Hello and welcome! In today's lesson, we will focus on visualizing the relationship between actual and predicted prices for diamonds using a Linear Regression model. This visualization is crucial for understanding how well our model is performing and identifying any issues or areas for improvement. By the end of this lesson, you will be able to create scatter plots to compare actual vs. predicted values and interpret these results effectively.

Visualization plays an essential role in data science and machine learning. It transforms raw data into graphical representations, making it easier to identify patterns, trends, and outliers. Comparing actual vs. predicted values helps us understand our model's performance:

1. Insight into Model Accuracy: Visualization helps to quickly grasp how close the predictions are to the actual values.
2. Identification of Patterns: It reveals whether the model captures the underlying trend or if there are specific areas where it fails.
3. Detection of Outliers: Visualization can help identify significant deviations that might indicate model weaknesses or data issues.

Once the model has been used to make predictions, we can create visualizations to deepen our analysis of the data. Let's create a scatter plot using Seaborn and Matplotlib to visualize the comparison between the actual and predicted prices.

In this code:

• We set up the plot's size for better visibility.