Svc.py

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

: Converting text into numerical data using techniques like TfidfVectorizer or CountVectorizer . svc.py

: Ensure the model uses class_weight='balanced' if your dataset has an uneven number of positive and negative samples. : Importing data (e

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

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

When reviewing this script, consider these specific technical aspects:

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