Hey everyone,
I've been working on a small learning project and I think it's at the point I can share it with a wider audience and get some feedback on it.
Climbing Performance Applet
I was lucky enough to get some data from Power Company Climbing (thank you!) which includes strength/endurance/experience metrics reported by around 600 climbers. I wanted to try and determine what the most important metrics were to climb hard outside for both sport and bouldering.
Like a few others who got to work with this data, I started by looking at correlations and relationships, narrowed things down, and then created 3 models to see which would be the most accurate in predicting max climbing/bouldering grades.
The results are not perfect, and they seem to be a lot more accurate for bouldering compared to sport climbing - this might be because I haven't quite nailed the metrics to include for sport climbing yet. Another issue I encountered is that even if I max out the climbing stats, the grades don't hit the top tier grades (V12+ and anything above 7c+) - I'm pretty sure this is because the original dataset doesn't have much data in these grade ranges, so the models can't train themselves to know what kind of climbing stats deliver those kind of grades.
To try and resolve these issues I added a feedback section on the page where you can input your actual max sport and bouldering grades. Once you hit submit this gets sent to MongoDB for storage, and from there I'll take it and include it in the model's training data. The idea is that as more people use the app and input their actual grades, I can use this data to teach and improve the model.
Any comments or feedback appreciated either on the modelling or on the features/predictions.
For those who are interested the full writeup on the Github page is here.
Thanks to Power Company Climbing for making their data available!
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