Application of Machine Learning to Clustering Countries Based on Dominant Parameters
Author`s Contribution:
- Panipat Institute of Engineering and Technology, India
- Seth Navrang Rai Lohia Jairam Girls College, India
Received: 28.09.2021Accepted: 31.10.2021Published: 24.12.2021
Background and aim of study:
Several researches have been done to study the role of various psychological factors to predict the
happiness. The terms happiness, well-being, quality of life is used interchangeably. There is no universal definition of
happiness, as happiness means different things to different people. It changes with time, age, place etc. Some of the
countries have started measuring growth rate on the basis of happiness index of their nation’s population
Research methods:
In this research work we have applied machine learning model on the datasets available for
the year 2018 to 2020 to find the correlation between various global parameters adapted for identifying the impact of
those parameters on the happiness index for the 156 countries.
Results:
With the help unsupervised K-means learning method, we can analyze the importance of various parameters
and can put the country in the class/cluster like excellent, moderate, average or below average. The above-mentioned
classes signify the placement of the country and accurate classification on basis of various parameters
Conclusion:
This paper discusses the current factors effecting the happiness index of the country. The concept of
happiness index and dimensions that effects the happiness index of the nations is always the matter of discussion and
contradiction. There is no such comprehensive work done on the global parameters of happiness index in the machine
learning field.
Keywords:
machine learning, classification, K-Means method, happiness, world record
Copyright:
© 2021 Athavale V. A., Arora S., Yadav R
DOI and UDC:
DOI: https://doi.org/10.26697/ijes.2021.3.1 JEL: I12, I21, I25, I28 UDC: 37.012.1/3:159.9.07