mirror of
https://github.com/ArthurDanjou/artsite.git
synced 2026-01-14 15:54:13 +01:00
Arthur DANJOU
ba91408b6d
- Created index.md for personal introduction and interests. - Added languages.json to specify language proficiencies. - Developed profile.md detailing academic background, skills, and career goals. - Introduced multiple project markdown files showcasing personal and academic projects, including ArtChat, ArtHome, and various data science initiatives. - Implemented skills.json to outline technical skills and competencies. - Compiled uses.md to document hardware and software tools utilized for development and personal projects.
1.7 KiB
1.7 KiB
slug, title, type, description, publishedAt, readingTime, status, tags, emoji
| slug | title | type | description | publishedAt | readingTime | status | tags | emoji | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| loan-ml | Machine Learning for Loan Prediction | Academic Project | Predicting loan approval and default risk using machine learning classification techniques. | 2025-01-24 | 2 | Completed |
|
💰 |
Machine Learning for Loan Prediction
Overview
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 can effectively 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 various 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
📁 Key Findings
[To be completed with your findings]
📚 Resources
- Code Repository: [Add link to your code]
- Dataset: [Add dataset information]
- Full Report: See embedded PDF below