Bike sharing has become a popular transportation option in many cities around the world. With increasing environmental awareness and the need for sustainable transportation options, bike sharing systems have seen significant growth. However, for these systems to operate efficiently, it is crucial to predict the demand for bikes at different stations and locations. The goal of this project is to develop a predictive model that can estimate bike sharing demand based on various factors such as weather, time of day, day of the week, and special events. Using data analytics and machine learning techniques, the project aims to provide a tool that helps bike sharing system operators optimize bike distribution and availability, thereby improving user experience and operational efficiency.

In this project, tasks will include reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model.

In this project, we will focus on analyzing COVID-19 case data using the R programming language. The main objective is to obtain updated COVID-19 data using web scraping techniques and manipulate this data to extract valuable information and generate informative visualizations.