A project to detect whether a given piece of news is fake or not. It used Passive aggressive classifier - an ML algorithm which classifies a given tweet as either Passive(real) or Aggressive(fake). For this purpose we have a labelled data with the following columns Id number - used to identify the news title - The title of the news text - the full text of the news label - stating whether the given news is real or fake
With this we split the data as training and test set. We convert the given text using tf-idf vectorizer TF (Term Frequency): The number of times a word appears in a document is its Term Frequency. A higher value means a term appears more often than others, and so, the document is a good match when the term is part of the search terms.
IDF (Inverse Document Frequency): Words that occur many times a document, but also occur many times in many others, may be irrelevant. IDF is a measure of how significant a term is in the entire corpus.
The TfidfVectorizer converts a collection of raw documents into a matrix of TF-IDF features.
Using SVM based hybridized decision tree with Artificial Bee Colony optimization. Then we test our model using our test data. We are having an accuracy of 92%