https://github.com/pegah-ardehkhani/predictive-maintenance-using-ml

A machine learning project for predictive maintenance, designed to forecast equipment failures and optimize maintenance schedules to reduce downtime and operational costs
https://github.com/pegah-ardehkhani/predictive-maintenance-using-ml

adaboost decision-tree deep-learning deep-neural-networks feature-importance knn logistic-regression machine-learning machine-learning-algorithms matplotlib numpy pandas plotly predictive-maintenance random-forest seaborn sklearn stacking svm tensorflow

Last synced: 2 days ago
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A machine learning project for predictive maintenance, designed to forecast equipment failures and optimize maintenance schedules to reduce downtime and operational costs

• Host: GitHub
• URL: https://github.com/pegah-ardehkhani/predictive-maintenance-using-ml
• Owner: Pegah-Ardehkhani
• License: mit
• Created: 2024-11-13T16:30:15.000Z (6 days ago)
• Default Branch: main
• Last Pushed: 2024-11-15T22:40:27.000Z (4 days ago)
• Last Synced: 2024-11-15T23:20:51.619Z (4 days ago)
• Topics: adaboost, decision-tree, deep-learning, deep-neural-networks, feature-importance, knn, logistic-regression, machine-learning, machine-learning-algorithms, matplotlib, numpy, pandas, plotly, predictive-maintenance, random-forest, seaborn, sklearn, stacking, svm, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.04 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Predictive Maintenance Using Machine Learning⚙️ ![license](https://img.shields.io/github/license/Pegah-Ardehkhani/Predictive-Maintenance-Using-ML.svg)  [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.org/github/Pegah-Ardehkhani/Predictive-Maintenance-Using-ML/blob/main/Predictive%20Maintenance.ipynb)

> **`Note`**: Use [**Google Colab**](https://colab.research.google.com/github/Pegah-Ardehkhani/Predictive-Maintenance-Using-ML/blob/main/Predictive%20Maintenance.ipynb) in order to view interactive plotly graphs. You can see all the codes and the outputs in nbviwer without running the whole code again.

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