I have been working with a manufacturing process. It would be very efficient to build a machine learning model for the kind of data that I have. So, my dataset has typically three inputs.
VAL_1 can be from 8 to 12 and it is a continuous variable.
VAL_2 is a number from 1 to 4. (not continuous)
VAL_3 is a number from 1 to 3. (not continuous)
The output is like a function $y = f(x)$, where $x$ is a continuous value in the range $(0,1)$
For example, a sample row of my dataset is as follows:
I want to build a model that predicts y(x) given VAL_1, VAL_2, VAL_3
How to deal with features that are both continuous and not continuous in the same model?
Any ideas or thoughts on how I should proceed with such a dataset would be greatly appreciated.
I did try the following:
Normalized VAL_1. I tried encoding features VAL_2, VAL_3.
Normalized the values of y(x).
Tried to fit linear regression, polynomial regression models. But the value of R_square is around 68%.
Are there any better ways to improve the model?