Higher Classification of Fake Political News Using Decision Tree Algorithm over Random Forest Algorithm

The current project aims to model and compare the performance of fake news detectors using machine learning algorithms recognize connected political topics with high accuracy. Decision Tree algorithm Random Forest are two algorithms. methods were developed evaluated on a dataset including 44,000 samples. Implemented each through programs performed ten iterations different scales false feeds factual classification identified. G-power test is around 80% accurate. For detecting news, had mean accuracy 99.6990, approach 98.6380, according trial results. significance p=0.001, indicating efficacy classifier. This research use novel strategy for contemporary Machine Learning Classifiers predict news. comparison results reveal that method outperforms technique.