Increasing model performance using hyperparameter tuning

[PrasDev4]

Pull Request for PyVerse 💡

Requesting to submit a pull request to the PyVerse repository.

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Issue Title

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Enhancing Diabetes Prediction Accuracy Through Hyperparameter Tuning in Random Forest.

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Info about the Related Issue

What's the goal of the project?
The goal is to enhance the accuracy of the diabetes prediction model by implementing hyperparameter tuning using GridSearchCV on the Random Forest algorithm and visualizing the impact of the tuned parameters.

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Name

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PRASANNA DEVIREDDY

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GitHub ID

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PrasDev4

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Email ID

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[email protected]

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Identify Yourself

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SWOC

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Closes

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Closes: #1156

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Describe the Add-ons or Changes You've Made

Give a clear description of what you have added or modified.
I have implemented hyperparameter tuning using GridSearchCV for the Random Forest classifier to optimize model parameters (n_estimators, max_depth, and min_samples_split). I have also added a visualization comparing model accuracy before and after tuning to showcase the improvement.

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Type of Change

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• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

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How Has This Been Tested?

Describe how your changes have been tested.
The changes have been tested on the diabetes dataset by splitting it into training and testing sets. The accuracy scores for both the untuned and tuned models were calculated and validated using visualizations.

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Checklist

Please confirm the following:

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

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