The main goal of this project is to build several models to predict customers' default behavior on credit card payment in a dataset with more than 30,000 customer transaction records. Used python and visualization package seaborn to explore data and do basic data analysis, such as visualizing data and calculating correlation matrix. Used sklearn to build machine learning models, such as Logistic Regression, Random forest, Gradient boost, Adaboost, Voting classifier (ie. the ensemble of random forest, Gradient boost, Adaboost) and use keras and tensorflow to build deep learning models, such as feed forward network. And use seaborn to visualize the accuracy of these models. Used Grid search and cross-validation method to optimize each algorithm, and finally determine the Voting classifier as the best model. Utilized:Python,Keras,Tensorflow,Seaborn,Grid Search,Adaboost,Machine Learning,Deep Learning,Logistic Regression,Random Forest,Gradient Boost,Voting Classifier,Cross-Validation