BIAM 500 WEEK 6 DATA MINING
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BIAM 500 Week 6 Complete Work DeVry
BIAM 500 WEEK 6 DATA MINING
Read the end-of-chapter application case “Coors Improves Beer Flavors with Neural Networks” at the end of Chapter 6 in our textbook, and respond to the following questions.
- Why is beer flavor important to Coors’ profitability?
- What is the objective of the neural network used at Coors?
- Why were the results of Coors’ neural network initially poor, and what was done to improve the results?
- What benefits might Coors derive if this project is successful?
- What modifications would you make to improve the results of beer flavor prediction?
BIAM 500 Week 6 Lab
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Step 1: Load and Prepare Data Workbook contains worksheets for the OldCustomer and NewCustomer data sets. Age column was added to both sheets with formula to calculate customer age. CustomerClassification column was added to both sheets, with formula to determine customer classification on the OldCustomers sheet only.
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Step 2: Define the Training/Testing and Prediction Data Sets A training and testing data set is defined based on the OldCustomer data, and a prediction data set is defined based on the NewCustomer data. Variable types are set appropriately for both data sets.
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Step 3: Train and Test Neural Network Neural network is trained based on the training and testing data set, and tested using a randomly selected sample of 20% of the cases. Individual case test results are in the OldCustomers sheet, and summary results are in the Lab6_yourlastname_Summary workbook.
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Step 4: Predict Classifications for New Customers Predicted classifications generated by the neural network are recorded in the NewCustomers sheet.
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Step 5: Write Opinion Paper and Submit Paper is in APA format, free of typographical, spelling, and grammar errors, and clearly states appropriate findings and recommendations from the analysis. Findings and recommendations are relevant to the strategic objective and are supported by the dashboard.
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Total Points: 70.0
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