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
Upload the data to an S3 bucket through the AWS Gateway so that SageMaker has access to the data.
Using Gradient Boosting, a supervised learning method, we will analyze the attributes of customers to create customer classifications. This is a multi-class classification problem so the key metric we will use is the f1-score. The simulating dataset only has one product, while Starbucks offers dozens of products. Therefore, this data set is a simplified version of the real Starbucks app.
- Cleaning
- Feature engineering
- Split
- Training
- Inference
To finish this project, we perform the following tasks:
- Upload Training Data: First you will have to upload the training data to an S3 bucket.
- Model Training Script: Once we have done that, we will have to write a script to train a model on that dataset.
- Train in SageMaker: Finally, we will have to use SageMaker to run that training script and train your model
- Model Deployment: Once we have trained your model, we can deploy our model to a SageMaker endpoint and then query it with an image to get a prediction.
- Hyperparameter Tuning: To improve the performance of our model, we can use SageMaker’s Hyperparameter Tuning to search through a hyperparameter space and get the value of the best hyperparameters.