• ARYAN KODTE
• MOKSHAGNA TEJA
• PEDDI NIKHIL
• N MADHAVI

In the realm of automotive economics, the accurate prediction of used car prices stands as a formidable challenge, necessitating a profound grasp of market intricacies and a mastery of advanced analytical techniques. This pioneering study heralds a new era in price forecasting through the deployment of a sophisticated ensemble of machine learning algorithms. Employing a multifaceted approach encompassing Linear Regression, Decision Tree, and Random Forest methodologies, augmented by meticulous cross-validation protocols, we unveil a transformative framework that transcends conventional limitations. By delving into a myriad of distinct attributes and harnessing the predictive power of state-of-the-art algorithms, our model not only achieves unprecedented levels of accuracy but also offers invaluable insights into the complex dynamics of the automotive marketplace. As the automotive landscape continues to evolve, our research paves the way for enhanced decision-making efficacy and strategic resource allocation, thereby shaping the future trajectory of automotive economics on a global scale.

DYNAMIC USED CAR PRICE FORECASTING USING MACHINE LEARNING

"DYNAMIC USED CAR PRICE FORECASTING USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.e779-e783, April-2024, Available :http://www.jetir.org/papers/JETIR2404486.pdf

"DYNAMIC USED CAR PRICE FORECASTING USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppe779-e783, April-2024, Available at : http://www.jetir.org/papers/JETIR2404486.pdf