https://github.com/sayande01/fake_news_detection_logisticregression

This project detects fake news using Logistic Regression with NLP techniques, including NLTK stopword removal, Porter Stemmer for text normalization, and TF-IDF vectorization for feature extraction. It achieves high accuracy and precision, offering a reliable solution to combat misinformation.
https://github.com/sayande01/fake_news_detection_logisticregression

logistic-regression nltk porter-stemmer stopwords tf-idf-vectorizer

Last synced: about 2 months ago
JSON representation

This project detects fake news using Logistic Regression with NLP techniques, including NLTK stopword removal, Porter Stemmer for text normalization, and TF-IDF vectorization for feature extraction. It achieves high accuracy and precision, offering a reliable solution to combat misinformation.

• Host: GitHub
• URL: https://github.com/sayande01/fake_news_detection_logisticregression
• Owner: sayande01
• Created: 2024-11-13T15:43:43.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-11-15T12:55:55.000Z (7 months ago)
• Last Synced: 2025-02-13T02:38:38.244Z (4 months ago)
• Topics: logistic-regression, nltk, porter-stemmer, stopwords, tf-idf-vectorizer
• Language: Jupyter Notebook
• Homepage:
• Size: 33.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
### **Title**  

**"Fake News Detection: A Logistic Regression Approach with NLP Techniques"**

---

### **Description**  

This project focuses on detecting fake news using a Logistic Regression model coupled with powerful NLP techniques. The system processes textual data with NLTK's stopword removal, Porter Stemmer for text normalization, and TF-IDF vectorization for feature extraction. Achieving high accuracy and precision, this solution provides a reliable approach to combating misinformation.  

Key highlights include:  

- **Text Preprocessing**: Includes stopword removal, stemming, and normalization using NLTK's Porter Stemmer.  

- **Feature Extraction**: Utilizes **TF-IDF Vectorizer** to convert text into numerical features for model input.  

- **Classification Model**: Logistic Regression model ensures efficient and interpretable predictions.  

- **Performance Metrics**: Demonstrates high accuracy, recall, and F1-Score, ensuring robust fake news detection.  

This system is a comprehensive, data-driven solution designed to help mitigate the spread of fake news effectively.

---

### **Objective**  

1. **Develop an NLP-based pipeline** for preprocessing news articles, including:  

   - Stopword removal using NLTK.  

   - Text normalization with Porter Stemmer.  

   - Feature extraction via TF-IDF Vectorizer.  

2. **Build and train a Logistic Regression model** to classify news as real or fake.  

3. **Achieve high classification performance**, with metrics such as:  

   - **Accuracy**: > 90%  

   - **Precision** and **Recall**: > 85%  

4. **Provide a scalable and interpretable model** for real-world fake news detection tasks.  

---