Logistic Regression Model for Loan Prediction: A Machine Learning Approach

Richa Manglani, Anuja Bokhare
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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.
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贷款预测的逻辑回归模型:一种机器学习方法
随着银行业空间的发展,许多个人地区单位申请贷款,但银行有自己有限的资源,它必须允许简单地限制人们,因此发现向谁提供预付款对银行来说是一个更安全的选择是一个正常的互动。因此,在本研究中,试图减少选择受保护个人背后的风险问题,以避免浪费不同的银行努力和资源。这可以通过提取先前承认并支持信用的人的记录信息来完成。这些记录/遭遇,机器准备利用人工智能模型,提供最精确的结果。本研究的主要目的是预测委托贷款给选定个人是否受到保护。在本研究中,利用区域单元逻辑回归的机器学习算法预测贷款知识。贷款预测是一个极其基本的生活问题,每个真正的银行在其一生中至少要面临一次。如果做得有效,它将为零售银行的高层节省大量的人力。
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