Now, let’s take a artistic take a look at how this mannequin could be utilized in a real-world state of affairs:
State of affairs: A financial institution needs to resolve whether or not to approve a mortgage for a brand new applicant, John Doe. John has the next particulars:
- Credit score Rating: 720
- Annual Earnings: $80,000
- Mortgage Quantity: $150,000
- Curiosity Price: 7%
- DTI Ratio: 35%
Utilizing the Random Forest mannequin educated on our dataset, we will predict the probability of John defaulting on the mortgage. The mannequin may output a low likelihood of default on account of John’s first rate credit score rating, sturdy earnings, and manageable DTI ratio, regardless that the mortgage quantity is comparatively giant. In a real-world utility, this prediction may help the financial institution make a extra knowledgeable determination, decreasing the chance of default.
In a extra artistic state of affairs, if the financial institution have been to supply a decrease rate of interest or regulate the mortgage quantity based mostly on the predictions, they might additional cut back the probability of default and provide a greater deal to the shopper.
