I have a data set of 100000 samples with binary output.
I would like to study the impact of
Col_A (a continuous feature) on the output result.
Col_A has values from
7000000 and when I add this feature to my classifier it gives bad accuracy. I have tried z-score and scalar number but it doesn't change a thing.
I would like to try another method to solve this problem.
I made some plot visualization to my data and I found that there is some range in which the majority of the outputs are negatives. ie. when
col_A is ranging between (0 - 200), between (2500 - 2800) and between (5200-5400) the majority of outputs are negatives.
I would like to create a new feature to specify which category my
Col_A is in, instead of using
PS: I can't use if--else rules because I don't know where to delimit these ranges, I have just analyzed the previous values from my plot. but I need to have a dynamic method for getting those categories.
Is there any type of clustering that can solve this problem?
Does neural network help for this case?