Credit-Card-Fraud-Detection

Using a dataset of of nearly 28,500 credit card transactions and multiple unsupervised anomaly detection algorithms, I identified transactions with a high probability of being credit card fraud. In this project, I built and deployed the following two machine learning algorithms:

1. Local Outlier Factor (LOF)

2. Isolation Forest Algorithm

Furthermore, I used metrics suchs as precision, recall, and F1-scores.

In addition, I explored parameter histograms and correlation matrices.