[2B1-GS-2] Machine learning: Text mining

[2B1-GS-2-05] Improvement of Used Vehicle Price Forecasting Accuracy by Building Models by Vehicle Type Group

〇Yusuke Nishiyama¹, Le Binh Thanh¹, Hiroyuki Dan¹, Yutaro Hayashi¹, Akira Matsushita², Atsushi Iwasaki³ (1. Aucnet, Inc., 2. University of Tokyo, 3. University of Electro-Communications)

[[Online]]

Keywords:machine learning, used car, price pridiction

AUCNET INC. provides a valuation support service for used car dealers across Japan, leveraging machine learning to predict future auction sale prices.
While our previous research significantly enhanced overall prediction accuracy, we found the problem that the prediction accuracy remains low for specific car models.
To address this issue, this study introduces a novel approach that segments data into three distinct vehicle categories and builds individual models for each group.
Upon comparison with our previous model, our new models improve the prediction accuracy when the partitioned dataset is relatively large.