# Credit Card Data Analysis-Class 12 IP Project

Credit Card Data Analysis – Class 12 IP project

The manager of XYZ bank was very much disturbed by why credit card-holders are leaving the services of his bank. He has some data on the customers.  The idea of this project is to analyze the data and predict the probable type of customers that would not leave their credit card services.

• Python CSV files handling Tutorial for the beginners

The whole project is divided into four major parts

1. Reading Data from the source
2.  Data Analysis using Pandas
3. Data Visualization using Matplotlib
4. Export data in other formats

## Source code

```#   project name        : credit card analysis
#   made by             : rakesh kumar
#   email               : rakesh@binarynote.com
#   session             : 2020-21

import pandas as pd
import time
import sqlalchemy
import matplotlib.pyplot as plt

df = pd.DataFrame()
csv_file = "C:/Users/rakesh/Desktop/IP_notes/12IP/Projects/BankChurners.csv"

def introduction():
msg = '''
A manager at the bank is disturbed with more and more customers leaving their credit card services.
They would really appreciate if one could predict for them who is gonna get churned so they can
proactively go to the customer to provide them better services and turn customers' decisions in
the opposite direction.

I got this dataset from a website with the URL as https://leaps.analyttica.com/home. I have been
using this for a while to get datasets and accordingly work on them to produce fruitful results.
The site explains how to solve a particular business problem.

Now, this dataset consists of 10,000 customers mentioning their age, salary, marital_status,
credit card limit, credit card category, etc. There are nearly 18 features.

We have only 16.07 % of customers who have churned. Thus, it's a bit difficult to train our model
to predict churning customers.

In this project we are going to analyse the same dataset using Python Pandas on windows machine but
the project can be run on any machine support Python and Pandas. Besides pandas we also used
matplotlib python module for visualization of this dataset.

The whole project is divided into four major parts ie reading, analysis, visualization and export. all these

NOTE: Python is case-SENSITIVE so type exact Column Name wherever required.

for x in msg:
print(x, end='')
time.sleep(0.002)
wait = input('Press any key to continue.....')

msg = '''
Credit Card Analysis made by    : xyx
Roll No                         : 1234
School Name                     : Your school name
session                         : 2020-21

Thanks for evaluating my Project.
\n\n\n
'''

for x in msg:
print(x, end='')
time.sleep(0.002)

wait = input('Press any key to continue.....')

print(df)

# name of function      : clear
# purpose               : clear output screen

def clear():
for x in range(65):
print()

while True:
clear()
print('\n\nD A T A   A N A L Y S I S   M E N U  ')
print('_'*100,'\n')
print('1.   Show Whole DataFrame')
print('2.   Show Columns')
print('3.   how Top Rows')
print('4.   Row Bottom Rows')
print('5.   Show Specific Column')
print('8.   Delete a Column')
print('9.   Delete a Record')
print('10.  Card Type User')
print('11.  Gender wise User')
print('12.  Data Summery')
print('13.  Exit (Move to main menu)')
if ch == 1:
print(df)
wait = input('\n\n\n Press any key to continuee.....')
if ch == 2:
print(df.columns)
wait = input('\n\n\n Press any key to continuee.....')
if ch == 3:
n = int(input('Enter Total rows you want to show :'))
wait = input('\n\n\n Press any key to continuee.....')
if ch == 4:
n = int(input('Enter Total rows you want to show :'))
print(df.tail(n))
wait = input('\n\n\n Press any key to continuee.....')
if ch == 5:
print(df.columns)
col_name = input('Enter Column Name that You want to print : ')
print(df[col_name])
wait = input('\n\n\n Press any key to continuee.....')
if ch == 6:
a = input('Enter Customer ID :')
b = input('Enter Customer Type :')
c = input(' Enter Customer Age:')
d = input('Enter Customer Gender :')
e = input('Enter Customer Dependent Count :')
f = input('Enter Education Level :')
g = input('Enter Marital Status :')
h = input('Enter Income Category :')
i = input('Enter Card Category :')
j = input('Enter Month on Book')
k = input('Enter Total Relationship count :')
l = input('Enter Total Month Inactive in last 12 month  :')
m = input('Enter Total Contacted in last 12 months :')
n = input('Enter Credit Limit :')
o = input('Enter Revolving Balance :')
p = input('Enter Average Open to Buy Card :')
q = input('Enter Total amount change Q4 to Q1 :')
r = input('Enter Total Transaction amount :')
s = input('Enter Total Transaction Credit:')
t = input('Enter Total Credit Change Q4 Q1 :')
u = input('Enter Average Utilization Ratio  :')

data = {'clientID': a, 'Type': b, 'age': c,
'gender': d, 'Dependent_count': e, 'Educational_Level': f, 'Marital_Status': g,
'Income_Category':h,'Card_Category':i,'Months_on_book':j,'Total_Relationship_count':k,
'Month_Inactive_12_month':l,'Contacts_count_12_mon':m,'Credit_Limit':n,
'Total_Trans_Ct':s,'Total_Ct_Chng_Q4_Q1':t,'Average_Utilization_Ration':u
}
df = df.append(data, ignore_index=True)
print(df)
wait = input('\n\n\n Press any key to continuee.....')
if ch == 7:
col_name = input('Enter new column name :')
col_value = int(input('Enter default column value :'))
df[col_name] = col_value
print(df)
print('\n\n\n Press any key to continue....')
wait = input()

if ch == 8:
col_name = input('Enter column Name to delete :')
del df[col_name]
print(df)
print('\n\n\n Press any key to continue....')
wait = input()

if ch == 9:
index_no = int(
input('Enter the Index Number that You want to delete :'))
df = df.drop(df.index[index_no])
print(df)
print('\n\n\n Press any key to continue....')
wait = input()

if ch == 10:
print(df.columns)
print(df['Type'].unique())
tipe = input('Enter Card Type ')
g = df.groupby('Type')
print('Card Type : ', tipe)
print(g['Type'].count())
print('\n\n\n Press any key to continue....')
wait = input()

if ch == 11:
df1 = df.Gender.unique()
print('Available Gender :', df1)
print('\n\n')
schName = input('Enter Gender Type :')
df1 = df[df.Gender == schName]
print(df1)
print('\n\n\n Press any key to continue....')
wait = input()

if ch == 12:
print(df.describe())
print("\n\n\nPress any key to continue....")
wait = input()
if ch == 13:
break

# name of function      : graph
# purpose               : To generate a Graph menu
def graph():
while True:
clear()
print('_'*100)
print('1.  Whole Data LINE Graph\n')
print('2.  Whole Data Bar Graph\n')
print('3.  Whole Data Scatter Graph\n')
print('4.  Whole Data Pie Chart\n')
print('5.  Bar Graph By Education Level\n')
print('6.  Bar Graph By Income Level\n')
print('7.  Exit (Move to main menu)\n')

if ch == 1:
g = df.groupby('Gender')
x = df['Gender'].unique()
y = g['Gender'].count()
#plt.xticks(rotation='vertical')
plt.xlabel('Gender')
plt.ylabel('Total Credit Card Users')
plt.title('Credit Card User- Gender wise')
plt.grid(True)
plt.plot(x, y)  #line graph
plt.show()

if ch == 2:
g = df.groupby('Gender')
x = df['Gender'].unique()
y = g['Gender'].count()
#plt.xticks(rotation='vertical')
plt.xlabel('Gender')
plt.ylabel('Total Credit Card Users')
plt.title('Credit Card User- Gender wise')
plt.bar(x, y)  #bar graph
plt.grid(True)
plt.show()
wait = input()

if ch == 3:
g = df.groupby('Gender')
x = df['Gender'].unique()
y = g['Gender'].count()
#plt.xticks(rotation='vertical')
plt.xlabel('Gender')
plt.ylabel('Total Credit Card Users')
plt.title('Credit Card User- Gender wise')
plt.grid(True)
plt.scatter(x, y)
plt.show()
wait = input()

if ch == 4:
g = df.groupby("Card_Category")
x = df['Card_Category'].unique()
y = g['Card_Category'].count()
plt.pie(y, labels=x, autopct='% .2f', startangle=90)  #pie graph
plt.xticks(rotation='vertical')
plt.show()

if ch == 5:
g = df.groupby("Education_Level")
x = df['Education_Level'].unique()
y = g['Education_Level'].count()
plt.bar(x, y)
#plt.xticks(rotation='vertical')
plt.grid(True)
plt.title('Education Level wise Card User')
plt.xlabel('Education Level')
plt.show()
wait = input()

if ch == 6:
g = df.groupby("Income_Category")
x = df['Income_Category'].unique()
y = g['Income_Category'].count()
plt.grid(True)
plt.title('Credit Card User- Income Group')
plt.xlabel('Income Group')
plt.ylabel('Card Users')
plt.bar(x,y)
plt.show()

if ch == 7:
break

# purpose                : function to generate export menu
while True:
clear()
print('_'*100)
print()
print('1.  CSV File\n')
print('2.  Excel File\n')
print('3.  MySQL Table\n')
print('4.  Exit (Move to main menu)')
ch = int(input('Enter your Choice : '))

if ch == 1:
df.to_csv('c:/backup/bankchurner_backup.csv')
print('\n\nCheck your new file "bankchurner_backup.csv"  on C: Drive.....')
wait = input('\n\n\n Press any key to continuee.....')

if ch == 2:
df.to_excel('c:/backup/bankchurner_backup.xlsx')
print('\n\nCheck your new file "bankchurner_backup.xlsx"  on C: Drive.....')
wait = input('\n\n\n Press any key to continuee.....')

if ch == 3:
engine = sqlalchemy.create_engine(
'mysql+pymysql://root:@localhost:3306/davschool')
df.to_sql(name='bankchurner_backup', con=engine,
index=False, if_exists='replace')
print('\n\nPlease check DAVSCHOOL database for bankchurner_backup table.....')
wait = input('\n\n\n Press any key to continuee.....')

if ch == 4:
break

clear()
introduction()
while True:
clear()
print('_'*100)
print()
print('4.  Export Data\n')
print('5.  Exit\n')
choice = int(input('Enter your choice :'))

if choice == 1:
wait = input(
'\n\n Press any key to continue....')

if choice == 2:
wait = input('\n\n Press any key to continue....')

if choice == 3:
graph()
wait = input('\n\n Press any key to continue....')
if choice == 4:
wait = input(
'\n\n Press any key to continue....')

if choice == 5:
break
clear()

```

### Python Modules used

1. Python Pandas  for Data analysis and various other purposes
2. matplotlib for data visualization.

## How to Run Credit Card Analysis Python Project

2. The folder contains two files   a Credit_card_analysis  b. bankchurners.csv file
3. Adjust the location of your CSV file in the python file as per the video attached to this post.
4. Open IDLE/IDE and run your project

## Output Produced by Credit Card Analysis Project

Sample output screens produced by this Pandas project. These are not in any specific sequence. You can get your own outputs

## Working of  Credit Card Data Analysis

Check this attached YouTube Video to understand the working of this project as well as the code.

### Limitations of the Project

Since the project was developed for the class 12 IP students, It is obvious that this project will not predict the future trends of the customers so can not be used for machine learning. It also does not have more graphs and data analysis options that a real-world situation requires.

The project is the perfect solution for class 12 IP students who have a limited syllabus and according to that syllabus, this is perfect for them. Use this project as a guideline for your Python Pandas project.  It is highly advisable to generate your very own data analysis project.