jason23322/high-accuracy-email-classifier

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

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

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

@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 - The CNN+GRU model trained on this dataset