![]() ![]() Then, by using R, I will arrange the data in the correct format to build the machine learning model and the Shiny application. To put a purpose to the web application, in the data understanding section, I will create several business questions that I will walk through to build the R Shiny. ![]() Then, I will create a prediction model to help the user of the application predict the number of total bike registration in the system by taking into consideration weather conditions and a specific day of the year.įurthermore, I will describe the data to get in the context of the information that the dataset contains. Further, I will develop an exploratory data analysis of bike-sharing data in the form of interactive graphs. The first segment of the article covers R Shiny basics, such as the explanation of its functionality. The objective of the present article is to provide a simple guide on how to develop an R Shiny application to analyze, explore, and predict variables within a dataset. A simple guide of R Shiny to analyze, explore, and predict bike-sharing registrations ![]()
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