Data handling refers to the processes involved in preparing and managing the data used for training, validating, and testing the model. Data handling includes several steps: first, data collection, where relevant information is gathered from various sources. Next, data cleaning removes any errors, duplicates, or irrelevant information to ensure the dataset is accurate. After that, data transformation is often necessary to convert the data into a format suitable for training, which might involve normalization or scaling.
