How I Built a US Visa Prediction ML Pipeline

The Problem

Predicting US Visa approval is a classic classification problem. I wanted to build not just a model, but a production-ready ML pipeline that follows industry best practices.

Architecture

The project follows a modular architecture with separate components for:

• Data Ingestion — Fetching and validating raw data
• Data Transformation — Feature engineering and preprocessing
• Model Training — Training with hyperparameter tuning
• Model Evaluation — Performance metrics and threshold optimization
• Model Deployment — Flask API with Docker containerization

Key Learnings

1. Modular code is essential for maintainability
2. Custom logging and exception handling saves debugging time
3. Docker makes deployment reproducible across environments
4. CI/CD pipelines automate the boring stuff

Tech Stack

Python, Scikit-learn, Pandas, Flask, Docker, MongoDB

Building an end-to-end pipeline taught me more about software engineering than any tutorial ever could.