In two previous articles (1, 2) I've explained how I've implemented the training of Support Vector Machines and Neural Network and shown that I obtain good results on several datasets. Training is great, but if there is no way to use the trained model that's pretty useless. So I implemented right away an export of the prediction as a C function and its driver, both together giving a ready to use and automatically generated CLI app.

The export toward C code is nice to integrate the prediction into another piece of software, but I also wanted something more user-friendly and easy to share. I then added another export function, toward HTML/Javascript code this time, and I can now automatically generate at the end of the training a web app implementing the prediction with no effort. One single file for a simple and neat GUI running everywhere without any installation or setup. Great!

Then, I'd like to share below those automatically generated web apps for a bunch of datasets. First, to illustrate the result; second, with the hope that at least some of them will be useful to someone. I also plan to update this article with new datasets little by little, and would be very happy to receive requests for datasets to process. Don't hesitate to contact me! Datasets below are in alphabetical order, with a link to the source. The percentage is the expected accuracy estimated from 10-fold cross validation.

Predictor	Source	Expected accuracy	Web app
Heart disease detection	OpenML	82.96%	link
Iris type from petal and sepal dimensions	OpenML	97.33%	link
Leptograpsus crabs species from body dimensions	OpenML	99.50%	link
North-american mushroom edibility	OpenML	100.0%	link
Phoneme identification from harmonics	OpenML	85.80%	link