Multi-Class Classification of Hindi Text using Machine Learning

Multi-Class Classification of Hindi Text using Machine Learning

G. Bharath Teja1, E.Bharghav Rao2, P.Bhargavi3, M.Bhargavi4, G.Vijay Bhaskar Reddy5,
Prof. Sameera Sultana 6

1Department of Artificial Intelligence and Machine Learning, Malla Reddy University 2111cs020085@mallareddyuniversity.ac.in ,

2Department of Artificial Intelligence and Machine Learning, Malla Reddy University 2111cs020086@mallareddyuniversity.ac.in,

3Department of Artificial Intelligence and Machine Learning, Malla Reddy University 2111cs020087@mallareddyuniversity.ac.in ,

4Department of Artificial Intelligence and Machine Learning, Malla Reddy University 2111cs020088@mallareddyuniversity.ac.in ,

5Department of Artificial Intelligence and Machine Learning, Malla Reddy University 2111cs020090@mallareddyuniversity.ac.in ,

6Department of Artificial Intelligence and Machine Learning, Malla Reddy University Sameera_sultana@mallareddyuniversity.ac.in

ABSTRACT
This study focuses on the multi-class classification of Hindi texts using ML techniques. The goal is to develop an efficient model that can accurately classify Hindi text documents into various predefined categories. We explore the preprocessing steps necessary for cleaning and preparing the text data, tokenization, stop-word removal, and stemming. Feature extraction methods such as TF-IDF and word embeddings are employed to represent the text data numerically. It includes ML algorithm like Supervised Learning algorithm (Naïve Bayes, SVM, Decision tree and Random Forest) are experimented