📰 Fake-O-Meter

deployed on streamlit: https://ms-fake-o-meter.streamlit.app/

This project is a Fake News Detector that leverages machine learning algorithms and natural language processing techniques to classify news articles as "Real" or "Fake". The application offers robust functionality with six different machine learning models and supports two vectorization techniques for text preprocessing.

Files (code-2.ipynb contains python code and app1.py has streamlit)

🚀 Features

• Machine Learning Models:

  • Support Vector Machine (SVM)
  • Logistic Regression
  • Multinomial Naive Bayes
  • Decision Tree
  • Random Forest
  • K-Nearest Neighbors (KNN)

• Vectorization Techniques:

  • TF-IDF Vectorizer
  • Hashing Vectorizer

• Interactive Web Interface:

  • Built using Streamlit for ease of use and intuitive design.
  • Allows users to input or upload news articles for real-time prediction.

🛠️ Technologies Used

• Python
• Machine Learning: Scikit-learn, NumPy, Pandas
• NLP: TF-IDF, Hashing
• Web Framework: Streamlit
• Data Visualization: Matplotlib, Seaborn (Optional for insights)

🔧 Requirements Before running the project, ensure you have the following dependencies installed: pandas numpy scikit-learn beautiful soup streamlit

📈 Evaluation Each machine learning model is evaluated using metrics like:

Accuracy Precision Recall F1-Score

🤝 Contributing Contributions are welcome! If you have ideas or suggestions to improve the project, feel free to open an issue or submit a pull request.