Merge pull request #27 from KOSASIH/deepsource-transform-dab6e7e3

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detect_anomaly.py

@@ -2,20 +2,21 @@
 from sklearn.ensemble import IsolationForest
 
 # Load historical user data
-data = pd.read_csv('user_data.csv')
+data = pd.read_csv("user_data.csv")
 
 # Train anomaly detection model
 model = IsolationForest(contamination=0.01)
 model.fit(data)
 
+
 # Real-time monitoring
 def detect_anomaly(user_data):
     # Predict anomaly score
     anomaly_score = model.decision_function(user_data)
-    
+
     # Set anomaly threshold
     threshold = -0.5
-    
+
     # Check if anomaly score exceeds threshold
     if anomaly_score < threshold:
         # Send alert message
@@ -24,8 +25,9 @@ def detect_anomaly(user_data):
     else:
         return None
 
+
 # Sample user data
-user_data = pd.DataFrame({'transaction_amount': [1000, 200, 3000, 500]})
+user_data = pd.DataFrame({"transaction_amount": [1000, 200, 3000, 500]})
 
 # Detect anomaly
 alert = detect_anomaly(user_data)