This is an end-to-end ML project, which aims at developing a classification model for the problem of predicting credit card frauds using a given labeled dataset.
The classifier used for this project is RandomForestClassifier.
Deployed in Railway.app.
Link to the application : https://credit-card-default.up.railway.app/
With the growing number of credit card users, banks have been facing an escalating credit card default rate. This increase in defaults causes losses to the financial institutions along with the card holders. As such data analytics can provide solutions to tackle the current phenomenon and management of credit defaults. This project discusses the implementation of an model which predicts if a given credit card holder has a probability of defaulting in the following month, using their demographic data and behavioral data from the past 6 months.
This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card holders from Taiwan from April 2005 to September 2005.
Link : https://www.kaggle.com/uciml/defaultof-credit-cardclients-dataset
The Code is written in Python 3.7. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip.
