Ensemble based Technique to detect Fake News

In today’s digital world, Fake news is the crucial problem. For the detection of fabricated news content. This paper is using different Machine Learning (ML) algorithms. This study focuses on the use of algorithms to determine whether the news is true or false. ML algorithm has the ability to learn from the previous data and predict the output for current data. It helps the model to learn and give accurate results. It is cost effective method. This paper test different methods such as Support Vector (SPV) Machine, Passive Aggressive (PSAG), Random Forest (RNF), Gradient boosting (GTB) and Logistic Regression (LOR) to find the best one. This paper combines the best of each approach into a comprehensive solution called the ensemble Model. The ensemble model uses a voting classifier for making predictions for fake news detection. The model is designed to be really good at detecting fake news in the fast-paced world of online information. By using this ensemble model, Detect fake news quickly and accurately. The goal is to make the news safer and more reliable for everyone.