https://github.com/prakashjha1/loan-eligibility-prediction

This repository contains the codebase and resources for a machine learning-based project aimed at predicting loan eligibility for individuals. The project utilizes various algorithms and data preprocessing techniques to build predictive models that assess the likelihood of an applicant being eligible for a loan based on historical data.
https://github.com/prakashjha1/loan-eligibility-prediction

data data-visualization exploratory-data-analysis loan-prediction-analysis machine-learning-algorithms naive-bayes-classification parameter-tuning python random-forest

Last synced: 2 days ago
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This repository contains the codebase and resources for a machine learning-based project aimed at predicting loan eligibility for individuals. The project utilizes various algorithms and data preprocessing techniques to build predictive models that assess the likelihood of an applicant being eligible for a loan based on historical data.

• Host: GitHub
• URL: https://github.com/prakashjha1/loan-eligibility-prediction
• Owner: prakashjha1
• Created: 2023-11-23T14:15:06.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2023-11-23T14:21:13.000Z (12 months ago)
• Last Synced: 2024-01-25T14:43:00.329Z (10 months ago)
• Topics: data, data-visualization, exploratory-data-analysis, loan-prediction-analysis, machine-learning-algorithms, naive-bayes-classification, parameter-tuning, python, random-forest
• Language: Jupyter Notebook
• Homepage:
• Size: 522 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Loan-Eligibility-Prediction

This repository contains the codebase and resources for a machine learning-based project aimed at predicting loan eligibility for individuals. The project utilizes various algorithms and data preprocessing techniques to build predictive models that assess the likelihood of an applicant being eligible for a loan based on historical data.

# Key Features

Data Preprocessing: Cleaning, transformation, and feature engineering of the dataset for model training.

Exploratory Data Analysis (EDA): Visualizations and analysis to gain insights into the data.

Model Development: Implementation of machine learning algorithms (e.g.,  decision trees, random forests, Naive Bayes) to predict loan eligibility.

Model Evaluation: Assessment of model performance using appropriate metrics and validation techniques.

# Contents

Jupyter Notebooks: Contains the code for data preprocessing, EDA, model training, and evaluation.

Datasets: Raw and processed datasets used in the project.