Credit-Card-Risk-Prediction

This project is created to predict risk of credit card loan of a bank using Classification Machine Learning Model • I have developed and trained a classification machine learning model using Logistic Regressor, Decision Tree, Random Forest, Adaboost, XGBoost, SVM, KNN, and Naive Bayes algorithms achieving an average accuracy of 75%. • Implemented the model into a Flask API to provide real-time predictions and streamline data analysis process for stakeholders. • Optimized the model's performance resulting in an increase in accuracy by 10% through feature engineering, hyperparameter tuning and 10-fold Cross Validation.