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A Comparative Analysis for Loan Approval Prediction using Machine Learning

Akash Badhan · Abhishek Rana · Simarjit Singh Malhi · Bishal Saha

22 citations0/0 files storedInternational Conference on Electrical Electronics and Computing Technologies (ICEECT)

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Financial Distress and Bankruptcy Prediction Data Mining and Machine Learning Applications Imbalanced Data Classification Techniques

Abstract

The financial sector of any nation is the key factor in elevating the economy of nation. The biggest issue that finances departments are facing is to approve loans for valid candidates only. Our proposed model for predicting the loan approval for valid candidates uses three different supervised learning algorithms for classification of loan approved or not, on the basis of candidate attribute values. In proposed study ensemble technique has been used to find out best classifier algorithm to train our model. In this paper we are comparing KNN, Decision Tree, Logistic Regression, SVC, and Naïve Bayes classifier algorithms based on their accuracy for provided data set to find out the highly accurate and efficient algorithm for designing a model for approving loan to valid candidate which will also help in minimizing the financial frauds.

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2020Bank Loan Prediction System using Machine Learning 2021Prediction of Modernized Loan Approval System Based on Machine Learning Approach 2022Comparative Analysis of Customer Loan Approval Prediction using Machine Learning Algorithms 2022Customer Loan Eligibility Prediction using Machine Learning Algorithms in Banking Sector 2021Retracted: Design and Simulation of Loan Approval Prediction Model using AWS Platform 2022Machine Learning Models for Predicting Bank Loan Eligibility 2022Machine Learning Based Model for Prediction of Loan Approval

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