A Machine Learning Approach to Analyze and Reduce Features to a Significant Number for Employee’s Turn Over Prediction Model

  • Mirza Mohtashim AlamEmail author
  • Karishma Mohiuddin
  • Md. Kabirul Islam
  • Mehedi Hassan
  • Md. Arshad-Ul Hoque
  • Shaikh Muhammad Allayear
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 857)


Turnover of employee considers as one of the major issue that every company faces. Especially, if the employee has advance skills at his/her working field, then the company faces great loss during that period. To find out the most dominant reasons of employee attrition, we approach by determining features and using machine learning algorithms where features have been processed and reduced beforehand. We have proposed a new model where particular attributes of employee turnover have been selected and adjusted accordingly. In first phase of our reduction method, Sequential Backward Selection Algorithm (SBS) has been used to reduce the features from a higher number to a relatively smaller significant number. After that Chi2 and Random Forest importance algorithm have been used together for the second phase of reduction to determine the common important features by both of the algorithms which can be considered as the foremost features that lead to employee turnover. Our two steps feature selection technique confirms that there are mainly three features that are responsible for employee’s departure. Later, these selected minimal features have been tested with state of the art algorithms of machine learning, such as Decision Tree, Random Forest, Support Vector Machine, Multi-layer Perceptron (MLP), K-Nearest Neighbor (kNN) and Gaussian Naïve Bayes. Lastly, the test result has been visualized by 3D representation to learn the features that are precisely involved for the employee’s turnover.


Component Machine learning SBS Chi2 Predictive model SVM Decision tree Random forest MLP Naïve bayes kNN 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mirza Mohtashim Alam
    • 1
    Email author
  • Karishma Mohiuddin
    • 2
  • Md. Kabirul Islam
    • 1
  • Mehedi Hassan
    • 2
  • Md. Arshad-Ul Hoque
    • 1
  • Shaikh Muhammad Allayear
    • 1
  1. 1.Department of Multimedia and Creative TechnologyDaffodil International UniversityDhakaBangladesh
  2. 2.Department of Computer Science and EngineeringBRAC UniversityDhakaBangladesh

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