Clustering and Classifying Customer Transaction Behavior with Machine Learning

A machine learning project applied clustering and classification techniques to 2,512 bank transactions to uncover hidden customer behavior patterns. Using K-Means clustering, customers were grouped into two segments: older, cautious professionals and younger, digitally active students. These clusters then trained a Random Forest classifier that achieved 100% accuracy, enabling banks to automatically categorize new customers and tailor financial products accordingly.