Performance Analysis of Spam Detection on Five Classification Algorithms
Keywords:Spam Detection, Exploratory Data Analysis, k-Nearest Neighbors, Support Vector Machine, Recurrent Neural Networks, Random Forest Classifier
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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Copyright (c) 2023 Muthu Abinesh, K. Bhunesh, Seemantula Nischal, Seemantula Namratha
This work is licensed under a Creative Commons Attribution 4.0 International License.