Telco es una empresa internacional que vende una variedad de servicios de telecomunicaciones. Churn es un undicador de la desection de clientes, fenomeno en el cual los cloentes cancelan sus servicios con la empresa. Elobjetivo de este trabajo es predecir si un cliente abandonara o se quedara en la empresa.
library(tidyverse)
library(caret)
library(e1071)telco <- read.csv("~/Downloads/Telco Customer Churn.csv")summary(telco)## customerID gender SeniorCitizen Partner
## Length:7043 Length:7043 Min. :0.0000 Length:7043
## Class :character Class :character 1st Qu.:0.0000 Class :character
## Mode :character Mode :character Median :0.0000 Mode :character
## Mean :0.1621
## 3rd Qu.:0.0000
## Max. :1.0000
##
## Dependents tenure PhoneService MultipleLines
## Length:7043 Min. : 0.00 Length:7043 Length:7043
## Class :character 1st Qu.: 9.00 Class :character Class :character
## Mode :character Median :29.00 Mode :character Mode :character
## Mean :32.37
## 3rd Qu.:55.00
## Max. :72.00
##
## InternetService OnlineSecurity OnlineBackup DeviceProtection
## Length:7043 Length:7043 Length:7043 Length:7043
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## TechSupport StreamingTV StreamingMovies Contract
## Length:7043 Length:7043 Length:7043 Length:7043
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## PaperlessBilling PaymentMethod MonthlyCharges TotalCharges
## Length:7043 Length:7043 Min. : 18.25 Min. : 18.8
## Class :character Class :character 1st Qu.: 35.50 1st Qu.: 401.4
## Mode :character Mode :character Median : 70.35 Median :1397.5
## Mean : 64.76 Mean :2283.3
## 3rd Qu.: 89.85 3rd Qu.:3794.7
## Max. :118.75 Max. :8684.8
## NA's :11
## Churn
## Length:7043
## Class :character
## Mode :character
##
##
##
## head(telco)## customerID gender SeniorCitizen Partner Dependents tenure PhoneService
## 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
## MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection
## 1 No phone service DSL No Yes No
## 2 No DSL Yes No Yes
## 3 No DSL Yes Yes No
## 4 No phone service DSL Yes No Yes
## 5 No Fiber optic No No No
## 6 Yes Fiber optic No No Yes
## TechSupport StreamingTV StreamingMovies Contract PaperlessBilling
## 1 No No No Month-to-month Yes
## 2 No No No One year No
## 3 No No No Month-to-month Yes
## 4 Yes No No One year No
## 5 No No No Month-to-month Yes
## 6 No Yes Yes Month-to-month Yes
## PaymentMethod MonthlyCharges TotalCharges Churn
## 1 Electronic check 29.85 29.85 No
## 2 Mailed check 56.95 1889.50 No
## 3 Mailed check 53.85 108.15 Yes
## 4 Bank transfer (automatic) 42.30 1840.75 No
## 5 Electronic check 70.70 151.65 Yes
## 6 Electronic check 99.65 820.50 Yesstr(telco)## 'data.frame': 7043 obs. of 21 variables:
## $ customerID : chr "7590-VHVEG" "5575-GNVDE" "3668-QPYBK" "7795-CFOCW" ...
## $ gender : chr "Female" "Male" "Male" "Male" ...
## $ SeniorCitizen : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Partner : chr "Yes" "No" "No" "No" ...
## $ Dependents : chr "No" "No" "No" "No" ...
## $ tenure : int 1 34 2 45 2 8 22 10 28 62 ...
## $ PhoneService : chr "No" "Yes" "Yes" "No" ...
## $ MultipleLines : chr "No phone service" "No" "No" "No phone service" ...
## $ InternetService : chr "DSL" "DSL" "DSL" "DSL" ...
## $ OnlineSecurity : chr "No" "Yes" "Yes" "Yes" ...
## $ OnlineBackup : chr "Yes" "No" "Yes" "No" ...
## $ DeviceProtection: chr "No" "Yes" "No" "Yes" ...
## $ TechSupport : chr "No" "No" "No" "Yes" ...
## $ StreamingTV : chr "No" "No" "No" "No" ...
## $ StreamingMovies : chr "No" "No" "No" "No" ...
## $ Contract : chr "Month-to-month" "One year" "Month-to-month" "One year" ...
## $ PaperlessBilling: chr "Yes" "No" "Yes" "No" ...
## $ PaymentMethod : chr "Electronic check" "Mailed check" "Mailed check" "Bank transfer (automatic)" ...
## $ MonthlyCharges : num 29.9 57 53.9 42.3 70.7 ...
## $ TotalCharges : num 29.9 1889.5 108.2 1840.8 151.7 ...
## $ Churn : chr "No" "No" "Yes" "No" ...La regresión lineal es un modelo que se utilixa para predecir la probabilidad de que ocurre un ebento badado en una o mas variables independeintes. Adiferencia de la regresion lineal que predice balores coninuos (como el precio de una casa), la regresion logistica predice resultadis binarios o categorias como un si/no, 1/0, verdadero/falso. Si la probabilidad es mayor que 0.5 el modelo predice que el evento ocurrira (ej. cliente se dara de baja). Si la probabilidad es menor que 0.5 predice que el evento no ocurrira
telco_limpia <- telco %>% select(-customerID)
telco_limpia <- telco_limpia %>%
mutate(across(where(is.character),as.factor))
str(telco_limpia)## 'data.frame': 7043 obs. of 20 variables:
## $ gender : Factor w/ 2 levels "Female","Male": 1 2 2 2 1 1 2 1 1 2 ...
## $ SeniorCitizen : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Partner : Factor w/ 2 levels "No","Yes": 2 1 1 1 1 1 1 1 2 1 ...
## $ Dependents : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 1 1 2 ...
## $ tenure : int 1 34 2 45 2 8 22 10 28 62 ...
## $ PhoneService : Factor w/ 2 levels "No","Yes": 1 2 2 1 2 2 2 1 2 2 ...
## $ MultipleLines : Factor w/ 3 levels "No","No phone service",..: 2 1 1 2 1 3 3 2 3 1 ...
## $ InternetService : Factor w/ 3 levels "DSL","Fiber optic",..: 1 1 1 1 2 2 2 1 2 1 ...
## $ OnlineSecurity : Factor w/ 3 levels "No","No internet service",..: 1 3 3 3 1 1 1 3 1 3 ...
## $ OnlineBackup : Factor w/ 3 levels "No","No internet service",..: 3 1 3 1 1 1 3 1 1 3 ...
## $ DeviceProtection: Factor w/ 3 levels "No","No internet service",..: 1 3 1 3 1 3 1 1 3 1 ...
## $ TechSupport : Factor w/ 3 levels "No","No internet service",..: 1 1 1 3 1 1 1 1 3 1 ...
## $ StreamingTV : Factor w/ 3 levels "No","No internet service",..: 1 1 1 1 1 3 3 1 3 1 ...
## $ StreamingMovies : Factor w/ 3 levels "No","No internet service",..: 1 1 1 1 1 3 1 1 3 1 ...
## $ Contract : Factor w/ 3 levels "Month-to-month",..: 1 2 1 2 1 1 1 1 1 2 ...
## $ PaperlessBilling: Factor w/ 2 levels "No","Yes": 2 1 2 1 2 2 2 1 2 1 ...
## $ PaymentMethod : Factor w/ 4 levels "Bank transfer (automatic)",..: 3 4 4 1 3 3 2 4 3 1 ...
## $ MonthlyCharges : num 29.9 57 53.9 42.3 70.7 ...
## $ TotalCharges : num 29.9 1889.5 108.2 1840.8 151.7 ...
## $ Churn : Factor w/ 2 levels "No","Yes": 1 1 2 1 2 2 1 1 2 1 ...telco_limpia <- na.omit(telco_limpia)set.seed(123)
renglones_entrenamiento_telco <- createDataPartition(telco_limpia$Churn, p=0.7, list = FALSE)
entrenamiento_telco <- telco_limpia[renglones_entrenamiento_telco, ]
prueba_telco <- telco_limpia[-renglones_entrenamiento_telco, ]modelo_telco <- glm(Churn~., data = entrenamiento_telco, family = binomial)
summary(modelo_telco)##
## Call:
## glm(formula = Churn ~ ., family = binomial, data = entrenamiento_telco)
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.0746695 0.9571525 1.123 0.261532
## genderMale -0.0482269 0.0771304 -0.625 0.531797
## SeniorCitizen 0.1200467 0.1014491 1.183 0.236683
## PartnerYes -0.0910761 0.0918164 -0.992 0.321228
## DependentsYes -0.0843260 0.1068161 -0.789 0.429849
## tenure -0.0621135 0.0074487 -8.339 < 2e-16 ***
## PhoneServiceYes 0.0233358 0.7635434 0.031 0.975618
## MultipleLinesNo phone service NA NA NA NA
## MultipleLinesYes 0.3794100 0.2078464 1.825 0.067935 .
## InternetServiceFiber optic 1.5613737 0.9372764 1.666 0.095741 .
## InternetServiceNo -1.5047493 0.9480893 -1.587 0.112481
## OnlineSecurityNo internet service NA NA NA NA
## OnlineSecurityYes -0.2149292 0.2098780 -1.024 0.305804
## OnlineBackupNo internet service NA NA NA NA
## OnlineBackupYes 0.0008494 0.2053088 0.004 0.996699
## DeviceProtectionNo internet service NA NA NA NA
## DeviceProtectionYes 0.1177891 0.2084585 0.565 0.572041
## TechSupportNo internet service NA NA NA NA
## TechSupportYes -0.1280168 0.2128673 -0.601 0.547579
## StreamingTVNo internet service NA NA NA NA
## StreamingTVYes 0.5187839 0.3819512 1.358 0.174386
## StreamingMoviesNo internet service NA NA NA NA
## StreamingMoviesYes 0.5783808 0.3840289 1.506 0.132045
## ContractOne year -0.8451411 0.1304035 -6.481 9.11e-11 ***
## ContractTwo year -1.4644194 0.2039949 -7.179 7.04e-13 ***
## PaperlessBillingYes 0.3020753 0.0884590 3.415 0.000638 ***
## PaymentMethodCredit card (automatic) -0.0093796 0.1357499 -0.069 0.944914
## PaymentMethodElectronic check 0.2897469 0.1124649 2.576 0.009985 **
## PaymentMethodMailed check -0.1002695 0.1367827 -0.733 0.463524
## MonthlyCharges -0.0336622 0.0372618 -0.903 0.366314
## TotalCharges 0.0003653 0.0000844 4.328 1.50e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5702.8 on 4923 degrees of freedom
## Residual deviance: 4117.1 on 4900 degrees of freedom
## AIC: 4165.1
##
## Number of Fisher Scoring iterations: 6prediccion_entrenamiento_telco <- predict(modelo_telco, entrenamiento_telco, type = "response")
resultado_prediccion_entrenamiento_telco <- ifelse(prediccion_entrenamiento_telco>=0.5,"Yes","No")
mcet <- confusionMatrix(factor(resultado_prediccion_entrenamiento_telco,levels = c("Yes","No")), entrenamiento_telco$Churn)## Warning in
## confusionMatrix.default(factor(resultado_prediccion_entrenamiento_telco, :
## Levels are not in the same order for reference and data. Refactoring data to
## match.mcet## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 3237 598
## Yes 378 711
##
## Accuracy : 0.8018
## 95% CI : (0.7904, 0.8128)
## No Information Rate : 0.7342
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4634
##
## Mcnemar's Test P-Value : 2.383e-12
##
## Sensitivity : 0.8954
## Specificity : 0.5432
## Pos Pred Value : 0.8441
## Neg Pred Value : 0.6529
## Prevalence : 0.7342
## Detection Rate : 0.6574
## Detection Prevalence : 0.7788
## Balanced Accuracy : 0.7193
##
## 'Positive' Class : No
## prediccion_prueba_telco <- predict(modelo_telco, prueba_telco, type = "response")
resultado_prediccion_prueba_telco <- ifelse(prediccion_prueba_telco>=0.5,"Yes","No")
mcet <- confusionMatrix(factor(resultado_prediccion_prueba_telco,levels = c("Yes","No")), prueba_telco$Churn)## Warning in confusionMatrix.default(factor(resultado_prediccion_prueba_telco, :
## Levels are not in the same order for reference and data. Refactoring data to
## match.mcet## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 1385 229
## Yes 163 331
##
## Accuracy : 0.814
## 95% CI : (0.7968, 0.8304)
## No Information Rate : 0.7343
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5048
##
## Mcnemar's Test P-Value : 0.001027
##
## Sensitivity : 0.8947
## Specificity : 0.5911
## Pos Pred Value : 0.8581
## Neg Pred Value : 0.6700
## Prevalence : 0.7343
## Detection Rate : 0.6570
## Detection Prevalence : 0.7657
## Balanced Accuracy : 0.7429
##
## 'Positive' Class : No
##