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

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

PRASANNA DEVIREDDY

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GitHub ID

PrasDev4

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Email ID

[email protected]

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Identify Yourself

SWOC

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Closes

Closes: #1157

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Describe the Add-ons or Changes You've Made

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.

• I have described my changes.

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Type of Change

New feature (non-breaking change which adds functionality)

• New feature (non-breaking change which adds functionality)

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How Has This 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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[UTSAVS26]

@PrasDev4 did you raise the issue first?

[PrasDev4]

yes and the issue number is 1144