- Core Tech: Python, Scikit-Learn, Random Forest, RandomizedSearchCV
- Impact: Developed a custom
ChargesRatiometric to achieve a 78% recall rate for identifying at-risk revenue - Analysis: Conducted Learning Curve analysis to ensure model stability and generalization
- Core Tech: Python, Pandas
- Impact: Automated inventory auditing between warehouse and sales records to eliminate "ghost stock" discrepancies
- Significance: Directly addresses data integrity challenges common in the logistics and supply chain industry
- Core Tech: Python, Large Language Models, JSON
- Impact: Engineered an AI pipeline to transform unstructured medical text into structured, analysis-ready JSON datasets
- Significance: Showcases ability to handle messy, real-world data and convert it into high-value digital assets
- Languages: Python, SQL
- Machine Learning: Random Forest, Model Tuning, Recall Optimization
- Data Analysis: Pandas, NumPy, Matplotlib, Seaborn
- Environments: GitHub, Google Colab, VS Code