to download project abstract

Technology has boosted the existence of human kind the quality of life they live. Every day we
are planning to create something new and different. We have a solution for every other problem
we have machines to support our lives and make us somewhat complete in the banking sector
candidate gets proofs/ backup before approval of the loan amount. The application approved or
not approved depends upon the historical data of the candidate by the system. Every day lots of
people applying for the loan in the banking sector but Bank would have limited funds. In this
case, the right prediction would be very beneficial using some classes-function algorithm. An
example the logistic regression, random forest classifier, support vector machine classifier, etc. A
Bank’s profit and loss depend on the amount of the loans that is whether the Client or customer is
paying back the loan. Recovery of loans is the most important for the banking sector. The
improvement process plays an important role in the banking sector. The historical data of
candidates was used to build a machine learning model using different classification algorithms.
The main objective of this paper is to predict whether a new applicant granted the loan or not
using machine learning models trained on the historical data set

Aim: To determine the loan approval system using machine learning algorithms.

Loan approval is a very important process for banking organizations. The systems
approved or reject the loan applications. Recovery of loans is a major contributing
parameter in the financial statements of a bank. It is very difficult to predict the possibility
of payment of loan by the customer. In recent years many researchers worked on loan
approval prediction systems. Machine Learning (ML) techniques are very useful in
predicting outcomes for large amount of data. In this paper different machine learning
algorithms are applied to predict the loan approval of customers..In this paper, various
machine learning algorithms that have been used in past are discussed and their accuracy is
evaluated. The main focus of this paper is to determine whether the loan given to a
particular person or an organization shall be approved or not.


The enhancement in the banking sector lots of people are applying for bank loans
but the bank has its limited assets which it has to grant to limited people only, so finding out to
whom the loan can be granted which will be a safer option for the bank is a typical process. In
existing process, they are use RF algorithm in loan approval system. But the efficiency and
accuracy was pretty low. Already banks are provide online transaction system, online bank
account opening system, etc,. But there is no loan approval system in the banking sector. Then
now we create a new system for loan approval. So now we move on to the proposed system.


*To apply the loan we need to go to bank to apply it


The proposed model focuses on predicting the credibility of customers for loan
repayment by analyzing their details. The input to the model is the customer details collected. On
the output from the classifier, decision on whether to approve or reject the customer request can
be made. Using different data analytics tools loan prediction and there severity can be forecasted.
In this process it is required to train the data using different algorithms and then compare user
data with trained data to predict the nature of loan. The training data set is now supplied to
machine learning model; on the basis of this data set the model is trained. Every new applicant
details filled at the time of application form acts as a test data set. After the operation of testing,

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