language identifier using naive bayes and random forest methods. created an ensemble model using soft voting classifier with a dataset of 22000 entries, 10000 entries for each language. used count vectorizer to convert the language text phrases into matrix of token counts to build the machine learning model. used label encoding for converting categorical variable(language column) to numerical values. deployed the model on a webpage using Flask framework from python.