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README.md

@@ -1,7 +1,23 @@
+---
+license: other
+task_categories:
+  - tabular-classification
+language:
+  - en
+tags:
+  - fraud
+  - fraud-detection
+  - finance
+  - synthetic
+  - tabular
+  - transactions
+size_categories:
+  - 10M<n<100M
+---
+
 # Zia Data Labs
 
 ## About Zia Data Labs
-
 We believe great data shouldn't cost a fortune or come with strings attached.
 
 At Zia Data Labs we do one thing — produce quality datasets and offer them at prices that make sense. No games. No gatekeeping. Just clean, production-grade data you can trust.
@@ -12,71 +28,10 @@ We love what we do — and we'd love to earn your business.
 
 Come find us. More is coming.
 
-*— The Zia Data Labs Team*
-
----
-
-## ⚠️ Important Notice
-
-This dataset is the exclusive property of Zia Data Labs. By downloading or using this dataset you agree that it is free to use for personal and professional research, model training, and experimentation. Redistribution, repackaging, and resale are strictly prohibited. Attribution to Zia Data Labs is required in any published work.
-
----
+— The Zia Data Labs Team
 
 ## Fraud Detection Dataset
+A production-grade, large-scale dataset containing 50 million transactions.
 
-A production-grade, large-scale dataset containing 50 million transactions designed to serve both the beginner data scientist and the enterprise data engineer.
-
-What is inside:
-- 50 million rows of high-fidelity synthetic transaction data
-- 19 carefully engineered fields covering core transaction data, velocity signals, geospatial context, device fingerprinting, and diagnostic fraud labels
-- A realistic 2.5% fraud rate simulating the exact needle in a haystack challenge faced by real fraud teams at major financial institutions
-- Granular fraud_type labels enabling multi-class classification, not just binary detection
-
-File formats provided:
-- sample_50rows.csv — No gate. Inspect immediately.
-- fraud_detection_50M.parquet — Full 50 million row file. No gate. Free access.
-
-Quick load example:
-
-```python
-import pandas as pd
-df = pd.read_parquet('fraud_detection_50M.parquet')
-print(df.head())
-```
-
-Schema Overview:
-
-| Block | Fields |
-|---|---|
-| Core | transaction_id, timestamp, user_id, amount, merchant_id, merchant_category, location_country, payment_method, is_fraud |
-| Velocity & Context | ip_address, device_id, user_age_days, is_first_transaction, time_since_last_txn, auth_result |
-| Diagnostic | fraud_type |
-| Metadata | currency_code, card_network |
-| Branding | data_source |
-
----
-
-## Our New Home
-
-Visit our official new home where our full portfolio of datasets lives and grows:
-
-Zia-Data-Labs on Hugging Face
-https://huggingface.co/Zia-Data-Labs
-
----
-
-## Contact
-
-zia.data.team@protonmail.com
-
----
-
-## License & Usage
-
-Zia Data Labs Open Use License
-
-This dataset is the exclusive property of Zia Data Labs. It is made freely available for personal research, academic and educational use, and model training and development.
-
-It is strictly prohibited to redistribute, repackage, resell, or commercially exploit this dataset in any form. Removing or altering Zia Data Labs attribution is strictly prohibited.
-
+## License
 © 2026 Zia Data Labs — All Rights Reserved