Ola Data Analysis and Ride Comparision

Abstract

With the rise of ride sharing we see that accurate fare prediction has become a key issue. In this study we focus on development of a fare prediction system via machine learning methods. We use and compare Two algorithms, Multiple Linear Regression and Random Forest, out of which we try to determine which is the superior model. We evaluate the models with the use of R-squared, Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and RMSLE. What we found is that the Random Forest model does in fact perform better in terms of accuracy and also does a better job with real world data than Linear Regression. This work also puts into light how machine learning may be used to improve pricing in transport services.

Introduction

The text describes a machine learning-based cab fare prediction and comparison system designed for ride-hailing services like Uber and Ola. The main problem addressed is that traditional fare estimation methods are not accurate because they fail to properly account for factors such as distance, time, traffic, and demand. Machine learning is proposed as a better solution for capturing these complex relationships.

The project aims to build a unified platform that allows users to compare fares across different cab services, reducing the need to check multiple apps. It also focuses on identifying key factors affecting fare prices and improving prediction accuracy using data analysis and machine learning.

The literature review shows that previous studies support the use of machine learning for ride data analysis and fare prediction. Among different approaches, Multiple Linear Regression provides a simple baseline model, while Random Forest is generally more accurate due to its ability to handle non-linear relationships. Exploratory Data Analysis (EDA) is also highlighted as important for understanding data patterns.

The methodology includes data collection from ride datasets and simulations, followed by preprocessing steps such as handling missing values, removing duplicates, and selecting important features like distance and time. Two models are implemented:

• Multiple Linear Regression for simple linear relationships
• Random Forest for more complex and accurate predictions

Model performance is evaluated using metrics such as R², MSE, RMSE, and RMSLE.

In the results, Random Forest outperforms Multiple Linear Regression by producing more accurate predictions, lower error rates, and better handling of complex data patterns. Therefore, Random Forest is identified as the more effective model for cab fare prediction.

Conclusion

The in this project we developed the Ride Fare Comparison which addresses a large issue that users have at the time of booking cab services that of comparing fares across many platforms. We integrated data analysis and machine learning into the system which in turn is able to determine ride fares and present a comparison of what different service providers charge in one interface. We used models like Multiple Linear Regression and Random Forest which helped us to study the relationship between key factors of distance, time and fare. Of these the Random Forest model did it’s job with greater accuracy. Overall, the project demonstrates how data-driven approaches can improve decision-making for users by saving time, reducing effort, and providing cost-effective ride options. It also highlights the practical application of machine learning in solving real-world problems.

References

[1] R. Srinivas et al., “Uber Related Data Analysis Using Machine Learning,” Proc. ICICCS, 2021. [2] A. P. Kumar et al., “A Novel Approach to Analyze Uber Data Using Machine Learning.” [3] G. Venkat Sai Taruna and P. Sriramyab, “Ola Data Analysis for Dynamic Price Prediction Using Multiple Linear Regression and Random Forest Regression.” [4] E. Camizuli and E. J. Carranza, “Exploratory Data Analysis (EDA).” [5] R. Goel et al., “Operation Analytics: Uber and Ola Logistics Optimization.”

Copyright

Copyright © 2026 Rudra Pratap, Harsh Kumar, Avnish Kumar Singh, Kunal Solanki, Dr. Lalit Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.