Socio-Economical Status of India using Machine Learning Algorithms
V. Balasankar1, P. Suresh Varma2
1V.Balasankar,*Research Scholar, Department of CSE, Adikavi Nannaya University, A.P., India,Associate Professor, Department of CSE,Aditya college of engineering, Surampalem, Andhra Pradesh, India.
2Dr. P. Suresh Varma, Professor, Department of CSE, Adikavi Nannaya University, Andhra Pradesh, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 3804-3813 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6610018520/2020©BEIESP | DOI: 10.35940/ijrte.E6610.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Machine Leaning, k-means, K-monodies, Socio-Economical, poverty.
Scope of the Article: Machine Leaning.