Email Spam or Non Spam Classification using Machine Learning

Email Spam or Non Spam Classification using Machine Learning

Vivek Thota

Vivek Thota

Hyderabad, Telangana

This project envisages the usage of Machine Learning to predict the Spam or Non Spam content in the Email

Artificial Intelligence

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Description

The wide range of concepts were used to train the classifier to predict the Spam content in the Emails and get the best training accuracy. The main concepts used in this project was Support Vector Machines. To use an SVM to classify emails into Spam v.s. Non-Spam, you first need to convert each email into a vector of features.Now, you will convert each email into a vector of features. This can be done by Feature Extraction. Later, we need to train a linear classifier to determine if an email is Spam or Not-Spam.After training the classifier, we can evaluate it on a test set by loading the data set. Since the model we are training is a linear SVM, we can inspect the weights learned by the model to understand better how it is determining whether an email is spam or not. Now that you've trained the spam classifier, you can use it on your own emails!

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Git Hub Repo for Spam Classifier with Support Vec. Machines

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Venkata Vivek T. created project Email Spam or Non Spam Classification using Machine Learning

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Email Spam or Non Spam Classification using Machine Learning

The wide range of concepts were used to train the classifier to predict the Spam content in the Emails and get the best training accuracy. The main concepts used in this project was Support Vector Machines. To use an SVM to classify emails into Spam v.s. Non-Spam, you first need to convert each email into a vector of features.Now, you will convert each email into a vector of features. This can be done by Feature Extraction. Later, we need to train a linear classifier to determine if an email is Spam or Not-Spam.After training the classifier, we can evaluate it on a test set by loading the data set. Since the model we are training is a linear SVM, we can inspect the weights learned by the model to understand better how it is determining whether an email is spam or not. Now that you've trained the spam classifier, you can use it on your own emails!

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