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Overview Is your organization overflowing with data but struggling to turn it into useful, timely insights that help people make better, faster decisions? It’s a common problem with potentially huge impacts. Without fact-based answers to complex questions, you’re forced to make best-guess decisions about:
• What products will different customers purchase and when?
• Which customers are leaving, and what can be done to keep them?
• How should prices be set to ensure profitability and competitiveness?
 • How are maintenance schedules affecting time to failure?
• How can fraudulent transactions be prevented?

To accurately answer such questions, you need powerful, multipurpose predictive analytics solutions that can turn data into insights using state-of-the-art statistical and machine-learning algorithms. That’s why SAS offers a variety of predictive analytics solutions. These integrated solutions are designed to meet the needs of all types of users – from business managers and business analysts to data stewards and data scientists to chief scoring officers and IT staff.
Our Approach:
 Whether you are just getting started with predictive analytics or you’ve been using it for years, our solutions can help you take analytical decision making to the next level. With SAS predictive analytics, accurate insights are delivered at the right time, dramatically increasing the reach and value of your data. And, you can make evidence-based decisions every day – instead of occasionally or re actively. SAS solutions can be easily configured for any line of business or group of users so they can:
• Discover relevant insights with speed and flexibility.
• Analyze data to find results that can be confidently acted upon.
 • Act quickly to make better decisions that drive better actions.
 • Monitor and evaluate predictions and decisions to verify continued relevance   and accuracy.
• Manage a growing portfolio of predictive assets effectively.

 SAS offers solutions that cross the predictive modeling life cycle, including data preparation, visualization, descriptive and predictive analysis, model deployment, governance and ope-rationalization.

Analytical Data Preparation:
 Data preparation and data quality are key enablers of predictive modeling processes. Your input data, which may span multiple platforms and contain multiple big data sources, must be integrated and synchronized into a clear, coherent format. But manually transforming data into properly structured, representative modeling tables can take a great deal of time. With SAS, you can save time by using the software to:

• Structure data sets, transpose them and aggregate values into representative modeling tables.
• Optionally sample a representative set of data to speed processing time.
 • Segment observations into groups, interactively bin variable values, filter outliers, replace missing values and derive new variable transformations.
• Automatically generate the required score code to apply predictive models to    new data, including all data preparation transformations, on many different   operational processing platforms, including Hadoop.

 Visualization and Exploration:
 Data discovery and visualization should be supported during each step of the modeling process so you can continually evaluate, explain and validate results. SAS enables you to interact with your data to explore relationships, spot trends, dig into areas that interest you and move in directions that you hadn’t considered before. Our solutions provide interactive, dynamic and visually appealing graphics to help you:

• Quickly understand data, see important relationships and trends, and make better, faster decisions.
 • Explore data from all angles and move rapidly from one visualization to another to identify relevant variables and discover the information needed to     fuel the next step in the analysis process.

Statistical Analysis:
 Powerful statistical analysis is the foundation of SAS solutions. Use it to perform everything from simple descriptive statistics to complex Bayesian analyses, including variance analysis, categorical data analysis, survival analysis, predictive modeling, experimental design, time series analysis, clustering and survey data analysis.
SAS predictive analytics key functional areas:
  • Analytical Data Preparation
  • Data Exploration and Data Discovery
  • Machine Learning and Predictive Algorithms
  • Automated Model Tournaments
  • Model Validation and Assessment
  • Batch or Real-Time Scoring
  • Automated and Governed Decision Flows

Predictive Analytics  -EpochSASTraining
Epoch Research Institute India
Largest and Oldest SAS Training Institute
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