{"title":"Logistic Regression Model for Loan Prediction: A Machine Learning Approach","authors":"Richa Manglani, Anuja Bokhare","doi":"10.1109/ETI4.051663.2021.9619201","DOIUrl":null,"url":null,"abstract":"With the advance in the banking space, many individual’s area unit putting up for loans however the banks have its own restricted resources that it must permit to restricted people simply, therefore discovering to whom the advance is conceded which will be a more secure choice for the bank is a normal interaction. Therefore in this study, an attempt to reduce this risk issue behind selecting the protected individual to avoid wasting different bank endeavors and resources. This can be finished by extracting the info of the records of people to whom the credit was conceded antecedently and supported. These records/encounters the machine was ready to utilize the AI model which provides the foremost precise outcome. The main goal of this study to anticipate whether or not delegating the loan to a selected individual are protected or not. During this study foresee the loan knowledge by utilizing machine learning algorithms that area unit logistical regression. Loan prediction is an extremely basic life issue that every genuine bank faces a minimum of once in its period. If done effectively, it will save loads of manhours at the top of a retail bank.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETI4.051663.2021.9619201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
With the advance in the banking space, many individual’s area unit putting up for loans however the banks have its own restricted resources that it must permit to restricted people simply, therefore discovering to whom the advance is conceded which will be a more secure choice for the bank is a normal interaction. Therefore in this study, an attempt to reduce this risk issue behind selecting the protected individual to avoid wasting different bank endeavors and resources. This can be finished by extracting the info of the records of people to whom the credit was conceded antecedently and supported. These records/encounters the machine was ready to utilize the AI model which provides the foremost precise outcome. The main goal of this study to anticipate whether or not delegating the loan to a selected individual are protected or not. During this study foresee the loan knowledge by utilizing machine learning algorithms that area unit logistical regression. Loan prediction is an extremely basic life issue that every genuine bank faces a minimum of once in its period. If done effectively, it will save loads of manhours at the top of a retail bank.