From c880ccfeb88ed84bb40ae984b182204e665d3be1 Mon Sep 17 00:00:00 2001
From: s2-gado <shekwoyeyilo2.gado@live.uwe.ac.uk>
Date: Wed, 26 Feb 2025 15:50:48 +0000
Subject: [PATCH] Upload New File
---
preprocessing.ipynb | 2827 +++++++++++++++++++++++++++++++++++++++++++
1 file changed, 2827 insertions(+)
create mode 100644 preprocessing.ipynb
diff --git a/preprocessing.ipynb b/preprocessing.ipynb
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--- /dev/null
+++ b/preprocessing.ipynb
@@ -0,0 +1,2827 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from scipy.stats import pearsonr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
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+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2149 rows × 35 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " PatientID Age Gender Ethnicity EducationLevel BMI Smoking \\\n",
+ "0 4751 73 0 0 2 22.927749 0 \n",
+ "1 4752 89 0 0 0 26.827681 0 \n",
+ "2 4753 73 0 3 1 17.795882 0 \n",
+ "3 4754 74 1 0 1 33.800817 1 \n",
+ "4 4755 89 0 0 0 20.716974 0 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "2144 6895 61 0 0 1 39.121757 0 \n",
+ "2145 6896 75 0 0 2 17.857903 0 \n",
+ "2146 6897 77 0 0 1 15.476479 0 \n",
+ "2147 6898 78 1 3 1 15.299911 0 \n",
+ "2148 6899 72 0 0 2 33.289738 0 \n",
+ "\n",
+ " AlcoholConsumption PhysicalActivity DietQuality ... \\\n",
+ "0 13.297218 6.327112 1.347214 ... \n",
+ "1 4.542524 7.619885 0.518767 ... \n",
+ "2 19.555085 7.844988 1.826335 ... \n",
+ "3 12.209266 8.428001 7.435604 ... \n",
+ "4 18.454356 6.310461 0.795498 ... \n",
+ "... ... ... ... ... \n",
+ "2144 1.561126 4.049964 6.555306 ... \n",
+ "2145 18.767261 1.360667 2.904662 ... \n",
+ "2146 4.594670 9.886002 8.120025 ... \n",
+ "2147 8.674505 6.354282 1.263427 ... \n",
+ "2148 7.890703 6.570993 7.941404 ... \n",
+ "\n",
+ " MemoryComplaints BehavioralProblems ADL Confusion \\\n",
+ "0 0 0 1.725883 0 \n",
+ "1 0 0 2.592424 0 \n",
+ "2 0 0 7.119548 0 \n",
+ "3 0 1 6.481226 0 \n",
+ "4 0 0 0.014691 0 \n",
+ "... ... ... ... ... \n",
+ "2144 0 0 4.492838 1 \n",
+ "2145 0 1 9.204952 0 \n",
+ "2146 0 0 5.036334 0 \n",
+ "2147 0 0 3.785399 0 \n",
+ "2148 0 1 8.327563 0 \n",
+ "\n",
+ " Disorientation PersonalityChanges DifficultyCompletingTasks \\\n",
+ "0 0 0 1 \n",
+ "1 0 0 0 \n",
+ "2 1 0 1 \n",
+ "3 0 0 0 \n",
+ "4 0 1 1 \n",
+ "... ... ... ... \n",
+ "2144 0 0 0 \n",
+ "2145 0 0 0 \n",
+ "2146 0 0 0 \n",
+ "2147 0 0 0 \n",
+ "2148 1 0 0 \n",
+ "\n",
+ " Forgetfulness Diagnosis DoctorInCharge \n",
+ "0 0 0 XXXConfid \n",
+ "1 1 0 XXXConfid \n",
+ "2 0 0 XXXConfid \n",
+ "3 0 0 XXXConfid \n",
+ "4 0 0 XXXConfid \n",
+ "... ... ... ... \n",
+ "2144 0 1 XXXConfid \n",
+ "2145 0 1 XXXConfid \n",
+ "2146 0 1 XXXConfid \n",
+ "2147 1 1 XXXConfid \n",
+ "2148 1 0 XXXConfid \n",
+ "\n",
+ "[2149 rows x 35 columns]"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a_df = pd.read_csv('alzheimers_disease_data.csv')\n",
+ "a_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " <th>Ethnicity</th>\n",
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+ " <th>AlcoholConsumption</th>\n",
+ " <th>PhysicalActivity</th>\n",
+ " <th>DietQuality</th>\n",
+ " <th>...</th>\n",
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+ " <th>Forgetfulness</th>\n",
+ " <th>Diagnosis</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
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+ " <td>2149.000000</td>\n",
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+ " <td>2149.000000</td>\n",
+ " <td>2149.000000</td>\n",
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+ " <td>5825.000000</td>\n",
+ " <td>74.908795</td>\n",
+ " <td>0.506282</td>\n",
+ " <td>0.697534</td>\n",
+ " <td>1.286645</td>\n",
+ " <td>27.655697</td>\n",
+ " <td>0.288506</td>\n",
+ " <td>10.039442</td>\n",
+ " <td>4.920202</td>\n",
+ " <td>4.993138</td>\n",
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+ " <td>0.208004</td>\n",
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+ " <td>4.982958</td>\n",
+ " <td>0.205212</td>\n",
+ " <td>0.158213</td>\n",
+ " <td>0.150768</td>\n",
+ " <td>0.158678</td>\n",
+ " <td>0.301536</td>\n",
+ " <td>0.353653</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>620.507185</td>\n",
+ " <td>8.990221</td>\n",
+ " <td>0.500077</td>\n",
+ " <td>0.996128</td>\n",
+ " <td>0.904527</td>\n",
+ " <td>7.217438</td>\n",
+ " <td>0.453173</td>\n",
+ " <td>5.757910</td>\n",
+ " <td>2.857191</td>\n",
+ " <td>2.909055</td>\n",
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+ " <td>2.892743</td>\n",
+ " <td>0.405974</td>\n",
+ " <td>0.363713</td>\n",
+ " <td>2.949775</td>\n",
+ " <td>0.403950</td>\n",
+ " <td>0.365026</td>\n",
+ " <td>0.357906</td>\n",
+ " <td>0.365461</td>\n",
+ " <td>0.459032</td>\n",
+ " <td>0.478214</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>4751.000000</td>\n",
+ " <td>60.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>15.008851</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.002003</td>\n",
+ " <td>0.003616</td>\n",
+ " <td>0.009385</td>\n",
+ " <td>...</td>\n",
+ " <td>0.000460</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.001288</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>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>5288.000000</td>\n",
+ " <td>67.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>21.611408</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>5.139810</td>\n",
+ " <td>2.570626</td>\n",
+ " <td>2.458455</td>\n",
+ " <td>...</td>\n",
+ " <td>2.566281</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>2.342836</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>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>5825.000000</td>\n",
+ " <td>75.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
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+ " <td>9.934412</td>\n",
+ " <td>4.766424</td>\n",
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+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>6362.000000</td>\n",
+ " <td>83.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>33.869778</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>15.157931</td>\n",
+ " <td>7.427899</td>\n",
+ " <td>7.558625</td>\n",
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+ " <td>7.546981</td>\n",
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+ " <td>0.000000</td>\n",
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+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>6899.000000</td>\n",
+ " <td>90.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>39.992767</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>19.989293</td>\n",
+ " <td>9.987429</td>\n",
+ " <td>9.998346</td>\n",
+ " <td>...</td>\n",
+ " <td>9.996467</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>9.999747</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>8 rows × 34 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " PatientID Age Gender Ethnicity EducationLevel \\\n",
+ "count 2149.000000 2149.000000 2149.000000 2149.000000 2149.000000 \n",
+ "mean 5825.000000 74.908795 0.506282 0.697534 1.286645 \n",
+ "std 620.507185 8.990221 0.500077 0.996128 0.904527 \n",
+ "min 4751.000000 60.000000 0.000000 0.000000 0.000000 \n",
+ "25% 5288.000000 67.000000 0.000000 0.000000 1.000000 \n",
+ "50% 5825.000000 75.000000 1.000000 0.000000 1.000000 \n",
+ "75% 6362.000000 83.000000 1.000000 1.000000 2.000000 \n",
+ "max 6899.000000 90.000000 1.000000 3.000000 3.000000 \n",
+ "\n",
+ " BMI Smoking AlcoholConsumption PhysicalActivity \\\n",
+ "count 2149.000000 2149.000000 2149.000000 2149.000000 \n",
+ "mean 27.655697 0.288506 10.039442 4.920202 \n",
+ "std 7.217438 0.453173 5.757910 2.857191 \n",
+ "min 15.008851 0.000000 0.002003 0.003616 \n",
+ "25% 21.611408 0.000000 5.139810 2.570626 \n",
+ "50% 27.823924 0.000000 9.934412 4.766424 \n",
+ "75% 33.869778 1.000000 15.157931 7.427899 \n",
+ "max 39.992767 1.000000 19.989293 9.987429 \n",
+ "\n",
+ " DietQuality ... FunctionalAssessment MemoryComplaints \\\n",
+ "count 2149.000000 ... 2149.000000 2149.000000 \n",
+ "mean 4.993138 ... 5.080055 0.208004 \n",
+ "std 2.909055 ... 2.892743 0.405974 \n",
+ "min 0.009385 ... 0.000460 0.000000 \n",
+ "25% 2.458455 ... 2.566281 0.000000 \n",
+ "50% 5.076087 ... 5.094439 0.000000 \n",
+ "75% 7.558625 ... 7.546981 0.000000 \n",
+ "max 9.998346 ... 9.996467 1.000000 \n",
+ "\n",
+ " BehavioralProblems ADL Confusion Disorientation \\\n",
+ "count 2149.000000 2149.000000 2149.000000 2149.000000 \n",
+ "mean 0.156817 4.982958 0.205212 0.158213 \n",
+ "std 0.363713 2.949775 0.403950 0.365026 \n",
+ "min 0.000000 0.001288 0.000000 0.000000 \n",
+ "25% 0.000000 2.342836 0.000000 0.000000 \n",
+ "50% 0.000000 5.038973 0.000000 0.000000 \n",
+ "75% 0.000000 7.581490 0.000000 0.000000 \n",
+ "max 1.000000 9.999747 1.000000 1.000000 \n",
+ "\n",
+ " PersonalityChanges DifficultyCompletingTasks Forgetfulness \\\n",
+ "count 2149.000000 2149.000000 2149.000000 \n",
+ "mean 0.150768 0.158678 0.301536 \n",
+ "std 0.357906 0.365461 0.459032 \n",
+ "min 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 0.000000 0.000000 \n",
+ "50% 0.000000 0.000000 0.000000 \n",
+ "75% 0.000000 0.000000 1.000000 \n",
+ "max 1.000000 1.000000 1.000000 \n",
+ "\n",
+ " Diagnosis \n",
+ "count 2149.000000 \n",
+ "mean 0.353653 \n",
+ "std 0.478214 \n",
+ "min 0.000000 \n",
+ "25% 0.000000 \n",
+ "50% 0.000000 \n",
+ "75% 1.000000 \n",
+ "max 1.000000 \n",
+ "\n",
+ "[8 rows x 34 columns]"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a_df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "PatientID 0\n",
+ "Age 0\n",
+ "Gender 0\n",
+ "Ethnicity 0\n",
+ "EducationLevel 0\n",
+ "BMI 0\n",
+ "Smoking 0\n",
+ "AlcoholConsumption 0\n",
+ "PhysicalActivity 0\n",
+ "DietQuality 0\n",
+ "SleepQuality 0\n",
+ "FamilyHistoryAlzheimers 0\n",
+ "CardiovascularDisease 0\n",
+ "Diabetes 0\n",
+ "Depression 0\n",
+ "HeadInjury 0\n",
+ "Hypertension 0\n",
+ "SystolicBP 0\n",
+ "DiastolicBP 0\n",
+ "CholesterolTotal 0\n",
+ "CholesterolLDL 0\n",
+ "CholesterolHDL 0\n",
+ "CholesterolTriglycerides 0\n",
+ "MMSE 0\n",
+ "FunctionalAssessment 0\n",
+ "MemoryComplaints 0\n",
+ "BehavioralProblems 0\n",
+ "ADL 0\n",
+ "Confusion 0\n",
+ "Disorientation 0\n",
+ "PersonalityChanges 0\n",
+ "DifficultyCompletingTasks 0\n",
+ "Forgetfulness 0\n",
+ "Diagnosis 0\n",
+ "DoctorInCharge 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#checking for null values\n",
+ "a_df.isna().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#dropping the columns\n",
+ "df =a_df.drop(['DoctorInCharge', 'PatientID'], axis=1, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
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+ " <td>1</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
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+ " <td>1</td>\n",
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+ " <td>1</td>\n",
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+ " <td>0</td>\n",
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+ " <td>0</td>\n",
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+ " </tbody>\n",
+ "</table>\n",
+ "<p>2149 rows × 33 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Age Gender Ethnicity EducationLevel BMI Smoking \\\n",
+ "0 73 0 0 2 22.927749 0 \n",
+ "1 89 0 0 0 26.827681 0 \n",
+ "2 73 0 3 1 17.795882 0 \n",
+ "3 74 1 0 1 33.800817 1 \n",
+ "4 89 0 0 0 20.716974 0 \n",
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+ "2144 61 0 0 1 39.121757 0 \n",
+ "2145 75 0 0 2 17.857903 0 \n",
+ "2146 77 0 0 1 15.476479 0 \n",
+ "2147 78 1 3 1 15.299911 0 \n",
+ "2148 72 0 0 2 33.289738 0 \n",
+ "\n",
+ " AlcoholConsumption PhysicalActivity DietQuality SleepQuality ... \\\n",
+ "0 13.297218 6.327112 1.347214 9.025679 ... \n",
+ "1 4.542524 7.619885 0.518767 7.151293 ... \n",
+ "2 19.555085 7.844988 1.826335 9.673574 ... \n",
+ "3 12.209266 8.428001 7.435604 8.392554 ... \n",
+ "4 18.454356 6.310461 0.795498 5.597238 ... \n",
+ "... ... ... ... ... ... \n",
+ "2144 1.561126 4.049964 6.555306 7.535540 ... \n",
+ "2145 18.767261 1.360667 2.904662 8.555256 ... \n",
+ "2146 4.594670 9.886002 8.120025 5.769464 ... \n",
+ "2147 8.674505 6.354282 1.263427 8.322874 ... \n",
+ "2148 7.890703 6.570993 7.941404 9.878711 ... \n",
+ "\n",
+ " FunctionalAssessment MemoryComplaints BehavioralProblems ADL \\\n",
+ "0 6.518877 0 0 1.725883 \n",
+ "1 7.118696 0 0 2.592424 \n",
+ "2 5.895077 0 0 7.119548 \n",
+ "3 8.965106 0 1 6.481226 \n",
+ "4 6.045039 0 0 0.014691 \n",
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+ "2144 0.238667 0 0 4.492838 \n",
+ "2145 8.687480 0 1 9.204952 \n",
+ "2146 1.972137 0 0 5.036334 \n",
+ "2147 5.173891 0 0 3.785399 \n",
+ "2148 6.307543 0 1 8.327563 \n",
+ "\n",
+ " Confusion Disorientation PersonalityChanges \\\n",
+ "0 0 0 0 \n",
+ "1 0 0 0 \n",
+ "2 0 1 0 \n",
+ "3 0 0 0 \n",
+ "4 0 0 1 \n",
+ "... ... ... ... \n",
+ "2144 1 0 0 \n",
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+ "2146 0 0 0 \n",
+ "2147 0 0 0 \n",
+ "2148 0 1 0 \n",
+ "\n",
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+ "3 0 0 0 \n",
+ "4 1 0 0 \n",
+ "... ... ... ... \n",
+ "2144 0 0 1 \n",
+ "2145 0 0 1 \n",
+ "2146 0 0 1 \n",
+ "2147 0 1 1 \n",
+ "2148 0 1 0 \n",
+ "\n",
+ "[2149 rows x 33 columns]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Age True\n",
+ "Gender False\n",
+ "Ethnicity False\n",
+ "EducationLevel False\n",
+ "BMI True\n",
+ "Smoking False\n",
+ "AlcoholConsumption True\n",
+ "PhysicalActivity True\n",
+ "DietQuality True\n",
+ "SleepQuality True\n",
+ "FamilyHistoryAlzheimers False\n",
+ "CardiovascularDisease False\n",
+ "Diabetes False\n",
+ "Depression False\n",
+ "HeadInjury False\n",
+ "Hypertension False\n",
+ "SystolicBP True\n",
+ "DiastolicBP True\n",
+ "CholesterolTotal True\n",
+ "CholesterolLDL True\n",
+ "CholesterolHDL True\n",
+ "CholesterolTriglycerides True\n",
+ "MMSE True\n",
+ "FunctionalAssessment True\n",
+ "MemoryComplaints False\n",
+ "BehavioralProblems False\n",
+ "ADL True\n",
+ "Confusion False\n",
+ "Disorientation False\n",
+ "PersonalityChanges False\n",
+ "DifficultyCompletingTasks False\n",
+ "Forgetfulness False\n",
+ "Diagnosis False\n",
+ "dtype: bool"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a_df.all()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Demographic Details\n",
+ "\n",
+ "Age: The age of the patients ranges from 60 to 90 years.\n",
+ "\n",
+ "Gender: Gender of the patients, where 0 represents Male and 1 represents Female.\n",
+ "\n",
+ "Ethnicity: The ethnicity of the patients, coded as follows:\n",
+ "\n",
+ "0: Caucasian\n",
+ "\n",
+ "1: African American\n",
+ "\n",
+ "2: Asian\n",
+ "\n",
+ "3: Other\n",
+ "\n",
+ "EducationLevel: The education level of the patients, coded as follows:\n",
+ "\n",
+ "0: None\n",
+ "\n",
+ "1: High School\n",
+ "\n",
+ "2: Bachelor's\n",
+ "\n",
+ "3: Higher"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Age</th>\n",
+ " <th>Gender</th>\n",
+ " <th>Ethnicity</th>\n",
+ " <th>EducationLevel</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>73</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>89</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>73</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>74</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>89</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2144</th>\n",
+ " <td>61</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2145</th>\n",
+ " <td>75</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2146</th>\n",
+ " <td>77</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2147</th>\n",
+ " <td>78</td>\n",
+ " <td>1</td>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2148</th>\n",
+ " <td>72</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2149 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Age Gender Ethnicity EducationLevel\n",
+ "0 73 0 0 2\n",
+ "1 89 0 0 0\n",
+ "2 73 0 3 1\n",
+ "3 74 1 0 1\n",
+ "4 89 0 0 0\n",
+ "... ... ... ... ...\n",
+ "2144 61 0 0 1\n",
+ "2145 75 0 0 2\n",
+ "2146 77 0 0 1\n",
+ "2147 78 1 3 1\n",
+ "2148 72 0 0 2\n",
+ "\n",
+ "[2149 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "demographicdf = a_df[['Age','Gender','Ethnicity','EducationLevel']]\n",
+ "demographicdf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: >"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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A8kc3LzE0uTf5yj3/XKIatjQoQBIh6HowfnIPYHLPP5doWpc4eOvfoQLduMm9C4YQQ6Mav7xwVcPWFvVhk+yo8CFEO0ZxD1CTuDR4qzHzlh+SP6MofIis8PaQyFt+DIFRwJYGbwGPt/wQYup4C0i8BTze8kPExdUocarREkLyQwGJ5EI1bGnwFuDpAYIQvvBWw9Y3uZdZhllLnEaJywINOhOXqRWGRH5M7ZrjrUmfyzKRatjyQDVsQoiUeCtTeAvwRFxcBWwKkIQQwi/aXlNaXC2cQgghYuJt6idv6RtDpYcxCtiiMIaLwZTQAxbhHW9N0NqSexM3lenSoiZxQohsyP2hUu755xI1iYuDLk5CCDEeXJbpFLDFQTVgeeGtuU7f6PokQpnaPUOkxVWTuLaoCV1cVJgQY8PbPS/3AM/jKHFaS1wiFICJIdH1Zvx4+415C8Da4jL/FLAJkR+5117I5/H2G9M1RAyJq0Fncp+yQfJHtRciFG+/MW8PECbJdJYS56uGzdvFTAFeXLz9voQIZWrXNPVhS4urUeK81Zh5yw8RF/1ehHe81eC5fEChgC0PFFCJEHT9EEPjLQATeZF1wOZtGUGSPyqsiLGha5oD1IctDQqQ0tJ34cPbIERChKJrWnrUh00AmF6TKW+FA2/5IUQouqaJEFwFbFMLkIQQQgSiJnFpUAAmhMiJ3Ju45Z5/gJrEJcNbDdvUHiCM4eYlxJDkfg/IPf+mhquATaRFNy8h+sXbQzFv+dEJNYmLg7cas7bknn9tmdpCNFwWPsSo8XbN8ZYfXTAK2NLgrUCXewDWFj3NE8IXumdIdlwFbN7w9gAhd1SYEFNHZYQeUA1bHHJfiINuLkKImOT+0Mrn5h96PwU3qA87H3LPv7ao+Y3wjrdrlLcNjkytzAJANWyxyP1iM4qLWQtyD8C8FeZEfLz9ZnLfHYu375PkT9Y1bLmnL3e8fZ9U+Bg/3q4J3vKjLX2XWdQkLi6qYROd0dM/MTTergne8qMtuecfkDZgL1myBLNmzUJiYiJq1qyJRYsWoUGDBnl+PikpCWPGjMG2bdvw6tUrlC1bFvPnz0e7du0KdD4aJU50RrULYmhyv+bkjsdBZ1LZuHEjQkNDERYWhoYNG2L+/Plo06YNbt68CScnp1yfT0tLQ6tWreDk5IQtW7bAxcUFDx48QJEiRQp8TgVjrMALsZpbuhQ4YWNgajV+KtzyJ/ff1xhQE7e8WBT31Ps5njb/WpR0nI/EaPX5hg0bon79+li8eDEAIDMzE66urggODsaoUaNyfT4sLAyzZs3CjRs3YGFhoVMeuerD1hYVoNKiwo0QeTOKe5gpREkmNTUVqampasesrKxgZWWV67NpaWm4cOECRo8erTqmVCrh5eWF06dPa0x/165daNy4MX766Sfs3LkTJUqUQI8ePTBy5EiYmZkVKI9c9WETaRnFzasFuj6NH/3GpKCmT5+OiRMnqh0bP348JkyYkOuzL168QEZGBpydndWOOzs748aNGxrTv3fvHg4fPoyePXti7969uHPnDgYOHIiPHz9i/PjxBcojVzVs3m4u3vJDxCX369MU0UBWcen7+5HTKPHRo0cjNDRU7Zim2rWuMjMz4eTkhD/++ANmZmaoW7cuHj16hFmzZvERsOnmkhfeasymVuMnhHc83mMsU5wm8byavzUpXrw4zMzM8PTpU7XjT58+RcmSJTX+P6VKlYKFhYVa83flypWRmJiItLQ0WFpafva8NEo8H6b2AEEPWIR3PAYMOaOHYt1YWlqibt26iI6Ohp+fH4BPNejo6GgMGjRI4//TtGlTrF+/HpmZmVAqlQCAW7duoVSpUgUK1gAFbGJAdLMToUwtwNBD7udJNQ87NDQUAQEBqFevHho0aID58+cjJSUFgYGBAAB/f3+4uLhg+vTpAIABAwZg8eLFGDx4MIKDg3H79m1MmzYNISEhBT4nVwGbLk5pmdpKZHS9GT+5/8Zyv2cM0oct0ihxbXXt2hXPnz/HuHHjkJiYiFq1amHfvn2qgWgJCQmqmjQAuLq6Yv/+/Rg6dChq1KgBFxcXDB48GCNHjizwObmah81bEytv+dE33goHfZP770U+z9QeQvWNx4D9b8MWoqRT5u/DoqSjT1yNEidECOqDN35UpkiLvn9pyXqUOBW4JDsqTIih8faQaIr3gFijxOVA1jVsCsDiosKBEO3QBjjSK3inrvzJuoZNxEXfPyEkPzz2YZsSWY8SpyZxQkyL3CsBcs8/j6hJXCK8XZwU4AnhC5UR4jKGBwgK2DIh95uFEKJfvJURvA06M4a1xE0JVwGbtyZxanIXF2/z5onx462M0BZv1zRv+QFo0JlkKEAaN/p9CeELbw8ouqAmcZHw+OMS8RjDzU6IKaN7Ul5oWhfRmak1BxJiaLyVoTz2YUu1lrgUuGoSpyZQeaE+aUL0i+6Bz5Nqty4pcBWwiXGjwocQvhhDJSmTatjikPsgI97ywxu5j8AlxNjQPWbcuKph89b/QvIn98KBrgdi6vR9D1Aftri42g9bW7zV8OQeAOQeUOmBj/COt3tMWzwOOrvxRTtR0ql0a68o6eiTrJvEqQA1bnIv3Ij4eHuIlvtDor5XRiPioiZxET8vd7zVaAkRircAry3eykRtGaZJXO+n4AZXAVvuNwtv+dcWBWBCTIsx3PO00plIeKuxyT2g6pvcfy9jKHyIvPDWZE2MG1d92NqiACwu3goH3gYVEpKTqV1DPA46o3nYIpH71nD0QCAvptaFYYrk/lDGW/55y48uTGlal6z7sHlby5oCgLzQ7ys/PAYMbfCWf97yQ/JHTeKEEGKieKsk6YJGiRNCCPksuQc83vKjC+rDlgjVmAkhcmIMAU/uqA+bED2Qe22EEKHoHiBCcBWweRsERDV+cVHhQ4SSe8DjbVwPb9+PLqgPmxBCOMRbgOEtQPL2/RgC9WGLRO41VN5q/PrGW+HDW36I9Hi7JuiaI4ZE07qICm+FD2/5IUQo3h44jAENOpMJWulMXqiwIqbO1CoxtDSpuLhqEje1Jmje8LaULCE5mdo1xFsZZ2rfP29k3SROxEW/FyHa4a3VyBTvYRMaJE5N4lKmTwiRN1MMkLyhJnGRyD1AmloApr19CdEOb/cMbzV+Ii5qEic6o9+XmDpTuwd43A+bRolLhLcaLW81fn3jrfCh2gLJibdrgrcatqmVWQCQKXUGDIiaxAkhssHbQxlv+dGW3PMPAAxUwyagBwKpGUNhQsTFWw2bEEPiqg+btwAp9/wTYuronsyfvr8fwyycovdTcEPWNWwKqNKi2g4xNLn3SfPGGO7JTGoSl4YxXDymhH4vwjtTC8D6xuMocVNCg86IzuT++9IDh/Hj7ZqTOx7vGRp0JhLeJv3T5h/5462Jm8fCgRAheLvHjAFN65IIb0/DvOVH30ytcJD770Xkx9TuMSIurgI2FaAkO952D6Prk+TEW6ugKT4QUJM4IRygEcEkJ94CmNzTN4ppXXo/Az8oYBNu8VY4E+nRbywtHr9/Ctgi4a3ApRpV/nj7vXhLn64f6fF2jcq9SZzHAEzyxtUocW3R9pri4u334q1wIyQnuT/0GUOTOPVhE8IBCsDE2PB2jcr9gQMAMk0nXlPAzo8xXMw84a2wot9Lfni7hnhD17Rxo4BNVHir0fKWH0KE4m3tCGNAa4mLhApcIgQNOiOmjrcykce1xE1osy6+athUIEqLt8JB3+h6I4ZmavcYERdXAZs3plagm1qLCNWwCdEOj/c8zcOWCG8FKG/5IYTwRe5lEI8BWFuZCurDJkRyplj4EHnh7ZqjAG/cuArYVEOVFt2MhPCFtxo8j2jQmUR4a4KmBwhpGUNhQogQNHXy86gPWyZ4C/Akf8ZQOBAiBG/3gDHcY7TSmUxQAJYXYygciLR4C3ja4i0/xrCWuCmR9eYf+kY1eEL4wluZIvcNbXj7PnUh5UpnS5YswaxZs5CYmIiaNWti0aJFaNCgwWf/v6ioKHTv3h3ffPMNduzYUeDzcbXSGW/knn+SP/p9iVDUZC09qQadbdy4EaGhoQgLC0PDhg0xf/58tGnTBjdv3oSTk1Oe/9/9+/cxfPhwfPWV9uWPrJvE9Y1q2PkzteZJU/t9jQH9ZuLicWlSqcydOxf9+/dHYGAgACAsLAx79uzBypUrMWrUKI3/T0ZGBnr27ImJEyfi+PHjSEpK0uqcXAVsKkClJfcATEhOVKbkzxjuebEGnaWmpiI1NVXtmJWVFaysrHJ9Ni0tDRcuXMDo0aNVx5RKJby8vHD69Ok8zzFp0iQ4OTmhb9++OH5c+++Sq4BNpMXbFBJtUeFMiHZ4DMDaEmta1/Tp0zFx4kS1Y+PHj8eECRNyffbFixfIyMiAs7Oz2nFnZ2fcuHFDY/onTpxAeHg4Ll++rHMeKWDngwp0cfFWOFCAJ4RkGT16NEJDQ9WOaapd6+Lt27fo3bs3li9fjuLFi+ucDlejxHkrEOWef0IIMXZiDTrLq/lbk+LFi8PMzAxPnz5VO/706VOULFky1+fv3r2L+/fvw8fHR3UsM/NT24C5uTlu3ryJcuXKffa8XI0Sl3uTrNzx9ntpS+75J0QoU7wHpFg4xdLSEnXr1kV0dDT8/Pw+5SMzE9HR0Rg0aFCuz1eqVAlXrlxROzZ27Fi8ffsWCxYsgKura4HOy1UNW1sUgMXF2xxRbRlD4UOIELyVucY8Sjw0NBQBAQGoV68eGjRogPnz5yMlJUU1atzf3x8uLi6YPn06rK2tUa1aNbX/v0iRIgCQ63h+TKqGrS1TeyDg7ffiLT+EGBtjuMekWku8a9eueP78OcaNG4fExETUqlUL+/btUw1ES0hIgFKpFPWcCsZYgbsAzC1dRD0570ytD5vHm5Encv99jQE9JMqLRXFPvZ9jWZleoqTzw79rRUlHn2TdJK4tbW9GUyugqbAiRDt0z0iP0eYf4pB7wKMatnGT++9FCDEtNA+bGAxvtRFTeyAj4pP7NWQMD+m0H7ZI5H4x85YfuTOGwoEQIeR+D/A4SpwCtkR4C/C85YcQwhfeWo30Te75lztZ92FTQCWESIm3AMbb/tyGqGFLtb2mFLiqYWuLArC8mFpthBBjw+M9KcVKZ1Lhqg9bWxSwxaXvgMrjzU6IELw9hNI9Ztxk3SROCCFSMrUAyWOTOA06Ewlva1PTwin5M7XChxCheKthm2IrGQVskeg74JlaQNU33gofQnjH2z3AW36IuLiqYesbBfj88fZ76RtdD8TUGcNDOo0SlwmaNkay461LhRDe8RiAtUWjxCVCBSIRwhgKHyIuKlPyp+/vhwadiYurPmzeBmQQQuSNWlGIMaEaNlHh7QGLEFNHZeLnUR+2ROhpWFoUgImxkXsZwds9yeP3mWlCIZurgM3bxcBbfggh2qFKgLh4e4AwNTStKx90sxNCxETdTuKjQWcikXuApABMCF/kHvB4W/1RWzwuTWo6DeKc1bB5C/C8PUDwhrfChBg/U7uGeCuDTO375w1XNWze0jc1vAVgKnwI0Y4pXtPUJC4SuRfoJH+muNEAkRZvD5Xaknv+eUQrnUmEArC8UGFCDI23a463AMxbfoi4uArYvPXXmBq6eQnRDm/3DG/5MQSah00IB6i2QHgn92tU7vkHaJS4aOReA5Z7/uWOx8KByAsNXMyf3PMP0KAz0fDWX6MtaqKXF/r+CdGOMezWZUq4ahKnApcYEj2QyQ+tpSAuY/h+qA+bEBkyhuY9kj/6jaXF4/dvOuGas4BtDE97RDzGMCCGEELEQoPOCLcoABOiHVN8yKVBZyKRe42Zt/zwRu6FA/2+xNjwNtDXEIPOqA9bJHIfIMJbfvTN1AKwqf2+hAjF2z1variqYes7fSpw8yf3m1Hu+SeEaM906tecDTrTFgVgcfFWw+YtP4QQ/lAftkgooMqLqQU8uj7lR+4PcaaWf1o4RVxc1bB5q7FRgW7cqEtFfngLYNriLf9yf4AAAGZCjeJcBWzeCkQq0KXFY+FASHZyD3i85UcX1CROTJLcCx9CDI3uAenRtC6R8HYxUw04f7x1SfB2/RBCiJRkPeiMCnRx6Tug0u9FhKKHPnEZw25dplO/lnmTONWYxUWFGzE2+i4j5P4AYQzjdKhJXCLGcPEQ+aDrx/jpu0zhLQBry9T+Xrnjqg+bClBCSH54CxhyD3i85UcXNEpcJLz1YdMDAcmOrh8ilDEEPLmjedgi0XeByFv/FCGEL3QPE2NCfdiEEKNFZUr+5N6kD1CTuGzwNqBE7jc7b10YhJg6uZcphkBN4iKhi01eKKASwhe6J0l2su7DlnsfOW/k3jwm9/yTz+PtN6b8SI+axAkA02sSlztjKHxI/ug3zh9vDwSGWOksk1GTuCh4qzETcZni0zwh2cn9mjaGe9h0wjXVsIkAPN68hBgTWt+fZCfrPmxi3Izh6Z8QIXi7prlsEjehOjZXNWze+l9MDW+FA2/5IdKjhziSE03rkgkKwPJChS0Riq4JadH3Ly1ZDzojhJgW3soU3vJjimhal0jkfnFSDV5ccp+XT0hOvF3Tpoj6sAkA0wsAvBUmvKUv99+XiI+3e0bfeBx0Zkq4Cti8FYi85UffqLZAeEfXkLR4/P5p0JlI5N6kyVt+eMPbzUsPEMTQ5H4NGcM9Q33YBIDpBWC537z0gEWIdni7h0n+uArYvBWgphYATK1J3NR+XyI+3u4B3lodDYFJuJb4kiVLMGvWLCQmJqJmzZpYtGgRGjRooPGzy5cvx5o1a3D16lUAQN26dTFt2rQ8P68JV6PEeWtCpwI6fzzevMS48XZP8haAecuPMa90tnHjRoSGhiIsLAwNGzbE/Pnz0aZNG9y8eRNOTk65Pn/06FF0794dTZo0gbW1NWbOnInWrVvjn3/+gYuLS4HOqWBaPJ6YWxYs0Sy8XTzaMrUaGG9/L28PBHL/fY0Bb2UK3TP5syjuqfdz+Lh1ECWd3Ql/avX5hg0bon79+li8eDEAIDMzE66urggODsaoUaM++/9nZGTA0dERixcvhr+/f4HOyVWTuLZ4u1lMjdwLT0IMjbdrlO6x/5OamorU1FS1Y1ZWVrCyssr12bS0NFy4cAGjR49WHVMqlfDy8sLp06cLdL53797h48ePKFq0aIHzyFXA5i2g8pYfU2PMhQPRDQUYafHYJC7WtK7p06dj4sSJasfGjx+PCRMm5PrsixcvkJGRAWdnZ7Xjzs7OuHHjRoHON3LkSJQuXRpeXl4FzqOsp3VpiwJw/nj7vbQl9/yTz6NWNXEZw/cpVh/26NGjERoaqnZMU+1aDDNmzEBUVBSOHj0Ka2vrAv9/XA060xZv/UE8XsymhAKw8ZN7HzNvTO3vzU9ezd+aFC9eHGZmZnj69Kna8adPn6JkyZL5/r+zZ8/GjBkzcOjQIdSoUUOrPFKTOFHhLeBRjZnkRL+xtHj8/qWY1mVpaYm6desiOjoafn5+AD4NOouOjsagQYPy/P9+++03TJ06Ffv370e9evW0Pq+sm8T1HeDpAUJaPBYOhBgTY7jHpFrpLDQ0FAEBAahXrx4aNGiA+fPnIyUlBYGBgQAAf39/uLi4YPr06QCAmTNnYty4cVi/fj3c3d2RmJgIALC3t4e9vX2BzinrJnF94+0BwtTw1uVB5EfuD/W8XaM8DjqTSteuXfH8+XOMGzcOiYmJqFWrFvbt26caiJaQkAClUqn6/NKlS5GWloZvv/1WLZ28BrZpotd52Pom94VZeMNb4aBvcv+9TBFv42LonsmfIQJ2a1dvUdI58HCfKOnoE1c1bN4KUN7yQ8Ql9+uTSI8CvPRoP2yJ8FaA8pYfU0MtIoR3vAVgfd8zvP29poarQWeEZKfvwoECvPzI/TeQe0DlsUlcys0/DI1q2ESFtxotPc2TnHgLSKZWBvF4T1KTuEgoABNCpMRbmcJbwNP39yOnpUnlgJrE8yH3/GuLasyEd9QKREwZNYnng7f86BtvhRVvhTORHgVIcRnD95lJfdjEFFFthBgbU3so4+0h1zBN4qZD1gFb3xcb3ezSolHiRChT+43podi4cRWwebtZTO1m15bcm8QJIdrh8R6jUeIikXvA4y0/+sZbkziPhQMhhC8UsEUi94An9wcOfePt+6EAT3jHWx+ztugekxZXTeK8XZxEXPT7EqHk3k3C27gMbXE56IxGiUuDtwKat/yYGt4KWyI9uiakxeP3T03iEuGtBsZbfvSNt+Y6U/v+CSEkP1wNOiPSkntzHW/NgYQQ/aOlSQkhhHyW3PvUjQH1YYuELmZCiDEztVYdHsto6sMWCY8/LiFEvnirBPA2jkZb+l790RCjxE2JrGvYNChJXkytsCXi460SwFt+9I3Hv5eaxEUi94DKW370jbeAqm9yvz5Nkaldo9ribSaGIWrY1CQuEt6ai3i7mHkj91HcxPjRNSQuuZdZpoarGra29H2xmdrFTLUXQuSNt0qMIdC0Lpng7eKUe4CX+/fJY2FCjJvcr1He8qOLTOrDlge5B0je8PZ9yr2Fhhg/egglhsRVHzZdnNIyte/f1FpQiPQowIuPmsQlQgWitHgrHHjLDyGGRtf051GTuEioBiMvVDgQ3vH2EMdbfvSNx2ldpkTWTeI0SlxeTK1wI+Lj7ZrgLT+8LVZlCNQkLhIef1wiHboeCCFioyZxmeBtAAfVyMVFNXLCO96uUd7KREOgGrZMUIAUF283L4+FAyHZyT1A0j0mL7IO2Ppmag8Eci98tGVqvy+RHm/3gDGgJnGRyL1ApyZx40a/LzE2ci9zdUFN4iLhrTlH33u/yp0p3uyECMHbPUP3pHHjqkmctwBJNTBx8Va4Efnh7RribZ63toxhe03GMvV+Dl6YVJM4BdT8mVofNpEfU7uGePt7ecsPQPthi4ZqqMaNx5uXkOyoTBEXjzVsU8JVkzghhIiJKg3i4vEhndEocUIIIXJjit1U1CQuEd6ehulpmxAiJ8YQgEneuArY2uJtkBQFeEL4wttDvdwDKo992NQkLhMUIAkh+eHtoZ43xlCG0kpnEjGGi4cQQqRiiq2ItNKZSOR+8fB4cZoSuV8/hBia3BeKIfnjqoatLd6au+QeAHhrDuQtfbn/vsaAt2uUSI/6sCVCBaK0qHAjvKOHLJITTesSCS01SrKj2hExNArwxJhw1YdNjBsFYGLq5P7QStO6pEU1bAnTlzu5Fz6EGBrdA+KjaV0S4S1AUnNa/qjwIbwztXtSW/T9yAtXTeIUIAkhYjK1MsUUy1xqEheJ3GtgPF6chBB+8BYgeZsKaQg0Slwkcg94xvD0KWe8FYaE5CT3AKnve4D2wxaXrGvYVOAaN94KQ7reiLExhnuAmsQJIYQQGaBR4iLh8WmMEEKkwluftLZ4yw9Am3+IhprECSHk//AY8LTB48IppoRq2MRg5F67IITwh5rECSkA3kZxU4AnRL94vMdo0BkhHOCxcCDEkPTdSqnvh2hqEhcXVwHbGKYYmBIKqMTQTK3VhbdxQDx+nzToTCK8BWDe8kMIIVLisYZNTeIywVufqNwDvDE8bRPjZmrXnL7vSVMr48S2ZMkSzJo1C4mJiahZsyYWLVqEBg0a5Pn5zZs349dff8X9+/dRoUIFzJw5E+3atSvw+bia1kUXAyGE/B/eHlB4yw8gXQ1748aNCA0NRVhYGBo2bIj58+ejTZs2uHnzJpycnHJ9/tSpU+jevTumT5+ODh06YP369fDz88PFixdRrVq1Ap1TwbT4a80tXQr+1xCjx+PNqw16QJQf6tPNH2/XtCGaxMWKS9rmtWHDhqhfvz4WL14MAMjMzISrqyuCg4MxatSoXJ/v2rUrUlJS8Oeff6qONWrUCLVq1UJYWFiBzslVDVtbch9ByRveCh99M7Xf1xTx9pvJvYnbmMuI1NRUpKamqh2zsrKClZVVrs+mpaXhwoULGD16tOqYUqmEl5cXTp8+rTH906dPIzQ0VO1YmzZtsGPHjoJnkgn04cMHNn78ePbhwwehSVH6lD6lb2TpG+IclL5xp28o48ePZwDUXuPHj9f42UePHjEA7NSpU2rHR4wYwRo0aKDx/7GwsGDr169XO7ZkyRLm5ORU4DwKDtivX79mANjr16+FJkXpU/qUvpGlb4hzUPrGnb6hfPjwgb1+/VrtlddDiFQBW9ajxAkhhBAx5NX8rUnx4sVhZmaGp0+fqh1/+vQpSpYsqfH/KVmypFaf10RZ4E8SQgghBJaWlqhbty6io6NVxzIzMxEdHY3GjRtr/H8aN26s9nkAOHjwYJ6f14Rq2IQQQoiWQkNDERAQgHr16qFBgwaYP38+UlJSEBgYCADw9/eHi4sLpk+fDgAYPHgwvv76a8yZMwft27dHVFQUzp8/jz/++KPA5xQcsK2srDB+/PgCNyVQ+pQ+pW866RviHJS+cafPq65du+L58+cYN24cEhMTUatWLezbtw/Ozs4AgISEBCiV/9eI3aRJE6xfvx5jx47FL7/8ggoVKmDHjh0FnoMNaDkPmxBCCCHSoD5sQgghRAYoYBNCCCEyQAGbEEIIkQEK2IQQQogMUMAmhAAAMjIy8PjxY6mzQQjJg87TutLS0hAfH49y5crB3Jymc2eXlJSEs2fP4tmzZ8jMzFR7z9/fX6JcEaFSUlIwfPhw7Nq1C2lpaWjZsiUWLVqEEiVKSJ01UVy9ehV16tRBRkaG1Fnh0o0bN+Dr64tbt25JnRViorSOtO/evUNwcDBWr14NALh16xY8PT0RHBwMFxcXjduKaevIkSNo3ry54HRy2rVrV4E+5+vrq/M5du/ejZ49eyI5ORmFChWCQqFQvadQKEQL2F9//TX69u2L7777DjY2NqKkmZ83b97g8OHDqFixIipXrqyXcyQlJaFIkSKipKWPa+jXX39FZGQkevbsCRsbG6xfvx7ff/89tm/fLjjtTp06Ffiz27ZtE3w+ffntt98QHBysuiZPnjyJevXqqebovn37FiNHjsTvv/8u6nkzMjJw5coVlC1bFo6OjqKmnSU1NRV3797VS9qGIOb9tW/fPtjb2+PLL78EACxZsgTLly9HlSpVsGTJEr39BiavwKuO/38hISGsbt267Pjx48zOzo7dvXuXMcbYjh07WK1atbRNTiNLS0vm6enJJk+ezBISEkRJkzHGFArFZ19KpVLQOSpUqMAGDx7MUlJSRMq1ZoMHD2YlSpRghQoVYv369WOnT58WNf3vvvuOLVq0iDHG2Lt371iFChWYhYUFMzc3Z1u2bBGc/owZM1hUVJTa+ZRKJStdujS7fPmy4PT1cQ25u7uzTZs2qf59/vx5Zm5uzj5+/Cg47T59+hT4pS+XL18WfP0rlUr29OlT1b8dHBxUZQRjjCUmJgo+B2Ofrv8VK1YwxhhLT09nTZs2ZQqFgtnZ2bEjR44ITl8TMb6fLOnp6WzFihWse/furGXLlqx58+ZqL6H0fX9Vq1aN7dmzhzHGWFxcHLOysmKjR49mjRo10us1auq0Dthubm6q4GBvb6+6GW/fvs0cHBxEydTz58/Z3LlzWc2aNZm5uTlr3bo127hxI0tNTRUlfX2ytbVVK6D06ePHj2zr1q3M19eXWVhYsMqVK7NZs2axxMREwWk7Ozurbux169ax8uXLs5SUFPb777+L8mDm7u7OTp48yRhj7MCBA6xIkSJs//79rG/fvqxVq1aC09fHNWRubs4ePXqkdszGxoY9ePBAaHa5IEZAUigUagE7exnBmHgB28XFhZ07d44xxtj27dtZ6dKl2c2bN9nYsWNZkyZNBKeviZgB+6effmJ2dnasS5cubPDgwWzIkCFqL6H0fX/Z2dmx+Ph4xtinbSk7d+7MGGPswoULzNnZWXD6RDOtA7aNjY3qBsx+M16+fJkVKlRI3NyxTxfAoEGDWLFixVixYsVYcHCwKE+I+tKxY0e2ceNGg5/36dOnbPLkycza2ppZWFiwb775hkVHR+ucnrW1tapm2rt3bzZy5EjGGGMPHjxgdnZ2gvObPf2QkBD2/fffM8YYu3nzJitSpIjg9LMT6xpSKpXs2bNnasccHBzYvXv3xMqqXsXGxub72rhxo2wCtpWVFXv48CFjjLH+/fuzwYMHM8YYu3fvnmgVh5zEDNjFihVT1VD1Qd/3l6OjI/vnn38YY4w1bdqULVu2jDHGWHx8PLOxsRGcPtFM6z7sevXqYc+ePQgODgYAVR/tihUrtNp1pKDq1KmDkiVLolixYpgxYwZWrlyJ33//HY0bN0ZYWBiqVq1a4LSOHTtWoM/973//0zW7aN++PUaMGIFr166hevXqsLCwUHtfSP94Xs6ePYtVq1YhKioKTk5O6NOnDx49eoQOHTpg4MCBmD17ttZpurq64vTp0yhatCj27duHqKgoAMB///0Ha2trwXl2dHTEw4cP4erqin379mHKlCkAAMaY6IOexLqGGGNo2bKl2iDLd+/ewcfHB5aWlqpjFy9eFJznLVu2YNOmTUhISEBaWprae7qmX6tWLSgUCjANqxFnHc8+5oJnzs7OuHbtGkqVKoV9+/Zh6dKlAD79HmZmZjql6ejomO/fn56erlO6mlhaWqJ8+fKipZeTvu+vL7/8EqGhoWjatCnOnj2LjRs3Avg0pqlMmTKC0yeaaR2wp02bhrZt2+LatWtIT0/HggULcO3aNZw6dQoxMTGiZezjx4/YuXMnVq5ciYMHD6JevXpYvHgxunfvjufPn2Ps2LH47rvvcO3atQKn2axZM9UNqanQAj4VXEIu6P79+wMAJk2aJHra2T179gyRkZFYtWoVbt++DR8fH2zYsAFt2rRR/Y19+vSBt7e3TgF7yJAh6NmzJ+zt7VG2bFk0a9YMwKeHnurVqwvOf6dOndCjRw9UqFABL1++RNu2bQEAly5dEq0gE/saGj9+fK5j33zzjSh5zW7hwoUYM2YM+vTpg507dyIwMBB3797FuXPn8NNPP+mcbnx8vIi5zNuKFStgb28P4FOQi4iIQPHixQF8GnQmhsDAQHTp0gWlSpWCQqGAl5cXAODvv/9GpUqVdEpz/vz5ouStIIYNG4YFCxZg8eLFenlI0vf9tXjxYgwcOBBbtmzB0qVL4eLiAgD466+/4O3tLTh9oplOm3/cvXsXM2bMQGxsLJKTk1GnTh2MHDlSlIIcAIKDg7FhwwYwxtC7d2/069cv144miYmJKF26dK5pU/kpVqwYHBwc0KdPH/Tu3VtViORUuHBhQfk3BEtLS5QrVw5BQUHo06ePxqlFb968wTfffIMjR47odI4LFy4gISEBrVq1UhXAe/bsQZEiRdC0aVNB+f/48SMWLFiAhw8fok+fPqhduzYAYN68eXBwcEC/fv0Epa+va8gQKlWqhPHjx6N79+5wcHBAbGwsPD09MW7cOLx69QqLFy+WOot5cnd3L1AAEuPhYcuWLXj48CG+++47Va1u9erVKFKkiF4epMTUsWNHHDlyBEWLFkXVqlVztcQJnQmg7/uLSIPL3bpatmyJfv36oVOnTnlu2Zaeno6TJ0/i66+/LnC6aWlp2L59O1auXInjx4+jXbt26Nu3L7y9vWXTFJjl+PHj+Oqrr/SS9sePH1GpUiX8+eefepvCpW/6uoY0SUtLQ1pamuqhRihbW1tcv34dZcuWhZOTEw4ePIiaNWvi9u3baNSoEV6+fCko/du3b2Pnzp24f/8+FAoFPDw84OfnB09PT1Hybwhr1qxB165dc/22aWlpiIqKEjR9kjGGCxcuqH0/tWvXFrWMyNozOS+rVq0SlP6bN29QqFAhje/duXNHp1p29jTfvHmT72fzOjcRSNtO79evX2t8vXnzRrRR3DExMRqnynz8+JHFxMSIco4HDx6wiRMnMk9PT+bi4sJ++eUXUabnMMbY0aNHWYcOHVi5cuVYuXLlmI+PDzt27JgoaWdp3rw5+++//3Idf/36tSjTQkqXLs2uXbsmOJ38rFmzhjVt2pSVKlWK3b9/nzHG2Lx589iOHTsEpZuWlsYCAwP1Mhhs5cqVbNCgQWzt2rWMMcZGjRrFLC0tmVKpZF5eXuzFixeCz+Hh4cEuXrzIGGOsbt26LCwsjDHG2P79+5mjo6OgtKdNm8bMzc2ZUqlkJUuWZM7OzkypVDILCws2a9YswXk3lJzTx7K8ePFC0MCww4cPMw8PD6ZUKtWmepYrV060sscQvvzyS/bhw4dcx2/cuMFcXFx0SjP7d571veR8iTE1luRN64Cd1w+V9XJzc2Pjxo1jGRkZOmdKXzejJvfu3WPNmzdnSqWSvXz5UnB6kZGRzNzcnHXp0oUtWLCALViwgHXp0oVZWFiwdevWiZDjT/L6jp4+fcrMzc0Fpz916lQWEBAg2kNMTr///jsrXrw4mzJlitrMg1WrVrFmzZoJTr9QoUKiB+ysvHp5ebGiRYuyH3/8kZUsWZLNmDGD/fbbb6xMmTLsxx9/FHyevn37sgkTJjDGGFu8eLHqnEWKFGFBQUE6p3v48GGmVCrZ+PHj2atXr1THX758yX799VdmZmYmOCidOnWK7d69W+3Y6tWrmbu7OytRogTr37+/xkCiLYVCkWvEPmOfRnLr+lBz+/ZtZmtry5o3b8527NjBbty4wa5fv862bt3Kvv76a7V1J8Ty7Nkzdvz4cXb8+HGNf4+uvL29Wdu2bdXu32vXrrGSJUuykJAQndI8evSoKr2jR4/m+yL6oXXAXr16NStTpgwbO3Ys27VrF9u1axcbO3Ysc3V1ZcuWLWNTpkxhRYoUYVOnTtU5U3ndjDdv3hRlysaHDx/YunXrWMuWLZmtrS377rvv2F9//SU4XcYYq1SpEps7d26u43PmzGGVKlUSnH7WFByFQsGOHDmiNi3n4sWLbNq0aaxs2bKCz+Pn58ccHBxYqVKlWOvWrVnHjh3VXkJVrlyZbd++nTGmPvXnypUrrFixYoLT9/f31/g7CFG+fHm2fv16xhhj586dY0qlUm0Rmb179zI3NzfB58nIyFAraDds2MCCg4PZwoULBbVidenSRTW9R5P+/fuzbt266Zw+Y58CxYwZM1T/jouLY+bm5qxfv35szpw5rGTJkmz8+PE6p1+rVi1Wu3ZtplQqWfXq1Vnt2rVVrxo1ajAHBwf23Xff6ZT2Tz/9xFq0aKHxvczMTNaiRQs2aNAgnfOeXXJyMgsMDGRmZmaqmry5uTkLCgoSZdGld+/esSZNmrAuXbqwzMxMduXKFebk5MSGDh0qQu6JVLQeJb569WrMmTMHXbp0UR3z8fFB9erVsWzZMkRHR8PNzQ1Tp07FL7/8olXaWcszKhQK9OnTR61/KiMjA3FxcWjSpIm2WVbJPv3J3d0dgYGB2LRpE4oWLapzmjndu3cPPj4+uY77+vpq/X1okjU1R6FQoEWLFrnet7GxwaJFiwSfp0iRIujcubPgdPISHx+vGgiTnZWVFVJSUgSnX6FCBUyaNAknT55E3bp1YWdnp/Z+SEiI1mkmJCSolmKsV68ezM3N1Qay1ahRA0+ePBGWcQD//vsvXF1dVf/u1q0bunXrBsYYHj58CDc3N53SPXv2LCIjI/N8v3fv3oKXzr18+TImT56s+ndUVBQaNmyI5cuXA/g0XXD8+PGYMGGCTun7+fmpztOmTRu1cQOWlpZwd3fX+bo9evQopk+frvE9hUKBIUOGYPTo0TqlnVNoaChiYmKwe/du1QDOEydOICQkBMOGDVNNU9OVjY0N9uzZg2bNmqFLly44duwY/P39MWvWLDGyT0uTSkTrgH3q1CmEhYXlOl67dm2cPn0awKc5egkJCVpnJmt0NmMMDg4OamtkW1paolGjRqppU7po1KgR3NzcEBISgrp16wL4dJPkJGSutKurK6Kjo3MN6jh06JBaIayr+Ph4MMbg6emJs2fPqo0Ot7S0hJOTk87zULMTOujlczw8PHD58mWULVtW7fi+fftEGegWHh6OIkWK4MKFC7hw4YLaewqFQqeA/fHjR7WHSEtLS7XRvebm5qJM2/Pw8MCTJ0/g5OSkdvzVq1fw8PDQ+RxPnz6Fu7t7vudNTEzUKe0s//33H5ydnVX/jomJUU0pAoD69evj4cOHOqefNbXO3d0dXbt2FWVNgCwJCQn5znSpVq0aHjx4IMq5tm7dii1btqimSwJAu3btYGNjgy5duugUsHMOBFMqldi4cSNatWqFzp0749dff1V9RuigsBEjRmDmzJkAgCtXriA0NBTDhg3DkSNHEBoaqvfyw1RpHbBdXV0RHh6OGTNmqB0PDw9XBaSXL1/q9ISV9SO7u7tj+PDhuWpFYkhISFCrAeQkdK70sGHDEBISgsuXL6taA06ePImIiAgsWLBA53SzZAU4Q0xFSk9Px9GjR3H37l306NEDDg4OePz4MQoVKiR4RHRoaCh++uknfPjwAYwxnD17Fhs2bMD06dOxYsUKwXnX15zja9euqYIaYww3btxAcnIyAODFixeinIPlsYBJcnKyoAD14cMHtQVecrKwsMi1SIu2nJ2dER8fD1dXV6SlpeHixYuYOHGi6v23b9/mmsKki4CAAACfRoVr2hVPl1aI5ORk2Nra5vm+ra0t3r17p3W6mrx7907twSaLk5OTzucoUqSIxuuGMYawsDAsW7ZMdW0JfbCMj49HlSpVAHx6+PDx8cG0adNw8eJFtGvXTlDaJG9aB+zZs2fju+++w19//YX69esDAM6fP4/r169j69atAIBz586ha9euOmdK0wIVYjBEkBswYABKliyJOXPmYNOmTQCAypUrY+PGjYLnhu7atQtt27aFhYXFZ3ceE7qi2oMHD+Dt7Y2EhASkpqaiVatWcHBwwMyZM5GamqqxlUUb/fr1g42NDcaOHYt3796hR48eKF26NBYsWIBu3boJSlufWrZsqbboTocOHQCIs1JYaGioKq1ff/1VLXhkZGTg77//Rq1atXROH1Bf1CQnMRY1adeuHUaNGoWZM2dix44dsLW1VZt+GBcXh3Llygk+z+3btxEUFIRTp06pHRcakLI/kOUk1gMZADRu3Bjjx4/HmjVrVA9h79+/x8SJE3VeMVLX9RZ0YWlpqXqwOHTokKorpWjRop+d8kV0p9M87Pv37yMsLEy1L2zFihXxww8/IDk5OdfiFAVVp04dREdHw9HR8bNzHoUu/fjy5UsUK1YMAPDw4UMsX74cHz58gI+Pj97mNotBqVQiMTERTk5OUCqVeX5OjCdoPz8/ODg4IDw8HMWKFVMt3nH06FH0798ft2/fFpR+du/evUNycnKuJmAhgoKC8n1/5cqVWqdZ0ObQnM38BZW1HWhMTAwaN26sVhvO6p8dPnw4KlSooFP6hljU5MWLF+jUqRNOnDgBe3t7rF69Gh07dlS937JlSzRq1AhTp07V+RwA0LRpU5ibm2PUqFGq1c6yq1mzptZpKpXKPJduzSLWaoVXr15FmzZtkJqaqsprbGwsrK2tsX//fq2WXJaCr68v0tLS0LRpU0yePBnx8fFwcXHBgQMHMGjQINozXE8EL5zy5s0bbNiwAStXrsT58+d1vpgnTpyIESNGwNbWVq0JTRNda+BXrlyBj48PHj58iAoVKiAqKgre3t5ISUmBUqlESkoKtmzZohrYYsqKFSuGU6dOoWLFimqrbd2/fx9VqlQR3DTYokULbNu2Ldf+vG/evIGfnx8OHz4sKP3sQQL41P989epVJCUlqc7Nq8DAQCxYsEDWi0+8fv0a9vb2ucZTvHr1Cvb29vk2zReEnZ0dLly4oPMypJoU5IHs7du3OldKcnr37h3WrVuHGzduAPjUEpe117pQ+h4UlpCQgIEDB+Lhw4cICQlB3759AQBDhw5FRkYGFi5cKPhvIBroOrw8JiaG+fv7Mzs7O1ahQgU2cuRIdvbsWSEj1vXO29ubdejQgZ04cYL98MMPzMXFhQUFBbGMjAyWkZHBBg4cyBo2bKh1uo6Ojuz58+eMMcaKFCnCHB0d83zJRZEiRVS78WSfdnX8+HHm5OQkOP2cuzplEWseuSYZGRns+++/ZzNnzhSUzsqVK9X2xc6yadMmFhERISjtnB4+fKjalYr8n3r16rHjx48b5Fxv3rxhy5YtYw0aNJDNoiA596u2tLSk/aqNgFY17MTERERERCA8PBxv3rxBly5dEBYWhtjYWNUABDGcO3cOmZmZaNiwodrxv//+G2ZmZqhXr55O6RYvXhyHDx9GjRo1kJycjEKFCuHcuXOqEeM3btxAo0aNkJSUpFW6q1evRrdu3WBlZYWIiIh8mx2zBsvoQpunVl1GQWfXtWtXFC5cGH/88QccHBwQFxeHEiVK4JtvvoGbm5vOo0Dj4uIAfJqedvjwYbUpdRkZGdi3bx+WLVuG+/fvC8p/Xm7evIlmzZoJmn71xRdfYNmyZaom7CwxMTH4/vvvcfPmTUF5zMzMxJQpUzBnzhzVgDYHBwcMGzYMY8aMybc7JD8FvX6EXDuf64rIokuXRHaHDx/G2LFjMW3aNI274onROnHs2DGEh4dj69atKF26NDp16oTOnTurxu5oy5BjUOzt7XH16lW4u7tjwoQJuHr1KrZs2aIaFCZ0NgDw6X7dsWMHrl+/DgCoWrUqfH19RZmlQvJQ0MjeoUMHVqhQIda9e3f2559/svT0dMYYY+bm5qqamFjq16/PNm/enOv41q1bWYMGDXRO11B79eqLu7t7gV4eHh6Cz/Xw4UNWpUoVVrlyZWZubs4aNWrEihUrxipWrKixZlxQ2VfKy1owIvvL1taWhYeHC85/Xvbs2cOKFy8uKA0rKysWHx+f63h8fDyztrYWlDZjn5Y7LVGiBPv9999Vi+IsWbKElShRgv3yyy86p5vzOjEzM2NlypQR9dpRKBTM3d2ddezYkfn5+eX5Eir7sqFiLo355MkTNn36dFa+fHnm5OTEBg0aJFoZl7380XTtZ/+bhNL3ftW3b99mFSpUYLa2tqqFa2xtbVnFihXZnTt3BKdPNCvwKPG//voLISEhGDBggM6DXgrq2rVrqFOnTq7jtWvX1mo7TU1y1n7F3vTDzMxM4xzaly9fwsnJSdCAFUNtjwgAZcqUQWxsLKKiohAXF4fk5GT07dtXcB+boeaRZ424zsIYw5MnT7Bnzx5BrRzAp6k3cXFxueY0x8bGqgYzCrF69WqsWLFCrZZVo0YNuLi4YODAgToP2Mp5/Tg4OCAmJkbUTT8GDBiADRs2ID4+HoGBgejVq5eoCxNl0ceIaB8fHxw7dgzt27fH/Pnz4e3tDTMzM8EzIrJkn6Wi7xkr+t6vOiQkBOXKlcOZM2dUv+/Lly/Rq1cvhISEYM+ePYLPQTQoaGQ/ffo069evH3NwcGANGjRgixYtYs+fP9dLDbto0aLs1KlTuY6fPHmSFSlSROd0FQoFa9eunWp5TXNzc7VlN9u1ayf46TavvtlHjx6JUvsiBdOsWTO1V4sWLVjXrl3ZsmXLBK+P/vPPP7OyZcuyw4cPs/T0dJaens6io6NZ2bJl2bBhwwTn3crKit28eTPX8Rs3boh6DeVsYRLLhw8f2Pr165mXl5dq6d99+/axzMxM0c8lJjMzMzZ06FB269YtteP6KONWr16tcU311NRUtnr1asHpP3jwgLVv357VqFGDrVixQnV8yJAhLDg4WHD6tra2LC4uLtfxy5cvMzs7O8HpE820HiWekpKCjRs3YuXKlTh79iwyMjIwd+5cBAUFwcHBQZSHiO7du+PJkyfYuXOnavWzpKQk+Pn5wcnJSTW/WVuf29Iuiy79s1n9g0OHDsXkyZPV5rpmZGTg2LFjuH//Pi5duqR12nn5999/sWvXLiQkJORa8GLu3LmC03/8+DFOnDihcWEKoX3kABAZGYmwsDDEx8fj9OnTKFu2LObNmwdPT0+u9zNOS0tD7969sXnzZpibf2qkyszMhL+/P8LCwgSPgG7YsCEaNmyYq885ODgY586dw5kzZwSlnyX76H99efDgASIiIrBmzRqkp6fjn3/+0XnRnbi4OFSrVg1KpVI1FiIvNWrU0Dr9M2fOIDw8HBs3bkTlypXRu3dvdOvWDaVKlRJ9nI4+W+IMoWjRovjzzz9zLRV98uRJ+Pj44NWrVxLlzMgJifY3btxgI0aMYCVLlmTW1tbMx8dHlKeIf//9l3l6erLChQurakhFihRhFStWZAkJCaKcQ2xZfYAKhYK5urqq9Qt+8cUXrHXr1uzMmTOine/QoUPM1taWVatWjZmbm7NatWqxIkWKsMKFC4uyveaqVauYpaUls7e3Z2XLlhW9j1zfu3UZws2bN9mmTZvY7t27VduDCuHh4cFevHjBjh49yuzs7FjlypVZUFAQCwoKYpUrV2b29vaibtOqrxp2dgkJCWzixInMw8ODubi4sLdv3+qcVs4+4LzGQQhtJUtOTmbh4eGsadOmzMLCgimVSjZ//nz25s0bQelmp4/dxvLy/v37XNshC9W7d29WtWpVdubMGZaZmckyMzPZ6dOnWbVq1VhAQIDwTBONBM/DBj7VIHfv3o2VK1d+dvRjQaWkpGDdunWIjY2FjY0NatSoge7du4uyrKE+NW/eHNu2bdP74vcNGjRA27ZtMXHiRFVNycnJCT179oS3tzcGDBggKH1XV1f8+OOPGD16tM6jkvNTpUoVTJs2TbVAS1ZN7+rVq2jWrJlOq0pps/iOvb09qlatil9++UWUNd7FkH1hnEePHuH3339Xm6M7cOBAlC5dWuf0c65AVaZMGZw4cSJXX7zQEdapqanYtm0bVq5ciRMnTqBDhw4IDAyEt7e3oGvpwYMHcHNzg0Kh+OycaV0Xr8np5s2bCA8PR2RkJJKSktCqVStBZVzWdRkbG4uqVauqWmiAT+VofHw8vL29dW5FzJKSkoKRI0di06ZNePnyZa73hdbgk5KSEBAQgN27d6vK5PT0dPj6+iIiIkLVMkrEJUrAJpplfbViD2wDPjVnXr58GeXKlYOjoyNOnDiBqlWrIjY2Ft98843gaVHFihXD2bNnRVlGUhMbGxvcuHEDZcuWVQvYt2/fRo0aNfD+/Xut09Rm8Z3U1FRER0fD2toaMTExn007NDQUkydPhp2dXa4BbTnp2h2RPWDrQ9ZKXllYjqVUmQjrTA8cOBBRUVFwdXVFUFAQevbsieLFiwvKNw/EqpRkXZcTJ07EsGHD8txtTGi3yk8//YQjR45g8uTJ6N27N5YsWYJHjx5h2bJlmDFjBnr27Cko/Sy3b99We6jMuekREZfWa4nriyHnKOpbeHg45s2bp1q+s0KFChgyZAj69esn2jns7OxU/dalSpXC3bt3VcsZirHmcd++fbF582aMGjVKcFqa6GO3ruwr4BVkNbzs39nnXLp0CR8/flT9d16EPpzt37//s7UTXa9/Q6w1HRYWBjc3N3h6eiImJibPhyGxVpq7du2axjEcYpcRZmZm8PPzE7wKoj53G8tu9+7dWLNmDZo1a4bAwEB89dVXKF++PMqWLYt169aJFrArVKig91lD5P9wU8M25DrZ+jRu3DjMnTsXwcHBqkX8T58+jcWLF2Po0KGYNGmSKOfx8/ND+/bt0b9/fwwfPhw7d+5Enz59VM3xhw4dEpR+RkYGOnTogPfv32tcmELooLYVK1ZgwoQJmDNnDvr27YsVK1bg7t27qt26DLUByOvXr7lpvitIczHv13+fPn0K9NAidPvFe/fuoWPHjrhy5Yra+t9Z5+b5OzIEe3t7XLt2DW5ubihTpgy2bduGBg0aID4+HtWrV1ctyKOrjIwMREREIDo6WuOgVKFLCxPNuKlhG3KOoj4tXboUy5cvR/fu3VXHfH19UaNGDQQHB4sWsOfOnau66SZOnIjk5GRs3LgRFSpUEGWE+PTp07F//35UrFgRgHrNUYwmfn3v1vX06VMMHz5cVaDkfC7NKtB5CdZZ9NUkrs0OSkL6sCMiInT+f7UxePBgeHh4IDo6Gh4eHjh79ixevnyJYcOGYfbs2QbJgxAZGRmYN28eNm3apLGFQOgoa09PT8THx8PNzQ2VKlXCpk2b0KBBA+zevTvX+v26GDx4MCIiItC+fXtUq1ZNL91+JDduatjGokiRIjh37lyuZqJbt26hQYMGWi97KhVHR0fMmzcPffr00fu59LFbV9u2bZGQkIBBgwZp3M1J22ljnTp1KvBndW3uzWuqjxhy9l9rIkYfdkGWJlUoFAgPD9f5HID6MsOFCxfG2bNnUbFiRRw+fBjDhg0TdfqkPowbNw4rVqzAsGHDMHbsWIwZMwb379/Hjh07MG7cOJ2nTWZdQ+vWrYOZmRlCQkJw6NAh+Pj4gDGGjx8/Yu7cuRg8eLCg/BcvXhxr1qyhva8NjJsadk7R0dF5NrcIXYdYn3r37o2lS5fmquX+8ccfovUbGYKVlRWaNm2q9/M8e/ZMtfa2QqFQW/lMiBMnTuD48eOC94/OYoiauD6fnQ21V3JERATKli2L2rVr6/XvycjIUK37ULx4cTx+/BgVK1ZE2bJlBa/lbgjr1q3D8uXL0b59e0yYMAHdu3dHuXLlUKNGDZw5c0bngJ31nQ8dOlR1zMvLCzdu3MCFCxdQvnx5neao52RpaUkDzCTAZcCeOHEiJk2ahHr16mmsHfEuPDwcBw4cQKNGjQB82rQkISEB/v7+aiOMhTRdf67GJLQPb/DgwVi0aJHetsl7+/YtBg4ciA0bNqgeyMzMzNC1a1csWbJEcIB0dXUVNWAI7XMtiICAAFG2VtTk66+/1ku6ORlqadJq1aohNjYWHh4eaNiwIX777TdYWlrijz/+0OtCMGJJTExE9erVAXzqb379+jUAoEOHDvj1119FP1/ZsmVFm+oGAMOGDcOCBQuwePFi2ZXPcsZlk3ipUqXw22+/oXfv3lJnRWs5d3DKi0KhEDQwY+fOnWr//vjxIy5duoTVq1dj4sSJqv1pddWxY0ccPnwYxYoVQ9WqVXMNOhM6yrdr1664dOkSFi1apDY4b/DgwahVqxaioqIEpX/gwAHMmTMHy5YtyzXPWCzPnz9X1eYqVqwoqHXAUH3MWZKSkhAeHq6201JQUJAoLQnZ52GfOnUK7du3R9++fdG6dWvRCvf9+/cjJSUFnTp1wp07d9ChQwfcunULxYoVw8aNG9GiRQtRzqMvFStWxJo1a9CwYUN8+eWX6NChA0aNGoWNGzciODgYz5490yldpVKJKVOmfHY1OaErFXbs2BFHjhxB0aJF9VI+EM24DNj6ngNszNavX4+NGzfmCuja+twyrkJrnHZ2dti/fz++/PJLtePHjx+Ht7c3UlJSBKXv6OiId+/eIT09Hba2trkKFCGDelJSUhAcHIw1a9aotQ74+/tj0aJFsLW11TpNQ/UxA8D58+fRpk0b2NjYoEGDBgA+bWn7/v17HDhwQOPGO7oSc2nSz3n16hUcHR1lUeMbNWoUChUqhF9++QUbN25Er1694O7ujoSEBAwdOhQzZszQKV2lUokyZcrku4GOQqHAvXv3dM06AP2XD0QzLgP2yJEjYW9vr5emIWN379491X7fPHNzc8OePXtUzYJZ4uLi0K5dO/z777+C0l+9enW+7wvZseuHH37AoUOHsHjxYlU//4kTJxASEoJWrVph6dKlWqdZkMVbsght3s6ak7t8+XLVSlvp6eno168f7t27h2PHjglKP7uHDx9i1apViIiIQFpaGm7cuKG3gC1nZ86cwalTp1ChQgX4+PjonI6+F98h0uIyYA8ePBhr1qxBjRo1UKNGDdHnAOvThw8fsGjRIhw5ckTjgLmLFy/q7dzv37/H6NGj8ddff3E/8OaPP/7A5s2bERkZiZIlSwL41K8XEBCATp064YcffpA4h3krXrw4tmzZgmbNmqkdP3LkCLp06YLnz59Lk7ECsrGxwaVLl1CpUiW149euXUO9evXw7t07Qenra2lSQ4zUN5Rjx46hSZMmakuTAp8enE6dOoX//e9/OqWrz5kGRHpcDjqLi4tTje69evWq2nu8N3f17dsXBw4cwLfffosGDRroLb85m/4YY3j79i1sbW2xdu1andLUZi1uXR48cqZ5+/ZtuLm5wc3NDQCQkJAAKysrPH/+XOeAXdC+YCH9wO/evYOzs3Ou405OToKDXRZ99jEXKlQICQkJuQL2w4cPBe+4l3Np0g0bNoi2NClvc+aFaN68ucbA+vr1azRv3lznbg9D1b/yKh8UCgWsra1Rvnx59OnTp8BjekjBcBmwDTUFRR/+/PNP7N27V+9ToubNm6d2wyiVSpQoUQINGzbUeeORb775BlZWVgAgeAlGTfSRZk5FihTJ90FDjH7gxo0bY/z48VizZo1qacn3799j4sSJqgF0QmjqY547dy6mTp0qSh9z165d0bdvX8yePVu1PeLJkycxYsQItQV/dKHPpUmNqV805zruWV6+fAk7Ozud0x0/frxBuhy8vb2xdOlSVK9eXW0cRFxcHPr06YNr167By8sL27Zt43qrXLnhsklczqpUqYKoqChR5joS7RW0L1hIP/CVK1fg7e2N1NRU1KxZEwAQGxsLa2tr7N+/v8Drk+dF333MaWlpGDFiBMLCwpCeng4AsLCwwIABAzBjxgzVQ5suDLU0aZbs8/grVqzIfVNwVrP+zp074e3trfZdZ2RkIC4uDhUrVsS+ffsEnSev/cKzasBubm6Cfuf+/fvDzc0t1zijKVOm4MGDB1i+fDnGjx+PPXv24Pz58zqfh6jjMmCnpKRgxowZeS6cInSEoz799ddfWLhwIcLCwkSd95jTuXPnsGHDBty6dQuWlpaoWLEi/P39dd44Q5OkpCRs2bIFd+/exYgRI1C0aFFcvHgRzs7OcHFxEe08cvTu3TusW7dObaeinj17ijKPWp99zBkZGTh58iSqV68OKysr3L17FwBQrlw5nUa3S+XNmzf46aefEBUVpWotEXMev75kja5evXo1unTpona9ZO3W1b9/f8HdCJ+bdWBhYYGuXbti2bJlOm1AUrhwYdVCLNnduXMHdevWxevXr3Hjxg3Ur18fb9++1Tp9kgf9bretm27durFSpUqxn3/+mc2bN4/Nnz9f7cWzZ8+esWbNmjGlUsns7e2Zo6Oj2ksMI0aMYAqFgjk4OLCaNWuymjVrMnt7e2ZmZsZmzJjBGPu0af3hw4d1PkdsbCwrUaIEK1++PDM3N2d3795ljDE2ZswY1rt3b8F/Q3p6Ops1axarX78+c3Z2FuV7ev36dYFfQsTExLCPHz/mOv7x40cWExMjKG3GGHNycmL79+/PdXzfvn3MyclJcPpWVlbs3r17gtORUpcuXViFChXYvn37VL/pvn37WMWKFVnXrl2lzt5nTZgwgSUnJ+st/R07drCKFSuyFStWsLi4OBYXF8dWrFjBKleuzKKiotjatWtZmTJl2LBhw3RK38nJia1evTrX8dWrV6uu0X/++YcVL15c0N9B1HEZsAsXLsxOnDghdTZ00rJlS1ahQgU2Y8YMtmrVKhYREaH2EioiIoJZW1uzRYsWsbS0NNXxtLQ0tmDBAmZjY8M2btzImjVrxiZPnizo7xgxYgRjjDF7e3tVwD558iQrW7asoL+BMcZ+/fVXVqpUKTZ79mxmbW3NJk+ezPr27cuKFSvGFixYoFOaCoWCKZXKAr2EUCqV7OnTp7mOv3jxQnDajDEWHBzMypQpw6KiolhCQgJLSEhgGzZsYGXKlGGDBw8WnH7dunXZoUOHBKcjJVtbW3b8+PFcx48dO8ZsbW0lyBFf6tevz/bt25fr+L59+1j9+vUZY4xt376deXp66pT+5MmTmY2NDQsJCWGRkZEsMjKShYSEMFtbWzZlyhTGGGNz585lXl5euv8RJBcuA7a7uzu7du2a1NnQiY2NDbt8+bLe0q9fvz6bO3dunu/PmTOHKZVKVqdOHfbq1Sudz1OoUCF2584dxph6wL5//z6zsrLSOd0snp6e7M8//1Sln3WuBQsWsO7du+uU5tGjR1WviIgIVrJkSTZq1Ci2c+dOtnPnTjZq1ChWqlQpwQ9OCoWCPXv2LNfxmzdvMgcHB0FpM8ZYamoqCwkJYZaWlqoHDCsrKzZkyBD24cMHwen/9ddfrFatWmz37t3s8ePHorY+GIqrqyuLi4vLdTw2Npa5uLhIkCPtJCYmsl69erFSpUoxMzMzUR8oGWPM2tqaXb9+Pdfx69evM2tra8YYY/Hx8czGxkbnc6xdu5Y1atRI1SrWqFEjtm7dOtX77969Y+/fv9c5fZIblwE7MjKSffvttywlJUXqrGitdu3a7PTp03pL39bWVhU8Nbl79y5TKBTsv//+E3SeEiVKsIsXLzLG1AP2gQMHWJkyZQSlzdinv+PBgweMMcZKlizJLly4wBj7lP9ChQoJTr9FixZs/fr1uY6vW7eOff311zql2bFjR9axY0emVCpZu3btVP/u2LEj8/X1Ze7u7qxNmzaC8p2ens5iYmLYq1evWEpKiqo5U8x7QaFQqF7Zg0TWv+Vg2bJlzMvLiz158kR17MmTJ6x169YsLCxMwpwVjLe3N6tSpQr7/fff2fbt29mOHTvUXkLVqlWLBQQEsNTUVNWxtLQ0FhAQwGrVqsUYY+zEiRPM3d1d8LmI4XAzrSvnvL47d+7A2dkZ7u7uuRZO0efiI0LNmDEDw4YNw9SpU1G9evVceRe6DrSZmVmuvXOz+/jxI+zt7QXveevr64tJkyZh06ZNAD6NLk1ISMDIkSPRuXNnQWkDQJkyZfDkyRO4ubmhXLlyqulK586dEzR6Ncvp06cRFhaW63i9evXQr18/ndLMGsjEGIODg0OuAUONGjVC//79dcvw/2dmZobWrVvj+vXr8PDwyLUSnBjkPG0yy9KlS3Hnzp085/EvW7ZM9Vkeywuxd5PLacmSJfD19UWZMmVUM1auXLmCjIwM/PnnnwA+Dd4dOHCgXs5P9IObgG2IObqG4O3tDQBo2bKl2nEm0jrQderUwbp16zB58mSN70dGRoqyFvScOXPw7bffwsnJCe/fv8fXX3+NxMRENG7cGFOnThWcfseOHREdHY2GDRsiODgYvXr1Qnh4uGotZaFcXV2xfPly/Pbbb2rHV6xYAVdXV53SzJqK5O7ujuHDhwuaL5ufatWq4d69e/Dw8NBL+obauUuf5F5eiL2bXE5NmjRBfHw81q1bh1u3bgEAvvvuO/To0UO1OI6QzZUyMjIwb948bNq0CQkJCbkqEULW6id543Jal5x9bh6w0MLyzz//hJ+fH0JDQzFs2DDViluJiYmYM2cO5s+fj23btglajzi7EydOIC4uDsnJyahTpw68vLxESTen06dP4/Tp04LXUs6yd+9edO7cGeXLl0fDhg0BAGfPnsXt27exdetWtGvXTue0379/D8aYahrUgwcPsH37dlSpUgWtW7cWnPd9+/Zh9OjRmDx5MurWrZvrwUCM3bqOHz+OZcuW4d69e9i8eTNcXFwQGRkJDw+PXBuyEPEZYjc5fRo3bhxWrFiBYcOGYezYsRgzZgzu37+PHTt2YNy4cYJ3AyOacRmwPT09ce7cORQrVkzteFJSEurUqcP1PGxDWLRoEYYPH4709HRVM+3r169hZmaG3377DUOGDJE2g5z4999/sXTpUtXynpUrV8aPP/6ocw07S+vWrdGpUyf8+OOPSEpKQsWKFWFpaYkXL15g7ty5GDBggKD0s6+3nXP5WTFaabZu3YrevXujZ8+eiIyMxLVr1+Dp6YnFixdj79692Lt3r6D0DS05OTnXWg1iPNTokz53k8ty+/btPPc0GDdunKC0y5Urh4ULF6J9+/ZwcHDA5cuXVcfOnDmD9evXC0qfaMZlwM5rx5mnT5/C1dU13z5cqX1uFSpdF/XP6d9//8XmzZtx+/ZtAECFChXw7bffCg5G2UVHR+e5eM3KlSu1Tm/Xrl1o27YtLCwssGvXrnw/6+vrq3X6hlK8eHHExMSgatWqWLFiBRYtWoRLly5h69atGDdunOoBQVf6bqWpXbs2hg4dCn9/fzg4OCA2Nhaenp64dOkS2rZti8TEREHpG0J8fDwGDRqEo0eP4sOHD6rjYj3U6Js+d5MDgOXLl2PAgAEoXrw4SpYsqfbgp1AoBPfr29nZ4fr163Bzc0OpUqWwZ88eVWWqdu3aeP36taD0iWbc9GEDUCvE9+/fr7ZaUUZGBqKjo/XWryeWnDs4Aeq1JLEKknv37iE4OFj03X6yTJw4EZMmTUK9evVQqlQpUTYx8fPzUz2I5dcHKWaB++7dO419bEKWjn337p2qH/DAgQPo1KkTlEolGjVqhAcPHgjKL6D/PuabN29qvD4KFy6MpKQkvZ5bLL169QJjDCtXroSzszP3mwLlJDQgf86UKVMwdepUjBw5Ui/p63vQKMmDJGPT85B9qkn2qScKhYJZWlqyL774gu3evVvqbOYrKSlJ7fX8+XN24MAB1rBhQ1EXq9D34h0lS5Zka9asEZyOVJ49e8bat2+vl4VTqlevzhYsWMASEhJYoUKF2KlTpxhjjJ0/f545OzuLkX127Ngx1rNnT9a4cWP277//MsYYW7NmjcbFQrTl4eHBDh48yBhTn7K3evVqVrlyZcHpG4KdnR27ceOG1NkQ5M6dO2zMmDGsW7duqnt579697OrVq4LTdnBwyHf6p1AjR45kU6dOZYwxFhUVxczNzVn58uWZpaUlGzlypN7Oa+q4CthZ3N3d2fPnz6XOhqiOHj3K6tSpI1p6+l68o2jRoqrFTMSWkZHBwsPDWfv27VnVqlVZtWrVmK+vL1u9ejXLzMwU5Rw9evRgTZs2ZefOnWN2dnbswIEDLDIyklWsWFG1YIuuNm/ezCwsLJhSqWStWrVSHZ82bRrz9vYWmnW2ZcsWZmNjw/r168esrKxUBe+iRYtY27ZtBac/bdo0VqVKFXbmzBnm4ODAjh8/ztauXctKlCjBFi5cKDh9Q2jWrJnqoUOOjh49ymxsbJiXlxeztLRU/cbTp09nnTt3Fpx+UFAQW7p0qeB0CurUqVNszpw5bNeuXQY7pyniKmC3bduWJSUlqf49ffp0tQVAXrx4IZsaQE7Xr19ndnZ2gtMxxOIdjDH2888/s0mTJglOJ6fMzEzWvn17plAoWK1atVi3bt1Y165dWY0aNZhCoWDffPONKOcpWbIk+/vvvxljn2obN2/eZIwxtnPnTta0aVPB6T958oRdvHiRZWRkqI79/fffGleX0latWrVU6zRnrwFfvHhRlBp8ZmYmmzJlCrOzs1O1YFlbW7OxY8cKTttQ7ty5w7y8vFhERAQ7f/48i42NVXvxrlGjRmzOnDmMMfXf+O+//xZlpbZp06ax4sWLs4CAADZ79my2YMECtReRJ64GneUcbFaoUCFcvnwZnp6eAD4NOitdujTXA0pybmvHGMOTJ08wY8YMpKen48SJE4LS1+duP6Ghoar/zszMxOrVq1GjRg3UqFEj1yjWuXPn6pT/VatWYfDgwdi5c2euze0PHz4MPz8/LF68GP7+/jqln6VQoUKIi4uDu7s7ypYti/Xr16Np06aIj49H1apVBe14tWrVKnTr1k2Unbk0sbW1xbVr1+Du7q42KOzevXuoUqWK2iArIdLS0nDnzh0kJyejSpUqBtlHWSxnzpxBjx49cP/+fdUxhUIhm0Fn9vb2uHLlCjw8PNR+4/v376NSpUqCf+P8xvooFAqdZtoYy6BROeNq0FlOHD1LFFitWrVUBUd2jRo10mlkdU76XLzj0qVLav/OWoXp6tWrop1jw4YN+OWXX3IFawBo0aIFRo0ahXXr1gkO2BUrVsTNmzfh7u6OmjVrqua7hoWFoVSpUoLSHjVqFAYPHozvvvsOffv2RZMmTQSll1PJkiVx586dXPNzT5w4oXp4FSIoKAgLFiyAg4MDqlSpojqekpKC4OBgUa5TfQsKCkLt2rWxYcMGWQ46K1KkCJ48eZIrsF66dEmUrWvj4+MFp5GTFINGSQ5SVu9zUigUagOpsjcVMfZpwXze1zq+f/++2ishIUFvC+B//PiRHTx4kIWFhbE3b94wxhh79OgRe/v2rV7OJwZnZ2d26dKlPN8Xq9k3MjKSrVq1ijH2aTBY8eLFmVKpZNbW1iwqKkpQ2h8/fmTbtm1jvr6+zMLCglWsWJHNmDFDbV1rIfTdx5zXgMXnz58zMzMzwekbgq2tLbt9+7bU2dDZsGHD2JdffsmePHnCHBwc2O3bt9mJEyeYp6cnmzBhgtTZI5ziKmArlUq1gVT29vZq+/bKIWBrInQjDk3u37/PKlWqxGxtbZmZmZnqwSYkJIT98MMPgtNfvXq1xh3T3r9/r3Ef3IKysLBgjx8/zvP9R48eMUtLS53Tz0tKSgq7cOGC6IMZExMT2ezZs1n16tWZhYUF8/HxYTt27FDr29aWvvqYX79+zZKSkphCoWB37txR26Hr1atXbPXq1axUqVKCzmEoHTp0YFu2bJE6GzpLTU1l/fr1Y+bm5kyhUKgGMfbq1Yulp6frlObQoUNVe2wPHTo035cQhhg0SjTjrg+7bdu2qnl8u3fvRosWLVTNvqmpqdi3bx/XzS0zZ86Eu7s7unbtCgDo0qULtmzZglKlSmHv3r2oWbOmKOfx8/ODg4MDwsPDUaxYMVUf2NGjR9G/f3/Vgiq6UiqVsLOzQ0REhNpmH0LHEZiZmSExMRElSpTQ+L7Y4xTS0tIQHx+PcuXK5ZqzLpa///4bK1euxOrVq1GqVCn8999/cHR0xKpVqzTOyy8osfuYlUplvk3HCoUCEydOxJgxYwSdxxD++OMPTJkyBUFBQRo32ZFLH2pCQgKuXr2K5ORk1K5dGxUqVNA5rebNm2P79u0oUqSIxi6n7HTdAIYxBh8fH1VZVqlSJTDGcP36dVy5cgW+vr7YsWOHTmmTz+MqYGcNqPqcrH5cHnl4eGDdunVo0qQJDh48iC5dumDjxo2qRfIPHDggynmKFSuGU6dOoWLFirkGrVSpUkXQoCrgU+E+e/ZsjB07Fj///DMmTJgAQHhAzflQlpNYD2Xv3r1DcHCwakWpW7duwdPTE8HBwXBxccGoUaMEpf/06VNERkZi1apVuHfvHvz8/NC3b194eXkhJSUFkyZNQlRUlE4LqWTvY85OaB9zTEwMGGNo0aIFtm7diqJFi6res7S0RNmyZVG6dGmd0ja07Mu35kR9qPpjqEGjRDOuArYxsLGxwa1bt+Dq6orBgwfjw4cPWLZsGW7duoWGDRviv//+E+U8jo6OOHnyJKpUqaIWsE+cOIHOnTvj6dOngtLPGrF/7949dOzYEU2bNkVkZCTevHkjKGAb6qFs8ODBOHnyJObPnw9vb2/ExcXB09MTO3fuxIQJE3INsNOGj48P9u/fjy+++AL9+vWDv7+/WvADgGfPnqFkyZK5lnQtCDMzMzx58iTX0rwvXrxAyZIlkZ6ernPegU+blbi5ucluoJbchYaGYvLkybCzs1ObkaGJrrMwsujroa9169aqwaGaTJs2DTExMdi/f79O6ZP8cT1KXI4cHR3x8OFDuLq6Yt++fZgyZQqAT01JYj71t27dGvPnz8cff/wB4FOtIjk5GePHjxe0E1WWrMK8UaNG+Pvvv+Hr64smTZpo3GNaG4ZqHdmxYwc2btyIRo0aqQWmqlWr4u7du4LSdnJyQkxMDBo3bpznZ0qUKKH1SN03b96AfRpXgrdv38La2lr1XkZGBvbu3ZsriOvi+vXrePjwoWpXriVLlmD58uWoUqUKlixZAkdHR8HnILldunQJHz9+BPBpj+68HpjEeJBavXo1ZsyYkStgv3//HmvWrNE5YMfFxeXasja7tm3bYuHChTqlTQpAmq5z4/XTTz+xsmXLMi8vL1asWDHViO0NGzaw2rVri3aehw8fsipVqrDKlSszc3Nz1qhRI1asWDFWsWJFjSOAtZVzxH5KSgrz8/NjDg4Oshj4Z2NjoxqIl322weXLl1mhQoV0SvPUqVO5lsZdvXo1c3d3ZyVKlGD9+/dnHz580DnPWcvy5vUyMzNjU6ZM0Tn9LNWqVWN79uxhjDEWFxfHLC0t2ejRo1mjRo1Ynz59BKdvKEePHmUdOnRg5cqVY+XKlWM+Pj7s2LFjUmdLUvoeWCjVoFHyCdWwRTZv3jy4u7vj4cOH+O2331QDhZ48eYKBAweKdp4yZcogNjYWUVFRqv2q+/bti549e4qyoMf48ePVBjnZ2tpi+/btGD9+/Gd3JONBvXr1sGfPHgQHBwP4v1rLihUr8q0Z52fSpElo1qwZOnToAAC4cuUK+vbtiz59+qBy5cqYNWsWSpcurerv19aRI0cM0sccHx+vmn+9detW+Pj4YNq0abh48aIorTOGsHbtWgQGBqJTp06qvZdPnjyJli1bIiIiAj169JA4h3n7+PEjbGxscPnyZVSrVk3UtIsUKQKFQgGFQoEvvvgi1/tZAwt1lZGRke/gTTMzM8FdNiRv1IdNjNKJEyfQtm1b9OrVCxEREfjhhx9w7do1nDp1CjExMahbt67WaZYqVQq7d+9GvXr1AABjxoxBTEyMavW6zZs3Y/z48bh27ZqgvOu7j7lo0aI4ceIEqlSpgi+//BL+/v74/vvvRRuwaAiVK1fG999/j6FDh6odnzt3LpYvXy54i1N98/T0xPbt20WbNZJF3wMLDTVolGhGAVsP9LVxvL6XBvxcmlkUCgV8fHy0Tt/Q7t69ixkzZiA2NhbJycmoU6cORo4cierVq+uUnrW1NW7fvq3ac/zLL79E27ZtVdOg7t+/j+rVq+Pt27eC8r1v3z7Y29vrrY/Z19cXaWlpaNq0KSZPnoz4+Hi4uLjgwIEDGDRoEG7duiUofUOwsrLCP//8g/Lly6sdv3PnDqpVqyba8q36Eh4ejm3btiEyMjLXgEUx6Ouhzxhm8sialO3xxuiPP/5gZmZmzNnZmdWsWZPVqlVL9RLah529Xznn9qPZX7r2MeeXphjpG0L2Prv8Xrpwc3NjMTExjLFPC1/Y2NiobZkaFxfHHB0dBf8N+u5jfvDgAWvfvj2rUaMGW7Fiher4kCFDWHBwsOD0DaFcuXIsLCws1/GlS5ey8uXLS5Aj7dSqVYvZ29szKysr9sUXX7DatWurvcSgzy1aiTSoD1tk+tw4PnttXZfpQtqkL1dZfXh5YQI2h2jXrh1GjRqFmTNnYseOHbC1tcVXX32lej8uLg7lypXTKd/Z6buP2c3NDX/++Weu4/PmzROctqEMGzYMISEhuHz5smot95MnTyIiIgILFiyQOHefl99a3GLYunUrevfujZ49e+LixYtITU0FALx+/RrTpk3D3r179Xp+oidSPzEYG31vHM+YtEsD/vfff2zRokV6PYcQR48eVb2OHDnCbGxs2Lp169SOHz16VKe0nz9/zr766iumUCiYg4MD27Ztm9r7LVq0YL/88ovgv8HR0ZH9888/jDHGmjZtypYtW8YYYyw+Pp7Z2NgITp+xT9tTjhkzhnXr1k3VarN371529epVUdI3hG3btrGmTZuyokWLsqJFi7KmTZuyHTt2SJ0tLuh7i1YiDQrYItP3xvGG2k86p0OHDrHu3bsza2trVrRoUb2cQx9ybiAjhqSkJI3rPb98+ZKlpqYKTt/Hx4e1adOGTZo0iVlYWKiaM/fv388qVKggOP2jR48yGxsb5uXlxSwtLVXfz/Tp01nnzp0Fp08K7vz58ywyMpJFRkayixcvipaujY0Ni4+PZ4yp3wN3795lVlZWop2HGBY1iYusfPny+PXXX3HmzBmNaxxnTUHRVUREBI4dO4bo6Og8lwZcs2aNKEsDPnz4EKtWrcKqVauQkJCAbt26Yfv27WjZsqXgtOWscOHCGo+LNXho8eLFGDhwILZs2YKlS5eqtlv866+/4O3tLTj9UaNGYcqUKQgNDVVbWKNFixZYvHix4PT16b///sPatWsREBCAQoUKqb33+vVrrFmzRuN7vHn27Bm6deuGo0ePokiRIgCApKQkNG/eHFFRUXmutV9Q+t6ilUhE6icGY+Pu7p7ny8PDQ3D6rVq1YtOnT8/z/alTp7LWrVvrnH5aWhrbtGkTa926NbOxsWEdO3ZkmzdvZubm5qpmWjnRRw1b7uzs7FS74GX/fuLj47mvfU2aNIl9++23eb7/3XffibK4jL516dKF1atXT21HvH/++YfVq1ePdevWTXD6+t6ilUiDArbM6Hs/6RIlSrCvvvqKLVu2jL169Up1XM4BO/sWrXKhzz5mFxcXdvLkScaYesDetm0b8/T0FJy+PtWsWVNtZH5Ohw4dYrVq1TJgjnRTqFAhdvbs2VzH//77b1a4cGHB6etri1YiLWoS1yP2/6e4izkX8tWrV3B2ds7zfWdnZ0EbjKSnp6tWSjIzM9M5Hal06tRJ7d8fPnzAjz/+qNqiNcu2bdsMmS2txMTEoG3btmjatCmOHTuGqVOnwsnJCbGxsQgPD8eWLVsEpd+tWzeMHDkSmzdvhkKhQGZmJk6ePInhw4dzv8vS3bt3892CskKFCoLXijeEzMzMXN1lAGBhYSHKbA2FQoExY8ZgxIgRom7RSqSV9x51RGdr1qxB9erVYWNjAxsbG9SoUQORkZGipK3vpQEfP36M77//Hhs2bEDJkiXRuXNnbN++XTY7OxUuXFjt1atXL5QuXTrXcZ5l9TEfPHgQlpaWquMtWrTAmTNnBKc/bdo0VKpUCa6urqqC/H//+x+aNGmCsWPHCk5fn8zMzPD48eM833/8+HG+W2/yokWLFhg8eLDa3/Lo0SMMHTpU1DEilpaWqFKlCho0aEDB2gjQSmcimzt3Ln799VcMGjQITZs2BfBpoMeSJUswZcqUXEspasuQSwPevXsXq1atwurVq/Ho0SN0794dffr0QYsWLWRZ+5YLe3t7XLlyBR4eHrn2Oq9UqZJoq3glJCTg6tWrSE5ORu3atfOtufKiefPmaNiwIWbMmKHx/ZEjR+Ls2bM4cuSIgXOmnYcPH8LX1xf//POPauW8hw8folq1ati1axfKlCmjdZo5W5fyw3MLE8kbNYmLbNGiRVi6dKla06Kvry+qVq2KCRMmCA7YAQEBn/2MWM2a5cqVw5QpUzBp0iTs378f4eHh6NChAxwcHPDixQtRzkFyK1KkCJ48eQIPDw+145cuXVKNGBeDm5sb3NzcREvPEAYNGoRu3bqhTJkyGDBggOrBMSMjA7///jvmzZuH9evXS5zLz3N1dcXFixdx6NAh3LhxA8Cn9dG9vLx0TpP3liMiHNWwRWZtbY2rV6/mWuP49u3bqF69OvdrHH/O8+fPERkZidDQUKmzYrSGDx+Ov//+G5s3b8YXX3yBixcv4unTp/D394e/vz/Gjx+vdZra/F5z587VOn1DGjNmDKZPnw4HBwfVFKV79+4hOTkZI0aMyLP2zbukpCTVFC9CNKGALbJq1aqhR48e+OWXX9SOT5kyBRs3bsSVK1ckypl2AgIC0LdvX/zvf/+TOismJy0tDT/99BMiIiJUYxYyMjLQo0cPRERE6NQdkXPOfl4UCgUOHz6sdfqGdvbsWaxbtw537twBYwxffPEFevTogQYNGkidtQKZOXMm3N3d0bVrVwBAly5dsHXrVpQsWRJ79+4VbRevZ8+e4ebNmwCAihUrwsnJSZR0iTQoYIts69at6Nq1K7y8vFR92CdPnkR0dDQ2bdqEjh07SpzDgvHz88PevXtRtmxZBAYGIiAgQNTmWPJ5cuxjJgXj4eGBdevWoUmTJjh48CC6dOmCjRs3YtOmTUhISMCBAwcEpf/mzRv89NNPiIqKUo1nMTMzQ9euXbFkyRJqPpcpCth6cOHCBcybN0+1J2/lypUxbNgw1K5dW+KcaSer+Xv16tW4du0avLy8EBQUBD8/P41TUoj8/PvvvwCg0yAnKbm7uyMoKAiBgYGqQVtyYmNjg1u3bsHV1RWDBw/Ghw8fsGzZMty6dQsNGzYUNDUTALp27YpLly5h0aJFaNy4MQDg9OnTGDx4MGrVqoWoqCgx/gxiYBSwSYFcvHgRq1atwooVK2Bvb49evXph4MCBVOsTiSH7mDMzMzFlyhTMmTMHycnJAAAHBwcMGzYMY8aMkcW0qPnz5yMiIgJXr15F8+bN0bdvX3Ts2DHP2RO8KV26NLZs2YImTZqgYsWKmDJlCr777jvcvHkT9evXx5s3bwSlb2dnh/3796v2VM9y/PhxeHt7IyUlRVD6RBr835ky8fjxYwwfPlzjjfb69WuMGDECT58+lSBnwj158gQHDx7EwYMHYWZmhnbt2uHKlSuoUqWKrLZk5NmlS5cK9Lp8+bLgc40ZMwaLFy/GjBkzVOlOmzYNixYtwq+//ir8jzGAIUOG4PLlyzh79iwqV66M4OBglCpVCoMGDcLFixelzt5nderUCT169ECrVq3w8uVLtG3bFsCn6yDngFVdFCtWTGOzd+HCheHo6Cg4fSIRKZZXM0bDhg1j/fv3z/P9H374gf38888GzJEwaWlpbMuWLax9+/bMwsKC1a1bly1dupS9fv1a9Zlt27axIkWKSJhLootSpUqxnTt35jq+Y8cOVrp0aQlyJFxaWhqbP38+s7KyYkqlktWsWZOFh4frfbtZXaWlpbFZs2axkJAQtV265s6dy5YvXy44/WXLljEvLy/25MkT1bEnT56w1q1bs7CwMMHpE2lQk7hIqlWrhrCwsFxNUFlOnTqF/v37459//jFwznRTvHhxZGZmonv37ujfvz9q1aqV6zNJSUmoXbs24uPjDZ9BE6GPPmZra2vExcXhiy++UDt+8+ZN1KpVC+/fvxftXPr28eNHbN++HatWrcLBgwfRqFEj9O3bF//++y+WLFmCFi1ayGJetthq166NO3fuIDU1VTXXPiEhAVZWVrm6seTQIkE+oYVTRBIfH5/vIhRlypTB/fv3DZchgebNm4fvvvsO1tbWeX6mSJEiFKz1QN99zDVr1sTixYuxcOFCteOLFy8WbTqRvmWNqdiwYQOUSiX8/f0xb948VKpUSfWZjh07on79+hLmUt2uXbvQtm1bWFhYYNeuXfl+1tfXV9C5/Pz8BP3/hFNSV/GNRbFixVhMTEye78fExLBixYoZMEe6S0tLY2ZmZuzKlStSZ8UkjRo1ipUoUYL9/vvvLDY2lsXGxrIlS5awEiVKsF9++UVw+kePHmV2dnascuXKLCgoiAUFBbHKlSsze3t7duzYMRH+Av1TKpWsTZs2bNOmTSwtLU3jZ5KTk1mfPn0MnLO8KRQK1c5rWTtoaXoplUqJc0p4RU3iImnfvj1Kly6N5cuXa3y/X79+ePz4Mfbu3WvgnOnG09MT27dvl02Ny5iULl0aYWFhuWpZO3fuxMCBA/Ho0SPB53j8+DGWLFmitizmwIEDUbp0acFp61tGRgbWrl0LX19fGkBVAMnJybl2ACtUqJBEuSGCSP3EYCwOHz7MzMzM2LBhw1hiYqLqeGJiIgsNDWVmZmYsOjpawhxqZ8WKFaxdu3bs5cuXUmfF5FhZWbGbN2/mOn7jxg1mbW0tQY74Y2VlJct9zhljLCMjg4WHh7P27duzqlWrsmrVqjFfX1+2evVq0QbJ3bt3j7Vr147Z2toypVKpelENXt6ohi2iZcuWYfDgwfj48SMKFSoEhUKB169fw8LCAvPmzcOAAQOkzmKBZQ1a+fjxI8qWLZtrP2kaqKI/DRs2RMOGDXP1MQcHB+PcuXOibLGZlJSEs2fP4tmzZ7lqX7zviQ0A9erVw8yZM0XditIQGGPw8fFRLT9aqVIlMMZw/fp1XLlyBb6+vtixY4fg8zRt2hSMMQwePBjOzs65tsf9+uuvBZ+DGB4FbJE9evQImzZtUlvj+Ntvv5XdSlITJ07M931dNqAgBRMTE4P27dvDzc1NbZWqhw8fYu/evfjqq68Epb9792707NkTycnJqgfLLAqFAq9evRKUviHs27cPo0ePxuTJk1G3bt1cD5S8NvmuWrUKgwcPxs6dO3Ot73748GH4+flh8eLFgh+a7O3tceHCBVSsWFFQOoQvFLAJ4ZA++5i/+OILtGvXDtOmTYOtra3g9KSQfaR89gcOxhgUCoUo+8HrQ+vWrdGiRQuMGjVK4/vTpk1DTEwM9u/fL+g8zZs3x5gxYwRt10n4QwFbBJ+bopGd0OkahnbhwgXVmuhVq1aV3XroJDc7OztcuXJFtTWlHMXExOT7Pq9NviVLlsS+ffs0rmsAfFrprG3btkhMTBR0nrt37+LHH39Er169UK1atVxr/9eoUUNQ+kQaNA9bBAWd88jzk39Oz549Q7du3XD06FHVHr1JSUlo3rw5oqKiUKJECWkzaOT02cfcpk0bnD9/XtYBm9eA/DmvXr2Cs7Nznu87OzsL3vgD+LRxz927dxEYGKg6plAouG+BIPmjGjbRqGvXrrh37x7WrFmDypUrAwCuXbuGgIAAlC9fHhs2bJA4h8ZLH33M2VuBnj9/jkmTJiEwMBDVq1fPVfuSSytQUlISwsPD1VqAgoKCuN460szMDImJiXk+8D59+hSlS5cWHFCrVKmCypUr4+eff9Y46Kxs2bKC0ifSoIBNNCpcuDAOHTqUa6Wos2fPonXr1khKSpImYyZAH33MBV0dTS61r/Pnz6NNmzawsbFBgwYNAADnzp3D+/fvceDAAdSpU0fiHGqmVCrRtm3bPHcVS01Nxb59+wT/BnZ2doiNjRVlIxHCD2oS14OYmBjMnj1b9eRfpUoVjBgxQvDoXkPKzMzUuOe1hYVFriZaIq5Hjx4hJCRE1AFhxvabDR06FL6+vli+fDnMzT8VY+np6ejXrx+GDBmCY8eOSZxDzQICAj77GTGm1bVo0YICthGiGrbI1q5di8DAQHTq1AlNmzYFAJw8eRLbt29HREQEevToIXEOC+abb75BUlISNmzYoBqZ/OjRI/Ts2ROOjo7Yvn27xDk0Xp06dUK3bt3QpUsXUdM9fPgwBg0ahDNnzuSa9vT69Ws0adIEYWFhsniwtLGxwaVLl9TWDgc+ddvUq1cP7969kyhnfPjjjz8wZcoUBAUFybrbg6ijgC2yypUr4/vvv8fQoUPVjs+dOxfLly9X1bp59/DhQ/j6+uKff/6Bq6ur6li1atWwa9cu2c0r550h+ph9fX3RvHnzXNdmloULF+LIkSOyeBhzdnZGZGQkWrdurXZ8//798Pf3l+3e82LJrwtELt0eJDcK2CKzsrLCP//8k6sp6s6dO6hWrRo+fPggUc60xxjDoUOH1OYC07xO/TBEH3PZsmWxb98+1SDCnG7cuIHWrVsjISFBp/QNKSQkBNu3b8fs2bPRpEkTAJ9askaMGIHOnTtj/vz50maQED2gPmyRubq6Ijo6OlfAPnTokKqmKgdr1qxB165d0apVK7Rq1Up1PC0tDVFRUbJYvlJODNHH/PTpU43jErKYm5vj+fPnes+HGGbPng2FQgF/f3+kp6cD+DS+YsCAAZgxY4bEuSNEP4RtrEtyGTZsGEJCQjBgwABERkYiMjISP/74I4YMGYLhw4dLnb0CCwwMxOvXr3Mdf/v2rdrcTiKew4cPo0qVKnjz5k2u916/fo2qVavi+PHjOqfv4uKCq1ev5vl+XFwcSpUqpXP6hmRpaYkFCxbgv//+w+XLl3H58mW8evUK8+bNy3MEtqmJiYmBj48Pypcvj/Lly8PX11fQ9UOkRwFbZAMGDEBUVBSuXLmCIUOGYMiQIbh69So2btyIH374QersFVjWAgs5/fvvv1zPc5Wz+fPno3///hrXwS5cuDB++OEHzJ07V+f027Vrh19//VVjt8z79+8xfvx4dOjQQef0pWBra4vq1aujevXqsl1mVR/Wrl0LLy8v2NraIiQkBCEhIbCxsUHLli2xfv16qbNHdER92ERN7dq1oVAoEBsbi6pVq6qmzACf9iGOj4+Ht7c3Nm3aJGEujZO++5ifPn2KOnXqwMzMDIMGDVJtDHHjxg0sWbIEGRkZuHjxYr4rcfHiw4cPWLRoEY4cOaJxNThT303OWAa/EnXUh60naWlpGgsSNzc3iXJUMFnLrF6+fBlt2rSBvb296j1LS0u4u7ujc+fOEuXOuOm7j9nZ2RmnTp3CgAEDMHr0aGQ9qysUCrRp0wZLliyRRbAGgL59++LAgQP49ttv0aBBA42tQabs3r178PHxyXXc19cXv/zyiwQ5ImKggC2y27dvIygoCKdOnVI7Lpc1fLO2zXR3d0e3bt2oP9CAsvqY81rsQow+5rJly2Lv3r3477//VFvAVqhQAY6OjoLSNbQ///wTe/fuVa11QNQZy+BXoo4Ctsj69OkDc3Nz/PnnnyhVqpRsn/xbtGiB58+fq+Zbnz17FuvXr0eVKlXw/fffS5w745TVx+zt7Q1ra2u198TuY3Z0dMy17KycuLi4wMHBQepscCtr8Ovly5fVpr1FRERgwYIFEueO6Ir6sEVmZ2eHCxcu5FqBSW6++uorfP/99+jduzcSExPxxRdfoFq1arh9+zaCg4Mxbtw4qbNodIypj1nf/vrrLyxcuBBhYWG0kUUetm/fjjlz5qj6qytXrowRI0bgm2++kThnRFcUsEVWv359zJs3D19++aXUWRHE0dERZ86cQcWKFbFw4UJs3LgRJ0+exIEDB/Djjz/i3r17UmfRKD148AADBgzA/v37NfYxe3h4SJxDPjx//hxdunTBsWPHYGtrm6vvX5cdzQjhHTWJiyD7vNmZM2fi559/xrRp0zQuK6lpyg6PPn78qOq/PnTokGo5zEqVKuHJkydSZs2oGUsfs751794djx49wrRp0zRuH2mq/vvvP6xduxYBAQEa14tfs2aNxveIPFANWwRKpVKtwNA0h1kug86yNGzYEM2bN0f79u3RunVrnDlzBjVr1sSZM2fw7bff4t9//5U6i8SE2dra4vTp06hZs6bUWeHK5MmTERcXh82bN2t8v0uXLqhZsybGjBlj4JwRMVANWwRHjhyROguimzlzJjp27IhZs2YhICBAVTDu2rVLtf8wIVKpVKkS3r9/L3U2uLN161bMmTMnz/d/+OEHDB8+nAK2TFENm+QpIyMDb968UWuOvX//PmxtbeHk5CRhzoipO3DgACZOnIipU6fKuutJbA4ODvjnn3/yXO8hISEB1apV07j8LeEf1bD1ICkpCeHh4arRmVWrVkVQUJDslvQ0MzPL1Xfq7u4uTWYIycbb2xsA0LJlS7Xjcut6EpuZmRkeP36cZ8B+/PhxgXeGI/yhgC2y8+fPo02bNrCxsVE1Hc+dOxdTp07FgQMHUKdOHYlzmLc6deogOjoajo6OqiVK82LqSz8SaRljN5QYateujR07dqBRo0Ya39++fTtq165t4FwRsVDAFtnQoUPh6+uL5cuXq9bhTk9PR79+/TBkyBAcO3ZM4hzm7ZtvvlGNDM9aopQQHn399ddSZ4FLgwYNQrdu3VCmTBkMGDAAZmZmAD51b/3++++YN28ebf4hY9SHLTIbGxtcunQp18Ip165dQ7169fDu3TuJckaIcTl+/DiWLVuGe/fuYfPmzXBxcUFkZCQ8PDxkvw6CEGPGjMH06dPh4OAAT09PAJ/WFk9OTsaIESNov3AZo84MkRUqVEjjbkoPHz6kpRQJEcnWrVtVXU8XL15EamoqgE9zjadNmyZx7qQ1depUnDlzBn369EHp0qVRqlQpBAYG4vTp0xSsZY5q2CILCQnB9u3bMXv2bLU1fEeMGIHOnTtj/vz50mbwMxwdHQu0CAWtJEWkVLt2bQwdOhT+/v5wcHBAbGwsPD09cenSJbRt2xaJiYlSZ5EQ0VEftshmz54NhUIBf39/pKenAwAsLCwwYMAAWTzdZn+gYIxhwIABmDRpEk3jIly5efMm/ve//+U6XrhwYSQlJRk+Q5xxd3dHUFAQAgMDaXcuI0I1bD159+4d7t69CwAoV64cbG1tJc6RbrLXXgjhhaenJ/744w94eXmpXaNr1qzBjBkzcO3aNamzKKn58+cjIiICV69eRfPmzdG3b1907NiRtsuVOerDFtnr16/x6tUr2Nraonr16qhevTpsbW3x6tUrWqyAEJH0798fgwcPxt9//w2FQoHHjx9j3bp1GD58OAYMGCB19iQ3ZMgQXL58GWfPnkXlypURHByMUqVKYdCgQTQlU84YEZW3tzdbsmRJruNLly5lbdu2lSBHwtjb27O7d+9KnQ1C1GRmZrIpU6YwOzs7plAomEKhYNbW1mzs2LFSZ41LaWlpbP78+czKyooplUpWs2ZNFh4ezjIzM6XOGtECNYmLrGjRojh58iQqV66sdvzGjRto2rQpXr58KVHOdENN4oRnaWlpuHPnDpKTk1GlShXY29tLnSWufPz4Edu3b8eqVatw8OBBNGrUCH379sW///6LJUuWoEWLFjQvW0Zo0JnIUlNTVYPNsvv48aMsNisIDQ1V+3daWhqmTp2aa1nVuXPnGjJbhAAAgoKCCvS5lStX6jknfLt48SJWrVqFDRs2QKlUwt/fH/PmzVNbH6Jjx46oX7++hLkk2qKALbIGDRrgjz/+wKJFi9SOh4WFoW7duhLlquAuXbqk9u8mTZrg3r17asdo72EilYiICJQtWxa1a9cGNQ7mrX79+mjVqhWWLl0KPz+/XJujAICHhwe6desmQe6IrqhJXGQnT56El5cX6tevr9qYIDo6GufOncOBAwfw1VdfSZxDQuTrp59+woYNG1C2bFkEBgaiV69eKFq0qNTZ4s6DBw9QtmxZqbNBREajxEXWtGlTnD59Gq6urti0aRN2796N8uXLIy4uTlbBmjZXIDxasmQJnjx5gp9//hm7d++Gq6srunTpgv3791ONO5vmzZtrHC+TlJRE41FkjGrYRCMrKyuUKVMGgYGBCAgIoMUXCJcePHiAiIgIrFmzBunp6fjnn39o4BkApVKJxMTEXAsePX36FG5ubqqlXIm8UB+2CLSZX12oUCE95kQ8jx49QmRkJFavXo2JEyeiRYsW6Nu3L/z8/GBpaSl19ggB8CkwKRQKMMZMdg/s7Hbt2qX67/3796sNFs3IyEB0dDTtaS9jVMMWQVahkR/GGBQKhSwLlewjTgGgR48e6Nu3L2rWrClxzogpSk1NxbZt27By5UqcOHECHTp0QGBgILy9vaFUmnYvX9bfn/UQk52FhQXc3d0xZ84cdOjQQYrsEYEoYIsgJiamQJ+7cuUKBg0apOfc6Mfjx4/xxx9/YMaMGTA3N8eHDx/QuHFjhIWFoWrVqlJnj5iIgQMHIioqCq6urggKCkLPnj1RvHhxqbPFHQ8PD5w7d46+GyNDAVvP3r59iw0bNmDFihW4cOGCrGrYHz9+xM6dO7Fy5UocPHgQ9erVQ9++fdG9e3c8f/4cY8eOxcWLF01+3WZiOEqlEm5ubqhdu3a+rVrbtm0zYK4IMQzqw9aTY8eOITw8HFu3bkXp0qXRqVMnLFmyROpsFVhwcDA2bNgAxhh69+6N3377DdWqVVO9b2dnh9mzZ6N06dIS5pKYGn9/f1oHIA8LFy4s8GdDQkL0mBOiL1TDFlFiYiIiIiIQHh6ON2/eoEuXLggLC0NsbCyqVKkidfa00rJlS/Tr1w+dOnXKc4ef9PR0nDx5El9//bWBc0cIycnDw6NAn1MoFLkWQyLyQAFbJD4+Pjh27Bjat2+Pnj17wtvbG2ZmZrCwsJBlwCaEEMIXahIXyV9//YWQkBAMGDAAFSpUkDo7Osk+JeRzfH199ZgTQgghOVHAFsmJEycQHh6OunXronLlyujdu7fs1un18/Mr0OfkOj2NEFPxuU1STH1zFLky7UmLImrUqBGWL1+OJ0+e4IcffkBUVBRKly6NzMxMHDx4EG/fvpU6i5+VmZlZoBcFa0L49t9//6m9nj17hsOHD2Pbtm1ISkqSOntER9SHrUc3b95EeHg4IiMjkZSUhFatWmnV7EwIIWLJzMzEgAEDUK5cOfz8889SZ4fogAK2AWRkZGD37t1YuXIl1wF74cKF+P7772Ftbf3ZKSI0LYQQ+bl58yaaNWuGJ0+eSJ0VogMK2ETFw8MD58+fR7FixfKdIkLTQgiRp7179yIgIADPnz+XOitEBzTojKjEx8dr/G9CiLyEhoaq/ZsxhidPnmDPnj0ICAiQKFdEKKphE0KIkWnevLnav5VKJUqUKIEWLVogKCgI5uZUV5MjCthEI8YYtmzZgiNHjuDZs2fIzMxUe5/WaiaEEMOiaV1EoyFDhqB3796Ij4+Hvb09ChcurPYihPArPj4et2/fznX89u3buH//vuEzRERBNWyiUdGiRbF27Vq0a9dO6qwQQrT09ddfIygoKFd/9dq1a7FixQocPXpUmowRQaiGTTQqXLgwPD09pc4GIUQHly5dQtOmTXMdb9SoES5fvmz4DBFRUMAmGk2YMAETJ07E+/fvpc4KIURLCoVC4+qKr1+/ppUKZYyaxIlG79+/R8eOHXHy5Em4u7vDwsJC7f2LFy9KlDNCyOf4+PjAxsYGGzZsgJmZGYBPCzh17doVKSkp+OuvvyTOIdEFje0nGgUEBODChQvo1asXnJ2doVAopM4SIaSAZs6cif/973+oWLEivvrqKwDA8ePH8ebNGxw+fFji3BFdUQ2baGRnZ4f9+/fjyy+/lDorhBAdPH78GIsXL0ZsbCxsbGxQo0YNDBo0CEWLFpU6a0RHFLCJRpUqVcKmTZtQo0YNqbNCCCEEFLBJHvbs2YNFixYhLCwM7u7uUmeHEPIZcXFxqFatGpRKJeLi4vL9LD2IyxMFbKKRo6Mj3r17h/T0dNja2uYadPbq1SuJckYI0USpVCIxMRFOTk5QKpVQKBTQVLwrFAoaKS5TNOiMaDR//nyps0AI0UJ8fDxKlCih+m9ifKiGTQghRqBOnTqIjo6Go6MjJk2ahOHDh8PW1lbqbBERUcAmn/XhwwekpaWpHStUqJBEuSGEaGJjY4Pbt2+jTJkyMDMzw5MnT+Dk5CR1toiIqEmcaJSSkoKRI0di06ZNePnyZa73qQ+MEL7UqlULgYGB+PLLL8EYw+zZs2Fvb6/xs+PGjTNw7ogYqIZNNPrpp59w5MgRTJ48Gb1798aSJUvw6NEjLFu2DDNmzEDPnj2lziIhJJubN29i/PjxuHv3Li5evIgqVapo3PdaoVDQSoUyRQGbaOTm5oY1a9agWbNmKFSoEC5evIjy5csjMjISGzZswN69e6XOIiEkD9lHjBPjQZt/EI1evXql2q2rUKFCqmlcX375JY4dOyZl1gghGtSpUwf//fcfAGD8+PF5NocT+aKATTTy9PRUTQ3JWvUMAHbv3o0iRYpImDNCiCbXr19HSkoKAGDSpElITk6WOEdEbDTojKi5d+8e3N3dERgYiNjYWHz99dcYNWoUfHx8sHjxYnz8+BFz586VOpuEkBxo0Jnxoz5soibndJCuXbti4cKF+PDhAy5cuIDy5cvTsoaEcIgGnRk/CthETc7BKg4ODoiNjVX1ZxNC+EeDzowTNYkTQoiRyczMlDoLRA8oYBM1CoUCCoUi1zFCCN927dqFtm3bwsLCArt27cr3s76+vgbKFRETNYkTNUqlEm3btoWVlRWAT6PCW7RoATs7O7XPbdu2TYrsEULykHO3rrzQbl3yRTVsoiYgIEDt37169ZIoJ4QQbWRvBqcmceNENWxCCDEimZmZiIiIwLZt23D//n0oFAp4enqic+fO6N27N3VxyRgFbEIIMRKMMfj4+GDv3r2oWbMmKlWqBMYYrl+/jitXrsDX1xc7duyQOptER9QkTgghRiIiIgLHjh1DdHQ0mjdvrvbe4cOH4efnhzVr1sDf31+iHBIhqIZNCCFGonXr1mjRogVGjRql8f1p06YhJiYG+/fvN3DOiBhoLXFCCDEScXFx8Pb2zvP9tm3bIjY21oA5ImKigE0IIUbi1atXcHZ2zvN9Z2dn1Y5eRH4oYBNCiJHIyMjQuH54FjMzM6SnpxswR0RMNOiMEEKMBGMMffr0US18lFNqaqqBc0TERAGbEEKMRM6FjzShEeLyRaPECSGEEBmgPmxCCCFEBihgE0IIITJAAZsQQgiRAQrYhBBCiAxQwCaEEEJkgAI2IYQQIgMUsAkhhBAZ+H/thcrgwY2t6QAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.heatmap(a_df == 0, yticklabels=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
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+ " AlcoholConsumption BMI Smoking PhysicalActivity DietQuality \\\n",
+ "0 13.297218 22.927749 0 6.327112 1.347214 \n",
+ "1 4.542524 26.827681 0 7.619885 0.518767 \n",
+ "2 19.555085 17.795882 0 7.844988 1.826335 \n",
+ "3 12.209266 33.800817 1 8.428001 7.435604 \n",
+ "4 18.454356 20.716974 0 6.310461 0.795498 \n",
+ "... ... ... ... ... ... \n",
+ "2144 1.561126 39.121757 0 4.049964 6.555306 \n",
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+ "2148 7.890703 33.289738 0 6.570993 7.941404 \n",
+ "\n",
+ " SleepQuality \n",
+ "0 9.025679 \n",
+ "1 7.151293 \n",
+ "2 9.673574 \n",
+ "3 8.392554 \n",
+ "4 5.597238 \n",
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+ "2147 8.322874 \n",
+ "2148 9.878711 \n",
+ "\n",
+ "[2149 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lifestyledf = a_df[['AlcoholConsumption','BMI','Smoking','PhysicalActivity', 'DietQuality', 'SleepQuality' ]]\n",
+ "lifestyledf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lifestyle Factors\n",
+ "\n",
+ "BMI: Body Mass Index of the patients, ranging from 15 to 40.\n",
+ "\n",
+ "Smoking: Smoking status, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "AlcoholConsumption: Weekly alcohol consumption in units, ranging from 0 to 20.\n",
+ "\n",
+ "PhysicalActivity: Weekly physical activity in hours, ranging from 0 to 10.\n",
+ "\n",
+ "DietQuality: Diet quality score, ranging from 0 to 10.\n",
+ "\n",
+ "SleepQuality: Sleep quality score, ranging from 4 to 10."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Medical History\n",
+ "\n",
+ "FamilyHistoryAlzheimers: Family history of Alzheimer's Disease, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "CardiovascularDisease: Presence of cardiovascular disease, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "Diabetes: Presence of diabetes, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "Depression: Presence of depression, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "HeadInjury: History of head injury, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "Hypertension: Presence of hypertension, where 0 indicates No and 1 indicates Yes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
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+ " </tbody>\n",
+ "</table>\n",
+ "<p>2149 rows × 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " FamilyHistoryAlzheimers CardiovascularDisease Diabetes Depression \\\n",
+ "0 0 0 1 1 \n",
+ "1 0 0 0 0 \n",
+ "2 1 0 0 0 \n",
+ "3 0 0 0 0 \n",
+ "4 0 0 0 0 \n",
+ "... ... ... ... ... \n",
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+ "2146 0 0 0 0 \n",
+ "2147 0 1 0 0 \n",
+ "2148 0 0 0 0 \n",
+ "\n",
+ " HeadInjury Hypertension \n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "... ... ... \n",
+ "2144 0 0 \n",
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+ "2146 0 0 \n",
+ "2147 0 0 \n",
+ "2148 0 0 \n",
+ "\n",
+ "[2149 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "medicaldf = a_df[['FamilyHistoryAlzheimers', 'CardiovascularDisease', 'Diabetes', 'Depression','HeadInjury','Hypertension']]\n",
+ "\n",
+ "medicaldf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Symptoms\n",
+ "\n",
+ "Confusion: Presence of confusion, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "Disorientation: Presence of disorientation, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "PersonalityChanges: Presence of personality changes, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "DifficultyCompletingTasks: Presence of difficulty completing tasks, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "Forgetfulness: Presence of forgetfulness, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "Diagnosis Information\n",
+ "Diagnosis: Diagnosis status for Alzheimer's Disease, where 0 indicates No and 1 indicates Yes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "</table>\n",
+ "<p>2149 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Confusion Disorientation PersonalityChanges \\\n",
+ "0 0 0 0 \n",
+ "1 0 0 0 \n",
+ "2 0 1 0 \n",
+ "3 0 0 0 \n",
+ "4 0 0 1 \n",
+ "... ... ... ... \n",
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+ "2145 0 0 0 \n",
+ "2146 0 0 0 \n",
+ "2147 0 0 0 \n",
+ "2148 0 1 0 \n",
+ "\n",
+ " DifficultyCompletingTasks Forgetfulness \n",
+ "0 1 0 \n",
+ "1 0 1 \n",
+ "2 1 0 \n",
+ "3 0 0 \n",
+ "4 1 0 \n",
+ "... ... ... \n",
+ "2144 0 0 \n",
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+ "2147 0 1 \n",
+ "2148 0 1 \n",
+ "\n",
+ "[2149 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "symptomsdf = a_df[['Confusion', 'Disorientation', 'PersonalityChanges', 'DifficultyCompletingTasks','Forgetfulness']]\n",
+ "\n",
+ "symptomsdf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.heatmap(lifestyledf == 0, yticklabels=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Clinical Measurements\n",
+ "\n",
+ "SystolicBP: Systolic blood pressure, ranging from 90 to 180 mmHg.\n",
+ "\n",
+ "DiastolicBP: Diastolic blood pressure, ranging from 60 to 120 mmHg.\n",
+ "\n",
+ "CholesterolTotal: Total cholesterol levels, ranging from 150 to 300 mg/dL.\n",
+ "\n",
+ "CholesterolLDL: Low-density lipoprotein cholesterol levels, ranging from 50 to 200 mg/dL.\n",
+ "\n",
+ "CholesterolHDL: High-density lipoprotein cholesterol levels, ranging from 20 to 100 mg/dL.\n",
+ "\n",
+ "CholesterolTriglycerides: Triglycerides levels, ranging from 50 to 400 mg/dL."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>SystolicBP</th>\n",
+ " <th>DiastolicBP</th>\n",
+ " <th>CholesterolTotal</th>\n",
+ " <th>CholesterolLDL</th>\n",
+ " <th>CholesterolHDL</th>\n",
+ " <th>CholesterolTriglycerides</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>142</td>\n",
+ " <td>72</td>\n",
+ " <td>242.366840</td>\n",
+ " <td>56.150897</td>\n",
+ " <td>33.682563</td>\n",
+ " <td>162.189143</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>115</td>\n",
+ " <td>64</td>\n",
+ " <td>231.162595</td>\n",
+ " <td>193.407996</td>\n",
+ " <td>79.028477</td>\n",
+ " <td>294.630909</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>99</td>\n",
+ " <td>116</td>\n",
+ " <td>284.181858</td>\n",
+ " <td>153.322762</td>\n",
+ " <td>69.772292</td>\n",
+ " <td>83.638324</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>118</td>\n",
+ " <td>115</td>\n",
+ " <td>159.582240</td>\n",
+ " <td>65.366637</td>\n",
+ " <td>68.457491</td>\n",
+ " <td>277.577358</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>94</td>\n",
+ " <td>117</td>\n",
+ " <td>237.602184</td>\n",
+ " <td>92.869700</td>\n",
+ " <td>56.874305</td>\n",
+ " <td>291.198780</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",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2144</th>\n",
+ " <td>122</td>\n",
+ " <td>101</td>\n",
+ " <td>280.476824</td>\n",
+ " <td>94.870490</td>\n",
+ " <td>60.943092</td>\n",
+ " <td>234.520123</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2145</th>\n",
+ " <td>152</td>\n",
+ " <td>106</td>\n",
+ " <td>186.384436</td>\n",
+ " <td>95.410700</td>\n",
+ " <td>93.649735</td>\n",
+ " <td>367.986877</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2146</th>\n",
+ " <td>115</td>\n",
+ " <td>118</td>\n",
+ " <td>237.024558</td>\n",
+ " <td>156.267294</td>\n",
+ " <td>99.678209</td>\n",
+ " <td>294.802338</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2147</th>\n",
+ " <td>103</td>\n",
+ " <td>96</td>\n",
+ " <td>242.197192</td>\n",
+ " <td>52.482961</td>\n",
+ " <td>81.281111</td>\n",
+ " <td>145.253746</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2148</th>\n",
+ " <td>166</td>\n",
+ " <td>78</td>\n",
+ " <td>283.396797</td>\n",
+ " <td>92.200064</td>\n",
+ " <td>81.920043</td>\n",
+ " <td>217.396873</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2149 rows × 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " SystolicBP DiastolicBP CholesterolTotal CholesterolLDL \\\n",
+ "0 142 72 242.366840 56.150897 \n",
+ "1 115 64 231.162595 193.407996 \n",
+ "2 99 116 284.181858 153.322762 \n",
+ "3 118 115 159.582240 65.366637 \n",
+ "4 94 117 237.602184 92.869700 \n",
+ "... ... ... ... ... \n",
+ "2144 122 101 280.476824 94.870490 \n",
+ "2145 152 106 186.384436 95.410700 \n",
+ "2146 115 118 237.024558 156.267294 \n",
+ "2147 103 96 242.197192 52.482961 \n",
+ "2148 166 78 283.396797 92.200064 \n",
+ "\n",
+ " CholesterolHDL CholesterolTriglycerides \n",
+ "0 33.682563 162.189143 \n",
+ "1 79.028477 294.630909 \n",
+ "2 69.772292 83.638324 \n",
+ "3 68.457491 277.577358 \n",
+ "4 56.874305 291.198780 \n",
+ "... ... ... \n",
+ "2144 60.943092 234.520123 \n",
+ "2145 93.649735 367.986877 \n",
+ "2146 99.678209 294.802338 \n",
+ "2147 81.281111 145.253746 \n",
+ "2148 81.920043 217.396873 \n",
+ "\n",
+ "[2149 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "clinicaldf = a_df[['SystolicBP', 'DiastolicBP', 'CholesterolTotal', 'CholesterolLDL','CholesterolHDL','CholesterolTriglycerides']]\n",
+ "\n",
+ "clinicaldf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: >"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.heatmap(clinicaldf == 0, yticklabels=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Cognitive and Functional Assessments\n",
+ "\n",
+ "MMSE: Mini-Mental State Examination score, ranging from 0 to 30. Lower scores indicate cognitive impairment.\n",
+ "\n",
+ "FunctionalAssessment: Functional assessment score, ranging from 0 to 10. Lower scores indicate greater impairment.\n",
+ "\n",
+ "MemoryComplaints: Presence of memory complaints, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "BehavioralProblems: Presence of behavioral problems, where 0 indicates No and 1 indicates Yes.\n",
+ "\n",
+ "ADL: Activities of Daily Living score, ranging from 0 to 10. Lower scores indicate greater impairment."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>MMSE</th>\n",
+ " <th>FunctionalAssessment</th>\n",
+ " <th>MemoryComplaints</th>\n",
+ " <th>BehavioralProblems</th>\n",
+ " <th>ADL</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>21.463532</td>\n",
+ " <td>6.518877</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1.725883</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>20.613267</td>\n",
+ " <td>7.118696</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2.592424</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>7.356249</td>\n",
+ " <td>5.895077</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>7.119548</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>13.991127</td>\n",
+ " <td>8.965106</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>6.481226</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>13.517609</td>\n",
+ " <td>6.045039</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.014691</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2144</th>\n",
+ " <td>1.201190</td>\n",
+ " <td>0.238667</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>4.492838</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2145</th>\n",
+ " <td>6.458060</td>\n",
+ " <td>8.687480</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>9.204952</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2146</th>\n",
+ " <td>17.011003</td>\n",
+ " <td>1.972137</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>5.036334</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2147</th>\n",
+ " <td>4.030491</td>\n",
+ " <td>5.173891</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>3.785399</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2148</th>\n",
+ " <td>11.114777</td>\n",
+ " <td>6.307543</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>8.327563</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2149 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " MMSE FunctionalAssessment MemoryComplaints BehavioralProblems \\\n",
+ "0 21.463532 6.518877 0 0 \n",
+ "1 20.613267 7.118696 0 0 \n",
+ "2 7.356249 5.895077 0 0 \n",
+ "3 13.991127 8.965106 0 1 \n",
+ "4 13.517609 6.045039 0 0 \n",
+ "... ... ... ... ... \n",
+ "2144 1.201190 0.238667 0 0 \n",
+ "2145 6.458060 8.687480 0 1 \n",
+ "2146 17.011003 1.972137 0 0 \n",
+ "2147 4.030491 5.173891 0 0 \n",
+ "2148 11.114777 6.307543 0 1 \n",
+ "\n",
+ " ADL \n",
+ "0 1.725883 \n",
+ "1 2.592424 \n",
+ "2 7.119548 \n",
+ "3 6.481226 \n",
+ "4 0.014691 \n",
+ "... ... \n",
+ "2144 4.492838 \n",
+ "2145 9.204952 \n",
+ "2146 5.036334 \n",
+ "2147 3.785399 \n",
+ "2148 8.327563 \n",
+ "\n",
+ "[2149 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cognitivedf = a_df[['MMSE', 'FunctionalAssessment','MemoryComplaints', 'BehavioralProblems', 'ADL']]\n",
+ "\n",
+ "cognitivedf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: >"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.heatmap(cognitivedf == 0, yticklabels=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='Diagnosis', ylabel='count'>"
+ ]
+ },
+ "execution_count": 44,
+ "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": [
+ "#0 indicates No and 1 indicates Yes\n",
+ "sns.countplot(x=\"Diagnosis\", hue=\"Diagnosis\", data=a_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#defining X nad y\n",
+ "X = a_df.drop('Diagnosis', axis = 1)\n",
+ "y = a_df['Diagnosis']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Correlation in Cognitive Features:\n",
+ "MemoryComplaints 0.306742\n",
+ "BehavioralProblems 0.224350\n",
+ "MMSE -0.237126\n",
+ "ADL -0.332346\n",
+ "FunctionalAssessment -0.364898\n",
+ "Name: Diagnosis, dtype: float64\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\sarah\\AppData\\Local\\Temp\\ipykernel_22224\\739675737.py:19: FutureWarning: \n",
+ "\n",
+ "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+ "\n",
+ " sns.barplot(x=correlationy.index, y=correlationy.values, palette=\"coolwarm\")\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Correlation in Symptoms Features:\n",
+ "DifficultyCompletingTasks 0.009069\n",
+ "Forgetfulness -0.000354\n",
+ "Confusion -0.019186\n",
+ "PersonalityChanges -0.020627\n",
+ "Disorientation -0.024648\n",
+ "Name: Diagnosis, dtype: float64\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\sarah\\AppData\\Local\\Temp\\ipykernel_22224\\739675737.py:19: FutureWarning: \n",
+ "\n",
+ "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+ "\n",
+ " sns.barplot(x=correlationy.index, y=correlationy.values, palette=\"coolwarm\")\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Correlation in Lifestyle Features:\n",
+ "BMI 0.026343\n",
+ "DietQuality 0.008506\n",
+ "PhysicalActivity 0.005945\n",
+ "Smoking -0.004865\n",
+ "AlcoholConsumption -0.007618\n",
+ "SleepQuality -0.056548\n",
+ "Name: Diagnosis, dtype: float64\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\sarah\\AppData\\Local\\Temp\\ipykernel_22224\\739675737.py:19: FutureWarning: \n",
+ "\n",
+ "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+ "\n",
+ " sns.barplot(x=correlationy.index, y=correlationy.values, palette=\"coolwarm\")\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\sarah\\AppData\\Local\\Temp\\ipykernel_22224\\739675737.py:19: FutureWarning: \n",
+ "\n",
+ "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+ "\n",
+ " sns.barplot(x=correlationy.index, y=correlationy.values, palette=\"coolwarm\")\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Correlation in Clinical Features:\n",
+ "CholesterolHDL 0.042584\n",
+ "CholesterolTriglycerides 0.022672\n",
+ "CholesterolTotal 0.006394\n",
+ "DiastolicBP 0.005293\n",
+ "SystolicBP -0.015615\n",
+ "CholesterolLDL -0.031976\n",
+ "Name: Diagnosis, dtype: float64\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Correlation in Medical Features:\n",
+ "Hypertension 0.035080\n",
+ "CardiovascularDisease 0.031490\n",
+ "Depression -0.005893\n",
+ "HeadInjury -0.021411\n",
+ "Diabetes -0.031508\n",
+ "FamilyHistoryAlzheimers -0.032900\n",
+ "Name: Diagnosis, dtype: float64\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\sarah\\AppData\\Local\\Temp\\ipykernel_22224\\739675737.py:19: FutureWarning: \n",
+ "\n",
+ "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+ "\n",
+ " sns.barplot(x=correlationy.index, y=correlationy.values, palette=\"coolwarm\")\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Correlation in Demographic Features:\n",
+ "Age -0.005488\n",
+ "Ethnicity -0.014782\n",
+ "Gender -0.020975\n",
+ "EducationLevel -0.043966\n",
+ "Name: Diagnosis, dtype: float64\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\sarah\\AppData\\Local\\Temp\\ipykernel_22224\\739675737.py:19: FutureWarning: \n",
+ "\n",
+ "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+ "\n",
+ " sns.barplot(x=correlationy.index, y=correlationy.values, palette=\"coolwarm\")\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "groups = {\"Cognitive\": cognitivedf,\"Symptoms\": symptomsdf,\"Lifestyle\": lifestyledf,\"Clinical\": clinicaldf,\"Medical\": medicaldf,\"Demographic\": demographicdf}\n",
+ "\n",
+ "for name, group in groups.items():\n",
+ " # Merge features with target variable\n",
+ " dfy = pd.concat([group, y], axis=1)\n",
+ "\n",
+ " # Compute correlation matrix\n",
+ " correlation_matrix = dfy.corr()\n",
+ "\n",
+ " # Extract correlation with target variable (Diagnosis)\n",
+ " correlationy = correlation_matrix['Diagnosis'].drop('Diagnosis').sort_values(ascending=False)\n",
+ "\n",
+ " # Display correlation values\n",
+ " print(f\"\\nCorrelation in {name} Features:\")\n",
+ " print(correlationy)\n",
+ "\n",
+ " # Visualize correlation with y\n",
+ " plt.figure(figsize=(10, 6))\n",
+ " sns.barplot(x=correlationy.index, y=correlationy.values, palette=\"coolwarm\")\n",
+ " plt.title(f\"Feature Correlation with Diagnosis {name}\")\n",
+ " plt.ylabel(\"Correlation Coefficient\")\n",
+ " plt.xlabel(\"Features\")\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Cross validation, gridsearch or randomizedgridsearch, KFold and array to store results and visualise the results"
+ ]
+ }
+ ],
+ "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": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--
GitLab