Loan Risk Anlaysis

Project: Loan Risk Analysis

In this project, I worked on analyzing a financial dataset to identify and predict potential loan risks. The goal was to help financial institutions make smarter lending decisions and reduce the chance of default.

Key steps included:

Data Cleaning & Preprocessing: handled missing values, normalized numerical features, and encoded categorical variables.

Exploratory Data Analysis (EDA): used visualizations to understand customer demographics, loan characteristics, and repayment behavior.

Machine Learning Models: applied classification algorithms (such as Logistic Regression, Decision Trees, and Random Forest) to predict loan default risk.

Dashboard & Insights: built an interactive dashboard to present the findings clearly and support decision-making.

This project demonstrated how data analysis and machine learning can provide valuable insights into financial risk, improve decision-making, and support more secure loan approval processes.