Database management systems and methods for datasets

Abstract

Systems and methods receive input(s) facilitating dataset management, the input(s) initiating a machine learning process configured to detect data redundancies of two or more datasets. Entity data stored to entity data storage location(s) are accessed and processed to conform with formatting requirements for the machine learning process. Validation is performed on the processed entity data, the validation ensuring the processed entity data satisfy the formatting requirements, where the validation produces training data that is inserted into an iterative training and testing loop. A model architecture is trained, based on weights and calculations, using the training data in the iterative training and testing loop to detect data redundancies, the training including predicting a target variable and iteratively adjusting the weights and calculations during each subsequent iteration to improve predictability of the target variable, where the model architecture is trained to identify data similarities among the two or more datasets.