Abstract

Payment plans are a key tactical area for fulfillment and growth of knowledge based industry and also optimum salary offer is essential to retain high performance employees. One of the challenges that industries face fairly often is finding such income facts, based on several information about a current employee or a future employee. Given the characteristics of a current employee or a future employee like his / her demographic profile alongside other information like performance level, qualification, etc., prediction of the salary class are often done by using many well-known machine learning algorithms. But unluckily, those details of employee of any industry are generally not presented publicly for performance evaluation of machine learning algorithms. In this paper, this limitation is overcome to some extent by employing a public database (UCI census data set) which has most of the attributes available for a segment of population for salary prediction. i.e., this paper aimed at examining and investigating three well-known supervised machine learning classifiers namely Gaussian Naive Bayes, KNN(K-Nearest Neighbors) and Decision Tree Classifier using the UCI census data set to find out the best classification algorithm out of above stated three well-known classifiers. It also aimed to determine the most effective classifier to be used in this area. Finally from the investigation we found that KNN(KNearest Neighbors) Classifier performed well in comparison with the opposite two classifiers.