- programming in Python
- processing and analyzing data sets, interpreting them for making business decisions
- training and deploying machine learning models
- communicating the results of analysis
Project goal is to deploy two different machine learning models that could potentially allow Starbucks to model their customers and personalize marketing efforts for much greater effectiveness. There are 3 raw data files - portfolio.json (10 offers x 6 fields), profile.json (17000 users x 5 fields) and transcript.json (306648 events x 4 fields). The files are not very straightforward to wrangle and require some creativity to process.
Please refer to PROJECT REPORT for a high-level overview of the project.
Amazon SageMaker Notebook Instance (requires AWS account)
OR
- Anaconda with latest Python 3 version (includes Python, Jupyter Notebook, and commonly used packages)
- Amazon SageMaker Python SDK
If you want to run the files in Amazon SageMaker Console, refer to this documentation.
OR
To install SageMaker Python SDK see this repo.
No other module installation is needed as long as you already have Anaconda on your machine. You can get it here.
See the open issues for a list of proposed features (and known issues).
The content of this project itself is licensed under the Creative Commons Attribution 3.0 Unported license, and the underlying source code used to format and display that content is licensed under the MIT license.