[USER (data scientist)]: Thanks! Can we use logistic regression to predict customer satisfaction based on the other variables? Please generate the code with the output in [dataframe, dataframe, series, series, model] type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd   
from sklearn.model_selection import train_test_split  
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

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

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

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

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

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure thing! First, let's get the data ready for logistic regression by splitting it into features (X) and target (y). Then, we'll split the data into training and testing sets. Here's the code for that:

# MY SOLUTION BEGIN:
