Business Problem
Organizations often have valuable information trapped in unstructured notes, reports, or comments.
Analytics Workflow
- Text collection
- cleaning
- entity extraction
- classification
- evaluation
- reporting
Methodology
Built a text-processing workflow to clean notes, extract entities, classify themes, and summarize patterns.
Key Findings
The NLP pipeline extracted structured entities from unstructured reports and grouped recurring operational themes.
Business Impact
Reduces manual review time and helps teams identify repeated issues or risks in text data.
Technical Highlights
Tokenization, entity extraction, classification metrics, prompt evaluation, error review.
What This Project Demonstrates
SQL, Python, data cleaning, dashboard design, machine learning or NLP where applicable, data engineering where applicable, business communication, and problem solving.
Interview Talking Points
How did you evaluate the NLP results? What errors were most important?
Lessons Learned
NLP needs careful validation because text outputs can look convincing while still being wrong.
Future Improvements
Add real evaluation metrics and example anonymized outputs.