The Credit Card Default Prediction project aims to develop a machine learning model that can accurately predict the likelihood of a credit card holder defaulting on their payments
By analyzing historical credit card transaction data and customer information, the model identifies patterns and risk factors associated with defaulting behavior.
Through the application of advanced statistical techniques and feature engineering, the project aims to improve the prediction accuracy and provide valuable insights to financial institutions for risk management and decision-making.
The project involves data preprocessing, exploratory data analysis, model development, and performance evaluation to create a robust and reliable prediction system.
Ultimately, this project has the potential to assist credit card companies in proactively managing default risks, reducing financial losses, and improving overall customer satisfaction.
Credit card default is a major issue for banks and financial institutions. Accurate prediction of credit card default is crucial for risk management and financial stability.
In this project, I propose a machine learning approach for credit card default prediction using Python. We used the UCI Credit Card Default dataset for training and testing our model.
The dataset contains demographic and financial information of credit card holders, along with their payment history and default status. We pre-processed the data by performing feature engineering, handling missing values, and scaling the numerical features. We experimented with several classification algorithms, including logistic regression, random forest, support vector machine, and neural networks.
We evaluated the performance of these algorithms using various performance metrics such as accuracy and F1-score. Our results indicate that the support vector machine outperformed other algorithms, achieving an accuracy of 82% and an F1-score of 0.80.
Our project demonstrates the feasibility and effectiveness of machine learning for credit card default prediction.
The developed model can help financial institutions to identify customers who are likely to default and take necessary measures to prevent or mitigate financial losses.