|- pic1.png <- Image resource for Jupyter notebook. |- CRISP-DM_Process_Diagram.png <- Image resource for Jupyter notebook. | |- train.py <- Python script to train models to training data. | |- predict.py <- Python script to predict values based on pretrained models. | |- offer_code_clf.joblib <- Pretrained sklearn model is stored for predictions. | |- influnce_clf.joblib <- Pretrained sklearn model is stored for predictions. | |- amount_clf.joblib <- Pretrained sklearn model is stored for predictions. | |- transcript_clean.csv <- Pivoted transcript data is stored in CSV format. | |- transcript.json <- Simulated transcript data provided by Starbucks | |- transaction.csv <- Pivoted transcript data is stored in CSV format. | |- profile_for_ml.csv <- Pivoted profile data is stored in CSV format. | |- profile.json <- Simulated profile data provided by Starbucks | |- portfolio_for_ml.csv <- Pivoted portfolio data is stored in CSV format. | |- portfolio.json <- Simulated portfolio data provided by Starbucks | | |- Wrangle.py <- Python script to wrangle all three data sources.
#Starbucks for life game code code
| | |- TranscriptWrangle.py <- Python utility script with code wrangle Transcript data. | | |- ProfileWrangle.py <- Python utility script with code wrangle Profile data. | | |- PortifolioWrangle.py <- Python utility script with code wrangle Portfolio data. | | |- Consolidate.py <- Python script to consolidated all three clean data sources into one consolidated data source. | |- wrangle <- Contains Data wrangling source code. | | |- analyze.py <- Python script with Plotly code to generate ad-hoc visualizations. | |- analyze <- Contains Data analysis source code. |- data <- Contains Data wrangling and analysis source code. | |- run.py <- Python script with Flask code to route HTTP requests. | | |- predict_amt.html <- Web page to get predicted total amount value. | | |- index.html <- Landing page for the Web Application. | |- templates <- Contains HTML files used in the Web Application. |- app <- Contains the source code to run Web Application. There will be an offer completion record in the data set however, the customer was not influenced by the offer because the customer never viewed the offer. The customer spends 15 dollars during those ten days. For example, a user might receive the "buy 10 dollars get 2 dollars off offer", but the user never opens the offer during the 10 day validity period. Customers do not opt into the offers that they receive in other words, a user can receive an offer, never actually view the offer, and still complete the offer. However, there are a few things to watch out for in this data set. If the customer accumulates at least 10 dollars in purchases during the validity period, the customer completes the offer. The offer is valid for 10 days from receipt. To give an example, a user could receive a discount offer buy 10 dollars get 2 off on Monday. 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. As an example, a BOGO offer might be valid for only 5 days. Not all users receive the same offer, and that is the challenge to solve with this data set.Įvery offer has a validity period before the offer expires. Some users might not receive any offer during certain weeks. 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). Once every few days, Starbucks sends out an offer to users of the mobile app. The last Starbucks for Life event started on November 30th, 2021 and ended on January 3rd, 2022, so that’s when we’re likely to see it happening again.This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app.
#Starbucks for life game code free
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