International Journal of Data Mining and Emerging Technologies
  • Year: 2018
  • Volume: 8
  • Issue: 2

Does Demonetisation and Cashless Economy Really Go Hand in Hand!

  • Author:
  • Akshat Jain1,, Sunil Kumar Singh2, Ankit Gupta3, Ashim Bhasin3, Swastikaa Moudgil4
  • Total Page Count: 11
  • Page Number: 132 to 142

1UG Scholar, CSE Department, Chandigarh College of Engineering and Technology (CCET), Chandigarh, India

2Professor and Head, CSE Department, Chandigarh College of Engineering and Technology (CCET), Chandigarh, India

3Assisstant Professor, CSE Department, Chandigarh College of Engineering and Technology (CCET), Chandigarh, India

4UG Scholar, CSE Department, Chandigarh College of Engineering and Technology (CCET), Chandigarh, India

*Corresponding author email id: akshat.jain.dec15@gmail.com

Abstract

This paper is an analytical study to infer about the various parameters associated with the decision of demonetisation by the Indian government in November 2016, and the introduction of cashless economy in India. A survey was conducted among 264 participants ofthe target audience in April 2017 and various data mining techniques such as multiple regression and correlation analysis (p value) were applied using a highly sophisticated data mining tool – Statistica. There is a brief introduction to the concept of demonetisation and its numerous occurrences in the past. An attempt has been made to investigate the popularity of the cashless modes of transaction among the Indian populace and the factors which influence their decision. The numerous observations made during the development of the paper reveal that majority of the population feels demonetisation could prove beneficial for the Indian economy and that the public is willing to accept the transition towards the cashless modes of payment. The paper also finds an intriguing relationship between the people's opinion on demonetisation and their shift to the cashless modes.

Keywords

Demonetisation, Regression, Economy, Data mining, Cashless mode, Data Elicitation, p value