#1 Credit-Card-Default-Prediction-Using-SVM-and-Logistic-Regression-A-Comparative-Analysis This project uses SVM and Logistic Regression to predict credit card defaults using the UCI Credit Card dataset. It includes data preprocessing, one-hot encoding, model training, accuracy evaluation, and hyperparameter optimization.

Documentation: Credit Card Default Prediction Software

Introduction:

The Credit Card Default Prediction Software is designed to accurately predict credit card defaults using state-of-the-art machine learning techniques. It leverages the power of Support Vector Machines (SVM) and Logistic Regression models to analyze the UCI Credit Card dataset, enabling financial institutions to make informed decisions regarding credit risk assessment.

Dataset:

The software utilizes the UCI Credit Card dataset, a comprehensive collection of credit card holder information. The dataset contains diverse features such as demographics, education, marital status, payment history, and default status. By processing this dataset, the software can generate meaningful insights to aid in credit risk management.

Data Preprocessing:

The software implements robust data preprocessing techniques, handling missing values and encoding categorical variables using one-hot encoding. The dataset is divided into training and testing sets, ensuring accurate model evaluation. Additionally, feature scaling is applied to enhance model performance.

Model Training and Evaluation:

The software employs both SVM and Logistic Regression models to train on the preprocessed data. Through rigorous model evaluation, including accuracy scoring and confusion matrix analysis, the software provides reliable predictions for credit card defaults. Logistic Regression serves as the baseline model, while the SVM model's hyperparameters are optimized using a grid search algorithm.

Comparative Analysis:

The software facilitates a comprehensive comparative analysis between the SVM and Logistic Regression models. By assessing their accuracy and performance metrics, financial institutions gain valuable insights into the strengths and weaknesses of each model. The generated confusion matrices offer a deeper understanding of true positives, true negatives, false positives, and false negatives, aiding in risk management decision-making.

Conclusion:

The Credit Card Default Prediction Software empowers financial institutions with advanced machine learning capabilities to predict credit card defaults effectively. Its user-friendly interface, combined with the comprehensive documentation, allows for seamless integration and understanding. By leveraging the software's insights, institutions can mitigate credit risk and make informed decisions, enhancing their overall portfolio management strategies.