Data Transformation

Data transformation is the process of converting raw data into a structured format that is optimized for analysis and machine learning. In this step, data undergoes various adjustments — such as encoding categorical variables, normalizing numerical values, and reshaping data structures — to ensure compatibility with ML algorithms. This transformation is vital for improving data quality and model performance, as well-transformed data helps algorithms learn patterns more effectively. Ultimately, data transformation lays the groundwork for reliable, accurate model training and predictive success.

The primary goal of data transformation is to enhance the data’s utility for ML algorithms. By converting raw inputs into suitable formats, data transformation improves the model’s ability to learn meaningful patterns and relationships. This process involves various steps, from encoding categorical variables and normalizing numerical features to reshaping data into the necessary structures for specific models. Each transformation is tailored to the type of data and the requirements of the algorithm, ultimately making the information more accessible and relevant.