Credit Score Cards are one of the common risk control methods in the financial industry which uses personal information and transactional records to identify and evaluate the creditworthiness of existing and potential customers. There are a number of different use cases leveraging this measure such as
- loan management,
- credit card approval,
- credit limit extension
The dataset used in this project is shared publicly via Kaggle, https://www.kaggle.com/rikdifos/credit-card-approval-prediction
Which includes 2 sub-sets: personal information and transactional records. Utilise and merge both files into one dataset to make the analysis more insightful and actionable.
Explanatory Data Analysis (EDA) and Feature Engineering Feature Scaling and Selection (+ Imbalanced Data Handling) Machine Learning Modelling