Toxic Comment Classification Challenge

Built a multi-label model which is capable of accurately detecting different types of toxicity like threats, obscenity, insults, and identity-based hate from comments made on social media posts. Used Data Transformation strategies such as Binary relevance, Label PowerSet, Classifier Chains and RaKel to solve multi-label problem. Used EDA, Text Data Preprocessing, Word Embedding, and Machine Learning models such as XGBoost and Logistic regression. Deep Learning models such as LSTM, and BERT.