ztqsteve / uber-rider-churn-analysis

Uber is interested in predicting rider retention. To help explore this question, they have provided a sample dataset of a cohort of users.

Uber Rider Churn Analysis

Project Overview

Uber is interested in predicting rider retention. To help explore this question, they have provided a sample dataset of a cohort of users who signed up for an account in January 2014. The data was pulled several months later.

Dataset Description

• 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 an luxury car in their first 30 days; False otherwise
• weekday_pct: the percent of the user’s trips occurring during a weekday