This is a Machine Learning-based web application built with Streamlit that detects whether a piece of news is Real or Fake. The app uses multiple ML models for prediction and provides visual insights like confusion matrices.

• Predicts news as Real or Fake using multiple models:
  • Logistic Regression
  • Decision Tree
  • Gradient Boosting
  • Random Forest
• Displays accuracy, confusion matrix, and classification report for each model
• Decision Tree visualization for interpretability
• User-friendly interface built with Streamlit

1. Clone this repository:

git clone <repository_url>
cd <repository_folder>

2. Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

1. Run the Streamlit app:

streamlit run app.py

2. Open the local URL shown in the terminal (usually http://localhost:8501)

3. Paste your news text in the text area and click Predict

4. The app will display predictions from all models and a majority-vote final verdict

• app.py — Main Streamlit application
• vectorizer.joblib — Saved TF-IDF vectorizer
• logistic_regression_model.joblib — Trained Logistic Regression model
• decision_tree_model.joblib — Trained Decision Tree model
• gradient_boosting_model.joblib — Trained Gradient Boosting model
• random_forest_model.joblib — Trained Random Forest model

The models are trained on a labeled dataset of news articles. The dataset contains text samples classified as either real or fake news.

• Python — Core programming language
• Streamlit — Web app framework
• scikit-learn — Machine learning models and preprocessing
• joblib — Model serialization
• pandas — Data manipulation
• matplotlib/seaborn — Visualization

Note: Make sure you have the trained model files (.joblib) in your project directory before running the app.