PRODIGY_ML_02

Segment your mall customers by spending patterns with this Python project! Uncover hidden groups and personalize marketing efforts. Dive in and explore!

This project dives into the world of customer segmentation, utilizing the power of Python and KMeans clustering. We'll explore the "Mall_Customers.csv" dataset, focusing on spending patterns and annual income to uncover hidden customer groups.

Armed with the "elbow curve" method, we'll identify the optimal number of clusters, representing distinct segments within the customer base. Visualizations will paint a clear picture, revealing how customers in each cluster behave in terms of their spending and income.

The project doesn't stop there! We'll export the clustered data as a convenient CSV file, ready for further analysis. Delve deeper by comparing demographics or behaviors across clusters, tailoring marketing strategies or product recommendations to each group's unique characteristics.

This project embraces Python's powerful libraries like numpy, pandas, sklearn, and matplotlib. It welcomes contributions and thrives on collaboration. Whether you're a seasoned data scientist or a curious beginner, jump in and let's uncover the hidden gems within these customers!