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Machine Learning for mining

During the second week of August we had a pleasure of running a #hack together with Cornish Lithium. The aim was to further explore our capability of developing #prospectivity #maps for the mining of the #criticalminerals. You can read how we developed our capability in this case study.

Here are some key outcomes:
– We managed to prepare and extract features from 9 different feature #datasets and several new sources of ground truth, and integrated them into the #data pipeline.
– We ran a number of #ML experiments with different feature sets and #Lithium thresholds, and produced some well performing models.
– We produced explainability outputs to help interpret the model predictions.

So all in all, it turned out to be a very fruitful #hackathon that fortunately ran fairly smoothly without being blocked by the issues some hacks face. And it was great for bringing experts from both companies together and make real strides while having a really interesting and enjoyable time.

We will continue to work on refining the methodology, but we are full of hope that in the future we’ll be able to take this from an early-stage capability to a very useful tool for the #batterymetals industry.  

#batterymaterials #greenenergy

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Decision Lab
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