Description
<p>An inventive machine learning project called the Fake News Detection System was created to categorize internet news stories as either authentic or fraudulent. Misinformation spreads swiftly on social media and other internet platforms in today’s digital age. By using <strong>Natural Language Processing (NLP)</strong> methods and <strong>AI algorithms</strong> to confirm the legitimacy of news material, this project offers a workable solution. This project is perfect for final-year students seeking practical experience in <strong>AI, Machine Learning, and Data Science</strong>. It was developed as a <strong>Python Django web application</strong> with a <strong>MySQL database</strong>. Text preparation methods like stopword elimination, lemmatization, and punctuation management are used by the Fake News Detection System. Then, using <strong>TF-IDF vectorization</strong>, the processed text is converted into numerical features and integrated with other characteristics such as capitalization ratio, punctuation %, and text length. Accurate predictions are produced by a <strong>Logistic Regression model</strong> that has been trained on labeled datasets of actual and fraudulent news. Evaluation measures that validate the model’s performance include <strong>classification report</strong> and <strong>accuracy score</strong>. Large text datasets are handled by the system effectively and without using too much memory thanks to the use of <strong>sparse matrix operations</strong></p>
