I have a project that's aimed to predict the amount of occupants at my local gym given the date and weather.

Here's my Kaggle kernel

I have two datasets, occupants on a given hour and weather on a given hour. My process is that I combine these two datasets, and using Occupants as the target. However, when I implement a regression algorithm I can only reach a prediction score of 57%.

I'd love any advice on how to modify my solution to achieve better predictions?

Thank you.

  • $\begingroup$ When you say given hour, does that includes the day of the week? because that could be an important feature. Btw what is your evaluation measure? I'm surprised to see 57% for a regression problem. $\endgroup$ – Erwan Sep 26 '19 at 0:52

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