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38 lines
1.0 KiB
38 lines
1.0 KiB
# Telco Customer Churn Analysis Script
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# Load required libraries
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library(ggplot2)
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library(dplyr)
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library(rpart)
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library(e1071)
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library(caret)
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library(pROC)
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# Load dataset
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telco <- read.csv("Telco-Customer-Churn.csv", stringsAsFactors = TRUE)
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telco$TotalCharges <- as.numeric(as.character(telco$TotalCharges))
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telco <- telco[!is.na(telco$TotalCharges), ]
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telco$Churn <- factor(telco$Churn, levels = c("No", "Yes"))
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# Split data
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set.seed(42)
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trainIndex <- createDataPartition(telco$Churn, p = 0.7, list = FALSE)
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train <- telco[trainIndex, ]
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test <- telco[-trainIndex, ]
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# Decision Tree model
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dt_model <- rpart(Churn ~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen,
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data = train, method = "class")
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dt_pred <- predict(dt_model, test, type = "class")
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dt_conf <- confusionMatrix(dt_pred, test$Churn)
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# Naive Bayes model
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nb_model <- naiveBayes(Churn ~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen,
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data = train)
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nb_pred <- predict(nb_model, test)
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nb_conf <- confusionMatrix(nb_pred, test$Churn)
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# ROC
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