Sleep Disorder Prediction using Machine Learning

This project aims to predict sleep disorders using patient lifestyle and health data. It demonstrates an end-to-end machine learning pipeline: from data analysis and preprocessing to model building and evaluation.

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Problem Statement

The goal is to analyze personal and health-related attributes (e.g., age, BMI, stress level, etc.) to classify individuals into:

• No Sleep Disorder
• Insomnia
• Sleep Apnea

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Key Features

• Performed exploratory data analysis (EDA) to uncover trends
• Handled missing values and categorical encoding
• Trained classification models like:
  • Random Forest
  • Logistic Regression
  • Decision Tree
• Achieved good accuracy and model performance
• Evaluated results using confusion matrix, classification report

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Tech Stack

• Python, Machine Learning
• Model Building, EDA, Preprocessing Data
• Jupyter Notebook
• Pandas, NumPy
• Scikit-learn
• Seaborn, Matplotlib

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Learnings

• Understood the impact of health/lifestyle factors on sleep quality.
• Gained hands-on experience in classification modeling.
• Learned how to build ML pipelines in the healthcare domain.