Sailing ship listing price forecast analysis based on support vector machine

The task of our project was to develop a mathematical model that explains the listing price of each sailboat in the provided dataset. To achieve this, we first performed data cleaning and normalization to ensure the accuracy of our model. We then utilized Principal Component Analysis to reduce the dimensionality of the dataset and extract the most relevant features. The resulting two main components were then used as predictors in a quadratic kernel Support Vector Machine model for predicting the listing price. This model achieved a high R-square value, indicating that it has high precision. We also identified the most notable features that contribute to the prediction of sailboat prices.