When Harry met Sally...oops When Laziness Meets Data There comes a day in your life where you glance at your expenses and you realize that you have spent more on food delivery than on groceries or even savings. It happened to me when I did the math and found that my total monthly spending was being utilized by 35% of feeding laziness. Yes, more than one-third of my money went to ordering food online. If my Swiggy account had a loyalty program, I’d be its Platinum Ambassador by now. Armed with this alarming realization, I dived into the data hoping for some clarity—or at least a good laugh at my own expense. What I found was a budgetary roast hotter than the dal makhni I paid a premium for. The Pie Chart of Poor Decisions When I sorted through my expenses, I was shocked to see food delivery as the absolute winner, consuming 35% of my budget. Rent and bills comprised 18%, and then came those impulsive buys from Nykaa that tallied at 15%. Everything else added up to 32%...
1. Introduction · The " The Loan Detective: Identifying Risks and Anomalies in Financial Data " aims to evaluate the risk associated with loan applicants using a comprehensive dataset containing customer and loan details. In the financial industry, accurately assessing loan risk is crucial for minimizing potential losses and ensuring the stability of lending institutions. This project leverages SQL to explore and analyze key risk factors, providing insights into customer profiles, creditworthiness, and potential default risks. The dataset used in this project includes a range of attributes such as customer demographics, loan amounts, credit scores, and repayment history. By examining these factors, we aim to identify high-risk customers, understand the characteristics of defaulted loans, and propose strategies for improving loan approval processes. The project not only focuses on the technical aspects of data analysis but also incorporat...