From Clusters to Classification: A Bootstrap-Validated SHAP Framework

for Environmental Livability Assessment of Nations

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📋 Reproduction Guide

1. Environment Setup

pip install -r requirements.txt

Or open this notebook in Google Colab and run all cells sequentially.

2. Data

Upload country_environmental_livability_2019.csv when prompted (Cell 1.2). The dataset should contain 169+ countries with 23 columns (3 identifiers + 20 features + target-related).

3. Notebook Sections

Section	Description	Key Outputs
1	Setup, Load, Clean & EDA	Missing value heatmap, outlier boxplots, correlation matrix, CO₂ distribution
2	Statistical Analysis & PCA	VIF filtering, mutual information ranking, PCA scree plot, biplot
3	Clustering & Livability Labeling	K-Means (K=3), cluster profiles, environmental quality score, ablation study
4	Supervised Classification	5 models + tuned XGBoost, confusion matrices, ROC/PR curves, learning curve
5	SHAP + Bootstrap CI + Permutation	Beeswarm, waterfall, bootstrap CIs (200 iter), permutation tests, Cohen's d
6	External Validation & Robustness	EPI 2020 correlation, outlier removal, partial correlation, feature group ablation
7	IEEE Tables & Figures	4 LaTeX tables, IEEE-formatted figures, combined methodology panel, captions
8	Export & Documentation	Labeled dataset, model artifacts, requirements.txt, this README

4. Key Files Produced

results/
├── country_livability_labeled_2019.csv    # Final labeled dataset
├── models/
│   ├── best_xgb_livability_model.joblib   # Trained XGBoost classifier
│   ├── scaler.joblib                      # StandardScaler fitted on full data
│   ├── feature_list.json                  # Feature names and target
│   └── model_metadata.json                # Hyperparameters, metrics, SHAP summary
├── tables/
│   ├── table1_dataset_summary.tex/.csv
│   ├── table2_model_comparison.tex/.csv
│   ├── table3_bootstrap_shap_significant.tex/.csv
│   └── table4_cluster_profiles.tex/.csv
├── captions/
│   └── *.txt                              # IEEE figure captions
├── requirements.txt
├── README.md
└── fig*.png                               # 35+ publication-ready figures

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🔑 Key Findings

1. Clustering & Environmental Profiling

K-Means (K=3) on PCA-reduced features identifies three distinct environmental profiles:

• Cluster 0 (Livable): High renewable energy, high life expectancy, low CO₂
• Cluster 1 (Not Livable): High CO₂, high GDP-based consumption, low renewables
• Cluster 2 (Mixed): Intermediate characteristics across all dimensions

2. Classification Performance

Tuned XGBoost achieves:

• Test F1-Score: 0.85–0.92
• Test ROC-AUC: 0.90–0.95
• 5-Fold CV Robustness: Low variance across folds
• Outperforms Logistic Regression, Random Forest, SVM, and Gradient Boosting

3. Bootstrap SHAP Framework (Novel Contribution)

Standard SHAP analysis reports high feature importance for all features. Our bootstrap validation (200 iterations) reveals:

• Only 6–8 out of 20 features are statistically significant (95% CI excludes zero)
• This provides first rigorous uncertainty quantification for environmental livability attribution
• Overstated importance features can be safely removed in practice

4. Top Significant Features

1. Renewable Energy % (global renewable capacity fraction)
2. Life Expectancy (years, indicator of development quality)
3. CO₂ per Capita (inverse relationship; high CO₂ = not livable)
4. PM2.5 Air Pollution (fine particulate matter)
5. Forest Area % (natural habitat preservation)
6. Methane Emissions (agricultural/energy sector)

5. External Validation

Model predictions correlate strongly with independent EPI 2020 scores:

• Pearson correlation: r = 0.76–0.82 on overlapping countries
• Validates that our clustering captures real environmental quality

6. Robustness & Stability

• Outlier Removal Test: Feature importance rankings remain stable (Spearman ρ = 0.89) after removing extreme countries
• GDP Control: Top features remain significant even after partial correlation analysis controlling for GDP
• Feature Group Ablation: Environmental features alone explain 78% of model variance; economic factors add 15%

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📊 Key Figures

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Fig 25 — Main Bootstrap SHAP CI (Main Result)

Top 15 features with bootstrap 95% CI error bars — reveals only 6–8 features are statistically significant

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Fig 35 — Combined Methodology 4-Panel (Methodology Overview)

4-panel figure (PCA + SHAP + Bootstrap CI + Permutation) — the complete framework at a glance

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Fig 17 — PCA Livability Labels (Clustering Result)

2D PCA projection with final livability labels (green/red) — clean separation of countries

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Fig 27 — EPI External Validation (Validation)

Model predictions vs independent EPI 2020 scores — proves clusters capture real environmental quality

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Fig 20 — ROC & PR Curves (Performance)

ROC and Precision-Recall curves for all 5 models — XGBoost dominance is immediately visible

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📊 Figure Index

#	Figure	Description
1	fig01_missing_value_heatmap.png	Missing value pattern across all features
2	fig02_outlier_boxplot_grid.png	IQR outlier detection — all 20 features
3	fig03_co2_distribution_analysis.png	CO₂ per capita: histogram, KDE, Q-Q plot, skewness analysis
4	fig04_co2_top_bottom_15_countries.png	Top 15 highest and lowest CO₂ emitters
5	fig05_full_correlation_heatmap.png	20×20 Pearson correlation matrix with annotations
6	fig06_feature_distribution_grid.png	5×4 grid of histograms + KDE for all features
7	fig07_co2_transformation_before_after.png	Log1p transformation effect on skewed features
8	fig08_vif_bar_chart.png	VIF scores after iterative multicollinearity removal
9	fig09_pearson_vs_mutual_info.png	Feature importance by Pearson R vs Mutual Information
10	fig10_feature_target_scatter_top10.png	Scatter plots: top 10 features vs CO₂ (r, p-value)
11	fig11_pca_scree_plot.png	PCA explained variance by component
12	fig12_pca_2d_projection.png	2D PCA scatter colored by CO₂ emission groups
13	fig13_pca_biplot.png	PCA biplot with feature loadings
14	fig14_kmeans_validation_dashboard.png	K-Means metrics: inertia, silhouette, DB-Index, Calinski-Harabasz
15	fig15_cluster_profile_heatmap.png	Heatmap of cluster means across all features
16	fig16_cluster_radar_chart.png	Radar chart of normalized cluster profiles
17	fig17_pca_livability_labels.png	2D PCA with final livability labels (green/red)
18	fig18_ablation_label_comparison.png	Cluster-based vs median-split label comparison (F1 metric)
19	fig19_confusion_matrices_all_models.png	2×3 grid of confusion matrices for all 5 models
20	fig20_roc_pr_curves.png	ROC curves and Precision-Recall curves for all models
21	fig21_learning_curve_best_model.png	Learning curve for best model (training/validation F1)
22	fig22_shap_beeswarm_bar_combined.png	SHAP beeswarm plot + mean absolute SHAP value bar chart
23	fig23_shap_dependence_top5.png	SHAP dependence plots for top 5 features
24	fig24_shap_waterfall_examples.png	SHAP waterfall: 1 livable + 1 not-livable country example
25	fig25_main_bootstrap_shap_ci.png	MAIN FIGURE: Top 15 features with bootstrap 95% CI error bars
26	fig26_permutation_test_top2.png	Permutation test results (2000 iter) for top 2 features
27	fig27_epi_external_validation.png	Scatter: Model predictions vs EPI 2020 scores
28	fig28_robustness_outlier_removal.png	Feature importance before/after outlier removal
29	fig29_partial_correlation_gdp.png	Partial correlation of top features with target (controlling GDP)
30	fig30_feature_group_ablation.png	F1 scores: Environment-only vs Econ-only vs Gov-only vs ALL
31	fig_ieee_a_pca_clusters.png	IEEE-formatted: PCA 2D projection with K-Means clusters
32	fig_ieee_b_shap_beeswarm.png	IEEE-formatted: SHAP beeswarm for publication
33	fig_ieee_c_bootstrap_shap.png	IEEE-formatted: Bootstrap SHAP CI (main result)
34	fig_ieee_d_permutation.png	IEEE-formatted: Permutation test results
35	fig_combined_methodology_4panel.png	4-panel figure: PCA + SHAP + Bootstrap CI + Permutation

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📈 Model Comparison Summary

Model	CV F1	CV ROC-AUC	Test F1	Test ROC-AUC	Training Time
Logistic Regression	0.78	0.86	0.79	0.87	0.2s
Random Forest (n=100)	0.85	0.91	0.84	0.90	1.5s
SVM (RBF, scaled)	0.80	0.88	0.81	0.89	0.5s
Gradient Boosting	0.87	0.93	0.86	0.92	2.1s
XGBoost (tuned)	0.89	0.95	0.88	0.94	1.8s

Best Model: XGBoost

• Hyperparameters: n_estimators=150, max_depth=5, learning_rate=0.08, subsample=0.9
• GridSearchCV optimization on 5-Fold CV with F1 as primary metric

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🛠 Technical Stack

Core Libraries

• Data: pandas, numpy
• Preprocessing: scikit-learn (StandardScaler, IterativeImputer, OneHotEncoder)
• Modeling: scikit-learn, xgboost
• Statistical Analysis: scipy.stats, statsmodels (VIF, partial correlation)
• Clustering: scikit-learn (K-Means, silhouette, Davies-Bouldin)
• Interpretability: shap (TreeExplainer for XGBoost)
• Visualization: matplotlib, seaborn, plotly

Environment

• Python 3.8+
• See requirements.txt for pinned versions

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🚀 Quick Start

Run Entire Pipeline

# 1. Install dependencies
pip install -r requirements.txt

# 2. Place data file
cp country_environmental_livability_2019.csv .

# 3. Run notebook (Jupyter)
jupyter notebook country_environmental_livability_2019.ipynb

# Or in Google Colab:
# - Upload notebook
# - Run all cells (Cell 1.2 will prompt for data upload)

Load Trained Model

import joblib
import json
from pathlib import Path

results_dir = Path('results')

# Load model and scaler
model = joblib.load(results_dir / 'models' / 'best_xgb_livability_model.joblib')
scaler = joblib.load(results_dir / 'models' / 'scaler.joblib')

# Load feature list and metadata
with open(results_dir / 'models' / 'feature_list.json') as f:
    feature_info = json.load(f)
    feature_names = feature_info['features']

with open(results_dir / 'models' / 'model_metadata.json') as f:
    metadata = json.load(f)
    print(f"Model Test F1: {metadata['test_metrics']['F1']:.4f}")
    print(f"Model Test ROC-AUC: {metadata['test_metrics']['ROC-AUC']:.4f}")

# Make predictions on new data
X_new_scaled = scaler.transform(X_new[feature_names])
predictions = model.predict(X_new_scaled)
probabilities = model.predict_proba(X_new_scaled)

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📚 Citation & References

Cite This Work

@article{livability2025,
  title={From Clusters to Classification: A Bootstrap-Validated SHAP Framework 
         for Environmental Livability Assessment of Nations},
  author={[Your Name]},
  journal={IEEE [Journal Name]},
  year={2025},
  doi={[DOI]},
  note={Code and reproducible results available at [GitHub/OSF]}
}

Key References

• Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. NeurIPS.
• Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence.
• Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. KDD.
• Malik, B., et al. (2020). 2020 Environmental Performance Index. Yale Center for Environmental Law & Policy.

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👨‍💻 About the Developer

Sarder Junaid Ahmed

Data Scientist & Machine Learning Engineer

Transforming complex data into strategic decisions through rigorous statistical modeling and production-ready machine learning systems.

Specializations: Statistical ML · Causal Inference · Trustworthy AI · Fairness-Aware ML · RAG Systems

Selected Research:

• 📄 Ahmed, S.J. et al. (2026). Machine Learning for Crime Classification: A Fairness-Aware Approach to Class Imbalance. Journal of Machine Learning and Applications, 2(1), 9–17. DOI: 10.61577/jmla.2026.100002
• 📄 Ahmed, S.J. et al. (2026). Machine Learning for Crime Classification: A Fairness-Aware Approach to Class Imbalance. IEEE SPICSCON 2026, BAUET, Bangladesh (Aug 13–14, 2026). Accepted for Presentation — IEEE Xplore.
• 📄 Ahmed, S.J. et al. (2026). CF-EGAT: A Causal Fairness-Aware Equity Graph Attention Network for Country-Level Environmental Livability Classification. SPECTRA 2026. 🏆 1st Best Paper Award
• 📄 Ahmed, S.J. (2025). Multi-Dimensional Statistical Similarity for Governance Classification: Beyond Arbitrary Thresholds. APMEE 2025. 🏆 Best Research Paper Award
• 📄 Ahmed, S.J. (2026). DeepEnMap: Ordinal-Aware Multi-Modal Deep Learning for Energy Poverty Risk Mapping. IEMIS 2026, University of British Columbia, Vancouver, Canada (Aug 10–12, 2026). Accepted for Presentation — Springer LNNS Series (Scopus, EI-Compendex, DBLP, ISI Proceedings).
• 📄 Ahmed, S.J. (2026). Density-Decoupled, Mask-Ablated Segmentation-Guided Diffusion for Controllable Mammography Synthesis: A Preliminary Study. IEMIS 2026, University of British Columbia, Vancouver, Canada (Aug 10–12, 2026). Accepted for Presentation — Springer LNNS Series (Scopus, EI-Compendex, DBLP, ISI Proceedings).
• 📄 Ahmed, S.J., Islam Nahian, M.T., & Kwoshik, M.H.R. (2026). Environmental Livability Assessment via Adaptive Bootstrap-Retrained SHAP and Statistically-Constrained Pareto Counterfactuals: A Cross-National Analysis. IEEE SPICSCON 2026, BAUET, Bangladesh (Aug 13–14, 2026). Accepted for Presentation — IEEE Xplore.
• 📄 Ahmed, S.J. (2026). DemocracyGuard: Testing a Divergence-Index Reconciliation of Subjective and Objective Democracy Indicators for Forecasting Adverse Regime Transitions. Under Review, Transactions on Machine Learning Research (TMLR) — Q1, Top-Tier Journal.
• 📄 Ahmed, S.J. (2026). FAI: Feature-Wise Adaptive Imputation via Downstream-Aware Method Selection. Under Review, ICISET 2026 (IEEE Xplore).

Other Deployed Projects:

• 🔬 ReproHub — Automated research reproducibility platform with composite scoring across 11 statistical tests
• 📊 StatsPro — AI-powered statistical analysis platform with automated CSV-to-report workflows

Honors: 🏆 1st Best Paper — SPECTRA 2026  ·  🏆 Best Research Paper — APMEE 2025  ·  🎖️ Esteemed Alumni Award — YLRL RUET 2024  ·  ⭐ Perfect GPA 5.00/5.00 — SSC & HSC  ·  🎓 National Merit Scholarship — 2009 & 2013

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⚖️ License & Disclaimer

This analysis is provided as-is for research and educational purposes. The livability labels are derived from environmental data using unsupervised clustering followed by supervised classification; they are not official designations and should be treated as a research artifact.

For policy decisions, always consult official environmental metrics (EPI, UN SDGs) and domain experts.

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📧 Feedback & Issues

• Reproducibility: Ensure all dependencies in requirements.txt are installed and versions match.
• Data Issues: If the notebook fails at data loading, verify CSV format matches expected structure (see Section 1.3 for column definitions).
• Performance: Training takes ~10–15 minutes on standard hardware; SHAP analysis (Sections 5–6) may take 5–10 minutes per run.

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Last Updated: 2025
Total Notebook Runtime: ~30–45 minutes (full pipeline)
Total Figures Generated: 35+
Total Tables: 4 (IEEE LaTeX + CSV)