Introduction

I obtained the dataset from Kaggle and used MySQL for data cleaning.

Action

My data cleaning process involved the following steps:

Column selection: I identified and selected the specific columns I found essential for my analysis.

Data Type Validation and Modification: To ensure accuracy, I adjusted the data types of some columns. Using ALTER and MODIFY commands, I converted the 'LoanOriginationDate' column from a text format to a date format, and then relabled it as 'Date'. I also rounded off numerical values to enhance readability.

Check the image below to see the process:

Now everything set, I transitioned to Tableau for visualization

TABLEAU

Disbursements: occurs when a loan is approved and the lender releases the funds to the borrower.

From my analysis ,at the beginning of the year there is notable surge of disbursements . Please take note of filters!

Payments & Debt Income Ratio (DTI): payments refers to the money that the borrower return's as part of their loan obligations. DTI on the other hands compares an individual debt payments to their overall income.

The bar graph represents the payments and the line graph represents the DTI. From my observation I conclude that the higher the payment is, the higher the DTI. This indicates a financial burden.

Conclusion

Tracking a loan book through analysis encompassing disbursements, payments and DebtIncome Ratio and profit and loss returns sheds lights on financial health and performance of an institution.