Research paper presented at the 4th Congress on Smart Computing Technologies (CSCT 2025) hosted by National Institute of Technology, Sikkim, India — December 13–14, 2025. Published under Springer.
Authors: Mahamat Hanga Derio, Chaitra P C, Sujatha Arun Kokatnoor Christ University, Bangalore, India
Loan approval decisions in financial institutions are often inconsistent, biased, or slow when handled manually. This study systematically benchmarks 10 ensemble machine learning algorithms to identify the most accurate and reliable model for automated loan approval prediction — with implications for microfinance institutions in data-scarce economies.
- Source: Kaggle — Loan Status Prediction
- Features: Gender, Marital Status, Dependents, Education, Employment, Income, Co-applicant Income, Loan Amount, Loan Term, Credit History, Property Area
- Target: Loan Status (Approved / Rejected)
- Preprocessing: Mode imputation (categorical), median imputation (numerical), IQR-based outlier removal, Label Encoding
| Model | Accuracy |
|---|---|
| LightGBM | 89% ✅ Best |
| Random Forest | 88% |
| CatBoost | 87% |
| XGBoost | 86% |
| Extra Trees | 86% |
| AdaBoost | 85% |
| Gradient Boosting | 84% |
| Bagging Classifier | 84% |
| Stacking Classifier | 82% |
| Voting Classifier | 79% |
Key finding: Gradient boosting variants (LightGBM, CatBoost, XGBoost) consistently outperform bagging-based and meta-ensemble approaches on this tabular financial dataset. LightGBM's leaf-wise tree growth gives it an edge in handling the income skew and categorical feature interactions present in loan data.
Raw Loan Dataset (Kaggle)
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Preprocessing — Imputation + Outlier Removal + Label Encoding
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Train/Test Split (80/20, random_state=42)
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Feature Importance Analysis (Random Forest)
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10 Ensemble Models Trained & Evaluated
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Accuracy + Confusion Matrix + Classification Report per model
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Final Comparison — Bar chart visualization
Credit History emerged as the strongest predictor of loan approval, followed by Applicant Income and Loan Amount — consistent with lending theory and prior literature.
# Clone
git clone https://github.com/Derio001/loan-prediction-ensemble-models.git
cd loan-prediction-ensemble-models
# Install dependencies
pip install pandas numpy matplotlib seaborn scikit-learn xgboost lightgbm catboost kagglehub
# Run notebook
jupyter notebook The_Project_Implementation.ipynbDataset is auto-downloaded via
kagglehub— no manual download needed.
| Detail | Info |
|---|---|
| Conference | 4th Congress on Smart Computing Technologies (CSCT 2025) |
| Venue | National Institute of Technology, Sikkim, India |
| Dates | December 13–14, 2025 |
| Paper ID | 872 |
| Publisher | Springer |
| Certificate | Presented by Mahamat Hanga Derio |
Mahamat Hanga Derio — M.Tech Data Science, Christ University, Bangalore Chaitra P C — Christ University, Bangalore Dr. Sujatha Arun Kokatnoor — Department of AIML & DS, Christ University, Bangalore
🔗 GitHub | LRI Health Project | GDP Intelligence Project
Part of ongoing research in applied machine learning for financial and development contexts.