Update README.md

README.md

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-# 🎓 Student Eligibility Prediction System
-
-An AI-powered web application that predicts student eligibility using a Convolutional Neural Network (CNN) model built with TensorFlow/Keras.
-
-## 🚀 Features
-
-- **Single Prediction**: Real-time prediction for individual students
-- **Batch Prediction**: Upload CSV files for bulk predictions
-- **Interactive Dashboard**: Beautiful visualizations with prediction confidence
-- **Model Analytics**: Performance metrics and feature information
-- **Responsive Design**: Works on desktop and mobile devices
-
-## 🤖 Model Information
-
-- **Architecture**: 1D Convolutional Neural Network
-- **Framework**: TensorFlow/Keras
-- **Input**: Student features (grades, scores, etc.)
-- **Output**: Binary classification (Eligible/Not Eligible) with probability
-
-## 📊 Usage
-
-### Single Prediction
-1. Enter student information in the input fields
-2. Click "Predict Eligibility" 
-3. View the prediction, probability, and confidence scores
-4. Analyze the interactive visualization dashboard
-
-### Batch Prediction
-1. Prepare a CSV file with the required columns
-2. Upload the file using the file uploader
-3. Click "Process Batch" to get predictions for all rows
-4. Download the results with predictions and confidence scores
-
-## 📁 Required Files for Deployment
-
-Make sure you have these files in your Hugging Face Space:
-
-1. `app.py` - Main application file
-2. `requirements.txt` - Python dependencies
-3. `final_model.h5` - Trained CNN model
-4. `scaler.pkl` - Fitted StandardScaler object
-5. `metadata.json` - Model metadata and performance metrics
-6. `README.md` - This documentation
-
-## 🔧 Setup Instructions
-
-1. Create a new Hugging Face Space
-2. Choose "Gradio" as the SDK
-3. Upload all required files
-4. The app will automatically deploy and be accessible via the provided URL
-
-## 📈 Model Performance
-
-The model achieves high accuracy on student eligibility prediction with:
-- Comprehensive feature analysis
-- Regularization techniques (Dropout, BatchNormalization)
-- Early stopping and learning rate scheduling
-- ROC-AUC scoring for balanced evaluation
-
-## 🤝 Contributing
-
-Feel free to fork this project and submit pull requests for improvements!
-
-## 📝 License
-
-This project is open source and available under the MIT License.
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference