Business intelligence

Home Blog Business intelligence

Business intelligence

Graded Discussion: Techniques for Predictive Modeling
When we think about various techniques for predictive modeling, always remember that the realm of predictive analytics is the use of data which is supported with various statistical algorithms and even machine learning techniques. When properly used, these methods and techniques can be used to identify the probability of future outcomes based on historical data.
When an organization needs to use predictive modeling or analytic techniques, they have many tools to consider and some include the following not limited to:
SAS Predictive Analytics (https://www.sas.com/en_us/insights/analytics/predictive-analytics.html)
IBM Predictive Analytics (https://reviews.financesonline.com/p/ibm-predictive-analytics/)
SAP Predictive Analytics (https://searchsap.techtarget.com/definition/SAP-Predictive-Analytics)
Rapid Miner Predictive Analytics (https://rapidminer.com/resource/operationalize-predictive-analytics/)
Altair Predictive Analytics (https://www.datawatch.com/in-action/angoss/)
Any many others
Using the list of predictive analytic tools listed above or using others of choice, compare at least three or more of these tools based on research and or experience and share with the class which you would prefer any why?
 
“Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!”

The post Business intelligence appeared first on Nursing Experts Help.

Add comment

Business Intelligence

I’m trying to learn for my Article Writing class and I’m stuck. Can you help?

Using APA in discussion posts is very similar to using APA in a paper. And it helps to think of your discussion post as a short APA paper without a cover page. You need to cite your sources in your discussion post both in-text and in a references section. If you need help forming in-text citations, check out your in-text citation page on the APA guide

Topic 1: Data Mining

1. What recent factors have increased the popularity of data mining?

2. What are the key differences between the major data mining methods?

3. Is data mining a new discipline? Explain.

Add comment

Academic Research Pro