👉 Input engineering is a critical phase in the development of machine learning models, focusing on designing and preparing the data that will be fed into the model. It involves selecting, transforming, and structuring raw data to ensure it is suitable for training, validating, or testing purposes. This process includes tasks such as data cleaning to remove inconsistencies and noise, feature engineering to create meaningful attributes from raw data, normalization or standardization to scale features appropriately, and encoding categorical variables into numerical formats. Effective input engineering enhances model performance by improving data quality and relevance, ultimately leading to more accurate and reliable predictions.
