Book ChapterDOI
Loan Default Prediction Using Machine Learning Techniques
- pp 529-535
TLDR
In this article , a better approach using machine learning approaches like KNN, decision tree, SVM and logistic regression to predict defaulters was proposed, which can help banks conserve their manpower and fiscal resources by reducing the number of steps they have to take in order to check if somebody is eligible for a loan.Abstract:
AbstractLoans are a very fundamental source of any bank’s revenue, so they work tirelessly to make sure that they only give loans to customers who will not default on the monthly payments. They pay a lot of attention to this issue and use various ways to detect and predict the default behaviors of their customers. However, a lot of the time, because of human error, they may fail to see some key information. This paper proposes a better approach using machine learning approaches like KNN, decision tree, SVM and logistic regression to predict defaulters. The accuracy of these methods will also be tested using metrics like log loss, Jaccard similarity coefficient and F1 Score. These metrics are compared to determine the accuracy of prediction. This can help banks conserve their manpower and fiscal resources by reducing the number of steps they have to take in order to check if somebody is eligible for a loan.KeywordsMachine learningLoan predictionBankingCredit risk managementPredictorClassifiersPython read more
Citations
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Proceedings ArticleDOI
Loan Payment Date Prediction Model Using Machine Learning Regression Algorithms
TL;DR: In this article , the authors make use of the data to forecast when a person or a company will pay back a loan, assisting the bank in creating the following business strategies, including personal costs, company expenses, and home purchases.
References
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Proceedings ArticleDOI
Cover trees for nearest neighbor
TL;DR: A tree data structure for fast nearest neighbor operations in general n-point metric spaces (where the data set consists of n points) that shows speedups over the brute force search varying between one and several orders of magnitude on natural machine learning datasets.
Journal ArticleDOI
Advances in Instance Selection for Instance-Based Learning Algorithms
Henry Brighton,Chris Mellish +1 more
TL;DR: This work introduces an algorithm that rivals the most successful existing algorithm for consistency and discusses the possibility of mechanisms that provide insights into the structure of class definitions that could be useful for the data miner.
Book ChapterDOI
Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection
TL;DR: An implementation of the approach, MCS, that performs a heuristic bestfirst search for the best hybrid classifier for a set of data and an empirical comparison of MCS to each of its primitive learning algorithms, and to the computationally intensive method of cross-validation, illustrates that automatic selection of learning algorithms using knowledge can be used to solve the selective superiority problem.
Proceedings ArticleDOI
Genetic algorithm based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset
TL;DR: This research work aims to improve the accuracy of existing diagnostic methods for the prediction of Type 2 Diabetes with machine learning algorithms.
Journal ArticleDOI
IOT based Air Quality Monitoring System Using MQ135 and MQ7 with Machine Learning Analysis
TL;DR: This paper deals with measuring the Air Quality using Mq135 sensor along with Carbon Monoxide CO using MQ7 sensor using Machine Learning analysis and proviing a reducement of the cost of components versus the state of the art.