Discuss the importance of data : Evaluating model performance in Python

The video tutorial explains how to evaluate a model's performance using two key metrics: mean squared error (MSE) and R-squared value. MSE measures the average squared deviation of predicted values from actual values, while R-squared indicates the goodness of fit, ranging from 0 to 1. The tutorial demonstrates how to calculate these metrics using sklearn, emphasizing the importance of evaluating models on test data to avoid overfitting. It also highlights that MSE is dataset-specific and cannot be compared across different projects, whereas R-squared values provide a relative measure of model performance.

What does an R-squared value of 0.65 indicate about a model's performance on test data?

Why is it important to evaluate model performance using test data that was not used during training?