Product Title Classification

🚀 Intelligent e-commerce product categorization using machine learning

Table of Contents

• Overview
• Problem Statement
• Key Features
• Technology Stack
• Models Implemented
• Installation & Setup
• Usage Guide
• Project Structure
• Configuration
• Documentation
• Contributing
• Roadmap
• Contact
• Acknowledgments

Overview

Problem Statement

The aim of this project is to develop a machine learning model that accurately classifies product titles into their respective categories. This solution helps organize products efficiently and improves the user experience on e-commerce platforms.

Key Features

• 🎯 Multi-level product categorization
• 🔍 Advanced text preprocessing
• 🤖 Multiple ML models (SVM, Random Forest, Decision Tree)
• 🌐 User-friendly web interface
• 📊 Real-time classification and performance visualization

Technology Stack

• Backend: Python 3.x, Flask
• Machine Learning: scikit-learn, NLTK, pandas, numpy
• Data Visualization: matplotlib, seaborn
• Development Tools: Jupyter Notebook, Git, Virtual Environment (venv)

Models Implemented

• Support Vector Machine (SVM)
• Random Forest Classifier
• Decision Tree Classifier

Installation & Setup

1. Clone the repository:

   git clone https://github.com/muhammadhamzagova666/product-title-classification.git
   cd product-title-classification

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # For Linux/Mac
   # or for Windows:
   .\venv\Scripts\activate

3. Install required packages:

   pip install -r requirements.txt

4. Download NLTK data:

   python -m nltk.downloader stopwords

Usage Guide

1. Start the Flask application:

   python Source\ Code/app.py

   Note: Depending on your setup, the path to app.py may differ.

2. Access the web interface:

   • Open your web browser.
   • Navigate to http://127.0.0.1:5000/.

3. Upload and classify product titles:

   • Use the provided interface to upload product titles and descriptions for classification.

Project Structure

Product Title Classification/
├── Source Code/
│   ├── app.py                  # Main Flask application
│   ├── Utilities.py            # Helper functions
│   ├── KNNImpute.py            # KNN imputation implementation
│   ├── templates/              # HTML templates
│   ├── static/                 # Static assets (CSS, images)
│   └── Svm_Models/             # Trained model files
├── data/                      # Training and validation datasets (CSV files)
├── notebooks/                 # Jupyter notebooks and model training scripts
└── other_files/               # Additional resources (e.g., labels.csv)

Note: Directory names may vary slightly between versions.

Configuration

Create a .env file in the root directory with the following content:

FLASK_APP=app.py
FLASK_ENV=development
DEBUG=True

Documentation

For more detailed information about the project and its underlying models, please refer to the documentation available in the Project Documentation directory.

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a feature branch:

   git checkout -b feature/YourFeature

3. Commit your changes:

   git commit -m 'Add some feature'

4. Push to the branch:

   git push origin feature/YourFeature

5. Open a Pull Request.

Contributors:

• Muhammad Hamza Gova
• Muhammad Salar
• Talha Bilal

Roadmap

• Add support for more languages
• Implement deep learning models
• Enhance API documentation
• Add batch processing capability

Contact

For any queries or further information, please contact:

• Muhammad Hamza Gova - @muhammadhamzagova666

Project Link: https://github.com/muhammadhamzagova666/product-title-classification

Acknowledgments

• Many thanks to all the contributors for their support.
• Special thanks to the scikit-learn team for their excellent machine learning library.
• Appreciation goes to the Flask team for providing a robust web framework.