A Comparative Analysis of Machine Learning Techniques for Spam Detection

Author:

Ali Rashida1,Rampurawala Ibrahim1,Wandhe Mayuri1,Shrikhande Ruchika1,Bhatkar Arpita1

Affiliation:

1. Anjuman College of Engineering and Technology, Nagpur, Maharashtra, India

Abstract

Internet provides a medium to connect with individuals of similar or different interests creating a hub. Since a huge hub participates on these platforms, the user can receive a high volume of messages from different individuals creating a chaos and unwanted messages. These messages sometimes contain a true information and sometimes false, which leads to a state of confusion in the minds of the users and leads to first step towards spam messaging. Spam messages means an irrelevant and unsolicited message sent by a known/unknown user which may lead to a sense of insecurity among users. In this paper, the different machine learning algorithms were trained and tested with natural language processing (NLP) to classify whether the messages are spam or ham.

Publisher

Naksh Solutions

Subject

General Medicine

Reference5 articles.

1. https://www.javatpoint.com/machine-learning-support-vector-machine-algorithm

2. https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/

3. https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_classification_algorithms_logistic_regression.htm

4. https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm

5. https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

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