Real Estate Listing Data and Interpretable machine learning

We are looking forward to the November edition of the Zurich R User Meetup, sponsored by Datahouse.

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Schedule:
06.15 pm Doors open
06.30 pm Introduction / Welcome by the organizing team
06.40 pm Talks (see below)
ca 07.45 Take the Stage: Pitching job ads, ideas, events, ...
ca 08.00 - 09.30 pm Apéro
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Andreas Thürlimann, Senior Data Scientist, Datahouse

Real Estate Listing Data: Unveiling Market Insights with R

Discover the power of real estate listing data as we explore its acquisition, cleansing, enrichment, and modeling using R. This talk demonstrates practical applications of data-driven decision-making in the real estate industry. We'll share real-world examples of integrating data into predictive models and strategies for presenting insights to clients. Gain the tools to stay ahead in the dynamic real estate market by joining us in this insightful session.

Interested in working for Datahouse? Check out this interesting traineeship.

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Andrea Farnham, Senior Researcher, Epidemiology, Biostatistics and Prevention Institute

Interpretable machine learning: applications in public health

Machine learning is increasingly being used in public health research. However, the black-box nature of many machine learning models can be a significant barrier to their adoption and trustworthiness in critical public health applications. This talk, heavily inspired by Christoph Molnar's Interpretable Machine Learning book, explores why interpretability is important and how to use the iml R package to introduce interpretability into your research. In particular, I will discuss a recent analysis I published using interpretable machine learning methods in predicting schistosomiasis incidence in Ghana based on satellite imagery.