Deep dive into multi-label classification..! --Karthik Nooney

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.

DISCLAIMER FROM THE DATA SOURCE: the dataset contains text that may be considered profane, vulgar, or offensive.

 

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.