CSS 300 SXU Linear Regression Models with Varying Test Percentages Lab

CSS 300 Module 5 Activity WorksheetUse this worksheet to complete your lab activity. Submit it to the applicable assignment
submission folder when complete.
Deliverable:
–
A word document answering the following questions
Using the Weather.csv dataset from Module 4
Part 1: Metrics for Evaluation
1. Calculate the following metrics: mean absolute error, mean squared error, root mean
squared error, and the R2 score. Use the following code samples:
print(‘Mean Absolute Error:’,
metrics.mean_absolute_error(y_test, y_pred))
print(‘Mean Squared Error:’, metrics.mean_squared_error(y_test,
y_pred))
print(‘Root Mean Squared Error:’,
np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
print(‘R-squared Score:’, regressor.score(X, y))
Part 2: Model Refinement
1. Rerun the linear regression model from Module 4, but change the percentage of records
that are used for testing. Try using 0.25 and 0.3.
2. Calculate the same metrics from above.
3. Use a scatter plot to visualize all three models.
4. Evaluate the three models. Are any of them underfit or overfit? Which % of testing data
performed best?