This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks.
Not all users receive the same offer, and that is the challenge to solve with this data set.
The basic task is to use the data to identify which groups of people are most responsive to each type of offer, and how best to present each type of offer.
-Python Data Analysis Library -Numpy
-Matplotlib
-seaborn: Statistical Data Visualization
-re: Regular expression operations
-os — Miscellaneous operating system interfaces
-scikit-learn: Machine Learning in Python
This project is trying to figure out: What factors mainly affect the usage of the offer from the customer? Should the company send out the offer or not? How possible will a customer open and use the offer sent to them? Are there any common characteristics of the customers who take the offer? From the result of the project, it’s likely to use machine learning model to predict whether the customer will respond to the offer or not, and the model also shows the main factors such as the length of membership, age, income which highly affect the possibility of customer’s responding to the offer.