[P] ML-based data understanding in formula 1 racing - Huggingface spaces demo

Hey r/MachineLearning,

Here is a super-simple example that lets you explore the F1 Montreal 2023 GP results on Huggingface:

[https://huggingface.co/spaces/renumics/f1\_montreal\_gp](https://huggingface.co/spaces/renumics/f1_montreal_gp)

In the default setting, we used a dimensionality reduction method (UMAP) on the brake telemetry to display all laps on a similarity map. You can use the similarity map and additional metadata to inspect different data segments (e.g. start, safety car).

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https://preview.redd.it/wbdyj00tabab1.png?width=1914&format=png&auto=webp&v=enabled&s=c06a38a03f3093e30e7e3ef10023bae0f88ee7ee

In real-world use cases more powerful ML models are typically used to compute embeddings and outlier scores. …

data demo dimensionality example formula 1 hey huggingface machinelearning map racing spaces telemetry umap understanding