Prediction And Investigation for U.S. Used Car Prices and Factors Related to Price
DOI:
https://doi.org/10.54097/02ddze05Keywords:
Used car; Linear Regression; Random Forest Regression; Factors; Price.Abstract
In contemporary society, as transportation infrastructure continues to advance, a growing number of families opt for car ownership as a means of commuting. However, constrained by budget considerations, many households opt for pre-owned vehicles. This underscores the significance of comprehending the dynamics of the used car market and delving into the factors that influence the pricing of these previously owned automobiles. The author found a data set of used cars in United States on Kaggle and used it for analysis. This dataset contains data about 762,091 used cars scraped from cars.com. The data was collected in Apr, 2023. The research question is how to predict the used car prices and what factors are related to the used car prices. The author uses Linear Regression and Random Forest Regression to predict used car prices. The author found the factors related to the price of used cars as well. The study found that the Random Forest Regression method is more accurate in predicting used car prices. A total of 17 used car factors correlated with used car price. Such as mileage, car age, mpg and so on.
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