[USER (data scientist)]:Hi Tapilot, I need your help to analyze the credit_customers dataset. Let's start with the first question: What is the distribution of age, employment status, and credit history among our customers in the credit_customers dataset? Please provide mean, median, mode, range and distribution plots for age, employment status, and credit history. 
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import numpy as np  
import matplotlib.pyplot as plt  
import seaborn as sns  
import pickle
from decision_company import read_csv_file, create_barplot, create_histogram, create_histogram_subplot, create_countplot, set_current_ticks, create_figure, set_plot_xlabel, create_figure, set_plot_title, set_yaxis_label, get_figure, configure_gridlines, show_plots, save_plot, fetch_column, avg, locate_mode, get_max, get_min_value, calculate_median, count_unique_values

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

print(f"Age - Mean: {age_mean}, Median: {age_median}, Mode: {age_mode}, Range: {age_range}") 

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

# save data
pickle.dump(age_median,open('./pred_result/age_median.pkl','wb'))

# save data
pickle.dump(age_mode,open('./pred_result/age_mode.pkl','wb'))

# save data
pickle.dump(age_range,open('./pred_result/age_range.pkl','wb'))
  
# YOUR SOLUTION BEGIN:
<code2>
[GENERATE YOUR CODE]
</code2>
# YOUR SOLUTION END

print("\nEmployment Status Distribution:")  
print(employment_counts)  

# save data
pickle.dump(employment_counts,open("./pred_result/employment_counts.pkl","wb"))
  
# YOUR SOLUTION BEGIN:
<code3>
[GENERATE YOUR CODE]
</code3>
# YOUR SOLUTION END

print("\nCredit History Distribution:")  
print(credit_history_counts)  

# save data
pickle.dump(credit_history_counts,open("./pred_result/credit_history_counts.pkl","wb"))
  
# Distribution plot for age  
create_figure((10, 6)) 

# YOUR SOLUTION BEGIN:
<code4>
[GENERATE YOUR CODE]
</code4>
# YOUR SOLUTION END

# save data
save_plot('./pred_result/histplot.png')  
show_plots()  
  
# Distribution plot for employment status  
create_figure((10, 6)) 

# YOUR SOLUTION BEGIN:
<code5>
[GENERATE YOUR CODE]
</code5>
# YOUR SOLUTION END

# save data
save_plot('./pred_result/countplot.png')  
show_plots()   
  
# Distribution plot for credit history  
create_figure((10, 6)) 
 
# YOUR SOLUTION BEGIN:
<code6>
[GENERATE YOUR CODE]
</code6>
# YOUR SOLUTION END

# save data
save_plot('./pred_result/countplot_2.png')  
show_plots()  

---END CODE TEMPLATE---

[YOU (AI assistant)]:Sure, let's start by calculating the descriptive statistics for age, employment status, and credit history. Then, we'll create distribution plots for each of these variables.

# MY SOLUTION BEGIN:
