About

This project builds a machine learning-powered customer spend prediction system that trains a Random Forest model on 10 retail customers to forecast next-month spending based on demographics and purchase history, evaluating performance with MAE and R² metrics while visualizing feature importance and correlations. The workflow executes the predictive model end-to-end and outputs comprehensive documentation explaining the business problem, technical approach, and actionable insights for retail optimization.