A used sailboat pricing model based on multiple linear regression

The value of sailboats varies depending on the region and market conditions. Brokers need a better understanding of the sailboat market and make informed decisions to mitigate potential risks. To address these issues, this paper employs methods of multiple linear regression analysis and the grey correlation forecasting method. By utilizing data analysis techniques, a commonly used sailboat pricing model is established. Specifically, we first preprocess the given dataset and encode the processed data for subsequent model use. Additionally, the paper selects the three most influential factors on the pricing model: length, make, and year, and employs the method of independent weighting coefficients for multiple linear regression analysis calculation. Furthermore, we analyze the regional impact on listing prices of used sailboats. Based on these analyses, the proposed model can provide data analysis and sales recommendation reports for brokers. Lastly, the model proposed in this paper demonstrates strong accuracy and robustness, making it easy to implement with other datasets.