This repository presents an in-depth analysis that was completed as a part of my CS252 - Data Analysis and Visualization course at Colby College.
The main objective of this project was to uncover distinctive spending patterns within mall customer data, sourced from Kaggle. Leveraging a self-implemented K-means algorithm, a powerful clustering technique, we meticulously categorized customers based on their behavior, revealing insightful preferences and tendencies.
The analysis is based on a carefully curated dataset available at: Customer Segmentation.
Central to this analysis is the innovative use of the K-means algorithm, which we developed from scratch. This technique enabled us to efficiently group customers with similar spending traits into distinct segments. Through this method, we gained a robust understanding of diverse spending behaviors exhibited by the mall's clientele.
The Mall Customer Analysis successfully illuminated diverse spending tendencies among mall customers based on genders, ages, and income, allowing for the development of targeted marketing strategies and personalized product offerings.