Fake News Detection using Machine Learning

Fake News Detection using Machine Learning

The rapid spread of fake news on the internet has become a major issue affecting public opinion and decision-making. This project aims to build a machine learning system that can automatically detect whether a news article is fake or real.

The model uses Natural Language Processing (NLP) techniques to analyze news text and classify it into fake or real categories. The text data is converted into numerical form using TF-IDF vectorization, and a Logistic Regression model is trained on the dataset.

Technologies Used: Python, Pandas, Scikit-learn, Natural Language Processing (NLP)

Methodology:

1. Data collection from a fake news dataset
2. Text preprocessing and cleaning
3. Feature extraction using TF-IDF
4. Training a Logistic Regression machine learning model
5. Predicting whether news is fake or real

This project demonstrates how machine learning and NLP can be used to combat misinformation online. The system can be further improved by using deep learning models and larger datasets.

Built With

• language
• natural
• pandas
• processing
• python
• scikit-learn
• tf-idf