Unique & Practical R Tool Tutorial 2 out of 5 Series

 Predict Customer Churn with R (Step-by-Step ML Guide)

Losing Customers? Stop Guessing—Start Predicting!

Every lost customer means lost revenue. But what if you could predict who’s about to leave—before they do?

With R, you can build a machine learning model in minutes—no PhD needed. I’ll walk you through it step-by-step, with real code you can run right now.

Ready to save your business from costly churn?

Why This Matters

Businesses lose revenue from customer churn. Predicting it early helps retain clients.

Real-Life Example

A telecom company wants to identify customers likely to cancel subscriptions.

R Code Example

R Tool

# Load libraries
library(tidyverse)
library(caret)
library(randomForest)

# Load data
data <- read.csv("customer_churn.csv")

# Preprocess data
data <- data %>% 
  mutate(Churn = as.factor(Churn)) %>%
  drop_na()

# Train-test split
set.seed(123)
train_index <- createDataPartition(data$Churn, p = 0.8, list = FALSE)
train_data <- data[train_index, ]
test_data <- data[-train_index, ]

# Train model
model <- randomForest(Churn ~ ., data = train_data)

# Predict & evaluate
predictions <- predict(model, test_data)
confusionMatrix(predictions, test_data$Churn)

Key Benefits

•  Identify at-risk customers early
•  Improve retention strategies
•  No need for expensive software

Recommendations

• Try xgboost for better accuracy

• Use shiny to build an interactive dashboard

Your Turn: Grab your customer data, paste the code, and see which customers are at risk. Take action before it’s too late!

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