A linear regression approach to predicting salaries with visualizations of job vacancies: a case study of Jobstreet Malaysia

Abstract

This study explicitly discusses helping job seekers predict salaries and visualize job vacancies related to their future careers. Jobstreet Malaysia is an ideal platform for discovering jobs across the country. However, it is challenging to identify these jobs, which are organized according to their respective and specific courses. Therefore, the linear regression approach and visualization techniques were applied to overcome the problem. This approach can provide predicted salaries, which is useful as this enables job seekers to choose jobs more easily based on their salary expectations. The extracted Jobstreet data runs the pre-processing, develops the model, and runs on real-world data. A web-based dashboard presents the visualization of the extracted data. This helps job seekers to gain a thorough overview of their desired employment field and compare the salaries offered. The system’s reliability as tested using mean absolute error, the functionality test was performed according to the use case description, and the usability test was performed using the system usability scale. The reliability results indicate a positive correlation with the actual values. The functionality test produced a successful result, and a score of 96.58% was achieved for the system usability scale result, proving the system grade is ‘A’ and usable.

Autoregressive Integrated Moving Average (ARIMA) Algorithm Adaptation for Business Financial Forecasting

“…(3) RMSE and MAE are suitable to be used together for evaluating the model because both are compared to determine whether the forecast contains large but infrequent errors and diagnose the variation in the errors in a set of forecasts [24]. The model's performance improves as the RMSE and MAE decrease [25].…”