Completed my dive into the wonderful world of machine learning with Scikit-learn (sklearn). Scikit is a machine learning library in the Python Programing Language that features various classification, regression and clustering algorithms. What made it stand out was the speed at which machine learning generated an analysis. Our objective was to train a supervised classification algorithm that had precision and recall scores equal or greater than 0.30. For an algorithm, I chose the Gradient Boosting Classifier, and it was well worth the extra effort. I manage outliers and NaN values with Pandas. I selected features with SelectKBest feature extraction. I then split the data between a training set, a testing set, and validation set. To keep my fits and bits together, I wrapped the classifier into a Pipeline and used cross-validated to test my algorithm. It was an excellent experience, and I've caught the bug for machine learning.
In this project, you will play detective, and put your machine learning skills to use by building an algorithm to identify Enron Employees who may have committed fraud based on the public Enron financial and email dataset.
This project will teach you the end-to-end process of investigating data through a machine learning lens.
It will teach you how to extract and identify useful features that best represent your data, a few of the most commonly used machine learning algorithms today, and how to evaluate the performance of your machine learning algorithms.
By the end of the project, you will be able to:
Deal with an imperfect, real-world dataset Validate a machine learning result using test data Evaluate a machine learning result using quantitative metrics Create, select and transform features Compare the performance of machine learning algorithms Tune machine learning algorithms for maximum performance Communicate your machine learning algorithm results clearly
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, you will play detective, and put your new skills to use by building a person of interest identifier based on financial and email data made public as a result of the Enron scandal. To assist you in your detective work, we've combined this data with a hand-generated list of persons of interest in the fraud case, which means individuals who were indicted, reached a settlement or plea deal with the government, or testified in exchange for prosecution immunity.
You should have python and sklearn running on your computer, as well as the starter code (both python scripts and the Enron dataset) that you downloaded as part of the first mini-project in the Intro to Machine Learning course. You can get the starter code on git: git clone https://github.com/udacity/ud120-projects.git
The starter code can be found in the final_project directory of the codebase that you downloaded for use with the mini-projects. Some relevant files:
poi_id.py : Starter code for the POI identifier, you will write your analysis here. You will also submit a version of this file for your evaluator to verify your algorithm and results.
final_project_dataset.pkl : The dataset for the project, more details below.
tester.py : When you turn in your analysis for evaluation by Udacity, you will submit the algorithm, dataset and list of features that you use (these are created automatically in poi_id.py). The evaluator will then use this code to test your result, to make sure we see performance that’s similar to what you report. You don’t need to do anything with this code, but we provide it for transparency and for your reference.
emails_by_address : this directory contains many text files, each of which contains all the messages to or from a particular email address. It is for your reference, if you want to create more advanced features based on the details of the emails dataset. You do not need to process the e-mail corpus in order to complete the project.
We will provide you with starter code that reads in the data, takes your features of choice, then puts them into a numpy array, which is the input form that most sklearn functions assume. Your job is to engineer the features, pick and tune an algorithm, and to test and evaluate your identifier. Several of the mini-projects were designed with this final project in mind, so be on the lookout for ways to use the work you’ve already done.
As preprocessing to this project, we've combined the Enron email and financial data into a dictionary, where each key-value pair in the dictionary corresponds to one person. The dictionary key is the person's name, and the value is another dictionary, which contains the names of all the features and their values for that person. The features in the data fall into three major types, namely financial features, email features and POI labels.
financial features: ['salary', 'deferral_payments', 'total_payments', 'loan_advances', 'bonus', 'restricted_stock_deferred', 'deferred_income', 'total_stock_value', 'expenses', 'exercised_stock_options', 'other', 'long_term_incentive', 'restricted_stock', 'director_fees'] (all units are in US dollars)
email features: ['to_messages', 'email_address', 'from_poi_to_this_person', 'from_messages', 'from_this_person_to_poi', 'shared_receipt_with_poi'] (units are generally number of emails messages; notable exception is ‘email_address’, which is a text string)
POI label: [‘poi’] (boolean, represented as integer)
You are encouraged to make, transform or rescale new features from the starter features. If you do this, you should store the new feature to my_dataset, and if you use the new feature in the final algorithm, you should also add the feature name to my_feature_list, so your evaluator can access it during testing. For a concrete example of a new feature that you could add to the dataset, refer to the lesson on Feature Selection.