predicting-diabetes-outcome.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/apttemi/predicting-diabetes-outcome?scriptVersionId=132244170\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","execution_count":1,"id":"d442ce48","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-06-04T12:01:23.649919Z","iopub.status.busy":"2023-06-04T12:01:23.649376Z","iopub.status.idle":"2023-06-04T12:01:25.475591Z","shell.execute_reply":"2023-06-04T12:01:25.47414Z"},"papermill":{"duration":1.84057,"end_time":"2023-06-04T12:01:25.47819","exception":false,"start_time":"2023-06-04T12:01:23.63762","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/pima-indians-diabetes-database/diabetes.csv\n"]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from scipy.stats import shapiro\n","from sklearn.datasets import make_classification\n","from sklearn.pipeline import make_pipeline\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_score\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import classification_report\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"markdown","id":"00b61b6a","metadata":{"papermill":{"duration":0.009271,"end_time":"2023-06-04T12:01:25.497293","exception":false,"start_time":"2023-06-04T12:01:25.488022","status":"completed"},"tags":[]},"source":["# This work is aimed at predicting diabetes outcome using Logistic regression"]},{"cell_type":"markdown","id":"995c6826","metadata":{"papermill":{"duration":0.009081,"end_time":"2023-06-04T12:01:25.515743","exception":false,"start_time":"2023-06-04T12:01:25.506662","status":"completed"},"tags":[]},"source":["Tasks to be completed:\n","* Perform EDA (data info, description and head)\n","* check for duplicates\n","* check for outliers\n","* test for normality\n","* check the distribution of the outcome variable\n","* plot the distribution of each variable by outcome\n","* perform logistic regression on the dataset and visualize the plot"]},{"cell_type":"markdown","id":"7e6e4caa","metadata":{"papermill":{"duration":0.008851,"end_time":"2023-06-04T12:01:25.535989","exception":false,"start_time":"2023-06-04T12:01:25.527138","status":"completed"},"tags":[]},"source":["# Load the dataset and perform EDA"]},{"cell_type":"code","execution_count":2,"id":"781f3860","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:25.557019Z","iopub.status.busy":"2023-06-04T12:01:25.556043Z","iopub.status.idle":"2023-06-04T12:01:25.58084Z","shell.execute_reply":"2023-06-04T12:01:25.579787Z"},"papermill":{"duration":0.038108,"end_time":"2023-06-04T12:01:25.583443","exception":false,"start_time":"2023-06-04T12:01:25.545335","status":"completed"},"tags":[]},"outputs":[],"source":["#read the file\n","data = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')"]},{"cell_type":"code","execution_count":3,"id":"08a03d47","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:25.604803Z","iopub.status.busy":"2023-06-04T12:01:25.604415Z","iopub.status.idle":"2023-06-04T12:01:25.630505Z","shell.execute_reply":"2023-06-04T12:01:25.629406Z"},"papermill":{"duration":0.039629,"end_time":"2023-06-04T12:01:25.632955","exception":false,"start_time":"2023-06-04T12:01:25.593326","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 768 entries, 0 to 767\n","Data columns (total 9 columns):\n"," #   Column                    Non-Null Count  Dtype  \n","---  ------                    --------------  -----  \n"," 0   Pregnancies               768 non-null    int64  \n"," 1   Glucose                   768 non-null    int64  \n"," 2   BloodPressure             768 non-null    int64  \n"," 3   SkinThickness             768 non-null    int64  \n"," 4   Insulin                   768 non-null    int64  \n"," 5   BMI                       768 non-null    float64\n"," 6   DiabetesPedigreeFunction  768 non-null    float64\n"," 7   Age                       768 non-null    int64  \n"," 8   Outcome                   768 non-null    int64  \n","dtypes: float64(2), int64(7)\n","memory usage: 54.1 KB\n"]}],"source":["#data info\n","data.info()"]},{"cell_type":"code","execution_count":4,"id":"c7a8f077","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:25.654727Z","iopub.status.busy":"2023-06-04T12:01:25.653639Z","iopub.status.idle":"2023-06-04T12:01:25.708811Z","shell.execute_reply":"2023-06-04T12:01:25.70771Z"},"papermill":{"duration":0.068451,"end_time":"2023-06-04T12:01:25.711137","exception":false,"start_time":"2023-06-04T12:01:25.642686","status":"completed"},"tags":[]},"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>Pregnancies</th>\n","      <th>Glucose</th>\n","      <th>BloodPressure</th>\n","      <th>SkinThickness</th>\n","      <th>Insulin</th>\n","      <th>BMI</th>\n","      <th>DiabetesPedigreeFunction</th>\n","      <th>Age</th>\n","      <th>Outcome</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","      <td>768.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>3.845052</td>\n","      <td>120.894531</td>\n","      <td>69.105469</td>\n","      <td>20.536458</td>\n","      <td>79.799479</td>\n","      <td>31.992578</td>\n","      <td>0.471876</td>\n","      <td>33.240885</td>\n","      <td>0.348958</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>3.369578</td>\n","      <td>31.972618</td>\n","      <td>19.355807</td>\n","      <td>15.952218</td>\n","      <td>115.244002</td>\n","      <td>7.884160</td>\n","      <td>0.331329</td>\n","      <td>11.760232</td>\n","      <td>0.476951</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.078000</td>\n","      <td>21.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>1.000000</td>\n","      <td>99.000000</td>\n","      <td>62.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>27.300000</td>\n","      <td>0.243750</td>\n","      <td>24.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>3.000000</td>\n","      <td>117.000000</td>\n","      <td>72.000000</td>\n","      <td>23.000000</td>\n","      <td>30.500000</td>\n","      <td>32.000000</td>\n","      <td>0.372500</td>\n","      <td>29.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>6.000000</td>\n","      <td>140.250000</td>\n","      <td>80.000000</td>\n","      <td>32.000000</td>\n","      <td>127.250000</td>\n","      <td>36.600000</td>\n","      <td>0.626250</td>\n","      <td>41.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>17.000000</td>\n","      <td>199.000000</td>\n","      <td>122.000000</td>\n","      <td>99.000000</td>\n","      <td>846.000000</td>\n","      <td>67.100000</td>\n","      <td>2.420000</td>\n","      <td>81.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n","count   768.000000  768.000000     768.000000     768.000000  768.000000   \n","mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n","std       3.369578   31.972618      19.355807      15.952218  115.244002   \n","min       0.000000    0.000000       0.000000       0.000000    0.000000   \n","25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n","50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n","75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n","max      17.000000  199.000000     122.000000      99.000000  846.000000   \n","\n","              BMI  DiabetesPedigreeFunction         Age     Outcome  \n","count  768.000000                768.000000  768.000000  768.000000  \n","mean    31.992578                  0.471876   33.240885    0.348958  \n","std      7.884160                  0.331329   11.760232    0.476951  \n","min      0.000000                  0.078000   21.000000    0.000000  \n","25%     27.300000                  0.243750   24.000000    0.000000  \n","50%     32.000000                  0.372500   29.000000    0.000000  \n","75%     36.600000                  0.626250   41.000000    1.000000  \n","max     67.100000                  2.420000   81.000000    1.000000  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["#data description\n","data.describe()"]},{"cell_type":"code","execution_count":5,"id":"25ee8b7e","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:25.73286Z","iopub.status.busy":"2023-06-04T12:01:25.732481Z","iopub.status.idle":"2023-06-04T12:01:25.74626Z","shell.execute_reply":"2023-06-04T12:01:25.74518Z"},"papermill":{"duration":0.027577,"end_time":"2023-06-04T12:01:25.748673","exception":false,"start_time":"2023-06-04T12:01:25.721096","status":"completed"},"tags":[]},"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>Pregnancies</th>\n","      <th>Glucose</th>\n","      <th>BloodPressure</th>\n","      <th>SkinThickness</th>\n","      <th>Insulin</th>\n","      <th>BMI</th>\n","      <th>DiabetesPedigreeFunction</th>\n","      <th>Age</th>\n","      <th>Outcome</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6</td>\n","      <td>148</td>\n","      <td>72</td>\n","      <td>35</td>\n","      <td>0</td>\n","      <td>33.6</td>\n","      <td>0.627</td>\n","      <td>50</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>85</td>\n","      <td>66</td>\n","      <td>29</td>\n","      <td>0</td>\n","      <td>26.6</td>\n","      <td>0.351</td>\n","      <td>31</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>8</td>\n","      <td>183</td>\n","      <td>64</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>23.3</td>\n","      <td>0.672</td>\n","      <td>32</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>89</td>\n","      <td>66</td>\n","      <td>23</td>\n","      <td>94</td>\n","      <td>28.1</td>\n","      <td>0.167</td>\n","      <td>21</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>137</td>\n","      <td>40</td>\n","      <td>35</td>\n","      <td>168</td>\n","      <td>43.1</td>\n","      <td>2.288</td>\n","      <td>33</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n","0            6      148             72             35        0  33.6   \n","1            1       85             66             29        0  26.6   \n","2            8      183             64              0        0  23.3   \n","3            1       89             66             23       94  28.1   \n","4            0      137             40             35      168  43.1   \n","\n","   DiabetesPedigreeFunction  Age  Outcome  \n","0                     0.627   50        1  \n","1                     0.351   31        0  \n","2                     0.672   32        1  \n","3                     0.167   21        0  \n","4                     2.288   33        1  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["#data head\n","data.head()"]},{"cell_type":"code","execution_count":6,"id":"02f26620","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:25.77079Z","iopub.status.busy":"2023-06-04T12:01:25.770413Z","iopub.status.idle":"2023-06-04T12:01:25.777101Z","shell.execute_reply":"2023-06-04T12:01:25.776061Z"},"papermill":{"duration":0.0206,"end_time":"2023-06-04T12:01:25.779532","exception":false,"start_time":"2023-06-04T12:01:25.758932","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(768, 9)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["data.shape"]},{"cell_type":"code","execution_count":7,"id":"5da391e5","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:25.802422Z","iopub.status.busy":"2023-06-04T12:01:25.802046Z","iopub.status.idle":"2023-06-04T12:01:25.812055Z","shell.execute_reply":"2023-06-04T12:01:25.811088Z"},"papermill":{"duration":0.024695,"end_time":"2023-06-04T12:01:25.814523","exception":false,"start_time":"2023-06-04T12:01:25.789828","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["No duplicates found!\n"]}],"source":["#check for duplicates\n","duplicates = data.duplicated()\n","if duplicates.any():\n","    print(\"Duplicates found!\")\n","    duplicate_rows = data[duplicates]\n","    print(duplicate_rows)\n","else:\n","    print(\"No duplicates found!\")"]},{"cell_type":"code","execution_count":8,"id":"4f0cedc1","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:25.837147Z","iopub.status.busy":"2023-06-04T12:01:25.836806Z","iopub.status.idle":"2023-06-04T12:01:25.845109Z","shell.execute_reply":"2023-06-04T12:01:25.843837Z"},"papermill":{"duration":0.022338,"end_time":"2023-06-04T12:01:25.847269","exception":false,"start_time":"2023-06-04T12:01:25.824931","status":"completed"},"tags":[]},"outputs":[],"source":["#get the feature variables\n","features_variables = data.iloc[:, 0:8]\n","#get the outcome variables\n","outcome_variable = data.iloc[:, -1]"]},{"cell_type":"code","execution_count":9,"id":"0a339254","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:25.86988Z","iopub.status.busy":"2023-06-04T12:01:25.869507Z","iopub.status.idle":"2023-06-04T12:01:25.889445Z","shell.execute_reply":"2023-06-04T12:01:25.88828Z"},"papermill":{"duration":0.034106,"end_time":"2023-06-04T12:01:25.891873","exception":false,"start_time":"2023-06-04T12:01:25.857767","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Feature variables\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Pregnancies</th>\n","      <th>Glucose</th>\n","      <th>BloodPressure</th>\n","      <th>SkinThickness</th>\n","      <th>Insulin</th>\n","      <th>BMI</th>\n","      <th>DiabetesPedigreeFunction</th>\n","      <th>Age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6</td>\n","      <td>148</td>\n","      <td>72</td>\n","      <td>35</td>\n","      <td>0</td>\n","      <td>33.6</td>\n","      <td>0.627</td>\n","      <td>50</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>85</td>\n","      <td>66</td>\n","      <td>29</td>\n","      <td>0</td>\n","      <td>26.6</td>\n","      <td>0.351</td>\n","      <td>31</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>8</td>\n","      <td>183</td>\n","      <td>64</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>23.3</td>\n","      <td>0.672</td>\n","      <td>32</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>89</td>\n","      <td>66</td>\n","      <td>23</td>\n","      <td>94</td>\n","      <td>28.1</td>\n","      <td>0.167</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>137</td>\n","      <td>40</td>\n","      <td>35</td>\n","      <td>168</td>\n","      <td>43.1</td>\n","      <td>2.288</td>\n","      <td>33</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>763</th>\n","      <td>10</td>\n","      <td>101</td>\n","      <td>76</td>\n","      <td>48</td>\n","      <td>180</td>\n","      <td>32.9</td>\n","      <td>0.171</td>\n","      <td>63</td>\n","    </tr>\n","    <tr>\n","      <th>764</th>\n","      <td>2</td>\n","      <td>122</td>\n","      <td>70</td>\n","      <td>27</td>\n","      <td>0</td>\n","      <td>36.8</td>\n","      <td>0.340</td>\n","      <td>27</td>\n","    </tr>\n","    <tr>\n","      <th>765</th>\n","      <td>5</td>\n","      <td>121</td>\n","      <td>72</td>\n","      <td>23</td>\n","      <td>112</td>\n","      <td>26.2</td>\n","      <td>0.245</td>\n","      <td>30</td>\n","    </tr>\n","    <tr>\n","      <th>766</th>\n","      <td>1</td>\n","      <td>126</td>\n","      <td>60</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>30.1</td>\n","      <td>0.349</td>\n","      <td>47</td>\n","    </tr>\n","    <tr>\n","      <th>767</th>\n","      <td>1</td>\n","      <td>93</td>\n","      <td>70</td>\n","      <td>31</td>\n","      <td>0</td>\n","      <td>30.4</td>\n","      <td>0.315</td>\n","      <td>23</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>768 rows × 8 columns</p>\n","</div>"],"text/plain":["     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n","0              6      148             72             35        0  33.6   \n","1              1       85             66             29        0  26.6   \n","2              8      183             64              0        0  23.3   \n","3              1       89             66             23       94  28.1   \n","4              0      137             40             35      168  43.1   \n","..           ...      ...            ...            ...      ...   ...   \n","763           10      101             76             48      180  32.9   \n","764            2      122             70             27        0  36.8   \n","765            5      121             72             23      112  26.2   \n","766            1      126             60              0        0  30.1   \n","767            1       93             70             31        0  30.4   \n","\n","     DiabetesPedigreeFunction  Age  \n","0                       0.627   50  \n","1                       0.351   31  \n","2                       0.672   32  \n","3                       0.167   21  \n","4                       2.288   33  \n","..                        ...  ...  \n","763                     0.171   63  \n","764                     0.340   27  \n","765                     0.245   30  \n","766                     0.349   47  \n","767                     0.315   23  \n","\n","[768 rows x 8 columns]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["print(\"Feature variables\")\n","features_variables"]},{"cell_type":"code","execution_count":10,"id":"c02e5d5e","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:25.915678Z","iopub.status.busy":"2023-06-04T12:01:25.915266Z","iopub.status.idle":"2023-06-04T12:01:25.925078Z","shell.execute_reply":"2023-06-04T12:01:25.923651Z"},"papermill":{"duration":0.024742,"end_time":"2023-06-04T12:01:25.927655","exception":false,"start_time":"2023-06-04T12:01:25.902913","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Outcome variable\n"]},{"data":{"text/plain":["0      1\n","1      0\n","2      1\n","3      0\n","4      1\n","      ..\n","763    0\n","764    0\n","765    0\n","766    1\n","767    0\n","Name: Outcome, Length: 768, dtype: int64"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["print(\"Outcome variable\")\n","outcome_variable"]},{"cell_type":"code","execution_count":11,"id":"14f147f1","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:25.952009Z","iopub.status.busy":"2023-06-04T12:01:25.951646Z","iopub.status.idle":"2023-06-04T12:01:27.401686Z","shell.execute_reply":"2023-06-04T12:01:27.40078Z"},"papermill":{"duration":1.466847,"end_time":"2023-06-04T12:01:27.405889","exception":false,"start_time":"2023-06-04T12:01:25.939042","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABKUAAAJOCAYAAABm7rQwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADPB0lEQVR4nOzdeVxUZfs/8M/IMqyios5AopCikKCiloQR4IKhuCG5ZbkWT9hCrqGm6JfgEXMpt7JHRVPUJ0MrzTU3DCrAXMM1UExGyhRQkPX+/eGP8zgyIOgwM8Dn/Xqdl8597jlznREuz1xzn/uWCSEEiIiIiIiIiIiIdKiRvgMgIiIiIiIiIqKGh0UpIiIiIiIiIiLSORaliIiIiIiIiIhI51iUIiIiIiIiIiIinWNRioiIiIiIiIiIdI5FKSIiIiIiIiIi0jkWpYiIiIiIiIiISOdYlCIiIiIiIiIiIp1jUYqIiIiIiIiIiHSORal6LjY2FjKZTNqMjY3RqlUrjB8/Hn/++ae+wzNIjo6OGDdunL7DIKIaOn36NCZOnIi2bdvC3Nwc5ubmcHZ2RkhICFJSUqR+ERERkMlkeoyUiPTt0esjmUyGFi1awNfXF7t27VLrK5PJEBERofMYMzIyIJPJEBsbK7WV56/yzdTUFE5OTnj//fdx584dncdIRLXjl19+wdChQ9G6dWvI5XIoFAq8+OKLmDp1qtTH0dERgYGBjz3Wk+QwTTlS0+bo6AgAGDduHKysrKp17CeJ58iRI5DJZNi+fXuNnkd1g7G+AyDdWL9+PVxcXFBQUIBjx44hOjoaR48exZkzZ2Bpaanv8AzKjh070LhxY32HQUQ18MUXX+Cdd95Bhw4d8P7776Njx46QyWRIS0vDli1b8Pzzz+Py5cto27atvkMlIgNSfn0khIBKpcKKFSswcOBAfPfddxg4cKC+w6vU3r17YWNjg7y8PPzwww/49NNP8euvvyIxMZFFd6I6bvfu3Rg0aBB8fX0RExMDOzs7ZGVlISUlBVu3bsXixYtrdLykpCS0atWqRs8ZMGAAkpKS1NpefPFFBAcHqxXG5HJ5jY77pPFQ/caiVAPh5uaG7t27AwD8/PxQWlqK//u//8POnTvx2muvVeifn58PCwsLXYdpEDw8PPQdAhHVwE8//YTQ0FAMGDAA27dvh6mpqbSvV69emDx5Mr7++muYm5vrMUoiMkQPXx8BwCuvvIKmTZtiy5YtBl2U6tatG5o3bw4A6Nu3L27duoWvvvoKiYmJ6Nmzp8bn1LVrOyEE7t+/z9xNDU5MTAycnJywb98+GBv/7+P6yJEjERMTU+PjeXp61vg5LVq0QIsWLSq0KxSKJzre08ZD9Rtv32ugypPB1atXpeGWZ86cgb+/P6ytrdG7d28AQFFRESIjI+Hi4gK5XI4WLVpg/Pjx+Ouvv9SOV1hYiKlTp0KpVMLCwgIvv/wyUlNTK9wKVz4U9PDhw3j77bfRvHlz2NraIigoCDdu3FA75rZt2+Dv7w87OzuYm5vD1dUVH374Ie7du6fWrzz+y5cvo3///rCysoKDgwOmTp2KwsLCCnEuWLAArq6uMDMzg62tLfz8/JCYmCj10XT7Xm5uLqZNmwYnJyeYmprimWeeQVhYWIVYvv76a/To0QM2NjawsLDAs88+iwkTJlT/H4aIaiwqKgpGRkb44osv1ApSD3v11Vdhb29f6TEqG0quKR/8+eefeOutt+Dg4ABTU1PY29sjODgYN2/elPpcu3YNY8aMQcuWLSGXy+Hq6orFixejrKxM7VirV69G586dYWVlBWtra7i4uGDWrFlqfVQqFUJCQtCqVSvpVp358+ejpKTkMe8MEdWUmZkZTE1NYWJiUmW/s2fPYvDgwWjatCnMzMzQpUsXbNiwoUK/6uaCGzduYPjw4bC2toaNjQ1GjBgBlUpV7bgfvq4DAF9fX7i5ueHYsWPw8vKChYWFdD2irWuasrIyREZGokOHDjA3N0eTJk3QqVMnfPrpp1KfcePGSbf3PEzTbdQymQzvvPMOPv/8c7i6ukIul0vv6aVLlzB69Gi193HlypXVfn+I6pJbt26hefPmagWpco0aVf3xfdWqVTA2Nsa8efOktkevcWryeaymqvN5TNM1V3WurR6Vm5uLfv36QaFQ4NdffwXwv9xy7tw5jBo1CjY2NlAoFJgwYQJycnLUni+EwKpVq9ClSxeYm5ujadOmCA4Oxh9//KHW77fffkNgYKCUf+zt7TFgwABcv35d6sPPgE+HI6UaqMuXLwN4UAW/ePEiioqKMGjQIISEhODDDz9ESUkJysrKMHjwYCQkJGDGjBnw8vLC1atXMW/ePPj6+iIlJUX69mr8+PHYtm0bZsyYgV69euH333/H0KFDkZubq/H1J02ahAEDBiAuLg6ZmZmYPn06xowZg0OHDkl9Ll26hP79+yMsLAyWlpY4f/48Fi5ciF9//VWtHwAUFxdj0KBBmDhxIqZOnYpjx47h//7v/2BjY4O5c+cCAEpKShAQEICEhASEhYWhV69eKCkpwc8//4xr167By8tLY6z5+fnw8fHB9evXMWvWLHTq1Annzp3D3LlzcebMGRw8eBAymQxJSUkYMWIERowYgYiICJiZmeHq1asVYiUi7SktLcXhw4fRvXt32NnZ1frr/fnnn3j++edRXFws5YNbt25h3759uH37NhQKBf766y94eXmhqKgI//d//wdHR0fs2rUL06ZNw5UrV7Bq1SoAwNatWxEaGop3330Xn3zyCRo1aoTLly/j999/l15PpVLhhRdeQKNGjTB37ly0bdsWSUlJiIyMREZGBtavX1/r50xUn5WWlqKkpARCCNy8eROLFi3CvXv3MHr06Eqfc+HCBXh5eaFly5b47LPPYGtri02bNmHcuHG4efMmZsyYAQDVzgUFBQXo06cPbty4gejoaLRv3x67d+/GiBEjqn0eD1/XlcvKysKYMWMwY8YMREVFoVGjRlq9pomJiUFERATmzJmDl19+GcXFxTh//vxTzW21c+dOJCQkYO7cuVAqlWjZsiV+//13eHl5oXXr1li8eDGUSiX27duH9957D3///bfah2+i+uDFF1/Ef/7zH7z33nt47bXX0LVr18cWyoUQmD59Oj777DP85z//qdb8uNX5PFYT1fk8pkl1rq0edf36dfTv3x9FRUVISkrCs88+q7Z/2LBhGDFiBCZOnIgzZ84gPDwcALBu3TqpT0hICGJjY/Hee+9h4cKF+Oeff7BgwQJ4eXnh1KlTUCgUuHfvHvr27QsnJyesXLkSCoUCKpUKhw8fRl5eHgDwM6A2CKrX1q9fLwCIn3/+WRQXF4u8vDyxa9cu0aJFC2FtbS1UKpUYO3asACDWrVun9twtW7YIAOKbb75Ra09OThYAxKpVq4QQQpw7d04AEDNnztT4/LFjx1aIJzQ0VK1vTEyMACCysrI0nkdZWZkoLi4WR48eFQDEqVOnpH3l8f/3v/9Ve07//v1Fhw4dpMcbN24UAMSXX35Z5XvWpk0btZijo6NFo0aNRHJyslq/7du3CwDihx9+EEII8cknnwgA4s6dO1Uen4i0R6VSCQBi5MiRFfaVlJSI4uJiaSsrKxNCCDFv3jzx6H9/AMS8efMqHOPRfDBhwgRhYmIifv/990pj+vDDDwUA8csvv6i1v/3220Imk4kLFy4IIYR45513RJMmTao8v5CQEGFlZSWuXr2q1l6eb86dO1fl84lIs/LrkUc3uVwuXd+UezQ/jBw5UsjlcnHt2jW1fgEBAcLCwkK6DqhuLli9erUAIL799lu1fm+++aYAINavXy+1lecvlUoliouLxe3bt8WmTZuEubm5cHBwEAUFBUIIIXx8fAQA8eOPP6odU5vXNIGBgaJLly6V7hfiwTVamzZtKrRXlodtbGzEP//8o9ber18/0apVK5GTk6PW/s477wgzM7MK/Ynqur///lu89NJLUl4yMTERXl5eIjo6WuTl5Un92rRpIwYMGCDy8/PFsGHDhI2NjTh48GCF4z2aw5708xgAMXnyZI37qvt5TFM81bm2Onz4sAAgvv76a/Hbb78Je3t74e3tLW7duqXWrzy3xMTEqLWHhoYKMzMz6VowKSlJABCLFy9W65eZmSnMzc3FjBkzhBBCpKSkCABi586dlcbGz4BPj7fvNRCenp4wMTGBtbU1AgMDoVQqsWfPHrXK87Bhw9Ses2vXLjRp0gQDBw5ESUmJtHXp0gVKpRJHjhwBABw9ehQAMHz4cLXnBwcHaxx2CgCDBg1Se9ypUycA/xt2DgB//PEHRo8eDaVSCSMjI5iYmMDHxwcAkJaWpvZ8mUxWYe6HTp06qR1vz549MDMzq/FQyl27dsHNzQ1dunRRex/69esHmUwmvQ/PP/88gAfvw3//+1+ubkikZ926dYOJiYm01XRiUE327NkDPz8/uLq6Vtrn0KFDeO655/DCCy+otY8bNw5CCOmbsxdeeAF37tzBqFGj8O233+Lvv/+ucKxdu3bBz88P9vb2avknICAAwP/yLxE9mY0bNyI5ORnJycnYs2cPxo4di8mTJ2PFihWVPufQoUPo3bs3HBwc1NrHjRuH/Px8aXLg6uaCw4cPw9rausK1UVWjtZRKJUxMTNC0aVOMGTMGXbt2xd69e2FmZib1adq0KXr16qX2PG1e07zwwgs4deoUQkNDsW/fvkpHx9dEr1690LRpU+nx/fv38eOPP2Lo0KGwsLBQi7l///64f/8+fv7556d+XSJDYmtri4SEBCQnJ+Pf//43Bg8ejIsXLyI8PBzu7u5q1wu3bt1Cr1698Ouvv+L48ePSFCzVUZ3PYzVRnc9jmlTn2qrcvn374O3tjZdffhkHDhxAs2bNNPbTdG73799HdnY2gAe5UCaTYcyYMWp5RalUonPnzlIubNeuHZo2bYqZM2fi888/VxvNXo6fAZ8ei1INRPlF12+//YYbN27g9OnTahNhWlhYVFhx7ubNm7hz5440t8LDm0qlkhLirVu3AKDC0EpjY2PY2tpqjOfR9vKVGwoKCgAAd+/ehbe3N3755RdERkbiyJEjSE5ORnx8vFq/h+N/+EKs/Jj379+XHv/111+wt7d/7L3Yj7p58yZOnz5d4T2wtraGEEJ6H15++WXs3LkTJSUleOONN9CqVSu4ublhy5YtNXo9Iqq+5s2bw9zcXOMFT1xcHJKTk/Hdd99p7fX++uuvx64Yc+vWLY23EpbPaVWeM19//XWsW7cOV69exbBhw9CyZUv06NEDBw4ckJ5z8+ZNfP/99xXyT8eOHQFAYyGLiKrP1dUV3bt3R/fu3fHKK6/giy++gL+/P2bMmFHpbWjV/R2vST9Nt6colcpK4z548CCSk5Nx8uRJ/P333zh+/Diee+45tT6aXlub1zTh4eH45JNP8PPPPyMgIAC2trbo3bs3UlJSKo37cR6N+datWygpKcHy5csrxNy/f38AzINUf3Xv3h0zZ87E119/jRs3buCDDz5ARkaG2mTnFy9exC+//IKAgAC4ubnV6PiP+zxWU9X5PKZJda6tyu3cuRMFBQV4++23q1z573HndvPmTQghoFAoKuSWn3/+WcorNjY2OHr0KLp06YJZs2ahY8eOsLe3x7x581BcXAyAnwG1gXNKNRDlF12V0bR8cPmkd3v37tX4HGtrawD/+6W/efMmnnnmGWl/SUmJdMFVU4cOHcKNGzdw5MgRaXQUgKeap6BFixY4fvw4ysrKalSYKv/Q+/A9yI/uLzd48GAMHjwYhYWF+PnnnxEdHY3Ro0fD0dERL7744hPHTkSaGRkZoVevXti/fz+ysrLUPtCUf0DLyMh47HHkcnmFiTgBVMhhLVq0UJvYUhNbW1tkZWVVaC+fPPThnDF+/HiMHz8e9+7dw7FjxzBv3jwEBgbi4sWLaNOmDZo3b45OnTrh448/1vhaVU3eTkRPplOnTti3bx8uXrxYYZQTUP3f8Zr0K5+k92FVTXTeuXNntVyiSWXXdtq6pjE2NsaUKVMwZcoU3LlzBwcPHsSsWbPQr18/ZGZmSh9QNeXWygpJj8bctGlTGBkZ4fXXX8fkyZM1PsfJyanS94CovjAxMcG8efOwdOlSnD17Vmp/8cUX8eqrr2LixIkAHiygUtMv4PWtOtdW5ZYuXYpt27YhICAAO3bsgL+//xO9ZvPmzSGTyZCQkKCxuPVwm7u7O7Zu3QohBE6fPo3Y2FgsWLAA5ubm+PDDDwHwM+DTqls/saRTgYGBuHXrFkpLS6VvER/eOnToAOBBdRh4sFrew7Zv3/7Eq0OVX5Q8miS++OKLJzoeAAQEBOD+/fuIjY2t0fMCAwNx5coV2NraanwfNK0qI5fL4ePjg4ULFwJ4sGoDEdWO8PBwlJaW4l//+pf0rVVNOTo64vTp02pthw4dwt27d9XaAgICcPjwYVy4cKHSY/Xu3Ru///47Tpw4oda+ceNGyGQy+Pn5VXiOpaUlAgICMHv2bBQVFeHcuXMAHuSfs2fPom3bthrzD4tSRNp38uRJANC4HDrw4He8/Muzh23cuBEWFhbSSnjVzQV+fn7Iy8urMKozLi5OG6ejprauaZo0aYLg4GBMnjwZ//zzj/RlgKOjI7Kzs9VW0CoqKsK+ffuqFa+FhQX8/Pzw22+/oVOnThpjrmxUPlFdpamYDfxv+pJH/+8fO3Ystm7divXr1+ONN95AaWlprceoTdW5tipnZmaG+Ph4BAYGYtCgQfj222+f6DUDAwMhhMCff/6pMa+4u7tXeI5MJkPnzp2xdOlSNGnSpEJuB/gZ8ElxpBRVauTIkdi8eTP69++P999/Hy+88AJMTExw/fp1HD58GIMHD8bQoUPRsWNHjBo1CosXL5ZGLZw7dw6LFy+GjY3NE1Xrvby80LRpU/zrX//CvHnzYGJigs2bN+PUqVNPfD6jRo3C+vXr8a9//QsXLlyAn58fysrK8Msvv8DV1RUjR47U+LywsDB88803ePnll/HBBx+gU6dOKCsrw7Vr17B//35MnToVPXr0wNy5c3H9+nX07t0brVq1wp07d/Dpp5+qzYVFRNrXs2dPrFy5Eu+++y66du2Kt956Cx07dkSjRo2QlZWFb775BgAq3KL8sNdffx0fffQR5s6dCx8fH/z+++9YsWIFbGxs1PotWLAAe/bswcsvv4xZs2bB3d0dd+7cwd69ezFlyhS4uLjggw8+wMaNGzFgwAAsWLAAbdq0we7du7Fq1Sq8/fbbaN++PQDgzTffhLm5OXr27Ak7OzuoVCpER0fDxsZGmp9gwYIFOHDgALy8vPDee++hQ4cOuH//PjIyMvDDDz/g888/r/aQdyKq6OzZs9IXaLdu3UJ8fDwOHDiAoUOHVjoCZ968edJ8b3PnzkWzZs2wefNm7N69GzExMVLeqG4ueOONN7B06VK88cYb+Pjjj+Hs7Iwffvih2oWbmtDmNc3AgQPh5uaG7t27o0WLFrh69SqWLVuGNm3awNnZGQAwYsQIzJ07FyNHjsT06dNx//59fPbZZzX60Pzpp5/ipZdegre3N95++204OjoiLy8Ply9fxvfff88Vrqje6devH1q1aoWBAwfCxcUFZWVlOHnyJBYvXgwrKyu8//77FZ4THBwMCwsLBAcHo6CgAFu2bIGpqakeoq+56lxbPczExARbtmzBpEmTEBwcjI0bN2LUqFE1es2ePXvirbfewvjx45GSkoKXX34ZlpaWyMrKwvHjx+Hu7o63334bu3btwqpVqzBkyBA8++yzEEIgPj4ed+7cQd++fQGAnwG1QX9zrJMulK+u8OgqKw8bO3assLS01LivuLhYfPLJJ6Jz587CzMxMWFlZCRcXFxESEiIuXbok9bt//76YMmWKaNmypTAzMxOenp4iKSlJ2NjYiA8++OCx8ZSvqHD48GGpLTExUbz44ovCwsJCtGjRQkyaNEmcOHGiwko0lcWvaWWXgoICMXfuXOHs7CxMTU2Fra2t6NWrl0hMTJT6PLralhBC3L17V8yZM0d06NBBmJqaChsbG+Hu7i4++OADoVKphBBC7Nq1SwQEBIhnnnlGmJqaipYtW4r+/fuLhIQEzW88EWnVyZMnxfjx44WTk5OQy+XCzMxMtGvXTrzxxhtqK1Bpyg2FhYVixowZwsHBQZibmwsfHx9x8uRJjfkgMzNTTJgwQSiVSmFiYiLs7e3F8OHDxc2bN6U+V69eFaNHjxa2trbCxMREdOjQQSxatEiUlpZKfTZs2CD8/PyEQqEQpqam0nFOnz6t9np//fWXeO+994STk5MwMTERzZo1E926dROzZ88Wd+/e1eI7SNRwaFp9z8bGRnTp0kUsWbJE3L9/X+oLDatznjlzRgwcOFDY2NgIU1NT0blzZ7Vrk3LVyQVCCHH9+nUxbNgwYWVlJaytrcWwYcNEYmJipavv/fXXX1Wen4+Pj+jYsaPGfdq6plm8eLHw8vISzZs3F6ampqJ169Zi4sSJIiMjQ+31fvjhB9GlSxdhbm4unn32WbFixYpKV9+rbGWv9PR0MWHCBPHMM88IExMT0aJFC+Hl5SUiIyOrfB+I6qJt27aJ0aNHC2dnZ2FlZSVMTExE69atxeuvv662Ql356nsPO3z4sLCyshKvvPKKyM/PF0JUvvpedT6PPayq39GafB7TlFMfd2318Op75crKysR7770nGjVqJK2uXlmOLD/n9PR0tfZ169aJHj16CEtLS2Fubi7atm0r3njjDZGSkiKEEOL8+fNi1KhRom3btsLc3FzY2NiIF154QcTGxkrH4GfApycTQgjdlcCoIUlMTETPnj2xefPmKleQISIiIiIiIqKGh0Up0ooDBw4gKSkJ3bp1g7m5OU6dOoV///vfsLGxwenTpyusxEBEREREREREDRvnlCKtaNy4Mfbv349ly5YhLy8PzZs3R0BAAKKjo1mQIiIiIiIiIqIKOFKKiIiIiIiIiIh0rubLohERERERERERET0lFqWIiIiIiIiIiEjnWJQiIiIiIiIiIiKdq5MTnZeVleHGjRuwtraGTCbTdzhE9JSEEMjLy4O9vT0aNar7tXLmKKL6g/mJiAxZfcpRzE9E9Ut181OdLErduHEDDg4O+g6DiLQsMzMTrVq10ncYT405iqj+YX4iIkNWH3IU8xNR/fS4/FQni1LW1tYAHpxc48aN9RwNET2t3NxcODg4SL/bdR1zFFH9wfxERIasPuUo5iei+qW6+alOFqXKh3M2btyYCYuoHqkvQ7WZo4jqH+YnIjJk9SFHMT8R1U+Py091+8ZjIiIiIiIiIiKqk1iUIiIiIiIiaqCOHTuGgQMHwt7eHjKZDDt37lTbL4RAREQE7O3tYW5uDl9fX5w7d06tT2FhId599100b94clpaWGDRoEK5fv67DsyCiuopFKTJYpaWlOHLkCLZs2YIjR46gtLRU3yGRAYuOjsbzzz8Pa2trtGzZEkOGDMGFCxfU+vCiioiI6jteP1FN3bt3D507d8aKFSs07o+JicGSJUuwYsUKJCcnQ6lUom/fvsjLy5P6hIWFYceOHdi6dSuOHz+Ou3fvIjAwkD9/VAFzFD2KRSkySPHx8WjXrh38/PwwevRo+Pn5oV27doiPj9d3aGSgjh49ismTJ+Pnn3/GgQMHUFJSAn9/f9y7d0/qw4sqIiKqz3j9RE8iICAAkZGRCAoKqrBPCIFly5Zh9uzZCAoKgpubGzZs2ID8/HzExcUBAHJycrB27VosXrwYffr0gYeHBzZt2oQzZ87g4MGDuj4dMmDMUaQJi1JkcOLj4xEcHAx3d3ckJSUhLy8PSUlJcHd3R3BwMJMWabR3716MGzcOHTt2ROfOnbF+/Xpcu3YNqampAHhRRURE9Ruvn6g2pKenQ6VSwd/fX2qTy+Xw8fFBYmIiACA1NRXFxcVqfezt7eHm5ib10aSwsBC5ublqG9VfzFFUGRalyKCUlpZi6tSpCAwMxM6dO+Hp6QkrKyt4enpi586dCAwMxLRp0zhqhR4rJycHANCsWTMAvKgiIqL6i9dPVFtUKhUAQKFQqLUrFAppn0qlgqmpKZo2bVppH02io6NhY2MjbQ4ODlqOngwFcxRVxVjfARA9LCEhARkZGdiyZQsaNVKvmTZq1Ajh4eHw8vJCQkICfH199RMkGTwhBKZMmYKXXnoJbm5uAKq+qLp69arU50kvqubPn6/NUyA9y8/Px/nz56vsU1BQgIyMDDg6OsLc3LzKvi4uLrCwsNBmiFTHHDt2DIsWLUJqaiqysrKwY8cODBkyBABQXFyMOXPm4IcffsAff/wBGxsb9OnTB//+979hb28vHaOwsBDTpk3Dli1bUFBQgN69e2PVqlVo1aqVns6KDAWvn6i2PbqkuxDiscu8P65PeHg4pkyZIj3Ozc1lYaqeYo6iqrAoRQYlKysLAKRCwqPK28v7EWnyzjvv4PTp0zh+/HiFfbyoouo4f/48unXrprXjpaamomvXrlo7HtU95RMJjx8/HsOGDVPbl5+fjxMnTuCjjz5C586dcfv2bYSFhWHQoEFISUmR+oWFheH777/H1q1bYWtrK33rnJqaCiMjI12fEhkQXj9RbVEqlQAefHFnZ2cntWdnZ0tf9CmVShQVFeH27dtqX+xlZ2fDy8ur0mPL5XLI5fJaipwMCXMUVYVFKTIo5f/ZnT17Fp6enhX2nz17Vq0f0aPeffddfPfddzh27Jja6AFeVFFNuLi4SPORVSYtLQ1jxozBpk2b4Orq+tjjUcMWEBCAgIAAjftsbGxw4MABtbbly5fjhRdewLVr19C6dWtpzruvvvoKffr0AQBs2rQJDg4OOHjwIPr161fr50CGi9dPVFucnJygVCpx4MABeHh4AACKiopw9OhRLFy4EADQrVs3mJiY4MCBAxg+fDiAB8WFs2fPIiYmRm+xk+FgjqKqsChFBsXb2xuOjo6IiorCzp071YZ3lpWVITo6Gk5OTvD29tZjlGSIhBB49913sWPHDhw5cgROTk5q+3lRRTVhYWFR7ZFNrq6uHAVFWpeTkwOZTIYmTZoAePycd5UVpQoLC1FYWCg95px39ROvn+hp3L17F5cvX5Yep6en4+TJk2jWrBlat26NsLAwREVFwdnZGc7OzoiKioKFhQVGjx4N4EFhfeLEiZg6dSpsbW3RrFkzTJs2De7u7lIRnRo25iiqCic6J4NiZGSExYsXY9euXRgyZIjaygxDhgzBrl278Mknn/A2Bapg8uTJ2LRpE+Li4mBtbQ2VSgWVSoWCggIAD27bK7+o2rFjB86ePYtx48ZVelH1448/4rfffsOYMWN4UUVEOnX//n18+OGHGD16NBo3bgyAEwlT1Xj9RE8jJSUFHh4e0pd2U6ZMgYeHB+bOnQsAmDFjBsLCwhAaGoru3bvjzz//xP79+2FtbS0dY+nSpRgyZAiGDx+Onj17wsLCAt9//z1/5ggAcxQ9hqiDcnJyBACRk5Oj71ColnzzzTfC0dFRAJA2Jycn8c033+g7NKoF2vidfvhn5eFt/fr1Up+ysjIxb948oVQqhVwuFy+//LI4c+aM2nEKCgrEO++8I5o1aybMzc1FYGCguHbtms7PhwxfamqqACBSU1P1HQrVotr4fQYgduzYoXFfUVGRGDx4sPDw8FB7zc2bNwtTU9MK/fv06SNCQkIqfa379++LnJwcacvMzGR+qsd4/dTw1Kdrjvp0LqQZc1TDUt3fad6+RwYpKCgIgwcPRkJCArKysmBnZwdvb29Wz6lSQojH9pHJZIiIiEBERESlfczMzLB8+XIsX75ci9ERET1ecXExhg8fjvT0dBw6dEgaJQVwzjuqHl4/EZEhY44iTViUIoNlZGTEJUGJiKhBKC9IXbp0CYcPH4atra3afs55R9XF6yciMmTMUfQoFqWIiIiIallVEwnb29sjODgYJ06cwK5du1BaWirNE9WsWTOYmppyImEiIiKql1iUIiIiIqplKSkp8PPzkx5PmTIFADB27FhERETgu+++AwB06dJF7XmHDx+WvlFeunQpjI2NMXz4cBQUFKB3796IjY3lbQ+kprS0lLfGEBFRncGiFBEREVEt8/X1rXLuu+rMi8c57+hx4uPjMXXqVGRkZEhtjo6OWLx4MYKCgvQXGBERUSUa6TsAIiIiIiJ6OvHx8QgODoa7u7vacuvu7u4IDg5GfHy8vkMkIiKqgEUpIiIiIqI6rLS0FFOnTkVgYCB27twJT09PWFlZwdPTEzt37kRgYCCmTZuG0tJSfYdKRESkhkUpIiIiIqI6LCEhARkZGZg1axYaNVK/vG/UqBHCw8ORnp6OhIQEPUVIRESkGYtSRERERER1WFZWFgDAzc1N4/7y9vJ+REREhoJFKSIiIiKiOszOzg4AcPbsWRQUFOCdd95Bv3798M4776CgoABnz55V60dERGQouPoeEREREVEd5u3tDUdHRwwdOhQqlUpq379/P1auXAmlUgknJyd4e3vrMUoiIqKKOFKKiIiIiKgOMzIyQosWLaBSqSCTyfD666/j5MmTeP311yGTyaBSqdC8eXMYGRnpO1QiIiI1HClFRERERFSHFRQUIDk5GcbGxnjmmWfw1Vdf4auvvgIAODo64vr160hOTkZBQQHMzc31HC0REdH/aH2k1LFjxzBw4EDY29tDJpNh586davvHjRsHmUymtnl6emo7DKoHSktLceTIEWzZsgVHjhzhMsZEREREGkyfPh0A0K1bN1y/fl1tX2ZmJrp27arWj4iIyFBovSh17949dO7cGStWrKi0zyuvvIKsrCxp++GHH7QdBtVx8fHxaNeuHfz8/DB69Gj4+fmhXbt2iI+P13doRERERAbl0qVLAIBffvkFzZs3x5dffomsrCx8+eWXaN68OX799Ve1fkRERIZC60WpgIAAREZGIigoqNI+crkcSqVS2po1a6btMKgOi4+PR3BwMNzd3ZGUlIS8vDwkJSXB3d0dwcHBLEwRERERPcTJyQkAYGFhgevXr2PSpElQKpWYNGkSrl+/DgsLC7V+REREhkIvE50fOXIELVu2RPv27fHmm28iOztbH2GQASotLcXUqVMRGBiInTt3wtPTE1ZWVvD09MTOnTsRGBiIadOm8VY+IiIiov+vvNhUVFSEsrIytX1lZWUoLCxU60dERGQodF6UCggIwObNm3Ho0CEsXrwYycnJ6NWrl/SfpSaFhYXIzc1V26h+SkhIQEZGBmbNmoVGjdR/PBs1aoTw8HCkp6cjISFBTxESERERGZbyeaRKSkpgbW2NmTNn4uLFi5g5cyasra2lL/MenW+KiIhI33S++t6IESOkv7u5uaF79+5o06YNdu/eXektf9HR0Zg/f76uQiQ9ysrKAvDgZ0OT8vbyfkREREQNXdu2bQEAXbp0wcmTJxETE4OYmBhpf6dOnXD69GmpHxERkaHQy+17D7Ozs0ObNm2qnHgxPDwcOTk50paZmanDCEmX7OzsAABnz57VuL+8vbwfERERUUMXGhoKY2NjZGVlITc3F5MnT4a/vz8mT56M3Nxc3Lx5E8bGxggNDdV3qERERGp0PlLqUbdu3UJmZmaVRQa5XA65XK7DqEhfvL294ejoiKioKOzcuVPtFr6ysjJER0fDyckJ3t7eeoySiIiISD/y8/Nx/vz5Cu2jR4/Gxo0b4ejoiEmTJmHgwIHIzMyEo6Mj/vnnH7zxxhsav/RzcXGRJkInIiLSNa0Xpe7evYvLly9Lj9PT03Hy5Ek0a9YMzZo1Q0REBIYNGwY7Oztp7qDmzZtj6NCh2g6F6iAjIyMsXrwYwcHBGDJkCMLDw+Hm5oazZ88iOjoau3btwvbt22FkZKTvUImIiIh07vz58+jWrVul+//55x+1W/fKbdy4ERs3bqzQnpqaiq5du2o1RiIiourSelEqJSUFfn5+0uMpU6YAAMaOHYvVq1fjzJkz2LhxI+7cuQM7Ozv4+flh27ZtsLa21nYoVEcFBQVh+/btmDp1Kry8vKR2JycnbN++vdK5x4iIiIjqOxcXF6Smpla6v6ioCCtWrMDmzZvx2muv4Z133oGpqWmVxyMiItIXrRelfH19IYSodP++ffu0/ZJUDwUFBWHw4MFISEhAVlYW7Ozs4O3tzRFSRERE1KBZWFg8dmSTqakpNm/ejClTpnAUFBERGTS9T3ROVJnS0lKcPHkSiYmJOHnypLScMRERERERERHVfXqf6JxIkxkzZmDp0qUoKSmR2qZPn44PPvhA4zwJRERERERERFS3cKQUGZwZM2Zg0aJFsLW1xZdffomsrCx8+eWXsLW1xaJFizBjxgx9h0hERFQjx44dw8CBA2Fvbw+ZTIadO3eq7RdCICIiAvb29jA3N4evry/OnTun1qewsBDvvvsumjdvDktLSwwaNAjXr1/X4VkQERERaReLUmRQioqKsHTpUigUCly/fh2TJk2CUqnEpEmTcP36dSgUCixduhRFRUX6DpWIiKja7t27h86dO2PFihUa98fExGDJkiVYsWIFkpOToVQq0bdvX+Tl5Ul9wsLCsGPHDmzduhXHjx/H3bt3ERgYyNvbiYiIqM5iUYoMyqpVq1BSUoLIyEgYG6vfXWpsbIwFCxagpKQEq1at0lOERERENRcQEIDIyEiNK8gKIbBs2TLMnj0bQUFBcHNzw4YNG5Cfn4+4uDgAQE5ODtauXYvFixejT58+8PDwwKZNm3DmzBkcPHhQ16dDRA1MSUkJ5syZAycnJ5ibm+PZZ5/FggULUFZWJvWpzohPIqJHsShFBuXKlSsAgMDAQI37y9vL+xEREdV16enpUKlU8Pf3l9rkcjl8fHyQmJgIAEhNTUVxcbFaH3t7e7i5uUl9NCksLERubq7aRkRUUwsXLsTnn3+OFStWIC0tDTExMVi0aBGWL18u9anOiE8iokexKEUGpW3btgCAXbt2adxf3l7ej4iIqK5TqVQAAIVCodauUCikfSqVCqampmjatGmlfTSJjo6GjY2NtDk4OGg5eiJqCJKSkjB48GAMGDAAjo6OCA4Ohr+/P1JSUgBUb8QnEZEmLEqRQQkNDYWxsTHmzJmjtvIe8GDY8Ny5c2FsbIzQ0FA9RUhERFQ7ZDKZ2mMhRIW2Rz2uT3h4OHJycqQtMzNTK7ESUcPy0ksv4ccff8TFixcBAKdOncLx48fRv39/ANUb8fkojuQkIoBFKTIwpqam+OCDD3Dz5k20atUKa9aswY0bN7BmzRq0atUKN2/exAcffABTU1N9h0pERKQVSqUSACqMeMrOzpZGTymVShQVFeH27duV9tFELpejcePGahsRUU3NnDkTo0aNgouLC0xMTODh4YGwsDCMGjUKQPVGfD6KIzmJCGBRigxQTEwMpk+fjlu3biEkJATPPPMMQkJCcOvWLUyfPh0xMTH6DpGIiEhrnJycoFQqceDAAamtqKgIR48ehZeXFwCgW7duMDExUeuTlZWFs2fPSn2IiGrLtm3bsGnTJsTFxeHEiRPYsGEDPvnkE2zYsEGtX01GfHIkJxEBgPHjuxDpXkxMDCIjI7Fq1SpcuXIFbdu2RWhoKEdIERFRnXT37l1cvnxZepyeno6TJ0+iWbNmaN26NcLCwhAVFQVnZ2c4OzsjKioKFhYWGD16NADAxsYGEydOxNSpU2Fra4tmzZph2rRpcHd3R58+ffR1WkTUQEyfPh0ffvghRo4cCQBwd3fH1atXER0djbFjx6qN+LSzs5OeV9VoTrlcDrlcXvvBE5FBY1GKDJapqSnCwsL0HQYREdFTS0lJgZ+fn/R4ypQpAICxY8ciNjYWM2bMQEFBAUJDQ3H79m306NED+/fvh7W1tfScpUuXwtjYGMOHD0dBQQF69+6N2NhYGBkZ6fx8iKhhyc/PR6NG6jfZGBkZoaysDID6iE8PDw8A/xvxuXDhQp3HS0R1B4tSRERERLXM19cXQohK98tkMkRERCAiIqLSPmZmZli+fLnaEuxERLowcOBAfPzxx2jdujU6duyI3377DUuWLMGECRMAPMhhjxvxSUSkCYtSREREREREVKnly5fjo48+QmhoKLKzs2Fvb4+QkBDMnTtX6lOdEZ9ERI/iROdEVG8cO3YMAwcOhL29PWQyGXbu3Km2f9y4cZDJZGqbp6enWp/CwkK8++67aN68OSwtLTFo0CBcv35dh2dBREREZFisra2xbNkyXL16FQUFBbhy5QoiIyPV5nstH/GZlZWF+/fv4+jRo3Bzc9Nj1ERUF7AoRUT1xr1799C5c2esWLGi0j6vvPIKsrKypO2HH35Q2x8WFoYdO3Zg69atOH78OO7evYvAwECUlpbWdvhEREREREQNCm/fI73Jz8/H+fPnq+xTUFCAjIwMODo6wtzcvMq+Li4usLCw0GaIVMcEBAQgICCgyj5yuVxaIeZROTk5WLt2Lb766itpNatNmzbBwcEBBw8eRL9+/bQeMxERERERUUPFohTpzfnz59GtWzetHS81NRVdu3bV2vGofjpy5AhatmyJJk2awMfHBx9//DFatmwJ4MHPUHFxMfz9/aX+9vb2cHNzQ2JiIotSREREREREWsSiFOmNi4sLUlNTq+yTlpaGMWPGYNOmTXB1dX3s8YiqEhAQgFdffRVt2rRBeno6PvroI/Tq1QupqamQy+VQqVQwNTVF06ZN1Z6nUCigUqkqPW5hYSEKCwulx7m5ubV2DkRERERERPUFi1KkNxYWFtUe2eTq6spRUPTURowYIf3dzc0N3bt3R5s2bbB7924EBQVV+jwhBGQyWaX7o6OjMX/+fK3GSkREREREVN9xonMiarDs7OzQpk0bXLp0CQCgVCpRVFSE27dvq/XLzs6GQqGo9Djh4eHIycmRtszMzFqNm4iIiIiIqD5gUYqIGqxbt24hMzMTdnZ2AIBu3brBxMQEBw4ckPpkZWXh7Nmz8PLyqvQ4crkcjRs3VtuIiIiIiIioarx9j4jqjbt37+Ly5cvS4/T0dJw8eRLNmjVDs2bNEBERgWHDhsHOzg4ZGRmYNWsWmjdvjqFDhwIAbGxsMHHiREydOhW2trZo1qwZpk2bBnd3d2k1PiIiIiIiItIOFqWIqN5ISUmBn5+f9HjKlCkAgLFjx2L16tU4c+YMNm7ciDt37sDOzg5+fn7Ytm0brK2tpecsXboUxsbGGD58OAoKCtC7d2/ExsbCyMhI5+dDRERERERUn7EoRUT1hq+vL4QQle7ft2/fY49hZmaG5cuXY/ny5doMjYiIiIiIiB7BOaWIiIiIiIiIiEjnWJQiIiIiIiIiIiKd4+17RETU4Fy6dAl5eXlPdYy0tDS1P5+GtbU1nJ2dn/o4RERERER1CYtSRETUoFy6dAnt27fX2vHGjBmjleNcvHiRhSkiIiIialBYlCIiogalfITUpk2b4Orq+sTHKSgoQEZGBhwdHWFubv7Ex0lLS8OYMWOeeuQWEREREVFdw6IUERE1SK6urujatetTHaNnz55aioaIiIiIqOHhROdERERERERERKRzLEoREREREREREZHOsShFREREpGclJSWYM2cOnJycYG5ujmeffRYLFixAWVmZ1EcIgYiICNjb28Pc3By+vr44d+6cHqMmIiIiejosShERERHp2cKFC/H5559jxYoVSEtLQ0xMDBYtWoTly5dLfWJiYrBkyRKsWLECycnJUCqV6Nu3LyfJJyIiojqLRSkiIiIiPUtKSsLgwYMxYMAAODo6Ijg4GP7+/khJSQHwYJTUsmXLMHv2bAQFBcHNzQ0bNmxAfn4+4uLi9Bw9ERER0ZNhUYqIiIhIz1566SX8+OOPuHjxIgDg1KlTOH78OPr37w8ASE9Ph0qlgr+/v/QcuVwOHx8fJCYm6iVmIiIioqdlrO8AiIiIiBq6mTNnIicnBy4uLjAyMkJpaSk+/vhjjBo1CgCgUqkAAAqFQu15CoUCV69erfS4hYWFKCwslB7n5ubWQvSkTZcuXXrqWzLT0tLU/nxa1tbWcHZ21sqxiIiIHsaiFBEREZGebdu2DZs2bUJcXBw6duyIkydPIiwsDPb29hg7dqzUTyaTqT1PCFGh7WHR0dGYP39+rcVN2nXp0iW0b99ea8cbM2aM1o518eJFFqaIiEjrWJQiIiIi0rPp06fjww8/xMiRIwEA7u7uuHr1KqKjozF27FgolUoAD0ZM2dnZSc/Lzs6uMHrqYeHh4ZgyZYr0ODc3Fw4ODrV0FvS0ykdIbdq0Ca6urk98nIKCAmRkZMDR0RHm5uZPFVNaWhrGjBnDCfWJiKhWsChFREREpGf5+flo1Eh9qk8jIyOUlZUBAJycnKBUKnHgwAF4eHgAAIqKinD06FEsXLiw0uPK5XLI5fLaC5xqhaurK7p27fpUx+jZs6eWoiEiIqo9LEoRERER6dnAgQPx8ccfo3Xr1ujYsSN+++03LFmyBBMmTADw4La9sLAwREVFwdnZGc7OzoiKioKFhQVGjx6t5+iJiIgefMFy/vz5x/aryWhOFxcXWFhYaCtEMkAsShERERHp2fLly/HRRx8hNDQU2dnZsLe3R0hICObOnSv1mTFjBgoKChAaGorbt2+jR48e2L9/P6ytrfUYORER0QPnz59Ht27dtHrM1NTUpx45SoaNRSkiIiIiPbO2tsayZcuwbNmySvvIZDJEREQgIiJCZ3EREZX7888/MXPmTOzZswcFBQVo37491q5dKxUhhBCYP38+1qxZIxXOV65ciY4dO+o5ctIVFxcXpKamPrZf+Vx11Zk/z8XFRVvhkYFiUYqIiIiIiIgqdfv2bfTs2RN+fn7Ys2cPWrZsiStXrqBJkyZSn5iYGCxZsgSxsbFo3749IiMj0bdvX1y4cIEjOhsICwuLGo1q0sb8eVT3sShFRERERERElVq4cCEcHBywfv16qc3R0VH6uxACy5Ytw+zZsxEUFAQA2LBhAxQKBeLi4hASEqLrkImojmj0+C5ERERERETUUH333Xfo3r07Xn31VbRs2RIeHh748ssvpf3p6elQqVTw9/eX2uRyOXx8fJCYmKiPkImojtB6UerYsWMYOHAg7O3tIZPJsHPnTrX9QghERETA3t4e5ubm8PX1xblz57QdBhEREREREWnBH3/8gdWrV8PZ2Rn79u3Dv/71L7z33nvYuHEjAEClUgEAFAqF2vMUCoW071GFhYXIzc1V24io4dF6UerevXvo3LkzVqxYoXF/+b3GK1asQHJyMpRKJfr27Yu8vDxth0JERERERERPqaysDF27dkVUVBQ8PDwQEhKCN998E6tXr1brJ5PJ1B4LISq0lYuOjoaNjY20OTg41Fr8RGS4tF6UCggIQGRkpHQv8cMevdfYzc0NGzZsQH5+PuLi4rQdChERERERET0lOzs7PPfcc2ptrq6uuHbtGgBAqVQCQIVRUdnZ2RVGT5ULDw9HTk6OtGVmZtZC5ERk6HQ60fnj7jWubAK8wsJCFBYWSo85tJOIiJ6UrOQ+PJSNYH7nInBD/1Mrmt+5CA9lI8hK7us7FCLSM0PLTwBzFD3Qs2dPXLhwQa3t4sWLaNOmDQDAyckJSqUSBw4cgIeHBwCgqKgIR48excKFCzUeUy6XQy6X127gRGTwdFqUqupe46tXr1b6vOjoaMyfP79WYyMioobB7O41nAixAo6FAMf0HQ3gCuBEiBXS7l4D4KXvcIhIjwwtPwHMUfTABx98AC8vL0RFRWH48OH49ddfsWbNGqxZswbAg9v2wsLCEBUVBWdnZzg7OyMqKgoWFhYYPXq0nqMnIkOm06JUuZrcaww8GNo5ZcoU6XFubi7vOSYioidy36o1un5xF5s3b4ari4u+w0Ha+fN47bXXsLZ/a32HQkR6Zmj5CWCOogeef/557NixA+Hh4ViwYAGcnJywbNkyvPbaa1KfGTNmoKCgAKGhobh9+zZ69OiB/fv3w9raWo+RE5Gh02lR6uF7je3s7KT2qu41Bji0k4iItEcYm+E3VRkKmrQH7LvoOxwUqMrwm6oMwthM36EQkZ4ZWn4CmKPofwIDAxEYGFjpfplMhoiICEREROguKCKq83R6s/rD9xqXK7/X2MuLw4GJiIiIiIiIiBoKrY+Uunv3Li5fviw9Tk9Px8mTJ9GsWTO0bt2a9xoTEREREREREZH2i1IpKSnw8/OTHpfPBTV27FjExsbyXmMiIiIiIiIiItJ+UcrX1xdCiEr3815jIiIiIiIiIiLS6ZxSREREREREREREAItSRERERERERESkByxKERERERERERGRzrEoRUREREREREREOseiFBERERERERER6RyLUkRUbxw7dgwDBw6Evb09ZDIZdu7cqbZfCIGIiAjY29vD3Nwcvr6+OHfunFqfwsJCvPvuu2jevDksLS0xaNAgXL9+XYdnQURERERE1DCwKEVE9ca9e/fQuXNnrFixQuP+mJgYLFmyBCtWrEBycjKUSiX69u2LvLw8qU9YWBh27NiBrVu34vjx47h79y4CAwNRWlqqq9MgIiIiIiJqEFiUIqJ6IyAgAJGRkQgKCqqwTwiBZcuWYfbs2QgKCoKbmxs2bNiA/Px8xMXFAQBycnKwdu1aLF68GH369IGHhwc2bdqEM2fO4ODBg7o+HSJqYP7880+MGTMGtra2sLCwQJcuXZCamirtr85oTyIiIqK6xFjfARAR6UJ6ejpUKhX8/f2lNrlcDh8fHyQmJiIkJASpqakoLi5W62Nvbw83NzckJiaiX79++gidiBqA27dvo2fPnvDz88OePXvQsmVLXLlyBU2aNJH6lI/2jI2NRfv27REZGYm+ffviwoULsLa21l/wpDX5+fkAgBMnTjzVcQoKCpCRkQFHR0eYm5s/1bHS0tKe6vlERERVYVGKiBoElUoFAFAoFGrtCoUCV69elfqYmpqiadOmFfqUP1+TwsJCFBYWSo9zc3O1FTYRNRALFy6Eg4MD1q9fL7U5OjpKf390tCcAbNiwAQqFAnFxcQgJCdF1yFQLzp8/DwB488039RxJRSx8EhFRbWBRiogaFJlMpvZYCFGh7VGP6xMdHY358+drJT6qfYY2EoGjEAgAvvvuO/Tr1w+vvvoqjh49imeeeQahoaFScaI6oz2p7hsyZAgAwMXFBRYWFk98nLS0NIwZMwabNm2Cq6vrU8dlbW0NZ2fnpz4OERHRo1iUIqIGQalUAngwGsrOzk5qz87OlkZPKZVKFBUV4fbt22qjpbKzs+Hl5VXpscPDwzFlyhTpcW5uLhwcHLR9CqQlhjoSgaMQGrY//vgDq1evxpQpUzBr1iz8+uuveO+99yCXy/HGG29Ua7SnJhzJWbc0b94ckyZN0trxXF1d0bVrV60dj4iISNtYlCKiBsHJyQlKpRIHDhyAh4cHAKCoqAhHjx7FwoULAQDdunWDiYkJDhw4gOHDhwMAsrKycPbsWcTExFR6bLlcDrlcXvsnQVphiCMROAqBysrK0L17d0RFRQEAPDw8cO7cOaxevRpvvPGG1K+moz05kpOIiIgMGYtSRFRv3L17F5cvX5Yep6en4+TJk2jWrBlat26NsLAwREVFwdnZGc7OzoiKioKFhQVGjx4NALCxscHEiRMxdepU2NraolmzZpg2bRrc3d3Rp08ffZ0WaRlHIpAhsrOzw3PPPafW5urqim+++QZA9UZ7asKRnERERGTIWJQionojJSUFfn5+0uPyD2Jjx45FbGwsZsyYgYKCAoSGhuL27dvo0aMH9u/fr3bb1NKlS2FsbIzhw4ejoKAAvXv3RmxsLIyMjHR+PkTUcPTs2RMXLlxQa7t48SLatGkDoHqjPTXhSE4iIiIyZCxKEVG94evrCyFEpftlMhkiIiIQERFRaR8zMzMsX74cy5cvr4UIiYg0++CDD+Dl5YWoqCgMHz4cv/76K9asWYM1a9YAeJC/Hjfak4iIiKiuYVGKiIiISM+ef/557NixA+Hh4ViwYAGcnJywbNkyvPbaa1Kf6oz2JCIiIqpLWJQiIiIiMgCBgYEIDAysdH91RnsSERER1SWN9B0AERERERERERE1PCxKERERERERERGRzrEoRUREREREREREOseiFBERERERERER6RyLUkREREREREREpHMsShERERERERERkc6xKEVERERERETVEh0dDZlMhrCwMKlNCIGIiAjY29vD3Nwcvr6+OHfunP6CJKI6g0UpIiIiIiIieqzk5GSsWbMGnTp1UmuPiYnBkiVLsGLFCiQnJ0OpVKJv377Iy8vTU6REVFewKEVERERERERVunv3Ll577TV8+eWXaNq0qdQuhMCyZcswe/ZsBAUFwc3NDRs2bEB+fj7i4uL0GDER1QUsShEREREREVGVJk+ejAEDBqBPnz5q7enp6VCpVPD395fa5HI5fHx8kJiYqOswiaiOMdZ3AERERERERGS4tm7dihMnTiA5ObnCPpVKBQBQKBRq7QqFAlevXq30mIWFhSgsLJQe5+bmailaIqpLOFKKiIiIiIiINMrMzMT777+PTZs2wczMrNJ+MplM7bEQokLbw6Kjo2FjYyNtDg4OWouZiOoOFqWIiIiIiIhIo9TUVGRnZ6Nbt24wNjaGsbExjh49is8++wzGxsbSCKnyEVPlsrOzK4yeelh4eDhycnKkLTMzs1bPg4gME2/fIyIiIiIiIo169+6NM2fOqLWNHz8eLi4umDlzJp599lkolUocOHAAHh4eAICioiIcPXoUCxcurPS4crkccrm8VmMnIsPHohQRERERERFpZG1tDTc3N7U2S0tL2NraSu1hYWGIioqCs7MznJ2dERUVBQsLC4wePVofIRNRHcKiFBERERERET2xGTNmoKCgAKGhobh9+zZ69OiB/fv3w9raWt+hEZGBY1GKiIiIiIiIqu3IkSNqj2UyGSIiIhAREaGXeIio7uJE50REREREREREpHMsShERERERERERkc6xKEVERERERERERDrHOaWoVly6dAl5eXlPfZy0tDS1P5+GtbU1nJ2dn/o4RERERERERPT0WJQirbt06RLat2+v1WOOGTNGK8e5ePEiC1NERGTwoqOjMWvWLLz//vtYtmwZAEAIgfnz52PNmjXS6lYrV65Ex44d9RssERER0RNiUYq0rnyE1KZNm+Dq6vpUxyooKEBGRgYcHR1hbm7+xMdJS0vDmDFjtDJ6i4iIqDYlJydjzZo16NSpk1p7TEwMlixZgtjYWLRv3x6RkZHo27cvLly4wGXXiYiIqE5iUYpqjaurK7p27frUx+nZs6cWoiEiIjJ8d+/exWuvvYYvv/wSkZGRUrsQAsuWLcPs2bMRFBQEANiwYQMUCgXi4uIQEhKir5CJiIiInhgnOiciIiIyEJMnT8aAAQPQp08ftfb09HSoVCr4+/tLbXK5HD4+PkhMTNR1mERERERawZFSRERERAZg69atOHHiBJKTkyvsU6lUAACFQqHWrlAocPXq1UqPWVhYiMLCQulxbm6ulqIlIiIienosShERERHpWWZmJt5//33s378fZmZmlfaTyWRqj4UQFdoeFh0djfnz52stTiIiari4wjrVBhaliIiIiPQsNTUV2dnZ6Natm9RWWlqKY8eOYcWKFbhw4QKAByOm7OzspD7Z2dkVRk89LDw8HFOmTJEe5+bmwsHBoRbOgIiI6jOusE61RS9FqYiIiArf2ikUCmloOhEREVFD0rt3b5w5c0atbfz48XBxccHMmTPx7LPPQqlU4sCBA/Dw8AAAFBUV4ejRo1i4cGGlx5XL5ZDL5bUaO+lWfn4+zp8/X2WfmoxCcHFxgYWFhVZiI6L6iyusU23R20ipjh074uDBg9JjIyMjfYVCREREpFfW1tZwc3NTa7O0tIStra3UHhYWhqioKDg7O8PZ2RlRUVGwsLDA6NGj9REy6cn58+fVRtRVpTqjEFJTU7WyWjIRNQxcYZ20TW9FKWNjYyiVSn29PBEREVGdMmPGDBQUFCA0NBS3b99Gjx49sH//flhbW+s7NNIhFxcXpKamVtmnJqMQXFxctBkeERFRjeitKHXp0iXY29tDLpejR48eiIqKwrPPPquxL1eOISIioobmyJEjao9lMhkiIiIQERGhl3jIMFhYWFRrlAJHIRARUV3QSB8v2qNHD2zcuBH79u3Dl19+CZVKBS8vL9y6dUtj/+joaNjY2EgbJ+gkIiIiIiIiIqrb9FKUCggIwLBhw+Du7o4+ffpg9+7dAIANGzZo7B8eHo6cnBxpy8zM1GW4RERERERERESkZXq7fe9hlpaWcHd3x6VLlzTu58oxRERERERERET1i15GSj2qsLAQaWlpsLOz03coRERERERERESkA3opSk2bNg1Hjx5Feno6fvnlFwQHByM3Nxdjx47VRzhE1EBERERAJpOpbQ+vAiqEQEREBOzt7WFubg5fX1+cO3dOjxETERERERHVX3q5fe/69esYNWoU/v77b7Ro0QKenp74+eef0aZNG32EQ0QNSMeOHXHw4EHpsZGRkfT3mJgYLFmyBLGxsWjfvj0iIyPRt29fXLhwgUuuExFRnVBUVIRVq1bhypUraNu2LUJDQ2FqaqrvsIiIiDTSS1Fq69at+nhZIiIYGxurjY4qJ4TAsmXLMHv2bAQFBQF4sPiCQqFAXFwcQkJCdB0qERFRjcyYMQNLly5FSUmJ1DZ9+nR88MEHiImJ0WNkREREmhnEnFJERLpy6dIl2Nvbw8nJCSNHjsQff/wBAEhPT4dKpYK/v7/UVy6Xw8fHB4mJifoKl4iIqFpmzJiBRYsWwdbWFl9++SWysrLw5ZdfwtbWFosWLcKMGTP0HSIREVEFLEoRUYPRo0cPbNy4Efv27cOXX34JlUoFLy8v3Lp1CyqVCgCgUCjUnqNQKKR9lSksLERubq7aRkREpCtFRUVYunQpFAoFrl+/jkmTJkGpVGLSpEm4fv06FAoFli5diqKiIn2HSkREpIZFKSJqMAICAjBs2DC4u7ujT58+2L17N4AHt+mVk8lkas8RQlRoe1R0dDRsbGykzcHBQfvBExERVWLVqlUoKSlBZGQkjI3VZ+cwNjbGggULUFJSglWrVukpQiIiIs1YlCKiBsvS0hLu7u64dOmSNM/Uo6OisrOzK4yeelR4eDhycnKkLTMzs9ZiJiIietSVK1cAAIGBgRr3l7eX9yMiIjIUepnonIjIEBQWFiItLQ3e3t5wcnKCUqnEgQMH4OHhAeDB7RBHjx7FwoULqzyOXC6HXC7XRchEREQVtG3bFgCwa9cuTJo0qcL+Xbt2qfUjIqopWcl9eCgbwfzOReCGYYxtMb9zER7KRpCV3Nd3KPQUWJQiogZj2rRpGDhwIFq3bo3s7GxERkYiNzcXY8eOhUwmQ1hYGKKiouDs7AxnZ2dERUXBwsICo0eP1nfoRERElQoNDcX06dMxZ84cjBs3Tu0WvpKSEsydOxfGxsYIDQ3VY5REVJeZ3b2GEyFWwLEQ4Ji+o3nAFcCJECuk3b0GwEvf4dATYlGKiBqM69evY9SoUfj777/RokULeHp64ueff0abNm0APFi5qKCgAKGhobh9+zZ69OiB/fv3w9raWs+RExERVc7U1BQffPABFi1ahFatWmHBggUIDAzErl27MHfuXNy8eRPTp0+HqampvkMlojrqvlVrdP3iLjZv3gxXFxd9hwMASDt/Hq+99hrW9m+t71DoKbAoRUQNxtatW6vcL5PJEBERgYiICN0ERAYrPz8f58+fr7JPWlqa2p9VcXFxgYWFhVZiIyLSJCYmBgCwZMkShISESO1GRkaYPn26tJ/oSURHRyM+Ph7nz5+Hubk5vLy8sHDhQnTo0EHqI4TA/PnzsWbNGunLvZUrV6Jjx456jJy0RRib4TdVGQqatAfsu+g7HABAgaoMv6nKIIzN9B0KPQUWpYiIiB5x/vx5dOvWrVp9x4wZ89g+qamp6Nq169OGRURUJU9PT7Rq1QpXr16V2lq1agVPT089RkX1wdGjRzF58mQ8//zzKCkpwezZs+Hv74/ff/8dlpaWAB4URpcsWYLY2Fi0b98ekZGR6Nu3Ly5cuMBR50RUKRaliIiIHuHi4oLU1NQq+xQUFCAjIwOOjo4wNzd/7PGIiGpTfHw8goODERgYiK1bt8LNzQ1nz55FVFQUgoODsX37dgQFBek7TKqj9u7dq/Z4/fr1aNmyJVJTU/Hyyy9DCIFly5Zh9uzZ0s/Zhg0boFAoEBcXpzZ6j4joYSxKERERPcLCwqJaI5t69uypg2iIiKpWWlqKqVOnIjAwEDt37kSjRg9WxvL09MTOnTsxZMgQTJs2DYMHD4aRkZGeo6X6ICcnBwDQrFkzAEB6ejpUKhX8/f2lPnK5HD4+PkhMTNRYlCosLERhYaH0ODc3t5ajJiJDZBhrORIRERER0RNJSEhARkYGZs2aJRWkyjVq1Ajh4eFIT09HQkKCniKk+kQIgSlTpuCll16Cm5sbAEClUgEAFAqFWl+FQiHte1R0dDRsbGykzcHBoXYDJyKDxKIUEREREVEdlpWVBQBSgeBR5e3l/YiexjvvvIPTp09jy5YtFfbJZDK1x0KICm3lwsPDkZOTI22ZmZm1Ei8RGTYWpYiIiIiI6jA7OzsAwNmzZzXuL28v70f0pN5991189913OHz4MFq1aiW1K5VKAKgwKio7O7vC6KlycrkcjRs3VtuIqOFhUYqIiIhIz6Kjo/H888/D2toaLVu2xJAhQ3DhwgW1PkIIREREwN7eHubm5vD19cW5c+f0FDEZEm9vbzg6OiIqKgplZWVq+8rKyhAdHQ0nJyd4e3vrKUKq64QQeOeddxAfH49Dhw7ByclJbb+TkxOUSiUOHDggtRUVFeHo0aPw8vLSdbhEVIdwonPSOlnJfXgoG8H8zkXghmHUPc3vXISHshFkJff1HQoREVEFXG6dnoaRkREWL16M4OBgDBkyBOHh4dLqe9HR0di1axe2b9/OSc7piU2ePBlxcXH49ttvYW1tLY2IsrGxgbm5OWQyGcLCwhAVFQVnZ2c4OzsjKioKFhYWGD16tJ6jJyJDxqIUaZ3Z3Ws4EWIFHAsBjuk7mgdcAZwIsULa3WsA+G0NEREZFi63Tk8rKCgI27dvx9SpU9VGpjg5OWH79u3Szw3Rk1i9ejUAwNfXV619/fr1GDduHABgxowZKCgoQGhoKG7fvo0ePXpg//79LJoTUZVYlCKtu2/VGl2/uIvNmzfD1cVF3+EAANLOn8drr72Gtf1b6zsUIiKix9LGcuvU8AQFBWHw4MFISEhAVlYW7Ozs4O3tzRFS9NSEEI/tI5PJEBERgYiIiNoPiIjqDRalSOuEsRl+U5WhoEl7wL6LvsMBABSoyvCbqgzC2EzfoRAREVWppsutX716tdJjFRYWorCwUHqcm5tbCxGTITEyMqowmoWIiMhQGcaEP0REREQEQHvLrQMPJlC3sbGRNgcHB63HS0RERPSkWJQiIiIiMhDaXG4dAMLDw5GTkyNtmZmZtRM4ERER0RNgUYqIiIhIz2pruXW5XI7GjRurbURERESGgnNKEREREekZl1snIiKihohFKSIiIiI943LrRERE1BCxKEVERESkZ1xunYiIiBoizilFREREREREREQ6x6IUERERERERERHpHItSRERERERERESkcyxKERERERERERGRzrEoRUREREREREREOseiFBERERERERER6RyLUkREREREREREpHMsShERERERERERkc6xKEVERERERERERDrHohQREREREREREekci1JERERERERERKRzLEoREREREREREZHOsShFREREREREREQ6Z6zvAKj+yc/PBwCcOHHiqY9VUFCAjIwMODo6wtzc/ImPk5aW9tSxEBEREREREZH2sChFWnf+/HkAwJtvvqnnSCqytrbWdwhEREREREREBBalqBYMGTIEAODi4gILC4unOlZaWhrGjBmDTZs2wdXV9amOZW1tDWdn56c6BhERERERERFpB4tSpHXNmzfHpEmTtHpMV1dXdO3aVavHJCIiIiIiIiL94UTnRERERERERESkcyxKERERERERERGRzrEoRUREREREREREOseiFBERERERERER6Zxei1KrVq2Ck5MTzMzM0K1bNyQkJOgzHCIiCfMTVSUnJwcvvfQSWrdujZdeegk5OTn6DokaEOYnqsratWshk8mkbe3atfoOiRoQ5iciqim9FaW2bduGsLAwzJ49G7/99hu8vb0REBCAa9eu6SskIiIAzE9UtXbt2qFJkyb46aefkJmZiZ9++glNmjRBu3bt9B0aNQDMT1QVmUxWYQXkSZMmQSaT6SkiakiYn4joSeitKLVkyRJMnDgRkyZNgqurK5YtWwYHBwesXr1aXyEREQFgfqLKtWvXDleuXAEAvPLKK0hKSsIrr7wCALhy5QoLU1TrmJ+oMo8Wntq3b1/lfiJtY34ioiehl6JUUVERUlNT4e/vr9bu7++PxMREfYRERASA+Ykql5OTIxWk7t27hz179sDT0xN79uzBvXv3ADwoTPFWPqotzE9UmYdv0du3bx+EELhw4QKEENi3b5/GfkTaxPxERE/KWB8v+vfff6O0tBQKhUKtXaFQQKVSVehfWFiIwsJC6XFubm6tx0i1Lz8/H+fPn6+yT1pamtqfVXFxcYGFhYVWYqOGq6b5CWCOaigGDBgA4MEIqUdzjYWFBfz9/bF//34MGDAAx48f10eIVM8xP1FlHr5lT1NR4OF+EydO1Flc1HAwP9V/+fn5AIATJ05U2qegoAAZGRlafV1HR0eYm5tr3Fedz4hk+PRSlCr36DBiIYTGocXR0dGYP3++rsIiHTl//jy6detWrb5jxox5bJ/U1FR07dr1acMiAlD9/AQwRzUU5XNizJs3T+P+OXPmYP/+/Zw7g2od8xNV5tFb9so5OTkhPT1dx9FQQ8T8VH+VDyZ488039RxJRdbW1voOgZ6CXopSzZs3h5GRUYWqeXZ2doXqOgCEh4djypQp0uPc3Fw4ODjUepxUu1xcXJCamlpln/Jqe1UV8oePR/S0apqfAOaohqJ169bIzMzE/PnzsWfPngr7IyMjpX5EtYH5iR7n4sWLGttZkKLaxvxU/w0ZMgRA1Xen6HqkFPCgIOXs7KzV1yTd0ktRytTUFN26dcOBAwcwdOhQqf3AgQMYPHhwhf5yuRxyuVyXIZIOWFhYVGtkU8+ePXUQDdEDNc1PAHNUQ7F79240adIEe/fuRX5+vtoFWX5+Pvbv3y/1I6oNzE9Umf/85z/SLXz79+9Xu2WvPDeV9yOqDcxP9V/z5s0rrO6pCT+7UU3pbfW9KVOm4D//+Q/WrVuHtLQ0fPDBB7h27Rr+9a9/6SskIiIAzE+kmY2NDdq2bQsAsLS0RL9+/ZCQkIB+/frB0tISANC2bVvY2NjoM0yq55ifSJOH54nq168fZDIZnn32WchkMvTr109jPyJtY34ioiehtzmlRowYgVu3bmHBggXIysqCm5sbfvjhB7Rp00ZfIRERAWB+ospdvnwZ7dq1w5UrV7B//361EQht27bF5cuX9RgdNQTMT1SZR+fuefSWPSGErkOiBob5iYiehEzUwf+hcnNzYWNjg5ycHDRu3Fjf4RDRU6pvv9P17XyoopycHAwYMADXrl1D69atsXv3bo6Qqqfq2+9zfTsfqmjt2rVqt9j85z//4Qipeqw+/U7Xp3Mhour/Tut19T0iIqK6yMbGBsePH9d3GEREFUycOJFFKCIiqjP0NqcUERERERERERE1XHVypFT5HYe5ubl6joSItKH8d7kO3k2sEXMUUf3B/EREhqw+5SjmJ6L6pbr5qU4WpfLy8gAADg4Oeo6EiLQpLy+vXszLwxxFVP8wPxGRIasPOYr5iah+elx+qpMTnZeVleHGjRuwtrZWW2WE6p/c3Fw4ODggMzOTEx7WY0II5OXlwd7eHo0a1f27ipmjGgbmp4aB+YnqIuanhqM+5Sjmp4aDOaphqG5+qpNFKWo4uAoHERkq5iciMlTMT0RkyJij6GF1u5xORERERERERER1EotSRERERERERESkcyxKkUGTy+WYN28e5HK5vkMhIlLD/EREhor5iYgMGXMUPYxzShERERERERERkc5xpBQREREREREREekci1JERERERERERKRzLEoREREREREREZHOsShFREREREREREQ6x6IUGaRjx45h4MCBsLe3h0wmw86dO/UdEhERAOYnIjJczE9EZMiYo0gTFqXIIN27dw+dO3fGihUr9B0KEZEa5iciMlTMT0RkyJijSBNjfQdApElAQAACAgL0HQYRUQXMT0RkqJifiMiQMUeRJhwpRUREREREREREOseiFBERERERERER6RyLUkREREREREREpHMsShERERERERERkc6xKEVERERERERERDrH1ffIIN29exeXL1+WHqenp+PkyZNo1qwZWrdurcfIiKihY34iIkPF/EREhow5ijSRCSGEvoMgetSRI0fg5+dXoX3s2LGIjY3VfUBERP8f8xMRGSrmJyIyZMxRpAmLUkREREREREREpHOcU4qIiIiIiIiIiHSORSkiIiIiIiIiItI5FqWIiIiIiIiIiEjnWJQiIiIiIiIiIiKdY1GKiIiIiIiIiIh0jkUpIiIiIiIiIiLSORaliIiIiIiIiIhI51iUIiIiIiIiIiIinWNRigyGr68vwsLC9B0GEZFGzFFEZKiYn4jIUDE/0eOwKEVaMXDgQPTp00fjvqSkJMhkMpw4cULHURERPcAcRUSGivmJiAwV8xPpAotSpBUTJ07EoUOHcPXq1Qr71q1bhy5duqBr1656iIyIiDmKiAwX8xMRGSrmJ9IFFqVIKwIDA9GyZUvExsaqtefn52Pbtm0YMmQIRo0ahVatWsHCwgLu7u7YsmVLlceUyWTYuXOnWluTJk3UXuPPP//EiBEj0LRpU9ja2mLw4MHIyMiQ9h85cgQvvPACLC0t0aRJE/Ts2VNjUiWi+o05iogMFfMTERkq5ifSBRalSCuMjY3xxhtvIDY2FkIIqf3rr79GUVERJk2ahG7dumHXrl04e/Ys3nrrLbz++uv45Zdfnvg18/Pz4efnBysrKxw7dgzHjx+HlZUVXnnlFRQVFaGkpARDhgyBj48PTp8+jaSkJLz11luQyWTaOGUiqkOYo4jIUDE/EZGhYn4inRBEWpKWliYAiEOHDkltL7/8shg1apTG/v379xdTp06VHvv4+Ij3339fegxA7NixQ+05NjY2Yv369UIIIdauXSs6dOggysrKpP2FhYXC3Nxc7Nu3T9y6dUsAEEeOHHn6kyOiOo85iogMFfMTERkq5ieqbRwpRVrj4uICLy8vrFu3DgBw5coVJCQkYMKECSgtLcXHH3+MTp06wdbWFlZWVti/fz+uXbv2xK+XmpqKy5cvw9raGlZWVrCyskKzZs1w//59XLlyBc2aNcO4cePQr18/DBw4EJ9++imysrK0dbpEVMcwRxGRoWJ+IiJDxfxEtY1FKdKqiRMn4ptvvkFubi7Wr1+PNm3aoHfv3li8eDGWLl2KGTNm4NChQzh58iT69euHoqKiSo8lk8nUhokCQHFxsfT3srIydOvWDSdPnlTbLl68iNGjRwMA1q9fj6SkJHh5eWHbtm1o3749fv7559o5eSIyeMxRRGSomJ+IyFAxP1FtYlGKtGr48OEwMjJCXFwcNmzYgPHjx0MmkyEhIQGDBw/GmDFj0LlzZzz77LO4dOlSlcdq0aKFWtX70qVLyM/Plx537doVly5dQsuWLdGuXTu1zcbGRurn4eGB8PBwJCYmws3NDXFxcdo/cSKqE5ijiMhQMT8RkaFifqLaxKIUaZWVlRVGjBiBWbNm4caNGxg3bhwAoF27djhw4AASExORlpaGkJAQqFSqKo/Vq1cvrFixAidOnEBKSgr+9a9/wcTERNr/2muvoXnz5hg8eDASEhKQnp6Oo0eP4v3338f169eRnp6O8PBwJCUl4erVq9i/fz8uXrwIV1fX2nwLiMiAMUcRkaFifiIiQ8X8RLWJRSnSuokTJ+L27dvo06cPWrduDQD46KOP0LVrV/Tr1w++vr5QKpUYMmRIlcdZvHgxHBwc8PLLL2P06NGYNm0aLCwspP0WFhY4duwYWrdujaCgILi6umLChAkoKChA48aNYWFhgfPnz2PYsGFo37493nrrLbzzzjsICQmpzdMnIgPHHEVEhor5iYgMFfMT1RaZePSGTiIiIiIiIiIiolrGkVJERERERERERKRzLEoREREREREREZHOsShFREREREREREQ6x6IUERERERERERHpHItSRERERERERESkcyxKERERERERERGRzrEoRUREREREREREOseiFBERERERERER6RyLUkREREREREREpHMsShERERERERERkc6xKEVERERERERERDrHohQREREREREREekci1JERERERERERKRzLEoREREREREREZHOsShFREREREREREQ6x6IUERERERERERHpHItS9MRiY2Mhk8mQkpKi1zh8fX3h6+ur1iaTyRAREaGXeIio7inPZw9vLVq0gK+vL3bt2qXWt3z/uHHjNB5rwYIFUp+MjAypfdy4cbCysqrFsyAiIiIiqltYlKJ6KSkpCZMmTdJ3GERUx6xfvx5JSUlITEzEmjVrYGRkhIEDB+L7779X62dtbY2vv/4aeXl5au1CCMTGxqJx48a6DJtIZx4t4JqZmUGpVMLPzw/R0dHIzs5W6x8REQGZTPZEr1Ubhdy4uDgsW7ZMq8fU5NEit42NDXx9fbF7926tvUb5v8XDxW9NX9QZovKfC03bihUr9BrbqlWrEBsbW6E9IyMDMplM4z4iAj777DPIZDK4ubnpOxSqY1iUonrJ09MTrVq10ncYRFTHuLm5wdPTEy+++CKGDh2KXbt2QS6XY8uWLWr9Bg8eDCEEtm7dqtZ+6NAhpKenY8SIEboMm0jnygu4Bw4cwMqVK9GlSxcsXLgQrq6uOHjwoNRv0qRJSEpK0mOk6nRVlAKA4OBgJCUl4aeffsLKlSuhUqkwcOBArRamHrVq1SqsWrWq1o6vbXv37kVSUpLa9uqrr+o1psqKUnZ2dkhKSsKAAQN0HxRRHbBu3ToAwLlz5/DLL7/oORqqS1iUIq0p/0bz8uXL6N+/P6ysrODg4ICpU6eisLBQre/q1avRuXNnWFlZwdraGi4uLpg1a5a0v7JvVjV9K6jJo7fvlT/v8OHDePvtt9G8eXPY2toiKCgIN27ceKrzJqL6y8zMDKampjAxMVFrt7GxwdChQ6ULsHLr1q1Dz5490b59e12GSaRz5QVcb29vDBs2DEuXLsXp06dhaWmJoKAg3Lx5EwDQqlUreHp66jla/VAoFPD09ISXlxfGjBmD3bt3QwhRq0Wx5557Ds8995zWjpefn6+1Y2nSrVs3eHp6qm0KhaJWX/NJyeVyeHp6okWLFvoOhcjgpKSk4NSpU1LRdu3atXqOiOoSFqVIq4qLizFo0CD07t0b3377LSZMmIClS5di4cKFUp+tW7ciNDQUPj4+2LFjB3bu3IkPPvgA9+7dq/X4Jk2aBBMTE8TFxSEmJgZHjhzBmDFjav11iahuKC0tRUlJCYqLi3H9+nWEhYXh3r17GD16dIW+EydOxM8//4y0tDQAwJ07dxAfH4+JEyfqOmwig9C6dWssXrwYeXl5+OKLLwBo/pJp27Zt8Pf3h52dHczNzeHq6ooPP/yw0uuAc+fOoXfv3rC0tESLFi3wzjvvVCiWCCGwatUqdOnSBebm5mjatCmCg4Pxxx9/SH3Kb5+7evWq2u1i5YqKihAZGQkXFxfI5XK0aNEC48ePx19//aX2WocOHYKvry9sbW1hbm6O1q1bY9iwYY8t4LRt2xYtWrTA1atXpbaUlBQMGjQIzZo1g5mZGTw8PPDf//63wnN//vln9OzZE2ZmZrC3t0d4eDiKi4sr9NN0+97169cRHBwMa2trNGnSBK+99hqSk5Mr3IpW/uXimTNn4O/vD2tra/Tu3btG7w3w4N/3xRdfhKWlJaysrNCvXz/89ttvVb43j6rqVrlHv3gs/xk7d+4cRo0aBRsbGygUCkyYMAE5OTlqzy0rK8Py5culn5MmTZrA09MT3333HQDA0dER586dw9GjR6WfD0dHxypjOn78OHr37g1ra2tYWFjAy8urwmg4fjlK9V15Eerf//43vLy8sHXr1go5sbq5CKh+bqT6wVjfAVD9UlRUhPnz50tDr3v37o2UlBTExcVh7ty5AICffvoJTZo0wWeffSY9r/yip7a98soraq/7zz//YMaMGVCpVFAqlTqJgYgM16MjOuRyOVasWIF+/fpV6Ovn5wcnJyesW7cOixYtQlxcHIyNjfHqq6/i888/11XIRAalf//+MDIywrFjxyrtc+nSJfTv3x9hYWGwtLTE+fPnsXDhQvz66684dOiQWt/i4mL0798fISEh+PDDD5GYmIjIyEhcvXpVba63kJAQxMbG4r333sPChQvxzz//YMGCBfDy8sKpU6egUCiwatUqvPXWW7hy5Qp27Nih9jplZWUYPHgwEhISMGPGDHh5eeHq1auYN28efH19kZKSAnNzc2RkZGDAgAHw9vbGunXr0KRJE/z555/Yu3cvioqKYGFhUel53759G7du3YKzszMA4PDhw3jllVfQo0cPfP7557CxscHWrVsxYsQI5OfnS4sp/P777+jduzccHR0RGxsLCwsLrFq1CnFxcY/997h37x78/Pzwzz//YOHChWjXrh327t1b6S3GRUVFGDRokPR+l5SUVPu9AYCoqCjMmTMH48ePx5w5c1BUVIRFixbB29sbv/76a4VRXOVfBJSTyWQwMjJ67HlpMmzYMIwYMQITJ07EmTNnEB4eDgBqI1rHjRuHTZs2YeLEiViwYAFMTU1x4sQJaQT+jh07EBwcDBsbG+k2SLlcXulrHj16FH379kWnTp2wdu1ayOVyrFq1CgMHDsSWLVsqvM+TJk3CgAEDEBcXh8zMTEyfPh1jxoyp8HNPVJcUFBRgy5YteP755+Hm5oYJEyZg0qRJ+PrrrzF27FgANctF1c2NVI8Ioie0fv16AUAkJycLIYQYO3askMlkoqCgQK3fhx9+KMzMzKTHGzduFADEyJEjxc6dO8Vff/1V4djz5s0Tmn48y18zPT1davPx8RE+Pj5q/QCIefPmVXje3r171frt3btXABA///xzdU+biOqh8hyxceNGkZycLJKTk8WePXvEW2+9JWQymVi+fLnUF4CYPHmyEEKI+fPnC4VCIYqLi0XXrl3FhAkThBBCLFq0qEKuGjt2rLC0tNTpeRFp26P/92uiUCiEq6urEKLy/8/LlZWVieLiYnH06FEBQJw6dUraN3bsWAFAfPrpp2rP+fjjjwUAcfz4cSGEEElJSQKAWLx4sVq/zMxMYW5uLmbMmCG1DRgwQLRp06ZCHFu2bBEAxDfffKPWnpycLACIVatWCSGE2L59uwAgTp48Wek5CfEgT4SGhori4mJRVFQk0tLSREBAgAAgVq5cKYQQwsXFRXh4eIji4mK15wYGBgo7OztRWloqhBBixIgRwtzcXKhUKqlPSUmJcHFxeew10cqVKwUAsWfPHrXXCAkJEQDE+vXrpbby93vdunVP9N5cu3ZNGBsbi3fffVetX15enlAqlWL48OFSW/nPxaPbM888I4QQIj09vUJ8D7+3D1/jlR8rJiZGrV9oaKgwMzMTZWVlQgghjh07JgCI2bNnVzjmwzp27FjhurKymDw9PUXLli1FXl6e1FZSUiLc3NxEq1atpNcu/70JDQ1VO2ZMTIwAILKysqqMiciQlX+2+/zzz4UQD37nrayshLe3t9SnJrmourmR6g/evkdaZWFhATMzM7U2uVyO+/fvS49ff/11rFu3DlevXsWwYcPQsmVL9OjRAwcOHKj1+GxtbSvEBjyo8BMRubq6onv37ujevTteeeUVfPHFF/D398eMGTNw586dCv3Lb1+JiorCiRMneOseER7cSleVP/74A6NHj4ZSqYSRkRFMTEzg4+MDANLtsA977bXX1B6X3057+PBhAMCuXbsgk8kwZswYlJSUSJtSqUTnzp1x5MiRx8a8a9cuNGnSBAMHDlQ7RpcuXaBUKqVjdOnSBaampnjrrbewYcMGtdsDH7Vq1SqYmJjA1NQUrq6uSExMxIIFCxAaGorLly/j/Pnz0rk9/Jr9+/dHVlYWLly4IJ1n79691eZaMjIyqtaCCkePHoW1tTVeeeUVtfZRo0ZV+pxhw4Y90Xuzb98+lJSU4I033lDrZ2ZmBh8fH43/DgcPHkRycrK0/fDDD489p8oMGjRI7XGnTp1w//59aUXIPXv2AAAmT578xK/xsHv37uGXX35BcHCw2iqRRkZGeP3113H9+nXp37CqGAGo3dJJVNesXbsW5ubmGDlyJADAysoKr776KhISEnDp0iUA1c9FNcmNVH+wKEV6MX78eCQmJiInJ0ea+DMwMFD6T7m8sPXoBOl///23zmMlooatU6dOKCgowMWLFyvsc3BwQJ8+fTB//nx06NABXl5eeoiQyHDcu3cPt27dgr29vcb9d+/ehbe3N3755RdERkbiyJEjSE5ORnx8PICKXxIZGxtX+EKp/Hb7W7duAQBu3rwJIQQUCgVMTEzUtp9//rla1w43b97EnTt3pIUNHt5UKpV0jLZt2+LgwYNo2bIlJk+ejLZt26Jt27b49NNPKxxz+PDhSE5ORkpKCi5cuIBbt27ho48+kl4PAKZNm1bh9UJDQwH875rn1q1bGqcYqM60A7du3dI4cXhlk4lbWFigcePGT/TelJ/T888/X6Hftm3bNP47dO7cWfoioHv37lKR5kk87ovHv/76C0ZGRlqbruH27dsQQsDOzq7CvvKf//Kf0erGSFTXXL58GceOHcOAAQMghMCdO3dw584dBAcHA/jf7bPVzUU1yY1Uf3BOKdIrS0tLBAQEoKioCEOGDMG5c+fQpk0baVLJ06dP4/nnn5f6Pzx/BBGRLpw8eRIAKl1xaerUqTA3N9f7MuZEhmD37t0oLS2tMNl2uUOHDuHGjRs4cuSINDoKgMaRiMCDb8lv3bql9mFepVIB+N8H/ObNm0MmkyEhIUHj/D9VzQlUrnzi6b1792rcb21tLf3d29sb3t7eKC0tRUpKCpYvX46wsDAoFApppADwIGd079690tcDgPDwcAQFBWns06FDB+k8y8/5YZraHmVra4tff/212s/VtPJxdd+b8nPavn072rRp89jYqlLZl5OPFnlqokWLFigtLYVKpdJYSKqppk2bolGjRsjKyqqwr3zy8vL3hKi+WrduHYQQ2L59O7Zv315h/4YNGxAZGVntXFST3Ej1B4tSpHNvvvkmzM3N0bNnT9jZ2UGlUiE6Oho2NjZSAap///5o1qyZNBGlsbExYmNjkZmZqefoiag+O3v2rDTp7q1btxAfH48DBw5g6NChcHJy0vgcf39/+Pv76zJMIoN07do1TJs2DTY2NggJCdHYp7zo8WihqHy1Pk02b96M9957T3pcPsF3eeErMDAQ//73v/Hnn39i+PDhVcYol8s1jkoJDAzE1q1bUVpaih49elR5jHJGRkbo0aMHXFxcsHnzZpw4cUKtKFWVDh06wNnZGadOnUJUVFSVff38/PDdd9/h5s2b0qiC0tJSbNu27bGv4+Pjg//+97/Ys2cPAgICpPatW7dWK06g+u9Nv379YGxsjCtXrlS4BbCmFAoFzMzMcPr0abX2b7/99omPGRAQgOjoaKxevRoLFiyotF9lPyOPsrS0RI8ePRAfH49PPvlEmuy9rKwMmzZtQqtWrdC+ffsnjpfI0JWWlmLDhg1o27Yt/vOf/1TYv2vXLixevBh79uypdi6qSW6k+oNFKdI5b29vxMbG4r///S9u376N5s2b46WXXsLGjRulkQiNGzfG3r17ERYWhjFjxqBJkyaYNGkSAgICMGnSJD2fARHVV+PHj5f+bmNjAycnJyxZskQaMk5ED5QXcEtKSpCdnY2EhASsX78eRkZG2LFjR6UjC728vNC0aVP861//wrx582BiYoLNmzfj1KlTGvubmppi8eLFuHv3Lp5//nlp9b2AgAC89NJLAICePXvirbfewvjx45GSkoKXX34ZlpaWyMrKwvHjx+Hu7o63334bAODu7o74+HisXr0a3bp1Q6NGjdC9e3eMHDkSmzdvRv/+/fH+++/jhRdegImJCa5fv47Dhw9j8ODBGDp0KD7//HMcOnQIAwYMQOvWrXH//n3p9pQ+ffrU6D384osvEBAQgH79+mHcuHF45pln8M8//yAtLQ0nTpzA119/DQCYM2cOvvvuO/Tq1Qtz586FhYUFVq5ciXv37j32NcaOHYulS5dizJgxiIyMRLt27bBnzx7s27cPANCo0eNn8qjue+Po6IgFCxZg9uzZ+OOPP/DKK6+gadOmuHnzJn799VdYWlpi/vz51XpvyucIW7duHdq2bYvOnTvj119/rdaKg5Xx9vbG66+/jsjISNy8eROBgYGQy+X47bffYGFhgXfffRfAg5+RrVu3Ytu2bXj22WdhZmYGd3d3jceMjo5G37594efnh2nTpsHU1BSrVq3C2bNnsWXLFo0jz4jqiz179uDGjRtYuHChxtGxbm5uWLFiBdauXYtNmzZVOxdVNzdSPaLPWdaJiIiIqO4oX0WsfDM1NRUtW7YUPj4+IioqSmRnZ6v117T6XmJionjxxReFhYWFaNGihZg0aZI4ceKExtXgLC0txenTp4Wvr68wNzcXzZo1E2+//ba4e/duhdjWrVsnevToISwtLYW5ublo27ateOONN0RKSorU559//hHBwcGiSZMmQiaTqcVWXFwsPvnkE9G5c2dhZmYmrKyshIuLiwgJCRGXLl0SQjxY6W/o0KGiTZs2Qi6XC1tbW+Hj4yO+++47tVjw0CqdVTl16pQYPny4aNmypTAxMRFKpVL06tVLWsWq3E8//SQ8PT2FXC4XSqVSTJ8+XaxZs6ZaKxJfu3ZNBAUFCSsrK2FtbS2GDRsmfvjhBwFAfPvttxXeb02q896U27lzp/Dz8xONGzcWcrlctGnTRgQHB4uDBw9Kfcp/LjStwFwuJydHTJo0SSgUCmFpaSkGDhwoMjIyKl1979FjaVqxubS0VCxdulS4ubkJU1NTYWNjI1588UXx/fffS30yMjKEv7+/sLa2FgCk1RorWxEwISFB9OrVS/q58/T0VDvew7E8umrl4cOHBQBx+PDhSt8HIkM1ZMgQYWpqWiHvP2zkyJHC2NhYqFSqauciIaqfG6l+kAnxmCVSiIiIiIio3oiKisKcOXNw7do1tGrVSt/hEFEDxVxEAG/fIyIiIiKqt1asWAEAcHFxQXFxMQ4dOoTPPvsMY8aM4YdAItIZ5iKqDItSRERERET1lIWFBZYuXYqMjAwUFhaidevWmDlzJubMmaPv0IioAWEuosrw9j0iIiIiIiIiItK5xy+5QUREREREREREpGUsShERERERERERkc6xKEVERESkY9HR0Xj++edhbW2Nli1bYsiQIbhw4UKVzzly5AhkMlmF7fz58zqKmoiIiEi76uRE52VlZbhx4wasra0hk8n0HQ4RPSUhBPLy8mBvb49Gjep+rZw5iqj+qK38dPToUUyePBnPP/88SkpKMHv2bPj7++P333+HpaVllc+9cOECGjduLD1u0aJFtV+X+YmofqlP11DMT0T1S3XzU52c6Pz69etwcHDQdxhEpGWZmZn1YklY5iii+qe289Nff/2Fli1b4ujRo3j55Zc19jly5Aj8/Pxw+/ZtNGnS5Ileh/mJqH6qD9dQzE9E9dPj8lOdHCllbW0N4MHJPfxNIRHVTbm5uXBwcJB+t+s65iii+kNX+SknJwcA0KxZs8f29fDwwP379/Hcc89hzpw58PPzq/brMD8R1S/16RqK+YmofqlufqqTRany4ZyNGzdmwiKqR+rLUG3mKKL6pzbzkxACU6ZMwUsvvQQ3N7dK+9nZ2WHNmjXo1q0bCgsL8dVXX6F37944cuRIpaOrCgsLUVhYKD3Oy8sDwPxEVN/Uh2soXj8R1U+Py091sihFREREVF+88847OH36NI4fP15lvw4dOqBDhw7S4xdffBGZmZn45JNPKi1KRUdHY/78+VqNl4iIiEhb6vZseERERER12LvvvovvvvsOhw8ffqL5YDw9PXHp0qVK94eHhyMnJ0faMjMznyZcIiIiIq3iSCkyWKWlpUhISEBWVhbs7Ozg7e0NIyMjfYdFRMT8RE9NCIF3330XO3bswJEjR+Dk5PREx/ntt99gZ2dX6X65XA65XP6kYVIdxPxERIaMOYoexaIUGaT4+HhMnToVGRkZUpujoyMWL16MoKAg/QVGRA0e8xNpw+TJkxEXF4dvv/0W1tbWUKlUAAAbGxuYm5sDeDDK6c8//8TGjRsBAMuWLYOjoyM6duyIoqIibNq0Cd988w2++eYbvZ0HGRbmJyIyZMxRpAlv3yODEx8fj+DgYLi7uyMpKQl5eXlISkqCu7s7goODER8fr+8QiaiBYn4ibVm9ejVycnLg6+sLOzs7adu2bZvUJysrC9euXZMeFxUVYdq0aejUqRO8vb1x/Phx7N69mxfyBID5iYgMG3MUVUYmhBD6DqKmcnNzYWNjg5ycHK7MUM+UlpaiXbt2cHd3x86dO9Go0f/qpmVlZRgyZAjOnj2LS5cucZhnPVLffqfr2/nQA8xPDVN9+32ub+dDDzA/NVy6+p0uKSlBREQENm/eDJVKBTs7O4wbNw5z5syRft6EEJg/fz7WrFmD27dvo0ePHli5ciU6duxoUOdCuscc1TBV93eaI6XIoCQkJCAjIwOzZs1SS1YA0KhRI4SHhyM9PR0JCQl6ipCIGirmJyIyVMxPVNsWLlyIzz//HCtWrEBaWhpiYmKwaNEiLF++XOoTExODJUuWYMWKFUhOToZSqUTfvn2Rl5enx8jJEDBHUVVYlCKDkpWVBQBwc3PTuL+8vbwfEZGuMD8RkaFifqLalpSUhMGDB2PAgAFwdHREcHAw/P39kZKSAuDBKKlly5Zh9uzZCAoKgpubGzZs2ID8/HzExcXpOXrSN+YoqgqLUmRQylcQOnv2rMb95e1VrTRERFQbmJ+IyFAxP1Fte+mll/Djjz/i4sWLAIBTp07h+PHj6N+/PwAgPT0dKpUK/v7+0nPkcjl8fHyQmJiol5jJcDBHUVVYlCKD4u3tDUdHR0RFRaGsrExtX1lZGaKjo+Hk5ARvb289RUhEDRXzExEZqofzU3FxMY4cOYItW7bgyJEjKC4uZn6ipzZz5kyMGjUKLi4uMDExgYeHB8LCwjBq1CgAkFYQVSgUas9TKBTSvkcVFhYiNzdXbaP6iddQVBUWpcigGBkZYfHixdi1axeGDBmitjLDkCFDsGvXLnzyySecAI+IdI75iYgM1cP5ycbGBn5+fhg9ejT8/PxgY2PD/ERPbdu2bdi0aRPi4uJw4sQJbNiwAZ988gk2bNig1k8mk6k9FkJUaCsXHR0NGxsbaXNwcKi1+Em/eA1FVTHWdwBEjwoKCsL27dsxdepUeHl5Se1OTk7Yvn07l74mIr1hfiIiQ6ZpUW2ZTKaxnagmpk+fjg8//BAjR44EALi7u+Pq1auIjo7G2LFjoVQqAUBama9cdnZ2hdFT5cLDwzFlyhTpcW5uLgtT9RivoagyLEqRQQoKCsLgwYORkJCArKws2NnZwdvbm9VzItI75iciMjSlpaWYOnUqBg4ciG+++QY//fSTlJ969uyJYcOGYdq0aRg8eDBzFT2R/Pz8CqumGRkZSbdiOTk5QalU4sCBA/Dw8AAAFBUV4ejRo1i4cKHGY8rlcsjl8toNnAwKr6FIkxrdvldSUoI5c+bAyckJ5ubmePbZZ7FgwQK1+0KFEIiIiIC9vT3Mzc3h6+uLc+fOqR2nsLAQ7777Lpo3bw5LS0sMGjQI169f184ZUb1hZGQEX19fjBo1Cr6+vkxWRGQwmJ+IyJBwuXWqbQMHDsTHH3+M3bt3IyMjAzt27MCSJUswdOhQAA9G5IWFhSEqKgo7duzA2bNnMW7cOFhYWGD06NF6jp4MCa+h6FE1Gim1cOFCfP7559iwYQM6duyIlJQUjB8/HjY2Nnj//fcBADExMViyZAliY2PRvn17REZGom/fvrhw4QKsra0BAGFhYfj++++xdetW2NraYurUqQgMDERqaip/KImIiIiIaqB8GfUrV65g1KhRyMjIkPY5OjoiMjJSrR9RTS1fvhwfffQRQkNDkZ2dDXt7e4SEhGDu3LlSnxkzZqCgoAChoaG4ffs2evTogf3790ufAYmINJGJGtxkHhgYCIVCgbVr10ptw4YNg4WFBb766isIIWBvb4+wsDDMnDkTwINRUQqFAgsXLkRISAhycnLQokULfPXVVxgxYgQA4MaNG3BwcMAPP/yAfv36PTaO3Nxc2NjYICcnB40bN67pORORgalvv9P17XyIGrL69vtc386HHjhy5Aj8/PwAPBjRMmvWLLi5ueHs2bOIiorC999/DwA4fPgwfH199RgpaVt9+p2uT+dCRNX/na7R7XsvvfQSfvzxR1y8eBEAcOrUKRw/fhz9+/cHAKSnp0OlUsHf3196jlwuh4+PDxITEwEAqampKC4uVutjb28PNzc3qc+juFwoEREREZFmXl5eMDY2hkKhQHx8PDw9PWFlZQVPT0/Ex8dDoVDA2NhYbXJhIiIiQ1CjotTMmTMxatQouLi4wMTEBB4eHggLC8OoUaMAPFhtAUCFFRYUCoW0T6VSwdTUFE2bNq20z6O4XCgRERERkWaJiYkoKSlBdnY2goKC1JZbDwoKQnZ2NkpKSir9ApiIiEhfalSU2rZtGzZt2oS4uDicOHECGzZswCeffIINGzao9ZPJZGqPhRAV2h5VVZ/w8HDk5ORIW2ZmZk3CJiIiIiKqt8rnivrqq69w5swZeHl5oXHjxvDy8sLZs2fx1VdfqfUjIiIyFDWa6Hz69On48MMPMXLkSACAu7s7rl69iujoaIwdOxZKpRLAg9FQdnZ20vOys7Ol0VNKpRJFRUW4ffu22mip7OzsSocUc7lQIiIiIiLNyq+727Zti8uXL1dYbv3XX39V60dERGQoajRSKj8/v8Iys0ZGRigrKwMAODk5QalU4sCBA9L+oqIiHD16VCo4devWDSYmJmp9srKycPbsWd7nTkRERERUQ97e3nB0dERUVBRkMpnacusymQzR0dFwcnKCt7e3vkMlIiJSU6ORUgMHDsTHH3+M1q1bo2PHjvjtt9+wZMkSTJgwAcCD2/bCwsIQFRUFZ2dnODs7IyoqChYWFhg9ejQAwMbGBhMnTsTUqVNha2uLZs2aYdq0aXB3d0efPn20f4ZERERERPWYkZERFi9ejODgYAwZMgTh4eHS6nvR0dHYtWsXtm/fDiMjI32HSkREpKZGRanly5fjo48+QmhoKLKzs2Fvb4+QkBDMnTtX6jNjxgwUFBQgNDQUt2/fRo8ePbB//35YW1tLfZYuXQpjY2MMHz4cBQUF6N27N2JjY/kfJRERERHREwgKCsL27dsxdepUtbsPnJycsH37dgQFBekxOiIiIs1kQgih7yBqKjc3FzY2NsjJyUHjxo31HQ4RPSVd/k7/+eefmDlzJvbs2YOCggK0b98ea9euRbdu3QA8WHRh/vz5WLNmjVRYX7lyJTp27Fjt12COIqo/6tvvc307H6qotLS0wpxS/OK3/qpPv9P16VyIqPq/0zUaKUVEVJfdvn0bPXv2hJ+fH/bs2YOWLVviypUraNKkidQnJiYGS5YsQWxsLNq3b4/IyEj07dsXFy5cUBvxSQ0bP/QRkaEyMjKCr6+vvsMgIiKqFhalyGDxQx9p28KFC+Hg4ID169dLbY6OjtLfhRBYtmwZZs+eLd3msGHDBigUCsTFxSEkJETXIZMBio+Px9SpU5GRkSG1OTo6YvHixbw9hoj0jtdPRERUl9Ro9T0iXYmPj0e7du3g5+eH0aNHw8/PD+3atUN8fLy+Q6M67LvvvkP37t3x6quvomXLlvDw8MCXX34p7U9PT4dKpYK/v7/UJpfL4ePjg8TExEqPW1hYiNzcXLWN6qf4+HgEBwfD3d0dSUlJyMvLQ1JSEtzd3REc/P/au/u4Kst83+PfJSqCAqUGC0YUTAwSKxWPipGYiUejZKPVSW2yBx8OaiE+7EH32RubBiZTowmz6JhahrXzsXE3BU2CONiMou7EQElBzSCqQ4CxBIV1/nCzNktARZG1gM/79bpfrvu6fuvmt3rFxc2P676uqYxRAGyK+ycAQFtDUQp2h1/6cKucOnVK69atk5+fnz7//HPNnTtXL7zwgt577z1JUnFxsSTJw8PD6n0eHh6WvsYkJCTIzc3Ncnh7e9+6DwGbqamp0aJFixQeHq6dO3dq5MiR6tGjh0aOHKmdO3cqPDxcixcvVk1Nja1TBdABcf8EAGiLWOgcdqWmpkYDBgzQ4MGDtXPnTnXq9N9109raWkVERCgnJ0f5+flMRW9HWut7umvXrgoKCrKa9fTCCy/owIED2r9/v7KysjR69Gh9//338vT0tMTMmjVLZ8+e1WeffdbodauqqlRVVWX1eby9vRmj2pn09HSNHTtW+/fv18iRIxv079+/X8HBwdqzZw/rubQj7e2eo719HlzG/VPH1Z6+p9vTZwFw/d/TzJSCXcnMzFRhYaGWLVtmdUMlSZ06dVJsbKwKCgqUmZlpowzRlnl6euruu++2agsICNCZM2ckSUajUZIazIoqKSlpMHuqPkdHR7m6ulodaH+KiookSYGBgY3217XXxQFAa+H+CQDQVlGUgl3hlz7cSqNHj9bx48et2k6cOKF+/fpJknx9fWU0GpWWlmbpr66uVkZGhoKDg1s1V9ifutlzOTk5qqmpUXp6urZs2aL09HTV1NQoJyfHKg4AWgv3TwCAtord92BX6v/S19jjMfzSh5uxcOFCBQcHKz4+Xo8//rj+8Y9/KDk5WcnJyZIkg8Gg6OhoxcfHy8/PT35+foqPj5ezs7OmTZtm4+xhayEhIfLx8dGCBQv0008/Ndh9r3fv3vL19VVISIjtkgTQIXH/BKCtYIdQXImZUrArdb/0xcfHq7a21qqvtrZWCQkJ/NKHGzZ8+HDt2LFDW7ZsUWBgoH7/+98rMTFR06dPt8QsXbpU0dHRioqKUlBQkM6dO6fU1FS5uLjYMHPYAwcHBz322GM6ePCgTCaTkpOT9f333ys5OVkmk0kHDx7U1KlTubEC0Oq4fwLQFrBDKBpDUQp2xcHBQatXr9bu3bsVERFhtXtMRESEdu/erVWrVvFLH25YeHi4jh49qgsXLig3N1ezZs2y6jcYDIqLi1NRUZEuXLigjIyMJh+HQMdSU1Ojjz/+WEFBQerWrZtmz54tLy8vzZ49W05OTgoKCtLWrVvZfQ9Aq+P+CYC9Y4dQNIXd92CXtm/frkWLFlk9HuPr66tVq1YpMjLSdonhlmhv39Pt7fPgsvq77w0fPrzB1PN//OMf7L7XDrW37+f29nlgjfunjqc9fU+3p88Ca+wQ2jFd7/c0a0rBLkVGRmry5Mk8bwzAbtRfSNjBwaFB4YmFhAHYGvdPAOxR3Q6hW7ZsaXKH0ODgYGVmZvKHvQ6IohTsVmO/9AGArbCQMIC2gPsnAPaGHUJxNawpBQDAdai/kPDFixeVnp6uLVu2KD09XRcvXmQhYTRLQkKChg8fLhcXF7m7uysiIkLHjx+/5vsyMjI0bNgwdevWTf3799dbb73VCtkCAHDj6v9hr6amxuoeqqamhj/sdXDMlAIA4DrULSQ8depUubm5yWQyWfqcnJx04cIFbd26lcdkcF0yMjI0b948DR8+XJcuXdLy5csVFhamb775Rt27d2/0PQUFBZo0aZJmzZqlzZs3629/+5uioqJ0xx13aMqUKa38CQB0JD4+Pjp9+nSD9qioKK1du1Zms1krVqxQcnKySktLNWLECK1du1aDBg2yQbawN3V/2FuwYIF++uknq3XvfHx81Lt3b/6w14ExUwoAgGZobH8Qg8HQaDvQlM8++0wzZ87UoEGDdO+992rDhg06c+aMsrOzm3zPW2+9pb59+yoxMVEBAQF6/vnn9eyzz2rVqlWtmDmAjujAgQMqKiqyHGlpaZKkxx57TJK0cuVKrVmzRklJSTpw4ICMRqPGjx+viooKW6YNO+Hg4KDHHntMBw8elMlkUnJysr7//nslJyfLZDLp4MGDmjp1Kn/Y66AoSsFuNTa1EwBspaamRosWLdIjjzyisrIy7dmzRykpKdqzZ49++eUXPfLII1q8eDFjFW5IWVmZJKlnz55Nxuzfv19hYWFWbRMmTNDBgwd18eLFRt9TVVWl8vJyqwMAmuuOO+6Q0Wi0HLt379add96pMWPGyGw2KzExUcuXL1dkZKQCAwO1adMmVVZWKiUlxdapww7U1NTo448/VlBQkBwdHTV79mx5eXlp9uzZ6tatm4KCgrR161buoTooilKwS9u3b9eAAQM0duxYTZs2TWPHjtWAAQO0fft2W6cGoIOq2zlm2bJl6tKli0JDQ/Xkk08qNDRUXbp0UWxsrAoKCpSZmWnrVNHGmM1mxcTE6P77729yEVhJKi4uloeHh1Wbh4eHLl26pJ9++qnR9yQkJMjNzc1yeHt7t2juADqe6upqbd68Wc8++6wMBoMKCgpUXFxsVTR3dHTUmDFjlJWV1eR1KJp3HHX3UHfddZfOnTtn1ffdd99p4MCB3EN1YBSlYHe2b9+uqVOnavDgwdq/f78qKiq0f/9+DR48WFOnTqUwBcAm6u8c09hMTnaOwY2aP3++vv76a23ZsuWasQaDweq87rHRK9vrxMbGqqyszHKcPXv25hMG0KHt3LlTv/zyi2bOnCnpcsFcUqNF87q+xlA07zjq7o0++OAD9e7dW++8846Kior0zjvvqHfv3pYZddxDdUwsdA67Uvd4THh4uHbu3KlOnS7XTUeOHKmdO3cqIiJCixcv1uTJk3nmGECrqtsRJikpSW+99ZbVgq/9+vXTnDlzrOKA67FgwQJ98skn2rt3r/r06XPVWKPR2OAXvJKSEnXu3Fm9evVq9D2Ojo5ydHRssXwBYP369Zo4caK8vLys2hsrmjdVMJcuF81jYmIs5+Xl5RSm2qm6n1E9e/bUd999p86dL5chnn/+ec2cOVPu7u4qLS1t8mcZ2jdmSsGu1H88pq4gVadTp048HgPAZkJCQuTu7q7Y2FiVlJRY9ZWUlGjZsmVyd3dn5xhcF7PZrPnz52v79u368ssv5evre833jBo1yrK4cJ3U1FQFBQWpS5cutypVALA4ffq0vvjiCz3//POWNqPRKEmNFs2vnD1Vn6Ojo1xdXa0OtE9Hjx6VJPXp00dms9lqtrnZbLYUI+vi0LEwUwp2pf7jMY3h8RgAtnThwgVJkslksmqvO6/rB65l3rx5SklJ0a5du+Ti4mL5Zc7NzU1OTk6SLs8iOHfunN577z1J0ty5c5WUlKSYmBjNmjVL+/fv1/r166/rsT90HDU1NcrMzFRRUZE8PT0VEhLC7HK0mA0bNsjd3V0PP/ywpc3X11dGo1FpaWkaMmSIpMvrTmVkZOiVV16xVaqwI4WFhZKkr7/+Wm5ublb3UU5OTpbzujh0LMyUgl2pe+wlJyen0f66dh6PAdDa0tPTLYuwXvk4Qt3MzvLycqWnp7d2amiD1q1bp7KyMoWGhsrT09NyfPTRR5aYoqIinTlzxnLu6+urTz/9VOnp6brvvvv0+9//Xn/60580ZcoUW3wE2CE2isGtVFtbqw0bNujpp5+2PH4lXf6ZGB0drfj4eO3YsUM5OTmaOXOmnJ2dNW3aNBtmDHtx5513NtlX/57qanFovyhKwa6EhITIx8dH8fHxqq2tteqrra1VQkKCfH19eTwGQKv761//Kkm6/fbbZTKZtGfPHqWkpGjPnj2qrKzU7bffbhUHXI3ZbG70qFs4WJI2btzYoMg5ZswYHTp0SFVVVSooKNDcuXNbN3HYLTaKwa32xRdf6MyZM3r22Wcb9C1dulTR0dGKiopSUFCQzp07p9TUVLm4uNggU9ibunU3u3btqu+++04REREaPHiwIiIidPbsWXXt2tUqDh0Lj+/Brjg4OGj16tWaOnWqIiIiFBsbq8DAQOXk5CghIUG7d+/W1q1bmYYOoNUdPHhQkvTMM8/I0dFRoaGhVv2//e1v9frrr1viAKC1sFEMWkNYWJhlx88rGQwGxcXFKS4urnWTQpvw97//XdLlxzrrL2Z+9OhR7dy50yruyvsrtH/MlILdiYyM1NatW3X06FEFBwfL1dVVwcHBysnJ0datWxUZGWnrFAF0QM7OzpIub8jQ2EzOrKwsqzgAaC1sFAPAnl3vesCsG9wxUZSCXYqMjNS3335r9XhMfn4+BSkANvPAAw9Ikg4cOKDJkydbPR4zefJkHThwwCoOAFoLG8UAsGc9e/aUJHXu3FkVFRV67bXXNH/+fL322muqqKiwrFFWF4eOhcf3YLccHByYvgnAbsyfP19LlixRbW2t/vrXv2r37t2Wvrrd0jp16qT58+fbKkUAHVT9jWJGjhzZoJ+NYgDY0q5duyRJPj4+cnZ2VnR0tKWvtrZW/fr108mTJ7Vr1y5NmDDBRlnCVpgpBQDAdejatasWLVokSaqqqrLqq66uliQtWrTIslgnALQWNooBYM9Onjxp+TciIsJqtnlERIROnTplFYeOhaIUAADXaeXKlVqyZInV9sV1lixZopUrV9ogKwAdXd1GMbt37270F77du3dr1apVLHIOwCb8/PwkSREREY2uGzx58mSrOHQsBnNTWyjYsfLycrm5uamsrEyurq62TgfATWpv39Pt7fOgoerqar355ps6efKk7rzzTkVFRTFDqp1qb9/P7e3zwNr27du1aNEiFRYWWtp8fX21atUq1uVsp9rT93R7+iwdVWVlpfLy8hq0m0wm3X///erSpYv27NmjQ4cOKS8vT/7+/ho6dKjGjh2rixcvat++fZYlEer4+/uziUwbdb3f06wpBQDAFZq6qapv+PDhuuOOO+Tj42NZr6Up3FABaA2RkZEKDw+naA7AJvLy8jRs2LAm+y9evKj777+/yf7G+rKzszV06NAWyQ/2iaIUAABXuNZNVXNxQwWgNTQ2U+r111/X6tWrmSkF4Jbz9/dXdnZ2k/0xMTHKyMho0D5mzBitWbOmyWuifaMoBQDAFa51UyVJubm5mjFjhjZv3qyAgIBrXg8AbqXt27dr6tSpCg8P15YtWxQYGKicnBzFx8dr6tSp2rp1K4UpALeUs7PzVf8Il56eLpPJpKeffloff/yxHnvsMW3atKnBI3voWChKwW7V1NQoMzNTRUVF8vT0VEhICAt0AmgV17qpqi8gIIBZUABsqqamRosWLVJ4eLh27typTp0u72U0cuRI7dy5UxEREVq8eLEmT57MvRQAm3JyctLvfvc7ffzxx/rd735HQQrN333v3LlzmjFjhnr16iVnZ2fdd999Vn9NNpvNiouLk5eXl5ycnBQaGqpjx45ZXaOqqkoLFixQ79691b17dz366KP67rvvbv7ToN3Yvn27BgwYoLFjx2ratGkaO3asBgwYoO3bt9s6NQAAALuSmZmpwsJCLVu2TGazWenp6dqyZYvS09NlNpsVGxurgoICZWZm2jpVAACsNKsoVVpaqtGjR6tLly76y1/+om+++UarV6/WbbfdZolZuXKl1qxZo6SkJB04cEBGo1Hjx49XRUWFJSY6Olo7duzQhx9+qH379un8+fMKDw9XTU1Ni30wtF11088HDx5staXx4MGDNXXqVApTAAAA9RQVFUmSTp482egf9U6dOmUVBwCAvWjW43uvvPKKvL29tWHDBkubj4+P5bXZbFZiYqKWL19ueWZ906ZN8vDwUEpKiubMmaOysjKtX79e77//vh566CFJ0ubNm+Xt7a0vvvhCEyZMaIGPhbaK6ecAAADN4+npKUl66qmnNGnSJE2ePFkmk0lOTk769ttv9dRTT1nFAQBgL5o1U+qTTz5RUFCQHnvsMbm7u2vIkCF65513LP0FBQUqLi5WWFiYpc3R0VFjxoxRVlaWpMs7EF28eNEqxsvLS4GBgZaYK1VVVam8vNzqQPtUf/p5XUGqTqdOnZh+DgAAcIXg4GB17txZTk5O+vzzz/X6668rOTlZr7/+uj7//HM5OTmpc+fOCg4OtnWqAABYaVZR6tSpU1q3bp38/Pz0+eefa+7cuXrhhRf03nvvSZKKi4slSR4eHlbv8/DwsPQVFxera9euuv3225uMuVJCQoLc3Nwsh7e3d3PSRhtSN608MDBQNTU1Vmsi1NTUKDAw0CoOAACgo8vKytKlS5dUWVmpTp066Xe/+53y8/P1u9/9Tp06dVJlZaUuXbrU5B+AAQCwlWY9vldbW6ugoCDFx8dLkoYMGaJjx45p3bp1+u1vf2uJMxgMVu8zm80N2q50tZjY2FjFxMRYzsvLyylMtVN108qTkpL09ttvq7Cw0NLn4+Oj2bNnW8UBAAB0dGfPnpUkubq66rbbbtMf//hH/fGPf5Qk9evXT6WlpSovL7fEAQBgL5o1U8rT01N33323VVtAQIDOnDkjSTIajZLUYMZTSUmJZfaU0WhUdXW1SktLm4y5kqOjo1xdXa0OtE8hISFyd3dXbGysAgMDrRY6DwwM1LJly+Tu7q6QkBBbpwoAAGAX/v73v0uSoqKidOrUKe3Zs0cpKSnas2ePTp48qblz51rFAQBgL5pVlBo9erSOHz9u1XbixAn169dPkuTr6yuj0ai0tDRLf3V1tTIyMizPsA8bNkxdunSxiikqKlJOTg7PuUPS5Vlz9V/XHQAAAGio7j4pOztbBoNBoaGhevLJJxUaGiqDwaDDhw9bxQEAYC+aVZRauHChvvrqK8XHx+vbb79VSkqKkpOTNW/ePEmXH9uLjo5WfHy8duzYoZycHM2cOVPOzs6aNm2aJMnNzU3PPfecFi1apL/+9a86fPiwZsyYocGDB1t240PHlZmZqR9//FEJCQmWQqWrq6uCg4N17NgxxcfHq6SkhIXOAQAA/oufn58kKS0tTREREVYzzSMiIix/DK6LAwDAXjRrTanhw4drx44dio2N1UsvvSRfX18lJiZq+vTplpilS5fKZDIpKipKpaWlGjFihFJTU+Xi4mKJee2119S5c2c9/vjjMplMGjdunDZu3CgHB4eW+2Rok+oWMJ8/f76WLFmizMxMFRUVydPTUyEhIaqsrNSyZctY6BwAAOC/REVFacmSJerevbv+8z//0+rpg379+snNzU2//vqroqKibJglAAANNasoJUnh4eEKDw9vst9gMCguLk5xcXFNxnTr1k1vvPGG3njjjeZ+ebRzdQuY5+TkaOTIkQoNDbXqz8nJsYoDAADo6Lp27aqFCxfq1VdfVbdu3RQTE6P+/fvr1KlT+uCDD1RWVqYlS5aoa9eutk4VAAArzXp8D7jVQkJC5OPjo/j4eNXW1lr11dbWKiEhQb6+vix0DgAAUM/KlSu1ZMkS/fzzz1qzZo3mz5+vNWvW6Oeff9aSJUu0cuVKW6eINu7cuXOaMWOGevXqJWdnZ913333Kzs629JvNZsXFxcnLy0tOTk4KDQ3VsWPHbJgxgLag2TOlgFvJwcFBq1ev1tSpU/Xoo49qwIABMplMcnJy0rfffqtPP/1UW7du5VFPAACAK4wcOVJeXl6WnbElycvLSyNHjrRhVmgPSktLNXr0aI0dO1Z/+ctf5O7urpMnT+q2226zxKxcuVJr1qzRxo0bNXDgQL388ssaP368jh8/brWUCwDUR1EKdicyMlKPPvqodu3a1aBv8uTJioyMtEFWAAAA9mv79u2aOnWqwsPD9dFHHykwMFA5OTmKj4/X1KlTtXXrVu6hcMNeeeUVeXt7a8OGDZY2Hx8fy2uz2azExEQtX77c8v/Zpk2b5OHhoZSUFM2ZM6e1UwbQRvD4HuzO0qVLtWvXLnXqZP2/Z6dOnbRr1y4tXbrURpkBAADYn5qaGi1atEjh4eHauXOnRo4cqR49emjkyJHauXOnwsPDtXjxYtXU1Ng6VbRRn3zyiYKCgvTYY4/J3d1dQ4YM0TvvvGPpLygoUHFxscLCwixtjo6OGjNmjLKysmyRMoA2gqIU7Ep1dbVWr14tSZo0aZLVlsaTJk2SJK1evVrV1dW2TBMAAMBuZGZmqrCwUMuWLWv0j3qxsbEqKChQZmamjTJEW3fq1CmtW7dOfn5++vzzzzV37ly98MILeu+99yRJxcXFkiQPDw+r93l4eFj6rlRVVaXy8nKrA0DHQ1EKdiUpKUm1tbW69957tWvXLqu/9O3atUv33HOPamtrlZSUZOtUAQAA7EJRUZEkKTAwsNH+uva6OKC5amtrNXToUMXHx2vIkCGaM2eOZs2apXXr1lnFGQwGq3Oz2dygrU5CQoLc3Nwsh7e39y3LH4D9oigFu1L3F7w//OEPjf6l7/e//71VHAAAQEfn6ekpScrJyWm0v669Lg5oLk9PT919991WbQEBAZZF9Y1GoyQ1mBVVUlLSYPZUndjYWJWVlVmOs2fP3oLMAdg7ilKwK3U7cxQUFDTaX1hYaBUHAADQ0YWEhMjHx0fx8fGqra216qutrVVCQoJ8fX0VEhJiowzR1o0ePVrHjx+3ajtx4oT69esnSfL19ZXRaFRaWpqlv7q6WhkZGQoODm70mo6OjnJ1dbU6AHQ8FKVgV5566ilJ0r/+67/q0qVLVn2XLl1SXFycVRwAAG3V3r179cgjj8jLy0sGg0E7d+68anx6eroMBkODIy8vr3USht1ycHDQ6tWrtXv3bkVERFityRkREaHdu3dr1apVcnBwsHWqaKMWLlyor776SvHx8fr222+VkpKi5ORkzZs3T9Llx/aio6MVHx+vHTt2KCcnRzNnzpSzs7OmTZtm4+wB2LPOtk4AqO/BBx+Um5ubSktL5eXlpaeeekp33nmnTp48qffff1+lpaVyc3PTgw8+aOtUAQC4Kb/++qvuvfdePfPMM5oyZcp1v+/48eNWMwruuOOOW5Ee2pjIyEht3bpVixYtspqZ4uvrq61btyoyMtKG2aGtGz58uHbs2KHY2Fi99NJL8vX1VWJioqZPn26JWbp0qUwmk6KiolRaWqoRI0YoNTWVJxwAXBVFKdgVBwcHvfvuu5oyZYp+/PFHrVmzpkHMu+++y1/6AABt3sSJEzVx4sRmv8/d3V233XZbyyeENi8yMlLh4eF68803dfLkSd15552KiopS165dbZ0a2oHw8HCFh4c32W8wGBQXF2d5sgEArgeP78EuGQwGOTk5WbU5OTk1uXsHcCMSEhIs083rmM1mxcXFycvLS05OTgoNDdWxY8dslyQAXGHIkCHy9PTUuHHjtGfPHlunAzuyfft2DRw4UAsXLlRSUpIWLlyogQMHavv27bZODQCARlGUgl2pqanRokWLFB4erh9//FHz5s1TWFiY5s2bpx9//FHh4eFavHixampqbJ0q2rgDBw4oOTlZ99xzj1X7ypUrtWbNGiUlJenAgQMyGo0aP368KioqbJQpAFzm6emp5ORkbdu2Tdu3b9ddd92lcePGae/evU2+p6qqSuXl5VYH2qft27drypQpKikpsWovKSnRlClTKEwBAOwSRSnYlczMTBUWFsrV1VW33Xab1q5dq9TUVK1du1a33XabXFxcVFBQoMzMTFunijbs/Pnzmj59ut555x3dfvvtlnaz2azExEQtX75ckZGRCgwM1KZNm1RZWamUlBQbZgwA0l133aVZs2Zp6NChGjVqlN588009/PDDWrVqVZPvSUhIkJubm+Xw9vZuxYzRWmpqajR37lxJl9fnXLt2rd59912tXbvWsg7n//7f/5s/6gEA7A5FKdiVoqIiSdIHH3ygXr166Z133lFRUZHeeecd9erVy1IYqIsDbsS8efP08MMP66GHHrJqLygoUHFxscLCwixtjo6OGjNmjLKyspq8HjMRANjKyJEjlZ+f32R/bGysysrKLMfZs2dbMTu0lvT0dP34448KCAhQTk6O5s2bp2effVbz5s1TTk6O/P39VVJSovT0dFunCgCAFYpSsCu9e/eWJN1+++06ffq0BgwYoD179mjAgAE6ffq0ZVZLXRzQXB9++KEOHTqkhISEBn3FxcWSJA8PD6t2Dw8PS19jmIkAwFYOHz4sT0/PJvsdHR3l6upqdaD9qSs25ebm6p577tH+/ftVUVGh/fv365577lFeXp5VHAAA9oLd92BXjh49KklydXXVgAED9N1331n6+vTpI1dXV5WWluro0aMaP368rdJEG3X27Fm9+OKLSk1NVbdu3ZqMu3JBfbPZfNVF9mNjYxUTE2M5Ly8vpzAF4JrOnz+vb7/91nJeUFCgI0eOqGfPnurbt69iY2N17tw5vffee5KkxMRE+fj4aNCgQaqurtbmzZu1bds2bdu2zVYfAXaitrZWkjRq1Cht27ZNf/vb3/TnP/9Znp6e2rZtm0JCQvT3v//dEgcAgL2gKAW7UlBQIEk6ffp0g776Baq6OKA5srOzVVJSomHDhlnaampqtHfvXiUlJen48eOSLs+Yqj/zoKSkpMHsqfocHR3l6Oh46xIH0C4dPHhQY8eOtZzXFbeffvppbdy4UUVFRTpz5oylv7q6WosXL9a5c+fk5OSkQYMG6T/+4z80adKkVs8d9qVnz56SLi9vcOedd1o9punt7a1OnTpZxQEAYC8oSsGu+Pj4tGgcUN+4ceMss/HqPPPMM/L399c///M/q3///jIajUpLS9OQIUMkXf4lMCMjQ6+88ootUgbQjoWGhspsNjfZv3HjRqvzpUuXaunSpbc4K7RFRqNRklRYWNigr36Bqi4OAAB7QVEKdmXAgAGW1xMmTJC/v79MJpOcnJyUl5enzz//vEEccL1cXFwUGBho1da9e3f16tXL0h4dHa34+Hj5+fnJz89P8fHxcnZ21rRp02yRMgAA13S9xSaKUgAAe0NRCnYlKSnJ8jo1NdVShJKs1/lJSkrS5MmTWzU3dAxLly6VyWRSVFSUSktLNWLECKWmpsrFxcXWqQEA0KgLFy5YXvft29fqsc9+/fpZlkWoHwcAgD2gKAW7Uv8mqiXigGu5cicig8GguLg4xcXF2SQfAACaKzEx0fK6pKTEqu+HH36winv44YdbKy0AAK6JohTsSp8+fXTixAk5Ozvrxx9/VHJysk6ePKk777xTs2fPVu/evWUymdSnTx9bpwoAAGAXfvnlF8vrK3eLrVvk/Mo4AADsQadrhwCtJywsTJJUWVmpxx57TCNGjFB8fLxGjBihxx57TCaTySoOAACgo6vbVdbZ2Vk//PCD5s2bp7CwMM2bN0/FxcVydna2igMAwF4wUwp2pf4MqE8//VSffvrpNeMAoLny8/NVUVFxU9fIzc21+vdmuLi4yM/P76avA6Bjmjx5st5++21VVlbK1dXV0p6amqq1a9daxQEAYE8oSsGu/OY3v2nROAC4Un5+vgYOHNhi15sxY0aLXOfEiRMUpgDckOt9LI/H9wAA9oaiFOxKSEiIfHx85ODgoIKCAtXW1lr6HBwc5OPjo9raWoWEhNgwSwBtWd0Mqc2bNysgIOCGr2MymVRYWCgfHx85OTnd8HVyc3M1Y8aMm565BaDj6t27t6TL60fVv3eqU9deFwcAgL2gKAW74uDgoMcee0yvvvqqPDw8NGPGDPXv31+nTp3S5s2bdfLkSS1ZskQODg62ThVAGxcQEKChQ4fe1DVGjx7dQtkAwI07evSoJKm2tlbdunXThQsXLH31z48eParx48fbJEcAABpDUQp2paamRh9//LGCgoL0448/avXq1ZY+Hx8fBQUFaevWrUpISKAwBQAAIOnkyZOW11VVVVZ99c/rxwEAYA8oSsGuZGZmqrCwUFu2bNHw4cOVmZmpoqIieXp6KiQkRP/4xz8UHByszMxMhYaG2jpdAAAAmzObzY2+vlYfAAC2RlEKdqWoqEiSFBgYKAcHhwaFp8DAQKs4AACAjq7+jntOTk4ymUyNntePAwDAHlCUgl3x9PSUJOXk5DQ6UyonJ8cqDgAAoKP77rvvLK8vXbqkIUOGyNnZWZWVlZZ7pyvjAACwBxSlYFfqdt9bsGCBfvzxR50+fdrS169fP91xxx3y9fVl9z0AAID/UrfjnsFg0MWLF3X48GGrfoPBILPZ3OjOfAAA2FInWycA1Fe3+97Bgwd14cIFJScn6/vvv1dycrIuXLiggwcPaurUqSxyDgAA8F86dbp8S9/UmlF17XVxQHPFxcXJYDBYHUaj0dJvNpsVFxcnLy8vOTk5KTQ0VMeOHbNhxgDaCn4ywa7U333PyclJs2fPlpeXl2bPni1nZ2fL7ns1NTW2ThUAAMAu/OY3v7G8vrLwVP+8fhzQXIMGDVJRUZHlOHr0qKVv5cqVWrNmjZKSknTgwAEZjUaNHz9eFRUVNswYQFtwU0WphIQEGQwGRUdHW9qup0peVVWlBQsWqHfv3urevbseffRRnnGHpP/efW/KlCkN/tpXW1uryMhIFRQUKDMz00YZAgAA2Jfc3FzL665du1r11T+vHwc0V+fOnWU0Gi3HHXfcIeny73+JiYlavny5IiMjFRgYqE2bNqmyslIpKSk2zhqAvbvhotSBAweUnJyse+65x6r9eqrk0dHR2rFjhz788EPt27dP58+fV3h4OLNfYNlVLzY2VoMHD9batWv17rvvau3atRo8eLCWLVtmFQcAANDRff/99y0aBzQmPz9fXl5e8vX11f/6X/9Lp06dkiQVFBSouLhYYWFhllhHR0eNGTNGWVlZTV6vqqpK5eXlVgeAjueGFjo/f/68pk+frnfeeUcvv/yypf3KKrkkbdq0SR4eHkpJSdGcOXNUVlam9evX6/3339dDDz0kSdq8ebO8vb31xRdfaMKECS3wsdBWubu7S5L8/f2Vk5Oj3bt3W/p8fHzk7++vvLw8SxwANJfh0gUNMXaS0y8npO9t/xS70y8nNMTYSYZLF2ydCoA2qmfPnpbXBoPBqq/+43v144DmGDFihN577z0NHDhQP/zwg15++WUFBwfr2LFjKi4uliR5eHhYvcfDw8Nq06IrJSQkaMWKFbc0bwD274aKUvPmzdPDDz+shx56yKooda0q+Zw5c5Sdna2LFy9axXh5eSkwMFBZWVmNFqWqqqpUVVVlOaeK3v7l5eXJycnJqu2HH36QyWSyUUYA2otu58/o0Jwe0t450l5bZyMFSDo0p4dyz5+RFGzrdADYucrKSuXl5Vm1Pfroo0pLS1OnTp3k5uZmdb/k5uamCxcuqLa2Vo8++qgOHTpk9V5/f385Ozu3Su5ouyZOnGh5PXjwYI0aNUp33nmnNm3apJEjR0pqWBA1m80N2uqLjY1VTEyM5by8vFze3t4tnDkAe9fsotSHH36oQ4cO6cCBAw36rqdKXlxcrK5du+r2229vEFP3/itRRe846v8/4OLiotdff13h4eHavXu3/uVf/sVyk9XU/ysAcC0XevTV0LfP64MPPlCAv7+t01FuXp6mT5+u9ZP62joVAG1AXl6ehg0b1mhfbW1tg3uk+kseLFiwoMF7srOzNXTo0JZNEu1e9+7dNXjwYOXn5ysiIkLS5ftzT09PS0xJSUmD3wvrc3R0lKOj461OFYCda1ZR6uzZs3rxxReVmpqqbt26NRnX3Cr5tWKoonccdTdSPj4+MpvNmj17tqXPx8dH/fr10+nTpylKAbhh5s7ddLi4VqbbBkpe99k6HZmKa3W4uFbmzk3/XAWAOv7+/srOzm7Q/uWXX2rJkiVNvu/VV1/Vgw8+2Oj1gOaqqqpSbm6uQkJC5OvrK6PRqLS0NA0ZMkSSVF1drYyMDL3yyis2zhSAvWtWUSo7O1slJSVWf52pqanR3r17lZSUpOPHj0u6epXcaDSqurpapaWlVrOlSkpKFBzc+GMLVNE7jv/3//6fpMs7xdR/ZFO6XLis20GmLg4AAKAjcXZ2bnRm09ChQ9W/f38tXLhQZ86csbT369dPa9assaz3CtyIxYsX65FHHlHfvn1VUlKil19+WeXl5Xr66actu7HHx8fLz89Pfn5+io+Pl7Ozs6ZNm2br1AHYuWat8Dpu3DgdPXpUR44csRxBQUGaPn26jhw5ov79+1uq5HXqquR1Badhw4apS5cuVjFFRUXKyclpsiiFjqNuMc4TJ07o7NmzVn1nz55Vfn6+VRwAAAAui4yM1KlTp/T2229Lkt5++22dPHmSghRu2nfffacnn3xSd911lyIjI9W1a1d99dVX6tevnyRp6dKlio6OVlRUlIKCgnTu3DmlpqbKxcXFxpkDsHfNminl4uKiwMBAq7bu3burV69elvZrVcnd3Nz03HPPadGiRerVq5d69uypxYsXa/DgwZbd+NBxhYSEWF537txZ1dXVlvMuXbpYZk/VjwMAAMBlDg4OCgoKkiQFBQXJwcHBxhmhPfjwww+v2m8wGBQXF6e4uLjWSQhAu3FDu+9dzdKlS2UymRQVFaXS0lKNGDGiQZX8tddeU+fOnfX444/LZDJp3Lhx2rhxIz80YeXK/x+YHQUAAAAAQPtx00Wp9PR0q/PrqZJ369ZNb7zxht54442b/fJoZzIzMy2vr1xTqv6sqczMTIWFhbVaXgAAAAAAoGUx9QR2pba2VpI0cODABjssent7a+DAgVZxAAAAAACgbWrxx/eAm9GzZ09Jl2fT/ed//qfeeustnTx5Unfeeafmzp2rESNGWMUBAAAAAIC2iaIU7IrRaJQkff311+rZs6dMJpOlb9myZZbzujgAAAAAANA28fge7MpvfvMby+sLFy5Y9dVfY6p+HAAAAAAAaHsoSsGuBAcHq3PnznJ2dm6w257BYJCzs7M6d+6s4OBgG2UIAEDL2Lt3rx555BF5eXnJYDBo586d13xPRkaGhg0bpm7duql///566623bn2iAAAAtwiP78GuZGVl6dKlS7p06VKDopTZbFZlZaUlLjQ01AYZAmjr6saRQ4cO3dR1TCaTCgsL5ePjIycnpxu+Tm5u7k3lgbbr119/1b333qtnnnlGU6ZMuWZ8QUGBJk2apFmzZmnz5s3629/+pqioKN1xxx3X9X4AAAB7Q1EKduXcuXOW11fusFf/vH4cADRHXl6eJGnWrFk2zsSai4uLrVNAK5s4caImTpx43fFvvfWW+vbtq8TERElSQECADh48qFWrVlGUAgAAbRJFKdiV4uLiFo0DgCtFRERIkvz9/eXs7HzD18nNzdWMGTO0efNmBQQE3FROLi4u8vPzu6lroP3bv3+/wsLCrNomTJig9evX6+LFi+rSpUuD91RVVVmtyVheXn7L8wQAALheFKVgV4qKiiyvJ06cqH/9139VYGCgcnJy9NJLL+kvf/lLgzgAaI7evXvr+eefb7HrBQQEaOjQoS12PaApxcXF8vDwsGrz8PDQpUuX9NNPP8nT07PBexISErRixYrWShEAAKBZWOgcduXDDz+0vHZwcJDZbLYcDg4OjcYBANBRGAwGq3Oz2dxoe53Y2FiVlZVZjrNnz97yHAEAAK4XM6VgVyoqKiRJRqNRR48etdplz8fHRx4eHvrhhx8scQAAdBRGo7HB4+slJSXq3LmzevXq1eh7HB0d5ejo2BrpAQAANBtFKdhMZWWlZcHhOm5ubiovL1dxcbHuv/9+RUZGqqKiQi4uLjpw4ID27dtnibty56ybXR8GAAB7NmrUKP35z3+2aktNTVVQUFCj60kBAADYO4pSsJm8vDwNGzasyf59+/ZZilBXOnv2bIP3Zmdns64LAKDNOH/+vL799lvLeUFBgY4cOaKePXuqb9++io2N1blz5/Tee+9JkubOnaukpCTFxMRo1qxZ2r9/v9avX68tW7bY6iMAAADcFIpSsBl/f39lZ2dbtdXU1GjUqFGqqalp8n0ODg7av3+/1RpTddcDAKCtOHjwoMaOHWs5j4mJkSQ9/fTT2rhxo4qKinTmzBlLv6+vrz799FMtXLhQa9eulZeXl/70pz9pypQprZ47AABAS6AoBZtxdnZudGbTv//7v1/1Bvvf//3fNXz48FuZGgAAt1xoaKhlofLGbNy4sUHbmDFjGjy+DgAA0Fax+x7sTmRkpLZt26a+fftatffr10/btm1TZGSkjTIDAAAAAAAthaIU7FJkZKROnTqlt99+W5L09ttv6+TJkxSkAAAAAABoJyhKwW45ODgoKChIkhQUFNRgDSkAAAAAANB2UZQCAAAAAABAq2OhcwAAAADAdUlISNCyZcv04osvKjExUZJkNpu1YsUKJScnq7S0VCNGjNDatWs1aNAg2yaLFpWfn6+Kioqbvk5ubq7VvzfDxcVFfn5+N30d2A5FKQAAAADANR04cEDJycm65557rNpXrlypNWvWaOPGjRo4cKBefvlljR8/XsePH5eLi4uNskVLys/P18CBA1v0mjNmzGiR65w4cYLCVBtGUQoAAAAAcFXnz5/X9OnT9c477+jll1+2tJvNZiUmJmr58uWWTYk2bdokDw8PpaSkaM6cObZKGS2obobU5s2bFRAQcFPXMplMKiwslI+Pj5ycnG74Orm5uZoxY0aLzN6C7VCUAgAAAABc1bx58/Twww/roYcesipKFRQUqLi4WGFhYZY2R0dHjRkzRllZWU0WpaqqqlRVVWU5Ly8vv3XJo8UEBARo6NChN32d0aNHt0A2aA9Y6BxAh5GQkKDhw4fLxcVF7u7uioiI0PHjx61izGaz4uLi5OXlJScnJ4WGhurYsWM2yhgAAMD2PvzwQx06dEgJCQkN+oqLiyVJHh4eVu0eHh6WvsYkJCTIzc3Ncnh7e7ds0gDaBIpSADqMjIwMzZs3T1999ZXS0tJ06dIlhYWF6ddff7XE1K2JkJSUpAMHDshoNGr8+PFMCwYAAB3S2bNn9eKLL2rz5s3q1q1bk3EGg8Hq3Gw2N2irLzY2VmVlZZbj7NmzLZYzgLaDx/cAdBifffaZ1fmGDRvk7u6u7OxsPfDAA6yJAAAAcIXs7GyVlJRo2LBhlraamhrt3btXSUlJllnnxcXF8vT0tMSUlJQ0mD1Vn6OjoxwdHW9d4gDaBGZKAeiwysrKJEk9e/aUdO01EZpSVVWl8vJyqwMAAKA9GDdunI4ePaojR45YjqCgIE2fPl1HjhxR//79ZTQalZaWZnlPdXW1MjIyFBwcbMPMAbQFzJQC0CGZzWbFxMTo/vvvV2BgoKSrr4lw+vTpJq+VkJCgFStW3LpkAQAAbMTFxcVyr1Sne/fu6tWrl6U9Ojpa8fHx8vPzk5+fn+Lj4+Xs7Kxp06bZImUAbQhFKQAd0vz58/X1119r3759DfpuZE2EmJgYy3l5eTmLdQIAgA5j6dKlMplMioqKUmlpqUaMGKHU1FS5uLjYOjUAdo6iFIAOZ8GCBfrkk0+0d+9e9enTx9JuNBolsSYCAADA1aSnp1udGwwGxcXFKS4uzib5AGi7WFMKQIdhNps1f/58bd++XV9++aV8fX2t+n19fVkTAQAAAABaCTOlAHQY8+bNU0pKinbt2iUXFxfLGlJubm5ycnKSwWBgTQRIkiorK5WXl3fVmNzcXKt/r8bf31/Ozs4tkhsAAADQXlCUAtBhrFu3TpIUGhpq1b5hwwbNnDlTEmsi4LK8vDyrra+vZsaMGdeMyc7O1tChQ282LQAAAKBdoSgFoMMwm83XjGFNBEiXZzZlZ2dfNcZkMqmwsFA+Pj5ycnK65vUAAAAAWKMoBQDAFZydna9rZtPo0aNbIRsAAACgfWrWQucJCQkaPny4XFxc5O7uroiICB0/ftwqxmw2Ky4uTl5eXnJyclJoaKiOHTtmFVNVVaUFCxaod+/e6t69ux599FF99913N/9pAAAAAAAA0CY0qyiVkZGhefPm6auvvlJaWpouXbqksLAw/frrr5aYlStXas2aNUpKStKBAwdkNBo1fvx4VVRUWGKio6O1Y8cOffjhh9q3b5/Onz+v8PBw1dTUtNwnAwAAAAAAgN1q1uN7n332mdX5hg0b5O7uruzsbD3wwAMym81KTEzU8uXLFRkZKUnatGmTPDw8lJKSojlz5qisrEzr16/X+++/r4ceekiStHnzZnl7e+uLL77QhAkTWuijAQAAAAAAwF7d1JpSZWVlkqSePXtKkgoKClRcXKywsDBLjKOjo8aMGaOsrCzNmTNH2dnZunjxolWMl5eXAgMDlZWV1WhRqqqqSlVVVZbz8vLym0kbAAAAsEv5+flWTxjciNzcXKt/b5aLi4v8/Pxa5FoAANR3w0Ups9msmJgY3X///QoMDJQkFRcXS5I8PDysYj08PHT69GlLTNeuXXX77bc3iKl7/5USEhK0YsWKG00VAAAAsHv5+fkaOHBgi11vxowZLXatEydOUJgCALS4Gy5KzZ8/X19//bX27dvXoM9gMFidm83mBm1XulpMbGysYmJiLOfl5eXy9va+gawBAAAA+1Q3Q2rz5s0KCAi44euYTCYVFhbKx8dHTk5ON5VTbm6uZsyYcdOztwAAaMwNFaUWLFigTz75RHv37lWfPn0s7UajUdLl2VCenp6W9pKSEsvsKaPRqOrqapWWllrNliopKVFwcHCjX8/R0VGOjo43kioAAADQpgQEBGjo0KE3dY3Ro0e3UDYAANw6zdp9z2w2a/78+dq+fbu+/PJL+fr6WvX7+vrKaDQqLS3N0lZdXa2MjAxLwWnYsGHq0qWLVUxRUZFycnKaLEoBAAAAAACgfWnWTKl58+YpJSVFu3btkouLi2UNKDc3Nzk5OclgMCg6Olrx8fHy8/OTn5+f4uPj5ezsrGnTpllin3vuOS1atEi9evVSz549tXjxYg0ePNiyGx8AAAAAAADat2YVpdatWydJCg0NtWrfsGGDZs6cKUlaunSpTCaToqKiVFpaqhEjRig1NVUuLi6W+Ndee02dO3fW448/LpPJpHHjxmnjxo1ycHC4uU8DAAAAAACANqHZj+81dtQVpKTLi5zHxcWpqKhIFy5cUEZGhmV3vjrdunXTG2+8oZ9//lmVlZX685//zMLlAACgw3nzzTfl6+urbt26adiwYcrMzGwyNj09XQaDocGRl5fXihkDAAC0nGYVpQAAANAyPvroI0VHR2v58uU6fPiwQkJCNHHiRJ05c+aq7zt+/LiKioosh5+fXytlDAAA0LIoSgEAANjAmjVr9Nxzz+n5559XQECAEhMT5e3tbVkuoSnu7u4yGo2Wg+UPAABAW0VRCgAAoJVVV1crOztbYWFhVu1hYWHKysq66nuHDBkiT09PjRs3Tnv27LmVaQIAANxSzVroHAAAADfvp59+Uk1NjTw8PKzaPTw8LLsbX8nT01PJyckaNmyYqqqq9P7772vcuHFKT0/XAw880Oh7qqqqVFVVZTkvLy9vuQ8BAABwkyhKAQAA2IjBYLA6N5vNDdrq3HXXXbrrrrss56NGjdLZs2e1atWqJotSCQkJWrFiRcslDAAA0IJ4fA8AAKCV9e7dWw4ODg1mRZWUlDSYPXU1I0eOVH5+fpP9sbGxKisrsxxnz5694ZwBdFzr1q3TPffcI1dXV7m6umrUqFH6y1/+Yuk3m82Ki4uTl5eXnJycFBoaqmPHjtkwYwBtBUUpAACAVta1a1cNGzZMaWlpVu1paWkKDg6+7uscPnxYnp6eTfY7OjpafomsOwCgufr06aM//vGPOnjwoA4ePKgHH3xQkydPthSeVq5cqTVr1igpKUkHDhyQ0WjU+PHjVVFRYePMAdg7Ht8DAACwgZiYGD311FMKCgrSqFGjlJycrDNnzmju3LmSLs9yOnfunN577z1JUmJionx8fDRo0CBVV1dr8+bN2rZtm7Zt22bLjwGgA3jkkUeszv/whz9o3bp1+uqrr3T33XcrMTFRy5cvV2RkpCRp06ZN8vDwUEpKiubMmWOLlAG0ERSlAAAAbOCJJ57Qzz//rJdeeklFRUUKDAzUp59+qn79+kmSioqKdObMGUt8dXW1Fi9erHPnzsnJyUmDBg3Sf/zHf2jSpEm2+ggAOqCamhp9/PHH+vXXXzVq1CgVFBSouLjYajdRR0dHjRkzRllZWRSlAFwVRSncEvn5+S0yXTc3N9fq35vh4uIiPz+/m74OAAAtJSoqSlFRUY32bdy40ep86dKlWrp0aStkBQANHT16VKNGjdKFCxfUo0cP7dixQ3fffbeysrIkqdHdRE+fPt3k9dgdFIBEUQq3QH5+vgYOHNii15wxY0aLXOfEiRMUpgAAAIBmuuuuu3TkyBH98ssv2rZtm55++mllZGRY+puzm6jE7qAALqMohRZXN0Nq8+bNCggIuKlrmUwmFRYWysfHR05OTjd8ndzcXM2YMYPFFgEAAIAb0LVrVw0YMECSFBQUpAMHDuj111/XP//zP0u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size 1200x600 with 8 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#plot the variables to check for outliers\n","fig, axs = plt.subplots(nrows=2, ncols=4, figsize=(12, 6))\n","\n","# Flatten the axs array to simplify indexing\n","axs = axs.flatten()\n","\n","for i, feature in enumerate(features_variables.columns):\n","    axs[i].boxplot(features_variables[feature])\n","    axs[i].set_title(feature)\n","    axs[i].set_xlabel('Values')\n","\n","# Remove any unused subplots\n","for j in range(len(features_variables.columns), len(axs)):\n","    fig.delaxes(axs[j])\n","\n","# Adjust spacing between subplots\n","fig.tight_layout()\n","\n","# Display the plot\n","plt.show()"]},{"cell_type":"code","execution_count":12,"id":"7dc6e319","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:27.443459Z","iopub.status.busy":"2023-06-04T12:01:27.442184Z","iopub.status.idle":"2023-06-04T12:01:29.370101Z","shell.execute_reply":"2023-06-04T12:01:29.367784Z"},"papermill":{"duration":1.949835,"end_time":"2023-06-04T12:01:29.372823","exception":false,"start_time":"2023-06-04T12:01:27.422988","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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size 1200x600 with 8 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#plot the variables to test for normality\n","fig, axs = plt.subplots(nrows=2, ncols=4, figsize=(12, 6))\n","\n","# Flatten the axs array to simplify indexing\n","axs = axs.flatten()\n","\n","for i, feature in enumerate(features_variables.columns):\n","    axs[i].hist(features_variables[feature])\n","    axs[i].set_title(feature)\n","    axs[i].set_xlabel('Values')\n","\n","# Remove any unused subplots\n","for j in range(len(features_variables.columns), len(axs)):\n","    fig.delaxes(axs[j])\n","\n","# Adjust spacing between subplots\n","fig.tight_layout()\n","\n","# Display the plot\n","plt.show()"]},{"cell_type":"code","execution_count":13,"id":"0d26be04","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:29.401967Z","iopub.status.busy":"2023-06-04T12:01:29.401565Z","iopub.status.idle":"2023-06-04T12:01:29.412893Z","shell.execute_reply":"2023-06-04T12:01:29.411384Z"},"papermill":{"duration":0.02859,"end_time":"2023-06-04T12:01:29.41529","exception":false,"start_time":"2023-06-04T12:01:29.3867","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Column: Pregnancies\n","Statistic: 0.9042831659317017\n","p_value: 1.6100101271064348e-21\n","The data does not follow normal distribution\n","\n","Column: Glucose\n","Statistic: 0.9701048731803894\n","p_value: 1.987464880170986e-11\n","The data does not follow normal distribution\n","\n","Column: BloodPressure\n","Statistic: 0.81892329454422\n","p_value: 1.5844936208677322e-28\n","The data does not follow normal distribution\n","\n","Column: SkinThickness\n","Statistic: 0.904627799987793\n","p_value: 1.751799708531821e-21\n","The data does not follow normal distribution\n","\n","Column: Insulin\n","Statistic: 0.7220208644866943\n","p_value: 7.915339984765649e-34\n","The data does not follow normal distribution\n","\n","Column: BMI\n","Statistic: 0.9499890208244324\n","p_value: 1.8407586602041262e-15\n","The data does not follow normal distribution\n","\n","Column: DiabetesPedigreeFunction\n","Statistic: 0.8365188837051392\n","p_value: 2.4777990069755762e-27\n","The data does not follow normal distribution\n","\n","Column: Age\n","Statistic: 0.874765932559967\n","p_value: 2.401829612553989e-24\n","The data does not follow normal distribution\n","\n"]}],"source":["#use shpairo-wilk test to test for normality\n","\n","alpha = 0.05 #set significance level\n","for column in features_variables.columns:\n","    column_data = features_variables[column]\n","    statistic, p_value = shapiro(column_data)\n","    #print the test result for the current column\n","    print(\"Column:\", column)\n","    print(\"Statistic:\", statistic)\n","    print(\"p_value:\", p_value)\n","    \n","    #check for normality per column\n","    if (p_value < alpha):\n","        print(\"The data does not follow normal distribution\")\n","    else:\n","        print(\"The data follows normal distribution\")\n","    print()"]},{"cell_type":"markdown","id":"b229a5ae","metadata":{"papermill":{"duration":0.012589,"end_time":"2023-06-04T12:01:29.440679","exception":false,"start_time":"2023-06-04T12:01:29.42809","status":"completed"},"tags":[]},"source":["Discussion on test for normality: From the test and histogram, none of the variables follow a normal distribution"]},{"cell_type":"code","execution_count":14,"id":"76a7eac2","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:29.468864Z","iopub.status.busy":"2023-06-04T12:01:29.467787Z","iopub.status.idle":"2023-06-04T12:01:29.691301Z","shell.execute_reply":"2023-06-04T12:01:29.690236Z"},"papermill":{"duration":0.240183,"end_time":"2023-06-04T12:01:29.693738","exception":false,"start_time":"2023-06-04T12:01:29.453555","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<Axes: xlabel='Outcome', ylabel='count'>"]},"execution_count":14,"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":["#plot the distribution of the outcome column\n","\n","sns.countplot(x = outcome_variable, data = data, palette=['brown', 'blue'])"]},{"cell_type":"markdown","id":"54dec772","metadata":{"papermill":{"duration":0.013629,"end_time":"2023-06-04T12:01:29.720771","exception":false,"start_time":"2023-06-04T12:01:29.707142","status":"completed"},"tags":[]},"source":["There are more negative outcomes than there are positive outcomes"]},{"cell_type":"code","execution_count":15,"id":"99fb10ca","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:29.750577Z","iopub.status.busy":"2023-06-04T12:01:29.749821Z","iopub.status.idle":"2023-06-04T12:01:30.393068Z","shell.execute_reply":"2023-06-04T12:01:30.391912Z"},"papermill":{"duration":0.661669,"end_time":"2023-06-04T12:01:30.396109","exception":false,"start_time":"2023-06-04T12:01:29.73444","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#check the correlation between the variables\n","column_corr = data.corr()\n","heatmap = sns.heatmap(column_corr, annot=True, cmap=\"BuPu\", fmt='.1g')"]},{"cell_type":"markdown","id":"1ddb4543","metadata":{"papermill":{"duration":0.014653,"end_time":"2023-06-04T12:01:30.425742","exception":false,"start_time":"2023-06-04T12:01:30.411089","status":"completed"},"tags":[]},"source":["Discussion of correlation:\n","There is a moderate positive linear relationship between Glucose and diabetes outcome as well as age and pregnancies"]},{"cell_type":"markdown","id":"737b57a4","metadata":{"papermill":{"duration":0.014452,"end_time":"2023-06-04T12:01:30.454927","exception":false,"start_time":"2023-06-04T12:01:30.440475","status":"completed"},"tags":[]},"source":["# Logistic regression"]},{"cell_type":"code","execution_count":16,"id":"e902772a","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:30.486713Z","iopub.status.busy":"2023-06-04T12:01:30.485947Z","iopub.status.idle":"2023-06-04T12:01:30.491849Z","shell.execute_reply":"2023-06-04T12:01:30.491106Z"},"papermill":{"duration":0.024228,"end_time":"2023-06-04T12:01:30.493832","exception":false,"start_time":"2023-06-04T12:01:30.469604","status":"completed"},"tags":[]},"outputs":[],"source":["X = data.iloc[:, :-1]  # Select all columns except the last one\n","y = data.iloc[:, -1]   # Select only the last column"]},{"cell_type":"code","execution_count":17,"id":"3f7c14d1","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:30.525333Z","iopub.status.busy":"2023-06-04T12:01:30.524714Z","iopub.status.idle":"2023-06-04T12:01:30.548366Z","shell.execute_reply":"2023-06-04T12:01:30.547257Z"},"papermill":{"duration":0.042267,"end_time":"2023-06-04T12:01:30.550738","exception":false,"start_time":"2023-06-04T12:01:30.508471","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["0.96"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["# build the model\n","X, y = make_classification(random_state=42)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n","\n","pipe = make_pipeline(StandardScaler(), LogisticRegression())\n","\n","# apply scaling on training data\n","pipe.fit(X_train, y_train) \n","\n","# apply scaling on testing data, without leaking training data.\n","pipe.score(X_test, y_test)  "]},{"cell_type":"code","execution_count":18,"id":"5415da63","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:30.5834Z","iopub.status.busy":"2023-06-04T12:01:30.582954Z","iopub.status.idle":"2023-06-04T12:01:30.588471Z","shell.execute_reply":"2023-06-04T12:01:30.587422Z"},"papermill":{"duration":0.024237,"end_time":"2023-06-04T12:01:30.590687","exception":false,"start_time":"2023-06-04T12:01:30.56645","status":"completed"},"tags":[]},"outputs":[],"source":[" # testing a prediction on the trained model\n","y_pred = pipe.predict(X_test)"]},{"cell_type":"code","execution_count":19,"id":"42a31fe5","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:30.623078Z","iopub.status.busy":"2023-06-04T12:01:30.622666Z","iopub.status.idle":"2023-06-04T12:01:30.630172Z","shell.execute_reply":"2023-06-04T12:01:30.629217Z"},"papermill":{"duration":0.026484,"end_time":"2023-06-04T12:01:30.632449","exception":false,"start_time":"2023-06-04T12:01:30.605965","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy: 0.96\n"]}],"source":[" # testing a prediction on the trained model\n","accuracy = accuracy_score(y_test, y_pred)\n","print(\"Accuracy:\", accuracy)\n"]},{"cell_type":"code","execution_count":20,"id":"5908d4b1","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:30.664722Z","iopub.status.busy":"2023-06-04T12:01:30.66358Z","iopub.status.idle":"2023-06-04T12:01:30.671861Z","shell.execute_reply":"2023-06-04T12:01:30.67106Z"},"papermill":{"duration":0.026832,"end_time":"2023-06-04T12:01:30.674026","exception":false,"start_time":"2023-06-04T12:01:30.647194","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["array([[14,  1],\n","       [ 0, 10]])"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":[" # evaluate the model using confusion matrix\n","cm = confusion_matrix(y_true=y_test, y_pred=y_pred)\n","cm"]},{"cell_type":"code","execution_count":21,"id":"81f2f36c","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:30.706396Z","iopub.status.busy":"2023-06-04T12:01:30.706008Z","iopub.status.idle":"2023-06-04T12:01:30.975043Z","shell.execute_reply":"2023-06-04T12:01:30.974207Z"},"papermill":{"duration":0.28827,"end_time":"2023-06-04T12:01:30.977258","exception":false,"start_time":"2023-06-04T12:01:30.688988","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot confusion matrix in a beautiful manner\n","\n","class_names=[0,1] # name  of classes\n","fig, ax = plt.subplots()\n","tick_marks = np.arange(len(class_names))\n","plt.xticks(tick_marks, class_names)\n","plt.yticks(tick_marks, class_names)\n","\n","sns.heatmap(cm, annot=True, cmap=\"Blues\" ,fmt='g')\n","plt.ylabel('Actual label')\n","plt.xlabel('Predicted label')\n","plt.title('Confusion matrix')\n","plt.show()"]},{"cell_type":"markdown","id":"3cc3f079","metadata":{"papermill":{"duration":0.014833,"end_time":"2023-06-04T12:01:31.007806","exception":false,"start_time":"2023-06-04T12:01:30.992973","status":"completed"},"tags":[]},"source":["Discussing the confusion matrix:\n","Correctly classified positive diabates outcome for 10 out of 10 samples. \n","Correctly classified negative diabetes outcome for 14 out of 15 samples.\n","1 negative outcome was wrongly classified classified as positive.\n","No positive outcome was wrongly classified as positive.\n"]},{"cell_type":"code","execution_count":22,"id":"c5ea420f","metadata":{"execution":{"iopub.execute_input":"2023-06-04T12:01:31.040956Z","iopub.status.busy":"2023-06-04T12:01:31.040332Z","iopub.status.idle":"2023-06-04T12:01:31.052182Z","shell.execute_reply":"2023-06-04T12:01:31.050868Z"},"papermill":{"duration":0.030834,"end_time":"2023-06-04T12:01:31.054257","exception":false,"start_time":"2023-06-04T12:01:31.023423","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["                  precision    recall  f1-score   support\n","\n","without diabetes       1.00      0.93      0.97        15\n","   with diabetes       0.91      1.00      0.95        10\n","\n","        accuracy                           0.96        25\n","       macro avg       0.95      0.97      0.96        25\n","    weighted avg       0.96      0.96      0.96        25\n","\n"]}],"source":["# evaluate the confusion matrix using classification report\n","target_names = ['without diabetes', 'with diabetes']\n","print(classification_report(y_test, y_pred, target_names=target_names))"]},{"cell_type":"markdown","id":"1ebb1189","metadata":{"papermill":{"duration":0.015331,"end_time":"2023-06-04T12:01:31.085192","exception":false,"start_time":"2023-06-04T12:01:31.069861","status":"completed"},"tags":[]},"source":["Discussing the classification report: \n","The accuracy is good at 96%.\n","Overall, the classification report shows that the model performs well, with high precision, recall, and F1-score values for both classes, indicating a good ability to distinguish between the \"with diabetes\" and \"without diabetes\" classes. The high accuracy and consistent performance across metrics suggest that the model is reliable for this classification task."]},{"cell_type":"markdown","id":"20930527","metadata":{"papermill":{"duration":0.015308,"end_time":"2023-06-04T12:01:31.115972","exception":false,"start_time":"2023-06-04T12:01:31.100664","status":"completed"},"tags":[]},"source":["# References"]},{"cell_type":"markdown","id":"20dc08d5","metadata":{"papermill":{"duration":0.015221,"end_time":"2023-06-04T12:01:31.147047","exception":false,"start_time":"2023-06-04T12:01:31.131826","status":"completed"},"tags":[]},"source":["Code for Confusion matrix: https://thatascience.com/learn-machine-learning/confusion-matrix/\n","\n","Code for data preprocessing and logistic regression: https://scikit-learn.org/stable/modules/preprocessing.html\n","\n","Code for decision matrix: https://www.datacamp.com/tutorial/understanding-logistic-regression-python?utm_source=google&utm_medium=paid_search&utm_campaignid=19589720827&utm_adgroupid=143216588617&utm_device=c&utm_keyword=&utm_matchtype=&utm_network=g&utm_adpostion=&utm_creative=657040116639&utm_targetid=dsa-1947282172981&utm_loc_interest_ms=&utm_loc_physical_ms=9046400&utm_content=dsa~page~community-tuto&utm_campaign=230119_1-sea~dsa~tutorials_2-b2c_3-uk_4-prc_5-na_6-na_7-le_8-pdsh-go_9-na_10-na_11-na-mayftyc23&gclid=CjwKCAjwg-GjBhBnEiwAMUvNWwQKKpWr3MTxY5kOjL951XSuByOf3GWV4wjwuTRUs1ysu-ukCo1tdRoC-JYQAvD_BwE"]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.10.10"},"papermill":{"default_parameters":{},"duration":19.868579,"end_time":"2023-06-04T12:01:32.185033","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-06-04T12:01:12.316454","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}