Machine Learning Optimization for Logistics Client

Objective Understanding: The primary goal was to reduce transport costs and improve service levels by determining the optimal locations for micro warehouses. This would enable the client to meet their delivery time key performance indicators (KPIs).

Data Analysis and Preparation: We analyzed the client's sales data and geographical distribution of points of sale to understand the existing logistics network and identify areas for improvement.

K-means Clustering Algorithm:

Integration with Google API:

Optimization and Strategy Development:

Lead and Collaboration:

This project demonstrated the power of combining analytical techniques with geospatial data to drive strategic decisions in logistics and supply chain management,