Linear Regresion
Romery Luna A01749837
2025-02-25
Contexto
Telco es una empresa internacional que vende una variedad de servicios de telecomunicaciones. Churn es un indicador de la diserción de clientes, fenómeno en el cuál los clientes cancelan sus servicios con la empresa. El objetivo de etse trabajo es predecir si un cliente abandonará o se quedará en la empresa.
Instalar paquetes y llamar librerías
#install.packages("tidyverse") #Manejo de datos
library(tidyverse)
library(caret) #Entreanamiento d edatos
#install.packages("e1071") #Matriz de Confusión
library(e1071)
library(dplyr)Importar la base de datos
telco <- read.csv("C:\\Users\\rylun\\Downloads\\Telco Customer Churn.csv")
View(telco)Entender la base de datos
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" ...Teoría de la Regresión Lógistica
La regresión lógistica es una modelo qye se utiliza para predecir la peobabilidad de que ocurra un evento, basado en una o más variables independientes. A diferencia de la regresión lineal, que predice valores continuos (como el precio de una casa), la regresión lógistica predice resultados binarios o categóricos, como un sí/no, 1/0, verdadero/false. Si la probabilidad es mayor que 0.5, el modelo predice que el event ocurrirá (Ej. el cliente se dará de baja). Si la probabilidad es menor que 0.5, predice que el evento no ocurrirá.
Preparar la base de datos
#Eliminar la columna de "CostumerID"
telco_limpia <- telco %>% select(-customerID)
#Convertir las variables caractér a factores
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 ...#Eliminar NA´s
telco_limpia <- na.omit(telco_limpia)Entrenar la base de datos
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) 1.6509079 1.5751655 1.048 0.294599
## genderMale 0.0264476 0.1214183 0.218 0.827568
## SeniorCitizen 0.4461618 0.1554043 2.871 0.004092 **
## PartnerYes 0.1978381 0.1488616 1.329 0.183846
## DependentsYes -0.2949084 0.1680871 -1.754 0.079345 .
## tenure -0.0591466 0.0116360 -5.083 3.71e-07 ***
## PhoneServiceYes 0.6806017 1.2466673 0.546 0.585109
## MultipleLinesNo phone service NA NA NA NA
## MultipleLinesYes 0.6307457 0.3425340 1.841 0.065561 .
## InternetServiceFiber optic 2.4248413 1.5412661 1.573 0.115654
## InternetServiceNo -2.7306763 1.5591530 -1.751 0.079880 .
## OnlineSecurityNo internet service NA NA NA NA
## OnlineSecurityYes -0.1550564 0.3452691 -0.449 0.653368
## OnlineBackupNo internet service NA NA NA NA
## OnlineBackupYes 0.1433349 0.3401124 0.421 0.673438
## DeviceProtectionNo internet service NA NA NA NA
## DeviceProtectionYes 0.2505036 0.3355537 0.747 0.455343
## TechSupportNo internet service NA NA NA NA
## TechSupportYes -0.2606770 0.3467243 -0.752 0.452155
## StreamingTVNo internet service NA NA NA NA
## StreamingTVYes 0.8757067 0.6331826 1.383 0.166657
## StreamingMoviesNo internet service NA NA NA NA
## StreamingMoviesYes 0.7430688 0.6300170 1.179 0.238222
## ContractOne year -0.2497583 0.1953523 -1.279 0.201072
## ContractTwo year -1.1522840 0.3598118 -3.202 0.001363 **
## PaperlessBillingYes 0.4795697 0.1409077 3.403 0.000665 ***
## PaymentMethodCredit card (automatic) -0.2356236 0.2140010 -1.101 0.270879
## PaymentMethodElectronic check 0.3454196 0.1764540 1.958 0.050282 .
## PaymentMethodMailed check 0.0536531 0.2145306 0.250 0.802513
## MonthlyCharges -0.0660192 0.0614360 -1.075 0.282553
## TotalCharges 0.0002680 0.0001317 2.035 0.041859 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2440.6 on 2107 degrees of freedom
## Residual deviance: 1685.7 on 2084 degrees of freedom
## AIC: 1733.7
##
## Number of Fisher Scoring iterations: 6Evaluar el modelo
#Predicción en entrenamiento
prediccion_entrenamiento_telco <- predict(modelo_telco, entrenamiento_telco,type="response")
resultado_prediccion_entrenamiento_telco <- ifelse(prediccion_entrenamiento_telco>=0.5,"Yes","No")
#Matriz de confusión
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 1389 226
## Yes 159 334
##
## Accuracy : 0.8174
## 95% CI : (0.8002, 0.8336)
## No Information Rate : 0.7343
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5133
##
## Mcnemar's Test P-Value : 0.0007691
##
## Sensitivity : 0.8973
## Specificity : 0.5964
## Pos Pred Value : 0.8601
## Neg Pred Value : 0.6775
## Prevalence : 0.7343
## Detection Rate : 0.6589
## Detection Prevalence : 0.7661
## Balanced Accuracy : 0.7469
##
## 'Positive' Class : No
## Ejemplo de predicción
info_cliente <- telco_limpia[2, ]
info_cliente <-info_cliente %>% select(-Churn)
probabilidad_cliente <-predict(modelo_telco,info_cliente,type="response")
probabilidad_cliente## 2
## 0.0471038prediccion_cliente <- ifelse(probabilidad_cliente>=0.5,"Yes","No")
prediccion_cliente## 2
## "No"