News is the most vital source of information for common people about what is happening around the world. Newspapers are an authentic source of news, but nowadays social networks have become the emerging source of news. Due to easy access to these social networks, the news can be easily manipulated which gives rise to fake news. Fake news can be used for economic as well as political benefits. It can be used as a weapon to spread hate among the community which can harm society. So it is crucial to detect fake news to avoid its consequences. There is no existing platform that can verify the news and categorize it. This paper proposes a system that can be used for real-time prediction of news to be real or fake. This system is based on natural language processing to extract features from the data and then these features are used for the training of machine learning classifiers such as Naive Bayes, Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent (SGD), and Logistic Regression (LR). Each of the classifier performance is evaluated on various parameters. Then the best performing classifier is deployed as a website using flask API for real-time prediction of the news