Personal loans are a lucrative revenue stream for banks. The typical interest rate of a two year loan in the United Kingdom is around 10%. This might not sound like a lot, but in September 2022 alone UK consumers borrowed around £1.5 billion, which would mean approximately £300 million in interest generated by banks over two years!
You have been asked to work with a bank to clean and store the data they collected as part of a recent marketing campaign, which aimed to get customers to take out a personal loan. They plan to conduct more marketing campaigns going forward so would like you to set up a PostgreSQL database to store this campaign's data, designing the schema in a way that would allow data from future campaigns to be easily imported.
They have supplied you with a csv file called "bank_marketing.csv", which you will need to clean, reformat, and split, in order to save separate files based on the tables you will create. It is recommended to use pandas for these tasks.
Lastly, you will write the SQL code that the bank can execute to create the tables and populate with the data from the csv files. As the bank are quite strict about their security, you'll provide the database design script as a .sql file that they can then run.
You have been asked to design a database that will have three tables:
client
| column | data type | description |
|---|---|---|
id | serial | Client ID - primary key |
age | integer | Client's age in years |
job | text | Client's type of job |
marital | text | Client's marital status |
education | text | Client's level of education |
credit_default | boolean | Whether the client's credit is in default |
housing | boolean | Whether the client has an existing housing loan (mortgage) |
loan | boolean | Whether the client has an existing personal loan |
campaign
| column | data type | description |
|---|---|---|
campaign_id | integer | Campaign ID |
client_id | serial | Client ID - references id in the client table |
number_contacts | integer | Number of contact attempts to the client in the current campaign |
contact_duration | integer | Last contact duration in seconds |
pdays | integer | Number of days since contact in previous campaign (999 = not previously contacted) |
previous_campaign_contacts | integer | Number of contact attempts to the client in the previous campaign |
previous_outcome | boolean | Outcome of the previous campaign |
campaign_outcome | boolean | Outcome of the current campaign |
last_contact_date | date | Last date the client was contacted |
economics
| column | data type | description |
|---|---|---|
client_id | serial | Client ID - references id in the client table |
emp_var_rate | float | Employment variation rate (quarterly indicator) |
cons_price_idx | float | Consumer price index (monthly indicator) |
euribor_three_months | float | Euro Interbank Offered Rate (euribor) three month rate (daily indicator) |
number_employed | float | Number of employees (quarterly indicator) |
import pandas as pd
import numpy as np
# Start coding here...# Split the data into three DataFrames using information provided about the desired tables as your guide: one with information about the client, another containing campaign data, and a third to store information about economics at the time of the campaign.# Read in bank_marketing.csv as a pandas DataFrame.
df = pd.read_csv("bank_marketing.csv")# Choosing columns for campaign
campaign = df[[ "client_id", "campaign", "duration", "pdays", "previous", "poutcome", "y"]]
campaign.rename(columns = {"duration": "contact_duration", "previous": "previous_campaign_contacts", "poutcome": "previous_outcome", "campaign":"number_contacts", "y": "campaign_outcome"}, inplace=True)
campaign.head()# Choosing columns for client
client = df[["client_id", "age", "job", "marital", "education", "credit_default", "housing", "loan"]]
client.rename(columns={"client_id":"id"}, inplace=True)
client.head()# Choosing columns for economics
economies = df[["client_id", "emp_var_rate", "cons_price_idx", "euribor3m", "nr_employed"]]
economies.rename(columns= {"euribor3m": "euribor_three_months", "nr_employed":"number_employed"}, inplace=True)df["education"].replace({"unknown": None, ".": "_"}, inplace=True)
df["job"].replace({".": ""}, inplace=True)campaign[["previous_outcome", "campaign_outcome"]].replace({"previous_outcome":"1", "campaign_outcome": "0"}, inplace=True)
campaign["previous_outcome"].replace({"nonexistent": None}, inplace=True)#Add a column called campaign_id in campaign, where all rows have a value of 1.
campaign["campaign_id"] = 1
campaign.head()# Convert month and day_of_week columns to appropriate data types
df['month'] = pd.Categorical(df['month'], categories=['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']).codes
df['day_of_week'] = pd.Categorical(df['day_of_week'], categories=['mon', 'tue', 'wed', 'thu', 'fri']).codes
df['month'] += 1
#campaign["last_contact_date"] = pd.to_datetime(df['month'].apply(lambda x: #f"2022-{x+1:02}-01")) + pd.to_timedelta(df['day_of_week'], unit='d')
campaign["last_contact_date"] = pd.to_datetime("2021-12-31")
campaign.tail(20)client.to_csv('client.csv', index=False)
campaign.to_csv('campaign.csv', index=False)
economies.to_csv('economics.csv', index=False)economies.head() # client
client_table = "CREATE TABLE client (id serial PRIMARY KEY, age INT, job TEXT, marital TEXT, education TEXT, credit_default boolean, housing boolean, loan boolean); \copy client from 'client.csv' DELIMITER ',' CSV HEADER"
# campaign
campaign_table = "CREATE TABLE campaign (campaign_id serial PRIMARY KEY, client_id SERIAL REFERENCES client (id), number_contacts INT, contact_duration INT, pdays INT, previous_campaign_contacts INT, previous_outcome boolean, campaign_outcome boolean, last_contact_date date, ); \copy campaign from 'campaign.csv' DELIMITER ',' CSV HEADER"
#economy
economics_table = "CREATE TABLE economics (client_id SERIAL REFERENCES client (id), emp_var_rate FLOAT, cons_price_idx FLOAT, euribor_three_months FLOAT, number_employed FLOAT); \copy economics from 'economics.csv' DELIMITER ',' CSV HEADER"