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Project: Cleaning Bank Marketing Campaign Data

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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

# Import the data from bank_marketing.csv

bank_marketing = pd.read_csv('bank_marketing.csv')
# load the first 5 rows of the data set
bank_marketing.head()
# checking the number of rows of the entire data set
bank_marketing.shape
# Extract the columns names contained in the data set
bank_marketing.columns
# Check the column datatypes
bank_marketing.dtypes
# start subsetting the columns for each dataframe by selecting the required columns

# select the columns required in the client dataframe
client_columns = ['client_id', 'age', 'job', 'marital', 'education', 'credit_default','mortgage']

# Generate the client dataframe
client = bank_marketing[client_columns]

# load the first 5 rows of the client dataframe
client.head()
# select the columns required in the campaign dataframe
campaign_columns = ['client_id', 'number_contacts', 'contact_duration', 'previous_campaign_contacts', 'previous_outcome', 'campaign_outcome','month', 'day']

# Generate the campaign dataframe
campaign = bank_marketing[campaign_columns]

# change "day" column datatype from int to str
campaign['day'] = campaign['day'].astype(str)

# change "month" column datatype from object to str
campaign['month'] = campaign['month'].astype(str)

# When converting to strings, ensure leading zeros for single-digit months and days for consistent date formatting
campaign['month'] = campaign['month'].apply(lambda x: x.zfill(2))
campaign['day'] = campaign['day'].apply(lambda x: x.zfill(2))

# create "last_contact_date" column from the "month" and "day" columns
campaign["last_contact_date"] = campaign['month'].apply(lambda x : x.capitalize()) + "-" + campaign['day']

# add a year value e.g. 2022 so that pd.to_datetime() can be able to convert the date
campaign["last_contact_date"] = pd.to_datetime("2022-" + campaign['month'] + "-" + campaign['day'], errors='coerce', format='%Y-%b-%d')

# drop the 'month' and the 'day' columns since we no longer need them
campaign.drop(columns=['month','day'],inplace=True)

# load the first 5 rows of the campaign dataframe
campaign.head()
# select the columns required in the campaign dataframe
economics_columns = ['client_id','cons_price_idx','euribor_three_months']

# Generate the economics dataframe
economics = bank_marketing[economics_columns]

# view the first 5 rows of the economics dataframe
economics.head()

2. Cleaning the data

client['education'] = client['education'].replace(['.','unknown'],\
                                                  ['-',np.NaN])
client.head()
# Check the education columns values after performing the replacement of 
# values above

client['education'].unique()
# check the values in 'job' column
client['job'].unique()
# replace the '.' periods in the 'job columns'
client['job'] = client['job'].replace(['.',''])



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