1. Load Libraries and Data

# Load required libraries
library(tidyverse)    # for data manipulation and visualization
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(janitor)      # for cleaning column names
## 
## Attaching package: 'janitor'
## 
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test
library(skimr)        # for quick data summary
library(DataExplorer) # optional, great for quick EDA

# Load dataset
churn_data <- read_csv("~/Desktop/DA Portfolio/telco-customer-churn-prediction/data/raw/raw_Telco-Customer-Churn.csv")
## Rows: 7043 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (17): customerID, gender, Partner, Dependents, PhoneService, MultipleLin...
## dbl  (4): SeniorCitizen, tenure, MonthlyCharges, TotalCharges
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Clean column names
churn_data <- clean_names(churn_data)

# View first few rows
head(churn_data)
## # A tibble: 6 × 21
##   customer_id gender senior_citizen partner dependents tenure phone_service
##   <chr>       <chr>           <dbl> <chr>   <chr>       <dbl> <chr>        
## 1 7590-VHVEG  Female              0 Yes     No              1 No           
## 2 5575-GNVDE  Male                0 No      No             34 Yes          
## 3 3668-QPYBK  Male                0 No      No              2 Yes          
## 4 7795-CFOCW  Male                0 No      No             45 No           
## 5 9237-HQITU  Female              0 No      No              2 Yes          
## 6 9305-CDSKC  Female              0 No      No              8 Yes          
## # ℹ 14 more variables: multiple_lines <chr>, internet_service <chr>,
## #   online_security <chr>, online_backup <chr>, device_protection <chr>,
## #   tech_support <chr>, streaming_tv <chr>, streaming_movies <chr>,
## #   contract <chr>, paperless_billing <chr>, payment_method <chr>,
## #   monthly_charges <dbl>, total_charges <dbl>, churn <chr>

2. Data Understanding

# Check structure of data
str(churn_data)
## spc_tbl_ [7,043 × 21] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ customer_id      : chr [1:7043] "7590-VHVEG" "5575-GNVDE" "3668-QPYBK" "7795-CFOCW" ...
##  $ gender           : chr [1:7043] "Female" "Male" "Male" "Male" ...
##  $ senior_citizen   : num [1:7043] 0 0 0 0 0 0 0 0 0 0 ...
##  $ partner          : chr [1:7043] "Yes" "No" "No" "No" ...
##  $ dependents       : chr [1:7043] "No" "No" "No" "No" ...
##  $ tenure           : num [1:7043] 1 34 2 45 2 8 22 10 28 62 ...
##  $ phone_service    : chr [1:7043] "No" "Yes" "Yes" "No" ...
##  $ multiple_lines   : chr [1:7043] "No phone service" "No" "No" "No phone service" ...
##  $ internet_service : chr [1:7043] "DSL" "DSL" "DSL" "DSL" ...
##  $ online_security  : chr [1:7043] "No" "Yes" "Yes" "Yes" ...
##  $ online_backup    : chr [1:7043] "Yes" "No" "Yes" "No" ...
##  $ device_protection: chr [1:7043] "No" "Yes" "No" "Yes" ...
##  $ tech_support     : chr [1:7043] "No" "No" "No" "Yes" ...
##  $ streaming_tv     : chr [1:7043] "No" "No" "No" "No" ...
##  $ streaming_movies : chr [1:7043] "No" "No" "No" "No" ...
##  $ contract         : chr [1:7043] "Month-to-month" "One year" "Month-to-month" "One year" ...
##  $ paperless_billing: chr [1:7043] "Yes" "No" "Yes" "No" ...
##  $ payment_method   : chr [1:7043] "Electronic check" "Mailed check" "Mailed check" "Bank transfer (automatic)" ...
##  $ monthly_charges  : num [1:7043] 29.9 57 53.9 42.3 70.7 ...
##  $ total_charges    : num [1:7043] 29.9 1889.5 108.2 1840.8 151.7 ...
##  $ churn            : chr [1:7043] "No" "No" "Yes" "No" ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   customerID = col_character(),
##   ..   gender = col_character(),
##   ..   SeniorCitizen = col_double(),
##   ..   Partner = col_character(),
##   ..   Dependents = col_character(),
##   ..   tenure = col_double(),
##   ..   PhoneService = col_character(),
##   ..   MultipleLines = col_character(),
##   ..   InternetService = col_character(),
##   ..   OnlineSecurity = col_character(),
##   ..   OnlineBackup = col_character(),
##   ..   DeviceProtection = col_character(),
##   ..   TechSupport = col_character(),
##   ..   StreamingTV = col_character(),
##   ..   StreamingMovies = col_character(),
##   ..   Contract = col_character(),
##   ..   PaperlessBilling = col_character(),
##   ..   PaymentMethod = col_character(),
##   ..   MonthlyCharges = col_double(),
##   ..   TotalCharges = col_double(),
##   ..   Churn = col_character()
##   .. )
##  - attr(*, "problems")=<externalptr>
# Quick data summary
skim(churn_data)
Data summary
Name churn_data
Number of rows 7043
Number of columns 21
_______________________
Column type frequency:
character 17
numeric 4
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
customer_id 0 1 10 10 0 7043 0
gender 0 1 4 6 0 2 0
partner 0 1 2 3 0 2 0
dependents 0 1 2 3 0 2 0
phone_service 0 1 2 3 0 2 0
multiple_lines 0 1 2 16 0 3 0
internet_service 0 1 2 11 0 3 0
online_security 0 1 2 19 0 3 0
online_backup 0 1 2 19 0 3 0
device_protection 0 1 2 19 0 3 0
tech_support 0 1 2 19 0 3 0
streaming_tv 0 1 2 19 0 3 0
streaming_movies 0 1 2 19 0 3 0
contract 0 1 8 14 0 3 0
paperless_billing 0 1 2 3 0 2 0
payment_method 0 1 12 25 0 4 0
churn 0 1 2 3 0 2 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
senior_citizen 0 1 0.16 0.37 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
tenure 0 1 32.37 24.56 0.00 9.00 29.00 55.00 72.00 ▇▃▃▃▆
monthly_charges 0 1 64.76 30.09 18.25 35.50 70.35 89.85 118.75 ▇▅▆▇▅
total_charges 11 1 2283.30 2266.77 18.80 401.45 1397.47 3794.74 8684.80 ▇▂▂▂▁ 
# Check missing values
colSums(is.na(churn_data))
##       customer_id            gender    senior_citizen           partner 
##                 0                 0                 0                 0 
##        dependents            tenure     phone_service    multiple_lines 
##                 0                 0                 0                 0 
##  internet_service   online_security     online_backup device_protection 
##                 0                 0                 0                 0 
##      tech_support      streaming_tv  streaming_movies          contract 
##                 0                 0                 0                 0 
## paperless_billing    payment_method   monthly_charges     total_charges 
##                 0                 0                 0                11 
##             churn 
##                 0

3. Data Cleaning

# Convert total_charges to numeric (some spaces cause NA)
churn_data <- churn_data %>%
  mutate(total_charges = as.numeric(trimws(total_charges)))

# Check again
summary(churn_data$total_charges)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    18.8   401.4  1397.5  2283.3  3794.7  8684.8      11
# How many NAs?
sum(is.na(churn_data$total_charges))
## [1] 11
# Remove rows with missing total_charges
churn_data <- churn_data %>%
  filter(!is.na(total_charges))

4. Exploratory Data Analyis (EDA)

Churn Rate

# Churn rate overall
churn_data %>%
  count(churn) %>%
  mutate(percent = n / sum(n) * 100)
## # A tibble: 2 × 3
##   churn     n percent
##   <chr> <int>   <dbl>
## 1 No     5163    73.4
## 2 Yes    1869    26.6

Churn Rate by Contract Type

# Churn rate by contract type
churn_data %>%
  group_by(contract) %>%
  summarise(
    churn_rate = mean(churn == "Yes") * 100,
    count = n()
  )
## # A tibble: 3 × 3
##   contract       churn_rate count
##   <chr>               <dbl> <int>
## 1 Month-to-month      42.7   3875
## 2 One year            11.3   1472
## 3 Two year             2.85  1685

Visualize Churn rate by Contract Type

# Bar plot: Churn rate by contract type
churn_data %>%
  group_by(contract) %>%
  summarise(churn_rate = mean(churn == "Yes") * 100) %>%
  ggplot(aes(x = contract, y = churn_rate, fill = contract)) +
  geom_col() +
  labs(title = "Churn Rate by Contract Type", y = "Churn Rate (%)", x = "Contract Type") +
  theme_minimal()

Churn by Tenure Group (as example of feature engineering)

# Create tenure group
churn_data <- churn_data %>%
  mutate(tenure_group = case_when(
    tenure <= 12 ~ "0-1 year",
    tenure <= 24 ~ "1-2 years",
    tenure <= 48 ~ "2-4 years",
    tenure <= 60 ~ "4-5 years",
    tenure > 60  ~ "5+ years"
  ))

# Churn by tenure group
churn_data %>%
  group_by(tenure_group) %>%
  summarise(
    churn_rate = mean(churn == "Yes") * 100,
    count = n()
  )
## # A tibble: 5 × 3
##   tenure_group churn_rate count
##   <chr>             <dbl> <int>
## 1 0-1 year          47.7   2175
## 2 1-2 years         28.7   1024
## 3 2-4 years         20.4   1594
## 4 4-5 years         14.4    832
## 5 5+ years           6.61  1407

Visualize Churn Rate by Tenure Group

# Bar plot: Churn rate by tenure group
churn_data %>%
  group_by(tenure_group) %>%
  summarise(churn_rate = mean(churn == "Yes") * 100) %>%
  ggplot(aes(x = tenure_group, y = churn_rate, fill = tenure_group)) +
  geom_col() +
  labs(title = "Churn Rate by Tenure Group", y = "Churn Rate (%)", x = "Tenure Group") +
  theme_minimal()

Quick Auto EDA

# Quick EDA report (DataExplorer)
plot_bar(churn_data, by = "churn")
## 1 columns ignored with more than 50 categories.
## customer_id: 7032 categories

Churn by Payment Method

# Churn by payment method
churn_data %>%
  group_by(payment_method) %>%
  summarise(
    churn_rate = mean(churn == "Yes") * 100,
    count = n()
  ) %>%
  arrange(desc(churn_rate))
## # A tibble: 4 × 3
##   payment_method            churn_rate count
##   <chr>                          <dbl> <int>
## 1 Electronic check                45.3  2365
## 2 Mailed check                    19.2  1604
## 3 Bank transfer (automatic)       16.7  1542
## 4 Credit card (automatic)         15.3  1521

Churn by Internet Service

# Churn by internet service
churn_data %>%
  group_by(internet_service) %>%
  summarise(
    churn_rate = mean(churn == "Yes") * 100,
    count = n()
  ) %>%
  arrange(desc(churn_rate))
## # A tibble: 3 × 3
##   internet_service churn_rate count
##   <chr>                 <dbl> <int>
## 1 Fiber optic           41.9   3096
## 2 DSL                   19.0   2416
## 3 No                     7.43  1520

Churn by Monthly Charges Viz

# Boxplot: Monthly charges vs. churn
ggplot(churn_data, aes(x = churn, y = monthly_charges, fill = churn)) +
  geom_boxplot() +
  labs(title = "Monthly Charges vs. Churn", x = "Churn", y = "Monthly Charges") +
  theme_minimal()

5. Convert Variables to Factors

# Convert categorical variables to factors
churn_data <- churn_data %>%
  mutate(
    churn = factor(churn),
    gender = factor(gender),
    partner = factor(partner),
    dependents = factor(dependents),
    phone_service = factor(phone_service),
    multiple_lines = factor(multiple_lines),
    internet_service = factor(internet_service),
    online_security = factor(online_security),
    online_backup = factor(online_backup),
    device_protection = factor(device_protection),
    tech_support = factor(tech_support),
    streaming_tv = factor(streaming_tv),
    streaming_movies = factor(streaming_movies),
    contract = factor(contract),
    paperless_billing = factor(paperless_billing),
    payment_method = factor(payment_method),
    tenure_group = factor(tenure_group)
  )

Split into Train/Test Sets

We will use 70% train / 30% test split.

library(caTools)

# Set random seed for reproducibility
set.seed(123)

# Split data
split <- sample.split(churn_data$churn, SplitRatio = 0.7)
train_data <- subset(churn_data, split == TRUE)
test_data  <- subset(churn_data, split == FALSE)

# Check sizes
nrow(train_data)
## [1] 4922
nrow(test_data)
## [1] 2110

6. Build the Model

# Build logistic regression model
churn_model <- glm(churn ~ contract + tenure + monthly_charges + payment_method + internet_service + paperless_billing + online_security + tech_support + tenure_group, 
                   data = train_data, 
                   family = binomial)

# Model summary
summary(churn_model)
## 
## Call:
## glm(formula = churn ~ contract + tenure + monthly_charges + payment_method + 
##     internet_service + paperless_billing + online_security + 
##     tech_support + tenure_group, family = binomial, data = train_data)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8572  -0.6669  -0.2786   0.6735   3.2204  
## 
## Coefficients: (2 not defined because of singularities)
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           -0.680902   0.222782  -3.056 0.002240 ** 
## contractOne year                      -0.718740   0.127592  -5.633 1.77e-08 ***
## contractTwo year                      -1.743919   0.231905  -7.520 5.48e-14 ***
## tenure                                -0.053200   0.009066  -5.868 4.41e-09 ***
## monthly_charges                        0.010767   0.003962   2.718 0.006569 ** 
## payment_methodCredit card (automatic) -0.049399   0.136062  -0.363 0.716557    
## payment_methodElectronic check         0.330577   0.112137   2.948 0.003199 ** 
## payment_methodMailed check            -0.065573   0.136118  -0.482 0.629995    
## internet_serviceFiber optic            0.533029   0.170086   3.134 0.001725 ** 
## internet_serviceNo                    -0.927831   0.186763  -4.968 6.77e-07 ***
## paperless_billingYes                   0.436720   0.089336   4.889 1.02e-06 ***
## online_securityNo internet service           NA         NA      NA       NA    
## online_securityYes                    -0.529635   0.102649  -5.160 2.47e-07 ***
## tech_supportNo internet service              NA         NA      NA       NA    
## tech_supportYes                       -0.356172   0.104719  -3.401 0.000671 ***
## tenure_group1-2 years                 -0.305155   0.164593  -1.854 0.063739 .  
## tenure_group2-4 years                  0.360018   0.284011   1.268 0.204934    
## tenure_group4-5 years                  1.168785   0.459498   2.544 0.010971 *  
## tenure_group5+ years                   1.573792   0.568618   2.768 0.005645 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5699.5  on 4921  degrees of freedom
## Residual deviance: 4054.5  on 4905  degrees of freedom
## AIC: 4088.5
## 
## Number of Fisher Scoring iterations: 6

7. Evaluate the Model

Predict on Test Data

# Predict probabilities
test_prob <- predict(churn_model, newdata = test_data, type = "response")
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
# Convert probabilities to Yes/No (threshold 0.5)
test_pred <- ifelse(test_prob > 0.5, "Yes", "No") %>% factor(levels = c("No", "Yes"))

# Confusion matrix
table(Predicted = test_pred, Actual = test_data$churn)
##          Actual
## Predicted   No  Yes
##       No  1377  271
##       Yes  172  290

Calculate Accuracy

# Calculate accuracy
mean(test_pred == test_data$churn)
## [1] 0.7900474

Summary of Findings and Business Recommendations

Business Problem

The company seeks to reduce customer churn, which negatively impacts revenue and profitability. This analysis identifies key drivers of churn and predicts which customers are at risk, enabling more targeted retention efforts.

Key Findings

  • The overall churn rate in the customer base is approximately 26%.
  • Contract type is a significant predictor of churn. Customers on Month-to-Month contracts are more likely to churn.
  • Tenure is also a key factor. Customers with short tenure (less than 1 year) exhibit higher churn rates.
  • Payment method influences churn. Customers using Electronic check show higher churn than those using automatic payments.
  • Internet service type and security features (online security, tech support) are significant predictors.
  • Customers with higher monthly charges tend to have slightly higher churn risk.

Model Performance

  • The logistic regression model achieved an accuracy of 0.7% on the test dataset.
  • The model can effectively identify customer segments at risk of churning.

Business Recommendations

  1. Target Month-to-Month customers with incentives to switch to longer-term contracts.
  2. Focus retention efforts on new customers (tenure < 1 year) with proactive engagement.
  3. Encourage automatic payment methods to reduce churn.
  4. Bundle security and support services with internet plans to increase stickiness.
  5. Develop a churn monitoring dashboard to track risk factors in real time.

Next Steps

  • Explore more advanced models (random forest, XGBoost) to improve predictive accuracy.
  • Conduct A/B tests on targeted interventions to measure impact.
  • Integrate model predictions into CRM system for ongoing use by marketing and customer service teams.

Exporting Cleaned Data

## Cleaned data exported as 'cleaned_churn_data_for_tableau.csv' in your working directory.