To build a model to accurately classify a piece of news as REAL or FAKE. The detection of fake news is done from saving oneselves from believing in false information. All the news that are read on social media cannot be trusted or relied upon. Hence the aim of our project is to detect such fake news by using tf-idf vectorization on news data sets, calculating the confusion matrix of our model and generating its accuracy for prediction of fake news. Using sklearn, we build a Tf-Idf Vectorizer on our dataset. Then, we initialize a PassiveAggressive Classifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares.
To build a model to accurately classify a piece of news as REAL or FAKE. The detection of fake news is done from saving oneselves from believing in false information. All the news that are read on social media cannot be trusted or relied upon. Hence the aim of our project is to detect such fake news by using tf-idf vectorization on news data sets, calculating the confusion matrix of our model and generating its accuracy for prediction of fake news. Using sklearn, we build a Tf-Idf Vectorizer on our dataset. Then, we initialize a PassiveAggressive Classifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares.