Text Classification

Abstract

1. Introduction

Referenced paper : Text Classification Algorithms: A Survey

In Natural Language Processing (NLP), most of the text and documents contain many words that are redundant for text classification, such as stopwords, miss-spellings, slangs, and etc. In this section, we briefly explain some techniques and methods for text cleaning and pre-processing text documents. In many algorithms like statistical and probabilistic learning methods, noise and unnecessary features can negatively affect the overall perfomance. So, elimination of these features are extremely important.

~ Random Projection ~ Random projection or random feature is a dimensionality reduction technique mostly used for very large volume dataset or very high dimensional feature space. Text and document, especially with weighted feature extraction, can contain a huge number of underlying features. Many researchers addressed Random Projection for text data for text mining, text classification and/or dimensionality reduction. We start to review some random projection techniques.

2. Objective

Text Classification Algorithms: A Survey

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/kk7nc/Text_Classification.git
cd Text_Classification

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by kk7nc and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.