The code was developed using Python 3.6.3. Necessary packages beyond the Python Standard Library are:
- numpy==1.12.1
- pandas==0.23.3
- scipy==1.2.1
- scikit-learn==0.19.1
- matplotlib==2.1.0
In this project, an A/B test on customer promotion is analyzed. Predefined KPIs are statistically evaluated.
KPIs:
- Incremental Response Rate (IRR)
- Net Incremental Revenue (NIR)
After exploratory and statistical analysis, a classification model based on customers' features is fitted to the data in order to optimize future promotion strategy. The goal is to target the most promising customers regarding IRR & NIR.
More details on the questions of interest can be taken from the Jupyter Notebook introduction.
Starbucks.ipynb
- notebook containing a detailed description of the questions of interest
- statistical analysis & optimization model
training.csv
- Starbucks training dataset used in the notebook (the dataset has been used for assessments of data scientist job interviews at Starbucks in the past)
test_results.py
- test on optimization model provided on Udacity.
Test.csv
- Starbucks test dataset used in test_results.py
The data used in this project was kindly provided by Starbucks in cooperation with Udacity in the Udacity Data Science Nanodegree program.