This project highlights the use of Python-driven analytics to improve supply chain efficiency.
By modeling key KPIs such as Days of Supply, Inventory Turnover, and Stockout Risk,
the notebook demonstrates how data insights can reduce holding costs, prevent shortages, and guide smarter inventory decisions.
Capital Markets Profitability Simulator
A Python-based financial modeling tool that simulates investment returns across global markets using macroeconomic data from kaggle. This project calculates Net Present Value (NPV) and Internal Rate of Return (IRR) based on country-specific interest rates, inflation, and political risk, helping identify optimal regions for capital deployment. Visualizations and risk-adjusted metrics provide actionable insights for strategic investment decisions.
Inventory Health Monitor for Automotive Stock
This project uses Python and Jupyter Notebook to analyze dealership vehicle inventory and evaluate supply chain performance. The analysis covers key metrics such as Days of Supply, Inventory Turnover, Stockout Risk, and Forecasted Inventory Depletion. The workflow includes data cleaning, integration of dealer insights, and visualization of trends to identify potential care gaps in inventory management.
By simulating reorder logic and highlighting risk areas, this notebook demonstrates how data-driven insights can improve stock allocation, reduce holding costs, and prevent vehicle shortages. The project showcases skills in data wrangling (Pandas), visualization (Matplotlib/Seaborn), and KPI modeling with an applied focus on supply chain analytics.
March Madness Metrics
Statistical deep dive into NCAA Men's Basketball performance using regression models, visualizations, and predictive analytics. Includes conference breakdowns, win forecasts, and model evaluations.