Synthetic data generation using bayesian models and machine learning techniques

A system, comprising:

• a memory storing instructions;
• one or more Input/Output (I/O) interfaces;
• and
• one or more processor(s) coupled to the memory via the one or more I/O interfaces, wherein the one or more processor (s) are configured by the instructions to:
• compute a plurality of prior probabilities, associated with occurrence of an event for a user behavioral trait of a plurality of users, from a data set;
• obtain a prior probability distribution of the plurality of users based on the computed plurality of prior probabilities;
• compute a plurality of posterior probabilities from the prior probability distribution using a Bayesian model;
• obtain a posterior probability distribution based on the computed plurality of posterior probabilities using the Bayesian model;
• obtain distribution parameters from the posterior probability distribution;
• determine percentage of occurrence of the event from the data set, for each user among the plurality of users;
• apply an oversampling technique over the data set to generate a plurality of rows comprising a first set of synthetic data for the user behavioral trait in accordance with the distribution parameters and the percentage of occurrence of the event;
• update the dataset with the plurality of rows of the first set of synthetic data;
• and
• provide the updated data set to a machine learning (ML) model for generating a second set of synthetic data corresponding to a plurality of features for each row of the updated data set based on an iterative process, wherein the iterative process terminates when the second set of synthetic data is generated for a plurality of features.

One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes a method for:

• computing a plurality of prior probabilities, associated with occurrence of an event for a user behavioral trait of a plurality of users, from a data set;
• obtaining a prior probability distribution of the plurality of users based on the computed plurality of prior probabilities;
• computing a plurality of posterior probabilities from the prior probability distribution using a Bayesian model;
• obtaining a posterior probability distribution based on the computed plurality of posterior probabilities using the Bayesian model;
• obtaining distribution parameters from the posterior probability distribution;
• determining a percentage of occurrence of the event from the data set, for each user among the plurality of users;
• applying an oversampling technique over the data set to generate a plurality of rows comprising a first set of synthetic data for the user behavioral trait in accordance with the distribution parameters and the percentage of occurrence of the event;
• updating the data set with the plurality of rows of the first set of synthetic data;
• and
• providing the updated data set to a machine learning (ML) model for generating a second set of synthetic data corresponding to a plurality of features for each row of the updated data set based on an iterative process, wherein the iterative process terminates when the second set of synthetic data is generated for a plurality of features.