The rise of fake news is a major concern to the society as media being influenced with wrong propaganda affecting the people negatively by making them believe wrong facts about an institution or organization causing harm to the image of the same. Fake news is considered to be the root cause of social disruptions and wrong beliefs about some communities as well. This leads to the introduction of a fake news analyzer and classifier to detect the major topics on which the fake news are mainly built upon as well as the classify them, which is built with the help of python and its packages which support the process of diving deep in the data. During the analysis of the data, major findings pointed towards the bias news spread across the paper, word-cloud and bigram pointing towards the words that are the most common in fake news. Hence, in the end, building a Random Forrest Classifier model to detect whether a piece of news is spam or not.