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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Shailesh Kurdekar

Solutions Architect at Capital One

I have worked for more than 15 years in Java and J2EE and have recently developed an interest in Big Data technologies and Machine learning due to a big need at my workspace. I was referred here by a... Read More

Arvind Sodhi

VP - Data Architect, CDO at Deutsche Bank

I have extensive experience in data management and data processing. Over the past few years I saw the data management technology transition into the Big Data ecosystem and I needed to follow suit. I... 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