Predicting bike rentals guided project

Hi everyone,
Please see my solution to the Predicting Bike Rentals guided project. I’d particularly appreciate your feedback on the conclusions I made after each model and at the end as I still find it a bit hard to interpret the metrics.

Predicting Bike Rentals.ipynb (226.4 KB)

Thanks in advance,
Ivelina

Click here to view the jupyter notebook file in a new tab

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Hi @ivelinagenova

Congrats! Project looks good. Interpret the metrics is hard for all newbies, i still don’t feel that i can interpret them at 100%, but in my experience:

  • Linear Regression: Works perfectly, both train and test error are similar. Obviously in MSE they are going to be really high because every value is squared, but RMSE have only a 0.4 point difference, so no problem at all
  • Decision Tree: As every decision tree, it’s overfitted. But it is normal in DT. When min_samples_leaf = 5, the difference between train and test reduced but it’s still overfitted
  • Random Tree: The first one is overfitted, the second one has a difference of 10 points i don’t consider it as an overfitted model but i’m not sure

A good explanation of overfit/underfit is found here

I’m agree with your final conclusion

That’s all i have to say, good luck!

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Thanks, @alegiraldo666! I really appreciate your comments and the link you provided. I will make sure to go through it.

Have a lovely day!
Ivelina