# Basic Usage

This tutorial guides you through the basic usage of the ConversionFlow library.

## Overview

ConversionFlow is a library for conversion goals analysis that employs a two-stage model pipeline:
1. Estimation stage using Bayesian Network modeling
2. Optimization stage using Genetic Algorithms

## Quick Start

Here's a minimal example to get you started:

```python
from conversionflow import ConversionFlow

# Initialize the ConversionFlow instance
cf = ConversionFlow(config_path="config.yml")

# Run the full pipeline (estimation and optimization)
cf.run_pipeline()

# Access the results
estimation_results = cf.get_estimation_results()
optimization_results = cf.get_optimization_results()

# Generate reports
cf.generate_reports()
```

## Understanding the Two-Stage Pipeline

### Estimation Stage

The Estimation stage builds a detailed probabilistic model to quantify customer journey dynamics:

1. Data Loading: Ingests relevant datasets from specified sources
2. Data Preprocessing: Cleans and transforms data for model training
3. Bayesian Network Model Inference: Constructs a probabilistic model and uses MCMC sampling

### Optimization Stage

The Optimization stage utilizes a Genetic Algorithm to maximize conversion value:

1. Parameter Loading: Loads parameter summaries from the Bayesian Network model
2. Budget Optimization: Uses Genetic Algorithm to find optimal budget allocation
3. Output Generation: Provides optimal budget distribution for implementation

## Next Steps

Now that you understand the basic usage, proceed to the [Building Your First Model](first_model.md) tutorial to learn how to create and customize a model for your specific use case.