Measurement Models For Sailboats Price vs. Features And Regional Areas
Jiaqi Weng Chunlin Feng Yihan Shao
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.