Description: Applying the Cross Industry Standard Process for Data Mining (CRISP-DM) framework

Post author By Doe, RN, BSN
Post date February 5, 2023

CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model that serves as the base for a data science process. It has six sequential phases:

Business understanding – What does the business need?
Data understanding – What data do we have / need? Is it clean?
Data preparation – How do we organize the data for modeling?
Modeling – What modeling techniques should we apply?
Evaluation – Which model best meets the business objectives?
Deployment – How do stakeholders access the results?

Review the following case studies found here:

https://www.knowledgehut.com/blog/data-science/top-data-science-case-studies

Choose one of the industries and create an APA-formatted white paper by following these steps:

Step 1. [BUSINESS UNDERSTANDING] Provide additional research and thoroughly outline the problem faced by the industry chosen. (1-page minimum)

Step 2. [DATA DISCOVERY/UNDERSTANDING] Locate data (e.g., Kaggle.com or similar) relevant to your chosen industry and produce a model (Use one of the tools RStudio, Python, Jupyter, RapidMiner, or Tableau) that you propose will help resolve the issues you discovered in your research and readings. (Minimum 1 graphic that adequately depicts your model)

Step 3. [POSTURING STATEMENT] Articulate what your proposed model is postured to accomplish. (1-page minimum)

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