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A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomialNB & GaussianNB to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer. It is implemented using LSTM and Word Embeddings to gain accuracy of 97.70% .

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Ambarish-224/SMS_SPAM_Classifier

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SMS_SPAM_Classifier

I have used an naive bayes model along with natural language processing to create this model.

Porter Stemmer, Bag of words are the nlp techniques used for text preprocessing This model will be able to classify a message as spam or ham with an accuracy of 97 percent.

The machine learning algorithm used here is GaussianNB and MultinomialNB which uses bayes theorem from probability.

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A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomialNB & GaussianNB to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer. It is implemented using LSTM and Word Embeddings to gain accuracy of 97.70% .

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