Credit Card Approval Prediction Using Machine Learning
In the modern banking industry, handling the high volume of credit card applications efficiently is crucial. Traditional manual review processes are not only time-consuming but also prone to errors. To address this challenge, I developed an automatic credit card approval predictor using advanced machine learning techniques.
Project Highlights:
Objective: To create a machine learning model that predicts credit card approval outcomes, simulating real-world banking systems.
Algorithms Used: Implemented and compared various models, including:
@ Logistic Regression @ Stochastic Gradient Descent (SGD) @ Support Vector Classifier (SVC) @ Decision Tree @ Random Forest @ XGBoost
Outstanding Performance: Achieved an exceptional accuracy of 99.5% using the Decision Tree algorithm, demonstrating its effectiveness in predicting credit card approvals.
Model Training & Testing: Trained and evaluated multiple models to select the most accurate and reliable one. The chosen model was saved in .pkl format for deployment.
Deployment: Integrated the model with a Flask application, enabling real-time predictions and easy access.
Key Takeaways:
High Accuracy: Achieved a notable 99.5% accuracy with Decision Tree, proving its robustness in credit approval predictions.
Automated System: Developed an automated solution that enhances efficiency and reduces manual errors in the approval process.
Deployment Skills: Gained experience in deploying machine learning models using Flask.
This project has been a significant milestone in applying machine learning to financial applications and achieving remarkable results.