Spamming involves posting irrelevant and unsolicited comments on social media platforms or video-sharing sites. Bots are used to post these messages, aiming to lower the ranking of the content or disrupt users’ viewing experience, which ultimately reduces the rank of the video or post. Besides that, spam comments can also include malicious links that can steal user’s sensitive data when clicked. Spamming, often manual, is prevalent on competitive platforms. While data mining can mitigate some forms of spam, this project automates spam comment detection on YouTube using machine learning techniques. We’ll leverage a dataset of YouTube spam comments from the UCI Machine Learning Repository and apply the Countvectorizer and Support Vector Machine algorithm for clustering on the given dataset using python programming.