This project involves building a machine learning model to classify news articles as real, fake, or AI-generated. The system uses text classification techniques to accurately identify misinformation and fake content.
Developed a text classification model to detect and filter misinformation in the digital era, addressing the challenge of identifying the authenticity of news articles.
Demonstrated proficiency in data preprocessing, text classification, machine learning techniques, and data visualization using Python, pandas, scikit-learn
Implemented TF-IDF vectorization to capture important words and improve the performance of the text classification models
Built various classification models, including Multinomial Naive Bayes, Decision Trees, and Random Forests, to classify news articles as Real, Fake and AI
Employed 10-fold cross-validation to evaluate and optimize the performance of the models and successfully classified articles with an 80% accuracy