Introduction To Predictive Modeling:
Core ideas in data mining - Supervised and unsupervised learning - Classification vs Prediction -Steps in data mining- SEMMA Approach - Sampling -Pre-processing - Data cleaning - Data Partitioning - Building a model - Statistical models - Statistical models for predictive analytics.
Predictive Modeling Basics:
Data splitting – Balancing- Overfitting –Oversampling –Multiple Regression - Artificial neural networks (MLP) - Variable importance- Profit/loss/prior probabilities - Model specification - Model selection - Multivariate Analysis.
Association Rules-Clustering Models –Decision Trees- Ruleset Models- K-Nearest Neighbors – Naive Bayes - Neural Network Model – Regression Models– Regression Trees – Classification & Regression Trees (CART) – Logistic Regression – Mulitple Linear Regression Scorecards –Support Vector Machines – Time Series Models - Comparison between models - Lift chart - Assessment of a single model.
Predictive Modeling Markup Language:
Introduction to PMML – PMML Converter - PMML Structure – Data Manipulation in PMML – PMML Modeling Techniques - Multiple Model Support – Model Verification.
Tools and Technologies:
Weka – RapidMiner – IBM SPSS Statistics- IBM SPSS Modeler – SAS Enterprise Miner – Apache Mahout – R Programming Language
Real time case study with modeling and analysis.
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