How I Built an AI System to Reduce Healthcare No-Shows Using Flask, Random Forest & SimPy

How I Built an AI System to Reduce Healthcare No-Shows Using Flask, Random Forest & SimPy. A walkthrough of my final year project — from problem statement to working simulation The Problem I Wanted to Solve Anyone who has visited a clinic knows the frustration — long wait times, overbooked doctors, and yet somehow, empty slots because patients didn't show up. No-shows are one of the biggest inefficiencies in healthcare. Clinics lose revenue. Doctors waste time. Other patients who actually needed that slot couldn't get one. I wanted to build something that tackles this with a data-driven approach. The result: an AI-Based Healthcare Appointment Scheduling Optimization System — my final year project built with Python, Flask, scikit-learn, and SimPy. Here's how I built it, what I learned, and what I'd do differently. What the System Does At its core, the system does three things: Predicts which patients are likely to miss their appointment (no-show prediction) Uses that prediction to assig