Customer Segmentation Based on Annual Income and Spending Behavior

In this project, I applied unsupervised machine learning techniques to segment mall customers into distinct groups based on their annual income and spending score. The goal was to help businesses understand different customer types and develop targeted marketing strategies.

I used the popular Mall Customer Segmentation dataset and applied K-Means Clustering to identify patterns in customer behavior. The project involved data preprocessing, visualization (scatter plots & cluster graphs), and evaluation using the elbow method to find the optimal number of clusters.

Tools used include Python, pandas, matplotlib, seaborn, and scikit-learn.