Defect Data Analysing

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Key Features
  • Collected data from the manufacturing process
  • Bulk Defect data analysing.
  • Developed predictive models to forecast defect.

Client Overview

A Pan India company which has business interests in fields as varied as steel rolling mills, flour mills, real estate, construction, plantation and medical care. The group has acquired many accreditations in its feather and one of the most respected Indian Family business houses today.

The group vision is to be an engineering and manufacturing conglomerate that will become the one-stop solutions provider to its customers in terms of machined castings, forgings, fabrications and assemblies.

  • Client Need This

  • Store the Data and provide data Security.
  • Wanted to reduce the number of defects and improve the quality of their products.
  • Accurately predict defect rates and optimise their production process.

Problems We Faced

The problem we faced was that the data was not organized in a way that was easy to analyze. We needed to develop a system to organize the data and make it easier to analyze. We need to develop a Custom prediction model for predicting defect. The analysis also provided insights into how the process could be improved to reduce the number of defects.

  • Solutions We made

We developed a data processing pipeline that organized the data into a format that was easier to analyze. We then used data science techniques to identify patterns in the data and develop predictive models to forecast defect rates and optimize production processes. Finally, we implemented the models and provided the client with the results.

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Tools & Technologies Used

Data Science

Flask

Python

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