python import BlaiseMath as bm dataset = bm.load_dataset("data.csv") preprocessed_data = bm.statistics.preprocess(dataset) mean = bm.statistics.mean(preprocessed_data) variance = bm.statistics.variance(preprocessed_data) correlation = bm.statistics.correlation(preprocessed_data) model = bm.neural_network.build_model() model.add_layer(bm.neural_network.Dense(128, activation='relu')) model.add_layer(bm.neural_network.Dense(64, activation='relu')) model.add_layer(bm.neural_network.Dense(10, activation='softmax')) optimizer = bm.optimizers.SGD(lr=0.01) model.compile(optimizer=optimizer, loss='categorical_crossentropy') model.fit(preprocessed_data, labels, epochs=10, batch_size=32)