README.md

---
language: en
license: other
tags:
- random-forest
- classification
- bert
- sector-classification
- machine-learning
inference: false
datasets:
- custom
model-index:
- name: RF 48 Sectors Classification Model
  results: []
---

# RF 48 Sectors Classification Model

## Overview

This machine learning model is a Random Forest classifier designed to categorize datasets into 48 predefined sectors based on column names. By leveraging BERT embeddings and a sophisticated Random Forest algorithm, the model provides intelligent sector classification for various types of datasets.

## Model Details

- **Model Type**: Random Forest Classifier
- **Embedding Method**: BERT (bert-base-uncased)
- **Number of Sectors**: 48
- **Classification Approach**: Column name embedding and prediction

## 48 Supported Sectors

The model can classify datasets into the following sectors:

1. Agriculture Sector
   - Crop Production
   - Livestock Farming
   - Agricultural Equipment
   - Agri-tech

2. Banking & Finance Sector
   - Retail Banking
   - Corporate Banking
   - Investment Banking
   - Digital Banking
   - Asset Management
   - Securities & Investments
   - Financial Planning & Advice

3. Construction & Infrastructure
   - Residential Construction
   - Commercial Construction
   - Industrial Construction
   - Infrastructure

4. Consulting Sector
   - Management Consulting
   - IT Consulting
   - Human Resources Consulting
   - Legal Consulting

5. Education Sector
   - Early Childhood Education
   - Primary & Secondary Education
   - Higher Education
   - Adult Education & Vocational Training

6. Engineering Sector
   - Civil Engineering
   - Mechanical Engineering
   - Electrical Engineering
   - Chemical Engineering

7. Entertainment & Media
   - Film & Television
   - Music Industry
   - Video Games
   - Live Events

8. Environmental Sector
   - Environmental Protection
   - Waste Management
   - Renewable Energy
   - Wildlife Conservation

9. Insurance Sector
   - General Insurance Services
   - Life Insurance
   - Health Insurance
   - Property & Casualty Insurance
   - Reinsurance

10. Food Industry
    - Food Processing
    - Food Retail
    - Food Services
    - Food Safety & Quality Control

11. Healthcare Sector
    - Hospitals
    - Clinics & Outpatient Care
    - Pharmaceuticals
    - Medical Equipment & Supplies

## Installation

```bash
pip install transformers torch joblib scikit-learn
```

## Usage

```python
from transformers import BertTokenizer, BertModel
import joblib
import torch

# Initialize model
bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
bert_model = BertModel.from_pretrained('bert-base-uncased', ignore_mismatched_sizes=True)

# Download and load the Random Forest model
model_path = hf_hub_download(repo_id="Mageswaran/rf_48_sectors", filename="model_48_sectors.pkl")
label_encoder_path = hf_hub_download(repo_id="Mageswaran/rf_48_sectors", filename="label_encoder_48_sectors.pkl")

rf = joblib.load(model_path)
label_encoder = joblib.load(label_encoder_path)

def predict_sector(column_names):
    # Convert column names to BERT embeddings
    embeddings = get_bert_embeddings([column_names])
    
    # Predict sector
    prediction = rf.predict(embeddings)
    return label_encoder.inverse_transform(prediction)[0]

# Example
column_names = "clinical_trail_duration, computer_analysis_score, customer_feedback_score"
sector = predict_sector(column_names)
print(f"Predicted Sector: {sector}")
```

## Model Performance

- **Embedding Technique**: BERT embeddings from 'bert-base-uncased'
- **Classification Algorithm**: Random Forest
- **Unique Feature**: Sector classification based on column name semantics

## Limitations

- Model performance depends on the semantic similarity of column names to training data
- Works best with column names that clearly represent the dataset's domain
- Requires careful preprocessing of column names

## Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

## License and Usage Restrictions

### Proprietary Usage Policy

**IMPORTANT: This model is NOT freely available for unrestricted use.**

#### Usage Restrictions
- Prior written permission is REQUIRED before using this model
- Commercial use is strictly prohibited without explicit authorization
- Academic or research use requires formal permission from the model's creator
- Unauthorized use, distribution, or reproduction is prohibited

#### Licensing Terms
- This model is protected under proprietary intellectual property rights
- Any use of the model requires a formal licensing agreement
- Contact the model's creator for licensing inquiries and permissions

### Permissions and Inquiries

To request permission for model usage, please contact:
- Email: [Your Contact Email]
- Hugging Face Profile: [Your Hugging Face Profile URL]

**Unauthorized use will result in legal action.**

## Contact

meyyappanmageswaran@gmail.com

## Citing this Model

If you use this model in your research, please cite it using the following BibTeX entry:

```bibtex
@misc{mageswaran_rf_48_sectors,
  title = {Random Forest 48 Sectors Classification Model},
  author = {Mageswaran},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Mageswaran/rf_48_sectors}}
}
```

## Additional Resources

- [Author's Hugging Face Profile](https://huggingface.co/Mageswaran)
- [Model Repository](https://huggingface.co/Mageswaran/rf_48_sectors)

## Acknowledgments

- Hugging Face Transformers