Create data_processing.py

ai-powered-risk-management-system/data_processing.py

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+import tensorflow as tf
+from sklearn.ensemble import IsolationForest
+
+def anomaly_detection(data):
+    # Implement anomaly detection using Isolation Forest
+    isolation_forest = IsolationForest(contamination=0.01)
+    isolation_forest.fit(data)
+    anomalies = isolation_forest.predict(data)
+    return anomalies
+
+def predictive_modeling(data):
+    # Implement predictive modeling using TensorFlow
+    model = tf.keras.models.Sequential([
+        tf.keras.layers.LSTM(units=50, return_sequences=True, input_shape=(data.shape[1], 1)),
+        tf.keras.layers.Dense(units=1)
+    ])
+    model.compile(loss='mean_squared_error', optimizer='adam')
+    model.fit(data, epochs=100)
+    predictions = model.predict(data)
+    return predictions
+
+def clustering_and_classification(data):
+    # Implement clustering and classification using scikit-learn
+    from sklearn.cluster import KMeans
+    from sklearn.metrics import accuracy_score
+    kmeans = KMeans(n_clusters=5)
+    kmeans.fit(data)
+    labels = kmeans.labels_
+    accuracy = accuracy_score(data, labels)
+    return labels, accuracy