The main goal of this project is to predict the credit card fraud using predictive analytics technique. The project is applied by using QDA (Quadratic Discriminant Analysis), LR (Logistic Regression), and SVM (Support Vector Machine) models to help detect Fraud Credit Card transactions. With the provided dataset, we have 492 frauds out of 284,807 transactions, or the positive class (frauds) account for 0.172% of all transactions. In addition, we have a high dimensional dataset with 30 features, including 28 PCA-ed features (V1, V2, ... V28), 'Amount', and 'Time' features. The benefits of this project are that Due to the highly unbalanced dataset, we will apply sampling technique to under-sample the majority class, and over-sample the minority class before the training process. Also, we used recursive feature elimination and cross-validated technique for features selection. Comparison of all three algorithms and which is the optimal one is also to be done. I will provide you the link from where you can download the data set. But i need this project within 7-10 days.