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Project-scriptv1.2.R

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+# Telco Customer Churn Analysis Script
+
+# Load required libraries
+
+library(ggplot2)
+library(dplyr)
+library(rpart)
+library(e1071)
+library(caret)
+library(pROC)
+
+# Load dataset
+
+telco <- read.csv("Telco-Customer-Churn.csv", stringsAsFactors = TRUE)
+telco\$TotalCharges <- as.numeric(as.character(telco\$TotalCharges))
+telco <- telco\[!is.na(telco\$TotalCharges), ]
+telco\$Churn <- factor(telco\$Churn, levels = c("No", "Yes"))
+
+# Exploratory Data Visualizations
+
+# Histogram for numeric variables
+
+numeric\_vars <- c("tenure", "MonthlyCharges", "TotalCharges")
+for (var in numeric\_vars) {
+  p <- ggplot(telco, aes\_string(x = var)) +
+    geom\_histogram(binwidth = 10, fill = "skyblue", color = "black") +
+    labs(title = paste("Histogram of", var), x = var, y = "Frequency") +
+    theme\_minimal()
+  ggsave(paste0("hist\_", var, ".png"), plot = p)
+}
+
+# Bar plot for churn
+
+p\_churn <- ggplot(telco, aes(x = Churn, fill = Churn)) +
+  geom\_bar() +
+  labs(title = "Churn Distribution", x = "Churn", y = "Count") +
+  theme\_minimal()
+ggsave("bar\_churn.png", plot = p\_churn)
+
+# Boxplot of MonthlyCharges by Churn
+
+p\_box <- ggplot(telco, aes(x = Churn, y = MonthlyCharges, fill = Churn)) +
+  geom\_boxplot() +
+  labs(title = "Monthly Charges by Churn", x = "Churn", y = "Monthly Charges") +
+  theme\_minimal()
+ggsave("boxplot\_monthlycharges\_churn.png", plot = p\_box)
+
+# Split data into training, validation1, and validation2
+
+set.seed(100)
+n <- nrow(telco)
+train.index <- sample(1\:n, size = round(0.70 \* n))
+remaining.index <- setdiff(1\:n, train.index)
+valid1.index <- sample(remaining.index, size = round(0.15 \* n))
+valid2.index <- setdiff(remaining.index, valid1.index)
+
+train.df <- telco\[train.index, ]
+valid1.df <- telco\[valid1.index, ]
+valid2.df <- telco\[valid2.index, ]
+
+# Logistic regression model (simplified)
+
+logit.reg <- glm(Churn \~ SeniorCitizen + Dependents + tenure + MultipleLines + InternetService + Contract +
+                   PaperlessBilling + PaymentMethod + MonthlyCharges + TotalCharges,
+                 data = train.df, family = "binomial")
+summary(logit.reg)
+
+# Evaluate on validation set
+
+valid1\_pred\_probs <- predict(logit.reg, newdata = valid1.df, type = "response")
+valid1\_pred <- factor(ifelse(valid1\_pred\_probs > 0.5, "Yes", "No"), levels = c("No", "Yes"))
+logit\_conf <- confusionMatrix(valid1\_pred, valid1.df\$Churn, positive = "Yes")
+logit\_roc <- roc(response = valid1.df\$Churn, predictor = valid1\_pred\_probs)
+
+# Decision Tree model
+
+dt\_model <- rpart(Churn \~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen, data = train.df, method = "class")
+dt\_pred <- predict(dt\_model, valid1.df, type = "class")
+dt\_conf <- confusionMatrix(dt\_pred, valid1.df\$Churn)
+
+# Naive Bayes model
+
+nb\_model <- naiveBayes(Churn \~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen, data = train.df)
+nb\_pred <- predict(nb\_model, valid1.df)
+nb\_probs <- predict(nb\_model, valid1.df, type = "raw")
+nb\_conf <- confusionMatrix(nb\_pred, valid1.df\$Churn)
+nb\_roc <- roc(response = valid1.df\$Churn, predictor = nb\_probs\[,"Yes"])
+
+# Print evaluations
+
+cat("\nLogistic Regression Confusion Matrix:\n")
+print(logit\_conf)
+cat("\nAUC (Logistic):", auc(logit\_roc), "\n")
+
+cat("\nDecision Tree Confusion Matrix:\n")
+print(dt\_conf)
+
+cat("\nNaive Bayes Confusion Matrix:\n")
+print(nb\_conf)
+cat("\nAUC (Naive Bayes):", auc(nb\_roc), "\n")
+
+# Save ROC curve plots
+
+png("logistic\_roc\_curve.png")
+plot(logit\_roc, main = "ROC Curve - Logistic Regression", col = "darkgreen")
+dev.off()
+
+png("naive\_bayes\_roc\_curve.png")
+plot(nb\_roc, main = "ROC Curve - Naive Bayes", col = "blue")
+dev.off()
+
+

Project/Project2.R

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+# Telco Customer Churn Analysis Script
+
+# Load required libraries
+
+library(ggplot2)
+library(dplyr)
+library(rpart)
+library(e1071)
+library(caret)
+library(pROC)
+
+# Load dataset
+
+telco <- read.csv("Telco-Customer-Churn.csv", stringsAsFactors = TRUE)
+telco\$TotalCharges <- as.numeric(as.character(telco\$TotalCharges))
+telco <- telco\[!is.na(telco\$TotalCharges), ]
+telco\$Churn <- factor(telco\$Churn, levels = c("No", "Yes"))
+
+# Exploratory Data Visualizations
+
+# Histogram for numeric variables
+
+numeric\_vars <- c("tenure", "MonthlyCharges", "TotalCharges")
+for (var in numeric\_vars) {
+p <- ggplot(telco, aes\_string(x = var)) +
+geom\_histogram(binwidth = 10, fill = "skyblue", color = "black") +
+labs(title = paste("Histogram of", var), x = var, y = "Frequency") +
+theme\_minimal()
+ggsave(paste0("hist\_", var, ".png"), plot = p)
+}
+
+# Bar plot for churn
+
+p\_churn <- ggplot(telco, aes(x = Churn, fill = Churn)) +
+geom\_bar() +
+labs(title = "Churn Distribution", x = "Churn", y = "Count") +
+theme\_minimal()
+ggsave("bar\_churn.png", plot = p\_churn)
+
+# Boxplot of MonthlyCharges by Churn
+
+p\_box <- ggplot(telco, aes(x = Churn, y = MonthlyCharges, fill = Churn)) +
+geom\_boxplot() +
+labs(title = "Monthly Charges by Churn", x = "Churn", y = "Monthly Charges") +
+theme\_minimal()
+ggsave("boxplot\_monthlycharges\_churn.png", plot = p\_box)
+
+# Split data into training, validation1, and validation2
+
+set.seed(100)
+n <- nrow(telco)
+train.index <- sample(1\:n, size = round(0.70 \* n))
+remaining.index <- setdiff(1\:n, train.index)
+valid1.index <- sample(remaining.index, size = round(0.15 \* n))
+valid2.index <- setdiff(remaining.index, valid1.index)
+
+train.df <- telco\[train.index, ]
+valid1.df <- telco\[valid1.index, ]
+valid2.df <- telco\[valid2.index, ]
+
+# Logistic regression model (simplified)
+
+logit.reg <- glm(Churn \~ SeniorCitizen + Dependents + tenure + MultipleLines + InternetService + Contract +
+PaperlessBilling + PaymentMethod + MonthlyCharges + TotalCharges,
+data = train.df, family = "binomial")
+summary(logit.reg)
+
+# Evaluate on validation set
+
+valid1\_pred\_probs <- predict(logit.reg, newdata = valid1.df, type = "response")
+valid1\_pred <- factor(ifelse(valid1\_pred\_probs > 0.5, "Yes", "No"), levels = c("No", "Yes"))
+logit\_conf <- confusionMatrix(valid1\_pred, valid1.df\$Churn, positive = "Yes")
+logit\_roc <- roc(response = valid1.df\$Churn, predictor = valid1\_pred\_probs)
+
+# Decision Tree model
+
+dt\_model <- rpart(Churn \~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen, data = train.df, method = "class")
+dt\_pred <- predict(dt\_model, valid1.df, type = "class")
+dt\_conf <- confusionMatrix(dt\_pred, valid1.df\$Churn)
+
+# Naive Bayes model
+
+nb\_model <- naiveBayes(Churn \~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen, data = train.df)
+nb\_pred <- predict(nb\_model, valid1.df)
+nb\_probs <- predict(nb\_model, valid1.df, type = "raw")
+nb\_conf <- confusionMatrix(nb\_pred, valid1.df\$Churn)
+nb\_roc <- roc(response = valid1.df\$Churn, predictor = nb\_probs\[,"Yes"])
+
+# Print evaluations
+
+cat("\nLogistic Regression Confusion Matrix:\n")
+print(logit\_conf)
+cat("\nAUC (Logistic):", auc(logit\_roc), "\n")
+
+cat("\nDecision Tree Confusion Matrix:\n")
+print(dt\_conf)
+
+cat("\nNaive Bayes Confusion Matrix:\n")
+print(nb\_conf)
+cat("\nAUC (Naive Bayes):", auc(nb\_roc), "\n")
+
+# Save ROC curve plots
+
+png("logistic\_roc\_curve.png")
+plot(logit\_roc, main = "ROC Curve - Logistic Regression", col = "darkgreen")
+dev.off()
+
+png("naive\_bayes\_roc\_curve.png")
+plot(nb\_roc, main = "ROC Curve - Naive Bayes", col = "blue")
+dev.off()
+

Project/tempt-code.R

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