The inputs of various sensors for different wafers have been provided. In electronics, a wafer (also called a slice or substrate) is a thin slice of semiconductor used for the fabrication of integrated circuits. The goal is to build a machine learning model which predicts whether a wafer needs to be replaced or not(i.e., whether it is working or not) based on the inputs from various sensors. There are two classes: +1 (working condition) and -1 (Not working to replace)
The pipeline of a project below image
- In this project we have used KMEANS, RANDOM FOREST and XGBOOST for Fault Detection.
- Download the dataset for custom training
- https://github.com/ameerkings123/Wafer_Sensors_Fault/tree/main/Training_Batch_Files
- Download the file and Place it into " Training_Batch_Files/ ". folder.
1.Clone or download the repo.
2.Open command prompt in the downloaded folder.
3.Create a virtual environment
$ pip install virtualenv$ virtualenv environment_nameInstall dependencies :-
$ pip install -r requirements.txtRun the application:
python main.py📖 Please Go through LLD Documents for more info.
- ABDUL AMEER NABILLA