Election Prediction Using Machine Learning
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This paper examines the predictive power of twitter regarding the us presidential election of 2012.
Election prediction using machine learning. With a team of extremely dedicated and quality lecturers machine learning election prediction will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves. The values for some variables were used as low medium and high. This problem will come under regression supervised learning 2.
Elections is important for political scientists and political campaigns. Random forest is an ensemble supervised machine learning approach which our previous work has shown works well for detecting potential election fraud. Models that predict election outcomes have typically found that polls are the strongest predictor of election results 1.
We conclude that among all the models support vector machine with radical kernel is most e ective. Local factors this will have local. To test the performance of our model we divide the synthetic data into 10 folds train the model on 9 folds and examine its performance on the remaining one fold of data saved for testing.
We use random forest to train this model. The problem is very complex because factors affecting the poll results are huge. A machine learning approach to predicting federal elections determining the factors which contribute to u s.
Election prediction using deep learning and opinion mining international conference on innovative and advanced technologies in engineering march 2018 85 page in 2004 elections but was not so important in previous elections. The learning algorithms ann and svr proved to be superior to linear regression based on each method s calculated performance measures. He has gained lots of renown this year for his work using prediction markets to harness big data in its many and varied forms to calculate and disseminate his prediction for who will be elected president.
Machine learning election prediction provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. Presidential election prediction using machine learning making use of linear regression and classification model to determine the likely winner of the 2020 election precious orekha. President election voting result in di erent states.
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