feat: add scikit-learn classification example using logistic regression

[Golixco]

📝 Description

Brief description of changes made.
Added a file named scikit_classification.py in the advanced folder.
This file demonstrates how to implement a basic classification model using Scikit-learn with Logistic Regression on a sample dataset.
It’s designed to help beginners understand how machine learning models are trained, tested, and evaluated in Python.

🎃 Hacktoberfest 2025

• This PR is for Hacktoberfest 2025

🎯 Type of Change

• 📝 Documentation update
• 🐛 Bug fix
• ✨ New feature
• 🎨 Code example/tutorial
• 🧪 Testing
• 🔧 Code refactoring

📋 Difficulty Level

• Beginner
• Intermediate
• Advanced

✅ Checklist

• My code follows the style guidelines
• I have performed a self-review
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes

🧪 Testing

Describe how you tested your changes.
Tested the classification example locally using sample datasets in scikit-learn.
Verified correct model training and prediction outputs.

📸 Screenshots (if applicable)

not included any screenshots

Add screenshots to help explain your changes.

📎 Additional Notes

Any additional information about this PR.
This PR introduces a clean and concise machine learning classification example under the advanced folder, focusing on clarity and educational value for beginners exploring Scikit-learn.
The script follows a structured pipeline — dataset loading, model initialization, training, evaluation, and prediction — making it a solid starting point for contributors interested in AI/ML fundamentals.
Created as part of Hacktoberfest 2025, this contribution aims to expand the repository’s collection of practical ML code samples.

[Golixco]

Hi! 👋
It seems the Hacktoberfest Auto-Label checks failed , likely due to a labeling or automation configuration issue in the repository, since the code changes themselves are clean and have no merge conflicts.
My contribution adds a scikit_classification.py example under the advanced section, showcasing a Scikit-learn Logistic Regression implementation for classification.
Please review and add the appropriate labels (e.g., hacktoberfest-accepted, enhancement, or new-feature) manually if required.
Thank you! 😊