README.md

metadata

language:
  - en
license: mit
tags:
  - finance
  - expense-tracker
  - text-classification
  - tabular
  - scikit-learn
  - tf-idf
datasets:
  - Ranjit0034/finee-dataset
  - Sumeetgpt/indian-transaction-categorization-synthetic
  - mitulshah/transaction-categorization
metrics:
  - accuracy
  - f1
pipeline_tag: text-classification
widget:
  - text: Uber ride from airport to downtown
  - text: Dominos Pizza slice and coke
  - text: Netflix monthly subscription fee
  - text: Monthly salary credited from employer
  - text: Apollo Pharmacy medicines and prescription

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