Measurement Models For Sailboats Price vs. Features And Regional Areas
This provides a machine learning-enhanced perspective to help prospective sailboat buyers make informed decisions, though it is incremental as it applies existing methods to a new dataset.
The study investigated how sailboat technical specifications and regional factors affect prices using machine learning models, finding that gradient descent performed best with the lowest MSE and MAE, and revealing that monohulls are cheaper than catamarans while length, beam, displacement, and sail area correlate with higher prices.
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.