  Recommendation engine suggest content, product or services to the user by
using machine learning algorithm. This paper proposed a content-based
recommendation engine for providing video suggestion to the user based on their
previous interests and choices. We will use TF-IDF text vectorization method to
determine the relevance of words in a document. Then we will find out the
similarity between each content by calculating cosine similarity between them.
Finally, engine will recommend videos to the users based on the obtained
similarity score value. In addition, we will measure the engine's performance
by computing precision, recall, and F1 core of the proposed system.
