News Headline Classification with Statistical Machine Learning

This project focuses on categorizing news headlines into six categories using a statistical machine learning approach. The dataset consists of 3000 headlines evenly distributed across six categories: Politics, Economy, Sports, Current Affairs, Health, and Technology.

Project Structure

• Data: The dataset contains 500 headlines for each of the six categories. The data has been cleaned, with stop words and numbers removed.
• Model: A Random Forest classifier is used.
• Evaluation: Cross-validation is employed to test the model, achieving a precision of 0.70 and an accuracy of 0.61.

Requirements

To set up the environment, the following packages need to be installed:

• requirements
• chardet
• gensim
• openpyxl

Installation

1. Clone the repository:

   git clone https://github.com/eraybuyukkanat/nlp_news_classification.git
   cd your-repo
   

2. Install the necessary packages:

    pip install -r requirements.txt

   pip install chardet

   pip install gensim

   pip install openpyxl

Usage

1. Ensure your dataset is properly formatted and placed in the appropriate directory.

2. Open and run the Jupyter Notebook to train and evaluate the model:

   jupyter notebook classification.ipynb

Results

The model achieves the following performance metrics:

Precision: 0.70 Accuracy: 0.61

_{## License}

This project is licensed under the MIT License - see the LICENSE file for details.