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Prediction or Classification Using Ensemble Methods

In this project, we are going to predict churn using a built-in dataset using Ensemble Methods.

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What will you learn

  • Where and why to apply ensemble methods
  • Bagging, 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.