Starbucks-Capstone-Challenge

This project is a part of Udacity Data Science Nanodegree

Table of Contents

1. Project Overview
2. Installation
3. Project Motivation
4. File Descriptions
5. Acknowledgements
6. Results

1. Project Overview

Starbucks provided simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. Not all users receive the same offer, and that is the challenge to solve with this data set. The task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type.

2. Installation

• The code should run with no issues using Python version 3.*.
• No extra besides the built-in libraries from Anaconda needed to run this project
• Data Processing & Machine Learning Libraries: Numpy, Scipy, Pandas, Sciki-learn
• Data Visualiztion: Matplotlib, Seaborn

3. Project Motivation

This project was completed as part of the Udacity Data Science Nanodegree.The data set used in this project contains simulated data that mimics customer behavior on the Starbucks reward mobile app. The main goal on this project is :

• How many customers were provided with a specific offer?
• Which type is most successful?

4. File Descriptions

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

5. Acknowledgements

This project was completed as part of the [Udacity Data Science Nanodegree]. The dataset used in this project contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Starbucks® Rewards program: Starbucks Coffee Company.

6. Results

The main observations of the code are published on medium here