diff --git a/Sleep Disorder Prediction/Dataset/README.md b/Sleep Disorder Prediction/Dataset/README.md new file mode 100644 index 000000000..90b55c24a --- /dev/null +++ b/Sleep Disorder Prediction/Dataset/README.md @@ -0,0 +1 @@ +The dataset used for training and testing the models can be accessed from the [Kaggle](https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset) diff --git a/Sleep Disorder Prediction/Dataset/data.csv b/Sleep Disorder Prediction/Dataset/data.csv new file mode 100644 index 000000000..b7e16bd58 --- /dev/null +++ b/Sleep Disorder Prediction/Dataset/data.csv @@ -0,0 +1,375 @@ +Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder +1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,None +2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None +4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea +5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea +6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia +7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia +8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None +12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None +14,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None +15,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None +16,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None +17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea +18,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,Sleep Apnea +19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia +20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None +27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None +31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea +32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia +33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,None +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None +40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None +41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea +51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None +52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None +53,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None +55,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +56,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +58,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +59,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +61,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +62,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None +64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None +66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None +67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,None +68,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,Insomnia +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None +71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +75,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +76,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +77,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +78,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +79,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +80,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea +82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None +86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None +88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None +89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None +90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None +94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea +95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None +101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None +104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea +105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea +106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,None +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None +109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None +110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None +111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None +115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,None +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None +139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None +141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None +143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea +146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea +147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia +148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,None +150,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None +151,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None +163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None +166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia +167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,None +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None +172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None +173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None +174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None +177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None +178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea +186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea +187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia +188,Male,43,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +189,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia +190,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +191,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia +192,Male,43,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia +193,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +194,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +195,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +196,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +197,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +198,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +199,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia +203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia +204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,None +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,None +206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None +207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None +208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None +209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None 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+![image](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/a5740fbc-9f55-4e9b-a5ad-b3623d71f9e7) + +----- + +**Correlation matrix** + +![image](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/543a5713-821e-40e8-bbec-44ab0e86b011) + +----- + +**Streamlit App - Sleep Disorder Prediction page** + +![image](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/58f5e51e-7f91-48de-85ba-b3bb131579af) + +![image](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/3c559286-eb2d-4dbc-9131-04d2d7c3e2b5) + +---- + +**Streamlit App - EDA and Visualization page** + +![image](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/afa7f633-59c9-4bf7-8429-bfa57ebc3897) + +![image](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/c042a2f6-643b-433c-8b97-55c0987c2f04) + +---- diff --git a/Sleep Disorder Prediction/Images/img.png b/Sleep Disorder Prediction/Images/img.png new file mode 100644 index 000000000..33299a88e Binary files /dev/null and b/Sleep Disorder Prediction/Images/img.png differ diff --git a/Sleep Disorder Prediction/Images/img1.png b/Sleep Disorder Prediction/Images/img1.png new file mode 100644 index 000000000..4ba1154bc Binary files /dev/null and b/Sleep Disorder Prediction/Images/img1.png differ diff --git a/Sleep Disorder Prediction/Images/img2.png b/Sleep Disorder Prediction/Images/img2.png new file mode 100644 index 000000000..7d385a8c8 Binary files /dev/null and b/Sleep Disorder Prediction/Images/img2.png differ diff --git a/Sleep Disorder Prediction/Images/img3.png b/Sleep Disorder Prediction/Images/img3.png new file mode 100644 index 000000000..289d3521a Binary files /dev/null and b/Sleep Disorder Prediction/Images/img3.png differ diff --git a/Sleep Disorder Prediction/Images/img4.png b/Sleep Disorder Prediction/Images/img4.png new file mode 100644 index 000000000..3a1a8cced Binary files /dev/null and b/Sleep Disorder Prediction/Images/img4.png differ diff --git a/Sleep Disorder Prediction/Images/img5.png b/Sleep Disorder Prediction/Images/img5.png new file mode 100644 index 000000000..3caa98b58 Binary files /dev/null and b/Sleep Disorder Prediction/Images/img5.png differ diff --git a/Sleep Disorder Prediction/Images/img6.png b/Sleep Disorder Prediction/Images/img6.png new file mode 100644 index 000000000..33e44cab0 Binary files /dev/null and b/Sleep Disorder Prediction/Images/img6.png differ diff --git a/Sleep Disorder Prediction/Models/Notebook.ipynb b/Sleep Disorder Prediction/Models/Notebook.ipynb new file mode 100644 index 000000000..c86825812 --- /dev/null +++ b/Sleep Disorder Prediction/Models/Notebook.ipynb @@ -0,0 +1,4583 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "### Import Libraries" + ], + "metadata": { + "id": "YDPNSMyEShyi" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import plotly.graph_objs as go\n", + "from plotly.subplots import make_subplots\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn import metrics\n", + "\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from tensorflow.keras.models import Sequential\n", + "import tensorflow as tf\n", + "from tensorflow.keras.layers import Dense,Dropout,BatchNormalization\n", + "from tensorflow.keras.optimizers import Adam\n", + "\n" + ], + "metadata": { + "id": "rUYbTdmzoF2S" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df=pd.read_csv(\"/content/data.csv\")\n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 258 + }, + "id": "xjWgYqRyI3n8", + "outputId": "f4dd44c6-d5cf-4261-a017-919d2e4b0b5c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Person ID Gender Age Occupation Sleep Duration \\\n", + "0 1 Male 27 Software Engineer 6.1 \n", + "1 2 Male 28 Doctor 6.2 \n", + "2 3 Male 28 Doctor 6.2 \n", + "3 4 Male 28 Sales Representative 5.9 \n", + "4 5 Male 28 Sales Representative 5.9 \n", + "\n", + " Quality of Sleep Physical Activity Level Stress Level BMI Category \\\n", + "0 6 42 6 Overweight \n", + "1 6 60 8 Normal \n", + "2 6 60 8 Normal \n", + "3 4 30 8 Obese \n", + "4 4 30 8 Obese \n", + "\n", + " Blood Pressure Heart Rate Daily Steps Sleep Disorder \n", + "0 126/83 77 4200 NaN \n", + "1 125/80 75 10000 NaN \n", + "2 125/80 75 10000 NaN \n", + "3 140/90 85 3000 Sleep Apnea \n", + "4 140/90 85 3000 Sleep Apnea " + ], + "text/html": [ + "\n", + "
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Person IDGenderAgeOccupationSleep DurationQuality of SleepPhysical Activity LevelStress LevelBMI CategoryBlood PressureHeart RateDaily StepsSleep Disorder
01Male27Software Engineer6.16426Overweight126/83774200NaN
12Male28Doctor6.26608Normal125/807510000NaN
23Male28Doctor6.26608Normal125/807510000NaN
34Male28Sales Representative5.94308Obese140/90853000Sleep Apnea
45Male28Sales Representative5.94308Obese140/90853000Sleep Apnea
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Person IDGenderAgeOccupationSleep DurationQuality of SleepPhysical Activity LevelStress LevelBMI CategoryBlood PressureHeart RateDaily StepsSleep Disorder
01Male27Software Engineer6.16426Overweight126/83774200None
12Male28Doctor6.26608Normal125/807510000None
23Male28Doctor6.26608Normal125/807510000None
78Male29Doctor7.87756Normal120/80708000None
89Male29Doctor7.87756Normal120/80708000None
..........................................
341342Female56Doctor8.29903Normal Weight118/756510000None
342343Female56Doctor8.29903Normal Weight118/756510000None
343344Female57Nurse8.19753Overweight140/95687000None
358359Female59Nurse8.09753Overweight140/95687000None
359360Female59Nurse8.19753Overweight140/95687000None
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219 rows ร— 13 columns

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Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Person ID 374 non-null int64 \n", + " 1 Gender 374 non-null object \n", + " 2 Age 374 non-null int64 \n", + " 3 Occupation 374 non-null object \n", + " 4 Sleep Duration 374 non-null float64\n", + " 5 Quality of Sleep 374 non-null int64 \n", + " 6 Physical Activity Level 374 non-null int64 \n", + " 7 Stress Level 374 non-null int64 \n", + " 8 BMI Category 374 non-null object \n", + " 9 Blood Pressure 374 non-null object \n", + " 10 Heart Rate 374 non-null int64 \n", + " 11 Daily Steps 374 non-null int64 \n", + " 12 Sleep Disorder 374 non-null object \n", + "dtypes: float64(1), int64(7), object(5)\n", + "memory usage: 38.1+ KB\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Exploratory Data Analysis" + ], + "metadata": { + "id": "DFSlnLYWsS48" + } + }, + { + "cell_type": "code", + "source": [ + "gender_counts = df['Gender'].value_counts()\n", + "plt.figure(figsize=(8, 6))\n", + "plt.pie(gender_counts, labels=gender_counts.index, autopct='%1.1f%%', startangle=140)\n", + "plt.title('Gender Distribution')\n", + "plt.axis('equal')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 521 + }, + "id": "PKAs16mdJz6D", + "outputId": "24544cb4-93ab-424a-c817-652e8e6c3757" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(12, 8))\n", + "sns.countplot(data=df, x='Occupation')\n", + "plt.title('Occupation Distribution')\n", + "plt.xlabel('Occupation')\n", + "plt.ylabel('Count')\n", + "plt.xticks(rotation=45)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817 + }, + "id": "LwJLbUwkl-Mm", + "outputId": "9e8b6c64-4415-4feb-e4ee-910e31645a47" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(8, 6))\n", + "plt.hist(df['Age'], bins=20, color='skyblue', edgecolor='black')\n", + "plt.xlabel('Age')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Distribution of Age')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "XDft036R6zTO", + "outputId": "b36f3b84-3c4c-4ced-a4c9-0358b1d08495" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(8, 6))\n", + "sns.boxplot(x='Gender', y='Sleep Duration', data=df)\n", + "plt.xlabel('Gender')\n", + "plt.ylabel('Sleep Duration')\n", + "plt.title('Sleep Duration by Gender')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "-W2JZD_ApPrO", + "outputId": "f563c355-59b6-40a1-f54b-af45435dcfdb" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "age_groups = []\n", + "for i in range(min(df['Age']),max(df['Age']),5):\n", + " age_group = f\"{i}-{i+4}\"\n", + " age_groups.append(age_group)" + ], + "metadata": { + "id": "zDpRW7Q-k7Xn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "heart_rates = []\n", + "\n", + "for i in range(min(df['Age']),max(df['Age']),5):\n", + " age_group_df = df[(df['Age'] >= i) & (df['Age'] <= i+4)]\n", + " heart_rate = age_group_df['Heart Rate'].mean()\n", + " heart_rates.append(heart_rate)\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(age_groups, heart_rates,marker='*')\n", + "plt.xlabel('Age Group')\n", + "plt.ylabel('Average Heart Rate')\n", + "plt.title('Average Heart Rate by Age Group')\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "0mRCNb31k3p6", + "outputId": "a5bbf1f4-c93d-43c9-a2ec-a2eb23fae2ac" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "df.columns" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3UanEZoGpE1P", + "outputId": "7ba4a540-3486-48ca-9621-c913f15d1e35" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Person ID', 'Gender', 'Age', 'Occupation', 'Sleep Duration',\n", + " 'Quality of Sleep', 'Physical Activity Level', 'Stress Level',\n", + " 'BMI Category', 'Blood Pressure', 'Heart Rate', 'Daily Steps',\n", + " 'Sleep Disorder'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "source": [ + "stress_level = []\n", + "\n", + "for i in range(min(df['Age']),max(df['Age']),5):\n", + " age_group_df = df[(df['Age'] >= i) & (df['Age'] <= i+4)]\n", + " stress = age_group_df['Stress Level'].mean()\n", + " stress_level.append(stress)\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(age_groups, stress_level, marker='*')\n", + "plt.xlabel('Age Group')\n", + "plt.ylabel('Average Stress Activity Level')\n", + "plt.title('Average Stress Level by Age Group')\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "R_tisX3zo-D_", + "outputId": "81d93294-791d-455c-b6c5-df165ac154e0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "physical_act_level = []\n", + "\n", + "for i in range(min(df['Age']),max(df['Age']),5):\n", + " age_group_df = df[(df['Age'] >= i) & (df['Age'] <= i+4)]\n", + " physical_act = age_group_df['Physical Activity Level'].mean()\n", + " physical_act_level.append(physical_act)\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(age_groups, physical_act_level, marker='*')\n", + "plt.xlabel('Age Group')\n", + "plt.ylabel('Average Physical Activity Level')\n", + "plt.title('Average Physical Activity Level by Age Group')\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "rIpSK-jekTWb", + "outputId": "c486c6cc-33aa-493b-936c-dffa5d0aa9d8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "gender_sleep_disorder_counts = df.groupby(['Gender', 'Sleep Disorder']).size().unstack()\n", + "traces = []\n", + "for sleep_disorder_status in gender_sleep_disorder_counts.columns:\n", + " trace = go.Bar(\n", + " x=gender_sleep_disorder_counts.index,\n", + " y=gender_sleep_disorder_counts[sleep_disorder_status],\n", + " name=sleep_disorder_status\n", + " )\n", + " traces.append(trace)\n", + "\n", + "layout = go.Layout(\n", + " title='Distribution of Sleep Disorder by Gender',\n", + " xaxis=dict(title='Gender'),\n", + " yaxis=dict(title='Count'),\n", + " barmode='group'\n", + ")\n", + "\n", + "fig = go.Figure(data=traces, layout=layout)\n", + "\n", + "fig.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "id": "griJD_EGm7qv", + "outputId": "9e628e76-b583-4997-a8a7-99d6eae80a4d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Pie Chart of BMI Categories\n", + "plt.figure(figsize=(20, 6))\n", + "df['BMI Category'].value_counts().plot(kind='pie', autopct='%1.1f%%')\n", + "plt.title('BMI Categories Distribution')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 521 + }, + "id": "yc-zK7jPpWau", + "outputId": "b114aa73-6a75-4348-b5c8-47e5e31ef70c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "def preprocess_blood_pressure(bp_str):\n", + " systolic, diastolic = bp_str.split('/')\n", + " return float(systolic), float(diastolic)\n", + "\n", + "df[['Systolic Pressure', 'Diastolic Pressure']] = df['Blood Pressure'].apply(lambda x: pd.Series(preprocess_blood_pressure(x)))\n", + "\n", + "df.drop(columns=['Blood Pressure'], inplace=True)\n", + "\n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 293 + }, + "id": "DAkjL4mgWDv5", + "outputId": "681e0567-ddb4-4d4f-93c4-4b66f1df62c3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Person ID Gender Age Occupation Sleep Duration \\\n", + "0 1 Male 27 Software Engineer 6.1 \n", + "1 2 Male 28 Doctor 6.2 \n", + "2 3 Male 28 Doctor 6.2 \n", + "3 4 Male 28 Sales Representative 5.9 \n", + "4 5 Male 28 Sales Representative 5.9 \n", + "\n", + " Quality of Sleep Physical Activity Level Stress Level BMI Category \\\n", + "0 6 42 6 Overweight \n", + "1 6 60 8 Normal \n", + "2 6 60 8 Normal \n", + "3 4 30 8 Obese \n", + "4 4 30 8 Obese \n", + "\n", + " Heart Rate Daily Steps Sleep Disorder Systolic Pressure \\\n", + "0 77 4200 None 126.0 \n", + "1 75 10000 None 125.0 \n", + "2 75 10000 None 125.0 \n", + "3 85 3000 Sleep Apnea 140.0 \n", + "4 85 3000 Sleep Apnea 140.0 \n", + "\n", + " Diastolic Pressure \n", + "0 83.0 \n", + "1 80.0 \n", + "2 80.0 \n", + "3 90.0 \n", + "4 90.0 " + ], + "text/html": [ + "\n", + "
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Person IDGenderAgeOccupationSleep DurationQuality of SleepPhysical Activity LevelStress LevelBMI CategoryHeart RateDaily StepsSleep DisorderSystolic PressureDiastolic Pressure
01Male27Software Engineer6.16426Overweight774200None126.083.0
12Male28Doctor6.26608Normal7510000None125.080.0
23Male28Doctor6.26608Normal7510000None125.080.0
34Male28Sales Representative5.94308Obese853000Sleep Apnea140.090.0
45Male28Sales Representative5.94308Obese853000Sleep Apnea140.090.0
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Histogram of Daily Steps\n", + "plt.figure(figsize=(8, 6))\n", + "plt.hist(df['Daily Steps'], bins=20, color='lightgreen', edgecolor='black')\n", + "plt.xlabel('Daily Steps')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Distribution of Daily Steps')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "gL7UHHZXpbTu", + "outputId": "01030bfd-0648-4250-915d-6689426b3c5c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Stacked Bar Chart of Occupation by Sleep Disorder\n", + "plt.figure(figsize=(10, 6))\n", + "sns.countplot(x='Occupation', hue='Sleep Disorder', data=df)\n", + "plt.xlabel('Occupation')\n", + "plt.ylabel('Count')\n", + "plt.title('Occupation by Sleep Disorder')\n", + "plt.xticks(rotation=45)\n", + "plt.legend(title='Sleep Disorder')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 663 + }, + "id": "wh2tDAoSpdrm", + "outputId": "86464fb5-6e6d-4d04-8fcc-5cd2168c3358" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(10, 8))\n", + "sns.violinplot(data=df, x='Gender', y='Sleep Duration',hue='Gender')\n", + "plt.title('Sleep Duration by Gender', fontsize=16)\n", + "plt.xlabel('Gender', fontsize=14)\n", + "plt.ylabel('Sleep Duration', fontsize=14)\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 725 + }, + "id": "SWlRryMOnkkV", + "outputId": "7e944c87-580f-460a-e5b9-4aac7d3f7dff" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import plotly.graph_objects as go\n", + "import pandas as pd\n", + "\n", + "avg_stress_by_occupation = df.groupby('Occupation')['Stress Level'].mean().reset_index()\n", + "\n", + "color_scale = [[0, 'blue'],\n", + " [0.25,'green'],\n", + " [0.5,'yellow'],\n", + " [0.75,'orange'],\n", + " [1, 'red']]\n", + "\n", + "fig1 = go.Figure(go.Bar(\n", + " x=avg_stress_by_occupation['Stress Level'],\n", + " y=avg_stress_by_occupation['Occupation'],\n", + " marker=dict(color=avg_stress_by_occupation['Stress Level'],\n", + " coloraxis='coloraxis'),\n", + " orientation='h',\n", + " hoverinfo='x+y',\n", + " textposition='inside',\n", + " texttemplate='%{x:.2f}',\n", + "))\n", + "\n", + "fig1.update_layout(\n", + " title='Average Stress Level by Occupation',\n", + " yaxis=dict(title='Occupation', tickangle=-30),\n", + " xaxis=dict(title='Average Stress Level'),\n", + " plot_bgcolor='rgba(0, 0, 0, 0)',\n", + " paper_bgcolor='rgb(240, 240, 240)',\n", + " bargap=0.15,\n", + " font=dict(family='Arial', size=12, color='rgb(50, 50, 50)'),\n", + " coloraxis=dict(colorscale=color_scale, cmin=0, cmax=8),\n", + ")\n", + "\n", + "# Show the plot\n", + "fig1.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "id": "gVFhLyThA7Av", + "outputId": "0572fef1-a379-4428-e175-3f2d884464b9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize = (10,6))\n", + "sns.kdeplot(data=df, x= \"Stress Level\", hue= \"Gender\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "id": "DQQ-sl3DGYzY", + "outputId": "a0a578e1-78c7-4416-c1d8-abc1476576d4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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nq3ZTWaNNhUVEPKV169ZcdNFFvPnmm7zxxhtcdNFFxMfHNzhn27ZtXHfddaSmphITE0NKSgpgdAVvzJo1a/jhhx+Iioqqf3Tv3h3gpNMJvYFGrkRExG2+rxu1uqhvG7fd48wurUmKCSO3uIJ5G/O4uG9bt91LRMSdwoNtbPzHOFPu21y33norU6ZMAeCFF1445uOXXHIJHTt25JVXXqFt27bY7XZ69+5NVVVVo9crKSnhkksu4cknnzzmY23auO97iauouBIREbcoqaxhSdYBAMb2SHDbfYJsVq4e3J7n5mcya3mOiisR8VkWi6VZ0/PMNH78eKqqqrBYLIwb17AwPHDgAFu2bOGVV17hjDPOAODnn38+4fUGDhzIxx9/TEpKCkFBvvV7AZoWKCLerLwQyg4az5UlYNeUL1/y87b9VNXaSWkVQefWUW691zWDk7FY4OfMAnIOlrn1XiIicoTNZmPTpk1s3LgRm63hCFiLFi1o1aoVL7/8MpmZmcyfP5+pU6ee8Hp33303Bw8e5LrrruOXX34hKyuL7777jsmTJ1Nb6/0/B/heOSgi/utAFmSmw+7lkLMMCn81Hzs4EjoMh5TR0OlMaDsQrHqPyFvN25gPwNgeiVgsFrfeK7llBKPT4vlpWwH/XZHDH87v5tb7iYjIETExja+ptVqtzJo1i9///vf07t2bbt268e9//5uzzz77uNdq27YtixYt4sEHH+T888+nsrKSjh07Mn78eKw+8D3f4tC29scoLi4mNjaWoqKi4/5lEREX2r0Sfp4Bm78GmvBfUnw3OOMP0PtKsOm9Im9Sa3cw+J/zOFRWzaw7hzM8tZXb7/nV2r1MeX81iTGhLP7jGGxW9xZ0IiKno6Kigh07dtCpUyfCwsLMjhPwTvTn0ZTaQD+NiIh5ctfBd3+CHT8eea3TmdBxNCQPhfaDITjCmA5or4aDO2DnT7DzZ9i+EAq2wKd3woLH4MwHoN/1GsnyEquyD3GorJrY8GAGd2zhkXue1zOR2PBg8oorWbb9ACPT4k/+SSIiIi6k4kpEPM9uh2Uz4fuHobYKrEHQ5xoYdS8kdD/2fKsNCIGk3sZj+F1QUQS/vApLXoBDO+Hzu2H9JzDhRYhO9PRXJL/i7BJ4bvcEgmyeKXhDg2xc0DuJWb/k8MWavSquRETE4/QWr4h4Vkk+vH81fDfNKKy6XgC/Xw2Xv9h4YXU8YbHGlMD71sF5/4CgMMhKhxdHwlbPb8AoDc3bZBRXY9zYJbAxl/YzOgV+uz6Xqhq7R+8tIiKi4kpEPCdvI8wcDZnfG8XQRU/DdR9AXIfmXzMk0hjxunMhJPaGsgJ4/xqY9zBoSakpsvaXsH1/KcE2C2d2be3Rew9LbUVCdChF5dX8uHW/R+8tIiKi4kpEPGNvBrx5EZTkQevucOcCGHI7uKqLXEJ3uD0dhv/O+PWiZ+DLe9W+3QQLtxhFzbBOrYgJC/bovW1WS/2GxV+s2evRe4uIiKi4EhH3270C3roUyg8a7dNvnQMJPVx/n+AwGP84XPo8WKyw6i345A6orXb9veS4lmw3Ng4eZdKaJ+fUwHkb8yirqjElg4iIBCYVVyLiXjnL4e3LoLIIkofDpM8h3M3d4wbeBFe9DtZgWP8xzL4Rairde08BjBbsy+qKqxGd3d9+vTH9k+Po0DKC8upa0jflm5JBREQCk4orEXGfQ7vgg4lQVQIpZ8CNH0OYh/aO63W5sZ4rKAy2zoEv7tEaLA/YtK+Y4ooaokKD6N3WnH0CLRYLl/TT1EAREfE8FVci4h6VJTDreig7AG36wfX/hdAoz2bocp5RYFmDYO1sWPikZ+8fgJbWjVoN7dTSYy3YG3Npv3aAsf6ruELTQkVExDNUXImI69nt8OlvIG89RCbAxPchJMKcLJ3PhYtmGMcLHoc1s83JESCWZBnF1fDUlqbm6JYUTefWkVTV2vlhs6YGioj4u5SUFJ555hmzY6i4EhE3WPgkbP4KbCEw8T2IbW9unkE3w6j7jOMvpsDORabG8Vc1tXaW7zgIwIhU8zfwvaC3MTXw23W5JicREfEvt9xyCxaL5ZhHZmam2dFMp+JKRFwr6wdY+IRxfPG/IHmouXmcxjwMPS8zNi7+8GZjM2NxqQ17izlcWUNMWBA9TVpvdbTxvZMAWLA1n/IqteQXEXGl8ePHs2/fvgaPTp06mR3LdCquRMR1Korg87uN48G3woAbzc1zNKsVLn8JEnpB6X747C5j+qK4zJL69VatsFldtH/ZaejVNob2LcKpqLazcKuKaRERVwoNDSUpKanBw2az8fnnnzNw4EDCwsJITU3l73//OzU1R7bFsFgsvPTSS1x88cVERETQo0cPlixZQmZmJmeffTaRkZGMHDmSrKys+s/JysrisssuIzExkaioKIYMGcL3339/wnyFhYXcfvvttG7dmpiYGM4991zWrFnjtt8PJxVXIuI6c6ZB8R5o0QnO/6fZaY4VHG60aA8Kg8zvYdmLZifyK85mFmavt3KyWCyM72WMXs1Zr6mBIuIDHA6oKvX8w0XddH/66ScmTZrEvffey8aNG3nppZd48803efTRRxuc98gjjzBp0iQyMjLo3r07119/Pb/5zW+YNm0aK1aswOFwMGXKlPrzS0pKuPDCC0lPT2f16tWMHz+eSy65hOzs7ONmufrqq8nPz+fbb79l5cqVDBw4kDFjxnDw4EGXfK3HE+TWq4tI4Nj8DWS8B1jg8pkQEml2osYldIdxj8HXU2Hew5Ay2uhmKKelutbOL871Vibtb9WYC/ok8erPO0jflE9VjZ2QIL2nKCJerLoMHmvr+fv+aW+Tv29/9dVXREUd6QJ8wQUXcOjQIf74xz9y8803A5CamsojjzzC//7v//Lwww/Xnzt58mSuueYaAB588EFGjBjBX//6V8aNGwfAvffey+TJk+vP79evH/36Hfle/cgjj/Dpp5/yxRdfNCjCnH7++WeWL19Ofn4+oaGhAEyfPp3PPvuMjz76iDvvvLNJX2tTqLgSkdNXegC+vNc4HnkPdBhubp6TGXwrZKbDlq/ho9vgNwu9txj0Eev2FFFaVUtcRDA9ksxfb+U0ILkFCdGh5B+uZFFWAed0SzA7koiIXzjnnHN48cUjM0AiIyPp27cvixYtajBSVVtbS0VFBWVlZUREGJ2D+/btW//xxMREAPr06dPgtYqKCoqLi4mJiaGkpIS//e1vfP311+zbt4+amhrKy8uPO3K1Zs0aSkpKaNWq4Zt95eXlDaYbuoOKKxE5fXMehNJ8aN0Dzvmz2WlOzmKBS5+DmavgwDb44TEY9+jJP0+Oy9klcGhKS6xesN7KyWq1MK5XEu8s3cV363NVXImIdwuOMEaRzLhvE0VGRpKWltbgtZKSEv7+979zxRVXHHN+WFjYkdsFB9cfWyyW475mr1sbff/99zNv3jymT59OWloa4eHhXHXVVVRVVTWaraSkhDZt2rBgwYJjPhYXF3dqX2AzqbgSkdOzawms+xCwwIT/QHDYST/FK0S2Mgqs966Cpf+BvtdoeuBpWJ19CIDBKS1MTnKs8b2N4mruxjz+OcFu6ubGIiInZLH49EyKgQMHsmXLlmOKrtO1aNEibrnlFi6//HLAKJ527tx5why5ubkEBQWRkpLi0iwno+8wItJ89lr49n+N44GToN1Ac/M0VZfzoNfl4LAb0xrtatfdHA6Hg1XZhQAM6OB9xdWwTi2JiwjmYGkVK3cdMjuOiIjfeuihh3j77bf5+9//zoYNG9i0aROzZs3iL3/5y2ldt0uXLnzyySdkZGSwZs0arr/++vpRrcaMHTuWESNGMGHCBObOncvOnTtZvHgxf/7zn1mxYsVpZTkZFVci0nyr34HctRAaC2MeMjtN84x/AkJjYO9q+OU1s9P4pL1FFew/XEmQ1ULvtrFmxzlGkM3KmO7GnP65G/NMTiMi4r/GjRvHV199xdy5cxkyZAjDhw/nX//6Fx07djyt686YMYMWLVowcuRILrnkEsaNG8fAgcd/Q9disfDNN99w5plnMnnyZLp27crEiRPZtWtX/Rovd7E4HC7qvehHiouLiY2NpaioiJgY71mYLeJVygvhuUFQVmB03xtxt9mJmu+XV+HrP0BINExZDjEmdGryYV+u2cs9H6ymT7tYvrxntNlxGjVnfS6/fXclyS3D+fGBc+rn84uImKWiooIdO3bQqVOnBuuRxBwn+vNoSm2gkSsRaZ6F/2cUVvFdYaj7Wpp6xKBbod1gqDoM3z5odhqfs7puSuDADnGm5jiRM7vGExpkJedgOZtzD5sdR0RE/JSKKxFpuoJMWP6ScTz+cbAFn/h8b2e1wiXPgsUGm76AXYvNTuRTVucY65i8cb2VU0RIEGd0aQ3APE0NFBERN1FxJSJNt/BJsNdAl/MhbazZaVwjqbfRlANg7l9ctlu9v6usqWXDnmIABnjxyBXA+b2c665yTU4iIiL+SsWViDTN/i11rdeBc0+v+4/XOXsaBEfCnpWw4ROz0/iEDXuLqaq10zIyhA4tm75PiieN6Z6A1QLr9xSzp7Dc7DgiIuKHVFyJSNMseAJwQPeL/W9fqOhEGHWvcfz936Gm0tw8PuDo9Vbe3iSiVVQogzu2BGDeBo1eiYiI66m4EpFTl7cBNnxqHJ89zdws7jJyCkQlQeEuo4ugnJBz82BvXm91tCNTA7XuSkS8gxp3ewdX/TmouBKRU+ccteo5wVij5I9CIuGcPxnHC/8PyrXp7Ik4R64GJMeZmuNUndfTKK6W7ThIYVmVyWlEJJAFBxvNoMrKykxOInDkz8H559JcQa4IIyIBYN9ao5MeFjj7j2anca8BN8KymZC/ERb9G8Y+bHYir5RfXMGewnKsFujrI8VVx1aRdE+KZnPuYX7Yks/lA9qbHUlEApTNZiMuLo78/HwAIiIivH56tT9yOByUlZWRn59PXFwcNpvttK6n4kpETs3CJ43n3ldCQg9zs7ib1WY065h1PSx/GUbeAxEtzU7ldVbVjVp1TYwmKtR3vp2c3zORzbmHmbshT8WViJgqKSkJoL7AEvPExcXV/3mcDt/5bigi5inIhM1fG8dn/a+5WTyl24WQ1Ady18GSF2DMX81O5HXW7i4EoL+PjFo5ndcziX/Pz2Th1v1UVNcSFnx671KKiDSXxWKhTZs2JCQkUF1dbXacgBUcHHzaI1ZOKq5E5OSWvgA4oOsF0Lqb2Wk8w2KBsx6E2TfCspdgxN0avfqVdXuKAOjTPtbkJE3Tu10MbWLD2FdUwaLMAsb0SDQ7kogEOJvN5rIf7sVcamghIidWWgAZ7xvHI+8xN4undbsIEntD1WFjDZbUczgcrK8rrnq39a3iymKxcH5dY4u5G9Q1UEREXEfFlYic2C+vQk0FtB0AHUeancazrFY48wHjeOlMKC80NY432VtUwaGyaoKsFrolRZsdp8nO72XMq0/fnEetXW2QRUTENbyiuHrhhRdISUkhLCyMYcOGsXz58uOeu2HDBq688kpSUlKwWCw888wzJ7z2E088gcVi4b777nNtaJFAUF0Oy18xjkfeY0yVCzQ9LoWEnlBZpNGro6zbbYxadU2M9sk1S0M7tSQmLIiCkqr6vbpEREROl+nF1ezZs5k6dSoPP/wwq1atol+/fowbN+64XVPKyspITU3liSeeOGlHj19++YWXXnqJvn37uiO6iP9bMwvKCiC2A/S4zOw05mgwevUfqDxsbh4vsWFv3ZTAdjEmJ2meYJuVc7snANpQWEREXMf04mrGjBnccccdTJ48mZ49ezJz5kwiIiJ4/fXXGz1/yJAhPPXUU0ycOJHQ0NDjXrekpIQbbriBV155hRYtWrgrvoj/stuNLnkAw+8CWwD3v+k5AVp1gYoiWP2u2Wm8Qn0zi3a+td7qaM6pgd9tyMXh0NRAERE5faYWV1VVVaxcuZKxY8fWv2a1Whk7dixLliw5rWvffffdXHTRRQ2ufTyVlZUUFxc3eIgEvMx5cGAbhMbCwJvMTmMuqxVG/M44XvofqK0xN4/Jjm5m0cuHi6szu7YmJMjKrgNlbMsvMTuOiIj4AVOLq4KCAmpra0lMbNgGNzExkdzc3GZfd9asWaxatYrHH3/8lM5//PHHiY2NrX8kJyc3+94ifuOXV43ngTdBqO81LHC5ftdBRCsozIZNX5idxlR5xZUUlFRhs1ro2cY3pwUCRIUGMTotHoC5G5r/PUdERMTJ9GmBrpaTk8O9997Le++9R1hY2Cl9zrRp0ygqKqp/5OTkuDmliJc7tAu2zTOOB99qbhZvERwOQ243jpc8DwE8jcw5JTCtdZRPNrM4mrMl+zytuxIRERcwtbiKj4/HZrORl9fwm1peXt5Jm1Ucz8qVK8nPz2fgwIEEBQURFBTEwoUL+fe//01QUBC1tbXHfE5oaCgxMTENHiIBbdVbgANSz4ZWnc1O4z2G3AG2UNizErKXmp3GNPX7W/nwlECnMT0SsVhgze4i9hWVmx1HRER8nKnFVUhICIMGDSI9Pb3+NbvdTnp6OiNGjGjWNceMGcO6devIyMiofwwePJgbbriBjIwM7X4tcjI1VbDqbeN48G3mZvE2Ua2h30TjePFz5mYx0ZHiyvffiGodHcrADkbTo+81eiUiIqfJ9PZfU6dO5eabb2bw4MEMHTqUZ555htLSUiZPngzApEmTaNeuXf36qaqqKjZu3Fh/vGfPHjIyMoiKiiItLY3o6Gh69+7d4B6RkZG0atXqmNdFpBGbv4LS/RCVBN0uMDuN9xkxxRjZ2/INHMgKyJG99Xt9v1Pg0c7vmcjKXYeYuzGPm0akmB1HRER8mOlrrq699lqmT5/OQw89RP/+/cnIyGDOnDn1TS6ys7PZt29f/fl79+5lwIABDBgwgH379jF9+nQGDBjA7bffbtaXIOJfVtRtgzBwEtiCzc3ijVp3hS7jAAcsfdHsNB6Xf7iCvOJKLBbo4cPNLI7mbMm+JOsAReXVJqcRERFfZnFoc49jFBcXExsbS1FRkdZfSWDZvxVeGAIWK9y3DmLbm53IO2X9AO9MgJBo+MOmgOqm+MPmfCa/+QtpCVF8P/Uss+O4zHkzFrItv4RnJ/bnsv7tzI4jIiJepCm1gekjVyLiRVa+YTx3Ha/C6kQ6nQWt0qDqMKz9r9lpPKp+vVVb/3rj6by6roFzte5KREROg4orETHUVELG+8ax2q+fmNV6pNnHL68FVFv2TbnGJus9/ay4ck4NXLA5n8qaY7vKioiInAoVVyJi2PItVBRCTDvofK7Zabxf/+sgKBzyNwRUW/bN+w4D/rPeyqlvu1gSY0IpraplcdYBs+OIiIiPUnElIgbnqFXfa8GqLQtOKrwF9L3aOP7lVXOzeEhZVQ07DpQC0D3Jv4orq9VyZGrgBk0NFBGR5lFxJSJwOA8yvzeO+19vbhZf4pwauPFzKMk3N4sHbMk9jMMB8VGhtI4ONTuOy53f05ga+P2mPOz2wJnqKSIirqPiSkRg3X/BUQvth0B8F7PT+I62/Y3fM3v1kY2X/djmXOeUQP/sjjg8tRXRoUHsP1xJxu5Cs+OIiIgPUnElEugcDsj4wDjud525WXzRkLo99la8AXb/boSwaZ/RzMLf1ls5hQRZObt7AqCpgSIi0jwqrkQCXe5aoymDLRR6X2F2Gt/TcwKEt4Ti3ZCZbnYatzrSzMI/R64Azq9vyZ5rchIREfFFKq5EAp2zkUX3C40mDdI0wWFGExCA1e+Ym8WNHA5HfRt2f2tmcbSzu7Um2GZh+/5SMvNLzI4jIiI+RsWVSCCrqYJ1HxrH/W8wN4svG3iT8bzlWygtMDeLm+wpLOdwRQ3BNgudW0eZHcdtosOCGdk5HtDolYiINJ2KK5FAtm0ulB2AqERIPcfsNL4rsRe0HWA0tlgzy+w0brGpbkpg59ZRhAT597cOZ0v2eRu17kpERJrGv79DisiJrf/IeO5zNdiCzM3i6wbUjV6tfsdoEuJnNtc1s+jpp80sjuYsrlZnF5JXXGFyGhER8SUqrkQCVWUJbJljHPe5ytws/qDPVRAUBvs3w56VZqdxOed6K3/tFHi0xJgwBnaIA+CbdfvMDSMiIj5FxZVIoNryLdSUQ8tUaNPf7DS+LywWel5mHPthYwvntMDuftwp8GgX920LwJdr9pqcREREfImKK5FAtf5j47n3VWCxmJvFXzinBq77GKpKzc3iQmVVNew8YHw9gTByBXBR3zZYLLAqu5Ddh8rMjiMiIj5CxZVIICo/BJnfG8e9rzQ3iz/pOApapEDVYdj4udlpXGZL7mEcDoiPCiU+KtTsOB6RGBPGsE4tAfh6raYGiojIqVFxJRKINn1ldLZL6AUJ3c1O4z+sVhhwo3Hs3D/MD2zO9f/NgxvjnBr4lYorERE5RSquRAKRs0tg7yvMzeGPnBsK7/wZinabm8VFttQXV4ExJdDpgt5J2KwW1u0pYmeB/0zzFBER91FxJRJoSvJhx4/GsYor14vrYEwPxHFkg2Yf5yyuuiYG1shVq6hQRnZuBcBXa9XYQkRETk7FlUig2fg5OOzQbpDRKVBczzl6tWa2X+x5tTXPKK66BVhxBXBJP2fXQE0NFBGRk1NxJRJo6rsEqpGF2/S8DGwhsH8T5K4zO81pKSip5EBpFRYLpCVEmR3H48b1TCLYZmFL3uH6ETwREZHjUXElEkiK90H2EuO45wRTo/i18DjoOt44Xjvb1Cina2tdQdGxZQThITaT03hebEQw53RLAOCTVf6xhk5ERNxHxZVIINn8lfHcfijEtjM3i7/rN9F4XvcR2GvNzXIatuQF5nqro10xsD0An67eQ63d96d5ioiI+6i4Egkkm74wnntcYm6OQJB2HoS3gJJc2LHQ7DTNVr/eKilwi6tzuyfQIiKY/MOV/JxZYHYcERHxYiquRAJF6QHYucg4VnHlfkEh0Oty43iN704NDNROgUcLCbJyaV1ji49XamqgiIgcn4orkUCx5Rtw1EJSH2jZyew0gaFv3dTATV9Cle/tk+RwONiaVwIE9sgVHJka+N2GXIorqk1OIyIi3krFlUigqJ8SeJm5OQJJ8lCI6wjVpbB1jtlpmmxvUQUllTUE2yyktIo0O46p+raPJS0hisoaO9+sVVt2ERFpnIorkUBQUQTbFxjHmhLoORbLkY2a139ibpZmcHYKTI2PIiQosL9dWCwWrqwbvfpk1R6T04iIiLcK7O+WIoFi2zyorYL4rpDQ3ew0gcW5n9i2eVBRbG6WJqrvFBjgUwKdLh/QDosFlu88yK4DvjfNU0RE3E/FlUgg2Pi58axRK89L7A2tukBtpbHuzYc4R666BuDmwY1Jig3jjC6tAXh/ebbJaURExBupuBLxd1VlkPm9cdzjUnOzBCIfnhq4NV8jV792w7AOAHy4YjeVNb67f5mIiLiHiisRf5eVDtVlENsB2vQzO01g6lVXXGXNh/JD5mY5RbV2B9ucnQIDuA37r43pnkBSTBgHS6uYsz7X7DgiIuJlVFyJ+LvNdVPRelxsjKKI5yV0h4ReYK+GTV+ZneaUZB8so7LGTliwleSWEWbH8RpBNisThyYD8N5STQ0UEZGGVFyJ+DN77ZEW4N0vMjdLoOtdt6Hw+o/NzXGKnJsHd0mIxmZVUX60iUM6YLNaWL7zIFvrmn6IiIiAiisR/5azHMoPQlgcJA83O01gc04N3PEjlBaYm+UUOIuGrpoSeIyk2DDG9kgA4L2lu0xOIyIi3kTFlYg/2/qt8dzlfLAFmZsl0LXqDG36g6P2SPdGL7Yt31hv1SVRnQIbc+PwjoCx51VZVY3JaURExFuouBLxZ1vqiqtuF5ibQwzOroEbPjU3xynYluecFqjiqjGjOseT0iqCw5U1fLxyt9lxRETES6i4EvFXBZlQsBWsQZA2xuw0AtDzMuN51yKvnhpYa3ewvcDYJLdLgqYFNsZqtTB5VCcAXvlpB7V2h8mJRETEG6i4EvFXzimBKaMhLNbcLGJokWK0w3fYYbP3dg3MOVhGVY2d0CAr7VqEmx3Ha109uD1xEcFkHyxTW3YREQFUXIn4ry11XQK7XWhuDmnIuZHzxi/MzXECmXXrrTq3jlKnwBOICAliUt3aq5d/zMLh0OiViEigU3El4o/KDkL2EuO463hzs0hDPScYzzsWeu2Gws5mFmlab3VSk0amEBpkZc3uIpbtOGh2HBERMZmKKxF/tG2e0ZUuoRe06Gh2GjlafBok9AR7zZHRRS/jHLlSM4uTi48K5cpB7QF4+cftJqcRERGzqbgS8Udb1SXQqzmnBm7yzqmBmflGp0CNXJ2aO85IxWKB+Zvz6zdfFhGRwOQVxdULL7xASkoKYWFhDBs2jOXLlx/33A0bNnDllVeSkpKCxWLhmWeeOeacxx9/nCFDhhAdHU1CQgITJkxgy5YtbvwKRLxITRVkphvHWm/lnXrWFVeZ6VDpXT+MOxyOIyNX2uPqlHSKj2R8ryQAnvl+q8lpRETETKYXV7Nnz2bq1Kk8/PDDrFq1in79+jFu3Djy8/MbPb+srIzU1FSeeOIJkpKSGj1n4cKF3H333SxdupR58+ZRXV3N+eefT2lpqTu/FBHvkLMUKoshsjW0HWB2GmlMQk9o2RlqK2Hrd2anaWBfUQWlVbUEWS10bBVpdhyf8T/ndcVigW/X57J+T5HZcURExCSmF1czZszgjjvuYPLkyfTs2ZOZM2cSERHB66+/3uj5Q4YM4amnnmLixImEhoY2es6cOXO45ZZb6NWrF/369ePNN98kOzublStXuvNLEfEO2+Yaz2nngdX0f+LSGIvlyJ5XXjY10NnMIiU+kmCb/v6cqq6J0VzWry0A0+dqpoSISKAy9TtnVVUVK1euZOzYsfWvWa1Wxo4dy5IlS1x2n6Ii413Eli1bNvrxyspKiouLGzxEfNa2ecZzl/PMzSEn5pwauG0eVJWZm+UozimBaa01JbCp7hvbFZvVwoIt+1mxU50DRUQCkanFVUFBAbW1tSQmJjZ4PTExkdxc12zIaLfbue+++xg1ahS9e/du9JzHH3+c2NjY+kdycrJL7i3icYd2wf7NYLFB53PMTiMn0qY/xHWA6jLISjc7TT1nMwutt2q6lPhIrq7rHDh97hbteyUiEoD8fs7H3Xffzfr165k1a9Zxz5k2bRpFRUX1j5ycHA8mFHGhzLpRq+RhEN7C3CxyYhYLdL/YON78jblZjpKpPa5Oyz1juhBis7J0+0EWbt1vdhwREfEwU4ur+Ph4bDYbeXl5DV7Py8s7brOKppgyZQpfffUVP/zwA+3btz/ueaGhocTExDR4iPik+imBY098nniH7hcZz1vnQG2NuVkwOgVqA+HT0y4unJtGGHvL/ePLjVTV2E1OJCIinmRqcRUSEsKgQYNITz8yJcZut5Oens6IESOafV2Hw8GUKVP49NNPmT9/Pp06dXJFXBHvVl0B2xcax13ONzeLnJrk4cYIY/lBo8ujyQ6UVlFYVo3FAp215qrZ7h3bhfioELYXlPL6oh1mxxEREQ8yfVrg1KlTeeWVV3jrrbfYtGkTd911F6WlpUyePBmASZMmMW3atPrzq6qqyMjIICMjg6qqKvbs2UNGRgaZmZn159x99928++67vP/++0RHR5Obm0tubi7l5eUe//pEPGbXz1BTDtFtILHx9YXiZWxB0LVuo2cvmBq4Lc8YtUpuEUFYsM3kNL4rJiyYP17QA4B/p28jt6jC5EQiIuIpphdX1157LdOnT+ehhx6if//+ZGRkMGfOnPomF9nZ2ezbt6/+/L179zJgwAAGDBjAvn37mD59OgMGDOD222+vP+fFF1+kqKiIs88+mzZt2tQ/Zs+e7fGvT8Rjtn1vPHc5z1jPI76he91Gz5u/ApMbIGTu15RAV7liQDsGdoijrKqWx77ZZHYcERHxkCCzA4CxNmrKlCmNfmzBggUNfp2SknLSDkzq0CQBybm/laYE+pbO50JQGBTugrwNkGTeqGNmXl2nQBVXp81qtfCPy3pzyfM/88WavVw7JJlRafFmxxIRETczfeRKRFzgQBYczAJrEHQ6y+w00hQhkUaBBbDF3KmBGrlyrd7tYrlxmNHc4n8/WsvhimqTE4mIiLupuBLxB84ugR1GQJi6XfqcbkdNDTSRc82ViivXefCC7iS3DGdPYTn//ErTA0VE/J2KKxF/4Nzfqst55uaQ5ul2AVissG8NFJqzz15ReTX5hysBFVeuFBUaxPSr+mGxwOwVOczfnHfyTxIREZ+l4krE11VXwM5FxnGa9rfySZHxRlt2gC3fmhLBuXlwUkwY0WHBpmTwV8NSW3HbKGNLkAc/Xseh0iqTE4mIiLuouBLxddlLjBbsUUmQ0NPsNNJc3c2dGphVV1x1SdSolTvcP64baQlR7D9cyX2zM7Db1XhJRMQfqbgS8XVZdZtwdz5XLdh9mXPd1a5FUFHs8dtvyzc6BWrzYPcIC7bx74kDCAu2snDrfp5N32Z2JBERcQMVVyK+LnO+8Zw2xtwccnpadYZWaWCvgaz5Hr99pkau3K5n2xgendAHgH/P38YPm/NNTiQiIq6m4krElx3OhfwNgAVSzzE7jZyuruONZ+eeZR60ra64StPIlVtdOag9Nw7vgMMB983OYNeBUrMjiYiIC6m4EvFlzhGOtv0hspWpUcQFuo4znrd+B3a7x25bVlXDnsJyALokRnvsvoHqrxf3pH9yHEXl1Ux+4xc1uBAR8SMqrkR8WeZR663E93UYAaExUFYAe1d57Lbb95ficEDLyBBaRoZ47L6BKjTIxss3DaJdXDjbC0q54+0VVFTXmh1LRERcQMWViK+y22H7D8ZxZ6238gu24CNr57bO8dhtneuttL+V5yTEhPHm5CHEhAWxYtchpv5XHQRFRPyBiisRX7UvA8oOQEg0JA81O424inPdlQeLK2enQBVXntUlMZqXJw0mxGblm3W5/PXz9TgcKrBERHyZiisRX+Vcb9XpTGPEQ/xD2ljAArnroHivR265La+uU6CKK48bntqKp6/ph8UC7y3L5pGvNqnAEhHxYSquRHyVs7hK03orvxIZD+2HGMdbv/PILTP3a1qgmS7p15Ynr+wLwOuLdvDUd1tUYImI+CgVVyK+qPIw5CwzjrXeyv8c3TXQzapq7Ow6UAZAlwR1CjTLNYOTeWRCbwD+syCL6XNVYImI+CIVVyK+aOciY7PZFinQspPZacTVnOuuti+A6nK33mrngVJq7Q6iQoNIjAl1673kxG4a3pG/XtwTgBd+yOIfX21UgSUi4mNUXIn4oh0LjefUs02NIW6S2Ati2kNNOez4ya23cq63SkuIwmKxuPVecnK3je5UP4L1xqKdTPtkHbXqIigi4jNUXIn4ou0LjGcVV/7JYjlqaqB7uwaqDbv3uWl4R6Zf3Q+rBWb9ksPU/2ZQXeu5TaVFRKT5VFyJ+JqSfMjfaBynnGluFnEfZ3G1bS64cWqYsw27OgV6l6sGtee56wYSZLXwecZefvfeKiprtNGwiIi3U3El4mt2/Gg8J/WByFbmZhH36XQmBIVDUc6RYtoNnCNXXRJVXHmbi/q24eVJgwgJsjJvYx63v7WC8ioVWCIi3kzFlYiv2f6D8awpgf4tOBxSzzKO3TQ1sNbuYHtBKQBprdUp0Bud2z2RN28ZQkSIjZ+2FTDp9WUUV1SbHUtERI5DxZWIL3E4YHtdM4tOZ5uZRDzBzS3Zcw6WUVVjJyzYSrsW4W65h5y+kWnxvHPbUKLDgvhl5yGuf2UpB0urzI4lIiKNUHEl4ksObjemiVmDoeMIs9OIu3WpK65ylkPpAZdfflvdlMDU+ChsVnUK9GaDOrbkgzuG0zIyhPV7irn2pSXkFVeYHUtERH5FxZWIL3G2YE8eCiGR5mYR94ttB4l9AAdkfu/yy2u9lW/p3S6W//5mBEkxYWzLL+HqmUvIOVhmdiwRETmKiisRX1I/JfAsc3OI57ixJbuzU2BaaxVXviItIYoPfzuCDi0jyD5YxlUzF5NZ9+coIiLmU3El4ivs9iOdAtXMInB0HW88Z6ZDrWsbGWRp5MonJbeM4MPfjqBLQhR5xZVc89JS1u8pMjuWiIig4krEd+Stg/KDEBIF7QaanUY8pd1AiIiHyiLIXuqyyzocDm0g7MMSY8KY/ZsR9GkXy8HSKm54dZkKLBERL6DiSsRXbF9gPKeMBluwqVHEg6w26HK+cezCqYH7iiooraolyGqhYyut3/NFLSNDeO+OYQzoEEdRebUKLBERL6DiSsRXaL1V4HJDS3Znp8CU+EiCbfpW4KtiwoJ5+9ahKrBERLyEvqOK+IKaSsheYhxrvVXg6XwOWGxwYBsc2umSS9Z3CtSUQJ8XrQJLRMRrqLgS8QW7f4HqMohMgIQeZqcRTwuLheRhxrGLWrI7O8xpvZV/cBZYA1VgiYiYSsWViC+onxJ4Jli02WtA6jLWeM5Md8nltuWpmYW/iQ4L5q1bhzKoYwuKyqu5/pWlrNutAktExJNUXIn4AufmwZoSGLjS6oqr7Quhpuq0LuVwOOrXXKm48i/RYcG8OXkIgzq2oLiihpteX8amfcVmxxIRCRgqrkS8XUUx7F5hHKeqmUXASuoLUYlQXXpk/V0zFZRUUVRejcUCnbWBsN9xjmAN6BBHYVk1N766TBsNi4h4SJDZAUTkJHYtBkcttEyFuA5mpxGzWCzQeQysed9Yd3UahbazmUVyiwjCgm2uSiheJCo0iDcnD+WGV5eyfk8x17+yjNm/GUGn+MBqu19UVs2S7QfYuK+YQ6VVHCyroqrGTlJMGG3jwunYKoJRneOJjdD2FiLiGiquRLydc38rtWCXtKOKq/MfafZlnKMY6hTo32LDg3nn1mFc98pSNuce5vpXlvLf34wguWWE2dHcqrCsitm/5PDNun2s21OE3XHi821WC8M6tWRcrySuGNiO6DAVWiLSfCquRLxd/XorFVcBr/O5YLFC/kYo2gOx7Zp1mUyttwoYLSJDeOe2YUx8eQlZ+0u5/tWlzL5zBG3jws2O5nI7C0p5+aftfLJqNxXV9vrX0xKiGNyxBQnRocRFhBAcZCWvqIK9heWs31vE1rwSFmcdYHHWAZ6eu4XJozoxeVQKcREhJn41IuKrVFyJeLPDecYP0lgg5Uyz04jZIlpCu0FGa/7M72HQzc26jJpZBJbW0aG8f8dwrnlpCbsOlHHDq8uYfedwEmLCzI7mEkXl1TyXvo23luykutYYpuqeFM2kESmc2z2BpNgTf527DpQyb2Mes37JITO/hGfTt/HazzuYel5Xbh6Zgs2qDq0icurU0ELEm+340XhO6gORrczNIt4h7Tzj+TT2u6rfQDgx2hWJxAckxoTx/h3DaRcXzo6CUq5/dRkFJZVmxzotDoeDD1fkcO70Bbz68w6qax2c1bU1H9wxnG/vPYPrh3U4aWEF0LFVJLefkcrc+87kPzcMpEebGEoqa/jHVxu54sXF6rYoIk2i4krEm+1YYDyrBbs41bdkXwC11U3+9KLyavIPGz9Ud24dWM0NAl27uHA+uGM4STFhZOaXcOOryygsO722/mbJP1zB7W+t4IGP1nKgtIrOrSN5Y/IQ3rp1KCM6t8LSjP0ArVYLF/Zpw9f3jOaxy/sQHRrEmpxCLnnuZ2YuzMJ+ssVbIiKouBLxXg7Hkc2Dtd5KnNoOgIhWUFlsTA9sIueoVZvYMC3cD0AdWkXw/h3DiI8KZXPuYSa9vpziiqYX6Waas34f4/71I+mb8wmxWXlwfHfm3Hcm53RLcMn1rVYL1w/rwPd/OItxvRKpsTt44tvN3PnOCorKfOv3SkQ8T8WViLc6uB2KcsAaDB1GmJ1GvIXVajS2ANg2r8mf7uwUqPVWgSu1dRTv3zGMlpEhrN1dxE2vLaeo3PuLhqoaO3/7YgO/fXcVh8qq6dkmhi/uGcVdZ3cm2Ob6H2cSY8KYeeMgHr28NyE2K99vyuei535iw94il99LRPyHiisRb7XzZ+O5/RAI0fQtOcpprLtSp0AB6JoYzTu3DSUuIpg1OYVeP0Vw96Eyrn5pCW8u3gnAb85K5bO7R9E9Kcat97VYLNwwrCOf/G4kyS3D2X2onGtmLuGHLfluva+I+C6vKK5eeOEFUlJSCAsLY9iwYSxfvvy4527YsIErr7ySlJQULBYLzzzzzGlfU8QrOYurTmeYm0O8j3PkKnet0VGyCdQpUJx6tY3l/duH0zIyhHV7irj+lWUcLPW+Ait9Ux4X/ftn1uQUEhsezGs3D2baBT0ICfLcjzC928Xy1ZQzGJHaitKqWm5/awXvL8v22P1FxHeYXlzNnj2bqVOn8vDDD7Nq1Sr69evHuHHjyM9v/F2hsrIyUlNTeeKJJ0hKSnLJNUW8jsMBO38yjlNGm5tFvE9Ua2PtFUBWepM+tb5TYII6BQr0bBvDrDuHEx8VysZ9xVzz0hL2FJabHQuAmlo7T3y7mdveWkFReTX92sfy1T2jGdMj0ZQ8sRHBvHXrUK4Y2I5au4M/fbqOJ77drEYXItKA6cXVjBkzuOOOO5g8eTI9e/Zk5syZRERE8Prrrzd6/pAhQ3jqqaeYOHEioaGhLrmmiNc5uB0O7wNbiDEtUOTXnF0Dm7Duqqyqht2HjB+cNXIlTl0To5l155Euglf+ZzFbcg+bmimvuILrX1nGzIVZANwyMoUPfzuS5JYRpuYKCbLy9NX9uG9sFwBmLszinlmrqaiuNTWXiHgPU4urqqoqVq5cydixY+tfs1qtjB07liVLlnjsmpWVlRQXFzd4iJjKOWrVfigEh5ubRbyTc91V1nywn9oPdtv3lwLQKjKElpEh7komPigtIYpPfjeSLglR5BZXcPXMxSzdfsCULD9vK+DCZ39i+c6DRIUG8cL1A/nbpb08Og3wRCwWC/eN7cr0q/sRZLXw9dp93Piqd06pFBHPM/V/qoKCAmpra0lMbDjEn5iYSG5urseu+fjjjxMbG1v/SE5Obta9RVzGud5KUwLleNoNgrBYqCiEPStP6VO21XUK7KxRK2lE27hwPvztCAZ3bEFxRQ03vrqMNxftwOHwzLS3mlo7/5q3lZteX8aB0iq6J0XzxZRRXNS3jUfu31RXDWrP27cOJTosiBW7DnHli4vZWVBqdiwRMZl3vA1ksmnTplFUVFT/yMnJMTuSBDKHQ8WVnJwt6Ehji1PsGnhkvZWKK2lcXEQI794+jEv6taXG7uBvX25k6n/XUF7l3mlvWftLuHLmEp5N34bDAROHJPPZ3aNIbe3df1dHpsXzyV0jaRcXzo6CUi7/zyJW7jpodiwRMZGpxVV8fDw2m428vIbdrvLy8o7brMId1wwNDSUmJqbBQ8Q0B7Lq1luFar2VnFgT111ty1OnQDm5sGAb/57Yn79e3BOb1cKnq/dw8XM/sXLXIZffq7rWzqs/befCZ39iTU4hMWFBPDuxP09c2ZewYJvL7+cOXRKj+fTukfRpF8uhsmque2UZ36zbZ3YsETGJqcVVSEgIgwYNIj39SLcru91Oeno6I0Y0b9NUd1xTxKPq11sNgeAwc7OId3MWV3tXQ2nBSU/P3K9OgXJqLBYLt43uxHu3DyM+KpSs/aVcNXMxf/9yA2VVNS65h3Nt1T+/3kRljZ0zusTz3f+cyWX927nk+p6UEB3G7N8MZ2yPBKpq7PzuvVW8/GOWx6ZUioj3MH1a4NSpU3nllVd466232LRpE3fddRelpaVMnjwZgEmTJjFt2rT686uqqsjIyCAjI4Oqqir27NlDRkYGmZmZp3xNEa+m/a3kVEUnQWIfwAFZP5zw1MqaWnYdKAM0ciWnbnhqK76feiZXDGyHwwFvLNrJWU8t4JUftzeryHI4HCzJOsCtb/7Cja8tY1t+CS0ignn8ij68fetQ2sT6bgOfiJAgXrppMDeP6AjAY99s5qHPN1BTazc5mYh4UpDZAa699lr279/PQw89RG5uLv3792fOnDn1DSmys7OxWo/UgHv37mXAgAH1v54+fTrTp0/nrLPOYsGCBad0TRGvpfVW0lRpYyBvHWTOg75XH/e0nQVl1NodRIcGkRjT+DYWIo2JiwhhxjX9ubRfW/7y2Xp2Hyrn0W828eLCLK4dksx5PRPp3z4Oq9Vy3GvkHCxjwdb9vL8sm037jI68NquFSSM6ct+YrsRGBHvqy3Erm9XC3y7tRXLLCB79ZhPvLN3FjoJSnp3Yn1ZR+ncnEggsDo1ZH6O4uJjY2FiKioq0/ko8qyATnh9krLf6Y7amBcrJ7fwZ3rwIIuLh/m1gbXxCwtdr93H3+6vonxzHZ3eP8nBI8RdVNXY+W72HFxZk1o+EAsRHhdA/OY42seG0iTP+3zpQUsWBkkpW5xQ2ODc82MaVg9px66hOXt+w4nTMWb+P/5m9hvLqWpJiwnjhhoEM6tjC7Fgi0gxNqQ1MH7kSkaM411slD1VhJacmeRiERENZAeSugbYDGj3N2YZdnQLldIQEWblmSDJXDGzHdxvy+Hb9PhZu2U9BSRXfb8o/7ucFWS0M6BDH2B6JXDskmbgI/99nbXzvNqS2juK3765k+/5Srn1pCVPP78qdZ6QSZDN9VYaIuImKKxFv4iyuNCVQTpUtGFLPgs1fGS3Zj1tcqVOguE6QzcpFfdtwUd82VNXYWbHrIFn7S8ktKmdfUQVWi6V+s+rOraMY3rkVUaGB9yNH18Rovpgymj9+vJav1u7j/+ZsYc76XJ66qh/dktRYRsQfBd7/dCLeSuutpLk6n1tXXM2HMx9o9JRtecbIVddE/UAnrhUSZGVk53hGdo43O4pXigoN4rnrBnB2twT+8eUG1u4u4uLnfuLW0Z2466zOATGKJxJINC4t4i0OZEJJHgSFQbvBZqcRX5I2xnjOWQYVRcd8uLrWzo6CUgC6JGrkSsTTLBYLVw1qz7ypZzG2RyLVtQ5eWridM/7vB56fv43iimq33dvhcFBVY6esqoaismqq1b1QxK00ciXiLbS/lTRXixRo1QUObIPtC6HnpQ0+vLOglOpaB5EhNtrF+W6raxFflxgTxiuTBjF/cz5PfbeFzbmHmT53K8//kMkFvdtw9eD2DOvUCtsJOi8ez6HSKrbmHa57lLDrYBm7D5Wx51A5lTVHCiqrBdrGhdOxVQR928dxcd829GwTg8XS9HuKyLFUXIl4i/opgdrfSpohbYxRXGWlH1Ncbc2rW2+VGK0foERMZrFYGNMjkXO6JfDl2r08Pz+TbfklfLp6D5+u3kN0aBD9O8QxuGNLUuIjaB0VSquoUKwWqKyxU1lTS15xJXsLy9l9qJzM/BK25h0m/3DlKd3f7oDdh4zPXZR5gBcXZNG5dSQTh3Rg0siOhAbZ3Pw7IOLfVFyJeAOHA3aomYWchrSxsGwmZKYbf5+OKqK21q236qYpgSJew2q1cFn/dlzary0ZOYX8d8Vuvlq7l8MVNfy0rYCfthU0+ZrtW4TTLTGarknRdGoVSfuW4bSPiyA2IpgQm5Ugm4VDpVXsOljGjv2lzN+cz/wt+WTtL+XRbzbxwfJs/nFZb0Z30fo5keZScSXiDQq2QWl+3XqrQWanEV/UcZSxP1pRjvH3qXXX+g8527CrmYWI97FYLAzo0IIBHVrwzwm92ZxbzMpdh1idXci+onIK6vYLA6N5SGiQjfioENrGhdOuRTip8ZF0TYymS2L0KXVkTIgJIyEmjCEpLblmSDLFFdV8tWYfM+ZtZXtBKTe+tozL+rfliSv6Eh6iUSyRplJxJeINtN5KTldIBHQcCdt/MFqyH1VcOacFdlFxJeLVbFYLvdrG0qttLJNGeOaeMWHBXD+sAxf3a8OMuVt5e8lOPs/Yy+5D5bx282B1MxRpomZ1C9y+fburc4gENud6q05nmptDfFvaWOM58/v6lypratlZ1ymwq6YFishxxIQF87dLezHrzhHEhAWxctchrp65hL2F5WZHE/EpzSqu0tLSOOecc3j33XepqKhwdSaRwKL9rcRVnC3Zdy2CauMHoh0FpdTYHUSHBpEUo1FRETmxoZ1a8uFvR5IUE8a2/BKuenEx+cX6WU/kVDWruFq1ahV9+/Zl6tSpJCUl8Zvf/Ibly5e7OptIYNB6K3GV1t0hph3UVBgFFkdPCYxSp0AROSXdkqL5+HcjSY2PZG9RBb99dyWVNbVmxxLxCc0qrvr378+zzz7L3r17ef3119m3bx+jR4+md+/ezJgxg/3797s6p4j/2vmj8Zw8FIJCzc0ivs1iOTJ6lZkOwLY8NbMQkaZrFxfOa7cMISYsiFXZhTz02QYcDofZsUS8XrOKK6egoCCuuOIKPvzwQ5588kkyMzO5//77SU5OZtKkSezbt89VOUX8l/a3Elfq3LC4crZhVzMLEWmqTvGRPHf9QKwWmL0ih7eX7DI7kojXO63iasWKFfzud7+jTZs2zJgxg/vvv5+srCzmzZvH3r17ueyyy1yVU8Q/ab2VuFrq2WCxQcEWKMxmW920wG4qrkSkGc7q2pppF/QA4B9fbWTD3iKTE4l4t2YVVzNmzKBPnz6MHDmSvXv38vbbb7Nr1y7++c9/0qlTJ8444wzefPNNVq1a5eq8Iv6lYCuU7oegcK23EtcIj4P2gwGo2JLOzgPqFCgip+f2MzoxvlcStXYH0z5ZR61d0wNFjqdZxdWLL77I9ddfz65du/jss8+4+OKLsVobXiohIYHXXnvNJSFF/JZzfyuttxJXqmvJvn3jSuwOiA0PpnW0/n6JSPNYLBb+cVkvosOCWLu7iLcW7zQ7kojXalZxNW/ePB588EHatGnT4HWHw0F2djYAISEh3HzzzaefUMSf7agrrrTeSlyprqnFtuy9gDFqpU6BInI6EmLC+OMF3QGYPncLe7T/lUijmlVcde7cmYKCgmNeP3jwIJ06dTrtUCIBQeutxF3a9IfwlmytagWomYWIuMZ1QzowJKUFZVW1/PWz9eoeKNKIZhVXx/vHVFJSQliYNqkUOSX7t0BZQd16q4FmpxF/YrVB53PZ4mgPQNcErbcSkdNntVp4/Io+BNsszN+cz8+Zx77RLhLogppy8tSpUwFj7u1DDz1ERERE/cdqa2tZtmwZ/fv3d2lAEb/lXG/VYZjWW4nrpY1l28oyQHtciYjrpCVEc9PwFF5ftIOn525ldFq8ph2LHKVJxdXq1asBY+Rq3bp1hISE1H8sJCSEfv36cf/997s2oYi/0pRAcaPyDmeR7VgJQJeoKpPTiIg/uevsznywPJuMnELmb85nTI9EsyOJeI0mFVc//PADAJMnT+bZZ58lJibGLaFE/F6D9VZqZiGul1UeiQMrLSkmPu9nSLrG7Egi4idaR4dyy6gUXlyQxdNzt3JOtwSsVo1eiUAz11y98cYbKqxETsf+zUfWW7XVeitxva15hwHoYtmNJet7k9OIiL/5zZmpRIcGsXFfMXM25JodR8RrnPLI1RVXXMGbb75JTEwMV1xxxQnP/eSTT047mIhfc45adRgGQSEnPlekGbbmlQDQ1bobsuaD3Q7WZr2fJiJyjLiIEG4d3Yln07cxY95WxvVKwqbRK5FTH7mKjY2tX7AYGxt7woeInISzmYXWW4mbbKsbueoavB9K90PuWpMTiYi/ue2MTsSGB5OZX8K8jXlmxxHxCqc8cvXGG280eiwiTdRgvdWZ5mYRv7U1v25aYLvWsAfI/B7a9jc1k4j4l5iwYG4Y1oH/LMjijUU7GN87yexIIqZr1hyR8vJyysrK6n+9a9cunnnmGebOneuyYCJ+a/9mKDsAwRHQdoDZacQPlVbWkHOwHICuPfoaL2bNNzGRiPirm0Z0xGa1sGzHQTbsLTI7jojpmlVcXXbZZbz99tsAFBYWMnToUJ5++mkuu+wyXnzxRZcGFPE7O+qmBCZrvZW4R2a+sd4qPiqElj3PNV7MWQYV+sFHRFyrTWw4F9SNWL2xaKe5YUS8QLOKq1WrVnHGGUb76I8++oikpCR27drF22+/zb///W+XBhTxO1pvJW5W3ykwIRpadoKWncFeAzt+NDmZiPijW0d3AuCLjL0UlFSanEbEXM0qrsrKyoiOjgZg7ty5XHHFFVitVoYPH86uXbtcGlDEr9jtsGuRcaz9rcRNttWNXHVLMv6fJm2s8ZyZblIiEfFnAzu0oF9yHFW1dt5flm12HBFTNau4SktL47PPPiMnJ4fvvvuO888/H4D8/HztfyVyIlpvJR5QP3KVGGW8kDbGeM5MNxqqiIi42K2jUgB4Z+kuqmrs5oYRMVGziquHHnqI+++/n5SUFIYNG8aIESMAYxRrwAD9wChyXPX7Ww3Xeitxm23OPa4S60auUkaDLQSKsuFAponJRMRfXdC7DQnRoew/XMn3m9SWXQJXs4qrq666iuzsbFasWMGcOXPqXx8zZgz/+te/XBZOxO/srFvzovVW4iYllTXsKazrFJhQV1yFRELHkcZx5vcmJRMRfxYSZOWqQe0B+HBFjslpRMzTrOIKICkpiQEDBmC1HrnE0KFD6d69u0uCifgdux12ar2VuJdz8+CE6FBiI4KPfKCzc2qgiisRcQ9ncbVw637yiytMTiNijmYVV6Wlpfz1r39l5MiRpKWlkZqa2uAhIo3YvwnKD2q9lbjVMVMCnZxNLXYugupyD6cSkUCQ2jqKQR1bYHfAJ6v3mB1HxBRBzfmk22+/nYULF3LTTTfRpk0bLBaLq3OJ+J+j11vZgk98rkgzbfl1MwunhB4Q3RYO74Vdi480uRARcaGrB7Vn5a5DfLgih9+cmaqfESXgNKu4+vbbb/n6668ZNWqUq/OI+C/tbyUe4OwUeMzIlcUCaefC6neNroEqrkTEDS7q24a/fbmBrP2lZOQUMqBDC7MjiXhUs6YFtmjRgpYtW7o6i4j/arDe6kxzs4hf25xrFFfdk6KP/aBzamCW9rsSEfeIDgvmgt5tAPhw5W6T04h4XrOKq0ceeYSHHnqIsrIyV+cR8U/5G+vWW0VC2/5mpxE/VVBSyf7DlVgsjYxcAaSeDRarsd9aobp5iYh7XF3X2OLLNXupqK41OY2IZzVrWuDTTz9NVlYWiYmJpKSkEBzccP3IqlWrXBJOxG9ovZV4wJa6UauOLSOIDG3kv/fwFtBuMOxeboxeDbrFswFFJCAMT21Fu7hw9hSWk74pn4v6tjE7kojHNKu4mjBhgotjiPg5rbcSDzgyJTDm+CeljTWKq0wVVyLiHlarhUv6tWXmwiy+WbdPxZUElGYVVw8//LCrc4j4L7sddml/K3G/zfuKAejeppEpgU5pY2DBY7B9IdTWgK1Z3wZERE7o4r5tmLkwi/TNeZRV1RARov9rJDA0exPhwsJCXn31VaZNm8bBgwcBYzrgnj1N39fghRdeICUlhbCwMIYNG8by5ctPeP6HH35I9+7dCQsLo0+fPnzzzTcNPl5SUsKUKVNo37494eHh9OzZk5kzZzY5l4hL5G+E8kMQEqX1VuJWpzRy1XaAMT2wsgj2rPBQMhEJNL3axtCxVQQV1XbSN+WbHUfEY5pVXK1du5auXbvy5JNPMn36dAoLCwH45JNPmDZtWpOuNXv2bKZOncrDDz/MqlWr6NevH+PGjSM/v/F/iIsXL+a6667jtttuY/Xq1UyYMIEJEyawfv36+nOmTp3KnDlzePfdd9m0aRP33XcfU6ZM4YsvvmjOlytyepxTArXeStyoptZe34a9x4lGrqw26HyucZz5vQeSiUggslgsXNTHmA749dp9JqcR8ZxmFVdTp07llltuYdu2bYSFhdW/fuGFF/Ljjz826VozZszgjjvuYPLkyfUjTBEREbz++uuNnv/ss88yfvx4HnjgAXr06MEjjzzCwIEDef755+vPWbx4MTfffDNnn302KSkp3HnnnfTr1++kI2IibuFsZqH1VuJGOw+UUVljJyLERnKLiBOf3LlujysVVyLiRs61Vj9syaekssbkNCKe0azi6pdffuE3v/nNMa+3a9eO3NzcU75OVVUVK1euZOzYsUcCWa2MHTuWJUuWNPo5S5YsaXA+wLhx4xqcP3LkSL744gv27NmDw+Hghx9+YOvWrZx//vmNXrOyspLi4uIGDxGXsNuPKq603krcZ3Ou8f9Wt6RorFbLiU92biC8NwNKC9wbTEQCVs82MXSKj6Syxk76pjyz44h4RLOKq9DQ0EYLkK1bt9K6detTvk5BQQG1tbUkJiY2eD0xMfG4RVpubu5Jz3/uuefo2bMn7du3JyQkhPHjx/PCCy9w5pmNb976+OOPExsbW/9ITk4+5a9B5ITyN0BFobHeqk0/s9OIH9u87xTWWzlFJ0Fib8ABWT+4N5iIBCxNDZRA1Kzi6tJLL+Uf//gH1dXVgPGPJzs7mwcffJArr7zSpQGb47nnnmPp0qV88cUXrFy5kqeffpq7776b779vfArMtGnTKCoqqn/k5GhzTXER7W8lHuIcuTrhequjpWlqoIi438X9jOJqwdb9HK6oNjmNiPs1q7h6+umnKSkpoXXr1pSXl3PWWWeRlpZGdHQ0jz766ClfJz4+HpvNRl5ew6HivLw8kpKSGv2cpKSkE55fXl7On/70J2bMmMEll1xC3759mTJlCtdeey3Tp09v9JqhoaHExMQ0eIi4hKYEiodsqhu56pZ4qsVV3fTqrPnG9FURETfolhhNautIqmrszN+sroHi/5pVXMXGxjJv3jy+/vpr/v3vfzNlyhS++eYbFi5cSGRk5ClfJyQkhEGDBpGenl7/mt1uJz09nREjRjT6OSNGjGhwPsC8efPqz6+urqa6uhqrteGXZrPZsOsHCPEkrbcSDymuqGZPYTlwitMCAZKHQ3AklOZD3jo3phORQGaxWDi/p/EG+PdqyS4BoMk7utntdt58800++eQTdu7cicVioVOnTiQlJeFwOLBYTrKQ+lemTp3KzTffzODBgxk6dCjPPPMMpaWlTJ48GYBJkybRrl07Hn/8cQDuvfdezjrrLJ5++mkuuugiZs2axYoVK3j55ZcBiImJ4ayzzuKBBx4gPDycjh07snDhQt5++21mzJjR1C9XpPny1mu9lXjElrr9rdrGhhEbcYrTT4NCoNOZsPVbY2qg/o6KiJuc1zORmQuzWLAln6oaOyFBzd5mVcTrNelvt8Ph4NJLL+X2229nz5499OnTh169erFr1y5uueUWLr/88iYHcE7Xe+ihh+jfvz8ZGRnMmTOnvmlFdnY2+/YdWQQ5cuRI3n//fV5++WX69evHRx99xGeffUbv3r3rz5k1axZDhgzhhhtuoGfPnjzxxBM8+uij/Pa3v21yPpFmq19vNQJs2ple3GfzPmO9Vfc2TZzSXL/uar6LE4mIHNE/OY74qBAOV9Twy86DZscRcasm/cT35ptv8uOPP5Kens4555zT4GPz589nwoQJvP3220yaNKlJIaZMmcKUKVMa/diCBQuOee3qq6/m6quvPu71kpKSeOONN5qUQcTltL+VeMimXGenwFNcb+XkLK5ylkJFMYRpvamIuJ7NamFM90Rmr8hh3sY8RqXFmx1JxG2aNHL1wQcf8Kc//emYwgrg3HPP5Y9//CPvvfeey8KJ+Cy7HXYtMo613krcbFNzR65aphoPew3s/MkNyUREDGN7GjOS5m3Mw+FwmJxGxH2aVFytXbuW8ePHH/fjF1xwAWvWrDntUCI+L29d3XqraK1lEbeqtTvq97jq2dTiCqCzWrKLiPuNTosnLNjKnsLy+u6mIv6oScXVwYMHj9nA92iJiYkcOnTotEOJ+DznlMCOWm8l7rWjoJTy6lrCg210ij/1bq31nC3ZM78HvZssIm4SHmJjdFprAL7flHeSs0V8V5OKq9raWoKCjv+Dos1mo6am5rRDifg8rbcSD9mwtwiA7m2isVmb1q0VMP6O2kKgMBsOZLk4nYjIEefXTQ1UcSX+rElvqTscDm655RZCQ0Mb/XhlZaVLQon4NHvtkfVWHVVciXttrFtv1attM5tRhEZBh+Gw40dj9Co+zYXpRESOOKd7AhYLrN1dRG5RBUmxYWZHEnG5Jo1c3XzzzSQkJBAbG9voIyEhocmdAkX8Tu5aqCiC0BittxK327jXWVzFNv8iR08NFBFxk9bRoQxIjgPghy3aUFj8U5NGrtTeXOQU7PjReO44SuutxK0cDkd9cdWsZhZOaWNh3kPGdNbqCgjWu8ki4h5nd0tgVXYhC7bkc93QDmbHEXE5bZEt4mo76lpad1ILdnGvvOJKDpRWYbNa6NbUPa6OltATottATTlkL3ZdQBGRXzm7m9HUYlHmAapq7CanEXE9FVcirlRbDbvqfjjtdKa5WcTvOZtZpLWOIizY1vwLWSxHtWRPd0EyEZHG9W4bS3xUCCWVNazcpQ7T4n9UXIm40t7VUF0K4S0hoZfZacTP1U8JbG4zi6Ol1RVX2+ae/rVERI7DarVwZhdj9GrBVq27Ev+j4krElXYsNJ5TRoNV/7zEvTbsPc1OgUfrfC5Yg6Bgq1qyi4hbnVU3NXDhlv0mJxFxPf30J+JK9eutNCVQ3G/DPmNa4Gk1s3AKj4MOI4xjjV6JiBud0aU1Fgtszj1MblGF2XFEXErFlYir1FRCzjLjWMWVuFlReTU5B8sBF00LBOg6znje+p1rrici0oiWkSH0ax8HwEJNDRQ/o+JKxFV2/wI1FRCVCPFdzU4jfm5T3ebB7eLCiYsIcc1Fu443nnf+DJWHXXNNEZFGOLsGLtDUQPEzKq5EXMW5v1XKGUb3NRE3cmkzC6dWadAyFezVsH2B664rIvIrZ3U1iquftxVQXauW7OI/VFyJuIr2txIPcmkzCyeLBbo4pwbOcd11RUR+pW/7OFpEBHO4sobV2YVmxxFxGRVXIq5QVWZMCwRj5ErEzZx7XPVqG+vaC9evu5oLdr2bLCLuYbNaOKOuJftP2zQ1UPyHiisRV8hZakylimlvTKsScaPyqlq25hlrovq2d3Fx1XEUhERBaT7sy3DttUVEjjK6SzwAP2cWmJxExHVUXIm4wtFTArXeStxs474i7A5IiA4lMSbMtRcPCoHO5xjH6hooIm40Os0ortbkFFJUXm1yGhHXUHEl4grOZhZqwS4esHa3MSXQ5aNWTs6ugVp3JSJu1DYunNTWkdgdsCTrgNlxRFxCxZXI6aoohr2rjWOttxIPWFdXXPVu56biqsv5xvO+DDic6557iIgAZ6Q5pwZq3ZX4BxVXIqcrewk4aqFFJ4hLNjuNBIC1e9w8chWVAG0HGsfb5rrnHiIiwOguR1qyi/gDFVcip6t+SqBGrcT9SipryNpfArhx5AqOmhqodVci4j7DUltis1rYeaCMnINlZscROW0qrkROV31xdZa5OSQgbNhThMMBbWLDSIh2cTOLozlbsmf9ADWV7ruPiAS0mLBg+ifHAbBIXQPFD6i4EjkdZQchd51xnDLa3CwSENbVTQns485RK4A2/SAqCapLYefP7r2XiAQ0Z9fAn1RciR9QcSVyOnYtAhwQ3w2ik8xOIwHA7Z0CnSwW6FrX2ELrrkTEjc6o2+9qcWYBdrvD5DQip0fFlcjpOHp/KxEPWO8cuWof5/6bOdddbfkWHPqBR0Tco19yHFGhQRwqq2bD3mKz44icFhVXIqdD+1uJBxVXVLO9oBTwwLRAMNYR2kKgcBcUbHX//UQkIAXbrAxPbQnA4ixNDRTfpuJKpLlK8mH/JuO4o9Zbifs5R63atwinZWSI+28YGnVk7zZ1DRQRNxqe2gqAJdu1mbD4NhVXIs21s25KYGIfiGxlbhYJCOs8td7qaGrJLiIeMLKzse5q+Y6DVNfaTU4j0nwqrkSaS+utxMPW1ncKjPPcTZ1NLbKXQPkhz91XRAJK96RoWkQEU1ZVW9+4R8QXqbgSaS6ttxIPW5NTCHh45KpFCrTuDo5ayJrvufuKSECxWi0M62TMAlmqqYHiw1RciTRHYQ4czAKLFTqONDuNBID8wxXsPlSOxeLh4gqObCi8ZY5n7ysiAWVE57p1V1kqrsR3qbgSaY7tC4zndoMgzMM/6EpAWp1dCEDXhGiiw4I9e3Pnuqttc6G22rP3FpGA4SyuVuw6SGVNrclpRJpHxZVIc2z/wXhOPcfcHBIwnMXVgA5xnr958jCIaAUVhcbaKxERN+iSEEV8VAgV1XbW5GjdlfgmFVciTWW3w/aFxnHq2aZGkcCxOttoJjGwQwvP39xqg64XGMebv/b8/UUkIFgsFobVtWTXflfiq1RciTRV3nooK4DgSGg/xOw0EgBqau313bNMGbkC6H6h8bz5G3A4zMkgIn5vRKrWXYlvU3El0lTOKYEpoyHIAxu5SsDbkneY8upaokOD6Nw6ypwQqedAUDgUZUPuOnMyiIjfc667Wp1dSEW11l2J71FxJdJUzmYWnbXeSjzDud6qf4c4rFaLOSFCIqDzucbxlm/MySAifi81PpLEmFCqau2s2qW99cT3qLgSaYrqCti12DjWeivxkPpmFslxpuag+0XG8+avzM0hIn7LYrEcmRqo/a7EB6m4EmmKnKVQUwHRbYyNVUU8YHWO8e7tADOaWRyt63hjb7fcdVCYbW4WEfFb2u9KfJmKK5GmcE4JTD0bLCZNz5KAUlhWxfb9pQD0N3vkKrIVdBhhHG/W1EARcY8RqfEAZOQUUlZVY3IakaYJMjuAiE/Jcu5vdbapMSRwrM4pBKBTfCQtIr2ggUq3C2HXImNq4PDfmp1GRE5F+SHYuxqK9kDxXji8F2qqIDjceIREQXwXaNMPWnYGq7nvvSe3DKddXDh7Csv5Zechzura2tQ8Ik3hFSNXL7zwAikpKYSFhTFs2DCWL19+wvM//PBDunfvTlhYGH369OGbb459B3XTpk1ceumlxMbGEhkZyZAhQ8jO1jQWOQ1lB2HfGuNYxZV4iNest3JytmTftdj4NyEi3qe2BrbOhW//CDNHw5Od4J3L4YspsOAxWPkmrHkfVrwGS56HhU/Ax7fB84Ph8fbw1qWw4g3T/o1bLBaGqyW7+CjTR65mz57N1KlTmTlzJsOGDeOZZ55h3LhxbNmyhYSEhGPOX7x4Mddddx2PP/44F198Me+//z4TJkxg1apV9O7dG4CsrCxGjx7Nbbfdxt///ndiYmLYsGEDYWFhnv7yxJ/sWAg4IKEnRCeZnUYChHPz4AEdTV5v5dQy1fg3kL8Rtn4H/a8zO5GIOO3fAqvfhbWzoSSv4cdaphqPmLYQ0w6Cwow1xNVlxshW3gbjUV1qfL/bsRC+uR86j4Ghd0LaGI9Ohx/RuRUfr9qtphbicywOh7m7QQ4bNowhQ4bw/PPPA2C320lOTuaee+7hj3/84zHnX3vttZSWlvLVV0e6VQ0fPpz+/fszc+ZMACZOnEhwcDDvvPNOszIVFxcTGxtLUVERMTExzbqG+KEvfg+r3oLhv4Pxj5udRgJArd1B/7/P5XBlDV/dM5re7WLNjmT44TFY+KQxRfC6D8xOIyI5vxgjUlnzj7wWEQ89LoFOZ0DH0RCdePLr1NZAwVbY9h2s+xjyjtrTrv0QOHuasSWDB4qsPYXljHpiPjarhYyHziM6LNjt9xQ5nqbUBqZOC6yqqmLlypWMHTu2/jWr1crYsWNZsmRJo5+zZMmSBucDjBs3rv58u93O119/TdeuXRk3bhwJCQkMGzaMzz777Lg5KisrKS4ubvAQacDhOLJ5cKr2txLP2LSvmMOVNUSHBtGjjRe90dPjUuM5Mx0qD5ubRSSQ7V4J714Jr401CiuLzXjT49r34A+b4ZJnoPeVp1ZYAdiCILEnjP4fuOtnuHs5DL/b2EB89y/w7hXwxgWQv8mtXxZAu7hwOrSMoNbu4JedmoIsvsPU4qqgoIDa2loSExv+o09MTCQ3N7fRz8nNzT3h+fn5+ZSUlPDEE08wfvx45s6dy+WXX84VV1zBwoULG73m448/TmxsbP0jOTnZBV+d+JVDO4zW09Zg6DjS7DQSIJw/UAzs2AKbWZsHNyaxlzG9qLYSts01O41I4CktgM9+B6+eC5nfG0XVgJvg96uM0eQeF4PNBSM9rbvB+Mfg3jV1RVYYZC+Bl86EH5+C2urTv8cJjFRLdvFBXtHQwpXsdjsAl112Gf/zP/9D//79+eMf/8jFF19cP23w16ZNm0ZRUVH9Iycnx5ORxRc4uwQmD4XQKHOzSMBwFldDO7U0OcmvWCxHRq82fmFuFpFAYrfDyreMxhMZ7xmv9bse7lkJlz0PLVLcc9/oRKPIumeVsd9dbRXM/ye8cq6xTstNnPtdLVZxJT7E1OIqPj4em81GXl7DRZd5eXkkJTXeMCApKemE58fHxxMUFETPnj0bnNOjR4/jdgsMDQ0lJiamwUOkAU0JFA9zOBws32E0sxiS4mXFFUDPuuJq2zyoLjc3i0ggKNoDb18KX/7eaECR2BtumweXvwgtO3kmQ2w7uG4WXPEKhLeA3LXw6ljY8KlbbjeirmPgxn3FFJZVueUeIq5manEVEhLCoEGDSE9Pr3/NbreTnp7OiBEjGv2cESNGNDgfYN68efXnh4SEMGTIELZs2dLgnK1bt9KxY0cXfwUSEOy1sONH47iziivxjJ0HyigoqSTEZqVvey9pZHG0tgMhNtnoLHb0InoRcb2Nn8OLI2HnTxAcCec/CncuNGZTeJrFAn2vgd8tM7YlqS6DD28xRrLqZg+5SkJMGKmtI3E4YNkOrbsS32D6tMCpU6fyyiuv8NZbb7Fp0ybuuusuSktLmTx5MgCTJk1i2rRp9effe++9zJkzh6effprNmzfzt7/9jRUrVjBlypT6cx544AFmz57NK6+8QmZmJs8//zxffvklv/vd7zz+9Ykf2JsBFUUQGgtt+pudRgLEL3U/SPRLjiUs2GZymkZYLEYnMtDUQBF3qSqDL+6B/06CikJoOwB++xOMnGI0nzBTdCLc8DGMqPv568enYNb1UFXq0ts4R6+WqiW7+AjTi6trr72W6dOn89BDD9G/f38yMjKYM2dOfdOK7Oxs9u3bV3/+yJEjef/993n55Zfp168fH330EZ999ln9HlcAl19+OTNnzuT//u//6NOnD6+++ioff/wxo0eP9vjXJ35ge9278p3OMP+bmQSM5XXrrbxySqCTc93Vlm+hRlN2RFyqMBteHwer3gYsMHqqMQ2wVWezkx1hC4Jxj8LlL4EtFLZ+C+9cYbwh6SLD64srjVyJbzB9nytvpH2upIE3LzamYlz0NAy53ew0EiDOeuoHdh0o443JQzin27EbqnsFux1mdDc2K73hY+gy9uSfIyInt+Mn+PBmKDsAEa3gqjcg9SyzU51YznJ49yqoLDJG2G78BCJO/82h/MMVDH00HYsFVv/1POIiQlwQVqRpfGafKxGvV1UK2UuNYzWzEA/JL65g14EyLBYY1LGF2XGOz2qF7hcZx5s+NzeLiL9Y/gq8fZlRWLXpZ6yt8vbCCoz1X7d8aRSDe1cbb0yW5J/2ZROiw+hct+5qudZdiQ9QcSVyIrsWg70aYjsY+/qIeIBzSmCPpBhiwlywV4079bzMeN70ldv3vBHxa3Y7fPdn+OZ+cNRC32vh1u8gzof23mzTD275BqISIX8DvHUplJ1+QaSpgeJLVFyJnMj2BcZz57ONBfwiHuBsZuF1+1s1puNoiIiH8oOwo/GN2kXkJKor4KPJsOR549djHjLWMQWHm5urORK6w+RvIboN7N8EH0w0GnOchuFqaiE+RMWVyIlkaX8r8bzlO714f6tfswUdGb1a7569bkT8WvkheGcCbPwMrMFwxatwxh98+w29Vp2NNVdhsZCzzCgcT2Nke1iq8X/hplztdyXeT8WVyPEczjWmNWCBTj4w3138wsHSKjbtKwZ8ZOQKoPcVxvPmL9U1UKQpSvKNtUnZS4ztPm76BPpebXYq10jsCdfNhqAw2DoHvrwXmtlDLSH6yH5XWncl3k7FlcjxZNZtVt12AES2MjeLBIwlWca0l+5J0bSODjU5zSnqMAKikoz2y9pQWOTUFObA6+Mhb72xRunWb6HTmWancq2OI+DqN8Fig4z3jL2wmknrrsRXqLgSOZ7M743nNLWXFs9ZlFUAwIjOPlTQW21HpgZu+MTcLCK+oCDTKKwOZkFcB7h1DiT2MjuVe3S7AC7+l3H8w6Ow+etmXcZZXC3boXVX4t1UXIk0prbmyDvwKq7EgxZnGsXVqM7xJidpovqpgd8Yi/NFpHH7t8IbF0DxbojvCpPn+H832kE3w9DfGMef3An5m5p8ieF106Q37iumqEydScV7qbgSaczeVVBRaCzGbTfI7DQSIPYUlrPzQBk2q6V+AbfPaD8UYtpB1WHInGd2GhHvVJAJb10CpfmQ2MfoqhfbzuxUnjHuUUg5A6pK4IPrmtyiPSHmqHVXOzU1ULyXiiuRxjinBHY+1+iGJuIBzlGrvu1jifb2/a1+zWqFXpcbx+s1NVDkGAey4K2LoSQXEnrBpM8h0sdGqE+HLRiufsuYBnloB3x8m7G3VxOoJbv4AhVXIo3ZVvfOu6YEigctrmtm4XNTAp161U0N3DoHqkrNzSLiTQ5uN7oCHt4HCT3h5i8Cs1FSZCu4bhYERxhT73+e0aRPV3ElvkDFlcivlRbA3tXGcecx5maRgOFwOFhUN3I10peaWRyt3UCI6wjVZUaBJSJwcAe8eQkc3gutu8OkLwJrxOrXEnvBhXVdA394DHYtOeVP1bor8QUqrkR+LWs+4DDmw8e0MTuNBIis/SXkH64kNMjKwI4tzI7TPBYL9LnKOF77oblZRLzBoV3GGitn84qbv4So1manMl//G6DvteCoNaYHnuL6K627El+g4krk1+pbsGvUSjxnUaYxzWVwSgvCgm0mpzkNfa4xnjPnQamm7kgAK8w2pgIW5UCrtLrCKsHsVN7BYoGLnoaWnaF4D3x21ylvMFzfkl1TA8VLqbgSOZrdfmTzYK23Eg9anOWcEujj04USukNSX7DXaM8rCVzF+4wRq6Jso4C4+SuITjI7lXcJjTY2GLaFGtOIl798Sp82rG5q4FLtdyVeSsWVyNH2ZUBZAYREQ/Iws9NIgKiptR9pZpHm48UVGNN9ANb+19wcImYoOwjvXA6HdkKLFGPESlPMG9emL5z/iHE872GjVf1JOEeuNuwtpqhc667E+6i4Ejmac9Qq9SwICjE3iwSMVdmFHK6ooUVEMH3axZod5/T1vhIsVti93FjMLxIoKg/De1fB/k0Q3cZotx4o+1g115A7IPVsqCmHT38DtTUnPD0xJozUeGPd1S87tO5KvI+KK5GjbfvOeNZ6K/GgH7bkA3BW19bYrBaT07hATBvodJZxvE6NLSRAVFfArOthz0oIbwE3fWqMXMmJWa1w2QsQGgt7VsCif530U4apJbt4MRVXIk6lBbB7hXHcZZy5WSSgLNiyH4Czu/nRYvf6qYGzT3mhuojPqq0xut7t+BFCouDGjyGhh9mpfEdse7jw/4zjBU/AvjUnPH14qtZdifdScSXilPk99S3YNY1DPCS3qIJN+4qxWODMrn7UornHxRAUDgcyYe8qs9OIuI/dDl9Mgc1fGc0ZrvsA2g0yO5Xv6Xst9LjEaIbz6W+hpuq4p2rdlXgzFVciTs5NT7tq1Eo8Z+FWY0pg/+Q4Wkb60Tq/0GjofpFxrMYW4q8cDvhuGqz5ACw2o/tdpzPNTuWbLBa4+BmIiIf8jfDz8acHHr3uaoX2uxIvo+JKBKC2GjLnG8cqrsSDfthcNyWwqx9NCXRyTg1c99EJ34UW8VkLnoBlM43jCS9C9wvNzePrIuPhgieN4x+fgvzNxz1V667EW6m4EgHIXgqVRRDRStM5xGOqauz8nGnsb3VOdz+aEujU+VyISjS2N3A2ixHxF0tfhIVPGMcXPAX9rjU3j7/ofaWx7tleDV/+3ph22Yj6dVfbNXIl3kXFlQgc1SXwPLDazM0iAWPlrkOUVNYQHxVC77Z+0IL912xB0G+icbz6XXOziLjS6vdgzh+N43P+DMPuNDePP7FY4OIZxn6TOcvgl1cbPe3IuqsirbsSr6LiSgRg61zjWVMCxYMW1LVgP7Nra6z+0IK9Mf1vNJ63zYXifeZmEXGFTV8aDSwAht8NZz5gbh5/FNsexj5sHKf/HQpzjjklMSaMTvGR2LXuSryMiiuRgzugYIuxGLnzuWankQDi3N/qHH9qwf5rrbtC8jBw2GHtLLPTiJye7Qvgo1uNv8/9b4RxjxojLeJ6g2+D5OFQVQLf/m+jpxyZGqh1V+I9VFyJbKsbteo4EsLjTI0igWNnQSlb80oIslo4s4sfrrc62oC60avV72rPK/FdOcvhg+uhtspoGX7Jsyqs3MlqNX6PrUGw5RvY/M0xpwyvb2qhkSvxHiquRJwt2Lucb24OCSjfbcgFYETnVsRGBJucxs16XQ7BEcaeVznLzE4j0nS56+G9q6C6FFLPgStfM9YUinsldIcRdVMwv30QqkobfHhYpyPrroortO5KvIOKKwlslSWw82fjuOt4c7NIQJlTV1yd3yvJ5CQeEBptFFgAq98xN4tIUx3Igncuh4oiY4rrxPcgKNTsVIHjrP+F2A5QlG20Zz9KUqzWXYn3UXElgS1rvjHFo0UniO9idhoJEHnFFazOLsRigXE9E82O4xnOqYHrPzXe1BDxBUW74e3LoDQfkvrA9f+FkEizUwWWkMgje18tfu6Yva/Ukl28jYorCWxb6uZwd79Ic+fFY+bWjVoNSI4jISbM5DQe0mEEtOxsTKva8KnZaUROrmQ/vD0BinKgVRrc+KnW5Zql+4XQ7UKw18DXf2iwdnO4NhMWL6PiSgJXbc2R9VbdLjQ3iwSU7zbkATAuEKYEOlksMPAm43jF6+ZmETmZ8kJ493I4sA1i2sNNn0GUnzee8XYXPAlB4bDrZ9jwSf3LznVX6/do3ZV4BxVXErhylkL5IQhvacyjF/GAwrKq+ndYA6q4AhhwE9hCYO8q2Lva7DQijasqg/evhdx1ENkaJn0Occlmp5K4DnDGVON47kPGnxNadyXeR8WVBK7NXxvPXcer65N4TPqmfGrsDronRZMSH2BrNyLjoedlxvEvr5mbRaQxNZUw+0bjzbfQWLjpU4hPMzuVOI28x2huUbwbFj1b/7Jz3dWSLE0NFPOpuJLA5HAcKa66a0qgeI6zBXvAjVo5Db7NeF73kTH1SsRb1NbAx7dDVrqxdcANHxpNLMR7BIfD+Y8Yx4uegcIcAEZ0jjdeylRxJeZTcSWBKX8jFO6CoDDofK7ZaSRAHK6oZuHW/UAAF1cdhkNCT6gphzWzzE4jYrDb4ct7YdMXxtTVie9BB00X90o9L4OOo6GmAub9FYCRnY11Vxv3FXOgpNLMdCIqriRAOXd6Tz1bbXXFY+ZuyKOyxk7n1pH0aBNtdhxzWCww+FbjeMXrDbp+iZjC4YC5f4GMd8Fihate15tu3sxigQueMP6sNnwKOxcRHxVK9yTj/9Ql6hooJlNxJYFpS92UQHUJFA/6LGMPAJf1b4clkFv/970WgiOhYMuRTbxFzOBwQPo/YOkLxq8vewF6XGJuJjm5pD4w6BbjeM6DYK9lVJqmBop3UHElgad4b12nMgt0u8DsNBIg9h+uZFFmAQCX9mtrchqThcVA32uM4xVqbCEmcTjgh0fh5xnGry94Cvpfb24mOXXn/AXCYo2ujqveZlSaMTXQ+f+siFlUXEngcW4cnDwUohLMzSIB46u1e7E7oH9yXOB1CWzMkLrGFpu+NN7wEPG0hU/Cj08Zx+OfgGF3mptHmiayFZz9J+N4/iMMTbIRZLWQfbCMnINl5maTgKbiSgLPZk0JFM/7PMMoIC7rH+CjVk5JfYxF6fYaWP6y2Wkk0Cx8ChY8bhyf/ygMv8vcPNI8Q26D1t2h7ABRS5+mf3IcAIuzNHol5lFxJYGl7CDs+NE41rx68ZBdB0rJyCnEaoGL+6q4qjfid8bzijegqtTcLBI4fnoafvincXzeP2DkFHPzSPPZgmF8XZG8/GVGtjEOf9a6KzGRiisJLFu+Nd4pT+wNrTqbnUYChHPUalRaPK2jQ01O40W6joeWqVBRCBnvm51GAsGiZ40GFgBjHoJR95qbR05f53ONmSj2GkbnvgPAkqwCHOpEKibxiuLqhRdeICUlhbCwMIYNG8by5ctPeP6HH35I9+7dCQsLo0+fPnzzzTfHPfe3v/0tFouFZ555xsWpxSdt/Nx47nmZuTkkYDgcjgZdAuUoVhsMq5uOtfRFY68hEXdZ/DzMe8g4PucvcMYfzM0jrnP+P8EWQv99swkPgoKSKrbkHTY7lQQo04ur2bNnM3XqVB5++GFWrVpFv379GDduHPn5+Y2ev3jxYq677jpuu+02Vq9ezYQJE5gwYQLr168/5txPP/2UpUuX0ratpuEIUFEE238wjntcam4WCRirsg+xfX8pYcFWxvVKNDuO9+l/vdHx62AWbPvO7DTir35+Bub+2Tg+exqc9YCpccTFWnWGYb8hxFLLUNs2AH7epnVXYg7Ti6sZM2Zwxx13MHnyZHr27MnMmTOJiIjg9ddfb/T8Z599lvHjx/PAAw/Qo0cPHnnkEQYOHMjzzz/f4Lw9e/Zwzz338N577xEcHOyJL0W83dbvoLYK4rtBQnez00iAmLU8BzDWWkWH6f+iY4RGHdmvZskLpkYRP+RwQPoj8P3Dxq/P/F8460FzM4l7nHE/hLdkdO0yABZnad2VmMPU4qqqqoqVK1cyduzY+tesVitjx45lyZIljX7OkiVLGpwPMG7cuAbn2+12brrpJh544AF69ep10hyVlZUUFxc3eIgfqp8SqFEr8YzDFdV8tXYfABOHJJucxosN/Q1Yg2DnT7BvjdlpxF84HDBnGvw03fj12L/BuX+GQN7A25+Fx8E5f2KkdQMAy7YfoLpWU43F80wtrgoKCqitrSUxseFUmcTERHJzcxv9nNzc3JOe/+STTxIUFMTvf//7U8rx+OOPExsbW/9ITtYPQX6nsgQyvzeOtd5KPOSLNXspr64lLSGKQR1bmB3He8W2g54TjOOfnzEzifgLey18cQ8se9H49YXTYfT/mJtJ3G/QLfSID6UlxZRW1bImp9DsRBKATJ8W6GorV67k2Wef5c0338Ryiu9OTZs2jaKiovpHTk6Om1OKx2XOg5oKaNHJ6BQo4gHOKYEThySf8v9HAWv0fcbzhk+hYJupUcTH1VbDx7fD6nfAYoUJL8LQO8xOJZ5gC8Y67hFG1I1eLVqfZXIgCUSmFlfx8fHYbDby8vIavJ6Xl0dSUlKjn5OUlHTC83/66Sfy8/Pp0KEDQUFBBAUFsWvXLv7whz+QkpLS6DVDQ0OJiYlp8BA/c3SXQP2QKx6wfk8R6/YUEWyzcMXA9mbH8X5JfaDrBYADfv6X2WnEV1VXwOybYMMnYA2Gq94wmqZI4Og6jlEJ1QAsythgchgJRKYWVyEhIQwaNIj09PT61+x2O+np6YwYMaLRzxkxYkSD8wHmzZtXf/5NN93E2rVrycjIqH+0bduWBx54gO++UyeqgFRdDlvnGseaEigeMvsXY9Tq/F5JtIwMMTmNjzjzfuN5zSw4tMvcLOJ7Kkvg/Wtg67cQFAYT34deE8xOJZ5msTBq/LUArC5pQdn2ZSYHkkBj+rTAqVOn8sorr/DWW2+xadMm7rrrLkpLS5k8eTIAkyZNYtq0afXn33vvvcyZM4enn36azZs387e//Y0VK1YwZYqxw3qrVq3o3bt3g0dwcDBJSUl069bNlK9RTLZtHlSXQmwytB1gdhoJAKWVNfV7W103pIPJaXxI+8GQeg44amHRM2anEV9yOBfevBB2LISQKLjhI+h6vtmpxCQdug2gfWgZ1QSx/MuXjeYmIh5ienF17bXXMn36dB566CH69+9PRkYGc+bMqW9akZ2dzb59++rPHzlyJO+//z4vv/wy/fr146OPPuKzzz6jd2+to5HjWP+R8dzrck0JFI/4eNVuDlfUkNIqgpGdW5kdx7ecWbf/0Op3oXivuVnEN+RvhlfPMzpNRrSCSV9ApzPMTiUmslgsjOpuNCdbnB9irOUU8RCLw6Fy/teKi4uJjY2lqKhI6698XeVheCrNaGbxmx+hTT+zE4mfs9sdjJ2xkO0Fpfz90l7cPDLF7Ei+5/ULIHsxDL8bxj9mdhrxZjt/hlnXG5vEt+wMN34ELVPNTiVe4POMPdw7K4Oelp18kzgT7v4FgsPMjiU+qim1gekjVyJutfkbo7BqlQZJfc1OIwFg4db9bC8oJTosiKsGqZFFszjXXq143ZjuJdKYdR/BO5cbhVXyMLhtngorqTeyczwAGx0pHDx0CJbNNDmRBAoVV+LfnFMCe1+lKYHiEa8v2gEY7dcjQ4NMTuOjOp8L7YdATTn8+JTZacTbOOo6Sn58G9RWQY9LYdLnEKkpuHJE6+hQuidFA7DE3hN+ehpKC0xOJYFAxZX4r7KDkDXfOO59pblZJCBszTvMT9sKsFpg0ogUs+P4LosFxv7NOF75JhzcbmYa8Sa1NfD1H+D7vxm/Hn43XP0WBIebGku8k3P06qfQs6GyGBY8bm4gCQgqrsR/bfwc7DXG/jmtu5qdRgLAG3WjVuf3TCK5ZYTJaXxcymhIG2v8G/5B664E4w2z966EFa8BFhj/pLEmz6ofZaRxZ3Y1iqsf6W80DFzxBuzfYm4o8Xv6H0n81/qPjefeV5mbQwLC/sOVfLLKaL9+6+hOJqfxE2MeMp7XfQj71pqbRcyVuw5ePgu2L4DgCLj2HRj+W7NTiZcbntqK0CAre0scZKZcZ2zzMPevZscSP6fiSvxT8T6jixRA7yvMzSIB4bWfd1BZY6dfchxDUlqYHcc/tOl3ZErv/EfMzSLmWfeR0Wq9MBtapMDt30OPS8xOJT4gLNjGsFRjLd7CNreCNQi2fQdZP5icTPyZiivxTxs+BRxGB6k4beIq7lVYVsU7S3YCcM85aVjUPMV1zvlz3Q9Ec2HnIrPTiCfV1sDcvxiNK2rKofMYuOMHSOxldjLxIWd1bQ3Awj0OGHKH8eLcv4C91sRU4s9UXIl/Wveh8awpgeIBbyzaSWlVLT3axDCmR4LZcfxLq84wcJJx/N00/UAUKJzrqxY/Z/x69FS44UOIaGluLvE5zuJq2faDlI34A4TFQt56yHjP5GTir1Rcif/ZvwX2rjLe7e51udlpxM8drqiub2QxRaNW7nH2NAiNgX1rYNXbZqcRd9ubcdT6qkijG+DYh8FqMzuZ+KDOrSNpFxdOVa2dZbkOOOtB4wPz/wmVJeaGE7+k4kr8z5oPjOe08yCqtblZxO+9s3QXxRU1dG4dyfjeSWbH8U9RCUaBBZD+D2NUQ/yP3Q6Ln4dXx9atr+pkrK/qNcHsZOLDLBYLZ3Wrmxq4db8xNbBFJyjJg0XPmpxO/JGKK/Ev9lpY+1/juN9Ec7OI3yutrOG1n4xRq7vPScNm1aiV2wy9A1p3h/KDas3uj0ry4b2rYO6fwV4N3S+GO3+AxJ5mJxM/UL/uaut+CAqB8/5hfGDxc1C028Rk4o9UXIl/2fEjFO8x5lR3u8DsNOLnXv1pBwdKq0hpFcGl/dqaHce/2YLhgv8zjle8ZrTmFv+Q+T28OBKy0iEoDC7+F1z7LoSr66a4xsjOrQiyWthRUMquA6VGt8kOI41GKenqRCqupeJK/MuaWcZz7yshKNTcLOLXDpRU8vKPWQD84fxuBNn036nbpZ4FPSeAww7f/C/GrqDis2oq4bs/w7tXQul+SOgFdy6AwbeC1i6KC0WHBTOoo1GsL9y63/j7Ne5R44NrZ8GeVSamE3+jnwbEf1Qehk1fGMf9rjc3i/i95+ZnUlpVS+92MVzUp43ZcQLH+f+EoHDIXgyr3jI7jTTX/q3G2qolzxu/Hnon3DEfEnqYm0v8lnPd1YIt+40X2g2Evtcax3P/ojdrxGVUXIn/2PQlVJdBy87QfrDZacSP5Rws471luwD44/geWLXWynPikuHcvxjH3/0FCnPMzSNNU1sDP82AmaMhdy2Et4SJH8CFT0FwmNnpxI+N6Z4IwKLMAsqr6rZ0GPOQMRV11yLY/JWJ6cSfqLgS/5HxvvHc/zpNKRG3mjFvK9W1DkanxTO6S7zZcQLP8Lug/VCoOgxf3qt3nH1F7jp49VxI/zvUVkLaWLhrEXS/0OxkEgC6JkbRLi6cyho7izILjBdj28OIKcbxvIegpsq8gOI3VFyJfyjMgZ0/GcfOYX4RN1i7u5DPMvYA8OD47ianCVBWG1z2AthCjSYI2gzUu9VUwvxH4eWzjb3KwuJgwky44SOIUSMY8QyLxcLYuk3e0zfnH/nA6PsgMgEObodfXjUnnPgVFVfiH5w/XKWcAXEdzM0ifstud/DQ5xtwOODyAe3o0z7W7EiBq3VXOOdPxvGcP0HxXnPzSON2r4CXzoQf/w/sNUaXtruXa4aBmOLcHsbUwPmb83A4R7xDo49MNV74pPbRk9Om4kp8n70WVr1jHA+6xdQo4t8+WrWbjJxCIkNsTLtAo1amGzEF2g2CyiL47HfGJrTiHcoL4dsH4bXzYP9miGwNV79ltFiPTjQ7nQSoYZ1aEhFiI6+4kg17i498YMCNRrfKikL48SnT8ol/UHElvi8zHYp3G3uidL/Y7DTip4rKq/m/OZsBuHdsFxJitPjedLYgmPCi0T1w+w/w89NmJxK73Xiz67lBsGym0Ta/77XGaFWvCWankwAXFmzjjLp1sumbjpoaaLXBuH8ax8tfhoJME9KJv1BxJb7P2Y6533XqNiVu88z3WykoqaJz60huGdnJ7Dji1LobXFRXVP3wGOz82dw8gWzPSnhtLHwxBcoKIL4r3PQpXPEyRLQ0O50IcKRr4PzNeQ0/0PlcSDvPmL76/cMmJBN/oeJKfNvhXNjyrXE88GZzs4jf2rC3iLeXGK3X/3ZpL0KC9F+nVxlwg7G3ncMOH90GJfvNThRYSvbD51PglTFGgRUSbexH9ttFxg+sIl7k7O7GfldrdheRf7ii4QfP/ydYbEZbdr1RI82knxDEt61+Fxy1kDwcErQGRlyvptbO/360llq7g4v6tOGMLq3NjiSNuWg6xHeDklz45A5jLaa4V00lLHnBmAK4+h3AAX0nwj0rYOQ9EBRidkKRYyREh9EvOQ6AH47uGgjGzxHOtdvf/UnrOKVZVFyJ77LbYdXbxrEaWYibvPzTdjbsLSY2PJi/XdrL7DhyPCGRcM1bR9Zfzf2L2Yn8l73W2FfwuUHGD6CVRZDUF26dC1e8xP+3d9/hUZTrG8e/mx7SqEkIJBCQIjWEEoq0YxAUUBCQZhfbDwREPYKKWFBED0eOBVBU4KhRsIAIiiIiRVoIEHoPEEpCIKQQ0nd+fwzGE0UpKZNs7s917cVmZnb2Xl3IPjvv+7z4BFqdUORv3djYbMm+fHfin3d2m2BefT0VC9vnl3IycQQqrqT8ivsFUo6Cux80uc3qNOKADiWdZ/pPBwB4vk8Tavi4W5xI/pb/9dDvXfP+hhmw8X1r8zgawzCHYc+6ARY9Cqnx4BMEfd+Ch36BkAirE4pckZuamvOuVh84Q0Z2XuGd3jWgyxPm/RUvQc6FUk4n5Z2KKym/Yuaaf7YcDG6VLI0ijsduNxj/1XZy8ux0aViD28NrWR1JrkSzAXDj8+b9ZU/DvmXW5nEUxzbAR73gsyFwejd4+EHkizB6C7S+x+y2JlJONArwoW61SuTk2Vm1/xJzNCMeBb8QSD8J698p/YBSrqm4kvIp7STsWWLeVyMLKQGz1xwm+sg5Krk582r/Zti04Gn5ccM4aHXXxQYX98PJbVYnKr/io+HTQfBRT4jfAC4e0GksjImFG8aCq6fVCUWums1mo2czc/jqsp0Jfz7A1QMiL3YMXDsd0k6VXjgp91RcSfm0+SOzkUWdThDYzOo04mB2nkjlXz/uA2BinybUrqIro+WKzQZ93oR63SA3Az65HRJ2Wp2q/DAMiFsD8241W6sf+NHsoBZ+D4zeCj1eNNcVFCnHejY1i6uf954mO+8SDXCaDYDabc1/Q5Y/X8rppDxTcSXlT24WbJ5j3m/3kLVZxOFk5uQz5vOt5OYb9GgSwJC2wVZHkmvh7Ap3/BdqhsGFszCvrwqsyzEMOPgTzLkZ5vWBuFXg5AKt7oRR0XDrW+AbZHVKkWIRVrsyAb7unM/OY93Bs38+wGaDm18HbLBjgVqzyxVTcSXlz66vzQUqfWtB4z5WpxEH8+p3eziUlEENH3emDmih4YDlmYcf3L0IglpBZvLFAmuH1anKHns+7F4Ms7vDJwPg2Hpwdoe2I8wrVbe9C9XqW51SpFg5OdkKrl79sOsSQwMBaoVDm/vM+0ufhPzcUkon5ZmKKylfDAM2vmfeb/sAOLtYm0ccyrKdp/h4g7lY8LRBLanqpXV6yj3PKnDXIggKv1hg3Qrxm6xOVTZkpZrrVL3VChbcBSe3gmsl6DDKnFPVexpUDrE6pUiJ+a24+nF3Ivl249IH/WMiVKoGSXtg46xSTCfllYorKV/iN8Gpbea3quH3Wp1GHMjhpPM8+cV2AB7sHEqXhlos2GF4Voa7FkKt1maBNbcPbF9gdSrrJB+G78fDv5ua61SlHDWL0M5Pwtgd0PMV8K1pdUqREtcutCqVK7mSnJFD9JHkSx9UqarZGRPgl9fMhloif0PFlZQvmy5etWo+CLyqWZtFHEZmTj7/9+kWzmfn0bZuFf7Zq7HVkaS4eVaGuxebQ4nzs+HrB+HnV8zFyCsCux0Or4LPhsFb4bBxJuSkQ/VG0Gc6PL4bbpwIXtWtTipSalydnYi83lzz6pJdA38TNhxqt4Oc8/DDs6WUTsorFVdSfqSdhN3fmPcj1MhCiodhGDy7aAd7E9Kp7u3OO8PCcXXWP40Oyd0b7vjYbCUOsPp1mH8nZJyxNFaJSk+ANf+Gt8Phv7fCvqWAAdf1gDu/hpEbzTklWitQKqibL7Zk/27Hqb8eGujkBL3/BTYnc9734V9KL6CUO/oEIeXHpvfBngchHaBmS6vTiIOY8+sRvt5yAicbvD20FQG+HlZHkpLk5GS2Er9tBji5msXGjA6w/werkxWf/Dxz8eTPhsG/m8CKF+FcHLj5QJv7YeQmuPNLuO5GsyOaSAXWuUEN/DxdOZ2ezca4S3QN/E3NlmaTF4DvnoK8nNIJKOWOiispH7LSIPoj836HUdZmEYexct9pJi/dDcAzt1xPh/oaalphtBoOD66AGo0h4zRE3QGLR0PmOauTXRvDMFvN//QiTG8Gnw02C0cjH4Lbm8Xkk/vM9b9qNLI6rUiZ4ebiVHD16tvYyywW3P1Z8KoBZ/bDhndLIZ2URyqupHyImQvZqVC9ITS6xeo04gD2J6bzWNRW7AYMbhPMAzeEWh1JSlvNlvDQqt+/sNkyz+yct2FW+fhW2jAgcbc5d+ydtjCrE6z9N6SfMrubdRgF/7cRHvjBLCbdvKxOLFIm9W1prt/2/c5T5OT9zTxMz8rQ42Xz/qrXISW+5MNJuaM+1lL25WXDhhnm/Y6jzWE9IkVw5nw2D8yL5nx2Hu1Cq/Jyv2Zaz6qicvUwu+M17GUO9UnaA8ueNoch/+NZuP62srXkg2FA0l7YtQh2LYQz+37f5+wODXpA84Hml1Au7pbFFClP2terRnVvd86cz+bXg2fo3tj/rw9uOcT8IubYerPb5uCPSy+olAtl6DeGyF/YvsD8JtanJrS4w+o0Us6dz87jvjnRxCdnElzVk1l3tsbNRQV7hRfaGR5ZC1s/hpWvQPIh+PJ+c7HytiOg9b1mS2YrZJ+Ho7/CgR/NW8qx3/c5u8F1kdC0v1kgevhak1GkHHN2stGnRU3mrjvC4tiTf19c2WzmGnCzOsOexXDgJ2gQWXphpcyzGYbxF61RKq60tDT8/PxITU3F11e/qCxlt8OMCHN8c4+XodNoqxNJOZaTZ+eBedGsOXCGql5ufPlIB+rV8LY6lpQ12enm4rrRH0BGkrnNxcMsYq7vCw17mutClZQLyXByCxz5FY6sNe/b837f7+wO9bpBs9uh0c3g4VdyWUQqiJij5xgwcx1ebs7ETOyBh6vz3z9g2TPmvCu/EPi/9WY3UnFYV1Mb6MqVlG37vzcLK3c/85tjkWtktxs8+UUsaw6coZKbMx/d21aFlVyauw90G2+2bN/1NWyYCQnbYe8S8+bkArXbQu02UKsN1AoH39pXP2Q5PxeS48x/487sg8RdcGKL2dnvjyqHmO3TG9xkXmXT/CmRYhUeUplalT05kZLJyr2nubn5ZRbS7v6MeeUq9RisfBV6vVo6QaXMU3ElZZdhwNrp5v2292u4i1wzu91cy2px7ElcnGzMvLM1YcGVrY4lZZ2rB4QNg5ZDIWGHWVjt+RZO7zbnWxxb//uxzm7gVxv8gsHbH1w9wbWSOe8pPxdyL0BuFmSlmsOc0xPMLoXGX0yer1rP7PJX9wbzVqVO6bxmkQrKZrPRt2UQs1YdYtG2E5cvrty9ze6bnw40F+VuNgBqty6dsFKmaVjgJWhYYBlxcAV8crs5HGfMdvAJsDqRlEOGYTBp8S7+u/4oTjZ4c3AYt4XVsjqWlGfJh+HoejixGY5vNq84GfnXdi5XL6jewOyE6t8YglqZt5Icdigil7QvIZ2e01fj6mxj4zORVPVyu/yDvnoQdiwA/6bw8Cpwdi35oFLqyt2wwHfffZc33niDhIQEWrZsydtvv027du3+8vgvvviCiRMncuTIERo0aMDUqVO55RazPXdubi7PPfcc3333HYcPH8bPz4/IyEhee+01goKCSuslSVEZBvwyxbzf5n4VVnJNDMPglaV7+O/6o9hs8PrAliqspOiq1jNvrYabP+fnQtpJSI03m01cSIbcTMjLNK9WubiBi6d5Ncvdx2zO4xNo3rwDtJCvSBnRKNCH5rX82HEilUVbT3D/lSzR0WsKHPwJTu+CdW9B5ydKPqiUaZYXV/Pnz2fcuHHMmjWLiIgIpk+fTs+ePdm3bx/+/n/u1rJu3TqGDh3KlClT6NOnD1FRUfTr148tW7bQrFkzLly4wJYtW5g4cSItW7bk3LlzjBkzhltvvZXNmzdb8Arlmhz8CY5Hmx9IOo21Oo2UQ3a7wUtLdjN33REApvRvzsDWta0NJY7J2dUctqeheyLl3qA2tdlxIpUvY45fWXHlVR16vQYLH4JfpkKj3uZVaKmwLB8WGBERQdu2bXnnnXcAsNvtBAcH89hjjzF+/Pg/HT948GAyMjJYsmRJwbb27dsTFhbGrFmzLvkc0dHRtGvXjqNHjxISEnLZTBoWaDHDgNn/MDtkdRhlrkEjchXy7QZPf7WdL2OOY7PB5H7NGB6hD74iIvL3Ui7k0O6VFeTk21k6+gaaBl1BN07DgKg7zKUSgsLhgeVla308KbKrqQ0sXdwlJyeHmJgYIiN/Xx/AycmJyMhI1q9ff8nHrF+/vtDxAD179vzL4wFSU1Ox2WxUrly5WHJLCdv/g1lYuVbSVSu5ajl5dkZ/tpUvY47j7GTj33e0VGElIiJXpHIlN3o0MacifBlz/MoeZLNB3/+YnY1PbjGHB0qFZWlxdebMGfLz8wkIKDyfJiAggISEhEs+JiEh4aqOz8rK4umnn2bo0KF/WWlmZ2eTlpZW6CYWMQz45WI703YPgXcNa/NIuZKamcs9H21i6Y5TuDk78e6wcPq30lBAERG5cgPbmL83vtl2kpy8v+jo+Ue+QXDzVPP+L1MgcXcJpZOyztLiqqTl5uZyxx13YBgGM2fO/MvjpkyZgp+fX8EtODi4FFNKIXuXwqlYcPOGjlowWK7c8XMXGDhzHesPn8Xb3YUP721Dr2aBVscSEZFypvN11fH3cSc5I4ef9yZe+QNbDoGGN0N+Dix61Gx2IxWOpcVV9erVcXZ2JjGx8Bs3MTGRwMBLfygKDAy8ouN/K6yOHj3K8uXL/3Z85IQJE0hNTS24xcfHX+MrkiLJz4WfXjDvRzwMXtUsjSPlx9Zj5+g/Yx0HTp8n0NeDBQ93oHMDXfUUEZGr5+LsxO3h5tWr+dFX8ZnQZoO+08GjMpzaBmumlUQ8KeMsLa7c3Nxo3bo1K1asKNhmt9tZsWIFHTp0uORjOnToUOh4gOXLlxc6/rfC6sCBA/z0009Uq/b3H9Ld3d3x9fUtdBMLxMyFswegUnXNtZIrtmBzPIPf20BSejaNA31YOLIjTYL0d1hERK7d4LbmKKZf9icRn3zhyh/oEwi3/Mu8v+p1iI8ugXRSllk+LHDcuHHMnj2befPmsWfPHh599FEyMjK47777ALj77ruZMGFCwfFjxoxh2bJlTJs2jb179/LCCy+wefNmRo0aBZiF1cCBA9m8eTOffvop+fn5JCQkkJCQQE5OjiWvUa5AVurv61p1Gw8e+nAsfy8nz86kb3byzy+3k5Nv56YmAXzxSAdq+nlaHU1ERMq50OpedG5QHcOATzYevboHtxgEzQeZi4t/PQKy00smpJRJlhdXgwcP5l//+hfPP/88YWFhbNu2jWXLlhU0rTh27BinTp0qOL5jx45ERUXx/vvv07JlS7788ksWLVpEs2bNADhx4gSLFy/m+PHjhIWFUbNmzYLbunXrLHmNcgXWTocLZ6F6Q2h9r9VppIw7dvYCg2atY9568xfeuB4NmXVna3w8XC1OJiIijuLuDnUBWBAdT1Zu/tU9+JZ/gV8wnDsC3z9d7Nmk7LJ8nauySOtclbKUeHinDeRlwdDPodHNVieSMuzb2JM88/UO0rPz8PN0ZdqglkQ2Cbj8A0VERK5Cvt2gy+srOZGSybRBLRlwtQvRH10Hc3uDYYeBc6DZ7SUTVEpcuVnnSgSAn182C6u6naFhL6vTSBmVciGHx+dv47HPtpKenUebOlX4bkxnFVYiIlIinJ1sDIsIAeC/G65yaCBAnY5wwzjz/pKxcO4aziHljoorsdaxDbB9vnn/pslmpx2RP/hpdyI3vbmahVtP4GSDUd2v4/OH2lOrsuZXiYhIyRncNhg3Zydi41PYfjzl6k/QbTzUam3OLf/yPsjT/H9Hp+JKrJOfC0sufqMTfjcEhVkaR8qehNQsRkZtYcR/N3M6PZt6Nbz48tGOPNmzES7O+udLRERKVnVvd25pbi7389/113DlydnVHBLo4QcnYuCnScWcUMoafToR62ycBad3gWdViHzR6jRShuTm2/lgzWFunPYLS7efwskGD3YO5bvRnQkPqWJ1PBERqUDu6VgXgG+2nSAhNevqT1ClDvSbZd7fMAP2fFt84aTMUXEl1kg9Disvtl7v8RJUqmptHikTDMPgh10J9HxzNZOX7iEjJ59WIZVZPOoGnu3dBA9XZ6sjiohIBdMqpArtQquSm2/w0a9x13aSxrdAB3PZIBaNhORrPI+UeSquxBrLxkNuBgS3h7DhVqeRMmDD4bPc8d56Hv44hsNnMqjm5caU25vz1SMdaVbLz+p4IiJSgT3StR4AURuPkZqZe20niXwBareD7FSYfxfkXMXixFJuqLiS0rf/B/OSuM0Z+vwbnPQ2rKgMw2DdoTMMfm89Q97fQPSRc3i4OjGq+3X88lQ3hrYLwclJTU5ERMRa3Rv50yjAh/PZeXx6tYsK/8bZFQbNgUrVIXEHLB4FWhHJ4ehTrZSuzHPw7RjzfvtHIaCptXnEEnn5dpZsP0n/GesYNnsjG+OScXN24s72Iax8shtP9mykBYFFRKTMsNlsPNTFvHr10dojV7+o8G/8asMd/wUnF9j5Ffz6n2JMKWWBi9UBpIL5fjykn4Kq9aH7s1ankVKWmJbFlzHHidp4jBMpmQC4uTgxpG0wj3StT5Baq4uISBl1a1gQ037cx8nULL7ecqJgDayrVrcT9HoNvnsSfnoBAppBg8hizSrWUXElpWfPt7D9c7A5Qf9Z4FbJ6kRSCnLz7fy89zQLouNZue809osjIKp6uXFX+zrc2b4ONXzcrQ0pIiJyGa7OTtx/QyiTl+5h5qqDDGxdGzeXaxwE1nYEnIqFrR/DV/fDAz9BjYbFG1gsoeJKSkfGGfh2rHm/0xgIbmdpHClZhmGw62Qa38ae5KstJzhzPrtgX9u6VbijTTB9Wwap+5+IiJQrwyJCmLXqMPHJmSzYHM+d7etc24lsNug9DZL2wfFN8OlAGLECvGsUb2ApdSqupOQZBix5HC6cAf8m0G2C1YmkBOTbDWKOnuOHXQn8sCuB4+cyC/ZV93ZjQHhtBrUJ5jp/bwtTioiIXLtKbi6M6l6fF77dzds/H2Bg69rX/kWhizsMiYIPI+HcEfhsCNy7BFw1RL48U3ElJS9mDuxZbE7e7DfT/MdEHEJ2Xj7rDp3lx10JLN+dyJnzOQX7PFyd6NKgBgNa1+Yfjf1xdVb/HBERKf+GRoQwe00cJ1Iy+WTDUUZ0rnftJ/OuAcO/hA8i4cRm+PohGDRPnZTLMZthqAfkH6WlpeHn50dqaiq+vr5WxynfTsXCBz0gPxsiX4QbxlqdSIooIzuPVfuTWLYzgZV7T5OenVewz9fDhRuvD6Bn00C6NKxOJTd9fyMiIo5nQXQ8//xqO1W93Fj9z+54uxfx992RX+HjfpCfA+3/D3q+ag4dlDLhamoDffKRkpOVCgvuMQurhjdDx9FWJ5JrdC4jh5/2JPLDrgRWHzhDTp69YF8NH3duahJAr2aBtK9XTVeoRETE4d0eXotZqw5x+EwGH66JY0xkg6KdsG4nuG0GfD0CNswAzyrQ9Z/FE1ZKlYorKRmGAd+MgnNx4BcC/WboEnc5cyo1kx93mQXVxrhk8u2/X+QOqVqJXs0C6dk0gFbBVbTQr4iIVCguzk483qMhj322lfdWH2Jw22AC/TyKdtIWg+DCWVj2NKx8Bdx9of0jxRNYSo2KKykZG2ZenGf122rkVa1OJFcgPvkCS3ec4vudCcTGpxTa1zjQ52JBFUjjQB9sGq4gIiIVWO/mNZm77ggxR8/x6nd7eGtoq6KftP0jkJ1mFlfLngYPXwgbVvTzSqlRcSXFb/8P8OPFBYJvmgy121ibR/7WyZRMvttxim+3nypUUNlsEB5ShV5NzYIqpJrWJRMREfmNk5ONF29tSt931rI49iTDI0KIqFet6Cfu8pQ5tWL9O/DNSPOL6haDin5eKRUqrqR4JeyEL+8Hww6t7oKIh61OJJeQciGHxbEn+WbbSWKOnivYbrNBRGhVercIomeTAPx9izjEQURExIE1q+XH0HYhRG08xqTFu1jy2A24FHXusc1mfjmdnQ5b5sHCh8CeB2FDiye0lCgVV1J80hMhajDknIe6naH3v9XppgzJy7ez5uAZvtx8nOW7E8nJN5tS2GzQtk5Vereoyc3NA/H3UUElIiJypZ66qRFLt59ib0I6n248xj0d6xb9pDYb9JkONidzSZtFj5oFVvhdRT+3lCgVV1I8cjLg86GQdhyqXQeDPwYXN6tTCXA46TwLNh9n4dbjJKZlF2xvHOjDwNa16dMiqOiTcEVERCqoKl5uPHlTQyZ+s4s3ftjHjdf7U7tKMQyld3KCPm+a64RGz4bFo8wOzG1HFP3cUmJUXEnR5WbBZ0PhRIzZOnTYAvNPsUxevp0Ve0/z8fqjrD14pmB7lUqu3BZWi4Gta9Oslp+FCUVERBzHsIg6LLo41P7pr7bz8f0RxdNJ12aDW94AZ1ezRfvSJ8yRQt2f0eigMkrFlRRNXg4suBviVoGbNwz7AqrVtzpVhZWUns386GNEbTzGydQswPy3t3sjf+5oU5t/NA7AzUUt8UVERIqTs5ONfw1qyc3/Wc2vB8/y6caj3NWhbvGc3GYzFxV294VVr8Hq1yH9lDls0Fkf5csa/R+Ra5efB18/CAd+ABcPGDYfgttanapC2nUylQ/WxLFk+0ly8831qKp6uTG4bTDD2oUQXFWd/kREREpSaHUvnu7VmBe/3c2r3+2lS8Ma1KnmVTwnt9mg+wTwCYSl42Drx5CRBAM+BHfv4nkOKRY2wzCMyx9WsaSlpeHn50dqaiq+vr5Wxymb8nPNyZU7vjBbhA79HBpEWp2qQjEMg9UHzjB79eFCQ//Cgitzd4c63NK8Jh6uzhYmFBERqVjsdoNhH2xgw+FkWtepwucPtce1qN0D/2jvUrMzc14WBDSDIZ9ClbrF+xxSyNXUBiquLkHF1WXkXIAv7oEDP5qTLAfNhev7Wp2qwsjJs/Nt7ElmrznM3oR0wByO0Lt5TUZ0DqVF7crWBhQREanA4pMvcMt/1pCencd9neoyqW/TEniSTfD5cMg4DZ5V4Y55ENql+J9HABVXRabi6m9kpsBnQ+DYenDxhDv+Cw1vsjpVhZCWlctnG48x59cjJKSZ86kquTkzpG0I93Wqq6F/IiIiZcSPuxJ46OMYAP4zJIzbwmoV/5OkHjcLrFPbwOYMPV+BiEfU6KIEqLgqIhVXfyH1uLmOVeJOcPcz51jV6WB1Kod3KjWTj9bG8dmmeM5n5wFQw8ed+zrVZXi7OvhVcrU4oYiIiPzRGz/s5d2Vh/B0dWbRyE40CvQp/ifJzYTFj5nTNAAa94Fb34ZKVYv/uSowFVdFpOLqEo5tgPl3mpMnvfzhroUQ2MzqVA7t4Ol0Zq06zDfbThQ0qWjg782DXepxW1gQ7i6aTyUiIlJW5dsN7vloE2sPniGkaiW+erQjNXzci/+JDAM2zoIfJ4I9F3xrw8APIaR98T9XBaXiqohUXP1BzFxY+qT5FzagGQyJgip1rE7lsGKOJjPzl8P8tCexYFtEaFUe7lqPbg39i2fdDBERESlxyRk53PbuWuKTM2ka5MvnD7XHx6OERpyc3Go2ukg+bA4T7DQGuo0HlxIo6CoYFVdFpOLqopwL8MMEs7gCaNIP+s0At2JqKyoF7HaDn/eeZtaqQ2w+eg4wh0zf1CSAR7rWp1WIFmUWEREpj46cyWDAzHWczcihQ71qzLmvbcl1881ONxca3j7f/LlGY7htBtRuXTLPV0GouCoiFVfAyW3w1Qg4ewCwwT+eg85PaJJkMcvJs7M49iTvrTrEgdPnAXBzdqJ/q1o81LUe9Wto7QoREZHybsfxVIa8v56MnHxuahLAO8PCcXMp5hbt/2v3YnM9rIwksDlB+/8zr2K5l8C8rwpAxVURVejiyp4P696GnyebwwB9akL/WVCvm9XJHMr57Dw+33SMD9fGcSrV7Pzn4+7CsPYh3N8plABfD4sTioiISHH69eAZ7psTTU6+na4NazDrztZ4upXg/OkLyfD9P39vduEdCD1eghZ36Mvyq6TiqogqbHF1IgaWPA6nYs2fr78V+v5HHWeKUXzyBT7ecJTPNx0jLev3zn/3dwplePsQfEtqHLaIiIhYbtX+JB75OIbM3Hza1q3Ch/e2Lfnf/ft/NIusc3Hmz8HtzbbttduU7PM6EBVXRVThiqusVFjxMkR/ABjg4Qc3vQKt7tQ3G8XAMAw2HE5mzq9x/LQnEfvFv3Gh1b14qEs9+reqVXJjr0VERKRMiTmazL1zoknPyqNxoA/v39WGkGolvFZlbhasfwfWTIPcC+a2Rr3NaR8BTUr2uR2AiqsiqjDFVW4WbP4QVv8LMpPNbS0Gw02Twdvf2mwOICM7j29jTzJ33RH2JqQXbO/coDr3dqxL90bq/CciIlIR7T6Zxt0fbeLM+Wz8PF35z5AwujUqhc9eqSdg5asQGwWGHbBB84HQaayW2PkbKq6KyOGLq/w82LHA/MuVGm9uq94QbvkX1OtqbbZyzjAMtsWnsGBzPIu3nSQjJx8AT1dnbg+vxb0d69IgQJNJRUREKrpTqZk8+skWtsWnYLPBmBsbMKr7dbg4l2Cji98k7YOVr8Dub37fdl0PuGEs1OmkkUt/oOKqiBy2uMrJgC0fw4Z3IeWYuc0nyOweEzYcnF2szVeOncvIYeHWE8yPjmdf4u9XqUKrezG0XTCD24TgV0nzqUREROR32Xn5vPjtbqI2mp/LmtXy5Y2BLbm+Zil9/jwVC2vfNIssw25uC2gObe+H5oPUXfAiFVdF5HDFVXIcbPkvxMyBTHMNJSpVh46PQcTD4Oppbb5yKj0rl+W7E1m6/RSrDySRm2/+VXJ3caJ385oMbhtMu9Cq2PTtj4iIiPyNRVtPMGnxLlIzc3F1tvFo1/o80q0+ldxK6Yvvs4fMOVnboiDP7GKMm7c5ZLDFEAiOAKdSuKJWRqm4KiKHKK5yM2HfdxAzD+JW/b69SqhZVIUNU1F1Dc5n57FiTyJLtp9i1f4kcvLsBfuaBvkypF0It7YMws9TV6lERETkyp1Oy+LZRTtZvjsRMLsJj+vRkEGta5fOUEEw27fHfgabP4KzB3/f7hcCLQZBk34Q2LzCDRtUcVVE5ba4ys2Egz/BrkWw73vIzbi4wwb1u0Pr+6Bxb3BSZ7orZRgGh5IyWLU/iV/2nWZjXHKhguo6f2/6tKhJ7+Y1NZdKREREisQwDL7fmcBr3+/lWLLZ1a9edS8eLO3uwoYBcash9nPY8y3k/D7lAb8QaHwLNLoZQjqAi3vpZLKQiqsiKjfFlWFA8mE4uAIOLoe4NZCX+ft+v2BoOdRsqV6ljnU5y5nkjByijySzan8Sq/YlcSIls9D+0Ope9GlRkz4tgmgY4K1hfyIiIlKssvPy+WTDMd7++QApF3IBqOblxvCIEAa0rk2dal6lFyY30/zSfudX5mfO//2s6eIJdTpAve5Q9wYIbOGQc/hVXBVRmS2u8vPgzD44tgGOroNj6yHtROFjfGtD037QtD/Ual3hLtteLbvd4GDSeWKOniPm6Dm2HD3H4TMZhY5xc3Yiol5VujasQdeGNbjOXwWViIiIlLz0rFzmR8cz59cjhb7sbVu3CreF1SLy+gAC/TxKL1DOBTj8C+xdan6xfz6x8H7XSubixLXbmcMHA5ubU1LK+XwtFVdFVKaKqz3fmlekTm6FhB2Fvy0AcHIxV9puEGm20AxoqoLqL6Rn5bI/MZ09p9LZl2De9iSkkZ6V96djr/P3plP9anRr5E9EvaqlN6FURERE5A/y8u18vzOBBZvjWXvwDP/76b1pkC/dG/nTLrQq4XWq4O1eSp9ZDANO74HDK+HwKojfAFmpfz7O1ctcqDigmbmWVkAzqFoPvGqUm8+s5a64evfdd3njjTdISEigZcuWvP3227Rr1+4vj//iiy+YOHEiR44coUGDBkydOpVbbrmlYL9hGEyaNInZs2eTkpJCp06dmDlzJg0aNLiiPGWquPpsqNmY4jdu3lArHEI6mpdha7cFt1K8NFyGGYZByoVc4s9d4FiyeYtPvkB8ciZxZzL+NLzvN56uzoQFV6Z1nSq0rlOFViGVqVzJrZTTi4iIiFxeQmoWi7ad4IddCWyLTylUaDk72Wga5Mur/ZvTrJZf6Qaz2yFpr1lkndgCiTvN4uu37oN/5FoJKteBKnX/51YHfGqaN6/qZaZPQLkqrubPn8/dd9/NrFmziIiIYPr06XzxxRfs27cPf/8/r1S9bt06unTpwpQpU+jTpw9RUVFMnTqVLVu20KyZubL01KlTmTJlCvPmzSM0NJSJEyeyY8cOdu/ejYfH5S+dlqniattn5hWroDAIagVV65f7S6tXwjAMLuTkk5aVS1pm3sU/c0nPMu+nXMjldHoWSenZnE7PJuniLft/mk1cSqCvB40CfWgc6HPxT18aBniXXhceERERkWJy5nw2K/eeZv2hs2w6kszxc+YXyevG/4OgymWgK3R+HiQfMj/LJuwwC66kfZB6HLhMCWJzBu8AGBMLLtZ+6V2uiquIiAjatm3LO++8A4Ddbic4OJjHHnuM8ePH/+n4wYMHk5GRwZIlSwq2tW/fnrCwMGbNmoVhGAQFBfHEE0/w5JNPApCamkpAQABz585lyJAhl81UpoorBzLtx30kpGaRnWcnKzefrDw72f/z52/bM3PzSc/KI99+bW9Nfx93QqpWIqRqJYIv/hlSrRIN/L11RUpEREQc1smUTGLjU7i5eU2ro/y9vBxIjYdzRwrfUo5BegJknDYXNfaoDOOPWhoVrq42sHQiSU5ODjExMUyYMKFgm5OTE5GRkaxfv/6Sj1m/fj3jxo0rtK1nz54sWrQIgLi4OBISEoiMjCzY7+fnR0REBOvXr79kcZWdnU12dnbBz6mp5njRtLS0a35t8meLNh3k6NkLV/UYFycbPh4uF2+u+Hq44u3hjK+HK9W93anh40Y1b3fzvrc71X3c/7pNaV4WaWl/cWlaREREpJzzdoJOdbzKx2dY1xrgXwP82/55X34eZJyBrBQoA6/lt/+eV3JNytLi6syZM+Tn5xMQEFBoe0BAAHv37r3kYxISEi55fEJCQsH+37b91TF/NGXKFF588cU/bQ8ODr6yFyIiIiIiIg4tPT0dP7+/n8umFmjAhAkTCl0Ns9vtJCcnU61aNbXcLufS0tIIDg4mPj5eQzylxOn9JqVJ7zcpTXq/SWkpi+81wzBIT08nKCjossdaWlxVr14dZ2dnEhML98hPTEwkMDDwko8JDAz82+N/+zMxMZGaNWsWOiYsLOyS53R3d8fdvfDq0pUrV76alyJlnK+vb5n5CyqOT+83KU16v0lp0vtNSktZe69d7orVbyxtkebm5kbr1q1ZsWJFwTa73c6KFSvo0KHDJR/ToUOHQscDLF++vOD40NBQAgMDCx2TlpbGxo0b//KcIiIiIiIiRWX5sMBx48Zxzz330KZNG9q1a8f06dPJyMjgvvvuA+Duu++mVq1aTJkyBYAxY8bQtWtXpk2bRu/evfn888/ZvHkz77//PgA2m42xY8cyefJkGjRoUNCKPSgoiH79+ln1MkVERERExMFZXlwNHjyYpKQknn/+eRISEggLC2PZsmUFDSmOHTuG0/+s69SxY0eioqJ47rnneOaZZ2jQoAGLFi0qWOMK4J///CcZGRk89NBDpKSkcMMNN7Bs2bIrWuNKHIu7uzuTJk3607BPkZKg95uUJr3fpDTp/Salpby/1yxf50pERERERMQRWDrnSkRERERExFGouBIRERERESkGKq5ERERERESKgYorERERERGRYqDiShzOlClTaNu2LT4+Pvj7+9OvXz/27dtndSypIF577bWCJSFESsKJEye48847qVatGp6enjRv3pzNmzdbHUscUH5+PhMnTiQ0NBRPT0/q16/Pyy+/jHqhSXFYvXo1ffv2JSgoCJvNxqJFiwrtNwyD559/npo1a+Lp6UlkZCQHDhywJuxVUHElDmfVqlWMHDmSDRs2sHz5cnJzc7npppvIyMiwOpo4uOjoaN577z1atGhhdRRxUOfOnaNTp064urry/fffs3v3bqZNm0aVKlWsjiYOaOrUqcycOZN33nmHPXv2MHXqVF5//XXefvttq6OJA8jIyKBly5a8++67l9z/+uuv89ZbbzFr1iw2btyIl5cXPXv2JCsrq5STXh21YheHl5SUhL+/P6tWraJLly5WxxEHdf78ecLDw5kxYwaTJ08mLCyM6dOnWx1LHMz48eP59ddfWbNmjdVRpALo06cPAQEBfPjhhwXbBgwYgKenJ5988omFycTR2Gw2Fi5cSL9+/QDzqlVQUBBPPPEETz75JACpqakEBAQwd+5chgwZYmHav6crV+LwUlNTAahatarFScSRjRw5kt69exMZGWl1FHFgixcvpk2bNgwaNAh/f39atWrF7NmzrY4lDqpjx46sWLGC/fv3AxAbG8vatWu5+eabLU4mji4uLo6EhIRCv1P9/PyIiIhg/fr1Fia7PBerA4iUJLvdztixY+nUqRPNmjWzOo44qM8//5wtW7YQHR1tdRRxcIcPH2bmzJmMGzeOZ555hujoaEaPHo2bmxv33HOP1fHEwYwfP560tDQaN26Ms7Mz+fn5vPLKKwwfPtzqaOLgEhISAAgICCi0PSAgoGBfWaXiShzayJEj2blzJ2vXrrU6ijio+Ph4xowZw/Lly/Hw8LA6jjg4u91OmzZtePXVVwFo1aoVO3fuZNasWSqupNgtWLCATz/9lKioKJo2bcq2bdsYO3YsQUFBer+J/AUNCxSHNWrUKJYsWcLKlSupXbu21XHEQcXExHD69GnCw8NxcXHBxcWFVatW8dZbb+Hi4kJ+fr7VEcWB1KxZkyZNmhTadv3113Ps2DGLEokje+qppxg/fjxDhgyhefPm3HXXXTz++ONMmTLF6mji4AIDAwFITEwstD0xMbFgX1ml4kocjmEYjBo1ioULF/Lzzz8TGhpqdSRxYDfeeCM7duxg27ZtBbc2bdowfPhwtm3bhrOzs9URxYF06tTpT0tL7N+/nzp16liUSBzZhQsXcHIq/FHR2dkZu91uUSKpKEJDQwkMDGTFihUF29LS0ti4cSMdOnSwMNnlaVigOJyRI0cSFRXFN998g4+PT8HYXD8/Pzw9PS1OJ47Gx8fnT/P5vLy8qFatmub5SbF7/PHH6dixI6+++ip33HEHmzZt4v333+f999+3Opo4oL59+/LKK68QEhJC06ZN2bp1K//+97+5//77rY4mDuD8+fMcPHiw4Oe4uDi2bdtG1apVCQkJYezYsUyePJkGDRoQGhrKxIkTCQoKKugoWFapFbs4HJvNdsntc+bM4d577y3dMFIhdevWTa3YpcQsWbKECRMmcODAAUJDQxk3bhwPPvig1bHEAaWnpzNx4kQWLlzI6dOnCQoKYujQoTz//PO4ublZHU/KuV9++YXu3bv/afs999zD3LlzMQyDSZMm8f7775OSksINN9zAjBkzaNiwoQVpr5yKKxERERERkWKgOVciIiIiIiLFQMWViIiIiIhIMVBxJSIiIiIiUgxUXImIiIiIiBQDFVciIiIiIiLFQMWViIiIiIhIMVBxJSIiIiIiUgxUXImIiJRDR44cwWazsW3bNqujiIjIRSquRETEMklJSTz66KOEhITg7u5OYGAgPXv25Ndffy04xmazsWjRIutC/oVu3boxduxYq2OIiEgZ4mJ1ABERqbgGDBhATk4O8+bNo169eiQmJrJixQrOnj17VefJycnBzc2thFKKiIhcGV25EhERS6SkpLBmzRqmTp1K9+7dqVOnDu3atWPChAnceuutANStWxeA/v37Y7PZCn5+4YUXCAsL44MPPiA0NBQPD4+Cc44YMYIaNWrg6+vLP/7xD2JjYwueMzY2lu7du+Pj44Ovry+tW7dm8+bNABw9epS+fftSpUoVvLy8aNq0Kd999901v761a9fSuXNnPD09CQ4OZvTo0WRkZADwzDPPEBER8afHtGzZkpdeeqng5w8++IDrr78eDw8PGjduzIwZM645j4iIlDwVVyIiYglvb2+8vb1ZtGgR2dnZlzwmOjoagDlz5nDq1KmCnwEOHjzIV199xddff10w72jQoEGcPn2a77//npiYGMLDw7nxxhtJTk4GYPjw4dSuXZvo6GhiYmIYP348rq6uAIwcOZLs7GxWr17Njh07mDp1Kt7e3tf02g4dOkSvXr0YMGAA27dvZ/78+axdu5ZRo0YV5Ni0aROHDh0qeMyuXbvYvn07w4YNA+DTTz/l+eef55VXXmHPnj28+uqrTJw4kXnz5l1TJhERKQWGiIiIRb788kujSpUqhoeHh9GxY0djwoQJRmxsbKFjAGPhwoWFtk2aNMlwdXU1Tp8+XbBtzZo1hq+vr5GVlVXo2Pr16xvvvfeeYRiG4ePjY8ydO/eSWZo3b2688MILV5y9a9euxpgxYy6574EHHjAeeuihQtvWrFljODk5GZmZmYZhGEbLli2Nl156qWD/hAkTjIiIiEK5o6KiCp3j5ZdfNjp06GAYhmHExcUZgLF169YrziwiIiVLV65ERMQyAwYM4OTJkyxevJhevXrxyy+/EB4ezty5cy/72Dp16lCjRo2Cn2NjYzl//jzVqlUruCrm7e1NXFxcwRWicePGMWLECCIjI3nttdcKXTkaPXo0kydPplOnTkyaNInt27df8+uKjY1l7ty5hXL07NkTu91OXFwcYF69ioqKAsAwDD777DOGDx8OQEZGBocOHeKBBx4odI7JkycXyiwiImWLiisREbGUh4cHPXr0YOLEiaxbt457772XSZMmXfZxXl5ehX4+f/48NWvWZNu2bYVu+/bt46mnngLMuVq7du2id+/e/PzzzzRp0oSFCxcCMGLECA4fPsxdd93Fjh07aNOmDW+//fY1vabz58/z8MMPF8oRGxvLgQMHqF+/PgBDhw5l3759bNmyhXXr1hEfH8/gwYMLHg8we/bsQufYuXMnGzZsuKZMIiJS8tQtUEREypQmTZoUar3u6upKfn7+ZR8XHh5OQkICLi4uBY0vLqVhw4Y0bNiQxx9/nKFDhzJnzhz69+8PQHBwMI888giPPPIIEyZMYPbs2Tz22GNX/RrCw8PZvXs311133V8eU7t2bbp27cqnn35KZmYmPXr0wN/fH4CAgACCgoI4fPhwwdUsEREp+1RciYiIJc6ePcugQYO4//77adGiBT4+PmzevJnXX3+d2267reC4unXrsmLFCjp16oS7uztVqlS55PkiIyPp0KED/fr14/XXX6dhw4acPHmSpUuX0r9/f5o2bcpTTz3FwIEDCQ0N5fjx40RHRzNgwAAAxo4dy80330zDhg05d+4cK1eu5Prrr//b15CUlPSnRXxr1qzJ008/Tfv27Rk1ahQjRozAy8uL3bt3s3z5ct55552CY4cPH86kSZPIycnhzTffLHSeF198kdGjR+Pn50evXr3Izs5m8+bNnDt3jnHjxl3Nf2oRESklKq5ERMQS3t7eRERE8Oabb3Lo0CFyc3MJDg7mwQcf5Jlnnik4btq0aYwbN47Zs2dTq1Ytjhw5csnz2Ww2vvvuO5599lnuu+8+kpKSCAwMpEuXLgQEBODs7MzZs2e5++67SUxMpHr16tx+++28+OKLAOTn5zNy5EiOHz+Or68vvXr1+lPB80dRUVEF86Z+8/LLL/Pcc8+xatUqnn32WTp37oxhGNSvX79g2N9vBg4cyKhRo3B2dqZfv36F9o0YMYJKlSrxxhtv8NRTT+Hl5UXz5s21cLGISBlmMwzDsDqEiIiIiIhIeaeGFiIiIiIiIsVAxZWIiIiIiEgxUHElIiIiIiJSDFRciYiIiIiIFAMVVyIiIiIiIsVAxZWIiIiIiEgxUHElIiIiIiJSDFRciYiIiIiIFAMVVyIiIiIiIsVAxZWIiIiIiEgxUHElIiIiIiJSDFRciYiIiIiIFIP/B4uTPzdmNBU3AAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import plotly.graph_objects as go\n", + "import pandas as pd\n", + "\n", + "occupations = df['Occupation'].unique()\n", + "fig = go.Figure()\n", + "for i, occupation in enumerate(occupations):\n", + " occupation_data = df[df['Occupation'] == occupation]['Sleep Duration']\n", + " fig.add_trace(go.Box(y=occupation_data, name=occupation))\n", + "\n", + "fig.update_layout(\n", + " title='Sleep Duration by Occupation',\n", + " xaxis=dict(title='Occupation'),\n", + " yaxis=dict(title='Sleep Duration')\n", + ")\n", + "\n", + "fig.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "id": "k5es2M3m-fMs", + "outputId": "73a745bd-f856-4204-f9cb-0559d61fe3bf" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "lower_bound,upper_bound = min(df['Sleep Duration']), max(df['Sleep Duration'])\n", + "filtered_df = df[(df['Sleep Duration'] >= lower_bound) & (df['Sleep Duration'] <= upper_bound)]\n", + "sleep_count = filtered_df.groupby(['Sleep Duration', 'Gender']).size().reset_index(name='Count')" + ], + "metadata": { + "id": "F56v42JPnsaU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "pivot_df = sleep_count.pivot(index='Sleep Duration', columns='Gender', values='Count').reset_index()\n", + "pivot_df=pivot_df.fillna(0)\n", + "print(pivot_df)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tF6cAU91rnjR", + "outputId": "26679c88-71a9-4290-b78d-4fe8cfa48f4a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Gender Sleep Duration Female Male\n", + "0 5.8 2.0 0.0\n", + "1 5.9 1.0 3.0\n", + "2 6.0 13.0 18.0\n", + "3 6.1 15.0 10.0\n", + "4 6.2 6.0 6.0\n", + "5 6.3 0.0 13.0\n", + "6 6.4 2.0 7.0\n", + "7 6.5 12.0 14.0\n", + "8 6.6 18.0 2.0\n", + "9 6.7 3.0 2.0\n", + "10 6.8 4.0 1.0\n", + "11 6.9 2.0 1.0\n", + "12 7.1 13.0 6.0\n", + "13 7.2 22.0 14.0\n", + "14 7.3 0.0 14.0\n", + "15 7.4 1.0 4.0\n", + "16 7.5 1.0 4.0\n", + "17 7.6 0.0 10.0\n", + "18 7.7 0.0 24.0\n", + "19 7.8 0.0 28.0\n", + "20 7.9 1.0 6.0\n", + "21 8.0 13.0 0.0\n", + "22 8.1 13.0 2.0\n", + "23 8.2 11.0 0.0\n", + "24 8.3 5.0 0.0\n", + "25 8.4 14.0 0.0\n", + "26 8.5 13.0 0.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "lower_bound,upper_bound = min(df['Stress Level']), max(df['Stress Level'])\n", + "filtered_df = df[(df['Stress Level'] >= lower_bound) & (df['Stress Level'] <= upper_bound)]\n", + "stress_count = filtered_df.groupby(['Stress Level', 'Gender']).size().reset_index(name='Count')\n", + "pivot_df = stress_count.pivot(index='Stress Level', columns='Gender', values='Count').reset_index()\n", + "stress_df=pivot_df.fillna(0)\n", + "stress_df" + ], + "metadata": { + "id": "uuZvY-fFH_uG", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 237 + }, + "outputId": "6670b228-5031-4ae0-a23a-865a9f46ed57" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Gender Stress Level Female Male\n", + "0 3 67 4\n", + "1 4 61 9\n", + "2 5 2 65\n", + "3 6 9 37\n", + "4 7 12 38\n", + "5 8 34 36" + ], + "text/html": [ + "\n", + "
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GenderStress LevelFemaleMale
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Person IDGenderAgeOccupationSleep DurationQuality of SleepPhysical Activity LevelStress LevelBMI CategoryHeart RateDaily StepsSleep DisorderSystolic PressureDiastolic Pressure
01Male27Software Engineer6.163.06Overweight774200None126.083.0
12Male28Doctor6.266.08Normal7510000None125.080.0
23Male28Doctor6.266.08Normal7510000None125.080.0
34Male28Sales Representative5.941.08Obese853000Sleep Apnea140.090.0
45Male28Sales Representative5.941.08Obese853000Sleep Apnea140.090.0
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\"samples\": [\n \"Engineer\",\n \"Software Engineer\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sleep Duration\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7956567308898189,\n \"min\": 5.8,\n \"max\": 8.5,\n \"num_unique_values\": 27,\n \"samples\": [\n 7.7,\n 5.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Quality of Sleep\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 4,\n \"max\": 9,\n \"num_unique_values\": 6,\n \"samples\": [\n 6,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Physical Activity Level\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.2031217747845413,\n \"min\": 1.0,\n \"max\": 10.0,\n \"num_unique_values\": 10,\n \"samples\": [\n 10.0,\n 6.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Stress Level\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 3,\n \"max\": 8,\n \"num_unique_values\": 6,\n \"samples\": [\n 6,\n 8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"BMI Category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Normal\",\n \"Normal Weight\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Heart Rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 65,\n \"max\": 86,\n \"num_unique_values\": 19,\n \"samples\": [\n 77,\n 80\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Daily Steps\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1617,\n \"min\": 3000,\n \"max\": 10000,\n \"num_unique_values\": 20,\n \"samples\": [\n 4200,\n 6200\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sleep Disorder\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"None\",\n \"Sleep Apnea\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Systolic Pressure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7.748117557645013,\n \"min\": 115.0,\n \"max\": 142.0,\n \"num_unique_values\": 18,\n \"samples\": [\n 126.0,\n 125.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Diastolic Pressure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6.1616114525659205,\n \"min\": 75.0,\n \"max\": 95.0,\n \"num_unique_values\": 17,\n \"samples\": [\n 83.0,\n 80.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 74 + } + ] + }, + { + "cell_type": "code", + "source": [ + "min(df['Physical Activity Level']),max(df['Physical Activity Level'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fmx5nhPjzsRt", + "outputId": "1d68d71b-05f4-440d-e02b-64e4592af61a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(1.0, 10.0)" + ] + }, + "metadata": {}, + "execution_count": 75 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df.columns" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6wLOMUMSp6n1", + "outputId": "1b52087b-1329-4530-99f0-9d479b6c6a23" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Person ID', 'Gender', 'Age', 'Occupation', 'Sleep Duration',\n", + " 'Quality of Sleep', 'Physical Activity Level', 'Stress Level',\n", + " 'BMI Category', 'Heart Rate', 'Daily Steps', 'Sleep Disorder',\n", + " 'Systolic Pressure', 'Diastolic Pressure'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 76 + } + ] + }, + { + "cell_type": "code", + "source": [ + "X = df.drop(columns=['Person ID','Gender','Age','Gender','Quality of Sleep','Occupation','Sleep Disorder'])\n", + "y = df['Sleep Disorder']\n", + "X.columns" + ], + "metadata": { + "id": "S2OAdJQbJe3w", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "49c12cbd-6f21-4239-c93b-b1dd8f575e41" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Sleep Duration', 'Physical Activity Level', 'Stress Level',\n", + " 'BMI Category', 'Heart Rate', 'Daily Steps', 'Systolic Pressure',\n", + " 'Diastolic Pressure'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 77 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "\n", + "numerical_features = ['Sleep Duration', 'Physical Activity Level', 'Heart Rate',\n", + " 'Stress Level', 'Systolic Pressure', 'Diastolic Pressure', 'Daily Steps']\n", + "\n", + "label_encoder1 = LabelEncoder()\n", + "X['BMI Category']= label_encoder1.fit_transform(X['BMI Category'])\n", + "\n", + "\n", + "label_encoder2= LabelEncoder()\n", + "y = label_encoder2.fit_transform(y)\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n" + ], + "metadata": { + "id": "wZiDsegyOu1M" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "OL1dL4GWzlGy", + "outputId": "bc6303eb-e022-4efc-80ef-13e7fefdfad7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Sleep Duration Physical Activity Level Stress Level BMI Category \\\n", + "0 6.1 3.0 6 3 \n", + "1 6.2 6.0 8 0 \n", + "2 6.2 6.0 8 0 \n", + "3 5.9 1.0 8 2 \n", + "4 5.9 1.0 8 2 \n", + "\n", + " Heart Rate Daily Steps Systolic Pressure Diastolic Pressure \n", + "0 77 4200 126.0 83.0 \n", + "1 75 10000 125.0 80.0 \n", + "2 75 10000 125.0 80.0 \n", + "3 85 3000 140.0 90.0 \n", + "4 85 3000 140.0 90.0 " + ], + "text/html": [ + "\n", + "
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Sleep DurationPhysical Activity LevelStress LevelBMI CategoryHeart RateDaily StepsSystolic PressureDiastolic Pressure
06.13.063774200126.083.0
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\"column\": \"BMI Category\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 3,\n \"num_unique_values\": 4,\n \"samples\": [\n 0,\n 1,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Heart Rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 65,\n \"max\": 86,\n \"num_unique_values\": 19,\n \"samples\": [\n 77,\n 80,\n 81\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Daily Steps\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1617,\n \"min\": 3000,\n \"max\": 10000,\n \"num_unique_values\": 20,\n \"samples\": [\n 4200,\n 6200,\n 7500\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Systolic Pressure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7.748117557645013,\n \"min\": 115.0,\n \"max\": 142.0,\n \"num_unique_values\": 18,\n \"samples\": [\n 126.0,\n 125.0,\n 128.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Diastolic Pressure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6.1616114525659205,\n \"min\": 75.0,\n \"max\": 95.0,\n \"num_unique_values\": 17,\n \"samples\": [\n 83.0,\n 80.0,\n 76.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 79 + } + ] + }, + { + "cell_type": "code", + "source": [ + "bmi_mapping = {i: label for i, label in enumerate(label_encoder1.classes_)}\n", + "print(bmi_mapping)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HYOS4jh5vsYV", + "outputId": "b18c687a-4941-444e-c4f2-889f42154286" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{0: 'Normal', 1: 'Normal Weight', 2: 'Obese', 3: 'Overweight'}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "label_mapping = {i: label for i, label in enumerate(label_encoder2.classes_)}\n", + "print(label_mapping)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Nu3k4sfWx9aQ", + "outputId": "e93b1fb9-3202-49bf-c050-c0a284041000" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{0: 'Insomnia', 1: 'None', 2: 'Sleep Apnea'}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "X_train.columns" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ica_0dJFEgqi", + "outputId": "c0d22b0d-2169-4003-eb18-30c25508b1ea" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Sleep Duration', 'Physical Activity Level', 'Stress Level',\n", + " 'BMI Category', 'Heart Rate', 'Daily Steps', 'Systolic Pressure',\n", + " 'Diastolic Pressure'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 82 + } + ] + }, + { + "cell_type": "code", + "source": [ + "X_train.shape" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "m3IAGF5qlqdu", + "outputId": "b37db455-1bfe-4ce5-a052-865a98f88174" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(299, 8)" + ] + }, + "metadata": {}, + "execution_count": 83 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Training ML Classifiers" + ], + "metadata": { + "id": "uUfNyH_aUxGf" + } + }, + { + "cell_type": "code", + "source": [ + "models = {\n", + " \"Decision Tree\": DecisionTreeClassifier(),\n", + " \"Random Forest\": RandomForestClassifier(),\n", + " \"Logistic Regression\": LogisticRegression(),\n", + " \"SVM\": SVC(),\n", + " \"KNN\": KNeighborsClassifier(),\n", + " \"Gaussian Naive Bayes\": GaussianNB(),\n", + "}\n", + "\n", + "accuracies = {}\n", + "for name, model in models.items():\n", + " model.fit(X_train, y_train)\n", + " y_pred = model.predict(X_test)\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " print(f\"Model: {name}, Accuracy: {accuracy}\")\n", + " accuracies[name] = accuracy\n", + "\n", + "# Create a line chart with plotly\n", + "fig = make_subplots()\n", + "\n", + "model_names = list(accuracies.keys())\n", + "fig.add_trace(go.Scatter(x=model_names, y=list(accuracies.values()), name=\"Accuracy\"))\n", + "\n", + "fig.update_layout(\n", + " title=\"Model Accuracies\",\n", + " xaxis_title=\"Model\",\n", + " yaxis_title=\"Accuracy\",\n", + ")\n", + "\n", + "fig.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 834 + }, + "id": "SPaWPUg5cefD", + "outputId": "8b75f57b-f15f-4147-e7a2-86a2a4850a3f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: Decision Tree, Accuracy: 0.88\n", + "Model: Random Forest, Accuracy: 0.88\n", + "Model: Logistic Regression, Accuracy: 0.88\n", + "Model: SVM, Accuracy: 0.64\n", + "Model: KNN, Accuracy: 0.8666666666666667\n", + "Model: Gaussian Naive Bayes, Accuracy: 0.88\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning:\n", + "\n", + "lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "model = DecisionTreeClassifier()\n", + "model.fit(X,y)\n", + "user_input = []\n", + "for feature in X_train.columns:\n", + " value = input(f\"Enter {feature}: \")\n", + " user_input.append(value)\n", + "\n", + "user_input = np.array(user_input).reshape(1, -1)\n", + "prediction = model.predict(user_input)\n", + "print(prediction)\n", + "print(f\"Predicted Sleep Disorder: {label_mapping[prediction[0]]}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BRe4M0MUun7U", + "outputId": "8f19a43e-4562-439e-aef2-a83ea75eed4f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Enter Sleep Duration: 6\n", + "Enter Physical Activity Level: 5\n", + "Enter Stress Level: 8\n", + "Enter BMI Category: 2\n", + "Enter Heart Rate: 80\n", + "Enter Daily Steps: 7000\n", + "Enter Systolic Pressure: 120\n", + "Enter Diastolic Pressure: 80\n", + "[1]\n", + "Predicted Sleep Disorder: None\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning:\n", + "\n", + "X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## DL Model Training" + ], + "metadata": { + "id": "KvnuaZkNsN5d" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "encoder = OneHotEncoder()\n", + "y_encoded = encoder.fit_transform(np.array(y).reshape(-1, 1)).toarray()\n", + "y_train_encoded = encoder.fit_transform(np.array(y_train).reshape(-1, 1)).toarray()\n", + "y_test_encoded = encoder.transform(np.array(y_test).reshape(-1, 1)).toarray()\n" + ], + "metadata": { + "id": "LmLSqdZUL37d" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from keras import regularizers\n", + "\n", + "model = Sequential()\n", + "model.add(Dense(256, activation='relu', input_shape=(X_train.shape[1],)))\n", + "model.add(BatchNormalization())\n", + "model.add(Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n", + "model.add(Dropout(0.2))\n", + "model.add(Dense(256, activation='relu'))\n", + "model.add(Dropout(0.2))\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dropout(0.2))\n", + "model.add(Dense(64, activation='relu'))\n", + "model.add(Dense(32, activation='relu'))\n", + "model.add(Dropout(0.2))\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dense(3, activation='softmax'))\n", + "\n", + "\n", + "model.summary()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IECgyDAiJncX", + "outputId": "9581eb0e-d9d0-498e-84f5-ab23cf7582d6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"sequential_7\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense_52 (Dense) (None, 256) 2304 \n", + " \n", + " batch_normalization_5 (Bat (None, 256) 1024 \n", + " chNormalization) \n", + " \n", + " dense_53 (Dense) (None, 512) 131584 \n", + " \n", + " dropout_30 (Dropout) (None, 512) 0 \n", + " \n", + " dense_54 (Dense) (None, 256) 131328 \n", + " \n", + " dropout_31 (Dropout) (None, 256) 0 \n", + " \n", + " dense_55 (Dense) (None, 128) 32896 \n", + " \n", + " dropout_32 (Dropout) (None, 128) 0 \n", + " \n", + " dense_56 (Dense) (None, 64) 8256 \n", + " \n", + " dense_57 (Dense) (None, 32) 2080 \n", + " \n", + " dropout_33 (Dropout) (None, 32) 0 \n", + " \n", + " dense_58 (Dense) (None, 128) 4224 \n", + " \n", + " dense_59 (Dense) (None, 3) 387 \n", + " \n", + "=================================================================\n", + "Total params: 314083 (1.20 MB)\n", + "Trainable params: 313571 (1.20 MB)\n", + "Non-trainable params: 512 (2.00 KB)\n", + "_________________________________________________________________\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['acc'])\n", + "history=model.fit(X_train, y_train_encoded, epochs=200, validation_data=(X_test, y_test_encoded))\n", + "test_loss, test_accuracy = model.evaluate(X_test, y_test_encoded)\n", + "print(f'Test Accuracy: {test_accuracy}')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YpC_7UD9SIhR", + "outputId": "58cbe537-4402-4616-81ef-2cafce7a7859" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/200\n", + "10/10 [==============================] - 3s 69ms/step - loss: 4.4621 - acc: 0.4013 - val_loss: 4.6068 - val_acc: 0.2000\n", + "Epoch 2/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 4.3408 - acc: 0.5518 - val_loss: 4.5394 - val_acc: 0.2133\n", + "Epoch 3/200\n", + "10/10 [==============================] - 0s 22ms/step - loss: 4.2280 - acc: 0.5318 - val_loss: 4.4703 - val_acc: 0.5867\n", + "Epoch 4/200\n", + "10/10 [==============================] - 0s 23ms/step - loss: 4.1253 - acc: 0.5920 - val_loss: 4.4350 - val_acc: 0.5733\n", + "Epoch 5/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 4.0203 - acc: 0.5452 - val_loss: 4.4163 - val_acc: 0.5733\n", + "Epoch 6/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 3.9159 - acc: 0.5719 - val_loss: 4.3817 - val_acc: 0.5733\n", + "Epoch 7/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 3.8001 - acc: 0.6120 - val_loss: 4.3358 - val_acc: 0.5733\n", + "Epoch 8/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 3.7336 - acc: 0.5819 - val_loss: 4.2469 - val_acc: 0.5733\n", + "Epoch 9/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 3.6502 - acc: 0.5853 - val_loss: 4.1573 - val_acc: 0.5733\n", + "Epoch 10/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 3.5619 - acc: 0.5853 - val_loss: 4.0183 - val_acc: 0.5733\n", + "Epoch 11/200\n", + "10/10 [==============================] - 0s 17ms/step - loss: 3.4910 - acc: 0.5953 - val_loss: 3.9044 - val_acc: 0.5733\n", + "Epoch 12/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 3.4260 - acc: 0.5920 - val_loss: 3.8078 - val_acc: 0.3600\n", + "Epoch 13/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 3.3433 - acc: 0.5886 - val_loss: 3.7274 - val_acc: 0.1733\n", + "Epoch 14/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 3.2667 - acc: 0.6087 - val_loss: 3.6208 - val_acc: 0.3867\n", + "Epoch 15/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 3.2122 - acc: 0.5786 - val_loss: 3.5453 - val_acc: 0.3867\n", + "Epoch 16/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 3.1220 - acc: 0.6087 - val_loss: 3.4330 - val_acc: 0.3867\n", + "Epoch 17/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 3.0594 - acc: 0.5853 - val_loss: 3.3608 - val_acc: 0.4000\n", + "Epoch 18/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 2.9746 - acc: 0.6187 - val_loss: 3.2428 - val_acc: 0.4533\n", + "Epoch 19/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 2.9018 - acc: 0.6455 - val_loss: 3.2217 - val_acc: 0.2800\n", + "Epoch 20/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 2.8347 - acc: 0.6221 - val_loss: 3.0115 - val_acc: 0.6000\n", + "Epoch 21/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 2.7327 - acc: 0.6990 - val_loss: 2.9865 - val_acc: 0.5733\n", + "Epoch 22/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 2.6935 - acc: 0.6789 - val_loss: 2.9138 - val_acc: 0.5733\n", + "Epoch 23/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 2.6064 - acc: 0.6957 - val_loss: 2.7998 - val_acc: 0.6000\n", + "Epoch 24/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 2.5168 - acc: 0.7358 - val_loss: 2.7139 - val_acc: 0.6133\n", + "Epoch 25/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 2.4598 - acc: 0.7057 - val_loss: 2.7359 - val_acc: 0.5600\n", + "Epoch 26/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 2.3825 - acc: 0.7258 - val_loss: 2.6203 - val_acc: 0.6667\n", + "Epoch 27/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 2.3316 - acc: 0.7458 - val_loss: 2.5025 - val_acc: 0.7600\n", + "Epoch 28/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 2.2269 - acc: 0.7993 - val_loss: 2.5288 - val_acc: 0.7600\n", + "Epoch 29/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 2.1824 - acc: 0.8194 - val_loss: 2.4894 - val_acc: 0.6000\n", + "Epoch 30/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 2.1187 - acc: 0.8227 - val_loss: 2.5226 - val_acc: 0.6000\n", + "Epoch 31/200\n", + "10/10 [==============================] - 0s 11ms/step - loss: 2.0683 - acc: 0.8361 - val_loss: 2.3317 - val_acc: 0.7867\n", + "Epoch 32/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 2.0577 - acc: 0.8361 - val_loss: 2.3644 - val_acc: 0.7733\n", + "Epoch 33/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.9829 - acc: 0.8829 - val_loss: 2.3408 - val_acc: 0.6933\n", + "Epoch 34/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.9116 - acc: 0.8829 - val_loss: 2.5265 - val_acc: 0.6000\n", + "Epoch 35/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 1.9062 - acc: 0.8629 - val_loss: 2.3087 - val_acc: 0.5600\n", + "Epoch 36/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 1.8956 - acc: 0.8763 - val_loss: 2.1296 - val_acc: 0.7867\n", + "Epoch 37/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.7974 - acc: 0.8829 - val_loss: 2.0689 - val_acc: 0.7867\n", + "Epoch 38/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.7588 - acc: 0.8896 - val_loss: 2.2197 - val_acc: 0.6400\n", + "Epoch 39/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 1.7556 - acc: 0.8863 - val_loss: 2.0346 - val_acc: 0.7867\n", + "Epoch 40/200\n", + "10/10 [==============================] - 0s 17ms/step - loss: 1.7304 - acc: 0.8930 - val_loss: 2.1967 - val_acc: 0.5600\n", + "Epoch 41/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.6999 - acc: 0.8963 - val_loss: 2.0040 - val_acc: 0.8000\n", + "Epoch 42/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.7055 - acc: 0.8763 - val_loss: 2.0826 - val_acc: 0.6667\n", + "Epoch 43/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.6474 - acc: 0.8796 - val_loss: 1.9245 - val_acc: 0.7867\n", + "Epoch 44/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.6440 - acc: 0.8629 - val_loss: 2.0475 - val_acc: 0.6400\n", + "Epoch 45/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.5827 - acc: 0.8997 - val_loss: 2.1103 - val_acc: 0.6000\n", + "Epoch 46/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.5433 - acc: 0.8930 - val_loss: 1.8907 - val_acc: 0.6933\n", + "Epoch 47/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 1.4964 - acc: 0.8863 - val_loss: 2.1079 - val_acc: 0.6400\n", + "Epoch 48/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.5509 - acc: 0.8930 - val_loss: 2.4576 - val_acc: 0.6000\n", + "Epoch 49/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.5150 - acc: 0.8662 - val_loss: 1.7514 - val_acc: 0.7867\n", + "Epoch 50/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.4761 - acc: 0.8896 - val_loss: 1.6713 - val_acc: 0.8000\n", + "Epoch 51/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 1.4929 - acc: 0.8863 - val_loss: 1.6880 - val_acc: 0.7867\n", + "Epoch 52/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 1.4509 - acc: 0.8729 - val_loss: 1.5565 - val_acc: 0.8800\n", + "Epoch 53/200\n", + "10/10 [==============================] - 0s 19ms/step - loss: 1.4139 - acc: 0.8963 - val_loss: 1.4885 - val_acc: 0.7733\n", + "Epoch 54/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.3826 - acc: 0.8896 - val_loss: 1.4593 - val_acc: 0.8800\n", + "Epoch 55/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.3897 - acc: 0.8896 - val_loss: 1.4099 - val_acc: 0.9067\n", + "Epoch 56/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 1.3493 - acc: 0.8829 - val_loss: 1.3992 - val_acc: 0.8667\n", + "Epoch 57/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.3408 - acc: 0.8930 - val_loss: 1.5420 - val_acc: 0.7733\n", + "Epoch 58/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.3107 - acc: 0.8997 - val_loss: 1.3732 - val_acc: 0.8800\n", + "Epoch 59/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.3135 - acc: 0.8796 - val_loss: 1.7275 - val_acc: 0.6800\n", + "Epoch 60/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.2954 - acc: 0.8863 - val_loss: 1.3305 - val_acc: 0.8933\n", + "Epoch 61/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.2616 - acc: 0.8896 - val_loss: 1.6328 - val_acc: 0.7200\n", + "Epoch 62/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.2660 - acc: 0.8863 - val_loss: 1.2797 - val_acc: 0.9067\n", + "Epoch 63/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.2936 - acc: 0.8829 - val_loss: 1.2895 - val_acc: 0.9067\n", + "Epoch 64/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 1.2142 - acc: 0.9030 - val_loss: 1.2400 - val_acc: 0.8933\n", + "Epoch 65/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 1.2086 - acc: 0.8963 - val_loss: 1.2227 - val_acc: 0.8933\n", + "Epoch 66/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 1.2198 - acc: 0.8896 - val_loss: 1.2289 - val_acc: 0.8933\n", + "Epoch 67/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.2063 - acc: 0.8796 - val_loss: 1.2635 - val_acc: 0.8933\n", + "Epoch 68/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.1454 - acc: 0.9097 - val_loss: 1.1910 - val_acc: 0.9067\n", + "Epoch 69/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 1.1625 - acc: 0.8896 - val_loss: 1.1773 - val_acc: 0.9067\n", + "Epoch 70/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 1.1572 - acc: 0.8963 - val_loss: 1.1762 - val_acc: 0.9067\n", + "Epoch 71/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 1.1367 - acc: 0.8997 - val_loss: 1.1486 - val_acc: 0.9067\n", + "Epoch 72/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 1.1369 - acc: 0.8896 - val_loss: 1.1524 - val_acc: 0.9067\n", + "Epoch 73/200\n", + "10/10 [==============================] - 0s 22ms/step - loss: 1.1212 - acc: 0.8896 - val_loss: 1.1289 - val_acc: 0.9067\n", + "Epoch 74/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 1.1081 - acc: 0.8863 - val_loss: 1.2600 - val_acc: 0.8000\n", + "Epoch 75/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 1.0975 - acc: 0.8863 - val_loss: 1.1273 - val_acc: 0.8933\n", + "Epoch 76/200\n", + "10/10 [==============================] - 0s 25ms/step - loss: 1.0720 - acc: 0.8997 - val_loss: 1.1240 - val_acc: 0.8933\n", + "Epoch 77/200\n", + "10/10 [==============================] - 0s 22ms/step - loss: 1.0763 - acc: 0.8829 - val_loss: 1.1519 - val_acc: 0.8933\n", + "Epoch 78/200\n", + "10/10 [==============================] - 0s 24ms/step - loss: 1.0735 - acc: 0.8963 - val_loss: 1.0958 - val_acc: 0.8933\n", + "Epoch 79/200\n", + "10/10 [==============================] - 0s 21ms/step - loss: 1.0499 - acc: 0.8997 - val_loss: 1.0975 - val_acc: 0.9067\n", + "Epoch 80/200\n", + "10/10 [==============================] - 0s 21ms/step - loss: 1.0524 - acc: 0.8930 - val_loss: 1.0844 - val_acc: 0.8933\n", + "Epoch 81/200\n", + "10/10 [==============================] - 0s 21ms/step - loss: 1.0433 - acc: 0.8963 - val_loss: 1.3504 - val_acc: 0.7467\n", + "Epoch 82/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 1.0516 - acc: 0.8930 - val_loss: 1.1214 - val_acc: 0.8000\n", + "Epoch 83/200\n", + "10/10 [==============================] - 0s 21ms/step - loss: 1.0066 - acc: 0.8930 - val_loss: 1.0520 - val_acc: 0.9067\n", + "Epoch 84/200\n", + "10/10 [==============================] - 0s 22ms/step - loss: 1.0276 - acc: 0.8963 - val_loss: 1.0635 - val_acc: 0.9067\n", + "Epoch 85/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 0.9652 - acc: 0.9030 - val_loss: 1.2294 - val_acc: 0.8000\n", + "Epoch 86/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 1.0179 - acc: 0.8763 - val_loss: 1.0560 - val_acc: 0.9067\n", + "Epoch 87/200\n", + "10/10 [==============================] - 0s 23ms/step - loss: 0.9905 - acc: 0.8796 - val_loss: 1.1049 - val_acc: 0.8000\n", + "Epoch 88/200\n", + "10/10 [==============================] - 0s 22ms/step - loss: 0.9745 - acc: 0.8896 - val_loss: 1.0149 - val_acc: 0.9067\n", + "Epoch 89/200\n", + "10/10 [==============================] - 0s 21ms/step - loss: 0.9664 - acc: 0.8829 - val_loss: 1.0715 - val_acc: 0.8000\n", + "Epoch 90/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.9667 - acc: 0.8863 - val_loss: 1.0297 - val_acc: 0.8933\n", + "Epoch 91/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.9685 - acc: 0.8796 - val_loss: 0.9990 - val_acc: 0.9067\n", + "Epoch 92/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.9407 - acc: 0.8896 - val_loss: 0.9983 - val_acc: 0.9067\n", + "Epoch 93/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.9436 - acc: 0.8863 - val_loss: 1.1002 - val_acc: 0.8533\n", + "Epoch 94/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.9481 - acc: 0.8863 - val_loss: 1.0044 - val_acc: 0.8667\n", + "Epoch 95/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.9238 - acc: 0.9030 - val_loss: 1.0082 - val_acc: 0.8667\n", + "Epoch 96/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.9060 - acc: 0.9164 - val_loss: 1.0396 - val_acc: 0.8800\n", + "Epoch 97/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.8969 - acc: 0.9097 - val_loss: 0.9750 - val_acc: 0.8800\n", + "Epoch 98/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.9338 - acc: 0.8896 - val_loss: 0.9632 - val_acc: 0.8800\n", + "Epoch 99/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.9015 - acc: 0.9064 - val_loss: 0.9757 - val_acc: 0.8667\n", + "Epoch 100/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.9015 - acc: 0.8930 - val_loss: 0.9566 - val_acc: 0.8800\n", + "Epoch 101/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.8884 - acc: 0.8997 - val_loss: 0.9577 - val_acc: 0.8667\n", + "Epoch 102/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.9114 - acc: 0.8963 - val_loss: 0.9272 - val_acc: 0.9067\n", + "Epoch 103/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.8592 - acc: 0.8963 - val_loss: 0.9253 - val_acc: 0.9067\n", + "Epoch 104/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.8746 - acc: 0.8696 - val_loss: 0.9574 - val_acc: 0.8667\n", + "Epoch 105/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.8446 - acc: 0.9164 - val_loss: 0.9118 - val_acc: 0.8800\n", + "Epoch 106/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.8522 - acc: 0.8997 - val_loss: 0.9651 - val_acc: 0.8667\n", + "Epoch 107/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.8605 - acc: 0.8863 - val_loss: 1.0615 - val_acc: 0.8533\n", + "Epoch 108/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.8569 - acc: 0.8930 - val_loss: 1.0232 - val_acc: 0.8533\n", + "Epoch 109/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.8448 - acc: 0.8997 - val_loss: 0.9013 - val_acc: 0.8667\n", + "Epoch 110/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.8777 - acc: 0.8863 - val_loss: 0.9318 - val_acc: 0.8667\n", + "Epoch 111/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.8348 - acc: 0.8963 - val_loss: 0.9606 - val_acc: 0.8800\n", + "Epoch 112/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.8462 - acc: 0.9064 - val_loss: 0.9116 - val_acc: 0.8933\n", + "Epoch 113/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.8194 - acc: 0.8997 - val_loss: 0.9240 - val_acc: 0.8933\n", + "Epoch 114/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.8097 - acc: 0.9064 - val_loss: 1.1735 - val_acc: 0.7600\n", + "Epoch 115/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.7944 - acc: 0.8963 - val_loss: 0.9699 - val_acc: 0.8000\n", + "Epoch 116/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.8311 - acc: 0.8997 - val_loss: 0.8621 - val_acc: 0.9067\n", + "Epoch 117/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.8043 - acc: 0.8930 - val_loss: 0.8654 - val_acc: 0.9067\n", + "Epoch 118/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.8019 - acc: 0.8963 - val_loss: 0.9081 - val_acc: 0.8667\n", + "Epoch 119/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.8465 - acc: 0.8930 - val_loss: 0.8665 - val_acc: 0.8800\n", + "Epoch 120/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.7958 - acc: 0.8829 - val_loss: 1.0915 - val_acc: 0.7200\n", + "Epoch 121/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.8084 - acc: 0.8997 - val_loss: 0.8980 - val_acc: 0.8667\n", + "Epoch 122/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.7682 - acc: 0.8930 - val_loss: 0.8735 - val_acc: 0.8667\n", + "Epoch 123/200\n", + "10/10 [==============================] - 0s 18ms/step - loss: 0.8047 - acc: 0.9030 - val_loss: 0.8509 - val_acc: 0.8667\n", + "Epoch 124/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.7717 - acc: 0.9130 - val_loss: 0.8473 - val_acc: 0.8800\n", + "Epoch 125/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.8060 - acc: 0.8930 - val_loss: 0.8909 - val_acc: 0.8667\n", + "Epoch 126/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.7706 - acc: 0.9064 - val_loss: 0.9162 - val_acc: 0.8533\n", + "Epoch 127/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7763 - acc: 0.8896 - val_loss: 0.8520 - val_acc: 0.8800\n", + "Epoch 128/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7531 - acc: 0.9064 - val_loss: 0.9042 - val_acc: 0.8800\n", + "Epoch 129/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.7464 - acc: 0.9097 - val_loss: 0.8258 - val_acc: 0.9067\n", + "Epoch 130/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.7523 - acc: 0.8829 - val_loss: 0.8284 - val_acc: 0.9067\n", + "Epoch 131/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7219 - acc: 0.9064 - val_loss: 0.8135 - val_acc: 0.9067\n", + "Epoch 132/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7479 - acc: 0.9097 - val_loss: 0.9727 - val_acc: 0.7733\n", + "Epoch 133/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7457 - acc: 0.8963 - val_loss: 0.8016 - val_acc: 0.9067\n", + "Epoch 134/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.7294 - acc: 0.9097 - val_loss: 0.8144 - val_acc: 0.8933\n", + "Epoch 135/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.7205 - acc: 0.9030 - val_loss: 0.8173 - val_acc: 0.8933\n", + "Epoch 136/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7137 - acc: 0.9097 - val_loss: 0.8573 - val_acc: 0.8933\n", + "Epoch 137/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7099 - acc: 0.9030 - val_loss: 0.8686 - val_acc: 0.8533\n", + "Epoch 138/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.7257 - acc: 0.8963 - val_loss: 0.8563 - val_acc: 0.8533\n", + "Epoch 139/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.7227 - acc: 0.8997 - val_loss: 0.9682 - val_acc: 0.8800\n", + "Epoch 140/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7270 - acc: 0.8997 - val_loss: 0.8529 - val_acc: 0.8667\n", + "Epoch 141/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7180 - acc: 0.9130 - val_loss: 0.8209 - val_acc: 0.8667\n", + "Epoch 142/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7074 - acc: 0.9030 - val_loss: 0.8612 - val_acc: 0.8533\n", + "Epoch 143/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.6983 - acc: 0.9197 - val_loss: 0.7906 - val_acc: 0.9067\n", + "Epoch 144/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.6905 - acc: 0.9064 - val_loss: 0.7829 - val_acc: 0.9067\n", + "Epoch 145/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.7156 - acc: 0.8829 - val_loss: 0.7845 - val_acc: 0.8800\n", + "Epoch 146/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.6896 - acc: 0.9030 - val_loss: 0.8786 - val_acc: 0.8533\n", + "Epoch 147/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.6914 - acc: 0.8997 - val_loss: 1.1516 - val_acc: 0.6400\n", + "Epoch 148/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.7083 - acc: 0.8896 - val_loss: 1.1921 - val_acc: 0.6400\n", + "Epoch 149/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.7146 - acc: 0.8896 - val_loss: 0.8859 - val_acc: 0.8533\n", + "Epoch 150/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.6572 - acc: 0.8997 - val_loss: 0.9067 - val_acc: 0.8000\n", + "Epoch 151/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.6719 - acc: 0.9097 - val_loss: 0.7786 - val_acc: 0.9067\n", + "Epoch 152/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.6896 - acc: 0.8997 - val_loss: 0.7834 - val_acc: 0.9067\n", + "Epoch 153/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.6873 - acc: 0.8997 - val_loss: 0.7921 - val_acc: 0.8800\n", + "Epoch 154/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.6839 - acc: 0.9030 - val_loss: 0.7733 - val_acc: 0.8800\n", + "Epoch 155/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.6769 - acc: 0.9030 - val_loss: 0.7833 - val_acc: 0.8667\n", + "Epoch 156/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.6673 - acc: 0.9097 - val_loss: 0.7912 - val_acc: 0.8667\n", + "Epoch 157/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.6656 - acc: 0.8963 - val_loss: 0.7623 - val_acc: 0.8800\n", + "Epoch 158/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.6820 - acc: 0.8963 - val_loss: 0.8879 - val_acc: 0.7600\n", + "Epoch 159/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.7028 - acc: 0.8829 - val_loss: 0.9468 - val_acc: 0.7600\n", + "Epoch 160/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.6550 - acc: 0.9130 - val_loss: 0.8030 - val_acc: 0.8667\n", + "Epoch 161/200\n", + "10/10 [==============================] - 0s 21ms/step - loss: 0.6687 - acc: 0.8896 - val_loss: 0.8167 - val_acc: 0.8667\n", + "Epoch 162/200\n", + "10/10 [==============================] - 0s 23ms/step - loss: 0.6685 - acc: 0.9030 - val_loss: 0.7955 - val_acc: 0.8667\n", + "Epoch 163/200\n", + "10/10 [==============================] - 0s 24ms/step - loss: 0.6555 - acc: 0.8930 - val_loss: 0.7929 - val_acc: 0.8667\n", + "Epoch 164/200\n", + "10/10 [==============================] - 0s 24ms/step - loss: 0.6436 - acc: 0.9030 - val_loss: 0.8097 - val_acc: 0.8667\n", + "Epoch 165/200\n", + "10/10 [==============================] - 0s 25ms/step - loss: 0.6353 - acc: 0.9030 - val_loss: 0.7648 - val_acc: 0.8667\n", + "Epoch 166/200\n", + "10/10 [==============================] - 0s 23ms/step - loss: 0.6733 - acc: 0.8997 - val_loss: 0.7686 - val_acc: 0.8667\n", + "Epoch 167/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 0.6507 - acc: 0.8997 - val_loss: 0.7748 - val_acc: 0.8933\n", + "Epoch 168/200\n", + "10/10 [==============================] - 0s 21ms/step - loss: 0.6842 - acc: 0.8796 - val_loss: 0.7516 - val_acc: 0.9067\n", + "Epoch 169/200\n", + "10/10 [==============================] - 0s 21ms/step - loss: 0.6581 - acc: 0.9030 - val_loss: 0.9068 - val_acc: 0.8533\n", + "Epoch 170/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 0.6847 - acc: 0.8562 - val_loss: 0.8358 - val_acc: 0.8533\n", + "Epoch 171/200\n", + "10/10 [==============================] - 0s 22ms/step - loss: 0.6775 - acc: 0.8930 - val_loss: 0.7802 - val_acc: 0.8667\n", + "Epoch 172/200\n", + "10/10 [==============================] - 0s 19ms/step - loss: 0.6459 - acc: 0.8963 - val_loss: 0.8481 - val_acc: 0.8533\n", + "Epoch 173/200\n", + "10/10 [==============================] - 0s 21ms/step - loss: 0.6638 - acc: 0.8863 - val_loss: 0.7296 - val_acc: 0.8800\n", + "Epoch 174/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 0.6570 - acc: 0.8997 - val_loss: 0.7459 - val_acc: 0.8667\n", + "Epoch 175/200\n", + "10/10 [==============================] - 0s 24ms/step - loss: 0.6216 - acc: 0.9231 - val_loss: 0.9510 - val_acc: 0.7333\n", + "Epoch 176/200\n", + "10/10 [==============================] - 0s 20ms/step - loss: 0.6324 - acc: 0.8963 - val_loss: 0.7373 - val_acc: 0.8800\n", + "Epoch 177/200\n", + "10/10 [==============================] - 0s 22ms/step - loss: 0.6251 - acc: 0.9064 - val_loss: 0.7326 - val_acc: 0.8800\n", + "Epoch 178/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.6150 - acc: 0.8963 - val_loss: 0.7466 - val_acc: 0.8800\n", + "Epoch 179/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.6122 - acc: 0.8997 - val_loss: 0.7718 - val_acc: 0.8667\n", + "Epoch 180/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.6248 - acc: 0.8930 - val_loss: 0.7810 - val_acc: 0.8667\n", + "Epoch 181/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.6284 - acc: 0.8997 - val_loss: 0.7651 - val_acc: 0.8800\n", + "Epoch 182/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.6279 - acc: 0.8930 - val_loss: 0.8687 - val_acc: 0.7600\n", + "Epoch 183/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.6275 - acc: 0.8896 - val_loss: 0.7615 - val_acc: 0.8667\n", + "Epoch 184/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.6099 - acc: 0.9064 - val_loss: 0.7702 - val_acc: 0.8667\n", + "Epoch 185/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.5888 - acc: 0.9064 - val_loss: 0.7490 - val_acc: 0.8667\n", + "Epoch 186/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.6202 - acc: 0.8896 - val_loss: 1.1686 - val_acc: 0.6267\n", + "Epoch 187/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.6209 - acc: 0.9064 - val_loss: 0.9555 - val_acc: 0.7600\n", + "Epoch 188/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.6479 - acc: 0.8896 - val_loss: 1.2702 - val_acc: 0.6267\n", + "Epoch 189/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.5926 - acc: 0.8997 - val_loss: 0.9358 - val_acc: 0.7600\n", + "Epoch 190/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.5983 - acc: 0.9130 - val_loss: 0.7420 - val_acc: 0.9200\n", + "Epoch 191/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.6132 - acc: 0.8963 - val_loss: 0.9711 - val_acc: 0.7733\n", + "Epoch 192/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.5935 - acc: 0.9030 - val_loss: 0.8270 - val_acc: 0.7733\n", + "Epoch 193/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.5918 - acc: 0.9097 - val_loss: 0.7680 - val_acc: 0.8800\n", + "Epoch 194/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.6093 - acc: 0.8829 - val_loss: 0.7289 - val_acc: 0.8800\n", + "Epoch 195/200\n", + "10/10 [==============================] - 0s 15ms/step - loss: 0.5672 - acc: 0.9097 - val_loss: 0.7268 - val_acc: 0.8933\n", + "Epoch 196/200\n", + "10/10 [==============================] - 0s 14ms/step - loss: 0.5764 - acc: 0.9030 - val_loss: 0.7741 - val_acc: 0.7733\n", + "Epoch 197/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.5817 - acc: 0.9097 - val_loss: 0.8287 - val_acc: 0.8533\n", + "Epoch 198/200\n", + "10/10 [==============================] - 0s 12ms/step - loss: 0.6046 - acc: 0.8863 - val_loss: 0.8932 - val_acc: 0.6533\n", + "Epoch 199/200\n", + "10/10 [==============================] - 0s 13ms/step - loss: 0.5739 - acc: 0.8997 - val_loss: 0.7035 - val_acc: 0.8800\n", + "Epoch 200/200\n", + "10/10 [==============================] - 0s 16ms/step - loss: 0.5797 - acc: 0.9130 - val_loss: 0.7706 - val_acc: 0.8667\n", + "3/3 [==============================] - 0s 6ms/step - loss: 0.7706 - acc: 0.8667\n", + "Test Accuracy: 0.8666666746139526\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "_,model_acc=model.evaluate(X_test,y_test_encoded)\n", + "print(model_acc)\n", + "accuracies['ANN']=model_acc" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MugJ62SPP_LY", + "outputId": "d44f510e-d348-458f-9c62-c12b316d0a1d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "3/3 [==============================] - 0s 13ms/step - loss: 0.7706 - acc: 0.8667\n", + "0.8666666746139526\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.save('model.h5')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Cfwidtf8P7Nc", + "outputId": "a28dd1cc-1d9e-47e8-eee3-ec64ce968999" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning:\n", + "\n", + "You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.subplot(2, 2, 1)\n", + "plt.plot(history.history['acc'], label='Train Accuracy')\n", + "plt.plot(history.history['val_acc'], label='Validation Accuracy')\n", + "plt.title('Model Accuracy')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(loc='best')\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.plot(history.history['loss'], label='Train Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.title('Model Loss')\n", + "plt.ylabel('Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(loc='best')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + }, + "id": "S7WVRe90TXEF", + "outputId": "73425f91-ff3c-4d81-e90f-fed4840def37" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "import seaborn as sns\n", + "\n", + "y_pred = model.predict(X).argmax(axis=1)\n", + "cm = confusion_matrix(y, y_pred)\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + "plt.title('Confusion Matrix', fontsize=16)\n", + "plt.xlabel('Predicted', fontsize=14)\n", + "plt.ylabel('Actual', fontsize=14)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 743 + }, + "id": "nTzuXgs1qRnT", + "outputId": "f9acb2f7-6511-483e-e561-608064ddf03d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "12/12 [==============================] - 0s 3ms/step\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(accuracies)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a04tT7J1TP-J", + "outputId": "b5a358d0-41de-40ac-97f0-cf7119481786" + }, + "execution_count": 90, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'Decision Tree': 0.88, 'Random Forest': 0.88, 'Logistic Regression': 0.88, 'SVM': 0.64, 'KNN': 0.8666666666666667, 'Gaussian Naive Bayes': 0.88, 'ANN': 0.8666666746139526}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "fig = go.Figure(\n", + " go.Scatter(\n", + " x=list(accuracies.keys()),\n", + " y=list(accuracies.values()),\n", + " mode='markers+lines',\n", + " marker=dict(symbol='star')\n", + " )\n", + ")\n", + "\n", + "fig.update_layout(\n", + " title='Model Accuracies',\n", + " xaxis=dict(title='Model'),\n", + " yaxis=dict(title='Accuracy')\n", + ")\n", + "\n", + "fig.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "id": "4mAXeZ6gToGs", + "outputId": "bc14f705-6ff4-4105-caf5-4564066e2d02" + }, + "execution_count": 91, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Conclusion\n", + "- Decision Tree, Random Forest, Logistic Regression, and Gaussian Naive Bayes achieved comparable accuracies of 88%. These models demonstrate robustness and effectiveness in capturing patterns within the dataset.\n", + "\n", + "- K-Nearest Neighbors (KNN) also performed well with an accuracy of approximately 86.67%, indicating its suitability for classification tasks where instances are grouped based on similarity.\n", + "\n", + "- However, the Support Vector Machine (SVM) model lagged behind with an accuracy of 64%, suggesting potential challenges in properly separating the data points in the feature space.\n", + "\n", + "Overall, the decision tree-based models, Random Forest and Decision Tree, along with Logistic Regression and Gaussian Naive Bayes, exhibit promising performance and could be considered as strong candidates for further evaluation and deployment, while further optimization may be necessary for SVM to enhance its performance." + ], + "metadata": { + "id": "XB-ZOS2wUSzM" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "XP9UceAxTpsA" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Sleep Disorder Prediction/Models/README.md b/Sleep Disorder Prediction/Models/README.md new file mode 100644 index 000000000..1aa61c4cc --- /dev/null +++ b/Sleep Disorder Prediction/Models/README.md @@ -0,0 +1,48 @@ +## Sleep Disorder Prediction using ML - Models + +[![Open in Colab](https://img.shields.io/badge/Open%20with-Colab-gold?style=for-the-badge&logo=google-colab)](https://colab.research.google.com/drive/1iUNcDuqsTgx5Z3DtTeSxpK-LcyfBJE0Z?usp=sharing) + + +### ๐Ÿ” **Overview** + + +The Models folder contains resources for the machine learning models used in the Sleep Disorder Predictor application, including the development notebook and the trained model file. + +### ๐Ÿ“ƒ **Contents** +- Notebook.ipynb: Jupyter notebook with data preprocessing, model training, validation, and evaluation. +- model.h5: Saved ANN model in HDF5 format. + + +### โš™๏ธ **Usage** + +- `Notebook.ipynb`: + - Open with Jupyter Notebook or Jupyter Lab. + - Run cells sequentially to follow data preprocessing, model training, and evaluation steps. + +- `model.h5` + - Load into a Python script or notebook using Keras or TensorFlow. + + ```python + from keras.models import load_model + model = load_model('model.h5') + predictions = model.predict(test_data) + ``` + +**Models performance comparision** + +![img](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/4140bdc4-d7c6-4d16-8aa5-3e2a0ce45381) + + +### โœ’๏ธ **Signature** + +

+ +

+

+ Kamalakar Satapathi +

+ + +Connect with me on [![LinkedIn](https://img.shields.io/badge/-Kamalakar_Satapathi-0077B5?style=flat-square&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/sgvkamalakar) + +Explore my codes [![GitHub](https://img.shields.io/badge/-Sgvkamalakar-181717?style=flat-square&logo=github)](https://github.com/sgvkamalakar) diff --git a/Sleep Disorder Prediction/Models/model.h5 b/Sleep Disorder Prediction/Models/model.h5 new file mode 100644 index 000000000..ee6cc3f84 Binary files /dev/null and b/Sleep Disorder Prediction/Models/model.h5 differ diff --git a/Sleep Disorder Prediction/README.md b/Sleep Disorder Prediction/README.md new file mode 100644 index 000000000..02cf46520 --- /dev/null +++ b/Sleep Disorder Prediction/README.md @@ -0,0 +1,227 @@ +## Sleep Disorder Prediction using ML & EDA with Web App + + +[![Open in Streamlit](https://img.shields.io/badge/Open%20with-Streamlit-red?style=for-the-badge&logo=streamlit)](https://sleeeepy.streamlit.app/) [![Open in Colab](https://img.shields.io/badge/Open%20with-Colab-gold?style=for-the-badge&logo=google-colab)](https://colab.research.google.com/drive/1iUNcDuqsTgx5Z3DtTeSxpK-LcyfBJE0Z?usp=sharing) + +

+ +

+ + +### ๐ŸŽฏ **Goal** + +The goal of the this project is to predict sleep disorders based on personal and lifestyle factors using machine learning techniques. Additionally, it aims to provide users with exploratory data analysis (EDA) visualizations to understand sleep health patterns better. By analyzing sleep duration, physical activity, stress levels, BMI, heart rate, and other factors, the project seeks to offer insights into sleep health and facilitate early detection of potential sleep disorders. + +### ๐Ÿงต **Dataset** + +The dataset used for training and testing the models can be accessed from the [Kaggle](https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset) + +### ๐Ÿงพ **Description** + +This project is designed to analyze sleep health and lifestyle data to predict sleep disorders and conduct exploratory data analysis (EDA). Through this project, users can input various personal and lifestyle factors such as sleep duration, physical activity level, stress level, BMI category, heart rate, daily walking steps, and blood pressure. Using these inputs, the project employs a Decision Tree Classifier model to predict the likelihood of a sleep disorder. + +In addition to the predictive aspect, the project also offers an EDA section where users can explore the dataset visually. This includes visualizations such as gender distribution, BMI category distribution, occupation distribution, average heart rate by age group, average stress activity level by age group, distribution of sleep disorder by gender, distribution of sleep disorder by occupation, and average stress level by occupation. These visualizations provide insights into various factors related to sleep health and lifestyle. + + +### ๐Ÿ“œ **Repo Structure** + +```bash +Sleep Disorder Predictor +|- Dataset + |- data.csv + |- README.md +|- Images + |- img1.png + |- img2.png + : + : + |- README.md +|- Models + |- Notebook.ipynb + |- model.h5 + |- README.md +|- Web_app + |- app.py + |- data.csv + |- Demo.mp4 + |- README.md +|- requirements.txt + +``` + + +### ๐Ÿงฎ **What I had done!** + +- **Data Cleaning**: + + - Load the dataset using pandas and inspect its structure, including the number of rows and columns, and the data types of each column. + - Check for missing values in the data that may affect model performance. + - Handle missing values by either removing them, imputing them with mean or median values, or using more advanced techniques like interpolation or prediction. + +- **Exploratory Data Analysis (EDA)**: + - The EDA phase involves exploring the dataset's characteristics and relationships between variables. + - Visualizations such as pie charts, histograms, box plots, and line plots are used to analyze distributions, correlations, and trends in the data. + - Key aspects examined include gender distribution, occupation distribution, age distribution, sleep duration by gender, average heart rate by age group, and average stress level by age group. + +- **Data Preprocessing**: + - Data preprocessing steps are implemented to prepare the dataset for model training. + - Categorical variables are encoded using one-hot encoding or label encoding to convert them into numerical representations. + - Numerical features are standardized using techniques like z-score normalization to ensure they are on a similar scale + +- **Training ML Classifiers**: + - Several machine learning classifiers are trained on the preprocessed data to predict sleep disorders. + - Models such as Decision Trees, Random Forests, Logistic Regression, SVM, KNN, and Gaussian Naive Bayes are trained and evaluated for their predictive performance. + - Model accuracies are calculated, and a line chart is created to visualize the accuracy of each model. + +- **Training ANN Model**: + - An Artificial Neural Network (ANN) model is built and trained using TensorFlow and Keras. + - The neural network architecture includes multiple layers with appropriate activation functions and regularization techniques.- The model is compiled with an optimizer, loss function, and evaluation metric before training on the preprocessed data. + +- **Evaluation and Comparison**: + + The performance of both traditional machine learning classifiers and the ANN model is compared using accuracy metrics. + +- **Building Streamlit Application**: + - After training and evaluating machine learning models, the project proceeds to build a user-friendly Streamlit application for practical use. + - The Streamlit app provides an intuitive interface for users to input their sleep-related information, such as sleep duration, physical activity level, stress level, BMI category, heart rate, daily steps, and blood pressure. + - Users can interact with sliders and input fields to input their data, and the application dynamically updates based on user input. + + ![image](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/6b1a70c3-aaa0-46d6-8c36-d68d54d8cb58) + + - Upon submission of user data, the trained machine learning model predicts the likelihood of sleep disorders based on the input features. + + ![image](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/f31dea03-67c5-4ec0-9df5-b37eb2b70b9c) + + + +### ๐Ÿš€ **Models Implemented** + +- Decision Tree Classifier +- Random Forest Classifier +- Logistic Regression +- K Nearest Neighbor Classifier +- Support Vector Machine Classifier +- Gaussian Naive Bayes Classifier +- Simple Artificial Neural Network (Feed Forward Neural Network) + + +### ๐Ÿ“š **Libraries Needed** + +- TensorFlow +- Streamlit +- Numpy +- Pandas +- Seaborn +- Sklearn +- Matplotlib +- Plotly Graph Objects + +### ๐Ÿ“Š **Exploratory Data Analysis Results** + + +

+ + + + +

+ + + +![newplot (23)](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/74c833f6-4d79-485b-a7d9-e263b2bc2d83) + +![newplot (24)](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/11043155-cfb2-4c2b-9473-54e760d4a3bb) + +![newplot (20)](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/d5170625-6045-4389-9ea9-65335bcc5f98) + +![newplot (19)](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/42e49ae0-80cb-4a8e-8783-271a0bc61559) + +### ๐ŸŽฅ Demo + +https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/447cc6f8-c122-4826-a9f3-7d30af0f6fd4 + +### โš™๏ธ **Usage** + +1. **Exploring Notebooks**: Navigate to the `Models/` directory to explore Jupyter notebooks. These notebooks cover data analysis, preprocessing, model training, and evaluation steps. + - Navigate to the `Models/Notebook.ipynb` + - Run all the cells + +2. **Trained Models**: The `Models/` directory contains trained Deep Learning model saved in HDF5 format. This model can be loaded and used for making predictions. + +3. **Streamlit App:** The `Web_app/app.py` contains the source code for the Streamlit web application. To run the app locally, follow the instructions below: + + ```bash + pip install -r requirements.txt + cd Web_app + streamlit run app.py + ``` + + +### ๐Ÿ“ˆ **Performance of the Models based on the Accuracy Scores** + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelValidation Accuracy
Decision Tree88.0%
Random Forest88.0%
Logistic Regression88.0%
Gaussian Naive Bayes88.0%
KNN86.67%
ANN86.67%
SVM64.0%
+
+ +![](https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/5b7f8211-6b6d-439c-a1ba-a5b802ea6de4) + + +### ๐Ÿ“ข **Conclusion** + +- Decision Tree, Random Forest, Logistic Regression, and Gaussian Naive Bayes achieved comparable accuracies of 88%. These models demonstrate robustness and effectiveness in capturing patterns within the dataset. + +- K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) also performed well with an accuracy of approximately 86.67%, indicating its suitability for classification tasks where instances are grouped based on similarity. + +- However, the Support Vector Machine (SVM) model lagged behind with an accuracy of 64%, suggesting potential challenges in properly separating the data points in the feature space. + +Overall, the decision tree-based models, Random Forest and Decision Tree, along with Logistic Regression and Gaussian Naive Bayes, exhibit promising performance and could be considered as strong candidates for further evaluation and deployment, while further optimization may be necessary for SVM to enhance its performance. + + +### โœ’๏ธ **Signature** + +

+ +

+

+ Kamalakar Satapathi +

+ + +Connect with me on [![LinkedIn](https://img.shields.io/badge/-Kamalakar_Satapathi-0077B5?style=flat-square&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/sgvkamalakar) + +Explore my codes [![GitHub](https://img.shields.io/badge/-Sgvkamalakar-181717?style=flat-square&logo=github)](https://github.com/sgvkamalakar) diff --git a/Sleep Disorder Prediction/Web_app/Demo.mp4 b/Sleep Disorder Prediction/Web_app/Demo.mp4 new file mode 100644 index 000000000..a176fb261 Binary files /dev/null and b/Sleep Disorder Prediction/Web_app/Demo.mp4 differ diff --git a/Sleep Disorder Prediction/Web_app/README.md b/Sleep Disorder Prediction/Web_app/README.md new file mode 100644 index 000000000..6bb6aface --- /dev/null +++ b/Sleep Disorder Prediction/Web_app/README.md @@ -0,0 +1,48 @@ +## Sleep Disorder Prediction using ML - Web App + +[![Open in Streamlit](https://img.shields.io/badge/Open%20with-Streamlit-red?style=for-the-badge&logo=streamlit)](https://sleeeepy.streamlit.app/) + +### ๐Ÿ” **Overview** + +The Streamlit app is a comprehensive tool designed to provide insights into sleep health and lifestyle. It features a user-friendly interface with a navigation bar allowing access to sleep disorder prediction and exploratory data analysis (EDA). On the Predict page, users input health metrics like sleep duration, physical activity, stress level, weight, height, heart rate, steps, and blood pressure to get a prediction of potential sleep disorders using a Decision Tree Classifier. The app automatically calculates BMI and categorizes it, offering immediate feedback. + +On the EDA page, users can explore the dataset through various interactive visualizations, including gender distribution, BMI categories, occupation distribution, heart rate and stress levels by age group, and sleep disorders by gender and occupation. Built with Streamlit, Pandas, NumPy, Plotly, and Scikit-learn, the app seamlessly integrates data processing, model training, and visualization to enhance user understanding of sleep health. + +### ๐Ÿ“ **Feautres** + +#### ๐Ÿ˜ด **Sleep Disorder Prediction** + +- **Navigation Bar**: Easily switch between "Predict" and "EDA" pages. +- **Sleep Disorder Prediction**: Input personal health metrics for prediction using a Decision Tree Classifier. +- **BMI Calculation**: Automatic BMI calculation and categorization with visual feedback. +- **Prediction Display**: Shows predicted sleep disorder and summary of input data. + +#### ๐Ÿ“Š **EDA and Visualization** +- **Dataset Overview**: View the first few rows and access the complete dataset. +- **Gender Distribution Visualization**: Pie chart of gender distribution. +- **BMI Categories Distribution**: Pie chart of BMI category distribution. +- **Occupation Distribution**: Bar chart of occupation distribution. +- **Average Heart Rate by Age Group**: Scatter plot of average heart rate across age groups. +- **Average Stress Level by Age Group**: Scatter plot of average stress levels across age groups. +- **Sleep Disorder by Gender**: Grouped bar chart of sleep disorder counts by gender. +- **Occupation by Sleep Disorder**: Grouped bar chart of sleep disorder counts across occupations. +- **Average Stress Level by Occupation**: Horizontal bar chart of average stress levels for occupations. +- **Stress Level by Gender**: Histogram of density of stress levels by gender. + +### ๐ŸŽฅ **Demo** + +https://github.com/Sgvkamalakar/SnoozeMonitor/assets/103712713/447cc6f8-c122-4826-a9f3-7d30af0f6fd4 + +### โœ’๏ธ **Signature** + +

+ +

+

+ Kamalakar Satapathi +

+ + +Connect with me on [![LinkedIn](https://img.shields.io/badge/-Kamalakar_Satapathi-0077B5?style=flat-square&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/sgvkamalakar) + +Explore my codes [![GitHub](https://img.shields.io/badge/-Sgvkamalakar-181717?style=flat-square&logo=github)](https://github.com/sgvkamalakar) diff --git a/Sleep Disorder Prediction/Web_app/app.py b/Sleep Disorder Prediction/Web_app/app.py new file mode 100644 index 000000000..26a3806e1 --- /dev/null +++ b/Sleep Disorder Prediction/Web_app/app.py @@ -0,0 +1,339 @@ +import streamlit as st +from streamlit_navigation_bar import st_navbar +import pandas as pd +import numpy as np +import plotly.graph_objs as go +from plotly.subplots import make_subplots +from sklearn.model_selection import train_test_split +from sklearn.tree import DecisionTreeClassifier +from sklearn.ensemble import RandomForestClassifier +from sklearn.linear_model import LogisticRegression +from sklearn.preprocessing import LabelEncoder + + +st.set_page_config(page_title="Snooze Monitor",page_icon="๐Ÿ˜ด",layout="wide") + +styles = { + "nav": { + "background-color": "#83c9ff", + }, + "div": { + "max-width": "32rem", + }, + "span": { + "border-radius": "0.5rem", + "color": "rgb(49, 51, 63)", + "margin": "0 0.125rem", + "padding": "0.4375rem 0.625rem", + }, + "active": { + "background-color": "#7bf8ff", + }, + "hover": { + "background-color": "rgba(255, 255, 255, 0.35)", + }, +} +selected_page = st_navbar(pages=["Predict","EDA"],styles=styles) + +df=pd.read_csv('data.csv') + +if selected_page=="Predict": + st.title("Sleep Disorder Prediction") + def preprocess_blood_pressure(bp_str): + systolic, diastolic = bp_str.split('/') + return float(systolic), float(diastolic) + + df[['Systolic Pressure', 'Diastolic Pressure']] = df['Blood Pressure'].apply(lambda x: pd.Series(preprocess_blood_pressure(x))) + df.drop(columns=['Blood Pressure'], inplace=True) + + X = df.drop(columns=['Person ID','Gender','Age','Gender','Quality of Sleep','Occupation','Sleep Disorder']) + y = df['Sleep Disorder'] + + label_encoder1 = LabelEncoder() + X['BMI Category']= label_encoder1.fit_transform(X['BMI Category']) + + bmi_mapping = {i: label for i, label in enumerate(label_encoder1.classes_)} + + + label_encoder2= LabelEncoder() + y = label_encoder2.fit_transform(y) + + label_mapping = {i: label for i, label in enumerate(label_encoder2.classes_)} + + model = DecisionTreeClassifier() + model.fit(X,y) + + with st.sidebar: + sleep=st.slider('Sleep Duration in hrs',value=6.5,min_value=4.0,max_value=10.0,step=0.5) + phy=st.slider('Physical Activity Level',value=5,min_value=1,max_value=10) + stress=st.slider('Stress Level',value=5,min_value=1,max_value=10) + weight=st.number_input("Weight in Kgs",value=60.0,min_value=40.0,max_value=150.0,step=0.5) + height=st.number_input("Height in cm",value=160.0,min_value=130.0,max_value=200.0,step=0.5) + bmi=weight/((height/100)**2) + if bmi<18.5: + bmi_label=0 + bmi_category="Under Weight" + st.warning(bmi_category) + elif 18.5<=bmi<=24.9: + bmi_label=1 + bmi_category="Healthy Weight" + st.success(bmi_category) + + elif 25<=bmi<=30: + bmi_label=3 + bmi_category="Over Weight" + st.info(bmi_category) + + else: + bmi_label=2 + bmi_category="Obese" + st.error(bmi_category) + + + heart=st.slider('Heart rate',value=70,min_value=60,max_value=120) + steps=st.slider('Daily Walking Steps',value=6000,min_value=2000,max_value=12000,step=500) + st.write("Enter Blood Pressure") + col1,col2,col3=st.columns([0.3,0.2,0.3]) + with col1: + sys=st.number_input(label="Systolic pressure",value=120,min_value=110,max_value=160) + with col2: + st.write(" ") + st.markdown("

/

", unsafe_allow_html=True) + with col3: + dia=st.number_input(label="Diastolic Pressure",value=80,min_value=60,max_value=100) + user_input=[sleep,phy,stress,bmi_label,heart,steps,sys,dia] + user_df = pd.DataFrame([user_input], columns=['Sleep Duration', 'Physical Activity Level', 'Stress Level', 'BMI Category', 'Heart Rate', 'Daily Steps', "Sis","Dia"]) + user_df["BMI Category"]=bmi_category + + user_df["Blood Pressure"]=str(sys)+'/'+str(dia) + user_df=user_df.drop(columns=["Sis","Dia"]) + + user_input = np.array(user_input).reshape(1, -1) + prediction = model.predict(user_input) + + # print(prediction) + # print(f"Predicted Sleep Disorder: {label_mapping[prediction[0]]}") + + st.subheader("User Data") + df_transposed = user_df.transpose().reset_index() + df_transposed.columns = ['Metric', 'Value'] + df_transposed['Value'] = df_transposed['Value'].apply(lambda x: f"{x:.1f}" if isinstance(x, (int, float)) else x) + st.table(df_transposed) + + pred_label=label_mapping[prediction[0]] + if stress>7: + pred_label="Insomnia" + if sleep<5: + pred_label="Sleep_Anemia" + res_length=len(pred_label) + width = 100 + font_size = 30 + if res_length>6: + width=200 + padding_top = 5 + padding_bottom = 2 + style = f"background-color: grey; height: 60px; width: {width}px; border-radius: 5px; padding-top: {padding_top}px; padding-bottom: {padding_bottom}px; margin-left: 200px; text-align: center; margin-top:-50px;" + + label_style = "font-weight: bold; color: white; font-size: {font_size}px;" + + label_style = label_style.format(font_size=font_size) + st.markdown(f"

Sleep Disorder Report:

", unsafe_allow_html=True) + +else: + st.title("Exploratory Data Analysis (EDA) - Sleep Health and Lifestyle Dataset") + with st.expander("About the Dataset"): + st.subheader("Dataset") + st.table(df.head()) + st.markdown("Get complete dataset from [here](https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset)") + + + + age_groups = [] + for i in range(min(df['Age']),max(df['Age']),5): + age_group = f"{i}-{i+4}" + age_groups.append(age_group) + col1,col2=st.columns(2) + with col1: + gender_counts = df['Gender'].value_counts() + custom_colors = ['#1f77b4', '#ff7f0e'] + fig = go.Figure( + go.Pie( + labels=gender_counts.index, + values=gender_counts.values, + hole=0.3, + textinfo='percent+label', + ) + ) + + fig.update_layout( + title='Gender Distribution', + showlegend=False + ) + + st.plotly_chart(fig) + + with col2: + bmi_count = df['BMI Category'].value_counts() + custom_colors=['#0068c9','#ffabab','#83c9ff','#ff2b2b'] + fig = go.Figure( + go.Pie( + labels=bmi_count.index, + values=bmi_count.values, + hole=0.3, + textinfo='percent+label', + marker=dict(colors=custom_colors) + ) + ) + + fig.update_layout( + title='BMI Categories Distribution', + showlegend=False + ) + + st.plotly_chart(fig) + + + occupation_counts = df['Occupation'].value_counts() + fig = go.Figure( + go.Bar( + x=occupation_counts.index, + y=occupation_counts.values + ) + ) + fig.update_layout( + title='Occupation Distribution', + xaxis_title='Occupation', + yaxis_title='Count', + xaxis_tickangle=-45 + ) + st.plotly_chart(fig) + + + heart_rates = [] + for i in range(min(df['Age']),max(df['Age']),5): + age_group_df = df[(df['Age'] >= i) & (df['Age'] <= i+4)] + heart_rate = age_group_df['Heart Rate'].mean() + heart_rates.append(heart_rate) + fig = go.Figure( + go.Scatter( + x=age_groups, + y=heart_rates, + mode='markers+lines', + marker=dict(symbol='star'))) + + fig.update_layout( + title='Average Heart Rate by Age Group', + xaxis_title='Age Group', + yaxis_title='Average Heart Rate' + ) + + st.plotly_chart(fig) + + stress_level = [] + for i in range(min(df['Age']),max(df['Age']),5): + age_group_df = df[(df['Age'] >= i) & (df['Age'] <= i+4)] + x = age_group_df['Stress Level'].mean() + stress_level.append(x) + fig = go.Figure( + go.Scatter( + x=age_groups, + y=stress_level, + mode='markers+lines', + marker=dict(symbol='star'))) + + fig.update_layout( + title='Average Stress Activity Level', + xaxis_title='Age Group', + yaxis_title='Average Stress Level' + ) + + st.plotly_chart(fig) + + + gender_sleep_disorder_counts = df.groupby(['Gender', 'Sleep Disorder']).size().unstack() + traces = [] + for sleep_disorder_status in gender_sleep_disorder_counts.columns: + trace = go.Bar( + x=gender_sleep_disorder_counts.index, + y=gender_sleep_disorder_counts[sleep_disorder_status], + name=sleep_disorder_status + ) + traces.append(trace) + + layout = go.Layout( + title='Distribution of Sleep Disorder by Gender', + xaxis=dict(title='Gender'), + yaxis=dict(title='Count'), + barmode='group' + ) + + fig = go.Figure(data=traces, layout=layout) + st.plotly_chart(fig) + + occupation_sleep_disorder_counts = df.groupby(['Occupation', 'Sleep Disorder']).size().unstack() + + traces = [] + for sleep_disorder_status in occupation_sleep_disorder_counts.columns: + trace = go.Bar( + x=occupation_sleep_disorder_counts.index, + y=occupation_sleep_disorder_counts[sleep_disorder_status], + name=sleep_disorder_status + ) + traces.append(trace) + + layout = go.Layout( + title='Occupation by Sleep Disorder', + xaxis=dict(title='Occupation'), + yaxis=dict(title='Count') + ) + + fig = go.Figure(data=traces, layout=layout) + + st.plotly_chart(fig) + + avg_stress_by_occupation = df.groupby('Occupation')['Stress Level'].mean().reset_index() + + color_scale = [[0, 'blue'], [0.25, 'green'], [0.5, 'yellow'], [0.75, 'orange'], [1, 'red']] + + fig1 = go.Figure(go.Bar( + x=avg_stress_by_occupation['Stress Level'], + y=avg_stress_by_occupation['Occupation'], + marker=dict(color=avg_stress_by_occupation['Stress Level'], + coloraxis='coloraxis'), + orientation='h', + hoverinfo='x+y', + textposition='inside', + texttemplate='%{x:.2f}', + )) + + fig1.update_layout( + title='Average Stress Level by Occupation', + yaxis=dict(title='Occupation', tickangle=-30), + xaxis=dict(title='Average Stress Level'), + bargap=0.15, + font=dict(family='Arial', size=12, color='rgb(50, 50, 50)'), + coloraxis=dict(colorscale=color_scale, cmin=1, cmax=10), + ) + + st.plotly_chart(fig1) + + + fig = go.Figure() + custom_colors={"Male":"#0068c9","Female":"#ff2b2b"} + for gender, group in df.groupby('Gender'): + fig.add_trace(go.Histogram( + x=group['Stress Level'], + histnorm='probability density', + name=gender, + marker=dict(color=custom_colors[gender]), + opacity=0.8 + )) + + fig.update_layout( + title='Stress Level by Gender', + xaxis=dict(title='Stress Level'), + yaxis=dict(title='Probability Density'), + barmode='overlay' + ) + + st.plotly_chart(fig) \ No newline at end of file diff --git a/Sleep Disorder Prediction/Web_app/data.csv b/Sleep Disorder Prediction/Web_app/data.csv new file mode 100644 index 000000000..b7e16bd58 --- /dev/null +++ b/Sleep Disorder Prediction/Web_app/data.csv @@ -0,0 +1,375 @@ +Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder +1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,None +2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None +3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None +4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea +5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea +6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia +7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia +8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None +12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None +14,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None +15,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None +16,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None +17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea +18,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,Sleep Apnea +19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia +20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None +21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None +27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None +29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None +30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None +31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea +32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia +33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,None +34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None +39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None +40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None +41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None +49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea +51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None +52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None +53,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None +55,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +56,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +58,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +59,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None +61,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +62,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None +63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None +64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None +65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None +66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None +67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,None +68,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,Insomnia +69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None +70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None +71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None +75,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +76,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +77,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +78,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +79,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +80,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None +81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea +82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea +83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None +84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None +85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None +86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None +88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None +89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None +90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None +91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None +92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None +93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None +94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea +95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia +96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None +100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None +101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None +102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None +103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None +104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea +105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea +106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia +107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,None +108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None +109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None +110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None +111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None +113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None +115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None +126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,None +127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None +137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None +139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None +141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None +143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None +145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea +146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea +147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia +148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia +149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,None +150,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None +151,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None +152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None +162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None +163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None +164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None +165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None +166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia +167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,None +168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None +169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None +170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None +171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None +172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None +173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None +174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None +175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None +176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None +177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None +178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None +185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea +186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea +187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia +188,Male,43,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +189,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia +190,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +191,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia +192,Male,43,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia +193,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +194,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +195,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +196,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +197,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +198,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +199,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia +202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia +203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia +204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,None +205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,None +206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None +207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None +208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None +209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None +210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None +211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None +212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None +213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None +214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None +215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None +216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None +217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None +218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None +219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea +220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea +221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia +223,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +224,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia +225,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +226,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +227,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +228,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +229,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +230,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +231,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +232,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +233,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +234,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +235,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +236,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +237,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia +238,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia +239,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +240,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia +241,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia +242,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +243,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia +244,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia +245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia +247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia +248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia +249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,None +250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,None +251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia +252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia +253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia +254,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia +255,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia +256,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia +257,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +258,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia +262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None +263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None +264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,None +265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia +266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia +268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,None +269,Female,49,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +272,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +273,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +274,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +275,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea +278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea +279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia +280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None +281,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,None +282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +283,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +284,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +285,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +286,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +287,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +288,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +289,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +290,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +291,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +292,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +293,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +294,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +295,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +296,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,None +304,Female,51,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea +307,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia +308,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia +309,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia +310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia +311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia +312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia +313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia +317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None +326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None +328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None +330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None +339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None +340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea +341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea +342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None +343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None +344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None +345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +348,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +349,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +350,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +351,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +352,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +353,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +354,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +355,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +356,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +357,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +358,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +359,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,None +360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None +361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +364,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +365,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +366,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +367,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +368,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +369,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +370,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +371,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +372,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +373,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea +374,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea \ No newline at end of file diff --git a/Sleep Disorder Prediction/requirements.txt b/Sleep Disorder Prediction/requirements.txt new file mode 100644 index 000000000..24b42581a --- /dev/null +++ b/Sleep Disorder Prediction/requirements.txt @@ -0,0 +1,7 @@ +streamlit +streamlit_navigation_bar +scikit-learn +tensorflow +pandas +numpy +plotly