This report presents a detailed customer segmentation analysis using clustering techniques on the "Mall Customers Segmentation" dataset from Kaggle. The dataset contains demographic and behavioral information for 200 customers. The analysis begins with exploratory data analysis (EDA), including data validation, summary statistics, and visualizations to explore relationships between age, gender, annual income, and spending score. We apply hierarchical clustering using Ward’s method and K-means clustering, identifying optimal cluster numbers and interpreting the resulting customer groups. The findings offer actionable insights into distinct customer profiles based on age, income, and spending behavior—valuable for targeted marketing and strategic decision-making in retail management. In general this project demonstrates how unsupervised learning can be leveraged to segment retail customers and support data-driven business decisions.

Exams

Logistic regression