Credit Card Fraud Detection using Machine Learning

1. CREDIT CARD FRAUD DETECTION BY: PYTHONGEEKS

2. Credit Card Fraud Detection using Machine Learning This project aims to build a machine-learning model to detect credit card fraud. The dataset used in this project is the “creditcard.csv” file which contains credit card transactions made in September 2013 by European cardholders. The data set contains a total of 284,807 transactions, out of which 492 transactions are fraudulent. The class distribution in the dataset is highly imbalanced, with the majority class being non-fraudulent transactions. To address this class imbalance, the minority class is oversampled using the RandomOverSampler technique. After oversampling, the features are scaled using the StandardScaler. A Logistic Regression model is then trained using GridSearchCV to optimize hyperparameters. The model’s performance is evaluated using metrics such as the confusion matrix, classification report, and ROC curve. Finally, the confusion matrix is plotted using seaborn to visualize the model’s performance.

3. What is Credit Card Fraud Detection? Credit card fraud detection is the process of identifying and preventing fraudulent transactions made using credit cards. Fraudulent transactions occur when a person or entity uses stolen or fraudulent credit card information to make purchases or obtain funds. Credit card fraud can result in significant financial losses for both the individuals whose credit card information was stolen and the financial institutions that issued the cards. Fraud detection systems use various algorithms and techniques to detect fraudulent transactions and prevent them from being processed, protecting consumers and financial institutions from financial losses.

4. About Dataset The dataset used for this classification problem is ‘creditcard.csv’. The dataset contains transactions made by credit cards in September 2013 by European cardholders. The dataset has 31 columns and 284,807 rows. The ‘Class’ column is the target variable, where 1 means fraud and 0 means not fraud. Prerequisites for Credit Card Fraud Detection Using Machine Learning 1) Pandas 2) Seaborn 3) Matplotlib 4) Scikit-learn 5) Imblearn

5. Steps to Develop Credit Card Fraud Classifier in Machine Learning The code executes the following steps to develop the credit card fraud classifier in machine learning: 1) Importing the required libraries 2) Reading the dataset 3) Exploratory data analysis 4) Splitting the dataset into features and labels 5) Oversampling the minority class using RandomOverSampler 6) Scaling the features using StandardScaler 7) Splitting the resampled dataset into training and testing sets 8) Creating the logistic regression model 9) Setting up the GridSearchCV to optimize hyperparameters 10) Training the model 11) Predicting the labels for the testing set 12) Evaluating the model performance by printing the confusion matrix, classification report, and accuracy score 13) Plotting the ROC curve 14) Plotting the confusion matrix

6. Summary The code develops a credit card fraud classifier using logistic regression and evaluates its performance using the confusion matrix, classification report, and ROC curve. The code also addresses the class imbalance issue in the dataset by oversampling the minority class using RandomOverSampler.