Svc.py

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

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

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

: Converting text into numerical data using techniques like TfidfVectorizer or CountVectorizer . : For large datasets

: Using sklearn.svm.SVC for classification.

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

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