PyOD

Load transaction data into a Pandas DataFrame.

Preprocess data by scaling numerical features.

Train an ECOD or Isolation Forest model on historical transaction data.

Predict anomaly scores for new transactions.

Flag transactions with high anomaly scores as potentially fraudulent.

Collect network traffic data.

Extract relevant features such as packet size and frequency.

Train a COPOD or PCA model on normal network traffic data.

Predict anomaly scores for real-time network traffic.

Alert administrators to any network traffic with high anomaly scores.

Gather patient health records including vital signs and lab results.

Clean and preprocess the data, handling missing values.

Apply a GMM or KDE model to learn normal health patterns.

Score new patient data against the learned model.

Flag patients with significantly anomalous health data for further investigation.

Collect sensor data from manufacturing equipment.

Train a LOF or OCSVM model on normal operating data.

Predict anomaly scores for real-time sensor data.

Flag products with high anomaly scores as potentially defective.

Remove defective products from the production line.

Collect time-series data from sensors or logs.

Apply a time-series decomposition technique to extract trend and seasonality.

Train an appropriate model on the residual component.

Predict anomaly scores for future time points.

Alert stakeholders to potential anomalies.