Hi, I’m Saanvi Bhagat, a full-stack developer with AI/ML experience, delivering end-to-end web features and rapid prototypes for clients. I build and integrate APIs, databases, and UIs; I’m comfortable with Python back ends and React front ends, and I’m actively building in Node.js/TypeScript and deploying to the cloud.
I’m strong in data pipelines (OCR/CV), clear documentation, and remote collaboration. I enjoy contributing at the planning and research stage with quick prototype builds, technology comparisons, and well-scoped deliverables.
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Built a data-driven framework to measure how short-term traffic measures (e.g., road closures) affect urban NO₂. Combined roadside NO₂ readings with meteorological data, traffic counts, and calendar features (school breaks, public holidays, weekends, hour-of-day) to control for natural variation. Introduced changepoint detection to identify structural shifts in the time series and engineered a novel feature, segment-mean NO, by integrating detected changepoints into the predictive models. Used both Random Forest and LSTM models to compare performance. Results showed localized NO₂ reductions at intervention sites and consistent accuracy gains from changepoint-derived features (RF benefited most from structural context; LSTM captured temporal dynamics and delayed rebounds). The approach offers a practical, context-aware tool for policymakers evaluating targeted air-quality actions.
Github repo for the project:
https://www.twine.net/signin
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