The aim of this script is to create in R the following bivariate SVR model (the observations are represented with blue dots and the predictions with the orange 3D surface) :

3D graph showing the SVR model

We start by setting the working folder and loading the dataset

setwd("[WORKING FOLDER]")
#loading the dataset
dataset = read.csv('dataset.csv')

whose first 40 rows over 1000 are as follows :

Diamond price (C=dollars/carat) VS Paleonium concentration (A=g/m^3) and pressure (B=kg/cm^2)

Then we build the SVR regressor for the dataset with the « eps-regression » type (in order to build a non linear model) :

#Fitting the SVR to the dataset
#install.packages('e1071')
library(e1071)
regressor = svm(formula = C ~ ., data = dataset, type = "eps-regression")


Finally, we display in a 3D space the observed and predicted Price along the z axis, where x and y axis correspond to Paleonium and Pressure.


#visualizing the observations and predictions in a 3D space
#install.packages('plotly')
library(plotly)
Paleonium = dataset$A
Pressure = dataset$B
Price = dataset$C
plot_ly(x=as.vector(Paleonium),y=as.vector(Pressure),z=Price, type="scatter3d", mode="markers", name = "Obs", marker = list(size = 3)) %>%
add_trace(x=as.vector(Paleonium),y=as.vector(Pressure),z=predict(regressor, newdata=dataset), type = "mesh3d", name = "Preds")


The above code produces the following 3D graph where the predictions take the shape of a orange 3D surface and the observations are represented with blue dots. The model is fitting relatively well the observations since the blue dots are wrapping the 3D surface very closely.

3D graph showing the SVR model