Data science and machine learning

I’m building a practical application for my final-semester data science course and need help turning my raw dataset into a polished data-visualization tool. My main goal is to demonstrate how machine-learning insights can be communicated clearly through interactive, well-designed visuals.

Here’s the flow I have in mind. I’ll give you a structured CSV (roughly 50 K rows) plus a short brief on the story I want to tell. You analyse, clean, and, if it adds value, run a light ML model (clustering or regression is enough) so that the visual output goes beyond simple descriptive charts. The heart of the project is the front-end: an intuitive dashboard where faculty can filter variables and immediately see the updated plots. I’m comfortable with Python, so a Jupyter Notebook or a lightweight Flask/Streamlit app suits me, but if you think Plotly Dash, Tableau Public, or another tool would show the data better, feel free to propose it—clarity and interactivity take priority.

Deliverables I need before submission day:
• The fully commented code/notebook or packaged app, ready to run on my laptop.
• A short “read-me” that walks through setup, the ML component, and how each visualization supports the findings.
• One-page reflection (academic style) summarising methods, results, and limitations so I can slot it straight into my report.

Please keep the explanations clear and focus on reproducibility—my professor will run everything locally. If you’ve recently built dashboards or visual analytics for academic projects, that’s exactly the experience I’m hoping to lean on.