I have been working on a individual project with an online fashion company dataset. I aim building a churn prediction model. In order to do that I set a churn criteria such that a customer turns out to be churn with 12 months inactivity. But I have a confusion deciding the timeline of the data that I will train my model. Since churn periods are customer specific I cannot set a specific date interval. My dataset is betweem 2015 and March 2018 and I thought that it would be fine to select a sample customer who has a transaction in 2016. Then I took the last available date in dataset which is a someday in March 2018 and look 12 months back to identify who has gone churn. Then I took those customers I select who made a transaction in 2016 and took their all transaction data during the available data (2015-2018). I also added a feature to the model checking if the customer has a transaction within the last 3 months as a binary variable. However, I feel there is a mistake here. I am a self taught individual and I could not find a proper guide to build the model on the internet. Most of the churn prediction models do not talk about the data preparation in detail enough. I hope someone share their valuable ideas with me
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