Predicting Employee Attrition along with Identifying High Risk Employees using Big Data and Machine Learning

Mhatre, Apurva; Mahalingam, A.; Narayanan, Mahadevan; Nair, Akash; Jaju, Suyash

doi:10.1109/icacccn51052.2020.9362933

Cited by 16 publications

(4 citation statements)

References 20 publications

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“…The G-Square Test helps employers gain psychological insights into their personnel, aiding in understanding performance variations and developing strategic focuses based on motivators. Mhatre et al [12] Explored the correlation between Salary, Rating, and Happiness Index to understand reasons for employee departure. Identified issues with senior management and highlighted the importance of direct communication and fair judgment in performance evaluation.…”

Section: Background Studymentioning

confidence: 99%

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Techniques to Predict Employee Attrition Using Optimized Levy Fruit Fly Optimization Algorithm

Preena

2024

Preprint

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Competent people are a valuable asset for strong businesses. The issue of retaining competent staff with expertise poses a challenge to business owners. Companies may incur losses due to employee turnover if they are unable to replace lost expertise and productivity. Consequently, this research suggests a new model that uses machine learning to forecast staff turnover. The datasets are collected from Kaggle resource. The dataset has been pre-processed using standard scalar with Label Encoding method. The dataset has been trained with ML algorithm. The best features are selected by using modified genetic algorithm (MGA). The classification has been done with KNN, Gradient Boosting and Extra tree classifier. Finally, the attrition prediction using optimized levy fruit fly optimization (OLFFO). The experimental results are compared with ML algorithms with classification metrics (Accuracy, Precision, recall and f-measure).

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Section: Background Studymentioning

confidence: 99%

“…The field of machine learning (ML) is booming, with new applications popping up in all sorts of real-world settings [10]- [12]. This study evaluates and contrasts DT, SVM, and ANN, three machine learning techniques for predicting employee turnover.…”

Section: Introductionmentioning

confidence: 99%

Techniques to Predict Employee Attrition Using Optimized Levy Fruit Fly Optimization Algorithm

Preena

2024

Preprint

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“…The decrease of employee engagement, monotonous work life, and low career expectations are among the causes of employee attrition (Mhatre et al, 2020). The causes of employee attrition are monotonous jobs, job stress, long working hours, and excessive workloads.…”

Section: Extended Abstractmentioning

confidence: 99%

Kurumlarin Engellenemeyen Olgusu: Çalişan Yipranmasi

YİĞİT¹

2022

joh

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Günümüz dinamik ve karmaşık iş dünyasında, zorlu rekabet koşulları, stres, belirsizlikler, kriz dönemleri gibi zorlukların hem kurumlara hem de çalışanlara zararlı olmaktadır. Çeşitli zorluklar sebebi ile çalışanlar mutsuz olmakta ve işlerinden ayrılmaktadır. Çalışan yıpranması, kişinin kendisini işe karşı isteksiz ve mutsuz hissetmesi, kişinin işe yönelik motivasyonunun kırılmasına işaret etmektedir. Kurumlarda çalışan yıpranması genellikle gönüllü ve gönülsüz yıpranma şeklinde görülmektedir. Gönüllü yıpranma çalışanın isteğine bağlı olarak gerçekleşir iken; gönülsüz yıpranma işverenin isteği doğrultusunda gerçekleşmektedir. Kurumlar için endişe ve sorun yaratmaktadır. Çalışan yıpranması bireylere zarar vermesinin yanı sıra, kurumlara ve müşterilere de zarar vermektedir. Monoton işler, işin doğasının stresli olması, uzun çalışma saatleri, aşırı iş yükü çalışan yıpranmasına neden olmaktadır. Verimliliğin, üretkenliğin düşmesi, hizmet kalitesinin azalması, kuruma eğitim maliyetleri, finansal kayıplar, müşteri kayıpları ile sonuçlanmaktadır. Çalışan yıpranmasını tahmin etmek bu durumu en aza indirmenin yollarından biridir. Bu noktada, kurumlar çalışan yıpranmasını ciddi bir sorun olarak düşünmeli ve ona göre stratejiler belirlemelidir. Bu çalışmada, yerli yazında sayıca çok az çalışılan "çalışan yıpranması" kavramı detaylı olarak ele alınmış ve konuya ilişkin öneriler sunulmuştur.

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“…In addition, big data nowadays is used to target specific markets instead of spending tremendous money on advertising which reduces the costs for the organisations. Identifying risks and foreseeing potential opportunities are now possible by using big data [9]. Eventually, big data will be in almost every industry with significant advantages [10].…”

Section: Introductionmentioning

confidence: 99%

Integrating Big Data Analytics into Business Process Modelling: Possible Contributions and Challenges

AL-Madhrahi,

Singh,

Yadegaridehkordi

2022

IJACSA

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Business Process Modelling (BPM) is a set of organised, structured, and related activities that boost the development and evolution of an organisation's success by understanding, improving, and automating existing business processes. Recently, the integration of Big Data Analytics (BDA) into BPM has widely gained more attention as a unique opportunity for organisations to enhance their efficiency, effectiveness, added value, and competitive advantage. However, some organisations still rely on old data-driven strategies and are late in integrating BDA into their BPM. This study aims to explore the possible contributions and challenges of integrating BDA into BPM. This study discovered that better decision making, embracing the organisation's performance, upgrading business process capabilities, and supporting supply chain management are the main contributions of BDA to BPM in organisations. However, poor data quality, shortage of BDA professionals, and data security and protection are the main challenges that hinder organisations from implementing BDA. This study provides valuable insights for organisations that intend to implement big data technologies in their business processes.

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Predicting Employee Attrition along with Identifying High Risk Employees using Big Data and Machine Learning

Cited by 16 publications

References 20 publications

Techniques to Predict Employee Attrition Using Optimized Levy Fruit Fly Optimization Algorithm

Techniques to Predict Employee Attrition Using Optimized Levy Fruit Fly Optimization Algorithm

Kurumlarin Engellenemeyen Olgusu: Çalişan Yipranmasi

Integrating Big Data Analytics into Business Process Modelling: Possible Contributions and Challenges

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