Abstract:
"The banking authorities are well mindful of how they grant financial loans to customers.
Despite several precautions and analysis of loan application data, bank loan approval is not
always right. This situation is a real-world financial decision-making difficulty that
machine learning, pattern recognition, and data mining algorithms may effectively tackle.
Prediction systems are designed to anticipate the outcome of a certain event. This study is
the result of an attempt to develop such a prediction system. LoanApprovalStatus, the
created solution, was constructed using previously obtained bank data to predict the
approval status of a financial loan.
LoanApprovalStatus is primarily concerned with classification and a machine learning
model that was developed by combining a voting classifier with a number of other
algorithms. A online graphical interface was created to connect with and guide the user.
The user's inputs are taken into account while making a prediction. The loan approval
prediction will be able to forecast one by one, with the outcomes shown individually. Prediction, Machine learning, Classification, Loan Approval"
