Build a model that predicts the number of hits per session.
The data in .csv format containing ~900k records must be kept in the data directory.
The columns should be understood as follows:
row_num: a number uniquely identifying each row.locale: the platform of the session.day_of_week: Mon-Fri, the day of the week of the session.hour_of_day: 00-23, the hour of the day of the session.agent_id: the device used for the session.entry_page: describes the landing page of the session.path_id_set: shows all the locations that were visited during the session.traffic_type: indicates the channel the user cane through eg. search engine, email, ...session_duration: the duration in seconds of the session.hits: the number of interactions with the trivago page during the session.
Predict the number of hits corresponding to the rows where the column hits has missing values. Predictions are evaluated by the metric of root mean square error.
The code hits_regression.py contains the main class that is used for training the model. The code can be executed from the CLI as well. By default the make_submission argument of the main method of the class is set to True, which will create a separate CSV file containing 2 columns - row_num and hits for the all those rows where predictions are to be made.