This project focuses on building a Fake News Detection System using Machine Learning and techniques. The goal is to classify news articles as Fake or Real based on their textual content. With the rapid spread of misinformation online, this system helps in identifying unreliable news automatically.
Text preprocessing (cleaning, tokenization, stopword removal)
Feature extraction using NLP techniques
Machine learning model training and evaluation
Manual testing for custom news input
Clear classification output: Fake News or Not Fake News
Python
Pandas
NumPy
Scikit-learn
NLTK
Jupyter Notebook
The dataset contains labeled news articles with:
Text – News content
Label –
0 → Fake News
1 → Real News
Load and explore the dataset
Clean and preprocess the news text
Convert text into numerical features
Train machine learning models
Evaluate model performance
Test new news articles manually
The model achieves good accuracy and effectively distinguishes between fake and real news articles using text-based features.