Used Car Price Prediction System

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

doi:10.32628/IJSRST

Authors

Dr. Pradeep N. Fale Assistant Professor Department of Information Technology Priyadarshini College of Engineering, Nagpur, Maharashtra, India Author
Pankaj S. Borkar Assistant Professor Department of Information Technology Priyadarshini College of Engineering, Nagpur, Maharashtra, India Author
Sangharsh S. Chandekar UG Student Department of Information Technology Priyadarshini College of Engineering, Nagpur, Maharashtra, India Author
Ishwar P. Borsare UG Student Department of Information Technology Priyadarshini College of Engineering, Nagpur, Maharashtra, India Author
Sanskruti S. Bhagat UG Student Department of Information Technology Priyadarshini College of Engineering, Nagpur, Maharashtra, India Author
Shravani M. Rode UG Student Department of Information Technology Priyadarshini College of Engineering, Nagpur, Maharashtra, India Author

DOI:

https://doi.org/10.32628/IJSRST

Keywords:

Kaggle, Car Buyers, Automotive Market, CarDheko, Quikr, Cars24

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.

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