Early Prediction of Employee Attrition using Data Mining Techniques

Yadav, Sandeep; Jain, Aman; Singh, Deepti

doi:10.1109/iadcc.2018.8692137

Cited by 50 publications

(18 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, decision tree classifier generates the rules to predict the class label. SandeepYadav [17,18] suggested some data mining techniques for prediction of employee attrition. The model contains 12 features that are related to the professional domain.…”

Section: Related Workmentioning

confidence: 99%

An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

Papineni¹,

Reddy²,

yarlagadda³

et al. 2021

IJETT

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

Papineni¹,

Reddy²,

yarlagadda³

et al. 2021

IJETT

View full text Add to dashboard Cite

show abstract

“…Soni et al (2018) investigated the factors that led to employee turnover and used artificial neural network (ANN) and adaptive neuro‐fuzzy inference System (ANFIS) to predict employee turnover. Yadav et al (2018) used basic classification techniques such as LR, SVM, RF, DT, and AdaBoost for predicting the employee churn, and the best performance was acquired from DT with 99.2% accuracy. Khera and Divya (2018) developed an SVM‐based model to predict employee attrition with data collected from human resources databases of three IT companies.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel deep learning model based on convolutional neural networks for employee churn prediction

Özmen

Özcan

2021

Journal of Forecasting

View full text Add to dashboard Cite

Employees are one of the most important resources of a company. The churn of valuable employees significantly affects a company's performance. The design of systems that predict employee churn is critical importance for companies. At this point, machine learning algorithms offer important opportunities for the diagnosis of employee churn. Nowadays, traditional classification algorithms have been replaced by deep learning models. In this study, firstly, a convolutional neural network (CNN) model was applied on a numerical data set for employee churn prediction in retailing. Later, because the data loss is too much in data transformations, a new hybrid extended convolutional decision tree model (ECDT) was proposed by improving the CNN algorithm. Finally, a novel model (ECDT‐GRID) was developed by applying grid search optimization to improve the classification accuracy of ECDT. Numerical results showed that the developed ECDT‐GRID model outperformed the CNN and ECDT models and basic classification algorithms in terms of classification accuracy, and this model provided an efficient methodology for prediction of employee churn.

show abstract

“…Every time hiring new talent and training them in current technologies involves a great amount of cost to the organization. Apart from this tangible expense, a fair amount of time we need to give the newly employed person to become a productive member of the project [3].…”

Section: After a Week Of Working In A Company Few Decide Onmentioning

confidence: 99%

Churn Prediction of Employees Using Machine Learning Techniques

Bandyopadhyay

Jadhav

2021

Teh. glas. (Online)

View full text Add to dashboard Cite

Employees are considered as the most valuable assets of any organization. Various policies have been introduced by the HR professionals to create a good working environment for them, but still, the rate of employees quitting the Technology Industry is quite high. Often the reason behind their early attrition could be due to company-related or personal issues, such as No satisfaction at the workplace, Fewer opportunities for learning, Undue Workload, Less Encouragement, and many others. This paper aims in discussing a structured way for predicting the churn rate of the employees by implementing various Classification techniques like SVM, Random Forest classifier, and Naives Bayes classifier. The performance of the classifiers was compared using metrics like Confusion Matrix, Recall, False Positive Rate, and Accuracy to determine the best model for the churn prediction. We found that among the models, the Random Forest classifier proved to be the best model for IT employee churn prediction. A Correlation Matrix was generated in the form of a heatmap to identify the important features that might impact the attrition rate.

show abstract

Early Prediction of Employee Attrition using Data Mining Techniques

Cited by 50 publications

References 7 publications

An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

A novel deep learning model based on convolutional neural networks for employee churn prediction

Churn Prediction of Employees Using Machine Learning Techniques

Contact Info

Product

Resources

About