🔍 AI-Driven Fake News Detection | An AI-based system using Logistic Regression, Decision Tree, Random Forest, XGBoost, and Gradient Boosting to detect fake news. Implements TF-IDF Vectorization & Regex for text processing. Evaluated with accuracy, precision & F1-score. Tech:Python, Pandas, Scikit-learn. Goal: Combat misinformation with AI. 🚀 Features ✔ Multi-model classification (Logistic Regression, Decision Tree, Random Forest, XGBoost, Gradient Boosting) ✔ TF-IDF Vectorization for text processing ✔ Regex-based text cleaning ✔ Performance evaluation using accuracy, precision, and F1-score
🛠 Tech Stack Programming Language: Python Libraries: Pandas, NumPy, Scikit-learn, NLTK ML Models: Logistic Regression, Decision Tree, Random Forest, XGBoost, Gradient Boosting
📊 Dataset The dataset consists of real and fake news articles. Preprocessed using TF-IDF Vectorization and Regex for text cleaning.
📈 Model Performance The model is evaluated using accuracy, precision, recall, and F1-score. Performance metrics ensure a robust fake news detection system.