On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses

Implementing supervised machine learning on the Hindi corpus for classification and prediction of verses is an untouched and useful area. Classifying and predictions benefits many applications like organizing a large corpus, information retrieval and so on. The metalinguistic facility provided by websites makes Hindi as a major language in the digital domain of information technology today. Text classification algorithms along with Natural Language Processing (NLP) facilitates fast, cost-effective, and scalable solution. Performance evaluation of these predictors is a challenging task. To reduce manual efforts and time spent for reading the document, classification of text data is important. In this paper, 697 Hindi poems are classified based on four topics using four eager machine-learning algorithms. In the absence of any other technique, which achieves prediction on Hindi corpus, misclassification error is used and compared to prove the betterment of the technique. Support vector machine performs best amongst all.