The loan providing companies find it hard to give loans to the people due to their insufficient or non-existent credit history. Because of that, some consumers use it as their advantage by becoming a defaulter. Suppose you work for a consumer finance company which specialises in lending various types of loans to urban customers. You have to use EDA to analyse the patterns present in the data. This will ensure that the applicants capable of repaying the loan are not rejected. When the company receives a loan application, the company has to decide for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision: If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company.
The data given below contains the information about the loan application at the time of applying for the loan. It contains two types of scenarios: The client with payment difficulties: he/she had late payment more than X days on at least one of the first Y instalments of the loan in our sample, All other cases: All other cases when the payment is paid on time.
When a client applies for a loan, there are four types of decisions that could be taken by the client/company): Approved: The Company has approved loan Application Cancelled: The client cancelled the application sometime during approval. Either the client changed her/his mind about the loan or in some cases due to a higher risk of the client he received worse pricing which he did not want. Refused: The company had rejected the loan (because the client does not meet their requirements etc.). Unused offer: Loan has been cancelled by the client but on different stages of the process.
In this case study, you will use EDA to understand how consumer attributes and loan attributes influence the tendency of default.
- Data Cleaning
- Missing values
- Incorrect value handling
- Checking datatype of columns
- Checking Outliers
- Checking duplicate rows
- Creating new derived columns
- Univariate Analysis
- Merging the data sets
- Bivariate Analysis
- Multivariate Analysis
- Applications_EDA.ipynb : Consists of EDA performed on current applications.csv
- PreviousApplications_EDA.ipynb : Consists of EDA performed on previous_applications.csv
- EDA_Slides : Consists of basic approach along with important visualizations and summarization