https://github.com/yosrak5/predictive_maintenance

End-To-End Predictive Analytics to predict Hardware Maintenance Before it occurs using data preprocessing and Machine Learning classification modeling (XGBOOST , Random Forest )
https://github.com/yosrak5/predictive_maintenance

data-factory data-perspective machine-learning-workbench matplotlib numpy pandas python random-forest seaborn xgboost

Last synced: 2 months ago
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End-To-End Predictive Analytics to predict Hardware Maintenance Before it occurs using data preprocessing and Machine Learning classification modeling (XGBOOST , Random Forest )

• Host: GitHub
• URL: https://github.com/yosrak5/predictive_maintenance
• Owner: yosrak5
• Created: 2024-10-10T11:54:01.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-10-28T19:56:53.000Z (3 months ago)
• Last Synced: 2024-12-07T07:40:12.248Z (2 months ago)
• Topics: data-factory, data-perspective, machine-learning-workbench, matplotlib, numpy, pandas, python, random-forest, seaborn, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 5.19 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Predictive_Maintenance

Predictive maintenance is a proactive maintenance strategy that utilizes data analysis, machine learning algorithms, and sensor technologies to predict equipment failures before they occur.

By analyzing real-time and historical data, predictive maintenance aims to identify patterns and anomalies that signal potential issues, allowing maintenance teams to schedule repairs or replacements at optimal times.