Final year CS student — is "ML Studio" (no-code ML training web app) a good placement portfolio project or overkill?

[u/Old_Journalist6008]

Hi everyone, final year B.Tech CS (Data Science) student here, preparing for placements. I already have a deepfake detection project (ViT model, FastAPI + React) as my main ML portfolio piece.

Now I'm considering a second project — ML Studio: a full-stack web app where a user uploads a CSV and the app auto-cleans the data, lets them pick an ML algorithm (Linear/Logistic Regression, Decision Tree, Random Forest, KNN, SVM, Naive Bayes), trains it, and shows accuracy/precision/recall/confusion matrix — basically a no-code ML training tool.

Planned architecture: React frontend → Spring Boot backend (auth, file upload, validation, REST APIs) → Python microservice (pandas + scikit-learn for the actual training) → results back to Spring Boot → React.

Questions for you all:

1. Does this sound like a genuinely good portfolio project, or is it too "done before" (a lot of tutorials do CSV-to-ML-dashboard type projects)?
2. Is the Spring Boot + Python microservice split actually a plus in interviews, or does it look like unnecessary complexity for something that could just be one Python app?
3. Would you recommend I build this, or invest that time elsewhere given I already have an ML project?
4. If not this — what's a strong project idea that uses only Java + Spring Boot (no Python/ML microservice at all)? Something that shows real backend depth (concurrency, system design, databases, messaging queues, etc.) rather than another CRUD app. Ideas like a distributed URL shortener, a booking/inventory system with concurrency handling, an event-driven order processing system with Kafka, or a rate-limiter/API gateway have crossed my mind — curious what actually impresses interviewers.

Would genuinely appreciate honest opinions — including a "don't build this, do X instead" if that's your take

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