RSK World - Fraud Detection Dataset | Financial Fraud ML | Anti-Fraud AI | Fraud Detection Deep Learning | RSK World

Fraud Detection Transaction Data Machine Learning XGBoost & LightGBM Python SHAP Ready

This project features a comprehensive Fraud Detection dataset designed for professional financial fraud detection, anomaly detection, and security applications. The dataset includes transaction records with features like transaction amount, location, time, merchant information, and fraud labels. Includes powerful Python scripts: generate_data.py for dataset generation, advanced_feature_engineering.py for feature engineering, train_model.py for model training, hyperparameter_tuning.py for optimization, model_evaluation_advanced.py for evaluation, shap_explainability.py for model explainability, and predict_pipeline.py for predictions. The package includes interactive demo website, comprehensive README.md, and MIT License. Perfect for data scientists, researchers, students, and developers working on fraud detection, financial security, anomaly detection, and machine learning projects.

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Dataset Overview

Complete fraud detection dataset with transaction records, user behavior patterns, and fraud labels for anti-fraud ML models.

Transaction records with comprehensive features
User behavior patterns and features
Fraud labels (binary classification)
Realistic 5% fraud ratio (imbalanced dataset)
Transaction amount, location, time features
Merchant category and device type information
Pre-labeled with fraud indicators
Ready for classification models
Advanced feature engineering included
Multiple ML models support
Perfect for fraud detection & ML training

Dataset Structure & Files

Well-organized project structure with transaction data, Python scripts for ML training, evaluation, and prediction pipelines.

fraud_detection_dataset.csv - Main dataset file
generate_data.py - Dataset generation script
advanced_feature_engineering.py - Feature engineering
train_model.py - Model training script
hyperparameter_tuning.py - Hyperparameter optimization
model_evaluation_advanced.py - Advanced evaluation
shap_explainability.py - SHAP model explainability
predict_pipeline.py - Production prediction pipeline
fraud_detection_analysis.ipynb - Jupyter notebook
Consistent naming convention
Easy to load with pandas
Scikit-learn/XGBoost/LightGBM ready format

Machine Learning Training

Complete training pipeline with support for fraud detection models, feature engineering, and multiple ML frameworks.

Random Forest classifier
XGBoost gradient boosting
LightGBM fast gradient boosting
SMOTE for imbalanced data handling
Scikit-learn compatibility
Advanced feature engineering
Hyperparameter tuning support
Cross-validation support
Model evaluation metrics
SHAP explainability integration
Performance metrics
Model training examples
Prediction pipeline utilities

Multiple File Formats

Dataset available in CSV format for maximum compatibility with different data science tools and ML frameworks.

CSV format (comma-separated values)
Excel compatible
Pandas DataFrame ready
NumPy array compatible
Scikit-learn compatible
XGBoost/LightGBM ready
Standard data science formats
Easy to import and process
Compatible with all ML libraries
Jupyter Notebook ready
Python pandas ready

Analysis & Visualization

Comprehensive analysis tools with visualization capabilities and interactive fraud data explorer.

Interactive Fraud Data Explorer
Transaction distribution charts
Fraud pattern visualization
Dataset statistics
Feature importance analysis
Fraud/non-fraud filtering
Performance benchmarking
Model evaluation metrics
SHAP visualization
Dataset analysis tools
Interactive Demo Website

Compatible Frameworks

Works with all major machine learning and data science frameworks out of the box.

Scikit-learn ML library
XGBoost gradient boosting
LightGBM fast boosting
SHAP model explainability
NumPy numerical computing
pandas data manipulation
matplotlib visualization
Jupyter Notebook support
Fraud detection models
Classification models
Ensemble methods

What You Get

Complete package with all files needed for professional fraud detection projects.

Transaction records with fraud labels
Python utility scripts
generate_data.py - Dataset generation
advanced_feature_engineering.py - Feature engineering
train_model.py - Model training
hyperparameter_tuning.py - Hyperparameter optimization
model_evaluation_advanced.py - Evaluation metrics
shap_explainability.py - Model explainability
predict_pipeline.py - Prediction pipeline
Interactive demo website
Complete documentation (README, ADVANCED_FEATURES)
MIT License

Interactive Demo Website

Beautiful demo website with fraud data explorer, transaction analytics dashboard, and comprehensive guide.

Modern animated design
Interactive Fraud Data Explorer
Transaction Analytics Dashboard
Model Performance Metrics
Filter by fraud status
Transaction pattern visualization
Fraud distribution charts
Dataset statistics display
Step-by-step usage guide
Dark theme with gradients
Fully responsive layout

Python Scripts Included

Professional Python scripts for fraud detection, feature engineering, and model training.

generate_data.py - Generate fraud detection dataset
advanced_feature_engineering.py - Advanced feature creation
train_model.py - Train ML models (RF, XGBoost, LightGBM)
hyperparameter_tuning.py - Optimize model parameters
model_evaluation_advanced.py - Comprehensive evaluation
shap_explainability.py - SHAP model explainability
predict_pipeline.py - Production prediction pipeline
verify_dataset.py - Dataset verification
Batch processing support
Feature engineering utilities
Model training tools
Complete code examples

Dataset Features

Comprehensive fraud detection dataset with transaction records and various behavioral features.

Transaction records - Amount, location, time, merchant
User behavior features - Advanced patterns
Fraud labels - Binary classification (0/1)
Realistic 5% fraud ratio - Imbalanced dataset
Advanced features - Time-based, statistical, interaction
High-quality transaction data
Accurate fraud labels
Ready for ML training
Feature engineering utilities
Easy to extend dataset
Organized project structure

Credits & Acknowledgments

This dataset is provided for educational and research purposes. Core technologies and libraries are credited below.

Python 3.8+ - Programming language (PSF License)
Scikit-learn - Machine learning library (BSD License)
XGBoost - Gradient boosting framework (Apache 2.0)
NumPy - Numerical computing (BSD License)
pandas - Data manipulation (BSD License)
matplotlib - Data Visualization (PSF License)
RSK World - Dataset creator and provider
GitHub Repository - Source code and releases
Author: Molla Samser | Designer: Rima Khatun
MIT License - Free for learning & research

Support & Contact

For commercial use, custom datasets, or integration help, please contact us.

Email: help@rskworld.in
Phone: +91 93305 39277
Website: RSKWORLD.in
Location: Nutanhat, Mongolkote, West Bengal, India
Author: Molla Samser
Designer & Tester: Rima Khatun
GitHub: Coming Soon
Fraud Detection Dataset Documentation
Technical Support Available
Custom Dataset Requests Welcome

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Fraud Detection Dataset

Dataset Overview

Dataset Structure & Files

Machine Learning Training

Multiple File Formats

Analysis & Visualization

Compatible Frameworks

What You Get

Interactive Demo Website

Python Scripts Included

Dataset Features

Credits & Acknowledgments

Support & Contact

Featured Content

Additional Sponsored Content

Download Free Source Code

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