https://github.com/gehad-ahmed30/loan-approval-classification

This project focuses on analyzing and classifying loan applications using the Credit Risk dataset. It applies data analysis and machine learning techniques and deep learning to predict loan approval based on applicants' financial factors.
https://github.com/gehad-ahmed30/loan-approval-classification

ann deep-learning descion-tree gridsearchcv logistic-regression machine-learning pipeline random-forest svm xgboost-classifier

Last synced: 13 days ago
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This project focuses on analyzing and classifying loan applications using the Credit Risk dataset. It applies data analysis and machine learning techniques and deep learning to predict loan approval based on applicants' financial factors.

• Host: GitHub
• URL: https://github.com/gehad-ahmed30/loan-approval-classification
• Owner: gehad-Ahmed30
• Created: 2025-03-11T08:09:41.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-03-11T13:30:27.000Z (7 months ago)
• Last Synced: 2025-03-11T14:27:14.287Z (7 months ago)
• Topics: ann, deep-learning, descion-tree, gridsearchcv, logistic-regression, machine-learning, pipeline, random-forest, svm, xgboost-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 245 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 📊 Loan Approval Prediction using Credit Risk Dataset  

## 🔍 Project Overview  

This project focuses on **analyzing, processing, and predicting loan approvals** using a **Credit Risk dataset**. It leverages **Data Analysis, Machine Learning, and Deep Learning** techniques to assess applicants' credit risk based on financial factors like income, credit history, employment status, and debt-to-income ratio.  

## 📌 Key Features  

- **Data Preprocessing:** Handling missing values, feature engineering, and normalization.  

- **Exploratory Data Analysis (EDA):** Visualizing trends and correlations in the dataset.  

- **Machine Learning & Deep Learning Models:** Implementing Logistic Regression, Decision Trees, Random Forest, XGBoost, and Neural Networks.  

- **Performance Evaluation:** Using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.  

- **Deployment:** The model can be integrated into a web application for real-time loan approval predictions.  

## 📂 Dataset  

The dataset consists of **various financial attributes** of loan applicants, including:  

- **Applicant Income**  

- **Credit Score**  

- **Loan Amount**  

- **Employment Status**  

- **Debt-to-Income Ratio**  

- **Previous Loan Defaults**  

This project aims to provide an efficient and accurate **loan approval prediction system** to support financial institutions in making data-driven decisions. 🚀