Employee Attrition Prediction in the USA: A Machine Learning Approach for HR Analytics and Talent Retention Strategies DOI Open Access

Md Sumon Gazi,

Md Nasiruddin,

Shuvo Dutta

и другие.

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 47 - 59

Опубликована: Май 18, 2024

In the dynamic business domain in USA, human capital is one of most instrumental assets for companies. Maintaining high performance and reducing employee attrition has become an utmost priority USA since costs related to can be significant. The chief objective this study was explore application machine learning terms forecasting its ramifications HR analytics talent retention strategies. study, investigator used Jupyter Notebook, interactive platform Python users, design algorithms. dataset utilized research attained from IBM Human Resource workforce survey dataset. current research, proposed array models, notably, Decision Tree, Ada-boost classifier, Random Forest, gradient-boosted classifier. By referring model’s evaluation, it apparent that Forest algorithm had highest accuracy, followed by Gradient Boosting Tree respectively. AdaBoost lowest accuracy. Concerning precision, again precision accordingly. implementing models’ organizations identify high-performing employees at risk quitting, subsequently take proactive steps retain them, saving significant organizational resources. Ultimately, techniques assist government maintaining employees, impact labor shortages, continuity.

Язык: Английский

Analyzing trade-offs, synergies, and driving factors of ecosystem services in Anhui Province using spatial analysis and XG-boost modeling DOI

Jianshen Qu,

Zhili Xu,

Bin Dong

и другие.

Ecological Indicators, Год журнала: 2025, Номер 171, С. 113098 - 113098

Опубликована: Янв. 24, 2025

Язык: Английский

Процитировано

1
Analysis of Ecosystem Service Bundles and Influencing Factors Based on Sofm and Xgboost Models: A Case Study of the Western Dabie Mountains, a Typical Forest Ecosystem in China DOI
Yong Cao,

B. A. C. DON,

Hao Wang

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
A Summary of Recent Advances in the Literature on Machine Learning Techniques for Remote Sensing of Groundwater Dependent Ecosystems (GDEs) from Space DOI Creative Commons
Chantel Chiloane, Timothy Dube, Mbulisi Sibanda

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(8), С. 1460 - 1460

Опубликована: Апрель 19, 2025

While groundwater-dependent ecosystems (GDEs) occupy only a small portion of the Earth’s surface, they hold significant ecological value by providing essential ecosystem services such as habitat for flora and fauna, carbon sequestration, erosion control. However, GDE functionality is increasingly threatened human activities, rainfall variability, climate change. To address these challenges, various methods have been developed to assess, monitor, understand GDEs, aiding sustainable decision-making conservation policy implementation. Among these, remote sensing advanced machine learning (ML) techniques emerged key tools improving evaluation dryland GDEs. This study provides comprehensive overview progress made in applying ML algorithms assess monitor It begins with systematic literature review following PRISMA framework, followed an analysis temporal geographic trends applications research. Additionally, it explores different their across types. The paper also discusses challenges mapping GDEs proposes mitigation strategies. Despite promise studies, field remains its early stages, most research concentrated China, USA, Germany. enable high-quality classification at local global scales, model performance highly dependent on data availability quality. Overall, findings underscore growing importance potential geospatial approaches generating spatially explicit information Future should focus enhancing models through hybrid transformative techniques, well fostering interdisciplinary collaboration between ecologists computer scientists improve development result interpretability. insights presented this will help guide future efforts contribute improved management

Язык: Английский

Процитировано

0
The Impact of Technology on Sales Performance in B2B Companies DOI Creative Commons

John Deep Smith

Deleted Journal, Год журнала: 2024, Номер 3(1), С. 246 - 261

Опубликована: Апрель 7, 2024

This article provides an in-depth exploration of the multifaceted impact technology on sales performance within B2B companies. It delves into how digital transformation and integration advanced technologies such as artificial intelligence, machine learning, big data analytics have revolutionized traditional processes, enhancing efficiency, customer engagement, ultimately, outcomes. The discussion spans several key areas, including pivotal role relationship management (CRM) systems in improving significance marketing reaching engaging with potential customers, transformative effects automation chatbots streamlining operations providing superior service. also touches emerging trend IoT-enabled selling its to offer personalized proactive experiences. Through a series case studies, illustrates successful implementations sales, showcasing tangible benefits improvements performance. However, it addresses challenges barriers adoption, resistance change difficulties, while offering strategies overcome these obstacles. future trends section anticipates further advancements tech-driven practices, highlighting ongoing evolution landscape driven by technological innovation.

Язык: Английский

Процитировано

2
Employee Attrition Prediction in the USA: A Machine Learning Approach for HR Analytics and Talent Retention Strategies DOI Open Access

Md Sumon Gazi,

Md Nasiruddin,

Shuvo Dutta

и другие.

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 47 - 59

Опубликована: Май 18, 2024

In the dynamic business domain in USA, human capital is one of most instrumental assets for companies. Maintaining high performance and reducing employee attrition has become an utmost priority USA since costs related to can be significant. The chief objective this study was explore application machine learning terms forecasting its ramifications HR analytics talent retention strategies. study, investigator used Jupyter Notebook, interactive platform Python users, design algorithms. dataset utilized research attained from IBM Human Resource workforce survey dataset. current research, proposed array models, notably, Decision Tree, Ada-boost classifier, Random Forest, gradient-boosted classifier. By referring model’s evaluation, it apparent that Forest algorithm had highest accuracy, followed by Gradient Boosting Tree respectively. AdaBoost lowest accuracy. Concerning precision, again precision accordingly. implementing models’ organizations identify high-performing employees at risk quitting, subsequently take proactive steps retain them, saving significant organizational resources. Ultimately, techniques assist government maintaining employees, impact labor shortages, continuity.

Язык: Английский

Процитировано

0