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Predictive Scoring Model Training

Predictive scoring, as a technique, evolved to help organisations decide whether to grant credit to customers who apply to them. Over time, scoring techniques have been applied extensively to different marketing areas like attrition prediction, response modelling, as well. From marketing perspective, scorecards are used for activation, attrition, cross selling, prospecting, spend forecasting etc., while for risk, scorecards are used at application, behavioural, collections, recovery and pricing stages.

This course covers the detailed steps of understanding the data needs to build a predictive model, the considerations for time periods of the information considered, building the predictive framework, the technical aspects of model building and finally model validations and finalisation.

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Course Code: CRS-N-0042514

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Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.

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Course Details

Module 1: The basic requirements of Predictive Scoring

  • Introduction
  • Data considerations
  • Dependant and Independent variables
  • Observation and prediction window

Module 2: Data Integrity

  • Types of data
  • How to treat outliers
  • Missing value treatments
  • Variable distribution

Module 3: Prepare for Model Building 

  • Model development framework
  • Sampling techniques for analysis
  • Development and Validation data construction

Module 4: Model Build and Validation

  • Type of models : understanding and differentiating between linear, non-linear, and truncated models
  • Model Building and Diagonistics
    • o Multi-collinearity
    • o Redundancy
    • o Bivariates, transformations and fine classing
  • • Model validation and finalization
    • o Rank ordering
    • o Validation statistics
    • o Out of time validation

Module 5: Live Model building

  • Real time exercise to build a model based on real world situation

Who Should Attend

  • Digital Marketers
  • Data Analysts




Data Science TrainerNirmal Palaparthi is an Analytics professional with 19 years of experience in building practices from ground up in Asia. He has Co-founded and successfully exited from two Analytics companies. The first was Fractal Analytics, India’s leading third party analytics provider and the second: Mobius Innovations, a context awareness platform company, which was acquired for building significant IP.

He has consulted for clients in 15 countries, across Banking, Retail, Telecom, Consumer Product and Enterprise Software verticals and has built digital ecosystems for multiple clients. He has an engineering degree in Computer Science from the Indian Institute of Technology, Madras and an MBA from the Indian Institute of Management, Ahmedabad.

Data Science Trainer
Currently, Abhisek leads the Japan analytics team in Visa Inc. This regional team is focused on developing analytical solutions for Visa. During his six years with Visa, Abhisek has held roles in both the Product Management and Analytics functions and is one of the founding members of Visa Analytics.

Abhisek holds a Master of Statistics from the Indian Statistical Institute, with experience in secured and unsecured lending, campaign management, CLC Management, Credit Risk and BASEL compliance.

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