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Instructions
- Run the telcoChurn-main.R to drive the R demo
- Run the telcoChurn-modelComparison.R to compare different algorithms that we tried to build churn models
Description
- telcoChurn-setUp.R - Setting up relevant R packages
- telcoChurn-evaluate.R - Defining pre-functions for model evaluation
- telcoChurn-dataExploration.R - Creating a Shiny application to explore and visualize the data
- telcoChurn-dataPreparation.R - Defining functions to do data pre-processing and spliting in order to generate suitable training and testing data sets
- telcoChurn-trainModel.R - Defining a function to train the telco churn model with rxDForest algorithm
- telcoChurn-main.R - Main R file driving the demo execution
- telcoChurn-modelComparison.R - R file to build and compare different tree-based classification models, including CRAN R algorithms - randomForest and xgboost, RevoScaleR algorithms – rxDForest and rxBTrees