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
• Files in the Repository
• Setup and Installation
• Usage
• Model Details
• Contributing
• Contact
• 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:

   git clone https://github.com/yourusername/loan-eligibility-prediction.git
   cd loan-eligibility-prediction

2. Create and activate a virtual environment (optional but recommended):

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Run the application:

   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: Mo7ammad244@gmail.com

License

This project is licensed under the MIT License - see the LICENSE file for details.