I built a Kaggle kernel for Text Classification. Text Classification is a part of Natural Language Processing in which text data is classified into different classes.
I have built a model based on the Multinomial Naive Bayes which is built in the Scikit-learn(sklearn) python library. The sklearn library is also used for feature extraction using TF-IDF method. One more important library being used is the Natural Language Toolkit (nltk) which is used for preprocessing and lemmatization of text data.
The dataset used is having headlines from different news sites and is used for classification as clickbait or non-clickbait.
The link to the kernel is here.
Please share your views in the comments below about improving the kernel.
Also, please upvote the Kernel on Kaggle so that I can make more such models in the future.
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