archive the model

script5_2.py

@@ -3,6 +3,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 from keras.models import Sequential
 from keras.layers import Dense
+import pickle
 
 # Load the dataset
 df = pd.read_csv('data.csv')
@@ -33,6 +34,13 @@ model.compile(loss='mean_squared_error', optimizer='adam')
 # Train the model
 model.fit(X_train, y_train, batch_size=64, epochs=10, validation_data=(X_test, y_test))
 
+# Save the model to a file
+model.save('model.h5')
+
+# Save the encoder to a file
+with open('encoder.pkl', 'wb') as f:
+    pickle.dump(encoder, f)
+
 # Make predictions on new data
 new_writer = 'Jim Cash'
 new_writer_encoded = encoder.transform([new_writer])