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104 lines
3.4 KiB
104 lines
3.4 KiB
# Load necessary libraries
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library(forecast)
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library(ggplot2)
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library(gplots)
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library(reshape)
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library(GGally)
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library(MASS)
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library(naniar)
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library(psych)
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# Load dataset
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setwd('/home/anpham/Nextcloud/cpp/Data Mining/Project')
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telco.df <- read.csv("Telco-Customer-Churn.csv", stringsAsFactors = FALSE)
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# Display first few rows
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head(telco.df)
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# Check structure
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str(telco.df)
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# Summary statistics
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describe(telco.df)
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# Convert categorical variables to factors
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categorical_vars <- c("gender", "SeniorCitizen", "Partner", "Dependents", "PhoneService",
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"MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup",
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"DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies",
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"Contract", "PaperlessBilling", "PaymentMethod", "Churn")
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telco.df[categorical_vars] <- lapply(telco.df[categorical_vars], factor)
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# Convert TotalCharges to numeric and handle missing values
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telco.df$TotalCharges <- as.numeric(telco.df$TotalCharges)
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telco.df$TotalCharges[is.na(telco.df$TotalCharges)] <- 0
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# Create new feature: Average Monthly Spend
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telco.df$AvgMonthlySpend <- with(telco.df, ifelse(tenure > 0, TotalCharges / tenure, MonthlyCharges))
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# Categorize tenure into groups
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telco.df$TenureCategory <- cut(telco.df$tenure,
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breaks = c(-Inf, 12, 48, Inf),
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labels = c("Short-Term", "Mid-Term", "Long-Term"))
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# Check missing values
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sum(is.na(telco.df))
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colSums(is.na(telco.df))
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# Visualize missing values
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gg_miss_var(telco.df)
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# Boxplots to detect outliers
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ggplot(telco.df, aes(y = MonthlyCharges)) +
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geom_boxplot(fill = "skyblue") +
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ggtitle("Boxplot of Monthly Charges") +
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theme_minimal()
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ggplot(telco.df, aes(y = TotalCharges)) +
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geom_boxplot(fill = "lightcoral") +
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ggtitle("Boxplot of Total Charges") +
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theme_minimal()
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# Histogram for numeric variables
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hist(telco.df$TotalCharges, col = "blue", main = "Distribution of TotalCharges")
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hist(telco.df$MonthlyCharges, col = "lightblue", border = "black", main="Distribution of Monthly Charges")
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# Density plot
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plot(density(telco.df$MonthlyCharges, na.rm = TRUE), col = "red", main = "Density of MonthlyCharges")
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# Boxplot of Monthly Charges by Churn status
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boxplot(telco.df$MonthlyCharges ~ telco.df$Churn,
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main = "Monthly Charges by Churn Status",
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xlab = "Churn (Yes/No)",
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ylab = "Monthly Charges",
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col = c("red", "blue"))
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# Scatter plot of tenure vs Monthly Charges
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plot(telco.df$tenure, telco.df$MonthlyCharges,
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xlab = "Tenure (Months)",
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ylab = "Monthly Charges ($)",
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main = "Tenure vs. Monthly Charges",
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col = "blue", pch = 16)
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# Scatter plot of tenure vs churn with jitter
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ggplot(telco.df, aes(x = Churn, y = tenure, color = Churn)) +
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geom_jitter(width = 0.2, alpha = 0.6) +
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labs(title = "Tenure by Churn Status",
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x = "Churn (Yes/No)",
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y = "Tenure (Months)") +
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theme_minimal()
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# Convert Tenure to years
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telco.df$TenureYears <- telco.df$tenure / 12
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# Ensure "DSL" is properly recognized in InternetService
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dsl_data <- subset(telco.df, InternetService == "DSL", select = c(TenureYears, MonthlyCharges))
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# Scatter plot for DSL customers
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ggplot(dsl_data, aes(x = TenureYears, y = MonthlyCharges)) +
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geom_point(color = "blue", alpha = 0.6) +
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labs(title = "Monthly Charges vs. Tenure (DSL Customers)",
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x = "Tenure (Years)",
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y = "Monthly Charges ($)") +
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theme_minimal()
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