Elucidation of Bangla Language Classification Using Neural Network Approach
Keywords:Machine learning, Neural network, Principal Component Analysis, Python, Term Frequency
Bangladesh has two principal languages called Sadu and Cholit. In the early times, Sadhu was operational and was composed of Sanskrit components but the current era has shifted to Cholit language, which is now being used most commonly. Sadhu was mostly used for formal documentation purposes and it is the need of the hour to translate them to Cholit language because it is more speaker friendly and can be easily understandable. Therefore, in this chapter efforts were done to familiarize the current era with the Sadhu language by creating software. Few sentences were selected and the final dataset was obtained by Principal Component Analysis (PCA). Python is used for different machine learning algorithms. Maximum work was done on Scikit Learn which is Term Frequency-Inverse Data Frequency (TF-DF) Vectorizer’s class. The best performance was given by Neural Network with high precision. Speed was also anticipated and values were determined through graphs. The results showed that it translated all words from Sadhu to Cholit efficiently and in a well-oriented way. Therefore, Sadhu’s complexity has been removed in this era.
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