I want to train a model on transaction data to predict whether a customer will buy the product in the next 90 days.
- I observed seasonality in the data, i.e. during certain months of the year, sale increases.
- Similarly, customers repeat the purchase in certain intervals. for example, for product A the purchase is repeated usually around 24 months. Whereas for product B, the purchase is repeated usually around 18 months.
How do I capture these complex patterns in the data? For seasonality, I want to create Month1, Month2,.., Month11 Boolean variables. I'm not sure how I capture the second scenario? Can someone suggest useful features for this and how these features help capture these patterns?. I'm planning to use Xgboost as the algorithm.