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TIVAN training

Predictive Analytics



Introduction to the world of predictive analytics.


Techniques presented

Abandonment prevention, propensity to purchase, behavioral segmentation, basket analysis and credit risk, predictive and classification analyses.



Minimum familiarity with the PC, with Windows and with the most common office automation tools is required.


Predictive analytics

  • Introduction to predictive analytics

  • CRISP-DM: a standard methodology for predictive analytics


Fields of analysis/case studies

  • The prevention of abandonment

  • The propensity to buy

  • behavioral segmentation

  • Basketball Analysis

  • Credit risk and scoring grids


The mining process

  • Discovering relationships between data

  • Data sampling, partitioning and balancing

  • The overtraining

  • Evaluation of models


Predictive analytics and classification

  • Decision trees

  • The neural networks

  • The linear regression

  • Logistic regression

  • K-means Clustering

  • Two-step clustering

  • Association Rules Apriori


Data mining workshops

  • Examples of modeling through the analysis methodology



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