Used Car Price Prediction System

Pradeep N. Fale, Pankaj S. Borkar, Sangharsh S. Chandekar, Ishwar P. Borsare, Sanskruti S. Bhagat and Shravani M. Rode

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 3, 347-354

Abstract: 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.

Keywords: Kaggle; Car Buyers; Automotive Market; CarDheko; Quikr; Cars24 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST24113108 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST24113108/IJSRST24113108 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i3:id:193

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().