Why Does Learning SAS Predictive Modeler Do Wonder for Your Career?
Why Does Learning SAS Predictive Modeler Do Wonder for Your Career?

Why Does Learning SAS Predictive Modeler Do Wonder for Your Career?

SAS Predictive Modeler Exam

This SAS Predictive Modeler exam will take you through the process of SAS Predictive Modeler/Predictive Analytics. A statistical technique or machine learning algorithm is utilized to predict an outcome better. We will be using Logistic Regression. This SAS Predictive Modeler exam proposes to start you on your journey to becoming a top data analyst. To do that, you must understand the methodology or methods at your disposal in solving these problems.

An Overview of SAS Predictive Modeler

SAS Predictive Modeler is beneficial for an organization as it gives futuristic insights that will help you maintain a competing advantage. How will you feed the predictive model?

The following are the causes that your analyst can use for the predictive model:

  • Survey Data/Polling
  • Promotion and Digital marketing data
  • Economic data
  • Web Traffic Stats
  • Data from sensors and beacons
  • Transaction data
  • CRM software data

To be the business leader, you must align the predictive model with your enterprise’s strategic aims. Data organization is another aspect that an analyst must focus on. The model must follow the data so that machines can create outputs and forecast for hypothesis testing. Then the business intelligence tools will provide you insights in visuals or graphs, or report format.

So, when you are combining predictive models in your business process modeling, the following are the things you require to consider:

  • Data-gathering
  • Benchmark analysis
  • Data-segmentation
  • Goals & KPIs evaluation
  • Plan execution
  • Process streamlining
  • Action plan as per the report

SAS Predictive Modeler Exam Prerequisites

The SAS Certified Predictive Modeler Using SAS Enterprise Miner 14 examination explains your information about data preparation and creates an understanding of evaluating and completing models. Also, you will learn to develop predictive models and make pattern analysis.

This examination will help you achieve heights in your career. However, before planning to perform this examination, you are expected to have the following mentioned skills:

Candidates should have a firm knowledge and mastery of the functionalities for predictive modeling in SAS Enterprise Miner.

Top Reasons Why SAS Predictive Modeler Is Important Today? 

1. Secure An Unique Competitive Stronghold

You can analyze your business’ strengths and competitors' weaknesses using SAS Predictive Modeler to develop valuable predictive models. The SAS Predictive Modeler produced using the enterprise data taps into an understanding, limiting only to the organization. So, each predictive model and the insights gained through them are beyond the reach of common knowledge.

2. Enhance Your Core Business Capabilities

Improving your business offerings' core competencies is a step ahead and a necessary step to grow beyond increasing sales. The ultimate goal is to focus on using SAS Predictive Modeler to enhance your market approach, whether promoting your product or service offerings. This can be done in different industrial segments, including predicting health risks for proactive healthcare and forecasting inventory demand in retail.

3. Develop Customer Maintenance Strategies

SAS offers a unique competitive advantage to a variety of customer-facing activities. Also, it gives the required knowledge to target customers at the right moment. Developing customer retention strategies is the primary significance for businesses even though churn modeling may be the most widespread SAS Predictive Modeler's business application. You can also effectively spend your marketing dollars by predictively scoring customers based on their next probable action.

Why Is Predictive Models Performance Evaluation Important?

Selection of metrics impacts how the performance of the SAS Predictive Modeler is measured and analyzed. But metrics can also be deceiving. If we are not using metrics that accurately measure how accurate the model predicts our problem, we might be fooled to believe that we built a robust model. Let’s take a look at an example to explain why that can be a problem.

For example, take the prediction of a rare disease that happens in 1% of the population. If we do a metric that only shows us whereby good the model is at making the correct prediction, we might end up with a 98% or 99% accuracy because the model will be right in 99% of the times by predicting that the person does not have the condition. That is, however, not the point of the model.

Instead, we might need to use a metric that estimates only the true positives and the false negatives and determines how well the model predicts the disease's case.

Proper Predictive Modeler evaluation is also necessary because we want our model to have the same predictive ability across many disparate data sets. In other words, the results need to be comparable, measurable, and reproducible, which are essential parts for many industries with heavy regulations, such as insurance and healthcare.

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