Presentation
Banks, insurance companies and other institutions are often interested in estimating the value of a single house or a large portfolio of houses. Machine learning methods like random forest and gradient boosted trees tend to outperform traditional statistical models for this task, but these models are often hard to interpret. In this talk, we experiment with different tree-based models for house price prediction on a data set of house transactions from Oslo, and compare them in terms of prediction accuracy and interpretability.
Speaker
Anders Hjort is an industrial PhD at the Statistics & Data Science group at the University of Oslo. The focus of his PhD work is to develop machine learning methods for the topic of house price prediction. The work is done in collaboration with the Norwegian financial technology company Eiendomsverdi, which is the leading provider of data about the Norwegian housing market.
Program
11:30 – Doors open and lunch is served
12:00 – "Estimating House Prices with Machine Learning" by Anders Hjort (PhD Candidate, Department of Mathematics, UiO)
This event is open for all PhD candidates and postdocs. No registration needed.
About the seminar series
Once a month, dScience will invite you to join us for lunch, soft drinks and professional talks at the Science Library. In addition to these, we will serve lunch to PhD candidates in our lounge in Kristine Bonnevies hus every Thursday. Due to limited space (40 people), this will be first come, first served. See how to find us here (download).
Our lounge can also be booked by PhDs and Postdocs on a regular basis, whether it is for a meeting or just to hang out – we have fresh coffee all day long! Read more about the seminar series here.