Prediction Model of Sailing Ship Price Based on Random Forest Regression
DOI:
https://doi.org/10.54097/hbem.v16i.10530Keywords:
Random forest, sailboat, markup, prediction.Abstract
The sailboat market is one of the emerging sunrise industries. Many people choose to buy a used sailboat rather than a brand new one. In this paper, various physical variables of sailboats are collected and Pearson correlation coefficient matrix and random forest model are used to filter the important variables and build a model to predict the price of sailboats based on random forest regression. A random forest regression model is used to build a model about the relationship between the independent and dependent variables to predict the prices of sailboats in the market. After training the model, it is used to make predictions on new data. The test set was tested and the results showed that the model was 80% accurate with absolute squared error and mean squared error values of only 0.05 and 0.01. It is concluded that the main factors affecting the price of used sailboats were country/region/state, draft (maximum) (meters), breed, width (meters), length (feet), year, and brand (increasing importance). Our model was assessed as a good predictor of sailboat prices with a small margin of error.
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