Pre-processing#

Pre-processing, in the context of machine learning and data analysis, refers to the techniques used to prepare raw data for further processing and analysis. It involves cleaning, organizing, and transforming data to enhance its quality and ensure it is in a suitable format for creating accurate models or gaining meaningful insights. This step is crucial as it directly impacts the performance and reliability of the subsequent analysis or machine learning tasks.

Below there is a list of related links:

• Data imputation
• Feature Engineering: Scaling, Normalization, and Standardization
• Feature Selection Techniques
• Data Augmentation

To visualize the Pre-processing resources of the project, open the Query Tool and run the "All Preprocessing resources" predifined query from the Query list, as illustrated in the picture below