SYSTEM 01 // PROJECTRole: FEATURED PROJECT
E-Commerce Sales & Customer Segmentation
Cleaned and analysed 96,477 real orders from the Olist dataset across 9 product categories and 3 years of transaction data to uncover revenue and behaviour trends. Applied statistical clustering (KMeans) to segment customers into 4 actionable groups (Champions, At-Risk, Hibernating, Lost) with a Silhouette Score of 0.525; found the top 20% of customers drove ~62% of revenue — a pricing/retention-relevant insight.
TechnologyPython
StackPandas
PlatformScikit-learn
OutcomeSegmented customers into 4 actionable groups with a Silhouette Score of 0.525, finding the top 20% of customers drove ~62% of revenue.Self-reported
Metric 02—Pending
Metric 03—Pending
View Project
// INTERVIEW ENGINE