CSS $300$ Module $5$ Activity Worksheet

Use 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

Calculate the following metrics: mean absolute error, mean squared error, root mean

squared error, and the $R^2$ score. Use the following code samples:

print$(\text{'}$Mean Absolute Error:$\text{'},$ metrics.mean$\_$absolute$\_$error$($y$\_$test,

y$\_$pred$))$

print$(\text{'}$Mean Squared Error:$\text{'},$ metrics.mean$\_$squared$\_$error$($y$\_$test,

y$\_$pred$))$

print$(\text{'}$Root Mean Squared Error:$\text{'},$

np$.$sqrt $($metrics$.$mean$\_$squared$\_$error$($y$\_$test, y$\_$pred$)))$

print$(\text{'}$R$-$squared Sc$$re:$\text{'},$ re$$ressor$.$score $($X$,$ y$))$

Part $2$: Model Refinement

Rerun the linear regression model from Module $4,$ but change the percentage of records

that are used for testing. Try using $0\mathrm{.}25$ and $0\mathrm{.}3.$

Calculate the same metrics from above.

Use a scatter plot to visualize all three models.

Evaluate the three models. Are any of them underfit or overfit? Which $\%$ of testing data

performed best? please show code in python jupyter and show codes