International Journal For Multidisciplinary Research

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Taxi Fare Prediction Using Various Machine Learning Models

Author(s) Suyash Agarwal, Tanay Joshi, Raghav Khare, Aaryan Agarwal, Parbhat Gupta
Country India
Abstract The current architecture of ours has been developed with the help of Random Forest, Lasso Regression, XGBoost(Abbr.), and Ridge Regression among other machine literacy techniques. We have predicted fares relatively well considering various factors such as the number of passengers traveling, date, time, pickup and dropoff latitudes, and longitudes, and so on. Each model is trained and estimated with the help of a dataset
consisting of past travel information. Two models were randomly selected to reduce overfitting and enhance conceptualization with regularization techniques like Lasso and Ridge Retrogression. Also, Random Forest and XGBoost(Abbr.), known to handle complicated, nonlinear connections,
were applied. Finally, based on a detailed comparison of performances of these models through evaluation metrics such as RMSE (Abbr.) and R2 score (Abbr.), the best method was chosen that could predict the fare. A multi-model method like this ensures delicacy and rigidity-an essential source of insight into dynamic pricing and optimization of mobility efficiency within public spaces.
Keywords Machine literacy, regression models, RMSE, MAE, R-squared, dynamic pricing, data-driven decision-making, and urban transportation
Field Engineering
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-28
Cite This Taxi Fare Prediction Using Various Machine Learning Models - Suyash Agarwal, Tanay Joshi, Raghav Khare, Aaryan Agarwal, Parbhat Gupta - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.35839
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.35839
Short DOI https://doi.org/g829qk
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