Predictive analytics

Predictive analytics helps connect data to effective action by drawing reliable conclusions about current conditions and future events. In Predictive Analytics we analyze the data using statistical, mathematical, and other algorithmic techniques. Data should be cleansed and transformed before using it. Cleansing and Transformation are the steps in KDD.

The techniques adopted in Predictive Analytics generate models for

  • Classification– Classification is  technique used to predict group membership for data instances,
  • Segmentation– People with similar attributes tend to display similar patterns in various ways,
  • Forecasting– process of making statements about events whose actual outcomes (typically) have not yet been observed,
  • pattern recognition– encompasses a variety of techniques from statistics, Data Mining and game theory that analyze current and historical facts to make predictions about future events.,
  • sequence and association detection-,
  • anomaly identification-,
  • profiling-,
  • propensity scoring-,
  • rule induction-,
  • text mining-, and
  • Advanced visualization-.

Tactically, predictive analytics identifies precisely whom to target, how to reach them, when to make contact, and what messages should be communicated. The benefits achieved from predictive modeling are as follows:

  • Targeting the optimal allocation of operational resources
  • Ways to acquire more customers who will perform like your most profitable customers
  • Optimizing agronomic indicators for treatments that will maximize crop yield
  • Accuracy improvements to inventory forecasting
  • Anticipating who is about to leave as a customer and the most effective treatment to retain
  • Focus auditing efforts for more effective loss prevention, etc.
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