One-hot encoding is a process of converting categorical data into a form that could be provided to machine learning algorithms to improve predictions. In this technique, each unique category value is transformed into a binary vector with all zero values except for a single one in the position of the category.
Applying One-Hot Encoding
One-hot encoding is essential for handling categorical data since many machine learning models, especially those based on linear assumptions, are designed to work better with numerical inputs. By converting categories to a binary format, the model can easily utilize this data for prediction and classification tasks.
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