The project revolves around a text document classification dataset comprising 2225 news articles categorized into five distinct classes: politics, sport, tech, entertainment, and business. With the aim of enhancing document organization and retrieval, this dataset presents an opportunity for document classification and clustering tasks. By leveraging machine learning algorithms and natural language processing techniques, the project seeks to develop models capable of accurately categorizing and grouping news articles based on their content. This endeavor not only facilitates efficient information retrieval but also holds potential for uncovering insights into the underlying patterns and themes prevalent in the dataset.
-
Notifications
You must be signed in to change notification settings - Fork 0
The project is about using machine learning and natural language processing methods to analyze a dataset of news articles that are classified into five categories: politics, sport, tech, entertainment, and business. The objective is to build models that can accurately assign labels to new articles based on their content.
Orestas41/news_article_classification
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
The project is about using machine learning and natural language processing methods to analyze a dataset of news articles that are classified into five categories: politics, sport, tech, entertainment, and business. The objective is to build models that can accurately assign labels to new articles based on their content.