| --- |
| 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 |
|
|
