An Experimental Analysis of Work-Life Balance Among The Employees using Machine Learning Classifiers

• URL: http://arxiv.org/abs/2105.07837v1
• Date: Wed, 28 Apr 2021 21:35:43 GMT
• Title: An Experimental Analysis of Work-Life Balance Among The Employees using Machine Learning Classifiers
• Authors: Karampudi Radha, Mekala Rohith
• Abstract summary: We have trained 80% of our data with Random Forest, SVM and Naive Bayes algorithms. Upon testing, the algorithms predict the WLB with 71.5% as the best accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Researchers today have found out the importance of Artificial Intelligence, and Machine Learning in our daily lives, as well as they can be used to improve the quality of our lives as well as the cities and nations alike. An example of this is that it is currently speculated that ML can provide ways to relieve workers as it can predict effective working schedules and patterns which increase the efficiency of the workers. Ultimately this is leading to a Work-Life Balance for the workers. But how is this possible? It is practically possible with the Machine Learning algorithms to predict, calculate the factors affecting the feelings of the worker's work-life balance. In order to actually do this, a sizeable amount of 12,756 people's data has been taken under consideration. Upon analysing the data and calculating under various factors, we have found out the correlation of various factors and WLB(Work-Life Balance in short). There are some factors that have to be taken into serious consideration as they play a major role in WLB. We have trained 80% of our data with Random Forest Classifier, SVM and Naive Bayes algorithms. Upon testing, the algorithms predict the WLB with 71.5% as the best accuracy.

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