Empirical Assessment of Profit Predicting Deep Learning Methods

The major problem with the establishment of income in these days’ jobholders is that there is no single formula that can be used for all of them. In calculating the pay of every employee, varying elements include duties, experience level, and special abilities. A further issue is that it may be very expensive to live in the country, thus making it difficult to pay people from different backgrounds appropriately. The third problems are the different types of benefits and incentives provided by different firms, which may not be easy to have exact salaries comparison between all categories. Lastly, often there are external factors that are able to influence the payments to the employees, like the costs of health insurance or the condition of the local economy, where employees live. To ensure that workers are paid fairly for their labor and that all exchange is at par with the market, it is important to know how much to pay the employees in terms of their income. To ensure the company can plan for payment of salaries, advantages, and other amounts related to recruiting and retaining workers, it is also prudent to know the employees’ income. We have developed a design based on the other LSTM models to cope with the problems. In this paper, the voting procedure is used for comparing a variety of methods of deep learning designed for predicting revenue. ConvLSTM, B LSTM, and Stacked LSTM architectures are used to predict income using Long Short-Term Memory. The Socio-Economic data set, which describes the monthly income over 26 years, is used for simulation processing. It is revealed in this study that deep learning methods have applications in income prediction.