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Build NLP models on customer complaints CFPB data on Kaggle
1) I have a customer complaint text data borrowed from kaggle that has to be classified in 18 classes
2) I need to build few models using glove embedding, tf-idf models like GBM, xgboost and neutral networks to classify the input text into one of 18 classes
3)now I need to run iterations such that I start with 3 classes in first iteration and at each iteration i add a new class and assess model performance on confusion matrix, classification report and logloss
4) finally i have to plot a lear
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Mira-Bhayandar
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