https://github.com/parag000/predictive-machine-maintenance

This project performs multiclass classification to identify machine failure types using a synthetic dataset with features like temperature, torque, and tool wear. It includes data analysis, feature engineering, and model training to optimize maintenance strategies and reduce downtime through predictive insights.
https://github.com/parag000/predictive-machine-maintenance

machine-maintenance multiclass-classification python

Last synced: 2 months ago
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This project performs multiclass classification to identify machine failure types using a synthetic dataset with features like temperature, torque, and tool wear. It includes data analysis, feature engineering, and model training to optimize maintenance strategies and reduce downtime through predictive insights.

• Host: GitHub
• URL: https://github.com/parag000/predictive-machine-maintenance
• Owner: Parag000
• Created: 2025-01-23T05:39:02.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-23T05:41:59.000Z (over 1 year ago)
• Last Synced: 2025-07-29T10:52:44.947Z (11 months ago)
• Topics: machine-maintenance, multiclass-classification, python
• Language: Jupyter Notebook
• Homepage:
• Size: 396 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🔧 Predictive Maintenance: Failure Classification

This project demonstrates predictive maintenance by classifying machine failures  

The project leverages a synthetic dataset designed to mimic real-world predictive maintenance scenarios.

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Google Colab Link

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1nce52pTxcI4u7oafOz3VoJEserRPFQCp?usp=sharing)

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## 🌟 Introduction

Predictive maintenance is a proactive approach that utilizes data analysis to predict when equipment failure might occur. By anticipating failures, timely maintenance can be performed, reducing downtime and saving costs.

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## 📊 Dataset Overview

The synthetic dataset simulates real-world maintenance scenarios with:

- **10,000 records** and **14 features**

- **Target variable**:

  - `Failure_Type`: Specifies the type of failure (multiclass labels).

- **Features** include:

  - Continuous: Temperature, rotational speed, torque, tool wear, etc.

  - Categorical: Product quality, serial numbers, etc.

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## 🚀 Project Workflow

1. **EDA & Data Preperations**:

   - Statistical Analysis

   - Visualizations

   - Handling missing values and outliers

   - Skewness Analysis

   - Correlation Analysis

2. **Feature Engineering & Normalization**:

   - Three new features were engineered

   - Ordinal and Standard normalization 

3. **Model Training**:

   - Multiclass classification using algorithms like Decision Trees, Gradient Boosting, etc.

4. **Evaluation**:

   - Metrics: Accuracy, Precision, Recall, F1-Score, Support

   - Classification report

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## 🛠️ Requirements

- Python 3.8 or above

- Libraries:

  - pandas

  - numpy

  - scikit-learn

  - matplotlib