🛌🎓 Student Sleep & Academic Performance Predictor

🚀 Live Demo

Access the live application here: sleep-predictor.streamlit.app

This project predicts sleep types and academic profiles based on lifestyle details such as study hours, screen time, caffeine intake, and physical activity.

Features

• Sleep Type Prediction: Categorizes users into "Night Owl", "Balanced Sleeper", or "Oversleeper".
• Academic Profile Prediction: Categorizes users into "Low", "Average", or "High" performers.
• Data Visualization: Explore feature distributions, correlations, and cluster visualizations.

Tech Stack

• Frontend: Streamlit
• Backend API: Flask (Deployed on Render)
• Machine Learning: KMeans, GMM (Scikit-Learn)

How to Run Locally

1. Clone the repository:

   git clone <your-repo-url>
   cd Sleep_Pattern_Prediction_AI-ML-main

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run streamlit_app.py

Deployment

The app is designed to be deployed on Streamlit Cloud. The backend is currently hosted at: https://flask-sleep-backend.onrender.com