Dynamic pricing model using demand elasticity and competitor signals - built with Python, tested across multiple pricing scenarios
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Updated
Apr 15, 2026
Dynamic pricing model using demand elasticity and competitor signals - built with Python, tested across multiple pricing scenarios
Quantifying HDD underestimation of heat pump peak demand: 2R1C thermal simulation on real EPW weather, a 240-run factorial Monte Carlo, Type II ANOVA, interaction regression with a Ramsey RESET test, and validation against 730 Electrification of Heat field-trial homes.
Insurance demand modelling. Conversion, retention, DML price elasticity, demand curves, FCA GIPP-compliant optimisation. CatBoost + Polars.
Constrained rate optimisation for insurance pricing — FCA ENBP compliance, demand modelling, efficient frontier, portfolio-level margin control
Deprecated — merged into insurance-optimise
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