Prediction of employee performance using machine learning techniques

Lather, Anu Singh; Malhotra, Ruchika; Saloni, Priya; Singh, Prabhjot; Mittal, Sarthak

doi:10.1145/3373477.3373696

Cited by 21 publications

(17 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, machine learning behavior also helps in predicting employee turnover [16]. The research studies of numerous research scholars have claimed that machine learning behavior always results in positive employee turnover [17]. Positive employee turnover is a result of efficient employee productivity within an organization.…”

Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Employee Productivity Is Boosted Psychologically by Keeping Attendance System, CSR, Entrepreneurial Intentions, and Machine Learning Behavior

Sun

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

This paper aims to explore the impact of maintaining an attendance system, CSR, entrepreneurial intention, and machine learning behavior on psychological improvement and employee productivity. The paper utilizes both primary and secondary research to explore the relationship between the variables under consideration. The findings show that CSR has a positive impact on the performance of an employee. The machine learning behavior allows employees to select the right career paths that result in high performance, satisfaction, and productivity. In addition, entrepreneurial intentions improve the productivity of employees within an organization as they desire to create new core values. The attendance management system also positively influences employee productivity as it improves the physiological presence of employees.

show abstract

Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Employee Productivity Is Boosted Psychologically by Keeping Attendance System, CSR, Entrepreneurial Intentions, and Machine Learning Behavior

Sun

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Some studies targeted the exploration of psychological, socioeconomic and creative factors on employee performance and motivation [2,12]. One important research study considered the use of prediction model construction algorithms, such as random forest, logistic regression, support vector machine, artificial neural network or naïve Bayes [13].…”

Section: Background and Related Workmentioning

confidence: 99%

Data Analytics for Optimizing and Predicting Employee Performance

Tanasescu,

Vines,

Bologa

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The need to increase employee performance and productivity has become vital in most companies nowadays, considering the number of changes that processes and people have faced during recent years in many organizations. This becomes even more important as it can sustain the growth of the company, as well as the competitiveness. This work will present multiple methods and comparisons between them for the process of building a machine learning algorithm to predict performance scores for employees in one organization; these methods include pre-processing the data, selecting the best variables, building the best algorithms for the available data, and tuning their hyperparameters. The current research aims to conclude on a collection of practices that will determine the best predictions for the given variables, so that human opinion can become less influential in employee appraisal, increasing objectivity and overall productivity.

show abstract

“…Lather et al [12] adopted supervised learning to predict employee performance denoted as three 3 classes (low to high). After trying Support Vector Machines, Random Forest, Naive Bayes, Neural Networks and Logistic Regression, with and the 10-fold validation technique, the best accuracy was obtained with Support Vector Machines.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Machine Learning Algorithm for the Prediction of It Industry Employees Performance

2023

IRJMETS

View full text Add to dashboard Cite

A vital factor influencing corporate governance is maintaining the employees' performance. In this sense, the role of Human Resources Management is crucial, starting from choosing the right person for the job, to implementing a system that monitors the performance over time. Nevertheless, on the one hand, traditional performance monitoring requires direct human observation which takes time and effort, can be unfair and induces a dissatisfactory feeling of tight supervision. On the other hand, electronic performance monitoring which records voluminous data through computer, video or phone monitoring, has proved to induce stress, anxiety, boredom, fatigue and dissatisfaction on the job. One way to reduce the time allocated for this mission and avoid high turnover rate can occur through prediction of outcomes. Machine learning models have been applied in business organizations, especially with the current availability of huge amounts of data. The objective of the present work is to implement a model that predicts employee's performance from several features, based on a survey that was distributed and anonymously filled by 1044 employees. Collected data included overall average performance rating (Manager Rating, Client Rating, Peers Rating), Job Title, Location, Headquarter Location, Work Experience, Current Monthly Salary and Age of Organization. Feature selection specified the most influencing factors affecting performance. After many trials, the best model was the LightGBM classifier that yielded the best accuracy (0.87) with a hamming loss of 0.124056. This work confirms the possibility of adopting Machine Learning based solutions to facilitate and accelerate decision making in organizations, in order to improve employee's working conditions and the company's outcomes.

show abstract

Prediction of employee performance using machine learning techniques

Cited by 21 publications

References 3 publications

[Retracted] Employee Productivity Is Boosted Psychologically by Keeping Attendance System, CSR, Entrepreneurial Intentions, and Machine Learning Behavior

[Retracted] Employee Productivity Is Boosted Psychologically by Keeping Attendance System, CSR, Entrepreneurial Intentions, and Machine Learning Behavior

Data Analytics for Optimizing and Predicting Employee Performance

Implementation of Machine Learning Algorithm for the Prediction of It Industry Employees Performance

Contact Info

Product

Resources

About