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Prediction or Classification Using Ensemble Methods in R

In this data science project, you will learn to predict churn on a built-in dataset using Ensemble Methods in R.

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What will you learn

  • Where and why to apply ensemble methods
  • Ensemble Bagging and Boosting models implementation
  • Stacking model as ensemble model
  • Implementation and inferences
  • Hands-on example using a standard sample dataset

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Project Description

Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. In the age of artificial intelligence and machine learning the ensemble, methods are becoming new norms, as a stand-alone model won't be sufficient to capture the dynamics of the data variability.