This project focuses on predictive maintenance in industrial settings, specifically classifying types of failures based on sensor data. Using Jupyter Notebook for data analysis and modeling, streamlit to deploy and CRISP-DM for project organization, we aim to enhance machine reliability and reduce downtime through maintenance strategies.
- Classify types of failures using machine learning models.
The analysis utilizes the Predictive Maintenance Dataset (AI4I 2020), containing sensor readings and failure information from industrial equipment.
- Jupyter Notebook: Contains data preprocessing, exploratory data analysis (EDA), modeling, and evaluation using Python libraries such as pandas, numpy, and scikit-learn.
- CRISP-DM Framework: Organizes the project into phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
- Streamlit App: Deploys the trained model for interactive predictions and analysis.
This repository contains the Jupyter Notebook and Streamlit app for the predictive maintenance project.
For a detailed description of the project, including methodologies, insights, and model performance, please check out the full article on Medium. The article provides a comprehensive overview of the project's objectives, methodologies, and results. You can find the article using the link below:
Feel free to reach out to me for any questions or feedback regarding the project or the analysis.
Contributions and suggestions to improve this project are welcome.
For inquiries or feedback, you can contact me via LinkedIn.