Skip to content

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 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 ensure it conforms to the specific structure and data types that they specify so that they can then use the cleaned data you provide to set up a PostgreSQL database, which will store this campaign's data and 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 the data, saving three final csv files. Specifically, the three files should have the names and contents as outlined below:

client.csv

columndata typedescriptioncleaning requirements
client_idintegerClient IDN/A
ageintegerClient's age in yearsN/A
jobobjectClient's type of jobChange "." to "_"
maritalobjectClient's marital statusN/A
educationobjectClient's level of educationChange "." to "_" and "unknown" to np.NaN
credit_defaultboolWhether the client's credit is in defaultConvert to boolean data type
mortgageboolWhether the client has an existing mortgage (housing loan)Convert to boolean data type

campaign.csv

columndata typedescriptioncleaning requirements
client_idintegerClient IDN/A
number_contactsintegerNumber of contact attempts to the client in the current campaignN/A
contact_durationintegerLast contact duration in secondsN/A
previous_campaign_contactsintegerNumber of contact attempts to the client in the previous campaignN/A
previous_outcomeboolOutcome of the previous campaignConvert to boolean data type
campaign_outcomeboolOutcome of the current campaignConvert to boolean data type
last_contact_datedatetimeLast date the client was contactedCreate from a combination of day, month, and a newly created year column (which should have a value of 2022);
Format = "YYYY-MM-DD"

economics.csv

columndata typedescriptioncleaning requirements
client_idintegerClient IDN/A
cons_price_idxfloatConsumer price index (monthly indicator)N/A
euribor_three_monthsfloatEuro Interbank Offered Rate (euribor) three-month rate (daily indicator)N/A
import pandas as pd
import numpy as np

# Start coding here...

# loading the banking_marketing dataset into pandas using pd.read_csv() as a variable called bank
bank = pd.read_csv("bank_marketing.csv")

# viewing first five rows of economics
print(bank.head())

# checking the shape of data: number of rows and columns
print(bank.shape)

# checking bank columns data type
print(bank.info())


###### creating economics.csv from bank

# creating a dataframe called economics from bank
economics = bank[["client_id", "cons_price_idx", "euribor_three_months"]]

# checking economics columns data type
print(economics.info())

# viewing first five rows of economics
print(economics.head())

# storing as a csv files
economics.to_csv('economics.csv', index=False)



###### creating client.csv from bank

# creating a dataframe called client from bank
client = bank[["client_id", "age", "job", "marital", "education", "credit_default", "mortgage"]]

# replacing "." with  "_" in job & education column
client["job"] = client["job"].str.replace(".", "_")
client["education"] = client["education"].str.replace(".", "_")

# replacing "unknown" with  np.NaN in education, mortgage & credit_default column
client["education"] = client["education"].replace("unknown", np.NaN)
client["mortgage"] = client["mortgage"].replace("unknown", np.NaN)
client["credit_default"] = client["credit_default"].replace("unknown", np.NaN)

# creating a mapping dictionary 
mapping_bool = {"yes": True, "no": False}

# mapping the created dictionary to mortgage & credit_default column using the map()
client["mortgage"] = client["mortgage"].map(mapping_bool)
client["credit_default"] = client["credit_default"].map(mapping_bool)

# changing mortgage & credit_default column data type from object to boolean
client["credit_default"] = client["credit_default"].astype(bool)
client["mortgage"] = client["mortgage"].astype(bool)


# checking client columns data type
print(client.info())

# viewing first five rows of client
print(client.head())

# storing as a csv files
client.to_csv("client.csv", index= False)



###### creating campaign.csv from bank

# creating a dataframe called campaign from bank
campaign = bank[["client_id", "number_contacts", "contact_duration", "previous_campaign_contacts", "previous_outcome", "campaign_outcome", "day", "month"]]

# creating a year column and assigning it to be 2022
campaign["year"] = 2022

# parsing year, month, day as datetime
campaign["last_contact_date"] = pd.to_datetime(campaign["year"].astype(str) + "-" + campaign["month"] + "-" + campaign["day"].astype(str))

# dropping day, month, year columns
campaign = campaign.drop(columns=["day", "month", "year"])

# creating a mapping dictionary
mapp_bool = {"success": True, "failure": False, "nonexistent": False, "no": False, "yes": True}

# mapping the created dictionary to previous_outcome & campaign_outcome column using the map()
campaign["previous_outcome"] = campaign["previous_outcome"].map(mapp_bool)
campaign["campaign_outcome"] = campaign["campaign_outcome"].map(mapp_bool)

# changing previous_outcome & campaign_outcome column data type from object to boolean
campaign["previous_outcome"] = campaign["previous_outcome"].astype(bool)
campaign["campaign_outcome"] = campaign["campaign_outcome"].astype(bool)

# checking client columns data type
print(campaign.info())

# viewing first five rows of campaign
print(campaign.head())

# storing as a csv files
campaign.to_csv("campaign.csv", index=False)