During the normalization and sequence creation process in the context of deep learning with recurrent neural networks (RNNs) for cryptocurrency prediction, handling missing or invalid values is important to ensure accurate and reliable model training. Missing or invalid values can significantly impact the performance of the model, leading to erroneous predictions and unreliable insights. In