ling process.</p>
    </section>

    <section>
      <h2>5. Evaluation of Plastic Classification Model</h2>
      <pre><code>
    # Evaluate model performance on validation set
    val_loss, val_accuracy = model.evaluate(data_gen.flow(val_data['image_path'], val_data['encoded_labels'], batch_size=32))
    print(f"Validation Loss: {val_loss:.2f}")
    print(f"Validation Accuracy: {val_accuracy:.2f}")
    
    # Visualize training history
    plt.plot(model.history.history['accuracy'], label='Training Accura