This project is a comprehensive time complexity performance benchmark comparing the execution speed and solution quality of various programming languages Python, C++, Rust, and Julia. It figures outs supply chain CVRP operations cost and efficiency depending on the software architecture.

A delivery optimization project using causal inference and logistic optimization

A machine learning-driven supply chain optimization project presented as a Jupyter Notebook. It integrates demand forecasting, inventory level optimization, and logistics route optimization using time series models, classification, and graph-based methods, with visualizations and performance metrics to guide decision-making.

Work on clients’ data to help it understand the primary causes of unfulfilled requests as well as come up with solutions that recommend drivers locations that increase the fraction of complete orders.

Work on clients’ data to help it understand the primary causes of unfulfilled requests as well as come up with solutions that recommend drivers locations that increase the fraction of complete orders.