Lesson 4

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:

**Insight into Model Accuracy:**Visualization helps to quickly grasp how close the predictions are to the actual values.**Identification of Patterns:**It reveals whether the model captures the underlying trend or if there are specific areas where it fails.**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.

Python`1import matplotlib.pyplot as plt 2import seaborn as sns 3 4# Visualizing the regression results 5plt.figure(figsize=(10,6)) 6sns.scatterplot(x=y_test, y=predictions, alpha=0.6) 7sns.lineplot(x=[y_test.min(), y_test.max()], y=[y_test.min(), y_test.max()], color='red') 8plt.title('Actual vs Predicted Prices') 9plt.xlabel('Actual Prices') 10plt.ylabel('Predicted Prices') 11plt.show()`

In this code:

- We set up the plot's size for better visibility.
- Create a scatter plot with actual prices on the x-axis and predicted prices on the y-axis.
- Add a red line to represent perfect predictions (where actual prices equal predicted prices). This is accomplished by setting
`x=y`

and plotting a line between`(min, min)`

and`(max, max)`

.

The output of the above code will be a scatter plot with the actual prices plotted against the predicted prices, along with a red line indicating the ideal scenario where the predicted prices match the actual prices perfectly. This visualization aids in assessing the accuracy of the Linear Regression model by visual inspection. Note that we are using the test dataset, which contains unseen data, providing a realistic assessment of the model's performance under real conditions.

**Interpreting the Visualization**

Interpreting this scatter plot helps us understand our model's performance:

**The Red Line:**This line represents the scenario where predicted prices perfectly match the actual prices.**Scatter Points:**Each point represents a prediction. Points close to the red line indicate accurate predictions.**Clusters and Outliers:**Clusters of points near the red line indicate good performance, while points further away (outliers) indicate larger errors.

By examining the scatter plot, we can quickly identify whether our model is predicting well overall or if there are specific price ranges where it struggles.

To gain deeper insights, we can further enhance our visualization by adding more features and details. This can help us diagnose specific issues or identify patterns that were not immediately visible. Here are a few enhancements we can make:

**Color by Error Magnitude:** We can color the scatter points by the magnitude of the prediction error to see if there are specific ranges where the model performs poorly.

Python`1# Calculating the residuals (errors) 2residuals = abs(y_test - predictions) 3 4# Creating an enhanced scatter plot with residuals 5plt.figure(figsize=(10,6)) 6scatter = sns.scatterplot(x=y_test, y=predictions, hue=residuals, palette='coolwarm', alpha=0.6) 7sns.lineplot(x=[y_test.min(), y_test.max()], y=[y_test.min(), y_test.max()], color='red') 8 9plt.title('Actual vs Predicted Prices with Residuals') 10plt.xlabel('Actual Prices') 11plt.ylabel('Predicted Prices') 12plt.show()`

**Highlighting Outliers:** Visually marking outliers that deviate significantly from the expected values.

Python`1# Defining a threshold for outliers 2threshold = 10000 # You can adjust this value based on your criteria 3# Calculating the residuals (errors) 4residuals = abs(y_test - predictions) 5 6# Plotting with outliers highlighted 7plt.figure(figsize=(10,6)) 8sns.scatterplot(x=y_test, y=predictions, alpha=0.6) 9sns.scatterplot(x=y_test[abs(residuals) > threshold], y=predictions[residuals > threshold], color='red', label='Outliers') 10sns.lineplot(x=[y_test.min(), y_test.max()], y=[y_test.min(), y_test.max()], color='green') 11plt.title('Actual vs Predicted Prices with Outliers Highlighted') 12plt.xlabel('Actual Prices') 13plt.ylabel('Predicted Prices') 14plt.legend() 15plt.show()`

These enhancements can provide additional context and help you better interpret your model’s performance. By identifying where the model performs well and where it doesn’t, you can make more informed decisions about how to improve it.

To summarize, in this lesson, we covered the importance of visualization in model evaluation, prepared the diamonds dataset, made predictions using a Linear Regression model, visualized the relationship between actual and predicted prices, and interpreted the results.

Next, you'll practice creating similar visualizations with other datasets. This will help solidify your understanding of how visualizations can effectively aid in evaluating and improving machine learning models. Visualization is a powerful tool, and mastering it will greatly enhance your data science skills.