The aim of this project is to develop an automated sensor fault detection system for Scania trucks that can identify and diagnose sensor faults in real-time. The system should be able to detect faults in a wide range of sensors, including those used to monitor engine performance, fuel efficiency, and safety features. The goal is to improve the overall reliability and safety of Scania trucks by quickly identifying and addressing sensor faults, reducing the risk of accidents and downtime caused by equipment failure.]

Steps to followed for Machine Learning projects:

1. create enviorment venv file :conda create --prefix venv python==3.8 -y & conda activate venv/
2. create README.md file
3. create requirements.txt file : pip install -r requirements.txt
4. create setup.py ,application.py file : python setup.py install
5. create gitignore file(blank)
6. Create src ,notebooks, templates ,artifacts folder (blank)
7. Create src subfoler anf file naming compomnets/ ,pipelines, init,exception,logger,utils
8. under src/components/ create file name init, data ingestionb,transformation and model_trainer file .
9. under src/pipelines/ create file name init , train_pipeline & predict_piplines
10. under noteboolks/ paste the daatset in csv format.
11. create repository in github and push the code to github
12. Now create an ipyn file and to the EDA and model training of the data in notebooks folder
13. while working in jupyter notebook file make sure use :pip install ipykernel to install kernel
14. Complete the utlis ,exception, logger file codings.
15. Create Model File notebooks