Compact Data Visualization for Feature Relationships and Class Separability

Data visualization is mandatory for different applications, such data interpretation, exploratory analysis, pattern identification, etc. It is rapidly used in the domain of Artificial Intelligence & Machine Learning (AI/ML), healthcare and medical analytics, cybersecurity, finance, IoT, etc. Although it is effectively used in several areas, but it has some issues such as large volume of data, choosing the right visualization technique, data quality, user interpretation, etc. In this paper, five visualization methods are used to illustrate a compact multi-view visualization in the form of an analytical. Finally, the results show that each chart illustrates a specific insight. Among all visualizations, scatter visualization provides richer information with the context of feature relationship and class separability.