🔧 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

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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