Fraud Credit Card Transaction Classification

Download Dataset from kaggle-fraud-transaction-classification

In the project, we will be working on machine learning project on credit card fraud

Table Of Contents

• Project Structure Overview
• Project Goal
• Data Set
• Version
• Author

Project Structure Overview

├── Fraud Credit Card Transaction Classification
|  ├── data          - this folder contains training & testing data.
|  |    └──  transactions.csv (Ommited)
|  │
└────── Fraud Transaction Classification.ipynb

Project Goal

The following steps are followed:

• Describe the structure of the date (statistical summary: number of records, counting max, min, null,...)
• Plotting histogram of transactionAmount column.
• We have duplicated transactions in the dataset - two types: reversed transaction and multi-swipe. You need to answer these questions:
  • Identify those types of duplicates
  • Estimate of the total number of transactions and total dollar amount

How do you estimate for the reversed transactions and multi-swipe transactions?

• The first transaction to be normal and exclude it from the number of transaction and dollar amount count

• Modeling - Provide modeling algorithms and say why you use those methods? Why used those features?

  • There is a type of transactions in the dataset has a field called isFraud. Build a predictive model to determine whether a given transaction will be fraudulent or not.
  • Use an estimate of performance using an appropriate sample

Version

1.0.0

Author

• Rahul Gaikwad - Initial work and development

I welcome your questions. Write to rahul.gaikwad2010@gmail.com