Online_Payments_Fraud_Detection

In This Project

We will train a machine learning model for classifying fraudulent and non-fraudulent payments.

Dataset used can be found here - https://www.kaggle.com/datasets/ealaxi/paysim1 Various Columns of dataset, used in project are:

• step: represents a unit of time where 1 step equals 1 hour
• type: type of online transaction
• amount: the amount of the transaction
• nameOrig: customer starting the transaction
• oldbalanceOrg: balance before the transaction
• newbalanceOrig: balance after the transaction
• nameDest: recipient of the transaction
• oldbalanceDest: initial balance of recipient before the transaction
• newbalanceDest: the new balance of recipient after the transaction
• isFraud: fraud transaction

Implementation Steps

• Extract the data.
• Check, if data has null values. UIf yes, clean the data.
• Check correlation between the features of the data with the isFraud column.
• Transform the categorical features into numerical.
  • Transform the values of the isFraud column into No Fraud and Fraud labels.
• Train a classification model to classify fraud and non-fraud transactions.
• Test it, classify whether a transaction is a fraud or not by feeding about a transaction into the model.