Text Classification | Davide Sidoti

A small project for text classification in Python, demonstrating data preprocessing, feature extraction, and model training using scikit-learn and NLTK. Ideal for beginners and practitioners looking for a clear example of building and evaluating text classification models.

Table of Contents

• Introduction
• Installation
• Usage
• Features
• Contributing
• License

Introduction

This repository contains a comprehensive example of how to perform text classification in Python. The project walks through the entire process from raw text data to a trained machine learning model, providing a hands-on approach to learning text classification.

Installation

To get started with this project, clone the repository and install the required dependencies:

git clone https://github.com/davidesidoti/text_classification
cd text-classification
pip install -r requirements.txt

Usage

Follow these steps to run the text classification:

1. Data Preprocessing: Clean and preprocess the text data.
2. Feature Extraction: Convert text data into numerical features using methods like TF-IDF.
3. Model Training: Train a machine learning model using scikit-learn.
4. Evaluation: Evaluate the model’s performance using appropriate metrics.

Run the example script to see the process in action:

python main.py

Features

• Data Preprocessing: Techniques for cleaning and preparing text data.
• Feature Extraction: Methods for converting text into numerical features.
• Model Training: Using popular algorithms for text classification.
• Evaluation: Metrics and methods for assessing model performance.

Contributing

Contributions are welcome! Please fork this repository and submit a pull request with your changes. Ensure your code follows the project’s coding standards and includes appropriate tests.

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature-branch)
5. Create a new Pull Request

License

This project is licensed under the MIT License. See the LICENSE file for more details.