---
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