Prediction or Classification Using Ensemble Methods in R

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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Prasanna Lakshmi T

Advisory System Analyst at IBM

Initially, I was unaware of how this would cater to my career needs. But when I stumbled through the reviews given on the website. I went through many of them and found them all positive. I would... Read More

Swati Patra

Systems Advisor , IBM

I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More

What will you learn

Understanding the problem statement
Importing the dataset
Performing basic EDA and preprocessing
Understanding what is bagging and boosting
Performing train_test_split
Applying ensemble model DecisionTreeClassifier
Applying Random Forest Classifier
Applying boosting model Adaboost Classifier along with Decision Tree Classifier
Applying Boosting model Gradient Boosting Tree classifier
Selecting the best model and using it for predictions

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.

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Curriculum For This Mini Project

02h 36m
02h 38m