Loan Eligibility Prediction using Machine Learning
- Credit Risk Assessment has relied on statistical models such as logistic regression and expert-driven rule-based systems.
- With the advancement of machine learning (ML), modern credit risk prediction models have achieved higher accuracy, efficiency, and scalability, making them an essential tool in financial decision-making.
- Machine Learning-based credit scoring systems outperform traditional statistical approaches by leveraging large datasets, automated feature selection, and nonlinear relationships in data .
- Algorithms such as Random Forest (RF), Gradient Boosting (GB), XGBoost, and Stacked Classifiers have gained popularity due to their robustness and interpretability in predicting loan defaults
- Credit Risk Prediction System using machine learning models, including Random Forest, Gradient Boosting, XGBoost, and Stacked Classifier, deployed through a Flask-based web application.
- Preprocessing techniques such as missing value imputation, categorical encoding, and feature normalization we
- Model Results indicate that machine learning-based credit risk prediction systems can significantly enhance decision-making processes in financial.
- The best-performing model, the Stacked Classifier, achieved an accuracy of 93.42%, outperforming other models. A user-friendly web interface was developed using Flask and Bootstrap, allowing users to input financial details and receive real-time predictions.
Loan Approval Prediction using Machine Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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