How do we handle missing or invalid values during the normalization and sequence creation process?
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 crucial 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
What are the two options for handling missing data in non-numerical columns?
Handling missing data in non-numerical columns is an essential step in data preprocessing for machine learning tasks. When dealing with non-numerical data, such as categorical or text data, there are two main options for handling missing values: imputation and deletion. In this answer, we will explore these options in detail and provide examples to illustrate