Credit Card Fraud Detection using Machine Learning

• A model is build using various machine learning algorithms to detect the fraudulent transactions among legit (normal) transactions with best results.
• The machine learning algorithms used to train the model are-
  • Logistic Regression
  • Naive Bayes
  • Decision Tree
  • Random Forest
  • K-nearest neighbors
  • Linear Discriminant Analysis
  • XGBoost (eXtreme Gradient Boosting)

Dataset

• The dataset used for this research is collected from Kaggle at https://www.kaggle.com/mlg-ulb/creditcardfraud.

• The datasets 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.