Fake news prediction using machine learning is an important application in modern media analytics.
This project analyzes news headlines and article text to classify whether the information is real or fake.
Using Python and machine learning, the model learns linguistic patterns, writing style, and word usage differences between genuine and fabricated news articles.
This supports better content moderation and helps reduce the spread of misinformation.
Project Overview
- Machine learning based text classification system
- Uses TF IDF vectorization to convert text into numeric features
- Uses Logistic Regression as the main classification model
- Built using Python and essential data science libraries
Libraries Used
- Pandas for data loading and manipulation
- NumPy for numerical operations
- Scikit Learn for preprocessing vectorization model training and evaluation
- TF IDF Vectorizer for text feature generation
- Train Test Split for performance validation
Dataset Details
The dataset contains labeled real and fake news articles.
Key columns include
- Title
- Text
- Subject
- Date
- Label where zero represents fake and one represents real
The textual content is the main feature used for training the model.
Preprocessing Steps
- Checked and removed missing values
- Cleaned text if required by removing symbols and unnecessary characters
- Converted article text into numerical vectors using TF IDF
- Split the dataset into input features and target labels
- Ensured consistent shapes for model input
Model Building
- TF IDF Vectorizer converts full text into weighted word frequency vectors
- Logistic Regression selected as the classification algorithm
- Model trained on training portion of the dataset and evaluated on the testing portion
- Logistic Regression learns decision boundaries separating real and fake news patterns
Performance and Accuracy
- Model predictions evaluated using accuracy score
- Confusion matrix and classification report used for detailed insight
- Typical accuracy ranges between ninety two percent and ninety five percent depending on dataset size and parameters
Prediction Flow
1 User provides a news headline or article text
2 Text is converted to a TF IDF vector
3 Logistic Regression model predicts classification output
- One means real news
- Zero means fake news
Deployment Possibilities
- Can be deployed using Flask or Streamlit for live predictions
- Users can paste or upload text and instantly receive classification results
- Useful for news verification tools and media integrity applications
Key Takeaways
- Complete NLP classification pipeline implemented successfully
- Demonstrates how logistic regression performs strongly in text classification tasks
- Shows practical potential for misinformation detection and media validation
Future Enhancements
- Test advanced NLP models such as BERT LSTM or transformer based architectures
- Add hyperparameter tuning and cross validation for improved accuracy
- Integrate visual analytics like word clouds and top feature importance
- Build a full interactive dashboard for public or newsroom use