Overview
Customer Segmentation represents a prominent application of unsupervised learning techniques. Leveraging clustering algorithms, such as K-means, enables the identification of distinct customer segments, facilitating targeted marketing efforts towards the potential user base. Segmentation involves grouping customers based on shared characteristics such as gender, age, interests, and spending habits, empowering businesses to tailor their marketing strategies effectively.
In this project, I employed K-means clustering to segment a dataset comprising mall customers frequenting a specific location. Utilizing features including age, gender, and spending score, I partitioned the customers into three, four, and five clusters, respectively. Additionally, I conducted visualizations to analyse the distributions of gender and age, followed by an analysis of annual incomes and spending scores within each cluster.
By potraying distinct customer segments, this project facilitates targeted marketing campaigns and strategic decision-making tailored to the unique preferences and behaviors of each group within the customer base.
Tech Stack
Python: Primary language used in this work.Anaconda: A distribution of python.