Data mining using SAS enterprise miner

Data mining using SAS enterprise miner

Follow these instructions please:

1) You have to use SAS enterprise miner
* Use this account:

User name: xxxxxx
Password: xxxxxx

Note: DO NOT REMOVE ALL THE WORK FROM THE ACCOUNT, KEEP IT THERE

• You should take a screen shoot for every step

• You will need to load the customer file, which is in CSV format, into your SAS Enterprise Miner workspace. From there you will analyse the data, develop a number of predictive models, evaluate these models to determine which one gives the best results and then to make your recommendations

• You will need to work through/develop a number of classification models. To do this you need to use the data mining tool used in class. In this tool you can have a number of different classification techniques and within each of these you can modify the various parameter settings.

• You will need to develop a number of classification models. When you have developed all of your models (using the appropriate classification techniques available in the tool), you will have to evaluate them and identify the classification model and configuration that gives the best or most appropriate answer.

About the report:
# produce a report (following the CRISP-DM report, as much as possible) detailing your work investigating the data and classifying the provided data.

* The first task you should complete is a data investigation exercise, where you will document the characteristics and other information that you can determine about each Feature.

# You should create one document/report containing all the material for each part of the assignment. Send me a word document
and another one the same document, but Convert this document into a PDF.

The report should clearly show your work in the following areas:

• Data description
• Summary of classification techniques used and various parameter settings
• Set-up and configuration of each algorithm
• Evaluation of the classification models
• Discussion of best or most appropriate model you would recommend
• Discussion of how you would implement the model

Required Tasks

You are required to produce a report (following the CRISP-DM report, as much as possible) detailing your work investigating the data and classifying the provided data.

The first task you should complete is a data investigation exercise, where you will document the characteristics and other information that you can determine about each Feature.

You will need to work through/develop a number of classification models. To do this you need to use the data mining tool used in class. In this tool you can have a number of different classification techniques and within each of these you can modify the various parameter settings.

You will need to develop a number of classification models. When you have developed all of your models (using the appropriate classification techniques available in the tool), you will have to evaluate them and identify the classification model and configuration that gives the best or most appropriate answer.

 

Deliverables

You will be required to document your approach to solving this classification problem. To help you with producing the report you should use the CRISP-DM report layout, where appropriate.

You may need to have additional sections and some of the sections in the CRISP-DM report may not be suitable or required.The report should clearly show your work in the following areas:

• Data description
• Summary of classification techniques used and various parameter settings
• Set-up and configuration of each algorithm
• Evaluation of the classification models
• Discussion of best or most appropriate model you would recommend
• Discussion of how you would implement the model

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