Svc.py

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

When reviewing this script, consider these specific technical aspects: svc.py

: Using sklearn.svm.SVC for classification. : For large datasets, LinearSVC is often preferred

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