BitViz-ML

Project Authors

• Name: Kaushal Patel
• Name: Noah Hassett
• GitHub Profile: Kaushal Patel
• GitHub Profile: Noah Hassett

Organization

Math and Computer Science Department at Western Carolina University

• Course: CS 495-496 - Capstone Project

Description

This repository contains the code for the BitViz-ML project. The project aims to develop a machine learning model that can predict whether a given Bitcoin transaction is fraudulent or not. The dataset is from Elliptic Data Set, which contains information about Bitcoin transactions and their labels. The dataset is available at https://www.kaggle.com/ellipticco/elliptic-data-set.

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We use Random Forest Classifier to predict the labels of the transactions. The model is trained on the features of the dataset. The features are extracted from the dataset and are used to train the model. The model is then used to predict the labels of the transactions. The predicted labels are then used to filter the dataset. The filtered dataset is then used to load into the Neo4J database.

Files

• src/ml.py: Contains the code for generating, training, and exporting the Machine Learning model.
• src/filter.py: Contains the code for filtering the dataset. Currently, it provides total of 7 filters.
• model/predicted_data_final.json: Contains the predicted data from the model.
• model/Final Predicted Data.rar: Contains the predicted data from the model in RAR format.
• data/elliptic_txs_features.csv: Contains the features of the dataset.
  • Used in src/ml.py for training the model.
• data/data_final.json: Contains the final data before machine learning.
  • Used in src/ml.py for training the model.
• data/filtered_models/data/*: Contains the JSON files of the filtered data post ML.
  • This is the data that is used to load into the Neo4J database. It is all the nodes and their group categorization (Illicit, Licit, or Unknown) and edges.
• data/filtered_models/metadata/*: Contains the metadata of the filtered data post ML.
  • This is the metadata that is used to load into the Neo4J database. It is all the timesteps and the number of illict, unknown, or licit nodes within each timestep.

Usage

1. Clone the repository.

   git clone https://github.com/Kbpatel3/BitViz-ML.git

2. Navigate to the project directory.

   cd BitViz-ML

3. Install the required packages.

   pip install -r requirements.txt

4. Run the ml.py file to generate, train, and export the Machine Learning model.

   python src/ml.py

5. Run the filter.py file to filter the dataset.

   python src/filter.py

Requirements

• Python 3.8 or higher
• pandas
• sklearn
• joblib