Machine Learning case study A ride-sharing company (Company X) is interested in predicting rider retention. To help explore this question a sample dataset of a cohort of users who signed up for an account in January 2014 is used. The data was pulled on July 1, 2014. So an assumption is made: a user is retained if they were “active” (i.e. took a trip) in the preceding 30 days (from the day the data was pulled). In other words, a user is "active" if they have taken a trip since June 1, 2014.

The task is not only to build a model that minimizes error, but also a model that allows to interpret the factors that contributed to the predictions.

A Model's performance is estimated on the train set and later the performance is evaluated on the unseen data in the test set.

Here is a detailed description of the data:

• city: city this user signed up in phone: primary device for this user
• signup_date: date of account registration; in the form YYYYMMDD
• last_trip_date: the last time this user completed a trip; in the form YYYYMMDD
• avg_dist: the average distance (in miles) per trip taken in the first 30 days after signup
• avg_rating_by_driver: the rider’s average rating over all of their trips
• avg_rating_of_driver: the rider’s average rating of their drivers over all of their trips
• surge_pct: the percent of trips taken with surge multiplier > 1
• avg_surge: The average surge multiplier over all of this user’s trips
• trips_in_first_30_days: the number of trips this user took in the first 30 days after signing up
• luxury_car_user: TRUE if the user took a luxury car in their first 30 days; FALSE otherwise
• weekday_pct: the percent of the user’s trips occurring during a weekday