pandas - multi-label supervised classification of text data -


i solving machine learning problem using python. knowledge in machine learning not much. problem has given training dataset. training dataset includes text samples , labels text samples. possible values of labels given. supervised problem. text samples don't have empty set of labels. have make model find labels given text data.

what have done is, have created pandas dataframe training data. dataframe has columns [text_data, label1, label2, label3, ..., labeln]. values of labels columns either 0 or 1. cleaned , tokenized text_data. removed stop words tokens. stemmed tokens using porterstemmer. split out dataframe training data , validation data 80:20. , trying make model predicting validation data's labels using training data. confused here how make model. tried few things naive bayes classifier didn't work or maybe did mistake. idea how should proceed now?


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