A Comparative Study of Classification Algorithm for Spam Data Analysis
Author(s):
Sakshi, Prince Kumar, Mohd. Bilal, Priyanka Bhardwaj
Keywords:
classification accuracy, Multinomial Naive Bayes, Ridge Regression, Linear Regression, CountVectorizer, sklearn, Pandas
Abstract
Spams are more than just annoying texts; they pose a severe threat to the online community as they often have malware or ransom ware embedded in them. Other than this direct cost, there is also the cost of reputation and loss of productivity. For these and many other reasons, spam filtering solutions are very much needed. In this paper, we conducted experiment in the chat box environment by using three algorithms namely Naïve Bayes, Ridge Regression and Linear Regression on the spam filter on chat box and late the three algorithms were compared in terms of classification accuracy. According to our simulation results the Naïve Bayes classifier outperforms the Ridge and Linear Regression in terms of classification accuracy.
Article Details
Unique Paper ID: 151982

Publication Volume & Issue: Volume 8, Issue 2

Page(s): 317 - 320
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