Add multinomial Naive Bayes text classification example

[Raghav8690]

Describe your change:

Add a new machine learning algorithm implementation for text classification using Multinomial Naive Bayes.

This PR adds

• A new file: naive_bayes_text_classification.py
• A class-based implementation with:
  • fit
  • predict_proba
  • predict
• Input validation with proper exceptions
• Comprehensive docstrings and doctests for:
  • Valid cases
  • Invalid cases
• A small toy text dataset example for demonstration
• Reference links to algorithm explanations and learning resources

Additional Notes

• The implementation is designed to be simple, readable, and beginner-friendly.
• The classifier supports basic text preprocessing and probabilistic prediction.
• Error handling has been added to improve robustness and usability.

Fixes #14664

• 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 [FEATURE] Add Naive Bayes Text Classification Algorithm #14664".

[AG141293]

@Raghav8690,
Hi, Raghav, great implementation overall! I noticed a few things while reviewing:

1. Type hint issue in predict() — probabilities.get has return type float | None, which may cause mypy to fail. Consider using key=lambda label: probabilities[label] instead.
2. Empty string input — predict_proba("") silently returns class priors with no warning. A doctest covering this edge case would help learners understand the behaviour.
3. Validation order in fit() — the empty-list check comes after the length check. Since empty lists trivially satisfy len(texts) == len(labels), it works correctly — but reversing the order (check empty first) would be more readable.
4. No printed demo in main — most files in this repo print example output so learners can run python naive_bayes_text_classification.py and see results. Worth adding a few print() calls after doctest.testmod().
   Overall the math and structure are solid. Nice work!