# An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

**Authors:** Swarajya lakshmi v papineni, A. Mallikarjuna Reddy, Sudeepti yarlagadda, Snigdha Yarlagadda, Haritha Akkinen  
**arXiv:** [2105.07855](https://arxiv.org/abs/2105.07855)  
**Date:** 2021-05-07  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2105.07855v1/

## Key points

- Many software employees are considering changing job roles due to high pay in fields such as data science, business analysis, and artificial intelligence
- Factors like work-life imbalances, low pay, and uneven shifts contribute to employees' desire for change
- A proposed system has been developed to address these issues and ensure efficient human resource management within the company
- The system utilizes a random forest algorithm to create a model that takes into account various employee parameters
- By identifying gaps and addressing them, the HR department can improve employee retention and facilitate smooth operations within the organization
- Integration of HR practices with data science enhances productivity and promotes collaboration and well-being among employees
- Strategies can be developed based on external and social factors to positively impact employee performance
- Several studies have explored different approaches for predicting employee attrition using machine learning techniques
- IshaanBallal et al. proposed a supervised ML classification approach utilizing the random forest algorithm to identify key features influencing employees' decision to leave their jobs [10-11]
- Sabina-Cristiana Necula developed a data science architecture that analyzes resumes using semantic web technologies and supervised machine learning algorithms to identify candidates with the right technical skills [12-16]
- Sandeep Yadav suggested data mining techniques for predicting employee attrition by analyzing 12 professional domain-related features [17-18]
- These studies highlight the importance of leveraging advanced analytics and machine learning algorithms in human resource management to enhance recruitment processes, retain valuable employees, and improve organizational performance.

## Summary

In a recent job survey, it was found that many software employees are considering changing their job roles due to high pay in fields such as data science, business analysis, and artificial intelligence. Factors like work-life imbalances, low pay, and uneven shifts also contribute to employees' desire for change. To address these issues and ensure efficient human resource management within the company, a proposed system has been developed. This system utilizes a random forest algorithm to create a model that takes into account various employee parameters. By identifying gaps and addressing them, the HR department can improve employee retention and facilitate smooth operations within the organization. The integration of HR practices with data science not only enhances productivity but also promotes collaboration and well-being among employees. Additionally, strategies can be developed based on external and social factors to positively impact employee performance. Furthermore, several studies have explored different approaches for predicting employee attrition using machine learning techniques. IshaanBallal et al. proposed a supervised ML classification approach that achieved good accuracy by utilizing the random forest algorithm to identify key features influencing employees' decision to leave their jobs [10-11]. Sabina-Cristiana Necula developed a data science architecture that analyzes resumes using semantic web technologies and supervised machine learning algorithms to identify candidates with the right technical skills [12-16]. Sandeep Yadav suggested data mining techniques for predicting employee attrition by analyzing 12 professional domain-related features [17-18]. Their model achieved high accuracy using decision tree classification. Overall, these studies highlight the importance of leveraging advanced analytics and machine learning algorithms in human resource management to enhance recruitment processes, retain valuable employees, and improve organizational performance.