Predicting Religion from Flag Attributes

Author

Zach Wilde

Abstract

This project applies multivariate statistical classification methods to predict the dominant religion of a country using physical attributes of national flags, along with geographic and demographic variables. Multiple models are compared to evaluate predictive performance and to explore whether symbolic flag characteristics provide meaningful signals about religion.

Introduction

The purpose of this project is to determine whether a country’s dominant religion can be predicted using observable attributes of its national flag, along with geographic and demographic characteristics.

Flags often contain colors, symbols, and patterns that may reflect cultural or religious identity. This project investigates whether those features contain enough statistical signal to distinguish between religions.

The central research question is:

  • Can flag attributes and country-level characteristics predict dominant religion?

Data

The dataset comes from the UCI Machine Learning Repository and includes 194 countries with 30 attributes.

Dataset Overview
Description Value
Number of countries 194
Number of variables 30
Response variable Religion (8 classes)
Train/Test split 80% / 20%

Religion Distribution

Distribution of Religion Classes
Religion Count
Catholic 40
Other Christian 60
Muslim 36
Buddhist 8
Hindu 4
Ethnic 27
Marxist 15
Other 4

Numeric Variable Summary

Mean of Numeric Variables
Variable Mean
Area 700.05
Population 23.27
Bars 0.45
Stripes 1.55
Colors 3.46
Circles 0.17
Crosses 0.15
Saltires 0.09
Quarters 0.15
Sunstars 1.39

Flag Attribute Frequencies

Methods

Three classification methods were used:

1. Multinomial Logistic Regression

Used because the response variable has multiple categories (8 religions). The model estimates probabilities for each class using predictor variables.

2. Discriminant Analysis

Used to separate observations into predefined groups. This method assumes underlying distributional structure and attempts to maximize separation between classes.

3. Decision Tree

Used to identify important variables and hierarchical splits. This method provides interpretability by showing how predictions are made step-by-step.

All models were evaluated using an 80/20 train-test split, and performance was measured using classification accuracy and confusion matrices.

Results

Model Performance

Model Performance Comparison
Model Accuracy CI_Lower CI_Upper
Multinomial Logistic Regression 0.5385 0.3718 0.6991
Discriminant Analysis 0.5641 0.3962 0.7219
Decision Tree 0.6923 0.5243 0.8298

Decision Tree

Discussion

The decision tree model performed the best, achieving approximately 69% accuracy, outperforming both logistic regression and discriminant analysis.

This suggests that geographic and structural variables such as landmass, region, and language play a larger role in predicting religion than purely visual flag attributes.

While logistic regression was expected to perform well due to regularization, it performed the worst. This may be due to the complexity of separating eight categories using a linear decision boundary.

Discriminant analysis performed slightly better, likely because it is designed specifically for classification between groups.

Overall, results indicate that religion is more strongly tied to location and cultural context than to flag design alone.

Conclusion

This project demonstrates that:

  • Religion can be predicted with moderate accuracy (~50–70%)
  • Geographic variables are stronger predictors than visual flag features
  • Decision trees provide both the best performance and interpretability

While flag attributes contain some signal, they are not sufficient on their own to fully classify religion.

Future Work

Future research could explore:

  • Symbolism in architecture and art instead of flags
  • Incorporating additional cultural variables
  • Using ensemble methods for improved prediction

References

  1. Johnson, R. A., & Wichern, D. W. Applied Multivariate Statistical Analysis
  2. glmnet documentation: https://glmnet.stanford.edu
  3. UCI Flags Dataset: https://archive.ics.uci.edu/ml/datasets/Flags

Appendix

Full modeling code available upon request