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Sleep Disorder Predictor - Added
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‎App.py

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- **spread1**, **spread2**, **PPE**: Nonlinear measures of fundamental frequency variation.
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"""
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},
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{
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"name": "Sleep Disorder Prediction",
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"description": "Assess your risk of developing a sleep disorder using advanced ML algorithms.",
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"details": """
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### Introduction
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Sleep disorders can have a significant impact on an individual's overall health and well-being. These disorders often result from a combination of poor sleep habits, lifestyle factors, stress, and underlying medical conditions.
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### Sleep Health and Lifestyle Dataset
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The dataset consists of sleep, lifestyle, and health metrics collected from 400 individuals. The main goal is to predict the likelihood of an individual having a sleep disorder using the "Sleep Disorder" column, which contains categorical values indicating the presence or absence of a sleep disorder.
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"""
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},
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{
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"name": "PDF Malware Detector",
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"description": "Identify and alert users about potential malware in PDF files.",

‎form_configs/sleep_prediction.json

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{
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"Sleep Prediction Form": {
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"Age": {
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"field_name": "Age",
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"type": "number",
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"min_value": 0,
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"max_value": 120,
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"default_value": 25,
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"step": 1
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},
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"Sleep_Duration": {
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"field_name": "Sleep_Duration",
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"type": "float",
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"min_value": 0.0,
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"max_value": 24.0,
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"default_value": 8.0,
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"step": 0.1
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},
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"Heart_Rate": {
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"field_name": "Heart_Rate",
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"type": "number",
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"min_value": 10,
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"max_value": 200,
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"default_value": 72,
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"step": 1
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},
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"Daily_Steps": {
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"field_name": "Daily_Steps",
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"type": "number",
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"min_value": 0,
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"max_value": 1000000,
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"default_value": 0,
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"step": 10
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},
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"Systolic": {
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"field_name": "Systolic",
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"type": "float",
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"min_value": 0.0,
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"max_value": 250.0,
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"default_value": 120.0,
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"step": 0.1
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},
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"Diastolic": {
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"field_name": "Diastolic",
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"type": "float",
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"min_value": 0.0,
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"max_value": 250.0,
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"default_value": 80.0,
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"step": 0.1
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},
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"Occupation": {
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"field_name": "Occupation",
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"type": "dropdown",
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"options": [
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"Software Engineer" ,"Doctor" ,"Sales Representative","Teacher" ,"Nurse",
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"Engineer" ,"Accountant" ,"Scientist", "Lawyer" ,"Salesperson" ,"Manager"
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],
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"default_value": "Software Engineer"
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},
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"Quality_of_Sleep": {
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"field_name": "Quality_of_Sleep",
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"type": "number",
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"min_value": 0,
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"max_value": 10,
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"default_value": 0,
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"step": 1
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},
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"Gender": {
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"field_name": "Gender",
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"type": "dropdown",
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"options": [
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"Male",
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"Female"
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],
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"default_value": "Male"
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},
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"Physical_Activity_Level": {
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"field_name": "Physical_Activity_Level",
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"type": "number",
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"min_value": 0,
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"max_value": 200,
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"default_value": 0,
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"step": 1
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},
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"Stress_Level": {
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"field_name": "Stress_Level",
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"type": "number",
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"min_value": 0,
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"max_value": 10,
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"default_value": 0,
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"step": 1
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},
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"BMI_Category": {
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"field_name": "BMI_Category",
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"type": "dropdown",
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"options": [
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"Normal Weight",
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"Obese",
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"Overweight"
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],
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"default_value": "Normal Weight"
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}
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}
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}

‎models/sleep_disorder_predictor/data/dataset.csv

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‎models/sleep_disorder_predictor/model.py

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import streamlit as st
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import pickle
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import pandas as pd # Import pandas to handle DataFrames
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import numpy as np
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import warnings
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warnings.filterwarnings("ignore")
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# Load the model and the scaler
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model_path = 'models/sleep_disorder_predictor/saved_models/Model_Prediction.sav'
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preprocessor_path = 'models/sleep_disorder_predictor/saved_models/preprocessor.sav'
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# Load the pre-trained model and scaler using pickle
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loaded_model = pickle.load(open(model_path, 'rb'))
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preprocessor = pickle.load(open(preprocessor_path, 'rb'))
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# Define the prediction function
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def disease_get_prediction(Age, Sleep_Duration,
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Heart_Rate, Daily_Steps,
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Systolic, Diastolic, Occupation, Quality_of_Sleep, Gender,
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Physical_Activity_Level, Stress_Level, BMI_Category):
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# Create a DataFrame with the features using correct column names
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features = pd.DataFrame({
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'Age': [int(Age)],
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'Sleep Duration': [float(Sleep_Duration)], # Changed to match expected name
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'Heart Rate': [int(Heart_Rate)], # Changed to match expected name
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'Daily Steps': [int(Daily_Steps)], # Changed to match expected name
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'Systolic': [float(Systolic)],
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'Diastolic': [float(Diastolic)],
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'Occupation': [Occupation],
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'Quality of Sleep': [int(Quality_of_Sleep)], # Changed to match expected name
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'Gender': [Gender],
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'Physical Activity Level': [int(Physical_Activity_Level)], # Changed to match expected name
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'Stress Level': [int(Stress_Level)], # Changed to match expected name
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'BMI Category': [BMI_Category] # Changed to match expected name
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})
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# Apply the preprocessor (make sure it expects a DataFrame)
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preprocessed_data = preprocessor.transform(features)
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# Make prediction
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prediction = loaded_model.predict(preprocessed_data)
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return prediction

‎models/sleep_disorder_predictor/notebooks/sleep-disorder.ipynb

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‎models/sleep_disorder_predictor/predict.py

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from models.sleep_disorder_predictor.model import disease_get_prediction
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def get_prediction(Age, Sleep_Duration,
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Heart_Rate, Daily_Steps,
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Systolic, Diastolic,Occupation,Quality_of_Sleep,Gender,
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Physical_Activity_Level, Stress_Level, BMI_Category):
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prediction = disease_get_prediction(Age, Sleep_Duration,
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Heart_Rate, Daily_Steps,
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Systolic, Diastolic,Occupation,Quality_of_Sleep,Gender,
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Physical_Activity_Level, Stress_Level, BMI_Category)
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message = ""
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# Provide message based on the prediction value
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if prediction==0:
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message= "Insomnia"
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elif prediction==1:
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message = "No disorder"
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elif prediction==2:
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message = "Sleep Apnea"
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else:
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message="Invalid details."
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return message+"\n\nRecommendation - To prevent sleep disorders, maintain a balanced lifestyle with regular exercise, a healthy diet, and stress management. Stick to a consistent sleep schedule, limit caffeine and alcohol, and create a relaxing bedtime routine."

‎models/sleep_disorder_predictor/saved_models/Model_Prediction.sav

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‎models/sleep_disorder_predictor/saved_models/preprocessor.sav

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‎pages/Sleep_Disorder_Predictor.py

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from page_handler import PageHandler
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page_handler = PageHandler("pages/pages.json")
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page_handler.render_page("Sleep Disorder Predictor")

‎pages/pages.json

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}
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]
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},
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"Sleep Disorder Predictor": {
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"title": "Sleep Disorder Predictor",
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"page_title": "Sleep Disorder Predictor",
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"model_predict_file_path": "models/sleep_disorder_predictor/predict.py",
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"model_function": "get_prediction",
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"model_detail_function": "model_details",
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"form_config_path": "form_configs/sleep_prediction.json",
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"tabs": [
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{
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"name": "Sleep Disorder Predictor",
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"type": "form",
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"form_name": "Sleep Prediction Form"
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},
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{
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"name": "Model Details",
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"type": "model_details",
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"problem_statement": "The model aims to predict the likelihood of an individual having a sleep disorder based on lifestyle, sleep quality, and health metrics.",
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"description": "Using a XGBoost classifier, the model predicts whether an individual is likely to have a sleep disorder based on features such as sleep duration, stress level, physical activity level, cardiovascular health metrics, and demographic information."
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}
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]
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},
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"Malware_Detection": {
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"title": "PDF Malware Detection",

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