Machine Learning
Fake News Detection System
A comprehensive fake news detection framework comparing traditional machine learning approaches with modern deep learning models. The project provides detailed performance analysis across multiple datasets and model architectures.
Tech Stack
7 technologies used
PythonScikit-learnTensorFlowKerasBERTHugging FaceNLP
Key Features
- Traditional ML models: Naive Bayes, Random Forest, Logistic Regression, Passive Aggressive
- Deep learning models: CNN and LSTM for text classification
- Fine-tuned BERT using Hugging Face's Trainer API
- TF-IDF and CountVectorizer feature extraction pipelines
- Evaluation across multiple datasets (BuzzFeed, LIAR, Kaggle, Reuters)
- Reproducible training and evaluation pipelines
Challenges Solved
- Handling class imbalance across different datasets
- Comparing models fairly given different computational requirements
- Generalizing across news from different domains and time periods
Outcomes & Impact
- Comprehensive benchmark of fake news detection approaches
- Insights into trade-offs between model complexity and performance
- Reusable pipeline for text classification experiments