Build a Web App with FastAPI: EMI Calculator (Hands-on)
Details
Level up your Python skills by building a real, production-style web application with FastAPI—from zero to a working EMI (Equated Monthly Installment) Calculator with advanced features. We’ll also use Cursor (AI pair-programmer) to accelerate code completion, code generation, and code review—so you experience a modern dev workflow end-to-end.
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Why Attend
- Learn where FastAPI fits among Python web frameworks (Flask, Django, FastAPI).
- See how Cursor speeds up development without sacrificing quality.
- Build and deploy a FastAPI app that calculates EMIs, generates amortization schedules, supports prepayments, compares multiple loans, and runs what-if analyses.
- Walk away with a clean, well-structured project you can reuse for your own apps.
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What we'll build
A FastAPI service with endpoints like:
- `/emi` – compute monthly EMI for principal, rate, and term
- `/schedule` – full amortization schedule (Month, EMI, Principal, Interest, YTD Principal/Interest, Outstanding Balance)
- `/prepayments` – schedule and impact of one-time or recurring prepayments
- `/compare` – compare two or more loan options side-by-side
- `/analysis` – sensitivity analyses (interest rate changes, loan term variations)
We’ll return JSON responses and (time permitting) show a minimal HTML or Swagger UI-driven front end for quick testing.
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Agenda (compact & hands-on)
- Overview of Python Web Frameworks – strengths/trade-offs of Flask, Django, FastAPI
- Cursor Deep Dive – code completion, generation, and inline code review
- FastAPI Essentials – routing, pydantic models, validation, typing, docs (OpenAPI/Swagger)
- EMI Calculator Domain – formulas, assumptions, schedule math, prepayment strategies
- Live Build – scaffold the project, implement endpoints, test with docs UI
- Advanced Features – prepayments, loan comparison, sensitivity/term analysis
- Packaging & Next Steps – structure, testing, simple deploy options
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Key takeaways
- A working FastAPI project you can run locally.
- Clean patterns for request/response models with pydantic.
- Techniques for prepayment modeling, loan comparisons, and sensitivity analysis.
- Practical tips for using Cursor effectively (prompts, code review, refactors).
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Who should attend
- Python developers, data/ML engineers, and tech-curious professionals
- Beginners to intermediate devs comfortable with basic Python
- Product folks wanting to prototype financial calculators quickly
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Prerequisites
- Basic Python (functions, dicts/lists, virtualenvs)
- Laptop with:
- Python 3.10+
- FastAPI and Uvicorn (`pip install fastapi uvicorn pydantic`)
- An editor (VS Code or Cursor recommended)
- (Optional) Git installed
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Format & materials
- Live coding + Q&A
- Starter template and final code will be shared during the session
- Slides/notes with formulas and endpoint specs included
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Outcomes
By the end, you’ll have:
- A modular EMI Calculator API with documented endpoints
- Full amortization schedules with and without prepayments
- Loan comparison outputs and sensitivity/term analysis
- A clear roadmap to extend the service or add a front end
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Organizer
ITVersity – Meetups & Workshops: Focused, practical sessions to help you ship real projects faster.
Join us to build something useful—and learn patterns you can reuse in any FastAPI project.