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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