This is the python/scikit-learn code I wrote during my stab at the Kaggle titanic competition. There is code for several different algorithms, but the primary and highest performing one is the RandomForest implemented in randomforest2.py.

Requirements:

• python (a 2.x release at least 2.6)
• scikit-learn/NumPy/SciPy (http://scikit-learn.org/stable/install.html)
• pandas (http://pandas.pydata.org/pandas-docs/stable/install.html)
• matplotlib (http://matplotlib.org/faq/installing_faq.html)

Usage:
> python randomforest2.py

Key files:

• loaddata.py: Contains all the feature engineering including options for generating different variable types, and performing PCA, clustering, and class balancing
• randomforest2.py: The code that executes the pipeline
• scorereport.py: Inspects and reports on the results of hyperparameter search
• learningcurve.py: Includes code to generate a learning curve
• roc_auc: Includes code to generate a ROC curve

Other files contain other algorithms that were used during experimentation and are in various stages of completeness. Only randomforest2 is 100% up to date