Text Classification
Joblib
English
finance
expense-tracker
tabular
scikit-learn
tf-idf

Expense Intelligence Model

Production-ready financial transaction classification and auxiliary intelligence suite for the Expense Tracker System.

Model Overview

  • Architecture: Dual-Gram TF-IDF Vectorizer (Word 1-2 grams + Char-WB 3-5 grams, 200,000 max features) coupled with Calibrated Multiclass SAGA Logistic Regression.
  • Inference Speed: Under 5ms per transaction on standard CPU.
  • Accuracy: 99.30% overall test accuracy (99.32% Macro-F1) across 240,000+ holdout transactions.
  • India Holdout: 98.85% accuracy (98.96% Macro-F1) on Indian banking and UPI transactions.

Supported Canonical Categories

  1. food_dining
  2. transportation
  3. shopping_retail
  4. entertainment_recreation
  5. healthcare_medical
  6. utilities_services
  7. financial_services
  8. income
  9. government_legal
  10. charity_donations

Auxiliary Artifacts Included

  • models/category-tfidf/: Primary category classifier
  • models/merchant-similarity/: Normalized merchant similarity search index
  • models/duplicate-similarity/: Near-duplicate transaction detection model
  • reports/: Complete validation and test metrics across countries (India, USA, UK, Canada, Australia)
  • manifest.json: Full training provenance, environment, and quality gate scores
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Datasets used to train Yoge-2004/expense-intelligence-model