AnaData: A Novel Approach for Data Analytics Using Random Forest Tree and SVM

Big Data has been coined to refer different types of automated and non-automated system, which generated huge amount of data like audio, video, PDF documents, medical, biometric, etc., in the form of structured, unstructured or semi-structured data. In this paper, we are representing data analytics using Random Forest Tree and SVM (Support Vector Machine). The Big Data Analytics is utilized after integrating with digital capabilities of business or other. As per our novel algorithm approach, we have modified a combination of two robust algorithms of data mining such as Random Forest Tree and SVM. To check the robustness and feasibility of our approach, we are using some statistical techniques like precision, recall, sensitivity, specificity and confusion matrix for proving accuracy and ability benchmark. At last, the accuracy and speed-up time for doing the analysis is low as compared to existing algorithm. As for the accuracy calculation, our approach 'AnaData' gives result as 95% approximately.