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?