[Python] How to effectively present a Kaggle loan approval prediction project with multiple...

I trained machine learning models (Logistic Regression, Decision Tree, and Random Forest) on the Kaggle Loan Approval Prediction dataset.

The Jupyter Notebook runs fine, and I already:

Performed EDA (distribution plots of applicant income, loan amount, assets, etc.)

Preprocessed data (handled missing values, label encoding, scaling)

Trained models and compared them with accuracy, precision, recall, F1-score

Visualized results using a confusion matrix and classification reports

Now I’d like to present this project (to classmates and in my portfolio). My notebook generates a lot of graphs (asset value distributions, confusion matrix, etc.), but I’m not sure about the best way to structure the presentation.

What I’ve tried Exported plots into a PowerPoint manually → works, but feels static.

Considered turning the Jupyter Notebook into a PDF → not very engaging.

Looked into Streamlit → could make it interactive, but I’m not sure how to structure it.

What I expected I’d like advice on the best practices for presenting this kind of project. Should I:

Use PowerPoint (with code snippets, plots, and explanations)?

Build a small web app (e.g., with Streamlit)?

Stick with a polished Jupyter Notebook (Markdown + outputs)?

Basically: What’s the most effective way to present a machine learning project with many visualizations and evaluation metrics to different audiences?

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