👉 Before computing, also known as pre-processing or data preparation, is a crucial stage in the machine learning pipeline where raw data is transformed into a format suitable for training models. This process involves cleaning the data by handling missing values, removing duplicates, and correcting errors; converting categorical variables into numerical representations using techniques like one-hot encoding or label encoding; normalizing or standardizing numerical features to ensure they contribute equally to the model's learning process; and splitting the dataset into training, validation, and test sets. The aim of pre-processing is to enhance the quality and consistency of the data, making it more efficient and effective for subsequent machine learning algorithms to learn from and make accurate predictions.
