Used Car Prices in India: What about Future?
- https://doi.org/10.2991/aebmr.k.220307.134How to use a DOI?
- Machine Learning; Gradient Boosting; Decision Tree; Used Car; Information Asymmetry
With the steady increase in the demand for cars and the shrinking supply of the new cars due to chip shortage in the worldwide market, the used car price has risen dramatically in recent years. This paper investigated Used Car prices in India market by applying multiple machine learning models to predict and analyze the typical characteristics of the used car market in India, which is a rapidly developing country that has an ill-informed used vehicle buyer market. Before building models, We checked important statistical attributes of our data, created a new variable called the average_cost_price, converted the unit of price to thousand dollars, visualized the data through different graphs, and dropped several potential outliers to eliminate the effects on the later models. Then, we applied four useful machine learning algorithms, including Linear Regression, Decision Tree Regression, Random Forest Regression and Gradient Boosting Regression, for model buildings and analysis. Based on the performance of our final optimal model, the final optimal Gradient Boosting Model can be used to predict used car prices in India market at the current point. Buyers can then get much more information about used car prices in India, and sellers are hard to hide information in face of our relatively comprehensive model.
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Xinru Chen AU - Shiyu Gu AU - Ximing Deng AU - Lei Huang PY - 2022 DA - 2022/03/26 TI - Used Car Prices in India: What about Future? BT - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) PB - Atlantis Press SP - 831 EP - 840 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.220307.134 DO - https://doi.org/10.2991/aebmr.k.220307.134 ID - Chen2022 ER -