DR. R. RAMAN

Title: Predict employee attrition by using predictive analytics Year: 2018
Authors: Ramakrishnan Raman, Sandeep Bhattacharya, Dhanya Pramod Journal: Benchmarking: An International Journal Publication date: 2018/12/21 Publisher: Emerald Publishing Limited URL: click here Abstract In the past, research papers focused on predicting employee attrition and not on the reasons behind it. However, this research paper uses predictive analytics for employee attrition by mapping out the intentions to quit. The domain chosen for research purposes is a business school. The paper aims to study email patterns and use predictive analysis for employee attrition to correlate with the real-world situation of leaving the business school. Correlation analysis, word count analysis, and sentiment analysis were executed using “R” programming, and sentiment “R” package was used to understand employee sentiments and its association in leaving the business school. By analyzing this study, business schools can predict the chance of faculty members leaving the business school who are of immense value. As a result, appropriate measures can be taken to retain and restrict attrition.

Title: Predict employee attrition by using predictive analytics Year: 2018

Authors: Ramakrishnan Raman, Sandeep Bhattacharya, Dhanya Pramod
Journal: Benchmarking: An International Journal
Publication date: 2018/12/21
Publisher: Emerald Publishing Limited
URL: click here

Abstract

In the past, research papers focused on predicting employee attrition and not on the reasons behind it. However, this research paper uses predictive analytics for employee attrition by mapping out the intentions to quit. The domain chosen for research purposes is a business school. The paper aims to study email patterns and use predictive analysis for employee attrition to correlate with the real-world situation of leaving the business school. Correlation analysis, word count analysis, and sentiment analysis were executed using “R” programming, and sentiment “R” package was used to understand employee sentiments and its association in leaving the business school. By analyzing this study, business schools can predict the chance of faculty members leaving the business school who are of immense value. As a result, appropriate measures can be taken to retain and restrict attrition.

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