Update to Code for case study – Customer Churn with Keras/TensorFlow and H2O
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This is an UPDATE to this old post with updated links & descriptions
This is code that accompanies a book chapter on customer churn that I have written for the German dpunkt Verlag. The book is in German, however.
The code you find below can be used to recreate all figures and analyses from this book chapter. Because the content is exclusively for the book, my descriptions around the code had to be minimal. But I’m sure, you can get the gist, even without the book. 😉
If you have questions or would like to talk about this article (or something else data-related), you can now book 15-minute timeslots with me (it’s free – one slot available per weekday):
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Customer churn
Customer churn describes the problem that customers may leave a business. The reasons for leaving can be manifold but some of the most common are:
- dissatisfaction with the product, service, etc.
- too expensive
- don’t need the service any longer
- …
Customer churn models generally fall into two categories:
- Aim to identify customers who are on the brink of leaving, so that appropriate measures can be taken to try and retain them (as a function over time).
A simple model as shown below takes a snapshot of data into account but as is quite obvious: customer churn likelihoods change over time. So, real models will most likely take into account features that describe changes over time, like customer engagement with the site or product, number of calls to customer service, etc.
- Aim to identify customer segments who are more likely to churn after a certain period of time (e.g. men being more likely to churn than women, or younger people changing contracts more often than older people, etc.).
Simple customer churn models use the customer’s information (see below for example data: demographic information, services that customers have signed up for and account information) to try to predict the likelihood of churn for each customer.
More advanced models will also try to classify the reason for potential churn (see above). Depending on the reason, specific actions can be taken to try and prevent the customer from churning (e.g. offering them a better deal when you think they might leave due to finding the service too costly).
Inspiration & Sources
Thank you to the following people for providing excellent code examples about customer churn:
Setup
All analyses are done in R using RStudio. For detailed session information including R version, operating system and package versions, see the sessionInfo() output at the end of this document.
All figures are produced with ggplot2.
- Libraries
# Load libraries library(tidyverse) # for tidy data analysis library(readr) # for fast reading of input files library(caret) # for convenient splitting library(mice) # mice package for Multivariate Imputation by Chained Equations (MICE) library(keras) # for neural nets library(lime) # for explaining neural nets library(rsample) # for splitting training and test data library(recipes) # for preprocessing library(yardstick) # for evaluation library(ggthemes) # for additional plotting themes library(corrplot) # for correlation theme_set(theme_minimal()) # Install Keras if you have not installed it before # follow instructions if you haven't installed TensorFlow install_keras()
Data preparation
The dataset
UPDATE: The old link doesn’t seem to exist any longer but the dataset is still available from Kaggle.
The Telco Customer Churn data set is the same one that Matt Dancho used in his post (see above). It was downloaded from Kaggle.
churn_data_raw <- read_csv("WA_Fn-UseC_-Telco-Customer-Churn.csv")
glimpse(churn_data_raw)
## Rows: 7,043
## Columns: 21
## $ customerID <chr> "7590-VHVEG", "5575-GNVDE", "3668-QPYBK", "7795-CFOCW…
## $ gender <chr> "Female", "Male", "Male", "Male", "Female", "Female",…
## $ SeniorCitizen <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ Partner <chr> "Yes", "No", "No", "No", "No", "No", "No", "No", "Yes…
## $ Dependents <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No"…
## $ tenure <dbl> 1, 34, 2, 45, 2, 8, 22, 10, 28, 62, 13, 16, 58, 49, 2…
## $ PhoneService <chr> "No", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", …
## $ MultipleLines <chr> "No phone service", "No", "No", "No phone service", "…
## $ InternetService <chr> "DSL", "DSL", "DSL", "DSL", "Fiber optic", "Fiber opt…
## $ OnlineSecurity <chr> "No", "Yes", "Yes", "Yes", "No", "No", "No", "Yes", "…
## $ OnlineBackup <chr> "Yes", "No", "Yes", "No", "No", "No", "Yes", "No", "N…
## $ DeviceProtection <chr> "No", "Yes", "No", "Yes", "No", "Yes", "No", "No", "Y…
## $ TechSupport <chr> "No", "No", "No", "Yes", "No", "No", "No", "No", "Yes…
## $ StreamingTV <chr> "No", "No", "No", "No", "No", "Yes", "Yes", "No", "Ye…
## $ StreamingMovies <chr> "No", "No", "No", "No", "No", "Yes", "No", "No", "Yes…
## $ Contract <chr> "Month-to-month", "One year", "Month-to-month", "One …
## $ PaperlessBilling <chr> "Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "No", …
## $ PaymentMethod <chr> "Electronic check", "Mailed check", "Mailed check", "…
## $ MonthlyCharges <dbl> 29.85, 56.95, 53.85, 42.30, 70.70, 99.65, 89.10, 29.7…
## $ TotalCharges <dbl> 29.85, 1889.50, 108.15, 1840.75, 151.65, 820.50, 1949…
## $ Churn <chr> "No", "No", "Yes", "No", "Yes", "Yes", "No", "No", "Y…
EDA
- Proportion of churn (customers who left within the last month):
churn_data_raw %>% count(Churn) ## # A tibble: 2 x 2 ## Churn n ## <chr> <int> ## 1 No 5174 ## 2 Yes 1869
- Plot categorical features (demographic information, services that customers have signed up for and account information):
churn_data_raw %>%
mutate(SeniorCitizen = as.character(SeniorCitizen)) %>%
select(-customerID) %>%
select_if(is.character) %>%
select(Churn, everything()) %>%
gather(x, y, gender:PaymentMethod) %>%
count(Churn, x, y) %>%
ggplot(aes(x = y, y = n, fill = Churn, color = Churn)) +
facet_wrap(~ x, ncol = 4, scales = "free") +
geom_bar(stat = "identity", alpha = 0.5) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "top") +
scale_color_tableau() +
scale_fill_tableau()
- Plot numerical features (demographic information, services that customers have signed up for and account information):
churn_data_raw %>%
select(-customerID) %>%
#select_if(is.numeric) %>%
select(Churn, MonthlyCharges, tenure, TotalCharges) %>%
gather(x, y, MonthlyCharges:TotalCharges) %>%
ggplot(aes(x = y, fill = Churn, color = Churn)) +
facet_wrap(~ x, ncol = 3, scales = "free") +
geom_density(alpha = 0.5) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "top") +
scale_color_tableau() +
scale_fill_tableau()
- Remove customer ID as it doesn’t provide information
churn_data <- churn_data_raw %>% select(-customerID)
Dealing with missing values
- Pattern of missing data
md.pattern(churn_data, plot = FALSE) ## gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines ## 7032 1 1 1 1 1 1 1 ## 11 1 1 1 1 1 1 1 ## 0 0 0 0 0 0 0 ## InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport ## 7032 1 1 1 1 1 ## 11 1 1 1 1 1 ## 0 0 0 0 0 ## StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod ## 7032 1 1 1 1 1 ## 11 1 1 1 1 1 ## 0 0 0 0 0 ## MonthlyCharges Churn TotalCharges ## 7032 1 1 1 0 ## 11 1 1 0 1 ## 0 0 11 11
- Option 1: impute missing data => NOT done here!
imp <- mice(data = churn_data, print = FALSE) train_data_impute <- complete(imp, "long")
- Option 2: drop missing data => done here because not too much information is lost by removing it
churn_data <- churn_data %>% drop_na()
Training and test split
- Partition data into training and test set
set.seed(42) index <- createDataPartition(churn_data$Churn, p = 0.7, list = FALSE)
- Partition test set again into validation and test set
train_data <- churn_data[index, ] test_data <- churn_data[-index, ] index2 <- createDataPartition(test_data$Churn, p = 0.5, list = FALSE) valid_data <- test_data[-index2, ] test_data <- test_data[index2, ] nrow(train_data) ## [1] 4924 nrow(valid_data) ## [1] 1054 nrow(test_data) ## [1] 1054
Pre-Processing
- Create recipe for preprocessing
A recipe is a description of what steps should be applied to a data set in order to get it ready for data analysis.
recipe_churn <- recipe(Churn ~ ., train_data) %>% step_dummy(all_nominal(), -all_outcomes()) %>% step_center(all_predictors(), -all_outcomes()) %>% step_scale(all_predictors(), -all_outcomes()) %>% prep(data = train_data)
- Apply recipe to three datasets
train_data <- bake(recipe_churn, new_data = train_data) %>% select(Churn, everything()) valid_data <- bake(recipe_churn, new_data = valid_data) %>% select(Churn, everything()) test_data <- bake(recipe_churn, new_data = test_data) %>% select(Churn, everything())
- For Keras create response variable as one-hot encoded matrix
train_y_drop <- keras::to_categorical(as.integer(as.factor(train_data$Churn)) - 1, 2)
colnames(train_y_drop) <- c("No", "Yes")
valid_y_drop <- keras::to_categorical(as.integer(as.factor(valid_data$Churn)) - 1, 2)
colnames(valid_y_drop) <- c("No", "Yes")
test_y_drop <- keras::to_categorical(as.integer(as.factor(test_data$Churn)) - 1, 2)
colnames(test_y_drop) <- c("No", "Yes")
- Because we want to train on a binary outcome, we can delete the “No” column
# if training with binary crossentropy train_y_drop <- train_y_drop[, 2, drop = FALSE] head(train_y_drop) ## Yes ## [1,] 0 ## [2,] 0 ## [3,] 1 ## [4,] 0 ## [5,] 1 ## [6,] 0 valid_y_drop <- valid_y_drop[, 2, drop = FALSE] test_y_drop <- test_y_drop[, 2, drop = FALSE]
- Remove response variable from preprocessed data (for Keras)
train_data_bk <- select(train_data, -Churn) head(train_data_bk) ## # A tibble: 6 x 30 ## SeniorCitizen tenure MonthlyCharges TotalCharges gender_Male Partner_Yes ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 -0.439 -1.28 -1.17 -1.00 -1.02 1.05 ## 2 -0.439 0.0678 -0.262 -0.173 0.983 -0.956 ## 3 -0.439 -1.24 -0.366 -0.965 0.983 -0.956 ## 4 -0.439 0.518 -0.751 -0.195 0.983 -0.956 ## 5 -0.439 -1.24 0.197 -0.946 -1.02 -0.956 ## 6 -0.439 -0.423 0.811 -0.146 0.983 -0.956 ## # … with 24 more variables: Dependents_Yes <dbl>, PhoneService_Yes <dbl>, ## # MultipleLines_No.phone.service <dbl>, MultipleLines_Yes <dbl>, ## # InternetService_Fiber.optic <dbl>, InternetService_No <dbl>, ## # OnlineSecurity_No.internet.service <dbl>, OnlineSecurity_Yes <dbl>, ## # OnlineBackup_No.internet.service <dbl>, OnlineBackup_Yes <dbl>, ## # DeviceProtection_No.internet.service <dbl>, DeviceProtection_Yes <dbl>, ## # TechSupport_No.internet.service <dbl>, TechSupport_Yes <dbl>, ## # StreamingTV_No.internet.service <dbl>, StreamingTV_Yes <dbl>, ## # StreamingMovies_No.internet.service <dbl>, StreamingMovies_Yes <dbl>, ## # Contract_One.year <dbl>, Contract_Two.year <dbl>, ## # PaperlessBilling_Yes <dbl>, PaymentMethod_Credit.card..automatic. <dbl>, ## # PaymentMethod_Electronic.check <dbl>, PaymentMethod_Mailed.check <dbl> valid_data_bk <- select(valid_data, -Churn) test_data_bk <- select(test_data, -Churn)
- Alternative to above, to convert response variable into numeric format where 1 = Yes and 0 = No
train_data$Churn <- ifelse(train_data$Churn == "Yes", 1, 0) valid_data$Churn <- ifelse(valid_data$Churn == "Yes", 1, 0) test_data$Churn <- ifelse(test_data$Churn == "Yes", 1, 0)
Modeling with Keras
- Define a simple MLP
model_keras <- keras_model_sequential()
model_keras %>%
layer_dense(units = 32, kernel_initializer = "uniform", activation = "relu",
input_shape = ncol(train_data_bk)) %>%
layer_dropout(rate = 0.2) %>%
layer_dense(units = 16, kernel_initializer = "uniform", activation = "relu") %>%
layer_dropout(rate = 0.2) %>%
layer_dense(units = 8, kernel_initializer = "uniform", activation = "relu") %>%
layer_dropout(rate = 0.2) %>%
layer_dense(units = 1,
kernel_initializer = "uniform", activation = "sigmoid") %>%
compile(
optimizer = 'adamax',
loss = 'binary_crossentropy',
metrics = c("binary_accuracy", "mse")
)
- Fit model (we could have used validation split on the trainings data instead of creating a validation set => see #)
fit_keras <- fit(model_keras,
x = as.matrix(train_data_bk),
y = train_y_drop,
batch_size = 32,
epochs = 20,
#validation_split = 0.30,
validation_data = list(as.matrix(valid_data_bk), valid_y_drop),
verbose = 2
)
Epoch 1/20
154/154 - 0s - loss: 0.6728 - binary_accuracy: 0.7342 - mse: 0.2399
154/154 - 1s - loss: 0.6728 - binary_accuracy: 0.7342 - mse: 0.2399 - val_loss: 0.6291 - val_binary_accuracy: 0.7343 - val_mse: 0.2183
Epoch 2/20
154/154 - 0s - loss: 0.5564 - binary_accuracy: 0.7342 - mse: 0.1877
154/154 - 0s - loss: 0.5564 - binary_accuracy: 0.7342 - mse: 0.1877 - val_loss: 0.4816 - val_binary_accuracy: 0.7343 - val_mse: 0.1586
Epoch 3/20
154/154 - 0s - loss: 0.4920 - binary_accuracy: 0.7342 - mse: 0.1630
154/154 - 0s - loss: 0.4920 - binary_accuracy: 0.7342 - mse: 0.1630 - val_loss: 0.4569 - val_binary_accuracy: 0.7343 - val_mse: 0.1515
Epoch 4/20
154/154 - 0s - loss: 0.4851 - binary_accuracy: 0.7342 - mse: 0.1603
154/154 - 0s - loss: 0.4851 - binary_accuracy: 0.7342 - mse: 0.1603 - val_loss: 0.4466 - val_binary_accuracy: 0.7343 - val_mse: 0.1486
Epoch 5/20
154/154 - 0s - loss: 0.4779 - binary_accuracy: 0.7342 - mse: 0.1582
154/154 - 0s - loss: 0.4779 - binary_accuracy: 0.7342 - mse: 0.1582 - val_loss: 0.4409 - val_binary_accuracy: 0.7343 - val_mse: 0.1465
Epoch 6/20
154/154 - 0s - loss: 0.4705 - binary_accuracy: 0.7342 - mse: 0.1557
154/154 - 0s - loss: 0.4705 - binary_accuracy: 0.7342 - mse: 0.1557 - val_loss: 0.4368 - val_binary_accuracy: 0.7343 - val_mse: 0.1452
Epoch 7/20
154/154 - 0s - loss: 0.4721 - binary_accuracy: 0.7342 - mse: 0.1553
154/154 - 0s - loss: 0.4721 - binary_accuracy: 0.7342 - mse: 0.1553 - val_loss: 0.4334 - val_binary_accuracy: 0.7343 - val_mse: 0.1438
Epoch 8/20
154/154 - 0s - loss: 0.4609 - binary_accuracy: 0.7342 - mse: 0.1535
154/154 - 0s - loss: 0.4609 - binary_accuracy: 0.7342 - mse: 0.1535 - val_loss: 0.4306 - val_binary_accuracy: 0.7343 - val_mse: 0.1429
Epoch 9/20
154/154 - 0s - loss: 0.4674 - binary_accuracy: 0.7342 - mse: 0.1540
154/154 - 0s - loss: 0.4674 - binary_accuracy: 0.7342 - mse: 0.1540 - val_loss: 0.4298 - val_binary_accuracy: 0.7343 - val_mse: 0.1426
Epoch 10/20
154/154 - 0s - loss: 0.4671 - binary_accuracy: 0.7342 - mse: 0.1540
154/154 - 0s - loss: 0.4671 - binary_accuracy: 0.7342 - mse: 0.1540 - val_loss: 0.4286 - val_binary_accuracy: 0.7343 - val_mse: 0.1422
Epoch 11/20
154/154 - 0s - loss: 0.4623 - binary_accuracy: 0.7342 - mse: 0.1524
154/154 - 0s - loss: 0.4623 - binary_accuracy: 0.7342 - mse: 0.1524 - val_loss: 0.4281 - val_binary_accuracy: 0.7343 - val_mse: 0.1420
Epoch 12/20
154/154 - 0s - loss: 0.4631 - binary_accuracy: 0.7342 - mse: 0.1533
154/154 - 0s - loss: 0.4631 - binary_accuracy: 0.7342 - mse: 0.1533 - val_loss: 0.4278 - val_binary_accuracy: 0.7343 - val_mse: 0.1419
Epoch 13/20
154/154 - 0s - loss: 0.4609 - binary_accuracy: 0.7342 - mse: 0.1520
154/154 - 0s - loss: 0.4609 - binary_accuracy: 0.7342 - mse: 0.1520 - val_loss: 0.4267 - val_binary_accuracy: 0.7343 - val_mse: 0.1414
Epoch 14/20
154/154 - 0s - loss: 0.4636 - binary_accuracy: 0.7342 - mse: 0.1529
154/154 - 0s - loss: 0.4636 - binary_accuracy: 0.7342 - mse: 0.1529 - val_loss: 0.4262 - val_binary_accuracy: 0.7343 - val_mse: 0.1412
Epoch 15/20
154/154 - 0s - loss: 0.4603 - binary_accuracy: 0.7342 - mse: 0.1510
154/154 - 0s - loss: 0.4603 - binary_accuracy: 0.7342 - mse: 0.1510 - val_loss: 0.4264 - val_binary_accuracy: 0.7343 - val_mse: 0.1414
Epoch 16/20
154/154 - 0s - loss: 0.4611 - binary_accuracy: 0.7342 - mse: 0.1519
154/154 - 0s - loss: 0.4611 - binary_accuracy: 0.7342 - mse: 0.1519 - val_loss: 0.4258 - val_binary_accuracy: 0.7343 - val_mse: 0.1410
Epoch 17/20
154/154 - 0s - loss: 0.4638 - binary_accuracy: 0.7342 - mse: 0.1522
154/154 - 0s - loss: 0.4638 - binary_accuracy: 0.7342 - mse: 0.1522 - val_loss: 0.4261 - val_binary_accuracy: 0.7343 - val_mse: 0.1411
Epoch 18/20
154/154 - 0s - loss: 0.4571 - binary_accuracy: 0.7342 - mse: 0.1507
154/154 - 0s - loss: 0.4571 - binary_accuracy: 0.7342 - mse: 0.1507 - val_loss: 0.4259 - val_binary_accuracy: 0.7343 - val_mse: 0.1410
Epoch 19/20
154/154 - 0s - loss: 0.4603 - binary_accuracy: 0.7498 - mse: 0.1513
154/154 - 0s - loss: 0.4603 - binary_accuracy: 0.7498 - mse: 0.1513 - val_loss: 0.4256 - val_binary_accuracy: 0.7875 - val_mse: 0.1408
Epoch 20/20
154/154 - 0s - loss: 0.4606 - binary_accuracy: 0.7738 - mse: 0.1512
154/154 - 0s - loss: 0.4606 - binary_accuracy: 0.7738 - mse: 0.1512 - val_loss: 0.4258 - val_binary_accuracy: 0.7856 - val_mse: 0.1409
Evaluation
- Predict classes and probabilities
pred_classes_test <- predict_classes(object = model_keras, x = as.matrix(test_data_bk)) pred_proba_test <- predict_proba(object = model_keras, x = as.matrix(test_data_bk))
- Create results table
test_results <- tibble( actual_yes = as.factor(as.vector(test_y_drop)), pred_classes_test = as.factor(as.vector(pred_classes_test)), Yes = as.vector(pred_proba_test), No = 1 - as.vector(pred_proba_test)) head(test_results) ## # A tibble: 6 x 4 ## actual_yes pred_classes_test Yes No ## <fct> <fct> <dbl> <dbl> ## 1 0 0 0.481 0.519 ## 2 0 0 0.0256 0.974 ## 3 0 0 0.383 0.617 ## 4 1 1 0.502 0.498 ## 5 0 0 0.0527 0.947 ## 6 1 1 0.502 0.498
- Calculate confusion matrix
test_results %>% conf_mat(actual_yes, pred_classes_test) ## Truth ## Prediction 0 1 ## 0 733 164 ## 1 41 116
- Calculate metrics
test_results %>% metrics(actual_yes, pred_classes_test) ## # A tibble: 2 x 3 ## .metric .estimator .estimate ## <chr> <chr> <dbl> ## 1 accuracy binary 0.806 ## 2 kap binary 0.420
- Area under the ROC curve
test_results %>% roc_auc(actual_yes, Yes) ## # A tibble: 1 x 3 ## .metric .estimator .estimate ## <chr> <chr> <dbl> ## 1 roc_auc binary 0.150
- Precision and recall
tibble(
precision = test_results %>% yardstick::precision(actual_yes, pred_classes_test) %>% select(.estimate) %>% as.numeric(),
recall = test_results %>% yardstick::recall(actual_yes, pred_classes_test) %>% select(.estimate) %>% as.numeric()
)
## # A tibble: 1 x 2
## precision recall
## <dbl> <dbl>
## 1 0.817 0.947
- F1-Statistic
test_results %>% yardstick::f_meas(actual_yes, pred_classes_test, beta = 1) ## # A tibble: 1 x 3 ## .metric .estimator .estimate ## <chr> <chr> <dbl> ## 1 f_meas binary 0.877
H2O
Shows an alternative to Keras!
- Initialise H2O instance and convert data to h2o frame
library(h2o) h2o.init(nthreads = -1) ## Connection successful! ## ## R is connected to the H2O cluster: ## H2O cluster uptime: 2 hours 34 minutes ## H2O cluster timezone: Europe/Berlin ## H2O data parsing timezone: UTC ## H2O cluster version: 3.32.0.1 ## H2O cluster version age: 5 months and 9 days !!! ## H2O cluster name: H2O_started_from_R_shiringlander_jfz275 ## H2O cluster total nodes: 1 ## H2O cluster total memory: 6.25 GB ## H2O cluster total cores: 16 ## H2O cluster allowed cores: 16 ## H2O cluster healthy: TRUE ## H2O Connection ip: localhost ## H2O Connection port: 54321 ## H2O Connection proxy: NA ## H2O Internal Security: FALSE ## H2O API Extensions: Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4 ## R Version: R version 4.0.4 (2021-02-15) h2o.no_progress() train_hf <- as.h2o(train_data) valid_hf <- as.h2o(valid_data) test_hf <- as.h2o(test_data) response <- "Churn" features <- setdiff(colnames(train_hf), response) # For binary classification, response should be a factor train_hf[, response] <- as.factor(train_hf[, response]) valid_hf[, response] <- as.factor(valid_hf[, response]) test_hf[, response] <- as.factor(test_hf[, response]) summary(train_hf$Churn, exact_quantiles = TRUE) ## Churn ## 0:3615 ## 1:1309 summary(valid_hf$Churn, exact_quantiles = TRUE) ## Churn ## 0:774 ## 1:280 summary(test_hf$Churn, exact_quantiles = TRUE) ## Churn ## 0:774 ## 1:280
- Train model with AutoML.
“During model training, you might find that the majority of your data belongs in a single class. For example, consider a binary classification model that has 100 rows, with 80 rows labeled as class 1 and the remaining 20 rows labeled as class 2. This is a common scenario, given that machine learning attempts to predict class 1 with the highest accuracy. It can also be an example of an imbalanced dataset, in this case, with a ratio of 4:1. The balance_classes option can be used to balance the class distribution. When enabled, H2O will either undersample the majority classes or oversample the minority classes. Note that the resulting model will also correct the final probabilities (“undo the sampling”) using a monotonic transform, so the predicted probabilities of the first model will differ from a second model. However, because AUC only cares about ordering, it won’t be affected. If this option is enabled, then you can also specify a value for the class_sampling_factors and max_after_balance_size options." http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/balance_classes.html
aml <- h2o.automl(x = features,
y = response,
training_frame = train_hf,
validation_frame = valid_hf,
balance_classes = TRUE,
max_runtime_secs = 3600)
# View the AutoML Leaderboard
lb <- aml@leaderboard
best_model <- aml@leader
h2o.saveModel(best_model, "/Users/shiringlander/Documents/Github/Data")
- Prediction
pred <- h2o.predict(best_model, test_hf[, -1])
- Mean per class error
h2o.mean_per_class_error(best_model, train = TRUE, valid = TRUE, xval = TRUE) ## train valid xval ## 0.2125416 0.2268365 0.2387021
- Confusion matrix on validation data
h2o.confusionMatrix(best_model, valid = TRUE) ## Confusion Matrix (vertical: actual; across: predicted) for max f1 @ threshold = 0.336658768072945: ## 0 1 Error Rate ## 0 644 130 0.167959 =130/774 ## 1 80 200 0.285714 =80/280 ## Totals 724 330 0.199241 =210/1054 h2o.auc(best_model, train = TRUE) ## [1] 0.8707153 h2o.auc(best_model, valid = TRUE) ## [1] 0.8531331 h2o.auc(best_model, xval = TRUE) ## [1] 0.8447738
- Performance and confusion matrix on test data
perf <- h2o.performance(best_model, test_hf) h2o.confusionMatrix(perf) ## Confusion Matrix (vertical: actual; across: predicted) for max f1 @ threshold = 0.362814272627094: ## 0 1 Error Rate ## 0 642 132 0.170543 =132/774 ## 1 76 204 0.271429 =76/280 ## Totals 718 336 0.197343 =208/1054
- Plot performance
plot(perf)
- More performance metrics extracted
h2o.logloss(perf) ## [1] 0.3997853 h2o.mse(perf) ## [1] 0.129026 h2o.auc(perf) ## [1] 0.8608204 metrics <- as.data.frame(h2o.metric(perf)) head(metrics) ## threshold f1 f2 f0point5 accuracy precision recall ## 1 0.8798453 0.007117438 0.004460303 0.01760563 0.7352941 1.0000000 0.003571429 ## 2 0.8719808 0.014184397 0.008912656 0.03472222 0.7362429 1.0000000 0.007142857 ## 3 0.8698321 0.021201413 0.013357079 0.05136986 0.7371917 1.0000000 0.010714286 ## 4 0.8658536 0.028169014 0.017793594 0.06756757 0.7381404 1.0000000 0.014285714 ## 5 0.8600962 0.035087719 0.022222222 0.08333333 0.7390892 1.0000000 0.017857143 ## 6 0.8515710 0.034965035 0.022202487 0.08223684 0.7381404 0.8333333 0.017857143 ## specificity absolute_mcc min_per_class_accuracy mean_per_class_accuracy tns ## 1 1.000000 0.05123624 0.003571429 0.5017857 774 ## 2 1.000000 0.07249342 0.007142857 0.5035714 774 ## 3 1.000000 0.08882817 0.010714286 0.5053571 774 ## 4 1.000000 0.10261877 0.014285714 0.5071429 774 ## 5 1.000000 0.11478595 0.017857143 0.5089286 774 ## 6 0.998708 0.09724979 0.017857143 0.5082826 773 ## fns fps tps tnr fnr fpr tpr idx ## 1 279 0 1 1.000000 0.9964286 0.00000000 0.003571429 0 ## 2 278 0 2 1.000000 0.9928571 0.00000000 0.007142857 1 ## 3 277 0 3 1.000000 0.9892857 0.00000000 0.010714286 2 ## 4 276 0 4 1.000000 0.9857143 0.00000000 0.014285714 3 ## 5 275 0 5 1.000000 0.9821429 0.00000000 0.017857143 4 ## 6 275 1 5 0.998708 0.9821429 0.00129199 0.017857143 5
- Plot performance metrics
metrics %>%
gather(x, y, f1:tpr) %>%
ggplot(aes(x = threshold, y = y, group = x)) +
facet_wrap(~ x, ncol = 2, scales = "free") +
geom_line()
- Examine prediction thresholds
# optimal threshold:
threshold <- metrics[order(-metrics$accuracy), "threshold"][1]
print(threshold)
## [1] 0.5436611
finalRf_predictions <- data.frame(actual = as.vector(test_hf$Churn),
as.data.frame(h2o.predict(object = best_model,
newdata = test_hf)))
head(finalRf_predictions)
## actual predict p0 p1
## 1 0 1 0.5300039 0.46999608
## 2 0 0 0.9695586 0.03044143
## 3 0 1 0.4360508 0.56394924
## 4 1 1 0.4275426 0.57245736
## 5 0 0 0.9369931 0.06300689
## 6 1 1 0.3377812 0.66221884
finalRf_predictions$accurate <- ifelse(finalRf_predictions$actual ==
finalRf_predictions$predict, "ja", "nein")
finalRf_predictions$predict_stringent <- ifelse(finalRf_predictions$p1 > threshold, 1,
ifelse(finalRf_predictions$p0 > threshold, 0, "unsicher"))
finalRf_predictions$accurate_stringent <- ifelse(finalRf_predictions$actual ==
finalRf_predictions$predict_stringent, "ja",
ifelse(finalRf_predictions$predict_stringent ==
"unsicher", "unsicher", "nein"))
finalRf_predictions %>%
group_by(actual, predict) %>%
dplyr::summarise(n = n())
## # A tibble: 4 x 3
## # Groups: actual [2]
## actual predict n
## <chr> <fct> <int>
## 1 0 0 620
## 2 0 1 154
## 3 1 0 71
## 4 1 1 209
finalRf_predictions %>%
group_by(actual, predict_stringent) %>%
dplyr::summarise(n = n())
## # A tibble: 6 x 3
## # Groups: actual [2]
## actual predict_stringent n
## <chr> <chr> <int>
## 1 0 0 681
## 2 0 1 51
## 3 0 unsicher 42
## 4 1 0 103
## 5 1 1 138
## 6 1 unsicher 39
finalRf_predictions %>%
gather(x, y, accurate, accurate_stringent) %>%
mutate(x = ifelse(x == "accurate", "Default Schwelle: 0.5",
paste("Angepasste Schwelle:", round(threshold, digits = 2)))) %>%
ggplot(aes(x = actual, fill = y)) +
facet_grid(~ x) +
geom_bar(position = "dodge") +
scale_fill_tableau()
df <- finalRf_predictions[, c(1, 3, 4)]
thresholds <- seq(from = 0, to = 1, by = 0.1)
prop_table <- data.frame(threshold = thresholds,
prop_p0_correct_pred = NA, prop_p0_wrong_pred = NA,
prop_p1_correct_pred = NA, prop_p1_wrong_pred = NA)
for (threshold in thresholds) {
# if prediction probability for churn (1) > threshold, predict 1, else 0 (no churn)
pred_1 <- ifelse(df$p1 > threshold, 1, 0)
# if prediction == actual, TRUE
pred_1_t <- ifelse(pred_1 == df$actual, TRUE, FALSE)
group <- data.frame(df,
"pred_true" = pred_1_t) %>%
group_by(actual, pred_true) %>%
dplyr::summarise(n = n())
# actual no churns
group_p0 <- filter(group, actual == "0")
prop_p0_t <- sum(filter(group_p0, pred_true == TRUE)$n) / sum(group_p0$n)
prop_p0_f <- sum(filter(group_p0, pred_true == FALSE)$n) / sum(group_p0$n)
prop_table[prop_table$threshold == threshold, "prop_p0_correct_pred"] <- prop_p0_t
prop_table[prop_table$threshold == threshold, "prop_p0_wrong_pred"] <- prop_p0_f
# actual churns
group_p1 <- filter(group, actual == "1")
prop_p1_t <- sum(filter(group_p1, pred_true == TRUE)$n) / sum(group_p1$n)
prop_p1_f <- sum(filter(group_p1, pred_true == FALSE)$n) / sum(group_p1$n)
prop_table[prop_table$threshold == threshold, "prop_p1_correct_pred"] <- prop_p1_t
prop_table[prop_table$threshold == threshold, "prop_p1_wrong_pred"] <- prop_p1_f
}
prop_table %>%
gather(x, y, prop_p0_correct_pred, prop_p1_correct_pred) %>%
rename(Schwellenwert = threshold) %>%
mutate(x = ifelse(x == "prop_p0_correct_pred", "prop true p0",
"prop true p1")) %>%
ggplot(aes(x = Schwellenwert, y = y, color = x)) +
geom_point() +
geom_line() +
scale_color_tableau()
Cost/revenue calculation per year
Let’s assume that
- a marketing campaign + employee time will cost the company 1000€ per year for every customer that is included in the campaign.
- the annual average revenue per customer is 2000€ (in more complex scenarios customers could be further divided into revenue groups to calculate how “valuable” they are and how harmful loosing them would be)
- investing into unnecessary marketing doesn’t cause churn by itself (i.e. a customer who isn’t going to churn isn’t reacting negatively to the add campaign - which could happen in more complex scenarios).
- without a customer churn model the company would target half of their customer (by chance) for ad-campaigns
- without a customer churn model the company would lose about 25% of their customers to churn
This would mean that compared to no intervention we would have
- prop_p0_correct_pred == customers who were correctly predicted to not churn did not cost anything (no marketing money was spent): +/-0€
- prop_p0_wrong_pred == customers that did not churn but were predicted to churn will be an empty investment: +/-0€ - 1500€
- prop_p1_wrong_pred == customer that were predicted to stay but churned: -2000€
- prop_p1_correct_pred == customers that were correctly predicted to churn:
- let’s say 100% of those could be kept by investing into marketing: +2000€ -1500€
- let’s say 50% could be kept by investing into marketing: +2000€ * 0.5 -1500€
- Let’s play around with some values:
# Baseline revenue <- 2000 cost <- 1000 ## number of customers who churn customers_churn <- filter(test_data, Churn == 1) customers_churn_n <- nrow(customers_churn) ## number of customers who don't churn customers_no_churn <- filter(filter(test_data, Churn == 0)) customers_no_churn_n <- nrow(customers_no_churn) ## number of customers customers <- customers_churn_n + customers_no_churn_n # percentage of customers randomly targeted for ad campaign ad_target_rate <- 0.5 ad_cost_default <- customers * ad_target_rate * cost churn_rate_default <- customers_churn_n / customers_no_churn_n ann_revenue_default <- customers_no_churn_n * revenue # net win per year: revenue from non-churn customers - ad costs net_win_default <- ann_revenue_default - ad_cost_default net_win_default ## [1] 1021000
- How much revenue can we gain from predicting customer churn with our model (assuming a conversion rate after ad campaign of 0.7):
# all customers predicted to churn will be targeted by ad campaign
# of those, 70% can be convinced to stay
conversion <- 0.7
net_win_table <- prop_table %>%
mutate(
# proportion of correctly predicted no-churns: make normal revenue at no cost
prop_p0_correct_pred_X = prop_p0_correct_pred * customers_no_churn_n * revenue,
# proportion of no-churns predicted to churn: make revenue but also cost ad money
prop_p0_wrong_pred_X = prop_p0_wrong_pred * customers_no_churn_n * (revenue - cost),
# proportion of churns predicted to not churn: revenue lost
prop_p1_wrong_pred_X = prop_p1_wrong_pred * customers_churn_n * 0,
# proportion of correctly predicted churns: 70% stay and make revenue but all of them cost ad money
prop_p1_correct_pred_X = prop_p1_correct_pred * customers_churn_n * ((revenue * conversion) - cost)) %>%
group_by(threshold) %>%
summarise(net_win = sum(prop_p0_correct_pred_X + prop_p0_wrong_pred_X + prop_p1_wrong_pred_X + prop_p1_correct_pred_X),
net_win_compared = net_win - net_win_default) %>%
arrange(-net_win_compared)
net_win_table
## # A tibble: 11 x 3
## threshold net_win net_win_compared
## <dbl> <dbl> <dbl>
## 1 0.7 1560800 539800
## 2 0.6 1555800 534800
## 3 0.8 1549600 528600
## 4 0.9 1548000 527000
## 5 1 1548000 527000
## 6 0.5 1536600 515600
## 7 0.4 1511400 490400
## 8 0.3 1460000 439000
## 9 0.2 1404000 383000
## 10 0.1 1268200 247200
## 11 0 886000 -135000
LIME
- Explaining predictions
Xtrain <- as.data.frame(train_hf)
Xtest <- as.data.frame(test_hf)
# run lime() on training set
explainer <- lime::lime(x = Xtrain,
model = best_model)
# run explain() on the explainer
explanation <- lime::explain(x = Xtest[1:9, ],
explainer = explainer,
n_labels = 1,
n_features = 4,
kernel_width = 0.5)
plot_explanations(explanation)
explanation %>% plot_features(ncol = 3)
sessionInfo() ## R version 4.0.4 (2021-02-15) ## Platform: x86_64-apple-darwin17.0 (64-bit) ## Running under: macOS Catalina 10.15.7 ## ## Matrix products: default ## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib ## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib ## ## locale: ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: ## [1] h2o_3.32.0.1 corrplot_0.84 ggthemes_4.2.4 yardstick_0.0.7 ## [5] recipes_0.1.15 rsample_0.0.9 lime_0.5.2 keras_2.3.0.0.9000 ## [9] mice_3.13.0 caret_6.0-86 lattice_0.20-41 forcats_0.5.1 ## [13] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4 readr_1.4.0 ## [17] tidyr_1.1.3 tibble_3.1.0 ggplot2_3.3.3 tidyverse_1.3.0 ## ## loaded via a namespace (and not attached): ## [1] colorspace_2.0-0 ellipsis_0.3.1 class_7.3-18 ## [4] base64enc_0.1-3 fs_1.5.0 rstudioapi_0.13 ## [7] listenv_0.8.0 furrr_0.2.2 farver_2.1.0 ## [10] bit64_4.0.5 prodlim_2019.11.13 fansi_0.4.2 ## [13] lubridate_1.7.10 xml2_1.3.2 codetools_0.2-18 ## [16] splines_4.0.4 knitr_1.31 zeallot_0.1.0 ## [19] jsonlite_1.7.2 pROC_1.17.0.1 broom_0.7.5 ## [22] dbplyr_2.1.0 tfruns_1.5.0 compiler_4.0.4 ## [25] httr_1.4.2 backports_1.2.1 assertthat_0.2.1 ## [28] Matrix_1.3-2 cli_2.3.1 htmltools_0.5.1.1 ## [31] tools_4.0.4 gtable_0.3.0 glue_1.4.2 ## [34] reshape2_1.4.4 Rcpp_1.0.6 cellranger_1.1.0 ## [37] jquerylib_0.1.3 vctrs_0.3.6 nlme_3.1-152 ## [40] blogdown_1.2 iterators_1.0.13 timeDate_3043.102 ## [43] gower_0.2.2 xfun_0.22 globals_0.14.0 ## [46] rvest_1.0.0 lifecycle_1.0.0 future_1.21.0 ## [49] MASS_7.3-53.1 scales_1.1.1 ipred_0.9-11 ## [52] hms_1.0.0 parallel_4.0.4 yaml_2.2.1 ## [55] reticulate_1.18 sass_0.3.1 rpart_4.1-15 ## [58] stringi_1.5.3 highr_0.8 tensorflow_2.2.0 ## [61] foreach_1.5.1 lava_1.6.9 shape_1.4.5 ## [64] bitops_1.0-6 rlang_0.4.10 pkgconfig_2.0.3 ## [67] evaluate_0.14 labeling_0.4.2 bit_4.0.4 ## [70] tidyselect_1.1.0 parallelly_1.23.0 plyr_1.8.6 ## [73] magrittr_2.0.1 bookdown_0.21 R6_2.5.0 ## [76] generics_0.1.0 DBI_1.1.1 pillar_1.5.1 ## [79] haven_2.3.1 whisker_0.4 withr_2.4.1 ## [82] RCurl_1.98-1.2 survival_3.2-7 nnet_7.3-15 ## [85] modelr_0.1.8 crayon_1.4.1 utf8_1.2.1 ## [88] rmarkdown_2.7 grid_4.0.4 readxl_1.3.1 ## [91] data.table_1.14.0 ModelMetrics_1.2.2.2 reprex_1.0.0 ## [94] digest_0.6.27 stats4_4.0.4 munsell_0.5.0 ## [97] glmnet_4.1-1 bslib_0.2.4
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