Project Overview
This is my first AI/machine learning project. The goal is to predict loan approval for applicants based on key financial and employment features. The model simulates a real-world loan evaluation scenario, helping to understand which factors influence approval decisions.
To ensure the model’s reliability, K-Fold Cross-Validation was used, providing robust performance evaluation and reducing the risk of overfitting.
Dataset
The dataset includes the following features:
Feature Description income Applicant’s income credit_score Creditworthiness score loan_amount Amount of loan requested years_employed Number of years the applicant has been employed points Additional scoring metric or internal evaluation points loan_approved Target variable indicating loan approval (Yes/No or 1/0)
Note: Name and city were removed as they are not relevant for prediction.
Tech Stack
Language: Python
Libraries: Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn
Models used: Logistic Regression, Decision Tree, Random Forest
Model Performance
Accuracy: 99% The model demonstrates excellent predictive performance, correctly identifying the vast majority of approvals and rejections. K-Fold Cross-Validation further confirmed the model’s consistency and robustness across different subsets of data.