Comparative Analysis of Machine Learning Techniques for the Prediction of Employee Performance

Adeniyi, Jide and Adeniyi, A. E. and Yetunde, J. O. and Egbedokun, G. O. and Ajagbe, K. D. and Obuzor, P. C. and Ajagbe, S. A. Comparative Analysis of Machine Learning Techniques for the Prediction of Employee Performance. ParadigmPlus, 3 (3). pp. 1-15.

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Abstract

Human Resources’ purpose is to assign the best people to the right job at the right time, train and 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 and most useful information from such large amounts of data is tough. Human Resource employees no longer need to manually handle vast amounts of data with the advent of data mining. Data mining’s primary 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 the prediction 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 employee performance.

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