Increasing the yield efficiency is a requirement that is very difficult to pursue manually, as it requires manual effort and a considerable time window to see the results. Hence, we try to build a model (based on Deep Learning) that can carry out the process many times faster and without the involvement of any real time queries on the processes. All we require is the data collected, pertaining to parameters and objects, prior to carrying out the processes/unit steps. However, the proposed model can only be applied on a single process for its optimisation.

For the purpose of data storage, the tool SQL Management Server 2014 was used. The data analysis was carried out in a python environment with some of the very helpful python3 libraries to carry out data science algorithms in an elegant and efficient manner.

The entire dataset can not be shared for it being sensitive to the organisation.

A detailed explaination can be seen in the final_report.pdf.