Predictive big data analytic on demonetization data using support vector machine

Predictive analytics is the branch of the advanced analytics which makes the user to predict the future events with current statistics. The patterns found in historical and transactional data can be used to identify risks and opportunities for future. Predictive analytics models capture relationships among many factors to assess risk with a particular set of conditions to assign a score. This paper provides predictive analysis on demonetization data using support vector machine approach (PAD-SVM). The proposed PAD-SVM system involved three stages including preprocessing stage, descriptive analysis stage, and prescriptive analysis. The pre-processing stage involves cleaning the obtained data, performing missing value treatment and splitting the necessary data from the tweets. The descriptive analysis stage involves finding the most influential people regarding this subject and performing analytical functionalities. Semantic analysis also is performed to find the sentiment values of the users and to find the compound polarity of each tweet. Predictive analysis is performed to view the current mindset of people and the society reacts to the issue in the current time. This analysis is performed to find out the overall view point of the society and their view may change in the near-future in regarding to the scheme of demonetization as well.