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

Abstract

HR's 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. HR employees no longer need to manually handle vast amounts of data with the advent of data mining. The basic purpose of data mining is to extract information from hidden patterns and trends in data to get near-optimal results. 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 PCA. 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 for the prediction of employee performance.