README.md

metadata

title: Blockchain Intelligence Dashboard
emoji: 🔗
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: true
license: mit
tags:
  - blockchain
  - cryptocurrency
  - analytics
  - cross-chain
  - visualization
datasets:
  - Omarrran/50k_Cryptocurrency_Transaction_Dataset_by_HNM
short_description: '8-dimension cross-chain analysis '

🔗 Blockchain Intelligence Dashboard

8-Dimension Cross-Chain Analysis of 50,000 Real Cryptocurrency Transactions

Interactive exploration of ETC, BTC, DOGE, BCH, and DASH across eight research dimensions:

#	Dimension	Key Finding
1	Fee Market Efficiency	BCH CV=15.89 (highest), BTC CV=3.12 (most stable)
2	Whale Concentration	DOGE Gini=0.998, top 1% controls 53-99% of volume
3	Network Reliability	ETC 99.93% success, failures unpredictable (AUC=0.499)
4	AML Pattern Detection	15,330 peeling chains, BTC risk rate 40.1%
5	Payment Velocity	12,400× gap: DOGE (30,978) vs BTC (2.49)
6	Gas Price & MEV	R²=0.269, moving averages = 96% importance
7	Cross-Chain Arbitrage	All 5 pairs cointegrated, 1,615 divergence signals
8	Privacy Leakage	ETC 55.6% address reuse, normalized entropy 0.715

Features

• Interactive Plots: Plotly-based visualizations for each research dimension
• Radar Comparison: Normalized cross-chain comparison across all metrics
• Custom Analysis: Upload your own CSV to get instant blockchain analytics
• Real Data: All results from verified blockchain transactions (Nov 2024)

Dataset

Uses Omarrran/50k_Cryptocurrency_Transaction_Dataset_by_HNM — 50,000 transactions (10K per chain) from live blockchain explorers.

Methods

• Statistical testing: Levene's, KS, Engle-Granger cointegration
• Machine learning: Random Forest, Gradient Boosting, Isolation Forest, DBSCAN
• Information theory: Shannon entropy, Gini coefficient
• All models use random seed 42 and temporal train/test splits

Citation

@misc{blockchain_intelligence_2026,
  title={Comprehensive Cross-Chain Cryptocurrency Analysis: Eight Dimensions of Blockchain Intelligence},
  author={Haq Nawaz, XXXX},
  year={2025},
  howpublished={HuggingFace Spaces}
}