Preprocessing Techniques

Preprocessing techniques in data cleaning involve transforming raw data into a more suitable format for machine learning models. This often includes handling missing values by either imputing them with estimated values or removing rows/columns containing them. It also encompasses scaling numerical features to a similar range to prevent features with larger values from dominating the model, and encoding categorical features into numerical representations that algorithms can understand. These steps aim to improve the quality and consistency of the data, leading to better model performance.