Project description -
Imagine you're a data analyst at a finance company that specializes in lending various types of loans to urban customers. Your company faces a challenge: some customers who don't have a sufficient credit history take advantage of this and default on their loans. Your task is to use Exploratory Data Analysis (EDA) to analyze patterns in the data and ensure that capable applicants are not rejected.
When a customer applies for a loan, your company faces two risks:
- If the applicant can repay the loan but is not approved, the company loses business.
- If the applicant cannot repay the loan and is approved, the company faces a financial loss.
The dataset you'll be working with contains information about loan applications. It includes two types of scenarios:
- Customers with payment difficulties: These are customers who had a late payment of more than X days on at least one of the first Y installments of the loan.
- All other cases: These are cases where the payment was made on time.
When a customer applies for a loan, there are four possible outcomes:
- Approved: The company has approved the loan application.
- Cancelled: The customer cancelled the application during the approval process.
- Refused: The company rejected the loan.
- Unused Offer: The loan was approved but the customer did not use it.
Your goal in this project is to use EDA to understand how customer attributes and loan attributes influence the likelihood of default.
Data Analytics Tasks:
- Identify Missing Data and Deal with it Appropriately
- Identify Outliers in the Dataset
- Analyze Data Imbalance
- Perform Univariate, Segmented Univariate, and Bivariate Analysis
- Identify Top Correlations for Different Scenarios