Create fraud_detection_neural_network.py

blockchain_integration/pi_network/autonomous_pi_network/neural_network_based_fraud_detection/fraud_detection_neural_network.py

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+# Import necessary libraries
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout
+
+# Define the neural network model
+model = Sequential()
+model.add(Dense(64, activation='relu', input_shape=(10,)))
+model.add(Dropout(0.2))
+model.add(Dense(32, activation='relu'))
+model.add(Dropout(0.2))
+model.add(Dense(1, activation='sigmoid'))
+
+# Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Load transaction data
+transaction_data = pd.read_csv('transactions.csv')
+
+# Preprocess data
+X = transaction_data.drop(['is_fraud'], axis=1)
+y = transaction_data['is_fraud']
+
+# Train the model
+model.fit(X, y, epochs=10, batch_size=32)
+
+# Use the model to predict fraud
+def predict_fraud(transaction):
+    input_data = pd.DataFrame([transaction], columns=X.columns)
+    prediction = model.predict(input_data)
+    return prediction[0][0] > 0.5