Starbucks_Capstone_project

Licensing, Authors, and Acknowledgements

Installation

Project Motivation

File Descriptions

Results

Licensing, Authors, and Acknowledgements

Installation:

Python versions 3.*.

• Libraries:
• Pandas.
• Scikit-learn.
• numpy.
• matplotlib.
• seaborn.

Project Motivation:

Keep customers satisfied is one of the most successful roles of business, there is no doubt Starbuckswork to increase customers loyalty. Furthermore, analyzing data is a method to follow the customer's behavior and guarantee to strive for their satisfaction. I analyzed this dataset to find interesting outcomes and find interesting results. I asked and answered for these:

• What are the rates of profiles per age on Starbucks?
• What is the age groups per gender that include in Starbucks profiles?
• What are the rates of incomes per ages in Starbucks profiles?
• Are there any increases in the number of profiles every month that depends on the rates of income for members?
• What are the rates of Starbucks members rewards every year?
• What are the rates of events In Transcripts?
• What is the highest Offers Type chosen by gender?
• What are the rates of completed promotion for each offer types?
• What is the rate of offer type which is a complete offer and type of promotion which is a Bogo promotion?
• What is the rate of offer type which is a complete offer and type of promotion which is a Discount promotion?

File Descriptions:

This project encompasses three Data Sets:

• portfolio.json - containing offer ids and metadata 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.

Results:

The main findings of the code can be found here.

Licensing, Authors, and Acknowledgements:

This project (Capstone Project) is part of Udacity’s Data Scientist Nanodegree program