In this study, we investigated the relationship between sailboat technical specifications and their prices, as well as regional pricing influences. Utilizing a dataset encompassing characteristics like length, beam, draft, displacement, sail area, and waterline, we applied multiple machine learning models to predict sailboat prices. The gradient descent model demonstrated superior performance, producing the lowest MSE and MAE. Our analysis revealed that monohulled boats are generally more affordable than catamarans, and that certain specifications such as length, beam, displacement, and sail area directly correlate with higher prices. Interestingly, lower draft was associated with higher listing prices. We also explored regional price determinants and found that the United States tops the list in average sailboat prices, followed by Europe, Hong Kong, and the Caribbean. Contrary to our initial hypothesis, a country's GDP showed no direct correlation with sailboat prices. Utilizing a 50% cross-validation method, our models yielded consistent results across test groups. Our research offers a machine learning-enhanced perspective on sailboat pricing, aiding prospective buyers in making informed decisions.

翻译：本研究探讨了帆船技术规格与其价格之间的关系，以及区域定价的影响。利用包含长度、船宽、吃水深度、排水量、帆面积和水线长度等特征的数据集，我们应用了多种机器学习模型来预测帆船价格。其中，梯度下降模型表现出色，产生了最低的均方误差（MSE）和平均绝对误差（MAE）。我们的分析揭示，单体船通常比双体船更经济实惠，且某些规格如长度、船宽、排水量和帆面积与较高价格直接相关。有趣的是，较低的吃水深度与较高的挂牌价格相关联。我们还探讨了区域价格决定因素，发现美国在帆船平均价格上位居榜首，其次是欧洲、香港和加勒比地区。与最初假设相反，国家的GDP与帆船价格没有直接相关性。采用50%交叉验证方法，我们的模型在测试组间得出一致结果。本研究提供了基于机器学习的帆船定价视角，有助于潜在买家做出明智决策。