Industrial Machinery Predictive Maintenance M.L. Model

This project builds a predictive maintenance model for industrial equipment using machine learning on time-series sensor data. The goal is to forecast potential machinery failures, minimize unplanned downtime, and improve operational reliability through data-driven insights.

🚀 Technologies Used:

• Python.
• Pandas.
• scikit-learn.
• Time Series Analysis.
• Jupyter Notebook.

📊 Project Highlights:

• Advanced feature engineering from raw time-series sensor data.
• Supervised machine learning model development, tuning, and evaluation.
• Predictive insights on equipment failure risk and maintenance schedules.
• Clear reporting of model performance and operational impact.

📂 Files:

• predictive_maintenance.csv — sensor dataset.
• Industrial_Machinery_Predictive_Maintenance_ML_Model.ipynb — Jupyter Notebook with full pipeline.

▶️ How to View & Run:

Click in the Industrial_Machinery_Predictive_Maintenance_ML_Model.ipynb Jupyter Notebook in this repository (recommended for non-technical people) OR Access the read-only executable version of the notebook in Google Colab:

This provides an interactive, executable review of the model development and analysis without requiring local setup.

📄 License

MIT License

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