Credit Card Approval Prediction Project
Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low-income levels, or too many inquiries on an individual's credit report, for example. Manually analysing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays.
Our goal is to look through this dataset and classify applications/applicants as being either worthy enough or not to be issued a credit card. In doing so, we help the applicants achieve a better and faster response rate while also enabling the service providers (banks/other financers) achieve a higher level of resource management.
Since, new applications have to be sorted through from start to finish based on the applicants’, the Machine Learning algorithm developed would help speed up this process for the stakeholders.
The notebook with the .ipynb label contains specified cells for data overview, cleaning, exploratory insight analysis, modelling, and evaluations respectively. The relevant graphs can also be found in the outputs of the respective code cells as well.
