Abstract :

Predictive Analytics Plays A Significant Role In Forecasting Future Events By Analyzing Historical Data And Identifying Meaningful Patterns. In Recent Years, The Growth Of Advanced Technologies In Machine Learning And Big Data Has Greatly Enhanced The Accuracy And Efficiency Of Predictive Models. These Techniques Are Widely Used Across Various Industries To Make Informed Decisions And Optimize Resource Planning. One Such Important Application Is Taxi Fare Prediction, Which Helps Estimate The Cost Of A Ride Based On Several Influencing Factors. The Fare Of A Taxi Ride Is Not Fixed And Depends On Multiple Variables Such As Distance Traveled, Time Taken, Traffic Conditions, Pickup And Drop Locations, Weather Conditions, And Time Of The Day. Accurately Predicting Taxi Fares Can Benefit Both Customers And Service Providers By Ensuring Transparency, Better Pricing Strategies, And Improved Customer Satisfaction. This Project Focuses On Developing A Machine Learning-based Model To Predict Taxi Fares Within A City. The System Uses Historical Trip Data To Analyze Patterns And Relationships Between Different Features Affecting The Fare. Various Preprocessing Techniques Are Applied To Clean And Prepare The Dataset, Followed By Feature Engineering To Extract Meaningful Insights. Machine Learning Algorithms Such As Linear Regression, Decision Tree, And Random Forest Are Implemented To Build Predictive Models. The Performance Of The Models Is Evaluated Using Appropriate Metrics, And The Bestperforming Model Is Selected For Fare Prediction. The Project Also Includes Data Visualization Techniques To Better Understand Trends And Patterns In Taxi Rides. Overall, This Work Demonstrates How Predictive Analytics And Machine Learning Can Be Effectively Used To Estimate Taxi Fares Accurately, Making Transportation Systems More Efficient And User-friendly.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1355-1361

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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