{
"cells": [
{
"cell_type": "markdown",
"id": "8a6a621a",
"metadata": {},
"source": [
"### <font color='289C4E'>Table of contents<font><a class='anchor' id='top'></a>\n",
"1. [Data Cleaning](#Data-cleaning)\n",
"2. [Data Scaling](#Data-scaling)\n",
"3. [Data Transformation](#Data-Transformation)\n",
"4. [Data Reduction](#Data-reduction)"
]
},
{
"cell_type": "markdown",
"id": "0d0582c1",
"metadata": {},
"source": [
"# Data preprocessing"
]
},
{
"cell_type": "markdown",
"id": "00afed32",
"metadata": {},
"source": [
"## Importing dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "aae84b5a",
"metadata": {},
"outputs": [],
"source": [
"# import libraries\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# Read the dataset\n",
"dataset_path = \"C:\\My\\Top-up Degree\\Data Science\\Data Science - Assignment\\Data set\\Kaggle\\Cardiovascular Diseases Risk Prediction Dataset\\CVD_cleaned.csv\"\n",
"data = pd.read_csv(dataset_path)"
]
},
{
"cell_type": "markdown",
"id": "7b1bb101",
"metadata": {},
"source": [
"## Data cleaning"
]
},
{
"cell_type": "markdown",
"id": "4de2bc46",
"metadata": {},
"source": [
"### Fixing any missing values"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6551a76e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'No missing values found'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import pandas as pd\n",
"import pandas as pd\n",
"\n",
"# Import display from IPython.display\n",
"from IPython.display import display\n",
"\n",
"# Calculate the missing data ratio for each column as percentages\n",
"missing_data_ratio = data.isnull().mean() * 100\n",
"\n",
"# Convert the Series to a DataFrame for better display\n",
"missing_data_df = pd.DataFrame({'Missing Data Ratio (%)': missing_data_ratio})\n",
"\n",
"# Check if there are any missing values in the entire dataset\n",
"if missing_data_df['Missing Data Ratio (%)'].any():\n",
" # Display the missing data ratio for each column as percentages in a scrollable DataFrame\n",
" with pd.option_context('display.max_rows', None):\n",
" display(missing_data_df)\n",
"else:\n",
" # Display a message that there are no missing values\n",
" display('No missing values found')"
]
},
{
"cell_type": "markdown",
"id": "c1ead635",
"metadata": {},
"source": [
"### Removing duplicates"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "03ea64c1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of duplicates: 80\n"
]
}
],
"source": [
"# Count duplicates\n",
"duplicate_count = data.duplicated().sum()\n",
"\n",
"# Print the count\n",
"print(\"Number of duplicates:\", duplicate_count)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c5988257",
"metadata": {},
"outputs": [],
"source": [
"# Remove duplicate rows\n",
"data = data.drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "248702e3",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of duplicates: 0\n"
]
},
{
"data": {
"text/plain": [
"(308774, 19)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Count duplicates\n",
"duplicate_count = data.duplicated().sum()\n",
"\n",
"# Print the count\n",
"print(\"Number of duplicates:\", duplicate_count)\n",
"data.shape"
]
},
{
"cell_type": "markdown",
"id": "22db0341",
"metadata": {},
"source": [
"### Dealing with outliers (Quantile Capping for columns with outliers)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5f7291ba",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(308774, 19)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lower_percentile = 1\n",
"upper_percentile = 99\n",
"\n",
"# Perform quantile capping on data_train_dia\n",
"lower_bound_dia = data[['Height_(cm)', 'Weight_(kg)', 'BMI']].quantile(lower_percentile/100)\n",
"upper_bound_dia = data[['Height_(cm)', 'Weight_(kg)', 'BMI']].quantile(upper_percentile/100)\n",
"data[['Height_(cm)', 'Weight_(kg)', 'BMI']] = data[['Height_(cm)', 'Weight_(kg)', 'BMI']].clip(lower_bound_dia, upper_bound_dia, axis=1)\n",
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "343bda42",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"\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>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>308774.000000</td>\n",
" <td>308774.000000</td>\n",
" <td>308774.000000</td>\n",
" <td>308774.000000</td>\n",
" <td>308774.000000</td>\n",
" <td>308774.000000</td>\n",
" <td>308774.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>170.625367</td>\n",
" <td>83.432232</td>\n",
" <td>28.575602</td>\n",
" <td>5.097557</td>\n",
" <td>29.834290</td>\n",
" <td>15.109517</td>\n",
" <td>6.297237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>10.382607</td>\n",
" <td>20.509949</td>\n",
" <td>6.223295</td>\n",
" <td>8.200434</td>\n",
" <td>24.877812</td>\n",
" <td>14.926912</td>\n",
" <td>8.583837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>150.000000</td>\n",
" <td>47.630000</td>\n",
" <td>18.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>163.000000</td>\n",
" <td>68.040000</td>\n",
" <td>24.210000</td>\n",
" <td>0.000000</td>\n",
" <td>12.000000</td>\n",
" <td>4.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>170.000000</td>\n",
" <td>81.650000</td>\n",
" <td>27.440000</td>\n",
" <td>1.000000</td>\n",
" <td>30.000000</td>\n",
" <td>12.000000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>178.000000</td>\n",
" <td>95.250000</td>\n",
" <td>31.850000</td>\n",
" <td>6.000000</td>\n",
" <td>30.000000</td>\n",
" <td>20.000000</td>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>193.000000</td>\n",
" <td>147.420000</td>\n",
" <td>49.492700</td>\n",
" <td>30.000000</td>\n",
" <td>120.000000</td>\n",
" <td>128.000000</td>\n",
" <td>128.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Height_(cm) Weight_(kg) BMI Alcohol_Consumption \\\n",
"count 308774.000000 308774.000000 308774.000000 308774.000000 \n",
"mean 170.625367 83.432232 28.575602 5.097557 \n",
"std 10.382607 20.509949 6.223295 8.200434 \n",
"min 150.000000 47.630000 18.010000 0.000000 \n",
"25% 163.000000 68.040000 24.210000 0.000000 \n",
"50% 170.000000 81.650000 27.440000 1.000000 \n",
"75% 178.000000 95.250000 31.850000 6.000000 \n",
"max 193.000000 147.420000 49.492700 30.000000 \n",
"\n",
" Fruit_Consumption Green_Vegetables_Consumption \\\n",
"count 308774.000000 308774.000000 \n",
"mean 29.834290 15.109517 \n",
"std 24.877812 14.926912 \n",
"min 0.000000 0.000000 \n",
"25% 12.000000 4.000000 \n",
"50% 30.000000 12.000000 \n",
"75% 30.000000 20.000000 \n",
"max 120.000000 128.000000 \n",
"\n",
" FriedPotato_Consumption \n",
"count 308774.000000 \n",
"mean 6.297237 \n",
"std 8.583837 \n",
"min 0.000000 \n",
"25% 2.000000 \n",
"50% 4.000000 \n",
"75% 8.000000 \n",
"max 128.000000 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8b1ad678",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>General_Health</th>\n",
" <th>Checkup</th>\n",
" <th>Exercise</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Skin_Cancer</th>\n",
" <th>Other_Cancer</th>\n",
" <th>Depression</th>\n",
" <th>Diabetes</th>\n",
" <th>Arthritis</th>\n",
" <th>Sex</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Smoking_History</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Poor</td>\n",
" <td>Within the past 2 years</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Female</td>\n",
" <td>70-74</td>\n",
" <td>150.0</td>\n",
" <td>47.63</td>\n",
" <td>18.01</td>\n",
" <td>Yes</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>16.0</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>70-74</td>\n",
" <td>165.0</td>\n",
" <td>77.11</td>\n",
" <td>28.29</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>60-64</td>\n",
" <td>163.0</td>\n",
" <td>88.45</td>\n",
" <td>33.47</td>\n",
" <td>No</td>\n",
" <td>4.0</td>\n",
" <td>12.0</td>\n",
" <td>3.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Poor</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>75-79</td>\n",
" <td>180.0</td>\n",
" <td>93.44</td>\n",
" <td>28.73</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>30.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>80+</td>\n",
" <td>191.0</td>\n",
" <td>88.45</td>\n",
" <td>24.37</td>\n",
" <td>Yes</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease Skin_Cancer \\\n",
"0 Poor Within the past 2 years No No No \n",
"1 Very Good Within the past year No Yes No \n",
"2 Very Good Within the past year Yes No No \n",
"3 Poor Within the past year Yes Yes No \n",
"4 Good Within the past year No No No \n",
"\n",
" Other_Cancer Depression Diabetes Arthritis Sex Age_Category \\\n",
"0 No No No Yes Female 70-74 \n",
"1 No No Yes No Female 70-74 \n",
"2 No No Yes No Female 60-64 \n",
"3 No No Yes No Male 75-79 \n",
"4 No No No No Male 80+ \n",
"\n",
" Height_(cm) Weight_(kg) BMI Smoking_History Alcohol_Consumption \\\n",
"0 150.0 47.63 18.01 Yes 0.0 \n",
"1 165.0 77.11 28.29 No 0.0 \n",
"2 163.0 88.45 33.47 No 4.0 \n",
"3 180.0 93.44 28.73 No 0.0 \n",
"4 191.0 88.45 24.37 Yes 0.0 \n",
"\n",
" Fruit_Consumption Green_Vegetables_Consumption FriedPotato_Consumption \n",
"0 30.0 16.0 12.0 \n",
"1 30.0 0.0 4.0 \n",
"2 12.0 3.0 16.0 \n",
"3 30.0 30.0 8.0 \n",
"4 8.0 4.0 0.0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"id": "b4f827ea",
"metadata": {},
"source": [
"#### Saving the cleaned Data for visualization"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "afecdb55",
"metadata": {},
"outputs": [],
"source": [
"data.to_csv('cvd_data_cleaned.csv', index=False)"
]
},
{
"cell_type": "markdown",
"id": "83dee627",
"metadata": {},
"source": [
"## Data scaling"
]
},
{
"cell_type": "markdown",
"id": "9d2e68f2",
"metadata": {},
"source": [
"### Using Normalization (MinMaxScaling)"
]
},
{
"cell_type": "markdown",
"id": "687fccfc",
"metadata": {},
"source": [
"<div class=\"alert alert-block alert-info\">\n",
"<b>Note:</b> This can help improve the performance of some machine learning algorithms that operate on a linear space or use a distance metric, such as KNN, linear regression, or K-means.\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7c347866",
"metadata": {},
"outputs": [
{
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" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
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" <td>0.09375</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
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" <td>70-74</td>\n",
" <td>0.348837</td>\n",
" <td>0.295420</td>\n",
" <td>0.326529</td>\n",
" <td>No</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.03125</td>\n",
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" <th>2</th>\n",
" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>60-64</td>\n",
" <td>0.302326</td>\n",
" <td>0.409059</td>\n",
" <td>0.491063</td>\n",
" <td>No</td>\n",
" <td>0.133333</td>\n",
" <td>0.100000</td>\n",
" <td>0.023438</td>\n",
" <td>0.12500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Poor</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>75-79</td>\n",
" <td>0.697674</td>\n",
" <td>0.459064</td>\n",
" <td>0.340504</td>\n",
" <td>No</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.234375</td>\n",
" <td>0.06250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>80+</td>\n",
" <td>0.953488</td>\n",
" <td>0.409059</td>\n",
" <td>0.202016</td>\n",
" <td>Yes</td>\n",
" <td>0.000000</td>\n",
" <td>0.066667</td>\n",
" <td>0.031250</td>\n",
" <td>0.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease Skin_Cancer \\\n",
"0 Poor Within the past 2 years No No No \n",
"1 Very Good Within the past year No Yes No \n",
"2 Very Good Within the past year Yes No No \n",
"3 Poor Within the past year Yes Yes No \n",
"4 Good Within the past year No No No \n",
"\n",
" Other_Cancer Depression Diabetes Arthritis Sex Age_Category \\\n",
"0 No No No Yes Female 70-74 \n",
"1 No No Yes No Female 70-74 \n",
"2 No No Yes No Female 60-64 \n",
"3 No No Yes No Male 75-79 \n",
"4 No No No No Male 80+ \n",
"\n",
" Height_(cm) Weight_(kg) BMI Smoking_History Alcohol_Consumption \\\n",
"0 0.000000 0.000000 0.000000 Yes 0.000000 \n",
"1 0.348837 0.295420 0.326529 No 0.000000 \n",
"2 0.302326 0.409059 0.491063 No 0.133333 \n",
"3 0.697674 0.459064 0.340504 No 0.000000 \n",
"4 0.953488 0.409059 0.202016 Yes 0.000000 \n",
"\n",
" Fruit_Consumption Green_Vegetables_Consumption FriedPotato_Consumption \n",
"0 0.250000 0.125000 0.09375 \n",
"1 0.250000 0.000000 0.03125 \n",
"2 0.100000 0.023438 0.12500 \n",
"3 0.250000 0.234375 0.06250 \n",
"4 0.066667 0.031250 0.00000 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"# Define the numerical features\n",
"numerical_features = ['Height_(cm)', 'Weight_(kg)', 'BMI', 'Alcohol_Consumption', 'Fruit_Consumption', 'Green_Vegetables_Consumption', 'FriedPotato_Consumption']\n",
"\n",
"# create a new dataframe for minmax scaling\n",
"data_minmax_scaled = data.copy()\n",
"\n",
"# Create a MinMaxScaler object\n",
"scaler = MinMaxScaler()\n",
"\n",
"# Fit and transform the numerical features using the scaler\n",
"data_minmax_scaled[numerical_features] = scaler.fit_transform(data_minmax_scaled[numerical_features])\n",
"\n",
"data_minmax_scaled.head()"
]
},
{
"cell_type": "markdown",
"id": "7fe1c75f",
"metadata": {},
"source": [
"### Using standardization"
]
},
{
"cell_type": "markdown",
"id": "42aa8620",
"metadata": {},
"source": [
"<div class=\"alert alert-block alert-info\">\n",
"<b>Note:</b> This can help reduce the effect of outliers and improve the performance of some machine learning algorithms that assume normality, such as SVM, logistic regression, or PCA\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "99b8ea8e",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>General_Health</th>\n",
" <th>Checkup</th>\n",
" <th>Exercise</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Skin_Cancer</th>\n",
" <th>Other_Cancer</th>\n",
" <th>Depression</th>\n",
" <th>Diabetes</th>\n",
" <th>Arthritis</th>\n",
" <th>Sex</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Smoking_History</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Poor</td>\n",
" <td>Within the past 2 years</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Female</td>\n",
" <td>70-74</td>\n",
" <td>-1.986534</td>\n",
" <td>-1.745606</td>\n",
" <td>-1.697753</td>\n",
" <td>Yes</td>\n",
" <td>-0.621621</td>\n",
" <td>0.006661</td>\n",
" <td>0.059656</td>\n",
" <td>0.664362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>70-74</td>\n",
" <td>-0.541808</td>\n",
" <td>-0.308252</td>\n",
" <td>-0.045893</td>\n",
" <td>No</td>\n",
" <td>-0.621621</td>\n",
" <td>0.006661</td>\n",
" <td>-1.012235</td>\n",
" <td>-0.267624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>60-64</td>\n",
" <td>-0.734438</td>\n",
" <td>0.244651</td>\n",
" <td>0.786465</td>\n",
" <td>No</td>\n",
" <td>-0.133842</td>\n",
" <td>-0.716876</td>\n",
" <td>-0.811255</td>\n",
" <td>1.130355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Poor</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>75-79</td>\n",
" <td>0.902919</td>\n",
" <td>0.487948</td>\n",
" <td>0.024810</td>\n",
" <td>No</td>\n",
" <td>-0.621621</td>\n",
" <td>0.006661</td>\n",
" <td>0.997561</td>\n",
" <td>0.198369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>80+</td>\n",
" <td>1.962384</td>\n",
" <td>0.244651</td>\n",
" <td>-0.675785</td>\n",
" <td>Yes</td>\n",
" <td>-0.621621</td>\n",
" <td>-0.877663</td>\n",
" <td>-0.744262</td>\n",
" <td>-0.733617</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease Skin_Cancer \\\n",
"0 Poor Within the past 2 years No No No \n",
"1 Very Good Within the past year No Yes No \n",
"2 Very Good Within the past year Yes No No \n",
"3 Poor Within the past year Yes Yes No \n",
"4 Good Within the past year No No No \n",
"\n",
" Other_Cancer Depression Diabetes Arthritis Sex Age_Category \\\n",
"0 No No No Yes Female 70-74 \n",
"1 No No Yes No Female 70-74 \n",
"2 No No Yes No Female 60-64 \n",
"3 No No Yes No Male 75-79 \n",
"4 No No No No Male 80+ \n",
"\n",
" Height_(cm) Weight_(kg) BMI Smoking_History Alcohol_Consumption \\\n",
"0 -1.986534 -1.745606 -1.697753 Yes -0.621621 \n",
"1 -0.541808 -0.308252 -0.045893 No -0.621621 \n",
"2 -0.734438 0.244651 0.786465 No -0.133842 \n",
"3 0.902919 0.487948 0.024810 No -0.621621 \n",
"4 1.962384 0.244651 -0.675785 Yes -0.621621 \n",
"\n",
" Fruit_Consumption Green_Vegetables_Consumption FriedPotato_Consumption \n",
"0 0.006661 0.059656 0.664362 \n",
"1 0.006661 -1.012235 -0.267624 \n",
"2 -0.716876 -0.811255 1.130355 \n",
"3 0.006661 0.997561 0.198369 \n",
"4 -0.877663 -0.744262 -0.733617 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# create a new dataframe for standard scaling\n",
"data_standard_scaled = data.copy()\n",
"\n",
"# Define the numerical features\n",
"numerical_features = ['Height_(cm)', 'Weight_(kg)', 'BMI', 'Alcohol_Consumption', 'Fruit_Consumption', 'Green_Vegetables_Consumption', 'FriedPotato_Consumption']\n",
"\n",
"# Create a StandardScaler object\n",
"scaler = StandardScaler()\n",
"\n",
"# Fit and transform the numerical features using the scaler\n",
"data_standard_scaled[numerical_features] = scaler.fit_transform(data_standard_scaled[numerical_features])\n",
"\n",
"data_standard_scaled.head()"
]
},
{
"cell_type": "markdown",
"id": "c9b606d7",
"metadata": {},
"source": [
"### Using Robust Scaling technique"
]
},
{
"cell_type": "markdown",
"id": "c992841a",
"metadata": {},
"source": [
"<div class=\"alert alert-block alert-info\">\n",
"<b>Note:</b> This can help improve the performance of some machine learning algorithms that are sensitive to outliers, such as DBSCAN, linear models with regularization, or neural networks.\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "cdb11ee9",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>General_Health</th>\n",
" <th>Checkup</th>\n",
" <th>Exercise</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Skin_Cancer</th>\n",
" <th>Other_Cancer</th>\n",
" <th>Depression</th>\n",
" <th>Diabetes</th>\n",
" <th>Arthritis</th>\n",
" <th>Sex</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Smoking_History</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Poor</td>\n",
" <td>Within the past 2 years</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Female</td>\n",
" <td>70-74</td>\n",
" <td>-1.333333</td>\n",
" <td>-1.250276</td>\n",
" <td>-1.234293</td>\n",
" <td>Yes</td>\n",
" <td>-0.166667</td>\n",
" <td>0.000000</td>\n",
" <td>0.2500</td>\n",
" <td>1.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>70-74</td>\n",
" <td>-0.333333</td>\n",
" <td>-0.166850</td>\n",
" <td>0.111257</td>\n",
" <td>No</td>\n",
" <td>-0.166667</td>\n",
" <td>0.000000</td>\n",
" <td>-0.7500</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Very Good</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>60-64</td>\n",
" <td>-0.466667</td>\n",
" <td>0.249908</td>\n",
" <td>0.789267</td>\n",
" <td>No</td>\n",
" <td>0.500000</td>\n",
" <td>-1.000000</td>\n",
" <td>-0.5625</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Poor</td>\n",
" <td>Within the past year</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>75-79</td>\n",
" <td>0.666667</td>\n",
" <td>0.433297</td>\n",
" <td>0.168848</td>\n",
" <td>No</td>\n",
" <td>-0.166667</td>\n",
" <td>0.000000</td>\n",
" <td>1.1250</td>\n",
" <td>0.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Good</td>\n",
" <td>Within the past year</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>80+</td>\n",
" <td>1.400000</td>\n",
" <td>0.249908</td>\n",
" <td>-0.401832</td>\n",
" <td>Yes</td>\n",
" <td>-0.166667</td>\n",
" <td>-1.222222</td>\n",
" <td>-0.5000</td>\n",
" <td>-0.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease Skin_Cancer \\\n",
"0 Poor Within the past 2 years No No No \n",
"1 Very Good Within the past year No Yes No \n",
"2 Very Good Within the past year Yes No No \n",
"3 Poor Within the past year Yes Yes No \n",
"4 Good Within the past year No No No \n",
"\n",
" Other_Cancer Depression Diabetes Arthritis Sex Age_Category \\\n",
"0 No No No Yes Female 70-74 \n",
"1 No No Yes No Female 70-74 \n",
"2 No No Yes No Female 60-64 \n",
"3 No No Yes No Male 75-79 \n",
"4 No No No No Male 80+ \n",
"\n",
" Height_(cm) Weight_(kg) BMI Smoking_History Alcohol_Consumption \\\n",
"0 -1.333333 -1.250276 -1.234293 Yes -0.166667 \n",
"1 -0.333333 -0.166850 0.111257 No -0.166667 \n",
"2 -0.466667 0.249908 0.789267 No 0.500000 \n",
"3 0.666667 0.433297 0.168848 No -0.166667 \n",
"4 1.400000 0.249908 -0.401832 Yes -0.166667 \n",
"\n",
" Fruit_Consumption Green_Vegetables_Consumption FriedPotato_Consumption \n",
"0 0.000000 0.2500 1.333333 \n",
"1 0.000000 -0.7500 0.000000 \n",
"2 -1.000000 -0.5625 2.000000 \n",
"3 0.000000 1.1250 0.666667 \n",
"4 -1.222222 -0.5000 -0.666667 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Import pandas and sklearn libraries\n",
"import pandas as pd\n",
"from sklearn.preprocessing import RobustScaler\n",
"\n",
"# create a new dataframe for standard scaling\n",
"data_robust_scaled = data.copy()\n",
"\n",
"# Define the numerical features\n",
"numerical_features = ['Height_(cm)', 'Weight_(kg)', 'BMI', 'Alcohol_Consumption', 'Fruit_Consumption', 'Green_Vegetables_Consumption', 'FriedPotato_Consumption']\n",
"\n",
"# Create a RobustScaler object\n",
"scaler = RobustScaler()\n",
"\n",
"# Fit and transform the numerical features using the scaler\n",
"data_robust_scaled[numerical_features] = scaler.fit_transform(data_robust_scaled[numerical_features])\n",
"\n",
"data_robust_scaled.head()"
]
},
{
"cell_type": "markdown",
"id": "2b60c5fc",
"metadata": {},
"source": [
"### Using the chosen scaling technique"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "473e8878",
"metadata": {},
"outputs": [],
"source": [
"# Overwrite the original data dataframe with the scaled dataframe\n",
"data = data_minmax_scaled"
]
},
{
"cell_type": "markdown",
"id": "e706df4e",
"metadata": {},
"source": [
"## Data Transformation"
]
},
{
"cell_type": "markdown",
"id": "c9b959c9",
"metadata": {},
"source": [
"### Checking the abnormal data before transforming them"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5a28862c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Very Good 110351\n",
"Good 95355\n",
"Excellent 55929\n",
"Fair 35808\n",
"Poor 11331\n",
"Name: General_Health, dtype: int64\n",
"\n",
"Within the past year 239295\n",
"Within the past 2 years 37210\n",
"Within the past 5 years 17442\n",
"5 or more years ago 13420\n",
"Never 1407\n",
"Name: Checkup, dtype: int64\n",
"\n",
"Yes 239305\n",
"No 69469\n",
"Name: Exercise, dtype: int64\n",
"\n",
"No 283803\n",
"Yes 24971\n",
"Name: Heart_Disease, dtype: int64\n",
"\n",
"No 278782\n",
"Yes 29992\n",
"Name: Skin_Cancer, dtype: int64\n",
"\n",
"No 278897\n",
"Yes 29877\n",
"Name: Other_Cancer, dtype: int64\n",
"\n",
"No 246875\n",
"Yes 61899\n",
"Name: Depression, dtype: int64\n",
"\n",
"No 259062\n",
"Yes 40170\n",
"No, pre-diabetes or borderline diabetes 6896\n",
"Yes, but female told only during pregnancy 2646\n",
"Name: Diabetes, dtype: int64\n",
"\n",
"No 207711\n",
"Yes 101063\n",
"Name: Arthritis, dtype: int64\n",
"\n",
"Female 160155\n",
"Male 148619\n",
"Name: Sex, dtype: int64\n",
"\n",
"65-69 33425\n",
"60-64 32409\n",
"70-74 31099\n",
"55-59 28048\n",
"50-54 25090\n",
"80+ 22269\n",
"40-44 21587\n",
"45-49 20963\n",
"75-79 20699\n",
"35-39 20598\n",
"18-24 18670\n",
"30-34 18425\n",
"25-29 15492\n",
"Name: Age_Category, dtype: int64\n",
"\n",
"No 183516\n",
"Yes 125258\n",
"Name: Smoking_History, dtype: int64\n",
"\n"
]
}
],
"source": [
"cols = ['General_Health', 'Checkup', 'Exercise','Heart_Disease','Skin_Cancer','Other_Cancer','Depression','Diabetes','Arthritis','Sex','Age_Category','Smoking_History']\n",
"for i in cols:\n",
" print(data[i].value_counts())\n",
" print()"
]
},
{
"cell_type": "markdown",
"id": "d5c057cf",
"metadata": {},
"source": [
"### Data transformation using OrdinalEncoder"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "609fc321",
"metadata": {},
"outputs": [
{
"data": {
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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>General_Health</th>\n",
" <th>Checkup</th>\n",
" <th>Exercise</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Skin_Cancer</th>\n",
" <th>Other_Cancer</th>\n",
" <th>Depression</th>\n",
" <th>Diabetes</th>\n",
" <th>Arthritis</th>\n",
" <th>Sex</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Smoking_History</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>Within the past 2 years</td>\n",
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" <td>0.250000</td>\n",
" <td>0.125000</td>\n",
" <td>0.093750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.0</td>\n",
" <td>Within the past year</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Yes</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>70-74</td>\n",
" <td>0.348837</td>\n",
" <td>0.295420</td>\n",
" <td>0.326529</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.031250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>Within the past year</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Yes</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>60-64</td>\n",
" <td>0.302326</td>\n",
" <td>0.409059</td>\n",
" <td>0.491063</td>\n",
" <td>0.0</td>\n",
" <td>0.133333</td>\n",
" <td>0.100000</td>\n",
" <td>0.023438</td>\n",
" <td>0.125000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>Within the past year</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Yes</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>75-79</td>\n",
" <td>0.697674</td>\n",
" <td>0.459064</td>\n",
" <td>0.340504</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.234375</td>\n",
" <td>0.062500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>Within the past year</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>80+</td>\n",
" <td>0.953488</td>\n",
" <td>0.409059</td>\n",
" <td>0.202016</td>\n",
" <td>1.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.066667</td>\n",
" <td>0.031250</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2.0</td>\n",
" <td>Within the past year</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>60-64</td>\n",
" <td>0.767442</td>\n",
" <td>1.000000</td>\n",
" <td>0.892554</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.100000</td>\n",
" <td>0.093750</td>\n",
" <td>0.093750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.0</td>\n",
" <td>Within the past year</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>No</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>60-64</td>\n",
" <td>0.581395</td>\n",
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" <td>1.0</td>\n",
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" <td>0.133333</td>\n",
" <td>0.062500</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2.0</td>\n",
" <td>Within the past year</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>No</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>65-69</td>\n",
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" <td>0.100000</td>\n",
" <td>0.250000</td>\n",
" <td>0.062500</td>\n",
" <td>0.062500</td>\n",
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" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>65-69</td>\n",
" <td>0.302326</td>\n",
" <td>0.249925</td>\n",
" <td>0.300165</td>\n",
" <td>1.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.100000</td>\n",
" <td>0.093750</td>\n",
" <td>0.031250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1.0</td>\n",
" <td>Within the past year</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Yes</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>70-74</td>\n",
" <td>0.302326</td>\n",
" <td>0.440926</td>\n",
" <td>0.529180</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.100000</td>\n",
" <td>0.093750</td>\n",
" <td>0.007812</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease \\\n",
"0 0.0 Within the past 2 years 0.0 0.0 \n",
"1 3.0 Within the past year 0.0 1.0 \n",
"2 3.0 Within the past year 1.0 0.0 \n",
"3 0.0 Within the past year 1.0 1.0 \n",
"4 2.0 Within the past year 0.0 0.0 \n",
"5 2.0 Within the past year 0.0 0.0 \n",
"6 1.0 Within the past year 1.0 1.0 \n",
"7 2.0 Within the past year 1.0 0.0 \n",
"8 1.0 Within the past year 0.0 0.0 \n",
"9 1.0 Within the past year 0.0 0.0 \n",
"\n",
" Skin_Cancer Other_Cancer Depression Diabetes Arthritis Sex \\\n",
"0 0.0 0.0 0.0 No 1.0 0.0 \n",
"1 0.0 0.0 0.0 Yes 0.0 0.0 \n",
"2 0.0 0.0 0.0 Yes 0.0 0.0 \n",
"3 0.0 0.0 0.0 Yes 0.0 1.0 \n",
"4 0.0 0.0 0.0 No 0.0 1.0 \n",
"5 0.0 0.0 1.0 No 1.0 1.0 \n",
"6 0.0 0.0 0.0 No 1.0 1.0 \n",
"7 0.0 0.0 0.0 No 1.0 0.0 \n",
"8 0.0 0.0 1.0 No 0.0 0.0 \n",
"9 0.0 0.0 0.0 Yes 1.0 0.0 \n",
"\n",
" Age_Category Height_(cm) Weight_(kg) BMI Smoking_History \\\n",
"0 70-74 0.000000 0.000000 0.000000 1.0 \n",
"1 70-74 0.348837 0.295420 0.326529 0.0 \n",
"2 60-64 0.302326 0.409059 0.491063 0.0 \n",
"3 75-79 0.697674 0.459064 0.340504 0.0 \n",
"4 80+ 0.953488 0.409059 0.202016 1.0 \n",
"5 60-64 0.767442 1.000000 0.892554 0.0 \n",
"6 60-64 0.581395 0.222668 0.150241 1.0 \n",
"7 65-69 0.348837 0.613589 0.696573 1.0 \n",
"8 65-69 0.302326 0.249925 0.300165 1.0 \n",
"9 70-74 0.302326 0.440926 0.529180 0.0 \n",
"\n",
" Alcohol_Consumption Fruit_Consumption Green_Vegetables_Consumption \\\n",
"0 0.000000 0.250000 0.125000 \n",
"1 0.000000 0.250000 0.000000 \n",
"2 0.133333 0.100000 0.023438 \n",
"3 0.000000 0.250000 0.234375 \n",
"4 0.000000 0.066667 0.031250 \n",
"5 0.000000 0.100000 0.093750 \n",
"6 0.000000 0.133333 0.062500 \n",
"7 0.100000 0.250000 0.062500 \n",
"8 0.000000 0.100000 0.093750 \n",
"9 0.000000 0.100000 0.093750 \n",
"\n",
" FriedPotato_Consumption \n",
"0 0.093750 \n",
"1 0.031250 \n",
"2 0.125000 \n",
"3 0.062500 \n",
"4 0.000000 \n",
"5 0.093750 \n",
"6 0.000000 \n",
"7 0.062500 \n",
"8 0.031250 \n",
"9 0.007812 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import OrdinalEncoder\n",
"\n",
"#First copy the original dataset to a new dataset before encoding\n",
"data_ordinal_encode = data.copy()\n",
"\n",
"enc = OrdinalEncoder()\n",
"data_ordinal_encode['Exercise'] = enc.fit_transform(data_ordinal_encode[['Exercise']])\n",
"data_ordinal_encode['Heart_Disease'] = enc.fit_transform(data_ordinal_encode[['Heart_Disease']])\n",
"data_ordinal_encode['Skin_Cancer'] = enc.fit_transform(data_ordinal_encode[['Skin_Cancer']])\n",
"data_ordinal_encode['Other_Cancer'] = enc.fit_transform(data_ordinal_encode[['Other_Cancer']])\n",
"data_ordinal_encode['Depression'] = enc.fit_transform(data_ordinal_encode[['Depression']])\n",
"data_ordinal_encode['Sex'] = enc.fit_transform(data_ordinal_encode[['Sex']])\n",
"data_ordinal_encode['Arthritis'] = enc.fit_transform(data_ordinal_encode[['Arthritis']])\n",
"data_ordinal_encode['Smoking_History'] = enc.fit_transform(data_ordinal_encode[['Smoking_History']])\n",
"\n",
"rank=['Poor','Fair','Good','Very Good','Excellent']\n",
"oe = OrdinalEncoder(categories=[rank])\n",
"data_ordinal_encode['General_Health']=oe.fit_transform(data_ordinal_encode[['General_Health']])\n",
"\n",
"data_ordinal_encode.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "1f3bf4d0",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>Checkup</th>\n",
" <th>Exercise</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Skin_Cancer</th>\n",
" <th>Other_Cancer</th>\n",
" <th>Depression</th>\n",
" <th>Diabetes</th>\n",
" <th>Arthritis</th>\n",
" <th>Sex</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Smoking_History</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>Within the past 2 years</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <th>1</th>\n",
" <td>3.0</td>\n",
" <td>Within the past year</td>\n",
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" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.03125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>Within the past year</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.409059</td>\n",
" <td>0.491063</td>\n",
" <td>0.0</td>\n",
" <td>0.133333</td>\n",
" <td>0.100000</td>\n",
" <td>0.023438</td>\n",
" <td>0.12500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>Within the past year</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>75-79</td>\n",
" <td>0.697674</td>\n",
" <td>0.459064</td>\n",
" <td>0.340504</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.234375</td>\n",
" <td>0.06250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>Within the past year</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.409059</td>\n",
" <td>0.202016</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease \\\n",
"0 0.0 Within the past 2 years 0.0 0.0 \n",
"1 3.0 Within the past year 0.0 1.0 \n",
"2 3.0 Within the past year 1.0 0.0 \n",
"3 0.0 Within the past year 1.0 1.0 \n",
"4 2.0 Within the past year 0.0 0.0 \n",
"\n",
" Skin_Cancer Other_Cancer Depression Diabetes Arthritis Sex \\\n",
"0 0.0 0.0 0.0 0 1.0 0.0 \n",
"1 0.0 0.0 0.0 1 0.0 0.0 \n",
"2 0.0 0.0 0.0 1 0.0 0.0 \n",
"3 0.0 0.0 0.0 1 0.0 1.0 \n",
"4 0.0 0.0 0.0 0 0.0 1.0 \n",
"\n",
" Age_Category Height_(cm) Weight_(kg) BMI Smoking_History \\\n",
"0 70-74 0.000000 0.000000 0.000000 1.0 \n",
"1 70-74 0.348837 0.295420 0.326529 0.0 \n",
"2 60-64 0.302326 0.409059 0.491063 0.0 \n",
"3 75-79 0.697674 0.459064 0.340504 0.0 \n",
"4 80+ 0.953488 0.409059 0.202016 1.0 \n",
"\n",
" Alcohol_Consumption Fruit_Consumption Green_Vegetables_Consumption \\\n",
"0 0.000000 0.250000 0.125000 \n",
"1 0.000000 0.250000 0.000000 \n",
"2 0.133333 0.100000 0.023438 \n",
"3 0.000000 0.250000 0.234375 \n",
"4 0.000000 0.066667 0.031250 \n",
"\n",
" FriedPotato_Consumption \n",
"0 0.09375 \n",
"1 0.03125 \n",
"2 0.12500 \n",
"3 0.06250 \n",
"4 0.00000 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_ordinal_encode['Diabetes'] = data_ordinal_encode['Diabetes'].replace({\n",
" 'Yes, but female told only during pregnancy':1,\n",
" 'No, pre-diabetes or borderline diabetes':0,\n",
" 'Yes':1,\n",
" 'No':0\n",
"})\n",
"data_ordinal_encode.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "3d272aa5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"65-69 33425\n",
"60-64 32409\n",
"70-74 31099\n",
"55-59 28048\n",
"50-54 25090\n",
"80+ 22269\n",
"40-44 21587\n",
"45-49 20963\n",
"75-79 20699\n",
"35-39 20598\n",
"18-24 18670\n",
"30-34 18425\n",
"25-29 15492\n",
"Name: Age_Category, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_ordinal_encode['Age_Category'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "14468652",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>General_Health</th>\n",
" <th>Checkup</th>\n",
" <th>Exercise</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Skin_Cancer</th>\n",
" <th>Other_Cancer</th>\n",
" <th>Depression</th>\n",
" <th>Diabetes</th>\n",
" <th>Arthritis</th>\n",
" <th>Sex</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Smoking_History</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>Within the past 2 years</td>\n",
" <td>0.0</td>\n",
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" <td>Within the past year</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>3.0</td>\n",
" <td>Within the past year</td>\n",
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" <td>0.023438</td>\n",
" <td>0.12500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>Within the past year</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>0.697674</td>\n",
" <td>0.459064</td>\n",
" <td>0.340504</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.234375</td>\n",
" <td>0.06250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
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" <td>0.409059</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease \\\n",
"0 0.0 Within the past 2 years 0.0 0.0 \n",
"1 3.0 Within the past year 0.0 1.0 \n",
"2 3.0 Within the past year 1.0 0.0 \n",
"3 0.0 Within the past year 1.0 1.0 \n",
"4 2.0 Within the past year 0.0 0.0 \n",
"\n",
" Skin_Cancer Other_Cancer Depression Diabetes Arthritis Sex \\\n",
"0 0.0 0.0 0.0 0 1.0 0.0 \n",
"1 0.0 0.0 0.0 1 0.0 0.0 \n",
"2 0.0 0.0 0.0 1 0.0 0.0 \n",
"3 0.0 0.0 0.0 1 0.0 1.0 \n",
"4 0.0 0.0 0.0 0 0.0 1.0 \n",
"\n",
" Age_Category Height_(cm) Weight_(kg) BMI Smoking_History \\\n",
"0 10.0 0.000000 0.000000 0.000000 1.0 \n",
"1 10.0 0.348837 0.295420 0.326529 0.0 \n",
"2 8.0 0.302326 0.409059 0.491063 0.0 \n",
"3 11.0 0.697674 0.459064 0.340504 0.0 \n",
"4 12.0 0.953488 0.409059 0.202016 1.0 \n",
"\n",
" Alcohol_Consumption Fruit_Consumption Green_Vegetables_Consumption \\\n",
"0 0.000000 0.250000 0.125000 \n",
"1 0.000000 0.250000 0.000000 \n",
"2 0.133333 0.100000 0.023438 \n",
"3 0.000000 0.250000 0.234375 \n",
"4 0.000000 0.066667 0.031250 \n",
"\n",
" FriedPotato_Consumption \n",
"0 0.09375 \n",
"1 0.03125 \n",
"2 0.12500 \n",
"3 0.06250 \n",
"4 0.00000 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create an instance of the encoder\n",
"encoder = OrdinalEncoder()\n",
"\n",
"# fit and transform the column\n",
"encoded_column = encoder.fit_transform(data_ordinal_encode['Age_Category'].values.reshape(-1, 1))\n",
"\n",
"# assign the encoded column back to the dataframe\n",
"data_ordinal_encode['Age_Category'] = encoded_column\n",
"\n",
"data_ordinal_encode.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "8e8fb07f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 -> 18-24\n",
"1 -> 25-29\n",
"2 -> 30-34\n",
"3 -> 35-39\n",
"4 -> 40-44\n",
"5 -> 45-49\n",
"6 -> 50-54\n",
"7 -> 55-59\n",
"8 -> 60-64\n",
"9 -> 65-69\n",
"10 -> 70-74\n",
"11 -> 75-79\n",
"12 -> 80+\n"
]
}
],
"source": [
"# loop through the categories and the encoded values\n",
"for category, value in zip(encoder.categories_[0], range(len(encoder.categories_[0]))):\n",
" # print the mapping\n",
" print(f\"{value} -> {category}\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "c1eddf5c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[array(['Never', 'Within the past year', 'Within the past 2 years',\n",
" 'Within the past 5 years', '5 or more years ago'], dtype=object)]\n"
]
}
],
"source": [
"# create an instance of the encoder with the specified order of categories\n",
"encoder = OrdinalEncoder(categories=[['Never', 'Within the past year', 'Within the past 2 years', 'Within the past 5 years', '5 or more years ago']])\n",
"\n",
"# fit and transform the column\n",
"encoded_column = encoder.fit_transform(data_ordinal_encode['Checkup'].values.reshape(-1, 1))\n",
"\n",
"# assign the encoded column back to the dataframe\n",
"data_ordinal_encode['Checkup'] = encoded_column\n",
"\n",
"print(encoder.categories_)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "d3cc376d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 -> Never\n",
"1 -> Within the past year\n",
"2 -> Within the past 2 years\n",
"3 -> Within the past 5 years\n",
"4 -> 5 or more years ago\n"
]
}
],
"source": [
"# loop through the categories and the encoded values\n",
"for category, value in zip(encoder.categories_[0], range(len(encoder.categories_[0]))):\n",
" # print the mapping\n",
" print(f\"{value} -> {category}\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "fd72478a",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <thead>\n",
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" <th>Age_Category</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>11.0</td>\n",
" <td>0.697674</td>\n",
" <td>0.459064</td>\n",
" <td>0.340504</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.234375</td>\n",
" <td>0.06250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease Skin_Cancer \\\n",
"0 0.0 2.0 0.0 0.0 0.0 \n",
"1 3.0 1.0 0.0 1.0 0.0 \n",
"2 3.0 1.0 1.0 0.0 0.0 \n",
"3 0.0 1.0 1.0 1.0 0.0 \n",
"4 2.0 1.0 0.0 0.0 0.0 \n",
"\n",
" Other_Cancer Depression Diabetes Arthritis Sex Age_Category \\\n",
"0 0.0 0.0 0 1.0 0.0 10.0 \n",
"1 0.0 0.0 1 0.0 0.0 10.0 \n",
"2 0.0 0.0 1 0.0 0.0 8.0 \n",
"3 0.0 0.0 1 0.0 1.0 11.0 \n",
"4 0.0 0.0 0 0.0 1.0 12.0 \n",
"\n",
" Height_(cm) Weight_(kg) BMI Smoking_History Alcohol_Consumption \\\n",
"0 0.000000 0.000000 0.000000 1.0 0.000000 \n",
"1 0.348837 0.295420 0.326529 0.0 0.000000 \n",
"2 0.302326 0.409059 0.491063 0.0 0.133333 \n",
"3 0.697674 0.459064 0.340504 0.0 0.000000 \n",
"4 0.953488 0.409059 0.202016 1.0 0.000000 \n",
"\n",
" Fruit_Consumption Green_Vegetables_Consumption FriedPotato_Consumption \n",
"0 0.250000 0.125000 0.09375 \n",
"1 0.250000 0.000000 0.03125 \n",
"2 0.100000 0.023438 0.12500 \n",
"3 0.250000 0.234375 0.06250 \n",
"4 0.066667 0.031250 0.00000 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_ordinal_encode.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "cf1a70b5",
"metadata": {},
"outputs": [],
"source": [
"# from sklearn.preprocessing import OrdinalEncoder\n",
"\n",
"# #First copy the original dataset to a new dataset before encoding\n",
"# data_ordinal_encode = data.copy()\n",
"\n",
"\n",
"# enc = OrdinalEncoder()\n",
"# data_ordinal_encode['Exercise'] = enc.fit_transform(data_ordinal_encode[['Exercise']])\n",
"# data_ordinal_encode['Heart_Disease'] = enc.fit_transform(data_ordinal_encode[['Heart_Disease']])\n",
"# data_ordinal_encode['Skin_Cancer'] = enc.fit_transform(data_ordinal_encode[['Skin_Cancer']])\n",
"# data_ordinal_encode['Other_Cancer'] = enc.fit_transform(data_ordinal_encode[['Other_Cancer']])\n",
"# data_ordinal_encode['Depression'] = enc.fit_transform(data_ordinal_encode[['Depression']])\n",
"# data_ordinal_encode['Sex'] = enc.fit_transform(data_ordinal_encode[['Sex']])\n",
"# data_ordinal_encode['Arthritis'] = enc.fit_transform(data_ordinal_encode[['Arthritis']])\n",
"# data_ordinal_encode['Smoking_History'] = enc.fit_transform(data_ordinal_encode[['Smoking_History']])\n",
"# data_ordinal_encode.head(10)\n",
"\n",
"# rank=['Poor','Fair','Good','Very Good','Excellent']\n",
"# oe = OrdinalEncoder(categories=[rank])\n",
"# data_ordinal_encode['General_Health']=oe.fit_transform(data_ordinal_encode[['General_Health']])\n",
"# data_ordinal_encode.head()\n",
"\n",
"# data_ordinal_encode['Diabetes'] = data_ordinal_encode['Diabetes'].replace({\n",
"# 'Yes, but female told only during pregnancy':1,\n",
"# 'No, pre-diabetes or borderline diabetes':0,\n",
"# 'Yes':1,\n",
"# 'No':0\n",
"# })\n",
"# data_ordinal_encode.head()\n",
"\n",
"\n",
"# # create an instance of the encoder with the specified order of categories\n",
"# encoder = OrdinalEncoder(categories=[['Never', 'Within the past year', 'Within the past 2 years', 'Within the past 5 years', '5 or more years ago']])\n",
"\n",
"# # fit and transform the column\n",
"# encoded_column = encoder.fit_transform(data_ordinal_encode['Checkup'].values.reshape(-1, 1))\n",
"\n",
"# # assign the encoded column back to the dataframe\n",
"# data_ordinal_encode['Checkup'] = encoded_column\n",
"\n",
"# print(encoder.categories_)\n",
"\n",
"# # loop through the categories and the encoded values\n",
"# for category, value in zip(encoder.categories_[0], range(len(encoder.categories_[0]))):\n",
"# # print the mapping\n",
"# print(f\"{value} -> {category}\")"
]
},
{
"cell_type": "markdown",
"id": "55dfc75a",
"metadata": {},
"source": [
"### Data transformation using Onehot Encoding"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ff70948e",
"metadata": {},
"outputs": [
{
"data": {
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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>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" <th>Exercise_Yes</th>\n",
" <th>Age_Category_25-29</th>\n",
" <th>Age_Category_30-34</th>\n",
" <th>...</th>\n",
" <th>General_Health_Good</th>\n",
" <th>General_Health_Poor</th>\n",
" <th>General_Health_Very Good</th>\n",
" <th>Diabetes_No, pre-diabetes or borderline diabetes</th>\n",
" <th>Diabetes_Yes</th>\n",
" <th>Diabetes_Yes, but female told only during pregnancy</th>\n",
" <th>Checkup_Never</th>\n",
" <th>Checkup_Within the past 2 years</th>\n",
" <th>Checkup_Within the past 5 years</th>\n",
" <th>Checkup_Within the past year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.125000</td>\n",
" <td>0.09375</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.348837</td>\n",
" <td>0.295420</td>\n",
" <td>0.326529</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.03125</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.302326</td>\n",
" <td>0.409059</td>\n",
" <td>0.491063</td>\n",
" <td>0.133333</td>\n",
" <td>0.100000</td>\n",
" <td>0.023438</td>\n",
" <td>0.12500</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.697674</td>\n",
" <td>0.459064</td>\n",
" <td>0.340504</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.234375</td>\n",
" <td>0.06250</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.953488</td>\n",
" <td>0.409059</td>\n",
" <td>0.202016</td>\n",
" <td>0.000000</td>\n",
" <td>0.066667</td>\n",
" <td>0.031250</td>\n",
" <td>0.00000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Height_(cm) Weight_(kg) BMI Alcohol_Consumption Fruit_Consumption \\\n",
"0 0.000000 0.000000 0.000000 0.000000 0.250000 \n",
"1 0.348837 0.295420 0.326529 0.000000 0.250000 \n",
"2 0.302326 0.409059 0.491063 0.133333 0.100000 \n",
"3 0.697674 0.459064 0.340504 0.000000 0.250000 \n",
"4 0.953488 0.409059 0.202016 0.000000 0.066667 \n",
"\n",
" Green_Vegetables_Consumption FriedPotato_Consumption Exercise_Yes \\\n",
"0 0.125000 0.09375 0 \n",
"1 0.000000 0.03125 0 \n",
"2 0.023438 0.12500 1 \n",
"3 0.234375 0.06250 1 \n",
"4 0.031250 0.00000 0 \n",
"\n",
" Age_Category_25-29 Age_Category_30-34 ... General_Health_Good \\\n",
"0 0 0 ... 0 \n",
"1 0 0 ... 0 \n",
"2 0 0 ... 0 \n",
"3 0 0 ... 0 \n",
"4 0 0 ... 1 \n",
"\n",
" General_Health_Poor General_Health_Very Good \\\n",
"0 1 0 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 1 0 \n",
"4 0 0 \n",
"\n",
" Diabetes_No, pre-diabetes or borderline diabetes Diabetes_Yes \\\n",
"0 0 0 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 0 1 \n",
"4 0 0 \n",
"\n",
" Diabetes_Yes, but female told only during pregnancy Checkup_Never \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Checkup_Within the past 2 years Checkup_Within the past 5 years \\\n",
"0 1 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Checkup_Within the past year \n",
"0 0 \n",
"1 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 \n",
"\n",
"[5 rows x 38 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Assuming 'data' is your original DataFrame\n",
"data_onehot = data.copy()\n",
"\n",
"# List of columns to one-hot encode\n",
"onehot_cols = [\n",
" 'Exercise', 'Age_Category', 'Heart_Disease', 'Skin_Cancer', 'Other_Cancer',\n",
" 'Depression', 'Sex', 'Arthritis', 'Smoking_History', 'General_Health',\n",
" 'Diabetes', 'Checkup'\n",
"]\n",
"\n",
"# Apply one-hot encoding using pandas.get_dummies()\n",
"data_onehot = pd.get_dummies(data_onehot, columns=onehot_cols, drop_first=True)\n",
"\n",
"# Print the one-hot encoded DataFrame\n",
"display(data_onehot.head())"
]
},
{
"cell_type": "markdown",
"id": "0bf96744",
"metadata": {},
"source": [
"## Data reduction"
]
},
{
"cell_type": "markdown",
"id": "da203f0f",
"metadata": {},
"source": [
"### Feature Selection\n",
"#### Using correlation"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "2a353aef",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>General_Health</th>\n",
" <th>Checkup</th>\n",
" <th>Exercise</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Skin_Cancer</th>\n",
" <th>Other_Cancer</th>\n",
" <th>Depression</th>\n",
" <th>Diabetes</th>\n",
" <th>Arthritis</th>\n",
" <th>Sex</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Smoking_History</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>General_Health</th>\n",
" <td>1.000000</td>\n",
" <td>0.088730</td>\n",
" <td>0.276080</td>\n",
" <td>-0.232484</td>\n",
" <td>-0.047079</td>\n",
" <td>-0.145614</td>\n",
" <td>-0.207533</td>\n",
" <td>-0.262990</td>\n",
" <td>-0.265911</td>\n",
" <td>0.018939</td>\n",
" <td>-0.167350</td>\n",
" <td>0.065967</td>\n",
" <td>-0.184094</td>\n",
" <td>-0.249477</td>\n",
" <td>-0.167538</td>\n",
" <td>0.118333</td>\n",
" <td>0.102602</td>\n",
" <td>0.119738</td>\n",
" <td>-0.031816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Checkup</th>\n",
" <td>0.088730</td>\n",
" <td>1.000000</td>\n",
" <td>0.035781</td>\n",
" <td>-0.084920</td>\n",
" <td>-0.081912</td>\n",
" <td>-0.088945</td>\n",
" <td>-0.033724</td>\n",
" <td>-0.129419</td>\n",
" <td>-0.155182</td>\n",
" <td>0.103296</td>\n",
" <td>-0.235454</td>\n",
" <td>0.096012</td>\n",
" <td>-0.009579</td>\n",
" <td>-0.062799</td>\n",
" <td>0.010875</td>\n",
" <td>0.048410</td>\n",
" <td>-0.042106</td>\n",
" <td>-0.036992</td>\n",
" <td>0.059084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exercise</th>\n",
" <td>0.276080</td>\n",
" <td>0.035781</td>\n",
" <td>1.000000</td>\n",
" <td>-0.096321</td>\n",
" <td>-0.003963</td>\n",
" <td>-0.054363</td>\n",
" <td>-0.084673</td>\n",
" <td>-0.138379</td>\n",
" <td>-0.124785</td>\n",
" <td>0.059355</td>\n",
" <td>-0.122334</td>\n",
" <td>0.091628</td>\n",
" <td>-0.088641</td>\n",
" <td>-0.156225</td>\n",
" <td>-0.093241</td>\n",
" <td>0.095028</td>\n",
" <td>0.136782</td>\n",
" <td>0.124983</td>\n",
" <td>-0.036904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Heart_Disease</th>\n",
" <td>-0.232484</td>\n",
" <td>-0.084920</td>\n",
" <td>-0.096321</td>\n",
" <td>1.000000</td>\n",
" <td>0.090835</td>\n",
" <td>0.092369</td>\n",
" <td>0.032494</td>\n",
" <td>0.172183</td>\n",
" <td>0.153891</td>\n",
" <td>0.072606</td>\n",
" <td>0.229027</td>\n",
" <td>0.016824</td>\n",
" <td>0.047409</td>\n",
" <td>0.044433</td>\n",
" <td>0.107757</td>\n",
" <td>-0.036614</td>\n",
" <td>-0.020045</td>\n",
" <td>-0.024027</td>\n",
" <td>-0.009249</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Skin_Cancer</th>\n",
" <td>-0.047079</td>\n",
" <td>-0.081912</td>\n",
" <td>-0.003963</td>\n",
" <td>0.090835</td>\n",
" <td>1.000000</td>\n",
" <td>0.150781</td>\n",
" <td>-0.013041</td>\n",
" <td>0.034466</td>\n",
" <td>0.136146</td>\n",
" <td>0.009658</td>\n",
" <td>0.272075</td>\n",
" <td>0.006679</td>\n",
" <td>-0.028367</td>\n",
" <td>-0.037468</td>\n",
" <td>0.032793</td>\n",
" <td>0.042734</td>\n",
" <td>0.024143</td>\n",
" <td>0.012894</td>\n",
" <td>-0.038945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Other_Cancer</th>\n",
" <td>-0.145614</td>\n",
" <td>-0.088945</td>\n",
" <td>-0.054363</td>\n",
" <td>0.092369</td>\n",
" <td>0.150781</td>\n",
" <td>1.000000</td>\n",
" <td>0.015861</td>\n",
" <td>0.066032</td>\n",
" <td>0.129320</td>\n",
" <td>-0.042061</td>\n",
" <td>0.234464</td>\n",
" <td>-0.044170</td>\n",
" <td>-0.020410</td>\n",
" <td>0.001875</td>\n",
" <td>0.053390</td>\n",
" <td>-0.008704</td>\n",
" <td>0.007992</td>\n",
" <td>-0.003215</td>\n",
" <td>-0.033326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Depression</th>\n",
" <td>-0.207533</td>\n",
" <td>-0.033724</td>\n",
" <td>-0.084673</td>\n",
" <td>0.032494</td>\n",
" <td>-0.013041</td>\n",
" <td>0.015861</td>\n",
" <td>1.000000</td>\n",
" <td>0.048940</td>\n",
" <td>0.121562</td>\n",
" <td>-0.141457</td>\n",
" <td>-0.103195</td>\n",
" <td>-0.093348</td>\n",
" <td>0.046350</td>\n",
" <td>0.109431</td>\n",
" <td>0.100215</td>\n",
" <td>-0.028200</td>\n",
" <td>-0.039938</td>\n",
" <td>-0.051134</td>\n",
" <td>0.018108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Diabetes</th>\n",
" <td>-0.262990</td>\n",
" <td>-0.129419</td>\n",
" <td>-0.138379</td>\n",
" <td>0.172183</td>\n",
" <td>0.034466</td>\n",
" <td>0.066032</td>\n",
" <td>0.048940</td>\n",
" <td>1.000000</td>\n",
" <td>0.133271</td>\n",
" <td>-0.000829</td>\n",
" <td>0.196006</td>\n",
" <td>-0.031031</td>\n",
" <td>0.160771</td>\n",
" <td>0.203496</td>\n",
" <td>0.054567</td>\n",
" <td>-0.113292</td>\n",
" <td>-0.018707</td>\n",
" <td>-0.028606</td>\n",
" <td>-0.002870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arthritis</th>\n",
" <td>-0.265911</td>\n",
" <td>-0.155182</td>\n",
" <td>-0.124785</td>\n",
" <td>0.153891</td>\n",
" <td>0.136146</td>\n",
" <td>0.129320</td>\n",
" <td>0.121562</td>\n",
" <td>0.133271</td>\n",
" <td>1.000000</td>\n",
" <td>-0.100047</td>\n",
" <td>0.370996</td>\n",
" <td>-0.098835</td>\n",
" <td>0.074759</td>\n",
" <td>0.140789</td>\n",
" <td>0.123128</td>\n",
" <td>-0.024968</td>\n",
" <td>-0.001983</td>\n",
" <td>-0.018803</td>\n",
" <td>-0.050994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sex</th>\n",
" <td>0.018939</td>\n",
" <td>0.103296</td>\n",
" <td>0.059355</td>\n",
" <td>0.072606</td>\n",
" <td>0.009658</td>\n",
" <td>-0.042061</td>\n",
" <td>-0.141457</td>\n",
" <td>-0.000829</td>\n",
" <td>-0.100047</td>\n",
" <td>1.000000</td>\n",
" <td>-0.060234</td>\n",
" <td>0.708497</td>\n",
" <td>0.362122</td>\n",
" <td>0.013432</td>\n",
" <td>0.073407</td>\n",
" <td>0.129311</td>\n",
" <td>-0.092486</td>\n",
" <td>-0.069169</td>\n",
" <td>0.130049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age_Category</th>\n",
" <td>-0.167350</td>\n",
" <td>-0.235454</td>\n",
" <td>-0.122334</td>\n",
" <td>0.229027</td>\n",
" <td>0.272075</td>\n",
" <td>0.234464</td>\n",
" <td>-0.103195</td>\n",
" <td>0.196006</td>\n",
" <td>0.370996</td>\n",
" <td>-0.060234</td>\n",
" <td>1.000000</td>\n",
" <td>-0.121451</td>\n",
" <td>-0.060041</td>\n",
" <td>-0.004520</td>\n",
" <td>0.133155</td>\n",
" <td>0.012833</td>\n",
" <td>0.043661</td>\n",
" <td>0.036030</td>\n",
" <td>-0.142761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Height_(cm)</th>\n",
" <td>0.065967</td>\n",
" <td>0.096012</td>\n",
" <td>0.091628</td>\n",
" <td>0.016824</td>\n",
" <td>0.006679</td>\n",
" <td>-0.044170</td>\n",
" <td>-0.093348</td>\n",
" <td>-0.031031</td>\n",
" <td>-0.098835</td>\n",
" <td>0.708497</td>\n",
" <td>-0.121451</td>\n",
" <td>1.000000</td>\n",
" <td>0.483410</td>\n",
" <td>-0.013834</td>\n",
" <td>0.052032</td>\n",
" <td>0.129726</td>\n",
" <td>-0.046750</td>\n",
" <td>-0.030994</td>\n",
" <td>0.109709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Weight_(kg)</th>\n",
" <td>-0.184094</td>\n",
" <td>-0.009579</td>\n",
" <td>-0.088641</td>\n",
" <td>0.047409</td>\n",
" <td>-0.028367</td>\n",
" <td>-0.020410</td>\n",
" <td>0.046350</td>\n",
" <td>0.160771</td>\n",
" <td>0.074759</td>\n",
" <td>0.362122</td>\n",
" <td>-0.060041</td>\n",
" <td>0.483410</td>\n",
" <td>1.000000</td>\n",
" <td>0.859020</td>\n",
" <td>0.048845</td>\n",
" <td>-0.031117</td>\n",
" <td>-0.092119</td>\n",
" <td>-0.076765</td>\n",
" <td>0.097414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BMI</th>\n",
" <td>-0.249477</td>\n",
" <td>-0.062799</td>\n",
" <td>-0.156225</td>\n",
" <td>0.044433</td>\n",
" <td>-0.037468</td>\n",
" <td>0.001875</td>\n",
" <td>0.109431</td>\n",
" <td>0.203496</td>\n",
" <td>0.140789</td>\n",
" <td>0.013432</td>\n",
" <td>-0.004520</td>\n",
" <td>-0.013834</td>\n",
" <td>0.859020</td>\n",
" <td>1.000000</td>\n",
" <td>0.025862</td>\n",
" <td>-0.110340</td>\n",
" <td>-0.078849</td>\n",
" <td>-0.072224</td>\n",
" <td>0.049331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Smoking_History</th>\n",
" <td>-0.167538</td>\n",
" <td>0.010875</td>\n",
" <td>-0.093241</td>\n",
" <td>0.107757</td>\n",
" <td>0.032793</td>\n",
" <td>0.053390</td>\n",
" <td>0.100215</td>\n",
" <td>0.054567</td>\n",
" <td>0.123128</td>\n",
" <td>0.073407</td>\n",
" <td>0.133155</td>\n",
" <td>0.052032</td>\n",
" <td>0.048845</td>\n",
" <td>0.025862</td>\n",
" <td>1.000000</td>\n",
" <td>0.100553</td>\n",
" <td>-0.093626</td>\n",
" <td>-0.034371</td>\n",
" <td>0.035824</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alcohol_Consumption</th>\n",
" <td>0.118333</td>\n",
" <td>0.048410</td>\n",
" <td>0.095028</td>\n",
" <td>-0.036614</td>\n",
" <td>0.042734</td>\n",
" <td>-0.008704</td>\n",
" <td>-0.028200</td>\n",
" <td>-0.113292</td>\n",
" <td>-0.024968</td>\n",
" <td>0.129311</td>\n",
" <td>0.012833</td>\n",
" <td>0.129726</td>\n",
" <td>-0.031117</td>\n",
" <td>-0.110340</td>\n",
" <td>0.100553</td>\n",
" <td>1.000000</td>\n",
" <td>-0.012542</td>\n",
" <td>0.060088</td>\n",
" <td>0.020503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fruit_Consumption</th>\n",
" <td>0.102602</td>\n",
" <td>-0.042106</td>\n",
" <td>0.136782</td>\n",
" <td>-0.020045</td>\n",
" <td>0.024143</td>\n",
" <td>0.007992</td>\n",
" <td>-0.039938</td>\n",
" <td>-0.018707</td>\n",
" <td>-0.001983</td>\n",
" <td>-0.092486</td>\n",
" <td>0.043661</td>\n",
" <td>-0.046750</td>\n",
" <td>-0.092119</td>\n",
" <td>-0.078849</td>\n",
" <td>-0.093626</td>\n",
" <td>-0.012542</td>\n",
" <td>1.000000</td>\n",
" <td>0.270426</td>\n",
" <td>-0.060302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <td>0.119738</td>\n",
" <td>-0.036992</td>\n",
" <td>0.124983</td>\n",
" <td>-0.024027</td>\n",
" <td>0.012894</td>\n",
" <td>-0.003215</td>\n",
" <td>-0.051134</td>\n",
" <td>-0.028606</td>\n",
" <td>-0.018803</td>\n",
" <td>-0.069169</td>\n",
" <td>0.036030</td>\n",
" <td>-0.030994</td>\n",
" <td>-0.076765</td>\n",
" <td>-0.072224</td>\n",
" <td>-0.034371</td>\n",
" <td>0.060088</td>\n",
" <td>0.270426</td>\n",
" <td>1.000000</td>\n",
" <td>0.003209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FriedPotato_Consumption</th>\n",
" <td>-0.031816</td>\n",
" <td>0.059084</td>\n",
" <td>-0.036904</td>\n",
" <td>-0.009249</td>\n",
" <td>-0.038945</td>\n",
" <td>-0.033326</td>\n",
" <td>0.018108</td>\n",
" <td>-0.002870</td>\n",
" <td>-0.050994</td>\n",
" <td>0.130049</td>\n",
" <td>-0.142761</td>\n",
" <td>0.109709</td>\n",
" <td>0.097414</td>\n",
" <td>0.049331</td>\n",
" <td>0.035824</td>\n",
" <td>0.020503</td>\n",
" <td>-0.060302</td>\n",
" <td>0.003209</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise \\\n",
"General_Health 1.000000 0.088730 0.276080 \n",
"Checkup 0.088730 1.000000 0.035781 \n",
"Exercise 0.276080 0.035781 1.000000 \n",
"Heart_Disease -0.232484 -0.084920 -0.096321 \n",
"Skin_Cancer -0.047079 -0.081912 -0.003963 \n",
"Other_Cancer -0.145614 -0.088945 -0.054363 \n",
"Depression -0.207533 -0.033724 -0.084673 \n",
"Diabetes -0.262990 -0.129419 -0.138379 \n",
"Arthritis -0.265911 -0.155182 -0.124785 \n",
"Sex 0.018939 0.103296 0.059355 \n",
"Age_Category -0.167350 -0.235454 -0.122334 \n",
"Height_(cm) 0.065967 0.096012 0.091628 \n",
"Weight_(kg) -0.184094 -0.009579 -0.088641 \n",
"BMI -0.249477 -0.062799 -0.156225 \n",
"Smoking_History -0.167538 0.010875 -0.093241 \n",
"Alcohol_Consumption 0.118333 0.048410 0.095028 \n",
"Fruit_Consumption 0.102602 -0.042106 0.136782 \n",
"Green_Vegetables_Consumption 0.119738 -0.036992 0.124983 \n",
"FriedPotato_Consumption -0.031816 0.059084 -0.036904 \n",
"\n",
" Heart_Disease Skin_Cancer Other_Cancer \\\n",
"General_Health -0.232484 -0.047079 -0.145614 \n",
"Checkup -0.084920 -0.081912 -0.088945 \n",
"Exercise -0.096321 -0.003963 -0.054363 \n",
"Heart_Disease 1.000000 0.090835 0.092369 \n",
"Skin_Cancer 0.090835 1.000000 0.150781 \n",
"Other_Cancer 0.092369 0.150781 1.000000 \n",
"Depression 0.032494 -0.013041 0.015861 \n",
"Diabetes 0.172183 0.034466 0.066032 \n",
"Arthritis 0.153891 0.136146 0.129320 \n",
"Sex 0.072606 0.009658 -0.042061 \n",
"Age_Category 0.229027 0.272075 0.234464 \n",
"Height_(cm) 0.016824 0.006679 -0.044170 \n",
"Weight_(kg) 0.047409 -0.028367 -0.020410 \n",
"BMI 0.044433 -0.037468 0.001875 \n",
"Smoking_History 0.107757 0.032793 0.053390 \n",
"Alcohol_Consumption -0.036614 0.042734 -0.008704 \n",
"Fruit_Consumption -0.020045 0.024143 0.007992 \n",
"Green_Vegetables_Consumption -0.024027 0.012894 -0.003215 \n",
"FriedPotato_Consumption -0.009249 -0.038945 -0.033326 \n",
"\n",
" Depression Diabetes Arthritis Sex \\\n",
"General_Health -0.207533 -0.262990 -0.265911 0.018939 \n",
"Checkup -0.033724 -0.129419 -0.155182 0.103296 \n",
"Exercise -0.084673 -0.138379 -0.124785 0.059355 \n",
"Heart_Disease 0.032494 0.172183 0.153891 0.072606 \n",
"Skin_Cancer -0.013041 0.034466 0.136146 0.009658 \n",
"Other_Cancer 0.015861 0.066032 0.129320 -0.042061 \n",
"Depression 1.000000 0.048940 0.121562 -0.141457 \n",
"Diabetes 0.048940 1.000000 0.133271 -0.000829 \n",
"Arthritis 0.121562 0.133271 1.000000 -0.100047 \n",
"Sex -0.141457 -0.000829 -0.100047 1.000000 \n",
"Age_Category -0.103195 0.196006 0.370996 -0.060234 \n",
"Height_(cm) -0.093348 -0.031031 -0.098835 0.708497 \n",
"Weight_(kg) 0.046350 0.160771 0.074759 0.362122 \n",
"BMI 0.109431 0.203496 0.140789 0.013432 \n",
"Smoking_History 0.100215 0.054567 0.123128 0.073407 \n",
"Alcohol_Consumption -0.028200 -0.113292 -0.024968 0.129311 \n",
"Fruit_Consumption -0.039938 -0.018707 -0.001983 -0.092486 \n",
"Green_Vegetables_Consumption -0.051134 -0.028606 -0.018803 -0.069169 \n",
"FriedPotato_Consumption 0.018108 -0.002870 -0.050994 0.130049 \n",
"\n",
" Age_Category Height_(cm) Weight_(kg) \\\n",
"General_Health -0.167350 0.065967 -0.184094 \n",
"Checkup -0.235454 0.096012 -0.009579 \n",
"Exercise -0.122334 0.091628 -0.088641 \n",
"Heart_Disease 0.229027 0.016824 0.047409 \n",
"Skin_Cancer 0.272075 0.006679 -0.028367 \n",
"Other_Cancer 0.234464 -0.044170 -0.020410 \n",
"Depression -0.103195 -0.093348 0.046350 \n",
"Diabetes 0.196006 -0.031031 0.160771 \n",
"Arthritis 0.370996 -0.098835 0.074759 \n",
"Sex -0.060234 0.708497 0.362122 \n",
"Age_Category 1.000000 -0.121451 -0.060041 \n",
"Height_(cm) -0.121451 1.000000 0.483410 \n",
"Weight_(kg) -0.060041 0.483410 1.000000 \n",
"BMI -0.004520 -0.013834 0.859020 \n",
"Smoking_History 0.133155 0.052032 0.048845 \n",
"Alcohol_Consumption 0.012833 0.129726 -0.031117 \n",
"Fruit_Consumption 0.043661 -0.046750 -0.092119 \n",
"Green_Vegetables_Consumption 0.036030 -0.030994 -0.076765 \n",
"FriedPotato_Consumption -0.142761 0.109709 0.097414 \n",
"\n",
" BMI Smoking_History Alcohol_Consumption \\\n",
"General_Health -0.249477 -0.167538 0.118333 \n",
"Checkup -0.062799 0.010875 0.048410 \n",
"Exercise -0.156225 -0.093241 0.095028 \n",
"Heart_Disease 0.044433 0.107757 -0.036614 \n",
"Skin_Cancer -0.037468 0.032793 0.042734 \n",
"Other_Cancer 0.001875 0.053390 -0.008704 \n",
"Depression 0.109431 0.100215 -0.028200 \n",
"Diabetes 0.203496 0.054567 -0.113292 \n",
"Arthritis 0.140789 0.123128 -0.024968 \n",
"Sex 0.013432 0.073407 0.129311 \n",
"Age_Category -0.004520 0.133155 0.012833 \n",
"Height_(cm) -0.013834 0.052032 0.129726 \n",
"Weight_(kg) 0.859020 0.048845 -0.031117 \n",
"BMI 1.000000 0.025862 -0.110340 \n",
"Smoking_History 0.025862 1.000000 0.100553 \n",
"Alcohol_Consumption -0.110340 0.100553 1.000000 \n",
"Fruit_Consumption -0.078849 -0.093626 -0.012542 \n",
"Green_Vegetables_Consumption -0.072224 -0.034371 0.060088 \n",
"FriedPotato_Consumption 0.049331 0.035824 0.020503 \n",
"\n",
" Fruit_Consumption Green_Vegetables_Consumption \\\n",
"General_Health 0.102602 0.119738 \n",
"Checkup -0.042106 -0.036992 \n",
"Exercise 0.136782 0.124983 \n",
"Heart_Disease -0.020045 -0.024027 \n",
"Skin_Cancer 0.024143 0.012894 \n",
"Other_Cancer 0.007992 -0.003215 \n",
"Depression -0.039938 -0.051134 \n",
"Diabetes -0.018707 -0.028606 \n",
"Arthritis -0.001983 -0.018803 \n",
"Sex -0.092486 -0.069169 \n",
"Age_Category 0.043661 0.036030 \n",
"Height_(cm) -0.046750 -0.030994 \n",
"Weight_(kg) -0.092119 -0.076765 \n",
"BMI -0.078849 -0.072224 \n",
"Smoking_History -0.093626 -0.034371 \n",
"Alcohol_Consumption -0.012542 0.060088 \n",
"Fruit_Consumption 1.000000 0.270426 \n",
"Green_Vegetables_Consumption 0.270426 1.000000 \n",
"FriedPotato_Consumption -0.060302 0.003209 \n",
"\n",
" FriedPotato_Consumption \n",
"General_Health -0.031816 \n",
"Checkup 0.059084 \n",
"Exercise -0.036904 \n",
"Heart_Disease -0.009249 \n",
"Skin_Cancer -0.038945 \n",
"Other_Cancer -0.033326 \n",
"Depression 0.018108 \n",
"Diabetes -0.002870 \n",
"Arthritis -0.050994 \n",
"Sex 0.130049 \n",
"Age_Category -0.142761 \n",
"Height_(cm) 0.109709 \n",
"Weight_(kg) 0.097414 \n",
"BMI 0.049331 \n",
"Smoking_History 0.035824 \n",
"Alcohol_Consumption 0.020503 \n",
"Fruit_Consumption -0.060302 \n",
"Green_Vegetables_Consumption 0.003209 \n",
"FriedPotato_Consumption 1.000000 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Calculate the correlation matrix\n",
"corr_matrix = data_ordinal_encode.corr()\n",
"\n",
"# Display the correlation matrix\n",
"display(corr_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "bf1f2555",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import matplotlib and seaborn libraries\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# Calculate the correlation matrix\n",
"corr_matrix = data_ordinal_encode.corr()\n",
"\n",
"# Create a heatmap of the correlation matrix\n",
"plt.figure(figsize=(10,10))\n",
"sns.heatmap(corr_matrix, annot=True, cmap=\"RdBu\")\n",
"plt.title(\"Correlation Matrix of Data Ordinal Encode\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "139714f2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>General_Health</th>\n",
" <th>Checkup</th>\n",
" <th>Exercise</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Skin_Cancer</th>\n",
" <th>Other_Cancer</th>\n",
" <th>Depression</th>\n",
" <th>Diabetes</th>\n",
" <th>Arthritis</th>\n",
" <th>Sex</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Smoking_History</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.125000</td>\n",
" <td>0.093750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.348837</td>\n",
" <td>0.295420</td>\n",
" <td>0.326529</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.031250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>0.302326</td>\n",
" <td>0.409059</td>\n",
" <td>0.491063</td>\n",
" <td>0.0</td>\n",
" <td>0.133333</td>\n",
" <td>0.100000</td>\n",
" <td>0.023438</td>\n",
" <td>0.125000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>0.697674</td>\n",
" <td>0.459064</td>\n",
" <td>0.340504</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.234375</td>\n",
" <td>0.062500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>12.0</td>\n",
" <td>0.953488</td>\n",
" <td>0.409059</td>\n",
" <td>0.202016</td>\n",
" <td>1.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.066667</td>\n",
" <td>0.031250</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308849</th>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.418605</td>\n",
" <td>0.340916</td>\n",
" <td>0.350669</td>\n",
" <td>0.0</td>\n",
" <td>0.133333</td>\n",
" <td>0.250000</td>\n",
" <td>0.062500</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308850</th>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>0.697674</td>\n",
" <td>0.222668</td>\n",
" <td>0.110219</td>\n",
" <td>0.0</td>\n",
" <td>0.266667</td>\n",
" <td>0.125000</td>\n",
" <td>0.468750</td>\n",
" <td>0.031250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308851</th>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.162791</td>\n",
" <td>0.136286</td>\n",
" <td>0.212180</td>\n",
" <td>1.0</td>\n",
" <td>0.133333</td>\n",
" <td>0.333333</td>\n",
" <td>0.062500</td>\n",
" <td>0.031250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308852</th>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>0.767442</td>\n",
" <td>0.318168</td>\n",
" <td>0.181687</td>\n",
" <td>0.0</td>\n",
" <td>0.100000</td>\n",
" <td>0.250000</td>\n",
" <td>0.093750</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308853</th>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.232558</td>\n",
" <td>0.336306</td>\n",
" <td>0.435160</td>\n",
" <td>0.0</td>\n",
" <td>0.033333</td>\n",
" <td>0.041667</td>\n",
" <td>0.093750</td>\n",
" <td>0.007812</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>308774 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" General_Health Checkup Exercise Heart_Disease Skin_Cancer \\\n",
"0 0.0 2.0 0.0 0.0 0.0 \n",
"1 3.0 1.0 0.0 1.0 0.0 \n",
"2 3.0 1.0 1.0 0.0 0.0 \n",
"3 0.0 1.0 1.0 1.0 0.0 \n",
"4 2.0 1.0 0.0 0.0 0.0 \n",
"... ... ... ... ... ... \n",
"308849 3.0 1.0 1.0 0.0 0.0 \n",
"308850 1.0 3.0 1.0 0.0 0.0 \n",
"308851 3.0 4.0 1.0 0.0 0.0 \n",
"308852 3.0 1.0 1.0 0.0 0.0 \n",
"308853 4.0 1.0 1.0 0.0 0.0 \n",
"\n",
" Other_Cancer Depression Diabetes Arthritis Sex Age_Category \\\n",
"0 0.0 0.0 0 1.0 0.0 10.0 \n",
"1 0.0 0.0 1 0.0 0.0 10.0 \n",
"2 0.0 0.0 1 0.0 0.0 8.0 \n",
"3 0.0 0.0 1 0.0 1.0 11.0 \n",
"4 0.0 0.0 0 0.0 1.0 12.0 \n",
"... ... ... ... ... ... ... \n",
"308849 0.0 0.0 0 0.0 1.0 1.0 \n",
"308850 0.0 0.0 1 0.0 1.0 9.0 \n",
"308851 0.0 1.0 1 0.0 0.0 2.0 \n",
"308852 0.0 0.0 0 0.0 1.0 9.0 \n",
"308853 0.0 0.0 0 0.0 0.0 5.0 \n",
"\n",
" Height_(cm) Weight_(kg) BMI Smoking_History \\\n",
"0 0.000000 0.000000 0.000000 1.0 \n",
"1 0.348837 0.295420 0.326529 0.0 \n",
"2 0.302326 0.409059 0.491063 0.0 \n",
"3 0.697674 0.459064 0.340504 0.0 \n",
"4 0.953488 0.409059 0.202016 1.0 \n",
"... ... ... ... ... \n",
"308849 0.418605 0.340916 0.350669 0.0 \n",
"308850 0.697674 0.222668 0.110219 0.0 \n",
"308851 0.162791 0.136286 0.212180 1.0 \n",
"308852 0.767442 0.318168 0.181687 0.0 \n",
"308853 0.232558 0.336306 0.435160 0.0 \n",
"\n",
" Alcohol_Consumption Fruit_Consumption Green_Vegetables_Consumption \\\n",
"0 0.000000 0.250000 0.125000 \n",
"1 0.000000 0.250000 0.000000 \n",
"2 0.133333 0.100000 0.023438 \n",
"3 0.000000 0.250000 0.234375 \n",
"4 0.000000 0.066667 0.031250 \n",
"... ... ... ... \n",
"308849 0.133333 0.250000 0.062500 \n",
"308850 0.266667 0.125000 0.468750 \n",
"308851 0.133333 0.333333 0.062500 \n",
"308852 0.100000 0.250000 0.093750 \n",
"308853 0.033333 0.041667 0.093750 \n",
"\n",
" FriedPotato_Consumption \n",
"0 0.093750 \n",
"1 0.031250 \n",
"2 0.125000 \n",
"3 0.062500 \n",
"4 0.000000 \n",
"... ... \n",
"308849 0.000000 \n",
"308850 0.031250 \n",
"308851 0.031250 \n",
"308852 0.000000 \n",
"308853 0.007812 \n",
"\n",
"[308774 rows x 19 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_ordinal_encode"
]
},
{
"cell_type": "markdown",
"id": "c77616d6",
"metadata": {},
"source": [
"## Feature importance with Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "8bd37d3b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"import numpy as np\n",
"\n",
"# Defining the features (X) and the target (y)\n",
"X = data_ordinal_encode.drop(\"Heart_Disease\", axis=1)\n",
"y = data_ordinal_encode[\"Heart_Disease\"]\n",
"\n",
"# Performing the train-test split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"\n",
"# Random Forest for feature importance\n",
"rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
"rf.fit(X_train, y_train)\n",
"\n",
"# Get feature importances\n",
"importances = rf.feature_importances_\n",
"\n",
"# Get feature names\n",
"feature_names = list(X_train.columns)\n",
"\n",
"# Sort feature importances in descending order\n",
"indices = np.argsort(importances)[::-1]\n",
"\n",
"# Plot the feature importances\n",
"plt.figure(figsize=(10, 6))\n",
"plt.title(\"Feature Importance\")\n",
"plt.bar(range(X_train.shape[1]), importances[indices])\n",
"plt.xticks(range(X_train.shape[1]), np.array(feature_names)[indices], rotation=90)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "3a61353f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" vertical-align: middle;\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>General_Health</th>\n",
" <th>Heart_Disease</th>\n",
" <th>Age_Category</th>\n",
" <th>Height_(cm)</th>\n",
" <th>Weight_(kg)</th>\n",
" <th>BMI</th>\n",
" <th>Alcohol_Consumption</th>\n",
" <th>Fruit_Consumption</th>\n",
" <th>Green_Vegetables_Consumption</th>\n",
" <th>FriedPotato_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.125000</td>\n",
" <td>0.093750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>10.0</td>\n",
" <td>0.348837</td>\n",
" <td>0.295420</td>\n",
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" <td>8.0</td>\n",
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" <td>0.409059</td>\n",
" <td>0.491063</td>\n",
" <td>0.133333</td>\n",
" <td>0.100000</td>\n",
" <td>0.023438</td>\n",
" <td>0.125000</td>\n",
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" <td>0.459064</td>\n",
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" <td>0.250000</td>\n",
" <td>0.234375</td>\n",
" <td>0.062500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>0.953488</td>\n",
" <td>0.409059</td>\n",
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" <td>0.066667</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>308849</th>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.418605</td>\n",
" <td>0.340916</td>\n",
" <td>0.350669</td>\n",
" <td>0.133333</td>\n",
" <td>0.250000</td>\n",
" <td>0.062500</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308850</th>\n",
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" <td>0.0</td>\n",
" <td>9.0</td>\n",
" <td>0.697674</td>\n",
" <td>0.222668</td>\n",
" <td>0.110219</td>\n",
" <td>0.266667</td>\n",
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" <td>0.031250</td>\n",
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" <td>0.136286</td>\n",
" <td>0.212180</td>\n",
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" <td>0.007812</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>308774 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" General_Health Heart_Disease Age_Category Height_(cm) Weight_(kg) \\\n",
"0 0.0 0.0 10.0 0.000000 0.000000 \n",
"1 3.0 1.0 10.0 0.348837 0.295420 \n",
"2 3.0 0.0 8.0 0.302326 0.409059 \n",
"3 0.0 1.0 11.0 0.697674 0.459064 \n",
"4 2.0 0.0 12.0 0.953488 0.409059 \n",
"... ... ... ... ... ... \n",
"308849 3.0 0.0 1.0 0.418605 0.340916 \n",
"308850 1.0 0.0 9.0 0.697674 0.222668 \n",
"308851 3.0 0.0 2.0 0.162791 0.136286 \n",
"308852 3.0 0.0 9.0 0.767442 0.318168 \n",
"308853 4.0 0.0 5.0 0.232558 0.336306 \n",
"\n",
" BMI Alcohol_Consumption Fruit_Consumption \\\n",
"0 0.000000 0.000000 0.250000 \n",
"1 0.326529 0.000000 0.250000 \n",
"2 0.491063 0.133333 0.100000 \n",
"3 0.340504 0.000000 0.250000 \n",
"4 0.202016 0.000000 0.066667 \n",
"... ... ... ... \n",
"308849 0.350669 0.133333 0.250000 \n",
"308850 0.110219 0.266667 0.125000 \n",
"308851 0.212180 0.133333 0.333333 \n",
"308852 0.181687 0.100000 0.250000 \n",
"308853 0.435160 0.033333 0.041667 \n",
"\n",
" Green_Vegetables_Consumption FriedPotato_Consumption \n",
"0 0.125000 0.093750 \n",
"1 0.000000 0.031250 \n",
"2 0.023438 0.125000 \n",
"3 0.234375 0.062500 \n",
"4 0.031250 0.000000 \n",
"... ... ... \n",
"308849 0.062500 0.000000 \n",
"308850 0.468750 0.031250 \n",
"308851 0.062500 0.031250 \n",
"308852 0.093750 0.000000 \n",
"308853 0.093750 0.007812 \n",
"\n",
"[308774 rows x 10 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_dropped_columns = data_ordinal_encode.copy()\n",
"data_dropped_columns = data_dropped_columns.drop(['Sex', 'Skin_Cancer','Smoking_History', 'Other_Cancer', 'Checkup', 'Depression', 'Exercise', 'Arthritis', 'Diabetes'], axis=1)\n",
"data_dropped_columns"
]
},
{
"cell_type": "markdown",
"id": "3736ed42",
"metadata": {},
"source": [
"### SelectKBest Method for feature selection"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "f3e51e5d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" feature score p-value\n",
"0 General_Health 17642.352807 0.000000e+00\n",
"9 Age_Category 17092.766758 0.000000e+00\n",
"6 Diabetes 9433.881563 0.000000e+00\n",
"7 Arthritis 7489.831691 0.000000e+00\n",
"13 Smoking_History 3627.435019 0.000000e+00\n",
"2 Exercise 2891.549432 0.000000e+00\n",
"4 Other_Cancer 2657.117300 0.000000e+00\n",
"3 Skin_Cancer 2568.860485 0.000000e+00\n",
"1 Checkup 2242.836339 0.000000e+00\n",
"8 Sex 1636.347832 0.000000e+00\n",
"11 Weight_(kg) 695.571837 4.064439e-153\n",
"12 BMI 610.820147 1.004189e-134\n",
"14 Alcohol_Consumption 414.490985 4.438285e-92\n",
"5 Depression 326.364158 6.491542e-73\n",
"16 Green_Vegetables_Consumption 178.362099 1.133331e-40\n",
"15 Fruit_Consumption 124.114486 8.053439e-29\n",
"10 Height_(cm) 87.422775 8.819184e-21\n",
"17 FriedPotato_Consumption 26.414514 2.756301e-07\n"
]
}
],
"source": [
"# Import pandas and sklearn libraries\n",
"import pandas as pd\n",
"from sklearn.feature_selection import SelectKBest, f_classif\n",
"\n",
"# Separate the features (X) and the target (y)\n",
"X = data_ordinal_encode.drop(\"Heart_Disease\", axis=1)\n",
"y = data_ordinal_encode[\"Heart_Disease\"]\n",
"\n",
"# Create a feature selector object\n",
"selector = SelectKBest(score_func=f_classif, k=\"all\")\n",
"\n",
"# Fit the selector to the data\n",
"selector.fit(X, y)\n",
"\n",
"# Get the scores and p-values of each feature\n",
"scores = selector.scores_\n",
"pvalues = selector.pvalues_\n",
"\n",
"# Create a dataframe of the feature names, scores, and p-values\n",
"feature_df = pd.DataFrame({\"feature\": X.columns, \"score\": scores, \"p-value\": pvalues})\n",
"\n",
"# Sort the dataframe by score in descending order\n",
"feature_df = feature_df.sort_values(by=\"score\", ascending=False)\n",
"\n",
"# Print the dataframe\n",
"print(feature_df)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "c2c10503",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" feature score\n",
"2 Exercise 0.061817\n",
"8 Sex 0.041916\n",
"0 General_Health 0.041514\n",
"1 Checkup 0.036737\n",
"9 Age_Category 0.036575\n",
"13 Smoking_History 0.032005\n",
"7 Arthritis 0.027391\n",
"6 Diabetes 0.013933\n",
"15 Fruit_Consumption 0.011096\n",
"16 Green_Vegetables_Consumption 0.008065\n",
"5 Depression 0.007394\n",
"3 Skin_Cancer 0.005377\n",
"14 Alcohol_Consumption 0.005152\n",
"11 Weight_(kg) 0.005096\n",
"10 Height_(cm) 0.004626\n",
"4 Other_Cancer 0.003841\n",
"12 BMI 0.003536\n",
"17 FriedPotato_Consumption 0.003392\n"
]
}
],
"source": [
"# Import pandas and sklearn libraries\n",
"import pandas as pd\n",
"from sklearn.feature_selection import SelectKBest, mutual_info_classif\n",
"\n",
"# Separate the features (X) and the target (y)\n",
"X = data_ordinal_encode.drop(\"Heart_Disease\", axis=1)\n",
"y = data_ordinal_encode[\"Heart_Disease\"]\n",
"\n",
"# Create a feature selector object\n",
"selector = SelectKBest(score_func=mutual_info_classif, k=\"all\")\n",
"\n",
"# Fit the selector to the data\n",
"selector.fit(X, y)\n",
"\n",
"# Get the scores of each feature\n",
"scores = selector.scores_\n",
"\n",
"# Create a dataframe of the feature names and scores\n",
"feature_df = pd.DataFrame({\"feature\": X.columns, \"score\": scores})\n",
"\n",
"# Sort the dataframe by score in descending order\n",
"feature_df = feature_df.sort_values(by=\"score\", ascending=False)\n",
"\n",
"# Print the dataframe\n",
"print(feature_df)"
]
},
{
"cell_type": "markdown",
"id": "7fe60ee2",
"metadata": {},
"source": [
"### Using cross validation to compare feature selection techinques"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "75a25416",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(308774, 19)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_ordinal_encode.shape"
]
},
{
"cell_type": "code",
"execution_count": 328,
"id": "ba32cb71",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SelectKBest_f_classif:\n",
"Number of features selected: 18\n",
"| feature | score | p-value |\n",
"|:-----------------------------|-----------:|-------------:|\n",
"| General_Health | 17642.4 | 0 |\n",
"| Age_Category | 17092.8 | 0 |\n",
"| Diabetes | 9433.88 | 0 |\n",
"| Arthritis | 7489.83 | 0 |\n",
"| Smoking_History | 3627.44 | 0 |\n",
"| Exercise | 2891.55 | 0 |\n",
"| Other_Cancer | 2657.12 | 0 |\n",
"| Skin_Cancer | 2568.86 | 0 |\n",
"| Checkup | 2242.84 | 0 |\n",
"| Sex | 1636.35 | 0 |\n",
"| Weight_(kg) | 695.572 | 4.06444e-153 |\n",
"| BMI | 610.82 | 1.00419e-134 |\n",
"| Alcohol_Consumption | 414.491 | 4.43828e-92 |\n",
"| Depression | 326.364 | 6.49154e-73 |\n",
"| Green_Vegetables_Consumption | 178.362 | 1.13333e-40 |\n",
"| Fruit_Consumption | 124.114 | 8.05344e-29 |\n",
"| Height_(cm) | 87.4228 | 8.81918e-21 |\n",
"| FriedPotato_Consumption | 26.4145 | 2.7563e-07 |\n",
"SelectKBest_mutual_info_classif:\n",
"Number of features selected: 18\n",
"| feature | score | p-value |\n",
"|:-----------------------------|-----------:|:----------|\n",
"| Exercise | 0.0625872 | |\n",
"| Sex | 0.0419496 | |\n",
"| General_Health | 0.0408466 | |\n",
"| Checkup | 0.0364467 | |\n",
"| Age_Category | 0.0359838 | |\n",
"| Smoking_History | 0.0313306 | |\n",
"| Arthritis | 0.0269889 | |\n",
"| Diabetes | 0.0137668 | |\n",
"| Alcohol_Consumption | 0.0136969 | |\n",
"| Depression | 0.00663824 | |\n",
"| Skin_Cancer | 0.00516473 | |\n",
"| FriedPotato_Consumption | 0.00503667 | |\n",
"| Other_Cancer | 0.00451621 | |\n",
"| Fruit_Consumption | 0.00405717 | |\n",
"| Green_Vegetables_Consumption | 0.00386828 | |\n",
"| Weight_(kg) | 0.0036666 | |\n",
"| Height_(cm) | 0.00315128 | |\n",
"| BMI | 0.00236402 | |\n",
"SelectKBest_f_regression:\n",
"Number of features selected: 18\n",
"| feature | score | p-value |\n",
"|:-----------------------------|-----------:|-------------:|\n",
"| General_Health | 17642.4 | 0 |\n",
"| Age_Category | 17092.8 | 0 |\n",
"| Diabetes | 9433.88 | 0 |\n",
"| Arthritis | 7489.83 | 0 |\n",
"| Smoking_History | 3627.44 | 0 |\n",
"| Exercise | 2891.55 | 0 |\n",
"| Other_Cancer | 2657.12 | 0 |\n",
"| Skin_Cancer | 2568.86 | 0 |\n",
"| Checkup | 2242.84 | 0 |\n",
"| Sex | 1636.35 | 0 |\n",
"| Weight_(kg) | 695.572 | 4.06444e-153 |\n",
"| BMI | 610.82 | 1.00419e-134 |\n",
"| Alcohol_Consumption | 414.491 | 4.43828e-92 |\n",
"| Depression | 326.364 | 6.49154e-73 |\n",
"| Green_Vegetables_Consumption | 178.362 | 1.13333e-40 |\n",
"| Fruit_Consumption | 124.114 | 8.05344e-29 |\n",
"| Height_(cm) | 87.4228 | 8.81918e-21 |\n",
"| FriedPotato_Consumption | 26.4145 | 2.7563e-07 |\n"
]
}
],
"source": [
"# Import pandas and sklearn libraries\n",
"import pandas as pd\n",
"from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif, f_regression\n",
"\n",
"# Separate the features (X) and the target (y)\n",
"X = data_ordinal_encode.drop(\"Heart_Disease\", axis=1)\n",
"y = data_ordinal_encode[\"Heart_Disease\"]\n",
"\n",
"# Define feature selection methods\n",
"methods = {\n",
" \"SelectKBest_f_classif\": SelectKBest(score_func=f_classif, k=\"all\"),\n",
" \"SelectKBest_mutual_info_classif\": SelectKBest(score_func=mutual_info_classif, k=\"all\"),\n",
" \"SelectKBest_f_regression\": SelectKBest(score_func=f_regression, k=\"all\")\n",
"}\n",
"\n",
"# Fit and transform each feature selection method\n",
"results = {}\n",
"for name, method in methods.items():\n",
" X_new = method.fit_transform(X, y)\n",
" scores = method.scores_\n",
" pvalues = method.pvalues_\n",
" results[name] = (X_new, scores, pvalues)\n",
"\n",
"# Print the results for each feature selection method\n",
"for name, (X_new, scores, pvalues) in results.items():\n",
" print(f\"{name}:\")\n",
" print(f\"Number of features selected: {X_new.shape[1]}\")\n",
" # Create a dataframe of the feature names, scores, and p-values\n",
" feature_df = pd.DataFrame({\"feature\": X.columns, \"score\": scores, \"p-value\": pvalues})\n",
" # Sort the dataframe by score in descending order\n",
" feature_df = feature_df.sort_values(by=\"score\", ascending=False)\n",
" # Print the dataframe with proper formatting\n",
" print(feature_df.to_markdown(index=False))"
]
},
{
"cell_type": "code",
"execution_count": 254,
"id": "24e978c2",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature Ranking (CFS):\n",
"| feature | score |\n",
"|:-----------------------------|-----------:|\n",
"| General_Health | 17642.4 |\n",
"| Age_Category | 17092.8 |\n",
"| Diabetes | 9433.88 |\n",
"| Arthritis | 7489.83 |\n",
"| Smoking_History | 3627.44 |\n",
"| Exercise | 2891.55 |\n",
"| Other_Cancer | 2657.12 |\n",
"| Skin_Cancer | 2568.86 |\n",
"| Checkup | 2242.84 |\n",
"| Sex | 1636.35 |\n",
"| Weight_(kg) | 695.572 |\n",
"| BMI | 610.82 |\n",
"| Alcohol_Consumption | 414.491 |\n",
"| Depression | 326.364 |\n",
"| Green_Vegetables_Consumption | 178.362 |\n",
"| Fruit_Consumption | 124.114 |\n",
"| Height_(cm) | 87.4228 |\n",
"| FriedPotato_Consumption | 26.4145 |\n"
]
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.feature_selection import SelectKBest, f_classif\n",
"\n",
"# Separate the features (X) and the target (y)\n",
"X = data_ordinal_encode.drop(\"Heart_Disease\", axis=1) # Replace \"Heart_Disease\" with any target column name\n",
"y = data_ordinal_encode[\"Heart_Disease\"]\n",
"\n",
"# Define the feature selection method (CFS)\n",
"cfs_selector = SelectKBest(score_func=f_classif, k=\"all\")\n",
"\n",
"# Fit and transform the CFS method\n",
"X_new = cfs_selector.fit_transform(X, y)\n",
"scores = cfs_selector.scores_\n",
"\n",
"# Create a dataframe of the feature names and scores\n",
"feature_df = pd.DataFrame({\"feature\": X.columns, \"score\": scores})\n",
"\n",
"# Sort the dataframe by score in descending order\n",
"feature_df = feature_df.sort_values(by=\"score\", ascending=False)\n",
"\n",
"# Print the sorted dataframe\n",
"print(\"Feature Ranking (CFS):\")\n",
"print(feature_df.to_markdown(index=False))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "9526b4a2",
"metadata": {},
"outputs": [],
"source": [
"# Drop the columns 'age' and 'gender' from the original dataframe\n",
"# data_ordinal_encode = data_ordinal_encode.drop(['FriedPotato_Consumption'], axis=1)\n",
"# data_ordinal_encode.head()"
]
},
{
"cell_type": "markdown",
"id": "d92e830d",
"metadata": {},
"source": [
"#### Saving preprocessed data"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "447d5875",
"metadata": {},
"outputs": [],
"source": [
"data_ordinal_encode.to_csv('cvd_data_preprocessed.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "be932438",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(308774, 19)"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_ordinal_encode.shape"
]
},
{
"cell_type": "code",
"execution_count": 325,
"id": "084e3ca5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy with RFE-selected features: 0.9188243866893369\n",
"Accuracy with Lasso-selected features: 0.9189701238766091\n",
"Accuracy with Random Forest-selected features: 0.9179985426281273\n"
]
}
],
"source": [
"# Import necessary libraries\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.feature_selection import SelectFromModel, RFE\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"X = data_ordinal_encode.drop(\"Heart_Disease\", axis=1)\n",
"y = data_ordinal_encode[\"Heart_Disease\"]\n",
"\n",
"# Split the data into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Method 1: Recursive Feature Elimination (RFE)\n",
"model_rfe = LogisticRegression(solver='liblinear')\n",
"rfe = RFE(model_rfe, n_features_to_select=10)\n",
"X_train_rfe = rfe.fit_transform(X_train, y_train)\n",
"X_test_rfe = rfe.transform(X_test)\n",
"\n",
"# Method 2: Lasso Regression (L1 Regularization)\n",
"model_lasso = LogisticRegression(penalty='l1', solver='liblinear', C=0.1)\n",
"model_lasso.fit(X_train, y_train)\n",
"sfm = SelectFromModel(model_lasso, threshold=0.25)\n",
"sfm.fit(X_train, y_train)\n",
"X_train_lasso = sfm.transform(X_train)\n",
"X_test_lasso = sfm.transform(X_test)\n",
"\n",
"# Method 3: Random Forest Feature Importance\n",
"model_rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
"model_rf.fit(X_train, y_train)\n",
"importances = model_rf.feature_importances_\n",
"sfm_rf = SelectFromModel(model_rf, threshold=0.05)\n",
"sfm_rf.fit(X_train, y_train)\n",
"X_train_rf = sfm_rf.transform(X_train)\n",
"X_test_rf = sfm_rf.transform(X_test)\n",
"\n",
"# Create and evaluate a simple model using selected features\n",
"def evaluate_model(X_train, X_test, y_train, y_test):\n",
" model = LogisticRegression(solver='liblinear')\n",
" model.fit(X_train, y_train)\n",
" y_pred = model.predict(X_test)\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" return accuracy\n",
"\n",
"# Evaluate the models\n",
"accuracy_rfe = evaluate_model(X_train_rfe, X_test_rfe, y_train, y_test)\n",
"accuracy_lasso = evaluate_model(X_train_lasso, X_test_lasso, y_train, y_test)\n",
"accuracy_rf = evaluate_model(X_train_rf, X_test_rf, y_train, y_test)\n",
"\n",
"# Print the results\n",
"print(\"Accuracy with RFE-selected features:\", accuracy_rfe)\n",
"print(\"Accuracy with Lasso-selected features:\", accuracy_lasso)\n",
"print(\"Accuracy with Random Forest-selected features:\", accuracy_rf)\n"
]
},
{
"cell_type": "code",
"execution_count": 175,
"id": "c3a26a13",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SelectFromModel_LogisticRegression: Mean accuracy = 0.9170, Std deviation = 0.0052\n",
"SelectFromModel_RandomForest: Mean accuracy = 0.9125, Std deviation = 0.0051\n",
"SelectFromModel_SVM: Mean accuracy = 0.9158, Std deviation = 0.0048\n"
]
}
],
"source": [
"# Import pandas and sklearn libraries\n",
"import pandas as pd\n",
"from sklearn.model_selection import cross_val_score, ShuffleSplit\n",
"from sklearn.feature_selection import SelectFromModel\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.svm import SVC\n",
"\n",
"# Use 20% of the data\n",
"data_ordinal_encode = data_ordinal_encode.sample(frac=0.2, random_state=42)\n",
"\n",
"# Separate the features (X) and the target (y)\n",
"X = data_ordinal_encode.drop(\"Heart_Disease\", axis=1)\n",
"y = data_ordinal_encode[\"Heart_Disease\"]\n",
"\n",
"# Define models using a pipeline\n",
"models = {\n",
" \"SelectFromModel_LogisticRegression\": Pipeline([\n",
" ('feature_selector', SelectFromModel(LogisticRegression(max_iter=1000))),\n",
" ('classifier', LogisticRegression(max_iter=1000)) # Adjust max_iter as needed\n",
" ]),\n",
" \"SelectFromModel_RandomForest\": Pipeline([\n",
" ('feature_selector', SelectFromModel(RandomForestClassifier())),\n",
" ('classifier', RandomForestClassifier())\n",
" ]),\n",
" # Add a new model with SVM and linear kernel\n",
" \"SelectFromModel_SVM\": Pipeline([\n",
" ('feature_selector', SelectFromModel(SVC(kernel='linear'))),\n",
" ('classifier', SVC(kernel='linear'))\n",
" ])\n",
"}\n",
"\n",
"# Define cross-validation settings\n",
"cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\n",
"\n",
"# Perform cross-validation and compare methods using parallelization\n",
"results = {}\n",
"for name, model in models.items():\n",
" scores = cross_val_score(model, X, y, cv=cv, scoring='accuracy', n_jobs=-1)\n",
" results[name] = scores\n",
"\n",
"# Print the cross-validation results\n",
"for name, scores in results.items():\n",
" print(f\"{name}: Mean accuracy = {scores.mean():.4f}, Std deviation = {scores.std():.4f}\")"
]
},
{
"cell_type": "markdown",
"id": "37705221",
"metadata": {},
"source": [
"# Testing with only features with only High importance"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "cbdca9a4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Logistic Regression\n",
"F1 Score: 0.07117960007338102\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.92 1.00 0.96 56677\n",
" 1.0 0.52 0.04 0.07 5078\n",
"\n",
" accuracy 0.92 61755\n",
" macro avg 0.72 0.52 0.51 61755\n",
"weighted avg 0.89 0.92 0.88 61755\n",
"\n",
"==========================================================\n",
"Model: Decision Tree\n",
"F1 Score: 0.20049893744802735\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.93 0.92 0.92 56677\n",
" 1.0 0.19 0.21 0.20 5078\n",
"\n",
" accuracy 0.86 61755\n",
" macro avg 0.56 0.57 0.56 61755\n",
"weighted avg 0.87 0.86 0.86 61755\n",
"\n",
"==========================================================\n",
"Model: Random Forest\n",
"F1 Score: 0.09153713298791019\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.92 0.99 0.96 56677\n",
" 1.0 0.37 0.05 0.09 5078\n",
"\n",
" accuracy 0.91 61755\n",
" macro avg 0.65 0.52 0.52 61755\n",
"weighted avg 0.88 0.91 0.88 61755\n",
"\n",
"==========================================================\n",
"Model: XGBoost\n",
"F1 Score: 0.04165103189493434\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.92 1.00 0.96 56677\n",
" 1.0 0.44 0.02 0.04 5078\n",
"\n",
" accuracy 0.92 61755\n",
" macro avg 0.68 0.51 0.50 61755\n",
"weighted avg 0.88 0.92 0.88 61755\n",
"\n",
"==========================================================\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"import numpy as np\n",
"# import libraries\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn import preprocessing\n",
"\n",
"# Importing necessary libraries for model development and evaluation\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, classification_report\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from xgboost import XGBClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"\n",
"# Defining the features (X) and the target (y)\n",
"X = data_dropped_columns.drop(\"Heart_Disease\", axis=1)\n",
"y = data_dropped_columns[\"Heart_Disease\"]\n",
"\n",
"# Performing the train-test split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Defining the function to apply models\n",
"def apply_model(model, X_train, y_train, X_test, y_test, name):\n",
" # Fit the model\n",
" model.fit(X_train, y_train)\n",
"\n",
" # Make predictions\n",
" y_pred = model.predict(X_test)\n",
" \n",
" # Calculate performance metrics\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" precision = precision_score(y_test, y_pred)\n",
" recall = recall_score(y_test, y_pred)\n",
" f1 = f1_score(y_test, y_pred)\n",
"\n",
" print(f\"Model: {name}\")\n",
" print(f\"F1 Score: {f1}\")\n",
" print(classification_report(y_test, y_pred))\n",
" print('==========================================================')\n",
" \n",
" # Compute ROC curve and ROC area\n",
" y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
" fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
" roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
"\n",
" return accuracy, precision, recall, f1, fpr, tpr, roc_auc\n",
"\n",
"# Defining the models\n",
"models = [\n",
" (\"Logistic Regression\", LogisticRegression(random_state=42, max_iter=500)),\n",
" (\"Decision Tree\", DecisionTreeClassifier(random_state=42)),\n",
" (\"Random Forest\", RandomForestClassifier(random_state=42)),\n",
" (\"XGBoost\", XGBClassifier(random_state=42))\n",
"]\n",
"\n",
"# Applying the models and storing the results\n",
"results = []\n",
"roc_curves = []\n",
"\n",
"for name, model in models:\n",
" accuracy, precision, recall, f1, fpr, tpr, roc_auc = apply_model(model, X_train, y_train, X_test, y_test, name)\n",
" results.append((name, accuracy, precision, recall, f1))\n",
" roc_curves.append((name, fpr, tpr, roc_auc))\n",
"\n",
"# Plotting the ROC curves for each model\n",
"plt.figure(figsize=(8, 6))\n",
"plt.plot([0, 1], [0, 1], 'k--')\n",
"plt.xlabel('False positive rate')\n",
"plt.ylabel('True positive rate')\n",
"plt.title('ROC curve comparison')\n",
"for name, fpr, tpr, roc_auc in roc_curves:\n",
" plt.plot(fpr, tpr, label=f\"{name} (area = {roc_auc:.4f})\")\n",
"plt.legend(loc='best')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "2d6385b6",
"metadata": {},
"source": [
"## With balanced sampling"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "b7f8c6b5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Logistic Regression\n",
"F1 Score: 0.31468065943532914\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.97 0.71 0.82 56677\n",
" 1.0 0.20 0.79 0.31 5078\n",
"\n",
" accuracy 0.72 61755\n",
" macro avg 0.59 0.75 0.57 61755\n",
"weighted avg 0.91 0.72 0.78 61755\n",
"\n",
"==========================================================\n",
"Model: Decision Tree\n",
"F1 Score: 0.19173718267794923\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.93 0.93 0.93 56677\n",
" 1.0 0.19 0.19 0.19 5078\n",
"\n",
" accuracy 0.87 61755\n",
" macro avg 0.56 0.56 0.56 61755\n",
"weighted avg 0.87 0.87 0.87 61755\n",
"\n",
"==========================================================\n",
"Model: Random Forest\n",
"F1 Score: 0.06391382405745062\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.92 0.99 0.96 56677\n",
" 1.0 0.36 0.04 0.06 5078\n",
"\n",
" accuracy 0.92 61755\n",
" macro avg 0.64 0.51 0.51 61755\n",
"weighted avg 0.87 0.92 0.88 61755\n",
"\n",
"==========================================================\n",
"Model: XGBoost\n",
"F1 Score: 0.04165103189493434\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.92 1.00 0.96 56677\n",
" 1.0 0.44 0.02 0.04 5078\n",
"\n",
" accuracy 0.92 61755\n",
" macro avg 0.68 0.51 0.50 61755\n",
"weighted avg 0.88 0.92 0.88 61755\n",
"\n",
"==========================================================\n"
]
},
{
"data": {
"image/png": 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e/PrrrxlejiGEEC9C1swKIYQQQohcS5JZIYQQQgiRa8kyAyGEEEIIkWvJzKwQQgghhMi1JJkVQgghhBC5liSzQgghhBAi13rpDk0wGo0EBQXh4uKSoaMRhRBCCCFE1lAUhdjYWAoVKvTUITP/9tIls0FBQRQuXDi7wxBCCCGEEP/hwYMH+Pn5Pfealy6ZfXK++YMHD3B1dc3maIQQQgghxL/FxMRQuHBhc972PC9dMvtkaYGrq6sks0IIIYQQOVh6loTKDWBCCCGEECLXkmRWCCGEEELkWpLMCiGEEEKIXEuSWSGEEEIIkWtJMiuEEEIIIXItSWaFEEIIIUSuJcmsEEIIIYTItSSZFUIIIYQQuZYks0IIIYQQIteSZFYIIYQQQuRakswKIYQQQohcS5JZIYQQQgiRa0kyK4QQQgghci1JZoUQQgghRK6VrcnsoUOHaN++PYUKFUKlUrFp06b/bPPnn39So0YNtFotJUqUYMmSJdYPVAghhBBC5EjZmszGx8dTpUoVFixYkK7rAwICaNu2LY0aNeLs2bOMGzeOoUOHsnHjRitHKoQQQgghciJNdg7epk0b2rRpk+7rlyxZQpEiRZgzZw4A5cuX56+//mLmzJl06tTJSlEKIYQQQuQ9xoQEDDEx6B8/xpiYiEFvID5Jh8FgwGgwoNfriU+OIywugqjI+xjDL1G111eULFUuu0NPJVuT2Yw6duwYrVq1SlXXunVrVqxYgU6nw9bW9qk2ycnJJCcnm8sxMTFWj1MIIYQQ4r8YjAqRCSkYjQpGBQyKgtGoYIiOxpCYSHxCMglxiRgfhWDQ6Ym5fBUbrRZFr8c24jGa2Cj0dlqi45KwVyn43DzPfaf8ONioUClGMCqoFAMqoxFdio7iMSHE2jtiY9TjqEtJV4waoODfXwA38y2i5LB51vqWvJBclcyGhIRQoECBVHUFChRAr9cTFhaGj4/PU22mT5/OlClTsipEIYQQQuQROoORJJ2BkOgkLgfFoFKBISkZYmIwpqRwLzQGm+hIlJRkFL2Bu6ExOGrUaI16vCKCSbTVgl5PeHQClSICiHZyxyU5jmKRgYRrXbHXp1AiJphYWwdsFCM2RgP2Rr15fFvA7R/x5HtGnL7/eFwuLvq5r8klOeGpukQ7cEiBB/lAUYHx768nj21UYIMCqCBfkfR++7JMrkpmAVQqVaqyoihp1j8xduxYRowYYS7HxMRQuHBh6wUohBBCiGxlMCok6QzEJet5HJWA7uYNIh4+wpiSjObeHR4kKDiqFZLjE3C6fZ1kj3yoDHowGoiPiqVCxF0eOnqhMRooHBuKgyGFwhp7HPXJqcapmIkYvRMizY9ddIlpXpNso0GvVuGk05FkqyEwvzMe0fHcKO6CzkaHbUoKMVodj93AqAaDCuz1EOT5d0KqBoW///w7MU2yU5HgqMHRwR2tswdaT2/c7T3x1HriYe9JEbcCeGndsT31Kz7n11AQA0kpCrsN9Wg3ZRN2WodMvGrryFXJbMGCBQkJCUlVFxoaikajwcvLK8029vb22NvbZ0V4QgghhMiEFL2RxBQDwTGJ6A0KBqOCUXnyhenj+Ihwoi9fIzAkkuSbN8HOjqDwWBzVKtwToij78ApxtqaEq0zUQ2wAG6DQP8Yp+O+B7z8dS4WkuFTlfyeyOo0dRhsbjGobHBLjiCtcAmxs0KPC3t4OG6MBdWI8utLlUWk06NUKtvFRJJTxI8mYRIKNjkh3G5JUiYTbJvHIJp4IXTRhuigeqWJItnsySafw/3TtSUz/XDJpupffxdYFTwdPvLReeGo98XL4+0+t11P1zrbOz5wEJOQSkavfwyPxLgAH7oO+zWzeeuf9tK/PAXJVMluvXj22bt2aqm737t3UrFkzzfWyQgghhMgZkvUGQmOSSdYbCYxKJCgqkdjAEML/2E1seDQlHwcQZ+eAjdGIRjFQ89F1Hrjk/7tspHhMsLkvd6DoC8RwzaMITk5a3KPDuOlXDk93J0CFXUoSqtJlUGtsUGs0KEA+VwecixXB1UmLxkaNnW8h1FotKnt7bDw8UFCISo4iIjGCiKQIwpPCTX8m/uvPpGtEJEWQqH8y+3oy7eAMmPJSewAVNiobPLQeeGo9n05O/y57ab3wcvDCQ+uBvU0mJ+70yXBoJsqRWXgY9UQmKiy87UuPmTspWqxY5vq2smxNZuPi4rh165a5HBAQwLlz5/D09KRIkSKMHTuWwMBA1qxZA8CAAQNYsGABI0aMoF+/fhw7dowVK1bw888/Z9dLEEIIIV4KiqIQ+yiM5LAIYu894FFUPJHRCdgYDWgeBaMYDDhfu0CIvStaNYTHJIKiEJ+Ygo1iBKORGqHXuedaEFujnqoxIf85ZrnIB899/q5vGbxjH5NSpSZoNLg4aVGjoLG3w6txQ+xcnLH19MC+ZElUtraU/0fb2mn0l2JIMSei4Unh3EgMJyLpvilRfZg6UY1KjsKgGDL0PdTaaM1JaKoENY1E1c3eDbUqi3ZQfXAKtgyGx9dQAUFuNfg+sQKf/jw7V0wWqpQni06zwcGDB2nWrNlT9b169WLVqlX07t2bu3fvcvDgQfNzf/75J8OHD+fy5csUKlSITz/9lAEDBqR7zJiYGNzc3IiOjsbV1dUSL0MIIYTIVZJ0BpJ1RlIMRmKTdITHp3ApMJqgq7dxDLqHfVwMLuEhGFRqCt2/TuH7V60fU9s3cIqPxr12TTR2dqhsNSg6PXZFi6DSaECjQa3VYl+yJGonp3T1qSgKcbq4f8yShhORmMYs6t/1sbrYDMftZu+Wro/2vbReONo6Zrh/q0qJh/1foBxfjAoFnLzh9ZlQoWN2R5ahfC1bk9nsIMmsEEKIvCQuWU+SzkCSzkBw4GMMiclE377Dnev3KRB0h8QHDzGiIjFFj1Zl5JWgqzxw9sZGMaJWjKgVBbVixC8+LN1jRto745EcxzWv4mjsbLG1t8U1JpzgouWw1aUQ4OFHkXzOpCjg6+lEAXdHHLS2ONjbogbsihRGZWeHXZEiaPLnz9Dr1Rv1RCVHmWdP0/5o3/RnRGIEKcb0bUH1hEateSohfdZMqofWA1t1zp+5TNOdgxg3D0EdbVow/PNVFU2n++NTokI2B2aSkXwtV62ZFUIIIV42+sQkNh69yYPQGM7cDuWV8DuEhkVjo9dTLfASlcNuE2PnhGdyLM5/t3EDnreBUunowOeOGe9bFKODE4pGQ1LhEqgxklyiLBU6t6OAT35s1Kabh8o/t5f0S9QnPjch/WfSGpUchULG5uGcbJ2eSkifNZPqauf67Juj8oLEKNj9GZz9ATVwL8pI/+1J1OzyKe8UKZPd0b0QSWaFEEKIbKAoCkpSEknXrqEkJpJ0J4CY3XuIN4Lhxg3UsdFoFCMAlf/+ev0ZfXkmp/54PEVjS4K9E6GehSioSiE2vy8O5cvi6umG1lGLPUbcShbDRmODysYGlUaDSq1G7eqKfYkSmX5tRsVITHKMOSkNTwpPO1n9O1H9/81R6aNCZb456p8JaVrrUT21nmg12ky/pjzh2nbYNgLiTOuV559MYfYFF5au/I1XX301m4N7cZLMCiGEEBamKArG+HiSrlzBGBtLvP8x9I8fk5SiI+XQIVTGZ9849GQrqX8zoELRaFABNnodCQ2a4eTsgL1ixKNlMxyrVMHGKx9qJ0erzCzqDLr/uGv//+XIpEj0iv6/O/0HO7Xd/5PRtLaY+kfS6m7vjo06re+SSFNcKOwcDZd/B+BamIEPtiRhV6oxR0+tTfPQqdxEklkhhBAiAwyxsaTcvUvCpctcvheG3Ul/bGIiSUlIwlYxYh8TiZ1B98z2aaWZUXZOPHL0JF9SNLfcfLlYpjbJ9g4UqVSajo3KUaSkHw5aO4u+DkVRSNAnpEpKn8yipnWTVExKxo+Dd7FzSTMpNa9D/UfS6mTrlLc/3s8OigIXNsCuTyExElQ2bI8uzdtLTzHms0l89tln2Njk/l8KJJkVQggh/sFgMHLnxn2SD+zHEBOD4Y8d6BwciU/U4RN8J9W16bl1KdLemccO7tgoRo4VrEiocz6SVTbE5i9EmaplaVS5MEXyu1DHxR4XrS11NWo0Ni+2JZPBaDDdHPW82dN/JKrJhuT/7vQfbFQ2z11v+u96W5tcenNUXhD1ALYNh1t7TOWCr0CHBTRzL8Oet87QsGHD7I3PgiSZFUII8dJQFIXIBB0nA8L5624kTkmxxD2OwG7LRjztVTS8dsR87T8/7ncA0rqfOsrOidPeZfHxdCKkUh0i7J0p5eNOglGhcLkSqJ1d0Nja4K5S4efhQEt3B7S2mZ8JUxSFRwmPuBx+mavhV7kSfoXrEdcJSwrD+Pc62/Ry0Dg896P9f97N72rvmnV7n4oXYzTC6e9hzyRIiSPFqObHB770Hr8Pta09jpCnElmQZFYIIUQeoigKDyISOfcwim3ng3B1sOXiw2jyxTym4r3zxEbE0OHOEYqmxKfrBKl7XoV54F0MF0VPVJXaxNo54lexNC4lilKtiAdFHGypZ2/dt1JFUQiMC+RqhClpfZK8RiZHpnm9ChXu9u7/uSn/kxnWHLf3qXhxYbdgyxC47w/AmTBb3l0Xyc3IGMr1O039+vWzOUDrkGRWCCFErpSkM5CYYmDftVCOXAni/s372N+9TZu7x3DSJ/G60YCt0cD7/zgG9VlSnFxQNLZoOryJxt2dol074eDlYbGtp9LLqBh5GPuQK+FXuBJxxZy8prVeVaPSUNK9JOW9ylPBqwLlPcvj5+KHu707GrW8vb9UDHo4tgAOTgd9EjqVHaN3JzDXP4ZCvr4cPPhznk1kQZJZIYQQOZDRqBAYlUhCioG74fHcCo3jwLVQnLUaAsLiSbz/kCaB53jv2m4qGvVUTG+/+b1xbt4cx3xeuL7WGltfX9SO2TMzaVSM3Iu5Z0pcw69wNeIq18KvpXkKlUatobR7aSp4VTB/lfYojb2NfTZELnKUkIuweRAEnwfgfHw+Oi4L4F60Qps2bVizZg358uXL5iCtS5JZIYQQ2c6o1+N/4R7H/C8T8NdFDEnJlPh7Y/+qYbdw17rTOyWOktFBxGu0OOmTntmXOr83jq9Uwu2Njtg4O6OytUXj44Odn19WvZynGIwGAqIDzEsFroRf4VrENRL0CU9da6e2o6xnWcp7/j3j6lWe0u6l5WYqkZo+GQ59A0dmg1EPWne+vZKfkT+cxsbGhhkzpvPJJ5+gVuf9Nc6SzAohhMhyKTo9N/Ye4cqqdeS7dx2fqBC8gHbPuL5IbKj58T8TWW21ajjVqY372+9gW8gHVQ5449YZddyJumOebb0SfoUbkTfSPBhAa6NNlbhW8KpACfcSufeIVJE1HpyEzYMh7LqpXL4DtJ1J81tBlD7ehdWrV1OvXr3sjTELSTIrhBDCagxGhVPXgzm65QAlj+4iJSqaV0JvAqadAl751/VGVKhRMNra4dK8GUpUFA7Vq6HSaLAvWRJsbNB4eaHJnx9bP79s35dUZ9BxM+qm+aasqxFXuRF5I80trxw0DqlmWyt4VqCYWzFZ3yrSLzkO9n8BJ5YACkbH/Jzz7U71LpMBqFatAFeuXEGjebn+Tb1cr1YIIYRVKIrCnbB4rgTFcDEwmiM3w9BfucTk49/jkRxH2+e0DfMtQf6ePSj8VnucXJyyLOaMSjYkczPyZqo1rjcjb6IzPn1AgrOtM+W9yqdKXou6FJVTq8SLu70ftg6DqPsAhPm1ptW3p7h0+0v8y7ajZs2aAC9dIguSzAohhHhBUQkpfH8kgD1XQ7kaHEOlsNu0eHCaN++d5M00rg91L4ihTHmUJs3J71eAMg1ronVyyPK40yNRn8iNyBuptsK6HXU7zSNaXe1czTsKVPA0LRXwc/GT/ViFZSRGwu7P4OyPAChuhdlkaEaXgUvR6XQUK1Yse+PLASSZFUIIkW6KorD/WijvrzpFvqRoSkc+pGPgOWYFnkv7ehdXXJs1xfer6ZTPAetZ05KgS+B65HXzjOuV8CsERAdgUAxPXetu755qR4HynuXxdfbN9uUOIo+6uhW2fwJxjwAVSVV60efHO6z7bQEAb731FitWrMDd3T1bw8xukswKIYRI05OlA5vOBnL00kMeBoVT/PFdJp9Yyc7ntHN57TVc27bBqX59bJydsyze9IhLieNqxFXTbGuEadY1IDoABeWpa720Xv9f3/r3rGtBp4KSuArriwuFHaPgyiZT2as0l0sP5PUBU7l37x52dnZ8++23DBo0SP49IsmsEEK89C4FRvM4Npl74fGkGIwERSbyaMcfFLt/lRqh12kbH/bcNa8qR0fcOrTH/c03cahSJcvi/i/RydFci7j2/6UCEVe4F3MvzWu9Hb3NSwSeJK/5HfJLoiCylqLA+XWwawwkRYHKBhp+DI1Hs332PO7du0fJkiXZsGED1atXz+5ocwxJZoUQ4iWSpDMwZ+9Ndl8JwVVry7kHUamerx1yhYknVmGjGJ/bj8e77+I9ehRqrdaK0aZfVFKU+cSsJ8nrw7iHaV7r4+RjXiLwJHnN55C3N5UXuUDUA9j2MdzaayoXrAwdF4JPZQBGjhyJSqWif//+uLq6Zl+cOZAks0IIkccZjAoXA6OZvecGf954nOo5j6QYmj84Q+moBzQJPP9UW7umzXCtVBG3dq9jl0NuNAlPDE+1h+vV8KsExQelea2vs+//17h6VqCcVzk8tZ5ZHLEQz2E0wl8rYO9kSIkDG3toOoajSnWm9h7Fb7/9hpOTE2q1mlGjRmV3tDmSJLNCCJHH6AxGTtyJ4NyDSGbtuYHxH8tB1UYD5SPuUTI2hI/O//bMPgpOnYJH585ZEO3zJeoTOfPoDBceXzDPvIYmhKZ5bVHXoqlmW8t7lsfN3i2LIxYiA8JuwpYhcP+YqVykHsZ2c/l6xW9MmNASg8HAtGnT+PLLL7M3zhxOklkhhMgDFEVh7Yn7fLbp0lPPaYx6KoXdYfyZtTgnxafZ3q5YMZzq18O1XTscqlXLtrWiiqJwM+om/oH+HA06yplHZ0gxpqS6RoWKYm7FUi0VKOdZDhc7l2yJWYgMM+jBfx4c/AoMyWDnDC0nE1q0PT169GL37t0AdO/enXHjxmVzsDmfJLNCCJHLHb8TTtfvjgOg1SdTPfQGpaIeUjE8gEoRAaiVp+/UB3B74w0ca9fG7c03svVGp4ikCI4FHcM/yJ9jQcd4nJh6KURBp4LUKlCLivkqUt6zPOU8y+Fo65hN0QqRScEXYMtgCP57WU/JFtB+DgfP3eHdatUJDg7GwcGBBQsW0KdPH7kJMR0kmRVCiFxIURSO3Apj9K8XCI5OwlGXyPCzG2gYdPG57Vzbt6fgZ+Oxccu+j991Bh3nHp/DP8gf/yB/roZfTbU1loPGgZoFalK/UH3q+9anuGtxeUMXuZ8uCQ7NgCNzQDGA1h1e+wqqdOXHtWvp1asXRqORChUqsGHDBipWrJjdEecakswKIUQukqw38P2Ru3y96xqOukSqPL7Nkr9+xM6Y+mQqm3z5sC9WDLsSJXBu3AjnJk1Q2dpmS8yKovAg9gFHg47iH+jPyZCTJOgTUl1T1qMs9X3rU79Qfap7V8fOxi5bYhXCKu6fMM3Ght0wlSt0hLYzwdkbgObNm+Pl5UW7du2YP38+Tk4591jnnEiSWSGEyOEuBUbz4/F7rDv1ABSFbtf3svX6HjRpbJ9lX7o0hZd9h23BgtkQ6f/FpsRyMvgk/kGmta+BcYGpnvfUelKvUD0aFGpAvUL1ZGsskTclx8G+qXDyO0AB5wKmJLZCB27cuEGZMqZktlChQpw/fx4fH5/sjTeXkmRWCCFykCSdgb/uRnIiIJwNfz0gPC4FbVI8b946xMywW1SMuJtmO6f69fCdNx8b5+yZ0TEYDVwJv8LRoKMcCzrG+cfnUx0Hq1FrqO5d3ZzAlvUsi1qVM4+3FcIibu2DrR9D9H1TuWp3aP0FelsXpk6cyLRp09iwYQOdOnUCkEQ2EySZFUKIbHb+QRSjf71AQJjpBC6AWiFXGXznCDVDr6fZRlu5Mp49uuPapg0qTfb8KA+JD+FY0DGOBh3lePBxopOjUz1fzLWYad1rofrUKlhLbtoSL4fESPhjPJxbayq7F4H2c6FkcwIDA3n33Tc4dOgQAMePHzcns+LFSTIrhBDZQFEUboXG0WbuYfR/bwTb5OFZ2gYco3L4nTTb2Hh64t6pE65t26AtXz4rwwVMe76efnTadONWoD+3o2+net7F1oU6PnXMa199nX2zPEYhstWVLbBjJMQ9AlRQpz80nwD2zuzatYsePXoQFhaGs7Mzy5Yto2vXrtkdcZ4gyawQQmSxBxEJNPl6H2Uj7jMi4CjNHp595rXOzZrh8W43nBo0QKXO2o/l/7nnq3+QP6cfnU6156tapaaSVyXq+9anQaEGVMpXCY1a3lbESyj2kSmJvbrFVM5XBjosgCJ10Ol0TBgzhq+//hqAatWqsX79ekqXLp2NAect8lNHCCGsLEln4M/LgYQd/4vHm7fS5P5ptht0z7w+38CBuLZvh33x4lkYpUlkUqR56cCz9nx9ctNWXZ+6csKWeLkpCpz/GXaNhaQoUGugwcfQeBTYagE4dOiQOZEdNGgQM2fORKvVZl/MeZAks0IIYQV6g5Htn83EbdcmCiRGUgQoksZ1Kq0WWz9fvPr0xaVFc2zc3bM0zid7vj5JYP+956vWRkvNgjVpUKgB9QvVp7ib7PkqBABR9003eN3eZyr7VDHNxvpUTnVZixYtGDduHNWqVePtt9/O+jhfAipFecbRMHlUTEwMbm5uREdH4+rqmt3hCCHyiGS9gctBMdx9HEfwlKk0v3EkzetSbO0JK1KGSsMHkr9BHdQODlkcKdyPuW/a8zXIn5PBT+/5WsajjCl59a1PNe9q2NvYZ3mMQuRYRiOcWg57J4MuHmzsodlYqDcEbDSkpKTw+eefM2DAAHx9Zd34i8pIviYzs0II8YIexyazyj+AreeDuR+RQLmIe3zhv4zy+qRU1+l6fkDZ997GrnDhLF/3CpCgS+BY8DHz2teHcQ9TPe+p9aSuT10a+Dagnk898jvmz/IYhcgVwm7C5sHwwHR8NEXqQ4f5kK8UAHfv3qVLly6cPHmSw4cPc+DAAfkkIwtIMiuEEBkQFpfM8TvhrD1+n2N3wlEpRjrePsKiS1ux+cfH87H5C1Fm6SLcK5TNljiNipHTj06z+dZm9tzbk2r2VaPWUM27mnnbrHKe5WTPVyGex6AD/3lw8GswJIOdM7ScDDXfh79/Qf3999/p27cvUVFRuLu7M3z4cElks4gks0II8RwR8Sks2H+Lk3fDuRQYA4CtQUfTh2dZdPswxWOCn2rjt2gRLs2bZXWogGkJwebbm9l2extB8UHmel9nXxr7NaZBoQay56sQGRF83jQbG3LBVC7VEtrNAffCACQnJzNq1Cjmz58PQN26dVm3bh1FixbNpoBfPpLMCiHEM3y96xqLD/5/L1UHXRId7xyh19VdT12rdnUl/5AheHTpjMrOLivDJCYlhj/u/sGWW1s49/icud7Z1pnWxVrTsVRHquavKrNEQmSELgn+/BqOzgXFAA4e8NpXULkL/P1/KTAwkI4dO3L69GkARo0axbRp07C1tc3OyF86kswKIcS/3HwUS9t5h9EZFFAUCseF0uXhCVpcP5T6QhsbPHv3Iv+QIaizeKsdvVHPsaBjbLm9hf3395v3f1Wr1NQrVI+OJTvSrHAztBrZAkiIDLt/3DQbG37TVK74JrSZAc7eqS5zd3cnMTERLy8vVq9ezeuvv54NwQpJZoUQAtPNXF/tvMbGM6abozwToxl36gcqRtx96lq1szN+ixbiVLt2FkcJNyNvsuX2Frbd2UZYYpi5vpR7KTqU7MDrJV7H29H7OT0IIZ4pORb2TYWTywAFnAvA67OgfDvzJUlJSdjZ2aFWq3FycuL333/H0dERPz+/7Iv7JSfJrBDipRafrGfi5stsPPMQr8RoBt7YR/sA/6eusytWDPty5cg/dCj2JbL2MIOIpAh23NnBlttbuBpx1Vzvbu9O2+Jt6VCqAxU8K8gyAiEy49Ze076x0Q9M5Wo9oNXnpuUFf7t+/TqdO3emW7dujBkzBoAyZcpkQ7DinySZFUK8lB5EJNBoxgFqPrpKrZBr7Aw4muZ1dqVKUnjhQuyy+GYOnUHHnw//ZPPtzRx5eAS9ogdMOxE08WtCh5IdaOTbCFsbWZsnRKYkRMAf4+H8T6ayexFoPw9Kpr6Jc+3atfTv35/4+HgeP37MkCFDcHJyyoaAxb9JMiuEyPNuP47j9L1IHkUnMW//TVQpKTR9eJadZzekeb1diRJ4dOmMR8+eWTrbqSgKl8Mvs/nWZnbe3Ul0crT5uYpeFelQsgNtirfBQ+vxnF6EEOl2ZTNsHwnxoYAK6gyA5p+BvbP5koSEBIYOHcqKFSsAaNq0KWvXrpVENgeRZFYIkWc9ikni9XlHCItLBqBU1ENmn9lAyZigp67NN3gw2vLlcG7ePMs/rn8U/4htd7ax5fYW7kTfMdd7O3jzesnX6ViyIyXdS2ZpTELkabEhsGMkXN1qKucrCx0XQOHU6+CvXr1K586duXTpEiqViokTJzJhwgRsbGyyIWjxLJLMCiHynC3ngxj689lUde8+9KfHX7+lqrMtUgTv4R/j2qZNVoYHQKI+kX3397H19laOBx/HqBgBsLexp3mR5nQs2ZG6PnWxUcubphAWoyhw7if4YywkRYNaAw2HQ+NRoEl9bHNMTAwNGjQgMjKSggULsnbtWpo3b55NgYvnkWRWCJGnfHfoNl/uuAZA3eBLtHhwmoZBF83Pq52d8ejWFa9+/bD5j/O+LU1RFE4/Os2W21vYfW838bp483PVvavTsVRHWhVthbOd83N6EUK8kMh7sHUY3DlgKvtUNc3GFnwlzctdXV2ZOnUqmzdv5scff6RAgQJZF6vIEJWiKMp/X5Z3xMTE4ObmRnR0NK5Z/EYmhLCOh5EJHL0VxsTNl0nWG7HXp7Bp27inrlO7ulLm6BFUWbyh+YPYB2y9vZUtt7cQGBdorvd19qVDyQ60L9Gewq6FszQmIV4aRiOcWgZ7p4AuHjRaaDoW6g0Gm9RzehcvXsRgMFC1alXA9Auooiio1XLcc1bLSL4mM7NCiFxr+eE7fLH9/1tVoSiM+etHmgSeT3Wda7t2uLRojsurr6LSZM2PPZ1Rx66AXfx641fOhJ4x1zvZOtGqaCs6lOxA9QLVUavkTVIIq3l8HbYMgQcnTOUi9aHDfMhXKtVliqKwfPlyhg4diq+vL2fOnMHV1RWVSiVb3uUCkswKIXKVJJ2B91ef4uitcHOdoy6JWYfmUzT2UaprHevWpeiqlVkaX7Ihmc23NvP9pe/Ns7AqVNT1qUuHUh1oUaQFDhqHLI1JiJeOQWc6hvbPr8GQAnbO8OoUqNEX/jXLGhsbS//+/fn5558BKF26NDqdLjuiFi9IklkhRK7w88n7fPPHdSLiU8x1asXIp6d+pHHQhVTX2nh4UGLHdjQeWbeFVbwunl+u/8LqK6vNJ3N5aj15t9y7dCzVkYJOBbMsFiFeakHnYMtgCPl7rXypV6HdbHB/einPuXPn6Ny5Mzdv3sTGxoZp06YxatQoWVaQy0gyK4TI0YKiEnlnyTECoxJT1XdIvMNHfyx66vpS+/dhW6hQVoVHdHI0a6+uZe3VtcSkxABQ0KkgvSv25q3Sb8ksrBBZRZdomok9Og8Ug+nkrte+hsqd4V9LBRRFYcmSJQwfPpzk5GQKFy7MunXrqF+/fjYFLzJDklkhRI4Ul6znvWXHOf8wOlX94Gal6KW7RdjI/yey2goVKLr2R9QOWZc4Pk54zJora1h/fT2JelOiXdS1KO9Xep92JdrJyVxCZKV7x0yzseG3TOWKb0Kbb8A5f5qXK4rCli1bSE5Opn379qxcuRIvL68sDFhYkiSzQogcIUln4LczgSz+8xYPIhKfev696j58uHUOCcNOEfaPer8F83Fp2TLL4nwY+5CVl1ay6dYmUoymJQ9lPcryQeUPeLXIq7IvrBBZKTnWtEvBqWWmsnNBeP1bKN/uuc3UajVr1qzhl19+4aOPPpKbvHI52ZpLCJGtDEaFd5b4c+Z+VJrPt6pQgLlvV+Ju9Wqp6m08PCj2yy/Y+flmQZRwO+o2Ky6uYEfADgyKAYCq+avSr3I/Gvk2kjdDIbLazb2w7WOIfmAqV+sBrb4AB/enLlUUhXnz5nHt2jUWL16cpWGKFyNbcwkhcoXHscm0mv0nkQmp7xzuXb8YXWoVpmwBFx7PmMHd6j3Mz9mXL0/hJUuwLeCdJTFeDrvMsovL2Hd/n7mufqH6fPDKB9QsUFOSWCGyWkIE/DEOzpt2H8C9KHSYByWapnl5ZGQkffv2ZdOmTQC88847cpJXHiPJrBAiW0zecplV/nfN5RL5ndg+pBEOdqaP6ZMDArheoU6qNrZ+fpT4PfWRtNagKAp/PfqL5ReX4x/kb65vWaQlH7zyARXzVbR6DEKIf1EUuLIZdoyE+MeACup+BM0/AzunNJucOHGCLl26cO/ePezs7Pj2229p1qxZ1sYtrE6SWSFEltIZjFSc+AcpBqO5bnXf2jQpY7pRI3rzZoI+HfNUu+KbfkdbrpxVY1MUhcOBh1l+cTlnQ88CYKOyoW3xtrz/yvuUdC9p1fGFEM8QGwLbP4Fr20zl/OWgwwIoXCvNyxVFYdasWYwZMwa9Xk/JkiVZv349NWrUyMKgRVaRZFYIkSUURWHZ4Tt8ueNaqvrDo5tR2NOR5Fu3uNOu/VPtHKpXp+iPP6Cy4r6PBqOBPff3sPzCcq5HXgfAVm3Lm6XepE+lPvi5+FltbCHEcygKnFtrWlaQFA1qDTQcAY1Hgsb+mc369u3LqlWrAOjcuTPfffcdbm5uWRS0yGqSzAohrMr/dhjTtl/lclBMqvrudYswtU0Zgsd/xtVt21I9p7K1xXfObFxatLBqbDqDjm13tvH9pe+5G3MXAAeNA13KdqFnhZ7kd0x7Wx8hRBaIvAtbh8Gdg6ayT1XouBAKVvrPpl26dGH9+vXMmjWL/v37y9r2PE6SWSGE1Xy87iybzgWlqvPzcGDRe9Upfusc16tUfapNgfHj8ezR3apxJemT2HhzI6suryIkPgQAVztXupfvzrvl38XNXmZwhMg2RgOcXAb7poAuATRaaDYO6g4Cm7TTFqPRyI0bNyj391Kk1157jYCAAAoUKJCVkYtsIsmsEMLiFEVh4YFbqRLZKoXdmdKhIhXtU7jzeisexsaan3Nq1AjvUSPRlilj1bjiUuJYd30dP1z5gYikCADyOeSjV4VevFP2HZxs076JRAiRRR5fh82D4eFJU7loA+gwH7yevV49NDSUnj17cvz4cc6ePUvx4sUBJJF9iUgyK4SwqC3ngxj689lUddc+fw2trQ2JFy9x6513Uj1XdO2POFr5pgxFUdh1dxczTs0gLNF05IKvsy99K/WlY6mO2Ns8e+2dECILGHRwdA78OQMMKWDnAq9OgRp94Dnr5f/880+6detGcHAwDg4OXLx40ZzMipeHJLNCCIuIjE+h/lf7SdQZUtUfHt0MexsVD/oPIO7PP831rh3aU+jLL1FprPtj6F7MPb44/gXHg48DUMSlCAOqDOC14q9hq5YjZ4XIdkFnTbOxjy6ZyqVbQbvZ4PbsGy8NBgPTpk1jypQpGI1Gypcvz4YNG6hU6b/X04q8R5JZIUSmXA+Jpc/KkwRFJ6Wqn9etGh2qFCLu8BGu9euX6rmiP63FsXp1q8aVbEhm+cXlrLi4Ap1Rh53ajn6V+9GnUh+ZiRUiJ9AlwsGvwH8+KAZw8IQ2X8Mr78BzbtgKCQmhe/fu7NtnOsikd+/eLFiwACcnWSb0spJkVgjxwjaefsgnv5xPVde4TH5W9q6F2mjg2iuVUXT/P91L7eJCGf+jqGytOyN6NPAo005M40Gs6ZjLBr4NGF97PIVdC1t1XCFEOt09CluGQMRtU7lSJ3jta3D+7x1E5s6dy759+3B0dGTx4sX07NnTysGKnE6SWSFEhi08cItv/riequ71V3z45p3KONppiDt8hAf/mo0t9M0M3No/vY+sJT2Kf8SMUzPYfW83AN4O3oyuPZpWRVvJ1jxC5ARJMaZdCk4tN5VdfOD1WVCubbq7mDRpEg8fPmT8+PHm3QvEy02SWSFEhjyMTHgqkT0z4VU8newAiN6+naBPRpqf83jvPQpO+MyqMemNetZdW8eCcwuI18WjVql5t9y7DKo6CGc7Z6uOLYRIp5t7YOvHEPPQVK7eC16dCg7uz20WGBjI7Nmz+eqrr9BoNGi1Wn744QerhytyD0lmhRDplqI30n7+EXN5x9BGVCjkCoAhJoZHX04netMm8/NZsWfs+cfn+eL4F1yLMJ0sVjl/ZSbUnUA5T5mxESJHSIiAXWPhwjpT2aMYtJ8HJZr8Z9Ndu3bRo0cPwsLCcHV1ZeLEidaNVeRKkswKIdLl1N0I3llyzFye27WqOZF9PH8BYQsXprq+zInj2Fjx+Mjo5GjmnJnDxhsbUVBwtXPl4xof06l0J9Qq6x19K4RIJ0WBy7/DjlGQEAYqNdQdaDoAwe75N2vpdDomTpzIV199BUDVqlXp2rVrVkQtciFJZoUQ/2nDqQeM3njBXO7fuAQdq/oCEDx5MlHr1pufc3u7EwVGjrRaIqsoClvvbOXbv741H3zQoWQHRtQYgZeDl1XGFEJkUEww7BgJ1/4+qjp/OdNRtH41/7PpgwcP6Nq1K/7+/gAMHDiQb7/9Fq1Wa82IRS4myawQ4rl6rDjB4Zth5vLGj+pTo6gHsfv28XDQ4P9fqFZTfNPvVj3F63bUbb44/gV/PfoLgJJuJfms7mfULPjfb5BCiCygKHD2B/jjM0iOBrUGGo2ERiNA899b4u3bt4/OnTsTERGBq6sry5cv551/HbQixL9JMiuESJOiKHz4w+lUieyx0U2x37aRq19OT3WtfYXyFF+/3mpbbiXqE1l6fimrL69Gr+jR2mgZUGUAPSv0xNZGDj4QIkeICICtwyDg78NRClUzzcYWqJjuLgoWLEhiYiI1atRg/fr1lCz57GNshXhCklkhxFMURaHrd8c5ERBhrrvycS3u1X96BrTw8uU4N2xgtVgOPjjI9BPTCYoPAqBp4aaMrT2WQs6FrDamECIDjAY4sRT2fw66BNBooflnUOcjsPnvNCM+Pt584EHFihXZt28f1atXx95eDjcR6SPJrBAilcQUA+Un7jKXNWoVx+oYuNc09Z3Hhb75Brf27awWR3BcMNNPTufAgwMA+Dj5MKb2GJoXaW61MYUQGRR6DbYMhoenTOWiDaHDPPBK34zqpk2b6NevH5s3b6Z+/foA1KtXz1rRijxKklkhhNm5B1G8sfCoufxunSJMrKQloH0Hc12Bzz7Ds/t7VotBURTWXFnDwnMLSdQnolFp6FmxJ/0r98fR1tFq4wohMkCfAkfnwKFvwJACdi7Q6nPT3rHq/95NJDk5mdGjRzNv3jwAZs+ebU5mhciobN+/ZtGiRRQvXhytVkuNGjU4fPjwc69fu3YtVapUwdHRER8fH/r06UN4eHgWRStE3nXmfmSqRHbEq2UY75uYKpEtvnmTVRPZFEMKM07NYOZfM0nUJ1Lduzq/tP+F4TWGSyIrRE4ReAaWNYMD00yJbOnWMOgE1OyTrkT29u3bNGjQwJzIjhw5kp9++snaUYs8LFuT2fXr1/Pxxx8zfvx4zp49S6NGjWjTpg33799P8/ojR47Qs2dP3n//fS5fvswvv/zCqVOn+OCDD7I4ciHylvG/X+StRf7m8ve9a9Iz8jz3e/Yy1/ktXoS2bFmrxXDx8UW6bOvCj1d/BGBAlQGsem0VpTxKWW1MIUQG6BJh9wRY3gIeXQJHL+i0At5dD26+6epiw4YNVKtWjdOnT+Pp6cm2bdv45ptvsLXSzaPi5aBSFEXJrsHr1KlD9erVWbx4sbmufPnyvPHGG0yfPv2p62fOnMnixYu5ffu2uW7+/PnMmDGDBw8epGvMmJgY3NzciI6OxtXVNfMvQohczGhUGPTTGXZeCjHXzXYP5JX9G9H945dKvyWLcWna1CoxJOoTWXh2IT9c/QGjYsRT68m4OuNoVbQVKpXKKmMKITLo7hHYMgQi7pjKld6GNl+DU750d3HgwAGaNzeteW/QoAE///wzhQsXtka0Ig/ISL6WbWtmU1JSOH36NGPGjElV36pVK/NGyf9Wv359xo8fz44dO2jTpg2hoaH8+uuvvP76688cJzk5meTkZHM5JibGMi9AiFwuPllPxUl/mMsNvW0Z/90wAHT/uK7s2TOoHRysEsOpkFNM8p/Eg1jTL6PtSrRjdK3ReGg9rDKeECKDkmJg7yT463tT2aUQtJsFZdtkuKumTZvSqVMnypQpw9SpU9Fo5LYdYRnZ9i8pLCwMg8FAgQIFUtUXKFCAkJCQNNvUr1+ftWvX0qVLF5KSktDr9XTo0IH58+c/c5zp06czZcoUi8YuRG63/PAdvth+1VRQFL4/vhCfR3dTXVNg3Fg8evSwyuxoXEocs0/PZsONDQB4O3ozqd4kGvs1tvhYQogXdGM3bPsYYgJN5Rq94dWpoE3/6X4bN27k1VdfxdXVFZVKxYYNG1CnY12tEBmR7f+i/v1GqSjKM988r1y5wtChQ5k4cSKnT59m165dBAQEMGDAgGf2P3bsWKKjo81f6V2OIEReNeqX8+ZE1jshgp2bR6VKZD3ee49yV6/g2bOnVRLZww8P8+aWN82J7Ntl3mZTx02SyAqRU8SHw8Z+8NM7pkTWozj02grt56Y7kU1ISKBfv368/fbbfPjhhzxZ0SiJrLCGbJuZzZcvHzY2Nk/NwoaGhj41W/vE9OnTadCgAaNGjQKgcuXKODk50ahRI7744gt8fHyeamNvby8bLwvxt5l/XOeX0w8BKBdxj9mH/v+phl3RohTfvAm1lc4/j0qKYsapGWy9sxUAP2c/ptSfQm2f2lYZTwiRQYoCl3+DHaMhIQxUaqg7EJqNB7v07yZy9epVOnfuzKVLl1CpVJQtW/a5E1VCZFa2JbN2dnbUqFGDPXv28Oabb5rr9+zZQ8eOHdNsk5CQ8NQaGxsbGwCy8T42IXKFfmv+Ys+VR6AoLDwynxLh/7/By+ujAXgPG2a1sXff3c20E9OISIpAhYruFbozuOpg2W5LiJwiJhi2j4DrO0xl7wrQYQH41chQN6tXr2bgwIEkJCRQoEAB1q5dS4sWLawQsBD/l62rr0eMGEGPHj2oWbMm9erV47vvvuP+/fvmZQNjx44lMDCQNWvWANC+fXv69evH4sWLad26NcHBwXz88cfUrl2bQoXkaEsh0hKbpKPjgqPcCYunWugNvvT/LtXzJXZsx75ECauMHZYYxrTj09h7f69pLLcSTG0wlSr5q1hlPCFEBikKnFlj2nIrORrUttB4JDQcARq7dHcTHx/PoEGDWL16NQAtWrTgxx9/pGDBgtaKXAizbE1mu3TpQnh4OFOnTiU4OJhKlSqxY8cOihYtCkBwcHCqPWd79+5NbGwsCxYs4JNPPsHd3Z3mzZvz9ddfZ9dLECJHi4xPodrne3BKSWTnjgmpnrMtUoSSf+yyykd/iqKw5fYWZpyaQUxKDBqVhr6v9KV/5f7Y2aT/DVIIYUURAbB1KAQcMpV9a5hmYwtUyHBXCQkJ7N69G7VazZQpUxg7dqz5k1MhrC1b95nNDrLPrHhZBEYl0uCr/dR8dJXPj61I9VzhpUtwbtLEKuMGxwUz5fgUjgaaThMr71meqQ2mUs6znFXGE0JkkNEAJ5bAvs9BnwgaB2j+GdT9CNQvnoAePnwYo9FIEyv9bBEvl1yxz6wQwnoW7L/JzN03mHJsObUfXTPXu7ZtS6FvZ1plNtaoGPnl+i/MOj2LBH0Cdmo7Pqr6Eb0q9sJWLaf7CJEjhF6FzYMh8C9TuVgj6DAPPDO21Cg2NpYBAwbQtm1b3nvPdMR1o0aNLB2tEOkiyawQeUhsko6aX+wlWW/k3Wu7UyWyxX79FYdKFa0ybmRSJKP+HMWJkBMAVM1flSkNplDCzTprcYUQGaRPgSOz4dA3YNSBvSu0+hyq94IM/nJ77tw5OnfuzM2bN9mxYwft27eXTzpFtpJkVog84sfj9/hs0yUAWt89QY9ru83Plbt4AZWVzj6PSopi2IFhnA09C8CY2mPoWrYrNpn4uFIIYUGBp2HzEAi9bCqXaWM6xcs1YzdOK4rCkiVLGD58OMnJyfj5+bFu3TpJZEW2k2RWiDzgnSX+nLobiVafzPAz62kcdMH8nDUT2Xsx9xi0bxD3Yu4BMKfZHFoUkW14hMgRUhLg4HQ4tgAUIzh6QZsZUKlThmdjo6Oj6devH7/88gsA7dq1Y9WqVXh5eVkjciEyRJJZIXKxA9dC6bPqFAB1gi8z+cTKVM+X/GOX1RLZ049OM+zAMKKTo/Fx8mFhi4WU9ihtlbGEEBl09whsGQIRd0zlV96B174Gp4wnn/Hx8dSoUYPbt2+j0Wj4+uuvGT58uByCIHIMSWaFyIUUReGrXddY+ucd1IqRz/2XUf3xTfPzdsWLU2z9Omys9PHf1ttbmeQ/CZ1RRyWvSsxvMZ98DvmsMpYQIgOSYmDvJPjre1PZpRC0mw1lX3vhLp2cnOjUqRPr169n/fr11KlTx0LBCmEZsjWXELnMg4gEGs04AEDpyAfM+3NuqudLHz6EJn9+q4ytKAqLzy9m8fnFALxa9FWmNZyGg8bBKuMJITLgxh+wbTjEBJrKNfrAq1NA65bhriIjI4mPj8fPzw8AnU5HXFwcHh4eloxYiGeSrbmEyKMO33xMjxUnQVFode8kw8/9kur5MidPWG02NtmQzMSjE9kRYDrusm+lvgyrPgy1Sm2V8YQQ6RQfDrvGwMUNprJHcegwH4q/2FZZJ06coEuXLhQsWJDDhw9ja2uLra2tJLIix5JkVohcIigqkR4rTqJSjMz+cz5lox6YnlCpKDTzG9xef91qY0cmRZp3LNCoNHxW9zM6lelktfGEEOmgKHBpI+wcDQnhoFJDvUHQdBzYOb5AdwqzZs1izJgx6PV6bGxsCAwMpFixYpaPXQgLkmRWiFzgfngCzb49iGdiNGv/+Nxcr3Z1pcSm37EtlLEtdjIiIDqAQfsG8SD2AS62LsxqNou6PnWtNp4QIh1igmD7J3Dd9EkJ3hWh43zTkbQvIDw8nN69e7Nt2zYA3nnnHZYtW4abW8aXKAiR1SSZFSKHuxocQ5u5h7Ez6Fi9e9r/n1CpKHP0iNV2KwA4FXKKjw98TExKDL7OvixqsYgS7nIQghDZRlHgzGrYPQGSY0BtC41HQcPhoLF7oS6PHj1K165defjwIfb29syZM4f+/fvLbgUi15BkVogc7oPVpmMnf9k+AY1iBMB79Gi8+vax6ribb21m8rHJ6I16quSvwtxmc/FykD0lhcg2EXdgy1C4e9hU9q0JHReAd/kX7lJRFIYPH87Dhw8pXbo0GzZsoGrVqpaJV4gsIsmsEDnY7D03CIxKZPzJ1dgZ9QDYly9v1UTWqBhZcHYByy4uA+C1Yq/xeYPP0Wq0VhtTCPEcRgMcXwz7vwB9ImgcoMUEqDMAMnnSnkqlYu3atXz99dfMnj0bFxcXCwUtRNaRrbmEyKG+2nmN7w7e5KsjS3gl/I653poneiXpk5hwdAK77u4CoN8r/RhcbbDsWCBEdnl0BbYMNh1JC1C8MbSfB57FX7jLP//8k3PnzjFs2DALBSmE5cnWXELkclvOB7F1+3HWH5yLsz4JALWLC6X/PGi1RDYiKYKh+4dy/vF5NGoNk+tNpmOpjlYZSwjxH/QpcGQWHJoJRh3Yu0KrL6B6zwwfRfuEwWDgyy+/ZPLkySiKQvXq1WnU6MW27xIiJ5FkVogc5sfj9zg4byXLz6w319nkz0eZw4etNmZAdAAf7f2IwLhAXO1cmdNsDrUK1rLaeEKI5wg8DZsHQ+gVU7lsW3j9W3B98V1LQkJC6N69O/v27QOgV69eVK9e3RLRCpHtJJkVIgcxGhV++HEPc/6RyDo3aYLfgvlWG/PMozMMPTCU6ORoCrsUZmGLhRR3e/GPMIUQLyglAQ5Mg+OLQDGCYz5oOwMqvvXCs7EA+/bt47333uPRo0c4OjqyaNEievXqZcHAhchekswKkYMsGfIFc/b9ZC6XPXMatWPGNz9Pr823NjPl2BR0Rh3lPcuzqOUi8jnks9p4QohnCDgMW4ZAZICpXLkLtJ4OTpnbQWT69OmMHz8eRVGoVKkS69evp0KFChYIWIicQ5JZIXIARVFo+tVelvwjkS28fLnVElmjYmTumbl8f+l7AFoWacm0htNwtLVe4iyESENSNOyZCKdXmcquvtBuNpRpbZHuvb29URSFDz74gLlz5+JoxV+OhcgukswKkQO88fUulqweYS7nW7YC54b1rTJWgi6BsYfHsv/BfkB2LBAi21zfBduGQ2yQqVzzfWg5GbSZ22knLi4OZ2dnAPr27UvZsmVp2LBhJoMVIueSZFaIbNZowma++2WMuezW6S3yN7JOIhsSH8LgfYO5HnkdW7UtU+pPoX3J9lYZSwjxDPFhsPNTuPSrqexZAjrMh2KZSzj1ej0TJkzg559/5syZM3h6eqJSqSSRFXmeJLNCZKMP1/zFB4dWmcsubdtSaNq0ZzfIhAuPLzB0/1DCk8Lx1Hoyt9lcqnpXtcpYQog0KApc2gg7R0NCOKjUUH8INB0Ltg6Z6vrBgwd069aNo0ePArBx40b69etniaiFyPEkmRUimyw8cIuyPy2k9qNrAOQbPZr8VjrZa2fATj478hkpxhRKe5RmQfMFFHJ+8W1+hBAZFB0I20fADdOBJHhXNB1F65v57bG2b99Oz549iYiIwNXVlWXLltG5c+dM9ytEbiHJrBDZYPQv56gybyLVH9801+XrbfmtcoyKkcXnF7Pk/BIAmvg14evGX+Nk62TxsYQQaTAa4cxq001eyTFgYweNR0ODYaCxy1TXKSkpjBs3jm+//RaAGjVqsH79ekqWLGmJyIXINSSZFSKLLdhyhj4T3ktVV/bcWVRqy96AlahPZMLRCfxx9w8AelfszcfVP8Ymk2e5CyHSKfw2bB0Gd/8+8MSvFnRYAN7lLNL95MmTzYns0KFDmTFjBvb29hbpW4jcRJJZIbJIQoqeChN28ev2CeY6ldaBMv5HUGu1Fh0rNCGUofuHcjn8Mhq1hol1J/Jm6TctOoYQ4hmMBtPBB/ungT4RbB2hxUSo/SFY8JfJkSNHsmPHDiZNmsSbb8r/b/HykmRWiCyy9MBNFu+fiZM+CQDnfh/iN+JjVJk42SctV8KvMGT/EEITQnG3d2dW01lyNK0QWeXRFdg8CILOmMrFm0D7ueCZ+VP1kpOTWb9+PT169EClUuHp6cmZM2dQW/hTHSFyG0lmhcgCyw7dwXfqSIrFPgLA5dWW+H0y3OLj7L23l7GHx5JkSKKEWwkWNF9AYdfCFh9HCPEv+hQ4/K3py6gDezdoPQ2qdc/UUbRP3Llzh86dO3P69GmSk5PNOxVIIiuEJLNCWN2YjRfYfegSa8PvAGBbvDh+8+dbfJx5Z+ax/OJyFBQaFGrAN02+wcXOxeLjCCH+5eFp02zs46umctnX4fVvwdXHIt3/+uuvvP/++8TExODp6YmPj2X6FSKvkGRWCCtacSSAdace8Nver811JbdstugYOqOOb059w8/XfgagTsE6LGixAI1a/nsLYVUpCXBgmml9rGIEp/zQ9huo8IZFZmOTkpL45JNPWLRoEQD169dn3bp1FC4sn7YI8U/ybieElYTGJrFkw1G+8/8OB0MKAO7duqKytbXYGFFJUYz8cyQnQk4AcjStEFkm4BBsGQKRd03lyl3hteng6GmR7m/evEnnzp05d+4cAGPGjGHq1KnYWvDnhxB5hSSzQlhJ11FrWHNwtrlsX7YsPpMmWaz/W5G3GLJ/CA/jHuKocWR6o+k0L9LcYv0LIdKQFA27J5j2jgVw9YP2c6D0qxYd5uHDh5w/f558+fLxww8/8Nprr1m0fyHyEklmhbCClbsvMe/Pueay9+jRePbpbbH+Dz44yKeHPiVBn4Cvsy/zms+jjEcZi/UvhEjD9Z2wbTjEBpvKtT6AFpNA62qR7hVFMe9u0qxZM1atWkWLFi3w9fW1SP9C5FXyWaQQFnY5KJrKI7pjoxgB8Jk+Ha++fSyyBZeiKCy7sIyh+4eSoE+gdsHa/Pz6z5LICmFN8WHwa1/4uaspkfUsCb13mG7yslAie/XqVRo2bMiNGzfMdT179pREVoh0kGRWCAuKik8hrnVzHPXJABhfa4/7m29YpO9EfSKjD41m3tl5KCh0LduVJa8uwUPrYZH+hRD/oihw4RdYUAsubQSVDTT4GD46CsUaWGyY1atXU7NmTfz9/Rk6dKjF+hXiZSHLDISwEKNRYd+rHaigSzDXVfh2ukX6DokPYej+oVyNuIpGpWFsnbF0LtvZIn0LIdIQ/RC2jYCbpuOgKfAKdJwPhapZbIj4+HgGDRrE6tWm9bfNmzdn1apVFutfiJeFJLNCWEBQVCJLeo6iW8Q9c125q1cssrTgXOg5Pj7wMeFJ4XjYezCr6SxqFqyZ6X6FEGkwGuHMKtg9EVJiwcYOmow2zcjaWG4ngUuXLtG5c2euXr2KWq1m8uTJjBs3Dhsbyx13K8TLQpJZITLp7q0HnOrVn27hAQAkuHpQ/eifFklkN93axNRjU9EZdZTxKMO85vPwdZY1dEJYRfht2DIU7h0xlf1qQ8cFkL+sRYc5ceIEzZo1IzExER8fH3766SeaNm1q0TGEeJlIMitEJih6PYntWlHp77JBbUP140dQZfKISb1Rz6zTs/jhyg8AtCzSkmkNp+Fo65jJiIUQTzHoTQcfHJgG+iSwdTTtUlC7H6gtP1NavXp1qlSpgqurKz/88APe3t4WH0OIl4kks0K8IGNKCteqVOXJ/Gu8VwGq796e6UQ2LiWOjw9+zIlg00EIH1X5iAFVBshBCEJYQ8gl2DIYgs6ayiWaQvu54FHMosNcuXKF0qVLY2tri62tLdu3b8fd3R11Jn9eCCFkNwMhXkjyzZtcr1wFlaIAsLFkY2ocOYDaySlT/eqMOtOJXsEn0NpomdV0FgOrDpREVghL0yfDgS/huyamRFbrBh0XQo9NFk1kFUVhyZIlVK9enfHjx5vrPT09JZEVwkJkZlaIDDImJXGnfQdz+VjBijSfOy3Ta2QVRWHa8WkcDTqK1kbL4paL5UYvIazhwSnTbOzja6ZyuXamPWNdClp0mJiYGPr168eGDRsA016yBoNBbvISwsIkmRUiA4yJiVyvVt1cnlP1HcYt/JSiXpmbkQVYcWkFG29uRK1S802TbySRFcLSUuJh/zTT+lgUcMoPbWdChY5ggRs2/+n06dN06dKF27dvo9FomD59OiNGjJDZWCGsQJJZITLgft/3zY/Xl27OZ4vGUNgz8zdl7bizg7lnTMffjqk9hqaFm2a6TyHEP9w5aNqpIOrv7fOqdIPWX4Kjp0WHURSFBQsWMHLkSFJSUihatCjr1q2jbt26Fh1HCPF/kswKkU6BI0eReNZ0k8iOYnXJN/xjiySypx+d5rOjnwHQs0JPupXrluk+hRB/S4yCPRPgzBpT2a0wtJsDpVtaZbjAwEDGjRtHSkoKb7zxBt9//z0eHnJKnxDWJMmsEOkQvXkzMdu2AfBHkdqEfTiC6c1LZ7rfgOgAhu4fis6oo2WRlnxS85NM9ymE+Nu17aZTvOJCTOVa/aDlJLB3sdqQfn5+LFu2jNDQUIYMGWKR/aaFEM8nyawQ/yFm1y6CPh0DQLSdI3Oqd+buW69kut/wxHAG7h1ITEoMlfNV5stGX8quBUJYQtxj2DkaLv9mKnuVgg7zoWh9iw+lKAqzZ8+mWrVqNGvWDICuXbtafBwhxLNJMivEc0Rfu0HQx8PN5b6vjuOvzzL/8WSSPomh+4fyMO4hfs5+zGs+DweNQ6b7FeKlpihwYQPs+hQSI0FlAw2GQpMxYKu1+HARERH07t2brVu3UrBgQa5cuSJLCoTIBpLMCvEMj6ISiHijo7n8QctPOT/jTWxtMjd7alSMjD08lgthF3C1c2VRy0V4OXhlNlwhXm7RD2HbcLi521Qu+Ap0WACFqlplOH9/f7p27cqDBw+wt7dn4sSJuLu7W2UsIcTzSTIrRBoURSGsXi3zqSLrG77Ln3N7oslkIgsw669Z7L2/F1u1LXObzaW4W/FM9ynES8tohNPfw57JkBILNnbQ5FNoMAxsbK0wnJFvvvmG8ePHYzAYKF26NBs2bKBq1aoWH0sIkT6SzAqRhm1d+lFKMQKQWKkak5dPsEi/P1/7mdVXVgPweYPPZS9ZITIj/DZsGQL3jprKheuYZmPzl7HKcImJiXTq1ImdO3cC0K1bN5YuXYqLi/VuKBNC/DdJZoX4l4Brdyl1wfTm+JdvJXr8+pNF+j344CBfnfwKgKHVhvJ6idct0q8QLx2DHo4tgIPTQZ8Etk6mXQpq9QMrHkqg1Wpxd3dHq9Uyb948PvjgA9mtQIgcQKUofx8u/5KIiYnBzc2N6OhoXF1dszsckcM8ikni1KuvUzI6iFhbB0oeO4aXs32m+70afpVeu3qRqE/krdJvMbneZHkTFOJFhFyEzYMh+JypXKIZtJ8LHkWtMpzBYCApKQknJ9Mpf7Gxsdy7d49KlSpZZTwhhElG8jWZmRXib8fvhHOjzwfUig4CwDhirEUSWYC5Z+eSqE+krk9dPqv7mSSyQmSUPhkOfQNHZoNRD1o3aD0dqr5r8aNon3j06BHdu3fH2dmZ3377DZVKhYuLiySyQuQwkswKAWw49YClK3aw4NE1AFJavU7dPu9YpO9dAbs4GmhatjC61mhs1Za/KUWIPO3BSdNsbNh1U7l8e2j7LbgUsNqQ+/fv57333iMkJARHR0euXbtG+fLlrTaeEOLFyQ7t4qV34HooC1buZsHBOea6ynO/sUjfpx+dZtyRcQB0L9+d0h6ZPzVMiJdGSjzsHAMrWpkSWSdv6LwGuvxotUTWYDAwadIkWrZsSUhICBUrVuTUqVOSyAqRg8nMrHipxSfrGbz0EL/un2muKzB+vEWWAfzzqNoWRVowsubITPcpxEvj9gHYOhSi7pvKVd6F1tPA0dNqQwYFBfHee+9x8OBBAN5//33mzZuHo6Oj1cYUQmSeJLPipdb3i438umOyuey3aBEuzZtlut+wxDA+2vuR+aja6Y2mY6O2yXS/QuR5iVGwezyc/dFUdisM7edAqcyfvPc8iqLQsWNH/vrrL5ycnFi6dCnvvfeeVccUQliGLDMQL60Td8IZ/ft0c9mlVSuLJLIJugSG7BtCYFwghV0KM7/FfDmqVoj0uLoNFtb5O5FVQe0PYeAxqyeyACqVinnz5lGjRg3OnDkjiawQuYjMzIqX1snPv6WlPhmAglOn4NG5c6b7NBgNfHr4Uy6FX8Ld3p1FLRbhqbXex6JC5AlxobBjFFzZZCp7lYYO86FoPasO+/DhQ86dO0e7du0AqFevHqdOnZLdRoTIZV5oZjYqKorly5czduxYIiIiADhz5gyBgYEWDU4IaxkyfjUtj/0OgNG3sEUSWUVR+OrkVxx8cBA7tR3zm8+nmFuxTPcrRJ6lKHB+HSysbUpkVTbQcAQMOGL1RHbHjh1UrVqVzp07c+nSJXO9JLJC5D4Znpm9cOECLVu2xM3Njbt379KvXz88PT35/fffuXfvHmvWrLFGnEJYzMHtRxi48Stzuex6y5zwtebKGtZdX4cKFdMbTaeqd1WL9CtEnhT1ALYNh1t7TOWCr0DHheBTxarD6nQ6xo8fzzffmHYsqV69Og4OsgxIiNwswzOzI0aMoHfv3ty8eROtVmuub9OmDYcOHbJocEJYmi40lAKf9DOXS2zbiiZfvkz3+8fdP5j5l2lHhE9qfkKrYq0y3acQeZLRCCeXwaK6pkTWxh5aTIR+B6yeyN67d4/GjRubE9khQ4bg7+9PyZIlrTquEMK6Mjwze+rUKZYuXfpUva+vLyEhIRYJSghrudW4ifmxw4zZ2Jcqlek+z4aeZdxh016y75Z7l54Vema6TyHypLBbsGUI3Pc3lQvXNa2NzV/G6kNv3ryZPn36EBkZiZubG99//z1vvfWW1ccVQlhfhpNZrVZLTEzMU/XXr18nf/78FglKCGu48d0q8+PNbT9kTIfXMt3n3ei7DNk/hBRjCs0KN2N0rdGy5k6IfzPo4dh8ODAdDMlg6wQtJ0OtD0CdNZvqnDlzhsjISGrXrs26desoXrx4lowrhLC+DCezHTt2ZOrUqWzYsAEwLZa/f/8+Y8aMoVOnThYPUAhLOL3rMI6zvgYgQWNP38kfZbrPkPgQ+u/pT3RyNK/ke4WvG38te8kK8W8hF2HzIAg+byqXbGHaN9a9iNWHVhTF/MvlxIkT8fb2pl+/ftjZ2Vl9bCFE1snwr8QzZ87k8ePHeHt7k5iYSJMmTShVqhQuLi5MmzbNGjEKkSnn74aRMnq4uZywYCXertrntPhvCboEhuwfQlB8EH7OfsxvLnvJCpGKLgn2fQ7fNTUlslp3eGMxdN+YJYnsxo0bad68OUlJSQDY2NgwaNAgSWSFyIMyPDPr6urKkSNH2L9/P2fOnMFoNFK9enVatrT+ptZCZFREfAprR3xJn5R4AGK+mE2jptUy1adRMTL+yHiuRVzDU+vJ8tbL8XLwskS4QuQN90/AlsEQdsNUrtAR2nwDLgWsPnRSUhIjR45k4cKFACxcuJBPPvnE6uMKIbJPhpPZNWvW0KVLF5o3b07z5s3N9SkpKaxbt46ePeXmF5Ez6AxGbtWtSx9doqn8VlfqvJ35dbILzy1k7/292KptmdNsDr7OvpnuU4g8ITkO9n8OJ5YCCjh5w+vfQoUOWTL8zZs36dKlC2fPngXg008/ZejQoVkythAi+2R4mUGfPn2Ijo5+qj42NpY+ffpYJCghMutqcAyvD1yKy9+JLMArU8Zlut8dd3bw3YXvAJhUbxLVvDM3yytEnnF7PyyuByeWAApU7Q6DT2ZZIrtu3TqqV6/O2bNnyZcvHzt27OCrr77C1tY2S8YXQmSfDM/M/nNB/T89fPgQNzc3iwQlRGYoikKn+YdY5b/MXFfu6pVM7zJw8fFFJhydAECfin3oWKpjpvoTIk9IjIQ/PoNzP5rKbkVMN3iVapFlIXz77beMHDkSgEaNGvHzzz/j6yufmAjxskh3MlutWjVUKhUqlYoWLVqg0fy/qcFgICAggNdey/xHuEJk1uEbj1m2fap5VrbIqpWZTmRD4kMYemAoKcYUmvg1YVj1YZYIVYjc7epW2P4JxD0CVFCnPzSfAPbOWRpGp06d+PLLLxk4cCCTJk1K9f4khMj70v0//o033gDg3LlztG7dGmfn//+wsrOzo1ixYrI1l8h2iqKw59MveDc5FgDXtm1xqls3U30m6BIYun8oYYlhlHIvJVtwCRH7CHaOgiubTeV8ZUyHHxTJ3P+1jDh79izVqpmW+RQrVoybN2/i6emZZeMLIXKOdCezkyZNAkw/NLp06ZLqKNvMWLRoEd988w3BwcFUrFiROXPm0KhRo2den5yczNSpU/nxxx8JCQnBz8+P8ePH07dvX4vEI3K3xV+v4d1rprPekytUodC3MzPVn1Ex8tnRz7gacRUPew8WtFiAk62TJUIVIvdRFDi/DnaNgaQoUNlAw+HQeBTYWuY94b/Ex8czZMgQVq5cyfbt22nbti2AJLJCvMQy/FlMr169LDb4+vXr+fjjj1m0aBENGjRg6dKltGnThitXrlCkSNr7EHbu3JlHjx6xYsUKSpUqRWhoKHq93mIxidwrJTyCZqu+Mpcrr8388oLF5xez594eNGqN7FwgXm5R92Hrx3B7n6lcsDJ0XAg+lbMshMuXL9O5c2euXLmCWq3m+vXr5mRWCPHyUimKomSkgcFgYPbs2WzYsIH79++TkpKS6vmIiIh091WnTh2qV6/O4sWLzXXly5fnjTfeYPr06U9dv2vXLrp27cqdO3de+LfwmJgY3NzciI6OxtXV9YX6EDmPoihcqVQZtcH0i43d0lWUbFInU33uCtjFqEOjAJhafypvln4z03EKkesYjfDXCtg7GVLiwMYemo2FekPAJmvWpiqKwsqVKxk8eDCJiYkULFiQn3/+maZNm2bJ+EKIrJeRfC3DW3NNmTKFWbNm0blzZ6KjoxkxYgRvvfUWarWayZMnp7uflJQUTp8+TatWrVLVt2rVCn9//zTbbNmyhZo1azJjxgx8fX0pU6YMI0eOJDExMc3rwbQsISYmJtWXyHsOd+1rTmQ3vto304nspbBLfHb0MwB6Vegliax4OYXdhFVtYcdIUyJbpB58dNS0tCCLEtm4uDh69uzJ+++/T2JiIq1ateL8+fOSyAohzDL802jt2rUsW7aM119/nSlTptCtWzdKlixJ5cqVOX78eLo3qA4LC8NgMFCgQOoTYQoUKEBISEiabe7cucORI0fQarX8/vvvhIWFMXDgQCIiIvj+++/TbDN9+nSmTJmSsRcpcpWwu4F4nT9hLo+aPSJT/YUmhPLhng9JNiTT2K8xw2sM/+9GQuQlBh34z4eDX4EhGeycoeVkqPk+qDM8B5Ipu3fv5scff8TGxobPP/+cTz/9FHUWxyCEyNky/BMhJCSEV155BQBnZ2fzAQrt2rVj+/btGQ7g32san7WPLYDRaESlUrF27Vpq165N27ZtmTVrFqtWrXrm7OzYsWOJjo42fz148CDDMYqcKzpBx/nO76LGtFrG+8gx7DUvvtOAzqhj1J+jiE2JNe1c0Eh2LhAvmeDzsKw57JtiSmRLtYSBx6B2vyxPZAHeeustxo4dy8GDBxk7dqwkskKIp2T4p4Kfnx/BwcEAlCpVit27dwNw6tQp7O3t091Pvnz5sLGxeWoWNjQ09KnZ2id8fHzw9fVNdThD+fLlURSFhw8fptnG3t4eV1fXVF8i75g+cwOFYkIBeDxmGl753DPV37d/fcuZ0DM42Toxq+ksnO2ydr9MIbKNLgn2TYXvmkHIBXDwgDeXwnu/gnvaN+RaQ0xMDIMGDSI0NNRc9+WXX9KwYcMsi0EIkbtkOJl988032bfPdDfrsGHDmDBhAqVLl6Znz54Z2h7Lzs6OGjVqsGfPnlT1e/bsoX79+mm2adCgAUFBQcTFxZnrbty4gVqtxs/PL6MvReRym84G8sqe9QAYbTQ07v1Wpvrbensra6+uBeDLhl9S3K14pmMUIle4fxyWNITD34JigApvwKCTUKUrZHJHkIw4c+YM1atXZ9GiRbz//vtZNq4QInfL8G4G/3bixAmOHj1KqVKl6NAhY2dwr1+/nh49erBkyRLq1avHd999x7Jly7h8+TJFixZl7NixBAYGsmbNGsB0I0D58uWpW7cuU6ZMISwsjA8++IAmTZqwbNmy/xjNRHYzyBsMRoVWA5ex4OBsAHxmfI17Bv/9/dOV8Cv03NmTZEMyA6oMYFDVQZYKVYicKznONBt78jtAAecC8Pq3UL59loahKAoLFy7kk08+ISUlhSJFirBu3Trq1auXpXEIIXKOjORrGboBTKfT8eGHHzJhwgRKlCgBmLbXqlPnxe4c79KlC+Hh4UydOpXg4GAqVarEjh07KFq0KADBwcHcv3/ffL2zszN79uxhyJAh1KxZEy8vLzp37swXX3zxQuOL3Ov3s4G8e820xIVSZTKVyEYmRfLxgY/NN3x9VOUjC0UpRA52a59p39jov3/GVusOrb4wLS/IQlFRUbz//vv89ttvAHTo0IGVK1fKIQhCiHTL8Mysu7s7Z86cMSezuY3MzOZ+OoOR6hO2seG3TwEoMG4snj17vlBfeqOeAXsGcCLkBEVcivBzu59xtZN/FyIPS4iA3Z/BOdOSGtyLQPt5ULJZlody7do12rZtS0BAALa2tnzzzTcMHTo004edCCFyP6vuM/vmm2+yadOmF41NiEyb+cd1phxYZC67ZWJWds7pOZwIOYGDxoG5zeZKIivytiubYWGdvxNZFdT5CD46li2JLEChQoWwsbGhePHiHD16lGHDhkkiK4TIsAzvM1uqVCk+//xz/P39qVGjBk5Oqc+pT+8+s0K8iNDYJO6t+Yk3Iu8BkG/QIGzc3V+or50BO1l9ZTUA0xpOo5RHKUuFKUTOEvvIdPDB1S2mcr6y0HEBFK6d5aHExMTg4uKCSqXC1dWVbdu2UaBAAdxf8P+xEEJkeJlB8eLPvsNbpVJx586dTAdlTbLMIPdK0hloM+sAi1YOAUDlV5hye3e/UF/XI67TfUd3kgxJvF/pfT6u8bEFIxUih1AUOPcT/DEOkqJArTGd3tV4FGjSv5WipRw7dowuXbowatQohgwZkuXjCyFyD6vdAAYQEBDwwoEJkRmtZh+i+ZHfzeUiX09/oX6ik6MZdmAYSYYk6heqz5Bq8qYq8qDIe7DtY7i931T2qWqajS34SpaHYjQamTlzJuPGjcNgMLB06VIGDBiAra1tlscihMh7suZwbSEyQVEU3l12gpigR3S8cxgA55YtcKxRI8N9GYwGRh8aTWBcIL7OvsxoPENO+BJ5i9EIp5bB3imgiweNFpqOhXqDwSbrf+Q/fvyYXr16sXPnTgC6du3K0qVLJZEVQliMJLMix5u85TLHboexc9cUc13BsWNfqK8Zp2bgH+SP1kbL3GZzcbN3++9GQuQWj2/AliHw4LipXKQ+dJgP+bJnPfihQ4fo1q0bQUFBaLVa5s2bxwcffCA3eQkhLEqSWZHj3QmLZ/D538zlQt98g62vb4b72RWwi5+u/QTA5w0+p6xnWYvFKES2Mujg6Fz482swpICdM7w6BWr0BXWGN62xiODgYFq1akVycjJly5Zlw4YNVK5cOVtiEULkbZLMihwtOlFH5LETvH73GACevXri1r5dhvu5F3OPSf6TAPjglQ94rfhrFo1TiGwTfB42D4KQi6ZyqVeh3WxwL5ytYfn4+DBlyhQuX77MokWLcHZ2ztZ4hBB5lySzIsdSFIU+K08y8vQ6ANTOzhR4geUFSfokPjn4CQn6BGoUqCFH1Yq8QZcEf34FR+eBYjCd3PXa11C5M2TTx/gHDhzA29ubihUrAjB69GgAWVYghLCqF/r86fDhw3Tv3p169eoRGBgIwA8//MCRI0csGpx4eSmKwpCfz1J29y8USIwEwHvkyBfqa8apGVyPvI6n1pMZjWegUcvvcCKXu3cMljSAI7NNiWzFN2HQKajSJVsSWYPBwOTJk2nRogWdO3cmPj4eMCWxksgKIawtw8nsxo0bad26NQ4ODpw9e5bk5GQAYmNj+fLLLy0eoHg5bbsQjP+Ja/S89gcAjnXq4NG1S4b72XFnB7/c+AUVKqY3nI63o7elQxUi6yTHwvaRsPI1CL8FzgWhy1p4ZxU458+WkIKDg3n11VeZMmUKiqJQt25dSWCFEFkqw8nsF198wZIlS1i2bFmqrVXq16/PmTNnLBqceHn9evohE06uNpf95s3NcB8B0QFMOWbaAaFf5X7U961vsfiEyHK39sKieqZttwCq9YBBJ6B8xteQW8qePXuoWrUqBw4cwMnJiR9++IEVK1bg6OiYbTEJIV4+Gf689fr16zRu3PipeldXV6KioiwRk3jJbTobiO+WtZSLvA+A96efYuOWsS20kvRJjPxzJAn6BGoVrMXAKgOtEaoQ1pcQYTrB6/zPprJ7UegwD0o0zbaQ9Ho9kydP5ssvv0RRFCpXrsz69espV65ctsUkhHh5ZXhm1sfHh1u3bj1Vf+TIEUqUKGGRoMTLS1EUtn73Cz2umY6p1RQsiGfvXhnu56uTX3Ej8gaeWk++bvS1HIwgcqfLm2Bh7b8TWRXUHQgDj2VrIgumtbBHjhxBURT69+/P8ePHJZEVQmSbDM/M9u/fn2HDhvH999+jUqkICgri2LFjjBw5kokTJ1ojRvES2Xk+kJEHlprLpfbtzfD6u213trHx5kZUqPi68dfkd8yetYRCvLDYENgxEq5uNZXzl4MOC6BwrWwNS1EUVCoVNjY2/PTTTxw5coTOnTtna0xCCJHhZHb06NFER0fTrFkzkpKSaNy4Mfb29owcOZLBgwdbI0bxklAUhfOTvqT432XfOXNQ2WRsRvVy2GUmHjX9UjWgygDq+tS1cJRCWJGiwLm1pmUFSdGg1kDDEdB4JGjssy0snU7H+PHjSU5OZu5c0/r1QoUKSSIrhMgRVIqiKC/SMCEhgStXrmA0GqlQoUKu2RA7JiYGNzc3oqOjcXV1ze5wxD+c/esq2u5vAaCuWp2y69ZmqL1RMdJ1W1euRlylTsE6LH11qSwvELlH5D3YOgzuHDCVfapCx4VQsFK2hnX//n26du3KsWOmg0vOnTtHlSpVsjUmIUTel5F8LcMzs6tXr+btt9/GycmJmjVrvnCQQvxb0shhaP9+XPr7ZRluv/nWZq5GXAVgcv3JksiK3MFogJPLYN9U0MWDRgvNxkHdQWCTvXsib9myhd69exMZGYmbmxsrVqyQRFYIkeNk+AawkSNH4u3tTdeuXdm2bRt6vd4acYmXzLUZc3APeQBAaI8BqDO4tU+8Lp55Z+cBMLLmSPxc/CweoxAW9/g6rGwDuz41JbJFG8BH/tBgWLYmsikpKQwfPpyOHTsSGRlJrVq1OHv2LJ06dcq2mIQQ4lkynMwGBwezfv16bGxs6Nq1Kz4+PgwcOBB/f39rxCdeAnFnz6F8//+bvmp+8lGG+1h2YRlhiWEUdS3Ku+XetWR4QlieQQeHvoElDeHBCbBzgddnQa9t4FUyW0NTFIX27dszZ84cAIYPH86RI0coXrz48xsKIUQ2yfCv/hqNhnbt2tGuXTsSEhL4/fff+emnn2jWrBl+fn7cvn3bGnGKPOzs2Cnk+/vxw+82UF5rl6H2D2IesObKGsA0K2trY/sfLYTIRkHnYPNgeHTRVC7dCtrNBrec8WmCSqWif//+nDp1ilWrVtGhQ4fsDkkIIZ4rU59jOTo60rp1ayIjI7l37x5Xr161VFziJRFy6Rr57l4D4NR7w+jZ+JUM9/Ht6W/RGXXU86lHE78mlg5RCMvQJcLBr8B/PigGcPCENl/DK+9ANh//mpSUxM2bN3nlFdP/v7feeovmzZvj7u6erXEJIUR6ZHiZAZh2Mli7di1t27alUKFCzJ49mzfeeINLly5ZOj6Rx10cNgqAex6+vDvuwwy3PxF8gn3392GjsmF0rdFyJrzIme75m5YUHJ1jSmQrdYJBJ6Fy52xPZG/dukX9+vVp3rw5gYGB5npJZIUQuUWGZ2a7devG1q1bcXR05J133uHgwYPUry9n3ouMO3v8In6BptPkDB8NQ2OTsd+t9EY9X5/6GoDOZTtTyqOUxWMUIlOSY2HvZDi13FR28TGtjS3XNlvDemL9+vX069eP2NhYvLy8CAgIwNfXN7vDEkKIDMlwMqtSqVi/fj2tW7dGo8nebWNE7qUoClHDhlAQeOiUj9d7ts9wH7/d/I2bkTdxtXNlYJWBlg9SiMy4uQe2fgwxD03l6r3g1ang4J6dUQGQmJjI8OHDWbrUdONlw4YN+fnnn/HzyxnrdoUQIiMynI3+9NNP1ohDvGRWDZlK3ehHABjbv5nh9tHJ0cw/Ox+AgVUH4q51t2R4Qry4hAjYNRYurDOVPYpB+3lQImes575+/TqdO3fmwoULqFQqxo0bx+TJk2VyQgiRa6Xrp9e8efP48MMP0Wq1zJs377nXDh061CKBibwrMiiEWnvXmx7bO9NgVMZnVZecX0JUchQl3UrSuawcqSlyAEWBK5tgxyiIfwwqNdQdaDoAwc4pu6Mzmzt3LhcuXMDb25sff/yRV199NbtDEkKITEnXcbbFixfnr7/+wsvL67l7DapUKu7cuWPRAC1NjrPNfn82ehXvx6aPXkufPIEmg38Pd6Lv0GlzJ/SKnqUtl1LfV9Zsi2wWGwLbP4Fr20zl/OVMR9H65bxTEuPj4xk+fDhTpkzBx8cnu8MRQog0Wfw424CAgDQfC5FRjzdtMSey2xp1YVQGE1lFUZhxagZ6RU8TvyaSyIrspShw9kf4YzwkR4NaA41GQqMRoLHP7ugAuHz5MkuXLmXOnDmo1WqcnJz47rvvsjssIYSwmAxvzTV16lQSEhKeqk9MTGTq1KkWCUrkTYqiEDbmUwBibB0ZtPCzDPex9/5ejgYexVZty8iaIy0dohDpF3kXfngDtgw2JbKFqkH/Q9BsbI5IZBVFYeXKldSqVYv58+f/5xIxIYTIrTKczE6ZMoW4uLin6hMSEpgyZYpFghJ506P5C8yPH/QfhaNdxm44idfF89XJrwDoW6kvxdyKWTI8IdLHaIDji2FRPbhzEDRaaPUFvL8XClTM7ugAiIuLo1evXvTt25fExERatWrFu+/KMc9CiLwpw7evKoqS5sb058+fx9PT0yJBibzp/g/rcME0K/tav7cz3H7xucWEJoTi5+zHB698YPkAhfgvoddgyxB4eNJULtoQOswDr5LZG9c/XLhwgc6dO3P9+nXUajWff/45Y8aMQa1+oTNyhBAix0t3Muvh4YFKpUKlUlGmTJlUCa3BYCAuLo4BAwZYJUiR+x1btBr32AgALk5eQB37jP0edSPyBj9e/RGAcXXGodVoLR6jEM9k0MGROXBoBhhSwM4FWn1u2js2ByWJ69evp3fv3iQlJeHr68vPP/9Mo0aNsjssIYSwqnRnFHPmzEFRFPr27cuUKVNwc3MzP2dnZ0exYsWoV6+eVYIUuV/imtW4//34/bfqZqitUTHyxfEvMCgGWhZpSSM/eXMWWSjoLGweDI/+Pq67dGtoNxvcct5JWaVKlcJoNNKmTRvWrFlDvnz5sjskIYSwunQns7169QJM23TVr18fW1tbqwUl8pYbR07jExUMwMP3h1M+g2fRb761mbOhZ3HQOPBp7U+tEaIQT9MlwsHp4D8fFCM4ekGbGVCpE2Tw37A1RUVF4e7uDkCNGjU4duwYVatWlWUFQoiXRrp+2sXExJgfV6tWjcTERGJiYtL8EuLf/vz+VwCinT14ddSHGWoblRTFrNOzABhYZSAFnQpaPD4hnnL3KCxuAEfnmhLZSm/DoJPwyts5JpFVFIWFCxdStGhRzpw5Y66vXr26JLJCiJdKumZmPTw8CA4OxtvbG3d39zRvAHtyY5jBYLB4kCL3SroTQD3/zQDENG2d4fZzz84lKjmKUu6leK/Ce5YOT4jUkmJg72T4a4Wp7FII2s2Csm2yNax/i4qKol+/fvz6q+kXxVWrVlG9evVsjkoIIbJHupLZ/fv3m3cqOHDggFUDEnmHotMR0LYtNph2MKjy6bAMtT//+Dy/3jC9WX9W9zNs1bK0RVjRjd2wbTjEmA71oEZveHUqaN2e2yyrnTp1ii5duhAQEICtrS0zZsxg2LCM/d8SQoi8JF3JbJMmTdJ8LMTz3P7qW/PjAx0/ZEx+93S31Rv1fHH8CwA6luxIjQI1LB2eECbx4fDHWLiw3lT2KG7abqt44+yN618URWHu3LmMHj0anU5H8eLFWb9+PbVq1cru0IQQIltleGHVrl27OHLkiLm8cOFCqlatyrvvvktkZKRFgxO5V8KZs+jWrgbgUr4SfDwpY/vCrr++nmsR13C1c2VEzRHWCFG87BQFLv0GC2ubElmVGuoNho/8c1wiC7Bx40aGDx+OTqejU6dOnDlzRhJZIYTgBZLZUaNGmW/0unjxIiNGjKBt27bcuXOHESMk6RAmobNnmx+HDRuP1tYm/W0TQpl/dj4Aw6oPw1Mrh3EIC4sJhnXvwa99ICEMvCuYTvBqPQ3sHLM7ujS99dZbdOjQgQULFvDLL7+YdzAQQoiXXYZPAAsICKBChQqAaaagffv2fPnll5w5c4a2bdtaPECR+xhiYkg8dQqAqbV7s7xD7Qy1n3lqJvG6eF7J9wpvl8n4SWFCPJOiwNkf4I/PIDka1LbQeCQ0HAEau+yOLhWj0ciKFSt47733cHR0RK1Ws2nTpjRvwBVCiJdZhpNZOzs7EhISANi7dy89e/YEwNPTU7bmEgAETplqfuzashlOGTjt61jQMXbe3Ylapeazup+hVskWQ8JCIgJg61AIOGQq+9aADgugQIXsjSsNYWFh9OrVix07dnD8+HFWrDDtriCJrBBCPC3DyWzDhg0ZMWIEDRo04OTJk6xfb7pp4saNG/j5+Vk8QJG7KAYD8du3A7CjWF2mvlE53W1TDCl8eeJLALqW7UoFr5yXZIhcyGiAE0th/+egSwCNAzT/DOp+BOr0L3/JKocPH6Zbt24EBgai1WqpU6eOeetDIYQQT8vwtNeCBQvQaDT8+uuvLF68GF9f05GOO3fu5LXXXrN4gCJ3CfhokPlxct+BFHDVprvtyksruRtzl3wO+RhcbbA1whMvm9CrsKKVabcCXQIUawQD/aH+4ByXyBqNRr788kuaNWtGYGAgZcuW5cSJE3z44YeSyAohxHNkeGa2SJEibNu27an62f+44Ue8vCLPXcQR2Fu4BsM6Vkt3uwexD1h2cRkAo2qOwsXOxUoRipeCPgWOzoE/Z4BRB/au0OpzqN4rx5zg9U+hoaH06NGD3bt3A9C9e3cWL16Ms7NzNkcmhBA5X4aTWQCDwcCmTZu4evUqKpWK8uXL07FjR2xsctZMh8hagX8exTEmAgB1v4HpXiurKArTT0wn2ZBMHZ86tCmes05bErlM4GnYPARCL5vKZdqYTvFyLZS9cT2HTqfjzJkzODg4sGDBAvr06SOzsUIIkU4ZTmZv3bpF27ZtzR+DKYrCjRs3KFy4MNu3b6dkyZLWiFPkcIqicHHGXIoCt90K8dHb9dLddv/9/RwOPIxGrWF8nfHyJi5eTEoCHJwOxxaAYgRHL2gzAyp1ypGzsf9cB+vr68svv/xC/vz5qVixYjZHJoQQuUuG18wOHTqUkiVL8uDBA86cOcPZs2e5f/8+xYsXZ+jQodaIUeQC8344SNHbFwFI6dITtTp9yUOCLoEvT5pu+upTsQ/F3YpbK0SRl909AksagP88UyL7yjsw6BS88naOTGRDQkJo2bIlv//+u7muadOmksgKIcQLyPDM7J9//snx48fx9Pz/RvZeXl589dVXNGjQwKLBidzh8M3HqH8wbR0U5pqPt0b0Snfb5ReXE5oQiq+zL/0q97NWiCKvSoqBvZPgr+9NZZdC0G42lM25N6Pu3buX9957j9DQUG7cuMHrr7+OnV3O2uNWCCFykwzPzNrb2xMbG/tUfVxcnPxAfglFxqewatIiWj44DUCZQR+mu63BaGDPvT0ANPZrjIPGwSoxijzqxh+wqO7/E9kafWDQ8RybyOr1ej777DNatWpFaGgolStXZu/evfJzUwghMinDyWy7du348MMPOXHiBIqioCgKx48fZ8CAAXTo0MEaMYoc7PezgYw8s85c9n6vW7rb7rq7i7sxd3Gxc5GtuET6xYfDxn7wU2eICQSP4tBrG7SfA1q37I4uTYGBgTRv3pxp06ahKAr9+/fn+PHjlC1bNrtDE0KIXC/DywzmzZtHr169qFevHra2toBpxqFDhw7MnTvX4gGKnCsqIYU5G0/x5FYv3zmzUWnS909Kb9Sz6NwiAHpX7I2rnauVohR5hqLApY2wczQkhINKDfUGQdNxYOeY3dE90+PHj6latSphYWG4uLjw3Xff0bVr1+wOSwgh8owMJ7Pu7u5s3ryZmzdvcvXqVQAqVKhAqVKlLB6cyLkURaHqlN3s3DkJAHW+/Lhm4NCMrbe3cj/2Ph72HrxX/j1rhSnyipgg2P4JXN9hKntXhI7zTUfS5nD58+enS5cu+Pv7s379ekqXLp3dIQkhRJ7yQvvMApQuXdqcwMpWSi+fiZsv0+HOEXPZoUz636BTDCksPr8YgPdfeR8nWyeLxyfyCEWBM6th9wRIjgG1LTQeBQ2HgybnrjW9f/8+tra2+Pj4APDtt9+iKApabfpPxBNCCJE+GV4zC7BixQoqVaqEVqtFq9VSqVIlli9fbunYRA5lNCr8cPwe7QL8AdD4+FDk+xXpbr/x5kaC44PJ75CfLmW7WCtMkdtF3IHV7WHrMFMi61sTBhyGpp/m6ER269atVK1alW7duqHX6wHTjbOSyAohhHVkeGZ2woQJzJ49myFDhlCvnmm15LFjxxg+fDh3797liy++sHiQImcZ+et5ykTep3DcYwB8Pv883W0T9Yl8d+E7AD6s/CFajbzBi38xGuD4Ytj/BegTQeMALSZAnQGgzrmnDKakpDB27FhmzZoFQEJCApGRkeTPnz+bIxNCiLwtw8ns4sWLWbZsGd26/f+u9Q4dOlC5cmWGDBkiyexL4OLDaD4/ZpqJty9dGqcG9dPddsP1DYQlhlHIqRCdSneyVogit3p0BbYMNh1JC1C8MbT/H3v3HRXF1QZw+Lf0JigggoogNuy9J3bF3mKLPUYTY4uaWBK7iZqYaDRFo8aSGAtGY0nsXey9QiwIYgEVRVBA2t7vj437ZQUUFFjA9zlnz2Fm7sy8uwPsO3du+R4cs/dkGkFBQXTr1o3jx48DMGLECL766isZdksIIbJAupPZpKQkqlWrlmx91apV9Y/URO6155+7xF27hkN8DAAFJoxPc5vp6IRoFl/QNUcYWHEg5qbmmRanyGES4+HgbDjwLWgTwNIemn0JVXpnyxm8/uvPP/+kX79+REZGki9fPpYtWybDFAohRBZKd5vZnj17Mn/+/GTrFy5cSI8e0is9t5ux5R/6XdqsX7atUSPN+/7u/zsRcRF42HvQplibzAhP5ES3T8HC+rBvhi6RLdUSBh+Dqn2yfSKbkJDAhAkTiIyMpHbt2pw5c0YSWSGEyGKvNJrB4sWL2bFjB7Vq1QLg6NGj3Lx5k969ezNy5Eh9uWdtx0TusO/yPa7efUz5B9cBMC9SJM37hseG8+PZHwEYVHEQZiavPJCGyC3iY2DvNDg6D5QWbJyh5Uwo2zHbJ7HPmJub4+vry8qVK5kyZYp+7G0hhBBZJ90ZxcWLF6lSpQoAgYGBgG4cxfz583Px4kV9ORmuK/dZc/ImHQIPYJMYB0CRRQvTvO/Xx78GoHje4jQvmj2nGxVZKMgPNg2FiCDdcoWu4DMDbJ2MG1carFmzhnv37jFkiG7WunLlyjF9+nQjRyWEEG+udCeze/fuzYw4RA5w4WYEX13dB4BVuXJYeHikab+bUTfZeWMnAEMqD8FE80ojwonc4Gkk7JwIp5bplu0LQevvoKSPUcNKi9jYWEaMGMGCBQswNTWlbt26VK5c2dhhCSHEG0+e9Yo0uXg7knb7VuIY9xgA14kT0rzv4ouLSVJJ1C1Ul8ZFGmdWiCK7u7wN/h4Bj+/olqu9D00mg1X2n8r48uXLdOnShfPnz6PRaBg7dizly5c3dlhCCCGQZFak0aiVJ5gdfAQAyxLFsa5QIU37hT4JZWPgRgA+rPBhpsUnsrHocNg6Bi6u1S07ekHbH8DzLePGlUa///47AwcOJDo6GhcXF37//XeaNm1q7LCEEEL8S5JZ8VJKKYqf2K1f9lyzJs37Lr20lERtIjVca1DZRR7JvlGUgovrYOtoiHkAGhOoMxQafAbm1saOLk0GDRqkH72lYcOGrFixQj9FrRBCiOxBklnxUn8cCWTQ+Q0AWLdug4l12hKR8Nhw1l1ZB+hm+xJvkMjbsHkkXNmmW3YpC+1+hEJVjBtXOnl7e6PRaJg0aRLjx4/H1DT7zkAmhBBvKklmxUsV+GSA/ueCwz9O837LLi4jXhtPxfwVqeGa9vFoRQ6m1cLpX3WdvOKiwNQC6o2Guh+DWc6YDevhw4c4OjoCMHToUOrXr0/FihWNHJUQQojUvFK38uXLl1O3bl0KFizIjRs3AJgzZw4bN27M0OCE8d24HIxLRBgA2mYtsShcKE37RTyNYM0VXXOEDyp8IEO1vQkeBMJvbeHv4bpEtnB1+NAP6o/KEYnskydP6NOnDzVr1iQqKgrQDTEoiawQQmRv6U5m58+fz8iRI2nZsiWPHj0iKSkJgLx58zJnzpyMjk8Y2ZlR/x+1oPTMaWneb7n/cmITYyntWJq3C72dGaGJ7EKbBId/gPl1IdgPzG2g+VfQbzu4eBs7ujS5cOEC1atX57fffuP69esyBKEQQuQg6U5mf/jhBxYtWsS4ceMM2o9Vq1aNCxcuZGhwwrhuHT1NqSsnAbg9bBwmVlZp2i8qPopV/6wCdCMYSK1sLnbXH35pAjvGQ2IsFK0PHx2GWh+BSfZvX6qUYtGiRdSoUYN//vmHQoUKsW/fPtq1a2fs0IQQQqRRutvMBgUFpThQuKWlJdHR0RkSlMgebn32GQ5AgsaUhgO6pXm/JReW8CThCcXzFqdhkYaZF6AwnsR48Jule2kTwNIBfKZB5Z45Zirax48f8+GHH7Jqle7Gq0WLFvz22284OzsbOTIhhBDpke6a2aJFi3L27Nlk67du3UqZMmUyIiaRDdzxO4pDaAgAt1p3w9Q8bfc9CUkJrPxnJQD9yvWT2b5yo1unYEE92P+VLpEt1QoGH4MqvXJMIgvwySefsGrVKkxNTZk5cyZ///23JLJCCJEDpTvTGDVqFIMHD8bX1xelFMePH2fatGl8/vnnjBo1Kt0BzJs3j6JFi2JlZUXVqlXx8/NL036HDh3CzMyMSpUqpfuc4sW0cXFEDnhPv1zns6Fp3ndj4EZiE2MBaO7ZPMNjE0YUHwPbx8HiJnA/AGycodNS6LYC7HPe2KtffvkltWrVws/Pj1GjRmFiIjdeQgiRE6W7mcF7771HYmIio0ePJiYmhu7du1OoUCHmzp1Lt25pfxQN4Ovry/Dhw5k3bx5169ZlwYIFtGjRAn9/f4oUKZLqfpGRkfTu3ZvGjRtz9+7d9L4F8RJnV2/i2Uiyp75cSE9HhzTtF5cUx8/nfgZgTPUxmJuaZ1KEIssFHYBNQyEiWLdcoauuk5eNo1HDSo/IyEjWrFnDgAG6oeZcXFw4fPiwtOkWQogcTqOUUq+6c3h4OFqtFhcXl1fav2bNmlSpUkU/ww5A6dKlad++PTNmzEh1v27dulGiRAlMTU3ZsGFDis0eUhMVFYWDgwORkZHY22f/OeGNYWvzTngGX2Jv4coM2rUyzfv97v87X5/4mgI2BdjccTOWppaZGKXIEk8jYccE3dixAPaFoPUcKNnMqGGl18mTJ+nSpQtBQUGsWLGC7t27GzskIYQQL5CefO21nqs5Ozu/ciIbHx/PqVOnaNbM8EuxWbNmHD58ONX9li5dSmBgIJMmTUrTeeLi4oiKijJ4idTFPo3HM/gSAB7tW6V5v5iEGBZdWATAwIoDJZHNDS5vhZ9q/j+RrfY+DDqaoxJZpRRz586lTp06BAUF4enpSfHixY0dlhBCiAyU7mYGRYsWfeFjuevXr6fpOOHh4SQlJVGgQAGD9QUKFCAsLCzFfa5evcrYsWPx8/PDzCxtoc+YMYMpU6akqayAOdN/o/2/P7/Vt2Oa91v5z0oePn2Iex532hWXYY1ytOhw2DoaLuqmIsaxGLT9ATzrGjeudIqIiKBfv35s2LABgI4dO7J48WLy5s1r1LiEEEJkrHQns8OHDzdYTkhI4MyZM2zbtu2VOoA9nxgrpVJMlpOSkujevTtTpkyhZMmSaT7+Z599xsiRI/XLUVFRuLu7pzvON8G2i2E03PCzftnBzvoFpf8vKj6KpReXAvBRxY8wN5G2sjmSUnBhrS6RjX0IGhOoMxQafAbmaftdyC6OHTtG165duXHjBhYWFsyaNYvBgwdL+1ghhMiF0p3Mfvzxxymu/+mnnzh58mSaj+Ps7IypqWmyWth79+4lq60F3ZiQJ0+e5MyZMwwZMgQArVaLUgozMzN27NhBo0aNku1naWmJpaU88k6LP/3+YUS8bqxg57Fj07zfb5d+Iyo+imIOxWhZtGVmhScyU+Qt+HskXN2uWy5QTlcbW6iKceN6RQ8ePODGjRsUK1aMNWvWUKVKznwfQgghXi7DxqJp0aIF69atS3N5CwsLqlatys6dOw3W79y5kzp16iQrb29vz4ULFzh79qz+NXDgQEqVKsXZs2epWbPma7+HN9mpGw8pvNlXv+zcp3ea9ot4GsFy/+UADK48GNMcMOuT+A+tFk4ugZ9q6RJZUwtoOB4+2JfjEtn/9mVt2bIlK1eu5PTp05LICiFELpfumtnUrF27FkfH9A3TM3LkSHr16kW1atWoXbs2CxcuJCQkhIEDBwK6JgK3b9/mt99+w8TEhHLlyhns7+LigpWVVbL1Iv2m/OVPhyf3ALCpWTPNj2OXXFxCTGIMpR1L06RIk8wMUWS0B4GwaRjcOKhbLlwd2v4ILt7GjesVHDx4kEGDBvHXX3/h4eEBwLvvvmvkqIQQQmSFdCezlStXNkh0lFKEhYVx//595s2bl65jde3alQcPHjB16lRCQ0MpV64cW7Zs0X8ZhYaGEhISkt4QRTpdDnvMleB71ArzB8Dp33E4XyYsOoxV/+imAh1SeYi0R8wpkhLh6DzYOw0Sn4K5DTSeCDU+gBxWs67Vavn666+ZMGECSUlJjB8/nuXLlxs7LCGEEFko3ePMPj8ygImJCfnz56dBgwZ4e2f/Gh0ZZza5lnP9KHx0FyPOrAHA2/8SmjTMhjTWbyybr2+miksVljVfJslsThB2ETYNgTtndMteDaDNXMjnacyoXsm9e/fo1asXO3bsAKBnz57Mnz8fOzs7I0cmhBDidaUnX0tXzWxiYiKenp74+Pjg6ur6WkGK7GHHpTD8Q6OY9W8im7dr1zQlsufun2Pz9c1o0DC6xmhJZLO7xDjwm6V7aRPB0gF8pkHlnpADr92+ffvo3r07oaGhWFtb89NPP9G3b1/5PRRCiDdQupJZMzMzPvroIwICAjIrHpHFvtt1lVIPb+iX7erXe+k+WqXl6+NfA9CueDvKOpXNtPhEBrh5Qlcbe/8f3bJ3a2j5Ldi7GTeuV7R161Zat26NVqulTJkyrFmzhrJl5XdQCCHeVOluM1uzZk3OnDmjb9cqcq57UU8JCI1iycn/T1mbJ4XhzZ63+fpmLoRfwMbMho+rpDxUm8gG4qNhzzRd+1gU2OaHlt9AmfY5sjb2mYYNG1KhQgUqV67MDz/8gK2trbFDEkIIYUTpTmYHDRrEJ598wq1bt6hatWqyL5IKFSpkWHAic60+cRPzpETyJujGlnXs0+el+8QkxDDn1BwABlQYgLO1c2aGKF7V9X26kQoe/VvrXvFd8JkONukbcSS7OHbsGNWqVcPU1BQrKysOHDhAnjx5jB2WEEKIbCDNyWy/fv2YM2cOXbt2BWDYsGH6bRqNRj9zV1JSUsZHKTJcklYxe+cV+gVswzrhKQAun37y0v0WX1zMvdh7FLYrTK8yvTI7TJFesY9g5wQ4/Ztu2b4wtJkDJZoaM6pXlpiYyJQpU5g2bRoTJ05k8uTJAJLICiGE0EtzMvvrr7/y1VdfERQUlJnxiCxy4Mp97OJj6HxtHwAOnd5BY/7iaWjvPLnDr5d+BeDTap9iaSozq2Ur/2zWzeL15N9Z9aoPgCaTwDJnJn63b9+me/fuHDhwAIC7d++mOt21EEKIN1eak9lnI3hJW9ncwffETbpf/v/sawXGjHnpPt+c+Ia4pDhquNagUZGXt60VWeTJfdg6Gi79qVt2LAbtfgSP5DPp5RTbtm2jV69ehIeHY2dnx6JFi+jWrZuxwxJCCJENpavNrNSI5A5JWoXfuWDWBvoBkK97d0xf8tj28J3D7ArZhYnGhNHVZSiubEEpOL8Gto2B2AjQmELdYVB/DJhbGzu6V5KQkMDEiRP56quvAN0kLb6+vpQoUcLIkQkhhMiu0pXMlixZ8qVJzMOHD18rIJH51py8Sfur+/XL+UcMf+k+C88vBKBzyc6UciyVWaGJtIq8BX+PgKu6CQMoUB7a/QAFKxs3rtd0/fp15syZA8DgwYP59ttvsbKyMm5QQgghsrV0JbNTpkzBwcEhs2IRWeTH3VeZFXQYAKcB/V9aK3v67mlO3T2FmYkZ/cv3z4oQRWq0Wji1BHZOhvjHYGqhq4mt+zGYvrjNc05QqlQpFixYgI2NDZ06dTJ2OEIIIXKAdCWz3bp1w8XFJbNiEVngaUISJS4exiE+Gq2ZGU4ffvjSfRZe0NXKtivWDldbmfnNaB4EwqahcOOQbrlwDV3b2Pw5t6Y8Pj6e8ePH06FDB2rXrg1A7969jRyVEEKInCTNyay0kcwdVh0PofPVvQA4de+O6Uvmsb/04BKHbh/CRGPC++Xez4oQxfOSEuHIj7BvBiQ+BXNb3SgF1fuDiamxo3tlwcHBdOvWjWPHjrFmzRr++ecfaVIghBAi3dI9moHI2Y5tPcjHUaFo0eD0Xt+Xll90fhEALYq2wN3ePZOjE8mEXYCNQyD0rG7ZqyG0mQv5cvaoIuvXr6dfv348evSIvHnzMnfuXElkhRBCvJI0J7NarTYz4xBZ4OLtSLxO7QNAWVpi7ub2wvLXIq6xO2Q3AAPKD8js8MR/JcbBgW/g4HegTQQrB/CZAZW65+ipaOPi4hg1ahQ//PADALVq1WL16tUy5J8QQohXlu7pbEXO1eb7A2z5t+NXnrfqvrT8Lxd/AaBJkSYUy1ssU2MT/3HzuK42Nvyybtm7NbSaBXlydnvliIgImjZtyqlTpwAYNWoU06ZNw/wlk3UIIYQQLyLJ7BviXtRTij+6rV8u8J/piFNyPfI6W4O2AtC/goxgkCXio2H3F3DsZ0CBrQu0+hbKtDN2ZBkib968FC5cmODgYH799VdatWpl7JCEEELkApLMviE2nL1Np387fllXroxVqZIvLP/D6R/QKi0N3BtQ1qlsVoT4ZgvcC38Ng0chuuWK3cFnGtg4Gjeu1/T06VMSExOxs7NDo9GwZMkSYmJiKFy4sLFDE0IIkUuYGDsAkTVmbrtMkcd3AbCu8uKB9c/fP6+f7evjyh9nRXhvrthHsHEwLG+vS2Qd3KHnOugwP8cnsleuXKFWrVp88MEH+g6kjo6OksgKIYTIUFIz+wa4dCeS4uHBeP6bzDr26JFqWaUUc07PAaCNVxuK5yueFSG+mQL+hs2fwJMw3XKND6DxRLB88SQWOcHKlSv58MMPefLkCXfu3OH27duSxAohhMgUksy+ASZuvESTkJO6BY0G84IFUy17+M5hToSdwMLEgsGVBmdRhG+YJ/dgyyjw36BbdioBbX8Aj9pGDSsjxMTE8PHHH/PLL7rOgw0aNGDFihUUfMHvnBBCCPE6JJnN5ZRSnLoRwfsPgwGwb9061bJapdXXynbz7oab3YuH7hLppBSc94VtYyE2AjSmumlo648B85w/xmpAQABdunTh4sWLaDQaJk6cyIQJEzA1zbkTOwghhMj+JJnN5U6HRJD36WOKRoUCkP/j1NvAbgvaxj8P/8HO3E7Glc1oj27C3yPg2k7dsmt5aPsjFKxk1LAySmJiIm3atCEwMBBXV1dWrFhBo0aNjB2WEEKIN4B0AMvltl+6S8XwawCYFy6MReFCKZbTKi3zz80HoG/ZvuS1yptVIeZuWi0cXwTzaukSWVNLXbvYAXtzTSILYGZmxsKFC/Hx8eHs2bOSyAohhMgyUjObyy08cJ1ll7YAYNewYarljoYeJTgqGFtzW3qW6ZlV4eVu4ddg01AI0U1UgXtNXW1s/hcPi5ZTXLhwgRs3btD636YrjRo1omHDhmhy8AxlQgghch5JZnMx/ztReEaGUiA2AoC8nTulWnb1P6sBaFusLbbmtlkSX66VlAhHfoC9MyApDsxtoclkqN4fTHL+wxClFIsXL2bo0KGYmZlx+vRpSpQoASCJrBBCiCwnyWwuduT6A3xuHNcvW5VMuUYw9Eko+2/tB6BbqW5ZEluuFXZBN25s6DndcrFG0HoO5PMwalgZ5fHjxwwcOJCVK1cC0Lx5c/LmzWvcoIQQQrzRJJnNxQ5cCGHsdT8AHPv0TrXcH1f+QKu01HStiVder6wKL3dJeAoHvoFDc0CbCFZ5ofkMqPgu5JLayrNnz9KlSxeuXr2Kqakp06dP59NPP8UkF9Q2CyGEyLkkmc3FPPf9rf85tVEM4pPiWXd1HaAbjku8gpBjsGkIhF/RLZduCy2/hTwFjBtXBvr5558ZPnw4cXFxuLu7s3r1aurUqWPssIQQQghJZnOr2xExtL+4HQD7DwdiYmOTYrkdN3bw8OlDXGxcaODeIAsjzAXinsCeL+DYAkCBrQu0+hbKtDN2ZBkuMDCQuLg42rRpw9KlS3FycjJ2SEIIIQQgyWyudXjnMcolJQDg2q9vquWedfzqXLIzZiby65BmgXvgr4/hUYhuuVIPaPYl2DgaN64MpNVq9U0Ipk+fTsWKFenRo4d08hJCCJGtSGO3XEgphel3X+uXTR0cUix39t5Zzt0/h5mJGZ1Kpj7SgfiP2AjYMBiWd9Alsg5FoOef0H5erklklVLMnTuXRo0akZCguyEyNzenZ8+eksgKIYTIdqQqLhca7nuWvlF3ADCtUSvVcgduHQCggE0BnK2dsyS2HC3gL9j8CTy5C2igxge6CRAs7YwdWYaJiIigX79+bNiwAYBVq1bRu3fqnQeFEEIIY5NkNpdJ0ip2nbnBgKREANw/GZ5iOaUU24K3ATCw4sCsCi9nenwXto4C/426ZacS0O5HKJL6jUJOdOzYMbp27cqNGzewsLBg1qxZ9OrVy9hhCSGEEC8kyWwuczL4Ie4RdzBFobGxwapChRTLXQy/yM3HN7E2s6aZR7MsjjKHUArOrYZtY+HpI9CYwlvDod5oMLcydnQZRqvV8t133zF27FgSExMpVqwYvr6+VK1a1dihCSGEEC8lyWwuczjwAR2v6SZAsCpdOtU2jpuDNgPQ0L0hNuYpj3TwRnsUAn8Nh8DdumXXCtDuJ3BL+eYgJxs9ejSzZs0CoEuXLixatAh7e3sjRyWEEEKkjXQAy2U2nL7J23fOA2BdsWKKZRK1iWwL0jUxaOXVKstiyxG0Wji+CObV1iWyppbQeBIM2JMrE1mAAQMG4OzszM8//8zq1aslkRVCCJGjSM1sLhIZk0D+f87olx379Emx3PGw4zx4+oC8lnmpXbB2VoWX/YVfhU1DIeSIbrlIbWj7AziXMG5cGUyr1XL48GHeeustAEqVKkVwcDC2trZGjkwIIYRIP6mZzUX+OHWTt2/ramUtihXDvIBLiuW2XN8CgI+nD+Ym5lkWX7aVlAB+s2F+XV0ia2Gnm8Gr75Zcl8jeu3ePli1bUr9+ffbt26dfL4msEEKInEpqZnORjWduM+q+bkpVx1SGU3qa+JRdIbsAaFm0ZZbFlm2FnoONQyBMdxNAscbQZg7kLWLUsDLD/v37effddwkNDcXa2prQ0FBjhySEEEK8Nklmc4mbD2OI9g8gf2wkmJhg3yrltrAHbh0gOiEaN1s3KrlUytogs5OEp3BgJhycAyoJrPJC86+gYjfIZRMDJCUlMX36dCZPnoxWq6V06dKsWbOGcuXKGTs0IYQQ4rVJMptL/Ho4mMY3TwFgU7MGpnYpPzbeEqRrYtCyaEtMNG9oK5OQo7ra2AdXdctl2umaFdil3CwjJwsLC6Nnz57s3q0blaFv3778+OOP0qxACCFEriHJbC6x7VIYM26fA8C8YMEUy0TGRepn/Wrp9QY2MYh7ArunwvGFgAK7AroktkxbY0eWabZu3cru3buxsbFh/vz5MpuXEEKIXEeS2Vzgwq1I7oZHYR8fDYBD23YpltsdspsEbQLF8xanZL6SWRmi8V3brRs3NjJEt1ypJ/h8Cdb5jBpWZuvbty/Xr1+ne/fulC5d2tjhCCGEEBnuDX3OnLtsvhBK+0A/LLSJaMzNsaleLcVyz0YxeKPGlo15CBsGwe8ddYls3iLQaz20/ylXJrJ37tyhZ8+eREREAKDRaPjiiy8kkRVCCJFrSc1sLvD3+TsMuq9r/2nXpDEak+T3KPdi7nE87DgALYq2yNL4jMZ/I2z+FKLvARqo+SE0mgCWdsaOLFNs27aNXr16ER4eDsDvv/9u5IiEEEKIzCfJbA537d5jbj+MpkTETQDyvftuiuW2Bm1FoajsUplCdoWyMsSs9/gubPkUAjbplp1LQtsfoUhN48aVSRITE5kwYQJfffUVAJUqVWLSpElGjkoIIYTIGpLM5nDf7bqKR9Rd7BKforGwwKZSpRTL/XcUg1xLKTi7ErZ/Dk8fgYkZ1B0O9UaBuZWxo8sUN2/e5N133+XQoUMADBo0iFmzZmFllTvfrxBCCPE8SWZzuAOX79Mm9AIAlsWLo7GwSFYmKDII/wf+mGpMaebZLKtDzBoRN+Dv4RC4R7fsVlFXG+tWwahhZaajR4/SqlUrHj58iL29PYsXL6ZTp07GDksIIYTIUpLM5mDhT+J4HJdIr3926Fak0FYWYFvwNgBqF6yNo5VjVoWXNbRaOLEIdk2BhGgwtYSGn0HtoWCau3+9S5Ysia2tLV5eXvj6+uLl5WXskIQQQogsl7u/7XM5v6v3KfT4nn7ZoW3K46XuCNYlu809m2dJXFnm/hXYNBRuHtUtF6kDbX8A5+LGjSsT3bt3j/z586PRaHB0dGT37t0UKVIES0tLY4cmhBBCGIUMzZWDLdh/nbfuXNAv5+vVM1mZ65HXufboGmYaMxq4N8jC6DJRUgIc+BZ+rqtLZC3sdJMf9N2cqxPZ9evXU6pUKZYsWaJfV6JECUlkhRBCvNEkmc2hEpK0/BP2mNqhFwHI27UrGo0mWbldN3YBULNgTRwsHbI0xkwReg4WNYQ9X0BSPBRvAoOOQo0BqTazyOni4uIYNmwYHTt25NGjR6xYsQKllLHDEkIIIbIFaWaQQ03bHIBFUgKlHv07JFeP7imWe9bEwMfDJ8tiyxQJT2H/V3Doe1BJugkPmn8FFbpCCkl8bhEYGEjXrl05deoUAJ9++inTp09P8cZFCCGEeBNJMpsDKaVYfvQG1e5dAcDE3h7L4skfr9+IusHliMuYakxp6N4wq8PMODeOwKYh8OCabrlsB2gxE+xcjBtXJvvjjz/o378/UVFRODo68ttvv9Gq1Rs0e5sQQgiRBpLM5kDX7j0hSatoEazr+GRVtkyKs37tvLETgJpuNclrlTcrQ8wYcY91oxScWKRbtisArWZD6dbGjSsLXLlyhW7duqHVaqlbty6rVq3C3d3d2GEJIYQQ2Y4ksznQ9kthmCgtFcMDdStSaD6plGJr0FYAmno0zcLoMsi1XfDXcIjUNaOgci9o9oWuecEboGTJkkycOJG4uDimTp2KmZn8qQohhBApkW/IHGjV8ZsUjQzFKikegIIzv05W5kL4Ba5EXMHCxCJnJbMxD3UzeJ1bpVvOWwTafA/FcnAziTRatWoV1apVo0SJEgAyJa0QQgiRBrmz+3cud/tRrL6JgVn+/Ji7JG87uubyGgCaF22ec0YxuLQBfqrxbyKrgVqDdCMV5PJENiYmhv79+9O9e3e6du3K06dPjR2SEEIIkWNIzWwOExufhInS0ir4CAD5ur+brExkXCTbg7cD0Llk5yyN75U8DoMtn0LAX7pl51LQ7kdwr2HcuLJAQEAAXbp04eLFi2g0Gtq0aYO5ubmxwxJCCCFyDElmc5gDV+9T+L+zfnXokKzM39f/5mnSU0rmK0nF/BWzMrz0UQrOrtA1K3gaCSZm8NZIqPcpmOX+iQB+/fVXBg0aRExMDAUKFGDFihU0btzY2GEJIYQQOYoksznM+tO3qfLvkFxWFStg7upqsF0ppW9i0KVkl+w7HmnEDfjrY7i+V7fsVklXG+ta3qhhZYWYmBg++ugjfvvtNwAaN27M77//jutz11IIIYQQLydtZnOY4AfRuMRGAKASEpJtP3X3FNcjr2NtZk0rr2w4Jqk2CY7+DPNq6xJZMytoOhX6734jElkAMzMz/vnnH0xMTPjiiy/Yvn27JLJCCCHEK5Ka2Rzkcthj/gl7zLD7VwGwq1cvWZk/rvwBQMuiLbGzsMvS+F7q/mXYNBRuHtMte9TVjVTgnHzCh9xGKYVSChMTEywsLPD19eXGjRvUr1/f2KEJIYQQOZokszmI39X72CTEUjQqDIB87xp2/opPimffzX0AdCiRvC2t0SQlwKE5sH8mJMWDRR5oOgWqvgcpTPaQ2zx+/JiBAwdSuHBhvv5aN4yap6cnnp6exg1MCCGEyAUkmc1BVh4LoUnIKQBMnZwwL1DAYPvxsOPEJMaQ3zo/5Z2zySP7O2dh4xC4e0G3XLwptJkDDoWNGVWWOXv2LF26dOHq1auYmZnx0UcfSRIrhBBCZKDcXy2WS8QlJnE9PJreAdsAsKlSOVmZPSF7AGjg3gATjZEvbUIs7JwEixrpEllrR+i4CHr88UYkskop5s+fT61atbh69SqFCxdm3759ksgKIYQQGUxqZnOIw9cekO9pFLaJugH1Hd97z2C7Vmn1TQwaFWmUxdE958ZhXdvYB9d0y2U7QouZYJffuHFlkcjISAYMGMAff+jaL7du3Zply5bh5ORk5MiEEEKI3EeS2RzityPB9Lu0GQDTfPmwrmxYM3sx/CL3Y+9ja25LDVcjTTYQ9xh2TYYTv+iW7Vyh9WzwzoajKmQSrVZL/fr1OXfuHGZmZnz99deMGDEi+w6RJoQQQuRw0swgB0hI0rL38n2a3NS1l83Xo0ey5GjnjZ0AvFXoLSxMLbI8Rq7uhJ9q/T+RrdIbBh97oxJZABMTE0aNGoWHhwcHDx5k5MiRksgKIYQQmUhqZnOAf0IfU+TfEQwAHHv3MtiulOJ3/98BaOjeMEtjI+YhbPsMzq/WLef1gLbfg1eDrI3DiCIiIrhx4waVKlUCoEePHnTo0AEbGxvjBiaEEEK8ASSZzQF+3HuVsg+DATB3d8fU3t5g+9n7Z0lUiQDUL5xF45YqBf4bYMsoiL4PaKDWIGg0DixssyaGbODYsWN07dqV+Ph4zp07R/78unbBksgKIYQQWUOaGeQAgfej6XRVN+2rXQqD7G+8thGA9sXbZ81ECY/DwLcn/NFXl8jm94b3d0Lz6W9MIquUYtasWbz11lvcuHEDa2tr7t27Z+ywhBBCiDeO1MzmALciYsj39DEAlsUNZ8uKTYxlW7BuuK52xdplbiBKwZnfYfs4iIsEEzN4+xPdy8wyc8+djTx48IC+ffvy999/A9C5c2cWLVqEg4ODkSMTQggh3jxGr5mdN28eRYsWxcrKiqpVq+Ln55dq2T///JOmTZuSP39+7O3tqV27Ntu3b8/CaLPeyeCHWDx5jHVSPAD2zX0Mtu8O2U10QjSF7QpTpUCVzAskIhiWt4dNQ3SJbMHK8MF+aPj5G5XIHjp0iEqVKvH3339jaWnJvHnz8PX1lURWCCGEMBKjJrO+vr4MHz6ccePGcebMGd5++21atGhBSEhIiuUPHDhA06ZN2bJlC6dOnaJhw4a0adOGM2fOZHHkWWfLhTBqhPkDoLGwwDRvXoPtz5oYtC3eNnMmStAmwdH5MK82XN8HZlbQ9At4fxe4lsv482Vz8+fP59atW5QoUYKjR4/y0UcfyWgFQgghhBFplFLKWCevWbMmVapUYf78+fp1pUuXpn379syYMSNNxyhbtixdu3Zl4sSJaSofFRWFg4MDkZGR2D/XkSo78hy7mQlHl1In7BL2LVtQaPZs/bY7T+7QfF1zFIpt72yjkF2hjD35vX90kx/cOq5b9nhLN1KBU7GMPU8OEhUVxZQpU5g8eTJ58uQxdjhCCCFErpSefM1oNbPx8fGcOnWKZs2aGaxv1qwZhw8fTtMxtFotjx8/xtHRMdUycXFxREVFGbxyitj4JEyUljphlwDI262bwfZ1V9ehUNR0rZmxiWxSAuz/Bha8rUtkLfJA6++gz19vXCK7f/9+Bg0axLN7Pnt7e2bNmiWJrBBCCJFNGK0DWHh4OElJSRQoUMBgfYECBQgLC0tlL0OzZs0iOjqaLl26pFpmxowZTJky5bViNZag8GiqhwUAYGJri/W/45gCJGgTWH91PQCdSnXKuJPeOQMbh8Ddi7rlEj66RNYhg2t9s7mkpCSmT5/O5MmT0Wq11KhRg759+xo7LCGEEEI8x+ijGTzf3lAplaY2iKtWrWLy5Mls3LgRFxeXVMt99tlnjBw5Ur8cFRWFu7v7qwechYLCoyn98AYAtnXrYmLx/5m9Dtw6wP3Y+zhaOdLYvfHrnywhFvbNgMM/gNKCtSO0mAnlO8Eb1iY0LCyMnj17snv3bgD69OlD586djRyVEEIIIVJitGTW2dkZU1PTZLWw9+7dS1Zb+zxfX1/ef/99/vjjD5o0afLCspaWllha5sze9lfuPqbQk/sAWFesYLDtjyt/ALqxZc1NzV/vRMGHdG1jHwbqlsu9o0tkbZ1f77g50O7du+nRowd3797FxsaGefPm0adPH2OHJYQQQohUGK3NrIWFBVWrVmXnzp0G63fu3EmdOnVS3W/VqlX07duXlStX0qpVq8wO06guB93lrdALAFiVK69ff/vJbQ7f1rUr7lTiNZsY7P8GlrXUJbJ53KDbKui05I1MZOfOnUvTpk25e/cu5cqV4+TJk5LICiGEENmcUZsZjBw5kl69elGtWjVq167NwoULCQkJYeDAgYCuicDt27f57bffAF0i27t3b+bOnUutWrX0tbrW1ta5bpzPJ3GJWOzaol+2rlRR//OW61t0Hb/cauJu/xpNJsKvwf6vdD9X7gk+08Eqd32O6VG9enVMTEx47733mDt3rkxJK4QQQuQARk1mu3btyoMHD5g6dSqhoaGUK1eOLVu24OHhAUBoaKjBmLMLFiwgMTGRwYMHM3jwYP36Pn36sGzZsqwOP1P9de4OXS/r2mzm8WmGyX+aSuy7uQ8AH0+fFPZMhwPfgDYRitSBtj++cW1jAe7evatv1lKnTh0uXryIt7e3kaMSQgghRFoZvQPYoEGDGDRoUIrbnk9Q9+3bl/kBZRMnrt2n5L+zfllX+H972fDYcM6HnwegfuH6r36C8GtwYY3u5+bT37hENjExkQkTJvDDDz9w7NgxypYtCyCJrBBCCJHDGD2ZFckppbi5Yw82iXEAOP5nSKhntbLlncvjYpP6KA4v5fetbtSCks11U9O+QW7evMm7777LoUOHAPjrr7/0yawQQgghchZJZrOhK3ef8J7/VgDMy5VHY2qq3/YsmW3g3uDVT/AgEM7/Wytbf8yrHycH2rx5M7179+bhw4fY29uzaNGiF45TLIQQQojsTZLZbMjv6n1Kxz0BwNLp/7ObxSTEcDT0KPCayeyBb0El6SZEKFTldULNMRISEvjss8+YNWsWAFWrVsXX15dixd6sGc3Em0er1RIfH2/sMIQQIhkLCwtMTF5/YC1JZrOhk5dDqRv3GACXTz/Rr/e77UdcUhyF7QpTIm+JVzv4g0A476v7ucGbUyu7ePFifSI7bNgwZs6cmWPHHxYireLj4wkKCkKr1Ro7FCGESMbExISiRYti8Z9JoV6FJLPZjFKKJ2fOAqC1tcOieHH9tu3B2wFo5tksTbOkpchv1r+1ss2gUNXXDTfH6N+/P9u3b6d379506NDB2OEIkemUUoSGhmJqaoq7u3uG1H4IIURG0Wq13Llzh9DQUIoUKfLqeQ2SzGY7/qFR1LxyBAAzCwv9xY1JiMHvlh+gS2ZfycPrcG617uf6Y1871uwsPj6euXPnMmzYMCwtLTEzM2P9+vXGDkuILJOYmEhMTAwFCxaUMZOFENlS/vz5uXPnDomJiZibv/psppLMZjMHroRTICYCAPum/5+q98DtAzxNekphu8KUcSzzigf/t1a2eFMonHtrZa9fv07Xrl05efIkISEh/PDDD8YOSYgsl5SUBPDaj++EECKzPPv/lJSU9FrJrDx3ymYuhYRT/sF1APL17KFfvyN4B/AaTQweBsG5VbqfG+TeWtm1a9dSuXJlTp48iaOjIz4+rzmxhBA53Os8uhNCiMyUUf+fJJnNZkIOn9L/bFlC18nrv00MXnnWL79/RzAo3gQKV3vtOLObp0+fMmjQIDp37kxUVBR169bl7NmztG7d2tihCSGEECITSTKbjTyMjqf03WsAaCpV0d+x/LeJQWnH0q9w4KBc3VY2MDCQ2rVrM3/+fADGjh3L3r17cXd3N3JkQojsyNPTkzlz5rzy/suWLSNv3rwZFk9u0qBBA4YPH54l55owYQIffPBBlpxLpN+FCxcoXLgw0dHRmX4uSWazkfn7rtHjH11zAsfaNfTrnzUx8PH0ebUqeb9ZoE2EYo3BvXqGxJqdmJiYEBQUhLOzM1u3bmXGjBmv1fZGCGE8ffv2pX379pl6jhMnTqQ5CUop8e3atStXrlx55fMvW7YMjUajfxUoUIA2bdpw6dKlVz5mdvHnn3/yxRdfZPp57t69y9y5c/n8888z/VzGEhcXx9ChQ3F2dsbW1pa2bdty69atF+6TmJjI+PHjKVq0KNbW1nh5eTF16lT98HwJCQmMGTOG8uXLY2trS8GCBenduzd37twxOM6HH35IsWLFsLa2Jn/+/LRr145//vkn2fk2b95MzZo1sba2xtnZmY4dO+q3lS9fnho1avDdd99lwKfxYpLMZiMhD2MwV/922ijiAWTAKAYRwbmyreyzzi0ARYsWZf369Zw9e5bmzZsbMSohRE6QP3/+1xrhwdraGheX15hOHLC3tyc0NJQ7d+6wefNmoqOjadWqVaZPcJGQkJCpx3d0dCRPnjyZeg7QjR1eu3ZtPD09X+s4mf15vI7hw4ezfv16Vq9ezcGDB3ny5AmtW7c2+P573tdff83PP//Mjz/+SEBAADNnzuSbb77Rd4SOiYnh9OnTTJgwgdOnT/Pnn39y5coV2rZta3CcqlWrsnTpUgICAti+fTtKKZo1a2Zw7nXr1tGrVy/ee+89zp07x6FDh+jevbvBcd577z3mz5//wpgzhHrDREZGKkBFRkYaO5RkGk7drPxLeSv/Ut4q4cEDpZRSW69vVeWWlVMt1rVQWq02/QfdOESpSfZK/dY+g6M1Hn9/f1WhQgW1detWY4ciRLYVGxur/P39VWxsrLFDSZc+ffqodu3apbp93759qnr16srCwkK5urqqMWPGqISEBP32qKgo1b17d2VjY6NcXV3V7NmzVf369dXHH3+sL+Ph4aG+++47/fKkSZOUu7u7srCwUG5ubmro0KFKKaXq16+vAIOXUkotXbpUOTg4GMS1ceNGVbVqVWVpaamcnJxUhw4dUn0PKe2/adMmBajz58/r1x06dEi9/fbbysrKShUuXFgNHTpUPXnyRL/9zp07qmXLlsrKykp5enqqFStWJHtvgJo/f75q27atsrGxURMnTtSfr0qVKsrS0lIVLVpUTZ482eBzTO0zUUqpn376SRUvXlxZWloqFxcX9c477+i3Pf9ZP3z4UPXq1UvlzZtXWVtbq+bNm6srV64k+yy2bdumvL29la2trfLx8VF37txJ9fNTSqny5curH3/80WDd1q1bVd26dZWDg4NydHRUrVq1UteuXdNvDwoKUoDy9fVV9evXV5aWlmrJkiVKKaWWLFmivL29laWlpSpVqpT66aefDI49evRoVaJECWVtba2KFi2qxo8fr+Lj418Y4+t49OiRMjc3V6tXr9avu337tjIxMVHbtm1Ldb9WrVqpfv36Gazr2LGj6tmzZ6r7HD9+XAHqxo0bqZY5d+6cAvSfZ0JCgipUqJD65ZdfXvg+4uLilKWlpdq9e3eK21/0fyo9+ZrUzGYTT+ISKeZ/TLdgaYVpvnwA7Ljx7ygGHq8wikHEDTi7UvdzLmkr+9tvv1GtWjXOnz/PqFGjZGYjIdJIKUVMfKJRXkqpDHkPt2/fpmXLllSvXp1z584xf/58Fi9ezJdffqkvM3LkSA4dOsSmTZvYuXMnfn5+nD59OtVjrl27lu+++44FCxZw9epVNmzYQPny5QHdI/PChQszdepUQkNDCQ0NTfEYmzdvpmPHjrRq1YozZ86we/duqlVLe0fbR48esXKl7n/1syZSFy5cwMfHh44dO3L+/Hl8fX05ePAgQ4YM0e/37PHwvn37WLduHQsXLuTevXvJjj9p0iTatWvHhQsX6NevH9u3b6dnz54MGzYMf39/FixYwLJly5g2bdpLP5OTJ08ybNgwpk6dyuXLl9m2bRv16tVL9b317duXkydPsmnTJo4cOYJSipYtWxrUiMbExPDtt9+yfPlyDhw4QEhICJ9++mmqx4yIiODixYvJPuPo6GhGjhzJiRMn2L17NyYmJnTo0CHZ98SYMWMYNmwYAQEB+Pj4sGjRIsaNG8e0adMICAhg+vTpTJgwgV9//VW/T548eVi2bBn+/v7MnTuXRYsWvfTxedmyZbGzs0v1VbZs2VT3PXXqFAkJCTRr9v8nsgULFqRcuXIcPnw41f3eeustdu/erW8Gc+7cOQ4ePEjLli1T3ScyMhKNRpNqO/Do6GiWLl1K0aJF9X1RTp8+ze3btzExMaFy5cq4ubnRokWLZE1lLCwsqFixIn5+fqmePyPIOLPZxPlbj6h+V9cexb5ZUzQazeuPYvCsraxXAyhSMwOjzXrR0dEMGTKEZcuWAdCoUSNWrFghsxoJkUaxCUmUmbjdKOf2n+qDjcXrf93MmzcPd3d3fvzxRzQaDd7e3ty5c4cxY8YwceJEoqOj+fXXX1m5ciWNGzcGYOnSpRQsWDDVY4aEhODq6kqTJk0wNzenSJEi1Kih67Pg6OiIqakpefLkwdXVNdVjTJs2jW7dujFlyhT9uooVK77wvURGRmJnZ6e7yYiJAaBt27Z4e3sD8M0339C9e3d9Z6oSJUrw/fffU79+febPn09wcDC7du3ixIkT+qTul19+oUSJ5FOdd+/enX79+umXe/XqxdixY+nTpw8AXl5efPHFF4wePZpJkya98DMJCQnB1taW1q1bkydPHjw8PKhcuXKK7/Hq1ats2rSJQ4cOUadOHQBWrFiBu7s7GzZsoHPnzoDuUf/PP/9MsWLFABgyZAhTp05N9bO7ceMGSqlk1/Wdd94xWF68eDEuLi74+/tTrlw5/frhw4cbtO384osvmDVrln5d0aJF9Un+s89o/Pjx+vKenp588skn+Pr6Mnr06FTj3LJlywubMbyob0dYWBgWFhbk+7di65kCBQoQFhaW6n5jxowhMjISb29vTE1NSUpKYtq0abz77rspln/69Cljx46le/fu2NvbG2ybN28eo0ePJjo6Gm9vb3bu3KkfF/b6dd0QopMnT2b27Nl4enoya9Ys6tevz5UrV3B0dNQfp1ChQgQHB6cac0aQZDabCLz3hLIPggBwaNsGgAO3dKMYuOdxx9vRO30HfBQCZ1fofs7htbIXL16kS5cuBAQEYGJiwuTJk/n8888xNTU1dmhCiCwUEBBA7dq1DZ5S1a1blydPnnDr1i0iIiJISEjQJ14ADg4OlCpVKtVjdu7cmTlz5uDl5UXz5s1p2bIlbdq0wcws7V+PZ8+eZcCAAel6L3ny5OH06dMkJiayf/9+vvnmG37++Wf99lOnTnHt2jVWrFihX6eUQqvVEhQUxJUrVzAzM6NKlSr67cWLF0+W/ADJajBPnTrFiRMn9DWxoOuH8PTpU2JiYl74mTRt2hQPDw/9tubNm9OhQ4cU2yAHBARgZmZGzZr/r0xxcnKiVKlSBAQE6NfZ2NjoE1kANze3FGuYn4mNjQXAysrKYH1gYCATJkzg6NGjhIeH62tkQ0JCDJLZ/34e9+/f5+bNm7z//vsG1zAxMREHBwf98tq1a5kzZw7Xrl3jyZMnJCYmJkv+nufh4fHC7a9CKfXCp7S+vr78/vvvrFy5krJly3L27FmGDx9OwYIF9Yn5MwkJCXTr1g2tVsu8efOSHatHjx40bdqU0NBQvv32W7p06cKhQ4ewsrLSf7bjxo3T30QsXbqUwoUL88cff/Dhhx/qj2Ntba2/YcssksxmE4GXQ6gW9xgA63/vcnfe2Am8YhODZ7WyReuDR+0MjTUrXb9+nRo1ahAbG4ubmxsrV66kQYMGxg5LiBzH2twU/6nGmUTE2jxjbjxT+iJ/1oRBo9EY/JxSmZS4u7tz+fJldu7cya5duxg0aBDffPMN+/fvT/OoKNbW1ul5G4BuFJbixYsD4O3tTVhYGF27duXAgQOAbt76Dz/8kGHDhiXbt0iRIly+fDnF46b0Xm1tbQ2WtVotU6ZMMaidfMbKyuqFn8mzJHzfvn3s2LGDiRMnMnnyZE6cOJHsMXVqn/vz1/H5z/m/1zIlzs7OgK65Qf78+fXr27Rpg7u7O4sWLaJgwYJotVrKlSuXrFPdfz+PZ0nZokWLDJJuQF9hcvToUX3Nu4+PDw4ODqxevZpZs2alGiPomhncuHEj1e0eHh6pjmDh6upKfHw8ERERBjco9+7d09dyp2TUqFGMHTuWbt26AboRBW7cuMGMGTMMktmEhAS6dOlCUFAQe/bsSTExd3BwwMHBgRIlSlCrVi3y5cvH+vXreffdd3FzcwOgTJn/z0hqaWmJl5cXISEhBsd5+PChwc1KZpBkNpswO3ZI/7OpnR1J2iSOhB4BoIF7g/Qd7NFNOPPv3XwOH8HAy8uLbt26cefOHX777bfX7kEsxJtKo9FkyKN+YypTpgzr1q0zSIYOHz5Mnjx5KFSoEHnz5sXc3Jzjx4/r2/ZFRUVx9epV6tevn+pxra2tadu2LW3btmXw4MF4e3tz4cIFqlSpgoWFxUt7YleoUIHdu3fz3nvvvfJ7GzFiBLNnz2b9+vV06NCBKlWqcOnSJX3C+zxvb28SExM5c+YMVavqpie/du0ajx49eum5qlSpwuXLl1M9Nrz4MzEzM6NJkyY0adKESZMmkTdvXvbs2ZMsOS5TpgyJiYkcO3ZMn4A9ePCAK1euULr0K4yZ/q9ixYphb2+Pv78/JUuW1B83ICCABQsW8PbbbwNw8ODBlx6rQIECFCpUiOvXr9OjR48Uyxw6dAgPDw/GjRunX/eiJPWZ12lmULVqVczNzdm5cyddunQBIDQ0lIsXLzJz5sxU94uJiUnW/M7U1NSg3fCzRPbq1avs3bsXJyenl74X0N2ExMXF6eOztLTk8uXLvPXWW/rjBgcHJ6uRvnjxIp06dUrTOV5Vzv7PlkskJmnxPq+7G+ffO7BLDy7xOP4xeSzyUM653Av2TsHB2aBNgKL1wCP1O7js6ty5cxQsWFB/xz1//nzMzc2lfawQb4jIyEjOnj1rsM7R0ZFBgwYxZ84chg4dypAhQ7h8+TKTJk1i5MiRmJiYkCdPHvr06cOoUaNwdHTExcWFSZMmYWJikurTrWXLlpGUlETNmjWxsbFh+fLlWFtb67+QPT09OXDgAN26dcPS0lJfK/hfkyZNonHjxhQrVoxu3bqRmJjI1q1bX9ie8nn29vb079+fSZMm0b59e8aMGUOtWrUYPHgwAwYMwNbWloCAAHbu3MkPP/yAt7c3TZo04YMPPtD/j/zkk0+wtrZ+6ZO8iRMn0rp1a9zd3encuTMmJiacP3+eCxcu8OWXX77wM/n777+5fv069erVI1++fGzZsgWtVptiU44SJUrQrl07BgwYwIIFC8iTJw9jx46lUKFCtGvXLs2fzfNMTExo0qQJBw8e1I9JnC9fPpycnFi4cCFubm6EhIQwdmzaKnMmT57MsGHDsLe3p0WLFsTFxXHy5EkiIiIYOXIkxYsXJyQkhNWrV1O9enU2b97M+vXrX3rc12lm4ODgwPvvv88nn3yCk5MTjo6OfPrpp5QvX54mTZroyzVu3JgOHTroOwa2adOGadOmUaRIEcqWLcuZM2eYPXu2vs10YmIinTp14vTp0/z9998kJSXp2+A6OjpiYWHB9evX8fX1pVmzZuTPn5/bt2/z9ddfY21tre9IZm9vz8CBA5k0aRLu7u54eHjwzTffAOjbQgMEBwdz+/Ztg5gzxUvHO8hlsuPQXLsuhaoj5asq/1Le6vaUqUoppeafna/KLSunRuwdkb6DRYQoNcVJNxxX0MFMiDbzaLVaNX/+fGVpaamaN2+ukpKSjB2SEDlWTh6ai+eGwwJUnz59lFKvNjRXjRo11NixY/Vl/jt81fr161XNmjWVvb29srW1VbVq1VK7du3Slz1y5IiqUKGCsrS0fOHQXOvWrVOVKlVSFhYWytnZWXXs2DHV95jS/kopdePGDWVmZqZ8fX2VUrohk5o2bars7OyUra2tqlChgpo2bZq+/J07d1SLFi2UpaWl8vDwUCtXrlQuLi7q559/1pcB1Pr165Oda9u2bapOnTrK2tpa2dvbqxo1aqiFCxe+9DPx8/NT9evXV/ny5VPW1taqQoUK+niVSn1oLgcHB2Vtba18fHxSHJrrv9avX69elp5s27ZNFSpUyOB7YufOnap06dLK0tJSVahQQe3bt8/g/T8bmuvMmTPJjrdixQr99cuXL5+qV6+e+vPPP/XbR40apZycnJSdnZ3q2rWr+u6771K8hhkpNjZWDRkyRDk6Oipra2vVunVrFRISYlDGw8NDTZo0Sb8cFRWlPv74Y1WkSBFlZWWlvLy81Lhx41RcXJxS6v+fQUqvvXv3KqV0Q4C1aNFCubi4KHNzc1W4cGHVvXt39c8//xicOz4+Xn3yySfKxcVF5cmTRzVp0kRdvHjRoMz06dOVj4/PC99jRgzNJclsNvDlD3/px5dNfPxYKaVU7y29Vbll5dSay2vSd7C/RugS2aWtMiHSzPPo0SPVpUsX/R9Vq1at1ON/PwshRPrl1GQ2oz158kQ5ODi8dDzM3ODmzZsKMEjGcyutVqtq1KihVq5caexQRCqePn2q3N3d1cGDqVesZVQyK80MsoGkWzcBeOjmiamdHXee3OHMvTMA1CmYjmYCkbfg9G+6n3NQW9lTp07RtWtXAgMDMTMzY8aMGfrHhkIIkR5nzpzhn3/+oUaNGkRGRuqHeHqdx9rZ1Z49e3jy5Anly5cnNDSU0aNH4+np+cJxX3MLjUbDwoULOX/+vLFDEam4ceMG48aNo27dupl+LklmjSw2PokEf38ArIp6ArD2yloUippuNSlkVyjtBzv4na6trOfb4PlWJkSbsZRS/Pjjj3z66afEx8fj4eHB6tWrqVWrlrFDE0LkYN9++y2XL1/GwsKCqlWr4ufnl2Jb15wuISGBzz//nOvXr5MnTx7q1KnDihUr0jwKQ05XsWLFl47nK4ynZMmS+g56mU2SWSMLfhBN16t7AHCvWp4EbQJ/Xv0TgC4lu6T9QJG3/18rW39MRoeZKaKjo5k7dy7x8fG0a9eOpUuXpjhGohBCpFXlypU5deqUscPIEj4+Pvj4GGe4NSGyE0lmjezG7Yd4KN2QGdZly3Ai9AQPnj7AycqJhkUapv1AB7+DpHjweAuKvp1J0WYsOzs7/RSNw4YNS/9YukIIIYR440kya2QBpy7xbPCOPA0acPrMj4Curay5SRofFUXehtP/ziGdjdvKKqWYM2cO1tbWDBw4ENCNVfdsjEQhhBBCiPSSZNbIoo6fBCDJWjcV4Nl7ZwGo5FIp7Qc5NOffWtm62bZW9uHDh/Tt25e//voLCwsLmjZtmukzggghhBAi95Nk1oieJiRhe1s3i4h5oUIkahM5H67rmVnZpXLaDhJ1B04t0/2cTdvKHj58mG7dunHz5k0sLS357rvv8PLyMnZYQgghhMgFZOwjIwoIjcIm8SkA9tWrcTniMrGJseSxyEOxvGmstTw4R1crW6SObsavbESr1fL1119Tr149bt68SYkSJTh69CgfffSRtI8VQgghRIaQmlkjCnkYg0tMBACWJYrrmxhUzF8RE00a7jOiQv9fK9tgDGSjBFGr1dK+fXv++usvAN599139dIZCCCGEEBlFamaN6PSNCIpGhQJg5V1aP1FCmpsYHJoDSXFQpDYUrZ9JUb4aExMTateujZWVFYsWLWLFihWSyAohshVPT0/mzJmT4WVzg8uXL+Pq6srjx4+NHYpIRfXq1fnzzz+NHUa2IMmsEe04fQObxDgATF3ypy+ZjQqFk0t1P9fPHrWySUlJ3L17V788ZswYLly4QP/+/aVZgRAiTfr27YtGo0Gj0WBubk6BAgVo2rQpS5YsQavVZui5Tpw4wQcffJDhZV/Ff993aq+sNG7cOAYPHpyrKyHWrVtHmTJlsLS0pEyZMqxfv/6F5YODg1O8Ltu2bdOX2bdvX4pl/vnnH32ZhIQEpk6dSrFixbCysqJixYoGxwCYPHlysmO4uroalJkwYQJjx47N8L+LnEiSWSN5GB1P0aAL+uVwBw33Yu5hqjGlnHO5lx/g0Fxdrax7LfBqkHmBptHdu3dp3rw5jRs3JiYmBtDVzhYvXtzIkQkhcprmzZsTGhpKcHAwW7dupWHDhnz88ce0bt2axMTEDDtP/vz5sbGxyfCyr2Lu3LmEhobqXwBLly5Ntu6Z+Pj4TIvl1q1bbNq0iffee++1jpOZMb6uI0eO0LVrV3r16sW5c+fo1asXXbp04dixYy/dd9euXQbXpVGjRsnKXL582aBMiRIl9NvGjx/PggUL+OGHH/D392fgwIF06NCBM2fOGByjbNmyBse4cOGCwfZWrVoRGRnJ9u3bX/FTyD0kmTWSdadu4RV5B4A8Pj6cvX8WAG9Hb6zNrF+88+MwOPVvrWw2aCu7Z88eKlasyK5duwgKCuL06dNGjUcIkQKlID7aOC+l0hWqpaUlrq6uFCpUiCpVqvD555+zceNGtm7dyrJly/TlIiMj+eCDD3BxccHe3p5GjRpx7tw5g2Nt2rSJatWqYWVlhbOzMx07dtRve77pwOTJkylSpAiWlpYULFiQYcOGpVo2JCSEdu3aYWdnh729PV26dDF4MjV58mQqVarE8uXL8fT0xMHBgW7duqX62N7BwQFXV1f9CyBv3rz65W7dujFkyBBGjhyJs7MzTZs2BcDf35+WLVtiZ2dHgQIF6NWrF+Hh4frjKqWYOXMmXl5eWFtbU7FiRdauXfvCz3/NmjVUrFiRwoUL69c9ePCAd999l8KFC2NjY0P58uVZtWqVwX4NGjR4pRi3bdvGW2+9Rd68eXFycqJ169YEBga+MMbXNWfOHJo2bcpnn32Gt7c3n332GY0bN05TUxInJyeDa2VhYZGsjIuLi0EZU1NT/bbly5fz+eef07JlS7y8vPjoo4/w8fFh1qxZBscwMzMzOEb+/PkNtpuamtKyZctk1+FNJB3AjOTszUdU1reXLcX+W/sBqO5a/eU7H5oLiU/BvSZ4pWOWsAyWlJTE1KlT+eKLL1BKUbZsWdasWUOZMmWMFpMQIhUJMTC9oHHO/fkdsLB9rUM0atSIihUr8ueff9K/f3+UUrRq1QpHR0e2bNmCg4MDCxYsoHHjxly5cgVHR0c2b95Mx44dGTduHMuXLyc+Pp7NmzenePy1a9fy3XffsXr1asqWLUtYWFiyxPgZpRTt27fH1taW/fv3k5iYyKBBg+jatSv79u3TlwsMDGTDhg38/fffRERE0KVLF7766iumTZv2Sp/Br7/+ykcffcShQ4dQShEaGkr9+vUZMGAAs2fPJjY2ljFjxtClSxf27NFNkz5+/Hj+/PNP5s+fT4kSJThw4AA9e/Ykf/781K+fcl+LAwcOUK1aNYN1T58+pWrVqowZMwZ7e3s2b95Mr1698PLyombNmq8VY3R0NCNHjqR8+fJER0czceJEOnTowNmzZzExSbnObfr06UyfPv2Fn9fWrVt5++2Ux14/cuQII0aMMFjn4+OTpmS2bdu2PH36lBIlSjBixAg6deqUrEzlypV5+vQpZcqUYfz48TRs+P/v6ri4OKysrAzKW1tbc/DgQYN1V69epWDBglhaWlKzZk2mT5+ebFjLGjVqMHPmzJfGnNtJMmskhwLDaRHzEACNlyf7by4DoIlHkxfv+PgunFyi+9mIbWXv3LlDjx499P+433//fb7//vtMfQwnhHizeXt7c/68bizuvXv3cuHCBe7du4elpSUA3377LRs2bGDt2rV88MEHTJs2jW7dujFlyhT9MSpWrJjisUNCQnB1daVJkyaYm5tTpEgRatSokWLZXbt2cf78eYKCgnB3dwd0tW1ly5blxIkTVK+uq5TQarUsW7ZM3+60V69e7N69+5WT2eLFixskLhMnTqRKlSoGSd2SJUtwd3fnypUrFCpUiNmzZ7Nnzx5q164NgJeXFwcPHmTBggWpJrPBwcHJZmYsVKgQn376qX556NChbNu2jT/++MMgmU1vjCVLluSdd94xONfixYtxcXHB39+fcuVSbnY3cOBAunTpkupn9Szm1ISFhVGgQAGDdQUKFCAsLCzVfezs7Jg9ezZ169bFxMSETZs20bVrV3799Vd69uwJgJubGwsXLqRq1arExcWxfPlyGjduzL59+6hXTzd8po+PD7Nnz6ZevXoUK1aM3bt3s3HjRpKSkvTnqlmzJr/99hslS5bk7t27fPnll9SpU4dLly7h5ORk8B5DQkLQarWpJv5vAklmjSAmPpFH0fE4xz4C4JpNFDGJMbjYuFDBucKLd35WK1u4BhRL3k4nqwwdOpR9+/Zha2vLggUL6NGjh9FiEUKkgbmNrobUWOfOAEopfUeoU6dO8eTJE4MvdoDY2Fj9I+qzZ88yYMCANB27c+fOzJkzBy8vL5o3b07Lli1p06YNZmbJvyYDAgJwd3fXJ7IAZcqUIW/evAQEBOiTWU9PT4MOVG5ubty7dy99b/o/nq8tPXXqFHv37sXOzi5Z2cDAQCIjI3n69Kn+cf8z8fHxVK6cekfj2NjYZDWHSUlJfPXVV/j6+nL79m3i4uKIi4vD1tawxj29MZYsWZLAwEAmTJjA0aNHCQ8P13doCgkJSTWZdXR0xNHRMdX3kBbPd6r77+9XSpydnQ1qc6tVq0ZERAQzZ87UJ7OlSpWiVKlS+jK1a9fm5s2bfPvtt/pkdu7cuQwYMABvb280Gg3FihXjvffeY+nSpfr9WrRoof+5fPny1K5dm2LFivHrr78ycuRI/TZra2u0Wi1xcXFYW7+kiWIuJsmsEfwT9hiX2Agc4mPAzIxzFro7wUr5K724x+p/a2WN3Fb2+++/JzIykp9++sngD1cIkU1pNK/9qN/YAgICKFq0KKCr9XRzczN4rP9M3rx5AdL15e7u7s7ly5fZuXMnu3btYtCgQXzzzTfs378fc3Nzg7KpJT3Pr39+P41G81o9z59PHLVaLW3atOHrr79OVtbNzY2LFy8CsHnz5mS1lM9qs1Pi7OxMRESEwbpZs2bx3XffMWfOHMqXL4+trS3Dhw9P1skrvTECtGnTBnd3dxYtWkTBggXRarWUK1fuhR3IXreZgaura7Ja2Hv37iWrrX2ZWrVq8csvv7y0zO+//65fzp8/Pxs2bODp06c8ePCAggULMnbsWP3vdkpsbW0pX748V69eNVj/8OFDbGxs3uhEFiSZNYobD6Kpeu8KAOZubhyO0PVgrOlW80W7weHvITEWCleHYo0zO0wDt27dYuPGjQwePBjQPdrYtWtXlsYghHhz7dmzhwsXLuhrxqpUqUJYWBhmZmZ4enqmuE+FChXYvXt3mnvlW1tb07ZtW9q2bcvgwYPx9vbmwoULVKlSxaBcmTJlCAkJ4ebNm/raWX9/fyIjIylduvSrv8l0qlKlCuvWrcPT0zPFGuRnw06FhISk2qQgJZUrV8bf399gnZ+fH+3atdPXQGq1Wq5evfrS9/uyGB88eEBAQAALFizQJ57Ptx1Nyes2M6hduzY7d+40qGndsWMHderUeem5/+vMmTP6pDy9ZaysrChUqBAJCQmsW7fuhe8nLi6OgICAZMn5xYsXk/1+vokkmTWCk8ER+iYGJs5OnLuv62RQy61W6js9uQcnFut+rj82S2tlt2zZQu/evXnw4AGFChWiffv2WXZuIcSbJy4ujrCwMP3Y1du2bWPGjBm0bt2a3r17A9CkSRNq165N+/bt+frrrylVqhR37txhy5YttG/fnmrVqjFp0iQaN25MsWLF6NatG4mJiWzdupXRo0cnO+eyZctISkqiZs2a2NjYsHz5cqytrfHw8EhWtkmTJlSoUIEePXowZ84cfQew+vXrJ3vMnpkGDx7MokWLePfddxk1ahTOzs5cu3aN1atXs2jRIvLkycOnn37KiBEj0Gq1vPXWW0RFRXH48GHs7Ozo06dPisf18fGhf//+JCUl6XvhFy9enHXr1nH48GHy5cvH7NmzCQsLe2ky+7IY8+XLh5OTEwsXLsTNzY2QkBDGjh370vf+us0MPv74Y+rVq8fXX39Nu3bt2LhxI7t27TJIpH/88UfWr1/P7t27AV3nNnNzcypXroyJiQl//fUX33//vUGt85w5c/D09KRs2bLEx8fz+++/s27dOtatW6cvc+zYMW7fvk2lSpW4ffs2kydPRqvVGvxefvrpp7Rp04YiRYpw7949vvzyS6KiopJdMz8/P5o1a/bKn0Nu8ea2FjaiC7cjKfxY124qqpIXidpEXKxdcM/jnvpOh+bqamULVYXiWVMrm5CQwOjRo2nVqhUPHjygSpUqlC9fPkvOLYR4c23btg03Nzc8PT1p3rw5e/fu5fvvv2fjxo365Eqj0bBlyxbq1atHv379KFmyJN26dSM4OFj/qLhBgwb88ccfbNq0iUqVKtGoUaNUxxHNmzcvixYtom7duvoa3b/++itZm9xn596wYQP58uWjXr16NGnSBC8vL3x9fTPvQ0lBwYIFOXToEElJSfj4+FCuXDk+/vhjHBwc9J2BvvjiCyZOnMiMGTMoXbo0Pj4+/PXXXy98pN2yZUvMzc0Nnr5NmDCBKlWq4OPjQ4MGDXB1dU1TxcbLYjQxMWH16tWcOnWKcuXKMWLECL755pvX/mxepk6dOqxevZqlS5dSoUIFli1bhq+vr0FntvDw8GRDhH355ZdUq1aN6tWrs3r1apYsWWJQuxsfH8+nn35KhQoVePvttzl48KB+VI1nnj59yvjx4ylTpgwdOnSgUKFCHDx4UN88BnRPQ999911KlSpFx44dsbCw4OjRowY3V7dv3+bw4cOvPR5wbqBRKp0DAOZwUVFRODg4EBkZib29vVFi8By7maU7puEaE8Gl8e8wJWkjzTyaMavBrJR3eHIf5pTXJbM91kKJpimXy0A3btygW7duHD16FNB1+Prmm29e2M5KCJF9PH36lKCgIIoWLZqsM48QLzNv3jw2btwoA/JnY6NGjSIyMpKFCxcaO5RX9qL/U+nJ16SZQRZ7mpCEXXwMrjG6xvWHHe7Bw5dMYXv431rZglWg+EuG7soAf//9N7169eLRo0c4ODiwZMkSg7tKIYQQudsHH3xAREQEjx8/ztVT2uZkLi4uBsOlvckkmc1iV+4+ptCT+wCYOjlxPFrXyL6SS6WUd3hy//9tZRt8liVtZePi4nj06BE1atRg9erVL3wcJYQQIvcxMzNj3Lhxxg5DvMCoUaOMHUK2IclsFjse9BDXfydLSLK2IDLuPlamVpRyTGV4q8Pf62buKVg5U5sXJCYm6nuavvPOO6xbt47WrVunOE2fEEIIIUR2IR3Aslh0XBJu0Q8AePpvnljOuRzmJuYpFA6HE/+OX5eJtbJr166lTJky3Lnz/wHVnzU4F0IIIYTIziSZzWInbzyk++WdANwoqhtcOtUmBga1shk/9MbTp08ZPHgwnTt35urVq1nSg1QIIYQQIiNJM4MsFhD6GIWuhjXQQtcJrFL+SskLRofD8X9rZTNhXNmrV6/StWtXzpzRTdgwZswYvvjiiww9hxBCCCFEZpNkNouFP4nDQpsIgJ/LI0BDhfwVkhc8/AMkRINbJSjpk6ExrF69mgEDBvDkyROcnZ357bffDOaBFkIIIYTIKSSZzUK3ImKwjY/VLz/MA0XyFCGfVT7DgtEP4Pgi3c8NMrZW9rffftPPIPL222+zatWqF075J4QQQgiRnUmb2Sx0Oewxle5f1S9H2KXSXvbIj//WylaEks0zNIZ33nmHsmXLMn78ePbs2SOJrBBCCCFyNElms9CF25HUv30WgHNV8oFGQy23WoaFYh7C8X9n88igtrI7d+5Eq9UCYGtry8mTJ/niiy/0Q3EJIYTQ8fT0ZM6cOcYOI8eJj4+nePHiHDp0yNihiFR8+umnDBs2zNhhZApJZrPQyeAIKoTr5nm+aRYJkDyZPfIjxD8B1wpQ6vXasUZHR/Pee+/RrFkzZs36/1S5MrWlECK76tu3LxqNBo1Gg5mZGUWKFOGjjz4iIiLC2KFlqsmTJ+vf939fu3btMmpMlSpVSlPZhQsX4uHhQd26dTM3KCO6cOEC9evXx9ramkKFCjF16lSUUqmW37dvX4rXVKPRcOLECX25EydO0LhxY/LmzUu+fPlo1qwZZ8+eNTjW9u3bqVWrFnny5CF//vy88847BAUFpXjeQ4cOYWZmluzajR49mqVLl6a6X04myWwWStIqHOKjAQgqAOWcypHfJv//C8Q8hGMLdD/XH/NatbKXLl2iRo0aLFu2DBMTExISEl4ndCGEyDLNmzcnNDSU4OBgfvnlF/766y8GDRpk7LAyXdmyZQkNDTV41atX75WOFR8fn8HRvdgPP/xA//79X+sY2fl7KioqiqZNm1KwYEFOnDjBDz/8wLfffsvs2bNT3adOnTrJrmf//v3x9PSkWrVqADx+/BgfHx+KFCnCsWPHOHjwIPb29vj4+Og/j+vXr9OuXTsaNWrE2bNn2b59O+Hh4SlOMx8ZGUnv3r1p3Lhxsm0uLi40a9aMn3/+OYM+lexDktksdPnO/2sWrhTU0KLoczWvR376t1a2PHi3eqVzKKVYsmQJ1atXx9/fH1dXV3bv3s3nn3/+OqELIXI4pRQxCTFGeb2o9iollpaWuLq6UrhwYZo1a0bXrl3ZsWOHfntSUhLvv/8+RYsWxdramlKlSjF37lyDY/Tt25f27dvz7bff4ubmhpOTE4MHDzZImO7du0ebNm2wtramaNGirFixIlksISEhtGvXDjs7O+zt7enSpQt3797Vb39We7lkyRKKFCmCnZ0dH330EUlJScycORNXV1dcXFyYNm3aS9+3mZkZrq6uBq9nk9dcuHCBRo0aYW1tjZOTEx988AFPnjxJ9n5nzJhBwYIFKVmyJAC3b9+ma9eu5MuXDycnJ9q1a0dwcLB+v3379lGjRg1sbW3JmzcvdevW5caNGyxbtowpU6Zw7tw5fW3ismXLUoz79OnTXLt2jVatDL+3xowZQ8mSJbGxscHLy4sJEyYYfP7//ey8vLywtLREKUVkZCQffPABLi4u2Nvb06hRI86dO6ffLzAwkHbt2lGgQAHs7OyoXr16ptdgr1ixgqdPn7Js2TLKlStHx44d+fzzz5k9e3aqv98WFhYG19LJyYlNmzbRr18/NP9WVl2+fJmIiAimTp1KqVKlKFu2LJMmTeLevXuEhIQAus83KSmJL7/8kmLFilGlShU+/fRTzp07l+wG4MMPP6R79+7Url07xZjatm3LqlWrMvCTyR6k0WQWiUtMwuxhuH75Xl6o6Vbz/wUyoFb2yZMnDBw4UP8PuVmzZixfvhwXF5fXCV0IkQvEJsZSc2XNlxfMBMe6H8PG3OaV9r1+/Trbtm3D3Pz/syRqtVoKFy7MmjVrcHZ25vDhw3zwwQe4ubnRpUsXfbm9e/fi5ubG3r17uXbtGl27dqVSpUoMGDAA0CWAN2/eZM+ePVhYWDBs2DDu3bun318pRfv27bG1tWX//v0kJiYyaNAgunbtyr59+/TlAgMD2bp1K9u2bSMwMJBOnToRFBREyZIl2b9/P4cPH6Zfv340btyYWrWea1qWBjExMTRv3pxatWpx4sQJ7t27R//+/RkyZIhBgrl7927s7e3ZuXOn7uYlJoaGDRvy9ttvc+DAAczMzPjyyy9p3rw558+fx8TEhPbt2zNgwABWrVpFfHw8x48fR6PR0LVrVy5evMi2bdv0iaKDg0OK8R04cICSJUtib29vsD5PnjwsW7aMggULcuHCBQYMGECePHkYPXq0vsy1a9dYs2YN69atw9TUFIBWrVrh6OjIli1bcHBwYMGCBTRu3JgrV67g6OjIkydPaNmyJV9++SVWVlb8+uuvtGnThsuXL1OkSJEUY/Tz83vpEJSff/55qhU/R44coX79+lhaWurX+fj48NlnnxEcHEzRokVfeGyATZs2ER4eTt++ffXrSpUqhbOzM4sXL+bzzz8nKSmJxYsXU7ZsWTw8PACoVq0apqamLF26lL59+/LkyROWL19Os2bNDP4uli5dSmBgIL///jtffvllijHUqFGDmzdvcuPGDf3xcwNJZrNI+JN4XKMfAnDPAWwt81A8b/H/Fzg6D+IfQ4HyUOrVamWvXLnCmjVrMDU15YsvvmDMmDGYmEjluxAiZ/n777+xs7MjKSmJp0+fAhg8zjU3N2fKlCn65aJFi3L48GHWrFljkMzmy5ePH3/8EVNTU7y9vWnVqhW7d+9mwIABXLlyha1bt3L06FFq1tQl+YsXL6Z06dL6/Xft2sX58+cJCgrC3d0dgOXLl1O2bFlOnDhB9erVAV1yvWTJEvLkyUOZMmVo2LAhly9fZsuWLZiYmFCqVCm+/vpr9u3b98Jk9sKFC9jZ2emXy5Qpw/Hjx1mxYgWxsbH89ttv2NrqZo788ccfadOmDV9//TUFChQAdB18f/nlF31t7pIlSzAxMeGXX37R1wQuXbqUvHnzsm/fPqpVq0ZkZCStW7emWLFiAAbv387OTl9b/CLBwcEULFgw2frx48frf/b09OSTTz7B19fXIJmNj49n+fLl5M+va3K3Z88eLly4wL179/SJ47fffsuGDRtYu3YtH3zwARUrVqRixYr6Y3z55ZesX7+eTZs2MWTIkBRjrFatWrJ2qM9zdHRMdVtYWBienp4G65597mFhYWlKZhcvXoyPj4/+dwl0Cf++ffto166dfuKikiVLsn37dn0nbU9PT3bs2EHnzp358MMPSUpKonbt2mzZskV/nKtXrzJ27Fj8/Pxe2Ln72QhGwcHBksyK9Au894QCMbpkNtweKuaviKmJ7i6UmIdw9N82LPVHwysmoFWqVGHBggWUKFGCt956KyPCFkLkEtZm1hzrfsxo506Phg0bMn/+fGJiYvjll1+4cuUKQ4cONSjz888/88svv3Djxg1iY2OJj49P1uGlbNmy+to+ADc3Ny5cuABAQEAAZmZm+raLAN7e3uTNm1e/HBAQgLu7u0HyUaZMGfLmzUtAQIA+mfX09CRPnjz6MgUKFMDU1NSgMqFAgQIGtb4pKVWqFJs2bdIvP0vmAgICqFixoj6RBahbty5arZbLly/rk6ry5cvrE1mAU6dOce3aNYPYQDeVeWBgIM2aNaNv3774+PjQtGlTmjRpQpcuXXBzc3thnM+LjY1NsWPx2rVrmTNnDteuXePJkyckJiYmq7318PDQJ7LPYn7y5AlOTk7JzhEYqOtAHR0dzZQpU/j777+5c+cOiYmJxMbG6h/Lp8Ta2prixYunuj0tNM89MX3WvOD59Sm5desW27dvZ82aNQbrY2Nj6devH3Xr1mXVqlUkJSXx7bff0rJlS06cOIG1tTVhYWH079+fPn368O677/L48WMmTpxIp06d9KMVde/enSlTpuibl6TG2lr3txgTE5Oet57tSTKbRcKinlIq4iYAN501huPLHp3/b61sOfBuneZjRkVFMWTIEEaMGEHlypUBeO+99zIybCFELqHRaF75UX9Ws7W11Sce33//PQ0bNmTKlCn6mqs1a9YwYsQIZs2aRe3atcmTJw/ffPMNx44ZJuv/fQQLus/g2TCFaUlElFIpbn9+fUrnedG5U2NhYZFiwpVaHM/H/99kF3Q1xlWrVk2xLfCzBHLp0qUMGzaMbdu24evry/jx49m5c2e6mkM4OzvrbxKeOXr0KN26dWPKlCn4+Pjg4ODA6tWrDUbWSS1mNzc3g2Yczzy70Rg1ahTbt2/n22+/pXjx4lhbW9OpU6cXdnp73WYGrq6uhIWFGax7dnPy7GbiRZYuXYqTkxNt27Y1WL9y5UqCg4M5cuSI/uZn5cqV5MuXj40bN9KtWzd++ukn7O3tmTlzpn6/33//HXd3d44dO4a3tzcnT57kzJkz+ppprVaLUgozMzN27NhBo0aNAHj4UFep9t8biNxAktkssv1iGENungTgWkENvV2q6DbERsCx9NfKnj59mi5duhAYGMjJkye5cOGCQQ2EEELkFpMmTaJFixZ89NFHFCxYED8/P+rUqWMwwsGzWru0Kl26NImJiZw8eZIaNWoAus44jx490pcpU6YMISEh3Lx5U1876+/vT2RkpMHj+MxWpkwZfv31V6Kjo/XJ36FDhzAxMXlhTVyVKlXw9fXVd6RKTeXKlalcuTKfffYZtWvXZuXKldSqVQsLCwuSkpJeGl/lypWZP3++QdJ96NAhPDw8GDdunL7cjRs3XnqsKlWqEBYWhpmZWbLH+s/4+fnRt29fOnToAOj6i/y3U1tKXreZQe3atfn888+Jj4/X137v2LGDggULphrnM0opli5dSu/evZPd5MTExGBiYmJwU/Js+dnNT0xMTLLv92fLWq0We3v7ZDcT8+bNY8+ePaxdu9agCcTFixcxNzenbNmyL4w5p5EGlVnELjoSqyRdr8OzXhrKOv/7i3R0PsRFgUtZ8G7z0uMopfjxxx+pXbs2gYGBFClShMWLF0siK4TItRo0aEDZsmWZPn06AMWLF+fkyZNs376dK1euMGHCBINxO9OiVKlSNG/enAEDBnDs2DFOnTpF//799Y9hAZo0aUKFChXo0aMHp0+f5vjx4/Tu3Zv69esbNE/IbD169MDKyoo+ffpw8eJF9u7dy9ChQ+nVq9cLawV79OiBs7Mz7dq1w8/Pj6CgIPbv38/HH3/MrVu3CAoK4rPPPuPIkSPcuHGDHTt2cOXKFX2i7unpSVBQEGfPniU8PJy4uLgUz9OwYUOio6O5dOmSfl3x4sUJCQlh9erVBAYG8v3337N+/fqXvtcmTZpQu3Zt2rdvz/bt2wkODubw4cOMHz+ekydP6o/9559/cvbsWc6dO0f37t1fWuv9rJnBi14vSma7d++OpaUlffv25eLFi6xfv57p06czcuRIfSJ6/PhxvL29uX37tsG+e/bsISgoiPfffz/ZcZs2bUpERASDBw8mICCAS5cu8d5772FmZkbDhg0BXYe4EydOMHXqVK5evcrp06d577338PDwoHLlypiYmFCuXDmDl4uLC1ZWVpQrV86g9tvPz4+3337b4Pc8N5BkNovE/OeO0N6tCLbmtrpa2aPzdSvTUCv76NEjOnXqxNChQ4mPj6dt27acOXMm1SE4hBAitxg5ciSLFi3i5s2bDBw4kI4dO9K1a1dq1qzJgwcPXmkc2qVLl+Lu7k79+vXp2LGjfjioZzQaDRs2bCBfvnzUq1ePJk2a4OXlha+vb0a+tZeysbFh+/btPHz4kOrVq9OpUycaN27Mjz/++NL9Dhw4QJEiRejYsSOlS5emX79+xMbGYm9vj42NDf/88w/vvPMOJUuW5IMPPmDIkCF8+OGHgG768+bNm9OwYUPy58+f6pBOTk5OdOzY0aA5Q7t27RgxYgRDhgyhUqVKHD58mAkTJrz0vWo0GrZs2UK9evXo168fJUuWpFu3bgQHB+sT9++++458+fJRp04d2rRpg4+PD1WqVEnrx/lKHBwc2LlzJ7du3aJatWoMGjSIkSNHMnLkSH2ZmJgYLl++nGy4rMWLF1OnTp0Ua/O9vb3566+/OH/+PLVr1+btt9/mzp07bNu2Td92uVGjRqxcuZINGzZQuXJlmjdvjqWlJdu2bUt3Urpq1Sr9aB65iUaldwDAHC4qKgoHBwciIyNf+Nglo33e6RN6XdzClYKwb0Jzvmv4HeydAfu/ApcyMPDQC5PZW7du8fbbbxMcHIy5uTnffPMNw4YNS1PDcyHEm+fp06cEBQVRtGhRmfVPZLoLFy7QpEmTFDuciexh8+bNjBo1ivPnz2eb6exf9H8qPfla9ng3uVySVuEWqRtk+0YBDSUdS0Lso3TVyhYsWJASJUqg0Wjw9fXV96IVQgghjK18+fLMnDmT4OBgypcvb+xwRAqio6NZunRptklkM1Lue0fZUERMPFZJul6WWqCofVFdp6+4SMhfGkq3S3G/hw8fYmVlhY2NDSYmJqxcuRIzMzODoWOEEEKI7KBPnz7GDkG8wH/HYM5tpM1sFgiLfErph7penOe8NLia2ekmSYBUa2UPHz5MpUqV+Pjjj/XrnJ2dJZEVQgghhPgPSWazwNmQCJyeRgEQkd+KUlf3w9NIyO8NZdoblNVqtcycOZN69epx8+ZN9u3bZzBUjBBCCCGE+D9JZrPAw3sP9T+XLl0H6+MLdAvP1crev3+f1q1bM2bMGJKSkujWrRunTp2S2lghhBBCiFRIm9ksoLkcAMAjWygd91hXK+tcyqBW1s/Pj27dunHnzh2srKyYO3cuAwYMkNEKhBBCCCFeQJLZLJB0/iwAT6zAM9BPt7L+aDDRTXQQExND586duXv3LqVKlWLNmjVUqFDBSNEKIYQQQuQc0swgC8RF6OZvjrDTUPrJQ3AuCWU76Lfb2NiwZMkSevXqxcmTJyWRFUIIIYRII6mZzWRKKdyjdVPbhRRR5E/SQv0x7N1/gNjYWFq2bAlAy5Yt9T8LIYQQQoi0kZrZTPYwOp6CT3QdwFT+BJRTCaasO0/jxo3p0aMHISEhRo5QCCFEdnL58mVcXV15/PixsUMRqahevTp//vmnscMQ/zJ6Mjtv3jz9NGZVq1bFz8/vheX3799P1apVsbKywsvLi59//jmLIn01j6LjKPAkFgAPq6dMO5jI5ClfoJSiY8eOODs7GzlCIYTIHpKSkqhTpw7vvPOOwfrIyEjc3d0ZP368wfp169bRqFEj8uXLh42NDaVKlaJfv36cOXNGX2bZsmVoNBr9y87OjqpVq2Z5ItKgQQOGDx+eprLjxo1j8ODBuXpa2HXr1lGmTBksLS0pU6YM69evf+k+27dvp1atWuTJk4f8+fPzzjvvEBQUpN/+559/0rRpU/Lnz4+9vT21a9dm+/btBse4dOkS77zzDp6enmg0GubMmZPiuV6Wm0yYMIGxY8ei1WrT/+ZFhjNqMuvr68vw4cMZN24cZ86c4e2336ZFixap1lYGBQXRsmVL3n77bc6cOcPnn3/OsGHDWLduXRZHnnYhl64AoNWA8+OnTPrjHLa2tixfvpzFixdjY2Nj5AiFECJ7MDU15ddff2Xbtm2sWLFCv37o0KE4OjoyceJE/boxY8bQtWtXKlWqxKZNm7h06RILFy6kWLFifP755wbHtbe3JzQ0lNDQUM6cOYOPjw9dunTh8uXLWfbe0urWrVts2rSJ995777WOEx8fn0ERZbwjR47QtWtXevXqxblz5+jVqxddunTh2LFjqe5z/fp12rVrR6NGjTh79izbt28nPDycjh076sscOHCApk2bsmXLFk6dOkXDhg1p06aNwc1NTEwMXl5efPXVV7i6uqZ4rrTkJq1atSIyMjJZsiyMRBlRjRo11MCBAw3WeXt7q7Fjx6ZYfvTo0crb29tg3Ycffqhq1aqV5nNGRkYqQEVGRqY/4Few+OtPlX8pb7W/hrd6t7yZqlChgvrnn3+y5NxCiDdXbGys8vf3V7GxsUoppbRarUqKjjbKS6vVpiv2uXPnqnz58qnbt2+rDRs2KHNzc3XmzBn99iNHjihAzZ07N8X9/3u+pUuXKgcHB4PtSUlJytzcXK1Zs0a/7uHDh6pXr14qb968ytraWjVv3lxduXLFYL+1a9eqMmXKKAsLC+Xh4aG+/fZbg+0//fSTKl68uLK0tFQuLi7qnXfeUUop1adPHwUYvIKCglKMfdasWapatWoG68LDw1W3bt1UoUKFlLW1tSpXrpxauXKlQZn69eurwYMHqxEjRignJydVr149pZRSly5dUi1atFC2trbKxcVF9ezZU92/f1+/39atW1XdunWVg4ODcnR0VK1atVLXrl1LMbaM0qVLF9W8eXODdT4+Pqpbt26p7vPHH38oMzMzlZSUpF+3adMmpdFoVHx8fKr7lSlTRk2ZMiXFbR4eHuq7775Ltj6tuUnfvn1Vr169Uj23eLnn/0/9V3ryNaN1AIuPj+fUqVOMHTvWYH2zZs04fPhwivscOXKEZs2aGazz8fFh8eLFJCQkYG5unmyfuLg44uLi9MtRUVEZEH3amfgfAMAsCRzqvMfR7+ZibW2dpTEIIYSKjeVylapGOXep06fQpOMp1NChQ1m/fj29e/fmwoULTJw4kUqVKum3r1q1Cjs7OwYNGpTi/i8anzspKYnffvsNgCpVqujX9+3bl6tXr7Jp0ybs7e0ZM2YMLVu2xN/fH3Nzc06dOkWXLl2YPHkyXbt25fDhwwwaNAgnJyf69u3LyZMnGTZsGMuXL6dOnTo8fPhQ/2h67ty5XLlyhXLlyjF16lQA8ufPn2J8Bw4coFq1agbrnj59StWqVRkzZgz29vZs3ryZXr164eXlRc2aNfXlfv31Vz766CMOHTqEUorQ0FDq16/PgAEDmD17NrGxsYwZM4YuXbqwZ88eAKKjoxk5ciTly5cnOjqaiRMn0qFDB86ePYtJClOtA0yfPp3p06en+hkDbN26lbfffjvFbUeOHGHEiBEG63x8fFJ95A9QrVo1TE1NWbp0KX379uXJkycsX76cZs2apfjdD7oZNR8/foyjo+MLY/2v9OQmNWrUYObMmWk+tsg8Rktmw8PDSUpKokCBAgbrCxQoQFhYWIr7hIWFpVg+MTGR8PBw3Nzcku0zY8YMpkyZknGBp5Nl6XI8DDjMjTqlmf/dQqPFIYQQOYVGo2H+/PmULl2a8uXLJ0ssrly5gpeXF2Zm//8Kmz17tkEzhNu3b+Pg4ADo2tza2dkBEBsbi7m5ub5JAqBPYg8dOkSdOnUAWLFiBe7u7mzYsIHOnTsze/ZsGjduzIQJEwAoWbIk/v7+fPPNN/Tt25eQkBBsbW1p3bo1efLkwcPDg8qVKwPg4OCAhYUFNjY2qT7afiY4OJiqVQ1vOgoVKsSnn36qXx46dCjbtm3jjz/+MEhmixcvbpBcTZw4kSpVqhgknkuWLMHd3Z0rV65QsmTJZO2TFy9ejIuLC/7+/pQrVy7FGAcOHEiXLl1e+D4KFSqU6rbUvstT++4H8PT0ZMeOHXTu3JkPP/yQpKQkateuzZYtW1LdZ9asWURHR7801v9KT25SqFAhQkJC0Gq1qSb+ImsYfWiu5++glVIvvKtOqXxK65/57LPPGDlypH45KioKd3f3Vw033d4ds5jEoTHUTOXOUQghsoLG2ppSp08Z7dzptWTJEmxsbAgKCuLWrVt4enoaHvO5//n9+vWjbdu2HDt2jJ49e+q/GwDy5MnD6dOnAV2byV27dvHhhx/i5OREmzZtCAgIwMzMzCAxdHJyolSpUgQE6GZwDAgIoF27dgbnrFu3LnPmzCEpKYmmTZvi4eGBl5cXzZs3p3nz5nTo0CHd/SJiY2OxsrIyWJeUlMRXX32Fr68vt2/f1j9xtLW1NSj3fI3uqVOn2Lt3rz6R/6/AwEBKlixJYGAgEyZM4OjRo4SHh+s7NIWEhKSazDo6OqartjMl6f3uDwsLo3///vTp04d3332Xx48fM3HiRDp16sTOnTuT7btq1SomT57Mxo0bcXFxyZT4rK2t0Wq1xMXFyRNXIzNaMuvs7IypqWmyO5179+4luyN6xtXVNcXyZmZmODk5pbiPpaUllpaWGRP0KzKTTl5CCCPTaDTpetRvTEeOHOG7775j69atzJw5k/fff59du3bpk4kSJUpw8OBBg+ZlefPmJW/evNy6dSvZ8UxMTChevLh+uUKFCuzYsYOvv/6aNm3aGCS+//XfBCalZCalhHnfvn3s2LGDiRMnMnnyZE6cOEHevHnT/N6dnZ2JiIgwWDdr1iy+++475syZQ/ny5bG1tWX48OHJOnk9n9xqtVratGnD119/new8z55ktmnTBnd3dxYtWkTBggXRarWUK1fuhR3IXreZQWrf5al99wP89NNP2NvbG9Q8//7777i7u3Ps2DFq1aqlX+/r68v777/PH3/8QZMmTV4Y5/PSk5s8fPgQGxsbSWSzAaPVi1tYWFC1alV27txpsH7nzp36xzzPq127drLyO3bsoFq1aqm2mRFCCJFzxMbG0qdPHz788EOaNGnCL7/8wokTJ1iwYIG+zLvvvsuTJ0+YN2/eK5/H1NSU2FjdsIllypQhMTHRoDf9gwcPuHLlCqVLl9aXOXjwoMExDh8+TMmSJTE11U1NbmZmRpMmTZg5cybnz58nODhY3zbVwsKCpKSkl8ZVuXJl/P39Ddb5+fnRrl07evbsScWKFfHy8uLq1asvPVaVKlW4dOkSnp6eFC9e3OBla2vLgwcPCAgIYPz48TRu3JjSpUsnS6RTMnDgQM6ePfvC1/O1xP+V2nd5at/9oKtRf/Y5P/Ns+b/DY61atYq+ffuycuVKWrVq9dL38rz05CYXL140aHctjCjj+qSl3+rVq5W5ublavHix8vf3V8OHD1e2trYqODhYKaXU2LFjDXoKXr9+XdnY2KgRI0Yof39/tXjxYmVubq7Wrl2b5nNm9WgGQghhDC/qJZydDRs2TBUrVkw9efJEv27hwoXKzs7OYASATz75RJmamqoRI0YoPz8/FRwcrI4cOaJ69uypNBqN/n/80qVLlb29vQoNDVWhoaHq+vXrasGCBcrU1NSgl3u7du1UmTJllJ+fnzp79qxq3ry5Kl68uL6n/KlTp5SJiYmaOnWqunz5slq2bJmytrZWS5cuVUop9ddff6m5c+eqM2fOqODgYDVv3jxlYmKiLl68qJRSasCAAap69eoqKChI3b9/36BX/n9t2rRJubi4qMTERP264cOHK3d3d3Xo0CHl7++v+vfvr+zt7VW7du30ZerXr68+/vhjg2Pdvn1b5c+fX3Xq1EkdO3ZMBQYGqu3bt6v33ntPJSYmqqSkJOXk5KR69uyprl69qnbv3q2qV6+uALV+/fr0Xro0O3TokDI1NVVfffWVCggIUF999ZUyMzNTR48e1Zf54YcfVKNGjfTLu3fvVhqNRk2ZMkVduXJFnTp1Svn4+CgPDw8VExOjlFJq5cqVyszMTP3000/66x0aGqoePXqkP05cXJw6c+aMOnPmjHJzc1OffvqpOnPmjLp69aq+zMtyk2fq16+vpk6dmlkf0xsho0YzMGoyq5RuKBMPDw9lYWGhqlSpovbv36/f1qdPH1W/fn2D8vv27VOVK1dWFhYWytPTU82fPz9d55NkVgjxJsiJyey+ffuUqamp8vPzS7atWbNmqlGjRgbDbvn6+qoGDRooBwcHZW5urgoXLqy6d+9ukBQtXbrUYEgsS0tLVbJkSTVt2jSDhPHZ0FwODg7K2tpa+fj4pDo0l7m5uSpSpIj65ptv9Nv8/PxU/fr1Vb58+ZS1tbWqUKGC8vX11W+/fPmyqlWrlrK2tn7h0FyJiYmqUKFCatu2bfp1Dx48UO3atVN2dnbKxcVFjR8/XvXu3fulyaxSSl25ckV16NBBP+SYt7e3Gj58uP5z3LlzpypdurSytLRUFSpUUPv27cv0ZFYp3VBbpUqVUubm5srb21utW7fOYPukSZOUh4eHwbpVq1apypUrK1tbW5U/f37Vtm1bFRAQoN9ev379ZEOgAapPnz76MkFBQSmWeT7XeFFuopRSt27dUubm5urmzZsZ8nm8qTIqmdUolUpjoVwqKioKBwcHIiMjsbe3N3Y4QgiRKZ4+fUpQUJB+FiORc8ybN4+NGzfKgPzZ2KhRo4iMjGThQhml6HW86P9UevI1o49mIIQQQoj/++CDD4iIiODx48e5ekrbnMzFxcVguDRhXJLMCiGEENmImZkZ48aNM3YY4gVGjRpl7BDEf8gov0IIIYQQIseSZFYIIYQQQuRYkswKIUQu9ob18RVC5CAZ9f9JklkhhMiFng0o/6KZnIQQwpie/X96fkKM9JIOYEIIkQuZmZlhY2PD/fv3MTc3x8RE6i6EENmHVqvl/v372NjYYGb2eumoJLNCCJELaTQa3NzcCAoK4saNG8YORwghkjExMaFIkSJoNJrXOo4ks0IIkUtZWFhQokQJaWoghMiWLCwsMuSpkSSzQgiRi5mYmMgMYEKIXE0aUQkhhBBCiBxLklkhhBBCCJFjSTIrhBBCCCFyrDeuzeyzAXqjoqKMHIkQQgghhEjJszwtLRMrvHHJ7OPHjwFwd3c3ciRCCCGEEOJFHj9+jIODwwvLaNQbNtehVqvlzp075MmT57XHNUurqKgo3N3duXnzJvb29llyTpFx5PrlfHINcz65hjmbXL+cL6uvoVKKx48fU7BgwZcO3/XG1cyamJhQuHBho5zb3t5e/ohzMLl+OZ9cw5xPrmHOJtcv58vKa/iyGtlnpAOYEEIIIYTIsSSZFUIIIYQQOZYks1nA0tKSSZMmYWlpaexQxCuQ65fzyTXM+eQa5mxy/XK+7HwN37gOYEIIIYQQIveQmlkhhBBCCJFjSTIrhBBCCCFyLElmhRBCCCFEjiXJrBBCCCGEyLEkmc0A8+bNo2jRolhZWVG1alX8/PxeWH7//v1UrVoVKysrvLy8+Pnnn7MoUpGa9FzDP//8k6ZNm5I/f37s7e2pXbs227dvz8JoRUrS+3f4zKFDhzAzM6NSpUqZG6B4qfRew7i4OMaNG4eHhweWlpYUK1aMJUuWZFG04nnpvX4rVqygYsWK2NjY4ObmxnvvvceDBw+yKFrxvAMHDtCmTRsKFiyIRqNhw4YNL90n2+QzSryW1atXK3Nzc7Vo0SLl7++vPv74Y2Vra6tu3LiRYvnr168rGxsb9fHHHyt/f3+1aNEiZW5urtauXZvFkYtn0nsNP/74Y/X111+r48ePqytXrqjPPvtMmZubq9OnT2dx5OKZ9F7DZx49eqS8vLxUs2bNVMWKFbMmWJGiV7mGbdu2VTVr1lQ7d+5UQUFB6tixY+rQoUNZGLV4Jr3Xz8/PT5mYmKi5c+eq69evKz8/P1W2bFnVvn37LI5cPLNlyxY1btw4tW7dOgWo9evXv7B8dspnJJl9TTVq1FADBw40WOft7a3Gjh2bYvnRo0crb29vg3UffvihqlWrVqbFKF4svdcwJWXKlFFTpkzJ6NBEGr3qNezatasaP368mjRpkiSzRpbea7h161bl4OCgHjx4kBXhiZdI7/X75ptvlJeXl8G677//XhUuXDjTYhRpl5ZkNjvlM9LM4DXEx8dz6tQpmjVrZrC+WbNmHD58OMV9jhw5kqy8j48PJ0+eJCEhIdNiFSl7lWv4PK1Wy+PHj3F0dMyMEMVLvOo1XLp0KYGBgUyaNCmzQxQv8SrXcNOmTVSrVo2ZM2dSqFAhSpYsyaeffkpsbGxWhCz+41WuX506dbh16xZbtmxBKcXdu3dZu3YtrVq1yoqQRQbITvmMWZaeLZcJDw8nKSmJAgUKGKwvUKAAYWFhKe4TFhaWYvnExETCw8Nxc3PLtHhFcq9yDZ83a9YsoqOj6dKlS2aEKF7iVa7h1atXGTt2LH5+fpiZyb9BY3uVa3j9+nUOHjyIlZUV69evJzw8nEGDBvHw4UNpN5vFXuX61alThxUrVtC1a1eePn1KYmIibdu25YcffsiKkEUGyE75jNTMZgCNRmOwrJRKtu5l5VNaL7JOeq/hM6tWrWLy5Mn4+vri4uKSWeGJNEjrNUxKSqJ79+5MmTKFkiVLZlV4Ig3S83eo1WrRaDSsWLGCGjVq0LJlS2bPns2yZcukdtZI0nP9/P39GTZsGBMnTuTUqVNs27aNoKAgBg4cmBWhigySXfIZqZJ4Dc7Ozpiamia787x3716yu5VnXF1dUyxvZmaGk5NTpsUqUvYq1/AZX19f3n//ff744w+aNGmSmWGKF0jvNXz8+DEnT57kzJkzDBkyBNAlRkopzMzM2LFjB40aNcqS2IXOq/wdurm5UahQIRwcHPTrSpcujVKKW7duUaJEiUyNWfzfq1y/GTNmULduXUaNGgVAhQoVsLW15e233+bLL7+Up5Q5QHbKZ6Rm9jVYWFhQtWpVdu7cabB+586d1KlTJ8V9ateunaz8jh07qFatGubm5pkWq0jZq1xD0NXI9u3bl5UrV0obLyNL7zW0t7fnwoULnD17Vv8aOHAgpUqV4uzZs9SsWTOrQhf/epW/w7p163Lnzh2ePHmiX3flyhVMTEwoXLhwpsYrDL3K9YuJicHExDAFMTU1Bf5fuyeyt2yVz2R5l7Nc5tlwJIsXL1b+/v5q+PDhytbWVgUHByullBo7dqzq1auXvvyzoSxGjBih/P391eLFi2VoLiNL7zVcuXKlMjMzUz/99JMKDQ3Vvx49emSst/DGS+81fJ6MZmB86b2Gjx8/VoULF1adcWt0swAACwRJREFUOnVSly5dUvv371clSpRQ/fv3N9ZbeKOl9/otXbpUmZmZqXnz5qnAwEB18OBBVa1aNVWjRg1jvYU33uPHj9WZM2fUmTNnFKBmz56tzpw5ox9eLTvnM5LMZoCffvpJeXh4KAsLC1WlShW1f/9+/bY+ffqo+vXrG5Tft2+fqly5srKwsFCenp5q/vz5WRyxeF56rmH9+vUVkOzVp0+frA9c6KX37/C/JJnNHtJ7DQMCAlSTJk2UtbW1Kly4sBo5cqSKiYnJ4qjFM+m9ft9//70qU6aMsra2Vm5ubqpHjx7q1q1bWRy1eGbv3r0v/G7LzvmMRimpzxdCCCGEEDmTtJkVQgghhBA5liSzQgghhBAix5JkVgghhBBC5FiSzAohhBBCiBxLklkhhBBCCJFjSTIrhBBCCCFyLElmhRBCCCFEjiXJrBBCCCGEyLEkmRVCiH8tW7aMvHnzGjuM16LRaNiwYcMLy/Tt25f27dtnSTxCCJHZJJkVQuQqffv2RaPRJHtdu3bN2KFlidDQUFq0aAFAcHAwGo2Gs2fPGpSZO3cuy5Yty/rg0mDfvn1oNBoePXpk7FCEEDmEmbEDEEKIjNa8eXOWLl1qsC5//vxGiiZrubq6vrSMg4NDFkRiKD4+HgsLiyw/rxAi95OaWSFErmNpaYmrq6vBy9TUlNmzZ1O+fHlsbW1xd3dn0KBBPHnyJNXjnDt3joYNG5InTx7s7e2pWrUqJ0+e1G8/fPgw9erVw9raGnd3d4YNG0Z0dHSqx5s8eTKVKlViwYIFuLu7Y2NjQ+fOnQ1qIbVaLVOnTqVw4cJYWlpSqVIltm3bpt8eHx/PkCFDcHNzw8rKCk9PT2bMmKHf/t9mBkWLFgWgcuXKaDQaGjRoABg2M1iwYAGFChVCq9UaxNq2bVv69OmjX/7rr7+oWrUqVlZWeHl5MWXKFBITE1N9r8/OMWPGDAoWLEjJkiUB+P3336lWrRp58uTB1dWV7t27c+/ePUBXk9ywYUMA8uXLh0ajoW/fvgAopZg5cyZeXl5YW1tTsWJF1q5dm+r5hRBvDklmhRBvDBMTE77//nsuXrzIr7/+yp49exg9enSq5Xv06EHhwoU5ceIEp06dYuzYsZibmwNw4cIFfHx86NixI+fPn8fX15eDBw8yZMiQF8Zw7do11qxZw19//cW2bds4e/YsgwcP1m+fO3cus2bN4ttvv+X8+fP4+PjQtm1brl69CsD333/Ppk2bWLNmDZcvX+b333/H09MzxXMdP34cgF27dhEaGsqff/6ZrEznzp0JDw9n7969+nURERFs376dHj16ALB9+3Z69uzJsGHD8Pf3Z8GCBSxbtoxp06a98L3u3r2bgIAAdu7cyd9//w3okvEvvviCc+fOsWHDBoKCgvQJq7u7O+vWrQPg8uXLhIaGMnfuXADGjx/P0qVLmT9/PpcuXWLEiBH07NmT/fv3vzAGIcQbQAkhRC7Sp08fZWpqqmxtbfWvTp06pVh2zZo1ysnJSb+8dOlS5eDgoF/OkyePWrZsWYr79urVS33wwQcG6/z8/JSJiYmKjY1NcZ9JkyYpU1NTdfPmTf26rVu3KhMTExUaGqqUUqpgwYJq2rRpBvtVr15dDRo0SCml1NChQ1WjRo2UVqtN8RyAWr9+/f/au7uQJts/DuDfLXSbsx0IlpvOCmOSUC4jyawkck5EC5evGQjZi1AIRtJZzgzpoIwYWNmBkC2iTiISPQgksgVaaS9KgiGN8CgMVpbavH/PQXTj0vU8/54/PEy/n5Ptuq+X+7runfx27/rdExGR8fFxASCDg4Mhbaqrq2Xfvn1qee/evXLo0CG1fO3aNUlISJBgMCgiIjt37pSWlpaQMTo7O8VsNi86h5/nWL16tczMzIRtIyLS398vAOTz588iItLb2ysA5NOnT2qbL1++iF6vF5/PF9K3pqZGKisrfzs+ES193DNLREvO7t27ceXKFbVsNBoBAL29vWhpacHIyAgCgQCCwSCmp6cxNTWltpnv5MmTOHz4MDo7O5Gbm4vS0lKkpKQAAJ4/f46xsTF4vV61vYhAURSMj49jw4YNi84tOTkZSUlJajkrKwuKomB0dBQxMTGYmJhAdnZ2SJ/s7Gy8fPkSwI+f7x0OB1JTU5Gfn4/CwkLk5eX94ZX6oaqqCkePHkVbWxt0Oh28Xi8qKiqwYsUKda0DAwMhd2Ln5uYwPT2Nr1+/IiYmZtFxN27cuGCf7ODgINxuN4aGhjA5Oalub/D7/UhLS1t0nJGREUxPT8PhcIQcn52dxebNm/943US0NDCYJaIlx2g0Yv369SHH3r9/j4KCAtTW1qK5uRlxcXHo6+tDTU0Nvn//vug4brcbBw4cQFdXF7q7u9HY2Ijbt2+juLgYiqLg2LFjqKurW9AvOTn5H89Vo9GEvP76HvgRJP88lpGRgfHxcXR3d+Phw4coKytDbm7uv9o/WlRUBEVR0NXVha1bt+Lx48dobW1V6xVFQVNTE1wu14K+er0+7Li/fkGYmppCXl4e8vLycPPmTcTHx8Pv98PpdGJ2djbsOD8D3q6uLiQmJobU6XS6f7RGIlq6GMwS0bLw7NkzBINBXLx4EVrtj3SBO3fu/G0/m80Gm82G+vp6VFZWoqOjA8XFxcjIyMDw8PCCoPnv+P1+TExMwGKxAACePn0KrVYLm80Gk8kEi8WCvr4+7Nq1S+3j8/mQmZmplk0mE8rLy1FeXo6SkhLk5+djcnIScXFxIef6eVd0bm7ut3MyGAxwuVzwer0YGxuDzWbDli1b1PqMjAyMjo7+z2v91du3b/Hx40ecP38eVqsVAEIS6sLNOS0tDTqdDn6/Hzk5Of9qDkS09DCYJaJlISUlBcFgEB6PB0VFRXjy5AmuXr0atv23b9/Q0NCAkpISrFu3Dh8+fMDAwAD2798PADh9+jS2bduG48eP48iRIzAajWqyk8fjCTuuXq9HdXU1Lly4gEAggLq6OpSVlamP1GpoaEBjYyNSUlJgt9vR0dGBoaEhdTvDpUuXYDabYbfbodVqcffuXSQkJCz6Zw+rVq2CwWBAT08PkpKSoNfrwz6Wq6qqCkVFRRgeHsbBgwdD6s6cOYPCwkJYrVaUlpZCq9Xi1atXeP36Nc6dO/fb6z5fcnIyoqOj4fF4UFtbizdv3qC5uTmkzZo1a6DRaPDgwQMUFBTAYDBg5cqVOHXqFOrr66EoCnbs2IFAIACfz4fY2NiQpy4Q0TL0X2/aJSL6f/o1uWm+1tZWMZvNYjAYxOl0yo0bN0KSjeYngM3MzEhFRYVYrVaJjo4Wi8UiJ06cCEnu6u/vF4fDIbGxsWI0GmXTpk0Lkrfma2xslPT0dGlraxOLxSJ6vV5cLpdMTk6qbebm5qSpqUkSExMlKipK0tPTpbu7W61vb28Xu90uRqNRTCaT7NmzR168eKHWY14CmIjI9evXxWq1ilarlZycnLDXKBgMitlsFgDy7t27BXPv6emR7du3i8FgEJPJJJmZmdLe3h52reE+h1u3bsnatWtFp9NJVlaW3L9/f0GS2tmzZyUhIUE0Go1UV1eLiIiiKHL58mVJTU2VqKgoiY+PF6fTKY8ePQo7ByJaHjQiIv9tOE1EtDy43W7cu3dvwT9yERHRn+NzZomIiIgoYjGYJSIiIqKIxW0GRERERBSxeGeWiIiIiCIWg1kiIiIiilgMZomIiIgoYjGYJSIiIqKIxWCWiIiIiCIWg1kiIiIiilgMZomIiIgoYjGYJSIiIqKI9Rd1CWPgE4U3oQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"import numpy as np\n",
"# import libraries\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn import preprocessing\n",
"\n",
"# Importing necessary libraries for model development and evaluation\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, classification_report\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from xgboost import XGBClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"\n",
"# Defining the features (X) and the target (y)\n",
"X = data_dropped_columns.drop(\"Heart_Disease\", axis=1)\n",
"y = data_dropped_columns[\"Heart_Disease\"]\n",
"\n",
"# Performing the train-test split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Defining the function to apply models\n",
"def apply_model(model, X_train, y_train, X_test, y_test, name):\n",
" # Fit the model\n",
" model.fit(X_train, y_train)\n",
"\n",
" # Make predictions\n",
" y_pred = model.predict(X_test)\n",
" \n",
" # Calculate performance metrics\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" precision = precision_score(y_test, y_pred)\n",
" recall = recall_score(y_test, y_pred)\n",
" f1 = f1_score(y_test, y_pred)\n",
"\n",
" print(f\"Model: {name}\")\n",
" print(f\"F1 Score: {f1}\")\n",
" print(classification_report(y_test, y_pred))\n",
" print('==========================================================')\n",
" \n",
" # Compute ROC curve and ROC area\n",
" y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
" fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
" roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
"\n",
" return accuracy, precision, recall, f1, fpr, tpr, roc_auc\n",
"\n",
"# Defining the models\n",
"models = [\n",
" (\"Logistic Regression\", LogisticRegression(class_weight='balanced', random_state=42, max_iter=500)),\n",
" (\"Decision Tree\", DecisionTreeClassifier(class_weight='balanced', random_state=42)),\n",
" (\"Random Forest\", RandomForestClassifier(class_weight='balanced', random_state=42)),\n",
" (\"XGBoost\", XGBClassifier(scale_pos_weight=1, eval_metric='logloss', random_state=42)),\n",
"]\n",
"\n",
"# Applying the models and storing the results\n",
"results = []\n",
"roc_curves = []\n",
"\n",
"for name, model in models:\n",
" accuracy, precision, recall, f1, fpr, tpr, roc_auc = apply_model(model, X_train, y_train, X_test, y_test, name)\n",
" results.append((name, accuracy, precision, recall, f1))\n",
" roc_curves.append((name, fpr, tpr, roc_auc))\n",
"\n",
"# Plotting the ROC curves for each model\n",
"plt.figure(figsize=(8, 6))\n",
"plt.plot([0, 1], [0, 1], 'k--')\n",
"plt.xlabel('False positive rate')\n",
"plt.ylabel('True positive rate')\n",
"plt.title('ROC curve comparison')\n",
"for name, fpr, tpr, roc_auc in roc_curves:\n",
" plt.plot(fpr, tpr, label=f\"{name} (area = {roc_auc:.4f})\")\n",
"plt.legend(loc='best')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5e448e76",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}