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• Implemented an interface with the Shiny package in R through which banks can create, register, and test models that detect financial crimes

• Used several machine learning models (Boosted Tree, Random Forest, Naive Bayes, Logistic Regression, etc.) on transactional data to assist banks in detecting these crimes

• Visualized models' results (ggplot2) and relative predictive powers to optimize model selection

• Formatted, cleaned, combined, and filtered millions of lines and hundreds of variables of flight booking data (R, SQL, MSSQL, Tableau)

• Analyzed and visualized said data to predict airlines' monthly revenues and determine the most profitable amount of company flight sales, presented this information to company executives (R, ggplot2)

• Helped create a clustering machine learning model to analyze customer lifetime value based on certain traits of customers