dc-x-2003 / loan-approval-prediction

Welcome to the Loan Approval Prediction project repository! This project focuses on predicting the approval of loan applications using various machine learning algorithms. By analysing applicant details and financial information, the model aims to assist financial institutions in making data-driven and reliable loan approval decisions.

Loan-Approval-Prediction

Introduction

Loan approval is a critical process for financial institutions, and making accurate predictions can save time and resources. This project leverages historical loan data to train machine learning models that can predict whether a loan application will be approved or not. The project includes data preprocessing, feature engineering, model training, evaluation, and optimization.

Dataset

The dataset used in this project contains information about loan applicants, such as:

1. Loan ID
2. Gender
3. Married
4. Dependents
5. Education
6. Self Employed
7. Applicant Income
8. Co-Applicant Income
9. Loan Amount
10. Loan Amount Term
11. Credit History
12. Property Area
13. Loan Status

Modeling and Prediction

The project uses various machine learning algorithms to predict loan approval, including:

1. Decision Tree Classifier
2. Naive Bayes Classifier

Results

The performance of the models is evaluated using metrics such as accuracy.

Contact Information

For any questions or suggestions, feel free to contact me:

Email: [email protected] GitHub: DC-x-2003