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Abstract
Car price prediction is an important step in the automotive industry because it can help consumers and dealers determine fair market value. However, many previous studies only used vehicle specifications. This study combines vehicle specifications and economic indicators to improve prediction accuracy. Three machine learning algorithms were tested: SVR, XGBoost, and Random Forest. The dataset comprises 2,498 car records from Kaggle, US GDP data from fred.stlouisfed.org, and US annual inflation data. The data preprocessing process includes handling missing values, removing outliers using the Interquartile Range (IQR) technique, encoding categorical data, and creating new features such as the ratio between GDP and inflation.
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DOI: https://doi.org/10.24167/proxies.v9i2.14956
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