A Big Data Analytical Approach for Analyzing Temperature Dataset using Machine Learning Techniques

Abstract

Machine Learning algorithms are used to predictive analytics. These algorithms are put into practice for measuring the temperature data. To capture these data spark framework is being exploited. Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a new set of requirements, none of which are difficult to achieve in isolation, but the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources. We assert that a new distributed execution framework is needed for such ML applications. This paper describes evaluation of these algorithms on Hadoop, an open-source for spark implementation. The proposed methodology uses a temperature data set for analyzing the machine learning algorithms on spark data frame.