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112 lines
3.6 KiB
112 lines
3.6 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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# Exploratory Data Visualizations
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# Histogram for numeric variables
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numeric\_vars <- c("tenure", "MonthlyCharges", "TotalCharges")
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for (var in numeric\_vars) {
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p <- ggplot(telco, aes\_string(x = var)) +
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geom\_histogram(binwidth = 10, fill = "skyblue", color = "black") +
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labs(title = paste("Histogram of", var), x = var, y = "Frequency") +
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theme\_minimal()
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ggsave(paste0("hist\_", var, ".png"), plot = p)
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}
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# Bar plot for churn
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p\_churn <- ggplot(telco, aes(x = Churn, fill = Churn)) +
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geom\_bar() +
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labs(title = "Churn Distribution", x = "Churn", y = "Count") +
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theme\_minimal()
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ggsave("bar\_churn.png", plot = p\_churn)
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# Boxplot of MonthlyCharges by Churn
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p\_box <- ggplot(telco, aes(x = Churn, y = MonthlyCharges, fill = Churn)) +
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geom\_boxplot() +
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labs(title = "Monthly Charges by Churn", x = "Churn", y = "Monthly Charges") +
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theme\_minimal()
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ggsave("boxplot\_monthlycharges\_churn.png", plot = p\_box)
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# Split data into training, validation1, and validation2
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set.seed(100)
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n <- nrow(telco)
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train.index <- sample(1\:n, size = round(0.70 \* n))
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remaining.index <- setdiff(1\:n, train.index)
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valid1.index <- sample(remaining.index, size = round(0.15 \* n))
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valid2.index <- setdiff(remaining.index, valid1.index)
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train.df <- telco\[train.index, ]
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valid1.df <- telco\[valid1.index, ]
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valid2.df <- telco\[valid2.index, ]
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# Logistic regression model (simplified)
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logit.reg <- glm(Churn \~ SeniorCitizen + Dependents + tenure + MultipleLines + InternetService + Contract +
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PaperlessBilling + PaymentMethod + MonthlyCharges + TotalCharges,
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data = train.df, family = "binomial")
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summary(logit.reg)
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# Evaluate on validation set
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valid1\_pred\_probs <- predict(logit.reg, newdata = valid1.df, type = "response")
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valid1\_pred <- factor(ifelse(valid1\_pred\_probs > 0.5, "Yes", "No"), levels = c("No", "Yes"))
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logit\_conf <- confusionMatrix(valid1\_pred, valid1.df\$Churn, positive = "Yes")
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logit\_roc <- roc(response = valid1.df\$Churn, predictor = valid1\_pred\_probs)
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# Decision Tree model
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dt\_model <- rpart(Churn \~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen, data = train.df, method = "class")
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dt\_pred <- predict(dt\_model, valid1.df, type = "class")
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dt\_conf <- confusionMatrix(dt\_pred, valid1.df\$Churn)
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# Naive Bayes model
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nb\_model <- naiveBayes(Churn \~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen, data = train.df)
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nb\_pred <- predict(nb\_model, valid1.df)
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nb\_probs <- predict(nb\_model, valid1.df, type = "raw")
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nb\_conf <- confusionMatrix(nb\_pred, valid1.df\$Churn)
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nb\_roc <- roc(response = valid1.df\$Churn, predictor = nb\_probs\[,"Yes"])
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# Print evaluations
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cat("\nLogistic Regression Confusion Matrix:\n")
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print(logit\_conf)
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cat("\nAUC (Logistic):", auc(logit\_roc), "\n")
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cat("\nDecision Tree Confusion Matrix:\n")
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print(dt\_conf)
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cat("\nNaive Bayes Confusion Matrix:\n")
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print(nb\_conf)
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cat("\nAUC (Naive Bayes):", auc(nb\_roc), "\n")
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# Save ROC curve plots
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png("logistic\_roc\_curve.png")
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plot(logit\_roc, main = "ROC Curve - Logistic Regression", col = "darkgreen")
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dev.off()
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png("naive\_bayes\_roc\_curve.png")
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plot(nb\_roc, main = "ROC Curve - Naive Bayes", col = "blue")
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dev.off()
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