Project: Cleaning Bank Marketing Campaign Data

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`

column	data type	description	cleaning requirements
`client_id`	`integer`	Client ID	N/A
`age`	`integer`	Client's age in years	N/A
`job`	`object`	Client's type of job	Change `"."` to `"_"`
`marital`	`object`	Client's marital status	N/A
`education`	`object`	Client's level of education	Change `"."` to `"_"` and `"unknown"` to `np.NaN`
`credit_default`	`bool`	Whether the client's credit is in default	Convert to boolean data type
`mortgage`	`bool`	Whether the client has an existing mortgage (housing loan)	Convert to boolean data type

`campaign.csv`

column	data type	description	cleaning requirements
`client_id`	`integer`	Client ID	N/A
`number_contacts`	`integer`	Number of contact attempts to the client in the current campaign	N/A
`contact_duration`	`integer`	Last contact duration in seconds	N/A
`previous_campaign_contacts`	`integer`	Number of contact attempts to the client in the previous campaign	N/A
`previous_outcome`	`bool`	Outcome of the previous campaign	Convert to boolean data type
`campaign_outcome`	`bool`	Outcome of the current campaign	Convert to boolean data type
`last_contact_date`	`datetime`	Last date the client was contacted	Create 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`

column	data type	description	cleaning requirements
`client_id`	`integer`	Client ID	N/A
`cons_price_idx`	`float`	Consumer price index (monthly indicator)	N/A
`euribor_three_months`	`float`	Euro Interbank Offered Rate (euribor) three-month rate (daily indicator)	N/A

import pandas as pd
import numpy as np

# Start coding here...


# CLIENT DATAFRAME
# colum for clients
columns = ['client_id', 'age', 'job', 'marital', 'education', 'credit_default', 'mortgage']

client = pd.read_csv('bank_marketing.csv', 
                     usecols=columns, 
                     index_col=None)

# print(client.info())
# print(dataframe.isna().sum())

# check the value inside the credit columns
# check = client['credit_default'].value_counts()

# # drop unknown value inside credit_default and mortgage column
# client = client[client['credit_default']!= 'unknown']

# client = client[client['mortgage']!= 'unknown']

# data type for each column
# print(client.dtypes)

# Mengubah nilai 'yes' dan 'no' menjadi boolean True dan False pada kolom 'credit_default' dan 'mortgage'
client['credit_default'] = client['credit_default'].replace({'yes': True, 'no': False, 'unknown': np.NaN})
client['mortgage'] = client['mortgage'].replace({'yes': True, 'no': False,'unknown': np.NaN})

# replace data type into boolean
client['credit_default'] = client['credit_default'].astype(bool)
client['mortgage'] = client['mortgage'].astype(bool)

# replace values in job column
client['job'] = client['job'].replace({'admin.': 'admin', 'blue-collar': 'blue_collar', 'self-employed': 'self_employed'})

# replace values in education column and unknown job into np.nan
client['education'] = client['education'].replace({'university.degree.': 'university_degree', 
                                                   'high.school': 'high_school', 
                                                   'professional.course': 'professional_course',
                                                 'basic.9y': 'basic_9y',
                                                 'basic.4y':'basic_4y',
                                                 'basic.6y':'basic_6y',
                                                  'unknown': np.NaN})

# save data into client.csv
print(client.to_csv('client.csv', index=False))



# CAMPAIGN DATAFRAME
# colum to be included
camp_col = ['client_id','number_contacts', 'contact_duration', 'previous_campaign_contacts', 'previous_outcome', 'campaign_outcome', 'month', 'day']

campaign = pd.read_csv('bank_marketing.csv', usecols=camp_col, index_col=None)

# change the object values into boolean valus
campaign['previous_outcome'] = campaign['previous_outcome'].replace({'success': True, 'failure': False, 'nonexistent': np.NaN})
campaign['campaign_outcome'] = campaign['campaign_outcome'].replace({'yes': True, 'no': False})

# replace data type into boolean
campaign['previous_outcome'] = campaign['previous_outcome'].astype(bool)
campaign['previous_outcome'] = campaign['previous_outcome'].astype(bool)

# create new year colum
campaign['year'] = 2022

# replace month column into the right format:
campaign['month'] = campaign['month'].replace ({'may':5,
                                                'jun':6,
                                                'jul':7,
                                                'aug':8,
                                                'oct':10,
                                                'nov':11,
                                                'dec':12,
                                                'mar':3,
                                                'apr':4,
                                                'sep':9})
# Menggunakan pandas untuk mengonversi kolom 'day', 'month', dan 'year' ke dalam kolom bertipe datetime
campaign['last_contact_date'] = pd.to_datetime(campaign[['year', 'month', 'day']])

# Memformat kolom baru menjadi format "YYYY-MM-DD"
campaign['last_contact_date'] = campaign['last_contact_date'].dt.strftime('%Y-%m-%d')

# Mengonversi kolom 'last_contact_date' menjadi tipe data datetime
campaign['last_contact_date'] = pd.to_datetime(campaign['last_contact_date'])

# Menghapus kolom 'year', 'month', dan 'day' dari DataFrame
campaign.drop(['year', 'month', 'day'], axis=1, inplace=True)

# save dataframe into campaign.csv
print(campaign.to_csv('campaign.csv', index=False))


# ECONOMICS DATAFRAME

# column to be included
eco_col = ['client_id', 'cons_price_idx', 'euribor_three_months']

economics = pd.read_csv('bank_marketing.csv', 
                        usecols=eco_col,
                       index_col=None)

# checking the csv file
# print(economics.info())

# save into economics.csv
print(economics.to_csv('economics.csv', index=False))

Project: Cleaning Bank Marketing Campaign Data

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campaign.csv

economics.csv

`client.csv`

`campaign.csv`

`economics.csv`