Getting value out of geodata with AI: train the model

At Cmotions we love to get inspired by working on projects that are just a little different than our day-to-day work, that is why we have our The Analytics Lab, where we organize our so called Project Fridays. This specific Project Friday was initiated by a question concerning wolf presence coming from a biologist. Starting with a small question and some enthusiastic colleagues, it ended up in a whole bunch of Python code, an understandable xgboost model to predict wolf presence in an area, a scientific article (please let us know if you are interested in reading this article) and a series of articles to share our approach (see links below). This article is part of that series, in which we show how we gather and prepare geographical data to use in a predictive model, visualize the predictions on a map and understand/unbox our model using SHAP.

This article is part of our series about working with geographical data. The entire series is listed here:

1. Getting value out of geodate with AI: getting started
2. Getting value out of geodate with AI: convert locations to their lat and lon
3. Getting value out of geodate with AI: data preparation
4. Getting value out of geodate with AI: train the model
5. Getting value out of geodate with AI: explainability using SHAP
6. Getting value out of geodate with AI: visualize the model predictions

This work has also resulted in a scientific article, published in the Environmental Management magazine.

If you want to use the notebooks that are the base of the articles in this series, check out our Cmotions Gitlab page.