Databases: Super users can perform basic management operations, as well as import data from CSV files into PostgreSQL databases or create new databases tailored to the project's needs.

Feature Creation: involves using existing columns in a dataset to generate useful new columns that can be utilized in the analysis phase. For example, a "Profit" column can be calculated and created using the "Sales Price" and "Cost" columns.

Outlier Detection: Outliers within the data that could negatively impact analysis results can be programmatically identified and removed. For example, removing all values outside the range of 0 to 110 in the Age column.

Machine learning algorithms generate statistical results by passing through anonymization filters defined for specific users and tables, instead of directly accessing the data. During the analysis phase, only this data is used to ensure the security of Personal Data Privacy.

Since the analysis algorithms use anonymized data, the machine learning model generated as a result of the analysis does not violate personal data privacy.

The analysis module can generate new models, import and reuse existing models, use these models to produce results on data, and facilitate the sharing of models between users based on their authorization levels.