An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

TL;DR

This paper presents a data-driven model using Random Forest to analyze employee parameters, aiming to improve HR strategies for better employee retention and organizational efficiency.

Contribution

It introduces a novel application of Random Forest in HR analytics to identify employee retention factors and enhance organizational management.

Findings

Improved employee retention prediction accuracy

Identification of key employee parameters affecting retention

Enhanced HR decision-making processes

Abstract

The current job survey shows that most software employees are planning to change their job role due to high pay for recent jobs such as data scientists, business analysts and artificial intelligence fields. The survey also indicated that work life imbalances, low pay, uneven shifts and many other factors also make employees think about changing their work life. In this paper, for an efficient organisation of the company in terms of human resources, the proposed system designed a model with the help of a random forest algorithm by considering different employee parameters. This helps the HR department retain the employee by identifying gaps and helping the organisation to run smoothly with a good employee retention ratio. This combination of HR and data science can help the productivity, collaboration and well-being of employees of the organisation. It also helps to develop strategies that have an impact on the performance of employees in terms of external and social factors.