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https://github.com/mohamedkhalidlx/loan-eligibility-prediction-web-app
Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.
https://github.com/mohamedkhalidlx/loan-eligibility-prediction-web-app
bernoulli-naive-bayes css csv decision-tree-classifier flask gaussian-naive-bayes-classifier gradient gradient-boosting-classifier html ipython-notebook knn-classifier logistic-regression machine-learning machine-learning-algorithms naive-bayes-classifier pickle python random-forest-classifier support-vector-classifier webapp
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Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.
- Host: GitHub
- URL: https://github.com/mohamedkhalidlx/loan-eligibility-prediction-web-app
- Owner: MohamedKhalidLx
- License: mit
- Created: 2024-05-21T01:46:47.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-06-01T12:23:28.000Z (7 months ago)
- Last Synced: 2024-10-12T14:42:59.471Z (2 months ago)
- Topics: bernoulli-naive-bayes, css, csv, decision-tree-classifier, flask, gaussian-naive-bayes-classifier, gradient, gradient-boosting-classifier, html, ipython-notebook, knn-classifier, logistic-regression, machine-learning, machine-learning-algorithms, naive-bayes-classifier, pickle, python, random-forest-classifier, support-vector-classifier, webapp
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/code/mohamedkhaledmahmoud/loan-eligibility-prediction-model
- Size: 681 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Loan Eligibility Prediction Model
This project is designed to predict the eligibility of a loan application based on various features using a machine learning model. The application is built using Python and leverages a pre-trained model to provide predictions.
## Table of Contents
- [Project Overview](#project-overview)
- [Files in the Repository](#files-in-the-repository)
- [Setup and Installation](#setup-and-installation)
- [Usage](#usage)
- [Model Details](#model-details)
- [Contributing](#contributing)
- [Contact](#contact)
- [License](#license)
## Project Overview
The Loan Eligibility Prediction Model is a machine learning application that determines whether a loan application is likely to be approved based on input features such as applicant income, loan amount, credit history, etc. The prediction model has been trained on historical loan data.
## Files in the Repository
- `app.py`: The main application script that runs the web server and handles prediction requests.
- `Loan Eligibility Prediction Model.ipynb`: Jupyter notebook containing the steps for data preprocessing, model training, and evaluation.
- `model.pkl`: The serialized pre-trained machine learning model used for making predictions.
- `Dataset/Data.csv`: The dataset used for training and evaluating the machine learning model.
- `static/style.css`: The CSS file for styling the web application.
- `templates/index.html`: The HTML template for the application's input form.
- `templates/result.html`: The HTML template for displaying the prediction results.
## Setup and Installation
To run this project locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/yourusername/loan-eligibility-prediction.git
cd loan-eligibility-prediction
```
2. Create and activate a virtual environment (optional but recommended):
```bash
python3 -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. Install the required packages:
```bash
pip install -r requirements.txt
```
4. Run the application:
```bash
python app.py
```
## Usage
1. Ensure that the web server is running by executing `python app.py`.
2. Open your web browser and navigate to `http://localhost:5000`.
3. Enter the required loan application details in the form provided.
4. Submit the form to receive the loan eligibility prediction.
## Model Details
The machine learning model used in this project is a [type of model, e.g., Logistic Regression, Random Forest, etc.], trained on a dataset containing various features related to loan applicants. The model was trained using the steps outlined in the `Loan Eligibility Prediction Model.ipynb` notebook.
### Key Features Used in the Model:
- Applicant Income
- Loan Amount
- Credit History
- Property Area
- And others...
## Contributing
Contributions are welcome! Please fork this repository and submit a pull request with your proposed changes.
## Contact
Name: Mohamed Khaled Mahmoud Sayed
E-mail: [email protected]
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.