Analyzing Migration Trends through Credit Card and Foot Traffic Data

Trautz, Hunter C.; Deyette, Katherine M.; Thompson, Lena S.

Student Work

Analyzing Migration Trends through Credit Card and Foot Traffic Data

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Through a collaboration with a prominent investment firm, a machine learning model was built to predict human migration patterns to inform real estate investments. The Agile Scrum methodology was applied in managing this project. Software development was primarily done using Python and Azure Databricks. Our work focused on two sets of data, which we attempted to combine using various statistical methodologies. Although this was not feasible, the data sets were still useful training sets for our model. The machine learning algorithm developed has the potential to be a great asset to the alternative investment firm, as it is able to predict the population within the United States to a high degree of confidence, even with the limited amount of initial training data.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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