I have been reading about imbalanced dataset lately and new to data science Domain. I came across this article which talks about various methods to solve this problem in R. Considering e-commerce data, we have Baseline sales(sales without promotion) and Incremental sales(sales with promotion). As this will be a true example of the imbalanced dataset, Supposedly in the hypothetical example, we have 2 million sales record for baselines for a year and 100K sales record for incremental sales. We can't Undersample and oversample as we will loose important information and won't be able to handle a large date with duplicates because of oversampling. If we want to tent the significance of promotion we can use SS3 ANNOVA but we will not have a true reflection. How do we find the lift of promotion or incremental sales over baseline sales in this scenario as CSL(cost sensitive learning) will not balance the data well. Any thoughts on how to handle this scenario? This lift analysis would help to analyze if promotion significantly helped in getting more sales FYI.
And regarding seasonality for year-long data, we can do seasonal adjustment using time decomposition in R and subtracting/dividing based on series.