Added notebook with model using scaled features

[tm-jace-peralta]

This PR adds the notebooks used to enhance cross-country model performance

1. 2023-02-06_crosscountry_initial_jace.ipynb - experimented on different scalers applied on the feature columns and wealth index. Performance was increased (R^2 = 0.41 -> 0.48) especially for the case where PH is the test country (R^2 = 0.25->0.42).

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alronlam commented on 2023-02-08T03:06:49Z ----------------------------------------------------------------

Line #1.    countries_data = pd.read_csv("../../data/countries_data.csv")

Jace, let's make sure the notebooks are replicable when you run from top to bottom. If I understood right, you commented out the data creation steps and instead loaded data from this file.

Understand it helps make the iteration faster, but since this is final and we're sharing it now, let's focus on replicability. Thanks!

review-notebook-app[bot] commented 1 year ago

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alronlam commented on 2023-02-08T03:08:40Z ----------------------------------------------------------------

Line #20.        plt.show()

Is this last part still relevant to the experiment? If not, let's just remove it!

alronlam commented 1 year ago

Oh one last thing, can you add a bit of discussion at the top and refer to the 2 reference papers we found that also normalized features and wealth index per country? Just so we remember the basis for doing something like this. Thanks!

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alronlam commented on 2023-02-08T03:08:40Z ----------------------------------------------------------------

Line #20.        plt.show()

Is this last part still relevant to the experiment? If not, let's just remove it!

alronlam commented 1 year ago

Oh one last thing, can you add a bit of discussion at the top and refer to the 2 reference papers we found that also normalized features and wealth index per country? Just so we remember the basis for doing something like this. Thanks!

• © Githubissues.
• Githubissues is a development platform for aggregating issues.

[alronlam]

Oh one last thing, can you add a bit of discussion at the top and refer to the 2 reference papers we found that also normalized features and wealth index per country? Just so we remember the basis for doing something like this. Thanks!