The objective of this challenge is to create a machine learning algorithm to determine if a user performs a specific action (such as making a purchase) in a 6-hour period over the course of a day, based on previous event data for seven weeks.

There are 4 200 customers, across five countries over a period of seven weeks. The training set is 7 weeks of data, and the last day of the final week will be the test set, where the target is whether a client performed event 14 during that day at 6-hour intervals.

This challenge is also unique in that the data is provided in the same way it would be encoded for machine learning - testing participants' data analysis and reasoning skills from the outset, having to discover the logical relationships before building models.

Variable Definitions

• eventdatetime: event date and time in “Y-m-d H:M:S” format
• event: name of the event (will be “signup”, “purchase”, etc)
• userid: the ID of the specific user logged in
• dayofweek: day of week the transaction was made on, you can use df['dayofweek'] = df['eventdatetime'].dt.day_name() to get the day of the week
• useremaildomain: user’s email provider (“gmail.com”, “yahoo.com”, etc)
• userrole: used to indicate user level – regular employee through to financial director etc
• companyprofileid: company ID
• country: jurisdiction of the user (South Africa, Botswana, etc)