Update README.md

Author: Pragya-19

README.md

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+# 🧠 NLP Model Testing Framework (Python | Pytest | AI/ML QA)
 
+![Python](https://img.shields.io/badge/Python-3.11-blue)
+![Pytest](https://img.shields.io/badge/Pytest-Automation-green)
+![NLP](https://img.shields.io/badge/NLP-Model--Testing-purple)
+![ML QA](https://img.shields.io/badge/ML-QA-orange)
+![Status](https://img.shields.io/badge/Status-Active-success)
+
+---
+
+## 🚀 Overview
+
+This project is a **NLP Model Testing Framework** designed to validate machine learning model behavior using **Python and Pytest**.
+
+Unlike traditional testing, this focuses on **AI/ML validation**, ensuring model predictions are:
+
+✔ Correct  
+✔ Consistent  
+✔ Robust  
+✔ Edge-case safe  
+
+---
+
+## 🧠 What This Project Covers
+
+- Sentiment Analysis Testing
+- NLP Text Preprocessing Validation
+- Data-driven Testing using CSV
+- Edge Case Handling
+- Prediction Accuracy Validation
+
+---
+
+## 🛠 Tech Stack
+
+- Python
+- Pytest
+- CSV (Data-driven testing)
+- NLP Basics (Text Processing + Classification)
+
+---
+
+## 📁 Project Structure
+
+
+NLP-Model-Testing-Framework-Python-Pytest/
+│
+├── nlp_model/
+
+  │ ├── init.py
+  
+  │ ├── sentiment_model.py
+  
+  │ └── text_preprocessor.py
+│
+├── tests/
+
+  │ ├── init.py
+  
+  │ ├── test_sentiment_prediction.py
+  
+  │ ├── test_text_preprocessing.py
+  
+  │ ├── test_edge_cases.py
+  
+  │ └── test_data_driven_sentiment.py
+│
+├── test_data/
+
+  │ └── sentiment_test_data.csv
+│
+├── requirements.txt
+
+└── README.md
+
+
+---
+
+## 🧪 Test Scenarios Covered
+
+### 1️⃣ Sentiment Prediction Testing
+
+```python
+def test_positive_sentiment():
+    assert predict_sentiment("I love this product") == "positive"
+
+2️⃣ Text Preprocessing
+def test_text_cleaning():
+    assert clean_text("HELLO!!!") == "hello"
+
+3️⃣ Edge Case Testing
+Empty input
+Numeric input
+Mixed case
+
+4️⃣ Data-Driven Testing
+
+CSV-based testing:
+
+text,expected_sentiment
+I love this,positive
+This is bad,negative
+
+▶️ How to Run
+pip install -r requirements.txt
+python -m pytest -v
+
+📊 Sample Output
+7 passed in 0.06s
+
+📸 Screenshot
+
+🎯 What This Project Demonstrates
+
+ML Model Testing Approach
+NLP Validation Techniques
+Data-driven QA Strategy
+Edge Case Handling in AI Systems
+
+🚀 Future Enhancements
+
+Real ML model integration (Scikit-learn / HuggingFace)
+Model accuracy metrics validation
+Confusion matrix validation
+CI/CD integration (GitHub Actions)
+
+👩‍💻 Author
+
+Pragya Kapil
+
+QA Automation | AI Testing | GenAI | NLP Testing