Studies of prediction techniques for big data analysis

Abstract

Nowadays, health prediction in modern life has become very much essential and it can be executed with the assistance of big data. Big data newlinerepresents a collection of complex and massive data sets and these data sets newlineinclude large volume of data, data management capabilities, social media newlineanalytics and real-time data. Big data and predictive analytics often go newlinetogether. Predictive analysis is a type of data analytics focused on making newlinepredictions about future consequences based on the historical data and newlineanalytics techniques such as statistical modeling and machine learning. newlineWith the view of these aspects, the present thesis has been pursued. newlineIn the first stage of the current work, a rainfall prediction system newlineusing generative adversarial networks has been developed and proposed to newlineanalyze the rainfall data of India as well as to predict the future rainfall. The newlineproposed system uses a GAN network in which Long Short-Term Memory newline(LSTM) network algorithm has been used as a generator and convolution newlineneural network model has been used as a discriminator. LSTM is much newlinesuitable to predict time series data such as rainfall data. The experimental newlineresults reveal that the proposed system predicts the results with 99% accuracy. newlineRainfall prediction helps the farmers to cultivate their crops and improve their newlineeconomy as well as country s economy. For estimating yearly rainfall newlineprojections, we developed a system that combines Generative Adversarial newlineNetworks with Convolution Neural Networks (CNN). newline