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45 lines
1.6 KiB
Markdown
45 lines
1.6 KiB
Markdown
---
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slug: ml-loan
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title: Machine Learning for Loan Prediction
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type: Academic Project
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description: Predicting loan approval and default risk using machine learning classification techniques.
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shortDescription: A project applying machine learning to predict loan approvals and assess default risk.
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publishedAt: 2025-01-24
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readingTime: 2
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status: Completed
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tags:
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- Python
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- Machine Learning
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- Regression
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- Finance
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- Data Science
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icon: i-ph-money-wavy-duotone
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---
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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.
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::BackgroundTitle{title="Project Objectives"}
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::
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- Build and compare multiple classification models for loan prediction
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- Identify key factors influencing loan approval decisions
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- Evaluate model performance using appropriate metrics
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- Optimize model parameters for better predictive accuracy
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::BackgroundTitle{title="Methodology"}
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::
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The study employs a range of machine learning approaches:
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- **Exploratory Data Analysis (EDA)** - Understanding applicant characteristics and patterns
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- **Feature Engineering** - Creating meaningful features from raw data
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- **Model Comparison** - Testing multiple algorithms (Logistic Regression, Random Forest, Gradient Boosting, etc.)
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- **Hyperparameter Tuning** - Optimizing model performance
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- **Cross-validation** - Ensuring robust generalization
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::BackgroundTitle{title="Detailed Report"}
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::
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<iframe src="/projects/loan-ml.pdf" width="100%" height="1000px">
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</iframe>
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