README.md

⚖️ Judgment Bias Detector

An AI-powered web application that analyzes court judgments and detects potential bias patterns using Natural Language Processing (NLP) and Machine Learning.

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🚀 Live Demo

🔗 https://judgment-bias-detector-c8wtvt49uxakhbqtejetj6.streamlit.app/

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📌 Features

• 🔍 Predicts case outcome (Guilty / Not Guilty)
• ⚖️ Analyzes potential bias based on gender and region
• 📊 Visualizes bias trends using charts
• 📂 Includes sample dataset for demo
• ⚡ Interactive UI built with Streamlit

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• NLTK
• Streamlit
• Matplotlib

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📂 Project Structure

judgment-bias-detector/
│
├── app/
│   └── streamlit_app.py
│
├── data/
│   └── sample_judgments.csv   # small demo dataset ✅
│
├── src/
├── requirements.txt
└── README.md

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⚙️ Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/Shrishti1701/judgment-bias-detector.git
cd judgment-bias-detector

2️⃣ Create virtual environment

python -m venv venv

3️⃣ Activate environment

venv\Scripts\activate

4️⃣ Install dependencies

pip install -r requirements.txt

5️⃣ Run the app

streamlit run app/streamlit_app.py

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📊 Dataset

• Includes a sample dataset (sample_judgments.csv) for demonstration
• You can replace it with real court judgment datasets for better results

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🧠 Model Details

• Text preprocessing using NLTK
• Feature extraction using TF-IDF
• Classification using Logistic Regression
• Bias detection using grouped statistical analysis

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📸 Screenshots

Add your app screenshots here

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🎯 Future Improvements

• Advanced NLP models (BERT / Transformers)
• More bias dimensions (caste, socioeconomic factors)
• Real-time legal data integration
• Model explainability (SHAP/LIME)

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⚠️ Disclaimer

This project is for educational purposes only and does not represent real legal judgments.

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📧 Contact

Shrishti Banshiar GitHub: https://github.com/Shrishti1701

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⭐ Support

If you like this project, give it a ⭐ on GitHub!