Massive Data Capturing and Transmission using Big Data Analytics Model with Machine Learning Prediction
Keywords:Deep learning, Big Data Analytics, Diabetes prediction, machine learning, Deep Neural Network, support vector machine
Deep learning techniques are widely used in many branches of research and engineering, including language processing, image classification, and speech recognition. Similar to this, processing vast amounts of data is restricted by a number of classical data processing approaches. To deal with information progressively with incredible exactness and effectiveness, huge information investigation requests spic and span, complex calculations in light of machine and profound learning methods. Recent research has, however, combined a variety of deep learning algorithms with hybrid learning and training processes to analyse data quickly. Bigdata investigation offers incredible opportunities to estimate future wellbeing status in light of wellbeing boundaries and to convey the best outcomes. To foresee diabetes, we utilized and differentiated Machine Learning methods (Direct Relapse, Guileless Bayes, Choice Tree). Also, we ran investigation on flight delays. This study's essential commitment is an outline of machine learning models and large information advancements. We feature a couple of measurements that let us select a model that is more exact. We utilized three machine learning models to foresee the improvement of diabetes, and afterward we assessed how well they performed. What is more, we saw flight delays and made a dashboard that might help flight organization the executives in getting a 360° viewpoint on their flights and going with vital choices.