Search Results for Employee Classification

Abstract

In the period of digital transformation, oil companies have to cope with management of huge volume staff data from different parts company everything from management, maintenance, engineering and geology to drill teams at heart workers front line standpoint. This research presents a complete study of big data accuracy and classification improvement in K-means Clustering Learning (KCL) management for 20,000 employees in an oil company. Data were auto-generated according to global standards and technical specifications. The data tables for formwork of human resources bars were 90% prepared by file laziness. In fact, the test kernel used in this research is also based on this data. The study focuses on important problems of work such as raising data quality and classification of employees according to various factors including practical experience, education levels technical expertise, competence achieved in performance evaluations (which may change over time) or safety training hours. Our methodology incorporates advanced preprocessing techniques, feature engineering and hyper parameter optimization in order to achieve better classification accuracy. The experimental results show that the optimized KNN algorithm is capable of 94.2 percent accuracy for employee classification, which represents a significant bat improvement over the traditional method. This research offers practical lessons for oil companies employing machine learning techniques in human resources management and improved operational efficiency learning and operational efficiency.