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Is your feature request related to a problem? Please describe.
Traditional taxi fare estimation methods struggle with accuracy, especially in dynamic environments where factors like time, distance, traffic, and route can significantly affect pricing. This inaccuracy impacts customer trust and transparency in fare estimation, which is critical in modern, data-driven transportation services. This project addresses the need for a reliable predictive model that can estimate taxi fares by analyzing historical data, with potential real-time applications to improve user experience in ride-hailing platforms.
Describe the solution you'd like
This project focuses on developing a predictive model to estimate taxi fares using deep learning, leveraging the Keras library. By training a neural network on historical trip data, including pickup/drop-off locations, distance, and trip duration, the model aims to provide accurate fare predictions. This solution has potential applications in ride-hailing services for real-time fare estimation and price transparency.
Additional context