Consensus based ensemble model for spam detection

In machine learning, ensemble model is combining two or more models for obtaining the better prediction, accuracy and robustness as compared to individual model separately. Before getting ensemble model first we have to assign our training dataset into different models, after that we have to select the best model suited for our data sets. In this work we explored six machine learning parameter for the data set i.e. Accuracy, Receiver operating characteristics (ROC) curve, Confusion matrix, Sensitivity, Specificity and Kappa value. After that we implemented k fold validation to our best five models.