Fix Gaussian Naive Bayes implementation

[krvipin15]

Describe your change:

Fixes the broken Gaussian Naive Bayes implementation that was using deprecated sklearn functions.

Issues in the old code:

• Used plot_confusion_matrix() which was deprecated and removed in scikit-learn 1.2+
• Only demonstrated sklearn's built-in implementation, not an actual algorithm
• Contained unnecessary time.sleep() calls
• Not suitable for TheAlgorithms repo (should implement algorithms from scratch)

What this PR does:

• Replaces the broken sklearn example with a complete from-scratch implementation
• Implements Gaussian Naive Bayes using only NumPy
• Includes fit(), predict(), and predict_proba() methods
• Uses proper mathematical formulas (Gaussian PDF and Bayes' theorem)
• Handles numerical stability with log probabilities
• Comprehensive docstrings with type hints
• Working doctests that pass automated testing
• Example usage with Iris dataset achieving 95%+ accuracy
• Reference: https://en.wikipedia.org/wiki/Naive_Bayes_classifier

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".