ankit3466 / starbuckscapstonechallenge

StarbucksCapstoneChallenge

Udacity Data Scientist Nanodegree

Medium article by me

Project Overview

This data set contains 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.

Your task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products.

Every offer has a validity period before the offer expires. As an example, a BOGO offer might be valid for only 5 days. You'll see in the data set that informational offers have a validity period even though these ads are merely providing information about a product; for example, if an informational offer has 7 days of validity, you can assume the customer is feeling the influence of the offer for 7 days after receiving the advertisement.

Results Summary

• Model ranking based on training data accuracy
  1. RandomForestClassifier model accuracy: 0.742
  2. GradientBoostingClassifier model accuracy: 0.736
  3. LogisticRegression model accuracy: 0.722
  4. Naive predictor accuracy: 0.471
• Model ranking based on training data F1-score
  1. RandomForestClassifier model f1-score: 0.735
  2. GradientBoostingClassifier model f1-score: 0.725
  3. LogisticRegression model f1-score: 0.716
  4. Naive predictor f1-score: 0.640
• Results suggest that the random forest model has the best training data accuracy and F1-score

Files

• Starbucks_Capstone_notebook.ipynb
  • Jupyter notebook that performs three tasks:
    • Combines offer portfolio, customer demographic, and customer transaction data
    • Generates training customer demographic data visualizations and computes summary statistics
    • Generates logistic regression, random forest, & gradient boosting models
• clean_data.py
  • Python software that combines offer portfolio, customer demographic, and customer transaction data
• exploratory_data_analysis.py
  • Generates training customer demographic data visualizations and computes summary statistics

Python Libraries Used

-Python Data Analysis Library
-Numpy
-Matplotlib
-seaborn: Statistical Data Visualization
-re: Regular expression operations
-os — Miscellaneous operating system interfaces
-scikit-learn: Machine Learning in Python
-Joblib: running Python functions as pipeline jobs