A Comparative Analysis of Machine Learning Algorithms with MongoDB-powered Uber Fare Prediction

DOI

10.2991/978-94-6463-948-3_4How to use a DOI?

Keywords

Machine Learning; Uber Data Analysis; Random Forest Regression; Linear Regression; SVR; Neural Network; Decision Tree Regression; KNN

Abstract

Efficient ride-demand forecasting has become essential for modern transportation systems that depend on real-time operational intelligence. This research introduces a machine learning–based approach that predicts Uber ride demand using a high-volume dataset managed through MongoDB Atlas. The dataset comprises detailed trip attributes including geospatial coordinates, travel distances, passenger information, fare components, and temporal indicators. After extensive preprocessing and feature engineering, multiple regression models—such as Linear Regression, Decision Tree, Random Forest, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Neural Networks—were trained and evaluated using Mean Squared Error (MSE). Random Forest and Neural Network models consistently delivered superior predictive accuracy by capturing complex nonlinear relationships within the data. To enhance interpretability, a Power BI dashboard was developed to provide interactive visualization of ride behavior and spatial trends. The integration of scalable NoSQL storage, machine learning, and business intelligence tools presents a robust pipeline for intelligent ride-demand forecasting, with strong applicability to fleet optimization, dynamic pricing, and smart city mobility planning.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ronak Umesh Bansal
AU  - Shakti Kinger
AU  - Swarup Shivaji Satav
PY  - 2026
DA  - 2026/01/06
TI  - A Comparative Analysis of Machine Learning Algorithms with MongoDB-powered Uber Fare Prediction
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 30
EP  - 47
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_4
DO  - 10.2991/978-94-6463-948-3_4
ID  - Bansal2026
ER  -