Tech Venture · Data + Machine Learning
Inequality data is scattered across dozens of disconnected indicators and dense reports. BeyondStats combines them into a single, interpretable score — uses machine learning to estimate the years official data is missing — and makes the whole thing explorable through interactive visuals.
↑ A scripted illustration with representative values — pick a country and year to explore the composite score and its dimensions.
01 — The problem
Gender inequality is usually presented through dozens of disconnected metrics and complex reports, which makes it hard to see patterns, compare countries, or engage meaningfully. BeyondStats lets people focus on understanding inequality instead of decoding numbers.
Multiple gender-related metrics combined into a single, interpretable number.
Machine learning estimates recent values where official indices are missing.
Insights through interactive maps and trends instead of raw tables.
02 — The machine-learning score
Trained on real historical Gender Inequality Index values, the model predicts GII for 2022, 2023, and 2024 — the years where no official data was available — turning dozens of indicators into one clear score.
Input features are grouped into three categories — economic, social, and physical/health. Without ML, users would have to manually compare dozens of indicators; the model reduces that to one number, making comparison and exploration intuitive.
03 — Key features
Country-level exploration at a glance.
Historical trends per country over time.
The composite, machine-learning-powered score front and centre.
Learning through interaction, not just reading.
Economic, social, and physical/health factors, broken out.
Put places side by side on one consistent scale.
04 — Stack & data