Started applying a SVM algorithm
Fit a linear SVC; lots of things left to do
Evaluated the prediction produced by a plain LinearSVC model
A dataset with float features and non-linearly separable classes
Introduced feature scaling; doesn't seem to improve the result
Experiment results: LinearSVC, float_features_dataset, scaling vs. no scaling
Applied the SVC algorithm with RBF kernel (Radial Basis Function)
Added a refactored version, which doesn't require the pandas library.
Removed temporary files (.py~, .csv~)
Update ml-project/learning-algorithms/README.md
Added .gitignore; changed the directory structure
Merge branch 'master' of github.com:traims/softml
Merge branch 'master' of github.com:traims/softml
Fixed feature scaling; skipped the first line of csv to avoid NaNs
Remove duplicated directory
Simplified the code; changed the file structure.
Experiment workflow: .yaml files now store experiment settings.
Update README.md
Merge branch 'master' of github.com:traims/softml
First commit of a splitted repository.