Fault diagnosis in steel plates using machine learning models and thereby optimizing the cost of product testing and also enhancing product quality.
Quality Control and Fault Diagnosis is an important aspect to enhance the quality of production and optimize the cost of product testing. Timely identification of the fault saves input cost. An analysis into the factors responsible for faulty steel plates during the manufacturing process will enable the manufacturer to identify the issue in the plate before packaging. The core of this analysis will involve pattern recognition and classification of faults in steel plates using Random Forest, Neural Networks and Multinomial Logistic Regression.
This objective is achieved through accomplishing the below tasks:\n
This project was done as part of the course ‘MSIS-5223 Programming for Data Science-II’ course in Spring 2020