Manufacturers can face significant costs due to product defects, which can be reduced by using a predictive model to assess product quality early in the production process.
A predictive model would allow the manufacturer to discard or subject high-risk semi-finished products to additional processing before advancing them.
The dataset is from an electronics manufacturer, with bi-secondly sensor readings at the first 2 stages of a production pipeline.
At the end of the pipeline, a critical signal strength measurement determines if the finished product is defective.
The goal is to build a model to predict this critical measurement using data from the first 2 stages.