Machine Learning Analytics: Logistic Regression and Machine Learning in R for Demography with End-to-End Case Studies
Demography is built on simple questions whose answers quietly shape entire societies. Will a child survive to age five? Which women are likely to have large families? Who is most likely to move from rural areas into cities, or from one region to another? Each of these outcomes can be expressed as a binary “yes/no” event, yet they are driven by a dense web of factors: age, education, wealth, geography, access to services, social norms, labour markets, housing conditions, and public policy. The way these forces combine at the individual and household level ultimately determines the population structure we see in census tables, projections, and planning documents.
This is exactly where logistic regression becomes a natural tool for demographers. Logistic regression links micro-level predictors—maternal age, education, rural–urban residence, wealth index, contraceptive access, unemployment history—to the probability of a demographic event: child survival, high parity, recent migration, and more. Unlike many black-box models, logistic regression remains interpretable. Its coefficients can be translated into odds ratios and clear narratives: how much an extra year of schooling reduces the odds of high parity, how improved water and sanitation increase child survival, or how unemployment history and housing costs shape migration decisions. That interpretability is crucial when findings need to be communicated to policymakers, programme designers, and other non-technical stakeholders.
At the same time, modern demography increasingly operates in a machine-learning workflow. Analysts work with large household surveys, administrative microdata, censuses, and panel datasets that demand more than a single regression run on the full sample. We need a disciplined approach: simulate or ingest data, conduct exploratory data analysis (EDA) to understand distributions and correlations, split the sample into training and test sets, scale continuous features, fit models, and then rigorously evaluate their performance using metrics and diagnostic plots that go beyond simple p-values.
This guide brings all of those elements together in R through three end-to-end simulated case studies: under-five survival, high parity fertility (3+ children), and recent internal migration. For each case, you begin with EDA—summary statistics, histograms, boxplots, and correlation heatmaps—to build intuition about the underlying population. You then fit logistic regression models, generate predicted probabilities, and assess performance using confusion matrices, ROC curves, calibration plots, coefficient bar charts, and binned event-rate graphs. By the end, you will have fully runnable R code and a reusable template for building transparent, machine-learning-ready logistic regression pipelines for your own demographic and population research.
Below is a step-by-step guide (with full R code) showing how to use logistic regression and a basic machine-learning workflow in R for demographic applications.
We’ll work through three simulated case studies:
Under-Five Survival – probability that a child survives to age 5
High Parity Fertility – probability that a woman has 3+ children
Recent Internal Migration – probability that an adult migrated in the last 5 years
Each case study includes:
Simulation of realistic demographic data
EDA (exploratory data analysis)
Logistic regression modelling
Multiple analytical figures (ROC, histograms, calibration, coefficients, binned event rates, etc.)
At the end you’ll get one complete script that you can save and run.
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