https://github.com/onome-joseph/nlp_cnn_classification

This project implements accurate tags or specifications to problems, or user complaints based on input text. The model is designed using neural networks to process data quickly and deliver high-performance results.
https://github.com/onome-joseph/nlp_cnn_classification

cnn-classification data-science deep-neural-networks multi-class-classification natural-language-processing

Last synced: 11 months ago
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This project implements accurate tags or specifications to problems, or user complaints based on input text. The model is designed using neural networks to process data quickly and deliver high-performance results.

• Host: GitHub
• URL: https://github.com/onome-joseph/nlp_cnn_classification
• Owner: Onome-Joseph
• License: mit
• Created: 2024-12-26T20:09:30.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-25T00:42:00.000Z (over 1 year ago)
• Last Synced: 2025-04-06T00:28:09.160Z (about 1 year ago)
• Topics: cnn-classification, data-science, deep-neural-networks, multi-class-classification, natural-language-processing
• Language: Jupyter Notebook
• Homepage:
• Size: 366 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# NLP Deep Learning Classification Model  

This project implements an **NLP Deep Learning Classification Model** that assigns accurate tags or specifications to products, problems, or user complaints based on input text. The model is designed to process data quickly and deliver high-performance results, making it ideal for various industries.  

## Key Features  

- **Accurate Classification**: Identifies the correct tag or specification with high precision.  

- **Fast Processing**: Ensures quick turnaround times for text inputs.

- **Algorithm used**: Makes use of Convulational Neural Network for maximuim accuracy. 

- **Scalable Solution**: Capable of handling large volumes of textual data.  

## Applications  

The NLP Deep Learning Classification Model has versatile applications:  

1. **Banking**:  

   - Classify customer complaints into categories like loan issues, transaction errors, or account management.  

   - Automate tagging for faster issue resolution.  

2. **E-Commerce**:  

   - Assign product categories based on user reviews or descriptions.  

   - Enhance product search and filtering capabilities.  

3. **Customer Support**:  

   - Tag and prioritize user complaints for efficient ticket resolution.  

   - Provide automated routing of issues to the correct departments.  

4. **Healthcare**:  

   - Categorize patient feedback or complaints to improve services.

   

  

**Cost-Effective**: Minimizes operational costs by automating repetitive tasks.