README.md

metadata

annotations_creators:
  - machine-generated
language:
  - en
license: mit
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
tags:
  - finance
  - credit-card
  - transactions
  - categorization
  - nlp
  - machine-learning
  - banking
  - fintech
  - supervised-learning
  - csv
task_categories:
  - text-classification
pretty_name: Transaction Categorization Dataset

Financial Transaction Categorization Dataset

A comprehensive worldwide dataset for financial transaction categorization with 4.5+ million records across 10 categories, 5 countries, and 5 currencies.

📊 Dataset Overview

• Total Records: 4,501,043 transactions
• Categories: 10 financial categories
• Countries: 5 countries (USA, UK, Canada, Australia, India)
• Currencies: 5 currencies (USD, GBP, CAD, AUD, INR)
• File Format: Parquet (optimized for fast loading and storage)
• Total Size: ~71 MB (compressed)

📁 Dataset Structure

The dataset is now consolidated into a single optimized parquet file:

File	Records	Size	Description
default/train/0000.parquet	4,501,043	71 MB	Complete dataset in parquet format
categories.json	-	5.3 KB	Category definitions and keywords
dataset_info.json	-	1.0 KB	Dataset metadata and statistics

🏷️ Categories

The dataset includes 10 comprehensive financial categories:

1. Food & Dining - Restaurants, groceries, fast food, coffee shops, food delivery
2. Transportation - Gas, rideshare, airlines, public transport, car rental
3. Shopping & Retail - Online shopping, electronics, retail, fashion, home & garden
4. Entertainment & Recreation - Streaming, gaming, movies, music, sports
5. Healthcare & Medical - Medical, pharmacy, dental, vision, fitness
6. Utilities & Services - Electricity, water, gas, internet & phone, cable
7. Financial Services - Banking, insurance, credit cards, investments, taxes
8. Income - Salary, freelance, business, investments, government benefits
9. Government & Legal - Taxes, licenses, legal services, government fees
10. Charity & Donations - Charitable, religious, community, political donations

🌍 Geographic Coverage

Country	Currency	Sample Transactions
USA	USD	McDonald's, Uber, Amazon, Netflix
UK	GBP	Tesco, Shell, ASDA, BBC iPlayer
Canada	CAD	Tim Hortons, Petro-Canada, Loblaws
Australia	AUD	Coles, Woolworths, Bunnings, Telstra
India	INR	Big Bazaar, Ola, Flipkart, Zomato

📋 Dataset Schema

Each record contains the following fields:

{
  "transaction_description": "string",
  "category": "string", 
  "country": "string",
  "currency": "string"
}

Example Records

transaction_description,category,country,currency
McDonald's #1234,Food & Dining,USA,USD
Uber Ride,Transportation,UK,GBP
Amazon Purchase,Shopping & Retail,CANADA,CAD
Netflix Subscription,Entertainment & Recreation,AUSTRALIA,AUD
Pharmacy Purchase,Healthcare & Medical,INDIA,INR

🚀 Usage

Loading the Dataset

Python (Pandas)

import pandas as pd

# Load the complete dataset
df = pd.read_parquet('default/train/0000.parquet')
print(f"Total records: {len(df):,}")
print(f"Columns: {list(df.columns)}")

Python (Hugging Face Datasets)

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("mitulshah/transaction-categorization")

# Access the data
train_data = dataset['train']
print(f"Dataset size: {len(train_data):,}")

Chunked Processing (Memory Efficient)

import pandas as pd

# Process large parquet file in chunks
for chunk in pd.read_parquet('default/train/0000.parquet', chunksize=10000):
    # Process each chunk
    print(f"Processing {len(chunk)} records...")
    # Your analysis code here

Data Analysis Examples

Category Distribution

import pandas as pd

# Load and analyze category distribution
df = pd.read_parquet('default/train/0000.parquet')
category_counts = df['category'].value_counts()
print(category_counts)

Country Analysis

# Analyze transactions by country
country_analysis = df.groupby(['country', 'category']).size().unstack(fill_value=0)
print(country_analysis)

🎯 Use Cases

This dataset is perfect for:

• Machine Learning: Train classification models for transaction categorization
• Financial Analysis: Study spending patterns across different regions
• NLP Research: Text classification and merchant name analysis
• Data Science: Exploratory data analysis and visualization
• Business Intelligence: Market research and consumer behavior analysis
• Academic Research: Financial behavior studies and economic research

📈 Dataset Statistics

Record Distribution

• Total Records: 4,501,043
• Unique Descriptions: ~1.4M unique transaction descriptions
• Category Balance: Well-distributed across all 10 categories
• Geographic Distribution: Balanced representation across 5 countries

File Sizes

• default/train/0000.parquet: 71 MB (4,501,043 records)
• Total: ~71 MB (compressed)

🔧 Technical Details

Data Quality

• ✅ No Duplicates: All records are unique
• ✅ Consistent Schema: All files follow the same structure
• ✅ Valid Categories: All categories match the defined taxonomy
• ✅ Country-Currency Pairs: Validated country-currency combinations

Performance Optimizations

• Parquet Format: Optimized columnar storage for fast loading and analysis
• Compression: Built-in compression reduces file size by ~66%
• Chunked Processing: Support for memory-efficient processing
• Fast Queries: Columnar format enables efficient filtering and aggregation

📚 Dataset Creation & Methodology

Curation Rationale

This dataset was created to address the need for a comprehensive, standardized dataset for financial transaction categorization that:

1. Covers multiple countries and currencies
2. Uses consistent categorization schema
3. Includes high-quality, manually curated data
4. Is suitable for both research and production use

Source Data

The dataset combines data from multiple sources:

• Synthetic Generation: 4.5M+ records generated using comprehensive merchant templates
• External Integration: Real transaction data from external Hugging Face datasets
• Country-specific Data: Curated data for USA, UK, Canada, Australia, and India
• Quality Validation: Duplicate prevention and data integrity checks

Data Quality Assurance

• ✅ No Duplicates: Hash-based duplicate detection implemented
• ✅ Schema Consistency: All files follow the same structure
• ✅ Category Validation: All categories match the defined taxonomy
• ✅ Country-Currency Pairs: Validated country-currency combinations
• ✅ Anonymized Data: No personally identifiable information

🎯 Use Cases & Applications

Direct Use

This dataset can be used directly for:

• Training transaction classification models
• Building personal finance applications
• Developing banking transaction categorization systems
• Research in financial NLP and text classification

Downstream Applications

Potential downstream applications include:

• Fraud detection systems
• Expense tracking applications
• Budgeting and financial planning tools
• Business intelligence and analytics
• Academic research in fintech and financial behavior

Out-of-Scope Use

This dataset should not be used for:

• Identifying specific individuals or accounts
• Training models that could compromise financial privacy
• Any application that requires access to actual financial data

⚠️ Bias, Risks, and Limitations

Known Limitations

1. Geographic Bias: The dataset focuses on 5 major countries and may not represent all global financial patterns
2. Currency Bias: Only 5 currencies are represented
3. Category Granularity: The 10-category schema may be too broad for some specialized applications
4. Temporal Bias: Data represents a specific time period and may not reflect current trends

Recommendations

Users should:

• Validate model performance on their specific data
• Consider fine-tuning for domain-specific applications
• Be aware of potential geographic and cultural biases
• Regularly update models with new data

📊 Training & Evaluation

Recommended Metrics

For model evaluation, consider these metrics:

• Accuracy: Overall classification accuracy
• F1-score: Macro and weighted F1-scores
• Precision and Recall: Per-category performance
• Confusion Matrix: Detailed error analysis

Model Training Tips

# Example training setup
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.ensemble import RandomForestClassifier

# Load and prepare data
df = pd.read_parquet('default/train/0000.parquet')
X = df['transaction_description']
y = df['category']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)

# Vectorize text
vectorizer = TfidfVectorizer(max_features=10000)
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train_vec, y_train)

📚 Additional Resources

• Categories: See categories.json for detailed category definitions and keywords
• Metadata: See dataset_info.json for complete dataset statistics

🤝 Contributing

This dataset is actively maintained. If you find issues or have suggestions:

1. Check the existing issues
2. Create a new issue with detailed description
3. Follow the contribution guidelines

📄 Citation

If you use this dataset in your research, please cite it as:

@dataset{financial_transaction_categorization_2025,
  title={Financial Transaction Categorization Dataset},
  author={Mitul Shah},
  year={2025},
  url={https://huggingface.co/datasets/mitulshah/transaction-categorization},
  note={A comprehensive worldwide dataset for financial transaction categorization with 4.5M+ records}
}

📄 License

This dataset is released under the MIT License. See the license file for details.

🙏 Acknowledgments

• Data Sources: Synthetic generation + real transaction data from external sources
• Categories: Based on comprehensive financial transaction taxonomy
• Validation: Duplicate prevention and data quality checks implemented

📞 Contact

• Dataset Maintainer: Mitul Shah
• Repository: mitulshah/transaction-categorization
• Last Updated: October 14, 2025

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