artsite/content/projects/ml-loan.md

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slug	title	type	description	shortDescription	publishedAt	readingTime	status	tags	icon
ml-loan	Machine Learning for Loan Prediction	Academic Project	Predicting loan approval and default risk using machine learning classification techniques.	A project applying machine learning to predict loan approvals and assess default risk.	2025-01-24	2	Completed	Python Machine Learning Regression Finance Data Science	i-ph-money-wavy-duotone

This project focuses on building machine learning models to predict loan approval outcomes and assess default risk. The objective is to develop robust classification models that identify creditworthy applicants.

📊 Project Objectives

• Build and compare multiple classification models for loan prediction
• Identify key factors influencing loan approval decisions
• Evaluate model performance using appropriate metrics
• Optimize model parameters for better predictive accuracy

🔍 Methodology

The study employs a range of machine learning approaches:

• Exploratory Data Analysis (EDA) - Understanding applicant characteristics and patterns
• Feature Engineering - Creating meaningful features from raw data
• Model Comparison - Testing multiple algorithms (Logistic Regression, Random Forest, Gradient Boosting, etc.)
• Hyperparameter Tuning - Optimizing model performance
• Cross-validation - Ensuring robust generalization

📄 Detailed Report