It consists of 3 models each with around 80% accuracy in detection of credit card defaults. One feeds in an excel file of customer information with the required features and gets either 'safe' or 'default' values as prediction. It also visualizes the patterns and correlations between the various features of the dataset
Python version 3.7-3.8 install streamlit install seaborn install pickle
Excute the command streamlit run credit_card.py in your cmd or terminal
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. The original dataset can be found at the UCI Machine Learning Repository.