Socio-Economical Status of India using Machine Learning Algorithms

Abstract

Data is everywhere and lots of data is openly available to people. We can analyze this data to find the hidden and unnoticed information to use it purposefully. One important source of information is census data and it provides data related to the people living in a country. Analyzing such data is useful for knowing the socio economic status of the country. Data mining and machine learning techniques can be used to analyze such large volumes of data. In this work Indian census 2011 is analyzed and identified the socio economic status of different states of India. To identify the social status of each state we studied literacy rate, categories of workers in different fields, gender wise working population. To identify economical status like people living below poverty and above poverty we used clustering techniques of machine learning. At first we pre-processed the data and later correlation based feature selection was applied, and on that result k-means and k-mediods clustering methods were implemented independently. Finally the clusters are evaluated to see the performance using confusion matrix. The final results show that k-mediod has better performance than K-means.