Fake News Detection System Using Logistic Regression, Decision Tree and Random Forest.

Oni Oluwabunmi Ayankemi; Idowu Oluwaferanmi Ruth; Bassir Abdullai Abiye

doi:10.52589/BJCNIT-IOYRPY7G

Fake News Detection System Using Logistic Regression, Decision Tree and Random Forest.

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British Journal of Computer, Networking and Information Technology

Vol. 7Issue 12024pp. 115–121Published 17 May 2024

DOI 10.52589/BJCNIT-IOYRPY7G

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Abstract:

The purpose of this study is to design a fake news detection system with these three machine learning models, namely: Decision Tree, Random Forest, and Logistic Regression. These three different models were analysed to determine the most efficient model for accurately detecting fake news. The result obtained showcased Logistic Regression with an accuracy of 98.80%, Decision Tree with an accuracy of 99.64% and Random Forest with an accuracy of 99.23%. It is evident as deduction from the comparative analysis that our best model came out to be Decision Tree with an accuracy of 99.64%.

Keywords:Internet Social media Fake news Classification Machine learning Data set Computational technique.

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