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    A web app for predicting voluntary employee attrition using R Shiny & Rstudio

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    hdip_sorenson_d_2020.pdf (1.627Mb)
    Author
    Sorenson, Douglas
    Date
    2020
    Degree
    Higher Diploma in Data Analytics
    URI
    https://esource.dbs.ie/handle/10788/3984
    Publisher
    Dublin Business School
    Rights holder
    http://esource.dbs.ie/copyright
    Rights
    Items in Esource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holde
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    Abstract
    Knowledge is one of the most critical resources that an organisation can possess. However, knowledge only constitutes an asset when it is shared and utilised effectively by firms. This capstone project shows how web applications, such as the R Shiny App, can assist the Human Resource (HR) function apply knowledge and insights from data mining to guide HR initiatives/programmes that can potentially mitigate attrition. Six supervised machine learning (ML) algorithms were applied to the IBM HR dataset to identify the determinants of, and predict, voluntary employee attrition, using R Shiny. Internal work-related factors such as working overtime, business travel and delayed promotional opportunities were identified as negative determinants of employee attrition. In contrast, behavioural dimensions of Human Capital (HC) were found to be positive determinants of employee attrition. The results showed that the best performing algorithm based on balanced accuracy score was Logistic Regression (AUC = 0.7573). However, Naïve Bayes performed best on the sensitivity metric (Sensitivity = 0.86) while the Decision Tree model achieved the highest specificity score (Specificity = 0.8607). These findings strongly suggested that the choice of ML model for predicting voluntary employee attrition should be guided by a firm’s HR retention strategy (if one such exists) and cost-benefit implications.
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