--> Analysing a credit card dataset, performed information preprocessing, investigated highlight designing methods, and assessed the execution of different machine learning models for credit card endorsement prediction.
--> Utilised oversampling methods, counting SMOTETomek, to handle class imbalance within the dataset and progress show performance.
--> Utilised machine learning models such as Logistic Regression, Random Forest Classification, CatBoost, and AdaBoost for credit card endorsement expectation.
--> Assessed the models utilising measurements such as accuracy, review, F1 score, and accuracy.
--> Based on the assessment comes about, the CatBoost show illustrated the most excellent with tall accuracy, review, F1 score, and exactness, making it the foremost suitable demonstrate for credit card endorsement expectation in this project.
--> The venture highlights the significance of strength and generalisation, emphasising the ought to survey models on inconspicuous information or real-world scenarios to guarantee dependable expectations past the preparing information.
Python, Pandas, Scikit-learn, CatBoost, Random Forest, AdaBoost, Matplotlib, Seaborn