Title

Implementing Machine Learning Techniques in Financial Modeling

Faculty Sponsor

Tiffany Kolba

College

Arts and Sciences

Department/Program

Data Science

Document Type

Poster Presentation

Symposium Date

Spring 5-4-2017

Abstract

The data science competition forum Kaggle, in conjunction with Two Sigma, proposed a financial modeling competition open to the public. The challenge is to predict an anonymous time-varying financial instrument based on anonymous features given in the dataset. To accomplish this task, we will demonstrate several machine learning techniques and show how well they perform in the prediction of the class variable. These techniques include Ridge Regression, Extreme Gradient Boosting, and Extremely Randomized Trees. We will review each of the techniques, and then show the results of how they worked independently and together.

Biographical Information about Author(s)

William Arloff is a senior Data Science Major at Valparaiso University. He plans on obtaining a job back in his home state of New York in the field of data science after graduation.

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