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PyData-EDA
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Identifying Fraud from Enron Email Dataset
Pranav e12d139cbb First Commit 6 years ago
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Enron_61702_Insiderpay.pdf e12d139cbb First Commit 6 years ago
Enron_Fraud.html e12d139cbb First Commit 6 years ago
Enron_Fraud.ipynb e12d139cbb First Commit 6 years ago
README.md e12d139cbb First Commit 6 years ago
feature_format.py e12d139cbb First Commit 6 years ago
final_project_dataset.pkl e12d139cbb First Commit 6 years ago
my_classifier.pkl e12d139cbb First Commit 6 years ago
my_dataset.pkl e12d139cbb First Commit 6 years ago
my_feature_list.pkl e12d139cbb First Commit 6 years ago
poi_email_addresses.py e12d139cbb First Commit 6 years ago
poi_id.py e12d139cbb First Commit 6 years ago
poi_names.txt e12d139cbb First Commit 6 years ago
tester.py e12d139cbb First Commit 6 years ago

README.md

P5: Identifying Fraud from Enron Email Dataset

With the help of machine learning techniques, a classification model is built to identify Enron Employees who may have committed fraud based on the public Enron financial and email dataset.

In 2000, Enron was one of the largest companies in the United States. By 2002, it had collapsed into bankruptcy due to widespread corporate fraud. In the resulting federal investigation, a significant amount of typically confidential information entered into the public record, including tens of thousands of emails and detailed financial data for top executives.

In this project, Enron employees who may have committed fraud are identified using machine learning algorithms on the email dataset. One of the challenges in this project was to optimize the algorithm for a small and skewed dataset, which made outlier removal highly crucial (done using EDA).

The activities implemented in this project are:

  1. Exploring the Enron Dataset: This involves data cleaning, outlier removal and analyzing.

  2. Feature Processing of the Enron Dataset: Includes creation, scaling, selection and transforming of features.

  3. Choosing the Algorithm(s): Multiple classification models are trained and tuned.

  4. Evaluation: Involves validation and overall performance check.

Files

  • Enron_61702_Insiderpay.pdf – Reference data.

  • Report.ipynb – iPython Report for the project.

  • Report.html – HTML export of the Jupyter notebook.

  • poi_id.py – Main project file.

  • tester.py – Script to test the algorithm performance.

  • All other files in this directory are helper codes/data.

Data Files
  • my_classifier.pkl – Developed classification model.
  • my_dataset.pkl – Modified dataset (during the analysis).
  • my_feature_list.pkl – List of features in classifier.
  • final_project_dataset.pkl – The dataset for the project.

Requirements

This project requires Python 3 with NumPy, Pandas, Seaborn and scikit-learn.

It is recommended to use Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

License

Modified MIT License © Pranav Suri