Foreseeing bugs, features, and questions on GitHub can be fun, especially when one is provided with a colossal dataset containing the GitHub issues. In this hackathon, we are challenging the MachineHack community to come up with an algorithm that can predict the bugs, features, and questions based on GitHub titles and the text body. With text data, there can be a lot of challenges especially when the dataset is big. Analyzing such a dataset requires a lot to be taken into account mainly due to the preprocessing involved to represent raw text and make them machine-understandable. Usually, we stem and lemmatize the raw information and then represent it using TF-IDF, Word Embeddings, etc. However, provided the state-of-the-art NLP models such as Transformer based BERT models one can skip the manual feature engineering like TF-IDF and Count Vectorizers. In this short span of time, we would encourage you to leverage the ImageNet moment (Transfer Learning) in NLP using various pre-trained models. Hackathon Link- https://www.machinehack.com/hackathons/predict_github_issues_embold_sponsored_hackathon/overview