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An Approach for Prediction of Loan Approval usingMachine Learning AlgorithmMohammad Ahmad SheikhSchool of Comuting Science &EngineeringGalgotias UniversityGreater Noida . India[email protected]AmitKumar GoelProfessor, School of Comuting Science& EngineeringGalgotias UniversityGreater Noida, India[email protected]Tapas KumarProfessor, School of Comuting Science& EngineeringGalgotias UniversityGreater Noida, India[email protected]AbstractIn our banking system, banks have many productsto sell but main source of income of any banks is on its credit line.So they can earn from interest of those loans which they credits.Abank’s profit or a loss depends to a large extent on loans i.e.whether the customers are paying back the loan or defaulting. Bypredicting the loan defaulters, the bank can reduce its Non-Performing Assets. This makes the study of this phenomenonvery important. Previous research in this era has shown thatthere are so many methods to study the problem of controllingloan default. But as the right predictions are very important forthe maximization of profits, it is essential to study the nature ofthe different methods and their comparison. A very importantapproach in predictive analytics is used to study the problem ofpredicting loan defaulters: The Logistic regression model. Thedata is collected from the Kaggle for studying and prediction.Logistic Regression models have been performed and thedifferent measures of performances are computed. The modelsare compared on the basis of the performance measures such assensitivity and specificity. The final results have shown that themodel produce different results.Model is marginally betterbecause it includes variables (personal attributes of customer likeage, purpose, credit history, credit amount, credit duration, etc.)other than checking account information (which shows wealth ofa customer) that should be taken into account to calculate theprobability of default on loan correctly. Therefore, by using alogistic regression approach, the right customers to be targetedfor granting loan can be easily detected by evaluating theirlikelihood of default on loan. The model concludes that a bankshould not only target the rich customers for granting loan but itshould assess the other attributes of a customer as well whichplay a very important part in credit granting decisions andpredicting the loan defaulters.Keywordsloan, outlier, Prediction, component,Overfitting,TransformI.INTRODUCTIONThis paper has taken the data of previous customers ofvarious banks to whom on a set of parameters loan wereapproved. So the machine learning model is trained on thatrecord to get accurate results. Our main objective of thisresearch is to predict the safety of loan [1][3]. To predict loansafety, the logistic regression algorithm is used. First the data iscleaned so as to avoid the missing values in the data set. To

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Term
Fall
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Tags
Machine Learning, Statistical classification, IEEE Xplore

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