generic machine learning model comparison

[MehekFatima]

Description

This PR adds a generic machine learning model comparison script. The script allows for easy comparison of various machine learning models on both normalized and standardized data. It includes functionalities such as model training, evaluation, hyperparameter tuning, and visualization of performance metrics.

Related Issues

Changes

• Added a Python script for comparing machine learning models.
• Implemented support for both normalization and standardization of data.
• Included model training, evaluation, hyperparameter tuning functionalities.
• Visualized performance metrics including ROC curves and confusion matrices.

Testing Instructions

1. Clone this branch.
2. Ensure Python is installed on your system.
3. Install the required dependencies using pip install.
4. Run the script python model_comparison.py.
5. Verify that the models are trained, evaluated, and visualized correctly.

Screenshots (if applicable)

N/A

Additional Context

This PR is part of my contribution to GSSOC.

Checklist

• [x] I have performed a self-review of my code
• [x] I have commented my code, particularly in hard-to-understand areas
• [x] My changes generate no new warnings
• [x] I am working on this issue under GSSOC

[MehekFatima]

Hi @SrijanShovit , I've submitted a pull request that adds a generic machine learning model comparison script. This script allows for easy comparison of various machine learning models on normalized and standardized data, including model training, evaluation, hyperparameter tuning, and visualization. I'd appreciate it if you could take a look whenever you have a moment.

Thank you!

[github-actions[bot]]

This issue has been automatically closed because it has been inactive for more than 7 days. If you believe this is still relevant, feel free to reopen it or create a new one. Thank you!

[MehekFatima]

Hi @SrijanShovit , I've submitted a pull request that adds a generic machine learning model comparison script. This script allows for easy comparison of various machine learning models on normalized and standardized data, including model training, evaluation, hyperparameter tuning, and visualization. I'd appreciate it if you could take a look whenever you have a moment.

Thank you!