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This project aims to perform customer segmentation, a crucial process for understanding and engaging with Starbucks' diverse customer base.

The primary objective of the project is to obtain the effective outreach for Starbucks’ marketing campaigns by determining the offers that should be targeted to different groups of customers based on the transactions and demographics data provided. In order to achieve this, we would perform customer segmentation to segment customers into groups that respond best to a particular marketing campaign. We would also like to study the likelihood of a customer responding to a purchase offer and to determine the possible level of response or user actions like offer received, offer viewed, transaction, offer completed etc using a multi-class classification model. Through this model, we hope to determine the best offer types for each customer by the likelihood of response. Hence, we want to focus on Customer Development and Retention related issues using these customer analytics techniques.

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Unsupervised learning project applying K-Means clustering on Starbucks Rewards data (~17k customers). Achieved Silhouette score ≈ 0.43, with insights visualized using Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn for targeted customer segmentation.

End-to-end customer segmentation project using Starbucks data. Includes data wrangling, EDA, K-Means clustering, and classification with Random Forest. Built with Python, scikit-learn, and visualized using Seaborn and Matplotlib.