ANALYTICS DRIVEN INFLUENCING METHODS FOR ELECTION STRATEGIES

Rutweek Sawant

Abstract


In today’s VUCA (volatility, uncertainty, complexity and ambiguity) world, to become winner, it is essential to keep up with the advancements and to inundate them in our work. Technology, not only helps us in bettering the current scenario but also to understand our future. It is not only applicable in health sciences, business, environment, etc., but also in culture, education and even politics. In this project, we will see how analytics helps us to understand the voters, analyze their patterns and figure out the factors that can influence their votes ranging from communities, religion, education, employment etc., which finally would help to secure maximum percentile of votes by a candidate/political party. Thus, this model uses analytics in vote prediction with statistical modelling consisting of regression analysis such as logistic regression and random forest approach, finding influential variables, prediction via hypothesis, accuracy testing using confusion matrix and a way to cluster using probability analysis.

Keywords


logistic regression; random forest; influential variables; prediction; hypothesis; confusion matrix; cluster.

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