Statistics – Banking Project
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Use the data in the google drive based on loan default.zip file for loan default project and present the findings in the following manner in word file and also in a PPT format.
Part-1
1) Understand and define the problem statement.
2) Get a preliminary understanding of data and perform exploratory data analysis.
&
1) Discuss the business context.
2) Data cleaning and pre – processing (like outlier treatment, missing value treatment etc.)
3) How to generate insights from EDA?
4) Discuss about any finer nuances that could be used to generate insights.
Part-2
1) Business Problem Understanding and Problem definition
2) Generate a data report.
3) Exploratory Data analysis and insights driven from it.
Part-3
1) Build various models and check their accuracy.
2) Discuss about model validation
3) Discuss about model tuning
4) Discuss about how to draw business insights & recommendations.
Part-4
1) Model Building and comparison
2) Model Tuning
3) Model Interpretation
Part-5
1) Business insights and recommendations
2) A structure for presentation.
Submit head wise all the above in a word document and make a PPT also which helps explain & communicate the data analysis to be used in the final presentation.
2
>Sheet
1 |
| # |
Fields |
Description |
1
| member_id |
A unique Id for the borrower member. |
2
| loan_amnt |
The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value. |
| 3 |
funded_amnt |
The total amount committed to that loan at that point in time. |
| 4 |
funded_amnt_inv |
The total amount committed by investors for that loan at that point in time. |
| 5 |
term |
The number of payments on the loan. Values are in months and can be either 3
| 6 |
or 60.
6
| int_rate |
Interest Rate on the loan |
| 7 |
installment |
The monthly payment owed by the borrower if the loan originates. |
| 8 |
grade |
Assigned loan grade |
| 9 |
emp_length |
Employment length in years. Possible values are between 0 and
| 10 |
where 0 means less than one year and 10 means ten or more years.
10
| home_ownership |
The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER. |
| 11 |
annual_inc |
The self-reported annual income provided by the borrower during registration. |
| 12 |
verification_status |
Status of the verification done |
| 13 |
issue_d |
The month which the loan was funded |
| 14 |
pymnt_plan |
Indicates if a payment plan has been put in place for the loan |
| 15 |
desc |
Loan description provided by the borrower |
| 16 |
purpose |
A category provided by the borrower for the loan request. |
| 17 |
addr_state |
The state provided by the borrower in the loan application |
| 18 |
dti |
A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income. |
| 19 |
delinq_2yrs |
The number of
| 30 |
+ days past-due incidences of delinquency in the borrower’s credit file for the past 2 years
| 20 |
earliest_cr_line |
The month the borrower’s earliest reported credit line was opened |
| 21 |
inq_last_6mths |
The number of inquiries in past 6 months (excluding auto and mortgage inquiries) |
| 22 |
mths_since_last_delinq |
The number of months since the borrower’s last delinquency. |
| 23 |
open_acc |
The number of open credit lines in the borrower’s credit file. |
| 24 |
revol_bal |
Total credit revolving balance |
| 25 |
revol_util |
Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit. |
| 26 |
total_acc |
The total number of credit lines currently in the borrower’s credit file |
| 27 |
out_prncp |
Remaining outstanding principal for total amount funded |
| 28 |
out_prncp_inv |
Remaining outstanding principal for portion of total amount funded by investors |
| 29 |
total_pymnt |
Payments received to date for total amount funded |
30
| total_pymnt_inv |
Payments received to date for portion of total amount funded by investors |
| 31 |
total_rec_prncp |
Principal received to date |
| 32 |
total_rec_int |
Interest received to date |
| 33 |
total_rec_late_fee |
Late fees received to date |
| 34 |
recoveries |
post charge off gross recovery |
| 35 |
collection_recovery_fee |
post charge off collection fee |
| 36 |
last_pymnt_d |
Last month payment was received |
| 37 |
last_pymnt_amnt |
Last total payment amount received |
| 38 |
next_pymnt_d |
Next scheduled payment date |
| 39 |
last_credit_pull_d |
The most recent month pulled credit for this loan |
| 40 |
application_type |
Indicates whether the loan is an individual application or a joint application with two co-borrowers |
| 41 |
loan_status |
Current status of the loan |
Many banks believed lending to individuals is the risk-free given thy are better placed with credit scores and sometimes the loans are backed by collateral. But recently the banking system has witnessed an increase in the loan default i.e. the borrower is not able to pay back the instalment on time. These loan defaults directly impact the revenues of a banking system.
Now a days, banks are scrutinizing each loan application to identify potential loan default cases so that they can predict which client is going to default the loan repayment and at which step.
Based upon the given data from a bank, build a model to predict default loan that will help the bank to take required actions.
The overall project working is fine.
In addition to the above can he make a sub-note with the following specific points as a separate assignment for the same data in 7 pages. Please charge me the fairest for this
Requirement of 7 pages notes is as under in detail:
1) Introduction
a) Defining problem statement
b) Need of the study/project
c) Understanding business/social opportunity
2)Data Report
a) Understanding how data was collected in terms of time, frequency and methodology
b) Visual inspection of data (rows, columns, descriptive details)
c) Understanding of attributes (variable info, renaming if required)
3) Exploratory data analysis
a) Univariate analysis (distribution and spread for every continuous attribute, distribution of data in categories for categorical ones)
b) Bivariate analysis (relationship between different variables , correlations)
a) Removal of unwanted variables
b) Missing Value treatment
d) Outlier treatment
e) Variable transformation (if applicable)
f) Addition of new variables
4) Insights from EDA
a) Is the data unbalanced ? If so, what can be done ?
b) Any insights using clustering (if applicable)
c) Any other Insights
