Starbucks Capstone Challenge

This repository deals with the Starbucks Capstone Challenge offered by udacity's datas science nanodegree program.

Included is a data set form Starbucks with simulated data containing transaction logs for persons interacting with offers distributed by Starbucks to end customers.

Project Motivation

The goal is to derive a predictive model from the data to suggest if there is a high chance that a person will spend money as a response to a discount offer or ‘by one get one free’ offer.

For more information read the corresponding blog post on medium: https://medium.com/@sopka/prediction-model-for-starbucks-bogo-and-discount-offers-e63383fd6469

Files Structure

├── data_aggregation.ipynb jupyter notebook for the data exploration, processing and aggregation and statistical evaluation

├── train_keras.py python script to train keras neural network model

├── train_model.ipynb jupyter notebook to construct and evaluate feature matrix and fit models with linear regression and radius near neighbors classification

├── data This directory contains a simulated data set that mimics customer behavior on the Starbucks rewards mobile app.

│   ├── profile.json Represents fictive or anonymized Rewards Program Users (17000 users x 5 fields)

• gender: (categorical) M, F, O, or null
• age: (numeric) missing value encoded as 118
• id: (string/hash)
• became_member_on: (date) format YYYYMMDD
• income: (numeric)

│   ├── portfolio.json Defines offers sent during 30-day test period (10 offers x 6 fields)

• reward: (numeric) money awarded for the amount spent
• channels: (list) web, email, mobile, social
• difficulty: (numeric) money required to be spent to receive reward
• duration: (numeric) time for offer to be open, in days
• offer_type: (string) bogo, discount, informational
• id: (string/hash)

│   ├── transcript.json Contains event logs (306648 events x 4 fields)

• person: (string/hash)
• event: (string) offer received, offer viewed, transaction, offer completed
• value: (dictionary) different values depending on event type
  • offer id: (string/hash) not associated with any "transaction"
  • amount: (numeric) money spent in "transaction"
  • reward: (numeric) money gained from "offer completed"
• time: (numeric) hours after start of test

Installation

All scripts and python source codes are tested under osx and linux only.

Initialize the python environment

You do not want to polute your local environment with new python libraries. Instead you should create a virtual python environment in the current directory. The only requirement is that python3.8 is preinstalled on your local system. Then just run:

python3 -m venv venv

And activate the virtual environment in your current shell:

source ./venv/bin/activate

Install necessary Libraries

We are using the following libraries:

• pandas - Python Data Analysis Library https://pandas.pydata.org/
• numpy - The fundamental package for scientific computing with Python https://numpy.org/
• matplotlib - Visualization with Python https://matplotlib.org/stable/index.html
• plotly - Plotly Python Open Source Graphing Library https://plotly.com/python/
• scikit-learn - Machine Learning in Python https://scikit-learn.org
• seaborn - Statistical data visualizationhttps://seaborn.pydata.org/
• keras - Built on top of TensorFlow 2.0, Keras is deep learning framework https://keras.io/
• tensorflow - An end-to-end open source machine learning platform https://www.tensorflow.org/

You can install the needed python libraries with pip in your venv environment:

pip install --upgrade pip
pip install pandas
pip install jupyterlab
pip install matplotlib
pip install plotly
pip install scikit-learn
pip install seaborn
pip install tensorflow
pip install keras

Project Analysis

Start with the jupyter notebook data_aggregation.ipynb. A complete run will aggregate the data into a CVS file in root of this directory called enriched_dtevent.csv.

This file is then used in the jupyter notebook train_model.ipynb to train models with linear regression and radius near neighbors classification.

The accuracy of these model will be around 65%.

There is also a python script called train_keras.py. It will also use the aggregated data from the CVS file enriched_dtevent.csv to train a simple neural network with keras.

The accurcy of this model will also be around 65%.

Acknowledgements

Thank you Starbucks and Udacity for providing the data sets!