Publication Venue Prediction Challenge

The objective of this assignment was to explore and apply machine learning and data mining techniques to tackle a real-world classification problem. The problem involved assigning research papers to predefined categories based on their content. Each research paper was associated with a citation network, along with its abstract, list of authors, and year of publication. The task at hand was to develop a model capable of predicting the class label for each research paper, where the class labels represented the publication venues. The application of machine learning and data mining techniques in this context has significant relevance in various domains, particularly in recommendation systems. The problem shares similarities with text categorization and node classification tasks, both of which have been extensively studied. The pipeline typically followed to address this problem resembles that of any classification problem, which involves learning the parameters of a classifier using a collection of training papers with known class information and subsequently using the learned model to predict the class labels of unlabeled papers