AI anomaly detector

The README.md file is the main documentation of the AI Memory Leak & Resource Anomaly Detection Platform. It gives developers and users a complete understanding of the project, including its purpose, features, architecture, technologies, installation process, and future development. It starts with an Overview, explaining that the application monitors system and process resources, detects abnormal memory behavior using Machine Learning, and uses a native C backend for performance. It then describes the major features, such as live CPU and memory monitoring, Isolation Forest-based anomaly detection, the Tkinter dashboard, real-time graphs, CSV logging, and automatic Python fallback. The Project Architecture section explains how the major components communicate. main.py starts the application, the logger handles application logs, backend.py manages the native C backend, monitoring modules collect telemetry, the anomaly detection module analyzes the data, and app.py launches the Tkinter dashboard. The README also documents the Technology Stack, including Python, C, Tkinter, Matplotlib, NumPy, Scikit-learn, and ctypes. The Folder Structure shows where important files such as main.py, backend.py, monitor.py, anomaly.py, utils.py, and the C source files are located. The Installation and Running sections provide the commands required to clone the repository, create a virtual environment, install dependencies, and start the application using python main.py. It also explains how to compile the native C backend for Windows, Linux, and macOS. The Machine Learning Pipeline explains how raw telemetry moves through feature extraction and Isolation Forest anomaly detection before producing an anomaly score and possible memory leak detection. The README also describes logging, generated reports, the development roadmap, contribution process, license, authors, acknowledgements, and future scope.