Hey, I'm Peter Mangoro Data Analytics Student

Built a production-ready sentiment analysis system for customer feedback analysis using AWS Free Tier services. Implemented serverless architecture with Lambda, DynamoDB, S3, and API Gateway, creating a cost-effective solution that processes thousands of feedback items with 75-80% accuracy.

A comprehensive machine learning system for detecting fraudulent transactions in mobile money services using unsupervised anomaly detection. Implements Isolation Forest algorithm to identify potentially fraudulent transactions with ~2% accuracy rate.

Developed an interactive fitness tracking application using Streamlit and Python. Features include workout analytics, progress visualization, and personalized recommendations based on user data patterns.

Built a predictive analytics model using machine learning to identify patients at high risk of readmission within 30 days. Utilized patient demographics, medical history, and treatment data to achieve 85% accuracy in predicting readmissions.

Conducted comprehensive analysis of traffic violation data using Jupyter Notebooks and Python. Identified patterns in violation types, locations, and time periods to help improve traffic safety and enforcement strategies.