Predicting Bike-Sharing Demand: A Machine Learning Approach for Urban Mobility Analysis

Due to the increasing environmental pollution and the greenhouse effect, individuals are increasingly inclined towards adopting environmentally friendly modes of transportation, such as electric cars and eco-friendly machinery. The bicycle is considered one of the most environmentally friendly modes of transportation due to its low cost and minimal environmental impact. The use of bicycles is steadily increasing daily, especially in urban areas. This study seeks to investigate the level of demand for bike-sharing services in urban areas. It will explore various feature engineering methods, thoroughly analyze ensemble machine learning techniques, and employ artificial intelligence for time analysis.