Unguided Project: Predicting Movie Revenues

[gbpignatti5]

I just finished another project. I’m quite proud of it because I was struggling mightily at the beginning but managed to eventually get it done.

I’m using a dataset I found on Kaggle which contains metadata about 3,000 movies. After performing data wrangling, EDA and feature engineering, I predict the movies revenues using 4 different regression models.

As usual, any feedback is appreciated. In particular, I would like to hear people’s opinion about how to handle the invalid budget values: I chose a drastic approach and dropped the rows with invalid values.

I would also like to know if my choice of dropping the popularity scores outliers is legitimate.

I tried the opposite actions (filling in missing budgets with median value and not dropping any outlier); unsurprisingly, the accuracy I obtained is lower.

At the same time, however, I’m worried the approach I used (dropping invalid data rather than replacing missing values and dropping outliers) exposes our models to overfitting.

Excited to hear what you think.

https://nbviewer.jupyter.org/urls/community.dataquest.io/uploads/short-url/lur8LO7Od2kVq92PR8CadPYGq8t.ipynb (1.7 MB)

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

[gbpignatti5]

@Sahil, thanks for fixing the link. I was trying to add the pretty link to nbviewer. I was able to do it last time but I forgot what was the procedure. You think you can help me with that?

[Elena_Kosourova]

Hi Giovan Battista,

First of all, congratulations on becoming a Community Champion for this week! Your project is really very impressive, great job!

As for your question on how to add a pretty link to nbviewer, in the case it wasn’t rendered automatically, you’ll find useful the following post:

Updating Your Guided Project in the Community

Hi there, Sharing your guided projects in the community is a fantastic way to get feedback. And this topic can help you with it. To make the sharing experience better, we have a handy feature in our community: Whenever you attach a .ipynb (Jupyter notebook file) in a new post, our system automatically edits your post to add a link to view your project in a new tab. It is handy as it will allow your peers to see your project without downloading the file. The only issue is that the preview l…

What you need from there is Option 2 (Super Cool).

Hope it helps!