Machine Learning Techniques for Effective Content- Based Approach in the Detection of Fake News

All functionalities required in the process of fake news detection, from data preparation to model building, have been done within the project, smoothly integrated into Flask for user interaction. The first step of data processing requires the import of the packages and the merging of real and fake news datasets. Data cleaning includes the removal of duplicates, text cleaning, and insightful visualization of characteristics using Seaborn and Matplotlib. Tokenization via TF-IDF Vectorization, Feature Selection, and Data Splitting paves the way for an extensive suite of Classification Models from Logistic Regression and SVC to KNN, Random Forest, Stacking, and even Voting Classifiers. Additionally, Flask provides functions for user registration, signing in, and an inbuilt SQLite database to store the user data. User input text is set to be translated and preprocessed for prediction using the best-fit pre-trained model. The outcome will then be shown to the user on the frontend. The novelty with the extension here is the models like the Voting Classifier (XGB + PA + Boosting) that have been introduced, which gives a $100 \%$ accuracy. More importantly, it extends the usability feature by allowing the user to provide text at the front end and get real-time predictions, thus enriching the overall user experience. In more ways than one, this project, from its approach toward fake news detection to its diversity of methodology and user-centered design, is going to be the most apropos project in the coming days to the emerging landscape of information integrity.