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LOAN PREDICTION
ABSTRACT
With 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. So in
this paper we try to reduce this risk factor behind selecting the safe person
so as to save lots of bank efforts and assets. This is done by mining the Big
Data of the previous records of the people to whom the loan was granted before
and on the basis of these records/experiences the machine was trained using the
machine learning model which give the most accurate result. The main objective
of this paper is to predict whether assigning the loan to particular person
will be safe or not. This paper is divided into four sections (i)Data
Collection (ii) Comparison of machine learning models on collected data (iii)
Training of system on most promising model (iv) Testing
INTRODUCTION
Distribution of the loans is the core business part
of almost every banks. The main portion the bank’s assets is directly came from
the profit earned from the loans distributed by the banks. The prime objective
in banking environment is to invest their assets in safe hands where it is.
Today many banks/financial companies approves loan after a regress process of
verification and validation but still there is no surety whether the chosen
applicant is the deserving right applicant out of all applicants. Through this
system we can predict whether that particular applicant is safe or not and the
whole process of validation of features is automated by machine learning
technique. The disadvantage of this model is that it emphasize different
weights to each factor but in real life sometime loan can be approved on the
basis of single strong factor only, which is not possible through this system.
Loan Prediction is very helpful for employee of banks as well as for the
applicant also. The aim of this Paper is to provide quick, immediate and easy
way to choose the deserving applicants. It can provide special advantages to
the bank. The Loan Prediction System can can automatically calculate the weight
of each features taking part in loan processing and on new test data same
features are processed with respect to their associated weight .A time limit
can be set for the applicant to check whether his/her loan can be sanctioned or
not. Loan Prediction System allows jumping to specific application so that it
can be check on priority basis. This Paper is exclusively for the managing
authority of Bank/finance company, whole process of prediction is done
privately no stakeholders would be able to alter the processing. Result against
particular Loan Id can be send to various department of banks so that they can
take appropriate action on application. This helps all others department to
carried out other formalities.
MODULES
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, model predict whether the new applicant is
a fit case for approval of the loan or not based upon the inference it conclude
on the basis of the training data sets.
Six machine learning classification models have been
used for prediction of android applications .The models are available in R open
source software. R is licensed under GNU GPL. The brief details of each model
are described below.
· Decision Trees (C5.0):
The basic algorithm of decision tree [7] requires all attributes or features
should be discretized. Feature selection is based on greatest information gain
of features. The knowledge depicted in decision tree can represented in the
form of IF-THEN rules. This model is an extension of C4.5 classification
algorithms described by Quinlan.
· Random Forest (RF):
Random forests [8] are a group learning system for characterization (and
relapse) that work by building a large number of Decision trees at preparing
time and yielding the class that is the mode of the classes yield by individual
trees.
· Support Vector Machine (SVM):
Support vector machines are administered learning models that uses association
r learning algorithm which analyze features and identified pattern knowledge,
utilized for application classification. SVM can productively perform a
regression utilizing the kernel trick, verifiably mapping their inputs into
highdimensional feature spaces.
· Linear Models (LM):
The Linear Model [10] is numerically indistinguishable to a various regression
analysis yet burdens its suitability for both different qualitative and
numerous quantitative variables.
· Neural Network (Nnet):
Neural networks [14] are non-linear statistical data modeling tools. They are
usually used to model complex relationships between inputs and outputs, to find
patterns in data, or to capture the statistical structure in an unknown joint
probability distribution between observed variables.
· Adaboost (ADB):
Adaboost short for " Adaptive Boosting ". It is delicate to noisy
information data and outliers. It is different from neural systems and SVM
because Adaboost preparing methodology chooses just those peculiarities known
to enhance the divining power of the model, decreasing dimensionality and
conceivably enhancing execution time as potentially features don't have to be
processed.
EXISTING
SYSTEM
With 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.
PROPOSED
SYSTEM
In the proposed system we try to reduce this risk
factor behind selecting the safe person so as to save lots of bank efforts and
assets. This is done by mining the Big Data of the previous records of the
people to whom the loan was granted before and on the basis of these
records/experiences the machine was trained using the machine learning model
which give the most accurate result. The main objective of this paper is to
predict whether assigning the loan to particular person will be safe or not.
This paper is divided into four sections (i)Data Collection (ii) Comparison of
machine learning models on collected data (iii) Training of system on most
promising model (iv) Testing
CONCLUSION
From a proper analysis of positive points and
constraints on the component, it can be safely concluded that the product is a
highly efficient component. This application is working properly and meeting to
all Banker requirements. This component can be easily plugged in many other
systems. There have been numbers cases of computer glitches, errors in content
and most important weight of features is fixed in automated prediction system,
So in the near future the so –called software could be made more secure,
reliable and dynamic weight adjustment .In near future this module of
prediction can be integrate with the module of automated processing system. the
system is trained on old training dataset in future software can be made such
that new testing date should also take part in training data after some fix
time.
BIBLIOGRAPHY
[2]. Aafer Y,
Du W &Yin H 2013, DroidAPIMiner: ‘Mining API-Level Features for Robust
Malware Detection in Android’, in: Security and privacy in Communication
Networks Springer, pp 86-103.
[3]. Ekta
Gandotra, Divya Bansal, Sanjeev Sofat 2014, ‘Malware Analysis and
Classification: A Survey’available from http:// www.scirp.org/journal/jis
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