Patent ID: 11954126
Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. A method for predictive analysis, the method comprising:
ingesting, by a processor, from a data lake, a plurality of data sets received from at least one of social media, web logs, and sensors, where each of the ingested plurality of data sets comprises of at least one of data tables, data sheets, and data matrices and wherein each of the data tables, the data sheets, and the data matrices has a plurality of attributes including at least one of a row, a column, and a list;
tagging, by the processor, at least a data set of the plurality of ingested data sets;
encoding, by the processor, the tagged data set into a pre-defined format such that the encoded data set has a machine-readable format;
parsing, by the processor, the encoded data to detect redundant occurrence of the plurality of attributes in each of the data tables, the data sheets, and the data matrices of the encoded data set, and where the detected redundant plurality of attributes are eliminated;
executing, by the processor, a first set of instructions on the encoded data set to obtain a transformed data set;
identifying, by the processor, a machine learning (ML) model from a set of pre-stored machine learning models, where the execution is done based on predefined second set of instructions stored in a database, wherein the ML model is identified based on at least one of an ability of the ML model to capture constant metamorphosis of input data sets, prediction accuracy of the ML model, ability of the ML model to capture long term and short-term trends and seasonality, training time and performance of the ML model, requirement of amount and type of available data by the ML model, and length of forecast horizon;

splitting, by the processor, the transformed data set into a plurality of groups, wherein the machine learning model is tested using one of the plurality of groups and trained with the remaining groups, wherein the steps of training and testing are repeated until each group of plurality of groups is used as test data set, where the step of training and testing is performed to conduct a predictive analysis on the transformed data set;
determining, by the processor, performance pertaining to anomalous data detection, false positives, and deployment time for the predictive analysis;
upon determining, that the predictive analysis yields a positive response for the transformed data set, validating, by the processor, the machine learning model based on performance metrics and accuracy of the predictive analysis according to a user-defined universal criteria comprising at least one of Root Mean Square Error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and confusion matrix;
transmitting, by the processor, the performance metrics of the predicted analysis to the user; and

calculating hyper parameters and tuning the hyper parameters with the encoded dataset using at least one of a grid search and a bayesian optimization, wherein the hyper-parameters correspond to a configuration external to the ML model, wherein values of the hyper-parameters are not estimated from the datasets.