Predicting Passenger Car Prices with Machine Learning Models
Year: 2024
Type: preprint
Abstract: The car resale industry is broad and precise price forecast is essential for preventing customers from overpaying to resellers. Machine learning is currently the most effective forecasting tool, which is why three machine learning models—Random Forest, CatBoost Regressor, and Support Vector Machine (SVM)—were chosen to analyze car features such as model, mileage, and year, as well as additional attributes such as color, conditioner, and car body type extracted from car advertisement descriptions. The data for the prediction was collected from the website mashina.kg[1] using a web scraper implemented in the Python programming language. The performances of multiple models were then evaluated to determine which one best suited the available data set. As a result, the model CatBoost provides the most effective way for accurate car price predictions in Bishkek, with an accuracy of 86.05%, making it a helpful tool for both customers and financial institutions in making well-informed decisions regarding cars price.
Source: Preprints.org
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405335643