A deep exploration of AI-driven foresight, how predictive models evolve into strategic collaborators. This repository presents Forecasting the Future of Forecasting, a professional essay on reflexive intelligence, human–AI collaboration, and the design of adaptive, explainable forecasting systems.

This article reframes pricing as a negotiation rather than a prediction, showing how price emerges from tensions between product reality, market dynamics, and buyer behavior. It introduces negotiation-aware ML, value decomposition, and equilibrium modeling to build transparent, human-aligned pricing systems.

A customer intelligence engine that predicts subscription probability, models purchase frequency, and computes a unified loyalty risk score. Includes explainability, segment insights, and a scenario simulator, all integrated into an interactive Streamlit dashboard.

An interpretable system that models the future of work as an equilibrium under AI-driven forces. Instead of predicting job loss, it decomposes workforce disruption into automation pressure, adaptability, skill transferability, demand, and AI augmentation to explain stability, tension, and transition paths by 2030.

An early-warning system that models disasters as instability transitions rather than isolated events. It combines force-based instability modeling with an interpretable ML escalation-risk layer to detect when hazards become disasters due to exposure growth, response delays, and buffer collapse.

CARLA Simulation and Data Analysis

A predictive modeling project that goes beyond simple classification. I built an interactive simulator that allows stakeholders to run "what-if" scenarios, visualizing the impact of specific retention actions on churn rates and calculating the potential ROI of intervention strategies.