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# 管理信息系统代写 MGT3002代写

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## MGT3002 Management Information Systems

### Submission:  管理信息系统代写

▪ This is an individual assignment – all work must be your own

The copying answers from others will be considered academic cheating and will be sanctioned according to University rules

▪ All submissions must be made electronically on Cyber Campus

Submission Details

• Weightage: 7% of the course grade.
• Upload the softcopy (docx, pdf) of your individual assignment report to Cyber Campus Assignment Submission Folder. Name your file with this format: [MGT32002]

### Part 1: Market Basket Analysis (2 Marks)  管理信息系统代写

#### Learning Objectives:

This assignment is designed to help you practice the concepts of market basket analysis discussed in class, understand how to interpret results, and reinforce the Excel methods covered in class.

#### Problem Framework:

The PC purchase data represents the configurations for the orders of laptops placed over the web. The main options from which customers can choose are the type of processor, screen size, memory, and hard drive. A “1” signifies that a customer selected a particular option. If the manufacturer can better understand what types of components are often ordered together, it can speed up final assembly by having partial completed laptops with the most popular combinations of components configured prior to order, thereby reducing delivery time and improving customer satisfaction.

• Go to Data Mining -> Associate -> Association Rules
• Input options:

o Data in binary matrix format: Choose this option if each column in the data represents a distinct item and the data are expressed as 0s and 1s.

• Specify the minimum support as to 5 and the minimum confidence threshold to80%.

#### Assignment Question:

• Q1: From the results, report the list of association rules. What is your interpretation of Rule 3 shown in the output?

### Part 2: Classification (5 Marks)   管理信息系统代写

#### Learning Objectives:

This assignment is designed to help you get familiarized with classification techniques.

#### Problem Framework:

Classification methods seek to classify a categorical outcome into one of two or more categories based on various data attributes. In credit approval decisions, the categorical variable of interest is the decision to approve or reject a credit application based on the predictor variables including credit score.

• To partition the data into training and validation sets, select Partition from the Data Mining Group and then choose Standard Partition. Among three options, select “Automatic percentages.” If you select this, 60% of the total number of records in the data set are assigned randomly to the training set and the rest to the validation set.

• Select Discriminant Analysis from the Classify menu in the Data Mining Group. Make sure the worksheet specified is the one with the data partition.
• In the first dialog, specify the input variables and the output variable. The “success” class

corresponds to the outcome value that you consider a success – in this case, the approval of the loan to which we assigned the value 1. The cutoff probability defaults to 0.5, and this is typically used.

• In the second dialog, select the Linear Discriminant: Display – Show LDA Model option to display the functions that define each class in the output.

• In the third dialog, check the boxes for Summary Report and Detail Report. To classify the new data in the Additional Data worksheet, click on Detailed report in the Score new data in Worksheet pane of the dialog.

Now, please do the following testing step:

• In the New Data (WS) dialog, select the Additional Data worksheet in the worksheet field and highlight the range of the new data in the Data range field, including headers. Because we use the same headers, click on Match By Name and then click Finish.

#### Assignment Question:

• Q2: Report the two classification functions displayed in the Linear DiscriminantFunctions section.
• Q3: From the Training: Classification Summary and Validation: ClassificationSummary section, report the two confusion matrices. What are the prediction accuracyrates of training and validation data?