A staggering number of individuals use social media platforms nowadays, which encompass a wide range of media. It is estimated that Twitter has 330 million monthly active users. Twitter is a social networking site where users may express their ideas and opinions on a variety of situations happening around the world, it may share information about the economy, stocks, important information about business, politics etc. With millions of users actively using it, it is one of the most widely used social networking sites worldwide. It is one of the fastest methods of information transfer. Can be said as one of the quickest ways to deliver information. A Twitter account that has been programmed to carry out social media tasks automatically by scheduling tweets is known as a Twitter bot. This found that a growing number of problematic bot accounts are disinformation, comedy, and promote unverified material, which can negatively affect several concerns. It may alter user confidence, public perceptions of a problem, or even the social order. Thus, it is mandatory to know that tweets are made by real humans or bots. For the implementation of our project, we have used different machine learning algorithms like KNN, Gaussian Naïve Bayes, Decision Tree Classifier, Random Forest. We will be training the dataset with these algorithms. As a result, we concluded Random Forest is the best algorithm which has highest accuracy among all the others. Random Forest machine learning technique is ultimately chosen because it can avoid most overfitting and produce a generalized model that can be used accurately right after training. The flask server was utilized to connect our model to the web content. Our analysis of our framework's results shows that we can reasonably determine if a user is a person or a bot.

Bot detection, Decision Tree Classifier, Gaussian Naïve Baye, KNN, Random Forest, Twitter Bot.