This is my Starbucks Capstone Project as part of the Udacity Machine Learning Engineer Nanodegree. Udacity partnered with Starbucks to provide a real-world business problem and simulated data mimicking their customer behavior.

Starbucks Corporation is an American coffeehouses company and it is one of the world`s largest coffeehouse chain. Once every few days, Starbucks sends out an offer to users of the Starbucks rewards mobile app. The offers contain three types: an advertisement for a drink, an actual offer such as a discount and BOGO (buy one get one free). Some users might not receive any offer during certain weeks. Starbucks wants to utilise technology to provide their customers a better personalised experience and boost their marketing performance. To be specific, they want to target the right customers who are most likely to make a purchase and also know about customers who might not want an offer.

The motivations of this project is to gain more machine learning hands-on practice and have a better understanding about how the technology can make an impact on the real business.

The data is contained in three files:

• 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

Here is the schema and explanation of each variable in the files:

portfolio.json

• id (string) - offer id
• offer_type (string) - type of offer ie BOGO, discount, informational
• difficulty (int) - minimum required spend to complete an offer
• reward (int) - reward given for completing an offer
• duration (int) - time for offer to be open, in days
• channels (list of strings)

profile.json

• age (int) - age of the customer
• became_member_on (int) - date when customer created an app account
• gender (str) - gender of the customer (note some entries contain 'O' for other rather than M or F)
• id (str) - customer id
• income (float) - customer's income

transcript.json

• event (str) - record description (ie transaction, offer received, offer viewed, etc.)
• person (str) - customer id
• time (int) - time in hours since start of test. The data begins at time t=0
• value - (dict of strings) - either an offer id or transaction amount depending on the record

• pandas==1.0.5
• numpy==1.16.4
• json==2.0.9
• progressbar==3.37.1
• sklearn==0.23.1
• keras==2.3.1
• seaborn==0.10.1
• matplotlib==3.2.2
• shap==0.35.0