https://github.com/manish506/loan-approval-prediction

Explore predictive modeling in this project by applying classification techniques to a loan approval dataset. Analyze and preprocess the data, then use models like K-Nearest Neighbors, Random Forest, SVC, and Logistic Regression to predict loan outcomes. Gain insights into approval factors and enhance prediction accuracy.
https://github.com/manish506/loan-approval-prediction

classification classification-models data-analysis data-science jupyter-notebook loan-approval-prediction machine-learning predictive-analytics predictive-modeling project python

Last synced: 9 days ago
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Explore predictive modeling in this project by applying classification techniques to a loan approval dataset. Analyze and preprocess the data, then use models like K-Nearest Neighbors, Random Forest, SVC, and Logistic Regression to predict loan outcomes. Gain insights into approval factors and enhance prediction accuracy.

• Host: GitHub
• URL: https://github.com/manish506/loan-approval-prediction
• Owner: manish506
• Created: 2024-09-13T11:18:14.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-09-13T11:20:28.000Z (5 months ago)
• Last Synced: 2025-02-11T22:55:31.360Z (9 days ago)
• Topics: classification, classification-models, data-analysis, data-science, jupyter-notebook, loan-approval-prediction, machine-learning, predictive-analytics, predictive-modeling, project, python
• Language: Jupyter Notebook
• Homepage: https://github.com/manish506/Loan-Approval-Prediction
• Size: 108 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Loan-Approval-Prediction

## Overview

This project aims to predict loan approval using machine learning models based on applicant features. The workflow includes data preprocessing, exploratory data analysis (EDA), and model evaluation.

## Steps

1. **Import Libraries**: Essential libraries for data manipulation, visualization, and machine learning are imported.

2. **Load Data**: The dataset is loaded and initial exploration is conducted.

3. **Identify Categorical Variables**: Categorical variables are detected and visualized.

4. **Encode Categorical Variables**: Categorical variables are converted to numerical values.

5. **Visualize Correlations**: Correlations between features are visualized using a heatmap.

6. **Handle Missing Values**: Missing values are imputed with column means.

7. **Split Data**: The dataset is split into training and testing sets.

8. **Train and Evaluate Models**: Several machine learning models are trained and evaluated for accuracy.

## Dataset

- **File**: `LoanApprovalPrediction.csv`

- **Description**: Contains loan application data with features and loan status.