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Prediction of Manufacturing Data using several machine learning approaches


This thesis is to meet the needs of developing automation process on defective testing in the

hard disk drives production process, focusing on machine learning and articial intelligence.

The objective is to to predict the defectives and improve the accuracy rate by using the tree

based algorithm and neural networks. The powerful models can help manufacturing process

improving time and labor efficiency.

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