Project 3 of the Udacity Machine Learning Engineer Nanodegree Program.
The data was initially 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
The stages of the project were Data Wrangling, Feature Engineering, machine learning application. Each major stage of the project was implemented in a separate notebook.
- Starbucks Capstone Challenge.ipynb
- Starbucks - Data Wrangling.ipynb
- Exploratory Analysis.ipynb
- Starbucks - Features Engineering.ipynb
- Starbucks - Linear Learner.ipynb
- Starbucks - XGBoost.ipynb
In addition to the Jupyter Notebooks files, there are two pdf files, one for proposal (proposal.pdf) and another for report (project.pdf).
UDACITY - Machine Learning Engineer Nanodegree: https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t