Agentic AI Class 7: Building a Loan Approval Agent with the PECAR Loop

Описание: Welcome to Module 7! Today, we bridge the gap between Data Science and Agentic AI. We aren't just writing "if-else" statements anymore; we are giving our agents a "Brain" powered by Machine Learning classifiers.

In this session, we build two sophisticated agents:

The LoanAgent: A Random Forest-powered agent that follows the full PECAR (Perception, Reasoning, Action, Reflection) loop to classify loan risk for different buyer profiles (Sophia, Liam, Olivia, and Ethan).

The LoanComparisonAgent: An advanced agent that runs a "Tournament" between three different mathematical models (Logistic Regression, Decision Trees, and Neural Networks) to autonomously decide which algorithm is best for our data.

What you will learn today:

PECAR in Practice: How to map Machine Learning predictions to "Reasoning" and "Reflection" steps.

Encoding & Processing: Handling categorical data (like loan purpose) inside an Agent's class.

Model Tournaments: Letting an agent evaluate its own tools (Neural Nets vs. Linear Models) to find the most accurate "Brain."

Autonomous Decision Making: Setting guardrails (like FICO score thresholds) that work alongside ML predictions.

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