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All Projects → ztqsteve → Uber-Rider-Churn-Analysis

ztqsteve / Uber-Rider-Churn-Analysis

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

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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
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