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. Here, I build a supervised learning algorithm to identify fraudulent employees using Enron dataset.

In 1912, the largest ship afloat at the time- RMS Titanic sank after colliding with an iceberg. Of the 2224 passengers and crew abroad 1502 died.

In this project, we will explore the training dataset (train) from kaggle. This dataset contains demographic and passenger information about 891 of the 2224 passengers and crew abroad. The most interesting question here is what features made people more likely to survive the sinking? Based on the available feature information can we build a classification algorithm that can reasonably predict survival?