Accurate Newspaper Article Classification Using Multi-Class Support Vector Machine
Objective
The main objective of this project is to present an comparison between some powerful machine learning algorithms for news article classification data which includes the categories like politics, entertainment, sports, etc.,
Abstract
With a diversity of sources on the internet producing a massive volume of daily news, it is necessary to categories the news items in order to make the information available to consumers quickly and efficiently. So the work of news classification begins with web scraping to collect real-time news items from news websites and then automatically classifying them using various classification algorithms. Thus, news categorization is a method of identifying themes of untracked news as well as making individual recommendations depending on the user's past interest. This task discusses various steps in news classification and implements a few algorithmic approaches such as Naive Bayes, Binary Classifier, SVM, Perceptron, and SGD for topic classification of news articles using the BBC News dataset, which contains articles from five different categories (Business, Entertainment, Politics, Sport, and Technology). The study examines the outcomes of several categorization algorithms and compares them with accuracy measures.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: Jupyter Notebook or Colaboratory
- Libraries Used: Pandas, Numpy, Sklearn, Seaborn
Learning Outcomes
- Scope of Real Time Application Scenarios
- What type of technology versions are used
- Working Procedure
- Introduction to basic technologies used for
- How project works.
- Input and Output modules
- Notebook using in work environment
- Datasets properties
- Data pre-processing techniques
- How to perform classification
- What are ML algorithms
- Project Development Skills:
- Problem analyzing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- Thesis writing skills.
Related Projects
Paper Publishing
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