🌙 Sleep Oracle: Anticipating Health & Lifestyle through Data

A Machine Learning project that predicts sleep disorders and health outcomes based on lifestyle and physiological data. Built with Python, scikit-learn, and deployed as a Flask web application.

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📌 Problem Statement

Sleep disorders affect millions of people and are often undiagnosed. This project uses machine learning to predict the likelihood of sleep disorders based on features such as physical activity, stress levels, BMI, heart rate, and daily steps — enabling early awareness and intervention.

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🔍 Project Phases

Phase	Description
Ideation	Brainstorming, idea prioritization, and empathy mapping
Planning	Project planning, timeline, and technology stack selection
Design	Solution architecture, data flow diagrams, user stories
Development	Data preprocessing, model training, Flask web app
Testing & Submission	Performance testing, final project report

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🧠 ML Model

• Dataset: Sleep Health and Lifestyle Dataset (374 records, 13 features)
• Algorithm: Random Forest Classifier
• Features used: Age, Gender, Occupation, Sleep Duration, Sleep Quality, Physical Activity Level, Stress Level, BMI Category, Heart Rate, Daily Steps, Blood Pressure
• Target: Sleep Disorder (None / Sleep Apnea / Insomnia)

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🛠️ Tech Stack

Layer	Technology
Language	Python 3.x
ML Libraries	scikit-learn, pandas, numpy, matplotlib, seaborn
Web Framework	Flask
Frontend	HTML, CSS, Bootstrap
Model Serialization	pickle
Notebook	Jupyter Notebook

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📂 Repository Structure

sleep-oracle/
├── dataset/          # Raw CSV dataset
├── notebooks/        # Jupyter notebooks (EDA + Model training + Flask prep)
├── app/              # Flask web application
│   └── templates/    # HTML frontend
├── docs/             # Phase-wise project documentation (PDFs)
│   ├── ideation/
│   ├── planning/
│   ├── design/
│   ├── development/
│   └── final/
└── assets/           # Static assets

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🚀 How to Run Locally

# 1. Clone the repository
git clone https://github.com/YOUR_USERNAME/sleep-oracle.git
cd sleep-oracle

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run the Flask app
cd app
python app.py

# 4. Open in browser
# Visit: http://127.0.0.1:5000

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📊 Results

• Model trained and evaluated with cross-validation
• Performance metrics documented in docs/final/Performance-Testing-ML.pdf
• Full project report available in docs/final/Project-Report.pdf

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📄 License

This project was developed as part of a guided project program.