Pull Request for PyVerse 💡

Requesting to submit a pull request to the PyVerse repository.

Issue Title

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[Code Addition Request]: Deep learning models

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Info about the Related Issue

What's the goal of the project?
PyVerse is an open-source collection of diverse Python projects, tools, and scripts, ranging from beginner to advanced, across various domains like machine learning, web development, and automation.

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Name

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Yash K. Saini

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GitHub ID

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yashksaini-coder

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Email ID

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[email protected]

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Identify Yourself

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GSSoC-Ext

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Closes

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Closes: #816

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Describe the Add-ons or Changes You've Made

Give a clear description of what you have added or modified.

This pull request primarily focuses on adding comprehensive documentation and necessary dependencies for the Fraud Transactions Prediction Neural Network project. The changes include a detailed README file and a requirements.txt file listing the needed Python libraries.

Documentation Enhancements:

• Deep_Learning/Fraud Transactions Prediction NN/README.md: Added a detailed README file that includes an overview of the project, technologies used, dataset description, installation instructions, usage guidelines, and steps for data exploration, model training, evaluation, and interpretation.

Dependency Management:

• Deep_Learning/Fraud Transactions Prediction NN/requirements.txt: Added a list of required Python libraries for the project, including scikit-learn, pandas, matplotlib, seaborn, numpy, scipy, statsmodels, imblearn, shap, tensorflow, missingno, xgboost, lightgbm, keras, and joblib.

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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

How Has This Been Tested?

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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.