This project was developed as part of my undergraduate thesis. It explores the use of various supervised machine learning algorithms for text classification. While the repository contains the core source code, it does not include the required datasets or APIs necessary to directly run the scripts.

Text classification is a key component in areas such as web search, data mining, ranking algorithms, and recommendation systems. This study investigates the performance of standard supervised classification techniques applied to various labeled text datasets.

• ✅ Multiple supervised algorithms tested including traditional classifiers and an Artificial Neural Network (ANN) using Backpropagation (BPN).
• ✅ Evaluates classification accuracy across datasets using benchmark methods.
• ✅ Emphasizes a modular and comparative setup for extensibility.

• Datasets and preprocessing steps are not included due to restrictions at the time of publication.
• API endpoints or tools that were used during the experiments are not available in the repo.

🔗 IEEE Xplore - Performance Analysis of Supervised ML Algorithms for Text Classification

If you use this work, please cite:

@inproceedings{mishu2016performance,
  title={Performance analysis of supervised machine learning algorithms for text classification},
  author={Mishu, Sadia Zaman and Rafiuddin, SM},
  booktitle={2016 19th International Conference on Computer and Information Technology (ICCIT)},
  pages={409--413},
  year={2016},
  organization={IEEE}
}

This project is licensed under the MIT License.