This project is a capstone requirement of Udacity Data Science NanoDegree. The project is about data exploration and building a model for Starbucks rewards app data, to determine what influence a customer to purchase or respond to an offer, based on customer’s transaction data, demographic profile, and offer type.
Install visuals.py and other Anaconda libraries of Python. The code should run with no issues using Python versions 3.*.
For this project, I was interested in using Starbucks rewards app data. The objective of this project is to understand what influence a customer to purchase or respond to an offer. The analysis is based on customer’s transaction data, demographic profile, and offer type. The full set of data was provided by Udacity.
There are 3 json files provided and available through Udacity's workspace:
portfolio.json - containing offer ids and meta data about each offer (duration, type, etc.)
profile.json - demographic data for each customer
transcript.json - records for transactions, offers received, offers viewed, and offers completed
The files included in this repo are:
- Starbucks_Capstone_Project.ipynb - Code in Jupyter Notebook
- Starbucks_Capstone_Project.html - HTML copy of code
- There is an additional visuals.py file that runs the necessary code, to run the Jupyter notebook.
The main findings can be found here: https://lovelina-richter.medium.com/monday-coffee-b98c62606e2
Must give credit to Udacity and Starbucks for the data.