Novel Detection of Accurate Spam Content using Logistic Regression Algorithm Compared with Gaussian Algorithm

Abstract

Aim: To detect the spam content over the internet and social media using Logistic Regression algorithm over Gaussian algorithm. Methods and Materials: Detection of spam content messages are performed using Logistic Regression algorithm and Gaussian algorithm (sample size=20) Where values are taken randomly. G-power was maintained to be 80%. Results and Discussion: This article is an attempt to improve the accuracy of spam content detection using the Logistic Regression algorithm, a machine learning algorithm. The AI based Application avoids overfitting. The proposed model has improved accuracy of 95% with p value which is less than 0.03(p<0.05) in spam detection than Gaussian algorithm having accuracy of 93%. Conclusion: The outcomes of the proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed algorithm seems to have higher accuracy than the Gaussian algorithm.