Fake News Detection

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🔍 Problem Statement

Fake news is a major issue in digital journalism and social media. The goal of this project is to automatically classify news articles as "Fake" or "Real" using Natural Language Processing (NLP) and Machine Learning/Deep Learning models.

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📊 Dataset

• Dataset Source: Kaggle – Fake and Real News Dataset
• Total Rows: ~44,000 articles
• Columns: title, text, subject, date, label

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⚙️ Features Implemented

✅ Data Preprocessing & EDA

• Merged and labeled True.csv and Fake.csv
• Cleaned HTML, URLs, punctuation, stopwords
• Analyzed text length, word count, NER entities
• Visualized class balance, word clouds, and NER distribution

✅ Text Representation

• TF-IDF Vectorization
• Word2Vec Embeddings
• GloVe 100d Pretrained Embeddings (for Deep Learning)

✅ Machine Learning Models

• Logistic Regression (with GridSearchCV)
• Naïve Bayes
• Support Vector Machine (SVM)
• Random Forest

✅ Deep Learning Model

• Bidirectional LSTM with:
  • Pretrained GloVe embeddings (100d)
  • Two stacked BiLSTM layers
  • Dropout & L2 regularization
  • EarlyStopping and ReduceLROnPlateau

✅ Evaluation

• Accuracy, Precision, Recall, F1-Score
• Confusion Matrix
• Cross-validation (for classical models)

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🧠 Model Performance Summary

Model	Accuracy
Logistic Regression	98.7%
Naïve Bayes	93.3%
SVM	99.4%
Random Forest	99.8%
BiLSTM (GloVe)	99.9% ✅

⚠️ Note: Models trained on padded & tokenized text with proper validation strategy.

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🧪 Tools & Technologies

• Languages: Python
• Libraries: NumPy, pandas, scikit-learn, TensorFlow/Keras, NLTK, Matplotlib, Seaborn
• Embeddings: GloVe (100d), Word2Vec
• EDA Tools: SpaCy, WordCloud
• Version Control: Git, GitHub

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🚀 Deployment (Optional)

Can be deployed via:

• Flask API for local predictions
• Streamlit web app for UI

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💡 Future Improvements

• Integrate BERT or DistilBERT for transformer-based classification
• Add SHAP/LIME for explainability
• Self-learning (active learning loop)
• Streamlit UI for production-level deployment
• Deploy on Render / HuggingFace Spaces / GCP

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🤝 Acknowledgements

• GloVe: Global Vectors for Word Representation
• Kaggle Dataset
• Inspired by the need for factual integrity in online journalism.

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👨‍💻 Author

Nikhil Reddy Banda
GitHub | LinkedIn