🚀 Intelligent CPU Scheduler

🚀 Intelligent CPU Scheduler using Machine Learning (Linear Regression) I recently developed an intelligent CPU scheduling system that leverages machine learning to optimize process execution in operating systems. Traditional scheduling algorithms like FCFS and SJF rely on static rules and often fail to adapt to dynamic workloads. To address this, I designed a system that uses a Linear Regression model to predict process burst times based on features such as arrival time and priority. 🔍 What it does: * Predicts CPU burst time using a trained ML model * Dynamically schedules processes based on predicted values * Simulates execution to compute key metrics like: * Waiting Time * Turnaround Time * Response Time ⚙️ Tech Stack: Python | FastAPI | Scikit-learn | NumPy 💡 Key Impact: * Reduces average waiting time compared to traditional scheduling * Demonstrates how ML can enhance core OS concepts * Bridges the gap between theoretical algorithms and intelligent systems This project highlights how even simple ML models like Linear Regression can bring meaningful improvements to classical system-level problems.