Empirical Statistical Analysis and Cluster Studies on Socio-Economic Status (SES) Dataset

Abstract

Socio-economic status (SES) levels and conditions are extremely influential variables in the study of a particular area of society or any society. Social factors, for instance, the position of caste, religion, marital status, education levels, give good assessment results for us about a person’s goals and the method of achieving their objectives. Generally economic status of any family is needy upon the social factors, for instance, the size of the family, educators in family and levels, and the level of the friendly environment in the family. SES with machine learning (ML) especially cluster analysis is important to identify important features or dimensions of the SES dataset, evaluate the rakings of dimensions and dimensional reductions. In this research, we collected 1742 samples (household information) as per socio-economic ratios and area (rural and urban) wise ratios with good questionnaires between 2018 and 2019 from Rajamahandravaram, East Godavari District, AP, India. We conduct the statistical analysis and cluster analysis for identifying the important factors of SES levels and their problem analysis. In cluster analysis, we apply k-means, hierarchal clustering (HC), and hierarchal with principal component analysis (PCA). The good projection results related to HC and PCA-HC specifies passements of SES class values.