E-Mail Classification ML Model

GOAL: Classify emails as spam or not-spam on the basis of the message.

DATASET: E-Mail classification NLP

WHAT I HAD DONE:

I have started with simple Exploratory Data Analysis(EDA), looked for some null or duplicate vales. Then splited the training and testing dataset using sklearn. Then I implemented different models to classify the message as spam or not-spam.

MODELS USED:

1. Naive Bayes (Multinominal)
2. Random Forest Classifier
3. XG Boost Classifier

LIBRARIES NEEDED:

1. Numpy
2. Pandas
3. Sklearn

CONCLUSION:

Models	Accuracy on Training Set	Accuracy on Testing Set
Naive Bayes (Multinominal)	0.99346	0.96875
Random Forest Classifier	1.0	0.91666
XG Boost Classifier	0.97908	0.95833

Naive Bayes (Multinominal) Training Set Accuracy	Naive Bayes (Multinominal) Testing Set Accuracy

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© 2021 Saurav Mukherjee