Saving and Reusing model



I am newbie in DataScience and doing a Churn Prediction as part of my academic work. Following is how i am thinking of how my predictor should work.

  1. I have 9 months of transaction starting from Nov to June
  2. I take 3 months of historical transactions(Nov, Dec, Jan) and predict for the 4th month(Feb)
  3. The problem is i have highly imbalanced data where only 1% of customers churn in a given month. So i have created a model evaluation technique where i do evaluate Classification model against 6 different imbalanced technique (Oversampling, Undersampling and Hybrid)

After one evaluation cycle i would like to repeat this procedure for the subsequent data sets like

Use Dec,Jan,Feb data and predict March and so forth.

I was thinking of reusing the trained model when ever i did subsequent analysis.

My question here is

  1. should i save the model after it has been trained and predicted with the imbalanced data?


Step 2 and Use Dec,Jan,Feb data and predict March and so forth are completely two different models right?

When you are balancing the sample why not consider your observation window to include more data, balance the data set, build a model and use that for predicting March and so forth.

If more data comes in the form of actual completed months, you can redo and build a newer model.

My explanation is based on my understanding you have 9 months transaction and want to predict tenth month and so on.