[USER (data scientist)]: Hey, I've got this dataset called "affordable_housing" and I'm trying to figure out the top 10 suburbs and regions with the most affordable properties sold. Can you help me out with the code? Please generate two lists from the 'melbourne_housing' DataFrame to identify the top 10 most common suburbs and regions, then save these lists using pickle for future reference.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import matplotlib.pyplot as plt
import pandas as pd
import pickle

melbourne_housing = pd.read_csv("melb_data.csv") 

# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]
</code1>
# YOUR SOLUTION END:

print(top_suburbs_list)

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

print(top_regions_list)

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Of course! Let's start by finding the top 10 suburbs and regions based on the number of affordable properties sold. We can use the value_counts() method to count how many times each unique value appears in the 'Suburb' and 'Regionname' columns. Here's what you can do: 
