Hello and welcome! In today's lesson, we will learn how to make predictions using a trained Linear Regression model and evaluate the model's performance using the Mean Squared Error (MSE) metric. We will use the diamonds dataset to demonstrate this process.

Before we dive into making predictions, let's briefly recap the steps we took to prepare and train our Linear Regression model.

First, we loaded the diamonds dataset using seaborn and prepared it by converting categorical variables into dummy variables for numerical compatibility. Next, we selected our features and target variable, and split the data into training and testing sets to ensure our model would generalize well to unseen data. Finally, we created and trained our Linear Regression model:

With the trained model ready, we can now move on to making predictions.

To make predictions with our trained model, we use the predict method provided by the LinearRegression class. This method will generate predicted values for our test data.