Welcome back! You're now ready to build and evaluate machine learning models. You have learned how to preprocess the mtcars dataset and how to split the data into training and testing sets. Now, let's take it a step further and construct a logistic regression model.

In this lesson, you will:

1. Train a logistic regression model using the mtcars dataset.
2. Understand the importance of logistic regression in binary classification tasks.
3. Display and interpret model details to evaluate their performance.
4. Interpret warnings generated during model training and understand their implications.

By the end of this lesson, you will be able to:

• Build a logistic regression model using the caret library in R.
• Print and interpret the details of the model, including key performance metrics.
• Explain common warnings that may arise during model training and their significance.

Here's a key snippet of the code you'll be working with: