An approach for prediction of loan approval using ML algorithm

Several individuals are looking for banking advancements resulting from the financial sector’s improvement, the bank can only offer certain people access to its limited resources. So, determining who can be granted credit is a common interaction and will be a more secure option for the bank. To conserve a tonne of bank resources and labour, we therefore try to lessen the element of risk involved in choosing the protected person in this assignment. To do this, it is necessary to examine the prior records of those who have previously received advances, base the machine’s preparation on these records and encounters, and use the model that yields the most accurate results. This project’s main goal is to determine whether giving someone a cash advance will protect them. Four pieces make up the project. Data gathering, ML model comparison using the information obtained, framework training using the most promising model, and testing are the first three steps.