Coding Resources for Udacity ud675 course

We have used Python scikit-learn and pybrain for analyzing the dataset. They implement many popular machine learning libraries. If you want to use these libraries, you will need to have some background in Python programming. However, the scikit documentation is a good resource that can help you get started.

• You will need Python version 2.7.5 or up to get started.
• Install scikit-learn. Use scikit-learn Installation Docs.
• Install pybrain. Use pybrain Installation Docs.
• Download project files directly using the .tar.gz or .zip links above.
• Hack!

• scikit-example.py - Start here. This shows you how to use the scikit-learn library to learn a decision tree model.
• complexity/ - Model complexity experiments for DT, NN, kNN, SVM and Boosting. For each algorithm, we change various complexity parameters and test algorithm performance against a test set.
• learning_curves/ - Learning curves for DT, NN, kNN, SVM and Boosting. For each algorithm, we observe the performance when we change the input sample size.

1. Fork it.
2. Create a branch (git checkout -b my_branch)
3. Commit your changes (git commit -am "Awesome feature")
4. Push to the branch (git push origin my_branch)
5. Open a Pull Request
6. Enjoy a refreshing Diet Coke and wait

• scikit-learn Reference Docs
• scikit-learn Installation Docs
• pybrain Installation Docs