This project tries to complete the Starbucks Take-Home Assignment provided by Udacity, in collaboration with Starbucks, in the context of their Data Scientist Nanodegree Program.

The results are shared on a Medium post.

In the experiment simulated by the data, an advertising promotion was tested to see if it would bring more customers to purchase a specific product priced at $10. Since it costs the company 0.15 to send out each promotion, it would be best to limit that promotion only to those that are most receptive to the promotion.

The task is to use the training data to understand what patterns in V1-V7 (abstract customers' features) indicate that a promotion should be provided to a user. Specifically, the goal is to maximize the following metrics:

• Incremental Response Rate (IRR): how many more customers purchased the product with the promotion, as compared to if they didn't receive the promotion
• Net Incremental Revenue (NIR): how much is made (or lost) by sending out the promotion.

1. Analyze the results of the experiment of the treatment on product purchase and Net Incremental Revenue
2. Build a model that selects the best customers to target that maximizes the IRR and the NIR