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Project Overview 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 f…
Building a model for identifying potential Enron fraudsters based on financial and e-mail data with the use of Python. From data exploration to building and validating an algorithm.
Machine Learning Project to build an algorithm which identifies Enron Employees who may have committed fraud based on the public Enron financial and email dataset.
Employed hyper-parameter tuning (Gridsearch CV) and ensemble methods (Voting Classifier) to combine the results of the best models. Data Cleaning and Exploration using Pandas. Stratified Cross Validation to model and validate the training data
Inspired by Dr. Jason Brownlee blog (https://machinelearningmastery.com/start-here/) which helped me to develop my predictive modeling skill.I took 14 days of mini-course after completing course.I could successfully developed this project with iris dataset.
Applied basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home