Predictive Modeling for Uber Ride Cancellation and Price Estimation: An Integrated Approach

In the realm of ridesharing services, exemplified by Uber, two formidable challenges have surfaced: ride cancellations and precise fare estimation. This research introduces an innovative, integrated approach that leverages predictive modeling to address both issues. By analyzing historical ride data, we identify the intricate factors influencing cancellations, and through machine learning techniques, we develop predictive models to forecast cancellation likelihood. Additionally, we pioneer a dynamic approach to fare estimation by considering historical data alongside real-time variables. By unifying these strategies, we aim to enhance user satisfaction, optimize driver allocation, and promote trust and transparency within the ridesharing ecosystem.