About Dataset

Context

Due to rapid growth in field of cashless or digital transactions, credit cards are widely used in all around the world. Credit cards providers are issuing thousands of cards to their customers. Providers have to ensure all the credit card users should be genuine and real. Any mistake in issuing a card can be reason of financial crises. Due to rapid growth in cashless transaction, the chances of number of fraudulent transactions can also increasing.

A Fraud transaction can be identified by analyzing various behaviors of credit card customers from previous transaction history datasets. If any deviation is noticed in spending behavior from available patterns, it is possibly of fraudulent transaction

The data is related to information about online payment fraud.

Content

The data contains historical information about fraudulent transactions which can be used to detect fraud in online payments.

• train.csv: 6,351,193 rows and 10 columns

Detailed Column Descriptions

	Name	Type	Desciption
1	step	Integer	maps a unit of time in the real world. In this case, 1 step is 1 hour of time.
2	type	String	CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER
3	amount	Decimal	amount of the transaction in local currency.
4	nameOrig	String	customer who started the transaction.
5	oldbalanceOrig	Decimal	initial balance before the transaction.
6	newbalanceOrig	Decimal	customer's balance after the transaction.
7	nameDest	String	recipient ID of the transaction.
8	oldbalanceDest	Decimal	initial recipient balance before the transaction.
9	newbalanceDest	Decimal	recipient's balance after the transaction.

Output variable (desired target):

10 - isFraud: fraud transaction (binary: "yes","no")

Missing Attribute Values: None

Usage

Top-level directory layout

├── config                        # Config files (alternatively `configs`)
├── docs                          # Documentation files (alternatively `doc`)
├── scripts                       # Bash files (alternatively `script`)
├── src                           # Source files 
(alternatively `lib` or `app`)
    ├── helpers                   # Contain several functions
    ├── loaders                   # Data transform/ Feature engineering / Custom Estimator
    ├── notebook                  # Notebook files 
        ├── BusinessUnderstanding.ipynb # BusinessBrainstom
        ├── DataUnderstanding.ipynb     # EDA data
        ├── DataPreparation.ipynb       # Data Intergration
        ├── Modeling.ipynb              # Modeling
        ├── 
├── tests                         # Automated tests (alternatively `spec` or `tests`)
├── LICENSE
└── README.md

Result:

Confusion Matrix

	Predict 0	Predict 1
Target 0	77221	62
Target 1	25	677

Metrics

	Model	F-beta	AUC	Avg Precision
0	XGBoost	0.9396	0.9997	0.9376
1	LightGBM	TODO	TODO	TODO