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. 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.
Analysis was done in Jupyter Notebook using these Python libraries - Pandas, Numpy, Matplotib and Seaborn
This analysis used various analytical steps and visualization:
- Data Handling and Cleaning
- Handling Outliers
- Univariate Analysis
- Multivariate Analysis
- Joining two datasets
- Histogram Chart
- Box Plots
- Pie Chart
- Bar Chart
- Scatter Plot
- Heatmaps
- Pair Plot