Spark-Ethereum-Analysis

Overview

This repository contains a comprehensive analysis of Ethereum transactions using Apache Spark. The project delves deep into various aspects of Ethereum transactions, from identifying high gas-consuming transactions to detecting potential scam activities and wash trading patterns.

Features

• Gas Guzzlers Analysis: Identify and analyze Ethereum transactions that consume significant amounts of gas.

• Scam Analysis: A dedicated module to detect potential scam activities within Ethereum transactions by identifying suspicious patterns and behaviors.

• Wash Trading Detection: Uncover wash trading activities, where there's an artificial inflation of trading volumes by simultaneously buying and selling assets.

Modules

1. Gas Guzzlers (gassguzzlers/gasguzzlers.py): Focuses on transactions consuming high amounts of gas.

2. Scam Analysis (scam/scamsanalysis1.py): Analyzes potential scam activities within Ethereum transactions.

3. Wash Trading Detection (washtrading/washtrading.py): Identifies wash trading activities within Ethereum transactions.

4. Part A Analysis (part a/parta.py & part a2/parta2.py): Initial steps in the Ethereum transaction analysis, setting up data structures or preliminary data processing.

5. Part B Analysis (part b/partb.py): Continuation of the analysis series.

6. Part C Analysis (part c/partc.py): Further steps in the Ethereum transaction analysis series.

Setup & Usage

1. Clone the repository:

git clone https://github.com/KarshVashi/Spark-Ethereum-Analysis.git

2. Navigate to the project directory:

cd Spark-Ethereum-Analysis

3. Experiment and run the desired analysis script. For example, to run the Gas Guzzlers analysis:

python gassguzzlers/gasguzzlers.py

Dependencies

• Apache Spark
• Python 3.x

Contributing

Feel free to fork this repository, make changes, and submit pull requests. Any contributions, no matter how minor, are greatly appreciated.