For any bank or financial organization, credit card fraud detection is of utmost importance. We have to spot potential fraud so that consumers can not bill for goods that they haven’t purchased. The aim is, therefore, to create a classifier that indicates whether a requested transaction is a fraud.
About Credit Card Fraud Detection
In this machine learning project, we solve the problem of detecting credit card fraud transactions using machine numpy, scikit learn, and few other python libraries. We overcome the problem by creating a binary classifier and experimenting with various machine learning techniques to see which fits better.
Credit Card Fraud Dataset
The dataset consists of 31 parameters. Due to confidentiality issues, 28 of the features are the result of the PCA transformation. “Time’ and “Amount” are the only aspects that were not modified with PCA.
There are a total of 284,807 transactions with only 492 of them being fraud. So, the label distribution suffers from imbalance issues.
Please download the dataset for credit card fraud detection project: Anonymized Credit Card Transactions for Fraud Detection
Tools and Libraries used
We use the following libraries and frameworks in credit card fraud detection project.
- Python – 3.x
- Numpy – 1.19.2
- Scikit-learn – 0.24.1
- Matplotlib – 3.3.4
- Imblearn – 0.8.0
- Collections, Itertools
Credit Card Fraud Project Code
Please download the source code of the credit card fraud detection project (which is explained below): Credit Card Fraud Detection Machine Learning Code
Steps to Develop Credit Card Fraud Classifier in Machine Learning
Our approach to building the classifier is discussed in the steps:
- Perform Exploratory Data Analysis (EDA) on our dataset
- Apply different Machine Learning algorithms to our dataset
- Train and Evaluate our models on the dataset and pick the best one.
Step 1. Perform Exploratory Data Analysis (EDA)
There are a total of 284,807 transactions with only 492 of them being fraud. Let’s import the necessary modules, load our dataset, and perform EDA on our dataset. Here is a peek at our dataset:
import pandas as pdfrom collections import Counterimport itertools ## Load the csv file dataframe = pd.read_csv ( “./Desktop/DataFlair/credit_card_fraud_detection/creditcard.csv” ) dataframe.head ()
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