Abstract: The examination of raw or crude data and drawing conclusions out of it is called data analytics. In this paper we will be analysing the employee turnover pattern and the factors contributing to it. Efforts will be made to create a model that can predict if a certain employee will leave the company or not. The goal is to create or improve different retention strategies on targeted employees. The first step in data analytics- data pre-processing is presented in the paper. Data pre-processing techniques convert crude data into useful format. Real world data are generally incomplete- noisy, inconsistent and contains many errors. Removing these factors improves the quality of analysis and prediction. The focus of data analytics lies in inference, the process of deriving conclusions. In this paper 2 out of top 3 strategies affecting employee turnover are being analysed and graphs plotted. The 3 top features include evaluation v/s exit, average monthly income v/s exit and satisfaction v/s exit.
Keywords: Examination of raw or crude data and drawing conclusions- data analytics, Employee turnover pattern, Data pre-processing, Evaluation v/s exit, Average monthly income v/s exit and Satisfaction v/s exit
| DOI: 10.17148/IJARCCE.2018.7913