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 some common scaling techniques available in Python, and how can they be applied using the 'scikit-learn' library?
Scaling is an important preprocessing step in machine learning, as it helps to standardize the features of a dataset. In Python, there are several common scaling techniques available that can be applied using the 'scikit-learn' library. These techniques include standardization, min-max scaling, and robust scaling. Standardization, also known as z-score normalization, transforms the data such
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Regression, Pickling and scaling, Examination review