Data preprocessing is a crucial step in any machine learning pipeline and requires as much attention as model development. Proper preprocessing converts raw data into a format suitable for model training, with a key aspect being the management of features with varying scales. Techniques like normalization and scaling are essential to address these issues. Normalization and sc aling adjust the range and distribution of data features to enhance algorithm performance. Standardization, or z -score normalization, is a standard method that transforms data to have a mean of zero and a standard deviation of one, ensuring that features with different units or scales contribute equally to the model.