https://github.com/karthikeyanav2003/loan-prediction

https://github.com/karthikeyanav2003/loan-prediction

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• Host: GitHub
• URL: https://github.com/karthikeyanav2003/loan-prediction
• Owner: karthikeyanav2003
• Created: 2023-11-30T04:18:18.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-30T04:21:55.000Z (about 1 year ago)
• Last Synced: 2023-12-01T04:37:11.129Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 603 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Loan-Prediction

Abstract:

In the domain of financial lending, accurately predicting loan repayment is a critical 

task for financial institutions. This project aims to develop predictive models for 

loan repayment using six distinct techniques: Simple Linear Regression, Multiple 

Linear Regression, Support Vector Machines (SVM), Random Forest, K-Nearest 

Neighbours (KNN), and Artificial Neural Networks (ANN). These models leverage 

various borrower characteristics, historical data, and financial features to make 

informed predictions.

Key Components:

Data Collection: 

Comprehensive data related to past loan transactions, 

borrower information, credit scores, and financial statements are collected from 

reliable sources, ensuring a diverse and representative dataset.

Data Preprocessing: 

The dataset undergoes thorough preprocessing, 

involving cleaning, handling missing values, and transforming data into a format 

suitable for model training. Features like credit scores, income, employment history, 

and debt-to-income ratios are prepared for analysis.

Feature Engineering: 

Relevant features are selected and engineered to 

create meaningful inputs for the predictive models. This includes creating new 

variables, handling categorical data, and considering historical repayment patterns.

Model Selection:

a. Simple Linear Regression: A baseline model is established to understand 

the linear relationship between individual features and the likelihood of loan 

repayment.

b. Multiple Linear Regression: This model extends simple linear regression to 

account for multiple independent variables, providing a more comprehensive view 

of the factors influencing loan repayment.

c. Support Vector Machines (SVM): SVM is employed to handle complex 

relationships and non-linear patterns in the data, offering robust predictive 

capabilities.

d. Random Forest: A Random Forest ensemble model is built to capture 

intricate patterns and interactions among various features, enhancing predictive 

accuracy.

e. K-Nearest Neighbours (KNN): KNN leverages instance-based learning to 

predict loan repayment based on the similarity of borrowers' characteristics.

f. Artificial Neural Networks (ANN): ANNs are used for complex modeling, 

learning non-linear relationships, and adapting to high-dimensional data, 

contributing to improved prediction capabilities