Step 01
Clean & normalize text
Strip noise from raw article text — casing, punctuation, and stopwords — so the vectorizer sees signal, not formatting.
How the pipeline works
Every stage is kept explicit and inspectable, from raw text to a labeled prediction.
Text cleaning
Lowercasing, punctuation stripping, and stopword removal bring raw article text into a consistent shape before vectorizing.
TF-IDF vectorization
Term frequency–inverse document frequency turns cleaned text into weighted feature vectors that reflect word importance, not just word count.
Multiple classifiers
Several classical classifiers are trained on the same features so their behavior can be compared directly, not just their headline score.
Metric comparison
Precision, recall, and F1 are reported per model — accuracy alone doesn’t tell the whole story on an imbalanced news dataset.
Reproducible split
A fixed train/test split keeps every classifier comparison fair and the results reproducible on a re-run.
Clear reporting
Results are laid out per classifier rather than buried in a single summary number, so trade-offs stay visible.
Workflow
Step 02
Vectorize with TF-IDF
Convert cleaned text into weighted feature vectors that emphasize words that are distinctive, not just frequent.
Step 03
Train & compare classifiers
Fit several classical classifiers on identical features and the same split, then compare precision, recall, and F1 side by side.
Full source
Explore the code
This case study covers the approach at a glance — the notebook with cleaning, vectorization, and classifier comparisons in full lives on GitHub.