This is a port of my thesis work from R to Python using the scikit-learn library.
Thesis: Prediction of Fault-Prone Software Modules.
Abstract: Evaluated the prediction performaces of Naive Bayes, SVM and k-Nearest Neighbor using the datasets from the NASA Metrics Data Program (MDP) repository. Applied the Principal Component Analisys (PCA) to extract the principal components. Compared the prediction performances with the full set of attributes and a subset of the principal components.
License:
Copyright (C) 2014 Francesco Marella
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.