  Social media is filled with toxic content. The aim of this paper is to build
a model that can detect insincere questions. We use the 'Quora Insincere
Questions Classification' dataset for our analysis. The dataset is composed of
sincere and insincere questions, with the majority of sincere questions. The
dataset is processed and analyzed using Python and its libraries such as
sklearn, numpy, pandas, keras etc. The dataset is converted to vector form
using word embeddings such as GloVe, Wiki-news and TF-IDF. The imbalance in the
dataset is handled by resampling techniques. We train and compare various
machine learning and deep learning models to come up with the best results.
Models discussed include SVM, Naive Bayes, GRU and LSTM.
