- Trained a Random Forest model on Continuous Glucose Monitoring (CGM) time-series sensor data to automatically classify when a diabetic patient eats a meal, achieving an accuracy of 94%.
- Preprocessed the data and extracted meaningful temporal and frequency-based features from the CGM time-series data.
- Applied K-means and DBSCAN algorithms to bin the CGM data into clusters for further analysis by doctors.