Shaik Subhani

Work place: Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telanganga-501301



Research Interests: Data Structures and Algorithms, Data Mining, Image Processing, Computer systems and computational processes


Dr. Shaik Subhani received his Ph. D in Computer Science and Engineering from ANU Guntur. His research focus is on Data Mining, Image Processing applications, Image Retrieval Systems, Semantic Analysis, Feedback Systems, Social Network Analysis, Face Recognition Systems, Machine Learning, Database Systems and Data Mining, ,Big Image data analysis, Computer Networks and Security, Mobile Adhoc and Sensor Networks, Green Computing and Communications, Cloud Computing and Information Retrieval Systems. He has published more than 50 Research papers in international journals and 20 papers published in National and International conferences. His areas of interest are Data Mining, Soft Computing and Image Processing. He received best teacher award in 2015-16 academic year

Author Articles
Predictive Analytics of Employee Attrition using K-Fold Methodologies

By V. Kakulapati Shaik Subhani

DOI:, Pub. Date: 8 Feb. 2023

Currently, every company is concerned about the retention of their staff. They are nevertheless unable to recognize the genuine reasons for their job resignations due to various circumstances. Each business has its approach to treating employees and ensuring their pleasure. As a result, many employees abruptly terminate their employment for no apparent reason. Machine learning (ML) approaches have grown in popularity among researchers in recent decades. It is capable of proposing answers to a wide range of issues. Then, using machine learning, you may generate predictions about staff attrition. In this research, distinct methods are compared to identify which workers are most likely to leave their organization. It uses two approaches to divide the dataset into train and test data: the 70 percent train, the 30 percent test split, and the K-Fold approaches. Cat Boost, LightGBM Boost, and XGBoost are three methods employed for accuracy comparison. These three approaches are accurately generated by using Gradient Boosting Algorithms.

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