Classify Support Tickets with TF-IDF

Business Context

ShopFlow, an e-commerce platform, receives thousands of customer support tickets each day. The operations team wants a lightweight text classification system that routes tickets to the correct queue without using large transformer models.

Data

You are given 420,000 historical support tickets labeled into 5 categories: Refund, Shipping Issue, Account Access, Product Defect, and Other.

• Text source: ticket subject + message body
• Text length: 5-350 words, median 42 words
• Language: English only
• Label distribution: Refund 28%, Shipping Issue 24%, Account Access 18%, Product Defect 15%, Other 15%
• Noise: HTML fragments, order IDs, URLs, repeated punctuation, misspellings, and copied email signatures

Success Criteria

A production-ready baseline should achieve:

• Macro F1 >= 0.82 on a held-out test set
• Recall >= 0.90 for Account Access tickets
• Inference latency < 20ms per ticket in batch scoring on CPU
• Clear feature interpretability for support operations review

Constraints

• Use a TF-IDF-based approach, not embeddings or transformers
• Solution must run in a standard Python service on CPU
• The pipeline should be reproducible and easy to retrain weekly

Requirements

1. Build an end-to-end multi-class text classification pipeline using TF-IDF features.
2. Define a realistic preprocessing strategy for noisy support text.
3. Choose and justify an appropriate classifier (for example, Logistic Regression or Linear SVM).
4. Show how you would tune TF-IDF parameters such as n-gram range, min_df, max_df, and sublinear_tf.
5. Evaluate the model with class-level metrics and confusion analysis.
6. Explain how you would inspect top weighted terms to validate routing behavior and detect spurious correlations.