# Amazon Personalize Data Science

### Diagnostics

Open the [diagnose/](diagnose/) folder to find an example on how to approach visualization of the key properties of your input datasets.

The key components we look out for include:
- Missing data, duplicated events, and repeated item consumptions
- Power-law distribution of categorical fields
- Temporal drift analysis for cold-start applicability
- Analysis on user-session distribution

### Offline Performance Evaluation

Open the [offline_performance_evaluation/](offline_performance_evaluation/) folder to find an example on how to approach the offline evaluation of your Amazon Personalize Campaign recommendations.

## License Summary

This sample code is made available under a modified MIT license. See the LICENSE file.