Using Linear Regression For Used Car Price Prediction
Yıl: 2023 Cilt: 9 Sayı: 1 Sayfa Aralığı: 11 - 16 Metin Dili: İngilizce DOI: 10.22399/ijcesen.1070505 İndeks Tarihi: 02-07-2023
Using Linear Regression For Used Car Price Prediction
Öz: Recently, there have been studies on the use of machine learning algorithms for price prediction in many different areas such as stock market, rent a house and used car sales. Studies give information about which algorithm is more successful in price prediction using different machine learning methods. The most commonly used method for price prediction is the linear regression model. In this study, the effectiveness of the linear regression model was examined for used car price prediction. The linear regression model was applied to the data set that includes the features and price information of vehicles in Turkey as the year 2020. As a result, when we selected 1/3 of the data set as the test data, it was observed that the R2 score for the prediction success of model was 73%. To improve the effectiveness of the results the dataset could be extend or preprocessing part be detailed.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
- [1] I. E. Naqa and M. J. Murphy. (2015). What Is Machine Learning ?. Machine Learning in Radiation Oncology 3;11 DOI 10.1007/978-3-319-18305-3_1.
- [2] N. S. Özen, S. Saraç and M. Koyuncu, (2021). COVID-19 Vakalarının Makine Öğrenmesi Algoritmaları ile Tahmini: Amerika Birleşik Devletleri Örneği. European Journal of Science and Technology (EJOSAT). 22;134-139 DOI: 10.31590/ejosat.855113.
- [3] F. F. Haque, A. Abdelgawad, V. P. Yanambaka and K. Yelamarthi. (2020). Crop Yield Analysis Using Machine Learning Algorithms. IEEE 6th World Forum on Internet of Things (WF-IoT). pp. 1-2, DOI: 10.1109/WF-IoT48130.2020.9221459.
- [4] I. Hapsari, I. Surjandari and K. (2018). Visiting Time Prediction Using Machine Learning Regression Algorithm. 6th International Conference on Information and Communication Technology (ICoICT). pp. 495-500, DOI: 10.1109/ICoICT.2018.8528810.
- [5] N. Nafi’iyah and K. F. Mauladi. (2021). Linear Regression Analysis and SVR in Predicting Motor Vehicle Theft. International Seminar on Application for Technology of Information and Communication (iSemantic), pp. 54-58 DOI: 10.1109/ISEMANTIC52711.2021.9573225.
- [6] Ms Kavita and P. Mathur. (2020). Crop Yield Estimation in India Using Machine Learning. 5th International Conference on Computing Communication and Automation (ICCCA), pp. 220- 224. DOI: 10.1109/ICCCA49541.2020.9250915.
- [7] J. K. Bae and B. Park. (2015). Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert systems with applications. 42(6);2928-2934, DOI: 10.1016/j.eswa.2014.11.040.
- [8] A. Varma, A. Sarma, S. Doshi and R. Nair. (2018). House Price Prediction Using Machine Learning and Neural Networks. Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1936-1939, DOI: 10.1109/ICICCT.2018.8473231.
- [9] I. Imran, U. Zaman, M. Waqar, A. Zaman. (2021) Using Machine Learning Algorithms for Housing Price Prediction: The Case of Islamabad Housing Data. Soft Computing and Machine Intelligence, 1(1);11-23.
- [10] B. Jia (2021). Computer mathematical statistics applied in the housing price investigation through machine learning and linear regression model. International Conference on Data Science and Computer Application (ICDSCA), pp. 769-772, DOI: 10.1109/ICDSCA53499.2021.9650136.
- [11] C. K.-S. Leung, R. K. MacKinnon and Y. Wang. (2014). A machine learning approach for stock price prediction. IDEAS '14: Proceedings of the 18th International Database Engineering & Applications Symposium, pp. 274-277, DOI:10.1145/2628194.2628211.
- [12] Z. D. Akşehir and E. Kılıç, (2019). Makine Öğrenmesi Teknikleri ile Banka Hisse Senetlerinin Fiyat Tahmini. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 12(2);30.
- [13] M. Nikou, G. Mansourfar, and J. Bagherzadeh (2019). Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(4)164-174, DOI: 10.1002/isaf.1459.
- [14] W. Lu, W. Ge, R. Li and L. Yang. (2021). A Comparative Study on the Individual Stock Price Prediction with the Application of Neural Network Models. International Conference on Computer Engineering and Artificial Intelligence (ICCEAI), pp. 235-238, DOI: 10.1109/ICCEAI52939.2021.00046,
- [15] B. Panwar and P. J. Gaurav Dhuriya. (2021). Stock Market Prediction Using Linear Regression and SVM. International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 629-631, DOI: 10.1109/ICACITE51222.2021.9404733.
- [16] V. Siddhi, S. Valecha and M. Shreya. (2018). Bitcoin price prediction using machine learning. 20th International Conference on Advanced Communication Technology (ICACT), pp. 144-147, DOI: 10.23919/ICACT.2018.8323676.
- [17] P. R. Kalehbasti, L. Nikolenko and H. Rezaei. (2021). Airbnb Price Prediction Using Machine Learning and Sentiment Analysis. International Cross-Domain Conference for Machine Learning and Knowledge Extraction, pp. 173-184, DOI: 10.1007/978-3-030-84060-0_11.
- [18] E. Gegic, B. Isakovic, D. Keco, Z. Masetic and J. Kevric, (2019). Car Price Prediction using Machine Learning Techniques. TEM Journal, 8(1)113, DOI: 10.18421/TEM81-16.
- [19] S. Selvaratnam, B. Yogarajah, T. Jeyamugan and N. Ratnarajah, (2021). Feature selection in automobile price prediction: An integrated approach. International Research Conference on Smart Computing and Systems Engineering (SCSE), 4;106- 112, DOI: 10.1109/SCSE53661.2021.9568288.
- [20] Namlı, E., Gül, E., and Ünlü, R., (2019). Fiyat Tahminlemesinde Makine Öğrenmesi Teknikleri ve Doğrusal Regresyon. Konya Mühendislik Bilimleri Dergisi, 7;806-821, DOI: 10.36306/konjes.654952.
- [21] Çelik, Ö and Osmanoğlu, U.Ö, (2019). Prediction of The Prices of Second-Hand Cars. European Journal of Science and Technology (EJOSAT), 16;77-83, DOI: 10.31590/ejosat.542884.
- [22] Amaresh, V., Singh, R. R., Kamal, R., & Kulkarni, A. (2022). Linear Regression Models based Housing Price Forecasting. 2022 International Conference on Industry 4.0 Technology (I4Tech) (pp. 1-5). IEEE.
- [23] Septianingrum, S. A., Dzikri, M. A., Soeleman, M. A., Pujiono, P., & Muslih, M. (2022). Performance Analysis of Multiple Linear Regression and Random Forest for an Estimate of the Price of a House. International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 415-418). IEEE.
- [24] Nouriani, A., & Lemke, L. (2022). Vision-based housing price estimation using interior, exterior & satellite images. Intelligent Systems with Applications, 14;200081.
- [25] M. Hankar, M. Birjali and A. Beni-Hssane (2022). Used Car Price Prediction using Machine Learning: A Case Study. 11th International Symposium on Signal, Image, Video and Communications (ISIVC), El Jadida, Morocco, pp. 1-4, doi: 10.1109/ISIVC54825.2022.9800719.
- [26] KAGGLE (10.10.2021), https://www.kaggle.com/alpertemel/turkey-car- market-2020.
- [27] G. V. Rossum, Python Development Team (2020). Python Tutorial Release 3.8.1 The Python Software Foundation.
- [28] L. Moreira, C. Dantas, L. Oliveira, J. Soares and E. Ogasawara, (2018). On Evaluating Data Preprocessing Methods for Machine Learning Models for Flight Delays. International Joint Conference on Neural Networks (IJCNN), pp. 1-8, DOI: 10.1109/IJCNN.2018.8489294.
- [29] D. Chicco, M. J. Warrens and G. Jurman, (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peer J Computer Science, 7; DOI: 10.7717/peerj-cs.623.
| APA | Muti S, Yıldız K (2023). Using Linear Regression For Used Car Price Prediction. , 11 - 16. 10.22399/ijcesen.1070505 |
| Chicago | Muti Sumeyra,Yıldız Kazım Using Linear Regression For Used Car Price Prediction. (2023): 11 - 16. 10.22399/ijcesen.1070505 |
| MLA | Muti Sumeyra,Yıldız Kazım Using Linear Regression For Used Car Price Prediction. , 2023, ss.11 - 16. 10.22399/ijcesen.1070505 |
| AMA | Muti S,Yıldız K Using Linear Regression For Used Car Price Prediction. . 2023; 11 - 16. 10.22399/ijcesen.1070505 |
| Vancouver | Muti S,Yıldız K Using Linear Regression For Used Car Price Prediction. . 2023; 11 - 16. 10.22399/ijcesen.1070505 |
| IEEE | Muti S,Yıldız K "Using Linear Regression For Used Car Price Prediction." , ss.11 - 16, 2023. 10.22399/ijcesen.1070505 |
| ISNAD | Muti, Sumeyra - Yıldız, Kazım. "Using Linear Regression For Used Car Price Prediction". (2023), 11-16. https://doi.org/10.22399/ijcesen.1070505 |
| APA | Muti S, Yıldız K (2023). Using Linear Regression For Used Car Price Prediction. International Journal of Computational and Experimental Science and Engineering, 9(1), 11 - 16. 10.22399/ijcesen.1070505 |
| Chicago | Muti Sumeyra,Yıldız Kazım Using Linear Regression For Used Car Price Prediction. International Journal of Computational and Experimental Science and Engineering 9, no.1 (2023): 11 - 16. 10.22399/ijcesen.1070505 |
| MLA | Muti Sumeyra,Yıldız Kazım Using Linear Regression For Used Car Price Prediction. International Journal of Computational and Experimental Science and Engineering, vol.9, no.1, 2023, ss.11 - 16. 10.22399/ijcesen.1070505 |
| AMA | Muti S,Yıldız K Using Linear Regression For Used Car Price Prediction. International Journal of Computational and Experimental Science and Engineering. 2023; 9(1): 11 - 16. 10.22399/ijcesen.1070505 |
| Vancouver | Muti S,Yıldız K Using Linear Regression For Used Car Price Prediction. International Journal of Computational and Experimental Science and Engineering. 2023; 9(1): 11 - 16. 10.22399/ijcesen.1070505 |
| IEEE | Muti S,Yıldız K "Using Linear Regression For Used Car Price Prediction." International Journal of Computational and Experimental Science and Engineering, 9, ss.11 - 16, 2023. 10.22399/ijcesen.1070505 |
| ISNAD | Muti, Sumeyra - Yıldız, Kazım. "Using Linear Regression For Used Car Price Prediction". International Journal of Computational and Experimental Science and Engineering 9/1 (2023), 11-16. https://doi.org/10.22399/ijcesen.1070505 |
