  This paper describes the system submitted by our team, KBCNMUJAL, for Task 2
of the shared task Hate Speech and Offensive Content Identification in
Indo-European Languages (HASOC), at Forum for Information Retrieval Evaluation,
December 16-20, 2020, Hyderabad, India. The datasets of two Dravidian languages
Viz. Malayalam and Tamil of size 4000 observations, each were shared by the
HASOC organizers. These datasets are used to train the machine using different
machine learning algorithms, based on classification and regression models. The
datasets consist of tweets or YouTube comments with two class labels offensive
and not offensive. The machine is trained to classify such social media
messages in these two categories. Appropriate n-gram feature sets are extracted
to learn the specific characteristics of the Hate Speech text messages. These
feature models are based on TFIDF weights of n-gram. The referred work and
respective experiments show that the features such as word, character and
combined model of word and character n-grams could be used to identify the term
patterns of offensive text contents. As a part of the HASOC shared task, the
test data sets are made available by the HASOC track organizers. The best
performing classification models developed for both languages are applied on
test datasets. The model which gives the highest accuracy result on training
dataset for Malayalam language was experimented to predict the categories of
respective test data. This system has obtained an F1 score of 0.77. Similarly
the best performing model for Tamil language has obtained an F1 score of 0.87.
This work has received 2nd and 3rd rank in this shared Task 2 for Malayalam and
Tamil language respectively. The proposed system is named HASOC_kbcnmujal.
