Explain OS usage gap via trees

Instagram usage is substantially higher among iOS than Android users. 1) Frame this as a supervised learning problem to identify discriminant variables: define target, candidate features (demographics, engagement, acquisition channel, device/app telemetry), and the modeling approach (decision tree vs. random forest). 2) Explain how to detect whether OS is a causal factor vs. a proxy for other variables; outline tests (feature ablation, conditional permutation importance, propensity stratification). 3) Specify additional telemetry you would collect (e.g., cold‑start latency, crash rate, time‑to‑first‑paint, battery drain) and how you’d compare distributions across OS versions. 4) Discuss handling missing values in consumer data: defend the '-1 sentinel' approach vs. model‑based imputation, and when each is appropriate. 5) Given two discovered segments—(a) Argentinians underperforming, (b) Indians <30 overperforming—propose concrete next steps for product/marketing and how to validate impact causally.