Loan Risk Prediction: Logistic Regression vs. Neural Networks - Couverture souple

Wanjale, Kirti; Wanjale, Aditya

Couverture souple

ISBN 10 : 6208420474 ISBN 13 : 9786208420475

Editeur : LAP LAMBERT Academic Publishing, 2025

Synopsis

Loan risk prediction is crucial for financial institutions to minimize lending risks. This study investigates the effectiveness of transaction data in loan risk prediction, comparing the performance of two popular algorithms: logistic regression and feed-forward neural networks. The research aims to assess the predictive capabilities, interpretability, and practical applicability of these models in identifying potential loan defaults based on transactional patterns. Transactional data, acquired from Kaggle, underwent rigorous preprocessing and feature engineering tailored to the unique characteristics of financial transaction records. Both models were extensively trained and evaluated using established metrics, encompassing accuracy, precision, recall, F1-score to comprehensively gauge their performance in predicting loan defaults. Findings indicate varied strengths between the models: logistic regression demonstrates commendable interpretability while achieving competitive performance metrics, whereas the feed-forward neural network exhibits higher predictive accuracy albeit with increased complexity and reduced interpretability.

Détails de l'édition

• Éditeur: LAP LAMBERT Academic Publishing
• Date d'édition: 2025
• Langue: anglais
• ISBN 10: 6208420474
• ISBN 13: 9786208420475
• Reliure: Broché
• Nombre de pages: 52
• Coordonnées du fabricant: non disponible
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