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LoanPredictor: A Streamlit Application for Interpretable and Fair Loan Approval Predictions

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
T. N. Ranganadham
SaiKrishna Ulavapati
Mg Pandu Ranga Reddy
SaiVinod Anandi
Published in
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering
Publisher
Springer Nature Singapore

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Abstract

LoanPredictor is a Streamlit application that leverages machine learning models to predict loan approval decisions with a focus on fairness, transparency, and interpretability. The application uses models like Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression to provide clear insights into the factors influencing loan decisions. The architecture of LoanPredictor includes data preprocessing, model training, and result visualization, ensuring a user-friendly interface for both borrowers and lenders. The text discusses the limitations of traditional lending systems and how LoanPredictor addresses these issues by incorporating bias mitigation strategies and fairness constraints. It also explores the dataset used for training the models and the user interface designed for easy interaction. The conclusion highlights the advantages of LoanPredictor, including its accuracy, fairness, and user engagement, setting a new standard for ethical lending practices in the finance industry.
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Title
LoanPredictor: A Streamlit Application for Interpretable and Fair Loan Approval Predictions
Authors
T. N. Ranganadham
SaiKrishna Ulavapati
Mg Pandu Ranga Reddy
SaiVinod Anandi
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering

Print ISBN: 978-981-9502-68-4

Electronic ISBN: 978-981-9502-69-1

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-0269-1_2
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