This repository contains code for predictive maintenance of turbofan engines using machine learning algorithms. The goal is to predict the Remaining Useful Life (RUL) of turbofan engines based on sensor data.

Predictive maintenance is crucial for ensuring the reliability and efficiency of turbofan engines. By predicting the remaining useful life of engines, maintenance tasks can be scheduled more effectively, reducing downtime and maintenance costs.

• Data loading and preprocessing
• Exploratory Data Analysis (EDA)
• Feature engineering
• Model training and evaluation
• Hyperparameter tuning
• Feature selection
• Model interpretation and visualization
• Saving trained models

The dataset used in this project consists of sensor data collected from turbofan engines. It includes information about engine settings and various sensor measurements. Additionally, the dataset contains labels indicating the remaining useful life of each engine.

To run the code in this repository, follow these steps:

1. Clone the repository:

git clone https://github.com/sayan18012004/Predictive-Maintenance.git

3. Run the Jupyter notebooks or Python scripts in the code directory.

• Dataset: Contains the dataset files.
• Models: Contains trained model files.
• readme.md: This file.

Contributions are welcome! If you find any bugs or have suggestions for improvement, please open an issue or submit a pull request.