In this lesson, we explore how to make predictions using a trained machine learning model and visualize the results. Understanding predictions and visualizations helps interpret model performance and make informed decisions.

Our goal is to build on the trained linear regression model from the last lesson, use it to predict, and visualize these predictions against actual data. This will provide a clear picture of your model's performance.

First, let's recap. In the last lesson, we trained a linear regression model to understand the relationship between the area of a house and its price using synthetic data. We will use the same code snippet for generating the data as we used in the previous lesson:

Now, imagine you have new data, like areas of new houses, and want to predict their prices using your trained model. This is where the predict method from Scikit-Learn comes into play.

The predict method takes input data and generates the predicted output based on the model.

We'll start by importing essential libraries, including NumPy, Pandas, , and . We'll use the same data generation script as in the previous lesson.