Anomaly detection with isolation forest and robust thresholds

                  import pandas as pd
from sklearn.ensemble import IsolationForest

df = pd.read_csv('service_metrics.csv')
features = df[['latency_p95', 'error_rate', 'throughput', 'cpu_utilization']]

detector = IsolationForest(
    n_estimators=300,
    contamination=0.02,
    random_state=42,
)
df['anomaly_score'] = detector.fit_predict(features)
anomalies = df[df['anomaly_score'] == -1]
print(anomalies.head())