Data Science

6 steps to data mining awesomeness

Have a data mining project on the horizon?  These 6 steps make up the Cross Industry Standard Process for Data Mining (CRISP-DM) and will help make it awesome!  datamining

  1. Gain an understanding of the business problem you are trying to solve. Are the business requirements well defined?
  2. Get to know the data. What data is available? Is it complete? What data is needed?  Now is also a good time to identify any data quality problems. 
  3. Prepare the data. Data is rarely clean or in the right format for your modeling tools. This step can be time consuming.   
  4. Create your model(s).  – Pick your modeling tool and build your model – Linear Regression, Classification, Clustering. Several techniques can be used to solve the same data mining problem. Now might also be a good time to revisit Step 3 if the data isn’t quite right. 
  5. Evaluate your results.  Are the results meaningful? Do they solve the problem you identified in Step 1?  Ultimately, a decision on the use of the results should be made.
  6. Deploy your model!  How should the model be deployed? What steps should be taken to maximize the benefit of the model and results?

That’s it!

Do you use a different process? I’d love to hear about it. Please leave a comment.

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