Performance Analysis of Spam Detection on Five Classification Algorithms

Authors

  • Muthu Abinesh Student, Department of Electronics and Communication Engineering, R.M.D. Engineering College, Chennai, India
  • K. Bhunesh Student, Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai, India
  • Seemantula Nischal Student, Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai, India
  • Seemantula Namratha Student, Department of Information Technology, S.S.N. College of Engineering, Chennai, India

Keywords:

Spam Detection, Exploratory Data Analysis, k-Nearest Neighbors, Support Vector Machine, Recurrent Neural Networks, Random Forest Classifier

Abstract

This paper aims to analyze the performance variances of 5 classification algorithms across Machine Learning, Deep Learning and Ensemble Learning Paradigms, namely, k-Nearest Neighbors, Support Vector Machine, Naïve Bayes, Recurrent Neural Networks and Random Forest Classifier on the Spam Detection dataset. Analysis of the dataset involves data cleaning, feature extraction, model training and evaluation. The goal is to develop a model that can accurately classify new emails as either spam or ham, which can be used to filter unwanted emails and improve the user experience.

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Published

12-09-2023

How to Cite

[1]
M. Abinesh, K. Bhunesh, S. Nischal, and S. Namratha, “Performance Analysis of Spam Detection on Five Classification Algorithms”, IJRESM, vol. 6, no. 9, pp. 13–17, Sep. 2023.

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Articles