A Study on the Pricing of Used Sailboats: Utilizing Random Forest Models to Analyze the Impact of Multiple Factors

This study explores the impact of various factors on the valuation of sailboats. The research delves into the mathematical correlation between the prices of secondhand sailboats and 11 specific factors. Initially, any missing values were removed, outliers were substituted, and box plots were utilized to validate the precision of the sample data. Furthermore, five supplementary parameters were sourced from public websites, encompassing displacement, draft, beam, material, and GDP. Subsequently, after organizing the data, categorical variables were encoded using sequence and label encoding. Following this, multiple linear regression models, ridge regression models, and random forest models were constructed using the entire dataset. Through a comparative analysis of the three models' accuracy, the random forest model was chosen as the ultimate model for elucidating the pricing of each sailboat. The predictive precision of the random forest model was scrutinized, yielding a prediction accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R²=0.786</tex> .