Integrating Data Analytics Platforms with Machine Learning Workflows: Enhancing Predictive Capability and Revenue Growth

Abstract

This research fills a major vacuum in the current body of literature on business intelligence by examining the revolutionary potential of predictive analytics for use in market performance. Evaluating prediction models' accuracy, dependability, scalability, and ethical implications is the main goal. The evaluation uses a qualitative approach that combines topic analysis and case studies to gather detailed information particular to each event. Deep Learning (DL), the most popular computational technique in the field of Machine Learning (ML) in recent years, has demonstrated incredible success on a variety of difficult cognitive tasks, matching or even surpassing human performance. Artificial neural networks, or ANNs, are the source of deep learning technology, which has become extremely popular in the computer industry due to its ability to learn from data. This article also discusses the various deep neural network systems and methodologies and the applicability of deep learning. Additionally, it provides an overview of real-world domains in which deep learning-based methodologies may be used. We wrap off with some research suggestions and possible features for the upcoming deep learning model versions. But the goal of this essay is to provide a comprehensive overview of deep learning models so that academics and practitioners in the field may utilise it as a reference. Lastly, in order to assist researchers in understanding the existing research gaps, we provide additional issues and possible solutions. Various techniques, deep learning structures, strategies, and applications are covered in this work.

This research fills a major vacuum in the current body of literature on business intelligence by examining the revolutionary potential of predictive analytics for use in market performance. Evaluating prediction models' accuracy, dependability, scalability, and ethical implications is the main goal. The evaluation uses a qualitative approach that combines topic analysis and case studies to gather detailed information particular to each event. Deep Learning (DL), the most popular computational technique in the field of Machine Learning (ML) in recent years, has demonstrated incredible success on a variety of difficult cognitive tasks, matching or even surpassing human performance. Artificial neural networks, or ANNs, are the source of deep learning technology, which has become extremely popular in the computer industry due to its ability to learn from data. This article also discusses the various deep neural network systems and methodologies and the applicability of deep learning. Additionally, it provides an overview of real-world domains in which deep learning-based methodologies may be used. We wrap off with some research suggestions and possible features for the upcoming deep learning model versions. But the goal of this essay is to provide a comprehensive overview of deep learning models so that academics and practitioners in the field may utilise it as a reference. Lastly, in order to assist researchers in understanding the existing research gaps, we provide additional issues and possible solutions. Various techniques, deep learning structures, strategies, and applications are covered in this work.