Comparison of the Performance of Machine Learning Techniques in the Prediction of Employee

ParadigmPlus

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Jide Kehinde Adeniyi¹  Abidemi EmmanuelAdeniyi²  Yetunde JosephineOguns³  Gabriel OlumideEgbedokun³  Kehinde Douglas Ajagbe⁴  Princewill Chima Obuzor⁵  Sunday Adeola Ajagbe⁶ 

[1]Landmark University

[2]Precious Cornerstone University

[3]The Polytechnic Ibadan

[4]Kogi State College of Education

[5]University of Salford

[6]Ladoke Akintola University of Technology LAUTECH

关键词: Machine learning;    Performance;    Human resources;    Employee;    Data mining;   

DOI  :  10.55969/paradigmplus.v3n3a1

学科分类：环境工程

来源: ITI Research Group

【 摘 要 】

Human Resources’ purpose is to assign the best people to the right job at the right time, trainand qualify them, and provide evaluation methods to track their performance and safeguard employees’ perspective skills. These data are crucial for decision-makers, but collecting the best andmost useful information from such large amounts of data is tough. Human Resource employees nolonger need to manually handle vast amounts of data with the advent of data mining. Data mining’sprimary goal is to uncover information hidden in data patterns and trends in order to produce results that are close to ideal. This study aims at comparing the performance of three techniques in theprediction of performance. The dataset undergoes preprocessing steps that include data cleaning,and data compression using Principal Component Analysis. After preprocessing, training and classification were done using Artificial Neural Network, Random Forest, and Decision tree algorithm.The result showed that Artificial Neural networks performed the best in the prediction of employeeperformance.

【 授权许可】

CC BY