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Loan Repayment Prediction Project
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Loan Repayment Prediction/Model/Loan Repayment Prediction.ipynb
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# Loan Repayment Prediction | ||
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## Overview | ||
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Welcome to the Loan Repayment Prediction Project! This project aims to build a predictive model to identify the likelihood of a loan application being approved. Leveraging machine learning techniques, this project helps financial institutions streamline their loan approval processes and minimize risks. | ||
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## Table of Contents | ||
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- [Project Description](#project-description) | ||
- [Data Description](#data-description) | ||
- [Installation](#installation) | ||
- [Usage](#usage) | ||
- [Modeling](#modeling) | ||
- [Results](#results) | ||
- [Contributing](#contributing) | ||
- [License](#license) | ||
- [Contact](#contact) | ||
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## Project Description | ||
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The Loan Repayment Prediction Project utilizes various machine learning algorithms to predict loan approvals based on applicant information. The primary goal is to create a model that accurately predicts whether a loan should be approved, thus aiding financial institutions in decision-making. | ||
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## Data Description | ||
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The dataset used in this project contains information about loan applicants, including: | ||
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- **Applicant Information**: Gender, Marital Status, Education, Number of Dependents, etc. | ||
- **Financial Information**: Applicant Income, Co-applicant Income, Loan Amount, Loan Amount Term, Credit History, etc. | ||
- **Loan Information**: Loan ID, Loan Status, Property Area, etc. | ||
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## Installation | ||
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To run this project, you'll need to have Python installed. Follow the steps below to set up the project: | ||
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1. Clone the repository: | ||
```bash | ||
git clone https://github.com/aviralgarg05/Loan-Repayment-Prediciton.git | ||
``` | ||
2. Navigate to the project directory: | ||
```bash | ||
cd Loan-Prediciton-Project | ||
``` | ||
3. Install the required dependencies: | ||
```bash | ||
pip install -r requirements.txt | ||
``` | ||
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## Usage | ||
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To use the project, follow these steps: | ||
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1. Preprocess the data by running the preprocessing script: | ||
```bash | ||
python preprocess.py | ||
``` | ||
2. Train the model using the training script: | ||
```bash | ||
python train_model.py | ||
``` | ||
3. Evaluate the model using the evaluation script: | ||
```bash | ||
python evaluate_model.py | ||
``` | ||
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## Modeling | ||
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This project explores various machine learning models, including: | ||
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- **Logistic Regression** | ||
- **Decision Trees** | ||
- **Random Forest** | ||
- **Gradient Boosting** | ||
- **Support Vector Machine** | ||
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Each model is evaluated based on its accuracy, precision, recall, and F1 score. The best-performing model is selected for predicting loan approvals. | ||
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## Results | ||
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1. The Loan Status is heavily dependent on the Credit History for Predictions. | ||
2. The Logistic Regression algorithm gives us the maximum Accuracy (79% approx) compared to the other Machine Learning Classification Algorithms. | ||
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| Model | Accuracy | | ||
|--------------------|--------------------| | ||
| Logistic Regression| 0.7852760736196319 | | ||
| SVM | 0.6503067484662577 | | ||
| Decision Tree | 0.7116564417177914 | | ||
| KNN | 0.6196319018404908 | | ||
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The final model demonstrates strong predictive power. | ||
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## Contributing | ||
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Contributions are welcome! If you'd like to contribute to this project, please follow these steps: | ||
1. Fork the repository. | ||
2. Create a new branch: | ||
```bash | ||
git checkout -b feature-branch | ||
``` | ||
3. Make your changes and commit them: | ||
```bash | ||
git commit -m 'Add new feature' | ||
``` | ||
4. Push to the branch: | ||
```bash | ||
git push origin feature-branch | ||
``` | ||
5. Create a Pull Request. | ||
## License | ||
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details. | ||
## Contact | ||
For any questions or suggestions, please feel free to contact: | ||
- **Name**: Aviral Garg | ||
- **Email**: [gargaviral99@gmail.com](mailto:gargaviral99@gmail.com) | ||
- **GitHub**: [aviralgarg05](https://github.com/aviralgarg05) |
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numpy==1.23.5 | ||
pandas==2.0.3 | ||
matplotlib==3.7.2 | ||
seaborn==0.12.2 | ||
scikit-learn==1.3.0 | ||
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