ENHANCING PREDICTIVE ANALYTICS:INTEGRATING AI/ML WITH BIG DATA FORREAL-TIME INSIGHTS
Keywords:
Big Data, Artificial Intelligence, ML , DLAbstract
This development of strong analytical frameworks to tap into the possibilities of today's data has been driven by the exponential expansion of data created from numerous sources. Big Data Driven Predictive Analytics powered by AI is a game-changer in this field, opening up previously unimaginable possibilities for deriving meaningful insights from massive, intricate datasets. Various industries, including healthcare, banking, marketing, and logistics, can benefit from improved decision-making with the help of predictive models developed through the combination of big data and AI. Starting with the basics, the study delves into the nature of big data, its properties (volume, velocity, diversity, and veracity), and the difficulties in handling and processing this type of data. After that, it explores how artificial intelligence (AI), and more specifically ML and DL algorithms, play a part in evaluating massive datasets. If you want to find trends, patterns, and correlations in your data that regular data processing methods can miss, then you need to use these AI techniques. Building and deploying big data-driven predictive analytics models is the meat and potatoes of this study. We test the efficacy of various ML and DL algorithms on a variety of prediction tasks, including forecasting, anomaly detection, and consumer behaviour prediction. The study also takes into account the significance of feature engineering, data preparation approaches, and model evaluation metrics in order to guarantee the dependability and accuracy of the prediction models. Also covered are some of the many industries that have already put big data-driven predictive analytics to use. By predicting the spread of diseases and tailoring treatment programs to individual patients, predictive models have the potential to enhance healthcare outcomes. The financial sector can benefit from these models' enhanced risk management and fraud detection capabilities; the marketing sector can optimise client segmentation and campaign plans using predictive analytics; and the logistics sector can increase demand forecasting and supply chain efficiency with these same tools.
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Copyright (c) 2021 Venkata Sai Swaroop Reddy, Nallapa Reddy (Author)
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