Bosques Aleatorios

Teoría

Instalar paquetes y llamar librerías

# install.packages("caret")
library(caret)
# install.packages("randomForest")
library(randomForest)

Importar la base de datos

churn <- read.csv("C:\\Users\\maria\\OneDrive\\Desktop\\TEC SEMESTRE 6\\customer_churn.csv")

Entender la base de datos

summary(churn)

##    CustomerID          Age           Gender              Tenure     
##  Min.   :     2   Min.   :18.00   Length:440833      Min.   : 1.00  
##  1st Qu.:113622   1st Qu.:29.00   Class :character   1st Qu.:16.00  
##  Median :226126   Median :39.00   Mode  :character   Median :32.00  
##  Mean   :225399   Mean   :39.37                      Mean   :31.26  
##  3rd Qu.:337739   3rd Qu.:48.00                      3rd Qu.:46.00  
##  Max.   :449999   Max.   :65.00                      Max.   :60.00  
##  NA's   :1        NA's   :1                          NA's   :1      
##  Usage.Frequency Support.Calls    Payment.Delay   Subscription.Type 
##  Min.   : 1.00   Min.   : 0.000   Min.   : 0.00   Length:440833     
##  1st Qu.: 9.00   1st Qu.: 1.000   1st Qu.: 6.00   Class :character  
##  Median :16.00   Median : 3.000   Median :12.00   Mode  :character  
##  Mean   :15.81   Mean   : 3.604   Mean   :12.97                     
##  3rd Qu.:23.00   3rd Qu.: 6.000   3rd Qu.:19.00                     
##  Max.   :30.00   Max.   :10.000   Max.   :30.00                     
##  NA's   :1       NA's   :1        NA's   :1                         
##  Contract.Length     Total.Spend     Last.Interaction     Churn       
##  Length:440833      Min.   : 100.0   Min.   : 1.00    Min.   :0.0000  
##  Class :character   1st Qu.: 480.0   1st Qu.: 7.00    1st Qu.:0.0000  
##  Mode  :character   Median : 661.0   Median :14.00    Median :1.0000  
##                     Mean   : 631.6   Mean   :14.48    Mean   :0.5671  
##                     3rd Qu.: 830.0   3rd Qu.:22.00    3rd Qu.:1.0000  
##                     Max.   :1000.0   Max.   :30.00    Max.   :1.0000  
##                     NA's   :1        NA's   :1        NA's   :1

str(churn)

## 'data.frame':    440833 obs. of  12 variables:
##  $ CustomerID       : int  2 3 4 5 6 8 9 10 11 12 ...
##  $ Age              : int  30 65 55 58 23 51 58 55 39 64 ...
##  $ Gender           : chr  "Female" "Female" "Female" "Male" ...
##  $ Tenure           : int  39 49 14 38 32 33 49 37 12 3 ...
##  $ Usage.Frequency  : int  14 1 4 21 20 25 12 8 5 25 ...
##  $ Support.Calls    : int  5 10 6 7 5 9 3 4 7 2 ...
##  $ Payment.Delay    : int  18 8 18 7 8 26 16 15 4 11 ...
##  $ Subscription.Type: chr  "Standard" "Basic" "Basic" "Standard" ...
##  $ Contract.Length  : chr  "Annual" "Monthly" "Quarterly" "Monthly" ...
##  $ Total.Spend      : num  932 557 185 396 617 129 821 445 969 415 ...
##  $ Last.Interaction : int  17 6 3 29 20 8 24 30 13 29 ...
##  $ Churn            : int  1 1 1 1 1 1 1 1 1 1 ...

head(churn)

##   CustomerID Age Gender Tenure Usage.Frequency Support.Calls Payment.Delay
## 1          2  30 Female     39              14             5            18
## 2          3  65 Female     49               1            10             8
## 3          4  55 Female     14               4             6            18
## 4          5  58   Male     38              21             7             7
## 5          6  23   Male     32              20             5             8
## 6          8  51   Male     33              25             9            26
##   Subscription.Type Contract.Length Total.Spend Last.Interaction Churn
## 1          Standard          Annual         932               17     1
## 2             Basic         Monthly         557                6     1
## 3             Basic       Quarterly         185                3     1
## 4          Standard         Monthly         396               29     1
## 5             Basic         Monthly         617               20     1
## 6           Premium          Annual         129                8     1

churn$Gender <- as.factor(churn$Gender)
churn$Subscription.Type <- as.factor(churn$Subscription.Type)
churn$Contract.Length <- as.factor(churn$Contract.Length)
churn$Churn <- as.factor(churn$Churn)

str(churn)

## 'data.frame':    440833 obs. of  12 variables:
##  $ CustomerID       : int  2 3 4 5 6 8 9 10 11 12 ...
##  $ Age              : int  30 65 55 58 23 51 58 55 39 64 ...
##  $ Gender           : Factor w/ 3 levels "","Female","Male": 2 2 2 3 3 3 2 2 3 2 ...
##  $ Tenure           : int  39 49 14 38 32 33 49 37 12 3 ...
##  $ Usage.Frequency  : int  14 1 4 21 20 25 12 8 5 25 ...
##  $ Support.Calls    : int  5 10 6 7 5 9 3 4 7 2 ...
##  $ Payment.Delay    : int  18 8 18 7 8 26 16 15 4 11 ...
##  $ Subscription.Type: Factor w/ 4 levels "","Basic","Premium",..: 4 2 2 4 2 3 4 3 4 4 ...
##  $ Contract.Length  : Factor w/ 4 levels "","Annual","Monthly",..: 2 3 4 3 3 2 4 2 4 4 ...
##  $ Total.Spend      : num  932 557 185 396 617 129 821 445 969 415 ...
##  $ Last.Interaction : int  17 6 3 29 20 8 24 30 13 29 ...
##  $ Churn            : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...

churn <- na.omit(churn)

Partir datos 80/20

set.seed(123)
renglones_entrenamiento <- createDataPartition(churn$Churn, p = 0.8, list = FALSE)
entrenamiento <- churn[renglones_entrenamiento, ]
prueba <- churn[-renglones_entrenamiento, ]

Modelo de Bosques Aleatorios

modelo <- randomForest(Churn~ . - CustomerID,
                        data =  entrenamiento,
                        ntree = 100,
                        importance = TRUE)

Matrices de confusión

resultado_entrenamiento <- predict(modelo, entrenamiento)
resultado_prueba <- predict(modelo, prueba)

# Matriz de Confusion del Resultado de Entrenamiento
mcre <- confusionMatrix(resultado_entrenamiento, entrenamiento$Churn)
mcre

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction      0      1
##          0 152667      0
##          1      0 200000
##                                    
##                Accuracy : 1        
##                  95% CI : (1, 1)   
##     No Information Rate : 0.5671   
##     P-Value [Acc > NIR] : < 2.2e-16
##                                    
##                   Kappa : 1        
##                                    
##  Mcnemar's Test P-Value : NA       
##                                    
##             Sensitivity : 1.0000   
##             Specificity : 1.0000   
##          Pos Pred Value : 1.0000   
##          Neg Pred Value : 1.0000   
##              Prevalence : 0.4329   
##          Detection Rate : 0.4329   
##    Detection Prevalence : 0.4329   
##       Balanced Accuracy : 1.0000   
##                                    
##        'Positive' Class : 0        
## 

# Matriz de Confusion del Resultado de Prueba
mcrp <- confusionMatrix(resultado_prueba, prueba$Churn)
mcrp

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction     0     1
##          0 38165     7
##          1     1 49992
##                                      
##                Accuracy : 0.9999     
##                  95% CI : (0.9998, 1)
##     No Information Rate : 0.5671     
##     P-Value [Acc > NIR] : <2e-16     
##                                      
##                   Kappa : 0.9998     
##                                      
##  Mcnemar's Test P-Value : 0.0771     
##                                      
##             Sensitivity : 1.0000     
##             Specificity : 0.9999     
##          Pos Pred Value : 0.9998     
##          Neg Pred Value : 1.0000     
##              Prevalence : 0.4329     
##          Detection Rate : 0.4329     
##    Detection Prevalence : 0.4330     
##       Balanced Accuracy : 0.9999     
##                                      
##        'Positive' Class : 0          
## 

Resultados

plot(modelo)

varImpPlot(modelo)