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

A well-structured svc.py usually includes the following stages: : For large datasets, LinearSVC is often preferred

When reviewing this script, consider these specific technical aspects: : For large datasets

: Check if the data is properly divided into training, validation, and test sets to ensure the model's reliability on new data.

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