An Intelligent Machine Learning Framework for Accurate Uber Ride Fare Prediction Using Gradi-ent Boosting Regression
Authors-P.Meghana Sri, Nagaram Jahnavi
Keyword-Uber Ride Prediction, Fare Estimation, Machine Learning, Gradient Boosting Regressor, Ran-dom Forest Regression, Linear Regression, Demand Forecasting, Ride-Hailing Services, Flask, Predictive Analytics.
The rapid expansion of app-based transportation services has increased the need for accurate ride fare prediction to improve customer satisfaction and operational efficiency. Reliable fare estima-tion enables passengers to plan their travel expenses while allowing ride-hailing companies to optimize pricing strategies and resource allocation. Conventional fare estimation methods often fail to capture the complex relationships among trip distance, travel time, traffic conditions, and temporal factors, resulting in inconsistent predictions. This research presents a machine learning-based framework for Uber ride fare prediction using historical trip information and regression algorithms. The proposed framework employs comprehensive data preprocessing, feature engi-neering, normalization, and model optimization to enhance prediction performance. Multiple regression algorithms, including Linear Regression, Random Forest Regression, and Gradient Boosting Regressor (GBR), are developed and compared to identify the most effective predictive model. Experimental evaluation demonstrates that the Gradient Boosting Regressor delivers superior prediction performance by effectively learning nonlinear relationships within ride data. The developed model is integrated into a Flask-based web application that provides real-time fare estimation based on user inputs. The proposed framework offers a scalable and intelligent solu-tion for ride fare prediction, improving pricing transparency, operational planning, and customer experience while maintaining the same implementation methodology and experimental evaluation presented in the original study.
Doi-[https://doi.org/10.5281/zenodo.21735287]