netflix decision tree how?

[netflix]

hi guys, im new here so sorry if this is the wrong category!

Netflix Movie and TV Shows (June 2021) | Kaggle

I have a netflix dataset and i'd like to predict the success of a tvseries with its age certificate is that possible ? because i couldnt do it ! can anyone help me please?

library(tidyverse)
library(forecast)
library(dbplyr)
library(stats)
library(caret)
library(pROC)
library(e1071)
library(rpart)
library(rpart.plot)
library(caret)
library(factoextra)
library(tidyr)
library(ggplot2)
library(cluster)

netflix <- read_csv("C:/Users/User/Desktop/netflix_list.csv", 
    col_types = cols(imdb_id = col_number(), 
        popular_rank = col_number(), startYear = col_number(), 
        endYear = col_number(), episodes = col_number(), 
        runtime = col_number(), rating = col_number(), 
        numVotes = col_number(), cast = col_skip(), 
        image_url = col_skip()))

serie <- netflix %>%
  select(popular_rank, type, certificate) %>%
  filter(type == "tvSeries")

serie_true <- serie %>%
  mutate( certificate = case_when(certificate == "13" ~ "jugendlich", certificate == "13+" ~ "jugendlich" , certificate == "12+" ~ "jugendlich" , certificate == "15" ~ "jugendlich", certificate == "15+" ~ "jugendlich", certificate == "7" ~ "jugendlich", certificate == "All" ~ "jugendlich", certificate == "UA" ~ "jugendlich", certificate == "U" ~ "jugendlich" , certificate == "7" ~ "jugendliche", TRUE ~ "volljährig")) 

serie_true <- serie_true %>%
  mutate(popular_rank = if_else(popular_rank <150, 1, 0)) 

serie_true <- serie_true %>%
  select(-type)

dummy_serie <- dummyVars("~certificate", data = serie_true, fullRank = TRUE)
dummy_certificate <- data.frame(predict(dummy_serie, newdata = serie_true))

serie_true <- serie_true %>%
  mutate(certificate = as.factor(certificate))%>%
  mutate(popular_rank = as.factor(popular_rank))

serie_subset <- cbind(serie_true, dummy_certificate)

# set seed
set.seed(456)

# train and test data
train_index <- createDataPartition(serie_subset$popular_rank, p = 0.8)$Resample1

train_serie <- serie_subset[train_index, ]
test_serie <- serie_subset[-train_index, ]

# check ratio of variables 
serie_true %>% 
  group_by(popular_rank, certificate) %>% 
  count()

train_serie %>% 
  group_by(popular_rank,  certificatevolljährig) %>% 
  count()

# load packages
library(rpart)
library(rpart.plot)

# train our model
tree_serie <- rpart(popular_rank ~ ., data = train_serie, method = "class")

# plot our first decision tree
rpart.plot(tree_serie)

# apply model to test data
dt_results_serie <- predict(tree_serie, test_serie[, -1], type = "prob")

dt_results_serie

# bind model data to titanic test data
model_dt_results_serie <- cbind(test_serie, dt_results_serie)

head(model_dt_results_serie)

[netflix]

this is supposed to be my "decision tree" but it's completely wrong