Used Car Price Prediction System
DOI:
https://doi.org/10.32628/IJSRSTKeywords:
Kaggle, Car Buyers, Automotive Market, CarDheko, Quikr, Cars24Abstract
Accurately predicting used car prices is crucial in the automotive market. This study introduces a robust system for estimating the value of used cars, utilizing linear regression to predict prices based on existing data. As new car prices increase due to manufacturer pricing and government taxes, the demand for used cars grows, particularly among middle-class buyers seeking affordable options. Our system meets this demand by providing reliable, user-friendly tools for potential car buyers.Operating as a computer program, it uses data from individuals selling or buying used cars and analyzes various parameters to ensure prediction accuracy. The primary goal is to deliver dependable estimates, giving buyers confidence in their investments. We trained the system using a dataset from Kaggle, analyzing it with different training and testing splits. Our model achieved an accuracy rate of approximately 97.53%, making it a highly reliable tool for used car price prediction.
Downloads
References
Siva, R.; M, A. Linear Regression Algorithm Based Price Prediction of Car and Accuracy Comparison with Support Vector Machine Algorithm. ECS Trans. 2022
Bharambe, P.P.; Bagul, B.; Dandekar, S.; Ingle, P. Used Car Price Prediction using Different Machine Learning Algorithms. Int. J. Res. Appl. Sci. Eng. Technol. 2022
Liu, E.; Li, J.; Zheng, A.; Liu, H.; Jiang, T. Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network. Sustainability 2022
Dholiya, M. et al. (2019) ‘Automobile Resale System Using Machine Learning’ , International Research Journal of Engineering and Technology(IRJET), 6(4), pp. 3122–3125.
Gegic, E. et al. (2019) ‘Car price prediction using machine learning techniques’ , TEM Journal, 8(1), pp. 113–118. doi: 10.18421/TEM81-16.
Pal, N. et al. (2019) ‘How Much is my car worth? A methodology for predicting used cars’ prices using random forest’ , Advances in Intelligent Systems and Computing, 886, pp. 413–422. doi: 10.1007/978-3- 030-03402-3_28.
Kuiper, S. (2008) ‘Introduction to Multiple Regression: How Much Is Your Car Worth?’ , Journal of Statistics Education, 16(3). doi: 10.1080/10691898.2008.11889579.
Pudaruth, S. (2014) ‘Predicting the Price of Used Cars using Machine Learning Techniques’ , International Journal of Information & Computation Technology, 4(7), pp. 753–764. Available at: http://www.irphouse.com
Fathalla, A.; Salah, A.; Li, K.; Li, K.; Francesco, P. Deep end-to-end learning for price prediction of second-hand items. Knowl. Inf. Syst. 2020, 62, 4541–4568.
de Prez, M. Used car market to soften in second-half of 2022. General News, 31 May 2022.
Statistics. Vehicle Center Croatia. Centar za vozila Hrvatske—Statistika, 2022. Available online: https://cvh.hr/gradani/tehnickipregled/statistika/ (accessed on 30 May 2022).
Noor, K.; Jan, S. Vehicle Price Prediction System using Machine Learning Techniques. Int. J. Comput. Appl. 2017, 167, 27–31.
Yang, R.R.; Chen, S.; Chou, E. AI Blue Book: Vehicle Price Prediction Using Visual Features. arXiv 2018, arXiv:1803.11227.
Khedr, A.E.; S.E.Salama.; Yaseen, N. Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis. Int. J. Intell. Syst. Appl. 2017, 9, 22–30.
Shastri, M.; Roy, S.; Mittal, M. Stock Price Prediction using Artificial Neural Model: An Application of Big Data. ICST Trans. Scalable Inf. Syst. 2018, 19, 156085.
Kalaiselvi, N.; Aravind, K.; Balaguru, S.; Vijayaragul, V. Retail price analytics using backpropogation neural network and sentimental analysis. In Proceedings of the 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), Chennai, India, 16–18 March 2017; pp. 1–6.
Ahmed, E.; Moustafa, M. House price estimation from visual and textual features. 2016, arXiv:1609.08399 .
Naumov, V.; Banet, K. Using Clustering Algorithms to Identify Recreational Trips within a Bike-Sharing System. In Reliability and Statistics in Transportation and Communication; Springer: Cham, Switzerland, 2020.
Banet, K.; Naumov, V.; Kucharski, R. Using city-bike stopovers to reveal spatial patterns of urban attractiveness. Curr. Issues Tour. 2022, 25, 2887–2904.
Pal, N.; Arora, P.; Sundararaman, D.; Kohli, P.; Palakurthy, S.S. How much is my car worth? A methodology for predicting used cars prices using Random Forest. arXiv 2017, arXiv:1711.06970.
Chen, C.; Hao, L.; Xu, C. Comparative analysis of used car price evaluation models. AIP Conf. Proc. 2017, 1839, 020165.
Moayedi, H.; Mehrabi, M.; Mosallanezhad, M.; Rashid, A.S.A.; Pradhan, B. Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng. Comput. 2019, 35, 967–984.
Nilashi, M.; Cavallaro, F.; Mardani, A.; Zavadskas, E.; Samad, S.; Ibrahim, O. Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique. Sustainability 2018, 10, 2707.
Drezewski, R.; Dziuban, G.; Paj ˛ak, K. The Bio-Inspired Optimization of Trading Strategies and Its Impact on the Efficient Market
Hypothesis and Sustainable Development Strategies. Sustainability 2018, 10, 1460.
Wu, J.D.; Hsu, C.C.; Chen, H.C. An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference. Expert Syst. Appl. 2009, 36, 7809–7817.
Zhou, X. The usage of artificial intelligence in the commodity house price evaluation model. J. Ambient. Intell. Humaniz. Comput. 2020, 11.
Liu, E.; Li, J.; Zheng, A.; Liu, H.; Jiang, T. Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network. Sustainability 2022, 14, 8993.
Samruddhi, K.; Kumar, R.A. Used Car Price Prediction using K-Nearest Neighbor Based Model. Int. J. Innov. Res. Appl. Sci. Eng. 2020, 4, 629–632.
Njuskalo.hr. 2022. Available online: https://www.njuskalo.hr/auti (accessed on 30 May 2022).
Botvinick, M.; Ritter, S.; Wang, J.X.; Kurth-Nelson, Z.; Blundell, C.; Hassabis, D. Reinforcement Learning, Fast and Slow. Trends Cogn. Sci. 2019, 23, 408–422.
Singh, A.; Thakur, N.; Sharma, A. A review of supervised machine learning algorithms. In Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 16–18 March 2016; pp. 1310–1315.
AlShared, A. Used Cars Price Prediction and Valuation using Data Mining Techniques. Master’s Thesis, Rochester Institute of Technology, Rochester, NY, USA, 2021.
Haijiao, J.; Jiancheng, L.; Wei, Y.; Chunyong, W.; Zhenhua, L. Theoretical distribution of range data obtained by laser radar and its applications. Opt. Laser Technol. 2013, 45, 278–284. .
Siva, R.; M, A. Linear Regression Algorithm Based Price Prediction of Car and Accuracy Comparison with Support Vector Machine Algorithm. ECS Trans. 2022, 107, 12953–12964.
Pudaruth, S. Predicting the Price of Used Cars using Machine Learning Techniques. Int. J. Inf. Comput. Technol. 2014, 4, 753–764.
Monburinon, N.; Chertchom, P.; Kaewkiriya, T.; Rungpheung, S.; Buya, S.; Boonpou, P. Prediction of prices for used car by using regression models. In Proceedings of the 2018 5th International Conference on Business and Industrial Research (ICBIR), Bangkok, Thailand, 17–18 May 2018; pp. 115–119.
Bharambe, P.P.; Bagul, B.; Dandekar, S.; Ingle, P. Used Car Price Prediction using Different Machine Learning Algorithms. Int. J. Res. Appl. Sci. Eng. Technol. 2022, 10, 773–778.
Puteri, C.K.; Safitri, L.N. Analysis of linear regression on used car sales in Indonesia. J. Phys. Conf. Ser. 2020, 1469, 012143.
Hankar, M.; Birjali, M.; Beni-Hssane, A. Used Car Price Prediction using Machine Learning: A Case Study. In Proceedings of the 2022 11th International Symposium on Signal, Image, Video and Communications (ISIVC), El Jadida, Morocco, 18–20 May 2022; pp. 1–4.
Miles, J. Tolerance and Variance Inflation Factor. In Book section: Wiley Statistics Reference Online; John Wiley & Sons: New York,NY, USA, 2015. ISBN 9781118445112.
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.