Data preparation is a critical phase in the data analysis workflow that involves cleaning, transforming, and organizing raw data into a format ready for analysis. This process encompasses essential tasks such as data collection, cleaning, transformation, integration, reduction, validation, and splitting. By ensuring the quality and reliability of the data, preparation lays the groundwork for accurate insights and effective modeling. It is particularly vital for data scientists, analysts, and machine learning practitioners seeking to enhance model performance and drive actionable results.
