README.md

---
license: mit
task_categories:
- text-classification
- text-generation
- question-answering
- summarization
language:
- en
tags:
- artificial-intelligence
- machine-learning
- deep-learning
- nlp
- computer-vision
- data-science
- technical-articles
- analytics-india-magazine
- ai-models
- programming
size_categories:
- 10K<n<100K
pretty_name: Analytics India Magazine Technical Articles Dataset

---

# Analytics India Magazine Technical Articles Dataset 🚀

## Dataset Description

This comprehensive dataset contains **25,685 high-quality technical articles** from Analytics India Magazine, one of India's leading publications covering artificial intelligence, machine learning, data science, and emerging technologies.

### ✨ Dataset Highlights

- **📚 Comprehensive Coverage**: Latest AI models, frameworks, and tools
- **🔬 Technical Depth**: Extracted keywords and complexity scoring
- **🏭 Industry Focus**: Real-world applications and insights
- **⚡ Multiple Formats**: JSON and optimized Parquet files
- **🎯 ML Ready**: Pre-processed and split for training

## Dataset Statistics

| Metric | Value |
|--------|-------|
| **Total Articles** | 25,685 |
| **Technical Articles** | 25,647 |
| **Average Word Count** | 724 words |
| **Language** | English |
| **Source** | [Analytics India Magazine](https://analyticsindiamag.com/) |

## 🎯 Technologies Covered

### AI & Machine Learning
- **Large Language Models**: GPT, Claude, Gemini, Llama
- **Frameworks**: TensorFlow, PyTorch, Hugging Face
- **MLOps Tools**: MLflow, Weights & Biases, Kubeflow
- **Agent Frameworks**: LangChain, AutoGen, CrewAI

### Programming & Tools
- **Languages**: Python, JavaScript, SQL
- **Cloud Platforms**: AWS, Azure, GCP
- **Development**: APIs, Docker, Kubernetes

## 📊 Dataset Structure

### Core Fields
- `title`: Article title
- `content`: Full article content (cleaned)
- `excerpt`: Article summary
- `author_name`: Article author
- `publish_date`: Publication date
- `url`: Original article URL

### Technical Metadata
- `extracted_tech_keywords`: Technical terms found in content
- `technical_depth`: Number of technical keywords
- `complexity_score`: Technical complexity (0-4)
- `word_count`: Article length
- `categories`: Article categories
- `tags`: Content tags

### Quality Indicators
- `has_code_examples`: Contains code snippets
- `has_tutorial_content`: Tutorial or how-to content
- `is_research_content`: Research or analysis
- `has_external_links`: Contains external references

## 📋 Dataset Splits

| Split | Examples | Purpose |
|-------|----------|---------|
| **Train** | 19,221 | Model training |
| **Validation** | 2,136 | Hyperparameter tuning |
| **Test** | 3,769 | Final evaluation |

## 🚀 Quick Start

### Using Hugging Face Datasets
```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("abhilash88/aim-technical-articles")

# Access splits
train_data = dataset["train"]
test_data = dataset["test"]

# Filter technical articles
technical_articles = dataset.filter(
    lambda x: x["technical_depth"] >= 3
)
```

### Using Pandas
```python
import pandas as pd

# Load from JSON
df = pd.read_json("aim_full_dataset.json")

# Load from Parquet (faster)
df = pd.read_parquet("aim_full_dataset.parquet")

# Convert list columns back from JSON strings
import json
df['categories'] = df['categories'].apply(json.loads)
df['extracted_tech_keywords'] = df['extracted_tech_keywords'].apply(json.loads)
```

## 🎯 Use Cases

### Machine Learning
- **Text Classification**: Topic classification, difficulty assessment
- **Content Generation**: Article summarization, content creation
- **Recommendation Systems**: Technical content recommendations
- **Question Answering**: Technical QA systems

### Business Intelligence
- **Trend Analysis**: Technology trend identification
- **Market Research**: Industry insights and analysis
- **Content Strategy**: Editorial planning and optimization

### Education & Research
- **Curriculum Development**: AI/ML course creation
- **Knowledge Mining**: Technical concept extraction
- **Academic Research**: Technology adoption studies

## 📦 Available Files

### Standard Formats
- `aim_full_dataset.json` - Complete dataset
- `aim_full_dataset.csv` - CSV format
- `aim_full_dataset.parquet` - Optimized Parquet format

### Specialized Subsets
- `aim_quality_dataset.json` - High-quality articles (300+ words)
- `aim_technical_dataset.json` - Highly technical content
- `aim_tutorial_dataset.json` - Educational content
- `aim_research_dataset.json` - Research and analysis articles

### ML-Ready Splits
- `train.json` / `train.parquet` - Training data
- `test.json` / `test.parquet` - Test data
- `validation.json` / `validation.parquet` - Validation data (if available)

## 📈 Content Quality

- **Duplicate Removal**: All articles are unique by ID
- **Content Filtering**: Minimum word count requirements
- **Technical Validation**: Verified technical keywords
- **Clean Processing**: HTML removed, text normalized
- **Rich Metadata**: Comprehensive article classification

## ⚖️ Ethics & Usage

### Licensing
- **License**: MIT License
- **Attribution**: Analytics India Magazine
- **Usage**: Educational and research purposes recommended

### Content Guidelines
- All content is publicly available from the source
- Original URLs provided for attribution
- Respects robots.txt and rate limiting
- No personal or private information included

## 📚 Citation

```bibtex
@dataset{aim_technical_articles_2025,
  title={Analytics India Magazine Technical Articles Dataset},
  author={Abhilash Sahoo},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/abhilash88/aim-technical-articles}
}
```

## 🤝 Contact & Support

- **Dataset Creator**: Abhilash Sahoo
- **Hugging Face**: [@abhilash88](https://huggingface.co/abhilash88)
- **Source**: [Analytics India Magazine](https://analyticsindiamag.com/)

For questions, issues, or suggestions, please open a discussion on the Hugging Face dataset page.

## 🔄 Updates & Versions

- **Version 2.0** (Current): Enhanced processing, technical depth scoring
- **Last Updated**: 2025-07-11
- **Processing Pipeline**: Optimized extraction with 2025 tech coverage

---

**🎯 Ready to power your next AI project with comprehensive technical knowledge!**

*This dataset captures the cutting edge of AI and technology discourse, perfect for training models, research, and building intelligent applications.*