This poject is about credit card fraud detection using diferents machine learning algorithms. We’ll compare all of them and find which one is best for this problem.
The following image shows the workflow for this analysis:
The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.
To analyze the data behavior, it will implement the following models:
- K Neighbors Classifier
- Support Vector Classification (SVC)
- Gaussian Naive Bayes
- Decision Tree Classifier
- Random Forest Classifier
- XGB Classifier
- LGBM Classifier
- Gradient Boosting Classifier
- Ada Boost Classifier
- Logistic Regression