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https://github.com/bniladridas/ml-loan-eligibility
Predicting loan eligibility using machine learning techniques.
https://github.com/bniladridas/ml-loan-eligibility
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Predicting loan eligibility using machine learning techniques.
- Host: GitHub
- URL: https://github.com/bniladridas/ml-loan-eligibility
- Owner: bniladridas
- License: mit
- Created: 2024-04-24T08:00:22.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-09-15T04:01:57.000Z (3 months ago)
- Last Synced: 2024-10-20T05:40:52.847Z (2 months ago)
- Language: Python
- Homepage:
- Size: 138 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🚀 Loan Eligibility Prediction
Welcome to the **Loan Eligibility Prediction** project, where machine learning is leveraged to predict loan eligibility based on the financial and demographic information of applicants. The dataset, sourced from Kaggle, includes a variety of features used to train a logistic regression model for accurate predictions.
## 💾 Dataset Overview
The dataset used in this project contains several key features:
- **Gender**
- **Marital Status**
- **Education**
- **Applicant's Income**
- **Co-applicant's Income**
- **Loan Amount**
- **Loan Term**
- **Credit History**
- **Loan Status** (Target Variable)
The dataset can be found [here](https://www.kaggle.com/datasets/zhijinzhai/loandata) on Kaggle.
## 🛠️ Tech Stack
This project is built using Python and several powerful libraries commonly used in machine learning:
- **[Python](https://www.python.org/)**
- **[NumPy](https://numpy.org/)** - Numerical computing
- **[Pandas](https://pandas.pydata.org/)** - Data manipulation and analysis
- **[scikit-learn](https://scikit-learn.org/)** - Machine learning model building and evaluation
- **[Matplotlib](https://matplotlib.org/)** - Data visualization
## 🧑💻 Methodology
This project follows a structured approach, covering the entire machine learning pipeline:
1. **Data Import**: The dataset is imported using `pandas.read_csv()`.
2. **Data Preprocessing**: Missing values are handled, categorical variables are encoded, and feature scaling is applied to ensure the model performs optimally.
3. **Model Training**: A logistic regression model is trained using `LogisticRegression` from `scikit-learn`.
4. **Model Evaluation**: Performance is measured using accuracy, precision, recall, and F1-score.
5. **Prediction**: Loan eligibility predictions are made using the trained model.
## 📊 Model Performance
### 🔥 **Accuracy**: 1.0
### 📈 **Classification Report**:
```
precision recall f1-score support
COLLECTION 1.00 1.00 1.00 24
COLLECTION_PAIDOFF 1.00 1.00 1.00 24
PAIDOFF 1.00 1.00 1.00 52
accuracy 1.00 100
macro avg 1.00 1.00 1.00 100
weighted avg 1.00 1.00 1.00 100
```
### 📊 **Confusion Matrix**:
```
[[24 0 0]
[ 0 24 0]
[ 0 0 52]]
```
## 📄 Report
🔗 [View Report](/doc/report.pdf)
## 💻 Usage
### Follow these steps to run the project:
1. **Clone the Repository**:
```bash
git clone https://github.com/niladrridas/ml-loan-eligibility.git
cd ml-loan-eligibility
```
2. **Install Required Libraries**:
```bash
pip install -r requirements.txt
```
3. **Run the Main Script**:
```bash
python main.py
```
4. **View Results and Predictions** directly in the terminal or through visual outputs.
## 📚 References
- **Dataset**: [Kaggle - Loan Eligibility Dataset](https://www.kaggle.com/datasets/zhijinzhai/loandata)
- **scikit-learn documentation**: [scikit-learn](https://scikit-learn.org/stable/)
- **pandas documentation**: [pandas](https://pandas.pydata.org/docs/)
---
Feel free to fork this repository, submit issues, or contribute with improvements. Happy coding! 🎉