Machine Learning
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Smartwatch Health Monitoring System
Collected health data from smartwatch sensors and applied machine learning algorithms to predict health scores with high accuracy.
Frontend
Python Streamlit / HTML Dash
Backend
Python, scikit-learn, Pandas, NumPy
Database
SQLite / CSV Data Pipeline
Project Overview
An intelligent health analytics platform that processes biometric data collected from smartwatch sensors (heart rate, step count, sleep patterns, body temperature). Applied supervised machine learning classification algorithms using Python and scikit-learn to analyze health trends and accurately predict wellness scores.
Key Features
- Biometric data processing and cleaning pipeline.
- Supervised ML model for health status classification.
- Visual trend analysis dashboard for health indicators.
- High accuracy predictions based on sensor patterns.
Challenges Faced
Handling missing sensor data readings and normalizing data from diverse hardware frequency intervals.
Implemented Solutions
Implemented robust Pandas preprocessing pipelines for missing data imputation and feature scaling using StandardScaler.