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New Employee Attrition prediction in the field of medical marketing personnel in Sri Lanka

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dc.contributor.author Alwis, Mewan
dc.date.accessioned 2020-07-24T18:12:45Z
dc.date.available 2020-07-24T18:12:45Z
dc.date.issued 2019
dc.identifier.citation Alwis, Mewan (2019) New Employee Attrition prediction in the field of medical marketing personnel in Sri Lanka. MSc. Dissertation Informatics Institute of Technology en_US
dc.identifier.other 2017036
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/479
dc.description.abstract Employees are considered as one of the most valuable assets of any organization. Modern day organizations invest considerably on them and therefore unexpected early departures would be costly in terms of money, time and loss of business. Many attempts have been made in the area of attrition prediction but lacks in the scope of new medical marketing rep hiring and the aim is to find a solution. Employee attrition prediction has been researched in the past and mix of algorithms, such as SVM, Logistic Regression, Random Forest and KNN, have been tested for their suitability. Having a unique situation and dataset, this research aims to find suitable algorithm through an optimum parameter selection. This research as able develop a classification model for new employee attrition and a unique dataset with fresh recruiter attrition that performs well at an 70% accuracy. The applications and implications of the classification model applied is debated and assessed in the project report. en_US
dc.subject Azure Machine Learning services en_US
dc.subject Machine Learning en_US
dc.subject Data mining en_US
dc.subject Supervised Learning en_US
dc.title New Employee Attrition prediction in the field of medical marketing personnel in Sri Lanka en_US
dc.type Thesis en_US


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