creditcard-fraud-detection's Introduction

Credit_Card_Fraud

Case Study

E-commerce has really changed everything, it gives us the chance to increase our sales but it exposes us to hackers and other types of frauds.
For this case study we will consider a E-commerce book store which has sold thousands in the last few years.
We are going to use transaction history for this project.
We are going to use a publicly available dataset for this project with real credit-card transactions that have been anonymized.
one of the biggest problem of credit card is that anyone can steal 15 or 16 digit card number with security number and expiration date.
Our job will be to tell apart that the transaction was from the real user who owns the credit card or the transaction was from the stolen credit card.
One of the major issues will be that most of the transactions will be non-fraudulent which means it will be difficult for us to detect underlying patterns in the infromation available.
It will make our dataset highly imbalance.
We will need to apply different sampling techniques and use different metrics.
It is estimated that only 0.1% of online credit card transactions are fraudulent.
But given the volume of transactions that occur everyday that means a lot of money.
We need to build the classifier system based on the dataset that can tell wether the classifier is fraudulent or non-fraudulent.
We will build a deep learning network and we are going to apply more traditional machine learning algorithm such as random forest.
We will consider only 2 labels in our classifier '0' for non-fraudulent transaction and '1' for fraud-leunt Transactions.

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