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I need to create a machine learning model to predict if a structure is an hotel or an apartment. I have a dataset structured as well:

ID | STATE  | ROOM | BEDROOMS |       COMFORT      |   CARD_ACCEPTED  |                   CONGRESS                          | OUTPUT
0  | ITALY  |   3  |    5     |  Park, Pool, Disco | Visa, Mastercard |  Number rooms 3, Min capacity 3, Max Capacity 110   | Hotel
1  | USA    |   2  |    2     |  Park, Pool        |                  |                                                     | Apartment
2  | ARG    |   1  |    4     |                    | Visa             |  Number rooms 1, Min capacity 3, Max Capacity 20    | Hotel

I would like to test different machine learning methods on it, so the first thing I wanna do is preprocess the data. My idea is to split the columns COMFORT and CARD_ACCEPTED to make something like COMFORT.Park, COMFORT.Pool etc, so I can transform them into numbers instead of categorial variables. My problem concerns the CONGRESS column, since it has particular data which wouldn't fit well like in the COMFORT and CARD_ACCEPTED case. What normalization method should I apply on it?

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I would one-hot encode STATE, COMFORT, and CARD_ACCEPTED, and I would parse what appears to be a string in CONGRESS into three columns: NUMROOMS, MINCAP, MAXCAP.

For all your one-hot columns, you don't need to normalize. For all your numeric values, you can reference this stack question

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