I built a modular Python preprocessing pipeline for a Network Intrusion Detection System, cleaning and transforming network-flow data before model training. I also developed a supervised classifier to flag traffic as benign or malicious and evaluated it with standard classification metrics.
For my Customer Churn Prediction API, I separated data processing from the prediction endpoint and served model predictions through a Flask REST API. My project work also includes NLTK-based sentiment analysis, and I contributed bug fixes and UI improvements to the “Pet Me” open-source application through GirlScript Summer of Code.
