karensheng / churning_prediction Goto Github PK

A ride-sharing company (Company X) is interested in predicting rider retention. To help explore this question, we have provided a sample dataset of a cohort of users who signed up for an account in January 2014. The data was pulled on July 1, 2014; we consider a user 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 data, churn.csv, is in the data folder. The data are split into train and test sets. You are encouraged to tune and estimate your model's performance on the train set, then see how it does on the unseen data in the test set at the end.

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