Predictive Maintenance Using Machine Learning Overview This project implements predictive maintenance using machine learning, focusing on ensemble models to predict equipment failures. The goal is to reduce downtime, optimize maintenance schedules, and enhance operational efficiency.
Features Data preprocessing and feature engineering
Model training using various ensemble methods
Performance evaluation and visualization
Deployment-ready structure for real-world applications
Dataset The dataset includes sensor readings and maintenance logs, used to train and evaluate models. Ensure you have the correct data format before running the models.
Installation Clone the repository and install dependencies:
bash Copy Edit git clone https://github.com/nic-stack/predictive-maintenance.git cd predictive-maintenance pip install -r requirements.txt Usage Run the Jupyter notebook to explore and execute the predictive models:
bash Copy Edit jupyter notebook Open the provided notebooks to analyze the data and train models.
Model Evaluation The project includes evaluation metrics such as accuracy, precision, recall, and F1-score to assess model performance.
Contributions Feel free to fork the repository, create pull requests, or report issues. Contributions are welcome!