| # Bayesian Networks Implementation | |
| A comprehensive implementation of Bayesian Networks for probabilistic modeling and inference, featuring educational content and practical applications using the Iris dataset. | |
| ## 📋 Project Overview | |
| This project provides a complete learning experience for Bayesian Networks, from theoretical foundations to practical implementation. It includes detailed explanations, step-by-step tutorials, and a working implementation that demonstrates probabilistic inference on real data. | |
| ## 🎯 Key Features | |
| - **Educational Content**: Comprehensive learning roadmap with real-life analogies | |
| - **Practical Implementation**: Working Bayesian Network using the Iris dataset | |
| - **Probabilistic Inference**: Multiple inference scenarios and predictions | |
| - **Visualization**: Network structure analysis and results visualization | |
| - **Model Persistence**: Trained models saved for reuse | |
| ## 📁 Project Structure | |
| ``` | |
| ├── implementation.ipynb # Main notebook with theory and implementation | |
| ├── README.md # This file | |
| ├── bayesian_network_model.pkl # Trained Bayesian Network model | |
| ├── bayesian_network_analysis.png # Network structure visualization | |
| ├── processed_iris_data.csv # Discretized Iris dataset | |
| ├── model_summary.json # Model architecture and performance metrics | |
| ├── inference_results.json # Inference scenarios and predictions | |
| └── bayesian_network_training.log # Training process logs | |
| ``` | |
| ## 🚀 Getting Started | |
| ### Prerequisites | |
| ```bash | |
| pip install numpy pandas scikit-learn pgmpy matplotlib seaborn jupyter | |
| ``` | |
| ### Running the Project | |
| 1. Open `implementation.ipynb` in Jupyter Notebook | |
| 2. Run all cells to see the complete learning experience | |
| 3. The notebook includes: | |
| - Theoretical explanations with real-life analogies | |
| - Step-by-step implementation | |
| - Model training and evaluation | |
| - Probabilistic inference examples | |
| ## 📊 Model Performance | |
| - **Dataset**: Iris (discretized) | |
| - **Accuracy**: 84.44% | |
| - **Nodes**: 5 (Species, Sepal_Length, Sepal_Width, Petal_Length, Petal_Width) | |
| - **Edges**: 5 probabilistic dependencies | |
| - **Parameters**: 57 learned parameters | |
| - **Inference Scenarios**: 4 different prediction scenarios | |
| ## 🧠 Learning Content | |
| The notebook includes comprehensive educational material: | |
| 1. **Graph Theory Foundations** - DAGs and network structure | |
| 2. **Probability Fundamentals** - Joint, marginal, and conditional probability | |
| 3. **Conditional Independence** - D-separation rules | |
| 4. **Network Construction** - Structure and parameter learning | |
| 5. **Inference Methods** - Exact and approximate inference | |
| 6. **Formula Memory Aids** - Real-life analogies for key concepts | |
| ## 🔍 Key Concepts Covered | |
| - **Bayes' Theorem**: Medical test accuracy analogy | |
| - **Chain Rule**: Recipe steps dependencies | |
| - **Conditional Independence**: Weather and clothing choice | |
| - **Probabilistic Inference**: Medical diagnosis scenarios | |
| ## 📈 Outputs | |
| - **Network Visualization**: Graphical representation of learned dependencies | |
| - **Inference Results**: Probabilistic predictions for various scenarios | |
| - **Model Metrics**: Performance evaluation and convergence analysis | |
| - **Training Logs**: Detailed learning process documentation | |
| ## 🎓 Educational Value | |
| This project serves as a complete learning resource for understanding Bayesian Networks, combining theoretical knowledge with practical implementation. Perfect for students, researchers, and practitioners looking to master probabilistic graphical models. |