Loan Repayment Prediction Project

Loan Repayment Prediction/README.md

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+# Loan Repayment Prediction
+
+## Overview
+
+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.
+
+## Table of Contents
+
+- [Project Description](#project-description)
+- [Data Description](#data-description)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Modeling](#modeling)
+- [Results](#results)
+- [Contributing](#contributing)
+- [License](#license)
+- [Contact](#contact)
+
+## Project Description
+
+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.
+
+## Data Description
+
+The dataset used in this project contains information about loan applicants, including:
+
+- **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.
+
+## Installation
+
+To run this project, you'll need to have Python installed. Follow the steps below to set up the project:
+
+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
+ ```
+
+## Usage
+
+To use the project, follow these steps:
+
+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
+ ```
+
+## Modeling
+
+This project explores various machine learning models, including:
+
+- **Logistic Regression**
+- **Decision Trees**
+- **Random Forest**
+- **Gradient Boosting**
+- **Support Vector Machine**
+
+Each model is evaluated based on its accuracy, precision, recall, and F1 score. The best-performing model is selected for predicting loan approvals.
+
+## Results
+
+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.
+
+| Model | Accuracy |
+|--------------------|--------------------|
+| Logistic Regression| 0.7852760736196319 |
+| SVM | 0.6503067484662577 |
+| Decision Tree | 0.7116564417177914 |
+| KNN | 0.6196319018404908 |
+
+The final model demonstrates strong predictive power.
+
+## Contributing
+
+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)

Loan Repayment Prediction/requirements.txt

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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
+