Create new feature and feature view

customer_high_value_txns.py

from feast import FeatureView, Field
from feast.types import Float64, Int64
from datetime import timedelta

# Get references
transaction_source = fs_banking.get_data_source("transaction_data_source")
customer = fs_banking.get_entity("customer")

customer_high_value_txns = FeatureView(
    name="customer_high_value_txns",
    entities=[customer],  # References the customer entity
    ttl=timedelta(hours=24),  # 24-hour TTL for real-time fraud detection
    schema=[
        Field(
            name="high_value_txn_24h",
            dtype=Int64,
            description="Count of transactions >$10,000 in last 24 hours - PRIMARY FRAUD INDICATOR"
        ),
    ],
    source=transaction_source,
    tags={
        "team": "risk_fraud",
        "owner": "risk_fraud_team@bank.com",
        "use_case": "fraud_detection_enhancement",
        "business_impact": "critical",
        "hypothesis": "high_value_txn_count_predicts_fraud",
        "model_version": "v2",
        "feature_importance": "high",
        "data_classification": "confidential",
    },
    description="High-value transaction features for enhanced fraud detection. Tracks frequency and amounts of transactions over $10,000 in the last 24 hours. Hypothesis: Sudden spikes in high-value transactions indicate fraudulent behavior. Based on analysis showing 114 high-value transactions (0.23%) with 4.39% fraud rate."
)

# Apply to remote registry
fs_banking.apply([customer_high_value_txns])
print("✓ Feature view created and registered\n")
fs_banking.refresh_registry()