Abstract

This research paper presents a data-driven approach for predicting cab fares, aiming to enhance the efficiency and transparency of transportation pricing. The study utilizes machine learning techniques to develop predictive models based on relevant features extracted from a comprehensive dataset of cab trips. Through rigorous experimentation and evaluation, the proposed models demonstrate promising accuracy and reliability in fare estimation. The findings contribute to improving the overall user experience for both passengers and service providers by facilitating informed decision-making and optimizing resource allocation in the transportation sector.