A Natural Language Processing application that automatically classifies news headlines into four categories: SPORTS, CRIME, EDUCATION, and COMEDY. Built using Multinomial Naive Bayes algorithm with CountVectorizer for text preprocessing, the application features a Gradio-based user interface and is deployed on Hugging Face Spaces for easy access.
Project Overview
Dataset / Input Data
Dataset Name / SourceNews Headlines Dataset
FormatText (CSV)
Size / InfoMultiple news headlines across 4 categories
Model / Approach
Algorithm / Model
Multinomial Naive Bayes
Tools / Frameworks
Tech Stack
Python
NLP
Naive Bayes
Gradio
HuggingFace
scikit-learn
Results / Output
Performance Metrics
Accuracy84%
Categories4
AlgorithmMultinomial Naive Bayes
Highlights / Improvements
- Achieved 84% accuracy on news headline classification
- Multinomial Naive Bayes with CountVectorizer preprocessing
- User-friendly Gradio interface
- Deployed on Hugging Face Spaces
- Real-time text classification capabilities
