Let's say this: I'm working on a machine learning project and I'm working on a dataset with a 4250,13 as shape and it is already grouped in 7 categories! Note that those categories can't be considered as predictor. Here is how my data is grouped in the categories feature 'FAC':
FSEG 32.852598 % of the dataset
FSTA 19.151644 % of the dataset
FD 19.003181 % of the dataset
FM 16.076352 % of the dataset
FT 5.132556 % of the dataset
PPSE 4.814422 % of the dataset
FSDC 2.969247 % of the dataset
I have a continuous output that I want to predict 'CGPA', so the task is a regression, my goal is to predict it in each category, and the final decision will be the category where the predicted output is maximized.
My approach to deal with this problem is to sample my dataset into 7 sub-datasets and train the model in all those 7 datasets. And for a new input, predict the output in each category, and the final category will be where the predicted output is maximal.
Now I want to know, is there any way to do it in one dataset and automatically predict the category where my output is max? With a single model instead of 7?
PS: I'm using python and scikit learn
Sound like random-forest but not sure that is it ... can someone help? Any help will be appreciated.
Here is my dataset with samples collected in each category. My final output is called CGPA and the category label is 'FAC'.