Data transformation is an important data normalization process. It's a possibility that bad data occurs, such as duplicates, missing values, non-numeric features, and so on. We need to normalize them by applying schema transformation so that data can be processed in a neural network. A neural network can only process numeric features. In this recipe, we will demonstrate the schema creation process.
Performing schema transformations
How to do it...
- Identify the outliers in the data: For a small dataset with just a few features, we can spot outliers/noise via manual inspection. For a dataset with a large number of features, we can perform Principal Component Analysis (PCA), as shown in the following code:
INDArray factor...
