With continuous increase in available data, there is a pressing need to organize it and modern classification problems often involve the prediction of multiple labels simultaneously associated with a single instance. Known as Multi-Label Classification, it is one such task which is omnipresent in many real world problems.
In this project, using a Kaggle problem as example, we explore different aspects of multi-label classification.
Bird’s-eye view of the project:
- Part-1: Overview of Multi-label classification.
- Part-2: Problem definition & evaluation metrics.
- Part-3: Exploratory data analysis (EDA).
- Part-4: Data pre-processing.
- Part-5: Multi-label classification techniques.