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A spam classifier is built using distinct tools with the objective of comparing the results of each approach.

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Name		Name	Last commit message	Last commit date
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LICENSE		LICENSE
README.md		README.md
Spam_Classifier_Embeddings.ipynb		Spam_Classifier_Embeddings.ipynb
Spam_Classifier_Keras.ipynb		Spam_Classifier_Keras.ipynb
Spam_Classifier_Spacy.ipynb		Spam_Classifier_Spacy.ipynb
spam.csv		spam.csv

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✉️⚠️ Spam Classifier: different approaches

◼️ First Approach: The first detector was built using the spacy module and the machine learning model Random Forest Classifier.

◼️ Second Approach: The second detector uses the tools offered by Keras to construct a sequential neural network.

◼️ Third Approach: The difference between this method and the other ones is that, besides utilizing Keras and building a neural network, it is also created an customized embedding layer and a tokenizer that transform the messages text into sequences of integers in a fixed length that will be read by the NN.

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A spam classifier is built using distinct tools with the objective of comparing the results of each approach.

machine-learning random-forest spacy neural-networks classification spam-detection nlp-machine-learning keras-neural-networks keras-tensorflow

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