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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.

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

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

Tech Stack

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

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.