Using Machine Learning to Identify High-Risk Schools: An Analysis of an Educational Inequality Prediction Project

This article analyzes a predictive analysis project built using Python, Pandas, and Scikit-learn. Through data cleaning, exploratory analysis, feature engineering, and machine learning models, it identifies high-risk schools in South Africa and provides data support for educational policy formulation. The project was published by Siyasanga Mudau on GitHub (link: https://github.com/SiyasangaMudau/education-inequality-ml-project) with the aim of improving educational equity through data-driven methods.

South Africa has long faced severe educational inequality issues. Legacy effects of apartheid, urban-rural resource gaps, and backward infrastructure in schools in poor communities have led to a quality gap between schools. Traditional resource allocation relies on administrative decisions or post-event responses, lacking a proactive risk identification mechanism. This project uses data-driven methods to identify schools in need of priority support before problems worsen, enabling precise resource allocation. This is a typical predictive analysis scenario where the target variable is the school's "risk status", and features cover multi-dimensional information such as infrastructure, teaching staff, student demographics, and geographical location.

Data Preparation

• Data Source Integration: Integrate heterogeneous data sources (e.g., student information systems, financial systems) to establish a unified view
• Missing Value Handling: Deletion (for high missing ratios), imputation (mean/median/prediction filling), marking (retain missing indicators)
• Outlier Detection: Use box plots and Z-scores to identify anomalies, and combine with domain knowledge to judge their authenticity
• Data Type Conversion: Convert categorical variables to model-processable formats and unify measurement units

Exploratory Data Analysis

• Univariate Analysis: Distributions of student-teacher ratio, infrastructure completeness, poverty index, etc.
• Bivariate Analysis: Regional differences in risk rates, correlation between student-teacher ratio and school performance
• Multivariate Relationships: Heatmaps display feature correlations to identify multicollinearity
• Geospatial Analysis: Visualize school locations to identify high-risk clustering patterns

Feature Engineering

• Construction: Resource adequacy index, historical trend features, relative location features, interaction features
• Selection: Variance threshold, correlation filtering, Recursive Feature Elimination (RFE), L1 regularization
• Scaling: Standardization/normalization of numerical features

Model Construction

• Baseline Models: Logistic regression and decision trees as references
• Candidate Models: Logistic regression (interpretability), random forest (non-linear), gradient boosting trees (XGBoost/LightGBM), support vector machines (high-dimensional space)
• Validation: Stratified cross-validation to address class imbalance
• Evaluation: F2 score (focus on recall), AUC-PR (for class imbalance), confusion matrix

• Interpretability Report: Influencing factors, risk causes for specific schools, prediction of intervention effects
• Risk Stratification: Output probability scores to support refined resource allocation
• Action Recommendations: Priority intervention areas, resource ROI prediction, pre-policy evaluation

Challenges and Limitations

• Data Quality: Inconsistent standards, update lags, and historical missingness affect generalization
• Causal Inference: Correlation does not imply causation; intervention features may not reduce risk
• Fairness: Potential systemic bias, requiring regular audits
• Dynamic Changes: School conditions change, requiring regular retraining

Tech Stack

Python ecosystem: Pandas (data processing), Scikit-learn (models), Matplotlib/Seaborn (visualization), Jupyter Notebook (interactive analysis)

Extended Applications

The framework can be extended to:

• Public health: Identify high-risk medical institutions/disease areas
• Social security: Predict welfare dependency risk
• Urban planning: Identify communities in need of infrastructure upgrades

Summary

The project demonstrates the potential of data science in social welfare, providing policy support through an end-to-end process. The tech stack is pragmatic, making it an excellent reference project for social impact data science. It is suitable for learners to understand how machine learning solves real social problems.