Data mining techniques

The actual proposal that you will be submitting should outline a problem that you want to solve and how you will use data mining techniques to solve it. A business problem from your work would be a likely candidate but it doesn’t have to be directly related to your work. The key is that this should not be a hypothetical example like something from the textbook, it should be something with which you have personal experience.

The draft of your proposal should include:

1. A description of the problem you want to support with a data science solution

2. Why the problem you selected can be solved with predictive modeling

3. The use you want to support with this solution and how it could add value to an organization

4. A description of the model and data mining techniques that will be used and why they are appropriate given the problem at hand. If doing supervised learning the target variable should be clear.

5. How and where you would get the data required to train your model. This should also include at least 5 attributes that would help your predictive modeling.

Note that you are not required to find real data nor perform any data analysis for this project, but you should write your proposal as if this were the start of a real project you were considering undertaking. When writing your proposal, consider the steps of the CRISP-DM process to help guide your thinking. However, the proposal should be written in paragraph form.