🐧 ML-Based Linux Update Stability Engine

A system-level project that collects real Linux update data, stores it in a structured database, and prepares a machine learning pipeline to analyze update stability and risk.

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📌 Problem Statement

Linux system updates—especially on rolling-release distributions—can sometimes introduce instability. Users often update their systems without knowing whether an update could potentially cause issues.

This project focuses on analyzing historical Linux update behavior and building a pipeline that can classify update risk using machine learning.

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🧠 What This Project Does

1. Reads real Linux update logs from the system
2. Extracts package and system update information
3. Stores structured update data in a SQLite database
4. Builds features required for machine learning
5. Trains a classification model when enough data exists

The project uses real system data, not fake or pre-made datasets.

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🏗️ System Architecture

Linux System → Pacman Logs (/var/log/pacman.log) → Data Collection Layer → SQLite Database → Feature Engineering → Machine Learning Pipeline

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⚙️ Technologies Used

• Python – core programming language
• SQLite – structured data storage
• Pandas & NumPy – data processing
• Scikit-learn – machine learning
• Linux (pacman) – real system data source

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📂 Project Structure

• src/
  • collectors/ – collects update data from Linux logs
  • features/ – feature engineering logic
  • models/ – machine learning model
  • utils/ – logging utilities
  • main.py – pipeline entry point
• sql/ – database schema
• notebooks/ – exploratory analysis
• requirements.txt – project dependencies
• README.md – project documentation

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▶️ How to Run the Project

Activate the virtual environment:
source .venv/bin/activate.fish

Collect real Linux update data:
python -m src.collectors.pacman

Run the machine learning pipeline:
python -m src.main

If there is not enough historical update data, the system safely skips ML training instead of failing.

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🤖 Machine Learning Overview

Problem Type: Classification
Model Used: Random Forest

Features:

• Number of packages updated
• Kernel update indicator

Output:

• Update risk classification (safe / risky)

The ML pipeline is designed to activate automatically when sufficient historical data is available.

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🔍 Key Highlights

• Uses real Linux system update logs
• End-to-end ML-ready pipeline
• Handles low-data scenarios safely
• Modular and explainable design
• Focused on system-level data engineering

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🚀 Future Improvements

• Time-series analysis of update history
• Support for multiple Linux distributions
• Background monitoring service
• Improved risk scoring logic
• Visualization dashboard

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👤 Author

Jagadheesan (Jd)
GitHub: https://github.com/jxgadheesan
Interests: Linux, Python, Machine Learning, System-Level Engineering