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Auto-converted to Parquet Duplicate
transaction_id
string
timestamp
string
amount
float64
transaction_type
string
channel
string
user_id
string
session_duration_sec
int64
pages_visited
int32
failed_attempts
int32
ip_country
string
device_fingerprint_match
int64
time_since_last_activity_hours
float64
anomaly_score
float64
is_anomaly
int64
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2025-12-24 17:00:00
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2025-03-26 17:00:00
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2025-04-07 20:00:00
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2025-05-06 22:00:00
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2025-03-20 20:00:00
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2025-02-25 07:00:00
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2025-05-02 19:00:00
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2025-01-01 05:00:00
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2025-06-02 05:00:00
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TXN-000040
2025-07-06 14:00:00
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2025-10-29 23:00:00
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2025-08-16 06:00:00
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2025-12-12 22:00:00
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2025-03-30 17:00:00
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USR-03910
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2025-12-07 19:00:00
434.14
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mobile
USR-03635
44
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OM
1
16.9
0
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TXN-000048
2025-01-27 00:00:00
225.35
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web
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105
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TXN-000049
2025-04-15 05:00:00
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167
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2025-09-18 22:00:00
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965
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2025-05-14 21:00:00
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mobile
USR-04189
529
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2025-04-30 13:00:00
1,122.84
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116
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TXN-000055
2025-03-02 22:00:00
494.29
adjustment
agent
USR-01323
3
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2025-11-13 07:00:00
3,613.97
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667
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2025-06-09 20:00:00
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575
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2025-03-04 18:00:00
321.1
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958
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1
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0
TXN-000059
2025-08-07 02:00:00
2,396.93
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web
USR-02705
316
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111.1
0.066
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TXN-000060
2025-04-11 00:00:00
319.42
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USR-03356
270
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TXN-000061
2025-12-26 08:00:00
4,092.55
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47
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2025-02-07 05:00:00
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276
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2025-02-19 22:00:00
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agent
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192
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2025-01-13 06:00:00
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493
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2025-01-25 03:00:00
1,363.73
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mobile
USR-03264
60
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TXN-000066
2025-08-28 10:00:00
5,860.79
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web
USR-01899
204
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SA
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TXN-000067
2025-01-22 22:00:00
848.69
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2025-09-05 19:00:00
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2025-03-16 08:00:00
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2025-06-30 03:00:00
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2025-04-17 07:00:00
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2025-02-24 18:00:00
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2025-05-08 03:00:00
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2025-01-22 02:00:00
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USR-04666
557
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TXN-000076
2025-08-14 05:00:00
1,718.46
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878
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2025-06-08 02:00:00
745.31
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2025-09-10 10:00:00
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2025-08-03 17:00:00
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2025-07-08 15:00:00
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2025-12-20 09:00:00
519.54
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agent
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406
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AE
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TXN-000090
2025-08-31 19:00:00
477.52
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638
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2025-03-02 12:00:00
391.5
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2025-12-29 06:00:00
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2025-08-11 12:00:00
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End of preview. Expand in Data Studio

Anomaly Detection Training

Training dataset for anomaly detection models. Contains transaction patterns and anomaly labels.

Dataset Details

  • Records: 20,000
  • Features: 14
  • Organization: GCC Insurance ML Models Hub

Features

Column Type Description
transaction_id object Feature for ML training
timestamp object Feature for ML training
amount float64 Feature for ML training
transaction_type object Feature for ML training
channel object Feature for ML training
user_id object Feature for ML training
session_duration_sec int64 Feature for ML training
pages_visited int32 Feature for ML training
failed_attempts int32 Feature for ML training
ip_country object Feature for ML training
... ... +4 more features

Usage

from datasets import load_dataset

dataset = load_dataset("gcc-insurance-ml-models/anomaly-detection-training")

License

Apache 2.0

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