Bike-sharing demand is highly relevant to related problems companies encounter, such as Uber, Lyft, and DoorDash. Predicting demand not only helps businesses prepare for spikes in their services but also improves customer experience by limiting delays.
Comprehensive exploration of the Bike Sharing Problem using advanced Machine Learning for demand forecasting and Mathematical Linear Optimization for strategic bike allocation. Includes detailed Jupyter notebooks covering model development, analysis, and optimization solutions.
Predicting bike sharing demand with AutoGluon - Udacity AWS Machine Learning Fundamentals Nanodegree, project 1
This project aims to enhance the mobility and convenience of the public through bike-sharing programs in metropolitan areas. One of the main challenges is maintaining a consistent supply of bikes for rental.
"Bike-Sharing-Algorithm" optimizes bike-sharing systems through advanced algorithms. This project employs data-driven approaches to enhance bike distribution, availability, and user experience.
Predict Bike Sharing Demand using AutoGluon
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