU1PzpcZjOCIiIqplzM3NAUAMSK8LIyMj8bG/DIYjIiKiWkYmk8HCwgINGzZEQUGBusupFtra2i99xUiO4YiIiKiW0tTUrLTA8DrhgmwiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiiRoVjpKSktChQwccPHhQbLt69Sp8fHzg4OAADw8PREVFKexTXFyM0NBQuLu7w97eHuPHj0dycrJCn/LGICIiIpKrMeGooKAA8+bNQ3Z2ttiWnp6OcePGoXnz5jhw4ABmzJiB9evX48CBA2Kf8PBw7Nu3D8uXL0dMTAxkMhl8fX2Rn5+v9BhEREREclrqLkBuw4YNqFu3rkLbF198AR0dHQQEBEBLSwutWrVCcnIytm7dCm9vb+Tn52Pbtm2YP38+unXrBgAICQmBu7s7jh49in79+pU7BhEREZFUhcLRo0ePEBUVhd9//x0PHjxAZGQkjh07Bmtra7z99tsqj3fu3DnExMTg66+/hoeHh9geHx8PJycnaGn9r0xXV1dEREQgLS0Nd+7cwdOnT+Hq6ipuNzQ0hI2NDc6dO4d+/fqVO4aJiUlFngIIgqBwlUtVMpmswvvWNoIgqLsEwrNzUk9PT91l1Bg5OTk8N4lqGUEQlPr7q3I4unXrFkaMGIG8vDx06tQJ165dQ1FREZKSkhAeHo7w8HCFgFOerKwsLFiwAH5+frCwsFDYlpKSAktLS4W2hg0bAgDu3r2LlJQUACixX8OGDXHv3j2lxqhoOCooKMDVq1crtK+2tjZs2rWDlqZmhfavTQqLipBw5QoKCgrUXcprT09PDzY2Nuouo8ZISkpCTk6Oussgokqmo6NTbh+Vw9Gnn34KExMT7N69G/r6+mjfvj0AYO3atcjLy8PmzZtVCkcBAQFwcHDAgAEDSmzLzc0t8SDq1KkDAMjLyxN/cZXWJzMzU6kxKkpbWxutW7eu0L4ymQxamprYc+lX3H+SWeEaXnWNDOpjpL072rRpw1foNQCvZipq0aIFz0uiWiYxMVGpfiqHo9jYWKxcuRKGhoYoKipS2DZ8+HDMmjVL6bG+/vprxMfH49ChQ6Vu19XVFRdWy8kDjb6+PnR1dQEA+fn54tfyPvLpgfLGqCiZTPZS+wPA/SeZuJP16KXGqA04lUM1Ec9LotpH2ReBFVpzpFnGdFB+fr5Krz4PHDiAtLS0EleaPvroI0RFReGNN95Aamqqwjb5940aNUJhYaHY1rRpU4U+1tbWAABzc/MXjkFEREQkpXI4cnR0xJYtW/DWW2+J01MymQzFxcX4/PPP0bFjR6XHCgoKQm5urkJbz549MXPmTPTt2xffffcd9u3bh6KiIjGQxcbGokWLFjAxMUG9evVgYGCAuLg4MRxlZWUhISEBPj4+AAAnJ6cXjkFEREQkpXI4mjt3LkaMGIGePXvCxcUFMpkMUVFR+Pfff5GcnIy9e/cqPVZZV25MTEzQuHFjeHt7IzIyEkuXLsXEiRPx559/YufOnQgMDATwbK2Rj48PgoKCYGxsjMaNG2PNmjUwNzdHjx49AKDcMYiIiIikVL4JpKWlJb788ku4uLggLi4Ompqa+P3339G0aVPs27cPbdu2rbTiTExMEBkZiaSkJAwePBhhYWFYsGABBg8eLPaZOXMmhg4dCj8/P4wYMQKampqIiooSF2ErMwYRERGRnEzg2zFUdvnyZQCAra3tS40TfPrwa70gu7GhMea49Vd3GfQcnpc8L4lqK2X/fis1rXbu3DmVDu7k5KRSfyIiIqKaQqlwNGrUqBLvQnv+gpNMJhPvPFnRmyMSERERqZtS4WjXrl1VXQcRERFRjaBUOHJ2dq7qOoiIiIhqhArdBDIhIQFbtmxBfHw8srKyYGJigs6dO2PKlClo0qRJZddIREREVG0q9PEhvr6+MDIyQrdu3WBiYoKHDx/i1KlT+PHHH/H555+X+KBXIiIioleFyuEoJCQEjo6OiIiIEO+QDTz7gNeJEyfis88+Q2RkZKUWSURERFRdVL4J5PXr1zFu3DiFYAQ8+4DXCRMm4Pz585VWHBEREVF1UzkcWVhY4Pbt26Vue/ToEYyNjV+6KCIiIiJ1UTkcLVy4EKGhofj2229RVFQktv/2229Yt24dlixZUqkFEhEREVUnldccffzxx8jPz8fChQuxZMkSNGjQAJmZmSgoKIAgCJg+fbrYVyaTISEhoVILJiIiIqpKKoejIUOGVEUdRERERDWCyuFIemWIiIiIqLap0E0g8/PzcfDgQcTFxSErKwsNGjSAo6MjBg8eXOJdbERERESvEpXDUVZWFkaPHo1r167hjTfegJmZGZKSknD48GHs2bMHe/fuRb169aqiViIiIqIqp/K71dauXYuUlBRER0fj559/RkxMDH7++WdER0cjLS0N69evr4o6iYiIiKqFyuHo+PHjmDVrFhwdHRXaHR0dMXPmTPz000+VVhwRERFRdVM5HD19+rTMD5dt0qQJMjIyXrYmIiIiIrVRORy1bNkSJ06cKHXb8ePH0axZs5cuioiIiEhdVF6QPWHCBMyZMwf5+fkYMGAATE1N8fDhQxw6dAj79+9HQEBAFZRJREREVD1UDkd9+/bFzZs3sXnzZuzfvx8AIAgCdHR0MG3aNAwfPrzSiyQiIiKqLhW6z9HUqVPh4+ODixcvIjMzE/Xr14e9vT3q169f2fURERERVSuV1xzJCYKAgoICPHnyBO3bt0daWhoEQajM2oiIiIiqXYWuHG3atAkRERHIzc2FTCaDnZ0dQkJCkJGRgW3btsHQ0LCy6yQiIiKqFipfOYqOjsaGDRswbtw4fPHFF+LVojFjxuDWrVu8CSQRERG90lQOR7t378akSZPw4Ycfol27dmK7u7s7Zs2ahZ9//rlSCyQiIiKqTiqHo7t378LZ2bnUbS1btsTDhw9fuigiIiIidVE5HFlYWODChQulbvvrr79gYWHx0kURERERqYvKC7KHDh2KDRs2QFdXFx4eHgCA7Oxs/Pjjj4iIiMC4ceMqu0YiIiKiaqNyOPL19cXt27cRFBSEoKAgAMDo0aMBAAMGDMDkyZMrt0IiIiKiaqRyOJLJZPj4448xbtw4xMXFISMjA/Xq1YOzszPatGlTFTUSERERVZsK3ecIAFq0aAEAyMrKgrGxMZo0aVJpRRERERGpS4XC0Z49e7Bp0yakpaWJbW+88Qbmzp2Lvn37VlpxRERERNVN5XC0Z88efPLJJ+jevTt69uwJExMTPHz4EEeOHMHcuXOho6ODt99+uypqJSIiIqpyKoejnTt34v3334e/v79C+zvvvAN/f39s3LiR4YiIiIheWSrf5+j+/fvo3r17qdt69eqFGzduvHRRREREROqicjiytbXFr7/+Wuq2CxcuwMrK6qWLIiIiIlIXlafVpkyZgjlz5uDJkycYOHAgGjZsiIyMDPz888/YsWMHlixZgnPnzon9nZycKrVgIiIioqqkcjiaMGECAODLL7/EgQMHxHZBEAAAgYGB4vcymQxXr16tjDqJiIiIqoXK4WjXrl1VUQcRERFRjaByOHJ2dq6KOoiIiIhqBJUXZBMRERHVZgxHRERERBIMR0REREQSSoWjb775Bunp6VVdCxEREZHaKRWOAgICkJSUBADo3r07rl27VqVFEREREamLUu9W09HRwTfffIPCwkLcuXMHFy9exOPHj8vszxs/EhER0atKqXA0bNgwREZG4osvvoBMJhNv9Pg83viRiIiIXnVKhaN58+Zh0KBBSE9Px+jRo+Hv74/WrVtXdW1ERERE1U7pm0C2adMGADB9+nR0794djRo1qpQC0tLSsHr1avz666/Iy8uDk5MTFixYIIavq1evYsWKFfjrr79gZGSEUaNGiR9hAgDFxcUICwvD/v37kZWVhU6dOuGjjz5Cs2bNxD7ljUFEREQkp/Jb+adPn44GDRpg3759mD17NiZMmIB58+Zh3759yMvLU7mAKVOm4NatW9i6dSu+/PJL6OrqYuzYscjJyUF6ejrGjRuH5s2b48CBA5gxYwbWr1+v8Jlu4eHh2LdvH5YvX46YmBjIZDL4+voiPz8fAJQag4iIiEhO5Y8PycrKwujRo3Ht2jW88cYbMDMzQ1JSEg4fPow9e/Zg7969qFevnlJjpaen480338SUKVPEK1NTp07FoEGD8M8//yA2NhY6OjoICAiAlpYWWrVqheTkZGzduhXe3t7Iz8/Htm3bMH/+fHTr1g0AEBISAnd3dxw9ehT9+vXDF1988cIxiIiIiKRUvnK0du1apKSkIDo6Gj///DNiYmLw888/Izo6GmlpaVi/fr3SYzVo0ADBwcFiMHr48CGioqJgbm6O1q1bIz4+Hk5OTtDS+l+Gc3V1RVJSEtLS0nDt2jU8ffoUrq6u4nZDQ0PY2Njg3LlzAFDuGERERERSKl85On78OGbNmgVHR0eFdkdHR8ycORPh4eHw8/NTuZBly5aJV3k2bdoEfX19pKSkwNLSUqFfw4YNAQB3795FSkoKAMDCwqJEn3v37gFAuWOYmJioXCvw7J152dnZFdpXJpNBT0+vQvvWRjk5ORAEQd1lvPZ4XirieUlU+8jfVV8elcPR06dP0aRJk1K3NWnSBBkZGaoOCQAYM2YMhg8fjs8//xzTpk3D3r17kZubCx0dHYV+derUAQDk5eUhJycHAErtk5mZCQDljlFRBQUFFb5lgZ6eHmxsbCp87NomKSlJ/FmS+vC8VMTzkqh2ej4TlEblcNSyZUucOHECbm5uJbYdP35c4V1iqpC/O+2TTz7BxYsXER0dDV1dXXFhtZw80Ojr60NXVxcAkJ+fL34t7yN/BVzeGBWlra1d4dsZKJNaXyctWrTgK/QagOelIp6XRLVPYmKiUv1UDkcTJkzAnDlzkJ+fjwEDBsDU1BQPHz7EoUOHsH//fgQEBCg9VlpaGmJjY9GnTx9oamoCADQ0NNCqVSukpqbC3NwcqampCvvIv2/UqBEKCwvFtqZNmyr0sba2BoByx6gomUz2UuGK/odTOVQT8bwkqn2UfRGocjjq27cvbt68ic2bN2P//v0Ans3h6ejoYNq0aRg+fLjSY6WmpmLu3LkwMTFB586dATybrkpISICXlxdMTU2xb98+FBUVieEpNjYWLVq0gImJCerVqwcDAwPExcWJ4SgrKwsJCQnw8fEB8OyjTF40BhEREZGUyuEIePZ2ex8fH1y8eBGZmZmoX78+7O3tUb9+fZXGsba2RpcuXRAYGIjly5fD0NAQmzdvRlZWFsaOHYs6deogMjISS5cuxcSJE/Hnn39i586d4seX6OjowMfHB0FBQTA2Nkbjxo2xZs0amJubo0ePHgAAb2/vF45BREREJFWhcAQ8e8t8165dX+rgMpkM69atw9q1azFr1iw8fvwYjo6O2LNnD9544w0AQGRkJFasWIHBgwfDzMwMCxYswODBg8UxZs6cicLCQvj5+SE3NxdOTk6IiooSF1yZmJiUOwYRERGRnEzgikOVXb58GQBga2v7UuMEnz6MO1mPKqOkV1JjQ2PMceuv7jLoOTwveV4S1VbK/v1W+SaQRERERLUZwxERERGRhMrhaPPmzfjnn3+qohYiIiIitVM5HEVGRoofzUFERERU26gcjpo3b84rR0RERFRrqfxWfg8PD4SEhODEiRNo06ZNiRspymQyTJs2rdIKJCIiIqpOKoejsLAwAEB8fDzi4+NLbGc4IiIioleZyuHo2rVrVVEHERERUY3wUm/lf/z4Mf7991/k5+ejqKiosmoiIiIiUpsKhaO4uDgMGzYMzs7OGDBgAP755x/MnTsXq1evruz6iIiIiKqVyuEoNjYWEyZMgK6uLubNmwf5p4/Y2Nhg165d2L59e6UXSURERFRdVA5H69atQ/fu3bF7926MGTNGDEeTJk3CxIkTsX///kovkoiIiKi6qByOrl69Cm9vbwDP3pkm5ebmhjt37lROZURERERqoHI4qlevHh48eFDqtnv37qFevXovXRQRERGRuqgcjrp3746QkBBcvnxZbJPJZEhJScHmzZvh4eFRmfURERERVSuV73M0d+5cXLp0Ce+++y5MTU0BAHPmzEFKSgosLCwwZ86cSi+SiIiIqLqoHI7q16+P/fv34+uvv8aZM2eQkZGBevXqYdSoURgyZAj09PSqok4iIiKiaqFyOAIAHR0dvPvuu3j33Xcrux4iIiIitapQOPr333+xadMmxMbGIjMzEyYmJnB1dcWUKVPQvHnzSi6RiIiIqPqoHI5iY2Ph6+uLBg0awMPDAyYmJnjw4AF++eUXHDt2DHv27IG1tXVV1EpERERU5VQORyEhIXByckJERAR0dHTE9qdPn2LixIlYuXIldu3aValFEhEREVUXld/Kf+3aNYwdO1YhGAFA3bp1MWnSJFy6dKnSiiMiIiKqbiqHIwsLC9y9e7fUbU+fPhXf3k9ERET0KlI5HM2fPx/r1q3DDz/8gKKiIrE9Li4OwcHBmDdvXqUWSERERFSdlFpzZG1trfA5aoIgYM6cOdDU1ISRkREeP36M/Px8aGpqYsWKFejTp0+VFUxERERUlZQKR9OmTSvxIbNEREREtZFS4WjGjBlVXQcRERFRjVChm0Dm5+fjxo0bePz4canbnZycXqooIiIiInWp0E0g586di/T0dADP1h8BgEwmgyAIkMlkuHr1auVWSURERFRNVA5HK1euRIMGDRAQEAAjI6MqKImIiIhIfVQOR//99x9CQkLg5eVVFfUQERERqZXK9zmysrISp9SIiIiIahuVrxwtWbIE8+bNg4aGBuzs7KCnp1eizxtvvFEpxRERERFVtwq/W23JkiVlbueCbCIiInpVqRyOAgICoKmpidmzZ8PMzKwqaiIiIiJSG5XD0Y0bN7B+/Xp4enpWRT1EREREaqXyguxmzZohJyenKmohIiIiUjuVw9GHH36IkJAQnD59Gk+fPq2KmoiIiIjURuVptbVr1+Lhw4eYOHFiqdtlMhkSEhJeujAiIiIidVA5HPXr168q6iAiIiKqEVQOR9OnT6+KOoiIiIhqBJXD0d27d8vtw5tAEhER0atK5XDk5eUFmUz2wj68CSQRERG9qlQORytXriwRjrKzs3H+/HmcOXMGK1eurLTiiIiIiKqbyuFoyJAhpbaPHDkSn376KQ4dOgQPD4+XrYuIiIhILVS+z9GLeHh44OTJk5U5JBEREVG1qtRwdPHiRWhpVeizbImIiIhqBJWTzOLFi0u0FRcX4969e4iPj8fQoUMrpTAiIiIidVA5HMXFxZVok8lkMDAwgK+vLz744INKKYyIiIhIHVQORz///HNV1EFERERUI1TqmqOKyMjIgL+/P7p27YqOHTtixIgRiI+PF7dfvXoVPj4+cHBwgIeHB6KiohT2Ly4uRmhoKNzd3WFvb4/x48cjOTlZoU95YxARERHJKXXlqLR1RmWRyWQq3etozpw5SEtLQ3BwMIyNjbF3715MmDABBw8ehLGxMcaNG4e3334bgYGBuHjxIgIDA2FkZARvb28AQHh4OPbt24dVq1ahUaNGWLNmDXx9fXH48GHo6OggPT293DGIiIiI5JQKR6WtM3peeno6cnJyVApHycnJOH36ND7//HN07NgRALB06VKcOnUKhw8fhq6uLnR0dBAQEAAtLS20atUKycnJ2Lp1K7y9vZGfn49t27Zh/vz56NatGwAgJCQE7u7uOHr0KPr164cvvvjihWMQERERSSkVjl60zqigoACbNm3Cli1bYGpqioCAAKUP3qBBA2zZsgXt27cX22QyGQRBQGZmJv766y84OTkp3B7A1dUVERERSEtLw507d/D06VO4urqK2w0NDWFjY4Nz586hX79+iI+Pf+EYJiYmStdLREREtd9L3ZTo6tWrWLRoEa5fv45+/fph2bJlqF+/vtL7Gxoaild85H744Qf8999/6NKlC0JCQmBpaamwvWHDhgCefQBuSkoKAMDCwqJEn3v37gEAUlJSXjhGRcORIAjIzs6u0L4ymQx6enoV2rc2ysnJgSAI6i7jtcfzUhHPS6LaRxCEcj8fFqhgOCosLMTGjRuxdetWGBkZISwsDN27d6/IUArOnz+PJUuWoHv37vDy8sKqVaugo6Oj0KdOnToAgLy8POTk5ABAqX0yMzMBALm5uS8co6IKCgoq/AG7enp6sLGxqfCxa5ukpCTxZ0nqw/NSEc9Lotrp+UxQGpXDUUJCAhYvXoy///4bAwcOhJ+fHwwNDStUoNSxY8cwb9482NvbIzg4GACgq6uL/Px8hX7yQKOvrw9dXV0AQH5+vvi1vI/8FXB5Y1SUtrY2WrduXaF9lUmtr5MWLVrwFXoNwPNSEc9LotonMTFRqX5Kh6PCwkKEhYUhMjISxsbG2LRpEzw9PStcoFR0dDRWrFiBHj16ICgoSEx15ubmSE1NVegr/75Ro0YoLCwU25o2barQx9raWqkxKkomk71UuKL/4VQO1UQ8L4lqH2VfBCp1n6MrV65g8ODBiIiIwIABA/D9999XWjDau3cvPvnkE4wcORLr1q1TuNzl5OSE8+fPo6ioSGyLjY1FixYtYGJiAmtraxgYGCi8my4rKwsJCQlwdHRUagwiIiIiKaWuHL377rsoLi5GvXr1cOfOHUydOrXMvjKZDDt37lTq4ElJSVi5ciV69OiByZMnIy0tTdymq6sLb29vREZGYunSpZg4cSL+/PNP7Ny5E4GBgQCezRv6+PggKCgIxsbGaNy4MdasWQNzc3P06NEDAModg4iIiEhKqXAkvwcRgHLn4FWZo//xxx9RUFCAo0eP4ujRowrbBg8ejNWrVyMyMhIrVqzA4MGDYWZmhgULFmDw4MFiv5kzZ6KwsBB+fn7Izc2Fk5MToqKixCtQJiYm5Y5BREREJCcTuOJQZZcvXwYA2NravtQ4wacP407Wo8oo6ZXU2NAYc9z6q7sMeg7PS56XRLWVsn+/1f7ZakREREQ1CcMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkUSNCkfh4eEYNWqUQtvVq1fh4+MDBwcHeHh4ICoqSmF7cXExQkND4e7uDnt7e4wfPx7JyckqjUFEREQkV2PC0Y4dOxAaGqrQlp6ejnHjxqF58+Y4cOAAZsyYgfXr1+PAgQNin/DwcOzbtw/Lly9HTEwMZDIZfH19kZ+fr/QYRERERHJa6i7g/v37WLp0Kc6fP48WLVoobPviiy+go6ODgIAAaGlpoVWrVkhOTsbWrVvh7e2N/Px8bNu2DfPnz0e3bt0AACEhIXB3d8fRo0fRr1+/cscgIiIiklL7laMrV66gfv36+Pbbb2Fvb6+wLT4+Hk5OTtDS+l+Gc3V1RVJSEtLS0nDt2jU8ffoUrq6u4nZDQ0PY2Njg3LlzSo1BREREJKX2K0deXl7w8vIqdVtKSgosLS0V2ho2bAgAuHv3LlJSUgAAFhYWJfrcu3dPqTFMTEwqVLcgCMjOzq7QvjKZDHp6ehXatzbKycmBIAjqLuO1x/NSEc9LotpHEATIZLJy+6k9HL1Ibm4udHR0FNrq1KkDAMjLy0NOTg4AlNonMzNTqTEqqqCgAFevXq3Qvnp6erCxsanwsWubpKQk8WdJ6sPzUhHPS6La6flMUJoaHY50dXXFhdVy8kCjr68PXV1dAEB+fr74tbyP/BVweWNUlLa2Nlq3bl2hfZVJra+TFi1a8BV6DcDzUhHPS6LaJzExUal+NTocmZubIzU1VaFN/n2jRo1QWFgotjVt2lShj7W1tVJjVJRMJnupcEX/w6kcqol4XhLVPsq+CFT7guwXcXJywvnz51FUVCS2xcbGokWLFjAxMYG1tTUMDAwQFxcnbs/KykJCQgIcHR2VGoOIiIhIqkaHI29vbzx58gRLly5FYmIiDh48iJ07d2Ly5MkAns0b+vj4ICgoCMePH8e1a9cwe/ZsmJubo0ePHkqNQURERCRVo6fVTExMEBkZiRUrVmDw4MEwMzPDggULMHjwYLHPzJkzUVhYCD8/P+Tm5sLJyQlRUVHigitlxiAiIiKSkwlccaiyy5cvAwBsbW1fapzg04dxJ+tRZZT0SmpsaIw5bv3VXQY9h+clz0ui2krZv981elqNiIiIqLoxHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERERFJMBwRERERSTAcEREREUkwHBERSdTT0YVQXKzuMmoEPg/0utJSdwFERDWJnrYOZBoayDz2OYrSU9VdjtpoNmiI+m+PUHcZRGrBcEREVIqi9FQUPryj7jKISA04rUZEREQkwXBEasO1HYr4XBAR1QycViO14dqO/+H6DiKimoPhiNSOazuIiKgm4bQaERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRxGsTjoqLixEaGgp3d3fY29tj/PjxSE5OVndZREREVMO8NuEoPDwc+/btw/LlyxETEwOZTAZfX1/k5+eruzQiIiKqQV6LcJSfn49t27ZhxowZ6NatG6ytrRESEoL79+/j6NGj6i6PiIiIapDX4g7Z165dw9OnT+Hq6iq2GRoawsbGBufOnUO/fv1UGq+goACCIODPP/+scE0ymQyddRuhSMeswmO86rSLtXD58mUUN3MGmrzmnyumoYE7ly9DEAS1lsHzkuelqIack0SVqaCgADKZrNx+r0U4SklJAQBYWFgotDds2BD37t1TeTz5E6vME/wiBjq6L7V/baGhZ6DuEmqMlz2nKgPPy2d4Xj5TE85Josoik8kYjuRycnIAADo6OgrtderUQWZmpsrjdejQoVLqIiIioprntVhzpKv77JXw84uv8/LyoKenp46SiIiIqIZ6LcKRfDotNTVVoT01NRXm5ubqKImIiIhqqNciHFlbW8PAwABxcXFiW1ZWFhISEuDo6KjGyoiIiKimeS3WHOno6MDHxwdBQUEwNjZG48aNsWbNGpibm6NHjx7qLo+IiIhqkNciHAHAzJkzUVhYCD8/P+Tm5sLJyQlRUVElFmkTERHR600m8CYWRERERKLXYs0RERERkbIYjoiIiIgkGI6IiIiIJBiOiIiIiCQYjoiIiIgkGI6IiIiIJBiO6KV5eXnBysoK27dvL3W7v78/rKyssGHDBqXHU7YvvZ5GjRoFKyurUv+tWLFCbXVZWVnh4MGDajs+1VxFRUXYu3cvhg4dig4dOsDR0RHvvfcevvrqK0jvqPPPP//g5MmT4vc8p9TjtbkJJFUtbW1tHDlyBOPGjVNoLywsxE8//QSZTKamyqi26tOnD5YuXVqinR8mTTVNYWEhpk6disuXL2P69Olwc3NDUVERTp8+jZUrV+L48eNYv349NDU1MXnyZAwePBgeHh7qLvu1xnBElaJz58749ddfce/ePfGDfgHgzJkz0NfX5x8sqnS6urowMzNTdxlE5dq8eTPOnz+PgwcPolmzZmJ7q1at4OzsjKFDhyIqKgqTJk1SY5UkxWk1qhR2dnZ44403cOTIEYX277//Hn369FG4cnTgwAG88847sLOzg4ODA0aNGoUrV66UOfYff/yBkSNHws7ODh4eHggMDMSTJ0+q7LHQq08QBGzduhXdu3eHvb09Bg0ahG+//VbcHhcXBxsbG5w5cwZ9+/aFra0thg8fjqSkJGzatAlvvfUWnJ2d8cknn4hTHoIgIDIyEn369EH79u3RqVMnTJ48Gbdu3SqzjhMnTmDIkCGws7NDjx49sG7dOuTn51f546eaQxAEREdHY/DgwQrBSM7a2hqDBg3C7t274eHhgTt37iAsLAyjRo0S+yQlJWHcuHGws7NDly5dEBERoTBGeeeZlZUVQkJC4OnpCTc3N9y4caPqHnAtwXBElaZPnz4K4Sg/Px/Hjh1Dv379xLajR4/io48+wtixY/HDDz9g586dyM3NLXV6BACuXbuGsWPHws3NDd9++y2CgoJw5coVjB8/HvzkGypLSEgI9u7dCz8/Pxw6dAijR49GQEAA9uzZI/YpKirC6tWrsXLlSnzxxRdIS0vDe++9h3///Re7d+/GnDlzEB0dLa7/2LlzJyIiIjB//nz8+OOPCA8PR1JSElavXl1qDadOncKHH36IYcOG4fDhw/joo4/www8/YP78+dXxFFANkZSUhPT0dHTs2LHMPp07d0ZqairWr18Pc3NzjB8/XmHdZXR0NAYNGoTvvvsO77//PoKDgxEbGwtA+fMsJiYGoaGh2LhxI1q2bFk1D7YWYTiiStOnTx9cunQJ9+7dAwCcPn0aDRo0gI2NjdjHyMgIy5cvxzvvvIPGjRvD3t4ew4YNw99//13qmFFRUejcuTOmTp2K5s2bw9HREWvXrsWlS5dw9uzZanlcVDMdOnQIHTp0UPg3fvx4ZGdnY8eOHVi4cCE8PT3RtGlTeHt7Y+zYsYiKilIY48MPP4SDgwPatm2Lnj174unTp/jkk0/QqlUrvPfeezA1NcU///wDAGjatClWr14NLy8vNG7cGC4uLujTp0+Z5+7mzZsxdOhQjBgxAk2bNkWXLl0QGBiII0eO4Pbt21X+/FDNkJGRAQBo0KBBmX3k2wRBgKamJvT19WFkZCRuHzFiBN555x00adIEU6dORb169fDXX38BUP48GzRoEGxtbeHg4FDpj7E24pojqjTt27dHkyZNxIXZ33//Pfr376/Qx8nJCcbGxggPD0dycjKSkpJw9epVFBcXlzpmQkICkpOT0aFDhxLb/v33X7i4uFTJY6Gaz8vLC/PmzVNo09XVRWJiIvLy8rBw4UIsXrxY3FZYWIj8/Hzk5uaKbS1atBC/1tPTg6mpqcL6uDp16iAvL0883qVLlxAaGork5GT8+++/+Oeff9CoUaNS60tISMCff/6Jr776SmyTX+38999/8eabb77Eo6dXhTzkPH78uMw+mZmZAMoOUNLzFAAMDQ3F81LZ86y0KT0qG8MRVSr51Nr777+P48ePY//+/Qrbv/vuOyxYsAD9+/eHnZ0dhg4diuvXr+Pjjz8udbzi4mIMGDAAH3zwQYltxsbGVfIY6NVQt27dUn/hp6SkAADWrVtX6vSBjo6O+LWWluKvQA2Nsi+mb926FRs2bMCQIUPg7OyMUaNG4fjx4/juu+9K7V9cXIyJEydi8ODBJbZxIfnro1mzZjAzM8PZs2fRs2fPUvvExcXBzMyszMCsqalZok0egJQ9z3R1dStS/muL02pUqeRTa19++SWaNGmCVq1aKWyXXwL+9NNPMXLkSDg5OYkLWktbQ9SmTRv8888/aNasmfivqKgIq1atEqfviKRatmwJLS0t3L17V+G8+eWXXxAVFfXCAPQimzZtwvTp0xEQEIDhw4fDwcEBN2/eLHPtW5s2bXDjxg2FGu7fv4/PPvsMT58+fZmHSK8QTU1NjB49Gl9++aU4RSt17do1fP3113j//fdLDUHl4XlWNRiOqFK1bdsWzZo1Q3BwsMJCbDkLCwv88ccfuHLlCv777z/s2LED0dHRAFDqu3jGjx+Pq1evwt/fH4mJibh06RLmzZuHpKQkNG/evKofDr2C6tWrh/feew/r1q3D119/jVu3buGrr77CmjVrYGpqWuFxLSwscPr0aSQmJuLGjRsICQnBTz/9VOa7z3x9ffHTTz9hw4YNSEpKQmxsLBYvXoysrCxeOXrNTJgwAe7u7vDx8cGePXuQnJyM5ORk7NmzB2PGjIGLi4v4Nv66devi5s2bePjwoVJj8zyrGgxHVOn69OmDJ0+eoG/fviW2LVu2DKampvDx8cGwYcNw4sQJfPbZZwCAS5culejv4OCAyMhIXL9+HUOGDMGkSZPQpEkTbN++XWF6hEhq8eLFGDt2LEJDQ9GnTx9s3LgR06dPx4wZMyo85meffYbc3Fx4e3vDx8cH169fR2BgINLS0kpdYN27d2+EhITg+PHjGDBgAObNm4fOnTsjLCzsZR4avYI0NTURGhqKBQsW4NChQ/D29saQIUNw6NAhzJs3DxEREeIU76hRo3Dy5EmMHz9eqbF5nlUNmcD3QxMRERGJeOWIiIiISILhiIiIiEiC4YiIiIhIguGIiIiISILhiIiIiEiC4YiIiIhIguGIiIiISILhiIiIiEiC4YiISAWLFi2Cl5eXussgoirEcEREREQkwXBEREREJMFwRESvlIKCAgQFBaFr166ws7PDhAkT8PXXX8PKykr8ANj4+Hj4+PjA3t4ezs7OWLhwIR49eiSOcfDgQdjY2ODSpUsYPnw4bG1t4eHhga1btyocKzMzE4sXL4aLiwucnJywZs0aFBcXl6jp2LFjGDJkCGxtbeHm5obly5cjOztb3L5hwwb06NEDYWFhcHFxwdtvv4309PQqeoaI6GUxHBHRK8Xf3x87d+6Ej48PNm7cCFNTUyxbtkzcfu7cOYwdOxa6urpYt24dlixZgrNnz2L06NHIzc0V+xUXF2PWrFno27cvtmzZgk6dOiEoKAi//vqruH3ixIk4efIk5s2bh08//RQXLlzA999/r1DPoUOHMG3aNLRs2RIbN27E9OnT8e2332Lq1KmQfq733bt3cfToUQQHB2PWrFlo0KBBFT9TRFRRWuougIhIWf/99x+++uorLFy4EOPGjQMAuLu74+HDh/jtt98AAGvXrkWLFi0QEREBTU1NAIC9vT369euHAwcOYOTIkQAAQRAwdepUDBs2DADQqVMnHD16FCdPnoS7uztOnTqFP//8ExEREfDw8AAAuLq6KizGFgQBQUFBcHd3R1BQkNjevHlzjB07Fr/88ou4b2FhIRYuXIi33nqrSp8jInp5vHJERK+MuLg4CIKA3r17K7T3798fAJCbm4tLly6hW7duEAQBhYWFKCwsRJMmTdCqVSucPn1aYb8OHTqIX+vo6MDY2FicDouPj4e2tja6du0q9tHX10e3bt3E72/cuIGUlBR4eXmJxyosLISTkxMMDAxKHM/S0rJynggiqlK8ckRErwz5uiETExOFdlNTUwDP1ggVFxdj69atJdYPAUCdOnUUvtfV1VX4XkNDQ5wKy8zMhJGRETQ0FF9DmpmZiV9nZGQAAAIDAxEYGFjieKmpqaXWSUQ1G8MREb0yGjVqBABIS0uDhYWF2J6WlgYAMDAwgEwmw9ixY9GvX78S++vp6Sl9rAYNGiA9PR1FRUXi9Bzwv0AEAIaGhgCABQsWwNnZucQY9evXV/p4RFRzcFqNiF4ZnTp1gqamJn766SeFdvn3devWhY2NDW7cuAFbW1vxX5s2bRAWFoa4uDilj9W5c2cUFhbi2LFjYlt+fr7CVFnLli1hYmKC27dvKxzP3Nwca9euRUJCwks+YiJSB145IqJXRpMmTeDt7Y3g4GAUFBTA2toaR48exYkTJwA8mxabM2cOJk2ahLlz52LgwIEoKirCtm3bcOnSJUyZMkXpY3Xu3BldunSBn58f0tLS0LhxY+zatQuPHj0Sp/U0NTUxe/Zs+Pv7Q1NTE56ensjKykJ4eDju37+Pdu3aVcnzQERVi+GIiF4py5Ytg76+PrZt24YnT56gc+fOmDJlCjZu3Ah9fX106dIFUVFRCAsLw8yZM6GtrY127dph+/btcHBwUOlYYWFhCAoKQmhoKPLy8tC3b1+8++67OH78uNhn2LBhqFu3LiIjIxETEwN9fX107NgRQUFBaNKkSSU/eiKqDjJBeiMOIqIaLCMjA6dOnYK7u7vCfYI+/fRTHDx4UKVpMyKisvDKERG9MvT09LBixQq0bdsWY8aMgb6+Pv744w/s3r0bH3zwgbrLI6JagleOiOiVcvXqVaxbtw4XL15ETk4OmjZtivfeew8jR46ETCZTd3lEVAswHBERERFJ8K38RERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQS/we0dFn3Hnc/IgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph\n",
    "gender_graph = sns.countplot(x=\"gender\", hue=\"stroke\", data=dataframe, palette=\"Set2\")\n",
    "\n",
    "# set the plot title and labels\n",
    "gender_graph.set_title('Number of people who had a stroke by gender')\n",
    "gender_graph.set_xlabel('gender')\n",
    "gender_graph.set_ylabel('Number of people')\n",
    "\n",
    "# save the plot\n",
    "gender_graph.figure.savefig('gender_graph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender  stroke\n",
       "Female  0         5706\n",
       "        1          282\n",
       "Male    0         4014\n",
       "        1          216\n",
       "Other   0            2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of people who had a stroke by gender\n",
    "dataframe['stroke'].groupby(dataframe['gender']).value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>Accroding to the dataset, <b>Female</b> individuals have a <b>higher</b> risk of having a stroke.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>Martial Status Plot</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph\n",
    "martial_status_graph = sns.countplot(x=\"ever_married\", hue=\"stroke\", data=dataframe, palette=\"Set2\")\n",
    "\n",
    "# set the plot title and labels\n",
    "martial_status_graph.set_title('Number of people who had a stroke by martial status')\n",
    "martial_status_graph.set_xlabel('martial status')\n",
    "martial_status_graph.set_ylabel('Number of people')\n",
    "\n",
    "# save the plot\n",
    "martial_status_graph.figure.savefig('martial_status_graph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ever_married  stroke\n",
       "No            0         3456\n",
       "              1           58\n",
       "Yes           0         6266\n",
       "              1          440\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of people who had a stroke by martial status\n",
    "dataframe['stroke'].groupby(dataframe['ever_married']).value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>Accroding to the dataset, individuals who are <b>married</b> have a <b>higher</b> risk of having a stroke.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>Work Type Plot</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph\n",
    "work_type_graph = sns.countplot(x=\"work_type\", hue=\"stroke\", data=dataframe, palette=\"Set2\")\n",
    "\n",
    "# set the plot title and labels\n",
    "work_type_graph.set_title('Number of people who had a stroke by martial status')\n",
    "work_type_graph.set_xlabel('work type')\n",
    "work_type_graph.set_ylabel('Number of people')\n",
    "\n",
    "# save the plot\n",
    "work_type_graph.figure.savefig('work_type_graph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "work_type      stroke\n",
       "Govt_job       0         1248\n",
       "               1           66\n",
       "Never_worked   0           44\n",
       "Private        0         5552\n",
       "               1          298\n",
       "Self-employed  0         1508\n",
       "               1          130\n",
       "children       0         1370\n",
       "               1            4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of people who had a stroke by work type\n",
    "dataframe['stroke'].groupby(dataframe['work_type']).value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>Accroding to the dataset, individuals work in <b>Private sector</b> have a <b>higher</b> risk of having a stroke.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>Residence Type Plot</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph\n",
    "residence_type_graph = sns.countplot(x=\"Residence_type\", hue=\"stroke\", data=dataframe, palette=\"Set2\")\n",
    "\n",
    "# set the plot title and labels\n",
    "residence_type_graph.set_title('Number of people who had a stroke by residencial type')\n",
    "residence_type_graph.set_xlabel('residencial type')\n",
    "residence_type_graph.set_ylabel('Number of people')\n",
    "\n",
    "# save the plot\n",
    "residence_type_graph.figure.savefig('residence_type_graph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Residence_type  stroke\n",
       "Rural           0         4800\n",
       "                1          228\n",
       "Urban           0         4922\n",
       "                1          270\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of people who had a stroke by residence type\n",
    "dataframe['stroke'].groupby(dataframe['Residence_type']).value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>Accroding to the dataset, the risk of having a stroke is <b>almost equal</b> for individuals who live in urban or rural areas.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>Smoking Status Plot</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph\n",
    "smoking_status_graph = sns.countplot(x=\"smoking_status\", hue=\"stroke\", data=dataframe, palette=\"Set2\")\n",
    "\n",
    "# set the plot title and labels\n",
    "smoking_status_graph.set_title('Number of people who had a stroke by residencial type')\n",
    "smoking_status_graph.set_xlabel('smoking status')\n",
    "smoking_status_graph.set_ylabel('Number of people')\n",
    "\n",
    "# save the plot\n",
    "smoking_status_graph.figure.savefig('smoking_status_graph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoking_status   stroke\n",
       "Unknown          0         2994\n",
       "                 1           94\n",
       "formerly smoked  0         1630\n",
       "                 1          140\n",
       "never smoked     0         3604\n",
       "                 1          180\n",
       "smokes           0         1494\n",
       "                 1           84\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of people who had a stroke by smoking status\n",
    "dataframe['stroke'].groupby(dataframe['smoking_status']).value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>Accroding to the dataset, individuals who <b>never smoked</b> have a <b>higher</b> risk of having a stroke.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='orange'>Which majority of the people who have never smoked got a higher risk of a stroke? </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id     gender  age   hypertension  heart_disease  ever_married  work_type      Residence_type  avg_glucose_level  bmi   smoking_status  stroke  age_range\n",
       "491    Female  74.0  0             0              Yes           Self-employed  Urban           74.96              26.6  never smoked    1       70-80        2\n",
       "58978  Female  70.0  0             1              Yes           Private        Rural           239.07             26.1  never smoked    1       60-70        2\n",
       "37651  Female  69.0  1             1              No            Self-employed  Urban           72.17              36.8  never smoked    1       60-70        2\n",
       "38829  Female  82.0  0             0              Yes           Private        Rural           59.32              33.2  never smoked    1       80-90        2\n",
       "39373  Female  82.0  1             0              Yes           Self-employed  Urban           196.92             22.2  never smoked    1       80-90        2\n",
       "                                                                                                                                                            ..\n",
       "28291  Female  79.0  0             1              Yes           Private        Urban           226.98             29.8  never smoked    1       70-80        2\n",
       "28484  Female  78.0  0             0              Yes           Self-employed  Rural           109.47             30.8  never smoked    1       70-80        2\n",
       "30456  Female  79.0  0             0              Yes           Private        Rural           93.05              24.2  never smoked    1       70-80        2\n",
       "32503  Female  80.0  0             0              Yes           Self-employed  Urban           76.57              34.1  never smoked    1       70-80        2\n",
       "72081  Female  57.0  1             0              Yes           Govt_job       Rural           67.41              32.9  never smoked    1       50-60        2\n",
       "Name: count, Length: 63, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of females who never smoked and had a stroke\n",
    "dataframe[(dataframe['gender'] == 'Female') & (dataframe['smoking_status'] == 'never smoked') & (dataframe['stroke'] == 1)].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='green'>The majority of people who have never smoked yet got a risk of a stroke are females. Which makes sense since on one hand smoking highers the risk of a stroke. And on the other hand females got a higher risk of having a stroke.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Numerical Data</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>BMI Plot</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 563.5x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph\n",
    "bmi_plot = sns.displot(x=\"bmi\", hue=\"stroke\", data=dataframe, palette=\"Set2\", kde = True)\n",
    "\n",
    "# set the plot title and labels\n",
    "bmi_plot.set(title='Number of people who had a stroke by BMI')\n",
    "bmi_plot.set(xlabel='BMI')\n",
    "bmi_plot.set(ylabel='Number of people')\n",
    "\n",
    "# save the plot\n",
    "bmi_plot.savefig('bmi_plot.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>Accroding to the dataset, Individuals' BMI who got a stroke is normally distributed where 95% of the values are between 20 and 40.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gray'>Glucose Level Plot</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j2O12NmzYwIgRIxgxYgQLFiwgOzub9evX079/f1JSUgKvObELwP/B7G9KLysrq/c9qqpKZWUlERER9cZ0/Pk1Gt/fKmp1E7zX6+W5557jrbfewmq10qFDBzIyMmp8aPnr6MT3nZSUVO81/T744AOee+45jh07RmxsLH369DnlfT1kyBBeeuklXnvtNVatWsWKFStISkpi+vTpXHfddSd9rf8egl8HhddXdzt27KhRlpeXx4ABA1i2bBkXXnhhoE4bci+ezInX99ejv14vuuginnzyST744ANuuukmPvnkk5O+z+LiYqDme63rOo1RWlpa67zHKysrC3SZ1XXt8vJyevXqxerVq3nppZf429/+xmuvvUZ0dDR/+MMfuOOOOwL1+eijj/Loo4/WOpd/7FJDYlVVtcYffCeexz8Gcfny5axfv55x48bx6aef8oc//KHGeRrzsy5aniQtpykmJob58+cza9YsXnjhhTqP8Q+48wt2oNnJVFRUoKpqjb8+CwsLAz9oUVFRdO/enQULFtT5+rp++dTHn9wUFhbWaBUAX/Jz4l9eDeH/5eXn7z9OSEggOjoagNdff73OD+SOHTsGxo40JCb/L7gT66q+X9BRUVGcffbZTJs2rdZzOl39Pzpjx47F5XKxdetWNm3axH333Uf//v2JjIxk8+bNrF+/vtZf3qcSExNDYWFhrfKCggLA11LTWP7kYP78+Zx//vmBRPCKK64IHOM/f2FhIR07dgyUn/j9O9GWLVu47777mDp1KjfeeCOpqakA/OUvfzll68To0aMZPXo0NpuNjRs38sYbb/Dkk08yePBgBg0a1KD35m+drK/uTqy3pUuXEhkZyW9/+1sWLlzIvHnzABp0L57M8a2jx8fjv/ciIiK44IIL+OSTT+jbty/l5eX89re/rfd8/oS3qKiIDh06BMobMv7iVL+PoqKiAknR8b7++mv69OnT4HsxIyODpUuX4nQ62bp1K2vXrmXFihX07t2bM844A/C1bA0fPrzWufy/a04lKioKi8XCG2+8Uefz3bp1C3wdPHgwn3zyCXq9npKSEiZNmlTjPI35WRctT7qHmsBvfvMbLrnkEl566aVaP+yRkZHk5ubWKPvhhx+a7NpOpzMw0BN8gwi/+uorRowYAcDw4cM5duwYCQkJDBw4MPBvw4YNrFy5Eq1W2+BrDRo0CIPBwIcfflijfMuWLRw9erTev3ZO5j//+U+Nx59++imdOnWia9euZGZmAlBSUlIj9tLSUp5//nlKS0uDisnlcvHNN98EHtvt9sCA2LoMHz6cffv20bdv38C1BwwYwGuvvca6devqfU9JSUn069ePt99+m4KCAoYPH45WqyUzM5N//OMfHDhwoEbXUENkZmby5Zdf1mg58ng8fPzxxwwcOBCDwRDU+Y63detWevXqxRVXXBFIWPLy8tizZ0+gpWfkyJEAtQaLfvnllyc997Zt2/B6vcyePTuQsHg8nkCT+/GzrI735z//mSuuuAJVVTGbzYwfPz6wkJx/Fou/xehk0tLSSEpKqnV/5OTksH379lr3bGJiIr169WLatGm89dZbbNu2DaBB9+LJHH/fAXz88ceYzeYaydcVV1zBnj17eOWVVxg5cuRJE6GhQ4ei1Wr57LPPapSf+PhEDfl9NGzYML755psaEwx2797NzTffzP/+9z8yMzPZtm1brZk1H3zwAUlJSXTr1o3XXnuNCRMm4HQ6MRgMnHXWWTz22GOA7/vXo0cPEhISOHz4cI36TE1N5dlnn63VAlaf4cOHY7VaUVW1xnn8S1IcP2PIPyPvo48+YvDgwXTv3r3GeRrzsy5anqSQTeThhx9m48aNtf4CGT9+PC+++CIrVqxg8ODBfPXVV6ecZhsMvV7Pgw8+yF133UVkZCQvvfQSdrudmTNnAnD55ZezevVqpk2bxi233EKHDh347rvvePnll5k6dWqt7pKTiY2N5eabb2bp0qXo9XrOPfdcDh8+zKJFi+jVqxeXX3550PG/9tprmEwmBg8ezGeffcaXX37Js88+C/jG2EyaNImHH36YI0eOMGDAALKysli4cCGdO3eme/fuaLXaoGJ68MEHufPOO0lISGDVqlVYrVZuvfXWOmObOXMmv//975kxYwZXX301RqORtWvX8vnnnwdmftRn3LhxLFu2jLS0tMBfxSNGjODpp58mOTm53u66+tx2222sX7+eP/7xj9x8880YDAZWr15NTk4OK1euDOpcJ8rIyGD58uW89NJLDB48mIMHD/Liiy/idDoDU7K7devGVVddxcKFC3G73fTt25d//vOf7N69+5TnBvi///s/pkyZQnl5OatXrw7MXLJarXV2i5511lm8+uqr3H///UyaNAmXy8XKlSuJjY0NJFDR0dFs27aNDRs20K9fvzqvr9FouOuuu3jggQeYM2cOv/3tbykpKWHp0qXExMTU+Zc1wKxZs/j444+ZN28e//jHPxp0L57MZ599RkpKSmAa9tq1a7njjjtqvPehQ4fSo0cPvv/++3pbRv26dOnClClTeO6553C5XPTp04d169YFksj6ErqG/D6aOXMmV111VaArzul0smjRIvr378+YMWMYNGgQH3zwAdOmTeO2224jLi6O999/n40bN/Lkk0+i0WgYOXIkCxYsYNasWUydOhWtVsuaNWswGAyMHz8erVbLnDlzeOSRR9BqtYwfP57y8nKWL19OXl5eg38+xo4dG5hOPXPmTHr27MlPP/3EkiVLOOecc2p07Vx88cU89dRTfPzxxzz00EO13nNjf9ZFy5KkpYnExsYyf/58brvtthrlM2bMoLi4mFdeeQWXy8W4ceN44okn6v2gDFZMTAxz585lwYIFFBQUMGjQIFavXk2PHj0A31iGt956i2effZZnnnmGiooKOnXqxN13380NN9wQ9PVuv/12EhMTWb16Ne+88w6xsbFccMEF3HnnnYHpu8F48MEH+cc//sGLL75Ijx49WLx4cY3BnU899RQvvvgia9asITc3l4SEBC666CLuvPPOQCtRMDHNnz+fJ598kuLiYs4880zefvvtQBPyifr06cNbb73FwoULuffee1FVlfT0dJYtW1bvlHA/f9JyfNO3v/Vr3LhxpxxMeqIzzjiDv/71rzz33HM8+OCDKIpCRkZGYJDr6ZgxYwYlJSW88cYbLFu2jA4dOnDZZZehKAovvvgiZWVlxMTE8Kc//SlQz2VlZYwePTowRb8+I0aM4JFHHuHVV1/l008/JTExkREjRrB06VJmzZrF1q1bGTt2bK3XjRkzhgULFvDKK68EBt8OHTqUN954I9Dlc8011/Dzzz8zffp0nnrqqVrjo/wuv/xyIiIiePHFF5k1axaRkZGMHj2au+66q94xOSaTiUceeYQZM2awYsUKZs+e3aB7sT73338/n376Ka+99hpJSUk88MADdY5ZGTduHAUFBUycOPGk5wPfH0oWi4VXXnmFyspKzjrrLG699VaWLVtW7zTphvw+6tevH2+++SbPPvssc+bMISIigrFjx3LPPfdgMBhISkri7bff5tlnn+WJJ54IJE3Lly8P/Fz06dOHFStWsGzZMu666y48Hg8DBgzglVdeCfxu+t3vfkdERAQrV65k7dq1WCwWzjzzTBYsWNDgrmaNRsNLL73EokWLePHFFykqKiIlJYXrr7+eWbNm1Tg2NjaWsWPH8vXXX9fqnj2dn3XRshRVlR3HRMvbtGkTf/zjH3njjTcCH+bNacmSJSxduvSULQNChIqqqlx66aWMGDHilOutlJaWsn79ekaPHl1jXM6f//xn3nvvPdkfR7RZ0tIihBAhVFlZyWuvvcb//vc/srOzT7oatZ/ZbOaJJ56gb9++XHfddVgsFn744QfefPNNbrnllhaIWojQkKRFCCFCyGQysWbNGrxeL0888QRdu3Y95WuMRiOvvfYazz//PPfffz82m42uXbty//33c80117RA1EKEhnQPCSGEECIsyJRnIYQQQoQFSVqEEEIIERYkaRFCCCFEWJCk5QS7d++WabFCCCFEKyRJywmcTmeN5avr49/Z1eFwtEBUbZ/UZ9ORumxaUp9NS+pTnA5JWhrJv+nYiZuPicaR+mw6UpdNS+qzaUl9itMhSYsQQgghwoIkLUIIIYQIC5K0CCGEECIsSNIihBBCiLAgSYsQQgghwoIkLUIIIYQIC5K0CCGEECIsSNIihBBCiLAgSYsQQgghwoIkLUIIIYQIC5K0CCGEECIsSNIihBBCiLAgSYsQQgghwoIkLUIIIYQIC5K0CCGEECIsSNIihBBCiLAgSYsQQgghwoIkLUIIIYQIC7pQByBaH5vbicPjrlFm1Oow6wwhikgIIYSQpEXUweFx803uvhplo1N7SdIihBAipKR7SAghhBBhQZIWIYQQQoQFSVqEEEIIERYkaRFCCCFEWJCkRQghhBBhQZIWIYQQQoQFSVqEEEIIERYkaRFCCCFEWGgVScv777/PRRddxMCBA7n44ov55JNPAs/t3LmTqVOnMnjwYMaNG8eqVatqvNbr9bJ48WJGjx7NoEGDuOGGGzh48GBLvwUhhBBCNLOQJy3//Oc/efDBB7nqqqv46KOPuOiii7jrrrvYtm0bJSUlTJs2je7du/Puu+9y++23s2jRIt59993A65cvX86aNWt4/PHHWbt2LYqiMH36dJxOZwjflRBCCCGaWkiX8VdVlUWLFnHddddx3XXXATBr1ix++OEHvv/+e77//nsMBgPz589Hp9PRs2dPDh48yMsvv8yUKVNwOp288sorzJ07l7FjxwKwcOFCRo8ezbp167j44otD+faEEEII0YRC2tJy4MABjhw5wqWXXlqjfNWqVcyYMYMtW7aQmZmJTvdrbjVy5EiysrIoKipi165dVFVVMXLkyMDz0dHR9OvXj82bN7fY+xBCCCFE8wtpS0t2djYAVquVG2+8kR07dtC5c2duvfVWJkyYQG5uLunp6TVek5ycDMDRo0fJzc0FoEOHDrWOOXbsWKPjUlUVq9V60mNsNluNr22JBw8ul6tmmcdzyjo5HW25Plua1GXTkvpsWm25Pi0WS6hDaPNCmrRUVlYCcN9993Hbbbdxzz338O9//5uZM2fy6quvYrfbMRhq7ixsNBoBcDgcgZu+rmPKysoaHZfL5WLnzp0NOtafeLUlCV06UlBQWKOsIroj2TlHm/3abbE+Q0XqsmlJfTattlifQ4cODXUIbV5Ikxa9Xg/AjTfeyOTJkwHo27cvO3bs4NVXX8VkMtUaUOtwOABfRmsymQBwOp2B//uPMZvNpxVXr169TnqMzWYjOzub7t27n9a1WiMbHpKSEmuURUVGkty3b/Ndsw3XZ0uTumxaUp9NS+pTnI6QJi2pqakAtbqAevXqxVdffUWnTp3Iz8+v8Zz/cUpKCm63O1DWtWvXGsf06dOn0XEpitLgZj6z2dzmmgSdDmsgofTTarVYjM3/PttifYaK1GXTkvpsWlKfojFCOhC3X79+RERE8OOPP9Yo37NnD127diUzM5OtW7fi8XgCz23YsIG0tDQSEhLo06cPkZGRbNq0KfB8eXk5O3bsYNiwYS32PoQQQgjR/ELa0mIymbjppptYtmwZKSkpZGRk8PHHH/Ptt9/y2muv0atXL1auXMlDDz3ETTfdxE8//cTrr7/Oo48+CvjGskydOpUFCxYQHx9Pp06deOaZZ0hNTWXixImhfGtCCCGEaGIhTVoAZs6cidlsZuHCheTl5dGzZ0+WLFnCiBEjAFi5ciVPPPEEkydPJikpiXvvvTcw/gVg9uzZuN1u5s2bh91uJzMzk1WrVtUanCuEEEKI8BbypAVg2rRpTJs2rc7nMjIyWLt2bb2v1Wq1zJ07l7lz5zZXeEIIIYRoBUK+jL8QQgghRENI0iKEEEKIsCBJixBCCCHCQqsY0yJCw+Z24vC4a5V7VG8IohFCCCFOTpKWdszhcfNN7r5a5SOT00IQjRBCCHFy0j0khBBCiLAgSYsQQgghwoIkLUIIIYQIC5K0CCGEECIsSNIihBBCiLAgSYsQQgghwoIkLUIIIYQIC5K0CCGEECIsSNIihBBCiLAgSYsQQgghwoIkLUIIIYQIC5K0CCGEECIsSNIihBBCiLAgSYsQQgghwoIkLUIIIYQIC5K0CCGEECIsSNIihBBCiLAgSYsQQgghwoIkLUIIIYQIC5K0CCGEECIsSNIihBBCiLAgSYsQQgghwoIu1AGI1sfucZFdUYRXVbHoDCSZIkMdkhBCCCFJi6jpYGUx/zm6B6vbGSiL0BnoE5tKrNESwsiEEEK0d9I9JAIOVRbzv+KjWN1OInQGOliiMWp0VLmdLN3xNTtLckMdohBCiHZMkhYBQKG9kv8VHwVgdGovxnY4g6GJXRnToReJpghcXg+rdn9HudMW4kiFEEK0V5K0CNxeL9uKDqMCHS0xnNepDxpFAcCo1ZGZ2I1UczQVLjsv7fyWYnsVpQ4rpQ4rFU574P/H/7Md170khBBCNAUZ0yI4UFGIw+PGotUzKL4TSnXC4qfVaLiyx5ks3fE1e8vzWbt/C50j4wAYmZzGxvysWuccndoLs87QIvELIYRoH6SlpZ1zeNzsLy8EoHdsClpN3bdEsjmK9JhkAPaU5eNV1RaLUQghhABJWtq9feUFeFQvMQYTHS0xJz22e2QCRo0Oq8dFTlVJC0UohBBC+EjS0o45PG5yKn3JR++YlFrdQifSaTT0ikkCYG9ZPh7V2+wxCiGEEH6StLRj24sO41a9RASxgFzXyDhMWh12j5tca3kzRyiEEEL8SpKWdkpV1cAA2m6R8adsZfHTKhq6RsYDvoXohBBCiJYiSUs7dbCymKPWMjQodI6IDeq1XSLiUIBih5U8m7S2CCGEaBmStLRT3+UdAKCDJRqDNriZ72adnhRzNACbCw42eWxCCCFEXSRpaYe8qpcfCnMAgm5l8etW3UW0reiwDMgVQgjRIiRpaYf2lhVQ4bJj0epJaOQOzommCExaHQ6PmwJbZRNHKIQQQtQmSUs79EPhIQD6xXUILNcfLEVRAuu6HLWWNVlsQgghRH0kaWlnju8aGhjf6bTO5U9a8mzluL3SRSSEEKJ5SdLSzuwvL6TcZcei09MrOum0zhVjMBNntOBRVfJtFU0UoRBCCFE3SVraAZvbGdh9eUv1bB/fCrind15FUciI6whIF5EQQojmJ0lLO+DwuPkmdx/f5O4LdA0BTbLp4YB4X9JSYK+QWURCCCGalSQt7Yjd7aLcZQdo8LL9p5Jqjsak1eNRVQrtVU1yTiGEEKIukrS0IwV239TkWIM56AXl6qMoCinmKAAZ1yKEEKJZSdLSjviTlqZqZfHzJy15tnLUJuhyEkIIIeoiSUs7oapqIGlJrk4ymkqCKQKtosHucQe6n4QQQoimFvKk5ciRI/Tu3bvWv3feeQeAnTt3MnXqVAYPHsy4ceNYtWpVjdd7vV4WL17M6NGjGTRoEDfccAMHD8p+OCcqddpweT3oFQ0xBnOTnluraEg0RQCQJ11EQgghmknTDGw4Dbt378ZoNPL555+jHDcHNyoqipKSEqZNm8ZvfvMbHn30UbZv386jjz5KbGwsU6ZMAWD58uWsWbOGp556ipSUFJ555hmmT5/ORx99hMFgCNXbanWKHL5BsvGmiEavgnsyKeYo8mwVFNgqSI9JbvLzCyGEECFPWvbs2UNaWhrJybU/6F5//XUMBgPz589Hp9PRs2dPDh48yMsvv8yUKVNwOp288sorzJ07l7FjxwKwcOFCRo8ezbp167j44otb+u20WkXVM3sSjBHNcv7E6nEy/hYdgFKHtcYxRq0Os04SSSGEEI0T8u6h3bt306tXrzqf27JlC5mZmeh0v+ZWI0eOJCsri6KiInbt2kVVVRUjR44MPB8dHU2/fv3YvHlzs8ceLjxeL8XVCYS/G6epWXQGInQGVKDQXoXL6wmsDeP/5/C4m+XaQggh2oeQJy179uyhqKiIP/zhD5x99tlcffXVfPPNNwDk5uaSmppa43h/i8zRo0fJzc0FoEOHDrWOOXbsWAtEHx6OWEvxqF70Gi1RelOzXcc/K6nQLrs+CyGEaHoh7R5yOp1kZ2djNpu59957sVgsfPDBB0yfPp1XX30Vu91ea1yK0WgEwOFwYLPZAOo8pqys8cvKq6qK1Wo96TH+a/u/tmb7ygoAiNObcbt/be1QVRWXy1Xr+LrKG3JsnN5MNr71Wuo63uPx1Fuv4VSfrZ3UZdOS+mxabbk+LRZLqENo80KatBgMBjZv3oxOpwskHgMGDGD//v2sWrUKk8mE0+ms8RqHwwH4bg6Tyddq4HQ6A//3H2M2N36GjMvlYufOnQ06Njs7u9HXaSl7FV/SYnB5KSgoDJS7U9JqPD5ZeUOO9W8LYPO4KLCW1zq+Iroj2TlHTxprONRnuJC6bFpSn02rLdbn0KFDQx1Cmxfygbh1Zabp6en897//JTU1lfz8/BrP+R+npKQEWg3y8/Pp2rVrjWP69OnT6Jj0en2942z8bDYb2dnZdO/e/bQSpObmUb28tn0fAF3ik4g+rntIp9WRlJRY6zV1lTf02ENFNkqcNrKrSmodHxUZSXLfvnXGGS71GQ6kLpuW1GfTkvoUpyOkScuuXbu4+uqrefnllxk2bFig/Oeff6ZXr1707duXNWvW4PF40Gq1AGzYsIG0tDQSEhKIiooiMjKSTZs2BZKW8vJyduzYwdSpUxsdl6IoDW7mM5vNrbpJ8FBlMU6vB52iId4cWWNauaIo6PX6Wq+pq7yhxyaaIn1JS2UxXSPjahyr1WqxGE9eV629PsOJ1GXTkvpsWlKfojFCOhA3PT2dM844g0cffZQtW7awf/9+nnrqKbZv384tt9zClClTqKys5KGHHmLfvn289957vP7668yYMQPwdS9NnTqVBQsW8MUXX7Br1y7mzJlDamoqEydODOVbazUC41mMlhoJS3NJqJ6dlF1RJEv6CyGEaFIhbWnRaDSsWLGCBQsWcOedd1JeXk6/fv149dVX6d27NwArV67kiSeeYPLkySQlJXHvvfcyefLkwDlmz56N2+1m3rx52O12MjMzWbVqlSwsV+1AhW9cSdwpWjiaSpzBgoJCucuO1eMiQtZlEUII0URCPqYlPj6eJ598st7nMzIyWLt2bb3Pa7Va5s6dy9y5c5sjvLC3v9zX0hLfQkmLVqMh1mCmxGml2F5FRKQkLUIIIZpGyNdpEc2nxGGl2GFFg0JsE+83dDIJJl+CVFy9dYAQQgjRFCRpacP8rSwdLNHoNNoWu2589VYBRZK0CCGEaEKStLRh/qSlW2R8i143zmhBAaxuFzZ37QXphBBCiMaQpKUN21/uG4TbLSqhRa+r12jpaIkBpItICCFE05GkpY1yeT0crioFqLVeSkvoXp0oSReREEKIpiJJSxtjczspdVjZVZKLR/Vi0RlqrILbUvxJS7H95Hs4CSGEEA0V8inPomk5PG6+yd1HdkURABE6A6FY4s0/jqbS7cDhcWPUyq0mhBDi9EhLSxtV6vTtoNqSU52PZ9EZiNL7duSWcS1CCCGagiQtbVSokxaABP/UZ7skLUIIIU6fJC1tkNvrodLlACDGGLqkJb56H6Jih4xrEUIIcfokaWmDypx2AExaHSZt7Z2ZW4q/paXcZcfl9YQsDiGEEG2DJC1t0K9dQ6Hd9t2o1WGp3jCxRFpbhBBCnCZJWtqgslYwnsUvrjoGSVqEEEKcLkla2qBSpy9BCOV4Fr+46t2l/a0/QgghRGNJ0tLGVLmcWKv3+2kVLS3VSUuJw4pXDcWKMUIIIdoKSVramMNVJYBvUTl9C+7sXJ8ovQmtosGtesm3VYQ6HCGEEGFMkpY2xr/fUEwraGUB0ChKoMXnUGVxiKMRQggRziRpaWP8LS2toWvIL656bM1BSVqEEEKcBkla2pic6paW2FYwCNcvrnrq9aHKkhBHIoQQIpxJ0tKGlDisVLjsKECMvvUkLbHVg3Hz7RVUuZwhjkYIIUS4kqSlDTlYvbNzpN6EVtN6vrXHLzKXVVEY4miEEEKEq9bzySZOW3b1mJFYgynEkdTmX2ROkhYhhBCNJUlLG+IfhNtaZg4dz79ey4FySVqEEEI0jiQtbUhO9UDX6FactGRVFMkic0IIIRpFkpY2otxpp9RpQwGi9cZQh1NLlN6EQaPF5nGRay0LdThCCCHCkCQtbYS/ayjBFImuFayEeyKNotA5Ig6AAzKuRQghRCNI0tJG+NdA6WiJCXEk9esWGQ/IuBYhhBCNI0lLG5FTPXOoNSctXSP9LS1FIY5ECCFEOJKkpY3wr4TbmpOWLtVJS661DFv1TtRCCCFEQ0nSEqZsbielDiulDit51nLybeUApFiiQhxZ/aL0JhKMEahAtrS2CCGECJIu1AGIxnF43HyTuw+AYocVFd/KsxG61jdz6HhpUQkUOarIqiiib1xqqMMRQggRRqSlpQ0od9oAiNG3vpVwT5QWnQhAtswgEkIIESRJWtqAMqcdaJ2Lyp0oLSoB8C0yp8oic0IIIYIgSUsbUO7ytbREt8I9h07UJSIOjaJQ7rJT7LCGOhwhhBBhRJKWMOdVVSqcDiA8uocMWl1gkTnZPFEIIUQwJGkJc5UuB15UdIoGi84Q6nAa5PguIiGEEKKhJGkJc/5BuNEGE4qihDiahpGkRQghRGNI0hLmylzVg3D1rX8Qrl9alG8G0aHKYjxeb4ijEUIIES4kaQlz5dUzh2LCYBCuX7I5CotOj8vr4Yi1NNThCCGECBONSlqKi4t55plnmDx5Mueccw67du1i6dKlfP75500dnzgJVVVrdA+FC42i0D3S10UkmycKIYRoqKCTlpycHCZNmsTf/vY3UlJSKCoqwuPxkJWVxezZs/nqq6+aIUxRF5vHhUv1oqAQpW/dK+GeyN9FJONahBBCNFTQy/j/+c9/JiEhgTfffBOLxcKAAQMAePbZZ3E4HKxYsYJx48Y1dZyiDv6uoSi9EY0SXj19adEyGFcIIURwgv6k27BhAzNnziQ6OrrWbJWrrrqKvXv3Nllw4uQqXP6kJXy6hvz83UN5tnKqXM4QRyOEECIcNOrPc61WW2e50+kMm2m3bUG5f+ZQGI1n8e9M7VG9xBstAOwpyw1xVEIIIcJB0N1Dw4YN46WXXuLss8/GaPSNo1AUBa/Xy9tvv82ZZ57Z5EGKuvlXwo0Ok/EsLq+HjflZgccmrR6A/eWF9LYkhiosIYQQYSLopOXuu+/m6quv5rzzzmPEiBEoisKqVavYv38/Bw8e5K9//WtzxClO4PJ6qHT7kpaoMGppOV6c0cJRaxk5lSWhDkUIIUQYCLp7KD09nb///e+MGDGCTZs2odVq+e677+jatStr1qyhb9++zRGnOEGerQIAg0aLURN07tkqxFbvSp1TVSI7PgshhDilRn3apaWl8eyzzzZ1LCIIx6xlgG8QbriOI4o2mNCgUOV2UuSUHZ+FEEKcXIOSls2bNwd10szMzEYFIxou11oOhNcg3BNpFQ3RBhOlThuHqkoIn40IhBBChEKDkpZrr7221l/zJzbnK4qCqqooisLOnTubLkJRp2M2X0tLdBhOdz5erMFMqdPGwapi+qAPdThCCCFasQYlLW+88UZzxyGCoKoqx6pbWsJ1EK5frNEClcUcrCqhD8mhDkcIIUQr1qCkZfjw4c0dhwhCucuO1e1bkC1KFx7TnesTVz0Y94i1DI8pKcTRCCGEaM0atbjcjh07uPPOOznnnHPIyMhg/PjxPPjgg+Tk5JxWMFlZWQwZMoT33nsvULZz506mTp3K4MGDGTduHKtWrarxGq/Xy+LFixk9ejSDBg3ihhtu4ODBg6cVR2t3pKoUgEidEa0mvJbvP5FFZ8CiM+BWvRR57aEORwghRCvWqGX8r7zySrZs2cLYsWO5/vrrOeuss1i/fj2//e1v2bNnT6MCcblc3HPPPVitv84iKSkpYdq0aXTv3p13332X22+/nUWLFvHuu+8Gjlm+fDlr1qzh8ccfZ+3atSiKwvTp03E62+7S8Ierk5YoQ3i3soBvLFSXiDgA8iVpEUIIcRJBT3leuHAhw4YN48UXXwysiAtgt9u56aab+Mtf/sLKlSuDDmTJkiVERETUKPvb3/6GwWBg/vz56HQ6evbsycGDB3n55ZeZMmUKTqeTV155hblz5zJ27NhAfKNHj2bdunVcfPHFQccRDo5U+RZjC/dBuH5dIuPYXZYnSYsQQoiTCrqlZc+ePUybNq1GwgJgMpm48cYb2bp1a9BBbN68mbVr1/LnP/+5RvmWLVvIzMxEp/s1txo5ciRZWVkUFRWxa9cuqqqqGDlyZOD56Oho+vXrF/Q07XDib2lpK0lL10BLiy3EkQghhGjNgm5p6dChA4cPH67zueLiYuLj44M6X3l5Offeey/z5s2jQ4cONZ7Lzc0lPT29Rllysm+GydGjR8nNzQ3EdOIxx44dCyqO46mqWqObqi42m63G15biUb2BheXMig6Xy1XjeVVVG1QWbHlznqNzQiwA5aqLfEcVUXgA32q/Wm+tw8UphOrebKukPptWW65Pi8US6hDavKCTlvvuu4/77ruPqKgoLr744sCOz//97395/vnnmT9/flDnmz9/PoMHD+bSSy+t9ZzdbsdgMNQo87fwOByOwE1f1zFlZWVBxXE8l8vV4LVmsrOzG32dxij2OvCoKkaNloriUipPWD/HnZJGQUHhKcuCLW/OcygpaRjR4MDL33ZsJEbj+36e3zODopyjtY4XDdPS92ZbJ/XZtNpifQ4dOjTUIbR5QSct//d//4fT6eS+++7jwQcfJC4ujrKyMlwuF6qqcttttwWOVRSFHTt21Huu999/ny1btvDhhx/W+bzJZKo1oNbh8G0SaLFYMJl83SNOpzPwf/8xZnPj11fV6/X06tXrpMfYbDays7Pp3r37aV0rWFuLcyArm1RzNMnRtacI67Q6kpIST1kWbHlznyPOaCHXUYnHqCMp1ndMVGQkybKXVdBCdW+2VVKfTUvqU5yOoJOWyy+/vMku/u6771JUVMS4ceNqlP/pT39i1apVdOzYkfz8/BrP+R+npKTgdrsDZV27dq1xTJ8+fRodl6IoDW7mM5vNLdokWJDna13qEBGLXl97BVlFUWqV11UWbHlzn8OftFR6nYFjtFotFqM0tzZWS9+bbZ3UZ9OS+hSNEXTScnxLyulasGABdnvNGSPnnXces2fP5qKLLuLjjz9mzZo1eDyeQDfUhg0bSEtLIyEhgaioKCIjI9m0aVMgaSkvL2fHjh1MnTq1yeJsTfwzhzpYonF5PSGOpunE6H1/cZU5bYHtIIQQQojjNWqXZ6fTyXvvvcemTZsoLy8nLi6OYcOGMXny5Fqzik4mJSWlzvKEhAQ6derElClTWLlyJQ899BA33XQTP/30E6+//jqPPvoo4BvLMnXqVBYsWEB8fDydOnXimWeeITU1lYkTJzbmrbV6R6oH4aaYozlcncC0BVF6IwrgUr1UuZ1E6sN/DRohhBBNK+ikpby8nD/+8Y/s2rWLjh07kpSURFZWFh999BFvvfUWf/3rX4mKimqS4BISEli5ciVPPPEEkydPJikpiXvvvZfJkycHjpk9ezZut5t58+Zht9vJzMxk1apVtQbntgU2t5MSh29WU6o5qk0lLRpFwYKOKtyUOm2StAghhKgl6KTl2WefJTc3l9WrVzNs2LBA+ZYtW5g9ezaLFi1i3rx5jQ5o9+7dNR5nZGSwdu3aeo/XarXMnTuXuXPnNvqa4eJodStLrMGMWdf2krIIRUeV6qbUYaVzRGyowxFCCNHKBL243BdffMGdd95ZI2EBGDZsGLNnz+azzz5rsuBETf71WTpaYkIcSfOwKL4cusTZ9tZvEEIIcfqCTlqqqqro0qVLnc916dKF0tLS041J1ONIVXXS0kZbISKqk5Zypx2PKqvKCSGEqCnopKVHjx58+eWXdT73xRdf0K1bt9MOSvzK5nZS6rBS6rByqLIY8HUPtcUPdQMaDBotKirlTtmHSAghRE1Bj2m58cYbueuuu3A6nVx66aUkJiZSWFjIhx9+yDvvvBP0irji5BweN9/k7gMgp9I38PaYtQyv2jmUYTULRVGI0ZkocFQFBhwLIYQQfkEnLRdddBHZ2dmsWLGCd955B/DtKWMwGJg1axZXXXVVkwcpwOlx4/D6FtOLasMza2L0ZgocVZTKuBYhhBAnaNQ6LTNnzmTq1Kls376dsrIyYmJiGDRoEDExbXOAaGtQ4fJtX2DW6tFptCGOpvnEGHzbMZQ6paVFCCFETUGPafHz79hbWVnJgAEDKCoqQlXVpoxNHKfC5RvjEaU3neLI8BZT/f6sbheV1YmaEEIIAY1saXnhhRd48cUXsdvtKIpCRkYGCxcupLS0lFdeeYXo6OimjrPd87e0RBnabtcQgF6jJVJnpNLtIKeyhM6RcaEOSQghRCsRdEvL6tWrWbJkCdOmTeNvf/tboHXluuuuIycnh0WLFjV5kOL4lpa2nbQAxBp9+xAdqioOcSRCCCFak6CTljfffJObb76ZO+64g/79+wfKR48ezZ133sl//vOfJg1Q+LriAi0tbbx7CCDO4Nv51T9bSgghhIBGJC1Hjx5l+PDhdT7Xo0cPCgsLTzsoUZPT6wns6Bypaz8tLTlVJXhlnJQQQohqQSctHTp0YNu2bXU+9/PPP9OhQ4fTDkrU5O8asugMaDWNHjsdNqL0JjSKgt3jJt9WHupwhBBCtBJBD8S94oorWLJkCSaTiXHjxgFgtVr597//zYsvvsi0adOaOsZ279euobbfygK+HZ9jDWaKHVayKopIbaN7LQkhhAhO0EnL9OnTOXz4MAsWLGDBggUA/PGPfwTg0ksvZcaMGU0boaDC2T6mOx/v+KTlrJQeoQ5HCCFEKxB00qIoCv/3f//HtGnT2LRpE6WlpURFRTF8+HDOOOOM5oix3WtvLS0AsUYLVBSRVSFjpIQQQvg0ap0WgLS0NADKy8uJj4+vd+dncXp8M4faX0tLnME3GPdwVSlOjxuDttG3qhBCiDaiUZ8Eb731Fi+88AJFRUWBso4dO3L33Xdz0UUXNVlwAspcdtyqFwWI1BtCHU6LMWn1ROmNVLgcHKosoVdMUqhDEkIIEWJBJy1vvfUWjz32GOeeey7nnXceCQkJFBYW8umnn3L33XdjMBj4zW9+0xyxtkt5Vt/smQidEY3S9mcO+SmKQpeIOHaU5pJVUShJixBCiOCTltdff50//OEPPPLIIzXKf/vb3/LII4+wbNkySVqaUJ6tAmhf41n8ukbGVyctRac+WAghRJsX9J/ueXl5nHvuuXU+d/7553PgwIHTDkr8Kt/uS1oi22HS0qV636FsSVqEEELQiKRl4MCBfPPNN3U+t23bNnr37n3aQYlfFdjab9LSOSIWBShyVFHutIU6HCGEECEWdPfQrbfeyl133UVlZSWTJk0iOTmZ0tJS/vOf//Daa6/x4IMPsnnz5sDxmZmZTRpwe6KqKvntOGkxafV0sMRw1FpGVkURgxI6hzokIYQQIRR00nLjjTcC8Pe//5133303UO7f7fnRRx8NPFYUhZ07dzZFnO1SpcuB1eMC2seeQ3VJi0qQpEUIIQTQiKTljTfeaI44RB2OVe+7Y9Hq28WeQ3VJi0rk27wDHCiXReaEECJY/gaEtiLopKW+HZ5F08u1lgEQ0Q67hvzSohMAOFhZhFf1tqtp30IIcTr27dvHvHnzWLNmzWmf6/Dhw5x77rk89dRTXH755U0QXePIJ0Ardqx6jZb2ON3Zr6MlBqNGh93jJtcqOz4LIURDffLJJ2zbti3UYTQpSVpasdzq7qH2OAjXT6No6BYVDyDrtQghRDsnSUsr5u8eas9JC0D3KF8XkSQtQghR0y+//MJ1113H0KFDGTJkCNdffz0//vgjS5YsYenSpQD07t2bJUuWBP6/dOlSpkyZwtChQ1m+fDkA2dnZzJ49m1GjRjF48GCuvfZatm7dWu91VVXl/vvvZ+DAgaxfvz5Q/vnnn3P55ZczcOBARo0axeOPP47Vam2y99ugpOWf//wnJSUlTXZRcWp2j4tih+8b3V5nDvmlRSUCyI7PQghxnMrKSm666Sbi4uJYvHgxCxcuxGazceONN3L55ZdzxRVXALB27Vp+97vfBV73wgsvcP755/Pcc89x7rnnsm/fPi6//HJycnKYN28eCxYsQFEUrrvuOr7//vs6r/3444/z0UcfsXTpUsaMGQPAhx9+yKxZs+jRowfLli3jtttu44MPPmDmzJmBGcanq0EDcefPn8+qVauIi4vj3HPPZdmyZfTp06dJAhB1y7P61meJ0Bna/Q7HadUtLUeqynB43BjbeX0IIQT4BtoWFxdz7bXXMnToUAB69OjBmjVr0Gg0pKamAjB48OAar8vIyODmm28OPL7zzjvR6/W88cYbREVFATBu3DguueQSnnnmGd55550ar3/22WdZu3YtS5YsYezYsYCv5WXBggWMHj2aBQsWBI7t3r07119/PV9//TXjxo077ffcoN/+BoOBf/7zn7jdbo4cOcL27dupqKio93hZUO70HbP5uoaSzVEhjiT04owWYg1mSp02DlYWkx6THOqQhBAi5M444wzi4+O59dZbufDCCxk7dixnnXUW995770lfl56eXuPx999/z/jx4wMJC4BOp+Piiy9m2bJlVFVVBcrfeustfv75Zy677DLGjx8fKD9w4AC5ubnMmDEDt9sdKM/MzCQyMpJvv/225ZKW3/3ud6xcuZK//e1vKIoSWEDuRLKgXNPxz5RJNknSAr4uom1FOWRVFErSIoQQQEREBG+99RYvvPAC//rXv1izZg1ms5lJkybx0EMP1fu6xMTEGo/LyspqlfmPU1WVysrKQNmuXbsYPXo0H330Eddddx39+/cHoLS0FPAtMFtXjpCfn9+Yt1hLg5KWe+65h8suu4ySkhL++Mc/8sgjj9CrV68mCUDUzZ+0JJkjQxxJ65AWlcC2ohyyy2UwrhBC+PXo0YNnnnkGj8fDTz/9xD//+U/efvttOndu+AriMTExFBbWHjNYUFAAQFxcXCDpuOOOO7juuuu45JJLmDdvHu+88w46nY7o6GgA7r333jrXc4uJiWnM26ulwYMDzjjjDABuu+02zj33XFJSUpokAFE3/8yhZFNUYKfn9ixNZhAJIUQNn376KfPnz+fDDz8kKSmJIUOGMGTIED7++GNyc3NJSEho0HkyMzP58ssvqaioCHQReTwePv74YwYOHIjBYAgcm5iYiNFo5JFHHuGmm27ilVde4eabb6ZHjx4kJCRw+PDhwHY/4Et85s6dy+9//3u6du162u856BGNt912G06nkzVr1rBp0ybKy8uJi4tj2LBhTJ48GaOxfc90aQoer5e86kQl2SxJC0DXqHgUFEqcVkodVmKNllCHJIQQIXXmmWfi9XqZNWsWN998MxEREXzyySdUVFRw3nnnsXfvXgA++ugjBg0aRJcuXeo8z2233cb69ev54x//yM0334zBYGD16tXk5OSwcuXKOl8zevRoLrzwQpYuXcp5551H9+7dmTNnDo888gharZbx48dTXl7O8uXLycvLC3Qjna6g12kpLy/nyiuvZP78+fz4449UVlbyww8/MH/+fK644oqTDtAVDVNgr8Srqhg1OmIM5lCH0yqYtHo6RfiaF6W1RQghIDk5mZUrVxIVFcVDDz3EjBkz+OWXX1iyZAkjR47kvPPOY+DAgdx///2sWrWq3vOcccYZ/PWvfyUxMZEHH3yQuXPnoqoqb7zxBmeffXa9r3vwwQfR6/U8/PDDqKrK7373O5599ll++OEHbrnlFubPn0/nzp158803602YghV0S8uzzz5Lbm4uq1evZtiwYYHyLVu2MHv2bBYtWsS8efOaJLj2yt81lGKJQtOGNro6Xd2jEjhcVUpWRRFDEpvmB0AIIcJZRkZGvQlJSkoKf//732uU7d69u85j+/bty8svv1zvdTp37lzrtcnJybUWoLvooou46KKLGhJ6owTd0vLFF19w55131khYAIYNG8bs2bP57LPPmiy49sq/u3OquWkGLrUVPWSROSGEaNeCTlqqqqrqbebp0qVLYNqTaDx/S0sHS3SII2ld/INxD1YU41W9IY5GCCFESws6aenRowdffvllnc998cUXdOvW7bSDau/8051TLdLScrxUSzQmrQ6H182RqrJQhyOEEKKFBT2m5cYbb+Suu+7C6XRy6aWXkpiYSGFhIR9++CHvvPMO8+fPb4Yw2w9VVQO7O6eapaXleBpFQ1pUIjtLc9lfXkCXyLhQhySEEKIFBZ20XHTRRWRnZ7NixYrAfgSqqmIwGJg1axZXXXVVkwfZnpQ6bdg9bjQoJJsjqXQ5Qh1Sq9IzOqk6aSlkXMf0U79ACCFEm9GonedmzpzJ1KlT2b59O2VlZcTExDBo0KAmW/GuPcu3+aaMJ5oi0Gm0IY6m9ekVnQTA/vKCEEcihBCipTV6u9zo6OjAdtSi6eTZ/IvKSddQXdKiElBQKHJUySJzQgjRzgQ9EFc0r/zq8SwpsrtznUw6PZ0jYgHYXy5Tn4UQoj2RpKWV8be0pEhLS716RvvWa5EuIiGEaF8kaWllfu0ekpaW+vSUcS1CCNEuBZ20rFixIrAJk2haHtVLQfXmiCkWSVoASh3WGv9sbmcgaTlUVYLT4w5xhEII0XJUVQ2r63q9XhYvXszo0aMZNGgQN9xwAwcPHmx0HEEPxF25ciX9+vXjjDPOaPRFRd2K7FV4VRW9RkusQQaYurweNuZn1SgbndqLeKOFWIOZUqeNrIoiesemYHM7cdSRwBi1Osw6Q61yIYQIR4qi8EPhoRZdDiNSb+TMxK6Neu3y5ctZs2YNTz31FCkpKTzzzDNMnz6djz76CIMh+N/NQSct3bt3Z+/evTJzqBnkHTcIVzZKrJ+iKPSMTmJr4SH2lxfSOzYFh8fNN7n7ah07OrWXJC1CiDal0uWgzGkLdRin5HQ6eeWVV5g7dy5jx44FYOHChYwePZp169Zx8cUXB33OoJOWcePGsXDhQr788kvOOOMMEhISajyvKAqzZs0KOhBx3HgWk3QNnUypw0onSwxbgd2luZydkoZH9iISQohWZdeuXVRVVTFy5MhAWXR0NP369WPz5s0tk7QsXboUgC1btrBly5ZazwebtBQVFfH000/zzTff4HA4yMzM5N5776VXr14A7Ny5kyeeeIKff/6Z2NhYrr32Wm688cbA671eL0uXLuWdd96hvLycoUOH8qc//Sks90DyLyyXLONZ6uXvMip1+P7K2F9ewPpjezkrpUeIIxNCCHG83NxcADp06FCjPDk5mWPHjjXqnEEnLbt27WrUhepz6623otFoePnll7FYLCxatIjrr7+edevWYbfbmTZtGr/5zW949NFH2b59O48++iixsbFMmTIFaPr+slDKl+nODRZtMKFVFFyqV7Y6EEKIVshm8/1xeeJnsdFopKyscZventaU54qKCvbv34/T6cTj8QT9+pKSEjp37sxjjz3GwIED6dmzJzNnzqSgoIC9e/fyt7/9DYPBwPz58+nZsydTpkzh+uuv5+WXXwZ+7S+7/fbbGTt2LH369GHhwoXk5eWxbt2603lrIZEnC8s1mEZRAoOVi53WEEcjhBDiRCaTCfB9Vh/P4XBgNpsbdc5GJS2bNm3id7/7HcOHD+fSSy9l79693H333Tz99NNBnScuLo7nnnsuMBOpsLCQVatWkZqaSq9evdiyZQuZmZnodL82CI0cOZKsrCyKiopO2V8WTpweN8UO34evJC0NE1+9hH+JQ5IWIYRobfzdQvn5+TXK8/PzSU1NbdQ5g05aNmzYwI033ojJZOKee+4JzN3u168fb7zxBq+++mqjAnn44YcZNWoUn376KU888QQWi4Xc3Nxabyw5ORmAo0ePNkt/WagU2CsBMGv1uDyewLokMsC0fnHVSUuxJC1CCNHq9OnTh8jISDZt2hQoKy8vZ8eOHQwbNqxR5wx6TMvzzz/Pueeey6JFi3C73TzzzDMA3HzzzVRWVvLOO+8wbdq0oAO57rrruOqqq3j77beZNWsWf/3rX7Hb7XX2hYGveak5+svAt4iO1XryD0L/tf1fT9ehUt/qrgaNli8P/zpu6JyOZ+ByueqMsaHlwRwbunP41ljxf23IOSI1egCsbicVTnud5/Z4PKf8XrY1TX1vtndSn02rLdenxdIy62tF6o0tcp3TvZ7BYGDq1KksWLCA+Ph4OnXqxDPPPENqaioTJ05s1DmDTlp27twZmB2knLCWyKhRo3j99dcbFYh/ttBjjz3G9u3bWb16NSaTqc6+MPDdHMf3l/n/7z+msf1lAC6Xi507dzbo2Ozs7EZf53i/uIoA0Lq9FBT8uhGgOyWtxuPGlIfDOUpLSwECXxt6DhNa7HjILi+s89wV0R3Jzjlaq7w9aKp7U/hIfTattlifQ4cObfZrqKra6IXeTve6J37mN8Ts2bNxu93MmzcPu91OZmYmq1atavREmaCTlqioKAoK6t7z5dixY0RFNXw8RlFRERs2bODCCy9Eq9UCoNFo6NmzZ6DPq66+MICUlBTcbnegrGvXrjWO6dOnT1Dv63h6vT6QRNXHZrORnZ1N9+7d602QPBpwemsPUDZotGhP6PXZnv0DFEF8RBRJUYmBcp1WR1JSIicKpjwczhEbG0tpaSmxsbHo9boGnyOxzM1haxmHbWV1njsqMpLkvn1rlbdlDbk3RcNJfTYtqc/T05jEIZTX1Wq1zJ07l7lz5zZJHEEnLeeeey4LFy4kPT2dfv36Ab43k5uby4oVKxg3blyDz5Wfn8/dd99NQkICZ511FuBr5dixYwcTJkwgMTGRNWvW4PF4AknNhg0bSEtLIyEhgaioqEB/mT9p8feXTZ06Ndi3FqAoSoOb+cxmc73HljqsfFeQVat8dGovok54TZHL11QabbSg1+trxHL848aUh8c5fLeiXq8LHNOQcySaIjlsLeNQZQmDEjrVOrdWq8VibJ9bIpzs3hTBk/psWlKfojGCHojrTzKuvPLKQIJy1113ccEFF6AoCnfddVeDz9WnTx/OOeccHn30UbZs2cKePXu47777KC8v5/rrr2fKlClUVlby0EMPsW/fPt577z1ef/11ZsyYAdTsL/viiy/YtWsXc+bMOa3+slDJr57uHKEPr7VlQs0/GPeotUwGLQshRBsXdEtLTEwM77zzDu+//z4bN26ktLSUqKgorr32Wi6//PKgmvsUReH555/n2Wef5c4776SiooJhw4bx1ltv0bFjR8C3QeMTTzzB5MmTSUpK4t5772Xy5MmBczR1f1koVLmcVFQvkBYh++QExaIzYNTocHjdlDltxBsjQh2SEEKIZhJ00gK+Fo4rr7ySK6+88rQDiIqKYv78+cyfP7/O5zMyMli7dm29r2/q/rJQyLf7Wlmi9SZ0Gm2IowkviqIQZ7SQayun2GGVpEUIIdqwRiUt+/fv54UXXmDDhg2UlZWRkJDAyJEjufXWW+nevXsTh9j2+TdKTDRFhjiS8ORPWmSROSGEaNuCTlo2bNjA9OnTiYuLY9y4cSQkJFBQUMDXX3/N559/zltvvXVaM3fai9LjPmAPVRQDkGCSVoLGOH5l3MZOyxNCCNH6BZ20LFy4kMzMTF588cUa40aqqqq46aabePLJJ3njjTeaNMi2xr9Tsd/OUt/KvgnStdEoMQYTOkWD0+uhyu1s8YWXhBBCtIygZw/t2rWL66+/vtZA14iICG6++WZ+/PHHJguuvahy+xbQi2+nU3NPl0bR0CkiFpB9iIQQoi0LOmnp0KEDR4/WvcJoVVUViYm1F/gSJ2cNJC3S0tJYXSPjAdmHSAgh2rKgk5a5c+fy/PPP88knn+Dx/Lra66ZNm3juuee45557mjTAts7p9eCqXjVXWloar1tkHCAtLUKIts2/SXE4Xnf58uVce+21p3WOBo1p6dOnT43Bjaqqctddd6HVaomNjaWiogKn04lWq+WJJ57gwgsvPK2g2hNbdSuLUaPDoG3UZC7Bry0tlW4HTo9b6lII0SYpioJ3z1aonnXaIsxRaNJPb1+l1157jcWLF5OZmXla52nQb/ZZs2bJjIxm4h/PYtHVXt5eNJxFZyBSZ6TS7aDEaSXFHB3qkIQQonnYKqCqLNRRNEheXh4PPfQQW7duJS0t7bTP16Ck5fbbbz/tC4m6WQNJi6yEe7rijGYq3Q6KHZK0CCFEa/DLL78QExPDBx98wLJlyzhy5Mhpna9RbehOp5MDBw5QUVF389TpNv+0J5K0NJ04YwQ5VaUyrkUIIVqJCRMmMGHChCY7X6MWl7v77rspKSkBfh2coyhKYGGvnTt3NlmAbZ3VJUlLU/EPZC512vCqXjRK0OPMhRBCtGJBJy1PPvkkcXFxzJ8/n9jY2GYIqX3xt7TIRomnL0JnQK/R4vJ6KHPaAztACyGEaBuCTloOHTrEwoULm7S5p73yqio2jwuQlpamoCgK8UYLebYKShxWSVqEEKKNCbr9vHfv3oGuIXF6bG4nKqBRFIwyRbdJ+BMVWWROCCHanqA/KR988EHuueceNBoNGRkZmM3mWsd07NixSYJr66zuX1tZZEp504g31Nw8UQghRNvR6NlDDz74YL3Py0DchgnMHNJK11BTiTGaUVBweN1Yq7vehBCiTTFHte3rnUTQScv8+fPRarXMmTOHpKSk5oip3fAvLBehl6SlqWgVDbEGEyVOGyWOqlCHI4QQTUpV1dNenbax1z3dHoGnn376tOMIOmk5cOAAixYtYvz48ad98fZO1mhpHnHGCEqcNortMq5FCNG2hGooQWsZwhD0QNxu3bphs9maI5Z2xypL+DeLeBmMK4QQbVLQScsdd9zBwoUL+fbbb6mqkub3xlJVVVpamok/aal0O6hyOUIcjRBCiKYSdPfQs88+S2FhITfddFOdzyuKwo4dO047sLbO5fXgVr2ADMRtagatjki9kUqXg4OVxXSKjAt1SEIIIZpA0EnLxRdf3BxxtDv+QbhGrQ6tRpabb2rxRguVLgfZFUWcndoz1OEIIYRoAkEnLbfddltzxNHuyPL9zSveaOFQZQlZlcWhDkUIIUQTCTppOXr06CmPkcXlTk3GszSveGMEAEeqSnB63BhkxWEhhAh7Qf8mnzBhwimnPsnicqcmSUvzMmv1GLU6HB432RVFpMemhDokIYQQp6lRuzyfmLRYrVa2bt3Kxo0befLJJ5ssuLbs+CX8RdNTFIUEYwRHrWXsKy+QpEUIIdqAoJOWyy+/vM7ya665hj//+c98+OGHjBs37nTjavOqZExLs4szWgJJixBCiPDXpNNWxo0bx1dffdWUp2yT3F4Pdo+0tDQ3/3ot+8sL8VZPLxdCCBG+mjRp2b59OzqdDHg8lVKnb0VhraLBoNGGOJq2K1pvwqjRYfe4OFJVFupwhBBCnKagM4wHHnigVpnX6+XYsWNs2bKFK664okkCa8uKqzfys+j0rWY/h7ZIURS6RcWzpyyffeX5dJFF5oQQIqwFnbRs2rSpVpmiKERGRjJ9+nRuueWWJgmsLfPviSNdQ82ve2SCL2kpK2B8x96hDkcIIcRpCDpp+c9//tMccbQrJdVJiwzCbX5pUQkA7CnLp8RehaIoGLU6zFL3QggRdmT9+BD4tXtIPjibW4o5CgWFcpedz47s5JvcfTg87lCHJYQQohEa1NJS1ziW+iiKImu1nIJ0D7Ucg1ZHjMFEqdNGscMqdS6EEGGsQUlLXeNYTlRSUoLNZpOk5RRUVQ10D8kHaMuIN0ZUJy1VdI6IDXU4QgghGqlBScvJxrG4XC5eeOEFXnrpJRITE5k/f35TxdYmOb0enF4PAGadPsTRtA/xRgsHKn5t4RJCCBGeTmtRlZ07d3L//fezZ88eLr74Yh5++GFiYmKaKrY2yb/nkFmrR6vIkKKW4F9krtLlwCnjWYQQImw1Kmlxu90sW7aMl19+mdjYWJYuXcq5557b1LG1SVWyUWKLM2h1ROqMVLodga45IYQQ4SfopGXHjh088MAD7N69m0mTJjFv3jyio6ObI7Y26dfdnaVrqCXFGy1Uuh3SRSSEEGGswUmL2+1m6dKlrFy5kvj4eF544QXGjx/fnLG1SVZpaQmJeKOFQ1UlgenmQgghwk+DkpZffvmF+++/n3379vHb3/6Whx56iMjIyOaOrU2SpCU04kwRAJQ67biqB0ILIYQILw1KWq688kq8Xi9RUVEcOXKEmTNn1nusoii8/vrrTRZgWyNjWkLDotVj1OpweNzkVJaQZI4KdUhCCCGC1KCk5cwzzwz8X1XVkx57qufbM4/XG1iNVZbwb1mKohBvtHDMWk52ZRFnJnUNdUhCCCGC1KCk5c0332zuONoFq8fXymLU6tBrtCGOpv2JN0b4kpaK4lCHIoQQohFkoZAW5B/PEm+woChKiKNpf/zrtRysLMKrekMcjRBCiGBJ0tKCrG4XAPHVg0JFy4rSm9ApGuweN0eqykIdjhBCiCBJ0tKCqly+lpY4gyXEkbRPGkUhrrq1ZV95foijEUIIESxJWlpQoHvIKElLqPjrfm9ZQYgjEUIIESxJWlqQfyBuvFG6h0LFX/d7yvJlppsQQoQZSVpaiKqqgZaWOGlpCZlYoxmdoqHCZSfPVh7qcIQQQgRBkpYW4vC48aoqChBrMIc6nHZLq2joFhUPwO4yGdcihBDhRJKWFuJvZTFr9Wg1Uu2h1CMqEYA9pXkhjkQIIUQwQv7pWVpayiOPPMKYMWM488wzufrqq9myZUvg+Z07dzJ16lQGDx7MuHHjWLVqVY3Xe71eFi9ezOjRoxk0aBA33HADBw8ebOm3cUqyfH/rEUhaZFyLEEKElZAnLXfddRc//vgjzz33HH//+9/p378/N954I/v376ekpIRp06bRvXt33n33XW6//XYWLVrEu+++G3j98uXLWbNmDY8//jhr165FURSmT5+O0+kM4buqLbBRol6SllDrEhmHXqOl3GUnz1YR6nCEEEI0UIOW8W8uBw8e5Ntvv+Xtt98O7G/00EMPsX79ej766CNMJhMGg4H58+ej0+no2bMnBw8e5OWXX2bKlCk4nU5eeeUV5s6dy9ixYwFYuHAho0ePZt26dVx88cWhfHs1yO7OrYdeoyUtKoE9ZfnsKcsj1RId6pCEEEI0QEhbWuLi4njppZcYMGBAoExRFFRVpaysjC1btpCZmYlO92tuNXLkSLKysigqKmLXrl1UVVUxcuTIwPPR0dH069ePzZs3t+h7ORVJWlqX9JgUwNdFJIQQIjyEtKUlOjo60ELi98knn3Do0CHOOeccFi5cSHp6eo3nk5OTATh69Ci5ubkAdOjQodYxx44da3RcqqpitVpPeozNZqvxtS4ePLhcvqX7/WNajGhQVTVQfuJ1TywP5tjwPodv92v/1+aMw+Px0M3ka13ZXZpHVVVVm9oLqiH3pmg4qc+m1Zbr02KR5SyaW0iTlhNt3bqVBx98kHPPPZcJEybw1FNPYTDUbJkwGo0AOByOwE1f1zFlZY3fW8blcrFz584GHZudnV3vcwldOlJQUIhHVXF6PQBUlZTj7uymoKCw1vHulLRa5XWVBVseDucoLS0FCHxtzjgqojtiy8lHi0K5y86mHT8Ro2l7LWAnuzdF8KQ+m1ZbrM+hQ4eGOoQ2r9UkLZ9//jn33HMPgwYN4rnnngPAZDLVGlDrcDgAX0ZrMpkAcDqdgf/7jzGbG78Wil6vp1evXic9xmazkZ2dTffu3eu9lg0PSUmJVLgcUFiMTtHQITkZnVZHUlJirePrKg/m2HA+R2xsLKWlpcTGxqLX65o1jqjISJL79af77mL2VxahJsfQN6l7rdeGq4bcm6LhpD6bltSnOB2tImlZvXo1TzzxBBMnTmTBggWBlpPU1FTy82uOOfA/TklJwe12B8q6du1a45g+ffo0Oh5FURrczGc2m+s91umwotfrcbp8LUIReiN6vR5FUdDr9XVe98TyYI4N73P4bkW9Xhc4prni0Gq1WIwW+sR3YH9lEdm2Es619Kv12nB3sntTBE/qs2lJfYrGCPmU57/+9a889thjXHPNNTz//PM1unoyMzPZunUrHo8nULZhwwbS0tJISEigT58+REZGsmnTpsDz5eXl7Nixg2HDhrXo+ziZXwfh1v5QFaHT+7jBuLJeixBCtH4hTVqysrJ48sknmThxIjNmzKCoqIiCggIKCgqoqKhgypQpVFZW8tBDD7Fv3z7ee+89Xn/9dWbMmAH4xrJMnTqVBQsW8MUXX7Br1y7mzJlDamoqEydODOVbq0EWlmud0qIS0CkaSp028u2yXosQQrR2Ie0e+ve//43L5WLdunWsW7euxnOTJ0/m6aefZuXKlTzxxBNMnjyZpKQk7r33XiZPnhw4bvbs2bjdbubNm4fdbiczM5NVq1bVGpwbSv6WlghJWloVg1ZHj+hE9pTls6s0jxSzrNcihBCtWUiTlltuuYVbbrnlpMdkZGSwdu3aep/XarXMnTuXuXPnNnV4TUbWaGm9+samsqcsn50luYztcEaowxFCCHESIR/T0tapqorN7VsnRJKW1qdvbCoAu8ty8areEEcjhBDiZCRpaWZ2jwsvKgq+HZ5F69I1Kh6zVo/V7eJQZUmowxFCCHESkrQ0M/8gXLPO0KZWXW0rtIqG3rG+WUQ7SnJDHI0QQoiTkaSlmVmru4ZkEG7r5e8i2lUqSYsQQrRmkrQ0MxmE2/r5k5b95QU4Pe5THC2EECJUJGlpZpK0tH7J5ijijRbcqpe95bLrsxBCtFaStDQzSVpaP0VRAq0tO0vyQhyNEEKI+kjS0syqAgvLycyh1qyPP2kpPRbiSIQQQtRHkpZmZHO7cHl9+yZJS0vr5k9aDleVUu60hzgaIYQQdZGkpRmVOKoAMGi06DTaEEcjTibaYKJzRCwAu2UWkRBCtEqStDSjouqkRVpZwkNgXIskLUII0SpJ0tKMih1WQJKWcNE3zpe07CjJRVXVEEcjhBDiRJK0NKPi6pYWWVguPJwRnYxeo6XEaeWItTTU4QghhDiBJC3NqMgu3UPhxKDV0ad6Sf//FR8NcTRCCCFOJElLM5LuofAzMK4TIEmLEEK0RpK0NBOP6qXEKUlLuBkY3xGAA+WFVLkcIY5GCCHE8SRpaSYlDiteVUWDgkmrC3U4ooHiTRF0tMSgovJLiSw0J4QQrYkkLc2kwFYJgFmnR1GUEEcjgjGgurVFuoiEEKJ1kaSlmRTYfUmLzBwKPwPjfeNafik5hlf1hjgaIYQQfpK0NJPC6qRFxrOEn57RiZi1eqrcDrIqikIdjhBCiGqStDQTh8cFQKTeGOJIRLC0ioZ+cR0A6SISQojWREaINpMLuvQn1mDG4XGHOhTRCAPjO7K18BA/Fx/l/M59a30fjVodZmlFE0KIFiVJSzOJM1o4K6UH3+TuC3UoohEGxHVEAXKqSsi3VfBT8ZEaz49O7SVJixBCtDDpHhKiDlEGE92jEgDYVZoX4miEEEKAJC1C1CvjuFlEQgghQk+SFiHqMSSxCwD7yvNxeT0hjkYIIYSMaRHtUmn1vlDHO3FwbQdLDB3M0RyzlZNnq6BzRGwLRiiEEOJEkrSIdsfl9bAxP6tWeV2Da4ckduFYzi8cs5ZJ0iKEECEmSYsQxzmxBeaM6CTAt8Kx2+tFp5EeVSGECBVJWoSoVlcLjKqqxBktlDis5Nsr6GiJCVF0Qggh5M9GIU5CURT6x/pWx821loc4GiGEaN8kaRHiFPpXL+mfZ6vAIxsoCiFEyEjSIsQpdIqIxaTV4VG9FNoqQx2OEEK0W5K0CHEKGkUh1RwNwDGbdBEJIUSoSNIiRAN0qB6Am2erwKuqIY5GCCHaJ0lahGiAeKMFo0aHy+uhwF4R6nCEEKJdkqRFiAZQFIWOEb7WliNVZSGORggh2idJWoRooE7VK+Lm2sqxuV2hDUYIIdohSVqEaKAYvYlInRGvqvJzydFQhyOEEO2OJC1CNJCiKHSq7iLaVpgT4miEEKL9kaRFiCD4u4gOVBRS7KgKbTBCCNHOSNIiRBAsOgPxRgsq8H1+dqjDEUKIdkWSFiGC1Lm6tWVjfjaqrNkihBAtRpIWIYLUwRKDTtFwzFpGTlVJqMMRQoh2Q5IWIYKk12jpG5cKwMb8rBBHI4QQ7YckLUI0wpkJXQDfuBa31xPiaIQQon2QpEWIRugdm0KMwUyFy8GPRUdCHY4QQrQLkrQI0QhaRcOolB4AfJO7L8TRCCFE+yBJixCNNCq1JwqwszSXAptsoiiEEM1NkhYhGinRFEnfuA4ArJfWFiGEaHaStAhxGsak9gLg29z9OD3uEEcjhBBtW6tKWpYvX861115bo2znzp1MnTqVwYMHM27cOFatWlXjea/Xy+LFixk9ejSDBg3ihhtu4ODBgy0ZtmjHMhI6EW+0UOV2srlA7jshhGhOrSZpee2111i8eHGNspKSEqZNm0b37t159913uf3221m0aBHvvvtu4Jjly5ezZs0aHn/8cdauXYuiKEyfPh2n09nSb0G0Q1pFw7gO6QB8eXSPrJArhBDNKORJS15eHjfddBOLFi0iLS2txnN/+9vfMBgMzJ8/n549ezJlyhSuv/56Xn75ZQCcTievvPIKt99+O2PHjqVPnz4sXLiQvLw81q1bF4q3I9qhUak90Wu05FSVsK+8INThCCFEmxXypOWXX34hJiaGDz74gEGDBtV4bsuWLWRmZqLT6QJlI0eOJCsri6KiInbt2kVVVRUjR44MPB8dHU2/fv3YvHlzi70H0b5F6o2MSO4OwLoju0IbjBBCtGG6Ux/SvCZMmMCECRPqfC43N5f09PQaZcnJyQAcPXqU3NxcADp06FDrmGPHjjVDtEL8qtRhDfx/RFJ3/pu7nx+LDpNrLSPVEhPCyIQQom0KedJyMna7HYPBUKPMaDQC4HA4sNlsAHUeU1ZW1ujrqqqK1Wo96TH+a/u/1sWDB5fLVef5G1oezLHhfQ7fzBv/19b+XpweN/89urdGWbIxknxHJf/K/pnfdx9S6zwtpSH3pmg4qc+m1Zbr02KxhDqENq9VJy0mk6nWgFqHwwH4bg6TyQT4xrb4/+8/xmw2N/q6LpeLnTt3NujY7Ozsep9L6NKRgoLCWuXulLQGlwdzbDifo7S0FCDwNRzfS6yqIR/YXHSIXlU6IjSh/fE62b0pgif12bTaYn0OHTo01CG0ea06aUlNTSU/P79Gmf9xSkoKbrc7UNa1a9cax/Tp06fR19Xr9fTq1eukx9hsNrKzs+nevXu9CZIND0lJibXKdVpdg8uDOTaczxEbG0tpaSmxsbHo9bqwfC9JgKNCx8HKYg5Fq1zepW+tc7WEhtybouGkPpuW1Kc4Ha06acnMzGTNmjV4PB60Wi0AGzZsIC0tjYSEBKKiooiMjGTTpk2BpKW8vJwdO3YwderURl9XUZQGN/OZzeZ6j3U6rOj1+jrP39DyYI4N73P4bkW9Xhc4Jhzfy8ROfVi5+zs2FGRzcfcM4oyhay4+2b0pgif12bSkPkVjhHz20MlMmTKFyspKHnroIfbt28d7773H66+/zowZMwDfWJapU6eyYMECvvjiC3bt2sWcOXNITU1l4sSJIY5etEe9opPoFZ2EW/Xy78M7Qh2OEEK0Ka06aUlISGDlypVkZWUxefJkli5dyr333svkyZMDx8yePZsrrriCefPmcfXVV6PValm1alWtwblCtARFUbi020AAvjm2jyJ7VYgjEkKItqNVdQ89/fTTtcoyMjJYu3Ztva/RarXMnTuXuXPnNmdoQjRY75gUeseksLssj/ezf+TGPmeHOiQhhGgTWnVLixDhSFEUrugxBAX4viCb7IqiUIckhBBtgiQtQjSDrpHxjEj2bUvxzoEfZE8iIYRoApK0CNFMLuuegUGjZV95ARvzs0IdjhBChD1JWoRoJvHGCC7p6huU+/cD26h0OUIckRBChDdJWoRoRr/p1IeOlhgq3Q7eOfBDqMMRQoiwJkmLEM1Iq9Ew9YzhKMDG/Cx+KMwJdUhCCBG2JGkRopn1jE7i/M79AFi993vKnG1vozghhGgJkrQI0QIu7TaQzhGxVLkdrNz1LR7VG+qQhBAi7EjSIkQL0Gm03NRnFEatjj1l+bybtS3UIQkhRNiRpEWIFtLBEsP16WcB8MWR3fw3d3+IIxJCiPAiSYsQLejMxC5c3GUA4Bvf8mPR4RBHJIQQ4UOSFiFa2KXdBnJ2Sg9UVF7e9S2/lBwNdUhCCBEWWtWGiUK0VTa3E4fHHXh8SdcBlDtt/FxyjOW/rGd633MYnNA5hBGKU/FooNRhrVVu1Oow605vV/kT74+mPLcQbYkkLUK0AIfHzTe5+2qUXd0zk3eztvFDUQ4rdnzDVT3PZHzH3iGKUJyK0+vhu4La2zGMTu112glHXfeH/9yStAjxK0lahAgRnUbDTX1H8dbe7/k27wBr9m/lSFUZV/Y4E4NWfjTDgVf1kmcrZ1N+Nm6vFxUVraJBr9FyTmpPOkbEotdoT+saJ7buSOuLaM/kN6MQTayuLoT61mXRKhquPWMEKZZo/pG1nW9y97G/vICregwl1RJd69gTzyMfYC3L5fWQb6ugyF5FidNKpcvBv3J21Hns+uqWk2i9iURTJImmCDpaYugVk0yXyDhMWn2DrnfiZpvS+iLaM0lahGhCdX3IAIxMTqv3NYqicH7nfnSJiOOV3d9x1FrG8z9/Sa/oRHrFJKFVNIFzyAdYy1NVlaMeK9/t38KO0ly8qDWe11W3rOg0GhQUPKoXl9eDW/XiVVXKXXbKXXYOVBQGXqMAqeZoukXF0y0ygRRzFKqqoihKC787IcKLJC1CtBL94jrw8JkX8cqu79hVlsfe8gKOWcvJSOhIvDEi1OG1G/5Bsaqqsqssj88O7+SoowyqN+mO0BlIMUeRYIwg2mBiXId0NhVk1zrPiKTufJO7jyq30/fP5UCv0XLMWk6J08oxWznHbOVszPe9Vq9oiDdFkGCMINEUSZTe2HJvWogwIUmLEK1IjMHM9ekj+duBH/i55CiVbgff5WXRyRJD75iUUIfXLjg8bj7J+YWfio9QXN3VpwDDErth0umI1ptqtIjU1zqiKAoGrQ6DVkec0QL4WsZijRbKnDYOVRZzsKKYrIoi9pbl4/C6ybNVkGerAMCg0ZJrq0ABkkyRaDWyQoUQkrQI0cooikLHiBgSTRHsLM0lp6qUI9YyFv38Jd2i4ukVnXTagztF3dxeD58f2cX6Y/vwoqJRFLpa4oi2q0zqNrDOFpXGiDGYGRjfiYHxnQAoslfy8aGfKXJUUWSvothhxen1sLXwEAAaRSHFHEUnSyxur+xbJdovSVqEaKUMWh2DEjrTLSqBHSXHKHZY2V9eSE5lCekxyXSNjA91iGHv+PVRDleWsDbrB/KrWzqSTZEMiO+IXlUocBSe7DSnTatoiDNaiDNa6BWdhFdVKXZUAQo/Fh3G5nFxzFrOMWs5O0qOMSypG+ek9qJblNwDon2RpEWIVi7WYOas5DRijRbez/6RKreTn0uOkVVRRJzRwujUXjKAs5EcHjfrj+3lUGUJv5Qcw4tKhM5A75gUOliiURQFl8vV4nFpFIVEUyQjk9NIMFood9k5Ut3iZvW4WJ+7j/W5++gWGc+YDr3ITOqOUabJi3ZA7nIhwoCiKPSNTaWkg5VDlcXsKcunyu3krX2b+TZ3P5PTBtMnNjXUYYYdp8fN9qLDHLGWAb4ZPTf0Poufio+EOLJfKYpCjMFMjMFM39hUOlhi+LH4CNsKczhYWcybe7/n3axtjEk9g/Ed04mtHj8jRFskSYsQYUSjKHSPSqBzRCz7yws5WFlMdmUxC//3H/rFpjI5bTCJGlOowwwLedZylu9YT66tHAXoE5tKj6gELM08hTyYdXxOpCgKZ8Qkk5ncnQqnne/yD/DNsX0U2Cv59PAO1h3ZxfCkbpzXuR8dI2LqPY9sGyDClSQtQoQhnUZL79gUruo5lP/mHuCb3H3sKM1lx7ZPGRLXid5e+eA5mR8KD/H6no3YPW6MGh1nJnYhwdT808obs45PfaIMJs7v3I+JnfryU/ER1h3eyb7yAjbkZ7ExP4thSd24qMsA4ozmWgmKR/XyXd6BWueUdX9EaydJixBhLEpv4upew/hNpz58cPBHvi84yLaSI2wHcg7BZT0GE2MwhzrMVsPj9fJe9nY+P7ILgLSoBHpGJzZoddrWSqMoDE7ozOCEzmRVFPLvnJ1sK8phc8FBthQcZFB8Z+KMZiKOW/elMUmSEK2BJC1ChNDpdBUcL8kcyY19RnFe5378ff9WdpXn821BFluKcji3U2/O69wPsy58P5hPl83tpMBWwVv7tpBdWQTA2NRe/KZzHzZVL+4WTurbbTotKpFb+o0mp7KEjw/9zLaiHLYXH0ZBIS0qnl4xyRhkurwIY5K0CBEiTdlV4NclMo4ZZ5zNFz9v5SdtJYesJfwr5xfWH9vHhV37M7bDGe1yjZcdJcd4bc9GnF4POkXDoITORBlMKITfrKv67pvju3a6RMYFkpe1+7eytzyfAxVF5FSVkh6TzPCk7i0ctRBNQ5IWIcLciX91e/Awsu9AztJo2FWez6eHd1Bgr+SdAz/w+ZFdXNYtgxHJ3dEobX+FVa+q8tnhHbyf/SMqEKU3MjSxK5HtZIn8LpFx3NTnbN7N2sbO0lwqXA5+KTnGip3f0CM6kVjpOhRhRpIWIcJYXX91u1wuRqaksfHYfvR6PZlJ3cipKmFPWT4lDiuv7dnIZ4d3Mrn7YAbGd2yza7yUO+28vmcjP5ccBaBzRCwD4zq2y+Xwk81RJJoiOVRZzK7SPI5ayzhqLSMtKoHeMcno2mHrmwhPkrQI0cZpFIVukfF0tsQC8HXuXo5ay1i242t6RSdxedpgekYnhTbIJvZLyVFe272RcpcdnaJhUrcMHB5Xm03QGsI/XT7VEk2+rZKfio+QVVFEnrWcQQmdW2T2lBCnS5IWIdoJrUbD6NReDE/uzlfH9vJt7n72lRfwlx/XMSi+E7/tPoiOEbGhDvO0OD1u/nnwp8DsoI6WGG7qM4oInYFvcveFOLrm15CB3Satnit7nIlZq+On4qNYPS425GfRIypRZhWJVk+SFiHaEVf1JnxReiNjO5zBnrJ8DleV8GPxEX4qPsLQxK5M7NSHWKMl7BYa21mSy1v7vqfAXgnAuA5nMCVtCAatrs4P87Ym2IHdSeYoxnToxY6SY+RUlXKgopAlv3zFTX1Gyb5WotWSpEWIdsqs0zMooROXdx/E2gM/kGsrZ0vhIX4ozKF7VDx/6JVJlzD48Cp32vl71g+BqcuxBjPX9BpORkKn0AYWBvQaLYMSOpNqjubH4iPk2Sp4avu/ubTrQM7v0g9tOxisLcKLJC1CtHNJ5iiGJXWlxGFlZ2kuxQ4rByqKeHr7Z4xMSWNip76kWqJDHWYtNreLz4/s5PMju7B73CjA+I7pXNZtEKZ2vCZNY6RYohlrtHDMWsbPJcf458Gf+LnkKNPSzybJHBnq8IQIkKRFCAFAnNHCWclpFNgr2VOWT6nTxn9z9/Nt7n4y4jsxtuMZ9I3tgCbEg1ntHhffHNvHJzk7qHI7AOgaGccfemWSFpUY0tjCmVGrY2qv4ewuy+ft/VvYX17I49v+xe97DmNkclq7HsQsWg9JWoQQAYqikGyOIskUSZfIOL7Ly+Kn4iP8WP0v0RTJmNRejExJa7HtAfyb++XZKtiYd4CthTk4vL69dFLM0VzWLYMhiV3QKEq9GwE2ZpXh9khRFEampNErJolXd29gX3kBr+3ZyE/FR7im1/B2s76NaL0kaRFC1KIoCmlRiaRFJfIbWx825WextfAQhfZK3svezj+yt3NGTDLDkroxJKEL0Ybm2Vm62FHFxrwsvsndR/Fxg2kjdAYu7NKfCZ161xh34fC465wlJLNiGq7UYUWnaLix99l8fWwvnx3ZyQ+FORwoL+S69JH0i+sQ6hBFOyZJSxujqiqq016rTIhgHT8bJc5oYXyHdI5Yyyh1WDlUvVjdnrJ83t63mS6RcfSOSaF3bAo9o5OwnGLWUV0tIkatDrfXy4GKQrIqithZcozsyuIax6SYo+gemUCiKYLMpG4yULSJnTgDSafRcHZKD3aX5lFgr2TRz19ybsfe/Lb7IAxa+fgQLU/uurYo72DNx536hCYO0aZoNRq6RsZxTa9MPKrK1sJDbC04SHZlMYcqSzhUWcK66vVRYg1mOlhiSLVEE603E6EzYNHpMWr1uFUPZQ4bO0qP4fC4sbldWD1OvKpaozUFQAG6RcZj1hnoaImuNQW71hYG0g3U5GINZmb3H8fHOb+wMT+LL47u5seiw/y+5zAGygwt0cIkaRFCBC3BFMF5nftyXue+lDlt7C7NY3dZHrtK8yi0V1LqtFHqtLGzNDfoc3ewxNAjKoEe0UkMjO+Iqqp1dvnUtS6JdAM1D0VRAq1bPxUfodBRxdIdXzMmtReXpw1p1zuIi5YlSYsQ4rTEGMwMjO9Iekwyl3YdiM3tIt9eQb6tgmJHFQ6Pmyq3E6vbidPjRqfRogBlTjsGrRazVo9FZ2Bkchp94lJrdS21h4XhwkWKOYpxHc5gZ2kuhypLWJ+7jx+Lj3B598EMT+4e8pllou2TpKWdOHGcC8hYF9F4dXXLfJd3oNZx53fuR6zRUufrT2w96RGdeMqxMCL09BotGfGduKBzP97P/pF8eyWv7tnAV8f2cFXPoTLtXDQrSVraA1WtPc4FZKyLaBTplhEAPaOTuGPAeL7J3c9/ju4mq3pBwjMTunBZ90GtckFCEf4kaRFCCBE0f/Kq02gYk9qLXWV5HK4q5YeiHLYVHWZEcncu7jqAZHNUqEMVbYgkLaLF1DUd21/ems8tTk9Ddh4W4c2k0zM4oTNpUQkU2avYUZrLxvwsNuVnc2ZiF87t1Jue0UmhDlO0AZK0tFWqit7twuB2oss7SGJZITqPG73b5fvqcWOxWelfVYLG60XxetB4vWi8HiJ1RobbK3ynUTSoQKQpkqFOG6qi+P5pNLi1esyR8aTbK/BodXi0+uqvOnTWKuKsZbgMRpwGE27/SppBdFMFveaMdIG1OsHuPCwJTniLMZi5pOtASp02Pjz4Ez+XHPNNjS88RFpUAhM69qa3JSHUYYowJklLCATTKlDrWFVF53KiUQ6TfHQ/ZmsFJnsVBrsVo72KGI+Hc8sKMLidaPzn+99/GVFXIEf3062eGOsaSpdSz7G96irc/iVnnfhedAYmKAounR6XTo9Db8ShN2JCS1enFaPdjcmg4omIwWmsXiJe1pxpN4JNcETr1T0qgdsHjOdwVQlfHNnN9/nZZFUUsWr3d1i0etKUCGKsHelk0NW57YJRq6u1Jo8QIElL6NTTKqA67SheD2ZrJRFVZZiOZdN/3zYsDhtmpw2zw4bO6wFgWD2nPv6b6tLq0JijqPK4cel0uLW//kvueAY5+QfxajSoigavouDVaEjrlsG+nB2ogIKKoqr07NqPAwd/qX4MGtWL1uuhS1I3co/uRev1oPN60Hq9aD1u4owWHFVl6F0O9C4HCqC4nZgBs8tRqy6G+P/vW5sMFVDNkYxGwaE3YDeYsBlMGM0xJLod2C2R2MxRePTyi000Xn2teceXab1e4iNbZp+ltqZzRBzXpY9kcvdBfH1sH9/m7afEYeUXSvll55ekmqOJMZjoYImpsa/R6NRekrSIOknSEiqqitHlINJeRYS9igi7lajcQ4zNz8bisP3aSgJE1PFyrymScq0Gm8GM3WDCoTfg0BtJ6zOSHw/9glNvxKEzoGo0jBxyHhu3fVbrHCOHnMfeOso79hnOEVtpjbLOfUaSYyuvdWzikPPYWc+5A9dUVXQeN8N7j+SXH/+D3uNC73ZhdDkwuhx0jk6irCQXbWU5kR4nRofNlxzZKokGsB134qP7GX7cQ5fOgN0ShTEmiYH2SmwGE/bqfzaDCU5MkIQ40Yl/QHTsXaPM6/XgtFqh56AWDqztiDaYubTbQC7u2p/tuQf5LOt/HPJWkWsrJ9dWzu6yfKL1JjpYoulgiZGxaKJekrQ0M73TTkRFCRGVpURUlhBRUUqMvYrzS/ICLSbH8/9t4dFoqDJGYOzQgxxbBVajGZvRjM1gwmYwM3zYhXUmIp279qO86HAzv6sgKQpunR5vdAJlkTG1no4783w2Ht1LQUEhSUmJ6HVaDA4bw8yx7P7pS0wuByanA5PTRgdTFPayfMy2Sl8rjtuJvrwIyovoWte1//dfJuqN2M2R2MyR2M2RmBK70Nnrwm6Owm7xlbll91rRAppiwHg4DDqva2ySv8unT0wKqrGYbmf0ZHv5Mb46tpdCeyXlLjvlZXZ2l+XzY9ERBsZ3pH98R/rEpGCSFXdFNUlamom6fzsRn73GRFtFvcd4UbAazVSZIqgyWUg+Yxg/FxyiymTBbjCBojByyHnsriM5aWu0HhfxkWa0Hheq6sGh0eJJ7EhhbM0ZB5Fnns/G6v1ttG4XJlslZnsV/U2RHNm7FbPTjslpD3zVe9yBLqqo8iLfSbJ/IeOE67u1eoiKY7jBn+BEYa9OcrQaA3qHDVf190SEKdWLzu1C53KidbvQuZ3oHE5SSvLQejxovR40qhfTj1/S68h+NKqvu1PxePC6HER+sZoh1nK0HjeKqqKoXlBVonRGRjqqUFQVqssVFSL0Bka7HL6B6yigKFgMJs6uKgsMaAcFVYGo6ESGuRx4tTq8Gi1erRavRos5Mp7e1jI8Wi1eje85Y95hOh/ZjVurw6PR4tZq8Wh0aCISMNqqcOv0eHT6kN2r9Y1NGp3aC4fHjQcPCV06oui0DEnsgs3jwulxk2ur4Ji1jCJ7FSVOK+tz97E+dx8aRaFnVBK9YpLoGe3beTxS/shot9pE0uL1elm6dCnvvPMO5eXlDB06lD/96U9061bfMNPmpxbkoKlOWGymCKoiY6iKiKEqIpbO3frxw+E9WI1mVM2vu9TG9B9F0baqUIUcOqqKNy8bp9WKzmJBq9H6yjv2rvNYf9O9B6iq/ufoPYJ91rJah581YCw/5/yCyVYZ+NdVhfKj+zA57ZicDgweFzqPC0rz6xyADDAR8CoanEYzDqMFc3QCGRoFh9GC02jBYbLgMFrQao0Y7FW4DCZU//sQjVOdZCiVpUSUF/uSDLcLbfVXQ/4R0gpzqst838MIvZlhVSXo3E60rurExO3E4HFzkdtV52VqjQ07uJM67jwozadDPaHG11Ne1wolcXUdWF5Mcj3n6FlHWZ0dVb98x7nHPXRrdXh0enRGC+dUt3Z6tL6ExhIZT1+XDY9Wj1unx6034Nbq0VVWkGAt9ZXpDL5ynQGaYAaXP5lxuVyBVtXRndIBMGh1dI2Mo2tkHG6vl1RLNNkVxewoPUa+rYK95fnsLc8PnCvZHEWPqES6RcbTKSKWDpYYog2mGteraydxkEG+4a5NJC3Lly9nzZo1PPXUU6SkpPDMM88wffp0PvroIwyG0NycyohLKO/Wn2/3bMJzwhbuKV37U1V0JCRxtTeqwURldAKV0b9Os0xI6s7mH/4deKz1uDE5HQzpPpCsY/swH5fgmGyVRDmsaGyVvr/C7VWY7FVQVkDneq75m+qvbq0Ol96E02DEEhnHmai4DKbAFHBjwVE62Cvx6HS4qz9MPDo9GnMMeocNj06PV6Nt3a07/hYHuxVDeTEarxutx4PG40brcaOzO0gpyqmRbGjdLix7tjGwotCXbLh85dGKwlhb5a/HHveBM7aey/eto6y+D38/r6LxfYDrDeiNEVQ4bb6p+hoNHo2WhITO5JXm4tFo8Cpa3Ao43B569hpMlq0cr0aLWj14XVUUekUnsyf7J1SqW08UUFHoc8Ywduz7AYXqFhigT9pg9hz4wdcqA9UtNiq9ug3gQPZPaFSvb+mB6q9dkrqRm7s/UKb1ekiMTqKs+Bja6rrWeT1oPW6MioLidPiuB+g8bl8dOmzUXpt2P3XPyfqyzpmGKgoTdb4kyK014NbriYiI5UyvB7fegCeQ4Ogx5uXQyV6BW2fAo9Pj0ht997UlFp3LgesUvVg6jYa+san0jU3lwi79KLJXsa+8gIOVxeRUlgT2tcq3VdRo0YnUGekYEUMHSwxJpkjMWj0HK4sw6wzoj/sDQgb5hrewT1qcTievvPIKc+fOZexY36+2hQsXMnr0aNatW8fFF18ckrgURUGNTqiVsIjWx6PVUWXW4e50BkcNtfvORyansSl3HwaHDaPditFhpa/BwuGCg4HHvinnViJcDjR2X2uZ70OjErO9EsqLSK3j2kPqKANfyw6AV1ECfx3rDSbGqF7fh6aiwavREmm0MNzj9HUpKBpUjRY3EG2OYoi9Cs1xLXkAEaZIMuyVJ5RFMNhajsbr8X2IVneT+Ls9NF5voFzr9WBS4TcuOxqP78PS/yH5G+o2tJ7yLnWU1fXTomo0uLS+JMOt1Vd/SOqJioiloLyoejacFo9WR+eu/dl3bN9xXSe+mXIDM8azpTy/ViI4Iqk7G49LYME3iPx/x3XJerwerFYrnQaN41BB7Vl/XZO6k3tcK4Bfz869KTrh+J7d+5NXUvsPlq7pwzhcVVyrvK6B7iOHnMfmuga/n3k+m/Kz0Hg9v3Z/uV1kRCaya+e31QmOG53HQ1pqGscO7URb/dif4MSaI7FbKwKv1bmcvkHx+NZ90rtdQPV4ldKCOu9pqKclCDiv+qtLo8WjN6I3RTBKUQIJj1vna9nRxfyPHKc10NoTrzMQrTPw+5Q0NKYIstx2DtirOGyv5KitnCJ7JZVuB3vK8tlTVvt7oddosegMWHR6yp12Ui3RxBkjiDOaiTNYiDaY0CiaWq8TrU/Yf6Lu2rWLqqoqRo4cGSiLjo6mX79+bN68OWRJi2g9ag9aDH7AoqrR4jBH4jBHAtAzOY0D8bX/ph+R1J2NWz8NzJDSu93o3U76de3Hwbws9E5HYIxNilZPRVUp2uO6N7RuF3qPG6W6lUGjqmjcTvRuJ9iriKwjtvq6tOocmAx1thB1rOfY+ro96uv48mg0eBStr5VCo8UYEUMFBLogPNUfTMnRSRyylfn+QtfpcWv1nJHag5+rigPH+P9Sz0zpycaju2tda2Sn3vz4Q80P78SBYzjsrj1I1RudgLOiADxu37+AYO4Fpc4BsI25n5qNovjGxWh1uKrXOnIndafwhPrrMPhcdqu1JwKMPG7MGACqisbjZkRSN37c/nl1cuNLcvp07Ud2/sHjuu1c6N1OkvUmyqpKfeXVrWg6txO924VSPflA7/Wgd1jBYaX20Hyf9JO8zX7V/8DXCuTUG8iNiOaoOZJccwSFBhNFBgOFOj1WrQ6X10OZ00aZ08Yxa+1ZkHEaHQ9lTiLqhC4m0fooamsact4In332Gbfffjs//vgjJtOvN9wdd9yB3W7nxRdfDOp8P/zwA6qqoteffLS6qqq43W50Oh1KPc33Kip2h61WudFoxtHA8mCODedz2B1WVFVFURQUlNC9lzrGPRh1+lrldZUFyoOJ42TXU9VAK4aiqhi0OpzHfWgqgF5nwOV1Vx9T/YSqotNqcXtqfyjptFrcJ8xa02n8Zb56V6tvZ71Gh8vt9JUdd7zBYMLpcVcPJPV9aBh1BhxOa+AcDXqPwdRHCO9TFRVVVTGbIlr9z1FQdR2SOKzVizB5UVAwGoy43C6oXv+J6nWhdIoGj9cTeAy+nwEN+Ma1VXe5NYSKgkdRfK2WJ3z14vuqALE6I5rTbBk3Go307l3niCjRRMK+pcVm8/3QnDh2xWg0UlZWe2DmqfgTkPoSkeOPO9V4GQUFi6muVVYIqrw9nCPCVFcbQgjeSz2zEuoqr/fYYOII4noApjr64uubR1Hf3VlXeX3HGuv5y7OuOCz1fQ+DqKcmqdNWcq+HJL5g6jokcdS+R4z1jC9pqg8npQnPJUIv7L+X/tYVp9NZo6XF4XBgNge/iuWQIfWNMhBCCCFEKIX9yKMOHXyTEPPzaw6+ys/PJzW1vmFiQgghhAg3YZ+09OnTh8jISDZt2hQoKy8vZ8eOHQwbVt/uPEIIIYQIN2HfPWQwGJg6dSoLFiwgPj6eTp068cwzz5CamsrEiRNPfQIhhBBChIWwT1oAZs+ejdvtZt68edjtdjIzM1m1alXIFpYTQgghRNML+ynPQgghhGgfwn5MixBCCCHaB0lahBBCCBEWJGkRQgghRFiQpEUIIYQQYUGSFiGEEEKEBUlahBBCCBEWJGkRQgghRFiQpOUUjhw5Qu/evWv9e+eddwDYuXMnU6dOZfDgwYwbN45Vq1aFOOLWa/ny5Vx77bU1yk5Vf16vl8WLFzN69GgGDRrEDTfcwMGDB1sy7Faprrp84IEHat2nY8aMCTwvdVlTaWkpjzzyCGPGjOHMM8/k6quvZsuWLYHn5d5suFPVpdybosmo4qS++OILdeDAgWpeXp6an58f+Gez2dTi4mJ1xIgR6kMPPaTu27dP/fvf/64OHDhQ/fvf/x7qsFudV199Ve3du7c6derUQFlD6m/JkiXqWWedpX711Vfqzp071RtuuEGdOHGi6nA4QvE2WoW66lJVVXXy5Mnqc889V+M+LSoqCjwvdVnTtGnT1EmTJqmbN29W9+/frz722GNqRkaGum/fPrk3g3SyulRVuTdF05Gk5RReeOEFddKkSXU+t2LFCnX06NGqy+UKlD377LPq+eef31LhtXq5ubnqjTfeqA4ePFi94IILanzQnqr+HA6HOmTIEPWvf/1r4PmysjI1IyND/eijj1ruTbQSJ6tLt9utDhw4UF23bl2dr5W6rCk7O1tNT09Xt27dGijzer3qxIkT1eeff17uzSCcqi7l3hRNSbqHTmH37t306tWrzue2bNlCZmYmOt2vWziNHDmSrKwsioqKWirEVu2XX34hJiaGDz74gEGDBtV47lT1t2vXLqqqqhg5cmTg+ejoaPr168fmzZtb7D20Fiery+zsbBwOBz179qzztVKXNcXFxfHSSy8xYMCAQJmiKKiqSllZmdybQThVXcq9KZpSm9gwsTnt2bOHpKQk/vCHP5CdnU23bt2YOXMmo0ePJjc3l/T09BrHJycnA3D06FESEhJCEXKrMmHCBCZMmFDnc6eqv9zcXAA6dOhQ65hjx441Q7St28nqcs+ePSiKwuuvv8769evRaDSMHTuWO++8k6ioKKnLE0RHRzN27NgaZZ988gmHDh3inHPOYeHChXJvNtCp6lLuTdGUpKXlJJxOJ9nZ2VRWVnLnnXfy0ksvMXDgQKZPn86GDRuw2+21dpI2Go0AOByOUIQcVk5VfzabDaDOY6R+a9q7dy8ajYZOnTqxYsUK7rvvPr7++mtmzpyJ1+uVujyFrVu38uCDD/5/e/cfU1X9P3D8eUkQEFQEp5RkoHI1QBO5XAUUd8lYIEm1AkFMiskmYYkm4KytaYSFEIKBoeAvdJXazKCcytAWhuaPJM2pwJgxRRAVSQ2B8/3DceoK/vw44O77emx34573+5zzPi/eZ/d13+9zziUgIACDwSB9839wdyylb4onSUZa7sPCwoLDhw/Tp08f9YRyd3ensrKSdevWYWlpSUtLi9E6HSeZtbV1t7fX1DwofpaWlsCd5LHj7446VlZW3ddQExAfH8+cOXPo378/AK6urgwePJiwsDAqKioklvexd+9eFi1axLhx40hPTwekbz6urmIpfVM8STLS8gDW1tadvgG4urpSV1fH0KFDuXTpklFZx/shQ4Z0WxtN1YPi1zFc3FWdoUOHdk8jTYRGo1E/FDp0TG9cvHhRYnkPmzdvJj4+nilTppCXl6d+aErffHT3iqX0TfEkSdJyH6dPn2b8+PFGzxsA+OOPPxg5ciQ6nY4jR47Q1tamlh08eBBnZ2e5nuUhPCh+o0ePxsbGhvLycrW8qamJU6dO4eXl1RNN7rUWLlzIO++8Y7SsoqICgJEjR0osu7BlyxaWLVtGZGQkX3zxhdGXE+mbj+Z+sZS+KZ6onr59qTdra2tT3njjDWX69OnK4cOHlXPnzikpKSmKu7u7cvr0aaWhoUHR6XRKYmKicvbsWWX79u2Kh4eHsmPHjp5ueq+UmJhodJvuw8QvPT1d8fb2Vvbu3as+v+Gll176f//8hrtjWVJSomi1WuXLL79UampqlNLSUsVgMCgJCQlqHYnlv6qqqhQ3NzclLi7O6Nkhly5dUpqamqRvPoIHxVL6pniSNIqiKD2dOPVmjY2NpKWlceDAAZqamnj++edZtGiR+g3gxIkTfPLJJ5w6dYrBgwfz9ttvM2vWrB5ude+UlJREbW0tmzZtUpc9KH5tbW2kp6ezY8cObt26hU6n46OPPmLYsGE9cQi9Rlex3L17N7m5uVRVVWFra0tISAjvv/++egGpxPJfubm5ZGRkdFn26quvkpqaKn3zIT1MLKVviidFkhYhhBBCmAS5pkUIIYQQJkGSFiGEEEKYBElahBBCCGESJGkRQgghhEmQpEUIIYQQJkGSFiGEEEKYBElahBBCCGESJGkRogdUVlaybNkyAgMDGTduHBMmTCAsLIzCwkJu375tVFer1ZKVldVDLX3yysvL0Wq1Ro9t7y5JSUkYDIZu368Q4smQX3kWopsVFxeTnJyMi4sL0dHRODs7c+vWLfbv38+nn37KgQMHyM3NRaPR9HRThRCiV5GkRYhuVFlZSXJyMj4+PmRlZdGnz7+noL+/P3q9nvnz51NUVMT06dN7sKVCCNH7yPSQEN1o7dq1mJmZsXz5cqOEpUNgYCChoaGYmXV9at5raiUqKoqoqCj1vaIoFBYWEhwczNixY5k2bRp5eXn891c7fvnlFyIiIpgwYQJ6vZ6FCxdy4cIFtby9vZ3MzEwMBgPu7u4YDAbS09ONpq/++ecfPvvsM/z9/XF3dyckJITi4uJHjsuZM2eIjY3F09MTT09P4uLiOH/+vLoPLy8vUlJSjNZpb2/Hz8+Pjz/+WF327bffEhwcjLu7O1OnTiUrK4vW1tZHbo8QoneSkRYhutG+ffuYOHEi9vb296yzYsWK/3k/6enprFu3jjlz5uDr68vJkyfJyMigpaWFuLg4du7cyeLFiwkKCiI2NpYrV66watUqwsLC+O6777C3tycvL4/CwkISExNxcnLi999/JyMjA3Nzc+Lj41EUhbi4OI4ePcr8+fMZMWIEe/bsYcGCBbS0tBAaGvpQba2uriY8PBwXFxdSU1Npa2sjJyeHmTNnsnPnTuzt7QkMDOTHH38kKSlJTejKy8upr69nxowZAKxZs4aMjAxmzZpFcnIyf/75J1lZWVy4cKFTwiOEME2StAjRTa5du8a1a9d47rnnOpXdPRqg0Wh46qmnHms/TU1NFBQUEBUVxeLFiwHw9fWlsbGRI0eO0N7ezueff46Pj4/Rr/N6enoSFBREfn4+H3zwAYcOHcLNzY3XX38dAG9vb6ysrLCxsQGgrKyMn3/+mYyMDIKCggCYPHkyN2/eJC0tjenTp3c5mnS37OxsLC0tWb9+vbrtSZMm8eKLL7J27VoSExOZMWMG27Zt47fffsPb2xuAXbt2MXz4cF544QWuX79OTk4OYWFhLF26FAA/Pz8GDhzI0qVLiY6OZtSoUY8VTyFE7yHTQ0J0k/b29i6X19TU4ObmZvSaNm3aY+/n+PHj3L59u9M2kpKSyM/Pp7q6mvr6ekJCQozKn332WcaPH69OPen1esrKyoiIiKCgoIDKykpmzZqljqAcPHgQjUaDv78/ra2t6stgMFBfX8/Zs2cfqr2//vorer0eS0tLdRs2NjZ4eXlRVlYGgE6n45lnnqGoqAiAlpYW9uzZwyuvvALAsWPHuHnzJgaDoVNb4M5UmBDC9MlIixDdxM7ODmtra2pra42WOzo6sm3bNvX96tWrOXPmzGPv5+rVqwAMGjTovuUODg6dyhwcHDh16hQAMTEx9OvXj+3bt7NixQpSU1NxdXVlyZIlTJo0iatXr6IoCp6enl3u59KlS4wZM+ah2ltcXNzltTAdx6DRaAgJCeGbb77hww8/5MCBAzQ1NalTQx3HNHfu3Hu2RQhh+iRpEaIbBQQEUFJSQnNzszoVYmFhgYeHh1pn4MCB91y/4zbou0dt/v77b/r16wdA//79AWhsbMTFxUWtc+HCBWpqarCzswOgoaGh0/br6+vVcjMzMyIjI4mMjOTy5cvs37+f3Nxc4uPjKSsrw9bWFmtrazZu3NhlW4cPH37fWHSwtbXFx8eH6OjoTmX/nV6aMWMGubm5lJeX88MPP+Dp6YmTk5PRMaelpXU5/dZVgiaEMD0yPSREN4qNjaWtrY0lS5bQ0tLSqfzWrVvqXTNd6Uh0/nuXz7Vr16isrFTfjx07FnNzc/bt22e07oYNG3jvvfdwdnZm8ODB7Nq1y6j8/PnzHD9+XB05CQ8PZ/ny5QDY29vz2muvERkZyfXr12lubsbb25sbN26gKAoeHh7q6+zZs6xevfqh79rx9vbm3LlzjBkzRt2Gu7s769evZ8+ePWo9FxcXPDw8KCoqorS0VB1lARg3bhzm5ubU1dUZtcXc3JyVK1fy119/PVRbhBC9m4y0CNGNRo0axcqVK0lMTCQ0NJQ333wTrVZLa2srx44dY9u2bTQ0NBATE9Pl+lqtFkdHR7Kzs7G1tcXMzIyvvvoKKysrtc6gQYOYPXs2GzZswMLCgokTJ1JRUcHmzZtJSEjAwsKChIQEkpOTWbBgAaGhoVy5coXs7GwGDBigjnjodDry8/NxcHBg/Pjx1NXVUVBQgLe3N4MGDcLf3x+dTse8efOYN28eI0aM4MSJE2RlZeHn53fP6am7zZs3j/DwcGJjY5k5cyZ9+/bl66+/Zu/evaxatcqobmhoKCkpKZiZmfHyyy+ry+3s7IiJiSEzM5Pm5mb0ej11dXVkZmai0WgYPXr0o/6rhBC9kEb574MbhBDdora2lq1bt1JaWkptbS2KouDk5ISvry/h4eFGUxxarZZ3332X+Ph4AE6cOEFKSgonT57EwcGBt956i6qqKqqrq9m0aRNw5zktBQUFbN26lYsXLzJs2DCioqKIiIhQt7t7927WrFnDmTNnsLGxYfLkySQkJODo6AjcuaMpJyeH77//nosXL2Jra4vBYGDhwoXqFNKNGzfIzMzkp59+4vLlywwZMoTg4GDi4uLo27dvl8deXl7O7Nmz2bhxI3q9HkC9Jfvo0aMoioKrqytz584lICDAaN3GxkamTJnC1KlTyc7O7rTtwsJCtmzZQk1NDQMGDGDSpEkkJCTw9NNPA3cuRj506BAlJSWP828TQvQwSVqEEEIIYRLkmhYhhBBCmARJWoQQQghhEiRpEUIIIYRJkKRFCCGEECZBkhYhhBBCmARJWoQQQghhEiRpEUIIIYRJkKRFCCGEECZBkhYhhBBCmARJWoQQQghhEiRpEUIIIYRJkKRFCCGEECbh/wClmtZ4e8/8tgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 563.5x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph\n",
    "glucose_level_plot = sns.displot(x=\"avg_glucose_level\", hue=\"stroke\", data=dataframe, palette=\"Set2\", kde = True)\n",
    "\n",
    "# set the plot title and labels\n",
    "glucose_level_plot.set(title='Number of people who had a stroke by glucose level')\n",
    "glucose_level_plot.set(xlabel='Glucose level')\n",
    "glucose_level_plot.set(ylabel='Number of people')\n",
    "\n",
    "# save the plot\n",
    "glucose_level_plot.savefig('glucose_level_plot.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='gold'>Accroding to the dataset, Individuals' whose average glucose level is between 50 and 100 have a higher risk of a stroke.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='silver'><i>Data Modeling</i></font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Creating a copy of the data frame</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy the dataframe\n",
    "train_set = dataframe.copy()\n",
    "\n",
    "# drop the age range column\n",
    "train_set = train_set.drop(['age_range'], axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='lightgreen'><b>In order to use Linear Regression, the values in the categorcial data should be converted to numerical values.</b></font>\n",
    "\n",
    "<font color='Silver'>That will be done by creating dictonnaries to map the categorical values to numeric values and then replacing the values into the dataframe.</font>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Train Set Values</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/4162418501.py:9: 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",
      "  train_set['work_type'] = train_set['work_type'].replace(work_type_dict)\n",
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/4162418501.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",
      "  train_set['smoking_status'] = train_set['smoking_status'].replace(smoking_status_dict)\n",
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/4162418501.py:11: 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",
      "  train_set['gender'] = train_set['gender'].replace(gender_dict)\n",
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/4162418501.py:12: 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",
      "  train_set['Residence_type'] = train_set['Residence_type'].replace(residence_type_dict)\n",
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/4162418501.py:13: 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",
      "  train_set['ever_married'] = train_set['ever_married'].replace(martial_dict)\n"
     ]
    }
   ],
   "source": [
    "# create a dictionary to map the values\n",
    "work_type_dict = {'Private': 1, 'Govt_job': 2, 'Self-employed': 3, 'children': 4, 'Never_worked': 5}\n",
    "smoking_status_dict = {'formerly smoked': 1, 'never smoked': 2, 'smokes': 3, 'Unknown': 4}\n",
    "gender_dict = {'Male': 1, 'Female': 2, 'Other': 3}\n",
    "residence_type_dict = {'Urban': 1, 'Rural': 2}\n",
    "martial_dict = {'Yes': 1, 'No': 2}\n",
    "\n",
    "# replace the values\n",
    "train_set['work_type'] = train_set['work_type'].replace(work_type_dict)\n",
    "train_set['smoking_status'] = train_set['smoking_status'].replace(smoking_status_dict)\n",
    "train_set['gender'] = train_set['gender'].replace(gender_dict)\n",
    "train_set['Residence_type'] = train_set['Residence_type'].replace(residence_type_dict)\n",
    "train_set['ever_married'] = train_set['ever_married'].replace(martial_dict)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Test Set Values</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/3380235350.py:18: 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",
      "  dataframe_test['work_type'] = dataframe_test['work_type'].replace(work_type_dict)\n",
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/3380235350.py:19: 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",
      "  dataframe_test['smoking_status'] = dataframe_test['smoking_status'].replace(smoking_status_dict)\n",
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/3380235350.py:20: 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",
      "  dataframe_test['gender'] = dataframe_test['gender'].replace(gender_dict)\n",
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/3380235350.py:21: 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",
      "  dataframe_test['Residence_type'] = dataframe_test['Residence_type'].replace(residence_type_dict)\n",
      "/var/folders/hc/74bmkd4951n2z4crcc57v9780000gn/T/ipykernel_78775/3380235350.py:22: 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",
      "  dataframe_test['ever_married'] = dataframe_test['ever_married'].replace(martial_dict)\n"
     ]
    }
   ],
   "source": [
    "# read the test set\n",
    "test_set = pd.read_csv('datasets/test_data.csv')\n",
    "test_table = pd.read_csv('datasets/test_data.csv')\n",
    "\n",
    "# Concatenating the datasets\n",
    "dataframe_test = pd.concat([test_set, test_table], axis=0, ignore_index=True)\n",
    "\n",
    "# create a dictionary to map the values\n",
    "work_type_dict = {'Private': 1, 'Govt_job': 2, 'Self-employed': 3, 'children': 4, 'Never_worked': 5}\n",
    "smoking_status_dict = {'formerly smoked': 1, 'never smoked': 2, 'smokes': 3, 'Unknown': 4}\n",
    "gender_dict = {'Male': 1, 'Female': 2, 'Other': 3}\n",
    "residence_type_dict = {'Urban': 1, 'Rural': 2}\n",
    "martial_dict = {'Yes': 1, 'No': 2}\n",
    "\n",
    "# Display the transposed DataFrame\n",
    "dataframe_test.head()\n",
    "# replace the values in dataframe_test\n",
    "dataframe_test['work_type'] = dataframe_test['work_type'].replace(work_type_dict)\n",
    "dataframe_test['smoking_status'] = dataframe_test['smoking_status'].replace(smoking_status_dict)\n",
    "dataframe_test['gender'] = dataframe_test['gender'].replace(gender_dict)\n",
    "dataframe_test['Residence_type'] = dataframe_test['Residence_type'].replace(residence_type_dict)\n",
    "dataframe_test['ever_married'] = dataframe_test['ever_married'].replace(martial_dict)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Checking if the changes were applied</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>9046</td>\n",
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       "      <td>67.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>202.21</td>\n",
       "      <td>30.96</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.50</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>2</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>2</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease  ever_married  work_type  \\\n",
       "0   9046       1  67.0             0              1             1          1   \n",
       "1  51676       2  61.0             0              0             1          3   \n",
       "2  31112       1  80.0             0              1             1          1   \n",
       "3  60182       2  49.0             0              0             1          1   \n",
       "4   1665       2  79.0             1              0             1          3   \n",
       "\n",
       "   Residence_type  avg_glucose_level    bmi  smoking_status  stroke  \n",
       "0               1             228.69  36.60               1       1  \n",
       "1               2             202.21  30.96               2       1  \n",
       "2               2             105.92  32.50               2       1  \n",
       "3               1             171.23  34.40               3       1  \n",
       "4               2             174.12  24.00               2       1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# head of the train set\n",
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender  age  hypertension  heart_disease ever_married  \\\n",
       "0   9046    Male   67             0              1          Yes   \n",
       "1  51676  Female   61             0              0          Yes   \n",
       "2  31112    Male   80             0              1          Yes   \n",
       "3  60182  Female   49             0              0          Yes   \n",
       "4   1665  Female   79             1              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \n",
       "0        Private          Urban             228.69  36.6  formerly smoked  \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked  \n",
       "2        Private          Rural             105.92  32.5     never smoked  \n",
       "3        Private          Urban             171.23  34.4           smokes  \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# head of the test set\n",
    "test_set.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='lightgray'>Predicting the Stroke Risk</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import linear regression from sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encode the categorical features in both training and test sets\n",
    "train_features = pd.get_dummies(train_set.drop('stroke', axis=1))\n",
    "test_features = pd.get_dummies(test_set)\n",
    "\n",
    "# Align the train and test datasets, ensuring they have the same columns\n",
    "train_features, test_features = train_features.align(test_features, join='left', axis=1)\n",
    "\n",
    "# Handle missing values by filling with the median (you can use mean or other strategies)\n",
    "imputer = SimpleImputer(strategy='median')\n",
    "train_features = imputer.fit_transform(train_features)\n",
    "test_features = imputer.transform(test_features)\n",
    "\n",
    "# Set the target for training\n",
    "train_target = train_set['stroke']\n",
    "\n",
    "# Create the linear regression model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Fit the model\n",
    "model.fit(train_features, train_target)\n",
    "\n",
    "# Make predictions\n",
    "predictions = model.predict(test_features)\n",
    "\n",
    "# Create a dataframe with the id and predictions\n",
    "output = pd.DataFrame({'id': test_set['id'], 'stroke': predictions})\n",
    "\n",
    "# Save the dataframe to a CSV file\n",
    "output.to_csv('stroke_predictions.csv', index=False)\n",
    "\n"
   ]
  }
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