The purpose of this work is to build a salary prediction system using machine learning techniques. The experiments are done using the data from 1994 census database which has 32,561 records of employee data. The techniques used in determining whether an employee salary is less than or greater than $50,000 are: logistic regression, decision tree, Naive Bayes classifier, K-nearest neighbor, and support vector machine. We implement these algorithms using original train data and oversampled train data. The results of these models are analyzed and compared with respect to accuracy. According to the experimental results, decision tree model outperforms the other models with original train data. © 2024 IEEE.