I’ve just finished another guided project on my Data Science path, and I would like to share it with the community! Any feedback would be appreciated
Here’s the URL of the last mission screen of the Guided Project and my notebook (.ipynb file):
Predicting the stock market.ipynb (219.9 KB)
Click here to view the jupyter notebook file in a new tab
One piece of feedback would be to challenge your conclusion - would you put money in the market with your models predictions if there is a RMSE of $246 between predicted and actual values? Not a commentary on your work, but it’s a limitation in the approach itself, which is a part of the lesson.
On a related note, Vik Paruchi (founder of dq) just did a webinar on an alternative approach to predicting the stock market, showing how he creates a model that he has actually ran against the market and sold to people who want to do algorithmic trading. Definitely worth the watch and following along if you have the time!
Hi @kevindarley2024 ,
Very interesting reflection on the limitations of this approach. It actually surprised me how adding features to the model didn’t improve RMSE values. It seems that 246$ was the minimum I could expect, with a model that probably over simplifies the stock market behavior.
That webinar sounds interesting! (if it’s not too advanced for me). Do you know where could I find or watch it?
Thanks for the video, @kevindarley2024 ! I just watched it and it’s very comprehensive! I think I’ll take a look to some other videos of DQ channel (I didn’t even know there was a DQ YouTube channel!).