Enhance t-SNE script: visualization, reproducibility, and parameter transparency (#13513)

This PR improves the t_stochastic_neighbour_embedding.py script by making it more user-friendly and reproducible:

• Added matplotlib scatter plot to visualize t-SNE results.
• Introduced random_state for consistent outputs.
• Exposed perplexity, learning_rate, and max_iter parameters for easier experimentation.
• Added import error handling for sklearn and matplotlib.
• Fixed doctest by replacing n_iter with max_iter.

These enhancements help learners understand and use t-SNE more effectively.

Screenshot

Here is an example output after running the script:

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 Feedback on t-SNE script: ideas to improve clarity and usability #13513".