ML Scenario

step aside it's tImE for bicc boi's worK.

99.99% germs to be killed

ML Scenario

Credit-card approving is a good machine learning scenario. Banks earn from late payment fees, overdraft, and interest if a credit-card owner pays late, but does not default. Hence, it could be lucrative if the bank can put a model to this. Running through the pointers as discussed previously

1. There is likely to be a pattern or trend of the type of people (demographics, lifestyle, and etc.) defaulting in credit-card payments

2. There is no straightforward mathematical formula to tell if a person will be defaulting in the future (sorry $\pi$)
3. A bank is likely to store a lot of historic data of credit-card holders whom have defaulted (and not)

Data Set

In a blatantly fictitious data set,

Name	Age	Gender	Salary	Debt	Default
Alice	23	Female	24000	-	1
Bob	32	Male	30000	10000	0
Charlie	34	Male	36000	5000	0
Danielle	25	Female	33000	5000	???

would we know if Danielle is likely to default in her credit-card payment? Stay-tuned.