Research Article

DOI :10.26650/JODA.1242645 IUP :10.26650/JODA.1242645 Full Text (PDF)

Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms

Merve Babaoğlu, Ahmet Coşkunçay, Tolga Aydın

The automobile sector is the locomotive of industrialized countries. The employment opportunities it creates are of great value because of its interconnectedness with other industries and the value it adds. Demand forecasting studies in such an important sector are one of the main drivers for the provision of raw materials and services needed in the future. In this study, 10 independent variables are used that directly or indirectly affect the level of car sales, which is our dependent variable. These variables are gross domestic product, real sector confidence index, capital expenditures, household consumption expenditures, inflation rate, consumer confidence index, percentage of one-year term deposits, and oil barrel, gold, and dollar prices. The dataset used consists of annual data between 2000 and 2021. To examine the sales forecast model, two variables that affect minimum sales are first extracted from the model using the least squares method. Linear Regression, Decision Tree, Random Forest, Ridge, AdaBoost, Elastic-net, and Lasso Regression algorithms are applied to build a predictive model with these variables. The Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2 ) are used to compare the performance of the predictive models. This study proposes an approach for sectors affected directly or indirectly by automotive sales to gain foresight on this issue.

Keywords: Automobile Sales, Regression, Demand Forecasting

References

Alper, C. E., Mumcu, A. (2000). Türkiye’de otomobil talebinin tahmini. Research Report, Boğaziçi University. http:// ideas.econ.boun.edu.tr/content/wp/ISS_EC_05_01.pdf. google scholar
Bulut, H. (2018). R uygulamaları ile çok değişkenli istatistiksel yöntemler. Nobel Academy. google scholar
Civelek, Ç. (2021). Tractor Sales Forcasting for Turkey Using Artificial Neural Network. European Journal of Science and Technology ,31 (1), 375-381. google scholar
Cutler, A., Cutler, D. R. And Stevens, J. R. (2012), Random forests, Ensemble Machine Learning Methods and Applications,45(1), 157-176. google scholar
Devhunter. (2022, May 05), Rastgele Orman (Random Forest) Algoritması, Https://Devhunteryz.Wordpress. Com/2018/09/20/Rastgele-Ormanrandom-Forest- Algoritmasi/Comment-Page-1/ google scholar
Dikmen, I. (2006). Otomotiv Sektörü Ve Rekabet Değerlendirme. Access Date: 10.12.2011, Http://Www.Kalder.Org.Tr/Genel/15kongre/Sunumlar/Isik_Dikmen.Doc. google scholar
Euronews. (2022, June 04). Türkiye’de 100 kişiye düşen otomobil sayısı 14, AB’de ise 51, Https://Tr.Euronews. Com/2019/12/30/Turkiye-De-100-Kisiye-Dusen-Arac-Say-S-28-Ab- De-Ise-51. google scholar
Freund, Y., Schapire, R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139. google scholar
Görener A., Görener Ö. (2008). The contributions of automotive industry in Turkish economy and sectoral-expectances about the future. Journal Of Yaşar University, 7(26), 306-319. google scholar
Hulsman, M., Borscheid, D., Friedrich, C.M., Reith, D. (2012). General sales forecast models for automobile markets and their analysis. Transactions On Machine Learning and Data Mining, 5(2), 65-86. google scholar
Karaatlı, M., Helvacıoğlu, Ö., Ömürbek, N., Tokgöz, G. (2012). An artificial neural network based automobile sales forecasting . International Journal of Management Economics and Business, 8(17), 87-100. google scholar
Kaya, K.S., Yildirim, Ö. (2020). A prediction model for automobile sales in Turkey using deep neural networks. Journal Of Industrial Engineering, 31(1), 57-74. google scholar
Kuvvetli, Y., Dağsuyu, C., Oturakçı, M. (2015). A prediction approach based on artificial neural networks with consideration of environmental and economic indicators for car sales in Turkey. Journal of Industrial Engineering, 26(3), 23-31. google scholar
Lin, Z.C., Wu, W.J. (1999). Multiple linearregression analysis of the overlay accuracy model zones. IEEE Trans. On Semiconductor Manufacturing, 12(2), 229 - 237. google scholar
Mmpvizyon 2023 Otomotiv Sektör Raporu,” Https://Tubitak.Gov.Tr/Tubitak_Content_Files/Vizyon2023/Mm/Ek3. Pdf, 22.05.2022. google scholar
Shahabuddin S. (2009). Forecasting automobile sales, Management Research News, 32(7), 670-682. google scholar
Sharma, R., Sinha, A.K. (2012). Sales forecast of an automobile industry. International Journal of Computer Applications, 53(12), 25-28. google scholar
Topal, İ. (2019). Çevrimiçi tüketici bütünleşmesi ve arama motoru verileri kullanılarak yapay sinir ağları ile otomobil satış tahmini, Nevşehir Hacı Bektaş Veli Üniversity Journal ofISS, 9(2), 534-551. google scholar

Citations

Copy and paste a formatted citation or use one of the options to export in your chosen format

EXPORT

APA

Babaoğlu, M., Coşkunçay, A., & Aydın, T. (2022). Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms. Journal of Data Applications, 0(0), -. https://doi.org/10.26650/JODA.1242645

AMA

Babaoğlu M, Coşkunçay A, Aydın T. Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms. Journal of Data Applications. 2022;0(0):-. https://doi.org/10.26650/JODA.1242645

ABNT

Babaoğlu, M.; Coşkunçay, A.; Aydın, T. Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms. Journal of Data Applications, [Publisher Location], v. 0, n. 0, p. -, 2022.

Chicago: Author-Date Style

Babaoğlu, Merve, and Ahmet Coşkunçay and Tolga Aydın. 2022. “Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms.” Journal of Data Applications 0, no. 0: -. https://doi.org/10.26650/JODA.1242645

Chicago: Humanities Style

Babaoğlu, Merve, and Ahmet Coşkunçay and Tolga Aydın. “Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms.” Journal of Data Applications 0, no. 0 (May. 2023): -. https://doi.org/10.26650/JODA.1242645

Harvard: Australian Style

Babaoğlu, M & Coşkunçay, A & Aydın, T 2022, 'Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms', Journal of Data Applications, vol. 0, no. 0, pp. -, viewed 28 May. 2023, https://doi.org/10.26650/JODA.1242645

Harvard: Author-Date Style

Babaoğlu, M. and Coşkunçay, A. and Aydın, T. (2022) ‘Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms’, Journal of Data Applications, 0(0), pp. -. https://doi.org/10.26650/JODA.1242645 (28 May. 2023).

MLA

Babaoğlu, Merve, and Ahmet Coşkunçay and Tolga Aydın. “Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms.” Journal of Data Applications, vol. 0, no. 0, 2022, pp. -. [Database Container], https://doi.org/10.26650/JODA.1242645

Vancouver

Babaoğlu M, Coşkunçay A, Aydın T. Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms. Journal of Data Applications [Internet]. 28 May. 2023 [cited 28 May. 2023];0(0):-. Available from: https://doi.org/10.26650/JODA.1242645 doi: 10.26650/JODA.1242645

ISNAD

Babaoğlu, Merve - Coşkunçay, Ahmet - Aydın, Tolga. “Modeling Automobile Sales in Turkiye with Regression-Based Machine Learning Algorithms”. Journal of Data Applications 0/0 (May. 2023): -. https://doi.org/10.26650/JODA.1242645

Issue 02022, P. -

TIMELINE

Submitted	26.01.2023
Accepted	15.03.2023
Published Online	26.04.2023

LICENCE

Attribution-NonCommercial (CC BY-NC)

This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.

Journal of Data Applications