You have literally 6 features; you do not need to do dimensionality reduction here. Further, PCA is used generally before performing a linear regression model, which assumes independence among variables. SVM's are nonlinear and so don't care (though you specify both a linear kernel and the RBF kernel in your code?). If the data was very high dimension you could do PCA and then SVM in order to increase speed at the expense of accuracy - there is no free lunch. I think that's what you see in your results.

As a side note, SVM's and Random Forests are generally considered the best 'out of the box' models. So you chose a good one to experiment with!

Also, since you already wrote the analysis code in Python, why not write the front-end in Python (rather than Node) as well?