README.md

---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- email-classification
- natural-language-processing
- classification
- spam-detection
- text-analysis
size_categories:
- 10K<n<100K
pretty_name: High-Accuracy Email Classification Dataset
---

# High-Accuracy Email Classification Dataset

## Dataset Description

This dataset contains **12,000+ emails** across **6 categories**, specifically curated for high-accuracy email classification tasks. The dataset achieves 98%+ classification accuracy with appropriate models.

## Categories

The dataset includes emails from the following categories:

| Category | Count | Description | Emoji |
|----------|--------|-------------|-------|
| **Forum** | ~2,000 | Forum posts, discussions, and community notifications | 🗣️ |
| **Promotions** | ~2,000 | Marketing emails, sales, offers, and advertisements | 🛒 |
| **Social Media** | ~2,000 | Notifications from social platforms | 📱 |
| **Spam** | ~2,000 | Unwanted emails, scams, and phishing attempts | ⚠️ |
| **Updates** | ~2,000 | System updates, security patches, maintenance notices | 🔄 |
| **Verify Code** | ~2,000 | Authentication codes and verification emails | 🔐 |

## Dataset Structure

### Data Splits

- **Training Set**: ~9,600 emails (80%)
- **Test Set**: ~2,400 emails (20%)

### Data Format

Each email contains the following fields:

- `id`: Unique identifier for each email
- `subject`: Email subject line
- `body`: Email body content  
- `text`: Combined subject and body text
- `category`: Email category label
- `category_id`: Numeric category identifier (0-5)

### Files

- `train.csv` / `train.json`: Training dataset
- `test.csv` / `test.json`: Test dataset  
- `full_dataset.csv` / `full_dataset.json`: Complete dataset
- `dataset_info.json`: Dataset metadata and statistics

## Usage

### Loading the Dataset

```python
import pandas as pd

# Load training data
train_df = pd.read_csv("train.csv")
test_df = pd.read_csv("test.csv")

# View categories
print(train_df['category'].value_counts())
```

### Example Email Samples

**Spam Email:**
```
Subject: Congratulations! You've won $1000!
Body: Click here to claim your prize now! Limited time offer.
Category: spam
```

**Verification Code:**
```
Subject: Your verification code
Body: Your verification code is 123456. Please enter this code to complete your login.
Category: verify_code
```

## Model Performance

When used with the companion CNN+GRU model:
- **Training Accuracy**: 98.13%
- **Validation Accuracy**: 98%+
- **Model Repository**: [jason23322/high-accuracy-email-classifier](https://huggingface.co/jason23322/high-accuracy-email-classifier)

## Data Quality

- **Balanced**: Each category contains approximately 2,000 emails
- **Diverse**: Wide variety of email content and styles
- **Clean**: Manually curated and validated
- **Realistic**: Based on common email patterns and templates

## Applications

This dataset is suitable for:

- Email classification and filtering
- Spam detection systems
- Email client automation
- Text classification research
- Natural language processing studies
- Cybersecurity research

## Citation

```bibtex
@misc{email_classification_dataset,
  title={High-Accuracy Email Classification Dataset},
  author={Email Classification Team},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/jason23322/email-classification-dataset}
}
```

## License

This dataset is released under the Apache 2.0 License.

## Related Models

- [High-Accuracy Email Classifier](https://huggingface.co/jason23322/high-accuracy-email-classifier) - The CNN+GRU model trained on this dataset