CRISP-DM Method: Project Management Activities for GE

2-1 MILESTONE ONE: PROJECT SUMMARY AND ANALYTIC PLAN
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PROJECT SUMMARY AND ANALYTIC PLAN
As the data analyst assigned to develop the predictive model for GE, my responsibility is to create a model that will support the business problem. The project goes through the CRISM-DM methodology. The project summary and analytic plan will identify the business problem, state the research question, and discuss the solution based on the six CRISP-DM phases.
CRISP-DM ANALYTIC PLAN
Business Understanding
Business understanding involves gaining a deeper insight into GE's business. The step involves data mining. The focus is on understanding GE's objectives (business problem) and project requirements. It also encompasses general project management activities based on the CRISP-DM model.
Project management activities
1 Determine business objectives
Phase I will involve gaining a thorough understanding of GE’s business model and perspective. There is a need to establish why high-profile employees leave the company. The overarching objective is to ascertain the reason for high employee attrition at GE. The second objective is to develop a predictive model that can accurately predict the possibility of high employee attrition (Piatetsky, 2014).
2 Situational Assessment
Identify data sources and check for their availability. Analyze project requirements, assess risks, and review the cost-benefit analysis (CBA).
3 Set data mining goals
Data mining goals will be defined and aligned to GE's business objectives. Define project deliverables from a data mining standpoint.
4 Develop a project plan
Data Analytics tools and technologies-R and SAS will be the data analytics and modeling tools of choice. A thorough understating of GE’s needs will be essential for project success.
* Data Understanding Phase
The data understanding phase will involve the identification, collection, and analysis of GE's data sets. Doing so will help understand the reason behind high-profile employee attrition. It will comprise four tasks:
1 Data collection-The data set will be loaded into SAS or R studio analytics tool.
2 Description-Evaluate and documented data e.g,...