Machine Learning Developer in Dublin, Ireland

This project produced a tool which allows urban planners and community members to interact with a physical model of a city district and see how different urban designs would influence the mobility patterns.

I built a Flask app which reads the state of the district design from an API and uses a discrete choice logit model to predict the changes in travel patterns and resulting environmental impacts at a regional level. The analysis results are exposed through the Flask API.

This project developed a regression model to explain and predict where dense clusters of urban activity form, based on the physical characteristics of the area. I first analyzed geolocated telecom data, which was obtained in the country of Andorra, to identify clusters of activity characterized in terms of their size, persistence, and diversity. I then built a lasso regularized multivariate linear regression model in R to order to identify associations between the formation of these clusters and various discrete urban features.

In this project, I built a Bayesian network in Python to predict trips made and traffic congestion in Andorra based on a combination of geolocated telecoms data and a small sample of traffic counts.