A step-by-step introduction to purchase prediction. Also applicable to survival analysis and churn prediction. Including implementation in PySpark.

When dealing with survival analysis, the model's success is predicting death correctly. But it can also predict an engine failure, abandonment, or even purchases.
In purchase prediction, survival analysis, or churn prediction, the data is usually labeled or artificially labeled by a set of rules- such as inactivity for 30 days equivalent to churn. But the data structure is different from classical machine learning, and the data handling and modeling are different accordingly.
In this lecture, we will cover the data structures and aggregations for such analysis focusing on time aggregations using pyspark and what NLP got to do with any of it.