Problem Statement

You have been hired by the stakeholders of Dream Housing Finance company to help them get visual insights on the company's operations. Using the data visualization techniques you learned, you have to help visualise the data and see if you can help the stakeholders arrive at some inference based on the visual plots.

About the Dataset

The snapshot of the data, you will be working on:

loan_data

The dataset has details of 614 customers with the following 13 features

Feature Description Loan_ID Unique Loan ID Gender Male/Female Married Applicant Married (Y/N) Dependents Number of dependents Education Graduate/Under Graduate Self_Employed Self employed (Y/N) ApplicantIncome Income of the applicant CoapplicantIncome Income of the co applicant LoanAmount Loan amount in thousands LoanAmountTerm Term of loan in months Credit_History credit hostory meets guidelines} Property_Area Urban/Semi-Urban/Rural Loan_Status Loan approved (Y/N) Why solve this project? After completing this project, you will have a better understanding of data visualization techniques. In this project, you will be applying the following concepts:

Bar plotting Scatter plotting Kernel Density plotting Subplot operations Axes modifications

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Instructions :

Step 1

Let's start with the simple task of visualizing the company's record with respect to loan approvals.

Save the value counts of Loan_Status in a variable called loan_status using value_counts()

Plot a bar graph of loan_status

Things to ponder upon:

• Company has more 'loan approvals' or 'loan disapprovals'?
• Can one of the company's health factors be its loan status distribution? data.iloc[25,1] shoule be Male data.iloc[53,9] should be 360 loan_status[0] should be 415 loan_status[1] should be 185

Step 2

Everyone needs money The company provides financial assistance across the different regions of the country. One interesting statistic that stakeholders want to see is the loan approval distribution across the regions.

Instructions :

Group the 'data' dataframe by Property_Area and Loan_Status and store it in a variable called 'property_and_loan'

Use the .size() method on 'property_and_loan' and then use .unstack() and save it back to 'property_and_loan'

Plot an unstacked bar plot of property_and_loan (It is similar to creating a stacked bar plot except change the parameter 'stacked' to False)

Name the x-axis as Property Area

Name the y-axis as Loan Status

Rotate the labels of x-axis by 45o

Things to ponder upon

• Which is the region with the highest no. of loan approvals? lowest no. of loan approvals?

• Which is the region with the maximum difference between loan approvals and loan rejections? property_and_loan['N'][1] should be 51 property_and_loan['Y'][0] should be 109

Step 3

Expensive Education Higher education has always been an expensive endeavour for people but it results in better career opportunities and stability in life. But does higher education result in a better guarantee in issuing loans?

Instructions :

Group the 'data' dataframe by Education and Loan_Status and store it in a variable called 'education_and_loan'

Use the .size() method on 'education_and_loan' and then use .unstack() and save it back to 'education_and_loan'

Plot an stacked bar plot of education_and_loan

Name the x-axis as Education Status

Name the y-axis as Loan Status

Rotate the labels of x-axis by 45o

Things to ponder upon

• Overall which group has asked for higher loan services irrespective of the approval? Graduate or Non-Graduate?

• Which group has had better approval to non-approval ratio? Graduate or Non-Graduate?

  Do the above conclusions make sense? Why? education_and_loan['N'][1] should be 52 education_and_loan['Y'][0] should be 333

Step 4

Smarter and Richer? After seeing the loan status distribution, let's check whether being graduate or not also leads to different loan amount distribution by plotting an overlapping density plot of two values

Instructions :

Create a dataframe called 'graduate' which is a subset of 'data' dataframe with the condition "data['Education'] == 'Graduate'"

Create a dataframe called 'not_graduate' which is a subset of 'data' dataframe with the condition "data['Education'] == 'Not Graduate'"

Plot a density plot LoanAmount of 'graduate' dataframe using "Series.plot()" and pass the parameter kind='density' and label='Graduate'

Do the same for LoanAmount of 'not_graduate' dataframe but with label='Not Graduate'

Things to ponder upon

• Do Graduate people get approved a higher amount than their Non Graduate counterparts?

• What's the average amount of loan approved for Graduate? for Non-Graduate? Is there a huge difference between the two?

• The conditionnp.all(graduate['Education']=='Graduate' should be satisfied that is all the values in the column Eductaion of the DataFrame should be Graduate

• The conditionnp.all(not_graduate['Education']=='Graduate' should be satisfied that is all the values in the column Eductaion of the DataFrame should be Not Graduate

Step 5

Income vs Loan For any financial institution to be successful in its loan lending system, there has to be a correlation between the borrower's income and loan amount he is lent. Let's see how our company fares in that respect:

Instructions :

Create three subplots with (nrows = 3 , ncols = 1) and store it in variable's fig ,(ax_1,ax_2,ax_3)

Since both are continuous variables, plot scatter plot between 'ApplicantIncome' and LoanAmount using ax_1. Set axis title as Applicant Income

Plot scatter plot between 'CoapplicantIncome' and LoanAmount using ax_2. Set axis title as Coapplicant Income

Create a new column in the dataframe called 'TotalIncome' which is a sum of the values of columns ApplicantIncome and CoapplicantIncome

Plot scatter plot between 'TotalIncome' and LoanAmount using ax_3. Set axis title as Total Income

Things to ponder upon

• Is there a correlation between 'ApplicantIncome' and 'LoanAmount'?(One way to know that is looking at the line formed when you connect the scatter plot dots?)

• Is the 'TotalIncome' better related to the 'LoanAmount' than 'ApplicantIncome'? Test Case:

The condition data['TotalIncome'][1] == 6091 should be satisfied.

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