[USER (data scientist)]: Hey there! I need some help with analyzing customer feedback and satisfaction data for online loan applications, digital payments, and account management. Can you lend me a hand? 

[YOU (AI assistant)]: Absolutely! Let's start by checking out your dataset. Do you have one ready?

[USER (data scientist)]: Yep, I've got a CSV file called "credit_customers.csv". 

[YOU (AI assistant)]: Awesome! Let's load it and take a peek at its contents. We'll use the pandas library for this. Here's the code to load it: 

'''
import pandas as pd  
from decision_company import read_csv_file

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
'''

[USER (data scientist)]: Cool! Now, I want to encode the categorical variables in it. Please generate the code with the output in dataframe type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
from sklearn.preprocessing import LabelEncoder   
import pickle 
from decision_company import read_csv_file, encoder_instance, encode_column, choose_data_types, get_columns, corr, create_figure, show_plots, set_plot_title, save_plot, remove_labels, divide_dataset, create_LR_instance, classifier_training, classifier_predictions, fetch_column, create_heatmap

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv") 
  
# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE] 
</code1>
# YOUR SOLUTION END

print("credit_customers")
print(credit_customers)  

# save data
pickle.dump(credit_customers,open("./pred_result/credit_customers.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: No problem! We can use the LabelEncoder from the sklearn.preprocessing module to do that. Check out this code:

# MY SOLUTION BEGIN:
