Performances evaluation of machine learning models on income forecasting

Abstract

Job seekers, especially those who are looking for their first job, often lack sufficient experience and guidance, which makes it difficult for them to obtain satisfactory salaries. Therefore, salary prediction is very important. For individuals, income ranges can be estimated; for companies, the use of such estimates can guide salary adjustments for employees and prevent the loss of talented personnel, increase company revenue, and reduce operating costs; for governments or countries, these estimates can provide a macro-level assessment of overall income for a large area, such as predicting GDP per capita in a city, making it easier to make economic adjustments and grasp macro development trends. This article uses three models: decision trees, random forests, and neural networks, to train relevant datasets. The dataset is Adult Income Dataset from Kaggle. A total of 32,561 adults are included, including 15 items of data including age, education level, occupation, marital status, working hours per week, and others. The training and test sets were divided into a 7:3 ratio, and the predictive result of each model was evaluated through following figures: accuracy, recall rate, and F1 score. The final conclusion was that the random forest model had the best performance. There is an inseparable relationship between residents' income and the development and happiness of individuals and social stability.