The main aim of this project is to build a classification methodology to predict the quality of wafer ( faulty or good) sensors based on the given training data. Client will send the data in multiple set of files in batches at a given location. Data will contain Wafer names and 590 columns of different sensor values for each wafer. Apart from prediction files, we also require a “schema” file from client which contains all the relevant information about the training files such as: Name of the files, Length of Date value in FileName, Length of Time value in FileName, Number of Columns, Name of the Columns and their datatype.

It help’s the client to easily classifiy the good and bad wafer sensor 90% accuracy. Deployed this Project for Internship(at Ineuro.ai) (as Freasher) Built a client facing API using streamli flask.

Project Work Flow :

1. Data Ingestion
2. Data Transformation
3. Model clustering
4. Model Selection & Training
5. Model Evaluation
6. Model Deployment

Link To the GitHub Repository

• scene