Performance analysis of various supervised algorithms on big data
Athira Unnikrishnan, Uma Narayanan, Shelbi Joseph · 2017
Big data analytics is the practice of analysing huge data sets to discover unknown patterns, hidden correlations, preferences and other useful information. Twitter is one of the famous social networks worldwide. Unstructured data comes from information which is not classified, Twitter tweets and other social media posts are Some of the central source of unstructured data. To find meaningful information from unstructured data is very difficult, with the help of classification technique it is possible to change unstructured data into organised form. Through this work we try to study the performance of various classification algorithms on Big Data. The data needed for the research is collected from Twitter using Twitter streaming API. We applied supervised machine-learning algorithms like Decision Tree, SVM, Naive Bayes, Neural Network and k-nearest neighbour to classify the data. The Result showed that SVM classifier has the highest accuracy compared to other classifiers.