Effectiveness of preprocessing strategies for work hours prediction based on machine learning model

Under the development of society, labor problems are increasingly concerning people. Workers try to figure out whether the salary and work hours set by their employers are fair. Additionally, companies also need to offer reasonable salaries and work hours to their workers to avoid disputes. Although there are a lot of studies conducted to do the prediction of salary and explore the best algorithm for the prediction, the research about work hours prediction and detecting other factors influencing the prediction are still deficient. This research applies linear regression to a dataset to predict work hours. Meanwhile, different methods of data pre-processing are also utilized to detect their effect on the regression. It can be discovered from the study that the prediction of work hours is feasible and linear regression can be leveraged for doing so. Meanwhile, different data pre-processing also influence the result of the regression, and one-hot encoding performs better than label encoding. Also, dropping features of the dataset seems to affect a lot when the features are not too many. The model using one-hot encoding and not dropping columns has the lowest Mean Square Error (MSE) of about 98.2, while other models all have MSE over 100. In conclusion, the study provides a baseline for work hours prediction and gives other methods to improve the accuracy of prediction besides finding the best algorithm.