I built a panel-trained model to predict year-to-year changes in suicide rates for South Africa. Structural socio-economic indicators explain long-run trends but provide limited predictive power for short-term fluctuations, which are dominated by unobserved shocks.
Please download the zip files and open in on Google Colab, or Jupyter Notebook
Predicting Year-to-Year Changes in Suicide Rates in South Africa
A Panel-Based Socioeconomic Modeling Approach
š Project Overview
This project develops a data-driven, statistically grounded machine learning model to predict year-to-year changes in suicide rates in South Africa, using socioeconomic signals learned from a multi-country panel dataset.
Rather than predicting absolute suicide levels, the model focuses on annual changes, which:
Reduces non-stationarity
Avoids spurious trend learning
Aligns with best practices in epidemiological and economic time-series modeling
The project emphasizes interpretability, robustness, and uncertainty awareness, making it suitable for research, policy analysis, and academic work.
šÆ Objectives
Predict annual changes in suicide rates for South Africa
Learn structural relationships from comparable countries using panel data
Avoid temporal leakage using time-aware validation
Quantify uncertainty in predictions
Produce an interpretable and defensible baseline model
š§ Methodological Summary
Target Variable
SuicideRate_diff
Year-over-year change in suicide mortality rate (per 100,000 population)
Predictors (3-Year Rolling Averages)
Alcohol consumption per capita
Intentional homicide rate
GDP per capita (current US$)
Rolling averages are used to capture structural trends while reducing short-term noise.
š Data Sources
All data comes from authoritative international sources:
Suicide mortality rate ā UN SDG / WHO
Alcohol consumption ā WHO / UN SDG
Homicide rate ā UN SDG
GDP per capita ā World Bank (NY.GDP.PCAP.CD)
Countries were selected based on:
Data availability (2000ā2024)
Reporting consistency
Socioeconomic comparability
South Africa is explicitly held out as the prediction target.
š§© Modeling Approach
Model type: Ridge Regression (L2-regularized linear model)
Training strategy:
Panel learning on multiple countries
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