This project aims to leverage EDA techniques to analyze loan application data and identify predictors of loan default. By systematically exploring missing data, outliers, imbalance, and relationships between variables, the analysis aims to uncover insights into default risk. The project culminates in a detailed report presenting findings, insights, and recommendations to inform loan approval processes and risk assessment strategies within the finance company. Through this analysis, the company aims to enhance decision-making, mitigate financial risks, and optimize lending practices.
-Business Objectives: The primary objective of this project is to identify factors that predict loan default, assisting the company in making informed decisions regarding loan approval. By understanding customer and loan attributes that contribute to default risk, the company can mitigate financial losses and optimize lending practices. This analysis aims to:
*Identify patterns indicating customers likely to face difficulty in repaying loans. *Inform decisions such as loan denial, reducing loan amounts, or adjusting interest rates for risky applicants. *Enhance risk assessment and improve the accuracy of loan approval processes.
Excel File Link -- https://docs.google.com/spreadsheets/d/1cquNRqZVHuITJp_hpYnZbDyP4OpI_-Qr/edit?usp=sharing&ouid=104826659982370286770&rtpof=true&sd=true