Develop a Predictive Analytics approach to analyze existing quality data on customer rejects

INSTRUCTIONS TO CANDIDATES

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Project Purpose

• Develop a Predictive Analytics approach to analyze existing quality data on customer rejects (remakes and repairs).

• Use historical quality data to create a statistical model that correlates product attributes and specifications with quality performance (reject rate).

• Apply this model to incoming orders to generate a risk score or rank and allow the production facilities to focus additional inspection resources on the  orders with the highest potential for failures.

. Use predictive analytics to make inspection process more efficient.

. Find out the main reasons why products are rejected

Project Outcomes/Deliverables

Expected Outcomes: Conduct classification predictive modelling to predict which  products will get rejected by customers in near future (1-90 days). The data contains over 4 million rows and 25 variables stored in txt file. Also use descriptive analytics to help find  meaningful trends such as customer behavior and seasonal or geographical trends as well as unsupervised analytics through clustering methods to increase data  understanding.

Deliverables: Report on the accuracy rate of the model and find the statistical technique  that can produce the highest accuracy. How do you tune the model and make model  features selection? We also would like to see results of a simulation that shows model  accuracy (sensitivity & precision), as well as root cause analysis. Finally, we would like to get recommendations on how we can implement the model and also use information we learn from the model to prevent customer rejects.

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