Card fraud occurs through various means such as unauthorized sharing of credit card information, lost or stolen cards, mail theft, and fraudulent activities by business employees. This dataset includes anonymized credit card transaction data, which is ideal for developing machine learning models to detect fraudulent activities. Given the rarity of fraud compared to legal transactions, this dataset presents a significant challenge. It is an excellent resource for enhancing skills in data exploration, EDA, data visualization, and machine learning algorithms, providing a robust foundation for advancing your machine learning journey.
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Dataset Features:
Anonymized credit card transactions
Suitable for fraud detection model development
Imbalanced dataset with more legal transactions than frauds
Ideal for data science projects focusing on:
Data exploration
Exploratory Data Analysis (EDA)
Data visualization
Machine learning algorithm implementation
Applications:
Fraud detection systems
Financial security analytics
Anomaly detection in transactions
Enhancements: This dataset can be enriched with additional features such as:
Time-based transaction patterns
Geographic location data
Customer behavior analytics
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