Research Paper
Year: 2023 | Month: May | Volume: 10 | Issue: 5 | Pages: 323-337
DOI: https://doi.org/10.52403/ijrr.20230539
Modelling the Employment Rate in Türkiye Using Machine Learning Methods
Dr. Cagatay Tuncsiper
PhD., Centrade Fulfillment Services Inc. co-founder, 1762. St. No. 9, 35600 Karsiyaka, Izmir, Turkiye
ABSTRACT
Employment rate is a crucial economic indicator that measures the percentage of the working-age population that is employed. Considering this importance, the employment rate of Türkiye is modelled in this work. The employment rate, gross domestic product, gross fixed capital formation, government expenditures, export and import data of the 1991-2021 period are taken from the World bank database and then the seasonal-trend decomposition of these data are performed in EViews software. After inspecting the nonlinearity of the data, a nonlinear machine learning model namely a feedforward artificial neural network model is developed in Python programming language for the modelling of the employment rate dependent on the gross domestic product, gross fixed capital formation, government expenditures, export and import data. The 70% of the available data is used as the training data and the developed feedforward artificial neural network is trained with a successful convergence despite the low number of samples thanks to its optimal structure. Then the remaining 30% of the available data are utilized as the test data. The actual employment rate data and the result of the developed feedforward neural network model are plotted on the same axis pair which show continuous overlap in a wide range. Apart from the visual inspection of the results of the developed model, the performance metrics are also calculated in Python programming language such as the mean absolute percentage error, coefficient of determination, root mean square error and the mean absolute error. The performance metrics of the results of the developed model also verify the high accuracy of the model. It is argued that the developed feedforward neural network model can also be used for the situations where low number of samples need to be modelled thanks to the optimal structure.
Keywords: Employment rate, modelling, machine learning, artificial neural networks, estimation.
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