Utilizing artificial intelligence to integrate data from multiple diverse sources into a data structure

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• one or more instructions that, when executed by one or more processors, cause the one or more processors to:
• receive, from a client device, a request to create a data structure from a plurality of different data sources;
• extract data files from the plurality of different data sources based on the request;
• process the data files to generate processed data files;
• extract data from the processed data files;
• process the data to generate processed data;
• train a first artificial intelligence model and a second artificial intelligence model based on historical data parameters,
• the historical data parameters being associated with previously processed historical data;
• utilize the trained first artificial intelligence model and the processed data to generate data parameters for the processed data;
• utilize the trained second artificial intelligence model, the processed data, and the data parameters to generate a data structure configuration;
• generate a data structure for the processed data based on the data parameters and the data structure configuration;
• store the processed data in the data structure;
• provide a user interface for accessing the data structure;
• utilize a trained third artificial intelligence model to test the data structure, prior to providing the user interface, and to identify one or more corrections to the data structure,
• wherein the trained third artificial trained model includes a decision tree machine learning model,
• wherein the decision tree machine learning model utilizes a decision tree associated with a target variable of an item;
• implement the one or more corrections in the data structure to generate an updated data structure;
• and
• provide another user interface for accessing the updated data structure.

A method, comprising:

• extracting, by a device, data files from a plurality of different data sources;
• extracting, by the device, data from the data files;
• training, by the device, a first artificial intelligence model and a second artificial intelligence model based on historical data parameters,
• the historical data parameters being associated with historical data previously processed by the device;
• utilizing, by the device, the trained first artificial intelligence model and the data to generate data parameters for the data;
• utilizing, by the device, the trained second artificial intelligence model, the data, and the data parameters to generate a data structure configuration;
• generating, by the device, a data structure for the data based on the data parameters and the data structure configuration;
• storing, by the device, the data in the data structure;
• providing, by the device, a user interface for accessing the data structure;
• utilizing, by the device, a trained third artificial intelligence model to test the data structure, prior to providing the user interface, and to identify one or more corrections to the data structure,
• wherein the trained third artificial trained model includes a decision tree machine learning model,
• wherein the decision tree machine learning model utilizes a decision tree associated with a target variable of an item;
• implementing, by the device, the one or more corrections in the data structure to generate an updated data structure;
• and
• providing, by the device, another user interface for accessing the updated data structure.