Extract Insurance Data from PDFs with spaCy & Python 📄

In this video, I’ll show you how to automatically extract important information from insurance or claim documents in PDF format using Python and the spaCy NLP library. You’ll learn how to process PDF files, use named entity recognition (NER) to detect key fields like policy numbers, claim amounts, dates, and more — and then export that structured data directly into an Excel sheet.

This is perfect for automating manual data entry tasks, saving hours of time, and improving accuracy in insurance data processing. Whether you’re a data analyst, Python developer, or just getting started with NLP, this tutorial will help you build a practical end-to-end pipeline.

👉 What you’ll learn:
✅ Read PDF files in Python
✅ Extract text data using pdfplumber or PyPDF2
✅ Perform Named Entity Recognition (NER) with spaCy
✅ Extract relevant fields like Policy Number, Claim Amount, Dates, and more
✅ Store the extracted data into Excel using pandas
✅ Tips for scaling this solution for multiple files

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NLP -https://www.youtube.com/watch?v=9gVGbm35jmM&list=PL27ymbRRTmMV0g1i-PJcxIIPkMbQedF3w

Data Extraction - https://www.youtube.com/watch?v=yqWmMhipt9Y&list=PL27ymbRRTmMXIupptMMtHmID8NwYD6nbG