Prediction of Employee Turnover Using Light GBM Algorithm


Authors : Rajat Keshri, Dr. Srividya P

Volume/Issue : Volume 5 - 2020, Issue 4 - April

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/3ca2iqa

Many employees leave the organisation or the company depending on various factors. This effects the growth and production of the company in many ways. The companies and many MNCs use machine learning methods to predict a turnover of workers to solve this problem. Such predictions help the company in success planning and employee retention. The dataset used in this paper for the above problem comes from the Human Resource Information Systems, which are usually different for different companies. Due to the differences of the dataset in different organisations, it results to a noisy data which makes the models to overfit or produce inaccurate results. This is the main issue which this paper focuses on, and one which has not been discussed traditionally. This paper discusses a new algorithm called the LightGBM, released by Microsoft in 2017. Here, we compare LighGBM with other existing algorithms. Data from the dataset is used to compare LightGBM and other classification algorithms and show LightGBM’s high accuracy of prediction.

Keywords : Machine Learning; Supervised Classification; Retention Prediction; Gradient Boosting

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