End-to-End ML Project (Problem to Deployment)

Summary

The flagship portfolio piece — take a real problem from data collection through modeling to a working deployed API, showing the full pipeline working together, not just a notebook.

How to Learn This

1Pick a problem with genuine substance and a dataset you can access cleanly.
2Work through EDA, feature engineering, modeling, evaluation, and a simple deployed API (Phase 12).
3Write a README explaining the problem, your approach, results, and what you'd improve next.

Presenting to Non-Technical Audiences

Kaggle Competition Submission

More projects in Build Your Data Science Portfolio

Kaggle Competition Submission NLP Project (Sentiment / Text Classification)Time Series Forecasting Project Computer Vision Project Business Case Study With ML Recommendation Portfolio Website / GitHub Showcase

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