Svc.py

: For large datasets, LinearSVC is often preferred over SVC because it is less computationally expensive and converges faster.

: Generating reports to check for overfitting (requires reducing polynomial degree) or underfitting (requires increasing degree). Key Areas to Check During Your Review svc.py

: Adhere to the PEP8 style guide —for instance, avoid using lower-case 'l' as a variable name to prevent confusion with the number '1'. Other Possible Contexts Depending on your project, svc.py might instead refer to: : For large datasets, LinearSVC is often preferred

A well-structured svc.py usually includes the following stages: : For large datasets

: Importing data (e.g., from CSV or JSON) and cleaning text by removing stop words and handling n-grams to improve accuracy.