Predicting Fraud at Enron: Who is Person of Interest?

Project report: IPython Notebook documenting the entire process. It includes more development scripts and work-in-progress output (charts and tables) than poi_id.py

poi_id.py: scripts for identifying POIs. Codes may not be in the exact same order as in the original file.

final_project_dataset.pkl: original dataset

Auxiliary codes:

• feature_format.py
• tester.py

Output from poi_id.py:

• my_classifier.pkl
• my_dataset.pkl
• my_feature_list.pkl

Resource: include references and additional materials coming with final_project folder.

Reference

• Parameter estimation using grid search with a nested cross-validation — scikit-learn 0.10 documentation
• Plotting Learning Curves — scikit-learn 0.16.1 documentation
• Model selection: choosing estimators and their parameters — scikit-learn 0.16.1 documentation
• 1.13. Feature selection — scikit-learn 0.16.0 documentation
• pylab_examples example code: demo_tight_layout.py — Matplotlib 1.4.3 documentation
• Accuracy and precision

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