This project predicts hourly bike rental demand using AutoGluon, an automated machine learning framework. Developed as part of the AWS Machine Learning Scholarship Program, it uses weather, time, and seasonal data to forecast bike usage. The project includes feature engineering, hyperparameter tuning, and Kaggle-formatted submissions, showcasing how AutoML can solve real-world transportation problems efficiently.
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