Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

With the advent of modern transaction technology, many are using online transactions to transfer money from one person to another. Credit Card Fraud, a rising problem in the financial department goes unnoticed most of the time. A lot of research is going on in this area.The Credit Card Fraud Detection project is developed to spot whether a new transaction is fraudulent or not with the knowledge of previousdata. We use various predictive models to ascertain how accurate they are in predicting whether a transaction is abnormalor regular. Techniques like Decision Tree, Logistic Regression, SVMand Naïve Bayes are the classification algorithms to detect non-fraud and fraud transactions. In modern conditions where data may vary in a matter of minutes or even seconds, conventional classification techniques may not perform well. When dataset involves huge numbers of differences in data distribution and also changing data with high dimensionality and volume issues supervised learning comes up short. Hence we may resort to unsupervised learning, semi-supervised or any other means to cope with that