https://github.com/anastasius21/fakenewsmodel
The repo contains the model for fake news detection and a streamlit app for its implementation.
https://github.com/anastasius21/fakenewsmodel
fake-news-detection machine-learning nlp pandas python scikit-learn
Last synced: about 2 months ago
JSON representation
The repo contains the model for fake news detection and a streamlit app for its implementation.
- Host: GitHub
- URL: https://github.com/anastasius21/fakenewsmodel
- Owner: anastasius21
- Created: 2025-04-14T17:45:14.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-14T18:43:27.000Z (about 1 year ago)
- Last Synced: 2025-04-15T04:15:30.468Z (about 1 year ago)
- Topics: fake-news-detection, machine-learning, nlp, pandas, python, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 13.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 📰 Fake News Detection using NLP & Streamlit
This project focuses on detecting fake news articles using Natural Language Processing (NLP) and Machine Learning. It includes both model training in Jupyter Notebook and a simple Streamlit app for interactive prediction.
Dataset: https://www.kaggle.com/c/fake-news/data
# Overview
This project focuses on building a binary classifier that uses text features from news articles to determine their authenticity.
The core workflow includes:
Preprocessing and cleaning text data.
Converting text into numerical features using TF-IDF.
Training a Decision Tree model to classify content as reliable or unreliable.
Deploying the model using Streamlit to make it accessible and interactive.
# 🧠 Features & Highlights
✔ End-to-end preprocessing pipeline using NLTK
✔ TF-IDF Vectorization for feature extraction
✔ Trained using a Decision Tree Classifier for interpretable, rule-based decisions
✔ Clean and minimal Streamlit interface for live prediction
✔ Exported model and vectorizer using pickle for efficient deployment
✔ Well-organized code structure for easy understanding and modification
# 🛠️ Libraries Used
Pandas
NLTK for NLP and text processing
Scikit-learn for model training and evaluation
Streamlit for app deployment