Loan approval prediction using KNN, decision Tree and Naïve Bayes models

Veeraballi Nagajyothi
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Abstract

In this modern world, financial institutions are playing a very crucial role. Nowadays, banks are developing their financial reserves by providing different kinds of loans to people who are in need. At the same time, there is also a massive increase in the count of individuals requesting loans. However, banks cannot provide loans for everyone as there are only limited reserves associated with each of them. So, banks must follow some stringent verification process to approve the loan, because if the one who got his/her loan approved failed to pay back his loan it may have a direct impact on the financial reserves of the bank and also onto the banking sector. So, banks started to provide loans only for a limited set of people who are capable of repaying their loans. But finding out who is eligible for the loan is a much typical and risky process. In this project, we will develop a model to predict who is eligible for a loan in order to reduce the risk associated with the decision process and to modify the typical loan approval process into a much easier one. Moreover, we will make use of previous data of loan decisions made by the company and with the help of various data mining techniques, we will develop a loan approval decision predicting model which can draw decisions for each individual based on the information provided by them. We will use a machine-learning-based KNN, Decision-tree, Naïve Bayes algorithms to train the model. This project primary goal is to develop a loan prediction model with a better accuracy rate.
利用KNN、决策树和Naïve贝叶斯模型进行贷款审批预测
在当今世界,金融机构扮演着至关重要的角色。如今,银行通过向有需要的人提供不同种类的贷款来发展他们的金融储备。与此同时,申请贷款的个人数量也在大幅增加。然而,银行不可能为每个人提供贷款,因为每个人的准备金都是有限的。因此,银行必须遵循一些严格的审核程序来批准贷款,因为如果获得贷款批准的人未能偿还贷款,这可能会对银行的财务储备产生直接影响,也会对银行业产生直接影响。因此,银行开始只向少数有能力偿还贷款的人提供贷款。但是找出谁有资格获得贷款是一个非常典型和有风险的过程。在这个项目中,我们将开发一个模型来预测谁有资格获得贷款,以减少与决策过程相关的风险,并将典型的贷款审批过程修改为一个更容易的过程。此外,我们将利用公司以往的贷款决策数据,借助各种数据挖掘技术,开发一个贷款审批决策预测模型,根据每个人提供的信息,为每个人做出决策。我们将使用基于机器学习的KNN,决策树,Naïve贝叶斯算法来训练模型。该项目的主要目标是开发一个准确率更高的贷款预测模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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