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@article{193765,
        author = {Mrs. M. Padmavathi and N Chandana and N Jaswanth and V Bharadwaj and G Chandra Sekhar and P  Mohith},
        title = {ANALYSIS AND PREDICTION OF UBER RIDE DEMAND USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1450-1460},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193765},
        abstract = {The ride demand prediction is very essential in the enhancement of Uber performance and customer satisfaction. This project explores machine learning techniques to forecast demands of Uber rides based on the historical data such as time, location, and environmental conditions such as the weather. Several algorithms, such as Linear Regression, Decision Trees, Random Forest, and Gradient Boosting Regressor were tested after preprocessing and feature engineering. Gradient Boosting model proved to be more effective in nonlinear relationship modeling. The suggested system will combine a web-based interface to predict the demand as well as the fare in real-time, which will enhance allocation of the drivers and it will also minimize the time that the passengers have to wait.},
        keywords = {Uber, ride demand forecasting, machine learning, GBR, fare prediction, transportation analytics.},
        month = {March},
        }