Multi source Customer Mart for Female Recommendations in Marketplace (E-Commerce |Recommendations)

This dataset represents a Gold Layer customer feature mart designed for female-oriented e-commerce recommendation systems. It includes 5,878 anonymized customer records with 17 engineered features derived from large-scale transactional, product, and review data.

Built using a data warehouse pipeline (extraction, cleaning, integration, and transformation), the dataset provides a denormalized, machine-learning-ready representation of customer behavior, spending patterns, retention signals, and product preferences. It originates from a star-schema model integrating customer, orders, product, time, and geographic dimensions.

Each record represents a unified customer profile supporting personalization, recommendation systems, churn prediction, customer segmentation, and customer lifetime value analysis. Key behavioral features include Recency, Frequency, and Total Orders. Monetary features include Total Spend, AvgOrderValue, and AvgPricePerItem. Preference features capture TotalItems, UniqueProducts, and quantity behavior. Retention features include CustomerLifespanDays and ChurnLabel. Profiling features include Country, Region, and JoinDate.

All customer identities are anonymized, and sensitive attributes are removed. The dataset is designed for research and industrial applications in e-commerce analytics, with emphasis on female-focused recommendation systems and personalization.