Dynamic Pricing System for Ride-Sharing Platforms

Dynamic pricing has emerged as a pivotal strategy in the ride-sharing industry, enabling platforms to adjust fares in real time based on fluctuating market conditions such as demand, supply, ride duration, and vehicle type. This paper presents the design and implementation of a dynamic pricing system for ride-sharing platforms, leveraging machine learning algorithms to analyze historical and real-time data for optimal price prediction. The proposed system utilizes a Random Forest Regressor to model complex relationships between key variables, ensuring responsive and data-driven fare adjustments. Implemented as a Flask web application, the platform provides an intuitive interface for both administrators and users, supporting functionalities such as user management, FAQ handling, and feedback integration. Data visualizations offer actionable insights into pricing distribution and profitability, empowering stakeholders to make informed decisions. By bridging advanced pricing algorithms with user-friendly interfaces, this work demonstrates an effective approach to maximizing platform profitability and customer satisfaction in a highly dynamic market environment.