🔍 Project Overview

Onchain Analytics is a comprehensive data analysis pipeline for blockchain transaction data. The project involves:

• Writing and executing SQL queries for blockchain data extraction.
• Preprocessing and cleaning raw data.
• Feature engineering to generate additional insights.
• Graph-based analysis to uncover relationships and clusters within transaction networks.

🖼️ Visualization

The project produces a graph-based visualization of transaction networks, identifying communities and key influencers.

Execute SQL Queries (Dune Analytics)

python queries/dune_data.sql
python queries/dune_eth.sql
python queries/dune_metrics.sql

Process & Analyze Data

python skript.py

Jupyter Notebook for Graph Analysis

jupyter notebook graph.ipynb

✨ Features

• 📡 Blockchain Data Extraction – Uses Dune Analytics to fetch transaction data.
• 🔄 Data Preprocessing – Cleans and formats raw blockchain transaction records.
• 🎯 Feature Engineering – Adds new metrics for deeper analysis.
• 🔗 Graph-Based Clustering – Uses NetworkX and Louvain clustering to detect transaction communities.
• 📈 Graph Visualization – Creates a network representation of crypto transactions.
• 🏆 Influencer Detection – Identifies key players using PageRank & centrality measures.

📂 Project Structure

├── output/                 # Processed data and visualizations
│   ├── graph.png           # Transaction network visualization
│   ├── processed_data.csv  # Processed dataset
├── queries/                # SQL queries for data extraction
│   ├── dune_data.sql       # SQL queries for Dune Analytics
│   ├── dune_eth.sql        # Ethereum-specific queries
│   ├── dune_metrics.sql    # Additional blockchain metrics
├── .gitignore              # Ignore unnecessary files
├── api.env                 # API keys and environment variables (ignored)
├── data.csv                # Raw transaction data
├── doc.pdf                 # Project documentation (ignored)
├── graph.ipynb             # Jupyter Notebook for graph analysis
├── metrics.csv             # Transaction metrics dataset
├── skript.py               # Main pipeline script
└── README.md               # Project documentation

🏆 Key Insights

• Transaction Networks: Visualizing transaction flows helps identify patterns of behavior.
• Cluster Detection: Louvain clustering reveals distinct transaction groups.
• Whale Identification: PageRank highlights influential crypto addresses.

👤 Author

• Konstantin Ždanović
• GitHub: @zdanovic
• LinkedIn: @zdanovic