Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Employee Attrition, Job Involvement, and Work Life Balance Prediction Using Machine Learning Classifier Models Subject The topic of the resource Attrition rate; Control variables; CRISP-DM method; Job Involvement; Low-code library approach; Machine learning; PyCaret; Ridge classifiers Description An account of the resource Employee performance is an integral part organizational success, for which Talent management is highly required, and the motivating factors of employee depend on employee performance. Certain variables have been observed as outliers, but none of those variables were operated or predicted. This paper aims at creating predictive models for the employee attrition by using classifier models for attrition rate, Job Involvement, and Work Life Balance. Job Involvement is specifically linked to the employee intentions to turn around that is minimal turnover rate. So, getting justifiable solution, this paper states the novel and accurate classification models. The Ridge Classifier model is the first one it has been used to classify IBM employee attrition, and it gave an accuracy of 92.7%. Random Forest had the highest accuracy for predicting Job Involvement, with accuracy rate of 62.3%. Similarly, Logistic Regression has been the model selected to predict Work Life Balance, and it has a 64.8% accuracy rate, making it an acceptable classification model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. Creator An entity primarily responsible for making the resource Kumar R.; Tople A. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-726 LNNS, pp. 907-915. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-99-3716-5_72" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-3716-5_72</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174449897&amp;doi=10.1007%2F978-981-99-3716-5_72&amp;partnerID=40&amp;md5=df9f57af1a244e8d51c315fcaa888a18" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174449897&amp;doi=10.1007%2f978-981-99-3716-5_72&amp;partnerID=40&amp;md5=df9f57af1a244e8d51c315fcaa888a18</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981993715-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar R., Christ (Deemed to be University), Lavasa, Pune, India; Tople A., Analytics and Insights at Tata Consultancy Services, Pune, India