Multi-label classification on images involves assigning multiple labels to a single image based on its content.
The text in text mining refers to written language that has some informational content. For example, newspaper articles, magazine articles, fiction and non-fiction books, emails, and online articles are all texts. The amount of text that exists today is vast, and it continues to grow. Although there are many techniques and approaches for text exploration, the overall goal is simple: to discover new and useful information contained in one or more textual documents. In practice, text exploration is carried out by running computer programs that read the documents and process them in various ways. The results are then interpreted by humans. On the other hand, multilabel classification is a predictive data mining task with multiple applications in the real world, including the automatic labeling of many resources such as texts. Learning from multilabel data can be achieved through various approaches, such as data transformation, method adaptation, and the use of classifier ensembles.
The mini project was completed during my Master's in Data Science at Aix-Marseille. Everything in the report(It was completed in French).