Portfolio Exercise: Starbucks
Background Information
The dataset you will be provided in this portfolio exercise was originally used as a take-home assignment provided by Starbucks for their job candidates. The data for this exercise consists of about 120,000 data points split in a 2:1 ratio among training and test files. 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. Each data point includes one column indicating whether or not an individual was sent a promotion for the product, and one column indicating whether or not that individual eventually purchased that product. Each individual also has seven additional features associated with them, which are provided abstractly as V1-V7.
Files Descriptions
- training.csv: Contains training data
- Test.csv: Contains test data
- Starbucks.ipynb: Contains code for the promotional strategy based on uplift models
- test_results.py: Contains functions to evaluate the IRR and NIR values. File is provided by Starbucks.
- README.md file
Libraries use
Instructions
Run the Starbucks.ipynb file, and read results.
Licensind
Apache License 2.0
See the LICENSE file for details