# 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.