https://github.com/relostar-devil/news-classification
Classifies news articles into categories like "News" and "Politics" using machine learning. The project processes fields such as title, text, subject, and date to uncover patterns in news content through various techniques which provides an efficient solution for organizing and analyzing large volumes of news data.
https://github.com/relostar-devil/news-classification
classification-model machine-learning natural-language-processing
Last synced: 11 months ago
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Classifies news articles into categories like "News" and "Politics" using machine learning. The project processes fields such as title, text, subject, and date to uncover patterns in news content through various techniques which provides an efficient solution for organizing and analyzing large volumes of news data.
- Host: GitHub
- URL: https://github.com/relostar-devil/news-classification
- Owner: Relostar-Devil
- Created: 2025-02-12T18:43:55.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-12T18:45:40.000Z (over 1 year ago)
- Last Synced: 2025-02-12T19:40:46.823Z (over 1 year ago)
- Topics: classification-model, machine-learning, natural-language-processing
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- Changelog: News Classification.ipynb
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README
## Overview
News Classification is a machine learning project that automatically categorizes news articles based on their textual content. By processing real-world data containing article titles, text, subjects, and dates, the project demonstrates how to transform raw news data into actionable insights through text preprocessing, feature extraction, and model training.
## Problem Statement
Given a dataset of news articles with fields such as title, text, subject, date, and a target label (e.g., 0 for general news and 1 for political news), the goal is to build a classifier that can accurately predict the category of each article. This automated classification helps in organizing and retrieving information efficiently.
## Conclusion
This project showcases an end-to-end text classification workflow applied to news data, offering insights into natural language processing and automated content categorization.