This is an in-depth data analysis and simulates data of how mobile app users respond to offers sent out by Starbucks. With a better understanding of customer behavior, Starbucks would be able to improve by promotional offers.
Starbucks_Capstone_notebook.ipynb: Notebook containing the data analysis. Starbucks_Capstone_notebook.html: An HTML file of the jupyter notebook. data/portfolio.json: containing offer ids and meta data about each offer (duration, type, etc.). data/profile.json: demographic data for each customer. data/transcript.json: records for transactions, offers received, offers viewed, and offers complete
-What is the most gender and age ? -What is the most registration members year ? -What is the most event in gender and age ? -What is the most viewed offer count in difficulty, duration, offer_type and reward ? -What is the most complet offer count in difficulty, duration, offer_type and reward ? -What is the most transcript in gender ,age and registration members for each year ? -What is the most offer type each age ? After Predict -What is the most gender and age in 2018 year ? -What is the most offer type of gender in 2018 year ? -What is the most offer type age in 2018 year ?
- Python3 is the main language.
- Numpy, Pandas, matplotlib, seaborn, and sklearn are the main packages that were used.
- Jupyter Notebook is where the code is hosted.