/Loan-Approval-Predictor

The project aims to predict loan approvals based on various factors, leveraging machine learning models and data pipelines.

Primary LanguageJupyter Notebook

Welcome to Loan Approval Predictor 👋

Documentation

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

✨ Demo

Install

npm install

Usage

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

Show your support

Give a ⭐️ if this project helped you!