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MaxPrestige/credit-card-fraud-CLEAN

📦 Dataset Card: credit-card-fraud-CLEAN Original Dataset: "https://huggingface.co/datasets/dazzle-nu/CIS435-CreditCardFraudDetection" New Features category (float): The category of the transaction. Mapping:{ 'entertainment': 0.0, 'food_dining': 1.0, 'gas_transport': 2.0, 'grocery_net': 3.0, 'grocery_pos': 4.0, 'health_fitness': 5.0, 'home': 6.0, 'kids_pets': 7.0, 'misc_net': 8.0, 'misc_pos': 9.0, 'personal_care': 10.0, 'shopping_net':… See the full description on the dataset page: https://huggingface.co/datasets/MaxPrestige/credit-card-fraud-CLEAN.

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Dataset Card

📦 Dataset Card: credit-card-fraud-CLEAN

Original Dataset: "https://huggingface.co/datasets/dazzle-nu/CIS435-CreditCardFraudDetection"

New Features

—category (float): The category of the transaction. Mapping:

python

      {
        'entertainment': 0.0,
        'food_dining': 1.0,
        'gas_transport': 2.0,
        'grocery_net': 3.0,
        'grocery_pos': 4.0,
        'health_fitness': 5.0,
        'home': 6.0,
        'kids_pets': 7.0,
        'misc_net': 8.0,
        'misc_pos': 9.0,
        'personal_care': 10.0,
        'shopping_net': 11.0,
        'shopping_pos': 12.0,
        'travel': 13.0
      }

—amt (float): The price amount of the transaction.
—gender (float): The gender of the the purchaser. Mapping:

python

      {
        'F': 0.0,
        'M': 1.0
      }

—state (float): The state the transaction took place in. Mapping:

python

      {
        'AK': 0.0,
        'AL': 1.0,
        'AR': 2.0,
        'AZ': 3.0,
        'CA': 4.0,
        'CO': 5.0,
        'CT': 6.0,
        'DC': 7.0,
        'DE': 8.0,
        'FL': 9.0,
        'GA': 10.0,
        'HI': 11.0,
        'IA': 12.0,
        'ID': 13.0,
        'IL': 14.0,
        'IN': 15.0,
        'KS': 16.0,
        'KY': 17.0,
        'LA': 18.0,
        'MA': 19.0,
        'MD': 20.0,
        'ME': 21.0,
        'MI': 22.0,
        'MN': 23.0,
        'MO': 24.0,
        'MS': 25.0,
        'MT': 26.0,
        'NC': 27.0,
        'ND': 28.0,
        'NE': 29.0,
        'NH': 30.0,
        'NJ': 31.0,
        'NM': 32.0,
        'NV': 33.0,
        'NY': 34.0,
        'OH': 35.0,
        'OK': 36.0,
        'OR': 37.0,
        'PA': 38.0,
        'RI': 39.0,
        'SC': 40.0,
        'SD': 41.0,
        'TN': 42.0,
        'TX': 43.0,
        'UT': 44.0,
        'VA': 45.0,
        'VT': 46.0,
        'WA': 47.0,
        'WI': 48.0,
        'WV': 49.0,
        'WY': 50.0
      }

—lat (float): The lattitude the transaction took place in.
—long (float): The longitude the transaction took place in.
—city_pop (float): The population of the city, the transaction took place in.
—merch_lat (float): The lattitude of the merchant that the transaction took place in.
—merch_long (float): The longitude of the merchant that the transaction took place in.
—is_fraud (float): Indication of the transaction as fraudulent (1.0) or non-fraudulent (0.0).

Intended Uses

—Fraudulent Credit Card Transaction prediction models
—Financial data analysis
—Educational and research purposes

Citation

If you use this dataset in your research or application, please cite the original source and this dataset card.