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Arthur DANJOU
b78d4ef983
- Implemented a new research project on Dropout strategies in deep learning, including detailed objectives, methodology, and usage instructions. - Created a project for predicting bike rentals using Generalized Linear Models, outlining objectives, methodology, and key findings. - Developed a machine learning project for loan prediction, detailing objectives, methodology, and a report on model performance. - Added a project focused on breast cancer detection using various classification models, including objectives, methodology, and resources. - Updated package.json with author information and upgraded dependencies.
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1.4 KiB
slug, title, type, description, publishedAt, readingTime, status, tags, icon
| slug | title | type | description | 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. | 2025-01-24 | 2 | Completed |
|
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 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