ChatGPT and Prompt Engineering With Advanced Data Analysis - Build a Machine Learning Model Using ChatGPT

The video tutorial discusses building a prediction model for loan defaulters in banking. It covers key factors to consider, such as credit history and income, and suggests machine learning algorithms like logistic regression and decision trees. The tutorial also provides guidance on evaluating model effectiveness using metrics like accuracy and F1 score.

What steps can a banking organization take based on the predictions from the model?

What additional factors could be introduced beyond the initial eight factors for predicting loan defaulters?

What is the importance of metrics like precision, recall, and F1 score in evaluating a model?