Feedback on t-SNE script: ideas to improve clarity and usability

Feature description

Hi! I tested your t_stochastic_neighbour_embedding.py script on my local setup and it runs well thanks for sharing it!

I have a few suggestions that might improve clarity and usability for others:

Visualization: Consider adding a matplotlib scatter plot to show the t-SNE result. It helps users see the clustering visually.

Reproducibility: Adding random_state=42 to the TSNE() constructor would make the output consistent across runs.

Parameter transparency: Explicitly setting perplexity, learning_rate, and n_iter could help learners understand how t-SNE behaves.

Import error handling: A simple try/except block for sklearn imports could guide users if they haven't installed the package.

Let me know if you'd like help implementing any of these — happy to collaborate!

Start by reading t_stochastic_neighbour_embedding.py and the current TSNE usage. Review the proposed visualization, reproducibility, parameter, and import-handling changes, then confirm which suggestions are in scope. Done means the selected improvements are implemented while the script continues to run as expected.