Helpppppp!!!!!!! For each of the following meetings, explain which phase in the

Question

Helpppppp!!!!!!!

For each of the following meetings, explain which phase in the CRISP-DM process is represented:

1. Managers wnt to know by next week whether deployment will take place. therefore, analysts meet to discuss how useful and accurate their model is.

2. The data mining project manager meets with the data warehousing manager to discuss how the data will be collected.

3. The data mining consultant meets with the Vice President for Marketing, who says that he would like to move forward with customer relationship management.

4. The data mining project manager meets with the production line supervisor, to discuss implementation of changes and improvements.

5. The analysits meeet to disscuss whether the neural network or desicision tree models should be applied.

6. Discuss the need for human direction of data mining. Describe the possible consequences of relying on completely automatic data analysis tools.

7. CRISP-DM is not the only standard process for data mining. Research an alternative methodology(Hint:SEMMA, from the SAS institute) Discuss the similarities and differences with CRISP-DM

Explanation / Answer

1. Managers want to know by next week whether deployment will take place. therefore, analysts meet to discuss how useful and accurate their model is.

Ans: This is the analysis innovate the CRISP-DM method. Within the analysis part the info mining analysts verify if the model and technique used meets business objectives established within the 1st part.

2. The data mining project manager meets with the data warehousing manager to discuss how the data will be collected.

Ans: This is the information Understanding introduces the CRISP-DM method. information the     warehouse is known as a resource throughout the Business Understanding phase; but the particular data assortment takes place throughout the information Understanding section. during this section knowledge is collected and accessed from the resources listed and known within the Business Understanding section.

3. The data mining consultant meets with the Vice President for Marketing, who says that he would like to move forward with customer relationship management.

Ans: The main objective of business is to review throughout the Business Understanding section. So, thus when the meeting it appears the info mining adviser gained success in convincing VP of promoting to supply approval for activity data processing on the client relationship management system.

4. the data mining project manager meets with the production line supervisor, to discuss implementation of changes and improvements.

Ans: The discussion of implementation of changes enhancements and within the project whether or not specific improvements or method changes square measure needed to make sure that each one necessary aspects of the business square measure accounted is performed underneath the analysis section. The meeting command with business objective to gather and cleanse the information to make sure the standard of knowledge.

6. Discuss the need for human direction of data mining. Describe the possible consequences of relying on completely automatic data analysis tools.

Ans: Many software system vendors market their analytical software system as being plug-and-play out of-the-box applications that may offer solutions to otherwise noncompliant issues

Without the requirement for human management or interaction. Some early definitions of knowledge mining followed this target automation. For instance, Berry and Linoff, in their

Book data processing Techniques for selling, Sales and client Support gave the following definition for knowledge mining: “Data mining is that the method of exploration and analysis, by automatic or semi-automatic means that, of huge quantities of knowledge in order to get significant patterns and rules”   3 years later, in their sequel, Mastering data processing   the authors go back their definition of data mining and state: “If there's something we have a tendency to regret, it's the phrase ‘by automatic or semi-automatic means’ ... as a result of we have a tendency to feel there has come back to be an excessive amount of focus on the automated techniques and not enough on the exploration and analysis.

7. CRISP-DM is not the only standard process for data mining. Research an alternative methodology (Hint: SEMMA, from the SAS institute) Discuss the similarities and differences with CRISP-DM.

Ans: The variations between CRISP-DM and SEMMA. Firstly, SEMMA was developed with a selected data processing computer code package in mind (Enterprise Miner), instead of designed to be applicable with a broader vary of information mining tools and therefore the general business setting. Since it's targeted on SAS Enterprise jack computer code and on model development specifically, it places less stress on the initial coming up with sections lined in CRISP-DM (Business Understanding and knowledge Understanding phases) and omits entirely the readying phase.

That said, there square measure some similarities additionally. The Sample and Explore stages of SEMMA roughly correspond with the info Understanding section of CRISP-DM; Modify interprets to the info Preparation section; Model is clearly the Modeling phase, and Assess parallels the analysis section of CRISP-DM. to boot, each models square measure supposed to be somewhat alternating instead of linear in scenery. The CRISP-DM model conjointly emphasizes data processing as a non-linear, adjective method.

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