Abstract :

Uber generates massive volumes of data every day, including trip details, driver behavior, customer demand, pricing, and location information. Analyzing this data using machine learning techniques helps in understanding ride demand patterns, predicting fares, and optimizing driver allocation. This project focuses on applying machine learning algorithms to analyze Uber ride data for demand prediction and trend identification. The proposed system uses data preprocessing, feature extraction, and supervised learning models to extract meaningful insights. By training models on historical Uber data, accurate predictions about ride demand and trip duration can be achieved. The system improves operational efficiency and decision-making for ride-sharing services. Experimental results show improved prediction accuracy and better data-driven insights. This approach demonstrates the effectiveness of machine learning in transportation analytics.