Employee Attrition Prediction Using Classification Models

Bhartiya, Namrata; Jannu, Sheetal; Shukla, Purvika; Chapaneri, Radhika

doi:10.1109/i2ct45611.2019.9033784

Cited by 11 publications

(3 citation statements)

References 5 publications

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“…The systematic flow for predicting employee attrition using machine learning techniques was proposed in this research study [24]. The machine learning models Naive Bayes, Random Forest, Decision Tree, Support Vector Machine, and K-Nearest Neighbor were applied using the python tool.…”

Section: Related Workmentioning

confidence: 99%

Predicting Employee Attrition Using Machine Learning Approaches

et al. 2022

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Employee attrition refers to the natural reduction in the employees in an organization due to many unavoidable factors. Employee attrition results in a massive loss for an organization. The Society for Human Resource Management (SHRM) determines that USD 4129 is the average cost-per-hire for a new employee. According to recent stats, 57.3% is the attrition rate in the year 2021. A research study needs to be implemented to find the causes of employee attrition and a learning framework to predict employee attrition. This research study aimed to analyze the organizational factors that caused employee attrition and the prediction of employee attrition using machine learning techniques. The four machine learning techniques were applied in comparison. The proposed optimized Extra Trees Classifier (ETC) approach achieved an accuracy score of 93% for employee attrition prediction. The proposed approach outperformed recent state-of-the-art studies. The Employee Exploratory Data Analysis (EEDA) was applied to determine the factors that caused employee attrition. Our study revealed that the monthly income, hourly rate, job level, and age are the key factors that cause employee attrition. Our proposed approach and research findings help organizations overcome employee attrition by improving the factors that cause attrition.

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Section: Related Workmentioning

confidence: 99%

Predicting Employee Attrition Using Machine Learning Approaches

et al. 2022

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“…Attributes like Gender, Education Field, and Performance Rate were visualized for Attrition parameters thus giving an idea on the relevant features. A comparison between the performance metrics of the classification models provided new insights on improving the work ethics [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Churn Prediction of Employees Using Machine Learning Techniques

Bandyopadhyay

Jadhav

2021

Teh. glas. (Online)

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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.

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“…Nowadays, the most fruitful and dynamic areas of study are data mining and machine learning. Classification, grouping, and prediction are three areas that make use of data mining techniques [1,2]. The significance of data mining and machine learning has led to the use of several supplementary methods in fields as diverse as human resource management, mobile gaming, education, and healthcare, finance, and security systems [3,4].…”

Section: Introductionmentioning

confidence: 99%

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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Employee Attrition Prediction Using Classification Models

Cited by 11 publications

References 5 publications

Predicting Employee Attrition Using Machine Learning Approaches

Predicting Employee Attrition Using Machine Learning Approaches

Churn Prediction of Employees Using Machine Learning Techniques

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

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