This project is a part of the Udacity Machine Leaning Engineer Nanodegree Program.
Nowadays, more and more shops, ecommerce are aiming to utilize the data in order to understand how the customers think, feel and decide, so the businesses can determine how best to market their products, services and which sales promotion strategies really work.
This project data is provided by Udacity in collaboration with Starbucks as the capstone project of Machine Learning Engineer Nanodegree. The data is simulated data that mimics customer behavior on the Starbucks rewards mobile app during a month experimen.
The goal of this project is to combine transaction, demographic and offer data to answer following questions:
- Which demographic groups are there and what the offers are that really excite them? In another word, what is the best offer, not just for the population as a whole but at an individual level?
- Which demographic groups will complete an offer even if they don't open an offer?
- Can we build a machine learning model to predict which action a customer may take?
- Python 3
- Python Modules in use:
- pandas
- numpy
- json
- matplotlib
- seaborn
- sklearn
Clone this repo (for help see this tutorial).
@ Hongxia Hou Licensed under MIT License