Bank loan prediction using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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| Định dạng: | Luận văn |
| Ngôn ngữ: | English |
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Brac University
2023
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/21984 |
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10361-21984 |
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Brac University |
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Institutional Repository |
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English |
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XGBoost Decision tree LIME XAI(Explainable artificial intelligence) Machine learning Artificial intelligence |
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XGBoost Decision tree LIME XAI(Explainable artificial intelligence) Machine learning Artificial intelligence Mahottam, Parthosarothi Anika, Antara Raida Jahan, Dilshad Lazika, Tanzina Afrin Bank loan prediction using machine learning |
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This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
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Nahim, Nabuat Zaman |
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Nahim, Nabuat Zaman Mahottam, Parthosarothi Anika, Antara Raida Jahan, Dilshad Lazika, Tanzina Afrin |
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Thesis |
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Mahottam, Parthosarothi Anika, Antara Raida Jahan, Dilshad Lazika, Tanzina Afrin |
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Mahottam, Parthosarothi |
| title |
Bank loan prediction using machine learning |
| title_short |
Bank loan prediction using machine learning |
| title_full |
Bank loan prediction using machine learning |
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Bank loan prediction using machine learning |
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Bank loan prediction using machine learning |
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bank loan prediction using machine learning |
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Brac University |
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2023 |
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http://hdl.handle.net/10361/21984 |
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AT mahottamparthosarothi bankloanpredictionusingmachinelearning AT anikaantararaida bankloanpredictionusingmachinelearning AT jahandilshad bankloanpredictionusingmachinelearning AT lazikatanzinaafrin bankloanpredictionusingmachinelearning |
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1814309590729228288 |
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10361-219842023-12-18T04:31:47Z Bank loan prediction using machine learning Mahottam, Parthosarothi Anika, Antara Raida Jahan, Dilshad Lazika, Tanzina Afrin Nahim, Nabuat Zaman Department of Computer Science and Engineering, Brac University XGBoost Decision tree LIME XAI(Explainable artificial intelligence) Machine learning Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (page 33). The Banking sector is a core foundation of the global economy as it facilitates the financial transactions while providing fundings in various purposes. One key aspect in the banking industry is the ability to accurately predict loan outcomes, which requires assessing the credit worthiness of the loan applicants. Traditional methods of loan prediction are often time consuming, it lacks transparency and interpretability which makes it challenging for the stakeholders to understand the factors that influence loan decisions. With the new addition of machine learning in technology there is an opening to enhance the loan eligibility prediction models and provide transparent insights into the decision-making process. This thesis aims to explore the application of machine learning models and XAI methods for bank loan prediction with a focus on improving accuracy and to get better experience on bank loan applications for both parties. The primary objective here is to develop a robust machine learning model that is capable of accurately predicting loan eligibility and to leverage XAI techniques to explain the reasoning behind these predictions.The research methodology involves a deep analysis from a dataset collected from the internet. The dataset contains various information such as : credit history, loan amount, self employment, earnings etc. Then initial data pre-processing techniques which includes data cleaning or filtering, feature selection and handling values are applied to ensure the quality and the consistency of the dataset. After that, few machine learning models were applied such as: Decision tree, Random forest, NNC etc to build the predictive model. These models are trained and evaluated using appropriate performance metrics such as accuracy, F1-score and AUC(Area under the ROC Curve) score. The goal is to identify the most effective algorithms by comparing them between each other for loan eligibility prediction based on dataset characteristics. Finally, to enhance the transparency and interpretability of the loan prediction models, XAI techniques are applied.These methods facilitate the comprehension of the factors influencing loan decisions, thereby mitigating issues of bias, discrimination, and unfairness. Interpretability techniques, such as analysing feature importance by employing LIME (Local Interpretable Model-agnostic Explanations), are utilised to offer clear and comprehensible explanations for the predictions made by the model. Furthermore, the thesis investigates the ethical implications and fairness considerations associated with loan prediction models. The experimental results demonstrate the efficacy of the proposed approach accurately predicting outcomes while providing interpretable explanations for these predictions. Finally, by utilising machine learning and XAI approaches, this thesis contributes to the subject of bank loan prediction. It provides a complete framework for constructing loan prediction models that are accurate, interpretable, and fair. Parthosarothi Mahottam Antara Raida Anika Dilshad Jahan Tanzina Afrin Lazika B.Sc. in Computer Science and Engineering 2023-12-17T05:51:23Z 2023-12-17T05:51:23Z 2023 2023-05 Thesis ID 19101291 ID 19101574 ID 18101453 ID 21141004 http://hdl.handle.net/10361/21984 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 40 pages application/pdf Brac University |