Enhance README with validation and project details

Added sections on input variability handling, data-driven validation, and edge case robustness. Expanded on project demonstrations and future enhancements.

Author: Pragya-19

README.md

@@ -130,23 +130,41 @@ In AI/ML systems, testing is not limited to functionality.
 We need to validate:
 
 Model prediction correctness
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 Input variability handling
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 Data-driven validation
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 Edge case robustness
 
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 This framework simulates real-world ML validation scenarios, bridging the gap between traditional QA and AI testing.
 
 🎯 What This Project Demonstrates
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 NLP Model Testing Approach
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 AI/ML Validation Techniques
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 Data-driven QA Strategy
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 Edge Case Handling in AI Systems
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 Python + Pytest Automation Skills
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 🚀 Future Enhancements
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 Integration with real ML models (Scikit-learn / HuggingFace)
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 Model accuracy metrics validation
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 Confusion matrix validation
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 CI/CD integration using GitHub Actions
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 Prompt-based NLP testing