Predicting Bike-Sharing Demand Based on Weather and Urban Events

RideWise is a Machine Learning-based bike-sharing demand prediction system that analyzes historical rental data, weather conditions, and time-related factors to predict the number of bikes required at different times. The project follows an end-to-end Machine Learning lifecycle, including data collection, exploratory data analysis, data preprocessing, feature engineering, model selection, training, evaluation, and hyperparameter tuning. Multiple regression algorithms are used to identify the best-performing model based on evaluation metrics such as MAE, RMSE, and R² score. The system aims to help bike-sharing companies optimize bike availability, improve resource allocation, and make data-driven operational decisions.