I am quite new to Data Science and learning things hands-on in the job. I am a fraud analyst and my job is to predict whether an application is fraudulent or not based on data.
Before moving on to many advanced models, I am asked to build decision trees on the dataset. Now the dataset which I have has 1500 columns; some categoricals and some numeric. Different categorical variables have different levels; some are binary and some have 100+ levels.
I came across the fact that scikit-learn can work only if the entire dataset comprises numeric variables (discrete or continuous). And the frequent work-around that I am seeing is around one-hot encoding like here - which I do not believe is pragmatic in my case because of a sheer number of columns and levels.
I asked my bosses to give me few weeks to understand most of the data so as to limit my variables and possibly do one-hot encoding but that's not flying well with them.
Has anyone any experience building classification decision trees on a mixed datatype dataset with large counts of variables?