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⚙️ Machine Failure Prediction Using Machine Learning

🚀 Project Overview

This project focuses on predicting machine failures using machine learning techniques to improve maintenance strategies and reduce downtime. By leveraging algorithms like Random Forest and Gradient Boosting, the model analyzes key machine parameters to determine operational status and predict failures.

🛠️ Tools and Technologies

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-Learn
• Algorithms: Random Forest, Gradient Boosting

✨ Key Features

• Data Preparation:
  • Extensive exploratory data analysis (EDA) and data cleaning to ensure high-quality inputs.
  • Transformation and feature engineering on parameters such as air temperature, process temperature, rotational speed, torque, and tool wear.
• Model Training and Evaluation:
  • Trained and fine-tuned Random Forest and Gradient Boosting classifiers.
  • Assessed performance using a confusion matrix and other evaluation metrics.
• Impact:
  • Enhanced maintenance strategies by predicting failures.
  • Reduced machine downtime, increasing overall operational efficiency.

📊 Dataset

• The dataset includes features such as:
  • Air Temperature
  • Process Temperature
  • Rotational Speed
  • Torque
  • Tool Wear
• Data was preprocessed to remove outliers, handle missing values, and normalize features for optimal model performance.

📂 Repository Structure

|-- README.md  
|-- machine_failure_prediction.ipynb   # Jupyter Notebook for model development  
|-- data/  
    |-- machine_data.csv               # Dataset used for training and testing  
|-- images/  
    |-- confusion_matrix.png           # Visual representation of model evaluation  

🖼️ Model Performance

The model achieved high accuracy in predicting machine failures. Below is the confusion matrix used to evaluate performance:

🚀 How to Use

1. Clone this repository:

   git clone https://github.com/username/machine-failure-prediction.git  

2. Install the required Python packages:

   pip install -r requirements.txt  

3. Run the Jupyter Notebook (machine_failure_prediction.ipynb) to explore the EDA, model training, and evaluation steps.

💡 Future Enhancements

• Integrate real-time data streaming for failure prediction.
• Experiment with deep learning techniques for improved accuracy.
• Build a user-friendly interface for predictive maintenance monitoring.

🤝 Contribution

Contributions are welcome! Fork the repository and submit a pull request with your improvements.

📬 Contact

For queries or feedback, reach out to Vinay Kumar at [[email protected]].

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