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https://github.com/rishitabansal9/loan-approval-predictor
The project aims to predict loan approvals based on various factors, leveraging machine learning models and data pipelines.
https://github.com/rishitabansal9/loan-approval-predictor
deployment eda end-to-end ipython-notebook loan-approval-prediction mlops pipelines python
Last synced: about 1 month ago
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The project aims to predict loan approvals based on various factors, leveraging machine learning models and data pipelines.
- Host: GitHub
- URL: https://github.com/rishitabansal9/loan-approval-predictor
- Owner: Rishitabansal9
- Created: 2023-11-24T12:45:44.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-14T17:23:00.000Z (about 1 year ago)
- Last Synced: 2025-01-12T06:07:59.653Z (about 1 month ago)
- Topics: deployment, eda, end-to-end, ipython-notebook, loan-approval-prediction, mlops, pipelines, python
- Language: Jupyter Notebook
- Homepage: https://loan-approval-predictor-es8r.onrender.com/
- Size: 21.6 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Welcome to Loan Approval Predictor 👋
> This project focuses on predicting loan approvals using machine learning techniques. It encompasses an end-to-end machine learning pipeline, exploratory data analysis (EDA) notebooks, and code for model deployment on the Render platform.
## Dataset
This data set has 207 rows and 15 columns.
Key Features:
- Demographics: Age, Gender, State, and City provide a snapshot of the applicant's background.
- Financial Information: Income, Credit Score, and Credit History Length offer insights into the applicant's financial stability and credit behavior.
- Loan Details: The dataset sheds light on the specifics of the loan the applicant is seeking, with details like Loan Amount, Loan Tenure, and Loan to Value (LTV) Ratio.
- Employment Information: The dataset includes both a general employment profile (e.g., Salaried, Self-Employed) and a specific occupation, giving a nuanced view of the applicant's employment status.
- Profile Score: A composite score, ranging from 0 to 100, represents the overall credit profile of the applicant. This score can serve as a quick reference for gauging the creditworthiness of an individual.
## Structure
```
Loan-Approval_Predictor/
│
├── artifacts/
│ ├── data.csv
│ ├── model.pkl
│ ├── preprocessor.pkl
│ ├── train.csv
│ └── test.csv
│
├── notebook/
│ ├── data/
│ │ └── credit_data.csv
│ └── Loan_approval.ipynb
│
├── src/
│ ├── components/
│ │ ├── __init__.py
│ │ ├── data_ingestion.py
│ │ ├── data_transformation.py
│ │ └── model_trainer.py
│ ├── pipeline/
│ │ ├── __init__.py
│ │ ├── predict_pipeline.py
│ │ └── train_pipeline.py
│ ├── __init__.py
│ ├── exception.py
│ ├── logger.py
│ └── utils.py
|
├── static/
│ └── style.css
|
├── templates/
│ └── index.html
|
├── .gitignore
|
├── README.md
|
├── app.py
|
├── requirements.txt
|
└── setup.py
```
### 🏠 [Homepage](https://github.com/Rishitabansal9/Loan-Approval-Predictor/blob/main/README.md)
### ✨ [Demo](https://loan-approval-predictor-es8r.onrender.com/)
## Install
```sh
npm install
```
## Usage
```sh
1. Clone the repository:
git clone https://github.com/YourUsername/Loan-Approval-Predictor.git
cd Loan-Approval-Predictor
2. Install dependencies:
pip install -r requirements.txt
3. Run:
python app.py
```
## Author
👤 **Rishita Bansal**
* Github: [@Rishitabansal9](https://github.com/Rishitabansal9)
* LinkedIn: [@rishita-bansal-589056143](https://linkedin.com/in/rishita-bansal-589056143)
## Show your support
Give a ⭐️ if this project helped you!