Predictive Analytics
Goals:
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.
Prerequisites:
Minimum familiarity with the PC, with Windows and with the most common office automation tools is required.
Predictive analytics
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Introduction to predictive analytics
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CRISP-DM: a standard methodology for predictive analytics
Fields of analysis/case studies
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The prevention of abandonment
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The propensity to buy
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behavioral segmentation
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Basketball Analysis
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Credit risk and scoring grids
The mining process
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Discovering relationships between data
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Data sampling, partitioning and balancing
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The overtraining
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Evaluation of models
Predictive analytics and classification
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Decision trees
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The neural networks
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The linear regression
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Logistic regression
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K-means Clustering
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Two-step clustering
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Association Rules Apriori
Data mining workshops
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Examples of modeling through the analysis methodology