Comparative Analysis of Linear Regression and Random Forest for Used Car Price Prediction - Muhammad Faris Adjil Syamsudi; Bimo Arya Daffa; Wisnu Jarodi; Nungky Awang Chandra (Journal Collabits, 2026)
Manual estimation is often subjective and prone to human bias because the used car market has a complex pricing structure with non-linear depreciation. Objective: This study conducted a comparative analysis between Linear Regression and Random Forest algorithms to develop a more objective pricing model. Methods: The Kaggle dataset contains 5,000 entries indicating features such as manufacturer, model, engine size, and mileage for this study. The methodology included data cleaning, feature engineering, and outlier removal using the IQR method. For training and testing, the data was split 80:20. Results: