https://github.com/abu14/loan-eligibility-prediction

The idea for this project and analysis is to predict whether a person is likely to receive a loan based on personal, financial, and other relevant factors
https://github.com/abu14/loan-eligibility-prediction

database machine-learning sklearn-library sklearn-metrics

Last synced: about 2 months ago
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The idea for this project and analysis is to predict whether a person is likely to receive a loan based on personal, financial, and other relevant factors

• Host: GitHub
• URL: https://github.com/abu14/loan-eligibility-prediction
• Owner: abu14
• Created: 2025-01-19T13:03:16.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-19T14:44:14.000Z (over 1 year ago)
• Last Synced: 2025-06-16T02:02:29.650Z (about 1 year ago)
• Topics: database, machine-learning, sklearn-library, sklearn-metrics
• Language: Jupyter Notebook
• Homepage:
• Size: 604 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
# Hi, I'm Abenezer Tesfaye! 👋

## 🚀 About Me

I'm a Machine Learning Engineer / Data Analyst

Currently working as a Data Analytics Developer.

## 🚀 Loan Eligibility Prediction Project

Welcome to my loan eligibility prediction project! Here, I take you on a journey from raw data to an intelligent solution that predicts whether a customer qualifies for a home loan. Using a logistic regression model, this project makes the process of loan approvals faster, smarter, and more reliable.

## 📖 Overview

Dream Housing Finance Company operates across urban, semi-urban, and rural areas, offering home loans to customers. When someone applies for a loan, their eligibility is determined based on details like income, credit history, education, and more. This project automates that decision-making process by identifying customers who are most likely to be eligible—helping the company save time and target the right audience.

## 🤔 Why It Matters

This project doesn’t just crunch numbers; it empowers businesses to make informed decisions faster, improving both customer experience and operational efficiency.

## 🌟 Key Features

Handles data preprocessing, including encoding, scaling, and missing value treatment.

Provides real-time predictions for customer loan eligibility.

Simple, interpretable, and scalable for real-world applications.

## Demo 

![Image](https://github.com/user-attachments/assets/6a3884ff-b408-4a30-bfda-00ba6ece4df1)

## Tools Used

- **Python:** allows me to analyze the data and find insights and patterns. Made use of the following Python libraries:

    - **Pandas Library** 

    - **Sklearn** 

    - **Numpy**  

- **SQL Lite** to load and save our data   

- **Visual Studio Code:** Executing my Python scripts.

- **Git & GitHub:** For version control and sharing my Python code and analysis.