Abstract: Enterprise culture is the soul of an enterprise, which is the key to obtain sustainable competitive advantage. For enterprise survival and development, enterprise culture is not the direct factor, but the most lasting decisive factor. In this paper, given the important role of enterprise culture in the process of human resource management practice, combining cultural construction with recruiting ,training, utilizing, and retaining talent to improving the level of human resource management to achieve benign interaction between culture construction (of the company) and human resource management. An effort is made in realizing a long-term sustainable competitive goal to obtain an invincible position in the present competitive market. Human Resource Management can be defined as planning, organizing, directing and compensating human resources resulting in the creation and development of human relation with a view to contribute proportionately to the organizational and individual goals. 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. In this paper we have taken up the challenge of predicting the exit vs. evaluation trends of the company, first using Logistic Regression method, and later using Random forest or Random decision forest method. We also implement SVM (Support Vector Machine) and KNN (K Nearest Neighbour) classification algorithm on the dataset and compare the accuracies of the model. Best model is selected on the basis of ROC graph and Accuracy Percentage.
Keywords: Logistic Regression, Random decision forest, Human Resource Management, 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, satisfaction v/s exit, planning, organizing, directing and compensating, competitive advantage, SVM (Support Vector Machine), KNN (K Nearest Neighbour) classification algorithm, ROC graph.
|
DOI:
10.17148/IJARCCE.2021.10776
