Predictive Analytics Using Statistics and Big Data: Concepts and Modeling

Predictive Analytics Using Statistics and Big Data: Concepts and Modeling

This book presents a selection of the latest and representative developments in predictive analytics using big data technologies. It focuses on some critical aspects of big data and machine learning ...
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An Epidemic Analysis of COVID-19 using Exploratory Data Analysis Approach

Pp. 99-111 (13)

DOI: 10.2174/9789811490491120010010

Author(s): Chemmalar Selvi G., Lakshmi Priya G. G.

Abstract

The outbreak of data has empowered the growth of the business by adding business values from the available digital information in recent days. Data is elicited from a diverse source of information systems to bring out certain kinds of meaningful inferences, which serve closer in promoting the business values. The approach used in studying such vital data characteristics and analyzing the data thoroughly is the Exploratory Data Analysis (EDA), which is the most critical and important phase of data analysis. The main objective of the EDA process is to uncover the hidden facts of massive data and discover the meaningful patterns of information which impact the business value. At this vantage point, the EDA can be generalized into two methods, namely graphical and non-graphical EDA’s. The graphical EDA is the quick and powerful technique that visualizes the data summary in a graphical or pictorial representation. The graphical visualization of the data displays the correlation and distribution of data before even attempting the statistical techniques over it. On the other hand, the non-graphical EDA presents the statistical evaluation of data while pursuing its’ key characteristics and statistical summary. Based on the nature of attributes, the above two methods are further divided as Univariate, Bivariate, and Multivariate EDA processes. The univariate EDA shows the statistical summary of an individual attribute in the raw dataset. Whereas, the bivariate EDA demonstrates the correlation or interdependencies between actual and target attributes; the multivariate EDA is performed to identify the interactions among more than two attributes. Hence, the EDA techniques are used to clean, preprocess, and visualize the data to draw the conclusions required to solve the business problems. Thus, in this chapter, a comprehensive synopsis of different tools and techniques can be applied with a suitable programming framework during the initial phase of the EDA process. As an illustration, to make it easier and understandable, the aforementioned EDA techniques are explained with appropriate theoretical concepts along with a suitable case study.

Keywords:

Bivariate analysis, Data visualization, Exploratory data analysis (EDA), Multivariate analysis, Statistical methods, Univariate analysis.