Predicting a Continous output in a dataset with categories

@EspyM wrote:

Let says 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 group in 7 categories!
Note that those categories can’t be considered as prectictor,
here is how my data is grouped in the categories

A       32.852598 % of the dataset
B       19.151644 % of the dataset
c       19.003181 % of the dataset
D       16.076352 % of the dataset
E       5.132556 % of the dataset
F       4.814422 % of the dataset
G       2.969247 % of the dataset

I have a continuos output that I want to predict , 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-dataset and train the model in all that 7 dataset. 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 ans scikit learn

Sound like random-forrest but not sure that is it …;
can someone help? Any help will be appreciated…

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