At Arcap REIT Ai Solutions, I developed and deployed a Healthcare Patient Risk Prediction System using Python, Scikit-learn, Random Forest, and XGBoost. The system achieved 85–95% predictive accuracy through data preprocessing, feature engineering, model training, and evaluation.
I deployed ML prediction workflows on AWS and Azure and built Flask and Streamlit dashboards with CloudWatch monitoring. These supported real-time predictions and performance tracking across 100+ prediction records.
On my Netflix Rating Analysis project, I engineered a Python data-processing pipeline with Pandas and NumPy to analyze 100M+ user ratings. I also developed a predictive recommendation model that achieved 85% estimation accuracy.
In my current Operations Associate role at Techademy, I manage and validate 100+ operational records weekly. I analyze learner activity and operational outcomes to track trends and support reporting and process improvement.
