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:
- Loan ID
- Gender
- Married
- Dependents
- Education
- Self Employed
- Applicant Income
- Co-Applicant Income
- Loan Amount
- Loan Amount Term
- Credit History
- Property Area
- Loan Status
Modeling and Prediction
The project uses various machine learning algorithms to predict loan approval, including:
- Decision Tree Classifier
- 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: chakrabortydeboqwerty@gmail.com GitHub: DC-x-2003