https://github.com/rohitinu6/loan-approval-prediction
This project predicts the likelihood of loan approval based on applicants' financial and demographic data.
https://github.com/rohitinu6/loan-approval-prediction
data-science eda finance finance-management financial-analysis loan-prediction loan-prediction-analysis loan-prediction-model machine-learning machine-learning-algorithms python visualization
Last synced: 8 months ago
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This project predicts the likelihood of loan approval based on applicants' financial and demographic data.
- Host: GitHub
- URL: https://github.com/rohitinu6/loan-approval-prediction
- Owner: rohitinu6
- Created: 2024-12-31T03:36:56.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-02-06T03:08:32.000Z (9 months ago)
- Last Synced: 2025-02-06T04:22:18.660Z (9 months ago)
- Topics: data-science, eda, finance, finance-management, financial-analysis, loan-prediction, loan-prediction-analysis, loan-prediction-model, machine-learning, machine-learning-algorithms, python, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 1.51 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Loan Approval Prediction
## 📌 Project Overview
This project predicts the likelihood of loan approval based on applicants' financial and demographic data. The goal is to assist financial institutions in making informed loan approval decisions using machine learning techniques.
## 🚀 Features
- Data preprocessing and exploratory data analysis (EDA)
- Feature engineering and selection
- Machine learning model training and evaluation
- Model interpretability and visualization
## 🛠 Tech Stack
- Python
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn
- Jupyter Notebook
## 📂 Dataset
The dataset includes:
- **Applicant Income**
- **Credit History**
- **Loan Amount**
- **Property Area**
- **Employment Status**
- **Other Financial Indicators**
## 💊 Machine Learning Models Used
- Logistic Regression
- Decision Trees
- Random Forest Classifier
- XGBoost
## 🔥 Results
The models are evaluated based on accuracy, precision, recall, and F1-score. The best model provides accurate predictions for loan approval likelihood.
## 📁 Repository Structure
```
📂 Loan-Approval-Prediction
👉 📂 data (Dataset & processed data)
👉 📂 notebooks (Jupyter Notebooks)
👉 📂 models (Trained models)
👉 📂 images (Code and Results Screenshots)
👉 📄 README.md (Project documentation)
```
## 🖼 Code and Results
Include images of code and results in the `images` folder. Example:
## 🐟 How to Run the Project
1. Clone the repository:
```bash
git clone https://github.com/rohitinu6/Loan-Approval-Prediction.git
```
2. Navigate to the project folder:
```bash
cd Loan-Approval-Prediction
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Run the Jupyter Notebook or Python scripts to train and test models.
## 📡 Links
- **GitHub Repository:** [Loan Approval Prediction](https://github.com/rohitinu6/Loan-Approval-Prediction.git)
- **Portfolio:** [Rohit Dubey](https://tinyurl.com/dubeyrohit)
- **GitHub Profile:** [rohitinu6](https://github.com/rohitinu6)
- **LinkedIn:** [Rohit Dubey](https://www.linkedin.com/in/rohit-dubey-d/)
- **Twitter/X:** [@rohitdubey003](https://x.com/rohitdubey003)
## 💖 Tags
`Machine Learning` `Loan Prediction` `Data Science` `Finance` `Python` `EDA`
## 📝 License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
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💡 **For any queries or collaboration opportunities, feel free to connect!** 🚀