This is the problem of binary classification: "1" - the subscriber is a driver (belongs to the segment of drivers), "0" - the subscriber is not a driver (does not belong to the segment of drivers).
The files tabular_data.csv and hashed_feature.csv ̶ here are descriptive characteristics for 4084 subscribers ("ID" is the subscriber ID). The train.csv file is data about the target label (subscriber id and corresponding binary target).
The tabular_data.csv file contains numerical data on the subscriber's activity for 12 periods. • period - period number (consecutive periods, 1 - the newest); • id - subscriber ID; • feature_0 - feature_49 - data on the subscriber's activity in the corresponding period.
The hashed_feature.csv file is a set of hashed values of one categorical variable for the subscriber. • id - subscriber ID; • feature_50 - hash from the value of the categorical variable.
I faced the following problems:
- How should I encode feature_50 for ML algorithm? This feature contains approximately 5k unique values and each user can have almost 1k of these values. As I think one hot encoding is not very helpful here, but what technique should I use in this case?
- NA filling strategy for feature_0 - feature_49 (I've only tried pandas filling strategies)
- Make use of period variable? I would appreciate any help.