29 skills found
EnsoBuild / TemperTemper your expectations - Ethereum Transaction Simulator
IBM / AMLSimThe AMLSim project is intended to provide a multi-agent based simulator that generates synthetic banking transaction data together with a set of known money laundering patterns - mainly for the purpose of testing machine learning models and graph algorithms. We welcome you to enhance this effort since the data set related to money laundering is critical to advance detection capabilities of money laundering activities.
telromellow88 / AegisAegis Web3 (2026) — Open-Source Transaction Simulation Engine & Anti-Drainer Shield. Protect your Crypto Assets (SOL, ETH, BSC) from Malicious Smart Contracts. Features Real-Time Metadata Auditing, Permission Revocation, and Stealth Simulation Mode.
walnuthq / WalnutWeb based transaction debugger and simulator for the EVM. Open source and self-hostable.
AmadiMichael / Evm Simulator RsCLI based transaction simulator supporting any EVM chain, written in Rust.
wallet-guard / Wallet Guard ExtensionA browser extension for detecting phishing attacks on crypto wallets
liveevilevilytish / USDTFlasherUSDT Flasher Tool - Secure Cryptocurrency Transaction Simulator | Free & Easy (2025)
shargri-la / Shargri LaA transaction-level sharding simulator for protocol testing against users' behavior on a sharded blockchain.
MarioPoneder / Web3 Transaction SimulatorSimulate a transaction on the current state of the mainnet and view its outcomes (compatible with MetaMask)
Kaiiser7 / Credit Card Fraud Detection Using Machine Learning With PythonIt is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification. Update (03/05/2021) A simulator for transaction data has been released as part of the practical handbook on Machine Learning for Credit Card Fraud Detection - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html. We invite all practitioners interested in fraud detection datasets to also check out this data simulator, and the methodologies for credit card fraud detection presented in the book. Acknowledgements The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project Please cite the following works: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019 Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019 Yann-Aël Le Borgne, Gianluca Bontempi Machine Learning for Credit Card Fraud Detection - Practical Handbook
Tenderly / Tenderly SnapTenderly TX Preview allows you to see the exact transaction outcomes before sending them on-chain using the Tenderly Simulation Infrastructure. 🦊
aanand300 / Blockchain Ns3Using the ns-3 simulator to implement wireless nodes of blockchain and analyse its performance in terms of transaction throughput, mining time, block propagation time
txrx-research / VorpalA Python simulator used to model cross shard transaction strategies for Ethereum 2.0
JEETAHIRWAR / Python ATM InterfacePython ATM Interface A Python-based command-line ATM simulator for depositing, withdrawing, and transferring funds, checking balances, and viewing transaction history.
Tenderly / Tenderly Metamask Snap Simulate Asset ChangesTenderly MetaMask Snap using the Tenderly Simulation API to showcase transaction asset changes. 🦊
Tenderly / Tenderly Rabby Transaction PreviewIntegrate the Tenderly Simulation API into Rabby Wallet to enable a transaction preview option for wallet users.
Vityapro453 / Cryptocurrency Transaction SimulatorNo description available
winnndixxie / WinnndixxieProfile repo for winnndixxie showcasing AI, Tokenization, and Bitcoin projects including python-agent-lab, token-simulator, and btc-transaction-visualizer. Serves as the central hub for open-source experiments and tooling.
sec-bit / Mfer NodeAn Ethereum transaction simulator based on go-ethereum
MariamGado0 / Starbucks Capstone Project ML Udacity Aws# Starbucks Promotions Project ### This project is the Capstone Project of Udacity's Machine Learning Engineering Nanodegree program.    ## Problem Statement This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. Not all users receive the same offer, and that is the challenge to solve with this data set. The task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products. Starbucks collects the customer data to understand their behaviour on the rewards and offers sent via the mobile-app. Once every few days, Starbucks sends the personalised offers to its customers. These customers can respond positively/negatively/neutrally. A key thing to note is that not all the customers receive the same offer. The task of this project is to combine transaction, demographic and offer data of the past (which is already provided) to determine which demographic groups respond best to which offer types. In order to develop this project, we needed to use some tools, packages, systems and services that could help us achieve our goals. #### Libraries First of all, we used **Python** to write our scripts not only for algorithm training and serving but also for the orchestration of the whole process. Important packages within this environment are listed below: This project is developed in Python 3.6. You will need install some libraries in order to run the code. Libraries are: * `pandas` so we could work with tabular data in dataframes; * `Ploty` so we could visualize our Dataset; * `matplotlib` for Dataset visualization; * `numpy` so we could easily manipulate arrays and data structures; * `seaborn` and `matplotlib` so we could generate insightful visualizations; * `sklearn` so we could build and develop our model pipeline; * `imblearn` so we could apply SMOTE to our training data; * `xgboost` so we could have our main classifier; * `sagemaker` so we could easily interact with AWS. * `json` for reading our Dataset Files. * `boto3` Finally, we used AWS environment in order to launch training jobs, deploy our model and serve predictions. The main services used are also listed below: * __AWS SageMaker__: training, hyperparameter tuning and endpoint serving; * __Amazon S3__: saving our data and model artifacts; ## Files Descriptions This project is structured as follows: #### 01. Proposal Project proposal documentation. #### 02. Data_Cleaning_[Dataset] Folder to perform data preparation and Dataset Cleaning and Prepare the Final Data for Further using in model algorithms. #### 03. Pre-processing Dataset Visualization Folder to perform final Pre-processing Dataset to be used in Visualization and exploration. #### 04. Dataset_Visualization Folder to perform Visualizations for the Pre-processed Dataset. #### 06. ORG_Starbucks_Capstone_Project.ipynb Jupyter notebook file that deploy final model and create an endpoint and orchestrates the end-to-end process in AWS SageMaker and also interacts with other services.
