Skip to content

Instantly share code, notes, and snippets.

@janduplessis883

janduplessis883/Framingham Cardiovascular Risk Logistic Regression.ipynb

Created May 10, 2023 16:44
Show Gist options
  • Save janduplessis883/a714b41bec7ae71ea3f67bc01ef4b9d2 to your computer and use it in GitHub Desktop.
Save janduplessis883/a714b41bec7ae71ea3f67bc01ef4b9d2 to your computer and use it in GitHub Desktop.
Download ZIP
Framingham Cardiovascular Risk Logistic Regression
Display the source blob
Display the rendered blob
Raw
Framingham Cardiovascular Risk Logistic Regression.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b59bcc18",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b19e76df2ef8436ea86e77b4be907443",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(Button(description='⚡️Connect', style=ButtonStyle(button_color='#e3e3e3')), Button(description=…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"\n",
" var code = \"an.search_code()\";\n",
" var cell = Jupyter.notebook.insert_cell_above('code');\n",
" cell.set_text(code);\n",
" cell.metadata.id = '9174b691-b218-4346-be0f-8e30b9306ceb';\n",
" "
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from autonote import AutoNote\n",
"an = AutoNote()"
]
},
{
"cell_type": "markdown",
"id": "e597cfdf",
"metadata": {
"id": "295f83b3-7607-4608-a888-32b68a90ea1e"
},
"source": [
"<div class='alert' style='background-color: #1c1a1e; color: #f5f4f0; padding:26px 26px; border-radius:15px; font-size:40px;'><B>Framingham Cardiovasc Prediction</B> </div><span style='color: #1c1a1e; padding:26px 26px; font-size:11px;'> Powered by <B>?AutoNote</B></span><div style='margin:4px 26px; color:#1c1a1e; font-size:17px;'>\n",
"<ol>\n",
"<li><B>Problem statement</B>: A clear description of the problem the project aims to solve.</li><BR>\n",
"<li><B>Data source</B>: Information on where the data used in the project is obtained from.</li><BR>\n",
"<li><B>Libraries used</B>: A list of the Python libraries used in the project and a brief explanation of their role. Include library version.</li><BR>\n",
"<li><B>Exploratory Data Analysis (EDA)</B>: A summary of the initial findings from exploring the data.</li><BR>\n",
"<li><B>Preprocessing</B>: Steps taken to clean and prepare the data for model building.</li><BR>\n",
"<li><B>Model building</B>: An overview of the model used and the reasoning behind its selection.</li><BR>\n",
"<br> Accuracy = $\\frac{\\text{correct predictions}}{\\text{total predictions}}$, Precision = $\\frac{\\text{true positives}}{\\text{true positives + false positives}}$, Recall = $\\frac{\\text{true positives}}{\\text{true positives + false negatives}}$, <br>F1 = $2 \\times \\frac{\\text{precision} \\times \\text{recall}}{\\text{precision + recall}}$</li><BR><BR>\n",
"<li><B>Model evaluation</B>: Evaluation metrics used to assess the performance of the model and results of the evaluation.</li><BR>\n",
"<li><B>Conclusion</B>: A summary of the findings and recommendations for further work.</li>\n",
"</ol>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "8da15493",
"metadata": {
"id": "805cf86c-2474-4b5c-a03b-ad7e5c6d10ba"
},
"source": [
"# Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "db2eb6ba",
"metadata": {
"id": "faaf891e-2cc1-4608-801f-f805e3c2529c"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import classification_report, confusion_matrix, accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "abf07322",
"metadata": {
"id": "9fcb296f-6dd8-4ae3-b3f1-d977b5104521"
},
"outputs": [],
"source": [
"data = pd.read_csv('../jan-datasets/framingham.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "96c7a985",
"metadata": {
"id": "0d5a54b7-8950-4f51-b9d5-74b8518ccf9a"
},
"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>male</th>\n",
" <th>age</th>\n",
" <th>education</th>\n",
" <th>currentSmoker</th>\n",
" <th>cigsPerDay</th>\n",
" <th>BPMeds</th>\n",
" <th>prevalentStroke</th>\n",
" <th>prevalentHyp</th>\n",
" <th>diabetes</th>\n",
" <th>totChol</th>\n",
" <th>sysBP</th>\n",
" <th>diaBP</th>\n",
" <th>BMI</th>\n",
" <th>heartRate</th>\n",
" <th>glucose</th>\n",
" <th>TenYearCHD</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>39</td>\n",
" <td>4.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>195.0</td>\n",
" <td>106.0</td>\n",
" <td>70.0</td>\n",
" <td>26.97</td>\n",
" <td>80.0</td>\n",
" <td>77.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>46</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>250.0</td>\n",
" <td>121.0</td>\n",
" <td>81.0</td>\n",
" <td>28.73</td>\n",
" <td>95.0</td>\n",
" <td>76.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>48</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>245.0</td>\n",
" <td>127.5</td>\n",
" <td>80.0</td>\n",
" <td>25.34</td>\n",
" <td>75.0</td>\n",
" <td>70.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>61</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>225.0</td>\n",
" <td>150.0</td>\n",
" <td>95.0</td>\n",
" <td>28.58</td>\n",
" <td>65.0</td>\n",
" <td>103.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>46</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>23.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>285.0</td>\n",
" <td>130.0</td>\n",
" <td>84.0</td>\n",
" <td>23.10</td>\n",
" <td>85.0</td>\n",
" <td>85.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>43</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>228.0</td>\n",
" <td>180.0</td>\n",
" <td>110.0</td>\n",
" <td>30.30</td>\n",
" <td>77.0</td>\n",
" <td>99.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>63</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>205.0</td>\n",
" <td>138.0</td>\n",
" <td>71.0</td>\n",
" <td>33.11</td>\n",
" <td>60.0</td>\n",
" <td>85.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>313.0</td>\n",
" <td>100.0</td>\n",
" <td>71.0</td>\n",
" <td>21.68</td>\n",
" <td>79.0</td>\n",
" <td>78.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>52</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>260.0</td>\n",
" <td>141.5</td>\n",
" <td>89.0</td>\n",
" <td>26.36</td>\n",
" <td>76.0</td>\n",
" <td>79.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>43</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>225.0</td>\n",
" <td>162.0</td>\n",
" <td>107.0</td>\n",
" <td>23.61</td>\n",
" <td>93.0</td>\n",
" <td>88.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>254.0</td>\n",
" <td>133.0</td>\n",
" <td>76.0</td>\n",
" <td>22.91</td>\n",
" <td>75.0</td>\n",
" <td>76.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0</td>\n",
" <td>43</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>247.0</td>\n",
" <td>131.0</td>\n",
" <td>88.0</td>\n",
" <td>27.64</td>\n",
" <td>72.0</td>\n",
" <td>61.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1</td>\n",
" <td>46</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>15.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>294.0</td>\n",
" <td>142.0</td>\n",
" <td>94.0</td>\n",
" <td>26.31</td>\n",
" <td>98.0</td>\n",
" <td>64.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0</td>\n",
" <td>41</td>\n",
" <td>3.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>332.0</td>\n",
" <td>124.0</td>\n",
" <td>88.0</td>\n",
" <td>31.31</td>\n",
" <td>65.0</td>\n",
" <td>84.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>226.0</td>\n",
" <td>114.0</td>\n",
" <td>64.0</td>\n",
" <td>22.35</td>\n",
" <td>85.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>221.0</td>\n",
" <td>140.0</td>\n",
" <td>90.0</td>\n",
" <td>21.35</td>\n",
" <td>95.0</td>\n",
" <td>70.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1</td>\n",
" <td>48</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>232.0</td>\n",
" <td>138.0</td>\n",
" <td>90.0</td>\n",
" <td>22.37</td>\n",
" <td>64.0</td>\n",
" <td>72.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0</td>\n",
" <td>46</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>291.0</td>\n",
" <td>112.0</td>\n",
" <td>78.0</td>\n",
" <td>23.38</td>\n",
" <td>80.0</td>\n",
" <td>89.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>195.0</td>\n",
" <td>122.0</td>\n",
" <td>84.5</td>\n",
" <td>23.24</td>\n",
" <td>75.0</td>\n",
" <td>78.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1</td>\n",
" <td>41</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>195.0</td>\n",
" <td>139.0</td>\n",
" <td>88.0</td>\n",
" <td>26.88</td>\n",
" <td>85.0</td>\n",
" <td>65.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>190.0</td>\n",
" <td>108.0</td>\n",
" <td>70.5</td>\n",
" <td>21.59</td>\n",
" <td>72.0</td>\n",
" <td>85.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0</td>\n",
" <td>43</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>185.0</td>\n",
" <td>123.5</td>\n",
" <td>77.5</td>\n",
" <td>29.89</td>\n",
" <td>70.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>234.0</td>\n",
" <td>148.0</td>\n",
" <td>78.0</td>\n",
" <td>34.17</td>\n",
" <td>70.0</td>\n",
" <td>113.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>215.0</td>\n",
" <td>132.0</td>\n",
" <td>82.0</td>\n",
" <td>25.11</td>\n",
" <td>71.0</td>\n",
" <td>75.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1</td>\n",
" <td>44</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>270.0</td>\n",
" <td>137.5</td>\n",
" <td>90.0</td>\n",
" <td>21.96</td>\n",
" <td>75.0</td>\n",
" <td>83.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1</td>\n",
" <td>47</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>294.0</td>\n",
" <td>102.0</td>\n",
" <td>68.0</td>\n",
" <td>24.18</td>\n",
" <td>62.0</td>\n",
" <td>66.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>260.0</td>\n",
" <td>110.0</td>\n",
" <td>72.5</td>\n",
" <td>26.59</td>\n",
" <td>65.0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1</td>\n",
" <td>35</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>225.0</td>\n",
" <td>132.0</td>\n",
" <td>91.0</td>\n",
" <td>26.09</td>\n",
" <td>73.0</td>\n",
" <td>83.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0</td>\n",
" <td>61</td>\n",
" <td>3.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>272.0</td>\n",
" <td>182.0</td>\n",
" <td>121.0</td>\n",
" <td>32.80</td>\n",
" <td>85.0</td>\n",
" <td>65.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>247.0</td>\n",
" <td>130.0</td>\n",
" <td>88.0</td>\n",
" <td>30.36</td>\n",
" <td>72.0</td>\n",
" <td>74.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" male age education currentSmoker cigsPerDay BPMeds prevalentStroke \n",
"0 1 39 4.0 0 0.0 0.0 0 \\\n",
"1 0 46 2.0 0 0.0 0.0 0 \n",
"2 1 48 1.0 1 20.0 0.0 0 \n",
"3 0 61 3.0 1 30.0 0.0 0 \n",
"4 0 46 3.0 1 23.0 0.0 0 \n",
"5 0 43 2.0 0 0.0 0.0 0 \n",
"6 0 63 1.0 0 0.0 0.0 0 \n",
"7 0 45 2.0 1 20.0 0.0 0 \n",
"8 1 52 1.0 0 0.0 0.0 0 \n",
"9 1 43 1.0 1 30.0 0.0 0 \n",
"10 0 50 1.0 0 0.0 0.0 0 \n",
"11 0 43 2.0 0 0.0 0.0 0 \n",
"12 1 46 1.0 1 15.0 0.0 0 \n",
"13 0 41 3.0 0 0.0 1.0 0 \n",
"14 0 39 2.0 1 9.0 0.0 0 \n",
"15 0 38 2.0 1 20.0 0.0 0 \n",
"16 1 48 3.0 1 10.0 0.0 0 \n",
"17 0 46 2.0 1 20.0 0.0 0 \n",
"18 0 38 2.0 1 5.0 0.0 0 \n",
"19 1 41 2.0 0 0.0 0.0 0 \n",
"20 0 42 2.0 1 30.0 0.0 0 \n",
"21 0 43 1.0 0 0.0 0.0 0 \n",
"22 0 52 1.0 0 0.0 0.0 0 \n",
"23 0 52 3.0 1 20.0 0.0 0 \n",
"24 1 44 2.0 1 30.0 0.0 0 \n",
"25 1 47 4.0 1 20.0 0.0 0 \n",
"26 0 60 1.0 0 0.0 0.0 0 \n",
"27 1 35 2.0 1 20.0 0.0 0 \n",
"28 0 61 3.0 0 0.0 0.0 0 \n",
"29 0 60 1.0 0 0.0 0.0 0 \n",
"\n",
" prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose \n",
"0 0 0 195.0 106.0 70.0 26.97 80.0 77.0 \\\n",
"1 0 0 250.0 121.0 81.0 28.73 95.0 76.0 \n",
"2 0 0 245.0 127.5 80.0 25.34 75.0 70.0 \n",
"3 1 0 225.0 150.0 95.0 28.58 65.0 103.0 \n",
"4 0 0 285.0 130.0 84.0 23.10 85.0 85.0 \n",
"5 1 0 228.0 180.0 110.0 30.30 77.0 99.0 \n",
"6 0 0 205.0 138.0 71.0 33.11 60.0 85.0 \n",
"7 0 0 313.0 100.0 71.0 21.68 79.0 78.0 \n",
"8 1 0 260.0 141.5 89.0 26.36 76.0 79.0 \n",
"9 1 0 225.0 162.0 107.0 23.61 93.0 88.0 \n",
"10 0 0 254.0 133.0 76.0 22.91 75.0 76.0 \n",
"11 0 0 247.0 131.0 88.0 27.64 72.0 61.0 \n",
"12 1 0 294.0 142.0 94.0 26.31 98.0 64.0 \n",
"13 1 0 332.0 124.0 88.0 31.31 65.0 84.0 \n",
"14 0 0 226.0 114.0 64.0 22.35 85.0 NaN \n",
"15 1 0 221.0 140.0 90.0 21.35 95.0 70.0 \n",
"16 1 0 232.0 138.0 90.0 22.37 64.0 72.0 \n",
"17 0 0 291.0 112.0 78.0 23.38 80.0 89.0 \n",
"18 0 0 195.0 122.0 84.5 23.24 75.0 78.0 \n",
"19 0 0 195.0 139.0 88.0 26.88 85.0 65.0 \n",
"20 0 0 190.0 108.0 70.5 21.59 72.0 85.0 \n",
"21 0 0 185.0 123.5 77.5 29.89 70.0 NaN \n",
"22 0 0 234.0 148.0 78.0 34.17 70.0 113.0 \n",
"23 0 0 215.0 132.0 82.0 25.11 71.0 75.0 \n",
"24 1 0 270.0 137.5 90.0 21.96 75.0 83.0 \n",
"25 0 0 294.0 102.0 68.0 24.18 62.0 66.0 \n",
"26 0 0 260.0 110.0 72.5 26.59 65.0 NaN \n",
"27 1 0 225.0 132.0 91.0 26.09 73.0 83.0 \n",
"28 1 0 272.0 182.0 121.0 32.80 85.0 65.0 \n",
"29 0 0 247.0 130.0 88.0 30.36 72.0 74.0 \n",
"\n",
" TenYearCHD \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 1 \n",
"4 0 \n",
"5 0 \n",
"6 1 \n",
"7 0 \n",
"8 0 \n",
"9 0 \n",
"10 0 \n",
"11 0 \n",
"12 0 \n",
"13 0 \n",
"14 0 \n",
"15 1 \n",
"16 0 \n",
"17 1 \n",
"18 0 \n",
"19 0 \n",
"20 0 \n",
"21 0 \n",
"22 0 \n",
"23 0 \n",
"24 0 \n",
"25 1 \n",
"26 0 \n",
"27 0 \n",
"28 1 \n",
"29 0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(30)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1767e15b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4238, 16)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "markdown",
"id": "63a57088",
"metadata": {
"id": "bc3c763f-6bff-4244-9a0d-13aa2296024b"
},
"source": [
"# Explore and Clean the Data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "998a3fab",
"metadata": {
"id": "8f4d8817-5e74-4602-bb39-e2551d98ab11"
},
"outputs": [
{
"data": {
"text/plain": [
"male 0\n",
"age 0\n",
"education 105\n",
"currentSmoker 0\n",
"cigsPerDay 29\n",
"BPMeds 53\n",
"prevalentStroke 0\n",
"prevalentHyp 0\n",
"diabetes 0\n",
"totChol 50\n",
"sysBP 0\n",
"diaBP 0\n",
"BMI 19\n",
"heartRate 1\n",
"glucose 388\n",
"TenYearCHD 0\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check for missing values\n",
"data.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "69d37309",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[<Axes: title={'center': 'glucose'}>,\n",
" <Axes: title={'center': 'education'}>],\n",
" [<Axes: title={'center': 'BPMeds'}>,\n",
" <Axes: title={'center': 'totChol'}>]], dtype=object)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data[['glucose', 'education', 'BPMeds', 'totChol']].hist()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cab7edf7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['male', 'age', 'education', 'currentSmoker', 'cigsPerDay', 'BPMeds',\n",
" 'prevalentStroke', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP',\n",
" 'diaBP', 'BMI', 'heartRate', 'glucose', 'TenYearCHD'],\n",
" dtype='object')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "45601e56",
"metadata": {},
"outputs": [],
"source": [
"data['BPMeds'] = data['BPMeds'].fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f4aed2a7",
"metadata": {},
"outputs": [],
"source": [
"missing_values = ['education','cigsPerDay', 'totChol','BMI', 'heartRate', 'glucose']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6a8fa7a0",
"metadata": {
"id": "f2eff417-d5bd-4515-b7d0-f62797042b58"
},
"outputs": [],
"source": [
"from sklearn.impute import SimpleImputer\n",
"imputer = SimpleImputer(strategy='mean')\n",
"imputed_data = imputer.fit_transform(data)\n",
"imputed_df = pd.DataFrame(imputed_data, columns=data.columns)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "819c4057",
"metadata": {
"id": "b14373b0-8452-413f-84fe-985e53ad4ae0"
},
"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>male</th>\n",
" <th>age</th>\n",
" <th>education</th>\n",
" <th>currentSmoker</th>\n",
" <th>cigsPerDay</th>\n",
" <th>BPMeds</th>\n",
" <th>prevalentStroke</th>\n",
" <th>prevalentHyp</th>\n",
" <th>diabetes</th>\n",
" <th>totChol</th>\n",
" <th>sysBP</th>\n",
" <th>diaBP</th>\n",
" <th>BMI</th>\n",
" <th>heartRate</th>\n",
" <th>glucose</th>\n",
" <th>TenYearCHD</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>39.0</td>\n",
" <td>4.0</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>0.0</td>\n",
" <td>195.0</td>\n",
" <td>106.0</td>\n",
" <td>70.0</td>\n",
" <td>26.97</td>\n",
" <td>80.0</td>\n",
" <td>77.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" <td>46.0</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>0.0</td>\n",
" <td>250.0</td>\n",
" <td>121.0</td>\n",
" <td>81.0</td>\n",
" <td>28.73</td>\n",
" <td>95.0</td>\n",
" <td>76.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>48.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>245.0</td>\n",
" <td>127.5</td>\n",
" <td>80.0</td>\n",
" <td>25.34</td>\n",
" <td>75.0</td>\n",
" <td>70.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>61.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>225.0</td>\n",
" <td>150.0</td>\n",
" <td>95.0</td>\n",
" <td>28.58</td>\n",
" <td>65.0</td>\n",
" <td>103.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>46.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>23.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>285.0</td>\n",
" <td>130.0</td>\n",
" <td>84.0</td>\n",
" <td>23.10</td>\n",
" <td>85.0</td>\n",
" <td>85.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" male age education currentSmoker cigsPerDay BPMeds prevalentStroke \n",
"0 1.0 39.0 4.0 0.0 0.0 0.0 0.0 \\\n",
"1 0.0 46.0 2.0 0.0 0.0 0.0 0.0 \n",
"2 1.0 48.0 1.0 1.0 20.0 0.0 0.0 \n",
"3 0.0 61.0 3.0 1.0 30.0 0.0 0.0 \n",
"4 0.0 46.0 3.0 1.0 23.0 0.0 0.0 \n",
"\n",
" prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose \n",
"0 0.0 0.0 195.0 106.0 70.0 26.97 80.0 77.0 \\\n",
"1 0.0 0.0 250.0 121.0 81.0 28.73 95.0 76.0 \n",
"2 0.0 0.0 245.0 127.5 80.0 25.34 75.0 70.0 \n",
"3 1.0 0.0 225.0 150.0 95.0 28.58 65.0 103.0 \n",
"4 0.0 0.0 285.0 130.0 84.0 23.10 85.0 85.0 \n",
"\n",
" TenYearCHD \n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 1.0 \n",
"4 0.0 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imputed_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a572ba39",
"metadata": {},
"outputs": [],
"source": [
"data = imputed_df "
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "1cfbfe8e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"male 0\n",
"age 0\n",
"education 0\n",
"currentSmoker 0\n",
"cigsPerDay 0\n",
"BPMeds 0\n",
"prevalentStroke 0\n",
"prevalentHyp 0\n",
"diabetes 0\n",
"totChol 0\n",
"sysBP 0\n",
"diaBP 0\n",
"BMI 0\n",
"heartRate 0\n",
"glucose 0\n",
"TenYearCHD 0\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "6151ffac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='Count'>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(data)"
]
},
{
"cell_type": "markdown",
"id": "dc56864d",
"metadata": {
"id": "c1fe4a31-b2ce-4339-bada-99084d1183e4"
},
"source": [
"# Feature Engineering"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "1fa8eede",
"metadata": {
"id": "5dde5da5-ad72-4bb7-9490-3d3251e2e7ed"
},
"outputs": [],
"source": [
"# Convert categorical features to numerical\n",
"data['heavy_smoker'] = (data['cigsPerDay']>= 20).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "176b069d",
"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>male</th>\n",
" <th>age</th>\n",
" <th>education</th>\n",
" <th>currentSmoker</th>\n",
" <th>cigsPerDay</th>\n",
" <th>BPMeds</th>\n",
" <th>prevalentStroke</th>\n",
" <th>prevalentHyp</th>\n",
" <th>diabetes</th>\n",
" <th>totChol</th>\n",
" <th>sysBP</th>\n",
" <th>diaBP</th>\n",
" <th>BMI</th>\n",
" <th>heartRate</th>\n",
" <th>glucose</th>\n",
" <th>TenYearCHD</th>\n",
" <th>heavy_smoker</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>39.0</td>\n",
" <td>4.0</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>0.0</td>\n",
" <td>195.0</td>\n",
" <td>106.0</td>\n",
" <td>70.0</td>\n",
" <td>26.97</td>\n",
" <td>80.0</td>\n",
" <td>77.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" <td>46.0</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>0.0</td>\n",
" <td>250.0</td>\n",
" <td>121.0</td>\n",
" <td>81.0</td>\n",
" <td>28.73</td>\n",
" <td>95.0</td>\n",
" <td>76.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>48.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>245.0</td>\n",
" <td>127.5</td>\n",
" <td>80.0</td>\n",
" <td>25.34</td>\n",
" <td>75.0</td>\n",
" <td>70.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" male age education currentSmoker cigsPerDay BPMeds prevalentStroke \n",
"0 1.0 39.0 4.0 0.0 0.0 0.0 0.0 \\\n",
"1 0.0 46.0 2.0 0.0 0.0 0.0 0.0 \n",
"2 1.0 48.0 1.0 1.0 20.0 0.0 0.0 \n",
"\n",
" prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose \n",
"0 0.0 0.0 195.0 106.0 70.0 26.97 80.0 77.0 \\\n",
"1 0.0 0.0 250.0 121.0 81.0 28.73 95.0 76.0 \n",
"2 0.0 0.0 245.0 127.5 80.0 25.34 75.0 70.0 \n",
"\n",
" TenYearCHD heavy_smoker \n",
"0 0.0 0 \n",
"1 0.0 0 \n",
"2 0.0 1 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(3)"
]
},
{
"cell_type": "markdown",
"id": "86a55754",
"metadata": {
"id": "eea560c6-3d20-4cbe-b3b2-226be23314bb"
},
"source": [
"# Plot a Learning Curve"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "321d58af",
"metadata": {
"id": "938f8a95-3ee9-4938-b278-c1f8d23139b1"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import learning_curve\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "6e4dffe1",
"metadata": {
"id": "e16a9cdc-b577-4049-a2dd-dea1082aabef"
},
"outputs": [],
"source": [
"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(0.1, 1.0, 5)):\n",
" plt.figure()\n",
" plt.title(title)\n",
" if ylim is not None:\n",
" plt.ylim(*ylim)\n",
" plt.xlabel(\"Training examples\")\n",
" plt.ylabel(\"Score\")\n",
" train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
" train_scores_mean = np.mean(train_scores, axis=1)\n",
" train_scores_std = np.std(train_scores, axis=1)\n",
" test_scores_mean = np.mean(test_scores, axis=1)\n",
" test_scores_std = np.std(test_scores, axis=1)\n",
" plt.grid()\n",
"\n",
" plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
" plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
" plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
" plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n",
"\n",
" plt.legend(loc=\"best\")\n",
" return plt"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "47161f78",
"metadata": {
"id": "c567d405-f6a7-477b-b7c4-67c187059b4d"
},
"outputs": [],
"source": [
"# Define the target variable and feature matrix\n",
"X = data.drop('TenYearCHD', axis=1)\n",
"y = data['TenYearCHD']\n",
"\n",
"# Split the dataset 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)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "1448869c",
"metadata": {
"id": "b728e88f-e009-4bcf-968d-a135e4878111"
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"logreg = LogisticRegression(solver='liblinear', random_state=42)\n",
"title = \"Learning Curves (Logistic Regression)\"\n",
"cv = 5 # Number of cross-validation folds\n",
"plot_learning_curve(logreg, title, X_train, y_train, cv=cv, n_jobs=1)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "d96eae71",
"metadata": {},
"source": [
"## Scale the data and repeat the Learning Curve"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "fd725a41",
"metadata": {
"id": "9c371f32-2298-4e78-b47c-ad81fad42474"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original DataFrame:\n",
" male age education currentSmoker cigsPerDay BPMeds \n",
"0 1.0 39.0 4.0 0.0 0.0 0.0 \\\n",
"1 0.0 46.0 2.0 0.0 0.0 0.0 \n",
"2 1.0 48.0 1.0 1.0 20.0 0.0 \n",
"3 0.0 61.0 3.0 1.0 30.0 0.0 \n",
"4 0.0 46.0 3.0 1.0 23.0 0.0 \n",
"... ... ... ... ... ... ... \n",
"4233 1.0 50.0 1.0 1.0 1.0 0.0 \n",
"4234 1.0 51.0 3.0 1.0 43.0 0.0 \n",
"4235 0.0 48.0 2.0 1.0 20.0 0.0 \n",
"4236 0.0 44.0 1.0 1.0 15.0 0.0 \n",
"4237 0.0 52.0 2.0 0.0 0.0 0.0 \n",
"\n",
" prevalentStroke prevalentHyp diabetes totChol sysBP diaBP BMI \n",
"0 0.0 0.0 0.0 195.0 106.0 70.0 26.97 \\\n",
"1 0.0 0.0 0.0 250.0 121.0 81.0 28.73 \n",
"2 0.0 0.0 0.0 245.0 127.5 80.0 25.34 \n",
"3 0.0 1.0 0.0 225.0 150.0 95.0 28.58 \n",
"4 0.0 0.0 0.0 285.0 130.0 84.0 23.10 \n",
"... ... ... ... ... ... ... ... \n",
"4233 0.0 1.0 0.0 313.0 179.0 92.0 25.97 \n",
"4234 0.0 0.0 0.0 207.0 126.5 80.0 19.71 \n",
"4235 0.0 0.0 0.0 248.0 131.0 72.0 22.00 \n",
"4236 0.0 0.0 0.0 210.0 126.5 87.0 19.16 \n",
"4237 0.0 0.0 0.0 269.0 133.5 83.0 21.47 \n",
"\n",
" heartRate glucose TenYearCHD heavy_smoker \n",
"0 80.0 77.000000 0.0 0 \n",
"1 95.0 76.000000 0.0 0 \n",
"2 75.0 70.000000 0.0 1 \n",
"3 65.0 103.000000 1.0 1 \n",
"4 85.0 85.000000 0.0 1 \n",
"... ... ... ... ... \n",
"4233 66.0 86.000000 1.0 0 \n",
"4234 65.0 68.000000 0.0 1 \n",
"4235 84.0 86.000000 0.0 1 \n",
"4236 86.0 81.966753 0.0 0 \n",
"4237 80.0 107.000000 0.0 0 \n",
"\n",
"[4238 rows x 17 columns]\n",
"\n",
"Scaled DataFrame:\n",
" male age education currentSmoker cigsPerDay BPMeds \n",
"0 1.0 0.184211 1.000000 0.0 0.000000 0.0 \\\n",
"1 0.0 0.368421 0.333333 0.0 0.000000 0.0 \n",
"2 1.0 0.421053 0.000000 1.0 0.285714 0.0 \n",
"3 0.0 0.763158 0.666667 1.0 0.428571 0.0 \n",
"4 0.0 0.368421 0.666667 1.0 0.328571 0.0 \n",
"... ... ... ... ... ... ... \n",
"4233 1.0 0.473684 0.000000 1.0 0.014286 0.0 \n",
"4234 1.0 0.500000 0.666667 1.0 0.614286 0.0 \n",
"4235 0.0 0.421053 0.333333 1.0 0.285714 0.0 \n",
"4236 0.0 0.315789 0.000000 1.0 0.214286 0.0 \n",
"4237 0.0 0.526316 0.333333 0.0 0.000000 0.0 \n",
"\n",
" prevalentStroke prevalentHyp diabetes totChol sysBP diaBP \n",
"0 0.0 0.0 0.0 0.149406 0.106383 0.232804 \\\n",
"1 0.0 0.0 0.0 0.242784 0.177305 0.349206 \n",
"2 0.0 0.0 0.0 0.234295 0.208038 0.338624 \n",
"3 0.0 1.0 0.0 0.200340 0.314421 0.497354 \n",
"4 0.0 0.0 0.0 0.302207 0.219858 0.380952 \n",
"... ... ... ... ... ... ... \n",
"4233 0.0 1.0 0.0 0.349745 0.451537 0.465608 \n",
"4234 0.0 0.0 0.0 0.169779 0.203310 0.338624 \n",
"4235 0.0 0.0 0.0 0.239389 0.224586 0.253968 \n",
"4236 0.0 0.0 0.0 0.174873 0.203310 0.412698 \n",
"4237 0.0 0.0 0.0 0.275042 0.236407 0.370370 \n",
"\n",
" BMI heartRate glucose TenYearCHD heavy_smoker \n",
"0 0.277024 0.363636 0.104520 0.0 0.0 \n",
"1 0.319680 0.515152 0.101695 0.0 0.0 \n",
"2 0.237518 0.313131 0.084746 0.0 1.0 \n",
"3 0.316045 0.212121 0.177966 1.0 1.0 \n",
"4 0.183228 0.414141 0.127119 0.0 1.0 \n",
"... ... ... ... ... ... \n",
"4233 0.252787 0.222222 0.129944 1.0 0.0 \n",
"4234 0.101066 0.212121 0.079096 0.0 1.0 \n",
"4235 0.156568 0.404040 0.129944 0.0 1.0 \n",
"4236 0.087736 0.424242 0.118550 0.0 0.0 \n",
"4237 0.143723 0.363636 0.189266 0.0 0.0 \n",
"\n",
"[4238 rows x 17 columns]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"# Get the column headers\n",
"column_headers = data.columns\n",
"\n",
"# Initialize the MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"\n",
"# Fit the scaler to the data and transform the data\n",
"scaled_values = scaler.fit_transform(data)\n",
"\n",
"# Convert the transformed data back to a DataFrame, preserving the column headers\n",
"scaled_data = pd.DataFrame(scaled_values, columns=column_headers)\n",
"\n",
"print(\"Original DataFrame:\")\n",
"print(data)\n",
"print(\"\\nScaled DataFrame:\")\n",
"print(scaled_data)\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "80673518",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='Count'>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAGdCAYAAADzOWwgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACb3klEQVR4nOzdd1RVx/rw8S8gIB0BFYwgCIpg76KxF7BdjS1RoxA7igYr9q7Ye4sNNOpVk2j0aqKxYUEsUbGiUUSPSUDECIhUgfcPX/bPE8AIoSnPZ62zrnvP7Ck7567zMDN7tkZ6eno6QgghhBDFmGZhN0AIIYQQorBJQCSEEEKIYk8CIiGEEEIUexIQCSGEEKLYk4BICCGEEMWeBERCCCGEKPYkIBJCCCFEsScBkRBCCCGKvRKF3YAPQVpaGn/++SdGRkZoaGgUdnOEEEII8R7S09N5+fIl5cqVQ1Pz3WNAEhC9hz///BNra+vCboYQQgghcuHJkyeUL1/+nXkkIHoPRkZGwJsbamxsXMitEUKIoik9PZ2UlJTCboYoZrS1tbOdvYmNjcXa2lr5HX8XCYjeQ8aNNjY2loBICCGykJycTFhYGGlpaYXdFFHMaGpqYmdnh46OTrZ53me5iwREQggh/pX09HTCw8PR0tLC2tr6H9dqCJFXMtb4hoeHY2Nj86/W+UpAJIQQ4l95/fo18fHxlCtXDn19/cJujihmSpcuzZ9//snr16/R1tbOdTkSxgshhPhXUlNTAd45ZSFEfsn43mV8D3NLAiIhhBB5QrYlEYUhr753EhAJIYQQotiTNURCCCHyhUqlIioqqsDqs7CwwMbGpsDqy4qHhwfR0dH8+OOPhdoOkXMSEAkhhMhzKpUKJycn4uPjC6xOfX19QkJCCj0oEh8mCYiEEELkuaioKOLj4/EfPw0nmwr5Xl+I6jEei+cQFRUlAZHIFQmIhBBC5BsnmwrUdnAs7GZkqUWLFlSvXh0tLS22bduGjo4Oc+fOpU+fPnh5efH9999TtmxZVq9eTfv27UlNTWXIkCGcPHmSiIgIbGxsGD58OF9//XW2daSlpbFw4UI2btxIREQElStXZtq0afTo0aMAeyrehyyqFkIIUWxt27YNCwsLLl26xMiRI/H09KRnz540btyYq1ev0q5dO/r160d8fDxpaWmUL1+e7777jjt37jB9+nQmT57M3r17sy3f19eX7du3s2HDBm7fvs3o0aP58ssvOX36dAH2UrwPGSESQghRbNWsWZOpU6cCMGnSJBYsWICFhQWDBw8GYPr06axfv54bN27QqFEjZs2apVxrZ2dHUFAQe/fupVevXpnKTkpKYv78+Rw/fhwXFxcAKlasyLlz5/jmm29o3rx5AfRQvC8JiIQQQhRbNWrUUP6tpaWFubk51atXV86VLVsWgMjISADWrl3L1q1bUalUJCQkkJycTK1atbIs+8GDB8THx9O2bVu188nJydSuXTuPeyL+LQmIhBBCFFt/f9WDhoaG2rmMTf/S0tLYvXs348aNY+nSpbi4uGBkZMTixYu5ePFilmXHxcUBcPjwYT755BO1NF1d3bzshsgDEhAJIYQQ7yEwMJDGjRszfPhw5VxoaGi2+Z2dndHV1UWlUsn02AdAAqIioEvPrkQ+f5ZlWhnz0hz47seCbZAQQohMKlWqxPbt2zl69Ch2dnZ8++23XL58GTs7uyzzGxkZMW7cOEaPHk1aWhqffvopMTExBAYGYmxsjLu7ewH3QLyLBERFQOTzZ7ivH5pl2jbPbwq4NUIIkXdCVI8/mnqGDh3KtWvX+Pzzz9HQ0KB3794MHz6cn3/+Odtr5syZQ+nSpfH19eXhw4eYmppSp04dJk+enO/tFTmjkZ6enl7YjSjqYmNjMTExISYmBmNj4zwv36VVk3cGREEnA/O8TiGEyCuJiYmEhYVhZ2dHyZIlAdmpWhScrL5/GXLy+y0jREIIIfKcjY0NISEhxe5dZuLDJQGREEKIfGFjYyMBivhgyE7VQgghhCj2JCASQgghRLEnU2ZFwP3Q+2zZvi3LtLDQ+wXcGiGEEKL4kYCoCHj9OpVaHdplmXbfP7hgGyOEEEIUQxIQFQHJr15xdPT6bNOEEEIIkb8kICoCNDWh+6ymWaZt6vtdAbdGCCGEKH5kUbUQQgghij0ZIRJCCJEvVCqVbMwoPhgSEAkhhMhzb17dUYX4+IQCq1NfX4+QkLsSFIlcKTIB0YIFC5g0aRJff/01K1asAN68n2Ts2LHs3r2bpKQkXF1dWbduHWXLllWuU6lUeHp6curUKQwNDXF3d8fX15cSJf6vawEBAYwZM4bbt29jbW3N1KlT8fDwKOAeCiFE8REVFUV8fALbfAZRxcYq3+u7qwrHfeFmoqKiJCASuVIkAqLLly/zzTffUKNGDbXzo0eP5vDhw3z33XeYmJjg5eVFt27dCAx887LT1NRUOnbsiKWlJefPnyc8PJz+/fujra3N/PnzAQgLC6Njx44MGzaMnTt3cuLECQYNGoSVlRWurq4F3lchhChOqthYUadShcJuRraOHDnC3LlzuXXrFlpaWri4uLBy5Urs7e0BOH/+PMOHD+fu3btUq1aNqVOn8tlnn3Ht2jVq1aoFwK1btxg/fjxnz57FwMCAdu3asXz5ciwsLAqxZyKnCn1RdVxcHH379mXTpk2UKlVKOR8TE8OWLVtYtmwZrVq1om7duvj5+XH+/HkuXLgAwC+//MKdO3fYsWMHtWrVon379syZM4e1a9eSnJwMwIYNG7Czs2Pp0qU4OTnh5eVFjx49WL58eaH0VwghRNHx6tUrxowZw6+//sqJEyfQ1NTks88+Iy0tjdjYWDp37kz16tW5evUqc+bMwcfHR+366OhoWrVqRe3atfn11185cuQIT58+pVevXoXUI5FbhT5CNGLECDp27EibNm2YO3eucv7KlSukpKTQpk0b5VyVKlWwsbEhKCiIRo0aERQURPXq1dWm0FxdXfH09OT27dvUrl2boKAgtTIy8nh7e2fbpqSkJJKSkpTj2NjYPOipEEKIoqZ79+5qx1u3bqV06dLcuXOHc+fOoaGhwaZNmyhZsiTOzs788ccfDB48WMm/Zs0aateurcxKZJRhbW3Nb7/9RuXKlQusL+LfKdQRot27d3P16lV8fX0zpUVERKCjo4Opqana+bJlyxIREaHkeTsYykjPSHtXntjYWBISsl7s5+vri4mJifKxtrbOVf+EEEIUbffv36d3795UrFgRY2NjbG1tgTfrU+/du0eNGjUoWbKkkr9BgwZq11+/fl1Zw5rxqVKlCgChoaEF1g/x7xXaCNGTJ0/4+uuvOXbsmNqXrSiYNGkSY8aMUY5jY2MlKBJCiI9Q586dqVChAps2baJcuXKkpaVRrVo1ZdnFP4mLi6Nz584sXLgwU5qVVf4vJhd5p9ACoitXrhAZGUmdOnWUc6mpqZw5c4Y1a9Zw9OhRkpOTiY6OVhslevr0KZaWlgBYWlpy6dIltXKfPn2qpGX8b8a5t/MYGxujp6eXZdt0dXXR1dX9130UQghRdD1//px79+6xadMmmjZ987aAc+fOKemOjo7s2LGDpKQk5Tfh8uXLamXUqVOHH374AVtbW7Wnm8WHp9D+67Vu3ZqbN2+qnfvqq6+oUqUKPj4+WFtbo62tzYkTJ5Q53nv37qFSqXBxcQHAxcWFefPmERkZSZkyZQA4duwYxsbGODs7K3l++ukntXqOHTumlCGEECL/3FWFF9l6SpUqhbm5ORs3bsTKygqVSsXEiROV9D59+jBlyhSGDBnCxIkTUalULFmyBAANDQ3gzTrYTZs20bt3byZMmICZmRkPHjxg9+7dbN68GS0trbzpoMh3hRYQGRkZUa1aNbVzBgYGmJubK+cHDhzImDFjMDMzw9jYmJEjR+Li4kKjRo0AaNeuHc7OzvTr149FixYRERHB1KlTGTFihBLNDxs2jDVr1jBhwgQGDBjAyZMn2bt3L4cPHy7YDgshRDFiYWGBvr4e7gs3F1id+vp6OXrUXVNTk927dzNq1CiqVauGo6Mjq1atokWLFgAYGxvzv//9D09PT2rVqkX16tWZPn06ffr0UZZ6lCtXjsDAQHx8fGjXrh1JSUlUqFABNzc3NDUL/UFukQNFenxv+fLlaGpq0r17d7WNGTNoaWlx6NAhPD09cXFxwcDAAHd3d2bPnq3ksbOz4/Dhw4wePZqVK1dSvnx5Nm/eLHsQCSFEPrKxsSEk5G6Rf3VHmzZtuHPnjtq59PR05d+NGzfm+vXryvHOnTvR1tZWq6dSpUrs27cvl60WRYVG+tv/5UWWYmNjMTExISYmBmNj4zwv39CsJIN39swybVPf74j7KzHP6xRCiLySmJhIWFgYdnZ2Re4hmX9r+/btVKxYkU8++YTr16/j5eVFixYt2LFjR2E3Tfx/7/r+5eT3u0iPEAkhhBCFKSIigunTpxMREYGVlRU9e/Zk3rx5hd0skQ8kIBJCCCGyMWHCBCZMmFDYzRAFQFZ8CSGEEKLYk4BICCGEEMWeBERCCCGEKPYkIBJCCCFEsScBkRBCCCGKPQmIhBBCCFHsyWP3Qggh8oVKpSryO1XnVEBAAC1btuTFixdqLx4vDLa2tnh7e+Pt7V2o7fhYSEAkhBAiz6lUKpycqhAfn1Bgderr6xEScjffg6KC5u/vj7e3N9HR0WrnL1++jIGBQeE06iMkAZEQQog8FxUVRXx8AmtnfEUlW6t8r+/+o3BGzPIjKirqowuIslO6dOnCbsJHRdYQCSGEyDeVbK2o4WiT75/cBl1paWn4+vpiZ2eHnp4eNWvW5Pvvv1fSf/rpJypXroyenh4tW7bk0aNHatfPnDmTWrVqqZ1bsWIFtra2aue2bt1K1apV0dXVxcrKCi8vLyVt2bJlVK9eHQMDA6ytrRk+fDhxcXHAmym6r776ipiYGDQ0NNDQ0GDmzJnAmymzFStWKOWoVCq6dOmCoaEhxsbG9OrVi6dPn2Zq67fffoutrS0mJiZ88cUXvHz5Mlf37mMjAZEQQohiy9fXl+3bt7NhwwZu377N6NGj+fLLLzl9+jRPnjyhW7dudO7cmeDgYAYNGsTEiRNzXMf69esZMWIEQ4YM4ebNmxw8eBAHBwclXVNTk1WrVnH79m22bdvGyZMnldeFNG7cmBUrVmBsbEx4eDjh4eGMGzcuUx1paWl06dKFv/76i9OnT3Ps2DEePnzI559/rpYvNDSUH3/8kUOHDnHo0CFOnz7NggULctynj5FMmQkhhCiWkpKSmD9/PsePH8fFxQWAihUrcu7cOb755htsbW2xt7dn6dKlADg6OnLz5k0WLlyYo3rmzp3L2LFj+frrr5Vz9evXV/799qJoW1tb5s6dy7Bhw1i3bh06OjqYmJigoaGBpaVltnWcOHGCmzdvEhYWhrW1NQDbt2+natWqXL58WakvLS0Nf39/jIyMAOjXrx8nTpyQF9YiAZEQQohi6sGDB8THx9O2bVu188nJydSuXZuEhAQaNmyolpYROL2vyMhI/vzzT1q3bp1tnuPHj+Pr68vdu3eJjY3l9evXJCYmEh8fj76+/nvVExISgrW1tRIMATg7O2NqakpISIgSENna2irBEICVlRWRkZE56tPHSqbMhBBCFEsZ63QOHz5McHCw8rlz547aOqJ30dTUJD09Xe1cSkqK8m89Pb13Xv/o0SM6depEjRo1+OGHH7hy5Qpr164F3gRmeU1bW1vtWENDg7S0tDyv50MkI0RCCCGKJWdnZ3R1dVGpVDRv3jxTupOTEwcPHlQ7d+HCBbXj0qVLExERQXp6OhoaGgAEBwcr6UZGRtja2nLixAlatmyZqY4rV66QlpbG0qVL0dR8M0axd+9etTw6Ojqkpqa+sy9OTk48efKEJ0+eKKNEd+7cITo6Gmdn53deK96QgEgIIUS+uf8ovMjWY2RkxLhx4xg9ejRpaWl8+umnxMTEEBgYiLGxMcOGDWPp0qWMHz+eQYMGceXKFfz9/dXKaNGiBc+ePWPRokX06NGDI0eO8PPPP2NsbKzkmTlzJsOGDaNMmTK0b9+ely9fEhgYyMiRI3FwcCAlJYXVq1fTuXNnAgMD2bBhg1odtra2xMXFceLECWrWrIm+vn6mqbQ2bdpQvXp1+vbty4oVK3j9+jXDhw+nefPm1KtXL8f3pjiSgEgIIUSes7CwQF9fjxGz/AqsTn19PSwsLHJ0zZw5cyhdujS+vr48fPgQU1NT6tSpw+TJk7GxseGHH35g9OjRrF69mgYNGjB//nwGDBigXO/k5MS6deuYP38+c+bMoXv37owbN46NGzcqedzd3UlMTGT58uWMGzcOCwsLevToAUDNmjVZtmwZCxcuZNKkSTRr1gxfX1/69++vXN+4cWOGDRvG559/zvPnz5kxY4by6H0GDQ0NDhw4wMiRI2nWrBmampq4ubmxevXqXNzJ4kkj/e+TnyKT2NhYTExMiImJUYv684qhWUkG7+yZZdqmvt8R91dintcphBB5JTExkbCwMOzs7ChZsqRy/mN8dYcoerL7/kHOfr9lhEgIIUS+sLGxkQBFfDDkKTMhhBBCFHsSEAkhhBCi2JOASAghhBDFngREQgghhCj2JCASQgghRLEnAZEQQgghij157L4ISE9L4+nTiGzThBBCCJG/JCAqIsoYlS7sJgghhBDFlgREQggh8oXsVP3xs7W1xdvbG29v78Juyr8mAZEQQog8p1KpcHKqQnx8QoHVqa+vR0jI3Y82KHr06BF2dnZcu3aNWrVqKefj4+OZM2cOe/fu5Y8//sDIyAhnZ2fGjBlDly5dCq/BH5hCDYjWr1/P+vXrefToEQBVq1Zl+vTptG/fHnjzFuHTp0+rXTN06FC1NwGrVCo8PT05deoUhoaGuLu74+vrS4kS/9e1gIAAxowZw+3bt7G2tmbq1Kl4eHjke/+EEKK4ioqKIj4+gSWzv8Lezirf6wsNC2fcdD+ioqIKLSBKTU1FQ0MDTU3155WSk5PR0dHJt3qHDRvGxYsXWb16Nc7Ozjx//pzz58/z/PnzfKszv+T3vXqXQn3KrHz58ixYsIArV67w66+/0qpVK7p06cLt27eVPIMHDyY8PFz5LFq0SElLTU2lY8eOJCcnc/78ebZt24a/vz/Tp09X8oSFhdGxY0datmxJcHAw3t7eDBo0iKNHjxZoX4UQojiyt7OiahWbfP/kNuhKS0tj0aJFODg4oKuri42NDfPmzSMgIAANDQ2io6OVvMHBwWhoaCh/xPv7+2NqasrBgwdxdnZGV1cXlUqFra0tc+bMoX///hgbGzNkyBAAzp07R9OmTdHT08Pa2ppRo0bx6tUrpXxbW1vmz5/PgAEDMDIywsbGho0bNyrpdnZ2ANSuXRsNDQ1atGgBwMGDB5k8eTIdOnTA1taWunXrMnLkSAYMGKBW9ty5c+nfvz+GhoZUqFCBgwcP8uzZM7p06YKhoSE1atTg119/Vbs/P/zwA1WrVkVXVxdbW1uWLl36zvu5efNmTE1NOXHiBAC3bt2iffv2GBoaUrZsWfr166c2jdqiRQu8vLzw9vbGwsICV1fX9/wvl/cKNSDq3LkzHTp0oFKlSlSuXJl58+ZhaGjIhQsXlDz6+vpYWloqn7ffVvvLL79w584dduzYQa1atWjfvj1z5sxh7dq1JCcnA7Bhwwbs7OxYunQpTk5OeHl50aNHD5YvX17g/RVCCFG0TJo0iQULFjBt2jTu3LnDrl27KFu27HtfHx8fz8KFC9m8eTO3b9+mTJkyACxZsoSaNWty7do1pk2bRmhoKG5ubnTv3p0bN26wZ88ezp07h5eXl1p5S5cupV69ely7do3hw4fj6enJvXv3ALh06RIAx48fJzw8nH379gFgaWnJTz/9xMuXL9/Z1uXLl9OkSROuXbtGx44d6devH/379+fLL7/k6tWr2Nvb079/f9LT0wG4cuUKvXr14osvvuDmzZvMnDmTadOm4e/vn2X5ixYtYuLEifzyyy+0bt2a6OhoWrVqRe3atfn11185cuQIT58+pVevXmrXbdu2DR0dHQIDA9VmgApakdmHKDU1ld27d/Pq1StcXFyU8zt37sTCwoJq1aoxadIk4uPjlbSgoCCqV6+u9uV1dXUlNjZWGWUKCgqiTZs2anW5uroSFBSUbVuSkpKIjY1V+wghhPi4vHz5kpUrV7Jo0SLc3d2xt7fn008/ZdCgQe9dRkpKCuvWraNx48Y4Ojqir68PQKtWrRg7diz29vbY29vj6+tL37598fb2plKlSjRu3JhVq1axfft2EhMTlfI6dOjA8OHDcXBwwMfHBwsLC06dOgVA6dJvnkY2NzfH0tISMzMzADZu3Mj58+cxNzenfv36jB49msDAwExt7dChA0OHDqVSpUpMnz6d2NhY6tevT8+ePalcuTI+Pj6EhITw9OlTAJYtW0br1q2ZNm0alStXxsPDAy8vLxYvXpypbB8fH1asWMHp06dp0KABAGvWrKF27drMnz+fKlWqULt2bbZu3cqpU6f47bfflGsrVarEokWLcHR0xNHR8b3vfV4r9IDo5s2bGBoaoqury7Bhw9i/fz/Ozs4A9OnThx07dnDq1CkmTZrEt99+y5dffqlcGxERkSmSzziOiIh4Z57Y2FgSErJe7Ofr64uJiYnysba2zrP+CiGEKBpCQkJISkqidevWuS5DR0eHGjVqZDpfr149tePr16/j7++PoaGh8nF1dSUtLY2wsDAl39tlaWhoYGlpSWRk5Dvb0KxZMx4+fMiJEyfo0aMHt2/fpmnTpsyZM0ct39tlZ/wuVq9ePdO5jPpCQkJo0qSJWhlNmjTh/v37pKamKueWLl3Kpk2bOHfuHFWrVlXrc8b63oxPlSpVAAgNDVXy1a1b9539KyiF/pSZo6MjwcHBxMTE8P333+Pu7s7p06dxdnZW5l3hzX80KysrWrduTWhoKPb29vnWpkmTJjFmzBjlODY2VoIiIYT4yOjp6WWblrEwOmP6CN6MBmVVhoaGRqbzBgYGasdxcXEMHTqUUaNGZcr79iJwbW1ttTQNDQ3S3mODXm1tbZo2bUrTpk3x8fFh7ty5zJ49Gx8fH2WR8ttlZ7Q5q3PvU9/bmjZtyuHDh9m7dy8TJ05UzsfFxdG5c2cWLlyY6Rorq/9b8/X3e1VYCj0g0tHRwcHBAXgTJV6+fJmVK1fyzTffZMrbsGFDAB48eIC9vT2WlpbKnGqGjKE+S0tL5X8zzr2dx9jYONv/M+jq6qKrq/vvOiaEEKJIq1SpEnp6epw4cSLTNFnG9FR4eDilSpUC3iyqzq06depw584d5fcuNzICm7dHZ7Lj7OzM69evSUxMzPVTW05OTpmm3gIDA6lcuTJaWlrKuQYNGuDl5YWbmxslSpRg3LhxwJs+//DDD9ja2qo9+V1UFbkWpqWlkZSUlGVaxpcxI7J0cXFh3rx5REZGKgvZjh07hrGxsTLt5uLiwk8//aRWzrFjx9TWKQkhhMgfoWHhRbaekiVL4uPjw4QJE9DR0aFJkyY8e/aM27dv079/f6ytrZk5cybz5s3jt99++8cnrN7Fx8eHRo0a4eXlxaBBgzAwMODOnTscO3aMNWvWvFcZZcqUQU9PjyNHjlC+fHlKliyJiYkJLVq0oHfv3tSrVw9zc3Pu3LnD5MmTadmypdqDSDk1duxY6tevz5w5c/j8888JCgpizZo1rFu3LlPexo0b89NPP9G+fXtKlCiBt7c3I0aMYNOmTfTu3ZsJEyZgZmbGgwcP2L17N5s3b1YLqoqCQg2IJk2aRPv27bGxseHly5fs2rWLgIAAjh49SmhoKLt27aJDhw6Ym5tz48YNRo8eTbNmzZR50Hbt2uHs7Ey/fv1YtGgRERERTJ06lREjRigjPMOGDWPNmjVMmDCBAQMGcPLkSfbu3cvhw4cLs+tCCPFRs7CwQF9fj3HT/QqsTn19PSwsLHJ0zbRp0yhRogTTp0/nzz//xMrKimHDhqGtrc1///tfPD09qVGjBvXr12fu3Ln07NkzV22rUaMGp0+fZsqUKTRt2pT09HTs7e35/PPP37uMEiVKsGrVKmbPns306dNp2rQpAQEBuLq6sm3bNiZPnkx8fDzlypWjU6dOalvQ5EadOnXYu3cv06dPZ86cOVhZWTF79uxs9/H79NNPOXz4MB06dEBLS4uRI0cSGBiIj48P7dq1IykpiQoVKuDm5pZpr6aiQCP97QnSAjZw4EBOnDhBeHg4JiYm1KhRAx8fH9q2bcuTJ0/48ssvuXXrFq9evcLa2prPPvuMqVOnqkW8jx8/xtPTk4CAAAwMDHB3d2fBggWZNmYcPXo0d+7coXz58kybNi1HGzPGxsZiYmJCTEzMv4q2s2NgqsPgLT2yTNs08HteRSfneZ1CCJFXEhMTCQsLw87OjpIlSyrn5dUdoiBk9/2DnP1+F+oI0ZYtW7JNs7a2zrRLdVYqVKiQaUrs71q0aMG1a9dy3D4hhBC5Z2NjIwGK+GAUvTErIYQQQogCJgGREEIIIYo9CYiEEEIIUexJQCSEEEKIYk8CIiGEEEIUexIQCSGEEKLYk4BICCGEEMWeBERCCCGEKPaK3LvMhBBCfBw+5J2qHz16hJ2dHdeuXaNWrVp5UqYo2iQgEkIIkedUKhVOTlWIj08osDr19fUICbmbJ0GRtbU14eHhOX43WnYyAqwMZmZm1K1bl4ULF1K7du1/VbaGhobyb319fcqVK0eTJk0YOXIkdevW/VdlFycSEAkhhMhzUVFRxMcnMHfeV9hVtMz3+sIeRjB1ih9RUVF5EhBpaWlhaZn37T5+/DhVq1bl999/Z9SoUbRv3567d+9iamqa47KSk5PR0dEBwM/PDzc3NxITE/ntt9/YuHEjDRs2ZOvWrfTv3z+Pe/FxkjVEQggh8o1dRUucnGzy/ZPboCstLY1Fixbh4OCArq4uNjY2zJs3j0ePHqGhoUFwcLCS9+DBg1SqVImSJUvSsmVLtm3bhoaGBtHR0cCbl4137tyZUqVKYWBgQNWqVTO9a9Pc3BxLS0vq1avHkiVLePr0KRcvXgTg3LlzNG3aFD09PaytrRk1ahSvXr1SrrW1tWXOnDn0798fY2NjhgwZoqSZmppiaWmJra0t7dq14/vvv6dv3754eXnx4sULAJ4/f07v3r355JNP0NfXp3r16vz3v/9Vyti+fTvm5uYkJSWptblr167069cvV/f3QyIBkRBCiGJr0qRJLFiwgGnTpnHnzh127dpF2bJlM+ULCwujR48edO3alevXrzN06FCmTJmilmfEiBEkJSVx5swZbt68ycKFCzE0NMy2bj09PeDNSE9oaChubm50796dGzdusGfPHs6dO4eXl5faNUuWLKFmzZpcu3aNadOmvbNvo0eP5uXLlxw7dgx481b4unXrcvjwYW7dusWQIUPo168fly5dAqBnz56kpqZy8OBBpYzIyEgOHz7MgAED3lnXx0CmzIQQQhRLL1++ZOXKlaxZswZ3d3cA7O3t+fTTT3n06JFa3m+++QZHR0cWL14MgKOjI7du3WLevHlKHpVKRffu3alevToAFStWzLbu6Oho5syZg6GhIQ0aNGDKlCn07dsXb29vACpVqsSqVato3rw569evp2TJkgC0atWKsWPHvlf/qlSpAqD05ZNPPmHcuHFK+siRIzl69Ch79+6lQYMG6Onp0adPH/z8/OjZsycAO3bswMbGhhYtWrxXnR8yCYiEEEIUSyEhISQlJdG6det/zHvv3j3q16+vdq5BgwZqx6NGjcLT05NffvmFNm3a0L17d2rUqKGWp3HjxmhqavLq1SsqVqzInj17KFu2LNevX+fGjRvs3LlTyZuenk5aWhphYWE4OTkBUK9evffuX3p6OvB/i65TU1OZP38+e/fu5Y8//iA5OZmkpCT09fWVawYPHkz9+vX5448/+OSTT/D398fDw0Nt4fbHSgIiIYQQxVLGlFVeGTRoEK6urhw+fJhffvkFX19fli5dysiRI5U8e/bswdnZGXNzc7WF1HFxcQwdOpRRo0ZlKvftReIGBgbv3Z6QkBAA5em2xYsXs3LlSlasWEH16tUxMDDA29ub5ORk5ZratWtTs2ZNtm/fTrt27bh9+zaHDx9+7zo/ZLKGSAghRLFUqVIl9PT0OHHixD/mdXR05Ndff1U7d/ny5Uz5rK2tGTZsGPv27WPs2LFs2rQpU7q9vX2mp8rq1KnDnTt3cHBwyPTJeJIsp1asWIGxsTFt2rQBIDAwkC5duvDll19Ss2ZNKlasyG+//ZbpukGDBuHv74+fnx9t2rTB2to6V/V/aGSESAghRL4JexhRZOspWbIkPj4+TJgwAR0dHZo0acKzZ8+4fft2pmm0oUOHsmzZMnx8fBg4cCDBwcH4+/sD/zcl5e3tTfv27alcuTIvXrzg1KlTylTXP/Hx8aFRo0Z4eXkxaNAgDAwMuHPnDseOHWPNmjX/eH10dDQREREkJSXx22+/8c033/Djjz+yfft2JfiqVKkS33//PefPn6dUqVIsW7aMp0+f4uzsrFZWnz59GDduHJs2bWL79u3v1f6PgQREQggh8pyFhQX6+npMneJXYHXq6+vleCPFadOmUaJECaZPn86ff/6JlZUVw4YNy5TPzs6O77//nrFjx7Jy5UpcXFyYMmUKnp6e6OrqAm/W6IwYMYLff/8dY2Nj3NzcWL58+Xu1o0aNGpw+fZopU6bQtGlT0tPTsbe35/PPP3+v67/66ivgTZD3ySef8Omnn3Lp0iXq1Kmj5Jk6dSoPHz7E1dUVfX19hgwZQteuXYmJiVEry8TEhO7du3P48GG6du36XvV/DDTSM1ZdiWzFxsZiYmJCTEwMxsbGeV6+gakOg7f0yDJt08DveRWdnGWaEEIUBYmJiYSFhWFnZ6c8DQUf9qs73se8efPYsGEDT548KbA6C0rr1q2pWrUqq1atKuym/KPsvn+Qs99vGSESQgiRL2xsbAo0QMlv69ato379+pibmxMYGMjixYsz7RP0oXvx4gUBAQEEBASwbt26wm5OgZKASAghhHgP9+/fZ+7cufz111/Y2NgwduxYJk2aVNjNylO1a9fmxYsXLFy4EEdHx8JuToGSgEgIIYR4D8uXL3/vNUEfqr9vSFmcyGP3QgghhCj2JCASQgghRLEnAZEQQgghij0JiIQQQghR7ElAJIQQQohiTwIiIYQQQhR78ti9EEKIfPGx71RdkAICAmjZsiUvXrzI9GJYkTcKdYRo/fr11KhRA2NjY4yNjXFxceHnn39W0hMTExkxYgTm5uYYGhrSvXt3nj59qlaGSqWiY8eO6OvrU6ZMGcaPH8/r16/V8gQEBFCnTh10dXVxcHBQXsgnhBAif6hUKpycqlC3bt0C+zg5VUGlUuWonR4eHmhoaCgfc3Nz3NzcuHHjhpLn7XQTExOaNGnCyZMnM5WR1TvQRowYgYaGBh4eHrm+l6JgFOoIUfny5VmwYAGVKlUiPT2dbdu20aVLF65du0bVqlUZPXo0hw8f5rvvvsPExAQvLy+6detGYGAg8OZFeh07dsTS0pLz588THh5O//790dbWZv78+QCEhYXRsWNHhg0bxs6dOzlx4gSDBg3CysoKV1fXwuy+Ij0dXr2KyzZNCCE+NFFRUcTHJzBpwVfYVLTM9/pUDyPwnehHVFRUjkeJ3Nzc8PN78xLaiIgIpk6dSqdOndSCKz8/P9zc3IiKimLKlCl06tSJW7duUbFiRQCsra3ZvXs3y5cvR09PD3jzR/2uXbs+2lGrj02hBkSdO3dWO543bx7r16/nwoULlC9fni1btrBr1y5atWoFvPlCOjk5ceHCBRo1asQvv/zCnTt3OH78OGXLlqVWrVrMmTMHHx8fZs6ciY6ODhs2bMDOzo6lS5cC4OTkxLlz51i+fHmRCYgADHT1C7sJQgiR52wqWlLZuWgHBLq6ulhavgnaLC0tmThxIk2bNuXZs2eULl0aAFNTUywtLbG0tGT9+vV88sknHDt2jKFDhwJQp04dQkND2bdvH3379gVg37592NjYYGdnp1ZfWloaCxcuZOPGjURERFC5cmWmTZtGjx7/95Lvn376CW9vb548eUKjRo1wd3dXK+Px48d4eXlx7tw5kpOTsbW1ZfHixXTo0CHf7tPHrsgsqk5NTWX37t28evUKFxcXrly5QkpKCm3atFHyVKlSBRsbG4KCggAICgqievXqlC1bVsnj6upKbGwst2/fVvK8XUZGnowyhBBCiAxxcXHs2LEDBwcHzM3Ns8yTMQKUnJysdn7AgAHKSBPA1q1b+eqrrzJd7+vry/bt29mwYQO3b99m9OjRfPnll5w+fRqAJ0+e0K1bNzp37kxwcDCDBg1i4sSJamWMGDGCpKQkzpw5w82bN1m4cCGGhob/qu/FXaEvqr558yYuLi4kJiZiaGjI/v37cXZ2Jjg4GB0dnUyLx8qWLUtERATwZmjz7WAoIz0j7V15YmNjSUhIUL7Yb0tKSiIpKUk5jo2N/df9FEIIUTQdOnRICSZevXqFlZUVhw4dQlMz85hBfHw8U6dORUtLi+bNm6ulffnll0yaNInHjx8DEBgYyO7duwkICFDyJCUlMX/+fI4fP46LiwsAFStW5Ny5c3zzzTc0b96c9evXY29vr8xsODo6KkFPBpVKRffu3alevbpShvh3Cj0gcnR0JDg4mJiYGL7//nvc3d2VKLmw+Pr6MmvWrEJtgxBCiILRsmVL1q9fD8CLFy9Yt24d7du359KlS1SoUAGA3r17o6WlRUJCAqVLl2bLli3UqFFDrZzSpUvTsWNH/P39SU9Pp2PHjlhYWKjlefDgAfHx8bRt21btfHJyMrVr1wYgJCSEhg0bqqVnBE8ZRo0ahaenJ7/88gtt2rShe/fumdojcqbQAyIdHR0cHBwAqFu3LpcvX2blypV8/vnnJCcnEx0drTZK9PTpU7W53kuXLqmVl/EU2tt5/v5k2tOnTzE2Ns5ydAhg0qRJjBkzRjmOjY3F2tr633VUCCFEkWRgYKD8DgFs3rwZExMTNm3axNy5c4E3b7pv06YNJiYmyrqirAwYMAAvLy8A1q5dmyk9Lu7NAzSHDx/mk08+UUvT1dV97zYPGjQIV1dXDh8+zC+//IKvry9Lly5l5MiR712GUFdk1hBlSEtLIykpibp166Ktrc2JEyeUtHv37qFSqZRI2cXFhZs3bxIZGankOXbsGMbGxjg7Oyt53i4jI8/fo+236erqKlsBZHyEEEIUDxoaGmhqapKQkKCcs7S0xMHB4Z3BELx5Yi05OZmUlJQsH9xxdnZGV1cXlUqFg4OD2ifjD28nJ6dMf+xfuHAhU1nW1tYMGzaMffv2MXbsWDZt2pSb7or/r1BHiCZNmkT79u2xsbHh5cuX7Nq1i4CAAI4ePYqJiQkDBw5kzJgxmJmZYWxszMiRI3FxcaFRo0YAtGvXDmdnZ/r168eiRYuUxyVHjBihRNrDhg1jzZo1TJgwgQEDBnDy5En27t3L4cOHC7PrQghRLKgeRhT5epKSkpR1py9evGDNmjXExcVlehL6fWhpaRESEqL8+++MjIwYN24co0ePJi0tjU8//ZSYmBgCAwMxNjbG3d2dYcOGsXTpUsaPH8+gQYO4cuVKpv3zvL29ad++PZUrV+bFixecOnUKJyennHdeKAo1IIqMjKR///6Eh4djYmJCjRo1OHr0qDK3unz5cjQ1NenevTtJSUm4urqybt065XotLS0OHTqEp6cnLi4uGBgY4O7uzuzZs5U8dnZ2HD58mNGjR7Ny5UrKly/P5s2bi9Qj90II8bGxsLBAX18P34l+/5w5j+jr62Vas/M+jhw5gpWVFfAmYKlSpQrfffcdLVq0yFU7/mlWYc6cOZQuXRpfX18ePnyIqakpderUYfLkyQDY2Njwww8/MHr0aFavXk2DBg2YP38+AwYMUMpITU1lxIgR/P777xgbG+Pm5sby5ctz1V7xhkZ6umz9909iY2MxMTEhJiYmX6bP9E10GLKxW5ZpG4fsIz4mOcs0IYQoChITEwkLC8POzo6SJUsq5+XVHaIgZPf9g5z9fhf6omoBkM6TPx5mmyaEEB8iGxsbCVDEB0MCoiKilPUn2aRcK9B2CCGEEMVRkXvKTAghhBCioElAJIQQQohiTwIiIYQQQhR7EhAJIYQQotiTgEgIIYQQxZ4EREIIIYQo9uSxeyGEEPlCNmYUHxIJiIQQQuQ5lUpFFacqJMQn/HPmPKKnr8fdkLsfRVCkoaHB/v376dq1a2E3JU89evQIOzs7rl27Rq1atQq7OWokIBJCCJHnoqKiSIhP4KvFQ7GsWC7f64t4+Cd+478hKirqowiI8oOtrS3e3t54e3urnd+0aRNr1qwhNDSUEiVKYGdnR69evZg0aRIAHh4eREdH8+OPPxZ8owuQBERCCCHyjWXFcthUtS3sZuSJ5ORkdHR0CrsZeWrr1q14e3uzatUqmjdvTlJSEjdu3ODWrVs5LislJQVtbe18aGXBkEXVQgghiqUWLVrg5eWFl5cXJiYmWFhYMG3aNDLeeW5ra8ucOXPo378/xsbGDBkyBIBz587RtGlT9PT0sLa2ZtSoUbx69QqAyZMn07Bhw0x11axZk9mzZwNw+fJl2rZti4WFBSYmJjRv3pyrV6++s61PnjyhV69emJqaYmZmRpcuXXj06JGS7uHhQdeuXVmyZAlWVlaYm5szYsQIUlJSlL4+fvyY0aNHo6GhgYaGBgAHDx6kV69eDBw4EAcHB6pWrUrv3r2ZN28eADNnzmTbtm0cOHBAuS4gIIBHjx6hoaHBnj17aN68OSVLlmTnzp2kpaUxe/Zsypcvj66uLrVq1eLIkSPZ9is1NZUBAwZQpUoVVCoVAAcOHKBOnTqULFmSihUrMmvWLF6/fv2P/z3/LQmIhBBCFFvbtm2jRIkSXLp0iZUrV7Js2TI2b96spC9ZsoSaNWty7do1pk2bRmhoKG5ubnTv3p0bN26wZ88ezp07h5eXFwB9+/bl0qVLhIaGKmXcvn2bGzdu0KdPHwBevnyJu7s7586d48KFC1SqVIkOHTrw8uXLLNuYkpKCq6srRkZGnD17lsDAQAwNDXFzcyM5OVnJd+rUKUJDQzl16hTbtm3D398ff39/APbt20f58uWZPXs24eHhhIeHA2BpacmFCxd4/PhxlnWPGzeOXr164ebmplzXuHFjJX3ixIl8/fXXhISE4OrqysqVK1m6dClLlizhxo0buLq68p///If79+9nKjspKYmePXsSHBzM2bNnsbGx4ezZs/Tv35+vv/6aO3fu8M033+Dv768EaPlJAiIhhBDFlrW1NcuXL8fR0ZG+ffsycuRIli9frqS3atWKsWPHYm9vj729Pb6+vvTt2xdvb28qVapE48aNWbVqFdu3bycxMZGqVatSs2ZNdu3apZSxc+dOGjZsiIODg1Lml19+SZUqVXBycmLjxo3Ex8dz+vTpLNu4Z88e0tLS2Lx5M9WrV8fJyQk/Pz9UKhUBAQFKvlKlSrFmzRqqVKlCp06d6NixIydOnADAzMwMLS0tjIyMsLS0xNLSEoAZM2ZgamqKra0tjo6OeHh4sHfvXtLS0gAwNDRET08PXV1d5bq3pw29vb3p1q0bdnZ2WFlZsWTJEnx8fPjiiy9wdHRk4cKF1KpVixUrVqj1KS4ujo4dO/Ls2TNOnTpF6dKlAZg1axYTJ07E3d2dihUr0rZtW+bMmcM333yTy//C708CIiGEEMVWo0aNlOkjABcXF+7fv09qaioA9erVU8t//fp1/P39MTQ0VD6urq6kpaURFhYGvBklygiI0tPT+e9//0vfvn2VMp4+fcrgwYOpVKkSJiYmGBsbExcXp0wZ/d3169d58OABRkZGSp1mZmYkJiaqjURVrVoVLS0t5djKyorIyMh39t/KyoqgoCBu3rzJ119/zevXr3F3d8fNzU0Jit7l7fsTGxvLn3/+SZMmTdTyNGnShJCQELVzvXv35tWrV/zyyy+YmJio9XX27Nlq93fw4MGEh4cTHx//j+35N2RRtRBCCJENAwMDteO4uDiGDh3KqFGjMuXNeLqtd+/e+Pj4cPXqVRISEnjy5Amff/65ks/d3Z3nz5+zcuVKKlSogK6uLi4uLmrTX3+vs27duuzcuTNTWsbICpBpQbOGhsZ7BTUA1apVo1q1agwfPpxhw4bRtGlTTp8+TcuWLd953d/vz/vq0KEDO3bsICgoiFatWinn4+LimDVrFt26dct0TcmSJXNV1/uSgEgIIUSxdfHiRbXjjDU9b4+0vK1OnTrcuXNHmf7KSvny5WnevDk7d+4kISGBtm3bUqZMGSU9MDCQdevW0aFDB+DNgul3bWBZp04d9uzZQ5kyZTA2Ns5J99To6OgoI1/v4uzsDKAsFH/f64yNjSlXrhyBgYE0b95cOR8YGEiDBg3U8np6elKtWjX+85//cPjwYSV/nTp1uHfv3jvvb36RgEgIIUS+iXj4Z5GuR6VSMWbMGIYOHcrVq1dZvXo1S5cuzTa/j48PjRo1wsvLi0GDBmFgYMCdO3c4duwYa9asUfL17duXGTNmkJycrLYmCaBSpUp8++231KtXj9jYWMaPH4+enl62dfbt25fFixfTpUsX5Qmux48fs2/fPiZMmED58uXfq6+2tracOXOGL774Al1dXSwsLPD09KRcuXK0atWK8uXLEx4ezty5cyldujQuLi7KdUePHuXevXuYm5urTXH93fjx45kxYwb29vbUqlULPz8/goODsxzdGjlyJKmpqXTq1Imff/6ZTz/9lOnTp9OpUydsbGzo0aMHmpqaXL9+nVu3bjF37tz36mduSUAkhBAiz1lYWKCnr4ff+PxfDJtBT18PCwuLHF3Tv39/EhISaNCgAVpaWnz99dfK4/VZqVGjBqdPn2bKlCk0bdqU9PR07O3t1abEAHr06IGXlxdaWlqZdpvesmULQ4YMoU6dOlhbWzN//nzGjRuXbZ36+vqcOXMGHx8funXrxsuXL/nkk09o3bp1jkaMZs+ezdChQ7G3tycpKYn09HTatGnD1q1bWb9+Pc+fP8fCwgIXFxdOnDiBubk5AIMHDyYgIIB69eoRFxfHqVOnsLW1zbKOUaNGERMTw9ixY4mMjMTZ2ZmDBw9SqVKlLPN7e3uTlpZGhw4dOHLkCK6urhw6dIjZs2ezcOFCtLW1qVKlCoMGDXrvfuaWRnrGhgsiW7GxsZiYmBATE/Ovhiuzo2+iTZ/NnbJM2zXoEPExKXlepxBC5JXExETCwsKws7NTW+dR1N9l1qJFiyyfgBIfluy+f5Cz328ZIRJCCJEvbGxs5DUa4oMhj90LIYQQotiTESIhhBDF0tubGgohI0RCCCGEKPYkIBJCCCFEsScBkRBCCCGKPQmIhBBCCFHsSUAkhBBCiGJPnjITQgiRL4r6xoxFmYaGBvv378+0y7XIPxIQCSGEyHMqlYoqTk4kxMcXWJ16+vrcDQn5aIKivGZra4u3tzfe3t7KuYCAAFq2bMmLFy8wNTX9x/wfs0INiHx9fdm3bx93795FT0+Pxo0bs3DhQhwdHZU8LVq04PTp02rXDR06lA0bNijHKpUKT09PTp06haGhIe7u7vj6+lKixP91LyAggDFjxnD79m2sra2ZOnUqHh4e+d5HIYQojqKiokiIj2fAoslYVayQ7/WFP3zM1gnziYqKyreAKDk5GR0dnXwpWxS+Ql1DdPr0aUaMGMGFCxc4duwYKSkptGvXjlevXqnlGzx4MOHh4cpn0aJFSlpqaiodO3YkOTmZ8+fPs23bNvz9/Zk+fbqSJywsjI4dO9KyZUuCg4Px9vZm0KBBHD16tMD6KoQQxZFVxQrYVK2c75/cBF0tWrTAy8sLLy8vTExMsLCwYNq0aWS84tPW1pY5c+bQv39/jI2NlZe+njt3jqZNm6Knp4e1tTWjRo1SfrcmT55Mw4YNM9VVs2ZNZs+eDcDly5dp27YtFhYWmJiY0Lx5c65evfrOtj558oRevXphamqKmZkZXbp04dGjR0q6h4cHXbt2ZcmSJVhZWWFubs6IESNISUlR+vr48WNGjx6NhoYGGhoaObpXAwYMoFMn9XdupqSkUKZMGbZs2fJe97OoK9SA6MiRI3h4eFC1alVq1qyJv78/KpWKK1euqOXT19fH0tJS+bz9grZffvmFO3fusGPHDmrVqkX79u2ZM2cOa9euJTk5GYANGzZgZ2fH0qVLcXJywsvLix49erB8+fIC7a8QQoiiZdu2bZQoUYJLly6xcuVKli1bxubNm5X0JUuWULNmTa5du8a0adMIDQ3Fzc2N7t27c+PGDfbs2cO5c+fw8vICoG/fvly6dInQ0FCljNu3b3Pjxg369OkDwMuXL3F3d+fcuXNcuHCBSpUq0aFDB16+fJllG1NSUnB1dcXIyIizZ88SGBiIoaEhbm5uyu8cwKlTpwgNDeXUqVPK4IC/vz8A+/bto3z58syePVsZXMiJQYMGceTIEbXrDh06RHx8PJ9//vl738+irEg9ZRYTEwOAmZmZ2vmdO3diYWFBtWrVmDRpEvFvzUkHBQVRvXp1ypYtq5xzdXUlNjaW27dvK3natGmjVqarqytBQUFZtiMpKYnY2Fi1jxBCiI+PtbU1y5cvx9HRkb59+zJy5Ei1P5ZbtWrF2LFjsbe3x97eHl9fX/r27Yu3tzeVKlWicePGrFq1iu3bt5OYmKj8gb9r1y6ljJ07d9KwYUMcHByUMr/88kuqVKmCk5MTGzduJD4+PtPykAx79uwhLS2NzZs3U716dZycnPDz80OlUqm9fqRUqVKsWbOGKlWq0KlTJzp27MiJEyeAN7+rWlpaGBkZKYMLbytfvjyGhoZqH5VKpaQ3btwYR0dHvv32W+Wcn58fPXv2xNDQ8L3vZ1FWZAKitLQ0vL29adKkCdWqVVPO9+nThx07dnDq1CkmTZrEt99+y5dffqmkR0REqAVDgHIcERHxzjyxsbEkJCRkaouvry8mJibKx9raOs/6KYQQouho1KiR2vSRi4sL9+/fJzU1FYB69eqp5b9+/Tr+/v5qgYOrqytpaWmEhYUBb0aJMgKi9PR0/vvf/9K3b1+ljKdPnzJ48GAqVaqEiYkJxsbGxMXFqQUgf6/zwYMHGBkZKXWamZmRmJioNhJVtWpVtLS0lGMrKysiIyPf6z6cPXuW4OBgtU+5cuXU8gwaNAg/Pz+lDz///DMDBgzI0f0syorMU2YjRozg1q1bnDt3Tu18xpwtQPXq1bGysqJ169aEhoZib2+fL22ZNGkSY8aMUY5jY2MlKBJCiGLIwMBA7TguLo6hQ4cyatSoTHkzFnP37t0bHx8frl69SkJCAk+ePFGbVnJ3d+f58+esXLmSChUqoKuri4uLi9r019/rrFu3Ljt37syUVrp0aeXf2traamkaGhqkpaW9Vz/t7OwyPWX29oNJAP3792fixIkEBQVx/vx57OzsaNq06XuV/yEoEgGRl5cXhw4d4syZM5QvX/6deTMWqz148AB7e3ssLS25dOmSWp6nT58CKEOClpaWyrm38xgbG6Onp5epDl1dXXR1dXPdHyGEEB+Gixcvqh1nrOl5e6TlbXXq1OHOnTvK9FdWypcvT/Pmzdm5cycJCQm0bduWMmXKKOmBgYGsW7eODh06AG8WTL9rv6Y6deqwZ88eypQpo7aGNqd0dHT+1UiNubk5Xbt2xc/Pj6CgIL766qtMeXJ6P4uSQp0yS09Px8vLi/3793Py5Ens7Oz+8Zrg4GDgzVAgvBmOu3nzptqw4LFjxzA2NsbZ2VnJkzGP+nYeFxeXPOqJEEKID5FKpWLMmDHcu3eP//73v6xevZqvv/462/w+Pj6cP38eLy8vgoODuX//PgcOHFAWVWfo27cvu3fv5rvvvlObLgOoVKkS3377LSEhIVy8eJG+fftm+cf522VZWFjQpUsXzp49S1hYGAEBAYwaNYrff//9vftqa2vLmTNn+OOPP3K9YeagQYPYtm0bISEhuLu7Z0rP6f0sSgp1hGjEiBHs2rWLAwcOYGRkpKz5MTExQU9Pj9DQUHbt2kWHDh0wNzfnxo0bjB49mmbNmlGjRg0A2rVrh7OzM/369WPRokVEREQwdepURowYoYzyDBs2jDVr1jBhwgQGDBjAyZMn2bt3L4cPHy60vgshRHEQ/vBxka6nf//+JCQk0KBBA7S0tPj666/Vlmr8XY0aNTh9+jRTpkyhadOmpKenY29vrzYlBtCjRw+8vLzQ0tLKtNv0li1bGDJkCHXq1MHa2pr58+czbty4bOvU19fnzJkz+Pj40K1bN16+fMknn3xC69atczRiNHv2bIYOHYq9vT1JSUm5ehy+TZs2WFlZUbVq1UxrjCDn97Mo0UgvxA0CstsHwc/PDw8PD548ecKXX37JrVu3ePXqFdbW1nz22WdMnTpV7Uvw+PFjPD09CQgIwMDAAHd3dxYsWJBpY8bRo0dz584dypcvz7Rp0957Y8bY2FhMTEyIiYn5V8OV2dE30abP5k5Zpu0adIj4mJQ8r1MIIfJKYmIiYWFh2NnZUbJkSeDD2Km6RYsW1KpVixUrVuRvwz4icXFxfPLJJ/j5+dGtWze1tMK6n1l9/zLk5Pc7VyNEFStW5PLly5ibm6udj46Opk6dOjx8+PC9yvmnWMza2jrbxxDfVqFCBX766ad35mnRogXXrl17r3YJIYT4d2xsbLgbEiLvMvtIpKWlERUVxdKlSzE1NeU///lPYTcpz+UqIHr06FGWC7OSkpL4448//nWjipsSyZqcnXUp2zQhhPgQ2djYSIDykVCpVNjZ2VG+fHn8/f0zPYH2MchRjw4ePKj8++jRo5iYmCjHqampnDhxAltb2zxrXHGho1kC3wHdskwbMm1rAbdGCCGKh7c3NRTvZmtr+4+zOh/6/cxRQJSxMExDQyPT6nJtbW1sbW1ZunRpnjVOCCGEEKIg5Cggytjgyc7OjsuXL2NhYZEvjRJCCCGEKEi5mgTM2J5cCCGEEOJjkOtVUSdOnODEiRNERkZm2hp861ZZ9yKEEEKID0euAqJZs2Yxe/Zs6tWrh5WVVbb7CQkhhBBCfAhyFRBt2LABf39/+vXrl9ftEUIIIYQocLkKiJKTk2ncuHFet0UIIcRHRKVSfZAbM76947KtrS3e3t54e3u/17X+/v54e3sTHR39r9shClauAqJBgwaxa9cupk2bltftEUII8RH4EF7d8T4uX76MgYFBnpX3vjQ0NNi/f3+m96CJ/JOrgCgxMZGNGzdy/PhxatSogba2tlr6smXL8qRxQgghPkxRUVEkxMczYOEcrOzt8r2+8NAwtvpMIyoqKk8DotKlS+dZWaJoy1VAdOPGDWrVqgXArVu31NJkgbUQQogMVvZ22DhXKexmZOvVq1d4enqyb98+jIyMMr11/u9TZsuWLcPPz4+HDx9iZmZG586dWbRoEYaGhmrX/fjjj4wfP54nT57QvHlzNm/ejLW1tZJ+4MABZs2axZ07dyhXrhzu7u5MmTKFEiVKKG98+Oyzz4A37+t89OjRP16Xnp7OrFmz2Lp1K0+fPsXc3JwePXqwatWq/Ll5H5lcBUSnTp3K63YIIYQQBW78+PGcPn2aAwcOUKZMGSZPnszVq1eVP/r/TlNTk1WrVmFnZ8fDhw8ZPnw4EyZMYN26dUqe+Ph45s2bx/bt29HR0WH48OF88cUXBAYGAnD27Fn69+/PqlWraNq0KaGhoQwZMgSAGTNmcPnyZcqUKYOfnx9ubm5oaWm913U//PADy5cvZ/fu3VStWpWIiAiuX7+ej3fv4/LxvZ1NCCGEeA9xcXFs2bKFHTt20Lp1awC2bdtG+fLls73m7cXVtra2zJ07l2HDhqkFRCkpKaxZs4aGDRsqZTo5OXHp0iUaNGjArFmzmDhxovIKrIoVKzJnzhwmTJjAjBkzlGk6U1NTLC0tlXL/6TqVSoWlpSVt2rRBW1sbGxsbGjRokDc3qxjIVUDUsmXLd06NnTx5MtcNEkIIIQpCaGgoycnJSuACYGZmhqOjY7bXHD9+HF9fX+7evUtsbCyvX78mMTGR+Ph49PX1AShRogT169dXrqlSpQqmpqaEhITQoEEDrl+/TmBgIPPmzVPypKamZirn7/7pup49e7JixQoqVqyIm5sbHTp0oHPnzh/lm+nzQ67u0t+HElNSUggODubWrVuZXvoqhBBCfAwePXpEp06d8PT0ZN68eZiZmXHu3DkGDhxIcnJytoHM38XFxTFr1iy6deuWKa1kyZK5vs7a2pp79+5x/Phxjh07xvDhw1m8eDGnT5/O9PCTyCxXAdHy5cuzPD9z5kzi4uL+VYOEEEKIgmBvb4+2tjYXL15Unkx78eIFv/32G82bN8+U/8qVK6SlpbF06VI0NTUB2Lt3b6Z8r1+/5tdff1Wmq+7du0d0dDROTk4A1KlTh3v37uHg4JBt27S1tUlNTVU79z7X6enp0blzZzp37syIESOoUqUKN2/epE6dOv9wN0SejqN9+eWXNGjQgCVLluRlsUIIIUSeMzQ0ZODAgYwfPx5zc3PKlCnDlClTlGDn7xwcHEhJSWH16tV07tyZwMBANmzYkCmftrY2I0eOZNWqVZQoUQIvLy8aNWqkBEjTp0+nU6dO2NjY0KNHDzQ1Nbl+/Tq3bt1i7ty5wJv1SSdOnKBJkybo6upSqlSpf7zO39+f1NRUGjZsiL6+Pjt27EBPT48KFSrk3038iORpQBQUFPTO4T6RjXR4eEaVbZoQQnyowkPDinQ9ixcvJi4ujs6dO2NkZMTYsWOJiYnJMm/NmjVZtmwZCxcuZNKkSTRr1gxfX1/69++vlk9fXx8fHx/69OnDH3/8QdOmTdmyZYuS7urqyqFDh5g9ezYLFy5EW1ubKlWqMGjQICXP0qVLGTNmDJs2beKTTz7h0aNH/3idqakpCxYsYMyYMaSmplK9enX+97//YW5unqt7U9xopKen5/gn9+/zl+np6YSHh/Prr78ybdo0ZsyYkWcNLApiY2MxMTEhJiYGY2PjPC/fwsCA3TP7ZJn2xcxdRL16led1CiFEXklMTCQsLAw7Ozvlj+KPZadqUfRl9f3LkJPf71yNEJmYmKgda2pq4ujoyOzZs2nXrl1uihRCCPERsbGx4W5IyAf5LjNRPOUqIPLz88vrdgghhPjI2NjYSIAiPhj/ag3RlStXCAkJAaBq1arUrl07TxolhBBCCFGQchUQRUZG8sUXXxAQEICpqSkA0dHRtGzZkt27d8vL8HIqHW4FRmSbJoQQQoj8lauAaOTIkbx8+ZLbt28r+yrcuXMHd3d3Ro0axX//+988beTHTkMDGjeyyDrtWAE3RgghhCiGchUQHTlyhOPHjyvBEICzszNr166VRdVCCCGE+OBkvfvUP0hLS8tyG3BtbW3S0tL+daOEEEIIIQpSrkaIWrVqxddff81///tfypUrB8Aff/zB6NGjlTcGCyGEEEK8LfTBA16npmSZVkJLG/t3vJYkv+UqIFqzZg3/+c9/sLW1xdraGoAnT55QrVo1duzYkacNFEIIIcTH4XVqChXKZ71m9vHvBbdnVVZyFRBZW1tz9epVjh8/zt27dwFwcnKiTZs2edo4IYQQHy6VSiUbM/7NzJkz+fHHHwkODs51GQEBAbRs2ZIXL14oT3qLfy9HAdHJkyfx8vLiwoULGBsb07ZtW9q2bQtATEwMVatWZcOGDTRt2jRfGiuEEOLD8KG8uqNFixbUqlWLFStW5Mk1P/zwA6tXr+batWukpqZSsWJFevTogZeXF2ZmZu9dhyh4OQqIVqxYweDBg7N8H4iJiQlDhw5l2bJl7x0Q+fr6sm/fPu7evYuenh6NGzdm4cKFODo6KnkSExMZO3Ysu3fvJikpCVdXV9atW0fZsmWVPCqVCk9PT06dOoWhoSHu7u74+vpSosT/dS8gIIAxY8Zw+/ZtrK2tmTp1Kh4eHjnpvhBCiPcUFRVFQnw8AxcuxLJixXyvL+LhQ7b4+BAVFVVoo0RTpkxh4cKFjB49mvnz51OuXDnu37/Phg0b+Pbbb/n6668LpV3i/eQoILp+/ToLFy7MNr1du3YsWbLkvcs7ffo0I0aMoH79+rx+/ZrJkyfTrl077ty5g4GBAQCjR4/m8OHDfPfdd5iYmODl5UW3bt0IDAwEIDU1lY4dO2Jpacn58+cJDw+nf//+aGtrM3/+fADCwsLo2LEjw4YNY+fOnZw4cYJBgwZhZWWFq6trTm6BEEKIHLCsWJEKzs6F3YwseXh4cPr0aU6fPs3KlSuBN78Xjx8/Zvz48Vy/fh0zMzPc3d2ZO3cuJUqUyPaayMhI5s+fz4oVK9QCH1tbW9q2bUt0dLRa3d9++y3Tpk3jxYsXtG/fnk2bNmFkZARAUlIS48ePZ/fu3cTGxlKvXj2WL19O/fr1C+bGFFM5euz+6dOnWT5un6FEiRI8e/bsvcs7cuQIHh4eVK1alZo1a+Lv749KpeLKlSvAm2m4LVu2sGzZMlq1akXdunXx8/Pj/PnzXLhwAYBffvmFO3fusGPHDmrVqkX79u2ZM2cOa9euJTk5GYANGzZgZ2fH0qVLcXJywsvLix49erB8+fKcdF8IIcRHZOXKlbi4uDB48GDCw8MJDw9HW1ubDh06UL9+fa5fv8769evZsmULc+fOzfYaa2trdu7ciaGhIcOHD8+yrrfX+oSGhvLjjz9y6NAhDh06xOnTp1mwYIGSPmHCBH744Qe2bdvG1atXcXBwwNXVlb/++itf70dxl6OA6JNPPuHWrVvZpt+4cQMrK6tcNyYmJgZAmWe9cuUKKSkpaou1q1Spgo2NDUFBQQAEBQVRvXp1tSk0V1dXYmNjuX37tpLn7wu+XV1dlTL+LikpidjYWLWPEEKIj4uJiQk6Ojro6+tjaWmJpaUl69atw9ramjVr1lClShW6du3KrFmzWLp0KWlpaVleo6Wlxf3796lYseI7Bw0ypKWl4e/vT7Vq1WjatCn9+vXjxIkTALx69Yr169ezePFi2rdvj7OzM5s2bUJPT48tW7bk9y0p1nIUEHXo0IFp06aRmJiYKS0hIYEZM2bQqVOnXDUkLS0Nb29vmjRpQrVq1QCIiIhAR0cn0yr6smXLEhERoeR5OxjKSM9Ie1ee2NhYEhISMrXF19cXExMT5ZOxtYAQQoiPW0hICC4uLmhoaCjnmjRpQlxcHL///nu216Wnv/+LJ21tbZXpMQArKysiIyOBN6NHKSkpNGnSREnX1tamQYMGysvURf7I0RqiqVOnsm/fPipXroyXl5ey+Pnu3busXbuW1NRUpkyZkquGjBgxglu3bnHu3LlcXZ+XJk2axJgxY5Tj2NhYCYqEEEJkq3Llypw7d46UlJR/HCX6e7qGhoa85aEIyNEIUdmyZTl//jzVqlVj0qRJfPbZZ3z22WdMnjyZatWqce7cuUwjMe/Dy8uLQ4cOcerUKcqXL6+ct7S0JDk5OdNitKdPn2Jpaankefr0aab0jLR35TE2NkZPTy9Te3R1dTE2Nlb7CCGE+Pjo6OiQmpqqHDs5OREUFKQ24hMYGIiRkZHy+/T3awD69OlDXFwc69aty7Kev/+OZcfe3h4dHR3lwSGAlJQULl++jHMRXZz+scjxu8wqVKjATz/9RFRUFBcvXuTChQtERUXx008/YWdnl6Oy0tPT8fLyYv/+/Zw8eTLT9XXr1kVbW1uZWwW4d+8eKpUKFxcXAFxcXLh586Yy3Ahw7NgxjI2NlS+Pi4uLWhkZeTLKEEIIUTzZ2tpy8eJFHj16RFRUFMOHD+fJkyeMHDmSu3fvcuDAAWbMmMGYMWPQ1NTM8pq0tDQaNmzIhAkTGDt2LBMmTCAoKIjHjx9z4sQJevbsybZt296rPQYGBnh6ejJ+/HiOHDnCnTt3GDx4MPHx8QwcODA/b0Wxl6udqgFKlSr1rx8BHDFiBLt27eLAgQMYGRkpa35MTEzQ09PDxMSEgQMHMmbMGMzMzDA2NmbkyJG4uLjQqFEj4M2j/s7OzvTr149FixYRERHB1KlTGTFiBLq6ugAMGzaMNWvWMGHCBAYMGMDJkyfZu3cvhw8f/lftF0II8W4RDx8W6XrGjRuHu7s7zs7OJCQkEBYWxk8//cT48eOpWbMmZmZmDBw4kKlTp77zGltbWxYuXEjdunVZu3YtGzZsIC0tDXt7e3r06IG7u/t7t2nBggWkpaXRr18/Xr58Sb169Th69CilSpXKVR/F+9FIz8lKsLyu/K1Fa2/z8/NTNk3M2Jjxv//9r9rGjBnTYQCPHz/G09OTgIAADAwMcHd3Z8GCBZk2Zhw9ejR37tyhfPnyTJs27b03ZoyNjcXExISYmJh8mT4rbWDA4Wm9skzrOGcvz169yvM6hRAiryQmJhIWFoadnR0lS5YEPpydqkXBuncv5J3vMnN0dMpxmVl9/zLk5Pc71yNEeeF9YrGSJUuydu1a1q5dm22ejGm8d2nRogXXrl3LcRuFEELknI2NDXdDQuRdZuKDUagBkRBCiI+XjY2NBCjig5HjRdVCCCGEEB8bCYiEEEIIUexJQCSEEEKIYk8CIiGEEEIUexIQCSGEEKLYk4BICCGEEMWeBERCCCGEKPZkHyIhhBD5QqVSycaMQk1SYhKPHz/NOi3ldQG3Rp0EREIIIfJccX51x6NHj9ReVq6trY2NjQ0eHh5MmTJFeW3VzJkzmTVrFgBaWlqUL1+ezz77jDlz5mBoaFgobc936VCxTOksk0J+Dy/gxqiTgEgIIUSei4qKIiE+noELlmNV0SHf6wt/+IAtE0cTFRVV6AFRhuPHj1O1alWSkpI4d+4cgwYNwsrKSu2t9VWrVuX48eO8fv2awMBABgwYQHx8PN98800htrx4koBICCFEvrGq6EAF52qF3Yxsff/998yaNYsHDx6gr69P7dq1mTZtGm3atOHJkydqLxL39vbmypUrnD17lsePH+Pl5cW5c+dITk7G1taWxYsX06FDByW/ubm5cn2FChXw8/Pj6tWragFRiRIllDyff/45J06c4ODBgxIQFQJZVC2EEKJYCg8Pp3fv3gwYMICQkBACAgLo1q0bdevWpWLFinz77bdK3pSUFHbu3MmAAQMAGDFiBElJSZw5c4abN2+ycOHCd05z/frrr1y5coWGDRu+s016enokJyfnTQdFjsgIkRBCiGIpPDyc169f061bNypUqABA9erVARg4cCB+fn6MHz8egP/9738kJibSq1cv4M0aqe7duyv5K1asmKn8xo0bo6mpSXJyMikpKQwZMoT+/ftn254rV66wa9cuWrVqlaf9FO9HRoiEEEIUSzVr1qR169ZUr16dnj17smnTJl68eAGAh4cHDx484MKFCwD4+/vTq1cvDAwMABg1ahRz586lSZMmzJgxgxs3bmQqf8+ePQQHB3P9+nX27t3LgQMHmDhxolqemzdvYmhoiJ6eHg0aNMDFxYU1a9bkc89FViQgEkIIUSxpaWlx7Ngxfv75Z5ydnVm9ejWOjo6EhYVRpkwZOnfujJ+fH0+fPuXnn39WpssABg0axMOHD+nXrx83b96kXr16rF69Wq18a2trHBwccHJyomfPnnh7e7N06VISExOVPI6OjgQHBxMSEkJCQgIHDx6kbNmyBXYPxP+RgEgIIUSxpaGhQZMmTZg1axbXrl1DR0eH/fv3A2+Cnj179rBx40bs7e1p0qSJ2rXW1tYMGzaMffv2MXbsWDZt2vTOurS0tHj9+rXaGiEdHR0cHBywtbVFR0cn7zso3pusISoC0tPh0vmsNy9LTy/gxgghRDFx8eJFTpw4Qbt27ShTpgwXL17k2bNnODk5AeDq6oqxsTFz585l9uzZatd6e3vTvn17KleuzIsXLzh16pRyXYbnz58TERHB69evuXnzJitXrqRly5YYGxsXWB/F+5OAqAjQ0IDW9c2yTJt1ooAbI4QQeSj84YMiW4+xsTFnzpxhxYoVxMbGUqFCBZYuXUr79u0B0NTUxMPDg/nz52daDJ2amsqIESP4/fffMTY2xs3NjeXLl6vladOmDfBmZMjKyooOHTowb968XPZQ5DcJiIQQQuQ5CwsL9PT12TJxdIHVqaevj4WFxXvnd3Jy4siRI+/M88cff9ChQwesrKzUzv99vdDbbG1tSX+P4f2ZM2cyc+bM92qryH8SEAkhhMhzNjY23A0J+WDfZRYTE8PNmzfZtWsXBw8ezJMyRdEmAZEQQoh8YWNjU2Reo5FTXbp04dKlSwwbNoy2bdsWdnNEAZCASAghhPibgICAwm6CKGDy2L0QQgghij0JiIQQQghR7ElAJIQQQohiTwIiIYQQQhR7EhAJIYQQotiTgEgIIYQQxZ48di+EECJfqFSqD3JjxhYtWlCrVi1WrFiBra0t3t7eeHt7//sGiiJNAiIhhBB5TqVSUcXJiYT4+AKrU09fn7shIXm6GeTly5cxMDB47/wBAQG0bNlSOS5ZsiQVK1bk66+/ZsiQIcp5Dw8Ptm3bBoC2tjY2Njb079+fyZMnU6KE/DQXhkK962fOnGHx4sVcuXKF8PBw9u/fT9euXZX0t78wGVxdXdXePfPXX38xcuRI/ve//6GpqUn37t1ZuXIlhoaGSp4bN24wYsQILl++TOnSpRk5ciQTJkzI9/4JIURxFRUVRUJ8PIN8N2BlVznf6wsP+43Nk4YRFRWVpwFR6dKlc3XdvXv3MDY2JiEhgf/97394enpib29P69atlTxubm74+fmRlJTETz/9xIgRI9DW1mbSpEl51XyRA4UaEL169YqaNWsyYMAAunXrlmWejC9MBl1dXbX0vn37Eh4ezrFjx0hJSeGrr75iyJAh7Nq1C4DY2FjatWtHmzZt2LBhAzdv3mTAgAGYmpqqRetCCCHynpVdZSo41yzsZmTr1atXeHp6sm/fPoyMjBg3bpxa+t+nzJYtW4afnx8PHz7EzMyMzp07s2jRIrU/wgHKlCmDqakpAKNGjWLVqlVcvXpVLSDS1dXF0tISAE9PT/bv38/BgwclICokhRoQtW/fnvbt278zz9tfmL8LCQnhyJEjXL58mXr16gFv3kDcoUMHlixZQrly5di5cyfJycls3boVHR0dqlatSnBwMMuWLZOASAghirnx48dz+vRpDhw4QJkyZZg8eTJXr16lVq1aWebX1NRk1apV2NnZ8fDhQ4YPH86ECRNYt25dlvnT09M5evQoKpWKhg0bvrMtenp6PH/+/N92SeRSkX/KLCAggDJlyuDo6Iinp6falyUoKAhTU1MlGAJo06YNmpqaXLx4UcnTrFkzdHR0lDyurq7cu3ePFy9eZFlnUlISsbGxah8hhBAfl7i4OLZs2cKSJUto3bo11atXZ9u2bbx+/Trba7y9vWnZsiW2tra0atWKuXPnsnfv3kz5ypcvj6GhITo6OnTs2JEZM2bQrFmzLMtMT0/n+PHjHD16lFatWuVZ/0TOFOmVW25ubnTr1g07OztCQ0OZPHky7du3JygoCC0tLSIiIihTpozaNSVKlMDMzIyIiAgAIiIisLOzU8tTtmxZJa1UqVKZ6vX19WXWrFn51CshhBBFQWhoKMnJyWojN2ZmZjg6OmZ7zfHjx/H19eXu3bvExsby+vVrEhMTiY+PR19fX8l39uxZjIyMSEpK4tKlS3h5eWFmZoanp6eS59ChQxgaGpKSkkJaWhp9+vRh5syZ+dJX8c+KdED0xRdfKP+uXr06NWrUwN7enoCAALV52Lw2adIkxowZoxzHxsZibW2db/UJIYQo+h49ekSnTp3w9PRk3rx5mJmZce7cOQYOHEhycrJaQGRnZ6esIapatSoXL15k3rx5agFRy5YtWb9+PTo6OpQrV06eLitkRX7K7G0VK1bEwsKCBw8eAGBpaUlkZKRantevX/PXX38p644sLS15+vSpWp6M4+zWJunq6mJsbKz2EUII8XGxt7dHW1tbWWIB8OLFC3777bcs81+5coW0tDSWLl1Ko0aNqFy5Mn/++ed71aWlpUVCQoLaOQMDAxwcHLCxsZFgqAj4oAKi33//nefPn2NlZQWAi4sL0dHRXLlyRclz8uRJ0tLSlCFQFxcXzpw5Q0pKipLn2LFjODo6ZjldJoQQongwNDRk4MCBjB8/npMnT3Lr1i08PDzQ1Mz6p9HBwYGUlBRWr17Nw4cP+fbbb9mwYUOWeSMjI4mIiODx48d89913fPvtt3Tp0iU/uyP+pUINSePi4pTRHoCwsDCCg4MxMzPDzMyMWbNm0b17dywtLQkNDWXChAk4ODjg6uoKgJOTE25ubgwePJgNGzaQkpKCl5cXX3zxBeXKlQOgT58+zJo1i4EDB+Lj48OtW7dYuXIly5cvL5Q+CyFEcRIelvVoS1GpZ/HixcTFxdG5c2eMjIwYO3YsMTExWeatWbMmy5YtY+HChUyaNIlmzZrh6+tL//79M+XNWIdUokQJrK2tGTp0qKwPKuI00tPT0wur8r/v6JnB3d2d9evX07VrV65du0Z0dDTlypWjXbt2zJkzR1kUDW82ZvTy8lLbmHHVqlXZbsxoYWHByJEj8fHxee92xsbGYmJiQkxMTL5Mn5U2MODMxB5ZpjVb8D3PXr3K8zqFECKvJCYmEhYWhp2dHSVLlgQ+np2qRd66ERyMU3mrLNNCfg+nRjbbHbxLVt+/DDn5/S7UEaIWLVrwrnjs6NGj/1iGmZmZsgljdmrUqMHZs2dz3D4hhBC5Y2Njw92QkA/yXWaieJJVXEIIIfKFjY2NBCjig/FBLaoWQgghhMgPEhAJIYQQotiTgEgIIYQQxZ4EREIIIYQo9mRRtRAfsF7du/DieWSWaaXMy7D3hwMF3CIhhPgwSUAkxAfsxfNIdvh6ZJn25ST/Am2LEEJ8yGTKTAghhBDFnowQCSGEyBcqlUo2ZhRq0tPSiM9m9/L0tLQCbo06CYiEEELkOZVKhZOTU7Y/fvlBX1+fkBy+usPDw4Nt27Ypx2ZmZtSvX59FixZRo0YNADQ0NAAICgqiUaNGSt6kpCTKlSvHX3/9xalTp2jRooWSf//+/XTt2vXfd+ojkw5ol9TNNq0wSUAkhBAiz0VFRREfH4/PbH9s7Krke32qsLssnO5BVFRUjkeJ3Nzc8PPzAyAiIoKpU6fSqVMnVCqVksfa2ho/Pz+1gGj//v0YGhry119/5U0nRKGSgEgIIUS+sbGrQqUqtQu7Ge+kq6uLpaUlAJaWlkycOJGmTZvy7NkzSpcuDbx56fiqVatYsWIFenp6AGzduhV3d3fmzJlTaG0XeUcWVQshhBD/X1xcHDt27MDBwQFzc3PlfN26dbG1teWHH34A3kwJnjlzhn79+hVWU0Uek4BICCFEsXbo0CEMDQ0xNDTEyMiIgwcPsmfPHjQ11X8iBwwYwNatWwHw9/enQ4cOygiS+PBJQCSEEKJYa9myJcHBwQQHB3Pp0iVcXV1p3749jx8/Vsv35ZdfEhQUxMOHD/H392fAgAGF1GKRHyQgEkIIUawZGBjg4OCAg4MD9evXZ/Pmzbx69YpNmzap5TM3N6dTp04MHDiQxMRE2rdvX0gtFvlBAiIhhBDiLRoaGmhqapKQkJApbcCAAQQEBNC/f3+0tLQKoXUiv8hTZkIIIYq1pKQkIiIiAHjx4gVr1qwhLi6Ozp07Z8rr5ubGs2fPMDY2LuhminwmAZEQQoh8owq7W+TrOXLkCFZWVgAYGRlRpUoVvvvuO2WjxbdpaGhgYWGR67pE0SUBkRBCiDxnYWGBvr4+C6d7FFid+vr6OQ5W/P398ff3f2ee9PTs91A2NTXNlP6u/AJSUlIKuwlZkoBICCFEnrOxsSEkJETeZSYy0dYqmqFH0WyVEEKID56NjY0EKOKDIU+ZCSGEEKLYk4BICCGEEMWeBERCCCGEKPYkIBJCCCFEsScBkRBCCCGKPQmIhBBCCFHsSUAkhBBCiGJP9iESQgiRL1QqVZHemLFFixbUqlWLFStW5F+jxAdDAiIhhBB5TqVS4eTkRHx8fIHVqa+vT0hISJHfDPLRo0fY2dlx7do1atWqpZyfOXMms2bNAkBTU5Ny5crRvn17FixYgJmZ2XuX7+HhQXR0ND/++GMet/zjVqgB0ZkzZ1i8eDFXrlwhPDyc/fv307VrVyU9PT2dGTNmsGnTJqKjo2nSpAnr16+nUqVKSp6//vqLkSNH8r///Q9NTU26d+/OypUrMTQ0VPLcuHGDESNGcPnyZUqXLs3IkSOZMGFCQXZVCCGKlaioKOLj41k01Z+KFarke30PH99lwlwPoqKiinRAlJyc/M70qlWrcvz4cVJTUwkJCWHAgAHExMSwZ8+eAmph8VWoa4hevXpFzZo1Wbt2bZbpixYtYtWqVWzYsIGLFy9iYGCAq6sriYmJSp6+ffty+/Ztjh07xqFDhzhz5gxDhgxR0mNjY2nXrh0VKlTgypUrLF68mJkzZ7Jx48Z8758QQhR3FStUoapj7Xz/5DboSktLY8KECZiZmWFpacnMmTOVtOjoaAYNGkTp0qUxNjamVatWXL9+XUkPDQ2lS5culC1bFkNDQ+rXr8/x48fVyre1tWXOnDn0798fY2NjhgwZgp2dHQC1a9dGQ0ODFi1aKPlLlCiBpaUln3zyCW3atKFnz54cO3ZMSU9NTWXgwIHY2dmhp6eHo6MjK1euVNJnzpzJtm3bOHDgABoaGmhoaBAQEADAkydP6NWrF6amppiZmdGlSxcePXqUq/v2MSrUgKh9+/bMnTuXzz77LFNaeno6K1asYOrUqXTp0oUaNWqwfft2/vzzT2UYMCQkhCNHjrB582YaNmzIp59+yurVq9m9ezd//vknADt37iQ5OZmtW7dStWpVvvjiC0aNGsWyZcsKsqtCCCGKoG3btmFgYMDFixdZtGgRs2fPVgKQnj17EhkZyc8//8yVK1eoU6cOrVu35q+//gIgLi6ODh06cOLECa5du4abmxudO3dGpVKp1bFkyRJq1qzJtWvXmDZtGpcuXQLg+PHjhIeHs2/fvizb9ujRI44ePYqOjo5yLi0tjfLly/Pdd99x584dpk+fzuTJk9m7dy8A48aNo1evXri5uREeHk54eDiNGzcmJSUFV1dXjIyMOHv2LIGBgRgaGuLm5vaPo1bFRZFdQxQWFkZERARt2rRRzpmYmNCwYUOCgoL44osvCAoKwtTUlHr16il52rRpg6amJhcvXuSzzz4jKCiIZs2aqX2hXF1dWbhwIS9evKBUqVKZ6k5KSiIpKUk5jo2NzadeCiGEKEw1atRgxowZAFSqVIk1a9Zw4sQJ9PT0uHTpEpGRkejq6gJvApsff/yR77//niFDhlCzZk1q1qyplDVnzhz279/PwYMH8fLyUs63atWKsWPHKsdaWloAmJubY2lpqdaemzdvYmhoSGpqqjIb8vYf8Nra2so6IwA7OzuCgoLYu3cvvXr1wtDQED09PZKSktTK3rFjB2lpaWzevBkNDQ0A/Pz8MDU1JSAggHbt2v27G/kRKLIBUUREBABly5ZVO1+2bFklLSIigjJlyqillyhRAjMzM7U8GcOTb5eRkZZVQOTr66v2hRNCCPFxqlGjhtqxlZUVkZGRXL9+nbi4OMzNzdXSExISCA0NBd6MEM2cOZPDhw8THh7O69evSUhIyDRC9PYf7f/E0dGRgwcPkpiYyI4dOwgODmbkyJFqedauXcvWrVtRqVQkJCSQnJystjg7K9evX+fBgwcYGRmpnU9MTFT6U9wV2YCoME2aNIkxY8Yox7GxsVhbWxdii4QQQuQHbW1ttWMNDQ3S0tKIi4vDyspKWX/zNlNTU+DN9NSxY8dYsmQJDg4O6Onp0aNHj0xTUAYGBu/dHh0dHRwcHABYsGABHTt2ZNasWcyZMweA3bt3M27cOJYuXYqLiwtGRkYsXryYixcvvrPcuLg46taty86dOzOllS5d+r3b9zErsgFRxlDf06dPsbKyUs4/ffpUiYQtLS2JjIxUu+7169f89ddfyvWWlpY8ffpULU/G8d+HKjPo6uoqQ6RCCCGKnzp16hAREUGJEiWwtbXNMk9gYCAeHh7KOti4uLj3WqScsYQjNTX1H/NOnTqVVq1a4enpSbly5QgMDKRx48YMHz5cyfP3ER4dHZ1MZdepU4c9e/ZQpkwZjI2N/7He4qjI7lRtZ2eHpaUlJ06cUM7FxsZy8eJFXFxcAHBxcSE6OporV64oeU6ePElaWhoNGzZU8pw5c4aUlBQlz7Fjx3B0dMxyukzkXq/PPqdtM9csP70++7ywmyeEEO+tTZs2uLi40LVrV3755RcePXrE+fPnmTJlCr/++ivwZs3Rvn37CA4O5vr16/Tp04e0tLR/LLtMmTLo6elx5MgRnj59SkxMTLZ5XVxcqFGjBvPnz1fq/PXXXzl69Ci//fYb06ZN4/Lly2rX2NracuPGDe7du0dUVBQpKSn07dsXCwsLunTpwtmzZwkLCyMgIIBRo0bx+++//4s79fEo1BGiuLg4Hjx4oByHhYURHByMmZkZNjY2eHt7M3fuXCpVqoSdnR3Tpk2jXLlyyl5FTk5OuLm5MXjwYDZs2EBKSgpeXl588cUXlCtXDoA+ffowa9YsBg4ciI+PD7du3WLlypUsX768MLr8UXvxPJq9E/+XZVqvBZ0LuDVCiKLg4eO7H2Q9Ghoa/PTTT0yZMoWvvvqKZ8+eYWlpSbNmzZR1qMuWLWPAgAE0btwYCwsLfHx83ushnBIlSrBq1Spmz57N9OnTadq0aZZTcxlGjx6Nh4cHPj4+DB06lGvXrvH555+joaFB7969GT58OD///LOSf/DgwQQEBFCvXj3i4uI4deoULVq04MyZM/j4+NCtWzdevnzJJ598QuvWrWXE6P/TSE9PTy+sygMCAmjZsmWm8+7u7vj7+ysbM27cuJHo6Gg+/fRT1q1bR+XKlZW8f/31F15eXmobM65atSrbjRktLCwYOXIkPj4+793O2NhYTExMiImJyZcvTmkDA85M7JFlWrMF3/Ps1as8rzM/tG3m+s6A6NiZowXcoo9f2xYu7PD1yDLty0n+HAsIKtgGiWIpMTGRsLAw7OzsKFmyJCA7VYusBV+9SuVyZbNM++3Pp9SqUyfHZWb1/cuQk9/vQh0hatGiBe+KxzQ0NJg9ezazZ8/ONo+ZmRm7du16Zz01atTg7NmzuW6nEEKInLGxsSEkJKRIv8tMiLcV2UXVQgghPmw2NjYSoIgPRpFdVC2EEEIIUVAkIBJCCCFEsSdTZiJHen32OS+eR2eZFnpfdjsVQgjxYZKASOTIux6tr+leOcvzQgghRFEnU2ZCCCGEKPYkIBJCCCFEsScBkRBCCCGKPVlDJIQQIl+oVKoPfmNGW1tbvL298fb2ztNyRdEjAZEQQog8J6/uEB8aCYiEEELkuaioKOLj4/lmnD+Vravke32/PbnL0CUeREVFSUAkckUCIiGEEPmmsnUVajrULuxmZOvly5cMGzaMH3/8EWNjYyZMmMCBAweoVasWK1asUMv76NEj7OzsuHbtGrVq1QIgOjqaUqVKKW+UB7h9+zY+Pj6cOXOG9PR0atWqhb+/P/b29qSlpTF37lw2btzIs2fPcHJyYsGCBbi5uQGQnJzMmDFj+OGHH3jx4gVly5Zl2LBhTJo0Salv3LhxHDhwgKSkJOrVq8fy5cupWbNmQd2yj5YsqhZCCFFsjRkzhsDAQA4ePMixY8c4e/YsV69ezXV5f/zxB82aNUNXV5eTJ09y5coVBgwYwOvXrwFYuXIlS5cuZcmSJdy4cQNXV1f+85//cP/+fQBWrVrFwYMH2bt3L/fu3WPnzp3Y2toq5ffs2ZPIyEh+/vlnrly5Qp06dWjdujV//fXXv7oPQkaIhBBCFFMvX75k27Zt7Nq1i9atWwPg5+dHuXLlcl3m2rVrMTExYffu3WhrawNQufL/bVq7ZMkSfHx8+OKLLwBYuHAhp06dYsWKFaxduxaVSkWlSpX49NNP0dDQoEKFCsq1586d49KlS0RGRqKrq6uU9+OPP/L9998zZMiQXLdbyAiREEKIYurhw4ekpKTQoEED5ZyJiQmOjo65LjM4OJimTZsqwdDbYmNj+fPPP2nSpIna+SZNmhASEgKAh4cHwcHBODo6MmrUKH755Rcl3/Xr14mLi8Pc3BxDQ0PlExYWRmiovDrp35IRIiEKSPceXfjreWSWaWbmZfjh+wMF3CIhRE5oar4ZQ0hPT1fOpaSkqOXR09P7V3XUqVOHsLAwfv75Z44fP06vXr1o06YN33//PXFxcVhZWREQEJDpOlNT039Vr5CASIgC89fzSJatcM8ybYz3tgJujRCiYsWKaGtrc/nyZeXJtJiYGH777TeaNWuWKX/p0qUBCA8Pp3btNwvFg4OD1fLUqFGDbdu2kZKSkmmUyNjYmHLlyhEYGEjz5s2V84GBgWqjVMbGxnz++ed8/vnn9OjRAzc3N/766y/q1KlDREQEJUqUUFtXJPKGBERCCCGKJSMjI9zd3Rk/fjxmZmaUKVOGGTNmoKmpiYaGRqb8enp6NGrUiAULFmBnZ0dkZCRTp05Vy+Pl5cXq1av54osvmDRpEiYmJly4cIEGDRrg6OjI+PHjmTFjBvb29tSqVQs/Pz+Cg4PZuXMnAMuWLcPKyoratWujqanJd999h6WlJaamprRp0wYXFxe6du3KokWLqFy5Mn/++SeHDx/ms88+o169egVy3z5WEhAJIYTIN789uVuk61m2bBnDhg2jU6dOymP3T548oWTJklnm37p1KwMHDqRu3bo4OjqyaNEi2rVrp6Sbm5tz8uRJxo8fT/PmzdHS0qJWrVrKuqFRo0YRExPD2LFjiYyMxNnZmYMHD1KpUiXgTZC2aNEi7t+/j5aWFvXr1+enn35Sput++uknpkyZwldffcWzZ8+wtLSkWbNmlC1bNlf9F/9HAiIhhBA50qt7F168tR6udJmyDPH8GkhDR1sXewcHLCws0NfXZ+gSjwJrl76+PhYWFjm6xsjISBmdAXj16hWzZs1Snth69OiRWn4nJyfOnz+vdu7tNUXwZtrs6NGjWdanqanJjBkzmDFjRpbpgwcPZvDgwe9s76pVq1i1alW2eUTuSEAkhBAiR148j2SHr4dynEJJovUMKF+mFOFRMQDY2NgQEhJS5N9ldu3aNe7evUuDBg2IiYlh9uzZAHTp0iU/miiKMAmIhBBC5AsbG5sP4jUaS5Ys4d69e+jo6FC3bl3Onj2b45Em8eGTgEgIIUSxVbt2ba5cuVLYzShWUv7/rt1FjQREQgghhCgw2lpFM/SQnaqFEEIIUexJQCSEEEKIYk8CIiGEEEIUe0VzIk8IoejZ7XOeP4/OMi3sgbzQUQgh8oIEREIUcc+fR7Nu7v+yTGvbw7KAWyOEEB8nCYiEEELkC5VKVeQ3ZhQiQ5EOiGbOnMmsWbPUzjk6OnL37pt31iQmJjJ27Fh2795NUlISrq6urFu3Tu2dLiqVCk9PT06dOoWhoSHu7u74+vpSokSR7nqh6vXZ57zIZoom9L5M0Qgh/plKpcLJyYn4+PgCq1NfX5+QkJD3Coqyennr22bMmMHMmTNz1Y5vv/2WYcOGcf36dRwcHJTzf/75J1WrVmXOnDl4eXnlquz3ce3aNebPn8+ZM2eIiYnB2tqaFi1aMH78eCpXrsyjR4+ws7Pj2rVr1KpVS+3aFi1aUKtWLVasWKEcnz59GgAdHR0sLCyoU6cOX331Fd26dcu3PhSGIh8VVK1alePHjyvHbwcyo0eP5vDhw3z33XeYmJjg5eVFt27dCAwMBCA1NZWOHTtiaWnJ+fPnCQ8Pp3///mhrazN//vwC78uH4sXzaPZOzHqKpqZ75QJujRDiQxQVFUV8fDz+o9dTpXylfK/v7u/38VjuSVRU1HsFROHh4cq/9+zZw/Tp07l3755yztDQMNdt6devH/v378fDw4MzZ84oL2YdPHgwdevWZcSIEbkuOzvJycno6Ohw6NAhunfvjqurKzt37sTe3p7IyEi+++47pk2bxp49e3Jc9uDBg5k9ezavX7/m999/Z//+/XzxxRd4eHiwcePGPO9LYSnyAVGJEiWwtMy8TiImJoYtW7awa9cuWrVqBYCfnx9OTk5cuHCBRo0a8csvv3Dnzh2OHz9O2bJlqVWrFnPmzMHHx4eZM2eio6NT0N0RQohipUr5StS2r1nYzcjk7d8VExMTNDQ01M5t3ryZpUuXEhYWhq2tLaNGjWL48OEAygjLDz/8wOrVq7l48SKVKlViw4YNuLi4APDNN99QtWpVli1bxrhx4/D39ycwMJCbN2+SnJzMlClT+O9//0t0dDTVqlVj4cKFtGjRAoDnz5/j5eXFmTNnePHiBfb29kyePJnevXsr7WvRogXVqlWjRIkS7Nixg+rVq3P48GG++uorOnTowP79+5W8dnZ2NGzYkOjo6FzdK319feXelC9fnkaNGlGlShUGDBhAr169aNOmTa7KLWqK/GP39+/fp1y5clSsWJG+ffuiUqkAuHLlCikpKWr/IapUqYKNjQ1BQUEABAUFUb16dbUpNFdXV2JjY7l9+3bBdkQIIcQHYefOnUyfPp158+YREhLC/PnzmTZtGtu2bVPLN2XKFMaNG0dwcDCVK1emd+/evP7/r6UoXbo0GzduZNq0aRw7dozRo0ezcuVKrK2t8fLyIigoiN27d3Pjxg169uyJm5sb9+/fB94sB6lbty6HDx/m1q1bDBkyhH79+nHp0iW1+rdt24aOjg6BgYFs2LCBo0ePEhUVxYQJE7Lsl6mpaZ7dI3d3d0qVKsW+ffvyrMzCVqRHiBo2bIi/vz+Ojo6Eh4cza9YsmjZtyq1bt4iIiEBHRyfTf+CyZcsSEREBQEREhFowlJGekZadpKQkkpKSlOPY2Ng86pEQQoiibsaMGSxdulRZI2NnZ8edO3f45ptvcHd3V/KNGzeOjh07AjBr1iyqVq3KgwcPqFKlCgBdu3alV69euLm50blzZ9zd3VGpVPj5+aFSqShXrpxSzpEjR/Dz82P+/Pl88sknjBs3Tqln5MiRHD16lL1799KgQQPlfKVKlVi0aJFyfODAAQCl/n/SuHFjZTovQ0JCQqZ1RVnR1NRU1iN9LIp0QNS+fXvl3zVq1KBhw4ZUqFCBvXv3oqenl2/1+vr6ZlrMLf6de/fu0baZa5ZppcxN2bs/5/PaQgiR1169ekVoaCgDBw5k8ODByvnXr19jYmKilrdGjRrKv62srACIjIxUC0imTZvG9u3bmTp1KgA3b94kNTWVypXV12MmJSVhbm4OvFn/On/+fPbu3csff/xBcnIySUlJ6Ovrq11Tt25dteP09PQc9XXPnj04OTmpnevbt+97X5+env6Pi9M/JEU6IPo7U1NTKleuzIMHD2jbti3JyclER0erjRI9ffpUmeu0tLTMNMT49OlTJS07kyZNYsyYMcpxbGws1tbWediTYiiNbBdq91rQuYAbI4QQWYuLiwNg06ZNNGzYUC1NS0tL7VhbW1v5d0ZgkJaWppYn40GgjP+Ni4tDS0uLK1euZCovYyH34sWLWblyJStWrKB69eoYGBjg7e1NcnKyWn4DAwO144wg6+7du8papnextrZWewoOeO/BhtTUVO7fv0/9+vXfK/+HoMivIXpbXFwcoaGhWFlZUbduXbS1tTlx4oSSfu/ePVQqlfJFcHFx4ebNm0RGRip5jh07hrGxMc7OztnWo6uri7GxsdpHCCHEx69s2bKUK1eOhw8f4uDgoPaxs7P71+XXrl2b1NRUIiMjM5Wf8Yd6YGAgXbp04csvv6RmzZpUrFiR33777R/LbteuHRYWFmrTaG/L7aLqrGzbto0XL17QvXv3HF8bF5eQ5aewFekRonHjxtG5c2cqVKjAn3/+yYwZM9DS0qJ3796YmJgwcOBAxowZg5mZGcbGxowcORIXFxcaNWoEvPlyODs7069fPxYtWkRERARTp05lxIgR6OrqFnLvCpfsNSSEEFmbNWsWo0aNwsTEBDc3N5KSkvj111958eKF2uxBblSuXJm+ffvSv39/li5dSu3atXn27BknTpygRo0adOzYkUqVKvH9999z/vx5SpUqxbJly3j69Ok7/5CHNyNGmzdvpmfPnvznP/9h1KhRODg4EBUVxd69e1GpVOzevTvHbY6PjyciIkLtsfvly5fj6elJy5Ytc1yesUHWT3j/+TLHReWpIh0Q/f777/Tu3Zvnz59TunRpPv30Uy5cuEDp0qUBWL58OZqamnTv3l1tY8YMWlpaHDp0CE9PT1xcXDAwMMDd3Z3Zs2cXVpeKDNlrSAhREO7+fv+Dq2fQoEHo6+uzePFixo8fj4GBAdWrV8fb2ztPyvfz82Pu3LmMHTuWP/74AwsLCxo1akSnTp0AmDp1Kg8fPsTV1RV9fX2GDBlC165diYmJ+ceyu3Tpwvnz5/H19aVPnz7Kko9WrVoxd+7cXLV306ZNbNq0CR0dHczNzalbty579uzhs88+y1V5RVWRDoj+KZItWbIka9euZe3atdnmqVChAj/99FNeN00IIcQ7WFhYoK+vj8dyzwKrU19fHwsLixxf5+HhgYeHh9q5Pn360KdPnyzz29raZlrAbGpqmuWi5qzyamtrM2vWrGwf3jEzM+PHH398Z5sDAgKyTatXrx4//PBDtulZtSm7ct9Vz8emSAdEQoj80bPb5zzPZsoUwNzclO/2yZN/IvdsbGwICQmRd5mJD4YERKJY6tKjB8/++ivLtNJmZhz4/vsCblHBev48mnVzs54yBRg+VZ78E/+ejY2NBCjigyEBkSiWnv31F1+tWZNl2qS2bWn8/18H83cfUrB0/0EojVu1zTLtwf1/fmJFCCGKEwmIxActP0Z6XqenZxss+eXjG6rzWsrr1wxcmfW01/h2tQu4NUIIUbRJQCQ+aO8a6fmQghchhBCF64PamFEIIYQQIj/ICJH4aP324AGNW7XJNk0IIYTIIAGR+Gi9fp3KgNWbs0wb17ZJAbdGCCFEUSYBkfhoJbx6xcJBWb+5OTGx8N+bI4QQouiQgEh8vLQ0aeO7OMukzV07sHDp8izT4uPjsy3y/oNQmrV0zTLNwsyUfT/IZoZCZFCpVEV6Y8YWLVpQq1YtVqxYkX+N+sgEBATQsmVLXrx4gampaWE3J09JQCQKRGxiFP+Z2izLtLCIsHde26VHT5799SLLtH+zFqhuV48sz6t++C7ba16/fs20xVlvaOje1ZlWzbMOlgAePJSX5oriQ6VS4eTk9M4/MPKavr4+ISEhshmkyBUJiESB0NRKY+HkflmmdRg8PtvFzwD3HzzA9+eALNPetRYoPS2Na9dv5qidAOmA37YdWabFvXyJ94isg54XL5+zbu6dbMtt2csyx20R4kMVFRVFfHw8/mMX42RdMd/rC3nyEI+l44mKipKA6AOTkpKCtrZ2YTdDAiJR+FLTUnmWkP2antiY6GzTEhMT2LLt2yzT0oEKFatlmXblH9rUrG3vLM//+s06Os9blWXakq4uNOxhl22ZqQlxbNm2Lcu0+0Xsqbdb9+9mu8t1abNSHPh+bwG3SHyonKwrUtuhamE3I1tpaWlMmDCBzZs3o6Ojw7Bhw5g5cyYA0dHRjBs3jgMHDpCUlES9evVYvnw5NWvWBCA0NJQxY8Zw4cIFXr16hZOTE76+vrRp8+YPvMmTJ3PixAkuXryoVmfNmjXp3r07LVq0oHXr1jx58gRLy//7g8nb25srV65w9uzZd7b98ePHeHl5ce7cOZKTk7G1tWXx4sV06NBBmdo6cuQIEydO5O7du7i4uLB7926uXLnCmDFj+OOPP+jUqRObN29GX18fgKSkJMaPH8/u3buJjY1V+ly/fv0s2xAfH0/37t2JjY3l8OHDmJqasnnzZpYuXUpYWBi2traMGjWK4cOHA/Dnn39Su25dtq9bw8bt33L5WjCrfefR7/OeOf+Pl8ckIBJFgEa2a30ANnd2zXZxdFpqGrXbZ/3erSvfrM+T1r0vzRIlcF/3c7bpfgPqU7tDhyzTvvvvsvxqVq68TifbXa63fP15rsp81wtl5WWyorBs27aNMWPGcPHiRYKCgvDw8KBJkya0bduWnj17oqenx88//4yJiQnffPMNrVu35rfffsPMzIy4uDg6dOjAvHnz0NXVZfv27XTu3Jl79+5hY2ND37598fX1JTQ0FHt7ewBu377NjRs3+OGHH3BwcKBixYp8++23jB8/HngzWrJz504WLVr0j20fMWIEycnJnDlzBgMDA+7cuYOhoaFanpkzZ7JmzRr09fXp1asXvXr1QldXl127dhEXF8dnn33G6tWr8fHxAWDChAn88MMPbNu2jQoVKrBo0SJcXV158OABZmZmamVHR0fTsWNHDA0NOXbsGPr6+uzcuZPp06ezZs0aateuzbVr1xg8eDAGBga4u7sr107zXcCC6VOpVa0qurq6/+q/YV6RgEgUeRpaWu9cHJ2babHCkA4Enr+YZVrCO0bIbt2/S58xWQdSCUmJedG0AvGuF8rKy2RFYalRowYzZvy/9s49Lspq6+O/mYEZxCN4IRASRFGyzPSjHEg9Shp+8HJMS4+c9CUw0VSstzgZeMXUlBDNNzMt770HozL12IG8hHLK26sZmkdQ5KKoycVE7peZefb7h8cpZO8HZmQGhlnfz2f6xLP23s+a5cDzm73XXjsWANC7d2989NFHSE1NRbt27XDmzBkUFRUZHtgJCQnYv38/9uzZg1mzZqF///6G2SIAWLFiBfbt24cDBw5g3rx56Nu3L/r374/du3djyZIlAIDExEQEBASgV69eAIAZM2Zgx44dBkH0zTffoKamBlOmTGnU9/z8fEyaNAn9+vUDAPTs2XBpcuXKlRg6dKjhXgsWLEBOTo6h7eTJk3Hs2DFER0ejsrISmzZtws6dOzFmzBgAwJYtW3DkyBFs27bN4CMAFBQUICQkBL1798bu3buhVqsBALGxsVi7di1eeuklAECPHj2QkZGBTz75pJ4gmhcxAxPHjmn0PVoSEkREK4DhwnmxqGEMOHmKLyTATF8Wk7vhoZREgUnO1vjQvXrzp50Z+07YR8eAkNjPubb4ad0bv6kFmTB5ijABng6UJVojzzzzTL2f3d3dUVRUhAsXLqCiogJdunSpZ6+urkZOzv0NEhUVFVi2bBmSk5Nx+/Zt6HQ6VFdXIz8/39B+2rRp2L59O5YsWQLGGD7//HNERUUZ7OHh4Vi8eDFOnz6NZ599Fjt37sSUKVPQvn37Rn1/4403MGfOHBw+fBhBQUGYNGlSg/fz+5/d3Nzg6OhYTzi5ubnhzJkzAO4vAWq1WoOAAgB7e3v4+/sjMzOz3rijRo2Cv78/vvjiC6hUKgBAZWUlcnJyMGPGDMycOdPQVqfTwdnZuV7/gf37Nfr+LA0JIqLZ+LW2DMGLR3NtWkmPvx/lJyozAE958UUNAJwF4CMQEuIU5kdAoYDvCP6Mxb+3bxbbtm1udOjr1/8tsDRBTVkBxXdL6EBZwqp4OJlXoVBAkiRUVFTA3d0daWlpDfo82G7+9ttv48iRI0hISECvXr3Qrl07TJ48GXV1dYa2L7/8MqKjo/HTTz+huroaN27cQEjIb8vOrq6uGD9+PHbs2IEePXrg22+/5d6TR0REBIKDg5GcnIzDhw9j9erVWLt2LV5//XXu+1MoFML3ayzjxo3D119/jYyMDMMMVUVFBYD7s0oBAQH12j8QTQ9o387R6HuaGxJERLPBlEqELt3CtS0M64v+gS/wOyb+ZEavWhedvZ4UWA4I6xtVt5EiknVVdzD9LX7phfxf5EsvEISlGThwIAoKCmBnZwdvb29umxMnTiA8PBwvvvgigPuC4Nq1a/XadOvWDYGBgUhMTER1dTVGjRoFV1fXem0iIiLw8ssvo1u3bvDx8ak3Q9MYnp6emD17NmbPno0FCxZgy5Yt9QSRMfj4+ECtVuPEiRPo3v3+7LNWq8XZs2fx5ptv1msbFxeHP/zhD3j++eeRlpaGp556Cm5ubvDw8EBubi6mTePnfbZmSBARFoExCaf/ta+l3TCg0lXj1NLZQpsQBhQWFprFJ1F9oz8/74F9h/hLZhIz/ptdS6FSMixfyi+98Jew+bSrjWhVBAUFYfDgwZg4cSLi4+Ph6+uLX375BcnJyXjxxRfh5+eH3r17Y+/evRg/fjwUCgWWLFnCnW2ZNm0aYmNjUVdXhw8+aFgQNjg4GE5OTli5ciWWL1/eZB/ffPNNjBkzBr6+vigpKcGxY8fw5JOiL12N0759e8yZMwfz589H586d4eXlhfj4eFRVVWHGjBkN2ickJECv12PkyJFIS0tDnz598O677+KNN96As7MzRo8ejdraWvz4448oKSmpt1TYGiFBRFgGhQLew/kPPCResKwvANSShPa1/FwXdSPTx507ugosDP/6fvcjesZDgWeH/ZVrubLrHTPcz/IwNP+uNqJ1kHkj1yrvo1AokJKSgkWLFmH69OkoLi5G165dMXz4cLi5uQEA1q1bh1dffRVDhgyBi4sLoqOjUVZW1mCsyZMnY968eVCpVJg4cWIDu1KpRHh4OFatWoVXXnmlyT7q9XpERkbi5s2bcHJywujRo7mCyxji4uIgSRJCQ0NRXl4OPz8/HDp0CJ06deK2/+CDD+qJooiICDg6OmLNmjWYP38+2rdvj379+jWYYWqNkCBqo0x5MQQlgi3OAJBztXVVTb6YJ8qtMQ9qpQIfRPCXb/5r6R7ZvqI8II22BhVHk4X9lFqx0GIA3ozk519J+jrudXMiVfyKj1/l+5N7+ZJwNudqC3yuaDt/68TFxQWOjo4IXzu/8cbNhKOjI1xcXJrcnpers3//fsP/d+jQAR9++CE+/JBfe8zb2xtHjx6tdy0yMrJBu44dO6KmRn5H6K1btzB27Fi4u7s37vh/2LBhg9D23HPPgT200yM8PBzh4eH1ri1btsxQdwkAHBwcZN8zb9yH20+dOhVTp07l9vfw8ED1retCv1sSEkRtlJJf7+HLGP4SDAD0D/O1oDeN4+UlLtwml2Gk0Vbj3KI5fFudabk3er2E0+9ECO8nygPSKBX42+Q/CcdduuaK7H1fWMnPv/poQl8cWv5nro1p9dghKExZW1MrvFdVVYVwKz8A2DE9Nv83/5vq2LnRwtmcvwUPEI6JWh2WLhZ8e63Vifs1Am3nb514eXkhMzOzVZ9l1hooLS3FxYsXsXv3bhw4cKCl3bFpSBARzYZWV4f174qne4vuFTX7PTVKBVbOeolri4yJM2lMe6USq2bza4C8seh92b7tnB+TtRfnX+JeV2olHJjPv6cGEqY/oeba1uQpEDiK/00s7rMzwlkee22VcCs/AMT/tfkfKmolsGsW/4iWoTHbTR5Xrk7TzVtXTR6XeHS8vLysTqBYmgkTJuDMmTOYPXs2Ro2qP/M6ZswYYbXqhQsXYuHChZZw0WYgQUQ0Gwo7JUYs4VdXTZo+Es5OfLGg1EpIf5c/ywPcn5UxFXMlQJuKkxd/Zk6tUCC8twPXtuYqoOneTTDiGeG97BXAa88N4NoWZJpezFKn1QqFFsp/FfZjADJy+Pd9lKIDcnWa3p/pJ+z315cmoOROMdfWyeUxJO39R7P2MxetzR/COOS22G/dulVYtPXhqtHEo0OCiDAKuVpDjJmWC6RWKrAigj/LAwDzFr+PyopKo8cF5BKg5Sk1w2yWLArAM1CQdJ5yViikAOCgoFAkAHTv2fzFz+yVCtnlNDlcu4t2wJwV9snP/BnBw4cI7dK920hcOI5vqxALtJI7xUhe0nDnDACMW7HNpH69QqOFvjYmTkwVNqa+D6L18/jjj7e0CzYFCSIbpazmDl5YzE8qLqsRr/nL1RpKDxskzAXiLxQ1HUeHxqu2NieNLX21Jp54bhLfkCQWGY8Gw/kLP/MtJhR4awylXid84AOAx8Rz2PAafzv/5AUxze6PLJLY18bEiTmEzZWsLJMEmpw4A4CsLNOWInOys6HXabk2lZ09fP5znAXRttHrTc8ZNCckiGwUpUrC+wv5D5E/R4h3hcjnCVlXteXaWtNmnczF3bumLe+dO2SOrf7y+HrzZ56s6RNwNTsb2wQJ6Vezsy3sjZkwUaDJiTMA6DXtbw9dYf/5L0NNTS2yLmc27IT7J6k/3YM/63H1F/EXMVOFlFy/xvrKQcLu0VCoFM063sO73kyFBJEVM2nyBNz9lb+0czXbPLU/5PKEdk3nL1uYm4rKCpP62WksO+vUGI4dTZuVkvvj8vNR/m4wxiSs/u+Rwn7KFij4uGsn/2iXGjMdYKvT6TFw9GS+7XBss98vJzsHIwP51cgBID+7dZXCMAYVtIBei7v3ysEkCZ4uztx2WTcKUFPHFxJ6SRJuTa+rq4W3K78OzrWiEpP6AUDWzUJkZvDnr1UqO3h1558VaKo/xH3xUqfj/31hjBkdO8YYiouLuceSGAsJIivm7q9FWLc+jGsbN9r03QdaSQfv6d5cm7quGodi+PeUS35WaiX84z3+A69Jp6LKUFcjnukRnx3Wdug6jL9zC3suoNMgfhkAxbc/Imh1knDMY68Oag7XjGLciJe51zO3LLCwJ/dFmEi8XLrws3BmSa7UgVanE5YHAIDgSU2vP9PaUEKCkzYPZfeAojsVsGd6bruiO3fFttIKQKHk2goLi8Bqqpq13wN/HnfhJyf/UloBvWAJ2FR/iouLIOn5YypVSjz2mGk5j9ZE8Z07UAmeFcWllXDIM/4YH4VCgW7dujU4L81YSBARDVEo8JdNX3FN+155HvFz+WfUzFss3pKuVirwflgA1/bqu4+2C0Yu30dUM6h1lgVrfiqLrpncN12QJwQAmYLdYuZAkvRCAXIfsaCWJL2wiKTczBNjEIqXYeM6CWeW2D9OC8esqakSnuX2wG7NaFglutRlYGbM/+BIAj93a2bM+zi0hp94v2rT19j6d/5uwfeWLMTmOfxcOVP7tTZ/Zm8U92tLTH3pJXw2i7+aEP1pMk6lpxs9pr29/SOLIYAEkc2iY5Ls6fPmwE71aNOZIrQ6y1dyNge/PyG7uZDbnSYHYwyl1/h5IAxAN6+nhX137BLMBJqMQihAAAD7Tsr2FRWRjBnqKewlSXphbSNTE8clSY/+z/JnDgAg+zJ/5sSaUELC7Vs34SDxZ8pu3bwhtBUXFcLBgV96oriosNn7tTZ/5Pq1JW7m50NZeU9oa8kY2JQg2rhxI9asWYOCggL0798fGzZsgL+/f0u71SIwQPb0ebnt85UyS1QtgZ2KX7QQMI/IMBf2duL30ewwCd/NE5+orQKD13BBTljK/yHjB/4MImMS8o99LbinsU42D6LcJHlhoxDWNpIrWilJTDibxQA8HSgWdl/v/FHGHzEXLpxHwlp+Xl91lXXPOtkScjsCAaopZQlsRhB98cUXiIqKwubNmxEQEID169cjODgYV65cgatr21+3fRilVo8PFq8X2CTh9vlsAO1kkpHz8k3L2SkpNU/dHzmRYY4t4taCQgG8ul5cDPOzmWuR9y/xMQLdhvHFkuLQWQxduolrOzJ1APJzBAf5MmDzUoFYeMQcM1Fu0qVPooXlAxpD3I+JZ7P2y81kmY7EJDw/kb8Ut+4H4/MxmkJtTS127TQ+j4qQQWZHICC/K7CtFBltaWxGEK1btw4zZ87E9OnTAQCbN29GcnIytm/fjpgYC9cqaSays3Pwvzv4337LKyvRM9xH2NdeocCKcT25tjlbS1BUYppA0TxmWpn+xur+5N9s/uRohSDxEbh/irRNowC6Dhcka3/7I67/wBdLjDEcWsxPulcC6NTjKcH9LsBtasNDMQGgNPY0jn8jPqCVAfjh4C6BP2LRq2B6VObxhY1CkPj7AFHZgUeBAWYpA2DKmHIlCQBAL+kxbgT/yJh1u02b6SJMxxxFRs1Z1FNX2zpFs00Iorq6Opw7dw4LFvy2W0WpVCIoKAinTp1q0L62tha1v/sHKy0tBQCUlZWZxT+JMVTU8Jd2JMYwbNgfubbS0hJUVvC3sCq0ejg6i7djV1dIULnyj4OQpLO4+uFiro1JOuQcEzwMJR2y4x+uT/JgTAklpff4NsZQLfOtkjGGDl34QovJ9GWMQVfFX96TGENVDT92EmOoreMXDmOMoepeidG+GO5ZwV/CkItBY7aaatP61VbJ+1paVs61Mcbg/Mdh/I4Hz6H/2+9yTRfmTMAvV/lbnCWJIXvXGuH9FD7PCH1l7DS6DhUcfpv6M1a9Hsi1qRiDUxfx752on1LS4/hhfu0nPWPY/xm/cKkkMVy4dI5re3DP2s7eXNu90tPwHz6Ca9Pp9ci9flM4pm8gfzbv3r7T6OTGz6NSVJQJ+wGAtO8EKqr4O4V0kh4fbd3BtVVX16CsUtBPpxP+jb2SlSUc80pWlrCfTqcT3g+4/29iij9y48r1y8q6gs1bP+Ha5GJjLn/k+mVevoznh/BTSjp2ccGORPEuVTkkxlAt+KIiMdbsz9kH4zWpVhGzAW7dusUAsJMnT9a7Pn/+fObv79+gfWxsLMP9L2z0ohe96EUvetHLyl83btxoVCvYxAyRsSxYsABRUVGGnyVJwt27d9GlSxcoFM1bYbOsrAyenp64ceMGnJycmnVs4jcozpaB4mwZKM6Wg2JtGcwVZ8YYysvL4eHh0WhbmxBELi4uUKlUDU4+LywsRNeuXRu012g00Gg09a517NjRnC7CycmJftksAMXZMlCcLQPF2XJQrC2DOeLs7OzcpHbirNI2hFqtxqBBg5Cammq4JkkSUlNTMXjw4Bb0jCAIgiCI1oBNzBABQFRUFMLCwuDn5wd/f3+sX78elZWVhl1nBEEQBEHYLjYjiEJCQlBcXIylS5eioKAAAwYMwMGDB+Hm5taifmk0GsTGxjZYoiOaF4qzZaA4WwaKs+WgWFuG1hBnBWOPWPWMIAiCIAjCyrGJHCKCIAiCIAg5SBARBEEQBGHzkCAiCIIgCMLmIUFEEARBEITNQ4LIAmzcuBHe3t5wcHBAQEAAzpw5I9v+q6++Qp8+feDg4IB+/fohJSXFQp5aN8bEecuWLRg2bBg6deqETp06ISgoqNF/F+I+xn6eH5CUlASFQoGJEyea18E2grFxvnfvHiIjI+Hu7g6NRgNfX1/629FEjI31+vXr8cQTT6Bdu3bw9PTEW2+9hZqaGgt5a318//33GD9+PDw8PKBQKLB///5G+6SlpWHgwIHQaDTo1asXdu7caXY/beIss5YkKSmJqdVqtn37dnbp0iU2c+ZM1rFjR1ZYWMhtf+LECaZSqVh8fDzLyMhgixcvZvb29uzixYsW9ty6MDbOU6dOZRs3bmTp6eksMzOThYeHM2dnZ3bz5k0Le25dGBvnB+Tl5bHHH3+cDRs2jE2YMMEyzloxxsa5traW+fn5sbFjx7Ljx4+zvLw8lpaWxs6fP29hz60PY2OdmJjINBoNS0xMZHl5eezQoUPM3d2dvfXWWxb23HpISUlhixYtYnv37mUA2L59+2Tb5+bmMkdHRxYVFcUyMjLYhg0bmEqlYgcPHjSrnySIzIy/vz+LjIw0/KzX65mHhwdbvXo1t/2UKVPYuHHj6l0LCAhgr732mln9tHaMjfPD6HQ61qFDB7Zr1y5zudgmMCXOOp2ODRkyhG3dupWFhYWRIGoCxsZ506ZNrGfPnqyurs5SLrYZjI11ZGQkGzlyZL1rUVFRbOjQoWb1s63QFEH0zjvvsL59+9a7FhISwoKDg83oGWO0ZGZG6urqcO7cOQQFBRmuKZVKBAUF4dSpU9w+p06dqtceAIKDg4XtCdPi/DBVVVXQarXo3Lmzudy0ekyN8/Lly+Hq6ooZM2ZYwk2rx5Q4HzhwAIMHD0ZkZCTc3Nzw9NNPY9WqVdDr9ZZy2yoxJdZDhgzBuXPnDMtqubm5SElJwdixYy3isy3QUs9Bm6lU3RLcuXMHer2+QTVsNzc3XL58mdunoKCA276goMBsflo7psT5YaKjo+Hh4dHgl5D4DVPifPz4cWzbtg3nz5+3gIdtA1PinJubi6NHj2LatGlISUlBdnY25s6dC61Wi9jYWEu4bZWYEuupU6fizp07+NOf/gTGGHQ6HWbPno2FCxdawmWbQPQcLCsrQ3V1Ndq1a2eW+9IMEWHzxMXFISkpCfv27YODg0NLu9NmKC8vR2hoKLZs2QIXF5eWdqdNI0kSXF1d8emnn2LQoEEICQnBokWLsHnz5pZ2rc2RlpaGVatW4eOPP8ZPP/2EvXv3Ijk5GStWrGhp14hHhGaIzIiLiwtUKhUKCwvrXS8sLETXrl25fbp27WpUe8K0OD8gISEBcXFx+O677/DMM8+Y002rx9g45+Tk4Nq1axg/frzhmiRJAAA7OztcuXIFPj4+5nXaCjHl8+zu7g57e3uoVCrDtSeffBIFBQWoq6uDWq02q8/WiimxXrJkCUJDQxEREQEA6NevHyorKzFr1iwsWrQISiXNMzwqouegk5OT2WaHAJohMitqtRqDBg1Camqq4ZokSUhNTcXgwYO5fQYPHlyvPQAcOXJE2J4wLc4AEB8fjxUrVuDgwYPw8/OzhKtWjbFx7tOnDy5evIjz588bXi+88AJGjBiB8+fPw9PT05LuWw2mfJ6HDh2K7Oxsg+AEgKysLLi7u5MYksGUWFdVVTUQPQ+EKKOjQZuFFnsOmjVlm2BJSUlMo9GwnTt3soyMDDZr1izWsWNHVlBQwBhjLDQ0lMXExBjanzhxgtnZ2bGEhASWmZnJYmNjadt9EzA2znFxcUytVrM9e/aw27dvG17l5eUt9RasAmPj/DC0y6xpGBvn/Px81qFDBzZv3jx25coV9s9//pO5urqylStXttRbsBqMjXVsbCzr0KED+/zzz1lubi47fPgw8/HxYVOmTGmpt9DqKS8vZ+np6Sw9PZ0BYOvWrWPp6ens+vXrjDHGYmJiWGhoqKH9g2338+fPZ5mZmWzjxo207b6tsGHDBubl5cXUajXz9/dnp0+fNtgCAwNZWFhYvfZffvkl8/X1ZWq1mvXt25clJydb2GPrxJg4d+/enQFo8IqNjbW841aGsZ/n30OCqOkYG+eTJ0+ygIAAptFoWM+ePdl7773HdDqdhb22ToyJtVarZcuWLWM+Pj7MwcGBeXp6srlz57KSkhLLO24lHDt2jPv39kFcw8LCWGBgYIM+AwYMYGq1mvXs2ZPt2LHD7H4qGKM5PoIgCIIgbBvKISIIgiAIwuYhQUQQBEEQhM1DgoggCIIgCJuHBBFBEARBEDYPCSKCIAiCIGweEkQEQRAEQdg8JIgIgiAIgrB5SBARBEEQBGHzkCAiCIIgCMLmIUFEEARBEITNQ4KIIAiCIAibhwQRQRAEQRA2z/8DLMblh4OZK0EAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(scaled_data)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "5342a37b",
"metadata": {},
"outputs": [],
"source": [
"data = scaled_data"
]
},
{
"cell_type": "markdown",
"id": "97f0a373",
"metadata": {
"id": "64f83ef0-6502-449c-925a-b2885b23df6d"
},
"source": [
"# Prepare data for modeling "
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "b03ece40",
"metadata": {
"id": "aa465aa8-9b73-456f-81f0-9458e525647a"
},
"outputs": [],
"source": [
"# Define the target variable and feature matrix\n",
"X = data.drop('TenYearCHD', axis=1)\n",
"y = data['TenYearCHD']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "2282362f",
"metadata": {
"id": "aa465aa8-9b73-456f-81f0-9458e525647a"
},
"outputs": [],
"source": [
"# Split the dataset 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)"
]
},
{
"cell_type": "markdown",
"id": "5a4936e3",
"metadata": {
"id": "a1d2e92a-dfa7-414b-95e4-24aa8f1a622e"
},
"source": [
"# Train the Model"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "671888c3",
"metadata": {
"id": "3b67313d-b1d3-4879-a766-11ade4010958"
},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
],
"text/plain": [
"LogisticRegression(random_state=42, solver='liblinear')"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logreg = LogisticRegression(solver='liblinear', random_state=42)\n",
"logreg.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"id": "2d10d5b6",
"metadata": {
"id": "bd839a48-c1df-432f-893b-e7cffe02b9e1"
},
"source": [
"# Make Predictions"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "0722b753",
"metadata": {
"id": "a9ae23de-6584-4878-8925-257368066ae3"
},
"outputs": [],
"source": [
"y_pred = logreg.predict(X_test)"
]
},
{
"cell_type": "markdown",
"id": "ad52a9e8",
"metadata": {
"id": "19b82267-d17a-4e60-9ba1-b1ffc618c08e"
},
"source": [
"# Evaluate the Model"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "003874e7",
"metadata": {
"id": "6a94fb2b-b003-4cbd-acdd-fa17d45cf978"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0.0 0.86 0.99 0.92 724\n",
" 1.0 0.60 0.07 0.13 124\n",
"\n",
" accuracy 0.86 848\n",
" macro avg 0.73 0.53 0.53 848\n",
"weighted avg 0.82 0.86 0.81 848\n",
"\n",
"Accuracy: 0.8573113207547169\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Classification report\n",
"print(classification_report(y_test, y_pred))\n",
"\n",
"# Confusion matrix\n",
"conf_matrix = confusion_matrix(y_test, y_pred)\n",
"sns.heatmap(conf_matrix, annot=True, cmap=\"YlGnBu\", fmt=\"d\")\n",
"\n",
"# Accuracy score\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"print('Accuracy:', accuracy)"
]
}
],
"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": "3.10.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment