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RohinM24/Predicting-Year-to-Year-Changes-in-Suicide-Rates-in-South-Africa-

Domain:

healthcaresocioeconomic

Record type:

model
Creator:
Roh
Host:
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 šŸ“Œ 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. šŸŽÆ 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 🧠 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. šŸ“Š Data Sources All data comes from authoritative international sources: Suicide mortality rate – UN SDG / WHO Alcohol consumption – WHO / UN SDG Homicide rate – UN SDG GDP per capita – World Bank (NY.GDP.PCAP.CD) Countries were selected based on: Data availability (2000–2024) Reporting consistency Socioeconomic comparability South Africa is explicitly held out as the prediction target. 🧩 Modeling Approach Model type: Ridge Regression (L2-regularized linear model) Training strategy: Panel learning on multiple countries Sou …

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