Abstract
IBM Watson Human Resource Employee Attrition Dataset is analysed to predict the employee attrition based on five selected attributes which are Gender, Distance from Home, Environment Satisfaction, Work Life Balance and Education Field out of 36 variables present in the dataset. Association Rule Algorithm ‘Apriori’ along with Decision Tree Algorithm ‘C5.0’ is used. The processing time taken to predict an attrition using the selected attributes using C5.0 with association is 0.02 ms while using traditional C5.0 is 2 ms. RAM consumption for C5.0 with association is 30.89 MB while for traditional C5.0, it is 48 MB. This is a new approach to predict the employee attrition which is better in efficiency than simply applying decision tree algorithms.
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Bindra, H., Sehgal, K., Jain, R. (2019). Optimisation of C5.0 Using Association Rules and Prediction of Employee Attrition. In: Bhattacharyya, S., Hassanien, A., Gupta, D., Khanna, A., Pan, I. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-13-2354-6_3
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DOI: https://doi.org/10.1007/978-981-13-2354-6_3
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