Process

Step 1: Feature Engineering

This involves

• Deep domain expertise results in creation of relevant derived attributes
• Enhanced variable selection using statistical modelling techniques
• Model selection, verification & validation for balanced fitment

Step 3: Parameterization & Scalability

This involves

• User defined parameters for consideration in the analytical framework
• Data quality monitoring
• Ability to support large volumes of data on both traditional and big data stacks

Step 2: Creating Integrated Solution

This involves

• Results of the data modelling are integrated with the workflow/reporting/visualization framework
• Superior visualization for ease of interpretation and decisioning
• Automated email of training and prediction results with complete details including error curves