CREDIT CARD FRAUD - ANOMALY DETECTION PROJECT

Problem : Predicting credit card fraud in the banking industry using machine learning algorithms.

Summary

I employed Exploratory Data Analysis (EDA) and various Anomaly Detection Algorithms, including Isolation Forest, One-Class SVM, Autoencoders, Local Outlier Factor (LOF) and DBSCAN to examine the dataset 'Credit Card Fraud Detection' from the Kaggle website, which is labeled as 'creditcard.csv'.

(https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud )

I attempted to explore the dataset comprehensively, examining various aspects and visualizing as much as possible to gain insights into the data. I employed ---five(5)-- Anomaly Detection Algorithms.