DATA ANALYSIS SYSTEM USING ARTIFICIAL INTELLIGENCE

1 . A data analysis system utilizing custom unsupervised machine learning processes over a communications network, the system comprising:

a repository of data connected to the communications network;

a web application deployed on a web server connected to the communications network, the web application including a data collection interface between the web server and the repository of data, wherein the web application is configured for:

a) providing a graphical user interface for modifying, by a user, a plurality of threshold parameters of a clustering algorithm for clustering the data, wherein the clustering algorithm comprises at least a machine learning logistic regression function;

b) executing the clustering algorithm with the plurality of threshold parameters that were modified by the user, thereby producing a set of results;

c) providing a graphical user interface for reviewing, by the user, the set of results of the clustering algorithm and re-executing steps a) through c) if the set of results are not useful;

d) copying the set of results into a deep learning software framework; and

e) executing a deep learning algorithm in the deep learning software framework on the set of results, thereby establishing relationships between the data, and providing generalizations of the data.

2 . The data analysis system of claim 1 , further comprising:

wherein the machine learning logistic regression function is configured for identifying a set of functional data comprised within the data.

3 . The data analysis system of claim 2 , further comprising:

wherein the machine learning logistic regression function is configured to calculate a metric of influence for each feature of a plurality of features associated with the data.

4 . The data analysis system of claim 3 , further comprising:

wherein the plurality of threshold parameters comprises a plurality of numerical values, wherein each numerical value comprises a decimal number.

5 . The data analysis system of claim 4 , further comprising:

wherein the graphical user interface for reviewing, by the user, the set of results comprises a supportive graphical user interface.

6 . The data analysis system of claim 5 , further comprising:

wherein the web application is further configured for generating a downloadable report comprising the set of results for review by the user.

7 . The data analysis system of claim 6 , further comprising:

wherein the set of results comprises a plurality of scores associated with each of the plurality of features associated with the data.

8 . A method for data analysis utilizing custom unsupervised machine learning processes over a communications network, the method comprising:

storing data in a repository connected to the communications network;

providing a web application deployed on a web server connected to the communications network, the web application including a data collection interface between the web server and the repository, wherein the web application is configured for:

a) providing a graphical user interface for modifying, by a user, a plurality of threshold parameters of a clustering algorithm for clustering the data, wherein the clustering algorithm comprises at least a machine learning logistic regression function;

b) executing the clustering algorithm with the plurality of threshold parameters that were modified by the user, thereby producing a set of results;

c) providing a graphical user interface for reviewing, by the user, the set of results of the clustering algorithm and re-executing steps a) through c) if the set of results are not useful;

d) copying the set of results into a deep learning software framework; and

e) executing a deep learning algorithm in the deep learning software framework on the set of results, thereby establishing relationships between the data, and providing generalizations of the data.

9 . The method of claim 8 , further comprising:

wherein the machine learning logistic regression function is configured for identifying a set of functional data comprised within the data.

10 . The method of claim 9 , further comprising:

wherein the machine learning logistic regression function is configured to calculate a metric of influence for each feature of a plurality of features associated with the data.

11 . The data method of claim 10 , further comprising:

wherein the plurality of threshold parameters comprises a plurality of numerical values, wherein each numerical value comprises a decimal number.

12 . The method of claim 11 , further comprising:

wherein the graphical user interface for reviewing, by the user, the set of results comprises a supportive graphical user interface.

13 . The method of claim 12 , further comprising:

wherein the web application is further configured for generating a downloadable report comprising the set of results for review by the user.

14 . The method of claim 13 , further comprising:

wherein the set of results comprises a plurality of scores associated with each of the plurality of features associated with the data.

15 . A data analysis system utilizing custom unsupervised machine learning processes over a communications network, the system comprising:

a repository of data connected to the communications network;

a web application deployed on a web server connected to the communications network, the web application including a data collection interface between the web server and the repository of data, wherein the web application is configured for:

a) providing a graphical user interface for modifying, by a user, a plurality of threshold parameters of a clustering algorithm for clustering the data, wherein the clustering algorithm comprises at least a machine learning logistic regression function;

b) executing the clustering algorithm with the plurality of threshold parameters that were modified by the user, thereby producing a set of results including a plurality of scores associated with at least one component of the data;

c) providing a graphical user interface for reviewing, by the user, the set of results of the clustering algorithm and re-executing steps a) through c) with an adjusted set of the plurality of threshold parameters, if the set of results are not useful;

d) copying the set of results into a deep learning software framework; and

e) executing a deep learning algorithm in the deep learning software framework on the set of results, thereby establishing relationships between the data, and providing generalizations of the data.

16 . The data analysis system of claim 15 , further comprising:

wherein the machine learning logistic regression function is configured for identifying a set of functional data comprised within the data.

17 . The data analysis system of claim 16 , further comprising:

wherein the machine learning logistic regression function is configured to calculate a metric of influence for each feature of a plurality of features associated with the data.

18 . The data analysis system of claim 17 , further comprising:

wherein the plurality of threshold parameters comprises a plurality of numerical values, wherein each numerical value comprises a decimal number.

19 . The data analysis system of claim 18 , further comprising:

wherein the graphical user interface for reviewing, by the user, the set of results comprises a supportive graphical user interface.

20 . The data analysis system of claim 19 , further comprising:

wherein the web application is further configured for generating a downloadable report comprising the set of results for review by the user.