Creating Your First Bayesian Network in Python

Our main goal in this lesson is to provide a high-level overview of the process of creating Bayesian networks using Python, laying the foundation for a deeper understanding in the subsequent lessons of this course.

Bayesian networks are a powerful tool for modeling uncertainty, representing probabilistic relationships between variables, and making inferences in complex domains. They have numerous applications in fields such as artificial intelligence, machine learning, statistics, and data science.

Create the network in Python using CausalNex

CausalNex is a Python library developed by QuantumBlack, a McKinsey company. It allows data scientists to utilize Bayesian networks for causal inference and probabilistic reasoning. Integrating traditional data science methods with graph theory, CausalNex offers tools for both structures learning from data and domain expertise, as well as predictions using the learned structures. This aids in understanding causality and reasoning behind data-driven decisions.

To use CausalNex, first, we import the necessary libraries and modules: