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Implementation of Linear and Multiple Regression Analysis for a Visual Analytic Approach to Understanding the Poverty Assessment Through Disaster Impacts in India

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 14)

Abstract

Data mining techniques is used to predict the data its relationships that have not previously been discovered. This paper is based on work which assesses the poverty caused due to the disasters occurred in various states of India and predict the future poverty that are likely to be incur using data mining techniques. The disasters like flood, landslide, earthquake, cyclone, hail storm etc. are studied through literature review considering the data of past five years in different parts of the country and then poverty is predicted. The Main reason for this work is that these disasters have devastated the people dwelling there and have made the area economically and socially weak, causing damage of infrastructure, land, crops also the death of animals and people of all communities no matter what their social status is. All these aspects are directly associated with the poverty. This study adapts the linear regression analysis and multiple regression analysis techniques of data mining. Linear regression efforts to predict the poverty are based by considering year wise feature selection. However, it lacks in predicting many relationships existing in data, so the multiple regressions are used instead. The data sets are normalized to get the values within a range for the purpose of estimation. The visual analytic approach is used to graphically represent the obtained results. Based on the history of the disasters, which are fetched from various sources in internet like news bulletin, news reports, etc., this system predicts the poverty that will probably be hitting the area in the upcoming years. This can be useful for surveyors, and government officials for prediction of poverty and also for sketching the risk management plans.

Keywords

Data mining Regression analysis Linear regression Multiple regression Regression errors Visual analytic approach 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceAmrita University, Mysore CampusMysoreIndia

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