This project aims to detect fake news articles using text mining and data analytics techniques. The objective is to classify news content as genuine or fake and support responsible information consumption through analytical and machine learning approaches.
Collect labeled news datasets from public sources.
Clean and preprocess text data.
Perform exploratory text analysis and word frequency analysis.
Extract features using TF-IDF or bag-of-words techniques.
Train classification models such as Naïve Bayes or SVM.
Evaluate model accuracy and performance.
Visualize classification results.
Document model behavior and ethical considerations.
