Bike Sharing : A Forecast using Machine Learning Techniques

Abstract

Bike sharing systems have emerged as sustainable and convenient transportation alternatives in urban environments. Understanding and predicting bike usage patterns is crucial for optimizing system efficiency and enhancing user experience. In this paper developed a machine learning model to predict bike sharing demand based on various factors, such as weather conditions, time of day, day of the week, and other relevant features. The dataset utilized for this paper includes historical bike sharing data, encompassing information on the number of bikes rented at different time intervals, weather conditions, and temporal attributes. The primary objective is to create a robust predictive model capable of forecasting bike demand accurately. The methodology involves preprocessing and cleaning the dataset, feature engineering to extract relevant information, and employing a machine learning algorithm, such as a regression model or ensemble method. The model will be trained on a subset of the dataset and validated using another portion to ensure generalizability.