This project aims to detect fraudulent transactions in the Ethereum blockchain using machine learning algorithms. Fraud detection is crucial for maintaining the
integrity and security of the blockchain, and can help prevent financial losses due to fraudulent activity.
Dataset
The dataset used for this project is a publicly available Ethereum transaction dataset. The dataset contains a total of 8,335 transactions, with each transaction having various features such as gas price, transaction fee, and timestamp.
Model:
The analysis is done using Python, and the models are built using the Scikit-learn and TensorFlow libraries. The project is divided into two parts: data preprocessing
and predictive modeling. In the data preprocessing part, the dataset is preprocessed and features are extracted from the transactions. In the predictive modeling part,
various machine learning algorithms, including decision trees, support vector machines, and neural networks, are used to classify the transactions as fraudulent or
legitimate.
Technologies Used
Python
Pandas Library
NumPy Library
Scikit-learn Library
TensorFlow Library
Matplotlib Library
Installation
pip install pandas
pip install numpy
pip install scikit-learn
pip install tensorflow
pip install matplotlib
Conclusion:
This project demonstrates how machine learning algorithms can be used to detect fraudulent transactions in the Ethereum blockchain. The project showcases how to
preprocess the dataset and extract features from the transactions. The results of this analysis can be used by blockchain developers and financial institutions to
prevent fraudulent activity and improve the security of the blockchain.