Use machine learning and natural language processing techniques to analyze the changes made in a project, and classify them in:
- Small / unimportant fix
- Big / important fix
- Small / important feature
- Big / important feature
For this project I will
- Generate a basic corpus of labeled data from a different set of project related with openSUSE
- Evaluate the best features to make a proper classification: n-gram, PoS tag, TF-IDF (with and without stemmer)
- Evaluate and measure the best classification model: Naive Bayes, Linear SVM, Max Entropy, ...
Looking for mad skills in:
nlp machinelearning git github
This project is part of:
Hack Week 10 Hack Week 11 Hack Week 12
The goal is to learn about [Kaggle](https://www...
UPDATE: it turns out that [people upstream ...
It is well-known that two git commits within a ...
https://www.gitbook.com/ allows you to combine ...
The goal of this project is to get an overview ...