shawn-y-sun/Customer_Analytics_Retail

Applies PCA-reduced dimensionality clustering to segment customers across seven demographic features (age, income, education, occupation, settlement size, marital status, sex), then builds predictive regression models on transaction data to estimate price elasticity across purchase probability, brand choice, and quantity—enabling data-driven pricing optimization per customer segment. Implements scikit-learn pipelines with standardization, K-means clustering (elbow method), and logistic/linear regression on Kaggle FMCG purchase datasets to identify revenue-maximizing price points for individual product brands.

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