Shiny Tutorial Episode 1. If you answered yes, then this article is for you as I will be showing you how to build your very first web application in R using the Shiny package.
Do you want to make your R code publicly available for others to use? If you answered yes, then this article is for you as I will be showing you how to build your very first web application in R using the Shiny package.
To supplement this article, you can also watch this tutorial video Building Your First Web Application in R that I made on my YouTube channel called the Data Professor.
The data science life cycle starts with the collection of data that will serve as the dataset and the core component of a data science project. Next, this dataset needs to be cleaned to void itself of missing data as well as any abnormalities. As the name implies, exploratory data analysis (EDA) will allow us to take a glimpse of the general characteristics of the dataset and this can be attained by performing descriptive statistics and data visualizations. Insights gained from EDA may provide starting points and ideas for building predictive models (e.g. classification, regression, clustering, association analysis, etc.). To complete the life cycle the best performing model can be deployed as an application programming interface (API) and web application.
So the question is, why would you want to build a web application? That’s a great question!
The deployed web application provides a graphical front-end (user interface or UI) that allow users to enter input parameters that the back-end (the server) will process and send back for display on the web front-end. In the context of machine learning, the deployment of a trained machine learning model as a web application allow users to easily make predictions by simply entering the input parameters into the form provided on the web front-end that will serve as input features to the trained machine learning model where the model will make a prediction.
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Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.