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Normalize training data #28

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nya-elimu opened this issue Sep 15, 2024 · 5 comments · May be fixed by #30
Open

Normalize training data #28

nya-elimu opened this issue Sep 15, 2024 · 5 comments · May be fixed by #30

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@nya-elimu
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nya-elimu commented Sep 15, 2024

https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html

Why?
Why normalize the training data? To check if that would improve the accuracy score.

@howdyDp
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howdyDp commented Oct 4, 2024

I was trying to normalize the data, i wanted to know what kind of normalization are you looking for ?
min max normalization or z-scaled normalization and this is my first open source contribution so if you have any advice do let me know

@jo-elimu
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jo-elimu commented Oct 8, 2024

@howdyDp We are looking for whichever normalization technique would result in an improved accuracy score.

Tip

Try running python run_all_steps.py for each kind of normalization you want to try, and see if that results in improvements in step3_2_accuracy_score.txt and step3_2_mean_absolute_error.txt

And if it turns out that normalization does not improve the accuracy, then we wouldn't use normalization at all.

@eve-b612
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eve-b612 commented Oct 9, 2024

Hi! I see the issue is still open, so I thought I would give this a try. This is my first contribution, I've normalised the data 3 ways and wanted to share the results. My work is in a jupyter notebook, I've forked the repo and created a new branch named 'normalisation-experiment'. I wanted to know which folder to upload this in the repo branch?

@jo-elimu
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jo-elimu commented Oct 9, 2024

@eve-b612 Feel free to open a pull request 🙂

You can add your code changes related to data processing to the step1_prepare folder.

@eve-b612
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eve-b612 commented Oct 9, 2024

Sent the pull request for review! :)

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